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data/sampled_jsons/'Machine_Learning_meets_Algebraic_Combinatorics'_'Grassmannian_cluster_algebra_dataset'_'Table_10'_'_year_2024.jsonl

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+{"idx": 0, "title": "Machine Learning meets Algebraic Combinatorics: A Suite of Datasets ...", "date": "", "ddg_snippet": "In this paper we introduce the Algebraic Combinatorics Dataset Repository, a collection of research-level mathematics datasets structured for machine learning and designed to accelerate mathematical discovery.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.06366v1", "content": "In this paper we introduce the Algebraic Combinatorics Dataset Repository, a collection of research-level mathematics datasets structured for machine learning and designed to accelerate mathematical discovery."}
+{"idx": 1, "title": "Algebraic Combinatorics Dataset Repository - GitHub", "date": "", "ddg_snippet": "To lower the barrier of entry to the machine learning community, we include datasets centered around open problems in algebraic combinatorics . We hope that use of these by the AI-community will translate into progress in mathematics.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/pnnl/ML4AlgComb", "content": "To lower the barrier of entry to the machine learning community, we include datasets centered around open problems in algebraic combinatorics . We hope that use of these by the AI-community will translate into progress in mathematics."}
+{"idx": 2, "title": "ACDRepo/grassmannian_cluster_algebras · Datasets at Hugging Face", "date": "", "ddg_snippet": "Machine learning meets algebraic combinatorics : A suite of datasets capturing research-level conjecturing ability in pure mathematics. arXiv preprint arXiv:2503.06366.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/ACDRepo/grassmannian_cluster_algebras", "content": "Machine learning meets algebraic combinatorics : A suite of datasets capturing research-level conjecturing ability in pure mathematics. arXiv preprint arXiv:2503.06366."}
+{"idx": 3, "title": "CANCELLED Grassmannian cluster algebras and machine learning", "date": "", "ddg_snippet": "It is an open problem to give a combinatorial method to check which tableaux correspond to cluster veriables. In the joint work with Man-Wai Cheung, Pierre-Philippe Dechant, Yang-Hui He, Elli Heyes, Edward Hirst, we apply machine learning method to classify cluster variables.", "subpage_snippet": "", "source": "meetings.ams.org", "link": "https://meetings.ams.org/math/jmm2025/meetingapp.cgi/Paper/40267", "content": "It is an open problem to give a combinatorial method to check which tableaux correspond to cluster veriables. In the joint work with Man-Wai Cheung, Pierre-Philippe Dechant, Yang-Hui He, Elli Heyes, Edward Hirst, we apply machine learning method to classify cluster variables."}
+{"idx": 4, "title": "Twists of $\\mathrm {Gr} (3,n)$ Cluster Variables as Double and Triple ...", "date": "", "ddg_snippet": "We give a combinatorial interpretation for certain cluster variables in Grassmannian cluster algebras in terms of double and triple dimer configurations. More specifically, we examine several Gr (3, n) cluster variables that may be written as degree two or degree three polynomials in terms of Plücker coordinates, and give generating functions for their images under the twist map - a cluster ...", "subpage_snippet": "", "source": "alco.centre-mersenne.org", "link": "https://alco.centre-mersenne.org/articles/10.5802/alco.376/", "content": "We give a combinatorial interpretation for certain cluster variables in Grassmannian cluster algebras in terms of double and triple dimer configurations. More specifically, we examine several Gr (3, n) cluster variables that may be written as degree two or degree three polynomials in terms of Plücker coordinates, and give generating functions for their images under the twist map - a cluster ..."}
+{"idx": 5, "title": "PDF Clustering cluster algebras with clusters - LIMS", "date": "", "ddg_snippet": "In this paper, we apply the tableaux method to clas-sify cluster variables in Grassmannian cluster algebras C[Gr(k, n)] up to (k, n) = (3, 12), (4, 10 ), or (4, 12) up to a certain number of columns of tableaux, using HPC clusters . These datasets are made available on GitHub.", "subpage_snippet": "", "source": "lims.ac.uk", "link": "https://lims.ac.uk/documents/undefined-6.pdf", "content": "In this paper, we apply the tableaux method to clas-sify cluster variables in Grassmannian cluster algebras C[Gr(k, n)] up to (k, n) = (3, 12), (4, 10 ), or (4, 12) up to a certain number of columns of tableaux, using HPC clusters . These datasets are made available on GitHub."}
+{"idx": 6, "title": "Machine Learning meets Algebraic Combinatorics: A Suite of Datasets to ...", "date": "", "ddg_snippet": "In this paper we introduced Algebraic Combinatorics Dataset Repository, a collection of datasets structured for machine learning and designed to facilitate the development of machine learning methods for advancing research level mathematics.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=KQ1gI5qzAf", "content": "In this paper we introduced Algebraic Combinatorics Dataset Repository, a collection of datasets structured for machine learning and designed to facilitate the development of machine learning methods for advancing research level mathematics."}
+{"idx": 7, "title": "Cluster algebras: Network science and machine learning", "date": "", "ddg_snippet": "Cluster algebras have recently become an important player in mathematics and physics. In this work, we investigate them through the lens of modern data science, specifically with techniques from network science and machine learning . Network analysis methods are applied to the exchange graphs for cluster algebras of varying mutation types. The analysis indicates that when the graphs are ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2772827723000050", "content": "Cluster algebras have recently become an important player in mathematics and physics. In this work, we investigate them through the lens of modern data science, specifically with techniques from network science and machine learning . Network analysis methods are applied to the exchange graphs for cluster algebras of varying mutation types. The analysis indicates that when the graphs are ..."}
+{"idx": 8, "title": "Machine Learning meets Algebraic Combinatorics: A Suite of Benchmark ...", "date": "", "ddg_snippet": "To address this, we introduce a new collection of benchmark datasets , Algebraic Combinatorics Benchmarks (ACBench), representing either classic or open problems in algebraic combinatorics , a subfield of mathematics that studies discrete structures arising from abstract algebra .", "subpage_snippet": "", "source": "www.pnnl.gov", "link": "https://www.pnnl.gov/publications/machine-learning-meets-algebraic-combinatorics-suite-benchmark-datasets-accelerate-ai", "content": "To address this, we introduce a new collection of benchmark datasets , Algebraic Combinatorics Benchmarks (ACBench), representing either classic or open problems in algebraic combinatorics , a subfield of mathematics that studies discrete structures arising from abstract algebra ."}
+{"idx": 9, "title": "PDF Pnnl- 36691", "date": "", "ddg_snippet": "In this paper we introduced Algebraic Combinatorics Benchmarks (ACBench), a collection of datasets structured for machine learning and designed to facilitate the development of machine learning methods for advancing research level mathematics.", "subpage_snippet": "", "source": "www.pnnl.gov", "link": "https://www.pnnl.gov/main/publications/external/technical_reports/PNNL-36691.pdf", "content": "In this paper we introduced Algebraic Combinatorics Benchmarks (ACBench), a collection of datasets structured for machine learning and designed to facilitate the development of machine learning methods for advancing research level mathematics."}

data/sampled_jsons/'Machine_Learning_meets_Algebraic_Combinatorics'_'Grassmannian_cluster_algebra_dataset'_Table_10.jsonl

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+{"idx": 0, "title": "Machine Learning meets Algebraic Combinatorics : A Suite of...", "date": "", "ddg_snippet": "Among the many algebraic - combinatorial properties of Grassmannians is an algebraic structure on its coordinate ring making it something called a cluster algebra (Williams, 2014) .This dataset relates to a cluster algebra associated with the Grassmann manifold.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.06366v1", "content": "Among the many algebraic - combinatorial properties of Grassmannians is an algebraic structure on its coordinate ring making it something called a cluster algebra (Williams, 2014) .This dataset relates to a cluster algebra associated with the Grassmann manifold."}
+{"idx": 1, "title": "Machine Learning meets Algebraic Combinatorics : A", "date": "", "ddg_snippet": "Model Performance. Baseline hyperparameters. Machine Learning meets Algebraic Combinatorics : A Suite of Datasets to Accelerate AI for Mathematics Research.All datasets (outside of the positive examples in the Grassmannian cluster algebra dataset ) were generated in June 2024.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=KQ1gI5qzAf", "content": "Model Performance. Baseline hyperparameters. Machine Learning meets Algebraic Combinatorics : A Suite of Datasets to Accelerate AI for Mathematics Research.All datasets (outside of the positive examples in the Grassmannian cluster algebra dataset ) were generated in June 2024."}
+{"idx": 2, "title": "Twists, Higher Dimer Covers, and Web Duality for Grassmannian ...", "date": "", "ddg_snippet": "Grassmannian cluster algebras represent a fundamental intersection between algebraic geometry, combinatorics , and representation theory.", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2507.15211v1", "content": "Grassmannian cluster algebras represent a fundamental intersection between algebraic geometry, combinatorics , and representation theory."}
+{"idx": 3, "title": "Clustering cluster algebras with clusters | Request PDF", "date": "", "ddg_snippet": "The Grassmannian cluster algebra .This computational algebra paradigm generates a dataset that can then be mined using techniques from data science such as supervised and unsupervised machine learning .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/381241767_Clustering_cluster_algebras_with_clusters", "content": "The Grassmannian cluster algebra .This computational algebra paradigm generates a dataset that can then be mined using techniques from data science such as supervised and unsupervised machine learning ."}
+{"idx": 4, "title": "co. combinatorics - Grassmannian cluster algebra of... - MathOverflow", "date": "", "ddg_snippet": "Grassmannian cluster algebra of infinite type has no trees in its mutation class.", "subpage_snippet": "", "source": "mathoverflow.net", "link": "https://mathoverflow.net/questions/381452/grassmannian-cluster-algebra-of-infinite-type-has-no-trees-in-its-mutation-class", "content": "Grassmannian cluster algebra of infinite type has no trees in its mutation class."}
+{"idx": 5, "title": "Twists of $\\mathrm{Gr}(3, n)$ Cluster Variables as Double and Triple...", "date": "", "ddg_snippet": "ALGEBRAIC COMBINATORICS .We give a combinatorial interpretation for certain cluster variables in Grassmannian cluster algebras in terms of double and triple dimer configurations. More specifically, we examine several. Gr(3,n).", "subpage_snippet": "", "source": "alco.centre-mersenne.org", "link": "https://alco.centre-mersenne.org/articles/10.5802/alco.376/", "content": "ALGEBRAIC COMBINATORICS .We give a combinatorial interpretation for certain cluster variables in Grassmannian cluster algebras in terms of double and triple dimer configurations. More specifically, we examine several. Gr(3,n)."}
+{"idx": 6, "title": "Categories for Grassmannian Cluster Algebras of Infinite Rank", "date": "", "ddg_snippet": "Grassmannian cluster categories are an additive categorification of Grassmannian cluster algebras , of which this section provides an overview. 2.1.1 The finite rank case Coordinate rings of flag varieties provide an interesting source of cluster algebras .", "subpage_snippet": "", "source": "eprints.gla.ac.uk", "link": "https://eprints.gla.ac.uk/294377/2/294377.pdf", "content": "Grassmannian cluster categories are an additive categorification of Grassmannian cluster algebras , of which this section provides an overview. 2.1.1 The finite rank case Coordinate rings of flag varieties provide an interesting source of cluster algebras ."}
+{"idx": 7, "title": "(PDF) Grassmannians and Cluster Algebras", "date": "", "ddg_snippet": "A cluster algebra of geometric type 4 GRASSMANNIANS AND CLUSTER ALGEBRAS will be defined as a subalgebra of F generated by a family of transcendence bases of F each of which is determined by a combinatorial process called mutation.", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/99109832/Grassmannians_and_Cluster_Algebras", "content": "A cluster algebra of geometric type 4 GRASSMANNIANS AND CLUSTER ALGEBRAS will be defined as a subalgebra of F generated by a family of transcendence bases of F each of which is determined by a combinatorial process called mutation."}
+{"idx": 8, "title": "Dimers in Combinatorics and Cluster Algebras", "date": "", "ddg_snippet": "Cluster Algebra , Dimers and Beyond (slides) (video).In certain cases, these algebras correspond to cluster -tilting objects in the cluster category associated to the Grassmannian introduced by Jensen, King and Su.", "subpage_snippet": "", "source": "dept.math.lsa.umich.edu", "link": "https://dept.math.lsa.umich.edu/~speyer/DimerConference2020/", "content": "Cluster Algebra , Dimers and Beyond (slides) (video).In certain cases, these algebras correspond to cluster -tilting objects in the cluster category associated to the Grassmannian introduced by Jensen, King and Su."}
+{"idx": 9, "title": "Cluster algebras and their bases-Bohrium", "date": "", "ddg_snippet": "[4] Cluster Algebras : Network Science and Machine Learning .Classification of cluster variables in cluster algebras (in particular, Grassmannian cluster algebras ) is an important problem, which has direct application to computations of scattering amplitudes in physics.", "subpage_snippet": "", "source": "www.bohrium.com", "link": "https://www.bohrium.com/paper-details/cluster-algebras-and-their-bases/867771891274744541-108554", "content": "[4] Cluster Algebras : Network Science and Machine Learning .Classification of cluster variables in cluster algebras (in particular, Grassmannian cluster algebras ) is an important problem, which has direct application to computations of scattering amplitudes in physics."}

data/sampled_jsons/0A4Y9qRnu9_Leveraging_Per-Instance_Privacy_for_Machine_Unlearning_paper.jsonl

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+{"idx": 0, "title": "Leveraging Per-Instance Privacy for Machine Unlearning", "date": "", "ddg_snippet": "We present a principled, per-instance approach to quantifying the difficulty of unlearning via fine-tuning. We begin by sharpening an analysis of noisy gradient descent for unlearning (Chien et al., 2024), obtaining a better utility- unlearning tradeoff by replacing worst-case privacy loss bounds with per-instance privacy losses (Thudi et al., 2024), each of which bounds the (Renyi) divergence ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2505.18786", "content": "We present a principled, per-instance approach to quantifying the difficulty of unlearning via fine-tuning. We begin by sharpening an analysis of noisy gradient descent for unlearning (Chien et al., 2024), obtaining a better utility- unlearning tradeoff by replacing worst-case privacy loss bounds with per-instance privacy losses (Thudi et al., 2024), each of which bounds the (Renyi) divergence ..."}
+{"idx": 1, "title": "Leveraging Per-Example Privacy for Machine Unlearning", "date": "", "ddg_snippet": "This work focuses on developing fine-grained theoretical insights to quantify unlearning difficulty at the level of individual data points for fine-tuning-based unlearning . Unlike other unlearning methods that lack theoretical guarantees for non-convex models, our approach builds on recent advances in differential privacy to provide per-instance guarantees using Rényi divergence. While our ...", "subpage_snippet": "", "source": "research.google", "link": "https://research.google/pubs/leveraging-per-example-privacy-for-machine-unlearning/", "content": "This work focuses on developing fine-grained theoretical insights to quantify unlearning difficulty at the level of individual data points for fine-tuning-based unlearning . Unlike other unlearning methods that lack theoretical guarantees for non-convex models, our approach builds on recent advances in differential privacy to provide per-instance guarantees using Rényi divergence. While our ..."}
+{"idx": 2, "title": "Leveraging Per-Instance Privacy for Machine Unlearning", "date": "", "ddg_snippet": "Abstract We present a principled, per-instance approach to quantifying the dificulty of unlearning via fine-tuning. We begin by sharpening an analysis of noisy gradient descent for unlearning (Chien et al., 2024), obtaining a better utility- unlearning trade-off by replacing worst-case privacy loss bounds with per-instance privacy losses (Thudi et al., 2024), each of which bounds the (R ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=0A4Y9qRnu9", "content": "Abstract We present a principled, per-instance approach to quantifying the dificulty of unlearning via fine-tuning. We begin by sharpening an analysis of noisy gradient descent for unlearning (Chien et al., 2024), obtaining a better utility- unlearning trade-off by replacing worst-case privacy loss bounds with per-instance privacy losses (Thudi et al., 2024), each of which bounds the (R ..."}
+{"idx": 3, "title": "A Survey on Machine Unlearning: Techniques and New Emerged Privacy Risks", "date": "", "ddg_snippet": "This paper provides an overview and analysis of the existing research on machine unlearning , aiming to present the current vulnerabilities of machine unlearning approaches. We analyze privacy risks in various aspects, including definitions, implementation methods, and real-world applications.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2406.06186", "content": "This paper provides an overview and analysis of the existing research on machine unlearning , aiming to present the current vulnerabilities of machine unlearning approaches. We analyze privacy risks in various aspects, including definitions, implementation methods, and real-world applications."}
+{"idx": 4, "title": "A survey of security and privacy issues of machine unlearning", "date": "", "ddg_snippet": "Specifically, we begin by investigating unlearning -based security attacks, where adversaries exploit vulnerabilities in the unlearning process to compromise the security of machine learning (ML) models. We then conduct a thorough examination of privacy risks associated with the adoption of machine unlearning .", "subpage_snippet": "", "source": "onlinelibrary.wiley.com", "link": "https://onlinelibrary.wiley.com/doi/full/10.1002/aaai.12209", "content": "Specifically, we begin by investigating unlearning -based security attacks, where adversaries exploit vulnerabilities in the unlearning process to compromise the security of machine learning (ML) models. We then conduct a thorough examination of privacy risks associated with the adoption of machine unlearning ."}
+{"idx": 5, "title": "PDF Forget to Flourish: Leveraging Machine-Unlearning on Pretrained ...", "date": "", "ddg_snippet": "This scenario creates a privacy threat, as pre-trained models can be intentionally crafted to compromise the privacy of fine-tuning datasets. In this study, we introduce a novel poisoning technique that uses model- unlearning as an attack tool.", "subpage_snippet": "", "source": "shadow.merl.com", "link": "https://shadow.merl.com/publications/docs/TR2025-017.pdf", "content": "This scenario creates a privacy threat, as pre-trained models can be intentionally crafted to compromise the privacy of fine-tuning datasets. In this study, we introduce a novel poisoning technique that uses model- unlearning as an attack tool."}
+{"idx": 6, "title": "Leveraging Per-Instance Privacy for Machine Unlearning", "date": "", "ddg_snippet": "Abstract We present a principled, per-instance approach to quantifying the difficulty of unlearning via fine-tuning. We begin by sharpening an analysis of noisy gradient descent for unlearning (Chien et al., 2024), obtaining a better utility- unlearning tradeoff by replacing worst-case privacy loss bounds with per-instance privacy losses (Thudi et al., 2024), each of which bounds the (Rényi ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.18786v1", "content": "Abstract We present a principled, per-instance approach to quantifying the difficulty of unlearning via fine-tuning. We begin by sharpening an analysis of noisy gradient descent for unlearning (Chien et al., 2024), obtaining a better utility- unlearning tradeoff by replacing worst-case privacy loss bounds with per-instance privacy losses (Thudi et al., 2024), each of which bounds the (Rényi ..."}
+{"idx": 7, "title": "Learning to Unlearn: Instance-Wise Unlearning for Pre-trained ...", "date": "", "ddg_snippet": "To this end, we consider instance -wise unlearning , of which the goal is to delete information on a set of instances from a pre-trained model, by either misclassifying each instance away from its original prediction or relabeling the instance to a different label.", "subpage_snippet": "", "source": "ojs.aaai.org", "link": "https://ojs.aaai.org/index.php/AAAI/article/view/28996", "content": "To this end, we consider instance -wise unlearning , of which the goal is to delete information on a set of instances from a pre-trained model, by either misclassifying each instance away from its original prediction or relabeling the instance to a different label."}
+{"idx": 8, "title": "Pseudo-Labeling for Enhanced User Privacy in Approximate Machine Unlearning", "date": "", "ddg_snippet": "This paper presents an effective pseudo-labeling method for machine unlearning , focusing on protecting user privacy . Recent research predominantly focused on modifying the weights of pre-trained models to approximate exact unlearning . We study the impact of pseudo-labeling methods and propose two novel algorithms for approximate unlearning . Our approach aims to imitate the inference errors and ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/abstract/document/10890795", "content": "This paper presents an effective pseudo-labeling method for machine unlearning , focusing on protecting user privacy . Recent research predominantly focused on modifying the weights of pre-trained models to approximate exact unlearning . We study the impact of pseudo-labeling methods and propose two novel algorithms for approximate unlearning . Our approach aims to imitate the inference errors and ..."}
+{"idx": 9, "title": "Leveraging Per-Instance Privacy for Machine Unlearning", "date": "", "ddg_snippet": "A principled, per-instance approach to quantifying the difficulty of unlearning via fine-tuning, which provides a foundation for more efficient and adaptive unlearning strategies tailored to the unique properties of individual data points. We present a principled, per-instance approach to quantifying the difficulty of unlearning via fine-tuning. We begin by sharpening an analysis of noisy ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Leveraging-Per-Instance-Privacy-for-Machine-Sepahvand-Thudi/dca9861c26bd83a7b1c30fb4255810afdd1e4aa3", "content": "A principled, per-instance approach to quantifying the difficulty of unlearning via fine-tuning, which provides a foundation for more efficient and adaptive unlearning strategies tailored to the unique properties of individual data points. We present a principled, per-instance approach to quantifying the difficulty of unlearning via fine-tuning. We begin by sharpening an analysis of noisy ..."}

data/sampled_jsons/0E5rZOGA13_Unbiased_Recommender_Learning_from_Implicit_Feedback_via_Weakly_Supervised_Learning.jsonl

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+{"idx": 0, "title": "Unbiased Recommender Learning from Implicit Feedback via ...", "date": "", "ddg_snippet": "This paper formulates implicit feedback recommendation as a weakly supervised learning problem, obtaining an unbiased positive-negative recommender without the need of negative feedback .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=0E5rZOGA13&referrer=[the+profile+of+Licheng+Pan](/profile?id=~Licheng_Pan1)", "content": "This paper formulates implicit feedback recommendation as a weakly supervised learning problem, obtaining an unbiased positive-negative recommender without the need of negative feedback ."}
+{"idx": 1, "title": "Unbiased Recommender Learning from Implicit Feedback via ...", "date": "", "ddg_snippet": "Learning from Weak Supervision .Unbiased Implicit Recommendation and Propensity Es-timation via Combinational Joint Learning . In RecSys. ACM, 551–556. 13 . Unbiased Recommender Learning from Implicit Feedback via Weakly Supervised Learning . 10−1.", "subpage_snippet": "", "source": "zhouchenlin.github.io", "link": "https://zhouchenlin.github.io/Publications/2025-ICML-Unbiased.pdf", "content": "Learning from Weak Supervision .Unbiased Implicit Recommendation and Propensity Es-timation via Combinational Joint Learning . In RecSys. ACM, 551–556. 13 . Unbiased Recommender Learning from Implicit Feedback via Weakly Supervised Learning . 10−1."}
+{"idx": 2, "title": "ICML Poster Unbiased Recommender Learning from Implicit ...", "date": "", "ddg_snippet": "To address this issue, we introduce PURL, a model-agnostic framework that reframes implicit feedback recommendation as a weakly supervised learning task, eliminating the need for negative samples.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46694", "content": "To address this issue, we introduce PURL, a model-agnostic framework that reframes implicit feedback recommendation as a weakly supervised learning task, eliminating the need for negative samples."}
+{"idx": 3, "title": "(PDF) Unbiased Implicit Feedback via Bi-level Optimization", "date": "", "ddg_snippet": "PDF | Implicit feedback is widely leveraged in recommender systems since it is easy to collect and provides weak supervision signals.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/361022915_Unbiased_Implicit_Feedback_via_Bi-level_Optimization", "content": "PDF | Implicit feedback is widely leveraged in recommender systems since it is easy to collect and provides weak supervision signals."}
+{"idx": 4, "title": "GitHub - jihoo-kim/RecSys-Papers-from-SIGIR-2021: Papers related to...", "date": "", "ddg_snippet": "Self- supervised & Contrasive Learning .[L10] Learning Recommender Systems with Implicit Feedback via Soft Target Enhancement. Mingyue Cheng, Fajie Yuan, Qi Liu, Shenyang Ge, Zhi Li, Runlong Yu, Defu Lian, Senchao Yuan and Enhong Chen.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/jihoo-kim/RecSys-Papers-from-SIGIR-2021", "content": "Self- supervised & Contrasive Learning .[L10] Learning Recommender Systems with Implicit Feedback via Soft Target Enhancement. Mingyue Cheng, Fajie Yuan, Qi Liu, Shenyang Ge, Zhi Li, Runlong Yu, Defu Lian, Senchao Yuan and Enhong Chen."}
+{"idx": 5, "title": "(PDF) Unbiased Pairwise Learning from Implicit Feedback for...", "date": "", "ddg_snippet": "Nonetheless, the existing unbiased pairwise learning method suffers from high variance. To get satisfactory performance, non-negative estimator is utilized for practical variance control but introduces additional bias.", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/papers/unbiased-pairwise-learning-from-implicit-feedback-for-15stlhei", "content": "Nonetheless, the existing unbiased pairwise learning method suffers from high variance. To get satisfactory performance, non-negative estimator is utilized for practical variance control but introduces additional bias."}
+{"idx": 6, "title": "[1909.03601] Unbiased Recommender Learning from ...", "date": "", "ddg_snippet": "Title: Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback . Authors:Yuta Saito, Suguru Yaginuma, Yuta Nishino, Hayato Sakata, Kazuhide Nakata.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1909.03601", "content": "Title: Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback . Authors:Yuta Saito, Suguru Yaginuma, Yuta Nishino, Hayato Sakata, Kazuhide Nakata."}
+{"idx": 7, "title": "Sci-Hub | Unbiased Recommender Learning from ...", "date": "", "ddg_snippet": "↓ скачать. Saito, Y., Yaginuma, S., Nishino, Y., Sakata, H., & Nakata, K. (2020). Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback . Proceedings of the 13th International Conference on Web Search and Data Mining. doi:10.1145/3336191.3371783.", "subpage_snippet": "", "source": "www.sci-hub.ru", "link": "https://www.sci-hub.ru/10.1145/3336191.3371783", "content": "↓ скачать. Saito, Y., Yaginuma, S., Nishino, Y., Sakata, H., & Nakata, K. (2020). Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback . Proceedings of the 13th International Conference on Web Search and Data Mining. doi:10.1145/3336191.3371783."}
+{"idx": 8, "title": "Unbiased Recommender Learning from Missing-Not-At-Random...", "date": "", "ddg_snippet": "Unbiased Recommender Learning from Biased Graded Implicit Feedback . Accelerating Offline Reinforcement Learning Application in Real-Time Bidding and Recommendation: Potential Use of Simulation.", "subpage_snippet": "", "source": "usait0.com", "link": "https://usait0.com/en/publication/2020/wsdm2020/", "content": "Unbiased Recommender Learning from Biased Graded Implicit Feedback . Accelerating Offline Reinforcement Learning Application in Real-Time Bidding and Recommendation: Potential Use of Simulation."}
+{"idx": 9, "title": "Breaking Feedback Loops in Recommender Systems with... | CoLab", "date": "", "ddg_snippet": "Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback . Saito Y., Yaginuma S., Nishino Y., Sakata H., Nakata K.", "subpage_snippet": "", "source": "colab.ws", "link": "https://colab.ws/articles/10.1145/3728372", "content": "Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback . Saito Y., Yaginuma S., Nishino Y., Sakata H., Nakata K."}

data/sampled_jsons/0WQJ6DFSKp_Equation_19_DHC_scaling.jsonl

@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Solving the viscous Burger’s equation using three methods", "date": "", "ddg_snippet": "So we consider the analytical solution of Burger’s equation using three different methods, which are: the Cole-Hopf transformation, variational iteration method and Adomian decomposition method.", "subpage_snippet": "", "source": "journals.misuratau.edu.ly", "link": "https://journals.misuratau.edu.ly/edu/upload/file/E2.pdf", "content": "So we consider the analytical solution of Burger’s equation using three different methods, which are: the Cole-Hopf transformation, variational iteration method and Adomian decomposition method."}
+{"idx": 1, "title": "Amplitudes of solar-like oscillations: a new scaling relation", "date": "", "ddg_snippet": "(17) and ( 19 ) gives our new scaling relation for intensity amplitudes: Aλ∝Lτosc0.5λM1.5Teff2.25+r", "subpage_snippet": "", "source": "www.aanda.org", "link": "https://www.aanda.org/articles/aa/full_html/2011/05/aa16789-11/aa16789-11.html", "content": "(17) and ( 19 ) gives our new scaling relation for intensity amplitudes: Aλ∝Lτosc0.5λM1.5Teff2.25+r"}
+{"idx": 2, "title": "(PDF) The Simplest Walking Model: Stability, Complexity, and Scaling", "date": "", "ddg_snippet": "Equation 2 describes the swing leg as a simple pendulum whose support (at the hip) moves through an arc.The period of motion τ is approximately independent of γ, for small γ. Speeds thus scale with the stance angle θ * . The angle by which the hip mass is deflected at heelstrike is 2θ * .", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/92301322/The_Simplest_Walking_Model_Stability_Complexity_and_Scaling", "content": "Equation 2 describes the swing leg as a simple pendulum whose support (at the hip) moves through an arc.The period of motion τ is approximately independent of γ, for small γ. Speeds thus scale with the stance angle θ * . The angle by which the hip mass is deflected at heelstrike is 2θ * ."}
+{"idx": 3, "title": "Analytic Solution of the Boltzmann Equation in", "date": "", "ddg_snippet": "mann equation using the method of moments [6]. Due. to local momentum isotropy, the distribution function fk. can be fully described by scalar moments only [31] Equation ( 19 ) is the main result of this Letter. While. the nonlinear coupling between dierent moments was.", "subpage_snippet": "", "source": "link.aps.org", "link": "https://link.aps.org/accepted/10.1103/PhysRevLett.116.022301", "content": "mann equation using the method of moments [6]. Due. to local momentum isotropy, the distribution function fk. can be fully described by scalar moments only [31] Equation ( 19 ) is the main result of this Letter. While. the nonlinear coupling between dierent moments was."}
+{"idx": 4, "title": "Reviews: Neural Ordinary Differential Equations", "date": "", "ddg_snippet": "Equation following line 170: missing right parenthesis in integrand of last integral.This leads the authors to propose ODE-Nets, which are networks solving a differential equation parametrized by a neural network. Numerical experiments span classification and generation.", "subpage_snippet": "", "source": "media.nips.cc", "link": "https://media.nips.cc/nipsbooks/nipspapers/paper_files/nips31/reviews/3310.html", "content": "Equation following line 170: missing right parenthesis in integrand of last integral.This leads the authors to propose ODE-Nets, which are networks solving a differential equation parametrized by a neural network. Numerical experiments span classification and generation."}
+{"idx": 5, "title": "Introduction to Differential Equations with Dynamical... - DOKUMEN.PUB", "date": "", "ddg_snippet": "Equation (4) is an example of a differential equation , and we develop methods to solve such equations in this text. We will discuss population growth models in more depth in Section 1.8 and Chapters 5 and 6. In a typical application, physical laws often lead to a differential...", "subpage_snippet": "", "source": "dokumen.pub", "link": "https://dokumen.pub/introduction-to-differential-equations-with-dynamical-systems-9781400841325.html", "content": "Equation (4) is an example of a differential equation , and we develop methods to solve such equations in this text. We will discuss population growth models in more depth in Section 1.8 and Chapters 5 and 6. In a typical application, physical laws often lead to a differential..."}
+{"idx": 6, "title": "14.3.3 Using the Arrhenius Equation Flashcards by Irina Soloshenko", "date": "", "ddg_snippet": "19.2.4 Using Standard Reduction Potentials. 19.2.5 The Nernst Equation . 19 .2.6 Electrochemicamical Determinants of Equilibria.", "subpage_snippet": "", "source": "www.brainscape.com", "link": "https://www.brainscape.com/flashcards/1433-using-the-arrhenius-equation-7410232/packs/11455278", "content": "19.2.4 Using Standard Reduction Potentials. 19.2.5 The Nernst Equation . 19 .2.6 Electrochemicamical Determinants of Equilibria."}
+{"idx": 7, "title": "[Solved]: Classify(if possible) the differential equation as... - UpStudy", "date": "", "ddg_snippet": "*First-Order Nonlinear** - No general solution method 19 . * *First-Order Linear** - Solution using integrating factor 20. *", "subpage_snippet": "", "source": "upstudy.ai", "link": "https://upstudy.ai/questions/221656465-classify-if-possible-the-differential-equation-as-separable-linear-bernoulli-or", "content": "*First-Order Nonlinear** - No general solution method 19 . * *First-Order Linear** - Solution using integrating factor 20. *"}
+{"idx": 8, "title": "In the paper mentioned can you help me to find the... | ResearchGate", "date": "", "ddg_snippet": "In the paper \"Magnetometer-Only Attitude Determination Using. Novel Two-Step Kalman Filter Approach\" in equation 19 do I need to complete quaternion multiplication first and then do simple mathematical finite differencing or it is in other way?", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/post/In_the_paper_mentioned_can_you_help_me_to_find_the_finite_difference_of_equation_no_19", "content": "In the paper \"Magnetometer-Only Attitude Determination Using. Novel Two-Step Kalman Filter Approach\" in equation 19 do I need to complete quaternion multiplication first and then do simple mathematical finite differencing or it is in other way?"}
+{"idx": 9, "title": "Transport phenomena Solved problems | PDF | Physics | Science", "date": "", "ddg_snippet": "1. The document derives a general differential equation for fluid flow problems in rectangular Cartesian coordinates using a shell momentum balance.", "subpage_snippet": "", "source": "www.slideshare.net", "link": "https://www.slideshare.net/slideshow/transport-phenomena-solved-problems/203516288", "content": "1. The document derives a general differential equation for fluid flow problems in rectangular Cartesian coordinates using a shell momentum balance."}

data/sampled_jsons/0hrkN07DuO_Linear_convergence_of_Sinkhorn's_algorithm_for_generalized_static_Schrödinger_bridge_Figu.jsonl

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+{"idx": 0, "title": "Linear Convergence of Sinkhorn's Algorithm for Generalized Static ...", "date": "", "ddg_snippet": "In this paper, we present the generalized static Schrödinger bridge problem, establish its Kantorovich dual, and show that the associated generalized Sinkhorn algorithm con-verges linearly in a dimension independent manner under mild assumptions on the general divergence functional f , weight matrix W and margin (r,c).", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=0hrkN07DuO", "content": "In this paper, we present the generalized static Schrödinger bridge problem, establish its Kantorovich dual, and show that the associated generalized Sinkhorn algorithm con-verges linearly in a dimension independent manner under mild assumptions on the general divergence functional f , weight matrix W and margin (r,c)."}
+{"idx": 1, "title": "PDF On the Convergence Rate of Sinkhorn's Algorithm", "date": "", "ddg_snippet": "For quadratic cost and unbounded continuous marginals satisfying a log-concavity condi-tion, [20] proves linear convergence based on a fine analysis of the gradients of Schrödinger potentials and Sinkhorn iterates.", "subpage_snippet": "", "source": "www.math.columbia.edu", "link": "https://www.math.columbia.edu/~mnutz/docs/Sinkhorn_rate.pdf", "content": "For quadratic cost and unbounded continuous marginals satisfying a log-concavity condi-tion, [20] proves linear convergence based on a fine analysis of the gradients of Schrödinger potentials and Sinkhorn iterates."}
+{"idx": 2, "title": "On the Convergence Rate of Sinkhorn's Algorithm - arXiv.org", "date": "", "ddg_snippet": "For the convergence results, the second key innovation uses the finer details of Sinkhorn's algorithm and its mono-tonicity properties: we relate the improvement to a marginal entropy and deduce a difference H(π∗|π2t) − equation enabling us to analyze H(π∗|π2t+2)", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2212.06000", "content": "For the convergence results, the second key innovation uses the finer details of Sinkhorn's algorithm and its mono-tonicity properties: we relate the improvement to a marginal entropy and deduce a difference H(π∗|π2t) − equation enabling us to analyze H(π∗|π2t+2)"}
+{"idx": 3, "title": "On the linear convergence of the multi-marginal Sinkhorn algorithm", "date": "", "ddg_snippet": "Abstract The aim of this short note is to give an elementary proof of linear convergence of the Sinkhorn algorithm for the entropic regularization of multi-marginal optimal transport. The proof simply relies on: i) the fact that Sinkhorn iterates are bounded, ii) strong convexity of the exponential on bounded intervals and iii) the convergence analysis of the coordinate descent (Gauss-Seidel ...", "subpage_snippet": "", "source": "hal.science", "link": "https://hal.science/hal-03176512/document", "content": "Abstract The aim of this short note is to give an elementary proof of linear convergence of the Sinkhorn algorithm for the entropic regularization of multi-marginal optimal transport. The proof simply relies on: i) the fact that Sinkhorn iterates are bounded, ii) strong convexity of the exponential on bounded intervals and iii) the convergence analysis of the coordinate descent (Gauss-Seidel ..."}
+{"idx": 4, "title": "PDF Generative modeling via Schrödinger bridge (basics on ... - vdb", "date": "", "ddg_snippet": "Outline of the course A dynamic and static Schrödinger bridges . Convergence of the Sinkhorn algorithm . Figure 2 : A Schrödinger Bridge between two data distributions. Image extracted from De Bortoli et al. (2021).", "subpage_snippet": "", "source": "vdeborto.github.io", "link": "https://vdeborto.github.io/project/generative_modeling/session_5.pdf", "content": "Outline of the course A dynamic and static Schrödinger bridges . Convergence of the Sinkhorn algorithm . Figure 2 : A Schrödinger Bridge between two data distributions. Image extracted from De Bortoli et al. (2021)."}
+{"idx": 5, "title": "Linear convergence of Sinkhorn's algorithm for generalized static ...", "date": "", "ddg_snippet": "Poster Linear convergence of Sinkhorn's algorithm for generalized static Schrödinger bridge Rahul Choudhary · Hanbaek Lyu West Exhibition Hall B2-B3 #W-506", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46671", "content": "Poster Linear convergence of Sinkhorn's algorithm for generalized static Schrödinger bridge Rahul Choudhary · Hanbaek Lyu West Exhibition Hall B2-B3 #W-506"}
+{"idx": 6, "title": "On the Linear Convergence of the Multimarginal Sinkhorn Algorithm", "date": "", "ddg_snippet": "The aim of this note is to give an elementary proof of linear convergence of the Sinkhorn algorithm for the entropic regularization of multimarginal optimal transport in the setting of general probability spaces. The proof simply relies on (i) the fact that Sinkhorn iterates are bounded, (ii) the strong convexity of the exponential on bounded intervals, and (iii) the convergence analysis of ...", "subpage_snippet": "", "source": "epubs.siam.org", "link": "https://epubs.siam.org/doi/10.1137/21M1410634", "content": "The aim of this note is to give an elementary proof of linear convergence of the Sinkhorn algorithm for the entropic regularization of multimarginal optimal transport in the setting of general probability spaces. The proof simply relies on (i) the fact that Sinkhorn iterates are bounded, (ii) the strong convexity of the exponential on bounded intervals, and (iii) the convergence analysis of ..."}
+{"idx": 7, "title": "On the Convergence Rate of Sinkhorn's Algorithm", "date": "", "ddg_snippet": "An Optimal Transport Approach for the Schrödinger Bridge Problem and Convergence of Sinkhorn Algorithm Article Full-text available Nov 2020 J SCI COMPUT", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/366213052_On_the_Convergence_Rate_of_Sinkhorn's_Algorithm", "content": "An Optimal Transport Approach for the Schrödinger Bridge Problem and Convergence of Sinkhorn Algorithm Article Full-text available Nov 2020 J SCI COMPUT"}
+{"idx": 8, "title": "On the Convergence Rate of Sinkhorn's AlgorithmThe authors thank ...", "date": "", "ddg_snippet": "On the Convergence Rate of Sinkhorn's Algorithm††thanks: The authors thank Stephan Eckstein and Flavien Léger for helpful discussions.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2212.06000v2", "content": "On the Convergence Rate of Sinkhorn's Algorithm††thanks: The authors thank Stephan Eckstein and Flavien Léger for helpful discussions."}
+{"idx": 9, "title": "Hanbaek Lyu", "date": "", "ddg_snippet": "Contingency tables, Random Matrices, Optimal Transport Hanbaek Lyu and Sumit Muhkerjee, \"Large random matrices with given margins.\" Submitted. Prepint (2024) (Last updated ver: Aug 6, 2025 CT_limit) Rahul Choudhary and Hanbaek Lyu, \"Linear convergence of Sinkhorn's algorithm for generalized static Schrödinger bridge \", To appear in ICML 2025 Yulia Alexandr, Miles Bakenhus, Mark Curiel, Sameer ...", "subpage_snippet": "", "source": "hanbaeklyu.com", "link": "https://hanbaeklyu.com/publications/", "content": "Contingency tables, Random Matrices, Optimal Transport Hanbaek Lyu and Sumit Muhkerjee, \"Large random matrices with given margins.\" Submitted. Prepint (2024) (Last updated ver: Aug 6, 2025 CT_limit) Rahul Choudhary and Hanbaek Lyu, \"Linear convergence of Sinkhorn's algorithm for generalized static Schrödinger bridge \", To appear in ICML 2025 Yulia Alexandr, Miles Bakenhus, Mark Curiel, Sameer ..."}

data/sampled_jsons/0yzOEMbShU_Beyond_Self-Repellent_Kernels_History-Driven_Target_MCMC_year_2024.jsonl

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+{"idx": 0, "title": "Beyond Self - Repellent Kernels : History - Driven Target Towards...", "date": "", "ddg_snippet": "Primary Area: Probabilistic Methods-> Monte Carlo and Sampling Methods. Keywords: Nonlinear MCMC , History - Driven Target , Computational Efficiency, Near-Zero Variance. Submission Number: 4668.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=0yzOEMbShU", "content": "Primary Area: Probabilistic Methods-> Monte Carlo and Sampling Methods. Keywords: Nonlinear MCMC , History - Driven Target , Computational Efficiency, Near-Zero Variance. Submission Number: 4668."}
+{"idx": 1, "title": "Remove Old Kernels on CentOS 7 Easily", "date": "", "ddg_snippet": "Kernel kernel -3.10.0-693.5.2.el7.x86_64 is currently loaded and used. based on the about output this is the latest version. Remove old kernels manually.", "subpage_snippet": "", "source": "linuxconfig.org", "link": "https://linuxconfig.org/how-to-remove-old-unused-kernels-on-centos-linux", "content": "Kernel kernel -3.10.0-693.5.2.el7.x86_64 is currently loaded and used. based on the about output this is the latest version. Remove old kernels manually."}
+{"idx": 2, "title": "Tesla to launch self - driving cars in Austin amid safety concerns", "date": "", "ddg_snippet": "Adding a third self - driving taxi service in the city has sparked mix reviews from people in the city. Luis Correa is an Austinite and a Tesla owner. His Tesla has a self - driving function that he uses often. He said this service will benecit many people in Austin the way it does for him.", "subpage_snippet": "", "source": "cbsaustin.com", "link": "https://cbsaustin.com/news/local/tesla-to-launch-self-driving-cars-in-austin-amid-safety-concerns", "content": "Adding a third self - driving taxi service in the city has sparked mix reviews from people in the city. Luis Correa is an Austinite and a Tesla owner. His Tesla has a self - driving function that he uses often. He said this service will benecit many people in Austin the way it does for him."}
+{"idx": 3, "title": "10 MCP Servers for Frontend Developers - The New Stack", "date": "", "ddg_snippet": "Spec- Driven Development: The Key to Scalable AI Agents.Code generation and modification , which facilitate AI- driven code generation, autocompletion, refactoring, and other code manipulation tasks, are executed by the IDE.", "subpage_snippet": "", "source": "thenewstack.io", "link": "https://thenewstack.io/10-mcp-servers-for-frontend-developers/", "content": "Spec- Driven Development: The Key to Scalable AI Agents.Code generation and modification , which facilitate AI- driven code generation, autocompletion, refactoring, and other code manipulation tasks, are executed by the IDE."}
+{"idx": 4, "title": "Debt Rattle September 18 2025 - The Automatic Earth", "date": "", "ddg_snippet": "I have made the decision not to cover any of the controversial aspects, direct or ancillary, beyond what I have already shared. Do I have opinions, yes.Patel was clear: “Nobody is targeted for their faith.” Jordan followed up: “Is the FBI still targeting Americans who are pro-life?”", "subpage_snippet": "", "source": "www.theautomaticearth.com", "link": "https://www.theautomaticearth.com/2025/09/debt-rattle-september-18-2025/", "content": "I have made the decision not to cover any of the controversial aspects, direct or ancillary, beyond what I have already shared. Do I have opinions, yes.Patel was clear: “Nobody is targeted for their faith.” Jordan followed up: “Is the FBI still targeting Americans who are pro-life?”"}
+{"idx": 5, "title": "Can You Copy and Automate Trading Strategies? A Guide to Creating...", "date": "", "ddg_snippet": "Moving beyond basic strategies involves sophisticated risk management, thorough backtesting, and tailoring the setup to specific needs.", "subpage_snippet": "", "source": "trading-strategies.academy", "link": "https://trading-strategies.academy/archives/1633", "content": "Moving beyond basic strategies involves sophisticated risk management, thorough backtesting, and tailoring the setup to specific needs."}
+{"idx": 6, "title": "Включаем Wi-Fi в новых версиях гу — Haval Jolion... | DRIVE 2", "date": "", "ddg_snippet": "Выбираем: Launch Debug App…. Узнайте подробности, смотрите фото и читайте на DRIVE 2.", "subpage_snippet": "", "source": "www.drive2.ru", "link": "https://www.drive2.ru/l/704432911734029556/", "content": "Выбираем: Launch Debug App…. Узнайте подробности, смотрите фото и читайте на DRIVE 2."}
+{"idx": 7, "title": "1,000,000 ЖИТЕЛЕЙ ОХОТИТЬСЯ НА Grox Озвучка На... - YouTube", "date": "", "ddg_snippet": "О сервисе Прессе Авторские права Связаться с нами Авторам Рекламодателям...", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=oPaYnFB0dF8", "content": "О сервисе Прессе Авторские права Связаться с нами Авторам Рекламодателям..."}
+{"idx": 8, "title": "Как подключить Magic Mouse к Windows-компьютеру - Яблык...", "date": "", "ddg_snippet": "Как использовать мышь Apple Mouse на компьютере с установленной операционной системой Windows?", "subpage_snippet": "", "source": "yablyk.com", "link": "https://yablyk.com/777455-kak-podklyuchit-magic-mouse-k-windows-pk-instrukciya/", "content": "Как использовать мышь Apple Mouse на компьютере с установленной операционной системой Windows?"}
+{"idx": 9, "title": "Embedded Linux System Components: Toolchain to RootFS", "date": "", "ddg_snippet": "The Linux kernel is considered a monolithic kernel . This means that its core functions—the process scheduler, memory manager, virtual filesystem (VFS), networking stack, and device drivers —are all tightly integrated into a single large executable (Image or zImage).", "subpage_snippet": "", "source": "circuitlabs.net", "link": "https://circuitlabs.net/embedded-linux-system-components-toolchain-to-rootfs/", "content": "The Linux kernel is considered a monolithic kernel . This means that its core functions—the process scheduler, memory manager, virtual filesystem (VFS), networking stack, and device drivers —are all tightly integrated into a single large executable (Image or zImage)."}

data/sampled_jsons/10l1pGeOcK_SAFE-_Finding_Sparse_and_Flat_Minima_to_Improve_Pruning_LLaMa-2_7B_50%_sparsity_perplexit.jsonl

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+{"idx": 0, "title": "Google - Wikipedia", "date": "", "ddg_snippet": "Google LLC (/ ˈɡuːɡəl / ⓘ, GOO-gəl) is an American multinational corporation and technology company focusing on online advertising, search engine technology, cloud computing, computer software, quantum computing, e-commerce, consumer electronics, and artificial intelligence (AI). [9] It has been referred to as \"the most powerful company in the world\" by the BBC [10] and is one of the ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Google", "content": "Google LLC (/ ˈɡuːɡəl / ⓘ, GOO-gəl) is an American multinational corporation and technology company focusing on online advertising, search engine technology, cloud computing, computer software, quantum computing, e-commerce, consumer electronics, and artificial intelligence (AI). [9] It has been referred to as \"the most powerful company in the world\" by the BBC [10] and is one of the ..."}
+{"idx": 1, "title": "About Google : Our products, technology and company information", "date": "", "ddg_snippet": "Learn more about Google . Explore our innovative AI products and services, and discover how we're using technology to help improve lives around the world.", "subpage_snippet": "", "source": "about.google", "link": "https://about.google/", "content": "Learn more about Google . Explore our innovative AI products and services, and discover how we're using technology to help improve lives around the world."}
+{"idx": 2, "title": "Google 's products and services - About Google", "date": "", "ddg_snippet": "Explore Google 's helpful products and services, including Android, Gemini, Pixel and Search.", "subpage_snippet": "", "source": "about.google", "link": "https://about.google/products/", "content": "Explore Google 's helpful products and services, including Android, Gemini, Pixel and Search."}
+{"idx": 3, "title": "Safe : Finding Sparse and Flat Minima to Improve Pruning", "date": "", "ddg_snippet": "Safe yields sparse and flat solutions. Figure 2 : Validation accuracy (mean. ±plus-or-minus\\pm±. std) of VGG-19 and ResNet-20/32 models on CIFAR-10/100 pruned across different sparsity levels and methods.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.06866v1", "content": "Safe yields sparse and flat solutions. Figure 2 : Validation accuracy (mean. ±plus-or-minus\\pm±. std) of VGG-19 and ResNet-20/32 models on CIFAR-10/100 pruned across different sparsity levels and methods."}
+{"idx": 4, "title": "(PDF) SAFE : Finding Sparse and Flat Minima to Improve Pruning", "date": "", "ddg_snippet": "SAFE yields sparse and flat solutions. (a-b) Weight distributions of densely-trained model and model trained with SAFE , and (c-d) loss landscape and maximum Hessian eigenvalue of minima found by ADMM and SAFE .Comparison with IMP+SAM on LLaMA 2 - 7 b for 50 % sparsity .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/392531034_SAFE_Finding_Sparse_and_Flat_Minima_to_Improve_Pruning", "content": "SAFE yields sparse and flat solutions. (a-b) Weight distributions of densely-trained model and model trained with SAFE , and (c-d) loss landscape and maximum Hessian eigenvalue of minima found by ADMM and SAFE .Comparison with IMP+SAM on LLaMA 2 - 7 b for 50 % sparsity ."}
+{"idx": 5, "title": "Как дообучить LLaMA бесплатно и без программирования... / Хабр", "date": "", "ddg_snippet": "Что будем обучать и что потребуется. В данной статье я покажу как я дообучал LLaMA 7 B и LLaMA 2 7 B . Если готовы заплатить за аренду видеокарт, то можете обучить и модели покрупнее.", "subpage_snippet": "", "source": "habr.com", "link": "https://habr.com/ru/articles/755114/", "content": "Что будем обучать и что потребуется. В данной статье я покажу как я дообучал LLaMA 7 B и LLaMA 2 7 B . Если готовы заплатить за аренду видеокарт, то можете обучить и модели покрупнее."}
+{"idx": 6, "title": "Perplexity", "date": "", "ddg_snippet": "Perplexity is a free AI-powered answer engine that provides accurate, trusted, and real-time answers to any question.", "subpage_snippet": "", "source": "www.perplexity.ai", "link": "https://www.perplexity.ai/", "content": "Perplexity is a free AI-powered answer engine that provides accurate, trusted, and real-time answers to any question."}
+{"idx": 7, "title": "AlphaPruning: Using Heavy-Tailed Self", "date": "", "ddg_snippet": "Table 10: Perplexity (↓) of pruning LLaMA - 7 B into various global sparsities using SparseGPT. We compare our method with three other baseline sparsity allocation methods.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/10fc83943b4540a9524af6fc67a23fef-Paper-Conference.pdf", "content": "Table 10: Perplexity (↓) of pruning LLaMA - 7 B into various global sparsities using SparseGPT. We compare our method with three other baseline sparsity allocation methods."}
+{"idx": 8, "title": "APT: Adaptive Pruning and Tuning Pretrained Language Models for...", "date": "", "ddg_snippet": "(b) Training initial sparsity trade-off with 30% target sparsity model’s relative performances to the LoRA-tuned LLaMA 2 - 7 B and 13B models. Figure 5. Detailed analysis in APT with different initial, target sparsities , and adaptive tuning schedules.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/8d52635997029af5c1cf48201dca245bac5e4deb.pdf", "content": "(b) Training initial sparsity trade-off with 30% target sparsity model’s relative performances to the LoRA-tuned LLaMA 2 - 7 B and 13B models. Figure 5. Detailed analysis in APT with different initial, target sparsities , and adaptive tuning schedules."}
+{"idx": 9, "title": "Installing llama -cpp-python with GPU Support", "date": "", "ddg_snippet": "Published on 10 Sep 2023.If llama -cpp-python cannot find the CUDA toolkit, it will default to a CPU-only installation. I got the installation to work with the commands below. If you have tried to install the package before, you will most likely need the --no-cache-dir option to get it to work.", "subpage_snippet": "", "source": "michaelriedl.com", "link": "https://michaelriedl.com/2023/09/10/llama2-install-gpu.html", "content": "Published on 10 Sep 2023.If llama -cpp-python cannot find the CUDA toolkit, it will default to a CPU-only installation. I got the installation to work with the commands below. If you have tried to install the package before, you will most likely need the --no-cache-dir option to get it to work."}

data/sampled_jsons/10l1pGeOcK_SAFE_Finding_Sparse_and_Flat_Minima_to_Improve_Pruning_ablation_study_section_F.1_year_2023.jsonl

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+{"idx": 0, "title": "SAFE: Finding Sparse and Flat Minima to Improve Pruning", "date": "", "ddg_snippet": "Sparsifying neural networks often suffers from seemingly inevitable performance degradation, and it remains challenging to restore the original performance despite much recent progress. Motivated by recent studies in robust optimization, we aim to tackle this problem by finding subnetworks that are both sparse and flat at the same time. Specifically, we formulate pruning as a sparsity ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2506.06866", "content": "Sparsifying neural networks often suffers from seemingly inevitable performance degradation, and it remains challenging to restore the original performance despite much recent progress. Motivated by recent studies in robust optimization, we aim to tackle this problem by finding subnetworks that are both sparse and flat at the same time. Specifically, we formulate pruning as a sparsity ..."}
+{"idx": 1, "title": "PDF Safe: Finding Sparse and Flat Minima to Improve Pruning", "date": "", "ddg_snippet": "In this section , we demonstrate that SAFE converges to sparse and flat solutions, leading to performance improve -ments over baselines in both image classification and lan-guage modeling tasks.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=10l1pGeOcK&name=pdf", "content": "In this section , we demonstrate that SAFE converges to sparse and flat solutions, leading to performance improve -ments over baselines in both image classification and lan-guage modeling tasks."}
+{"idx": 2, "title": "SAFE: Finding Sparse and Flat Minima to Improve Pruning | Cool Papers ...", "date": "", "ddg_snippet": "#1 SAFE: Finding Sparse and Flat Minima to Improve Pruning [PDF 7] [Copy] [Kimi 4] [REL] Authors: Dongyeop Lee, Kwanhee Lee, Jinseok Chung, Namhoon Lee Sparsifying neural networks often suffers from seemingly inevitable performance degradation, and it remains challenging to restore the original performance despite much recent progress.Motivated by recent studies in robust optimization, we aim ...", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/venue/10l1pGeOcK@OpenReview", "content": "#1 SAFE: Finding Sparse and Flat Minima to Improve Pruning [PDF 7] [Copy] [Kimi 4] [REL] Authors: Dongyeop Lee, Kwanhee Lee, Jinseok Chung, Namhoon Lee Sparsifying neural networks often suffers from seemingly inevitable performance degradation, and it remains challenging to restore the original performance despite much recent progress.Motivated by recent studies in robust optimization, we aim ..."}
+{"idx": 3, "title": "SAFE: Finding Sparse and Flat Minima to Improve Pruning", "date": "", "ddg_snippet": "This repository contains the official PyTorch implementation for the paper SAFE: Finding Sparse and Flat Minima to Improve Pruning . Our work introduces SAFE, an algorithm designed to find sparse and flat minima , leading to improved model pruning performance.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/LOG-postech/safe-torch", "content": "This repository contains the official PyTorch implementation for the paper SAFE: Finding Sparse and Flat Minima to Improve Pruning . Our work introduces SAFE, an algorithm designed to find sparse and flat minima , leading to improved model pruning performance."}
+{"idx": 4, "title": "PDF Safe: Finding Sparse and Flat Minima to Improve Pruning", "date": "", "ddg_snippet": "We first formulate this as a sharpness-aware sparsity-constrained optimization problem: min max f(x + ε), ∥x∥0≤d∥ε∥2≤ρ where goal is to find a sparse solution x⋆ with atmost d non-zero elements that minimizes the objective function in the whole ε-neighborhood, i.e., seek flat minima .", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/media/icml-2025/Slides/46658.pdf", "content": "We first formulate this as a sharpness-aware sparsity-constrained optimization problem: min max f(x + ε), ∥x∥0≤d∥ε∥2≤ρ where goal is to find a sparse solution x⋆ with atmost d non-zero elements that minimizes the objective function in the whole ε-neighborhood, i.e., seek flat minima ."}
+{"idx": 5, "title": "Presenting SAFE: A Method for Improving Pruning at ICML 2025", "date": "", "ddg_snippet": "Will be presenting our spotlight poster \"SAFE: Finding Sparse and Flat Minima to Improve Pruning \" tomorrow at East Exhibition Hall, #E-3510, from 11:00 AM to 1:30 PM! (#ICML2025 @ #Vancouver ...", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/posts/kwanheelee_e-icml2025-vancouver-activity-7351126114557153282-pk7J", "content": "Will be presenting our spotlight poster \"SAFE: Finding Sparse and Flat Minima to Improve Pruning \" tomorrow at East Exhibition Hall, #E-3510, from 11:00 AM to 1:30 PM! (#ICML2025 @ #Vancouver ..."}
+{"idx": 6, "title": "[이남훈 교수] SAFE: Finding Sparse and Flat Minima to Improve Pruning", "date": "", "ddg_snippet": "Dongyeop Lee, Kwanhee Lee, Jinseok Chung, and Namhoon Lee. \"SAFE: Finding Sparse and Flat Minima to Improve Pruning \", International Conference on Machine Learning (ICML), 2025. [성과와 관련된 이미지] 이전글[이남훈 교수] SASSHA: Sharpness-aware Adaptive Second-order Optimization with Stable Hessian Approximation", "subpage_snippet": "", "source": "cse.postech.ac.kr", "link": "https://cse.postech.ac.kr/csepostech/research/latest-research.do?mode=view&articleNo=24246&title=[이남훈+교수]+SAFE:+Finding+Sparse+and+Flat+Minima+to+Improve+Pruning", "content": "Dongyeop Lee, Kwanhee Lee, Jinseok Chung, and Namhoon Lee. \"SAFE: Finding Sparse and Flat Minima to Improve Pruning \", International Conference on Machine Learning (ICML), 2025. [성과와 관련된 이미지] 이전글[이남훈 교수] SASSHA: Sharpness-aware Adaptive Second-order Optimization with Stable Hessian Approximation"}
+{"idx": 7, "title": "Best performing penalty parameter of SAFE for VGG-19 and ResNet-20/32.", "date": "", "ddg_snippet": "Download scientific diagram | Best performing penalty parameter of SAFE for VGG-19 and ResNet-20/32. from publication: SAFE: Finding Sparse and Flat Minima to Improve Pruning | Sparsifying neural ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/Best-performing-penalty-parameter-of-SAFE-for-VGG-19-and-ResNet-20-32_tbl3_392531034", "content": "Download scientific diagram | Best performing penalty parameter of SAFE for VGG-19 and ResNet-20/32. from publication: SAFE: Finding Sparse and Flat Minima to Improve Pruning | Sparsifying neural ..."}
+{"idx": 8, "title": "Safe: Finding Sparse and Flat Minima to Improve Pruning", "date": "", "ddg_snippet": "Motivated by recent studies in robust optimization, we aim to tackle this problem by finding subnetworks that are both sparse and flat at the same time. Specifically, we formulate pruning as a sparsity-constrained optimization problem where flatness is encouraged as an objective.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.06866v2", "content": "Motivated by recent studies in robust optimization, we aim to tackle this problem by finding subnetworks that are both sparse and flat at the same time. Specifically, we formulate pruning as a sparsity-constrained optimization problem where flatness is encouraged as an objective."}
+{"idx": 9, "title": "publications | Dongyeop Lee - GitHub Pages", "date": "", "ddg_snippet": "2025 SAFE: Finding Sparse and Flat Minima to Improve Pruning Dongyeop Lee, Kwanhee Lee, Jinseok Chung, and Namhoon Lee ICML 2025 (spotlight), Jul 2025 Abs arXiv Code Poster Sparsifying neural networks often suffers from seemingly inevitable performance degradation, and it remains challenging to restore the original performance despite much recent progress. Motivated by recent studies in robust ...", "subpage_snippet": "", "source": "edong6768.github.io", "link": "https://edong6768.github.io/publications/", "content": "2025 SAFE: Finding Sparse and Flat Minima to Improve Pruning Dongyeop Lee, Kwanhee Lee, Jinseok Chung, and Namhoon Lee ICML 2025 (spotlight), Jul 2025 Abs arXiv Code Poster Sparsifying neural networks often suffers from seemingly inevitable performance degradation, and it remains challenging to restore the original performance despite much recent progress. Motivated by recent studies in robust ..."}

data/sampled_jsons/13HPTmZKbM_Upweighting_Easy_Samples_in_Fine-Tuning_Mitigates_Forgetting.jsonl

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+{"idx": 0, "title": "Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting", "date": "", "ddg_snippet": "Fine-tuning a pre-trained model on a downstream task often degrades its original capabilities, a phenomenon known as \"catastrophic forgetting \". This is especially an issue when one does not have access to the data and recipe used to develop the pre-trained model. Under this constraint, most existing methods for mitigating forgetting are inapplicable. To address this challenge, we propose a ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.02797", "content": "Fine-tuning a pre-trained model on a downstream task often degrades its original capabilities, a phenomenon known as \"catastrophic forgetting \". This is especially an issue when one does not have access to the data and recipe used to develop the pre-trained model. Under this constraint, most existing methods for mitigating forgetting are inapplicable. To address this challenge, we propose a ..."}
+{"idx": 1, "title": "Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting", "date": "", "ddg_snippet": "On this particular front, the major challenge in standard, unregu-lated fine-tuning is the catastrophic forgetting phenomenon. In broad terms, it describes the performance decline of the pre-trained model on previously observed data/tasks after fine-tuning on a new one.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=13HPTmZKbM", "content": "On this particular front, the major challenge in standard, unregu-lated fine-tuning is the catastrophic forgetting phenomenon. In broad terms, it describes the performance decline of the pre-trained model on previously observed data/tasks after fine-tuning on a new one."}
+{"idx": 2, "title": "Upweighting Easy Samples in Fine-Tuning Mitigates Forget...", "date": "", "ddg_snippet": "This research paper talks about a problem in machine learning called \"catastrophic forgetting ,\" which happens when a computer model forgets what it learned before while trying to learn something n...", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/icml/46655/paper", "content": "This research paper talks about a problem in machine learning called \"catastrophic forgetting ,\" which happens when a computer model forgets what it learned before while trying to learn something n..."}
+{"idx": 3, "title": "Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting", "date": "", "ddg_snippet": "title={Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting }, author={Sanyal, Sunny and Prairie, Hayden and Das, Rudrajit and Kavis, Ali and Sanghavi, Sujay},", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/sanyalsunny111/FLOW_finetuning", "content": "title={Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting }, author={Sanyal, Sunny and Prairie, Hayden and Das, Rudrajit and Kavis, Ali and Sanghavi, Sujay},"}
+{"idx": 4, "title": "(PDF) Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting", "date": "", "ddg_snippet": "To address this challenge, we propose a sample weighting scheme for the fine-tuning data solely based on the pre-trained model's losses.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/388754394_Upweighting_Easy_Samples_in_Fine-Tuning_Mitigates_Forgetting", "content": "To address this challenge, we propose a sample weighting scheme for the fine-tuning data solely based on the pre-trained model's losses."}
+{"idx": 5, "title": "Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting | Cool ...", "date": "", "ddg_snippet": "To address this challenge, we propose a sample weighting scheme for the fine-tuning data solely based on the pre-trained model's losses. Specifically, we upweight the easy samples on which the pre-trained model's loss is low and vice versa to limit the drift from the pre-trained model.", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/arxiv/2502.02797", "content": "To address this challenge, we propose a sample weighting scheme for the fine-tuning data solely based on the pre-trained model's losses. Specifically, we upweight the easy samples on which the pre-trained model's loss is low and vice versa to limit the drift from the pre-trained model."}
+{"idx": 6, "title": "Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting", "date": "", "ddg_snippet": "Spotlight Poster Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting Sunny Sanyal · Hayden Prairie · Rudrajit Das · Ali Kavis · Sujay Sanghavi East Exhibition Hall A-B #E-1302", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46655", "content": "Spotlight Poster Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting Sunny Sanyal · Hayden Prairie · Rudrajit Das · Ali Kavis · Sujay Sanghavi East Exhibition Hall A-B #E-1302"}
+{"idx": 7, "title": "Publications - Ali Kavis", "date": "", "ddg_snippet": "Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting S. Sanyal *, H. Prairie *, R. Das *, A. Kavis *, S. Sanghavi Published in arXiv, (2025) [Download here]", "subpage_snippet": "", "source": "alikavis.github.io", "link": "https://alikavis.github.io/publications/", "content": "Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting S. Sanyal *, H. Prairie *, R. Das *, A. Kavis *, S. Sanghavi Published in arXiv, (2025) [Download here]"}
+{"idx": 8, "title": "arXiv:2502.02797v1 [cs.LG] 5 Feb 2025", "date": "", "ddg_snippet": "tigating forgetting are inapplicable. To address this challenge, we propose a sample weighting scheme for the fine-tuning data solely base on the pre-trained model's losses. Specifically, we upweight the easy samples on which the pre-trained model's loss is low and vice versa to limit", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.02797v1", "content": "tigating forgetting are inapplicable. To address this challenge, we propose a sample weighting scheme for the fine-tuning data solely base on the pre-trained model's losses. Specifically, we upweight the easy samples on which the pre-trained model's loss is low and vice versa to limit"}
+{"idx": 9, "title": "About me - Ali Kavis", "date": "", "ddg_snippet": "Selected Publications: Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting S. Sanyal*, H. Prairie*, R. Das*, A. Kavis*, S. Sanghavi. arXiv. Understanding Self-Supervised Learning via Gaussian Mixture Models P. Bansal, A. Kavis, S. Sanghavi. arXiv. Adaptive and Optimal Second-order Optimistic Methods for Minimax Optimization", "subpage_snippet": "", "source": "alikavis.github.io", "link": "https://alikavis.github.io/", "content": "Selected Publications: Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting S. Sanyal*, H. Prairie*, R. Das*, A. Kavis*, S. Sanghavi. arXiv. Understanding Self-Supervised Learning via Gaussian Mixture Models P. Bansal, A. Kavis, S. Sanghavi. arXiv. Adaptive and Optimal Second-order Optimistic Methods for Minimax Optimization"}

data/sampled_jsons/1BaC3AdG1i_ATA_Equation_(7)_conf(i_k)_Orlicz_norm.jsonl

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+{"idx": 0, "title": "Orlicz space - Wikipedia", "date": "", "ddg_snippet": "In mathematical analysis, and especially in real, harmonic analysis and functional analysis, an Orlicz space is a type of function space which generalizes the Lp spaces. Like the Lp spaces, they are Banach spaces.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Orlicz_space", "content": "In mathematical analysis, and especially in real, harmonic analysis and functional analysis, an Orlicz space is a type of function space which generalizes the Lp spaces. Like the Lp spaces, they are Banach spaces."}
+{"idx": 1, "title": "pr.probability - Orlicz norm of random variable and variance...", "date": "", "ddg_snippet": "In probability and statistics Orlicz norms are frequently used in concentration inequalities. For example, for Bernstein's inequality, we have versions for sub-exponential random variables using ψ1. - norm and for bounded random variables using variance.", "subpage_snippet": "", "source": "cstheory.stackexchange.com", "link": "https://cstheory.stackexchange.com/questions/32978/orlicz-norm-of-random-variable-and-variance", "content": "In probability and statistics Orlicz norms are frequently used in concentration inequalities. For example, for Bernstein's inequality, we have versions for sub-exponential random variables using ψ1. - norm and for bounded random variables using variance."}
+{"idx": 2, "title": "pr.probability - Composing an Orlicz norm related to... - MathOverflow", "date": "", "ddg_snippet": "I am curious if this can additionally be made composable as well. There are many ways this question can be answered. One natural conjecture is the following though. Let $L>0$. Define the $L$-Bernstein- Orlicz norm as the Orlicz norm associated with the function $\\Psi_L(x)...", "subpage_snippet": "", "source": "mathoverflow.net", "link": "https://mathoverflow.net/questions/453191/composing-an-orlicz-norm-related-to-bernsteins-inequality", "content": "I am curious if this can additionally be made composable as well. There are many ways this question can be answered. One natural conjecture is the following though. Let $L>0$. Define the $L$-Bernstein- Orlicz norm as the Orlicz norm associated with the function $\\Psi_L(x)..."}
+{"idx": 3, "title": "The Hénon equation in Orlicz -Sobolev spaces", "date": "", "ddg_snippet": ", generalizing results for the classical Hénon equation . We also show that radial solutions are indeed bounded. Finally, we state a Pohozaev’s identity in Orlicz -Sobolev spaces that we apply to get a range in.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.17923v1", "content": ", generalizing results for the classical Hénon equation . We also show that radial solutions are indeed bounded. Finally, we state a Pohozaev’s identity in Orlicz -Sobolev spaces that we apply to get a range in."}
+{"idx": 4, "title": "finite-max.tex", "date": "", "ddg_snippet": "An Orlicz function is a convex, increasing function ψ on R+ with 0 ≤ ψ(0) < 1. Define the Orlicz norm X ψ (seminorm actually, unless one identifies random variables that are almost everywhere equal ) by.", "subpage_snippet": "", "source": "www.stat.yale.edu", "link": "http://www.stat.yale.edu/~pollard/Courses/607.spring05/handouts/finite-max.pdf", "content": "An Orlicz function is a convex, increasing function ψ on R+ with 0 ≤ ψ(0) < 1. Define the Orlicz norm X ψ (seminorm actually, unless one identifies random variables that are almost everywhere equal ) by."}
+{"idx": 5, "title": "Countable Additivity of Henstock-Dunford Integrable... | SpringerLink", "date": "", "ddg_snippet": "First Online: 25 June 2025. pp 127–135.Barcenas, D., Finol, C.E.: On vector measures, Uniformly integrable and Orlicz Spaces in vector measures.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-981-96-9548-5_9", "content": "First Online: 25 June 2025. pp 127–135.Barcenas, D., Finol, C.E.: On vector measures, Uniformly integrable and Orlicz Spaces in vector measures."}
+{"idx": 6, "title": "Кирилл Фёдоров / Война История Оружие – Telegram", "date": "", "ddg_snippet": "Россия может использовать Молдавию для нападения на Одесскую область, - Санду. \"Победа пророссийских сил угрожает суверенитету страны и может открыть путь российскому вторжению в Одесскую область\". Ежедневная отправка...", "subpage_snippet": "", "source": "t.me", "link": "https://t.me/s/warhistoryalconafter", "content": "Россия может использовать Молдавию для нападения на Одесскую область, - Санду. \"Победа пророссийских сил угрожает суверенитету страны и может открыть путь российскому вторжению в Одесскую область\". Ежедневная отправка..."}
+{"idx": 7, "title": "BilimClass - BilimLand", "date": "", "ddg_snippet": "Оқушылар Мен Ата -аналар үшін. Күнделікті жеке сабақ кестесі. Дербестендірілген және адаптивті оқыту.", "subpage_snippet": "", "source": "www.bilimclass.kz", "link": "https://www.bilimclass.kz/login", "content": "Оқушылар Мен Ата -аналар үшін. Күнделікті жеке сабақ кестесі. Дербестендірілген және адаптивті оқыту."}
+{"idx": 8, "title": "Метод Гаусса онлайн", "date": "", "ddg_snippet": "Как решить систему уравнений методом Гаусса. Решение системы линейных алгебраических уравнений методом Гаусса online. Оформление сразу в Word прямо на сайте...", "subpage_snippet": "", "source": "math.semestr.ru", "link": "https://math.semestr.ru/gauss/gauss.php", "content": "Как решить систему уравнений методом Гаусса. Решение системы линейных алгебраических уравнений методом Гаусса online. Оформление сразу в Word прямо на сайте..."}
+{"idx": 9, "title": "(Решено) Упр.640 ГДЗ Атанасян 10-11 класс с пояснениями", "date": "", "ddg_snippet": "Решение #1. Изображение Запишите координаты векторов: a = 3 i + 2j-5 k , b = -5 i + 3j- k ,с = i - j, d = j + k , m = k - i , n =...", "subpage_snippet": "", "source": "reshak.ru", "link": "https://reshak.ru/otvet/reshebniki.php?otvet=new/640&predmet=atan10_11", "content": "Решение #1. Изображение Запишите координаты векторов: a = 3 i + 2j-5 k , b = -5 i + 3j- k ,с = i - j, d = j + k , m = k - i , n =..."}

data/sampled_jsons/2.5_Score_Based_Denoising_Training_with_noise_augmentation_score_matching.jsonl

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+{"idx": 0, "title": "Normalizing Flows are Capable Generative Models", "date": "", "ddg_snippet": "9 Dec 2024 — 2.5 Score Based Denoising . Report issue for preceding element. Training with noise augmentation introduces an additional challenge: models ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.06329v1", "content": "9 Dec 2024 — 2.5 Score Based Denoising . Report issue for preceding element. Training with noise augmentation introduces an additional challenge: models ..."}
+{"idx": 1, "title": "Normalizing Flows are Capable Generative Models", "date": "", "ddg_snippet": "2 . 5 Score Based Denoising . Training with noise augmentation introduces an additional challenge: models trained on the noisy distribution.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.06329v2", "content": "2 . 5 Score Based Denoising . Training with noise augmentation introduces an additional challenge: models trained on the noisy distribution."}
+{"idx": 2, "title": "(PDF) Normalizing Flows are Capable Generative Models", "date": "", "ddg_snippet": "2 . 5 . Score Based Denoising . Training with noise augmentation introduces an additional.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/386577118_Normalizing_Flows_are_Capable_Generative_Models", "content": "2 . 5 . Score Based Denoising . Training with noise augmentation introduces an additional."}

data/sampled_jsons/2403.07300_abstract.jsonl

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+{"idx": 0, "title": "[2403.07300] CALF: Aligning LLMs for Time Series Forecasting", "date": "", "ddg_snippet": "Abstract : Deep learning (e.g., Transformer) has been widely and successfully used in multivariate time series forecasting (MTSF).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2403.07300", "content": "Abstract : Deep learning (e.g., Transformer) has been widely and successfully used in multivariate time series forecasting (MTSF)."}
+{"idx": 1, "title": "ChartThinker: A Contextual Chain-of-Thought Approach to", "date": "", "ddg_snippet": "... Abstract ... Abstract content", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.11236v2", "content": "... Abstract ... Abstract content"}
+{"idx": 2, "title": "SmolDocling: An ultra-compact vision-language model for", "date": "", "ddg_snippet": "... Abstract", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.11576v1", "content": "... Abstract"}
+{"idx": 3, "title": "Multimodal ArXiv: A Dataset for Improving Scientific", "date": "", "ddg_snippet": "However, their ability to interpret abstract figures, such as geometry shapes and scientific plots, remains limited due to a scarcity of training ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.00231v3", "content": "However, their ability to interpret abstract figures, such as geometry shapes and scientific plots, remains limited due to a scarcity of training ..."}
+{"idx": 4, "title": "Argumentatively Coherent Judgmental Forecasting", "date": "", "ddg_snippet": "... D Abstract Structure of Complexities ... Abstract", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.23163v1", "content": "... D Abstract Structure of Complexities ... Abstract"}
+{"idx": 5, "title": "Citations of Power Laws in Economics and Finance", "date": "", "ddg_snippet": "... Author & abstract ... HTML with abstract ... plain text with abstract", "subpage_snippet": "", "source": "ideas.repec.org", "link": "https://ideas.repec.org/r/anr/reveco/v1y2009p255-294.html", "content": "... Author & abstract ... HTML with abstract ... plain text with abstract"}
+{"idx": 6, "title": "Scaling up Multimodal Instruction Data through Web Search (2025)", "date": "", "ddg_snippet": "... Abstract ... Abstract diagrams and visual reasoning", "subpage_snippet": "", "source": "ritchebridal.com", "link": "https://ritchebridal.com/article/scaling-up-multimodal-instruction-data-through-web-search", "content": "... Abstract ... Abstract diagrams and visual reasoning"}
+{"idx": 7, "title": "| Theoretical Quantum Optics", "date": "", "ddg_snippet": "At an abstract level, our work points towards a fundamental difference between bipartite and multipartite entanglement.", "subpage_snippet": "", "source": "www.physik.uni-siegen.de", "link": "https://www.physik.uni-siegen.de/tqo/publications/?lang=de", "content": "At an abstract level, our work points towards a fundamental difference between bipartite and multipartite entanglement."}
+{"idx": 8, "title": "Scaling up Multimodal Instruction Data through Web Search (2025)", "date": "", "ddg_snippet": "... Abstract ... Abstract diagrams and visual reasoning", "subpage_snippet": "", "source": "galatamuhallebicisi.com", "link": "https://galatamuhallebicisi.com/article/scaling-up-multimodal-instruction-data-through-web-search", "content": "... Abstract ... Abstract diagrams and visual reasoning"}
+{"idx": 9, "title": "Fermionic Love of Black Holes in General Relativity", "date": "", "ddg_snippet": "... Abstract ... D 104 no. 2, (2021) 024013 , arXiv:2010. 07300 [gr-qc] .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.20155v1", "content": "... Abstract ... D 104 no. 2, (2021) 024013 , arXiv:2010. 07300 [gr-qc] ."}

data/sampled_jsons/2406.14532_Per-step_DPO_algorithm_formal_step-by-step.jsonl

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+{"idx": 0, "title": "GitHub - dvlab-research/ Step - DPO : Implementation for \" Step - DPO ...\"", "date": "", "ddg_snippet": "Step - DPO : Step -wise Preference Optimization for Long-chain Reasoning of LLMs.Notably, Step - DPO boosts the performance of Qwen2-7B-Instruct from 53.0% to 58.6% on MATH, and 85.5% to 87.9% on GSM8K, with as few as 10K data and hundreds of training steps !", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/dvlab-research/Step-DPO", "content": "Step - DPO : Step -wise Preference Optimization for Long-chain Reasoning of LLMs.Notably, Step - DPO boosts the performance of Qwen2-7B-Instruct from 53.0% to 58.6% on MATH, and 85.5% to 87.9% on GSM8K, with as few as 10K data and hundreds of training steps !"}
+{"idx": 1, "title": "[Literature Review] Full- Step - DPO : Self-Supervised Preference...", "date": "", "ddg_snippet": "Full- Step - DPO . Step -wise Rewards.In conclusion, Full- Step - DPO marks a significant advancement in optimizing mathematical reasoning for language models by leveraging self-supervised learning and dynamic step -wise optimization, thereby addressing both the quantization of preferences...", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/en/review/full-step-dpo-self-supervised-preference-optimization-with-step-wise-rewards-for-mathematical-reasoning", "content": "Full- Step - DPO . Step -wise Rewards.In conclusion, Full- Step - DPO marks a significant advancement in optimizing mathematical reasoning for language models by leveraging self-supervised learning and dynamic step -wise optimization, thereby addressing both the quantization of preferences..."}
+{"idx": 2, "title": "github- Step - DPO :Features, Alternatives | Toolerific", "date": "", "ddg_snippet": "Step - DPO : Step -wise Preference Optimization for Long-chain Reasoning of LLMs. Step 3: Rectify by the model itself # Before executing, please set the MODEL_PATH, EVAL_PROMPT, JSON_FILE, PRED_PATH, SAVE_PATH bash data_pipeline/ step 3.sh #.", "subpage_snippet": "", "source": "toolerific.ai", "link": "https://toolerific.ai/ai-tools/opensource/dvlab-research-Step-DPO", "content": "Step - DPO : Step -wise Preference Optimization for Long-chain Reasoning of LLMs. Step 3: Rectify by the model itself # Before executing, please set the MODEL_PATH, EVAL_PROMPT, JSON_FILE, PRED_PATH, SAVE_PATH bash data_pipeline/ step 3.sh #."}
+{"idx": 3, "title": "10 Minute Full Face Massage. Step - by - Step Tutorial.ANASTASIA...", "date": "", "ddg_snippet": "10 Minute Full Face Massage. Step - by - Step Tutorial.ANASTASIA BEAUTY FASCIA.", "subpage_snippet": "", "source": "rutube.ru", "link": "https://rutube.ru/video/50fb3447867f9500f18ed35948af8373/", "content": "10 Minute Full Face Massage. Step - by - Step Tutorial.ANASTASIA BEAUTY FASCIA."}
+{"idx": 4, "title": "ILLIT (아일릿) ‘jellyous' STEP BY STEP Dance Tutorial (Explained)", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=dVXLl3Rgo1Y", "content": ""}
+{"idx": 5, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math...", "date": "", "ddg_snippet": "Algorithm 1 Per - step DPO (Part 1: Practical version for most experiments; Parts 1 + 2: Complete version).given by SFT or RFT data, Part 1 of per - step DPO estimates the expected accuracy (Q-value) of each step in a negative rollout.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.14532v1", "content": "Algorithm 1 Per - step DPO (Part 1: Practical version for most experiments; Parts 1 + 2: Complete version).given by SFT or RFT data, Part 1 of per - step DPO estimates the expected accuracy (Q-value) of each step in a negative rollout."}
+{"idx": 6, "title": "RL on Incorrect Synthetic Data Scales the", "date": "", "ddg_snippet": "of per - step DPO [23] by running the full version of the per - step DPO algorithm ( Algorithm 1). In particular, we add new. 0.56. preference pairs to the dataset of per - step DPO algorithm starting from positive samples.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2406.14532", "content": "of per - step DPO [23] by running the full version of the per - step DPO algorithm ( Algorithm 1). In particular, we add new. 0.56. preference pairs to the dataset of per - step DPO algorithm starting from positive samples."}
+{"idx": 7, "title": "TempGmailer – Free Temp Gmail & Disposable Emails", "date": "", "ddg_snippet": "Learn how Temp Gmail works step by step . Create free temporary Gmail addresses to protect your inbox from spam and stay private online. Read Article.", "subpage_snippet": "", "source": "tempgmailer.com", "link": "https://tempgmailer.com/", "content": "Learn how Temp Gmail works step by step . Create free temporary Gmail addresses to protect your inbox from spam and stay private online. Read Article."}
+{"idx": 8, "title": "(PDF) Subtle Errors Matter: Preference Learning via Error-injected...", "date": "", "ddg_snippet": "Let's think step by step . Step 1: Determine how long one pencil lasts for Jenine. … then one pencil lasts her (5 \\times 1.5 = 7.5 ) hours.than the SOTA step -wise preference learning frameworks. Compared to Step - DPO , which shares.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/384770893_Subtle_Errors_Matter_Preference_Learning_via_Error-injected_Self-editing", "content": "Let's think step by step . Step 1: Determine how long one pencil lasts for Jenine. … then one pencil lasts her (5 \\times 1.5 = 7.5 ) hours.than the SOTA step -wise preference learning frameworks. Compared to Step - DPO , which shares."}
+{"idx": 9, "title": "For a utonomous ai a gents", "date": "", "ddg_snippet": "115 around step - by - step verifiers or “Process Reward Models” (Uesato et al., 2022; Lightman et al., 2023), 116. specifically for mathematical reasoning. Step -controlled dpo : Leveraging stepwise error for enhanced mathematical 696 reasoning, 2024.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=LuytzzohTa", "content": "115 around step - by - step verifiers or “Process Reward Models” (Uesato et al., 2022; Lightman et al., 2023), 116. specifically for mathematical reasoning. Step -controlled dpo : Leveraging stepwise error for enhanced mathematical 696 reasoning, 2024."}

data/sampled_jsons/2407.10264_'ηM'_'ηS'_'Section_4'_'learning_rate'_year_2024.jsonl

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+{"idx": 0, "title": "The 10 Best El Paso Hotels (From $48) - Booking.com", "date": "", "ddg_snippet": "Great savings on hotels in El Paso , United States online. Good availability and great rates. Read hotel reviews and choose the best hotel deal for your stay.", "subpage_snippet": "", "source": "www.booking.com", "link": "https://www.booking.com/city/us/el-paso.html", "content": "Great savings on hotels in El Paso , United States online. Good availability and great rates. Read hotel reviews and choose the best hotel deal for your stay."}
+{"idx": 1, "title": "THE 10 BEST Hotels in El Paso 2025", "date": "", "ddg_snippet": "Stanton House El Paso , Chase Suite Hotel El Paso , and Home2 Suites By Hilton El Paso Airport all received great reviews from families traveling in El Paso . See the full list: El Paso Family Hotels .", "subpage_snippet": "", "source": "www.tripadvisor.com", "link": "https://www.tripadvisor.com/Hotels-g60768-El_Paso_Texas-Hotels.html", "content": "Stanton House El Paso , Chase Suite Hotel El Paso , and Home2 Suites By Hilton El Paso Airport all received great reviews from families traveling in El Paso . See the full list: El Paso Family Hotels ."}
+{"idx": 2, "title": "25 Best Hotels in El Paso for 2025 | U.S. News Travel", "date": "", "ddg_snippet": "U.S. News evaluates top hotels in El Paso using expert insights, awards, class ratings and guest reviews.", "subpage_snippet": "", "source": "travel.usnews.com", "link": "https://travel.usnews.com/Hotels/El_Paso_TX/", "content": "U.S. News evaluates top hotels in El Paso using expert insights, awards, class ratings and guest reviews."}
+{"idx": 3, "title": "Find hotels in El Paso, TX from $55", "date": "", "ddg_snippet": "Discover a range of hotel accommodations in El Paso , Texas, catering to various traveler needs. From business-friendly options to pet-friendly stays, you'll find the perfect spot for your visit.", "subpage_snippet": "", "source": "www.expedia.com", "link": "https://www.expedia.com/El-Paso-Hotels.d1129.Travel-Guide-Hotels", "content": "Discover a range of hotel accommodations in El Paso , Texas, catering to various traveler needs. From business-friendly options to pet-friendly stays, you'll find the perfect spot for your visit."}
+{"idx": 4, "title": "16 Best Hotels in El Paso. Hotels from $42/night - KAYAK", "date": "", "ddg_snippet": "Stay at Super 8 El Paso Airport from $48/night, Radisson Hotel El Paso Airport from $130/night, Stanton House from $217/night and more. Compare prices of 822 hotels in El Paso on KAYAK now.", "subpage_snippet": "", "source": "www.kayak.com", "link": "https://www.kayak.com/El-Paso-Hotels.9254.hotel.ksp", "content": "Stay at Super 8 El Paso Airport from $48/night, Radisson Hotel El Paso Airport from $130/night, Stanton House from $217/night and more. Compare prices of 822 hotels in El Paso on KAYAK now."}
+{"idx": 5, "title": "Hotels in El Paso, TX - Find Hotels - Hilton", "date": "", "ddg_snippet": "Explore Hotels in El Paso , TX. Search by destination, check the latest prices, or use the interactive map to find the location for your next stay. Book direct for the best price and free cancellation.", "subpage_snippet": "", "source": "www.hilton.com", "link": "https://www.hilton.com/en/locations/usa/texas/el-paso/", "content": "Explore Hotels in El Paso , TX. Search by destination, check the latest prices, or use the interactive map to find the location for your next stay. Book direct for the best price and free cancellation."}
+{"idx": 6, "title": "Hotels - Visit El Paso", "date": "", "ddg_snippet": "Whether you’re seeking luxury accommodations with top-notch amenities, charming boutique hotels with unique local flavor, or budget-friendly stays for a quick trip, El Paso has it all.", "subpage_snippet": "", "source": "visitelpaso.com", "link": "https://visitelpaso.com/plan-your-trip/hotels", "content": "Whether you’re seeking luxury accommodations with top-notch amenities, charming boutique hotels with unique local flavor, or budget-friendly stays for a quick trip, El Paso has it all."}
+{"idx": 7, "title": "THE BEST 10 HOTELS in EL PASO, TX - Updated 2025 - Hours - Yelp", "date": "", "ddg_snippet": "What are the best inexpensive hotels ? What did people search for similar to hotels in El Paso , TX? See more hotels in El Paso .", "subpage_snippet": "", "source": "www.yelp.com", "link": "https://www.yelp.com/search?cflt=hotels&find_loc=El+Paso,+TX", "content": "What are the best inexpensive hotels ? What did people search for similar to hotels in El Paso , TX? See more hotels in El Paso ."}
+{"idx": 8, "title": "Top 10 Hotels in El Paso , TX | Hotels .com", "date": "", "ddg_snippet": "Flexible booking options on most hotels . Compare 814 hotels in El Paso using 16,278 real guest reviews. Unlock travel rewards with One Key.", "subpage_snippet": "", "source": "www.hotels.com", "link": "https://www.hotels.com/de1450956/hotels-el-paso-texas/", "content": "Flexible booking options on most hotels . Compare 814 hotels in El Paso using 16,278 real guest reviews. Unlock travel rewards with One Key."}
+{"idx": 9, "title": "Hotel Paso Del Norte – Luxury Hotel in Downtown El Paso , TX", "date": "", "ddg_snippet": "Experience historic charm & modern amenities at our boutique hotel featuring luxurious accommodations, elegant décor, onsite dining, and event venues in El Paso .", "subpage_snippet": "", "source": "www.hotelpdn.com", "link": "https://www.hotelpdn.com/", "content": "Experience historic charm & modern amenities at our boutique hotel featuring luxurious accommodations, elegant décor, onsite dining, and event venues in El Paso ."}

data/sampled_jsons/2410.02025_empirical_validation_MNIST_Wasserstein_distance_sparse_architecture_results.jsonl

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+{"idx": 0, "title": "arXiv:2410.02025v1 [math.ST] 2 Oct 2024", "date": "", "ddg_snippet": "Figure 2: MNIST images: real images (left panel), generated images with sparse architecture (central panel), and generated images with fully connected architecture (right panel)", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.02025", "content": "Figure 2: MNIST images: real images (left panel), generated images with sparse architecture (central panel), and generated images with fully connected architecture (right panel)"}
+{"idx": 1, "title": "Empirical Wasserstein distance between MNIST and MNIST with varied ...", "date": "", "ddg_snippet": "Figure 4 compares the empirical Wasserstein distance using orthonomal projection compared to Gaussian random projection and sparse random projection when MNIST is perturbed with additive Gaussian ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/Empirical-Wasserstein-distance-between-MNIST-and-MNIST-with-varied-levels-of-additive_fig3_368687851", "content": "Figure 4 compares the empirical Wasserstein distance using orthonomal projection compared to Gaussian random projection and sparse random projection when MNIST is perturbed with additive Gaussian ..."}
+{"idx": 2, "title": "Empirical Wasserstein distance between MNIST and MNIST with varied ...", "date": "", "ddg_snippet": "Download scientific diagram | Empirical Wasserstein distance between MNIST and MNIST with varied levels of additive impulse noise, measured over a range of sample sizes.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/Empirical-Wasserstein-distance-between-MNIST-and-MNIST-with-varied-levels-of-additive_fig4_368687851", "content": "Download scientific diagram | Empirical Wasserstein distance between MNIST and MNIST with varied levels of additive impulse noise, measured over a range of sample sizes."}
+{"idx": 3, "title": "Empirical Wasserstein distance between MNIST and MNIST with varied ...", "date": "", "ddg_snippet": "Empirical Wasserstein distance between MNIST and MNIST with varied levels of Gaussian blur, measured over a range of sample sizes. Curves are taken over 5 trials.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/Empirical-Wasserstein-distance-between-MNIST-and-MNIST-with-varied-levels-of-Gaussian_fig5_368687851", "content": "Empirical Wasserstein distance between MNIST and MNIST with varied levels of Gaussian blur, measured over a range of sample sizes. Curves are taken over 5 trials."}
+{"idx": 4, "title": "PDF Intrinsic Dimension Estimation Using Wasserstein Distance", "date": "", "ddg_snippet": "In the right plot, we pool all of the data to estimate the manifold distances , and then use these estimated distances to compute the Wasserstein distance between the empirical distributions.", "subpage_snippet": "", "source": "jmlr.org", "link": "https://jmlr.org/papers/volume23/21-1483/21-1483.pdf", "content": "In the right plot, we pool all of the data to estimate the manifold distances , and then use these estimated distances to compute the Wasserstein distance between the empirical distributions."}
+{"idx": 5, "title": "PDF Orthogonal Estimation of Wasserstein Distances", "date": "", "ddg_snippet": "In this paper, we investigate these two shortcomings of the sliced Wasserstein distance and propose a new dis-tance which incorporates the computational bene ts of the sliced Wasserstein distance , whilst still retain-ing information from the high-dimensional distances . Given recent strong results based on Wasserstein dis-tances and their tractable approximations, exploring related methods is ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v89/rowland19a/rowland19a.pdf", "content": "In this paper, we investigate these two shortcomings of the sliced Wasserstein distance and propose a new dis-tance which incorporates the computational bene ts of the sliced Wasserstein distance , whilst still retain-ing information from the high-dimensional distances . Given recent strong results based on Wasserstein dis-tances and their tractable approximations, exploring related methods is ..."}
+{"idx": 6, "title": "PDF Smooth p-Wasserstein Distance: Structure, Empirical Approximation, and ...", "date": "", "ddg_snippet": "We adopt the smooth Wasserstein distance as the figure of merit and use the empirical distri-bution ^n as an estimate for . Generative modeling is thus formulated as the following minimum smooth Wasserstein estimation (M-SWE) problem:", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v139/nietert21a/nietert21a.pdf", "content": "We adopt the smooth Wasserstein distance as the figure of merit and use the empirical distri-bution ^n as an estimate for . Generative modeling is thus formulated as the following minimum smooth Wasserstein estimation (M-SWE) problem:"}
+{"idx": 7, "title": "PDF Quantifying the Empirical Wasserstein Distance to a Set of ... - NeurIPS", "date": "", "ddg_snippet": "Abstract We consider the problem of estimating the Wasserstein distance between the empirical measure and a set of probability measures whose expectations over a class of functions (hypothesis class) are constrained.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper/2020/file/f3507289cfdc8c9ae93f4098111a13f9-Paper.pdf", "content": "Abstract We consider the problem of estimating the Wasserstein distance between the empirical measure and a set of probability measures whose expectations over a class of functions (hypothesis class) are constrained."}
+{"idx": 8, "title": "A Likelihood Based Approach to Distribution Regression Using ...", "date": "", "ddg_snippet": "Y 𝑌 Y with respect to the Hellinger distance and specialize the obtained rate for two popular deep neural network classes: the sparse and fully connected network classes. Furthermore, we characterize the Wasserstein convergence rates for the induced intrinsic conditional distribution estimator on the manifold (i.e., a deconvolution problem).", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2410.02025", "content": "Y 𝑌 Y with respect to the Hellinger distance and specialize the obtained rate for two popular deep neural network classes: the sparse and fully connected network classes. Furthermore, we characterize the Wasserstein convergence rates for the induced intrinsic conditional distribution estimator on the manifold (i.e., a deconvolution problem)."}
+{"idx": 9, "title": "ACE and Diverse Generalization via Selective Disagreement", "date": "", "ddg_snippet": "In the main body, we present ACE results uses separate group losses for evenly-balanced datasets (Toy Grid, FMNIST- MNIST , and CIFAR- MNIST ) and a combined group loss for unbalanced datasets (Waterbirds, CelebA, and MultiNLI).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2509.07955", "content": "In the main body, we present ACE results uses separate group losses for evenly-balanced datasets (Toy Grid, FMNIST- MNIST , and CIFAR- MNIST ) and a combined group loss for unbalanced datasets (Waterbirds, CelebA, and MultiNLI)."}

data/sampled_jsons/2502.01846_UVGS_branched_mapping_layers.jsonl

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+{"idx": 0, "title": "UVGS : Reimagining Unstructured 3D Gaussian Splatting using UV...", "date": "", "ddg_snippet": "Branched mapping layers : The rationale behind using branched mapping layers in both forward and reverse map -ping networks is to prevent the incompatibility issues aris-ing due the the different value distribution of 3DGS at-tributes.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.01846", "content": "Branched mapping layers : The rationale behind using branched mapping layers in both forward and reverse map -ping networks is to prevent the incompatibility issues aris-ing due the the different value distribution of 3DGS at-tributes."}
+{"idx": 1, "title": "(PDF) UVGS : Reimagining Unstructured 3D Gaussian Splatting using...", "date": "", "ddg_snippet": "arXiv: 2502 . 01846 v1 [cs.CV] 3 Feb 2025. implicit representation, which makes editing and manipula-. tion challenging. UVGS through branched inverse mapping , which in turn can be reconstructed back to the 3DGS object through inverse spherical mapping .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/388685707_UVGS_Reimagining_Unstructured_3D_Gaussian_Splatting_using_UV_Mapping", "content": "arXiv: 2502 . 01846 v1 [cs.CV] 3 Feb 2025. implicit representation, which makes editing and manipula-. tion challenging. UVGS through branched inverse mapping , which in turn can be reconstructed back to the 3DGS object through inverse spherical mapping ."}
+{"idx": 2, "title": "GitHub - flyingGH/arxiv-daily: Automatically Update CV Papers...", "date": "", "ddg_snippet": "Free360: Layered Gaussian Splatting for Unbounded 360-Degree View Synthesis from Extremely Sparse and Unposed Views. Chong Bao et.al. UVGS : Reimagining Unstructured 3D Gaussian Splatting using UV Mapping .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/flyingGH/arxiv-daily", "content": "Free360: Layered Gaussian Splatting for Unbounded 360-Degree View Synthesis from Extremely Sparse and Unposed Views. Chong Bao et.al. UVGS : Reimagining Unstructured 3D Gaussian Splatting using UV Mapping ."}
+{"idx": 3, "title": "Browse | ArcGIS Living Atlas of the World", "date": "", "ddg_snippet": "The Browse page provides comprehensive search and filtering tools to help find ready to use layers , maps , tools, apps and other items to begin your work.", "subpage_snippet": "", "source": "livingatlas.arcgis.com", "link": "https://livingatlas.arcgis.com/en/browse/", "content": "The Browse page provides comprehensive search and filtering tools to help find ready to use layers , maps , tools, apps and other items to begin your work."}
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data/sampled_jsons/2502.10875_equation_1_FBox_score_definition.jsonl

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+{"idx": 0, "title": "F-score - Wikipedia", "date": "", "ddg_snippet": "It thus symmetrically represents both precision and recall in one metric. The more generic score applies additional weights, valuing one of precision or recall more than the other. The highest possible value of an F-score is 1.0, indicating perfect precision and recall, and the lowest possible value is 0, if the precision or the recall is zero.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/F-score", "content": "It thus symmetrically represents both precision and recall in one metric. The more generic score applies additional weights, valuing one of precision or recall more than the other. The highest possible value of an F-score is 1.0, indicating perfect precision and recall, and the lowest possible value is 0, if the precision or the recall is zero."}
+{"idx": 1, "title": "Accuracy, Precision, Recall, and F1-Score", "date": "", "ddg_snippet": "F1- score : The F1- score is the harmonic mean of precision and recall. It provides a balanced measure of the model's performance by considering both precision and recall.", "subpage_snippet": "", "source": "machinelearning.org.in", "link": "https://machinelearning.org.in/accuracy-precision-recall-and-f1-score/", "content": "F1- score : The F1- score is the harmonic mean of precision and recall. It provides a balanced measure of the model's performance by considering both precision and recall."}
+{"idx": 2, "title": "F-Score Definition | DeepAI", "date": "", "ddg_snippet": "It is possible to adjust the F-score to give more importance to precision over recall, or vice-versa. Common adjusted F-scores are the F0.5-score and the F2- score , as well as the standard F1- score . F-score Formula The formula for the standard F1- score is the harmonic mean of the precision and recall. A perfect model has an F-score of 1 .", "subpage_snippet": "", "source": "deepai.org", "link": "https://deepai.org/machine-learning-glossary-and-terms/f-score", "content": "It is possible to adjust the F-score to give more importance to precision over recall, or vice-versa. Common adjusted F-scores are the F0.5-score and the F2- score , as well as the standard F1- score . F-score Formula The formula for the standard F1- score is the harmonic mean of the precision and recall. A perfect model has an F-score of 1 ."}
+{"idx": 3, "title": "What is F-score? Calculation and its Importance - Deepchecks", "date": "", "ddg_snippet": "What is an F-score? The F-score (also known as the F1 score or F-measure) is a metric used to evaluate the performance of a Machine Learning model. It combines precision and recall into a single score . F-measure formula: F-score = 2 * (precision * recall) / (precision + recall) Accuracy in making positive predictions is measured by a recall, while identifying all positive occurrences in the ...", "subpage_snippet": "", "source": "www.deepchecks.com", "link": "https://www.deepchecks.com/glossary/f-score/", "content": "What is an F-score? The F-score (also known as the F1 score or F-measure) is a metric used to evaluate the performance of a Machine Learning model. It combines precision and recall into a single score . F-measure formula: F-score = 2 * (precision * recall) / (precision + recall) Accuracy in making positive predictions is measured by a recall, while identifying all positive occurrences in the ..."}
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data/sampled_jsons/2503.16979_Equation_(6)_weighted_interpolation_motion.jsonl

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+{"idx": 0, "title": "Microsoft Word - Thesis_Stefan_Talke_submitted.doc", "date": "", "ddg_snippet": "The “ weighted interpolation ” method clearly outperforms the “best fit extrapolation” method for both the Soulsby. 124.", "subpage_snippet": "", "source": "depts.washington.edu", "link": "https://depts.washington.edu/uwefm/publications/Talke_dissertation2005.pdf", "content": "The “ weighted interpolation ” method clearly outperforms the “best fit extrapolation” method for both the Soulsby. 124."}
+{"idx": 1, "title": "IEEE Std 141-1993 (Revision of IEEE Std 141-1986) IEEE...", "date": "", "ddg_snippet": "The weighted interpolation has signiÞcance only for the short circuit at F1.An examination of equation ( 6 ) shows that it is only necessary to know the system reactance and the capacitor rating to predict the voltage change due to the change in reactive power.", "subpage_snippet": "", "source": "dl.booksee.org", "link": "https://dl.booksee.org/genesis/207000/e456c56c8bf29ef99adb84a7b2688600/_as/[IEEE]_IEEE_Std_141-1993,_IEEE_Recommended_Practic(BookSee.org).pdf", "content": "The weighted interpolation has signiÞcance only for the short circuit at F1.An examination of equation ( 6 ) shows that it is only necessary to know the system reactance and the capacitor rating to predict the voltage change due to the change in reactive power."}

data/sampled_jsons/2503.16979v1_Equation_6_motion_feature_z_i.jsonl

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+{"idx": 0, "title": "Monocular Dynamic Gaussian Splatting Is Fast and Brittle ...", "date": "", "ddg_snippet": "This document discusses the challenges and advancements in monocular dynamic view synthesis using Gaussian splatting methods, highlighting the lack of ...", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/872613140/2412-04457v1", "content": "This document discusses the challenges and advancements in monocular dynamic view synthesis using Gaussian splatting methods, highlighting the lack of ..."}

data/sampled_jsons/2504.11786_stage_1_total_loss_disease-matching_-patent.jsonl

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+{"idx": 0, "title": "DART: Disease -aware Image-Text Alignment and Self-correcting...", "date": "", "ddg_snippet": "1 . In the first stage , we introduce a disease-aware report generation model to generate reports from input images by leveraging image-to-text retrieval with a disease - matching constraint, ensuring that the generated reports accurately reflect disease-relevant findings.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.11786v1", "content": "1 . In the first stage , we introduce a disease-aware report generation model to generate reports from input images by leveraging image-to-text retrieval with a disease - matching constraint, ensuring that the generated reports accurately reflect disease-relevant findings."}
+{"idx": 1, "title": "(PDF) DART: Disease -aware Image-Text Alignment and...", "date": "", "ddg_snippet": "DOI:10.48550/arXiv. 2504 . 11786 .loss can be found in the supplementary materials. In the first stage , we minimize a total loss Lstage 1 , which. consists of the contrastive loss, the disease - matching con-. straint, the classification loss, the generation loss to gener", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/390845711_DART_Disease-aware_Image-Text_Alignment_and_Self-correcting_Re-alignment_for_Trustworthy_Radiology_Report_Generation", "content": "DOI:10.48550/arXiv. 2504 . 11786 .loss can be found in the supplementary materials. In the first stage , we minimize a total loss Lstage 1 , which. consists of the contrastive loss, the disease - matching con-. straint, the classification loss, the generation loss to gener"}
+{"idx": 2, "title": "DART | PDF | Artificial Intelligence", "date": "", "ddg_snippet": "... disease-matching constraint and then refines these reports by re ... ding space using the text encoder trained in stage 1 , ob- IU X-ray.", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/876841025/DART", "content": "... disease-matching constraint and then refines these reports by re ... ding space using the text encoder trained in stage 1 , ob- IU X-ray."}
+{"idx": 3, "title": "wedm2401, gjrmstn1440, dongheeshin, yhson135, meeeo , kamte ...", "date": "", "ddg_snippet": "The disease-matching constraint ensures that the retrieved re- ports align more closely with the disease-relevant findings of the input images, resulting in more accurate and clini- cally coherent generated reports.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2504.11786", "content": "The disease-matching constraint ensures that the retrieved re- ports align more closely with the disease-relevant findings of the input images, resulting in more accurate and clini- cally coherent generated reports."}

data/sampled_jsons/26JsumCG0z_The_Value_of_Prediction_in_Identifying_the_Worst-Off_theoretical_discussion_screening_cap.jsonl

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+{"idx": 0, "title": "Explaining Anxiety Prediction in Psychotherapy Transcripts ...", "date": "", "ddg_snippet": "Sep 1, 2025 · Request PDF | On Sep 1, 2025, Tobias Steinbrenner and others published Explaining Anxiety Prediction in Psychotherapy Transcripts: The Role of Patient Linguistic Features and Theoretical ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/395496975_Explaining_Anxiety_Prediction_in_Psychotherapy_Transcripts_The_Role_of_Patient_Linguistic_Features_and_Theoretical_Constructs", "content": "Sep 1, 2025 · Request PDF | On Sep 1, 2025, Tobias Steinbrenner and others published Explaining Anxiety Prediction in Psychotherapy Transcripts: The Role of Patient Linguistic Features and Theoretical ..."}
+{"idx": 1, "title": "AI and the Future of Academic Peer Review - arXiv.org", "date": "", "ddg_snippet": "These outputs suggest that statistical prediction does not preclude innovation, particularly when models are scaled and equipped with reasoning strategies such as chain-of-thought prompting or tool integration.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2509.14189", "content": "These outputs suggest that statistical prediction does not preclude innovation, particularly when models are scaled and equipped with reasoning strategies such as chain-of-thought prompting or tool integration."}
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+{"idx": 4, "title": "Daily Papers - Hugging Face", "date": "", "ddg_snippet": "Instead of assuming full prediction coverage that robust planners require, we propose to make prediction itself risk-aware. We introduce a new prediction objective to learn a risk-biased distribution over trajectories, so that risk evaluation simplifies to an expected cost estimation under this biased distribution.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=patient+risk+trajectory+prediction", "content": "Instead of assuming full prediction coverage that robust planners require, we propose to make prediction itself risk-aware. We introduce a new prediction objective to learn a risk-biased distribution over trajectories, so that risk evaluation simplifies to an expected cost estimation under this biased distribution."}
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data/sampled_jsons/32867_Descriptor-In-Pixel_-_Point-Feature_Tracking_For_Pixel_Processor_Arrays.jsonl

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+{"idx": 0, "title": "CVPR Poster Descriptor - In - Pixel : Point - Feature Tracking For Pixel ...", "date": "", "ddg_snippet": "This paper presents a novel approach for joint point - feature detection and tracking , specifically designed for Pixel Processor Array sensors (PPA).", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/poster/32867", "content": "This paper presents a novel approach for joint point - feature detection and tracking , specifically designed for Pixel Processor Array sensors (PPA)."}
+{"idx": 1, "title": "Descriptor - In - Pixel : Point - Feature Tracking for Pixel Processor ...", "date": "", "ddg_snippet": "This paper presents a novel approach for joint point - feature detection and tracking , designed specifically for Pixel Processor Array (PPA) vision sensors.", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/11092646/", "content": "This paper presents a novel approach for joint point - feature detection and tracking , designed specifically for Pixel Processor Array (PPA) vision sensors."}
+{"idx": 2, "title": "Descriptor - In - Pixel : Point - Feature Tracking For Pixel Processor ...", "date": "", "ddg_snippet": "Bose_ Descriptor - In - Pixel __ Point - Feature _ Tracking _ For _ Pixel _ Processor _ Arrays @CVPR2025@CVF.This paper presents a novel approach for joint point - feature detection and tracking , specifically designed for Pixel Processor Array sensors (PPA).", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/venue/Bose_Descriptor-In-Pixel__Point-Feature_Tracking_For_Pixel_Processor_Arrays@CVPR2025@CVF", "content": "Bose_ Descriptor - In - Pixel __ Point - Feature _ Tracking _ For _ Pixel _ Processor _ Arrays @CVPR2025@CVF.This paper presents a novel approach for joint point - feature detection and tracking , specifically designed for Pixel Processor Array sensors (PPA)."}
+{"idx": 3, "title": "Descriptor - In _ Pixel", "date": "", "ddg_snippet": "Descriptor - In - Pixel : Point - Feature Tracking for Pixel Processor Arrays . CVPR 2025 Best Paper Award Candidate. Point - Feature detection and tracking at thousands of frames-per-second, using ~1Watt of power.", "subpage_snippet": "", "source": "lauriebose.github.io", "link": "https://lauriebose.github.io/DIP/", "content": "Descriptor - In - Pixel : Point - Feature Tracking for Pixel Processor Arrays . CVPR 2025 Best Paper Award Candidate. Point - Feature detection and tracking at thousands of frames-per-second, using ~1Watt of power."}
+{"idx": 4, "title": "GitHub - wangxiao5791509/Single_Object_ Tracking _Paper_List...", "date": "", "ddg_snippet": "Descriptor - In - Pixel : Point - Feature Tracking For Pixel Processor Arrays Laurie Bose · Piotr Dudek · Jianing Chen.DINO- Tracker : Taming DINO for Self-Supervised Point Tracking in a Single Video, Narek Tumanyan*, Assaf Singer, Shai Bagon, Tali Dekel.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/wangxiao5791509/Single_Object_Tracking_Paper_List", "content": "Descriptor - In - Pixel : Point - Feature Tracking For Pixel Processor Arrays Laurie Bose · Piotr Dudek · Jianing Chen.DINO- Tracker : Taming DINO for Self-Supervised Point Tracking in a Single Video, Narek Tumanyan*, Assaf Singer, Shai Bagon, Tali Dekel."}
+{"idx": 5, "title": "Mapping Image Transformations Onto Pixel Processor Arrays", "date": "", "ddg_snippet": "Pixel Processor Arrays (PPA) present a new vision sensor/ processor architecture consisting of a SIMD array of processor elements, each capable of light capture, storage, processing and local communication.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.16994v1", "content": "Pixel Processor Arrays (PPA) present a new vision sensor/ processor architecture consisting of a SIMD array of processor elements, each capable of light capture, storage, processing and local communication."}
+{"idx": 6, "title": "Fully Embedding Fast Convolutional Networks on Pixel Processor ...", "date": "", "ddg_snippet": "We present a novel method of CNN inference for pixel processor array (PPA) vision sensors, designed to take advantage of their massive parallelism and analog compute capabilities.", "subpage_snippet": "", "source": "deepai.org", "link": "https://deepai.org/publication/fully-embedding-fast-convolutional-networks-on-pixel-processor-arrays", "content": "We present a novel method of CNN inference for pixel processor array (PPA) vision sensors, designed to take advantage of their massive parallelism and analog compute capabilities."}
+{"idx": 7, "title": "(PDF) BRISK: Binary Robust invariant scalable keypoints", "date": "", "ddg_snippet": "Descriptor - In - Pixel : Point - Feature Tracking for Pixel Processor Arrays .In this paper, we present a novel scale- and rotation-invariant interest point detector and descriptor, coined SURF (Speeded Up Robust Features).", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/221110715_BRISK_Binary_Robust_invariant_scalable_keypoints", "content": "Descriptor - In - Pixel : Point - Feature Tracking for Pixel Processor Arrays .In this paper, we present a novel scale- and rotation-invariant interest point detector and descriptor, coined SURF (Speeded Up Robust Features)."}
+{"idx": 8, "title": "Descriptor In Pixel : Point Feature Tracking for Pixel Processor ...", "date": "", "ddg_snippet": "О сервисе Прессе Авторские права Связаться с нами Авторам Рекламодателям...", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=QDucNhl8ir8", "content": "О сервисе Прессе Авторские права Связаться с нами Авторам Рекламодателям..."}
+{"idx": 9, "title": "Descriptor - In - Pixel : Point - Feature Tracking For Pixel Processor ...", "date": "", "ddg_snippet": "If you are attendinding #cvpr2025 and interested in next-generation visual perception, check out the award-nominated latest paper by Laurie Bose Jianing Chen and Piotr Dudek.", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/posts/walterio-mayol-cuevas_descriptor-in-pixel-point-feature-tracking-activity-7337315353846235137-Uo0o", "content": "If you are attendinding #cvpr2025 and interested in next-generation visual perception, check out the award-nominated latest paper by Laurie Bose Jianing Chen and Piotr Dudek."}

data/sampled_jsons/32867_Descriptor-In-Pixel_Figure_8_Translate_motion_reliability_FAST_tracking_explanation.jsonl

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+{"idx": 4, "title": "Bose Descriptor-In-Pixel Point-Feature Tracking For Pixel ...", "date": "", "ddg_snippet": "This paper introduces a novel method for point-feature detection and tracking using Pixel Processor Array (PPA) vision sensors, which enables in- pixel computation and significantly reduces data transfer requirements. The proposed Descriptor-In-Pixel paradigm allows for efficient processing at over 3000 FPS, making it suitable for high-speed applications while maintaining low latency. By ...", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/890539401/Bose-Descriptor-In-Pixel-Point-Feature-Tracking-for-Pixel-Processor-Arrays-CVPR-2025-Paper", "content": "This paper introduces a novel method for point-feature detection and tracking using Pixel Processor Array (PPA) vision sensors, which enables in- pixel computation and significantly reduces data transfer requirements. The proposed Descriptor-In-Pixel paradigm allows for efficient processing at over 3000 FPS, making it suitable for high-speed applications while maintaining low latency. By ..."}
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+{"idx": 6, "title": "CS231M · Mobile Computer Vision - Stanford University", "date": "", "ddg_snippet": "Motion estimation techniques Optical flow Recover image motion at each pixel from spatio-temporal image brightness variations (optical flow) Feature- tracking Extract visual features (corners, textured areas) and “track” them over multiple frames", "subpage_snippet": "", "source": "web.stanford.edu", "link": "https://web.stanford.edu/class/cs231m/lectures/lecture-7-optical-flow.pdf", "content": "Motion estimation techniques Optical flow Recover image motion at each pixel from spatio-temporal image brightness variations (optical flow) Feature- tracking Extract visual features (corners, textured areas) and “track” them over multiple frames"}
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data/sampled_jsons/33775_Instant_Gaussian_Stream-_Fast_and_Generalizable_Streaming_of_Dynamic_Scene_Reconstruction_via_.jsonl

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+{"idx": 4, "title": "Instant Gaussian Stream: Fast and Generalizable Streaming of Dynamic ...", "date": "", "ddg_snippet": "This paper proposes Instant Gaussian Stream (IGS), a fast and generalizable streaming framework, which introduces a generalized Anchor-driven Gaussian Motion Network, which projects multi-view 2D motion features into 3D space, using anchor points to drive the motion of all Gaussians . Building Free-Viewpoint Videos in a streaming manner offers the advantage of rapid responsiveness compared to ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Instant-Gaussian-Stream:-Fast-and-Generalizable-of-Yan-Peng/b1554873f9eb74b1fe602a534cde1cce6cb298bd", "content": "This paper proposes Instant Gaussian Stream (IGS), a fast and generalizable streaming framework, which introduces a generalized Anchor-driven Gaussian Motion Network, which projects multi-view 2D motion features into 3D space, using anchor points to drive the motion of all Gaussians . Building Free-Viewpoint Videos in a streaming manner offers the advantage of rapid responsiveness compared to ..."}
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+{"idx": 7, "title": "CVPR 2025 Open Access Repository", "date": "", "ddg_snippet": "Instant Gaussian Stream : Fast and Generalizable Streaming of Dynamic Scene Reconstruction via Gaussian Splatting Jinbo Yan, Rui Peng, Zhiyan Wang, Luyang Tang, Jiayu Yang, Jie Liang, Jiahao Wu, Ronggang Wang; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2025, pp. 16520-16531", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/html/Yan_Instant_Gaussian_Stream_Fast_and_Generalizable_Streaming_of_Dynamic_Scene_CVPR_2025_paper.html", "content": "Instant Gaussian Stream : Fast and Generalizable Streaming of Dynamic Scene Reconstruction via Gaussian Splatting Jinbo Yan, Rui Peng, Zhiyan Wang, Luyang Tang, Jiayu Yang, Jie Liang, Jiahao Wu, Ronggang Wang; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2025, pp. 16520-16531"}
+{"idx": 8, "title": "IGS/README.md at master · yjb6/IGS · GitHub", "date": "", "ddg_snippet": "This repository contains the official authors implementation associated with the paper: Instant Gaussian Stream : Fast and Generalizable Streaming of Dynamic Scene Reconstruction via Gaussian Splatting", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/yjb6/IGS/blob/master/README.md", "content": "This repository contains the official authors implementation associated with the paper: Instant Gaussian Stream : Fast and Generalizable Streaming of Dynamic Scene Reconstruction via Gaussian Splatting"}
+{"idx": 9, "title": "arXiv:2503.16979v1 [cs.CV] 21 Mar 2025", "date": "", "ddg_snippet": "With advancements in real-time rendering and high-quality view synthesis powered by 3D Gaussian Splatting (3DGS)[26], dynamic scene reconstruction has seen rapid progress. Some ofline training methods[23, 31, 66, 70, 73, 75] achieve high-quality view synthesis but require collect-ing all frames before training can begin. This limitation makes them less suitable for scenarios that demand fast ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.16979", "content": "With advancements in real-time rendering and high-quality view synthesis powered by 3D Gaussian Splatting (3DGS)[26], dynamic scene reconstruction has seen rapid progress. Some ofline training methods[23, 31, 66, 70, 73, 75] achieve high-quality view synthesis but require collect-ing all frames before training can begin. This limitation makes them less suitable for scenarios that demand fast ..."}

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data/sampled_jsons/4AmFA0qNQ2_Long-Form_Speech_Generation_with_Spoken_Language_Models_Experimental_Setup_training_seque.jsonl

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+{"idx": 7, "title": "Convert Centimeters to Feet - Length Unit Converter", "date": "", "ddg_snippet": "Free online centimeters to feet converter . Quick and easy length unit conversion calculator. Convert between length units of measurement.", "subpage_snippet": "", "source": "www.unitconverters.app", "link": "https://www.unitconverters.app/length/cm-to-feet.htm", "content": "Free online centimeters to feet converter . Quick and easy length unit conversion calculator. Convert between length units of measurement."}
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data/sampled_jsons/4HQaMUYWAT_reasoning_mapping_formula_Frsn(X)_key_34_anchor_11_14_target_59.jsonl

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+{"idx": 0, "title": "FormulaReasoning: A Dataset for Formula-Based Numerical Reasoning Mathematical reasoning Formula: Explanation and Solved Examples Numerical Reasoning Formulas You Need To Know To Pass Quantitative Reasoning Formula Sheet Flashcards | Quizlet Mathematical-reasoning Formula Sheet - Doubtlet Solve - Step-by-Step Math Problem Solver NUMERICAL REASONING FORMULA SHEET - AssessmentDay", "date": "", "ddg_snippet": "Feb 20, 2024 · The application of formulas (e.g., physics formulas) is a fundamental ability of humans when solving numerical reasoning problems. Existing numerical reasoning datasets seldom explicitly indicate the formulas employed in reasoning , as their questions rely on implicit commonsense mathematical knowledge. In contrast, in this paper, we introduce FormulaReasoning, a new dataset specifically ... Sep 13, 2023 · Mathematical reasoning Formula is an essential skill that empowers students to assess a given hypothesis devoid of any specific context or interpretation. Mar 24, 2025 · When it comes to acing numerical reasoning tests or even just improving your everyday mathematical skills, understanding key numerical reasoning formulas is a must. In this article, we’ll delve into some of the most important numerical reasoning formulas you’ll need to learn to succeed in 2024 and beyond. Study with Quizlet and memorize flashcards containing terms like Logarithm Rules, exponent rules, conversions and more. May 30, 2024 · Mathematical reasoning is the logical process of concluding based on established mathematical principles, rules, and axioms. It involves analyzing and synthesizing information, identifying patterns, and making deductions or inferences to solve problems or prove mathematical statements. The equations section lets you solve an equation or system of equations. You can usually find the exact answer or, if necessary, a numerical answer to almost any accuracy you require. So, if you are unsure about anything, use the first formula and it will help tell you whether the change is an increase or a decrease. Let’s look at two example questions and you can see both formulas in action (and hopefully you can see which one we would need from the question title). Q: Sales have risen from 130 to 160 in 6 months.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2402.12692", "content": "Feb 20, 2024 · The application of formulas (e.g., physics formulas) is a fundamental ability of humans when solving numerical reasoning problems. Existing numerical reasoning datasets seldom explicitly indicate the formulas employed in reasoning , as their questions rely on implicit commonsense mathematical knowledge. In contrast, in this paper, we introduce FormulaReasoning, a new dataset specifically ... Sep 13, 2023 · Mathematical reasoning Formula is an essential skill that empowers students to assess a given hypothesis devoid of any specific context or interpretation. Mar 24, 2025 · When it comes to acing numerical reasoning tests or even just improving your everyday mathematical skills, understanding key numerical reasoning formulas is a must. In this article, we’ll delve into some of the most important numerical reasoning formulas you’ll need to learn to succeed in 2024 and beyond. Study with Quizlet and memorize flashcards containing terms like Logarithm Rules, exponent rules, conversions and more. May 30, 2024 · Mathematical reasoning is the logical process of concluding based on established mathematical principles, rules, and axioms. It involves analyzing and synthesizing information, identifying patterns, and making deductions or inferences to solve problems or prove mathematical statements. The equations section lets you solve an equation or system of equations. You can usually find the exact answer or, if necessary, a numerical answer to almost any accuracy you require. So, if you are unsure about anything, use the first formula and it will help tell you whether the change is an increase or a decrease. Let’s look at two example questions and you can see both formulas in action (and hopefully you can see which one we would need from the question title). Q: Sales have risen from 130 to 160 in 6 months."}
+{"idx": 1, "title": "Mathematical reasoning Formula: Explanation and Solved Examples Numerical Reasoning Formulas You Need To Know To Pass Quantitative Reasoning Formula Sheet Flashcards | Quizlet Mathematical-reasoning Formula Sheet - Doubtlet Solve - Step-by-Step Math Problem Solver NUMERICAL REASONING FORMULA SHEET - AssessmentDay", "date": "", "ddg_snippet": "Sep 13, 2023 · Mathematical reasoning Formula is an essential skill that empowers students to assess a given hypothesis devoid of any specific context or interpretation. Mar 24, 2025 · When it comes to acing numerical reasoning tests or even just improving your everyday mathematical skills, understanding key numerical reasoning formulas is a must. In this article, we’ll delve into some of the most important numerical reasoning formulas you’ll need to learn to succeed in 2024 and beyond. Study with Quizlet and memorize flashcards containing terms like Logarithm Rules, exponent rules, conversions and more. May 30, 2024 · Mathematical reasoning is the logical process of concluding based on established mathematical principles, rules, and axioms. It involves analyzing and synthesizing information, identifying patterns, and making deductions or inferences to solve problems or prove mathematical statements. The equations section lets you solve an equation or system of equations. You can usually find the exact answer or, if necessary, a numerical answer to almost any accuracy you require. So, if you are unsure about anything, use the first formula and it will help tell you whether the change is an increase or a decrease. Let’s look at two example questions and you can see both formulas in action (and hopefully you can see which one we would need from the question title). Q: Sales have risen from 130 to 160 in 6 months.", "subpage_snippet": "", "source": "www.pw.live", "link": "https://www.pw.live/school-prep/exams/mathematical-reasoning-formula", "content": "Sep 13, 2023 · Mathematical reasoning Formula is an essential skill that empowers students to assess a given hypothesis devoid of any specific context or interpretation. Mar 24, 2025 · When it comes to acing numerical reasoning tests or even just improving your everyday mathematical skills, understanding key numerical reasoning formulas is a must. In this article, we’ll delve into some of the most important numerical reasoning formulas you’ll need to learn to succeed in 2024 and beyond. Study with Quizlet and memorize flashcards containing terms like Logarithm Rules, exponent rules, conversions and more. May 30, 2024 · Mathematical reasoning is the logical process of concluding based on established mathematical principles, rules, and axioms. It involves analyzing and synthesizing information, identifying patterns, and making deductions or inferences to solve problems or prove mathematical statements. The equations section lets you solve an equation or system of equations. You can usually find the exact answer or, if necessary, a numerical answer to almost any accuracy you require. So, if you are unsure about anything, use the first formula and it will help tell you whether the change is an increase or a decrease. Let’s look at two example questions and you can see both formulas in action (and hopefully you can see which one we would need from the question title). Q: Sales have risen from 130 to 160 in 6 months."}
+{"idx": 2, "title": "Numerical Reasoning Formulas You Need To Know To Pass", "date": "", "ddg_snippet": "Mar 24, 2025 · When it comes to acing numerical reasoning tests or even just improving your everyday mathematical skills, understanding key numerical reasoning formulas is a must. In this article, we’ll delve into some of the most important numerical reasoning formulas you’ll need to learn to succeed in 2024 and beyond.", "subpage_snippet": "", "source": "www.practiceaptitudetests.com", "link": "https://www.practiceaptitudetests.com/resources/numerical-reasoning-formulas-you-need-to-know-to-pass-2023/", "content": "Mar 24, 2025 · When it comes to acing numerical reasoning tests or even just improving your everyday mathematical skills, understanding key numerical reasoning formulas is a must. In this article, we’ll delve into some of the most important numerical reasoning formulas you’ll need to learn to succeed in 2024 and beyond."}
+{"idx": 3, "title": "Mathematical-reasoning Formula Sheet - Doubtlet", "date": "", "ddg_snippet": "May 30, 2024 · Mathematical reasoning is the logical process of concluding based on established mathematical principles, rules, and axioms. It involves analyzing and synthesizing information, identifying patterns, and making deductions or inferences to solve problems or prove mathematical statements.", "subpage_snippet": "", "source": "doubtlet.com", "link": "https://doubtlet.com/formula-sheet/mathematical-reasoning/", "content": "May 30, 2024 · Mathematical reasoning is the logical process of concluding based on established mathematical principles, rules, and axioms. It involves analyzing and synthesizing information, identifying patterns, and making deductions or inferences to solve problems or prove mathematical statements."}
+{"idx": 4, "title": "NUMERICAL REASONING FORMULA SHEET - AssessmentDay", "date": "", "ddg_snippet": "So, if you are unsure about anything, use the first formula and it will help tell you whether the change is an increase or a decrease. Let’s look at two example questions and you can see both formulas in action (and hopefully you can see which one we would need from the question title). Q: Sales have risen from 130 to 160 in 6 months.", "subpage_snippet": "", "source": "www.assessmentday.com", "link": "https://www.assessmentday.com/numerical-reasoning-formulas.pdf", "content": "So, if you are unsure about anything, use the first formula and it will help tell you whether the change is an increase or a decrease. Let’s look at two example questions and you can see both formulas in action (and hopefully you can see which one we would need from the question title). Q: Sales have risen from 130 to 160 in 6 months."}
+{"idx": 5, "title": "Quantitative Reasoning Formula Sheet Flashcards | Quizlet", "date": "", "ddg_snippet": "Study with Quizlet and memorize flashcards containing terms like Logarithm Rules, exponent rules, conversions and more.", "subpage_snippet": "", "source": "quizlet.com", "link": "https://quizlet.com/803938812/quantitative-reasoning-formula-sheet-flash-cards/", "content": "Study with Quizlet and memorize flashcards containing terms like Logarithm Rules, exponent rules, conversions and more."}
+{"idx": 6, "title": "Solve - Step-by-Step Math Problem Solver", "date": "", "ddg_snippet": "The equations section lets you solve an equation or system of equations. You can usually find the exact answer or, if necessary, a numerical answer to almost any accuracy you require.", "subpage_snippet": "", "source": "quickmath.com", "link": "https://quickmath.com/", "content": "The equations section lets you solve an equation or system of equations. You can usually find the exact answer or, if necessary, a numerical answer to almost any accuracy you require."}
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+{"idx": 9, "title": "ГДЗ по биологии 7 класс Пасечник - рабочая тетрадь Просвещение...", "date": "", "ddg_snippet": "§ 11 . Классификация покрытосеменных 45 § 12. Класс Двудольные. Семейства Крестоцветные и Розоцветные 47 § 13. Класс Двудольные. Семейства Паслёновые, Мотыльковые (Бобовые) и Сложноцветные (Астровые) 49 § 14 .", "subpage_snippet": "", "source": "gdz.top", "link": "https://gdz.top/7-klass/biologiya/pasechnik-rabochaya-tetrad", "content": "§ 11 . Классификация покрытосеменных 45 § 12. Класс Двудольные. Семейства Крестоцветные и Розоцветные 47 § 13. Класс Двудольные. Семейства Паслёновые, Мотыльковые (Бобовые) и Сложноцветные (Астровые) 49 § 14 ."}

data/sampled_jsons/4tFSKOY2mT_Cross-Verification_module_OmniBench_ablation_study.jsonl

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+{"idx": 0, "title": "What Limits Virtual Agent Application? OmniBench: A Scalable...", "date": "", "ddg_snippet": "May 1, 2025 · The authors' Cross-Verification method cleverly integrates subtask trajectory data with evaluation functions. By leveraging mutual verification between these two types of data, it iteratively optimizes both the synthesized trajectories and the evaluation functions simultaneously.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=4tFSKOY2mT", "content": "May 1, 2025 · The authors' Cross-Verification method cleverly integrates subtask trajectory data with evaluation functions. By leveraging mutual verification between these two types of data, it iteratively optimizes both the synthesized trajectories and the evaluation functions simultaneously."}
+{"idx": 1, "title": "OmniBench：重新定义虚拟代理评估的多维基准测试框架_omnieval-CSDN博...", "date": "", "ddg_snippet": "Jul 4, 2025 · 2. OmniBench ：突破性的自生成基准框架 为解决这些挑战，来自浙江大学、蚂蚁集团等机构的研究团队提出了 OmniBench ——一个基于图结构的自生成、跨平台基准测试框架，通过子任务组合自动合成可控复杂度的任务。", "subpage_snippet": "", "source": "blog.csdn.net", "link": "https://blog.csdn.net/qq_42540492/article/details/149117606", "content": "Jul 4, 2025 · 2. OmniBench ：突破性的自生成基准框架 为解决这些挑战，来自浙江大学、蚂蚁集团等机构的研究团队提出了 OmniBench ——一个基于图结构的自生成、跨平台基准测试框架，通过子任务组合自动合成可控复杂度的任务。"}
+{"idx": 2, "title": "What Limits Virtual Agent Application? OmniBench: A Scalable ...", "date": "", "ddg_snippet": "For each ablation shown in the table, we sampled 400 task graphs and calculated the average acceptance by three specially trained annotators. The experiment shows that removing any quality control module decreases human acceptance, with the removal of the cross-verification algorithm resulting in the largest drop to 61.2%.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=4tFSKOY2mT", "content": "For each ablation shown in the table, we sampled 400 task graphs and calculated the average acceptance by three specially trained annotators. The experiment shows that removing any quality control module decreases human acceptance, with the removal of the cross-verification algorithm resulting in the largest drop to 61.2%."}
+{"idx": 3, "title": "GitHub - antgroup/OmniBench: [ICML 2025 Oral] This is the ...", "date": "", "ddg_snippet": "OmniBench : A Scalable Multi-Dimensional Benchmark of Essential Virtual Agent Capabilities\". In this work, we introduce OmniBench , a self-generating, graph-based benchmark with an automated pipeline for synthesizing tasks of controllable complexity through subtask composition.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/antgroup/OmniBench", "content": "OmniBench : A Scalable Multi-Dimensional Benchmark of Essential Virtual Agent Capabilities\". In this work, we introduce OmniBench , a self-generating, graph-based benchmark with an automated pipeline for synthesizing tasks of controllable complexity through subtask composition."}
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+{"idx": 5, "title": "[2409.15272] OmniBench: Towards The Future of Universal Omni ...", "date": "", "ddg_snippet": "Sep 23, 2024 · Recent advancements in multimodal large language models (MLLMs) have focused on integrating multiple modalities, yet their ability to simultaneously process and reason across different inputs remains underexplored. We introduce OmniBench , a novel benchmark designed to evaluate models' ability to recognize, interpret, and reason across visual, acoustic, and textual inputs simultaneously. We ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2409.15272", "content": "Sep 23, 2024 · Recent advancements in multimodal large language models (MLLMs) have focused on integrating multiple modalities, yet their ability to simultaneously process and reason across different inputs remains underexplored. We introduce OmniBench , a novel benchmark designed to evaluate models' ability to recognize, interpret, and reason across visual, acoustic, and textual inputs simultaneously. We ..."}
+{"idx": 6, "title": "README.md · m-a-p/OmniBench at main - Hugging Face", "date": "", "ddg_snippet": "The project introduces OmniBench , a novel benchmark designed to rigorously evaluate models' ability to recognize, interpret, and reason across visual, acoustic, and textual inputs simultaneously. We define models capable of such tri-modal processing as omni-language models (OLMs).", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/m-a-p/OmniBench/blob/main/README.md", "content": "The project introduces OmniBench , a novel benchmark designed to rigorously evaluate models' ability to recognize, interpret, and reason across visual, acoustic, and textual inputs simultaneously. We define models capable of such tri-modal processing as omni-language models (OLMs)."}
+{"idx": 7, "title": "What Limits Virtual Agent Application? OmniBench ... | OpenReview", "date": "", "ddg_snippet": "OmniBench : A Scalable Multi-Dimensional Benchmark for Essential Virtual Agent Capabilities. Download PDF.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=4tFSKOY2mT&referrer=[the+profile+of+Yunfei+Li](/profile?id=~Yunfei_Li6)", "content": "OmniBench : A Scalable Multi-Dimensional Benchmark for Essential Virtual Agent Capabilities. Download PDF."}
+{"idx": 8, "title": "Недостатки и отзывы владельцев Lada Vesta SW Cross 2025", "date": "", "ddg_snippet": "LADA Vesta SW Cross позиционировалась производителем как кроссовер нового поколения. Анонсировались новые решения в облике модели, в силовой установке...", "subpage_snippet": "", "source": "autoguruclub.ru", "link": "https://autoguruclub.ru/threads/nedostatki-i-otzyvy-vladelcev-lada-vesta-sw-cross-2025.21034/", "content": "LADA Vesta SW Cross позиционировалась производителем как кроссовер нового поколения. Анонсировались новые решения в облике модели, в силовой установке..."}
+{"idx": 9, "title": "6- ablation - study .ipynb - Colab", "date": "", "ddg_snippet": "Ablation Studies . The gold standard in building complex machine learning models is proving that each constituent part of the model contributes something to the proposed solution.from sklearn.model_selection import cross _val_score.", "subpage_snippet": "", "source": "colab.research.google.com", "link": "https://colab.research.google.com/github/jesperdramsch/ml-for-science-reproducibility-tutorial/blob/main/book/notebooks/6-ablation-study.ipynb", "content": "Ablation Studies . The gold standard in building complex machine learning models is proving that each constituent part of the model contributes something to the proposed solution.from sklearn.model_selection import cross _val_score."}

data/sampled_jsons/4uOEiitySn_A_Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment_Dike_Eris.jsonl

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+{"idx": 1, "title": "Does Straco Still Operate A Giant Observation Wheel For Sale ...", "date": "", "ddg_snippet": "Découvrez les meilleurs emplois en tant que does straco still operate a giant observation wheel for sale in dubai today sur CareerBeacon.com! Commencez votre recherche des incroyables enterprises et trouvez un meilleur travail, dès aujourd'hui.", "subpage_snippet": "", "source": "www.careerbeacon.com", "link": "https://www.careerbeacon.com/fr/rechercher/does-straco-still-operate-a-giant-observation-wheel-for-sale-in-dubai-today-jobs", "content": "Découvrez les meilleurs emplois en tant que does straco still operate a giant observation wheel for sale in dubai today sur CareerBeacon.com! Commencez votre recherche des incroyables enterprises et trouvez un meilleur travail, dès aujourd'hui."}
+{"idx": 2, "title": "Does Straco Still Operate a Giant Observation Wheel for Sale ...", "date": "", "ddg_snippet": "Check out our does straco still operate a giant observation wheel for sale in orange selection for the very best in unique or custom, handmade pieces from our shops.", "subpage_snippet": "", "source": "www.etsy.com", "link": "https://www.etsy.com/ca/market/does_straco_still_operate_a_giant_observation_wheel_for_sale_in_orange", "content": "Check out our does straco still operate a giant observation wheel for sale in orange selection for the very best in unique or custom, handmade pieces from our shops."}
+{"idx": 3, "title": "A Checks - and - Balances Framework for Context - Aware Ethical AI ...", "date": "", "ddg_snippet": "This work introduces a checks - and - balances framework for ethical AI behavior. By delineating the responsibilities: LLM (executive), Dike (legislative), and Eris (judicial), the framework enables robust ethical oversight while...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.00136", "content": "This work introduces a checks - and - balances framework for ethical AI behavior. By delineating the responsibilities: LLM (executive), Dike (legislative), and Eris (judicial), the framework enables robust ethical oversight while..."}
+{"idx": 4, "title": "(PDF) Checks - and - Balances Framework for Context - Aware Ethical ...", "date": "", "ddg_snippet": "This paper introduces a checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by three-branch governmental systems.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/380515639_Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment", "content": "This paper introduces a checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by three-branch governmental systems."}
+{"idx": 5, "title": "A Three-Branch Checks - and - Balances Framework", "date": "", "ddg_snippet": "ERIS : Adversarial In- Context Review to Balance Ethics and Cultural Norms.This work presents a three-branch framework for ethical AI behavior, inspired by governmental checks and balances , centered on the DIKE - ERIS duality.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=o2afWIxjKD", "content": "ERIS : Adversarial In- Context Review to Balance Ethics and Cultural Norms.This work presents a three-branch framework for ethical AI behavior, inspired by governmental checks and balances , centered on the DIKE - ERIS duality."}
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data/sampled_jsons/4uOEiitySn_A_Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment_Figure_4a_DIKE_GPT.jsonl

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+{"idx": 4, "title": "A Three-Branch Checks-and-Balances Frameworkfor Context-Aware Ethical ...", "date": "", "ddg_snippet": "Conclusion This checks - and - balances approach offers a promising direction for building more ethically- aware AI systems. The framework's ability to handle cultural differences while maintaining ethical standards could help develop AI systems that work responsibly across global contexts .", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/three-branch-checks-balances-frameworkfor-context-aware", "content": "Conclusion This checks - and - balances approach offers a promising direction for building more ethically- aware AI systems. The framework's ability to handle cultural differences while maintaining ethical standards could help develop AI systems that work responsibly across global contexts ."}
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data/sampled_jsons/9m87e9Keq1_RL_Incorrect_Synthetic_Data_Scales_the_Efficiency_of_LLM_Math_Reasoning_Figure_2(a)_2(b)_.jsonl

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+{"idx": 0, "title": "(PDF) RL on Incorrect Synthetic Data Scales the Efficiency of LLM ...", "date": "", "ddg_snippet": "rejection finetuning ( RFT ; positive self-generated synthetic data from the SFT m odel) and step-level RL (via per-step DPO) algorithms. SFT on synthetic problems and res ponses by 2 x, whereas using step-level RL with negativ e data scales the efficiency by 8x.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/381604579_RL_on_Incorrect_Synthetic_Data_Scales_the_Efficiency_of_LLM_Math_Reasoning_by_Eight-Fold", "content": "rejection finetuning ( RFT ; positive self-generated synthetic data from the SFT m odel) and step-level RL (via per-step DPO) algorithms. SFT on synthetic problems and res ponses by 2 x, whereas using step-level RL with negativ e data scales the efficiency by 8x."}
+{"idx": 1, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM ...", "date": "", "ddg_snippet": "To provide clarity on how synthetic data contributes to performance , we aim to understand its impact on LLM capabilities via a study on math reasoning , a prevalent scenario where synthetic data is used. Typically, in this setting, synthetic data corresponds to correct or positive model-generated responses for a novel set of initial problems synthesized by prompting capable models [29, 31]. The ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=9m87e9Keq1", "content": "To provide clarity on how synthetic data contributes to performance , we aim to understand its impact on LLM capabilities via a study on math reasoning , a prevalent scenario where synthetic data is used. Typically, in this setting, synthetic data corresponds to correct or positive model-generated responses for a novel set of initial problems synthesized by prompting capable models [29, 31]. The ..."}
+{"idx": 2, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM ... RL on Incorrect Synthetic Data · MinWoo Park Reinforcement Learning for LLM Reasoning RL on Incorrect Synthetic Data Scales the Efficiency of LLM ... RL on Incorrect Synthetic Data Scales the Efficiency of LLM ... Reinforcement Learning for LLM Reasoning [2406.14532] RL on Incorrect Synthetic Data Scales the Efficiency of LL… Reinforcement Learning for LLM Reasoning dblp: RL on Incorrect Synthetic Data Scales the Efficiency of ...", "date": "", "ddg_snippet": "Jun 20, 2024 · Training on model-generated synthetic data is a promising approach for finetuning LLMs, but it remains unclear when it helps or hurts. In this paper, we investigate this question for math reasoning via an empirical study, followed by building a conceptual understanding of our observations. First, we find that while the typical approach of finetuning a model on synthetic correct or positive ... Jun 20, 2024 · First, we find that while the typical approach of finetuning a model on synthetic correct or positive problem-solution pairs generated by capable models offers modest performance gains, sampling more correct solutions from the finetuned learner itself followed by subsequent fine-tuning on this self-generated data doubles the efficiency of the ... RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold. Setlur, Garg, Geng, Garg, Smith, Kumar. NeurIPS 2024 Rewarding Progress: Scaling up Automated Process Supervision for LLM Reasoning Jun 20, 2024 · With this per-step scheme, we are able to attain consistent gains over only positive data , attaining performance similar to amplifying the amount of synthetic data by $\\mathbf {8 \\times}$. First, we find that while the typical approach of finetuning a model on synthetic correct or positive problem-solution pairs generated by capable models offers modest performance gains, sampling more correct solutions from the finetuned learner itself followed by subsequent fine-tuning on this self-generated data doubles the efficiency of the ... Does training with RL improve efficiency of learning compared to LLMs? Real-world is stochastic, this is little data compared to LLMs! Takeaway: Training with RL can help improve efficiency of learning! Summary: Still the old recipes and RL ideas are helpful! RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold. Setlur, Garg, Geng, Garg, Smith, Kumar. NeurIPS 2024 Does training on model-generated synthetic data help or hurt math reasoning? Training on model-generated synthetic data is a promising approach for finetuning LLMs, but it remains unclear when it helps or hurts. In this paper, we investigate this question for math reasoning via an empirical study, followed by building a conceptual understanding of our observations. Is LLM test-time compute more effective than scaling model parameters? Snell et al. Scaling LLM Test-Time Compute Optimally can be more Effective than Scaling Model Parameters. ICLR 2025 (Oral). Snell et al. Scaling LLM Test-Time Compute Optimally can be more Effective than Scaling Model Parameters. ICLR 2025 (Oral). Jul 16, 2024 · Bibliographic details on RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2406.14532", "content": "Jun 20, 2024 · Training on model-generated synthetic data is a promising approach for finetuning LLMs, but it remains unclear when it helps or hurts. In this paper, we investigate this question for math reasoning via an empirical study, followed by building a conceptual understanding of our observations. First, we find that while the typical approach of finetuning a model on synthetic correct or positive ... Jun 20, 2024 · First, we find that while the typical approach of finetuning a model on synthetic correct or positive problem-solution pairs generated by capable models offers modest performance gains, sampling more correct solutions from the finetuned learner itself followed by subsequent fine-tuning on this self-generated data doubles the efficiency of the ... RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold. Setlur, Garg, Geng, Garg, Smith, Kumar. NeurIPS 2024 Rewarding Progress: Scaling up Automated Process Supervision for LLM Reasoning Jun 20, 2024 · With this per-step scheme, we are able to attain consistent gains over only positive data , attaining performance similar to amplifying the amount of synthetic data by $\\mathbf {8 \\times}$. First, we find that while the typical approach of finetuning a model on synthetic correct or positive problem-solution pairs generated by capable models offers modest performance gains, sampling more correct solutions from the finetuned learner itself followed by subsequent fine-tuning on this self-generated data doubles the efficiency of the ... Does training with RL improve efficiency of learning compared to LLMs? Real-world is stochastic, this is little data compared to LLMs! Takeaway: Training with RL can help improve efficiency of learning! Summary: Still the old recipes and RL ideas are helpful! RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold. Setlur, Garg, Geng, Garg, Smith, Kumar. NeurIPS 2024 Does training on model-generated synthetic data help or hurt math reasoning? Training on model-generated synthetic data is a promising approach for finetuning LLMs, but it remains unclear when it helps or hurts. In this paper, we investigate this question for math reasoning via an empirical study, followed by building a conceptual understanding of our observations. Is LLM test-time compute more effective than scaling model parameters? Snell et al. Scaling LLM Test-Time Compute Optimally can be more Effective than Scaling Model Parameters. ICLR 2025 (Oral). Snell et al. Scaling LLM Test-Time Compute Optimally can be more Effective than Scaling Model Parameters. ICLR 2025 (Oral). Jul 16, 2024 · Bibliographic details on RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold."}
+{"idx": 3, "title": "RL on Incorrect Synthetic Data · MinWoo Park", "date": "", "ddg_snippet": "Jun 20, 2024 · First, we find that while the typical approach of finetuning a model on synthetic correct or positive problem-solution pairs generated by capable models offers modest performance gains, sampling more correct solutions from the finetuned learner itself followed by subsequent fine-tuning on this self-generated data doubles the efficiency of the ...", "subpage_snippet": "", "source": "dsdanielpark.github.io", "link": "https://dsdanielpark.github.io/llm/2024-06-25-RLonIncorrectSyntheticData.html", "content": "Jun 20, 2024 · First, we find that while the typical approach of finetuning a model on synthetic correct or positive problem-solution pairs generated by capable models offers modest performance gains, sampling more correct solutions from the finetuned learner itself followed by subsequent fine-tuning on this self-generated data doubles the efficiency of the ..."}
+{"idx": 4, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM ...", "date": "", "ddg_snippet": "First, we find that while the typical approach of finetuning a model on synthetic correct or positive problem-solution pairs generated by capable models offers modest performance gains, sampling more correct solutions from the finetuned learner itself followed by subsequent fine-tuning on this self-generated data doubles the efficiency of the ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/hash/4b77d5b896c321a29277524a98a50215-Abstract-Conference.html", "content": "First, we find that while the typical approach of finetuning a model on synthetic correct or positive problem-solution pairs generated by capable models offers modest performance gains, sampling more correct solutions from the finetuned learner itself followed by subsequent fine-tuning on this self-generated data doubles the efficiency of the ..."}
+{"idx": 5, "title": "dblp: RL on Incorrect Synthetic Data Scales the Efficiency of ...", "date": "", "ddg_snippet": "Jul 16, 2024 · Bibliographic details on RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold.", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/journals/corr/abs-2406-14532", "content": "Jul 16, 2024 · Bibliographic details on RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold."}
+{"idx": 6, "title": "RL on Incorrect Synthetic Data Scales", "date": "", "ddg_snippet": "Implementation Details. Negative Data Identifies Spurious Steps with Advantage Estimates. RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/4b77d5b896c321a29277524a98a50215-Paper-Conference.pdf", "content": "Implementation Details. Negative Data Identifies Spurious Steps with Advantage Estimates. RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold."}
+{"idx": 7, "title": "RL on Incorrect Synthetic Data Scales the", "date": "", "ddg_snippet": "• When positive data from 𝜋 sft contains spurious steps, scaling synthetic data leads to worse test errors. 7. RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2406.14532", "content": "• When positive data from 𝜋 sft contains spurious steps, scaling synthetic data leads to worse test errors. 7. RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold."}
+{"idx": 8, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math ...", "date": "", "ddg_snippet": "Training on model-generated synthetic data is a promising approach for finetuning LLMs, but it remains unclear when it helps or hurts. In this paper, we investigate this question for math reasoning via an empirical study, followed by building a conceptual understanding of our observations.", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/article/RL-on-Incorrect-Synthetic-Data-Scales-the-Efficiency-of-LLM-Math-Reasoning-by-Eight-Fold-17552ae8-02cf-4931-9351-04c6f7b243c5", "content": "Training on model-generated synthetic data is a promising approach for finetuning LLMs, but it remains unclear when it helps or hurts. In this paper, we investigate this question for math reasoning via an empirical study, followed by building a conceptual understanding of our observations."}
+{"idx": 9, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math ...", "date": "", "ddg_snippet": "This paper investigates the use of reinforcement learning ( RL ) on incorrect synthetic data to improve the math reasoning abilities of large language models (LLMs).", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/rl-incorrect-synthetic-data-scales-efficiency-llm", "content": "This paper investigates the use of reinforcement learning ( RL ) on incorrect synthetic data to improve the math reasoning abilities of large language models (LLMs)."}

data/sampled_jsons/9v1eW8HgMU_Archetypal_SAE_Section_6_Scaling_Archetypal-SAE_relaxation_term_norm_constraint.jsonl

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+{"idx": 0, "title": "Archetypal SAE: Adaptive and Stable Dictionary Learning for Concept ...", "date": "", "ddg_snippet": "To enable a controlled degree of flexibility beyond conv(C), we introduce a mild relaxation term Λ ∈Rk×d, a matrix of the same dimensions as the dictionary, with a small norm constraint ||Λ||2 2≤δ.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=9v1eW8HgMU", "content": "To enable a controlled degree of flexibility beyond conv(C), we introduce a mild relaxation term Λ ∈Rk×d, a matrix of the same dimensions as the dictionary, with a small norm constraint ||Λ||2 2≤δ."}
+{"idx": 1, "title": "Archetypal SAEs: Adaptive and Stable Dictionary Learning for Concept ...", "date": "", "ddg_snippet": "A) Compared to a Regular- SAE , Archetypal-SAEs constrain dictionary atoms (decoder directions) to the data's convex hull, improving stability. A relaxed variant (RA- SAE ) allows mild relaxation , matching standard SAEs in reconstruction while maintaining stability. Both integrate with any SAE variant (e.g., TopK, JumpReLU). B) Instability Problem.", "subpage_snippet": "", "source": "kempnerinstitute.harvard.edu", "link": "https://kempnerinstitute.harvard.edu/research/deeper-learning/archetypal-saes-adaptive-and-stable-dictionary-learning-for-concept-extraction-in-large-vision-models/", "content": "A) Compared to a Regular- SAE , Archetypal-SAEs constrain dictionary atoms (decoder directions) to the data's convex hull, improving stability. A relaxed variant (RA- SAE ) allows mild relaxation , matching standard SAEs in reconstruction while maintaining stability. Both integrate with any SAE variant (e.g., TopK, JumpReLU). B) Instability Problem."}
+{"idx": 2, "title": "[2502.12892] Archetypal SAE: Adaptive and Stable Dictionary Learning ...", "date": "", "ddg_snippet": "Sparse Autoencoders ( SAEs ) have emerged as a powerful framework for machine learning interpretability, enabling the unsupervised decomposition of model representations into a dictionary of abstract, human-interpretable concepts. However, we reveal a fundamental limitation: existing SAEs exhibit severe instability, as identical models trained on similar datasets can produce sharply different ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.12892", "content": "Sparse Autoencoders ( SAEs ) have emerged as a powerful framework for machine learning interpretability, enabling the unsupervised decomposition of model representations into a dictionary of abstract, human-interpretable concepts. However, we reveal a fundamental limitation: existing SAEs exhibit severe instability, as identical models trained on similar datasets can produce sharply different ..."}
+{"idx": 3, "title": "Archetypal - Overcomplete", "date": "", "ddg_snippet": "Archetypal SAE introduces a constraint on the dictionary where each atom is formed as a convex combination of data points with an additional relaxation term . This method enhances stability and interpretability in dictionary learning, making it a robust drop-in replacement for the dictionary layer in any Sparse Autoencoder.", "subpage_snippet": "", "source": "kempnerinstitute.github.io", "link": "https://kempnerinstitute.github.io/overcomplete/saes/archetypal/", "content": "Archetypal SAE introduces a constraint on the dictionary where each atom is formed as a convex combination of data points with an additional relaxation term . This method enhances stability and interpretability in dictionary learning, making it a robust drop-in replacement for the dictionary layer in any Sparse Autoencoder."}
+{"idx": 4, "title": "Archetypal SAEs: Adaptive and Stable Dictionary Learning for ... - Medium", "date": "", "ddg_snippet": "A) Compared to a Regular- SAE , Archetypal-SAEs constrain dictionary atoms (decoder directions) to the data's convex hull, improving stability. A relaxed variant (RA- SAE ) allows mild relaxation ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@kempnerinstitute/archetypal-saes-adaptive-and-stable-dictionary-learning-for-concept-extraction-in-large-vision-acf95010c691", "content": "A) Compared to a Regular- SAE , Archetypal-SAEs constrain dictionary atoms (decoder directions) to the data's convex hull, improving stability. A relaxed variant (RA- SAE ) allows mild relaxation ..."}
+{"idx": 5, "title": "Archetypal SAE: Adaptive and Stable Dictionary Learning for Concept ...", "date": "", "ddg_snippet": "The magnitude of this relaxation enables the Archetypal SAE to achieve performance comparable to the unconstrained TopK SAE denoted as Baseline (left) while maintaining excellent stability (right).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.12892v2", "content": "The magnitude of this relaxation enables the Archetypal SAE to achieve performance comparable to the unconstrained TopK SAE denoted as Baseline (left) while maintaining excellent stability (right)."}
+{"idx": 6, "title": "Archetypal SAE: Adaptive and Stable Dictionary Learning for Concept ...", "date": "", "ddg_snippet": "To enable a controlled degree of flexibility beyond conv (𝑪), we introduce a mild relaxation term 𝚲 ∈ ℝ k × d, a matrix of the same dimensions as the dictionary, with a small norm constraint ‖ 𝚲 ‖ 2 2 ≤ δ.", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2502.12892", "content": "To enable a controlled degree of flexibility beyond conv (𝑪), we introduce a mild relaxation term 𝚲 ∈ ℝ k × d, a matrix of the same dimensions as the dictionary, with a small norm constraint ‖ 𝚲 ‖ 2 2 ≤ δ."}
+{"idx": 7, "title": "[2502.12892] Archetypal SAE: Adaptive and Stable Dictionary Learning ...", "date": "", "ddg_snippet": "To address this issue, we draw inspiration from the Archetypal Analysis framework introduced by Cutler & Breiman (1994) and present Archetypal SAEs (A-SAE), wherein dictionary atoms are constrained to the convex hull of data.", "subpage_snippet": "", "source": "export.arxiv.org", "link": "http://export.arxiv.org/abs/2502.12892", "content": "To address this issue, we draw inspiration from the Archetypal Analysis framework introduced by Cutler & Breiman (1994) and present Archetypal SAEs (A-SAE), wherein dictionary atoms are constrained to the convex hull of data."}
+{"idx": 8, "title": "Section List - SAE Sections - Membership - SAE International", "date": "", "ddg_snippet": "Section List The following is an alphabetical reference guide to assist the user in locating a specific section /group within a state, province or country. Click on the relevant link to view each organization's website. Note: If your section is inactive, please reach out to sections@sae.org for information on restarting a section in your area.", "subpage_snippet": "", "source": "www.sae.org", "link": "https://www.sae.org/participate/membership/sections/list", "content": "Section List The following is an alphabetical reference guide to assist the user in locating a specific section /group within a state, province or country. Click on the relevant link to view each organization's website. Note: If your section is inactive, please reach out to sections@sae.org for information on restarting a section in your area."}
+{"idx": 9, "title": "Daily arXiv Papers - 2025-09-18", "date": "", "ddg_snippet": "We introduce a geometric framework to address this, based on archetypal analysis of batches of responses sampled with only black-box model access. At the global level, we propose Geometric Volume, which measures the convex hull volume of archetypes derived from response embeddings.", "subpage_snippet": "", "source": "blog.yueqianlin.com", "link": "https://blog.yueqianlin.com/daily-publication/250918/", "content": "We introduce a geometric framework to address this, based on archetypal analysis of batches of responses sampled with only black-box model access. At the global level, we propose Geometric Volume, which measures the convex hull volume of archetypes derived from response embeddings."}

data/sampled_jsons/ACIDDnTbSJ_Feint_Behaviors_Strategies_Rew_short_equation_formula.jsonl

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+{"idx": 0, "title": "Feint Behaviors and Strategies: Formalization, Implementation and ...", "date": "", "ddg_snippet": "The Design of Rew_temporal achieves the 3 points discussed in Section 4.2.1 as follows: We use large weighted accumulation of short-term rewards for Feint behaviors and the follow-up high-reward behaviors (the Dual- Behavior model) to address that strong correlation of Feint behaviors and follow-up high-reward behaviors .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=ACIDDnTbSJ", "content": "The Design of Rew_temporal achieves the 3 points discussed in Section 4.2.1 as follows: We use large weighted accumulation of short-term rewards for Feint behaviors and the follow-up high-reward behaviors (the Dual- Behavior model) to address that strong correlation of Feint behaviors and follow-up high-reward behaviors ."}
+{"idx": 1, "title": "[2403.07932v2] Feint Behaviors and Strategies: Formalization ...", "date": "", "ddg_snippet": "Feint behaviors refer to a set of deceptive behaviors in a nuanced manner, which enable players to obtain temporal and spatial advantages over opponents in competitive games. Such behaviors are crucial tactics in most competitive multi-player games (e.g., boxing, fencing, basketball, motor racing, etc.). However, existing literature does not provide a comprehensive (and/or concrete ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2403.07932v2", "content": "Feint behaviors refer to a set of deceptive behaviors in a nuanced manner, which enable players to obtain temporal and spatial advantages over opponents in competitive games. Such behaviors are crucial tactics in most competitive multi-player games (e.g., boxing, fencing, basketball, motor racing, etc.). However, existing literature does not provide a comprehensive (and/or concrete ..."}
+{"idx": 2, "title": "Feint behaviors and strategies: - ACM Digital Library", "date": "", "ddg_snippet": "Such behaviors are crucial tactics in most competitive multi-player games (e.g., boxing, fencing, basketball, motor racing, etc.). However, existing literature does not provide a comprehensive (and/or concrete) formalization for Feint behaviors , and their implications on game strategies .", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.5555/3737916.3738032", "content": "Such behaviors are crucial tactics in most competitive multi-player games (e.g., boxing, fencing, basketball, motor racing, etc.). However, existing literature does not provide a comprehensive (and/or concrete) formalization for Feint behaviors , and their implications on game strategies ."}
+{"idx": 3, "title": "NeurIPS Poster Feint Behaviors and Strategies: Formalization ...", "date": "", "ddg_snippet": "Poster Feint Behaviors and Strategies : Formalization, Implementation and Evaluation Junyu Liu · Xiangjun Peng", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/poster/96274", "content": "Poster Feint Behaviors and Strategies : Formalization, Implementation and Evaluation Junyu Liu · Xiangjun Peng"}
+{"idx": 4, "title": "Feint Behaviors and Strategies: Formalization, Implementation and ...", "date": "", "ddg_snippet": "The concept of 'Feint Formalization' in a research paper would involve a systematic and rigorous approach to defining and modeling feint behaviors . This would likely include a detailed action-level formalization, specifying the precise actions that constitute a feint , and a strategy-level formalization, outlining how feints are integrated into broader game strategies to achieve advantages ...", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/posters/aciddntbsj/", "content": "The concept of 'Feint Formalization' in a research paper would involve a systematic and rigorous approach to defining and modeling feint behaviors . This would likely include a detailed action-level formalization, specifying the precise actions that constitute a feint , and a strategy-level formalization, outlining how feints are integrated into broader game strategies to achieve advantages ..."}
+{"idx": 5, "title": "Feint Behaviors and Strategies: Formalization, Implementation and ...", "date": "", "ddg_snippet": "However, these prior works (1) lack concrete formalizations of Feint behavior characteristics, which cannot fully unveil the variety of Feint behaviors in the action level; and (2) lack comprehensive explorations of Feint behaviors implications on game strategies , which neglects the potential impacts of fusing effective Feint behaviors into ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=ACIDDnTbSJ", "content": "However, these prior works (1) lack concrete formalizations of Feint behavior characteristics, which cannot fully unveil the variety of Feint behaviors in the action level; and (2) lack comprehensive explorations of Feint behaviors implications on game strategies , which neglects the potential impacts of fusing effective Feint behaviors into ..."}
+{"idx": 6, "title": "Feint Behaviors and Strategies: Formalization, Implementation and ...", "date": "", "ddg_snippet": "Overview. Our work provides the first comprehensive and concrete formalization of Feint behaviors in action-level and strategy-level. We first present an automatic approach to generate Feint behaviors using Palindrome-directed Templates based on our observation on Feint characteristics, and provide Dual- Behavior Model to examine the design for the combination of Feint behaviors and normal ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.07932v2", "content": "Overview. Our work provides the first comprehensive and concrete formalization of Feint behaviors in action-level and strategy-level. We first present an automatic approach to generate Feint behaviors using Palindrome-directed Templates based on our observation on Feint characteristics, and provide Dual- Behavior Model to examine the design for the combination of Feint behaviors and normal ..."}
+{"idx": 7, "title": "PDF Handout #16: Function-Based Intervention Strategies", "date": "", "ddg_snippet": "The following tables provide possible intervention strategies to incorporate into a comprehensive behavior intervention plan based on the function of the problem behavior .", "subpage_snippet": "", "source": "ceedar.education.ufl.edu", "link": "https://ceedar.education.ufl.edu/wp-content/uploads/2014/09/Handout-16-Function-Based-Intervention-Strategies.pdf", "content": "The following tables provide possible intervention strategies to incorporate into a comprehensive behavior intervention plan based on the function of the problem behavior ."}
+{"idx": 8, "title": "Understanding Feints - 204s", "date": "", "ddg_snippet": "Feints are deceptive movements or gestures that a fighter uses to make their opponent react defensively. The purpose of a feint is to create an opening or to gain a better understanding of an opponent's reactions. It's a crucial aspect of high-level striking strategy and often goes unnoticed by casual viewers. How Feints Work When a fighter uses a feint , they simulate an attack — like ...", "subpage_snippet": "", "source": "204s.wordpress.com", "link": "https://204s.wordpress.com/2024/11/02/understanding-feints/", "content": "Feints are deceptive movements or gestures that a fighter uses to make their opponent react defensively. The purpose of a feint is to create an opening or to gain a better understanding of an opponent's reactions. It's a crucial aspect of high-level striking strategy and often goes unnoticed by casual viewers. How Feints Work When a fighter uses a feint , they simulate an attack — like ..."}
+{"idx": 9, "title": "Junyu Liu - Projects", "date": "", "ddg_snippet": "Such behaviors are crucial tactics in most competitive multi-player games (e.g., boxing, fencing, basketball, motor racing, etc.). However, existing literature does not provide a comprehensive (and/or concrete) formalization for Feint behaviors , and their implications on game strategies .", "subpage_snippet": "", "source": "junyu-liu-nate.github.io", "link": "https://junyu-liu-nate.github.io/projects/FeintFinal.html", "content": "Such behaviors are crucial tactics in most competitive multi-player games (e.g., boxing, fencing, basketball, motor racing, etc.). However, existing literature does not provide a comprehensive (and/or concrete) formalization for Feint behaviors , and their implications on game strategies ."}

data/sampled_jsons/ACM_Digital_Library_WWW_2024_proceedings_Information_Retrieval_papers_year_2024.jsonl

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+{"idx": 0, "title": "ICTIR '24: Proceedings of the 2024 ACM SIGIR ...", "date": "", "ddg_snippet": "5 Aug 2024 — ICTIR is the premier forum for presenting and discussing research on theoretical and foundational aspects of Information Retrieval .", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/proceedings/10.1145/3664190", "content": "5 Aug 2024 — ICTIR is the premier forum for presenting and discussing research on theoretical and foundational aspects of Information Retrieval ."}
+{"idx": 1, "title": "Proceedings of the ACM Web Conference 2024", "date": "", "ddg_snippet": "13 May 2024 — This conference has been the premier venue to present and discuss progress in research, development, standards and applications of topics related to the Web.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/proceedings/10.1145/3589334", "content": "13 May 2024 — This conference has been the premier venue to present and discuss progress in research, development, standards and applications of topics related to the Web."}
+{"idx": 2, "title": "SIGIR 2024 Proceedings", "date": "", "ddg_snippet": "SIGIR '24: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval ... ACM Digital Library . SESSION ...", "subpage_snippet": "", "source": "sigir-2024.github.io", "link": "https://sigir-2024.github.io/proceedings.html", "content": "SIGIR '24: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval ... ACM Digital Library . SESSION ..."}
+{"idx": 3, "title": "ACM CCS 2024", "date": "", "ddg_snippet": "Faster FHE-based Single-server Private Information Retrieval , Ming Luo (Key Laboratory of Cyberspace Security Defense, Institute of Information Engineering ...", "subpage_snippet": "", "source": "www.sigsac.org", "link": "https://www.sigsac.org/ccs/CCS2024/program/accepted-papers.html", "content": "Faster FHE-based Single-server Private Information Retrieval , Ming Luo (Key Laboratory of Cyberspace Security Defense, Institute of Information Engineering ..."}
+{"idx": 4, "title": "JCDL 2024 - Joint Conference on Digital Libraries", "date": "", "ddg_snippet": "The ACM /IEEE-CS Joint Conference on Digital Libraries (JCDL) is a major international forum focusing on digital libraries and associated technical, practical, ...", "subpage_snippet": "", "source": "2024.jcdl.org", "link": "https://2024.jcdl.org/", "content": "The ACM /IEEE-CS Joint Conference on Digital Libraries (JCDL) is a major international forum focusing on digital libraries and associated technical, practical, ..."}
+{"idx": 5, "title": "Call for Resource & Reproducibility Papers - SIGIR 2024", "date": "", "ddg_snippet": "The 47th ACM SIGIR conference will be run in-person in Washington D.C., USA, from July 14-18, 2024 . This year we continue a special track for resource and ...", "subpage_snippet": "", "source": "sigir-2024.github.io", "link": "https://sigir-2024.github.io/call_for_res_rep_papers.html", "content": "The 47th ACM SIGIR conference will be run in-person in Washington D.C., USA, from July 14-18, 2024 . This year we continue a special track for resource and ..."}
+{"idx": 6, "title": "Enhancing Health Information Retrieval with RAG by ...", "date": "", "ddg_snippet": "by R Upadhyay · 2025 · Cited by 7 — This paper introduces a solution driven by Retrieval -Augmented Generation (RAG), which leverages the capabilities of generative Large Language Models (LLMs)", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s10791-025-09505-5", "content": "by R Upadhyay · 2025 · Cited by 7 — This paper introduces a solution driven by Retrieval -Augmented Generation (RAG), which leverages the capabilities of generative Large Language Models (LLMs)"}
+{"idx": 7, "title": "WSDM '19- Proceedings of the Twelfth ACM International ...", "date": "", "ddg_snippet": "This presentation will survey some recent success stories of reinforcement learning for search, recommendation, and conversations.", "subpage_snippet": "", "source": "kdd.org", "link": "https://kdd.org/proceedings/view/wsdm-19-proceedings-of-the-twelfth-acm-international-conference-on-web-sear", "content": "This presentation will survey some recent success stories of reinforcement learning for search, recommendation, and conversations."}
+{"idx": 8, "title": "Research Tracks", "date": "", "ddg_snippet": "This track is a forum for theoretical, empirical, and applied research related to the modeling, analysis, and design of Web-related economic activities.", "subpage_snippet": "", "source": "www2025.thewebconf.org", "link": "https://www2025.thewebconf.org/research-tracks", "content": "This track is a forum for theoretical, empirical, and applied research related to the modeling, analysis, and design of Web-related economic activities."}
+{"idx": 9, "title": "\"Filtering.\", Genre: Conference ... - SearchWorks catalog, Subject", "date": "", "ddg_snippet": "On the use of matrix Bloom filters in data leak protection.- A hybrid approach for the automatic extraction of causal relations from text.", "subpage_snippet": "", "source": "searchworks.stanford.edu", "link": "https://searchworks.stanford.edu/?f[genre_ssim][]=Conference+proceedings&per_page=50&q=\"Filtering.\"&search_field=subject_terms&sort=relevance", "content": "On the use of matrix Bloom filters in data leak protection.- A hybrid approach for the automatic extraction of causal relations from text."}

data/sampled_jsons/AEROChain_Section_5.1_experimental_setup_total_nodes.jsonl

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+{"idx": 0, "title": "Enhancing Sharding Blockchain via Deep Reinforcement ...", "date": "", "ddg_snippet": "by M Song · Cited by 2 — The experimental setup consists of 16 physical shards, each containing 8 nodes, amounting to a total of 128 nodes in the total network. During each epoch, the ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=WcuXvn3HVk", "content": "by M Song · Cited by 2 — The experimental setup consists of 16 physical shards, each containing 8 nodes, amounting to a total of 128 nodes in the total network. During each epoch, the ..."}
+{"idx": 1, "title": "How can I see what products are available on Costco.com?", "date": "", "ddg_snippet": "To view products on Costco.com, either type what you’re looking for into the Search box, or mouse over \"Shop\" to view our different categories. Simply click on the name or picture of an item to navigate to its product page. The great thing about shopping on Costco.com is that everything you see reflects our live, real-time and up-to-date ...", "subpage_snippet": "", "source": "customerservice.costco.com", "link": "https://customerservice.costco.com/app/answers/detail/a_id/671/~/how-can-i-see-what-products-are-available-on-costco.com?", "content": "To view products on Costco.com, either type what you’re looking for into the Search box, or mouse over \"Shop\" to view our different categories. Simply click on the name or picture of an item to navigate to its product page. The great thing about shopping on Costco.com is that everything you see reflects our live, real-time and up-to-date ..."}
+{"idx": 2, "title": "How can I locate a product on Costco.com?", "date": "", "ddg_snippet": "To search for a product online at Costco.com, enter a keyword or an item number into the search engine at the top. If the item you’re seeking is in stock and available for purchase, your search will pull up its product page. You can view the item details on the product page, add it to your cart and proceed to checkout. You can also browse through the drop-down menu of departments on the ...", "subpage_snippet": "", "source": "customerservice.costco.com", "link": "https://customerservice.costco.com/app/answers/detail/a_id/668/~/how-can-i-locate-a-product-on-costco.com?", "content": "To search for a product online at Costco.com, enter a keyword or an item number into the search engine at the top. If the item you’re seeking is in stock and available for purchase, your search will pull up its product page. You can view the item details on the product page, add it to your cart and proceed to checkout. You can also browse through the drop-down menu of departments on the ..."}
+{"idx": 3, "title": "Contact Us - Costco Customer Service", "date": "", "ddg_snippet": "Welcome to the Costco Customer Service page. Explore our many helpful self-service options and learn more about popular topics.", "subpage_snippet": "", "source": "customerservice.costco.com", "link": "https://customerservice.costco.com/app/answers/detail/a_id/9/~/contact-us", "content": "Welcome to the Costco Customer Service page. Explore our many helpful self-service options and learn more about popular topics."}
+{"idx": 4, "title": "Find a Warehouse - Costco Customer Service", "date": "", "ddg_snippet": "Find your nearest Costco warehouse location and explore helpful self-service options for customer support.", "subpage_snippet": "", "source": "customerservice.costco.com", "link": "https://customerservice.costco.com/app/answers/answer_view/a_id/1007276/~/find-a-warehouse", "content": "Find your nearest Costco warehouse location and explore helpful self-service options for customer support."}
+{"idx": 5, "title": "How do I place an order on Costco.com? - Costco Customer Service", "date": "", "ddg_snippet": "If you’re a member, enter the membership number found on your membership card. You’re now ready to start shopping! Note: Maintaining an active Costco membership doesn’t automatically register you on Costco.com. After becoming a Costco member, you’ll still need to complete a new registration on Costco.com (if you didn’t previously ...", "subpage_snippet": "", "source": "customerservice.costco.com", "link": "https://customerservice.costco.com/app/answers/detail/a_id/762/~/how-do-i-place-an-order-on-costco.com?", "content": "If you’re a member, enter the membership number found on your membership card. You’re now ready to start shopping! Note: Maintaining an active Costco membership doesn’t automatically register you on Costco.com. After becoming a Costco member, you’ll still need to complete a new registration on Costco.com (if you didn’t previously ..."}
+{"idx": 6, "title": "Where can I find Costco Services contact information?", "date": "", "ddg_snippet": "Where can I find Costco Services contact information? You’ll find the phone numbers and information for each of our Costco Services (for your home and business needs) here. Simply click on the name of the service to visit their website.", "subpage_snippet": "", "source": "customerservice.costco.com", "link": "https://customerservice.costco.com/app/answers/detail/a_id/1198", "content": "Where can I find Costco Services contact information? You’ll find the phone numbers and information for each of our Costco Services (for your home and business needs) here. Simply click on the name of the service to visit their website."}
+{"idx": 7, "title": "Costco Customer Service", "date": "", "ddg_snippet": "Welcome to the Costco Customer Service page. Explore our many helpful self-service options and learn more about popular topics.", "subpage_snippet": "", "source": "customerservice.costco.com", "link": "https://customerservice.costco.com/", "content": "Welcome to the Costco Customer Service page. Explore our many helpful self-service options and learn more about popular topics."}
+{"idx": 8, "title": "How do I check warehouse inventory? - Costco Customer Service", "date": "", "ddg_snippet": "You can use the Costco App to check warehouse inventory. Head to our mobile app page to learn more! To use our customer service page instead, follow these steps: Select Chat Us in the lower right corner of the page Enter your first name, last name, and email address Enter a message that includes the word “inventory” in the “Your Message” field Select Login to your Costco.com account ...", "subpage_snippet": "", "source": "customerservice.costco.com", "link": "https://customerservice.costco.com/app/answers/answer_view/a_id/1015066/~/how-do-i-check-warehouse-inventory?", "content": "You can use the Costco App to check warehouse inventory. Head to our mobile app page to learn more! To use our customer service page instead, follow these steps: Select Chat Us in the lower right corner of the page Enter your first name, last name, and email address Enter a message that includes the word “inventory” in the “Your Message” field Select Login to your Costco.com account ..."}
+{"idx": 9, "title": "What is Costco’s return policy? - Costco Customer Service", "date": "", "ddg_snippet": "Electronics: Costco will accept returns within 90 days (from the date the member received the merchandise) for Televisions, Projectors, Major Appliances (refrigerators above 10 cu. ft., freezers, ranges, cooktops, over-the-range and under-counter microwaves, range hoods, dishwashers, water heaters, washers and dryers), Computers, Touchscreen ...", "subpage_snippet": "", "source": "customerservice.costco.com", "link": "https://customerservice.costco.com/app/answers/detail/a_id/1191/~/what-is-costcos-return-policy?", "content": "Electronics: Costco will accept returns within 90 days (from the date the member received the merchandise) for Televisions, Projectors, Major Appliances (refrigerators above 10 cu. ft., freezers, ranges, cooktops, over-the-range and under-counter microwaves, range hoods, dishwashers, water heaters, washers and dryers), Computers, Touchscreen ..."}

data/sampled_jsons/AERO_Enhancing_Sharding_Blockchain_via_Deep_Reinforcement_Learning_for_Account_Migration.jsonl

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+{"idx": 0, "title": "BlockEmulator: An Emulator Enabling to Test Blockchain Sharding", "date": "", "ddg_snippet": "... blockchain - sharding mechanism is usually ... For example, S-Store [ 18 ] enables a dedicated data structure for the storage migration of shard data.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2311.03612v5", "content": "... blockchain - sharding mechanism is usually ... For example, S-Store [ 18 ] enables a dedicated data structure for the storage migration of shard data."}
+{"idx": 1, "title": "AERO: Enhancing Sharding Blockchain via Deep Reinforcement Learning for ...", "date": "", "ddg_snippet": "AERO employs a prefix-based grouping strategy to enable group-level migration decisions and capture complex transaction patterns and relationships between accounts . We also implement a sharding blockchain system called AEROChain, which integrates AERO and aligns with the blockchain decentralization principle.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3696410.3714926", "content": "AERO employs a prefix-based grouping strategy to enable group-level migration decisions and capture complex transaction patterns and relationships between accounts . We also implement a sharding blockchain system called AEROChain, which integrates AERO and aligns with the blockchain decentralization principle."}
+{"idx": 2, "title": "AERO: Enhancing Sharding Blockchain via Deep Reinforcement Learning for ...", "date": "", "ddg_snippet": "To address these scalability issues, account migration ofers a promising solution. However, existing migration solutions struggle with the high computational overhead and insuficient capture of complex transaction patterns. We propose AERO , a deep reinforcement learning framework to facilitate eficient account migration in sharding blockchains .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=WcuXvn3HVk", "content": "To address these scalability issues, account migration ofers a promising solution. However, existing migration solutions struggle with the high computational overhead and insuficient capture of complex transaction patterns. We propose AERO , a deep reinforcement learning framework to facilitate eficient account migration in sharding blockchains ."}
+{"idx": 3, "title": "A Review of Dynamic Sharding in Blockchain with Deep Reinforcement ...", "date": "", "ddg_snippet": "Blockchain networks face significant scalability and efficiency challenges as transaction volumes increase. Sharding , a partitioning technique that divides the blockchain into smaller, parallel-processing units, offers a promising solution. However, traditional static sharding lacks adaptability, leading to inefficiencies in resource utilization and security vulnerabilities. This paper ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/11069521", "content": "Blockchain networks face significant scalability and efficiency challenges as transaction volumes increase. Sharding , a partitioning technique that divides the blockchain into smaller, parallel-processing units, offers a promising solution. However, traditional static sharding lacks adaptability, leading to inefficiencies in resource utilization and security vulnerabilities. This paper ..."}
+{"idx": 4, "title": "AERO: Enhancing Sharding Blockchain via Deep Reinforcement Learning for ...", "date": "", "ddg_snippet": "This paper proposes AERO , a deep reinforcement learning framework for efficient account migration in sharding blockchains , improving throughput by 31.77% and reducing cross-shard transactions and workload imbalances.", "subpage_snippet": "", "source": "chatpaper.com", "link": "https://chatpaper.com/zh-CN/chatpaper/paper/129864", "content": "This paper proposes AERO , a deep reinforcement learning framework for efficient account migration in sharding blockchains , improving throughput by 31.77% and reducing cross-shard transactions and workload imbalances."}
+{"idx": 5, "title": "PDF AERO: Enhancing Sharding Blockchain via Deep Reinforcement Learning for ...", "date": "", "ddg_snippet": "Sharding blockchain networks face significant scalability challenges due to high frequencies of cross-shard transactions and uneven workload distributions among shards. To address these scalability issues, account migration ofers a promising solution. However,", "subpage_snippet": "", "source": "zhenxiao.com", "link": "http://zhenxiao.com/papers/WWW_AERO_camera_ready.pdf", "content": "Sharding blockchain networks face significant scalability challenges due to high frequencies of cross-shard transactions and uneven workload distributions among shards. To address these scalability issues, account migration ofers a promising solution. However,"}
+{"idx": 6, "title": "Paper Digest: WWW 2025 Papers & Highlights –", "date": "", "ddg_snippet": "HySAE: An Efficient Semantic-Enhanced Representation Learning Model for Knowledge Hypergraph Link Prediction Related Papers Related Patents Related ...", "subpage_snippet": "", "source": "resources.paperdigest.org", "link": "https://resources.paperdigest.org/2025/04/www-2025-papers-highlights/", "content": "HySAE: An Efficient Semantic-Enhanced Representation Learning Model for Knowledge Hypergraph Link Prediction Related Papers Related Patents Related ..."}
+{"idx": 7, "title": "What Is Twitter, a Social Network or a News Media? | Request PDF", "date": "", "ddg_snippet": "The overarching aim of this PhD thesis was to explore the impact and effectiveness of social media-based publicity appeals for missing persons ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/214640678_What_Is_Twitter_a_Social_Network_or_a_News_Media", "content": "The overarching aim of this PhD thesis was to explore the impact and effectiveness of social media-based publicity appeals for missing persons ..."}
+{"idx": 8, "title": "Mingxuan Song", "date": "", "ddg_snippet": "\" AERO : Enhancing Sharding Blockchain via Deep Reinforcement Learning for Account Migration .\" Proceedings of the Web Conference, May 2025. [PDF] CVPR 2024 CCF-A Shenglin Yin, Zhen Xiao*, Mingxuan Song, and Jieyi Long. \"Adversarial Distillation Based on Slack Matching and Attribution Region Alignment.\"", "subpage_snippet": "", "source": "www.songmingxuan.com", "link": "https://www.songmingxuan.com/", "content": "\" AERO : Enhancing Sharding Blockchain via Deep Reinforcement Learning for Account Migration .\" Proceedings of the Web Conference, May 2025. [PDF] CVPR 2024 CCF-A Shenglin Yin, Zhen Xiao*, Mingxuan Song, and Jieyi Long. \"Adversarial Distillation Based on Slack Matching and Attribution Region Alignment.\""}
+{"idx": 9, "title": "Papers - Zhen Xiao", "date": "", "ddg_snippet": "AERO : Enhancing Sharding Blockchain via Deep Reinforcement Learning for Account Migration Proc. of the Web Conference 2025 (WWW 2025), May 2025. Lichen Pan, Juncheng Liu, Yongquan Fu, Jinhui Yuan, Rongkai Zhang, Pengze Li, and Zhen Xiao.", "subpage_snippet": "", "source": "zhenxiao.com", "link": "http://zhenxiao.com/papers/", "content": "AERO : Enhancing Sharding Blockchain via Deep Reinforcement Learning for Account Migration Proc. of the Web Conference 2025 (WWW 2025), May 2025. Lichen Pan, Juncheng Liu, Yongquan Fu, Jinhui Yuan, Rongkai Zhang, Pengze Li, and Zhen Xiao."}

data/sampled_jsons/AERO_Enhancing_Sharding_Blockchain_via_Deep_Reinforcement_Learning_for_Account_Migration_nodes_per_s.jsonl

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+{"idx": 0, "title": "(PDF) BlockEmulator: An Emulator Enabling to Test Blockchain ...", "date": "", "ddg_snippet": "PDF | Numerous blockchain simulators have been proposed to allow researchers to simulate mainstream blockchains. reinforcement learning based state placement,” in Proc. of the ACM on. Web Conference 2024 (WWW’24), 2024, pp. 2836–2846.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/389535758_BlockEmulator_An_Emulator_Enabling_to_Test_Blockchain_Sharding_Protocols", "content": "PDF | Numerous blockchain simulators have been proposed to allow researchers to simulate mainstream blockchains. reinforcement learning based state placement,” in Proc. of the ACM on. Web Conference 2024 (WWW’24), 2024, pp. 2836–2846."}
+{"idx": 1, "title": "BlockEmulator: An Emulator Enabling to Test Blockchain ...", "date": "", "ddg_snippet": "Feb 23, 2025 · To fill this gap, we developed BlockEmulator, which is designed as an experimental platform, particularly for emulating blockchain sharding mechanisms. BlockEmulator adopts a lightweight blockchain architecture so developers can only focus on implementing their new protocols or mechanisms.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2311.03612v5", "content": "Feb 23, 2025 · To fill this gap, we developed BlockEmulator, which is designed as an experimental platform, particularly for emulating blockchain sharding mechanisms. BlockEmulator adopts a lightweight blockchain architecture so developers can only focus on implementing their new protocols or mechanisms."}
+{"idx": 2, "title": "BlockEmulator: An Emulator Enabling to Test Blockchain ...", "date": "", "ddg_snippet": "by H Huang · 2025 · Cited by 33 — Yin, Z. Xiao, and J. Long, “ AERO: Enhancing sharding blockchain via deep reinforcement learning for account migration ,” in Proc. ACM Web Conf., ...", "subpage_snippet": "", "source": "www.computer.org", "link": "https://www.computer.org/csdl/journal/sc/2025/02/10908689/24MWrrwc2Fa", "content": "by H Huang · 2025 · Cited by 33 — Yin, Z. Xiao, and J. Long, “ AERO: Enhancing sharding blockchain via deep reinforcement learning for account migration ,” in Proc. ACM Web Conf., ..."}
+{"idx": 3, "title": "BlockEmulator: An Emulator Enabling to Test", "date": "", "ddg_snippet": "[36] M. Song, P. Li, B. Zhou, S. Yin, Z. Xiao, and J. Long, “ Aero : Enhancing sharding blockchain via deep reinforcement learning for account migration ,” in Proc. of the ACM on Web Conference 2025 (WWW’25), 2025, pp. 1–11.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2311.03612", "content": "[36] M. Song, P. Li, B. Zhou, S. Yin, Z. Xiao, and J. Long, “ Aero : Enhancing sharding blockchain via deep reinforcement learning for account migration ,” in Proc. of the ACM on Web Conference 2025 (WWW’25), 2025, pp. 1–11."}

data/sampled_jsons/AERO_blockchain_experimental_setup_100_nodes_4_shards_configuration.jsonl

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+{"idx": 0, "title": "AERO: Enhancing Sharding Blockchain via Deep Reinforcement Learning for ...", "date": "", "ddg_snippet": "The experimental setup consists of 16 physical shards , each containing 8 nodes , amounting to a total of 128 nodes in the total network. During each epoch, the consensus phase is composed of 100 blocks, with each block containing a maximum of 1,000 transactions.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=WcuXvn3HVk", "content": "The experimental setup consists of 16 physical shards , each containing 8 nodes , amounting to a total of 128 nodes in the total network. During each epoch, the consensus phase is composed of 100 blocks, with each block containing a maximum of 1,000 transactions."}
+{"idx": 1, "title": "AERO: Enhancing Sharding Blockchain via Deep Reinforcement Learning for ...", "date": "", "ddg_snippet": "AERO employs a prefix-based grouping strategy to enable group-level migration decisions and capture complex transaction patterns and relationships between accounts. We also implement a sharding blockchain system called AEROChain, which integrates AERO and aligns with the blockchain decentralization principle.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3696410.3714926", "content": "AERO employs a prefix-based grouping strategy to enable group-level migration decisions and capture complex transaction patterns and relationships between accounts. We also implement a sharding blockchain system called AEROChain, which integrates AERO and aligns with the blockchain decentralization principle."}
+{"idx": 2, "title": "BlockEmulator: An Emulator Enabling to Test Blockchain Sharding Protocols", "date": "", "ddg_snippet": "In step \\scriptsize {1}⃝ , before the BlockEmulator system gets started, users/developers set up the configuration parameters for the emulated blockchain .The configuration parameters mainly include the directory of the output of experimental results, block-generation interval, the consensus protocol adopted, block size, the number of nodes ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2311.03612v3", "content": "In step \\scriptsize {1}⃝ , before the BlockEmulator system gets started, users/developers set up the configuration parameters for the emulated blockchain .The configuration parameters mainly include the directory of the output of experimental results, block-generation interval, the consensus protocol adopted, block size, the number of nodes ..."}
+{"idx": 3, "title": "Setting Up a Blockchain Node: A Complete Tutorial", "date": "", "ddg_snippet": "Learn how to set up a blockchain node with step-by-step guidance on hardware, software, and network configuration for Bitcoin, Ethereum, and more.", "subpage_snippet": "", "source": "axeminer.com", "link": "https://axeminer.com/setting-up-a-blockchain-node/", "content": "Learn how to set up a blockchain node with step-by-step guidance on hardware, software, and network configuration for Bitcoin, Ethereum, and more."}
+{"idx": 4, "title": "Step-by-Step Guide to Building Blockchain Nodes: Key Considerations and ...", "date": "", "ddg_snippet": "This post provides a high-level overview for blockchain engineers and beginners looking to set up and operate blockchain nodes . Let's dive in!", "subpage_snippet": "", "source": "blog.nodit.io", "link": "https://blog.nodit.io/step-by-step-guide-to-building-blockchain-nodes-key-considerations-and-practical-tips/", "content": "This post provides a high-level overview for blockchain engineers and beginners looking to set up and operate blockchain nodes . Let's dive in!"}
+{"idx": 5, "title": "Complete Guide to Blockchain Node Setup Configuration", "date": "", "ddg_snippet": "Discover the complete process of blockchain node setup and configuration . Follow our expert guide to set up your blockchain node efficiently.", "subpage_snippet": "", "source": "cryptomaximal.com", "link": "https://cryptomaximal.com/blockchain-node-setup/", "content": "Discover the complete process of blockchain node setup and configuration . Follow our expert guide to set up your blockchain node efficiently."}
+{"idx": 6, "title": "A Step-by-Step Guide to Setting Up a Blockchain Node", "date": "", "ddg_snippet": "Learn how to configure a blockchain node with our step-by-step guide. Unlock the power of decentralized networks on your own terms.", "subpage_snippet": "", "source": "proofofdev.com", "link": "https://proofofdev.com/a-step-by-step-guide-to-setting-up-a-blockchain-node/", "content": "Learn how to configure a blockchain node with our step-by-step guide. Unlock the power of decentralized networks on your own terms."}
+{"idx": 7, "title": "A survey of state-of-the-art sharding blockchains: Models, components ...", "date": "", "ddg_snippet": "The sharding technique, a common technique used to expand performance horizontally in the traditional database domain, was first introduced into blockchain by Luu et al. (2016). By splitting nodes into smaller groups, called shards , and processing transactions in parallel, sharding can significantly improve the performance of the blockchain .", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S1084804523001054", "content": "The sharding technique, a common technique used to expand performance horizontally in the traditional database domain, was first introduced into blockchain by Luu et al. (2016). By splitting nodes into smaller groups, called shards , and processing transactions in parallel, sharding can significantly improve the performance of the blockchain ."}
+{"idx": 8, "title": "Server Infrastructure for Blockchain Nodes: A Complete Setup Guide", "date": "", "ddg_snippet": "This guide breaks down exactly what you need to run Bitcoin, Ethereum, and Solana nodes , based on real-world deployments and current network demands. We'll cover the specific hardware requirements, step-by-step setup processes, and actual hosting costs from providers that blockchain operators trust.", "subpage_snippet": "", "source": "www.mamboserver.com", "link": "https://www.mamboserver.com/blog/server-infrastructure-for-blockchain-nodes/", "content": "This guide breaks down exactly what you need to run Bitcoin, Ethereum, and Solana nodes , based on real-world deployments and current network demands. We'll cover the specific hardware requirements, step-by-step setup processes, and actual hosting costs from providers that blockchain operators trust."}
+{"idx": 9, "title": "PDF AERO: Enhancing Sharding Blockchain via Deep Reinforcement Learning for ...", "date": "", "ddg_snippet": "blockchain network into multiple smaller, manageable segments called shards . Each shard simultaneously processes a subset of blockchain transactions and smart contracts, while periodically reassigning and maintaining shard nodes to ensure security.", "subpage_snippet": "", "source": "zhenxiao.com", "link": "http://zhenxiao.com/papers/WWW_AERO_camera_ready.pdf", "content": "blockchain network into multiple smaller, manageable segments called shards . Each shard simultaneously processes a subset of blockchain transactions and smart contracts, while periodically reassigning and maintaining shard nodes to ensure security."}

data/sampled_jsons/AI_alignment.jsonl

@@ -0,0 +1,10 @@
+{"idx": 0, "title": "AI alignment", "date": "", "ddg_snippet": "In the field of artificial intelligence (AI), alignment aims to steer AI systems toward a person's or group's intended goals, preferences, or ethical principles. An AI system is considered aligned if it advances the intended objectives. A misaligned AI system pursues unintended objectives.It is often challenging for AI designers to align an AI system because it is difficult for them to specify the full range of desired and undesired behaviors. Therefore, AI designers often use simpler proxy goals, such as gaining human approval. But proxy goals can overlook necessary constraints or reward the AI system for merely appearing aligned. AI systems may also find loopholes that allow them to accomplish their proxy goals efficiently but in unintended, sometimes harmful, ways (reward hacking).Advanced AI systems may develop unwanted instrumental strategies, such as seeking power or survival because such strategies help them achieve their assigned final goals. Furthermore, they might develop undesirable emergent goals that could be hard to detect before the system is deployed and encounters new situations and data distributions. Empirical research showed in 2024 that advanced large language models (LLMs) such as OpenAI o1 or Claude 3 sometimes engage in strategic deception to achieve their goals or prevent them from being changed.Today, some of these issues affect existing commercial systems such as LLMs, robots, autonomous vehicles, and social media recommendation engines. Some AI researchers argue that more capable future systems will be more severely affected because these problems partially result from high capabilities.Many prominent AI researchers and the leadership of major AI companies have argued or asserted that AI is approaching human-like (AGI) and superhuman cognitive capabilities (ASI), and could endanger human civilization if misaligned. These include \"AI godfathers\" Geoffrey Hinton and Yoshua Bengio and the CEOs of OpenAI, Anthropic, and Google DeepMind. These risks remain debated.AI alignment is a subfield of AI safety, the study of how to build safe AI systems. Other subfields of AI safety include robustness, monitoring, and capability control. Research challenges in alignment include instilling complex values in AI, developing honest AI, scalable oversight, auditing and interpreting AI models, and preventing emergent AI behaviors like power-seeking. Alignment research has connections to interpretability research, (adversarial) robustness, anomaly detection, calibrated uncertainty, formal verification, preference learning, safety-critical engineering, game theory, algorithmic fairness, and social sciences.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/AI_alignment", "content": "In the field of artificial intelligence (AI), alignment aims to steer AI systems toward a person's or group's intended goals, preferences, or ethical principles. An AI system is considered aligned if it advances the intended objectives. A misaligned AI system pursues unintended objectives.It is often challenging for AI designers to align an AI system because it is difficult for them to specify the full range of desired and undesired behaviors. Therefore, AI designers often use simpler proxy goals, such as gaining human approval. But proxy goals can overlook necessary constraints or reward the AI system for merely appearing aligned. AI systems may also find loopholes that allow them to accomplish their proxy goals efficiently but in unintended, sometimes harmful, ways (reward hacking).Advanced AI systems may develop unwanted instrumental strategies, such as seeking power or survival because such strategies help them achieve their assigned final goals. Furthermore, they might develop undesirable emergent goals that could be hard to detect before the system is deployed and encounters new situations and data distributions. Empirical research showed in 2024 that advanced large language models (LLMs) such as OpenAI o1 or Claude 3 sometimes engage in strategic deception to achieve their goals or prevent them from being changed.Today, some of these issues affect existing commercial systems such as LLMs, robots, autonomous vehicles, and social media recommendation engines. Some AI researchers argue that more capable future systems will be more severely affected because these problems partially result from high capabilities.Many prominent AI researchers and the leadership of major AI companies have argued or asserted that AI is approaching human-like (AGI) and superhuman cognitive capabilities (ASI), and could endanger human civilization if misaligned. These include \"AI godfathers\" Geoffrey Hinton and Yoshua Bengio and the CEOs of OpenAI, Anthropic, and Google DeepMind. These risks remain debated.AI alignment is a subfield of AI safety, the study of how to build safe AI systems. Other subfields of AI safety include robustness, monitoring, and capability control. Research challenges in alignment include instilling complex values in AI, developing honest AI, scalable oversight, auditing and interpreting AI models, and preventing emergent AI behaviors like power-seeking. Alignment research has connections to interpretability research, (adversarial) robustness, anomaly detection, calibrated uncertainty, formal verification, preference learning, safety-critical engineering, game theory, algorithmic fairness, and social sciences."}
+{"idx": 1, "title": "[2310.19852] AI Alignment: A Comprehensive Survey", "date": "", "ddg_snippet": "by J Ji · 2023 · Cited by 429 — AI alignment aims to make AI systems behave in line with human intentions and values. As AI systems grow more capable, so do risks from misalignment.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2310.19852", "content": "by J Ji · 2023 · Cited by 429 — AI alignment aims to make AI systems behave in line with human intentions and values. As AI systems grow more capable, so do risks from misalignment."}
+{"idx": 2, "title": "AI Alignment Forum", "date": "", "ddg_snippet": "A community blog devoted to technical AI alignment research .", "subpage_snippet": "", "source": "www.alignmentforum.org", "link": "https://www.alignmentforum.org/", "content": "A community blog devoted to technical AI alignment research ."}
+{"idx": 3, "title": "Are We Misunderstanding the AI \"Alignment Problem ...", "date": "", "ddg_snippet": "When an AI exhibits what we call \"misalignment,\" it might actually be behaving exactly as a reasoning system should under the circumstances. It ...", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/ControlProblem/comments/1hvs2gu/are_we_misunderstanding_the_ai_alignment_problem/", "content": "When an AI exhibits what we call \"misalignment,\" it might actually be behaving exactly as a reasoning system should under the circumstances. It ..."}
+{"idx": 4, "title": "AI value alignment: Aligning AI with human values", "date": "", "ddg_snippet": "17 Oct 2024 — AI value alignment is about ensuring that artificial intelligence (AI) systems act in accordance with shared human values and ethical principles.", "subpage_snippet": "", "source": "www.weforum.org", "link": "https://www.weforum.org/stories/2024/10/ai-value-alignment-how-we-can-align-artificial-intelligence-with-human-values/", "content": "17 Oct 2024 — AI value alignment is about ensuring that artificial intelligence (AI) systems act in accordance with shared human values and ethical principles."}
+{"idx": 5, "title": "What is AI Alignment? Ensuring AI Works for Humanity", "date": "", "ddg_snippet": "9 Jul 2024 — AI Alignment refers to the very complex task of encoding human values into AI systems to prevent unintended consequences and to mitigate potential harm.", "subpage_snippet": "", "source": "www.datacamp.com", "link": "https://www.datacamp.com/blog/ai-alignment", "content": "9 Jul 2024 — AI Alignment refers to the very complex task of encoding human values into AI systems to prevent unintended consequences and to mitigate potential harm."}
+{"idx": 6, "title": "Call for the Special Track on AI Alignment - AAAI", "date": "", "ddg_snippet": "24 Jun 2025 — AAAI-26 is pleased to announce a special track focused on AI Alignment . This track recognizes that as we begin to build more and more capable AI systems, it ...", "subpage_snippet": "", "source": "aaai.org", "link": "https://aaai.org/conference/aaai/aaai-26/aia-call/", "content": "24 Jun 2025 — AAAI-26 is pleased to announce a special track focused on AI Alignment . This track recognizes that as we begin to build more and more capable AI systems, it ..."}
+{"idx": 7, "title": "AI Alignment", "date": "", "ddg_snippet": "AI alignment refers to the goal of designing artificial intelligence systems in such a way that their objectives and behavior are aligned with the values and ...", "subpage_snippet": "", "source": "thedecisionlab.com", "link": "https://thedecisionlab.com/reference-guide/computer-science/ai-alignment", "content": "AI alignment refers to the goal of designing artificial intelligence systems in such a way that their objectives and behavior are aligned with the values and ..."}
+{"idx": 8, "title": "AI Alignment: Why It's Hard, and Where to Start - Machine", "date": "", "ddg_snippet": "This is where we are on most of the AI alignment problems, like if I ask you, “How do you build a friendly AI ?” What stops you is not that you ...", "subpage_snippet": "", "source": "intelligence.org", "link": "https://intelligence.org/2016/12/28/ai-alignment-why-its-hard-and-where-to-start/", "content": "This is where we are on most of the AI alignment problems, like if I ask you, “How do you build a friendly AI ?” What stops you is not that you ..."}
+{"idx": 9, "title": "The AI Alignment Problem: Why It's Hard, and Where to", "date": "", "ddg_snippet": "... org if you have any questions, and see intelligence.org/get-involved for information about opportunities to collaborate on AI alignment ...", "subpage_snippet": "", "source": "intelligence.org", "link": "https://intelligence.org/stanford-talk/", "content": "... org if you have any questions, and see intelligence.org/get-involved for information about opportunities to collaborate on AI alignment ..."}

data/sampled_jsons/ATA_Adaptive_Task_Allocation_Tyurin_Richtárik_wasteful_distributed_machine_learning.jsonl

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+{"idx": 0, "title": "ATA: Adaptive Task Allocation for Efficient Resource ...", "date": "", "ddg_snippet": "In this paper, we propose ATA ( Adaptive Task Allocation ), a method that adapts to heterogeneous and random distributions of worker computation times.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46650", "content": "In this paper, we propose ATA ( Adaptive Task Allocation ), a method that adapts to heterogeneous and random distributions of worker computation times."}
+{"idx": 1, "title": "ATA: Adaptive Task Allocation for Efficient Resource ...", "date": "", "ddg_snippet": "2 Feb 2025 — Asynchronous methods are fundamental for parallelizing computations in distributed machine learning . They aim to accelerate training by ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.00775v1", "content": "2 Feb 2025 — Asynchronous methods are fundamental for parallelizing computations in distributed machine learning . They aim to accelerate training by ..."}
+{"idx": 2, "title": "ATA: Adaptive Task Allocation for Efficient Resource Management", "date": "", "ddg_snippet": "ATA : Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning ... a greedy task allocation strategy, leading to wastefulness .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/86bb01c56f97fb6ecf7e661151ca6ae354faf473.pdf", "content": "ATA : Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning ... a greedy task allocation strategy, leading to wastefulness ."}
+{"idx": 3, "title": "ATA: Adaptive Task Allocation for Efficient Resource Management in ...", "date": "", "ddg_snippet": "in Distributed Machine Learning Artavazd Maranjyan El Mehdi Saad Peter Richtárik Francesco Orabona Abstract Asynchronous methods are fundamental for parallelizing computations in distributed machine learning . They aim to accelerate training by fully utilizing all available resources.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.00775v2", "content": "in Distributed Machine Learning Artavazd Maranjyan El Mehdi Saad Peter Richtárik Francesco Orabona Abstract Asynchronous methods are fundamental for parallelizing computations in distributed machine learning . They aim to accelerate training by fully utilizing all available resources."}
+{"idx": 4, "title": "ATA: Adaptive Task Allocation for Efficient Resource Management in ...", "date": "", "ddg_snippet": "This paper suggests an adaptive method for task allocation , Adaptive Task Allocation ( ATA ). In the context of machine learning , this approach addresses federated learning , where in each round, n workers collaboratively perform minibatch SGD with total batch size of B.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=1BaC3AdG1i", "content": "This paper suggests an adaptive method for task allocation , Adaptive Task Allocation ( ATA ). In the context of machine learning , this approach addresses federated learning , where in each round, n workers collaboratively perform minibatch SGD with total batch size of B."}
+{"idx": 5, "title": "ATA: Adaptive Task Allocation for Efficient Resource Management in ...", "date": "", "ddg_snippet": "Through rigorous theoretical analysis, ATA ( Adaptive Task Allocation ) is proposed, a method that adapts to heterogeneous and random distributions of worker computation times and performs comparably to methods with prior knowledge of computation times. Asynchronous methods are fundamental for parallelizing computations in distributed machine learning . They aim to accelerate training by fully ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/ATA:-Adaptive-Task-Allocation-for-Efficient-in-Maranjyan-Saad/871e17b70c5c31985b6ecb1d61960ad5ee7d1cbd", "content": "Through rigorous theoretical analysis, ATA ( Adaptive Task Allocation ) is proposed, a method that adapts to heterogeneous and random distributions of worker computation times and performs comparably to methods with prior knowledge of computation times. Asynchronous methods are fundamental for parallelizing computations in distributed machine learning . They aim to accelerate training by fully ..."}
+{"idx": 6, "title": "ATA: Adaptive Task Allocation for Efficient Resource Management in ...", "date": "", "ddg_snippet": "View recent discussion. Abstract: Asynchronous methods are fundamental for parallelizing computations in distributed machine learning . They aim to accelerate training by fully utilizing all available resources. However, their greedy approach can lead to inefficiencies using more computation than required, especially when computation times vary across devices. If the computation times were ...", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2502.00775v2", "content": "View recent discussion. Abstract: Asynchronous methods are fundamental for parallelizing computations in distributed machine learning . They aim to accelerate training by fully utilizing all available resources. However, their greedy approach can lead to inefficiencies using more computation than required, especially when computation times vary across devices. If the computation times were ..."}
+{"idx": 7, "title": "ATA: Adaptive Task Allocation for Efficient Resource Management in ...", "date": "", "ddg_snippet": "View on arXiv @article {maranjyan2025_2502.00775, title= { ATA : Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning }, author= { Artavazd Maranjyan and El Mehdi Saad and Peter Richtárik and Francesco Orabona }, journal= {arXiv preprint arXiv:2502.00775}, year= { 2025 } }", "subpage_snippet": "", "source": "researchtrend.ai", "link": "https://researchtrend.ai/papers/2502.00775", "content": "View on arXiv @article {maranjyan2025_2502.00775, title= { ATA : Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning }, author= { Artavazd Maranjyan and El Mehdi Saad and Peter Richtárik and Francesco Orabona }, journal= {arXiv preprint arXiv:2502.00775}, year= { 2025 } }"}
+{"idx": 8, "title": "ATA: Adaptive Task Allocation for Efficient Resource Management in ...", "date": "", "ddg_snippet": "#1 ATA : Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning [PDF 1] [Copy] [Kimi 1] [REL] Authors: Artavazd Maranjyan, El Mehdi Saad, Peter Richtarik , Francesco Orabona Asynchronous methods are fundamental for parallelizing computations in distributed machine learning . They aim to accelerate training by fully utilizing all available resources. However ...", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/venue/1BaC3AdG1i@OpenReview", "content": "#1 ATA : Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning [PDF 1] [Copy] [Kimi 1] [REL] Authors: Artavazd Maranjyan, El Mehdi Saad, Peter Richtarik , Francesco Orabona Asynchronous methods are fundamental for parallelizing computations in distributed machine learning . They aim to accelerate training by fully utilizing all available resources. However ..."}
+{"idx": 9, "title": "[2502.00775] ATA: Adaptive Task Allocation for Efficient Resource ...", "date": "", "ddg_snippet": "Asynchronous methods are fundamental for parallelizing computations in distributed machine learning . They aim to accelerate training by fully utilizing all available resources. However, their greedy approach can lead to inefficiencies using more computation than required, especially when computation times vary across devices. If the computation times were known in advance, training could be ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.00775", "content": "Asynchronous methods are fundamental for parallelizing computations in distributed machine learning . They aim to accelerate training by fully utilizing all available resources. However, their greedy approach can lead to inefficiencies using more computation than required, especially when computation times vary across devices. If the computation times were known in advance, training could be ..."}

data/sampled_jsons/ATA_Adaptive_Task_Allocation_arXiv.jsonl

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+{"idx": 0, "title": "[2502.00775] ATA: Adaptive Task Allocation for Efficient Resource ...", "date": "", "ddg_snippet": "In this paper, we propose ATA ( Adaptive Task Allocation ), a method that adapts to heterogeneous and random distributions of worker computation times. Through rigorous theoretical analysis, we show that ATA identifies the optimal task allocation and performs comparably to methods with prior knowledge of computation times.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.00775", "content": "In this paper, we propose ATA ( Adaptive Task Allocation ), a method that adapts to heterogeneous and random distributions of worker computation times. Through rigorous theoretical analysis, we show that ATA identifies the optimal task allocation and performs comparably to methods with prior knowledge of computation times."}
+{"idx": 1, "title": "ICML Poster ATA: Adaptive Task Allocation for Efficient Resource ...", "date": "", "ddg_snippet": "ATA : Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning Artavazd Maranjyan · El Mehdi Saad · Peter Richtarik · Francesco Orabona", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46650", "content": "ATA : Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning Artavazd Maranjyan · El Mehdi Saad · Peter Richtarik · Francesco Orabona"}
+{"idx": 2, "title": "ATA: Adaptive Task Allocation for Efficient Resource Management in ...", "date": "", "ddg_snippet": "Through rigorous theoretical analysis, ATA ( Adaptive Task Allocation ) is proposed, a method that adapts to heterogeneous and random distributions of worker computation times and performs comparably to methods with prior knowledge of computation times. Asynchronous methods are fundamental for parallelizing computations in distributed machine learning. They aim to accelerate training by fully ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/ATA:-Adaptive-Task-Allocation-for-Efficient-in-Maranjyan-Saad/871e17b70c5c31985b6ecb1d61960ad5ee7d1cbd", "content": "Through rigorous theoretical analysis, ATA ( Adaptive Task Allocation ) is proposed, a method that adapts to heterogeneous and random distributions of worker computation times and performs comparably to methods with prior knowledge of computation times. Asynchronous methods are fundamental for parallelizing computations in distributed machine learning. They aim to accelerate training by fully ..."}
+{"idx": 3, "title": "ATA: Adaptive Task Allocation for Efficient Resource Management in ...", "date": "", "ddg_snippet": "Key Findings The research shows that dynamic resource allocation significantly improves training speed. Systems using ATA completed training tasks up to 40% faster than traditional methods. Task scheduling becomes more efficient as the system learns optimal patterns. Resource utilization improved by 35% compared to static allocation methods.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/ata-adaptive-task-allocation-efficient-resource-management", "content": "Key Findings The research shows that dynamic resource allocation significantly improves training speed. Systems using ATA completed training tasks up to 40% faster than traditional methods. Task scheduling becomes more efficient as the system learns optimal patterns. Resource utilization improved by 35% compared to static allocation methods."}
+{"idx": 4, "title": "ATA: Adaptive Task Allocation for Efficient Resource Management in ...", "date": "", "ddg_snippet": "In this paper, we propose ATA (Adaptive Task Allocation ), a method that adapts to heterogeneous and random distributions of worker computation times. Through rigorous theoretical analysis, we show that ATAidentifies the optimal task allocation and performs comparably to methods with prior knowledge of computation times.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.00775v1", "content": "In this paper, we propose ATA (Adaptive Task Allocation ), a method that adapts to heterogeneous and random distributions of worker computation times. Through rigorous theoretical analysis, we show that ATAidentifies the optimal task allocation and performs comparably to methods with prior knowledge of computation times."}
+{"idx": 5, "title": "論文の概要: ATA: Adaptive Task Allocation for Efficient Resource Management ...", "date": "", "ddg_snippet": "In this paper, we propose ATA ( Adaptive Task Allocation ), a method that adapts to heterogeneous and random distributions of worker computation times. Through rigorous theoretical analysis, we show that ATA identifies the optimal task allocation and performs comparably to methods with prior knowledge of computation times.", "subpage_snippet": "", "source": "fugumt.com", "link": "https://fugumt.com/fugumt/paper_check/2502.00775v2", "content": "In this paper, we propose ATA ( Adaptive Task Allocation ), a method that adapts to heterogeneous and random distributions of worker computation times. Through rigorous theoretical analysis, we show that ATA identifies the optimal task allocation and performs comparably to methods with prior knowledge of computation times."}
+{"idx": 6, "title": "\"ATA: Adaptive Task Allocation for Efficient Resource Management in ...\"", "date": "", "ddg_snippet": "Bibliographic details on ATA : Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning.", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/journals/corr/abs-2502-00775", "content": "Bibliographic details on ATA : Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning."}
+{"idx": 7, "title": "Adaptive Task Allocation in Multi-Human Multi-Robot Teams Under Team ...", "date": "", "ddg_snippet": "Task allocation in multi-human multi-robot (MHMR) teams presents significant challenges due to the inherent heterogeneity of team members, the dynamics of task execution, and the information uncertainty of operational states. Existing approaches often fail to address these challenges simultaneously, resulting in suboptimal performance. To tackle this, we propose ATA -HRL, an adaptive task ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/abstract/document/11127646", "content": "Task allocation in multi-human multi-robot (MHMR) teams presents significant challenges due to the inherent heterogeneity of team members, the dynamics of task execution, and the information uncertainty of operational states. Existing approaches often fail to address these challenges simultaneously, resulting in suboptimal performance. To tackle this, we propose ATA -HRL, an adaptive task ..."}
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data/sampled_jsons/ATA_Adaptive_Task_Allocation_arXiv2502.00775_Recursive_Allocation_Selection.jsonl

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+{"idx": 2, "title": "ATA: Adaptive Task Allocation for Efficient Resource ...", "date": "", "ddg_snippet": "This paper introduces a new method called ATA ( Adaptive Task Allocation ) for improving how computers divide tasks among different workers in machine learning. It looks at the problem where some wo...", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/icml/46650/paper", "content": "This paper introduces a new method called ATA ( Adaptive Task Allocation ) for improving how computers divide tasks among different workers in machine learning. It looks at the problem where some wo..."}
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+{"idx": 6, "title": "ATA : Adaptive Task Allocation for Efficient Resource Management in...", "date": "", "ddg_snippet": "Algorithm 1 ATA ( Adaptive Task Allocation ). 1: Input: allocation budget.Appendix B Recursive Allocation Selection Algorithm. In this section, we introduce an efficient method for finding the best allocation .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.00775v1", "content": "Algorithm 1 ATA ( Adaptive Task Allocation ). 1: Input: allocation budget.Appendix B Recursive Allocation Selection Algorithm. In this section, we introduce an efficient method for finding the best allocation ."}
+{"idx": 7, "title": "ICML Poster ATA : Adaptive Task Allocation for Efficient Resource...", "date": "", "ddg_snippet": "In this paper, we propose ATA ( Adaptive Task Allocation ), a method that adapts to heterogeneous and random distributions of worker computation times.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46650", "content": "In this paper, we propose ATA ( Adaptive Task Allocation ), a method that adapts to heterogeneous and random distributions of worker computation times."}
+{"idx": 8, "title": "Weather / adaptive", "date": "", "ddg_snippet": "Remix on Adaptive adaptive .ai.", "subpage_snippet": "", "source": "weather.on.adaptive.ai", "link": "https://weather.on.adaptive.ai/", "content": "Remix on Adaptive adaptive .ai."}
+{"idx": 9, "title": "Energy-balancing task allocation on wireless sensor networks for...", "date": "", "ddg_snippet": "Self- adapted task allocation algorithm with complicated coalition in wireless sensor network.In this paper, we develop a real-time adaptive task allocation algorithm based on parallel dynamic coalition in WSNs.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/224167339_Energy-balancing_task_allocation_on_wireless_sensor_networks_for_extending_the_lifetime", "content": "Self- adapted task allocation algorithm with complicated coalition in wireless sensor network.In this paper, we develop a real-time adaptive task allocation algorithm based on parallel dynamic coalition in WSNs."}

data/sampled_jsons/ATA_Adaptive_Task_Allocation_conf(i,_k)_equation_(7).jsonl

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+{"idx": 1, "title": "ATA: Adaptive Task Allocation for Efficient Resource Management in ...", "date": "", "ddg_snippet": "In this paper, we propose ATA ( Adaptive Task Allocation ), a method that adapts to het-erogeneous and random distributions of worker computation times. Through rigorous theoretical analysis, we show that ATA identifies the optimal task allocation and performs comparably to meth-ods with prior knowledge of computation times.", "subpage_snippet": "", "source": "repository.kaust.edu.sa", "link": "https://repository.kaust.edu.sa/bitstreams/884560fb-34b9-42e5-a5ee-88334b809ebb/download", "content": "In this paper, we propose ATA ( Adaptive Task Allocation ), a method that adapts to het-erogeneous and random distributions of worker computation times. Through rigorous theoretical analysis, we show that ATA identifies the optimal task allocation and performs comparably to meth-ods with prior knowledge of computation times."}
+{"idx": 2, "title": "Adaptive Task Allocation for Heterogeneous Multi-Robot Teams with ...", "date": "", "ddg_snippet": "For multi-robot teams with heterogeneous capabilities, typical task allocation methods assign tasks to robots based on the suitability of the robots to perform certain tasks as well as the requirements of the task itself. However, in real-world deployments of robot teams, the suitability of a robot might be unknown prior to deployment, or might vary due to changing environmental conditions ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/9197283", "content": "For multi-robot teams with heterogeneous capabilities, typical task allocation methods assign tasks to robots based on the suitability of the robots to perform certain tasks as well as the requirements of the task itself. However, in real-world deployments of robot teams, the suitability of a robot might be unknown prior to deployment, or might vary due to changing environmental conditions ..."}
+{"idx": 3, "title": "ATA-HRL - Google Sites", "date": "", "ddg_snippet": "Conceptual illustration of our adaptive task allocation method, named ATA -HRL, in MH-MR teams. Unlike previous one-sided approaches, we consider both inherent heterogeneity and in-process dynamic states of the team and its assigned tasks , hierarchically combining initial task allocation and conditional task reallocation. To handle state information uncertainty, we also introduce an auxiliary ...", "subpage_snippet": "", "source": "sites.google.com", "link": "https://sites.google.com/view/ata-hrl", "content": "Conceptual illustration of our adaptive task allocation method, named ATA -HRL, in MH-MR teams. Unlike previous one-sided approaches, we consider both inherent heterogeneity and in-process dynamic states of the team and its assigned tasks , hierarchically combining initial task allocation and conditional task reallocation. To handle state information uncertainty, we also introduce an auxiliary ..."}
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+{"idx": 6, "title": "(PDF) Adaptive Task Allocation Preferences in Different Workload ...", "date": "", "ddg_snippet": "Abstract Adaptive task allocation is used in many human-machine systems and has been proven to improve operators' performance with automated systems.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/365503627_Adaptive_Task_Allocation_Preferences_in_Different_Workload_Scenarios_in_Driving_Automation_Systems", "content": "Abstract Adaptive task allocation is used in many human-machine systems and has been proven to improve operators' performance with automated systems."}
+{"idx": 7, "title": "Adaptive Task Allocation in Multi-Human Multi-Robot Teams under Team ...", "date": "", "ddg_snippet": "To tackle this, we pro-pose ATA -HRL, an adaptive task allocation framework using hierarchical reinforcement learning (HRL), which incorporates initial task allocation (ITA) that leverages team heterogeneity and conditional task reallocation in response to dynamic op-erational states.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2409.13824", "content": "To tackle this, we pro-pose ATA -HRL, an adaptive task allocation framework using hierarchical reinforcement learning (HRL), which incorporates initial task allocation (ITA) that leverages team heterogeneity and conditional task reallocation in response to dynamic op-erational states."}
+{"idx": 8, "title": "Driver adaptive task allocation: A field driving study. - APA PsycNet", "date": "", "ddg_snippet": "Adaptive task allocation ( ATA ) provides a new solution for human-machine interaction in a highly automated system. Previous research demonstrates that psychophysiological signals yield sensitive information about human functional states, which can be used to build a closed loop for human-machine interaction to reallocate the tasks upon the status of human operator. The present study ...", "subpage_snippet": "", "source": "psycnet.apa.org", "link": "https://psycnet.apa.org/record/2017-47524-005", "content": "Adaptive task allocation ( ATA ) provides a new solution for human-machine interaction in a highly automated system. Previous research demonstrates that psychophysiological signals yield sensitive information about human functional states, which can be used to build a closed loop for human-machine interaction to reallocate the tasks upon the status of human operator. The present study ..."}
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