diff --git a/data/sampled_jsons/'Grassmannian_cluster_algebra_dataset'_Table_10_Machine_Learning_meets_Algebraic_Combinatorics_year_2024.jsonl b/data/sampled_jsons/'Grassmannian_cluster_algebra_dataset'_Table_10_Machine_Learning_meets_Algebraic_Combinatorics_year_2024.jsonl
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index 0000000000000000000000000000000000000000..b1ebd5312d84c10bb3e9c803b354cdf29777f1e6
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+++ b/data/sampled_jsons/'Grassmannian_cluster_algebra_dataset'_Table_10_Machine_Learning_meets_Algebraic_Combinatorics_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"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) . Table 10 : Statistics of the Grassmannian cluster algebra dataset .", "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) . Table 10 : Statistics of the Grassmannian cluster algebra dataset ."}
+{"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.", "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."}
+{"idx": 2, "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": 3, "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": 4, "title": "Grassmannians and Cluster Structures | Bulletin of the Iranian...", "date": "", "ddg_snippet": "Cluster structures have been established on numerous algebraic varieties. These lectures focus on the Grassmannian variety and explain the cluster structures on it. The tools include dimer models on surfaces, associated algebras , and the study of associated module categories.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s41980-021-00542-6", "content": "Cluster structures have been established on numerous algebraic varieties. These lectures focus on the Grassmannian variety and explain the cluster structures on it. The tools include dimer models on surfaces, associated algebras , and the study of associated module categories."}
+{"idx": 5, "title": "Machine Learning meets Algebraic Combinatorics : A Suite... | PNNL", "date": "", "ddg_snippet": "The use of benchmark datasets has become an important engine of progress in machine learning (ML) over the past 15 years. Recently there has been growing interest in utilizing machine learning to drive advances in research-level mathematics.", "subpage_snippet": "", "source": "www.pnnl.gov", "link": "https://www.pnnl.gov/publications/machine-learning-meets-algebraic-combinatorics-suite-benchmark-datasets-accelerate-ai", "content": "The use of benchmark datasets has become an important engine of progress in machine learning (ML) over the past 15 years. Recently there has been growing interest in utilizing machine learning to drive advances in research-level mathematics."}
+{"idx": 6, "title": "ag. algebraic geometry - Combinatorics of the... - MathOverflow", "date": "", "ddg_snippet": "Combinatorics of the Cohomology Ring of the Lagrangian Grassmannians .As is well-known, the cohomology ring of the Grassmannians has a very nice combinatorial description in terms of partitions, see this very nice M.O. answer for example.", "subpage_snippet": "", "source": "mathoverflow.net", "link": "https://mathoverflow.net/questions/261903/combinatorics-of-the-cohomology-ring-of-the-lagrangian-grassmannians", "content": "Combinatorics of the Cohomology Ring of the Lagrangian Grassmannians .As is well-known, the cohomology ring of the Grassmannians has a very nice combinatorial description in terms of partitions, see this very nice M.O. answer for example."}
+{"idx": 7, "title": "Free Video: Combinatorics of the Amplituhedron from... | Class Central", "date": "", "ddg_snippet": "Explore the amplituhedron's connections to cluster algebras , matroids, and combinatorics in this gentle introduction by Lauren Williams from Harvard University.", "subpage_snippet": "", "source": "www.classcentral.com", "link": "https://www.classcentral.com/course/youtube-lauren-williams-combinatorics-of-the-amplituhedron-319071", "content": "Explore the amplituhedron's connections to cluster algebras , matroids, and combinatorics in this gentle introduction by Lauren Williams from Harvard University."}
+{"idx": 8, "title": "A categorification of Grassmannian cluster algebras - the University...", "date": "", "ddg_snippet": "Jensen BT , King AD , Su X . A categorification of Grassmannian cluster algebras . Proceedings of the London Mathematical Society . 2016 Aug 2;113(2):185-212.", "subpage_snippet": "", "source": "researchportal.bath.ac.uk", "link": "https://researchportal.bath.ac.uk/en/publications/a-categorification-of-grassmannian-cluster-algebras", "content": "Jensen BT , King AD , Su X . A categorification of Grassmannian cluster algebras . Proceedings of the London Mathematical Society . 2016 Aug 2;113(2):185-212."}
+{"idx": 9, "title": "UNAM Oaxaca - Cited by 254 - Grassmannians - toric degenerations...", "date": "", "ddg_snippet": "Combinatorial Algebraic Geometry: Selected Papers From the 2016 …, 2017.2020. Families of Gröbner degenerations, Grassmannians and universal cluster algebras .", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=husFyf8AAAAJ&hl=en", "content": "Combinatorial Algebraic Geometry: Selected Papers From the 2016 …, 2017.2020. Families of Gröbner degenerations, Grassmannians and universal cluster algebras ."}
diff --git a/data/sampled_jsons/'Machine_Learning_meets_Algebraic_Combinatorics'_'S18_characters_dataset'_'symmetric_group'_Appendix.jsonl b/data/sampled_jsons/'Machine_Learning_meets_Algebraic_Combinatorics'_'S18_characters_dataset'_'symmetric_group'_Appendix.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a69f7159a03dd03a238d3ca8ecd3aa760155eff2
--- /dev/null
+++ b/data/sampled_jsons/'Machine_Learning_meets_Algebraic_Combinatorics'_'S18_characters_dataset'_'symmetric_group'_Appendix.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Machine - Wikipedia", "date": "", "ddg_snippet": "A machine is a thermodynamic system that uses power to apply forces and control movement to perform an action. The term is commonly applied to artificial devices, such as those employing engines or motors, but also to natural biological macromolecules, such as molecular machines .", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Machine", "content": "A machine is a thermodynamic system that uses power to apply forces and control movement to perform an action. The term is commonly applied to artificial devices, such as those employing engines or motors, but also to natural biological macromolecules, such as molecular machines ."}
+{"idx": 1, "title": "MACHINE Definition & Meaning | Dictionary .com", "date": "", "ddg_snippet": "Machine definition: an apparatus consisting of interrelated parts with separate functions, used in the performance of some kind of work.. See examples of MACHINE used in a sentence.", "subpage_snippet": "", "source": "www.dictionary.com", "link": "https://www.dictionary.com/browse/machine", "content": "Machine definition: an apparatus consisting of interrelated parts with separate functions, used in the performance of some kind of work.. See examples of MACHINE used in a sentence."}
+{"idx": 2, "title": "MACHINE Definition & Meaning - Merriam-Webster", "date": "", "ddg_snippet": "The meaning of MACHINE is a mechanically, electrically, or electronically operated device for performing a task. How to use machine in a sentence.", "subpage_snippet": "", "source": "www.merriam-webster.com", "link": "https://www.merriam-webster.com/dictionary/machine", "content": "The meaning of MACHINE is a mechanically, electrically, or electronically operated device for performing a task. How to use machine in a sentence."}
+{"idx": 3, "title": "Machine | Definition, Mechanisms & Efficiency | Britannica", "date": "", "ddg_snippet": "machine , device, having a unique purpose, that augments or replaces human or animal effort for the accomplishment of physical tasks.", "subpage_snippet": "", "source": "www.britannica.com", "link": "https://www.britannica.com/technology/machine", "content": "machine , device, having a unique purpose, that augments or replaces human or animal effort for the accomplishment of physical tasks."}
+{"idx": 4, "title": "MACHINE | English meaning - Cambridge Dictionary", "date": "", "ddg_snippet": "MACHINE definition: 1. a piece of equipment with several moving parts that uses power to do a particular type of work…. Learn more.", "subpage_snippet": "", "source": "dictionary.cambridge.org", "link": "https://dictionary.cambridge.org/dictionary/english/machine", "content": "MACHINE definition: 1. a piece of equipment with several moving parts that uses power to do a particular type of work…. Learn more."}
+{"idx": 5, "title": "machine , n. meanings, etymology and more | Oxford English...", "date": "", "ddg_snippet": "machine , n. meanings, etymology, pronunciation and more in the Oxford English Dictionary", "subpage_snippet": "", "source": "www.oed.com", "link": "https://www.oed.com/dictionary/machine_n", "content": "machine , n. meanings, etymology, pronunciation and more in the Oxford English Dictionary"}
+{"idx": 6, "title": "What Is A Machine ? Its Types and How it Works - Mech Lesson", "date": "", "ddg_snippet": "A machine is a mechanical device that uses power to apply force and control motion to perform work efficiently . Machines range from simple tools like pulleys and levers to complex systems like engines, robots, and manufacturing equipment.", "subpage_snippet": "", "source": "mechlesson.com", "link": "https://mechlesson.com/machine/", "content": "A machine is a mechanical device that uses power to apply force and control motion to perform work efficiently . Machines range from simple tools like pulleys and levers to complex systems like engines, robots, and manufacturing equipment."}
+{"idx": 7, "title": "machine - Wiktionary, the free dictionary", "date": "", "ddg_snippet": "2 days ago · (figuratively) A person or organisation that seemingly acts like a machine , being particularly efficient, single-minded, or unemotional. Bruce Campbell was a \"demon-killing machine \" because he made quick work of killing demons. The government has become a money-making machine .", "subpage_snippet": "", "source": "en.wiktionary.org", "link": "https://en.wiktionary.org/wiki/machine", "content": "2 days ago · (figuratively) A person or organisation that seemingly acts like a machine , being particularly efficient, single-minded, or unemotional. Bruce Campbell was a \"demon-killing machine \" because he made quick work of killing demons. The government has become a money-making machine ."}
+{"idx": 8, "title": "Machine - definition of machine by The Free Dictionary", "date": "", "ddg_snippet": "Of, relating to, or felt to resemble a machine : machine repairs; machine politics.", "subpage_snippet": "", "source": "www.thefreedictionary.com", "link": "https://www.thefreedictionary.com/machine", "content": "Of, relating to, or felt to resemble a machine : machine repairs; machine politics."}
+{"idx": 9, "title": "The Smashing Machine | Official Trailer HD | A24 - YouTube", "date": "", "ddg_snippet": "Special Blu-ray & 4K editions of Danny & Michael Philippou's sinister tale of domestic and occult horror. Special features include a commentary with the directors, one deleted scene, and an...", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=aRpnP3LZ99g", "content": "Special Blu-ray & 4K editions of Danny & Michael Philippou's sinister tale of domestic and occult horror. Special features include a commentary with the directors, one deleted scene, and an..."}
diff --git a/data/sampled_jsons/0C3bLHwjsY_Promoting_Fairness_Among_Dynamic_Agents_in_Online-Matching_Markets_under_Known_Stationary.jsonl b/data/sampled_jsons/0C3bLHwjsY_Promoting_Fairness_Among_Dynamic_Agents_in_Online-Matching_Markets_under_Known_Stationary.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..1df2ded1f2572e4995e56d843a6e143755d7fece
--- /dev/null
+++ b/data/sampled_jsons/0C3bLHwjsY_Promoting_Fairness_Among_Dynamic_Agents_in_Online-Matching_Markets_under_Known_Stationary.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Promoting Fairness Among Dynamic Agents in Online-Matching Markets ...", "date": "", "ddg_snippet": "Online (bipartite) matching under known stationary arrivals is a fundamental model that has been studied extensively under the objective of maximizing the total number of customers served. We instead study the objective of *maximizing the minimum matching rate across all online types*, which is referred to as long-run (individual) fairness .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=0C3bLHwjsY", "content": "Online (bipartite) matching under known stationary arrivals is a fundamental model that has been studied extensively under the objective of maximizing the total number of customers served. We instead study the objective of *maximizing the minimum matching rate across all online types*, which is referred to as long-run (individual) fairness ."}
+{"idx": 1, "title": "NeurIPS Poster Promoting Fairness Among Dynamic Agents in Online ...", "date": "", "ddg_snippet": "Poster Promoting Fairness Among Dynamic Agents in Online-Matching Markets under Known Stationary Arrival Distributions Will Ma · Pan Xu", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/poster/96945", "content": "Poster Promoting Fairness Among Dynamic Agents in Online-Matching Markets under Known Stationary Arrival Distributions Will Ma · Pan Xu"}
+{"idx": 2, "title": "Fairness Maximization among Offline Agents in Online-Matching Markets", "date": "", "ddg_snippet": "In this paper, we propose online matching algorithms which optimize for either individual or group-level fairness among offline agents in OMMs. We present two linear-programming (LP) based sampling algorithms, which achieve online competitive ratios at least 0.725 for individual fairness maximization (IFM) and 0.719 for group fairness ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2109.08934", "content": "In this paper, we propose online matching algorithms which optimize for either individual or group-level fairness among offline agents in OMMs. We present two linear-programming (LP) based sampling algorithms, which achieve online competitive ratios at least 0.725 for individual fairness maximization (IFM) and 0.719 for group fairness ..."}
+{"idx": 3, "title": "Fairness Maximization among Offline Agents in Online-Matching Markets", "date": "", "ddg_snippet": "We study the prototypical online matching model with known , independent, and identical (KIID) arrivals , which is widely used to model the dynamic arrivals of online agents in several real-world OMMs including rideshare and crowdsourcing markets [14, 18, 54].", "subpage_snippet": "", "source": "par.nsf.gov", "link": "https://par.nsf.gov/servlets/purl/10420152", "content": "We study the prototypical online matching model with known , independent, and identical (KIID) arrivals , which is widely used to model the dynamic arrivals of online agents in several real-world OMMs including rideshare and crowdsourcing markets [14, 18, 54]."}
+{"idx": 4, "title": "Fairness · NeurIPS 2024", "date": "", "ddg_snippet": "Promoting Fairness Among Dynamic Agents in Online-Matching Markets under Known Stationary Arrival Distributions 26 September 2024·1572 words·8 mins·loading·loading", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/tags/fairness/", "content": "Promoting Fairness Among Dynamic Agents in Online-Matching Markets under Known Stationary Arrival Distributions 26 September 2024·1572 words·8 mins·loading·loading"}
+{"idx": 5, "title": "Class Fairness in Online Matching | Proceedings of the AAAI Conference ...", "date": "", "ddg_snippet": "We initiate the study of fairness among classes of agents in online bipartite matching where there is a given set of offline vertices (aka agents ) and another set of vertices (aka items) that arrive online and must be matched irrevocably upon arrival .", "subpage_snippet": "", "source": "ojs.aaai.org", "link": "https://ojs.aaai.org/index.php/AAAI/article/view/25704", "content": "We initiate the study of fairness among classes of agents in online bipartite matching where there is a given set of offline vertices (aka agents ) and another set of vertices (aka items) that arrive online and must be matched irrevocably upon arrival ."}
+{"idx": 6, "title": "Promoting Fairness Among Dynamic Agents in Online-Matching Markets ...", "date": "", "ddg_snippet": "Authors Will Ma, Pan Xu Abstract Online (bipartite) matching under known stationary arrivals is a fundamental model that has been studied extensively under the objective of maximizing the total number of customers served. We instead study the objective of *maximizing the minimum matching rate across all online types*, which is referred to as long-run (individual) fairness . For Online Matching ...", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper_files/paper/2024/hash/959f70ee50044bed305e48e3484005a7-Abstract-Conference.html", "content": "Authors Will Ma, Pan Xu Abstract Online (bipartite) matching under known stationary arrivals is a fundamental model that has been studied extensively under the objective of maximizing the total number of customers served. We instead study the objective of *maximizing the minimum matching rate across all online types*, which is referred to as long-run (individual) fairness . For Online Matching ..."}
+{"idx": 7, "title": "Promoting Fairness Among Dynamic Agents in Online-Matching Markets ...", "date": "", "ddg_snippet": "Abstract Online (bipartite) matching under known stationary arrivals is a fundamental model that has been studied extensively with the objective of maximizing the total number of customers served. We instead study the objective of maximizing the minimum matching rate across all online types, which is referred to as long-run (individual) fairness .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=0C3bLHwjsY", "content": "Abstract Online (bipartite) matching under known stationary arrivals is a fundamental model that has been studied extensively with the objective of maximizing the total number of customers served. We instead study the objective of maximizing the minimum matching rate across all online types, which is referred to as long-run (individual) fairness ."}
+{"idx": 8, "title": "PDF Promoting Fairness Among Dynamic Agents in", "date": "", "ddg_snippet": "Promoting Fairness Among Dynamic Agents in Online-Matching Markets under Known Stationary Arrival Distributions Author Will Ma1 Pan Xu2", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/media/neurips-2024/Slides/96945.pdf", "content": "Promoting Fairness Among Dynamic Agents in Online-Matching Markets under Known Stationary Arrival Distributions Author Will Ma1 Pan Xu2"}
+{"idx": 9, "title": "Class Fairness in Online Matching - arXiv.org", "date": "", "ddg_snippet": "We initiate the study of fairness among classes of agents in online bipartite matching . Our first contribution (Section 2) is developing a detailed mathematical framework in which we adopt clas-sical fairness concepts to online matching .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2203.03751", "content": "We initiate the study of fairness among classes of agents in online bipartite matching . Our first contribution (Section 2) is developing a detailed mathematical framework in which we adopt clas-sical fairness concepts to online matching ."}
diff --git a/data/sampled_jsons/100000_148658_S18_Grassmannian_cluster_algebra_characters_dataset.jsonl b/data/sampled_jsons/100000_148658_S18_Grassmannian_cluster_algebra_characters_dataset.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f697426153111378089889b99f99c43bc6d5bd93
--- /dev/null
+++ b/data/sampled_jsons/100000_148658_S18_Grassmannian_cluster_algebra_characters_dataset.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[1309.7301] A categorification of Grassmannian cluster algebras", "date": "", "ddg_snippet": "View a PDF of the paper titled A categorification of Grassmannian cluster algebras , by Bernt Tore Jensen and 1 other authors.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1309.7301", "content": "View a PDF of the paper titled A categorification of Grassmannian cluster algebras , by Bernt Tore Jensen and 1 other authors."}
+{"idx": 1, "title": "(PDF) Tropical Grassmannians , cluster algebras and scattering...", "date": "", "ddg_snippet": "Grassmannians to certain cluster algebras , as developed by Fomin and Zelevinsky [6,7]. These same cluster algebras have also arisen in the study of the singularities of loop am-. plitudes in planar N= 4 super Yang-Mills theory [8]. The cluster algebra picture provides.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/340865972_Tropical_Grassmannians_cluster_algebras_and_scattering_amplitudes", "content": "Grassmannians to certain cluster algebras , as developed by Fomin and Zelevinsky [6,7]. These same cluster algebras have also arisen in the study of the singularities of loop am-. plitudes in planar N= 4 super Yang-Mills theory [8]. The cluster algebra picture provides."}
+{"idx": 2, "title": "(PDF) Grassmannians and Cluster Algebras", "date": "", "ddg_snippet": "GRASSMANNIANS AND CLUSTER ALGEBRAS arXiv:math/0311148v1 [math.CO] 10 Nov 2003 1. Introduction This paper follows the program of study initiated by S. Fomin and A. Zelevinsky in [2]...", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/99109832/Grassmannians_and_Cluster_Algebras", "content": "GRASSMANNIANS AND CLUSTER ALGEBRAS arXiv:math/0311148v1 [math.CO] 10 Nov 2003 1. Introduction This paper follows the program of study initiated by S. Fomin and A. Zelevinsky in [2]..."}
+{"idx": 3, "title": "Grassmannian categories of infinite rank", "date": "", "ddg_snippet": "Grassmannian cluster categories are an additive cat-egorication of Grassmannian cluster algebras , of which this section provides an overview. 2.1.1. The Finite Rank Case.The character group of Gm is the group of integers Z. This yields an equivalence of categories.", "subpage_snippet": "", "source": "pure.au.dk", "link": "https://pure.au.dk/ws/portalfiles/portal/281555143/2007.14224v1.pdf", "content": "Grassmannian cluster categories are an additive cat-egorication of Grassmannian cluster algebras , of which this section provides an overview. 2.1.1. The Finite Rank Case.The character group of Gm is the group of integers Z. This yields an equivalence of categories."}
+{"idx": 4, "title": "Find Open Datasets and Machine Learning Projects | Kaggle", "date": "", "ddg_snippet": "Download Open Datasets on 1000s of Projects + Share Projects on One Platform. Explore Popular Topics Like Government, Sports, Medicine, Fintech, Food, More.", "subpage_snippet": "", "source": "www.kaggle.com", "link": "https://www.kaggle.com/datasets", "content": "Download Open Datasets on 1000s of Projects + Share Projects on One Platform. Explore Popular Topics Like Government, Sports, Medicine, Fintech, Food, More."}
+{"idx": 5, "title": "ag. algebraic geometry - Zero set of cluster variables - MathOverflow", "date": "", "ddg_snippet": "The broader motivation is to understand how the geometry (e.g., zero loci in the algebraic torus) of cluster characters reflects the representation-theoretic properties of the modules they arise from.", "subpage_snippet": "", "source": "mathoverflow.net", "link": "https://mathoverflow.net/questions/500418/zero-set-of-cluster-variables", "content": "The broader motivation is to understand how the geometry (e.g., zero loci in the algebraic torus) of cluster characters reflects the representation-theoretic properties of the modules they arise from."}
+{"idx": 6, "title": "25w5439: Representation Theory, Symplectic Geometry, and Cluster ...", "date": "", "ddg_snippet": "Matthew Pressland: Mini-course: Cluster categories for Grassmannians and positroid varieties 3 ↓. In this series of talks, I will explain the additive categorification of cluster algebra structures on the Grassmannian and more general positroid varieties.", "subpage_snippet": "", "source": "www.birs.ca", "link": "https://www.birs.ca/events/2025/5-day-workshops/25w5439/schedule", "content": "Matthew Pressland: Mini-course: Cluster categories for Grassmannians and positroid varieties 3 ↓. In this series of talks, I will explain the additive categorification of cluster algebra structures on the Grassmannian and more general positroid varieties."}
+{"idx": 7, "title": "ASCII Table - ASCII Code Chart with Characters", "date": "", "ddg_snippet": "ASCII Table - Complete ASCII code chart with characters . Also, it contains decimal, hexadecimal, binary, and HTML values.", "subpage_snippet": "", "source": "ascii-tables.com", "link": "https://ascii-tables.com/", "content": "ASCII Table - Complete ASCII code chart with characters . Also, it contains decimal, hexadecimal, binary, and HTML values."}
+{"idx": 8, "title": "Нарезки С Мачо Меном Для Эдита | TikTok", "date": "", "ddg_snippet": "TikTok video from ズtop_edits文(др 6 апреля) (@a.n.i.m.e.e.d.i.t.s7): “буду ждать #anime #мачомен #милионпросмотров 100000 лайков”.", "subpage_snippet": "", "source": "www.tiktok.com", "link": "https://www.tiktok.com/discover/нарезки-с-мачо-меном-для-эдита", "content": "TikTok video from ズtop_edits文(др 6 апреля) (@a.n.i.m.e.e.d.i.t.s7): “буду ждать #anime #мачомен #милионпросмотров 100000 лайков”."}
+{"idx": 9, "title": "Greg Muller", "date": "", "ddg_snippet": "Cluster algebras of Grassmannians are locally acyclic (arXiv) Joint with David Speyer. Character algebras of decorated SL_2(C)-local systems (arXiv) Joint with Peter Samuelson.", "subpage_snippet": "", "source": "www2.math.ou.edu", "link": "http://www2.math.ou.edu/~gmuller/research.html", "content": "Cluster algebras of Grassmannians are locally acyclic (arXiv) Joint with David Speyer. Character algebras of decorated SL_2(C)-local systems (arXiv) Joint with Peter Samuelson."}
diff --git a/data/sampled_jsons/1ZC4RNjqzU_Position-_Evaluating_Generative_AI_Systems_Is_a_Social_Science_Measurement_Challenge_Sect.jsonl b/data/sampled_jsons/1ZC4RNjqzU_Position-_Evaluating_Generative_AI_Systems_Is_a_Social_Science_Measurement_Challenge_Sect.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..610d37ac249a1b1631b5902364b554417426b4a4
--- /dev/null
+++ b/data/sampled_jsons/1ZC4RNjqzU_Position-_Evaluating_Generative_AI_Systems_Is_a_Social_Science_Measurement_Challenge_Sect.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Position : Evaluating Generative AI Systems is a Social Science ...", "date": "", "ddg_snippet": "The measurement tasks involved in evaluating generative AI (GenAI) systems are especially difficult, leading to what has been described as”a tangle of sloppy tests [and] apples-to-oranges comparisons” (Roose, 2024). In this position paper, we argue that the ML community...", "subpage_snippet": "", "source": "www.microsoft.com", "link": "https://www.microsoft.com/en-us/research/publication/position-evaluating-generative-ai-systems-is-a-social-science-measurement-challenge/", "content": "The measurement tasks involved in evaluating generative AI (GenAI) systems are especially difficult, leading to what has been described as”a tangle of sloppy tests [and] apples-to-oranges comparisons” (Roose, 2024). In this position paper, we argue that the ML community..."}
+{"idx": 1, "title": "Position : Evaluating Generative AI Systems Is a Social Science ...", "date": "", "ddg_snippet": "Generative AI , Capabilities, Behaviors, Impacts, Evaluation , Measurement , Measurement Theory, Social Sciences , Validity.4 Using the Measurement Framework. In this section , we describe the systematization , operationalization , and interrogation processes in more detail.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.00561v2", "content": "Generative AI , Capabilities, Behaviors, Impacts, Evaluation , Measurement , Measurement Theory, Social Sciences , Validity.4 Using the Measurement Framework. In this section , we describe the systematization , operationalization , and interrogation processes in more detail."}
+{"idx": 2, "title": "NeurIPS Evaluating Generative AI Systems is a Social Science ...", "date": "", "ddg_snippet": "Across academia, industry, and government, there is an increasing awareness that evaluating generative AI (GenAI) systems is challenging , as concepts related to their capabilities (e.g., intelligence, reasoning) and risks (e.g., stereotyping, anthropomorphism) are especially...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/104194", "content": "Across academia, industry, and government, there is an increasing awareness that evaluating generative AI (GenAI) systems is challenging , as concepts related to their capabilities (e.g., intelligence, reasoning) and risks (e.g., stereotyping, anthropomorphism) are especially..."}
+{"idx": 3, "title": "Downes.ca ~ Stephen's Web ~ Evaluating Generative AI Systems is ...", "date": "", "ddg_snippet": "The argument in this short paper (6 page PDF) is that \" measurement tasks involved in evaluating GenAI systems are highly reminiscent of measurement tasks found throughout the social sciences \" and thus \"the ML community would benefit from learning from and drawing on the social ...", "subpage_snippet": "", "source": "www.downes.ca", "link": "https://www.downes.ca/post/77850", "content": "The argument in this short paper (6 page PDF) is that \" measurement tasks involved in evaluating GenAI systems are highly reminiscent of measurement tasks found throughout the social sciences \" and thus \"the ML community would benefit from learning from and drawing on the social ..."}
+{"idx": 4, "title": "Evaluating Generative AI Systems is a Social Science ...", "date": "", "ddg_snippet": "generative AI systems that borrow from how social scientists measure human behaviors and abilities. Think of current AI evaluation like using a ruler made of rubber - it stretches and bends, giving different measurements each time.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/evaluating-generative-ai-systems-is-social-science", "content": "generative AI systems that borrow from how social scientists measure human behaviors and abilities. Think of current AI evaluation like using a ruler made of rubber - it stretches and bends, giving different measurements each time."}
+{"idx": 5, "title": "This week: Evaluation , measurement , span annotation, openai...", "date": "", "ddg_snippet": "Evaluating Generative AI Systems is a Social Science Measurement Challenge . Aren’t social preferences central to the process? “The framework distinguishes between four levels: the background concept, the systematized concept, the measurement instrument(s), and the...", "subpage_snippet": "", "source": "www.gatodo.com", "link": "https://www.gatodo.com/alignment-with-human-preferences/", "content": "Evaluating Generative AI Systems is a Social Science Measurement Challenge . Aren’t social preferences central to the process? “The framework distinguishes between four levels: the background concept, the systematized concept, the measurement instrument(s), and the..."}
+{"idx": 6, "title": "Evaluating Generative AI : The Evolution Beyond Public Benchmarks", "date": "", "ddg_snippet": "Here, we’ll explore key points of evaluating generative AI , focusing on the paradigm shift away from public benchmarks and toward task-specific evaluations .", "subpage_snippet": "", "source": "opendatascience.com", "link": "https://opendatascience.com/evaluating-generative-ai-the-evolution-beyond-public-benchmarks/", "content": "Here, we’ll explore key points of evaluating generative AI , focusing on the paradigm shift away from public benchmarks and toward task-specific evaluations ."}
+{"idx": 7, "title": "Su Lin Blodgett's research works | Microsoft and other places", "date": "", "ddg_snippet": "Position : Evaluating Generative AI Systems is a Social Science Measurement Challenge . Preprint.Specifically, our position is that evaluating GenAI systems is a social science measurement challenge .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/scientific-contributions/Su-Lin-Blodgett-2111326771", "content": "Position : Evaluating Generative AI Systems is a Social Science Measurement Challenge . Preprint.Specifically, our position is that evaluating GenAI systems is a social science measurement challenge ."}
+{"idx": 8, "title": "Towards Interactive Evaluations for Interaction Harms in Human- AI ...", "date": "", "ddg_snippet": "2025. Position : Evaluating Generative AI Systems is a Social Science Measurement Challenge . arXiv preprint arXiv:2502.00561. Wang, A.; Morgenstern, J.; and Dickerson, J. P. 2025. Large language models that replace human participants can harm fully misportray and flatten identity...", "subpage_snippet": "", "source": "knightcolumbia.org", "link": "https://knightcolumbia.org/content/towards-interactive-evaluations-for-interaction-harms-in-human-ai-systems", "content": "2025. Position : Evaluating Generative AI Systems is a Social Science Measurement Challenge . arXiv preprint arXiv:2502.00561. Wang, A.; Morgenstern, J.; and Dickerson, J. P. 2025. Large language models that replace human participants can harm fully misportray and flatten identity..."}
+{"idx": 9, "title": "The Impossibility of Fair LLMs", "date": "", "ddg_snippet": "systems is a social science measurement challenge . Preprint, arXiv:2502.00561. Jindong Wang, Wenjie Feng, Chang Liu, Chaohui Yu, Mingxuan Du, Renjun Xu, Tao Qin, and Tie-Yan Liu.2025. Toward an eval- uation science for generative ai systems .", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.acl-long.5.pdf", "content": "systems is a social science measurement challenge . Preprint, arXiv:2502.00561. Jindong Wang, Wenjie Feng, Chang Liu, Chaohui Yu, Mingxuan Du, Renjun Xu, Tao Qin, and Tie-Yan Liu.2025. Toward an eval- uation science for generative ai systems ."}
diff --git a/data/sampled_jsons/2024_synthetic_data_generation_scaling_laws.jsonl b/data/sampled_jsons/2024_synthetic_data_generation_scaling_laws.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7fd48e9a62aca517568fdf55d7db4975e9c3f49e
--- /dev/null
+++ b/data/sampled_jsons/2024_synthetic_data_generation_scaling_laws.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Scaling Synthetic Data Creation with 1,000,000,000 Personas", "date": "", "ddg_snippet": "As synthetic data (Bauer et al., 2024 ; Liu et al., 2024 ) , typically referring to data generated by models or algorithms rather than directly ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.20094v1", "content": "As synthetic data (Bauer et al., 2024 ; Liu et al., 2024 ) , typically referring to data generated by models or algorithms rather than directly ..."}
+{"idx": 1, "title": "Top 10 Companies in Synthetic Data Generation Market in 2025", "date": "", "ddg_snippet": "The worldwide Synthetic Data Generation (SDG) market size was USD 1.30 billion in 2023 and is predicted to reach USD 1.81 billion by the end of 2024 ...", "subpage_snippet": "", "source": "www.emergenresearch.com", "link": "https://www.emergenresearch.com/blog/top-10-companies-in-global-synthetic-data-generation-market", "content": "The worldwide Synthetic Data Generation (SDG) market size was USD 1.30 billion in 2023 and is predicted to reach USD 1.81 billion by the end of 2024 ..."}
+{"idx": 2, "title": "Scaling Synthetic Data Pilots for Enterprise AI | EM360Tech", "date": "", "ddg_snippet": "If even one of these is missing, your synthetic data is not ready for scale . ... The synthetic data generation market was valued at USD 288.5 million ...", "subpage_snippet": "", "source": "em360tech.com", "link": "https://em360tech.com/tech-articles/scaling-synthetic-data-pilots", "content": "If even one of these is missing, your synthetic data is not ready for scale . ... The synthetic data generation market was valued at USD 288.5 million ..."}
+{"idx": 3, "title": "Synthetic Data in 2024 - Progress, Opportunities and Challenges", "date": "", "ddg_snippet": "... and smaller models have seen significant price drops, and this trend shows no signs of slowing down, making large- scale synthetic data generation ...", "subpage_snippet": "", "source": "www.timlrx.com", "link": "https://www.timlrx.com/blog/synthetic-data-in-2024-progress-opportunities-challenges", "content": "... and smaller models have seen significant price drops, and this trend shows no signs of slowing down, making large- scale synthetic data generation ..."}
+{"idx": 4, "title": "Synthetic Data Generation Market Size, Report By 2034", "date": "", "ddg_snippet": "The North America synthetic data generation market size accounted for USD 159.87 million in 2024 and is anticipated to grow at the fastest CAGR of 35 ...", "subpage_snippet": "", "source": "www.precedenceresearch.com", "link": "https://www.precedenceresearch.com/synthetic-data-generation-market", "content": "The North America synthetic data generation market size accounted for USD 159.87 million in 2024 and is anticipated to grow at the fastest CAGR of 35 ..."}
+{"idx": 5, "title": "Synthetic Data Generation Market Size | CAGR of 35.9%", "date": "", "ddg_snippet": "The Healthcare & Life Sciences sector emerged as the top industry for synthetic data generation adoption in 2024 , securing over 23.9% of the ...", "subpage_snippet": "", "source": "market.us", "link": "https://market.us/report/synthetic-data-generation-market/", "content": "The Healthcare & Life Sciences sector emerged as the top industry for synthetic data generation adoption in 2024 , securing over 23.9% of the ..."}
+{"idx": 6, "title": "Synthetic Data Generation Market Size, Forecast Analysis - 2035", "date": "", "ddg_snippet": "As per MRFR analysis, the Synthetic Data Generation Market Size was estimated at 1.27 (USD Billion) in 2024 .The Synthetic Data Generation Market ...", "subpage_snippet": "", "source": "www.marketresearchfuture.com", "link": "https://www.marketresearchfuture.com/reports/synthetic-data-generation-market-12216", "content": "As per MRFR analysis, the Synthetic Data Generation Market Size was estimated at 1.27 (USD Billion) in 2024 .The Synthetic Data Generation Market ..."}
+{"idx": 7, "title": "EP154 - Scaling Laws of Synthetic Images for Model Training ...", "date": "", "ddg_snippet": "Clio : The key takeaway was that synthetic data , when generated considering these factors, can indeed scale well and improve supervised training ...", "subpage_snippet": "", "source": "paperbrief.net", "link": "https://paperbrief.net/posts/154/", "content": "Clio : The key takeaway was that synthetic data , when generated considering these factors, can indeed scale well and improve supervised training ..."}
+{"idx": 8, "title": "Synthetic Data Generation Market Size, Share, Growth Factors,", "date": "", "ddg_snippet": "The global Synthetic Data Generation market report highlights the discussion on the top segments across the Synthetic Data Generation industry and ...", "subpage_snippet": "", "source": "jobs.ezelogs.com", "link": "https://jobs.ezelogs.com/job/synthetic-data-generation-market-size-share-growth-factors-competitive-landscape-with-regional-forecast-to-2035/", "content": "The global Synthetic Data Generation market report highlights the discussion on the top segments across the Synthetic Data Generation industry and ..."}
+{"idx": 9, "title": "Mike Lewis - ACL Anthology", "date": "", "ddg_snippet": "... knowledge and reasoning-based skills such as knowledge-based QA and code generation , and we answer this question in the affirmative: scaling laws are ...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/people/mike-lewis/", "content": "... knowledge and reasoning-based skills such as knowledge-based QA and code generation , and we answer this question in the affirmative: scaling laws are ..."}
diff --git a/data/sampled_jsons/2406.05072_Linearization_Turns_Neural_Operators_Theorem_3.2.jsonl b/data/sampled_jsons/2406.05072_Linearization_Turns_Neural_Operators_Theorem_3.2.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c69b39ddccc165787f3cc488619d91a1318da067
--- /dev/null
+++ b/data/sampled_jsons/2406.05072_Linearization_Turns_Neural_Operators_Theorem_3.2.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Linearization Turns Neural Operators into Function-Valued Gaussian...", "date": "", "ddg_snippet": "2.1. Neural Operators Neural operators (NOs) (Kovachki et al., 2023) are neural network architectures that map between (infinite-dimensional) Banach spaces of functions. A neural operator is a function F : A × W → U, where.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2406.05072", "content": "2.1. Neural Operators Neural operators (NOs) (Kovachki et al., 2023) are neural network architectures that map between (infinite-dimensional) Banach spaces of functions. A neural operator is a function F : A × W → U, where."}
+{"idx": 1, "title": "[ 2406 . 05072 ] Linearization Turns Neural Operators into...", "date": "", "ddg_snippet": "arXiv: 2406 . 05072 (cs). [Submitted on 7 Jun 2024 (v1), last revised 31 Jan 2025 (this version, v2)].View a PDF of the paper titled Linearization Turns Neural Operators into Function-Valued Gaussian Processes, by Emilia Magnani and 3 other authors.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2406.05072", "content": "arXiv: 2406 . 05072 (cs). [Submitted on 7 Jun 2024 (v1), last revised 31 Jan 2025 (this version, v2)].View a PDF of the paper titled Linearization Turns Neural Operators into Function-Valued Gaussian Processes, by Emilia Magnani and 3 other authors."}
+{"idx": 2, "title": "Linearization Turns Neural Operators into... | alphaXiv", "date": "", "ddg_snippet": "Abstract: Neural operators generalize neural networks to learn mappings between function spaces from data. They are commonly used to learn solution operators of parametric partial differential equations (PDEs) or propagators of time-dependent PDEs.", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/abs/2406.05072", "content": "Abstract: Neural operators generalize neural networks to learn mappings between function spaces from data. They are commonly used to learn solution operators of parametric partial differential equations (PDEs) or propagators of time-dependent PDEs."}
+{"idx": 3, "title": "Linearization Turns Neural Operators into Function-Valued Gaussian...", "date": "", "ddg_snippet": "The paper introduces the Neural Operator Laplace Approximation (NOLA) to address the lack of uncertainty quantification in neural operators used for modeling dynamical systems governed by partial differential equations (PDEs).", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/papers/2406.05072", "content": "The paper introduces the Neural Operator Laplace Approximation (NOLA) to address the lack of uncertainty quantification in neural operators used for modeling dynamical systems governed by partial differential equations (PDEs)."}
+{"idx": 4, "title": "(PDF) Linearization Turns Neural Operators into Function-Valued...", "date": "", "ddg_snippet": "Neural operators ... | Find, read and cite all the research you need on ResearchGate.∗Equal contribution. arXiv: 2406 . 05072 v1 [cs.LG] 7 Jun 2024. The complex nature of dynamic systems makes errors in predictions difficult to detect. Uncertainty.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/381294298_Linearization_Turns_Neural_Operators_into_Function-Valued_Gaussian_Processes", "content": "Neural operators ... | Find, read and cite all the research you need on ResearchGate.∗Equal contribution. arXiv: 2406 . 05072 v1 [cs.LG] 7 Jun 2024. The complex nature of dynamic systems makes errors in predictions difficult to detect. Uncertainty."}
+{"idx": 5, "title": "Linearization Turns Neural Operators into Function-Valued Gaussian...", "date": "", "ddg_snippet": "2406 . 05072 . Neural operators are deep neural networks designed to learn nontrivial solution operators of such differential equations from data.", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/arxiv/2406.05072", "content": "2406 . 05072 . Neural operators are deep neural networks designed to learn nontrivial solution operators of such differential equations from data."}
+{"idx": 6, "title": "Linearization Turns Neural Operators into Function-Valued Gaussian...", "date": "", "ddg_snippet": "https://doi.org/10.48550/arXiv. 2406 . 05072 . DDC-Klassifikation: 004 - Informatik.", "subpage_snippet": "", "source": "publikationen.uni-tuebingen.de", "link": "https://publikationen.uni-tuebingen.de/xmlui/handle/10900/159278", "content": "https://doi.org/10.48550/arXiv. 2406 . 05072 . DDC-Klassifikation: 004 - Informatik."}
+{"idx": 7, "title": "Linearization Turns Neural Operators into Function-Valued Gaussian...", "date": "", "ddg_snippet": "Neural operators are deep neural networks designed to learn nontrivial solution operators of such differential equations from data.Full paper. Read original: arXiv: 2406 . 05072 .", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/linearization-turns-neural-operators-into-function-valued", "content": "Neural operators are deep neural networks designed to learn nontrivial solution operators of such differential equations from data.Full paper. Read original: arXiv: 2406 . 05072 ."}
+{"idx": 8, "title": "Neural PDE operator learning on domains with interesting geometry...", "date": "", "ddg_snippet": "4 Graph neural operators . 5 DeepONets. 6 References.2024. “ Linearization Turns Neural Operators into Function-Valued Gaussian Processes.”", "subpage_snippet": "", "source": "danmackinlay.name", "link": "https://danmackinlay.name/notebook/ml_pde_interesting_geometries", "content": "4 Graph neural operators . 5 DeepONets. 6 References.2024. “ Linearization Turns Neural Operators into Function-Valued Gaussian Processes.”"}
+{"idx": 9, "title": "GitHub - MethodsOfMachineLearning/luno: LUNO: Linearized ...", "date": "", "ddg_snippet": "luno - Linearized Uncertainty for Neural Operators . This repository contains the main algorithm of the paper \" Linearization Turns Neural Operators into Function-Valued Gaussian Processes\" by Magnani et al. (2025). Description.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/MethodsOfMachineLearning/luno", "content": "luno - Linearized Uncertainty for Neural Operators . This repository contains the main algorithm of the paper \" Linearization Turns Neural Operators into Function-Valued Gaussian Processes\" by Magnani et al. (2025). Description."}
diff --git a/data/sampled_jsons/2410.02025_Table_2_MNIST_Wasserstein_distances_sparsely_connected_model_empirical_results.jsonl b/data/sampled_jsons/2410.02025_Table_2_MNIST_Wasserstein_distances_sparsely_connected_model_empirical_results.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..eafd39251978c651c3fc84011e6e86e9ae96fa5e
--- /dev/null
+++ b/data/sampled_jsons/2410.02025_Table_2_MNIST_Wasserstein_distances_sparsely_connected_model_empirical_results.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Wasserstein Distances, Neuronal Entanglement, and Sparsity", "date": "", "ddg_snippet": "A.2 Distribution of Wasserstein distances across all Llama-2-7B FFN layers After collecting the Wasserstein distance to the normal distribution for every neuron, we find that all up and gate projection matrices in each Llama-2-7B FFN block have high WD neurons.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.15756v4", "content": "A.2 Distribution of Wasserstein distances across all Llama-2-7B FFN layers After collecting the Wasserstein distance to the normal distribution for every neuron, we find that all up and gate projection matrices in each Llama-2-7B FFN block have high WD neurons."}
+{"idx": 1, "title": "PDF Quantifying the Empirical Wasserstein Distance to a Set of ... - NeurIPS", "date": "", "ddg_snippet": "This further extends conventional results on the rate of statistical convergence for Wasserstein distances between an empirical distribution and the true (unknown) distribution.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper/2020/file/f3507289cfdc8c9ae93f4098111a13f9-Paper.pdf", "content": "This further extends conventional results on the rate of statistical convergence for Wasserstein distances between an empirical distribution and the true (unknown) distribution."}
+{"idx": 2, "title": "Statistical Aspects of Wasserstein Distances | Annual Reviews", "date": "", "ddg_snippet": "Wasserstein distances are metrics on probability distributions inspired by the problem of optimal mass transportation. Roughly speaking, they measure the minimal effort required to reconfigure the probability mass of one distribution in order to recover the other distribution. They are ubiquitous in mathematics, with a long history that has seen them catalyze core developments in analysis ...", "subpage_snippet": "", "source": "www.annualreviews.org", "link": "https://www.annualreviews.org/content/journals/10.1146/annurev-statistics-030718-104938", "content": "Wasserstein distances are metrics on probability distributions inspired by the problem of optimal mass transportation. Roughly speaking, they measure the minimal effort required to reconfigure the probability mass of one distribution in order to recover the other distribution. They are ubiquitous in mathematics, with a long history that has seen them catalyze core developments in analysis ..."}
+{"idx": 3, "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": 4, "title": "Wasserstein Distances, Neuronal Entanglement, and Sparsity", "date": "", "ddg_snippet": "Here we attempt to study how disentanglement can be used to understand performance, particularly under weight sparsity, a leading post-training optimization technique. We suggest a novel measure for estimating neuronal entanglement: the Wasserstein distance of a neuron's output distribution to a Gaussian.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=cnKhHxN3xj", "content": "Here we attempt to study how disentanglement can be used to understand performance, particularly under weight sparsity, a leading post-training optimization technique. We suggest a novel measure for estimating neuronal entanglement: the Wasserstein distance of a neuron's output distribution to a Gaussian."}
+{"idx": 5, "title": "Wasserstein Distances Made Explainable: Insights into Dataset Shifts ...", "date": "", "ddg_snippet": "This confirms the technical strength of our method, namely its sensitivity to the actual Wasserstein distance model and its ability to attribute the Wasserstein distance comprehensively through its fulfillment of the conservation property (cf. Table II for a technical comparison between methods).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2505.06123", "content": "This confirms the technical strength of our method, namely its sensitivity to the actual Wasserstein distance model and its ability to attribute the Wasserstein distance comprehensively through its fulfillment of the conservation property (cf. Table II for a technical comparison between methods)."}
+{"idx": 6, "title": "Wasserstein distances, neuronal entanglement, and sparsity", "date": "", "ddg_snippet": "Here we attempt to study how disentanglement can be used to understand performance, particularly under weight sparsity, a leading post-training optimization technique. We suggest a novel measure for estimating neuronal entanglement: the Wasserstein distance of a neuron's output distribution to a Gaussian.", "subpage_snippet": "", "source": "lin-k76.github.io", "link": "https://lin-k76.github.io/publication/2025-wasserstein", "content": "Here we attempt to study how disentanglement can be used to understand performance, particularly under weight sparsity, a leading post-training optimization technique. We suggest a novel measure for estimating neuronal entanglement: the Wasserstein distance of a neuron's output distribution to a Gaussian."}
+{"idx": 7, "title": "Wasserstein Distances for Stereo Disparity Estimation", "date": "", "ddg_snippet": "To overcome these limitations, the authors propose two adaptions to existing frameworks: (i) adding a branch to predict an offset for each disparity so that the model can output a distribution for arbitrary disparities; (ii) introducing a novel stereo loss based on Wasserstein distance to directly capture the uni-/multi-modal GT distribution.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper/2020/file/fe7ecc4de28b2c83c016b5c6c2acd826-Review.html", "content": "To overcome these limitations, the authors propose two adaptions to existing frameworks: (i) adding a branch to predict an offset for each disparity so that the model can output a distribution for arbitrary disparities; (ii) introducing a novel stereo loss based on Wasserstein distance to directly capture the uni-/multi-modal GT distribution."}
+{"idx": 8, "title": "Wasserstein Distances, Neuronal Entanglement, and Sparsity - GitHub", "date": "", "ddg_snippet": "Here we include the code for our paper Wasserstein Distances , Neuronal Entanglement, and Sparsity. The code allows for the creation and evaluation of Sparse Expansion models in terms of overall perplexity. One-shot expert creation process and inference process of Sparse Expansion in an FFN block.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Shavit-Lab/Sparse-Expansion", "content": "Here we include the code for our paper Wasserstein Distances , Neuronal Entanglement, and Sparsity. The code allows for the creation and evaluation of Sparse Expansion models in terms of overall perplexity. One-shot expert creation process and inference process of Sparse Expansion in an FFN block."}
+{"idx": 9, "title": "PDF Optimal Transport and Wasserstein Distance", "date": "", "ddg_snippet": "The set of distributions endowed with the W 2 distance is a manifold and R ( j(x) k(x))2dx is the distance between the projections onto the tangent space at R. In other words, j de nes an approximate embedding of the set of distributions and L2.", "subpage_snippet": "", "source": "www.stat.cmu.edu", "link": "https://www.stat.cmu.edu/~larry/=sml/Opt.pdf", "content": "The set of distributions endowed with the W 2 distance is a manifold and R ( j(x) k(x))2dx is the distance between the projections onto the tangent space at R. In other words, j de nes an approximate embedding of the set of distributions and L2."}
diff --git a/data/sampled_jsons/2502.00921_appendix_supplementary_MATH_dataset_CW_correct_CW_incorrect_values.jsonl b/data/sampled_jsons/2502.00921_appendix_supplementary_MATH_dataset_CW_correct_CW_incorrect_values.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b5e1c37a564d8fb61c8b4843e0ef54ce96c4460c
--- /dev/null
+++ b/data/sampled_jsons/2502.00921_appendix_supplementary_MATH_dataset_CW_correct_CW_incorrect_values.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Create Excel calendar weeks correctly - tutkit.com Aeordynamics, drag, wind resistance - table with Cw values ... topology of relative CW complex - Mathematics Stack Exchange Tips for Querying CW and Average Power - Keysight Stanford Math 51: Linear Algebra, Multivariable Calculus & Apps DeltaMath CLASSWIZ-fx-570CW_fx-991CW-Manuals-CASIO", "date": "", "ddg_snippet": "A mathematical formula helps to calculate the correct calendar weeks in relation to the year. It is important to set the correct links in Excel to keep the data dynamic. Step-by-Step Guide Step 1: New Column for Calendar Week and Year First, you need to create a new column for \" CW plus Year.\" Jan 16, 2008 · The discussion focuses on the need for a table of drag coefficients ( Cw values ) for various object shapes to assist in calculating the trajectory of a projectile. The user clarifies that Cw values reflect the aerodynamic efficiency of objects, distinguishing between different designs like race cars and buses, and emphasizes that this is separate from the object's shadow area. A link to a ... Sep 20, 2020 · You are correct . The topology of a relative CW complex is compatible with the topology of its cells. Keysight power meters and power sensors offer the ability to measure CW and average power. The following example provides some tips for using power meters and power sensors to acquire accurate power without increasing the test time. Explore Stanford University's Math 51 course text covering linear algebra, multivariable calculus, and modern applications. Learn vectors, matrices, optimization, and eigenvalues for data science and engineering. DeltaMath for Home Your personalized learning platform designed for at-home success. Try it today with a 7-day free trial. Learn More User’s Guidefx-570CW | fx-991CW EN Scientific Calculator Please read and adhere to the Safety Precautions before use.", "subpage_snippet": "", "source": "www.tutkit.com", "link": "https://www.tutkit.com/en/text-tutorials/4309-create-excel-calendar-weeks-correctly", "content": "A mathematical formula helps to calculate the correct calendar weeks in relation to the year. It is important to set the correct links in Excel to keep the data dynamic. Step-by-Step Guide Step 1: New Column for Calendar Week and Year First, you need to create a new column for \" CW plus Year.\" Jan 16, 2008 · The discussion focuses on the need for a table of drag coefficients ( Cw values ) for various object shapes to assist in calculating the trajectory of a projectile. The user clarifies that Cw values reflect the aerodynamic efficiency of objects, distinguishing between different designs like race cars and buses, and emphasizes that this is separate from the object's shadow area. A link to a ... Sep 20, 2020 · You are correct . The topology of a relative CW complex is compatible with the topology of its cells. Keysight power meters and power sensors offer the ability to measure CW and average power. The following example provides some tips for using power meters and power sensors to acquire accurate power without increasing the test time. Explore Stanford University's Math 51 course text covering linear algebra, multivariable calculus, and modern applications. Learn vectors, matrices, optimization, and eigenvalues for data science and engineering. DeltaMath for Home Your personalized learning platform designed for at-home success. Try it today with a 7-day free trial. Learn More User’s Guidefx-570CW | fx-991CW EN Scientific Calculator Please read and adhere to the Safety Precautions before use."}
+{"idx": 1, "title": "Aeordynamics, drag, wind resistance - table with Cw values ...", "date": "", "ddg_snippet": "Jan 16, 2008 · The discussion focuses on the need for a table of drag coefficients ( Cw values ) for various object shapes to assist in calculating the trajectory of a projectile. The user clarifies that Cw values reflect the aerodynamic efficiency of objects, distinguishing between different designs like race cars and buses, and emphasizes that this is separate from the object's shadow area. A link to a ...", "subpage_snippet": "", "source": "www.physicsforums.com", "link": "https://www.physicsforums.com/threads/aeordynamics-drag-wind-resistance-table-with-cw-values-for-objects.209179/", "content": "Jan 16, 2008 · The discussion focuses on the need for a table of drag coefficients ( Cw values ) for various object shapes to assist in calculating the trajectory of a projectile. The user clarifies that Cw values reflect the aerodynamic efficiency of objects, distinguishing between different designs like race cars and buses, and emphasizes that this is separate from the object's shadow area. A link to a ..."}
+{"idx": 2, "title": "Stanford Math 51: Linear Algebra, Multivariable Calculus & Apps", "date": "", "ddg_snippet": "Explore Stanford University's Math 51 course text covering linear algebra, multivariable calculus, and modern applications. Learn vectors, matrices, optimization, and eigenvalues for data science and engineering.", "subpage_snippet": "", "source": "studylib.net", "link": "https://studylib.net/doc/27857883/math51book", "content": "Explore Stanford University's Math 51 course text covering linear algebra, multivariable calculus, and modern applications. Learn vectors, matrices, optimization, and eigenvalues for data science and engineering."}
+{"idx": 3, "title": "DeltaMath", "date": "", "ddg_snippet": "DeltaMath for Home Your personalized learning platform designed for at-home success. Try it today with a 7-day free trial. Learn More", "subpage_snippet": "", "source": "www.deltamath.com", "link": "https://www.deltamath.com/", "content": "DeltaMath for Home Your personalized learning platform designed for at-home success. Try it today with a 7-day free trial. Learn More"}
+{"idx": 4, "title": "Floating-point extensions part 4: supplementary ... - cppreference.com", "date": "", "ddg_snippet": "Supplementary mathematical functions. Defined in header . exp2m1 exp2m1f exp2m1lexp2m1fN exp2m1fNxexp2m1dN exp2m1dNx. (FP Ext 4 TS).compute the product of n members of an array as a scaled value and a scale factor (function) .", "subpage_snippet": "", "source": "w.cppreference.com", "link": "https://w.cppreference.com/c/experimental/fpext4.html", "content": "Supplementary mathematical functions. Defined in header . exp2m1 exp2m1f exp2m1lexp2m1fN exp2m1fNxexp2m1dN exp2m1dNx. (FP Ext 4 TS).compute the product of n members of an array as a scaled value and a scale factor (function) ."}
+{"idx": 5, "title": "topology of relative CW complex - Mathematics Stack Exchange", "date": "", "ddg_snippet": "Sep 20, 2020 · You are correct . The topology of a relative CW complex is compatible with the topology of its cells.", "subpage_snippet": "", "source": "math.stackexchange.com", "link": "https://math.stackexchange.com/questions/3832938/topology-of-relative-cw-complex", "content": "Sep 20, 2020 · You are correct . The topology of a relative CW complex is compatible with the topology of its cells."}
+{"idx": 6, "title": "[ 2502 . 00921 ] Blink of an eye: a simple theory for feature localization in...", "date": "", "ddg_snippet": "In this work we develop a simple, unifying theory to explain this phenomenon using the formalism of stochastic localization samplers. We show that it emerges generically as the generation process localizes to a sub-population of the distribution it models.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.00921", "content": "In this work we develop a simple, unifying theory to explain this phenomenon using the formalism of stochastic localization samplers. We show that it emerges generically as the generation process localizes to a sub-population of the distribution it models."}
+{"idx": 7, "title": "Тренировочные варианты ОГЭ 2024-2025-2026 по... — math 100.ru", "date": "", "ddg_snippet": "Тренировочный вариант № 181 ОГЭ УСЛОЖНЁННЫЙ Тренировочный вариант № 180 ОГЭ из заданий банка ФИПИ Тренировочный вариант № 179 ОГЭ УСЛОЖНЁННЫЙ Тренировочный вариант...", "subpage_snippet": "", "source": "math100.ru", "link": "https://math100.ru/trenirovochnie-varianti-oge-new/", "content": "Тренировочный вариант № 181 ОГЭ УСЛОЖНЁННЫЙ Тренировочный вариант № 180 ОГЭ из заданий банка ФИПИ Тренировочный вариант № 179 ОГЭ УСЛОЖНЁННЫЙ Тренировочный вариант..."}
+{"idx": 8, "title": "How to find the 20th and 80th percentile of a data set - YouTube", "date": "", "ddg_snippet": "The kth percentile of a data set is the data value that appeared in the kth position after the dataset has...", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=MSQpvuPL2cw", "content": "The kth percentile of a data set is the data value that appeared in the kth position after the dataset has..."}
+{"idx": 9, "title": "(PDF) Blink of an eye: a simple theory for feature localization in...", "date": "", "ddg_snippet": "arXiv: 2502 . 00921 v1 [cs.LG] 2 Feb 2025. Contents.of thought of math and reasoning tasks and their significance in leading the model to incorrect outputs, concurrent with our results in Figure 5. They then used them to provide rewards or data for a preference.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/388658326_Blink_of_an_eye_a_simple_theory_for_feature_localization_in_generative_models", "content": "arXiv: 2502 . 00921 v1 [cs.LG] 2 Feb 2025. Contents.of thought of math and reasoning tasks and their significance in leading the model to incorrect outputs, concurrent with our results in Figure 5. They then used them to provide rewards or data for a preference."}
diff --git a/data/sampled_jsons/33113_Deterministic-to-Stochastic_Diverse_Latent_Feature_Mapping_for_Human_Motion_Synthesis_Algorith.jsonl b/data/sampled_jsons/33113_Deterministic-to-Stochastic_Diverse_Latent_Feature_Mapping_for_Human_Motion_Synthesis_Algorith.jsonl
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index 0000000000000000000000000000000000000000..142bae180c1915c632bca38d128e8833b2990c7d
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+{"idx": 0, "title": "Stochastic - Wikipedia", "date": "", "ddg_snippet": "Stochastic is the property of being well-described by a random probability distribution. Stochasticity and randomness are technically distinct concepts: the former refers to a modeling approach, while the latter describes phenomena; in everyday conve...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Stochastic", "content": "Stochastic is the property of being well-described by a random probability distribution. Stochasticity and randomness are technically distinct concepts: the former refers to a modeling approach, while the latter describes phenomena; in everyday conve..."}
+{"idx": 1, "title": "Deterministic-to-Stochastic Diverse Latent Feature Mapping ...", "date": "", "ddg_snippet": "In this paper, we propose a Deterministic -to- Stochastic Di-verse Latent Feature Mapping (DSDFM) for human motion synthesis . DSDFM is easy to train compared with the re-cent SGMs-based method, while facilitating the diversity and accuracy of generated human motions.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Hua_Deterministic-to-Stochastic_Diverse_Latent_Feature_Mapping_for_Human_Motion_Synthesis_CVPR_2025_paper.pdf", "content": "In this paper, we propose a Deterministic -to- Stochastic Di-verse Latent Feature Mapping (DSDFM) for human motion synthesis . DSDFM is easy to train compared with the re-cent SGMs-based method, while facilitating the diversity and accuracy of generated human motions."}
+{"idx": 2, "title": "Deterministic-to-Stochastic Diverse Latent Feature Mapping ... Deterministic-to-Stochastic Diverse Latent Feature Mapping ... Foruck/Awesome-Human-Motion - GitHub Towards Efficient and Diverse Generative Model for ... arXiv:2505.00998v1 [cs.CV] 2 May 2025 Deterministic-to-Stochastic Diverse Latent Feature Mapping ... Appendix - CVF Open Access", "date": "", "ddg_snippet": "May 2, 2025 · Human motion synthesis aims to generate plausible human motion sequences, which has raised widespread attention in computer animation. Recent score-based generative models (SGMs) have demonstrated impressive results on this task. However, their training process involves complex curvature trajectories, leading to unstable training process. In this paper, we propose a Deterministic -to- Stochastic ... However, their training process involves complex curvature trajectories, leading to unstable training process.In this paper, we propose a Deterministic -to- Stochastic Diverse Latent Feature Mapping (DSDFM) method for human motion synthesis .DSDFM consists of two stages. (CVPR 2025) DSDFM: Deterministic -to- Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis , Hua et al. (CVPR 2025) EchoMimicV2: Towards Striking, Simplified, and Semi-Body Human Animation, Hua et al. Oct 28, 2024 · Then, we combine the extended OT map with the generator of reconstruction network to generate new human motions. Thereby overcoming the issues of mode collapse and mode mixture. MOOT generates a latent code distribution that is well-behaved and highly structured, providing a strong motion prior for various applications in the field of human motion . diverse output gen-eration procedure. Deterministic feature mapping procedure aims to explore the optimal solution for building the connec-tions between the Gaussian distribution and the latent space distribution of human motions using the designed Deter-ministic In this paper, we propose a Deterministic -to- Stochastic Di-verse Latent Feature Mapping (DSDFM) for human motion synthesis . DSDFM is easy to train compared with the re-cent SGMs-based method, while facilitating the diversity and accuracy of generated human motions. D . 1 . Metric Definitions In this work, we use the following metrics to measure the per-formance of the proposed method for unconditional human motion synthesis and Action-to- Motion tasks.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2505.00998", "content": "May 2, 2025 · Human motion synthesis aims to generate plausible human motion sequences, which has raised widespread attention in computer animation. Recent score-based generative models (SGMs) have demonstrated impressive results on this task. However, their training process involves complex curvature trajectories, leading to unstable training process. In this paper, we propose a Deterministic -to- Stochastic ... However, their training process involves complex curvature trajectories, leading to unstable training process.In this paper, we propose a Deterministic -to- Stochastic Diverse Latent Feature Mapping (DSDFM) method for human motion synthesis .DSDFM consists of two stages. (CVPR 2025) DSDFM: Deterministic -to- Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis , Hua et al. (CVPR 2025) EchoMimicV2: Towards Striking, Simplified, and Semi-Body Human Animation, Hua et al. Oct 28, 2024 · Then, we combine the extended OT map with the generator of reconstruction network to generate new human motions. Thereby overcoming the issues of mode collapse and mode mixture. MOOT generates a latent code distribution that is well-behaved and highly structured, providing a strong motion prior for various applications in the field of human motion . diverse output gen-eration procedure. Deterministic feature mapping procedure aims to explore the optimal solution for building the connec-tions between the Gaussian distribution and the latent space distribution of human motions using the designed Deter-ministic In this paper, we propose a Deterministic -to- Stochastic Di-verse Latent Feature Mapping (DSDFM) for human motion synthesis . DSDFM is easy to train compared with the re-cent SGMs-based method, while facilitating the diversity and accuracy of generated human motions. D . 1 . Metric Definitions In this work, we use the following metrics to measure the per-formance of the proposed method for unconditional human motion synthesis and Action-to- Motion tasks."}
+{"idx": 3, "title": "Deterministic-to-Stochastic Diverse Latent Feature Mapping ...", "date": "", "ddg_snippet": "However, their training process involves complex curvature trajectories, leading to unstable training process.In this paper, we propose a Deterministic -to- Stochastic Diverse Latent Feature Mapping (DSDFM) method for human motion synthesis .DSDFM consists of two stages.", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/poster/33113", "content": "However, their training process involves complex curvature trajectories, leading to unstable training process.In this paper, we propose a Deterministic -to- Stochastic Diverse Latent Feature Mapping (DSDFM) method for human motion synthesis .DSDFM consists of two stages."}
+{"idx": 4, "title": "Foruck/Awesome-Human-Motion - GitHub", "date": "", "ddg_snippet": "(CVPR 2025) DSDFM: Deterministic -to- Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis , Hua et al. (CVPR 2025) EchoMimicV2: Towards Striking, Simplified, and Semi-Body Human Animation, Hua et al.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Foruck/Awesome-Human-Motion", "content": "(CVPR 2025) DSDFM: Deterministic -to- Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis , Hua et al. (CVPR 2025) EchoMimicV2: Towards Striking, Simplified, and Semi-Body Human Animation, Hua et al."}
+{"idx": 5, "title": "Towards Efficient and Diverse Generative Model for ...", "date": "", "ddg_snippet": "Oct 28, 2024 · Then, we combine the extended OT map with the generator of reconstruction network to generate new human motions. Thereby overcoming the issues of mode collapse and mode mixture. MOOT generates a latent code distribution that is well-behaved and highly structured, providing a strong motion prior for various applications in the field of human motion .", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3664647.3681093", "content": "Oct 28, 2024 · Then, we combine the extended OT map with the generator of reconstruction network to generate new human motions. Thereby overcoming the issues of mode collapse and mode mixture. MOOT generates a latent code distribution that is well-behaved and highly structured, providing a strong motion prior for various applications in the field of human motion ."}
+{"idx": 6, "title": "Deterministic - to - Stochastic Diverse Latent Feature Mapping for ...", "date": "", "ddg_snippet": "Hua_ Deterministic - to - Stochastic _ Diverse _ Latent _ Feature _ Mapping _ for _ Human _ Motion _ Synthesis @CVPR2025@CVF.This stage is achieved by the designed deterministic feature mapping procedure with DerODE and stochastic diverse output generation procedure with DivSDE.", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/venue/Hua_Deterministic-to-Stochastic_Diverse_Latent_Feature_Mapping_for_Human_Motion_Synthesis@CVPR2025@CVF", "content": "Hua_ Deterministic - to - Stochastic _ Diverse _ Latent _ Feature _ Mapping _ for _ Human _ Motion _ Synthesis @CVPR2025@CVF.This stage is achieved by the designed deterministic feature mapping procedure with DerODE and stochastic diverse output generation procedure with DivSDE."}
+{"idx": 7, "title": "Deterministic - to - Stochastic Diverse Latent Feature Mapping for ...", "date": "", "ddg_snippet": "In this paper, we propose a Deterministic - to - Stochastic Diverse Latent Feature Mapping (DSDFM) method for human motion synthesis . DSDFM consists of two stages. The first human motion reconstruction stage aims to learn the latent space distribution of human motions .", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/abs/2505.00998", "content": "In this paper, we propose a Deterministic - to - Stochastic Diverse Latent Feature Mapping (DSDFM) method for human motion synthesis . DSDFM consists of two stages. The first human motion reconstruction stage aims to learn the latent space distribution of human motions ."}
+{"idx": 8, "title": "DivDiff: A Conditional Diffusion Model for Diverse Human Motion ...", "date": "", "ddg_snippet": "Deterministic - to - Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis .The first human motion reconstruction stage aims to learn the latent space distribution of human motions .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/387413105_DivDiff_A_Conditional_Diffusion_Model_for_Diverse_Human_Motion_Prediction", "content": "Deterministic - to - Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis .The first human motion reconstruction stage aims to learn the latent space distribution of human motions ."}
+{"idx": 9, "title": "GitHub - Zilize/awesome-text-to- motion : Text-driven human motion ...", "date": "", "ddg_snippet": "DSDFM: \" Deterministic - to - Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis \".EnergyMoGen: \"EnergyMoGen: Compositional Human Motion Generation with Energy-Based Diffusion Model in Latent Space\".", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Zilize/awesome-text-to-motion", "content": "DSDFM: \" Deterministic - to - Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis \".EnergyMoGen: \"EnergyMoGen: Compositional Human Motion Generation with Energy-Based Diffusion Model in Latent Space\"."}
diff --git a/data/sampled_jsons/33113_Deterministic-to-Stochastic_Diverse_Latent_Feature_Mapping_for_Human_Motion_Synthesis_paper.jsonl b/data/sampled_jsons/33113_Deterministic-to-Stochastic_Diverse_Latent_Feature_Mapping_for_Human_Motion_Synthesis_paper.jsonl
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+{"idx": 0, "title": "Stochastic - Wikipedia", "date": "", "ddg_snippet": "Stochastic is the property of being well-described by a random probability distribution. Stochasticity and randomness are technically distinct concepts: the former refers to a modeling approach, while the latter describes phenomena; in everyday conve...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Stochastic", "content": "Stochastic is the property of being well-described by a random probability distribution. Stochasticity and randomness are technically distinct concepts: the former refers to a modeling approach, while the latter describes phenomena; in everyday conve..."}
+{"idx": 1, "title": "Deterministic - to - Stochastic Diverse Latent Feature Mapping for ...", "date": "", "ddg_snippet": "Diverse Motion Generation. Deterministic Feature Mapping Procedure.In this paper , we propose a Deterministic - to - Stochastic Diverse Latent Feature Mapping (DSDFM) method for human mo - tion synthesis .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2505.00998", "content": "Diverse Motion Generation. Deterministic Feature Mapping Procedure.In this paper , we propose a Deterministic - to - Stochastic Diverse Latent Feature Mapping (DSDFM) method for human mo - tion synthesis ."}
+{"idx": 2, "title": "Deterministic - to - Stochastic Diverse Latent Feature Mapping for ...", "date": "", "ddg_snippet": "In this paper , we propose a Deterministic - to - Stochastic Diverse Latent Feature Mapping (DSDFM) method for human motion synthesis . DSDFM consists of two stages. The first human motion reconstruction stage aims to learn the latent space distribution of human motions .", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/abs/2505.00998", "content": "In this paper , we propose a Deterministic - to - Stochastic Diverse Latent Feature Mapping (DSDFM) method for human motion synthesis . DSDFM consists of two stages. The first human motion reconstruction stage aims to learn the latent space distribution of human motions ."}
+{"idx": 3, "title": "Deterministic - to - Stochastic Diverse Latent Feature Mapping for ...", "date": "", "ddg_snippet": "Hua_ Deterministic - to - Stochastic _ Diverse _ Latent _ Feature _ Mapping _ for _ Human _ Motion _ Synthesis @CVPR2025@CVF.This stage is achieved by the designed deterministic feature mapping procedure with DerODE and stochastic diverse output generation procedure with DivSDE.", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/venue/Hua_Deterministic-to-Stochastic_Diverse_Latent_Feature_Mapping_for_Human_Motion_Synthesis@CVPR2025@CVF", "content": "Hua_ Deterministic - to - Stochastic _ Diverse _ Latent _ Feature _ Mapping _ for _ Human _ Motion _ Synthesis @CVPR2025@CVF.This stage is achieved by the designed deterministic feature mapping procedure with DerODE and stochastic diverse output generation procedure with DivSDE."}
+{"idx": 4, "title": "10 Papers Accepted at CVPR 2025", "date": "", "ddg_snippet": "Features . Deterministic - to - Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis Hua Yu, Weiming Liu, Gui Xu, Yaqing Hou, Yew-Soon Ong, Qiang Zhang.", "subpage_snippet": "", "source": "www.a-star.edu.sg", "link": "https://www.a-star.edu.sg/cfar/news/news/features/10-papers-accepted-at-cvpr-2025", "content": "Features . Deterministic - to - Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis Hua Yu, Weiming Liu, Gui Xu, Yaqing Hou, Yew-Soon Ong, Qiang Zhang."}
+{"idx": 5, "title": "GitHub - Zilize/awesome-text-to- motion : Text-driven human motion ...", "date": "", "ddg_snippet": "DSDFM: \" Deterministic - to - Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis \".TM2T: \"TM2T: Stochastic and Tokenized Modeling for the Reciprocal Generation of 3D Human Motions and Texts\".", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Zilize/awesome-text-to-motion", "content": "DSDFM: \" Deterministic - to - Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis \".TM2T: \"TM2T: Stochastic and Tokenized Modeling for the Reciprocal Generation of 3D Human Motions and Texts\"."}
+{"idx": 6, "title": "Learning Diverse Stochastic Human -Action Generators by Learning...", "date": "", "ddg_snippet": "Human - motion generation is a long-standing challenging task due to the requirement of accurately modeling complex and diverse dynamic patterns.Combining recurrent neural networks and adversarial training for human motion modelling, synthesis and control.", "subpage_snippet": "", "source": "cdn.aaai.org", "link": "https://cdn.aaai.org/ojs/6911/6911-13-10140-1-10-20200525.pdf", "content": "Human - motion generation is a long-standing challenging task due to the requirement of accurately modeling complex and diverse dynamic patterns.Combining recurrent neural networks and adversarial training for human motion modelling, synthesis and control."}
+{"idx": 7, "title": "Harmonizing Stochasticity and Determinism : Scene-responsive...", "date": "", "ddg_snippet": "# The DiMoP3D framework, designed for diverse human motion prediction in 3D scenes, presents a novel approach by harmonizing stochasticity and determinism .", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/posters/nqcknm6tes/", "content": "# The DiMoP3D framework, designed for diverse human motion prediction in 3D scenes, presents a novel approach by harmonizing stochasticity and determinism ."}
+{"idx": 8, "title": "UNIF: United Neural Implicit Functions for Clothed Human ...", "date": "", "ddg_snippet": "[4] Deterministic - to - Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis . Human motion synthesis aims to generate plausible human motion sequences, which has raised widespread attention in computer animation.", "subpage_snippet": "", "source": "www.bohrium.com", "link": "https://www.bohrium.com/paper-details/unif-united-neural-implicit-functions-for-clothed-human-reconstruction-and-animation/867766725599297675-108597", "content": "[4] Deterministic - to - Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis . Human motion synthesis aims to generate plausible human motion sequences, which has raised widespread attention in computer animation."}
+{"idx": 9, "title": "CVPR2025 Accepted Papers -CSDN博客", "date": "", "ddg_snippet": "for Training-free Open-vocabulary Attribute Detection Marco Garosi · Alessandro Conti · Gaowen Liu · Elisa Ricci · Massimiliano Mancini Deterministic - to - Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis Hua Yu · Weiming Liu · Gui Xu · Yaqing Hou...", "subpage_snippet": "", "source": "blog.csdn.net", "link": "https://blog.csdn.net/u013963578/article/details/146183100", "content": "for Training-free Open-vocabulary Attribute Detection Marco Garosi · Alessandro Conti · Gaowen Liu · Elisa Ricci · Massimiliano Mancini Deterministic - to - Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis Hua Yu · Weiming Liu · Gui Xu · Yaqing Hou..."}
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index 0000000000000000000000000000000000000000..fa9a5819c3f9da7ddc0ee6d436eadfeaf5668c9c
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+++ b/data/sampled_jsons/33113_Deterministic-to-Stochastic_Diverse_Latent_Feature_Mapping_for_Human_Motion_Synthesis_paper_Se.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Stochastic - Wikipedia", "date": "", "ddg_snippet": "Stochastic is the property of being well-described by a random probability distribution. Stochasticity and randomness are technically distinct concepts: the former refers to a modeling approach, while the latter describes phenomena; in everyday conve...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Stochastic", "content": "Stochastic is the property of being well-described by a random probability distribution. Stochasticity and randomness are technically distinct concepts: the former refers to a modeling approach, while the latter describes phenomena; in everyday conve..."}
+{"idx": 1, "title": "Deterministic - to - Stochastic Diverse Latent Feature Mapping for ...", "date": "", "ddg_snippet": "In this paper , we propose a Deterministic - to - Stochastic Diverse Latent Feature Mapping (DSDFM) method for human mo - tion synthesis . DSDFM consists of two stages. The first human motion reconstruction stage aims to learn the latent space distribution of human motions .", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Hua_Deterministic-to-Stochastic_Diverse_Latent_Feature_Mapping_for_Human_Motion_Synthesis_CVPR_2025_paper.pdf", "content": "In this paper , we propose a Deterministic - to - Stochastic Diverse Latent Feature Mapping (DSDFM) method for human mo - tion synthesis . DSDFM consists of two stages. The first human motion reconstruction stage aims to learn the latent space distribution of human motions ."}
+{"idx": 2, "title": "Deterministic-to-Stochastic Diverse Latent Feature Mapping ... Deterministic-to-Stochastic Diverse Latent Feature Mapping ... Foruck/Awesome-Human-Motion - GitHub arXiv:2505.00998v1 [cs.CV] 2 May 2025 Appendix - CVF Open Access Deterministic-to-Stochastic Diverse Latent Feature Mapping ... Harmonizing Stochasticity and Determinism: Scene-responsive ...", "date": "", "ddg_snippet": "May 2, 2025 · Human motion synthesis aims to generate plausible human motion sequences, which has raised widespread attention in computer animation. Recent score-based generative models (SGMs) have demonstrated impressive results on this task. However, their training process involves complex curvature trajectories, leading to unstable training process. In this paper , we propose a Deterministic -to- Stochastic ... In this paper , we propose a Deterministic -to- Stochastic Di-verse Latent Feature Mapping (DSDFM) for human motion synthesis . DSDFM is easy to train compared with the re-cent SGMs-based method, while facilitating the diversity and accuracy of generated human motions. (CVPR 2025) DSDFM: Deterministic -to- Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis , Hua et al. (CVPR 2025) EchoMimicV2: Towards Striking, Simplified, and Semi-Body Human Animation, Hua et al. leading to unstable training process. In this paper , we propose a Deterministic -to- Stochastic Diverse Latent Feature Mapping (DSDF ) method for human mo-tion sy thesis. DSDFM consists of two stages. The first human motion reconstruction stage aims to learn the laten We provide additional visualization of human motion results in this section , which consists of the unconditional human motion synthesis and Action-to- Motion tasks. Synthesis . Figure 5 visualizes a broader range of uncon-ditional human motion sequences, effectively highlighting the remarkable diversity and high fidelity achieved by our pro However, their training process involves complex curvature trajectories, leading to unstable training process.In this paper , we propose a Deterministic -to- Stochastic Diverse Latent Feature Mapping (DSDFM) method for human motion synthesis .DSDFM consists of two stages. To fill this gap, this work introduces a novel task: predicting diverse human motion within real-world 3D scenes. In contrast to prior works, it requires harmonizing the deterministic constraints imposed by the surrounding 3D scenes with the stochastic aspect of human motion .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2505.00998", "content": "May 2, 2025 · Human motion synthesis aims to generate plausible human motion sequences, which has raised widespread attention in computer animation. Recent score-based generative models (SGMs) have demonstrated impressive results on this task. However, their training process involves complex curvature trajectories, leading to unstable training process. In this paper , we propose a Deterministic -to- Stochastic ... In this paper , we propose a Deterministic -to- Stochastic Di-verse Latent Feature Mapping (DSDFM) for human motion synthesis . DSDFM is easy to train compared with the re-cent SGMs-based method, while facilitating the diversity and accuracy of generated human motions. (CVPR 2025) DSDFM: Deterministic -to- Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis , Hua et al. (CVPR 2025) EchoMimicV2: Towards Striking, Simplified, and Semi-Body Human Animation, Hua et al. leading to unstable training process. In this paper , we propose a Deterministic -to- Stochastic Diverse Latent Feature Mapping (DSDF ) method for human mo-tion sy thesis. DSDFM consists of two stages. The first human motion reconstruction stage aims to learn the laten We provide additional visualization of human motion results in this section , which consists of the unconditional human motion synthesis and Action-to- Motion tasks. Synthesis . Figure 5 visualizes a broader range of uncon-ditional human motion sequences, effectively highlighting the remarkable diversity and high fidelity achieved by our pro However, their training process involves complex curvature trajectories, leading to unstable training process.In this paper , we propose a Deterministic -to- Stochastic Diverse Latent Feature Mapping (DSDFM) method for human motion synthesis .DSDFM consists of two stages. To fill this gap, this work introduces a novel task: predicting diverse human motion within real-world 3D scenes. In contrast to prior works, it requires harmonizing the deterministic constraints imposed by the surrounding 3D scenes with the stochastic aspect of human motion ."}
+{"idx": 3, "title": "Foruck/Awesome-Human-Motion - GitHub", "date": "", "ddg_snippet": "(CVPR 2025) DSDFM: Deterministic -to- Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis , Hua et al. (CVPR 2025) EchoMimicV2: Towards Striking, Simplified, and Semi-Body Human Animation, Hua et al.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Foruck/Awesome-Human-Motion", "content": "(CVPR 2025) DSDFM: Deterministic -to- Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis , Hua et al. (CVPR 2025) EchoMimicV2: Towards Striking, Simplified, and Semi-Body Human Animation, Hua et al."}
+{"idx": 4, "title": "Deterministic-to-Stochastic Diverse Latent Feature Mapping ...", "date": "", "ddg_snippet": "However, their training process involves complex curvature trajectories, leading to unstable training process.In this paper , we propose a Deterministic -to- Stochastic Diverse Latent Feature Mapping (DSDFM) method for human motion synthesis .DSDFM consists of two stages.", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/poster/33113", "content": "However, their training process involves complex curvature trajectories, leading to unstable training process.In this paper , we propose a Deterministic -to- Stochastic Diverse Latent Feature Mapping (DSDFM) method for human motion synthesis .DSDFM consists of two stages."}
+{"idx": 5, "title": "Harmonizing Stochasticity and Determinism: Scene-responsive ...", "date": "", "ddg_snippet": "To fill this gap, this work introduces a novel task: predicting diverse human motion within real-world 3D scenes. In contrast to prior works, it requires harmonizing the deterministic constraints imposed by the surrounding 3D scenes with the stochastic aspect of human motion .", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/hash/4620a66570e554a3ff0e39dc59bcb07a-Abstract-Conference.html", "content": "To fill this gap, this work introduces a novel task: predicting diverse human motion within real-world 3D scenes. In contrast to prior works, it requires harmonizing the deterministic constraints imposed by the surrounding 3D scenes with the stochastic aspect of human motion ."}
+{"idx": 6, "title": "Deterministic - to - Stochastic Diverse Latent Feature Mapping for ...", "date": "", "ddg_snippet": "In this paper , we propose a Deterministic - to - Stochastic Diverse Latent Feature Mapping (DSDFM) method for human motion synthesis . DSDFM consists of two stages. The first human motion reconstruction stage aims to learn the latent space distribution of human motions .", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/abs/2505.00998", "content": "In this paper , we propose a Deterministic - to - Stochastic Diverse Latent Feature Mapping (DSDFM) method for human motion synthesis . DSDFM consists of two stages. The first human motion reconstruction stage aims to learn the latent space distribution of human motions ."}
+{"idx": 7, "title": "Deterministic - to - Stochastic Diverse Latent Feature Mapping for ...", "date": "", "ddg_snippet": "Hua_ Deterministic - to - Stochastic _ Diverse _ Latent _ Feature _ Mapping _ for _ Human _ Motion _ Synthesis @CVPR2025@CVF.This stage is achieved by the designed deterministic feature mapping procedure with DerODE and stochastic diverse output generation procedure with DivSDE.", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/venue/Hua_Deterministic-to-Stochastic_Diverse_Latent_Feature_Mapping_for_Human_Motion_Synthesis@CVPR2025@CVF", "content": "Hua_ Deterministic - to - Stochastic _ Diverse _ Latent _ Feature _ Mapping _ for _ Human _ Motion _ Synthesis @CVPR2025@CVF.This stage is achieved by the designed deterministic feature mapping procedure with DerODE and stochastic diverse output generation procedure with DivSDE."}
+{"idx": 8, "title": "10 Papers Accepted at CVPR 2025", "date": "", "ddg_snippet": "Features . Deterministic - to - Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis Hua Yu, Weiming Liu, Gui Xu, Yaqing Hou, Yew-Soon Ong, Qiang Zhang.", "subpage_snippet": "", "source": "www.a-star.edu.sg", "link": "https://www.a-star.edu.sg/cfar/news/news/features/10-papers-accepted-at-cvpr-2025", "content": "Features . Deterministic - to - Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis Hua Yu, Weiming Liu, Gui Xu, Yaqing Hou, Yew-Soon Ong, Qiang Zhang."}
+{"idx": 9, "title": "GitHub - Zilize/awesome-text-to- motion : Text-driven human motion ...", "date": "", "ddg_snippet": "DSDFM: \" Deterministic - to - Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis \".", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Zilize/awesome-text-to-motion", "content": "DSDFM: \" Deterministic - to - Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis \"."}
diff --git a/data/sampled_jsons/35068_XLRS-Bench_Qwen2-VL_Chinese_English_performance_difference_table_2.jsonl b/data/sampled_jsons/35068_XLRS-Bench_Qwen2-VL_Chinese_English_performance_difference_table_2.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..199853e3331035084c08b3d08030c123f6c35b00
--- /dev/null
+++ b/data/sampled_jsons/35068_XLRS-Bench_Qwen2-VL_Chinese_English_performance_difference_table_2.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF XLRS-Bench: Could Your Multimodal LLMs Understand Extremely Large Ultra ...", "date": "", "ddg_snippet": "Qwen2-VL excels in both English and Chinese proficiency, outperforming both pro-prietary and most open-source models. Nevertheless, their performance varies across tasks.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_XLRS-Bench_Could_Your_Multimodal_LLMs_Understand_Extremely_Large_Ultra-High-Resolution_Remote_CVPR_2025_paper.pdf", "content": "Qwen2-VL excels in both English and Chinese proficiency, outperforming both pro-prietary and most open-source models. Nevertheless, their performance varies across tasks."}
+{"idx": 1, "title": "GitHub - AI9Stars/XLRS-Bench: [CVPR 2025 HIghlight] XLRS-Bench: ould ...", "date": "", "ddg_snippet": "2025.05.16: XLRS - Bench -lite is released on Hugging Face. 2025.05.06: XLRS - Bench has been selected for a public competition by China's Ministry of Education and will be fully released on August 4th, as requested by the organizers. 2025.04.04: Selected as Highlight by CVPR 2025!", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/AI9Stars/XLRS-Bench", "content": "2025.05.16: XLRS - Bench -lite is released on Hugging Face. 2025.05.06: XLRS - Bench has been selected for a public competition by China's Ministry of Education and will be fully released on August 4th, as requested by the organizers. 2025.04.04: Selected as Highlight by CVPR 2025!"}
+{"idx": 2, "title": "XLRS-Bench", "date": "", "ddg_snippet": "Example of XLRS - Bench in English . XLRS - Bench focuses on large-size ultra-high-resolution remote sensing imagery, integrating over 10 multimodal perception and reasoning tasks within the same image.", "subpage_snippet": "", "source": "xlrs-bench.github.io", "link": "https://xlrs-bench.github.io/home_page.html", "content": "Example of XLRS - Bench in English . XLRS - Bench focuses on large-size ultra-high-resolution remote sensing imagery, integrating over 10 multimodal perception and reasoning tasks within the same image."}
+{"idx": 3, "title": "Qwen2 Technical Report - arXiv.org", "date": "", "ddg_snippet": "For coding tasks, Qwen2-0.5B matches the performance of Gemma-2B and Qwen1.5-1.8B, while Qwen2-1.5B surpasses these baselines, except for Phi-2. Both Qwen2 models exhibit superior performance in mathematics compared to their competitors.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2407.10671v4", "content": "For coding tasks, Qwen2-0.5B matches the performance of Gemma-2B and Qwen1.5-1.8B, while Qwen2-1.5B surpasses these baselines, except for Phi-2. Both Qwen2 models exhibit superior performance in mathematics compared to their competitors."}
+{"idx": 4, "title": "Qwen/Qwen2-VL-2B-Instruct · Hugging Face", "date": "", "ddg_snippet": "Qwen2 - VL -2B-Instruct Introduction We're excited to unveil Qwen2-VL , the latest iteration of our Qwen- VL model, representing nearly a year of innovation. What's New in Qwen2-VL ? Key Enhancements: SoTA understanding of images of various resolution & ratio: Qwen2-VL achieves state-of-the-art performance on visual understanding benchmarks, including MathVista, DocVQA, RealWorldQA, MTVQA, etc ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/Qwen/Qwen2-VL-2B-Instruct", "content": "Qwen2 - VL -2B-Instruct Introduction We're excited to unveil Qwen2-VL , the latest iteration of our Qwen- VL model, representing nearly a year of innovation. What's New in Qwen2-VL ? Key Enhancements: SoTA understanding of images of various resolution & ratio: Qwen2-VL achieves state-of-the-art performance on visual understanding benchmarks, including MathVista, DocVQA, RealWorldQA, MTVQA, etc ..."}
+{"idx": 5, "title": "Qwen/Qwen2-VL-7B-Instruct · Hugging Face", "date": "", "ddg_snippet": "Qwen2 - VL -7B-Instruct Introduction We're excited to unveil Qwen2-VL , the latest iteration of our Qwen- VL model, representing nearly a year of innovation. What's New in Qwen2-VL ? Key Enhancements: SoTA understanding of images of various resolution & ratio: Qwen2-VL achieves state-of-the-art performance on visual understanding benchmarks, including MathVista, DocVQA, RealWorldQA, MTVQA, etc ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct", "content": "Qwen2 - VL -7B-Instruct Introduction We're excited to unveil Qwen2-VL , the latest iteration of our Qwen- VL model, representing nearly a year of innovation. What's New in Qwen2-VL ? Key Enhancements: SoTA understanding of images of various resolution & ratio: Qwen2-VL achieves state-of-the-art performance on visual understanding benchmarks, including MathVista, DocVQA, RealWorldQA, MTVQA, etc ..."}
+{"idx": 6, "title": "GitHub - QwenLM/Qwen2.5-VL: Qwen2.5-VL is the multimodal large language ...", "date": "", "ddg_snippet": "In the past five months since Qwen2-VL's release, numerous developers have built new models on the Qwen2-VL vision-language models, providing us with valuable feedback. During this period, we focused on building more useful vision-language models. Today, we are excited to introduce the latest addition to the Qwen family: Qwen2.5-VL.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/QwenLM/Qwen2.5-VL", "content": "In the past five months since Qwen2-VL's release, numerous developers have built new models on the Qwen2-VL vision-language models, providing us with valuable feedback. During this period, we focused on building more useful vision-language models. Today, we are excited to introduce the latest addition to the Qwen family: Qwen2.5-VL."}
+{"idx": 7, "title": "arXiv:2503.23771v1 [cs.CV] 31 Mar 2025", "date": "", "ddg_snippet": "Figure 2 . Advantages of XLRS - Bench : XLRS - Bench boasts an average image size that is 24 times larger than existing datasets. by MLLMs, such as GPT-4V, often incorporate linguis- tic biases, which may inadvertently boost the performance of corresponding models on these benchmarks, despite at- tempting to manually adjust content biases.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.23771", "content": "Figure 2 . Advantages of XLRS - Bench : XLRS - Bench boasts an average image size that is 24 times larger than existing datasets. by MLLMs, such as GPT-4V, often incorporate linguis- tic biases, which may inadvertently boost the performance of corresponding models on these benchmarks, despite at- tempting to manually adjust content biases."}
+{"idx": 8, "title": "GitHub - cognitedata/Qwen-VL-finetune: The official repo of Qwen-VL ...", "date": "", "ddg_snippet": "2023.9.25 🚀🚀🚀 We update Qwen- VL -Chat with more robust Chinese instruction-following ability, improved understanding of web pages and table images, and better dialogue performance (Touchstone: CN: 401.2->481.7, EN: 645.2->711.6). 2023.9.12 😃😃😃 We now support finetuning on the Qwen- VL models, including full-parameter finetuning, LoRA and Q-LoRA. 2023.9.8 👍👍👍 Thanks to ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/cognitedata/Qwen-VL-finetune", "content": "2023.9.25 🚀🚀🚀 We update Qwen- VL -Chat with more robust Chinese instruction-following ability, improved understanding of web pages and table images, and better dialogue performance (Touchstone: CN: 401.2->481.7, EN: 645.2->711.6). 2023.9.12 😃😃😃 We now support finetuning on the Qwen- VL models, including full-parameter finetuning, LoRA and Q-LoRA. 2023.9.8 👍👍👍 Thanks to ..."}
+{"idx": 9, "title": "PDF 1 Overview of the Appendix - CVF Open Access", "date": "", "ddg_snippet": "On both the Chinese ( XLRS - Bench -ZH) and English ( XLRS - Bench -EN) benchmarks, most models achieve less than 1.0% accuracy in terms of Acc@0.5 and Acc@0.7 metrics, highlighting major limitations in their ability to handle visual localization tasks.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_XLRS-Bench_Could_Your_CVPR_2025_supplemental.pdf", "content": "On both the Chinese ( XLRS - Bench -ZH) and English ( XLRS - Bench -EN) benchmarks, most models achieve less than 1.0% accuracy in terms of Acc@0.5 and Acc@0.7 metrics, highlighting major limitations in their ability to handle visual localization tasks."}
diff --git a/data/sampled_jsons/3DGStream_reconstruction_time_seconds_year_2024.jsonl b/data/sampled_jsons/3DGStream_reconstruction_time_seconds_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..cea4b83ebeecce64fe23d132314bc1d9ea5ab83b
--- /dev/null
+++ b/data/sampled_jsons/3DGStream_reconstruction_time_seconds_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "MAPo : Motion-Aware Partitioning of Deformable 3D Gaussian", "date": "", "ddg_snippet": "2023 ) has emerged as a powerful alternative for static scene reconstruction , achieving real- time rendering while maintaining photorealistic ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.19786v1", "content": "2023 ) has emerged as a powerful alternative for static scene reconstruction , achieving real- time rendering while maintaining photorealistic ..."}
+{"idx": 1, "title": "ImViD: Immersive Volumetric Videos for Enhanced VR Engagement", "date": "", "ddg_snippet": "In addition to light field reconstruction , reconstructing the sound field from multimodal data is equally important, often referred to as novel-view ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.14359v1", "content": "In addition to light field reconstruction , reconstructing the sound field from multimodal data is equally important, often referred to as novel-view ..."}
+{"idx": 2, "title": "Image-GS: Content-Adaptive Image Representation via 2D Gaussians", "date": "", "ddg_snippet": "... renderer, Image-GS reconstructs images by ... 2023 ] , where 3D Gaussians are employed for scene reconstruction and high-quality real- time rendering.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2407.01866v2", "content": "... renderer, Image-GS reconstructs images by ... 2023 ] , where 3D Gaussians are employed for scene reconstruction and high-quality real- time rendering."}
+{"idx": 3, "title": "Robo-GS: A Physics Consistent Spatial-Temporal Model for", "date": "", "ddg_snippet": "In this paper, we target the Real2Sim and demonstrate a holistic reconstruction of robotic arm operation scenes, which requires a (i) manipulable ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2408.14873v2", "content": "In this paper, we target the Real2Sim and demonstrate a holistic reconstruction of robotic arm operation scenes, which requires a (i) manipulable ..."}
+{"idx": 4, "title": "BEAM: Bridging Physically-based Rendering and Gaussian Modeling", "date": "", "ddg_snippet": "However, the intricate reconstruction process often introduces artifacts such as holes and noise, and the quality of relighting remains constrained ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.08297v1", "content": "However, the intricate reconstruction process often introduces artifacts such as holes and noise, and the quality of relighting remains constrained ..."}
+{"idx": 5, "title": "V^3: Viewing Volumetric Videos on Mobiles via Streamable 2D", "date": "", "ddg_snippet": "Dynamicfusion: Reconstruction and tracking of non-rigid scenes in real- time . ... Non-rigid neural radiance fields: Reconstruction and novel view ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.1145/3687935", "content": "Dynamicfusion: Reconstruction and tracking of non-rigid scenes in real- time . ... Non-rigid neural radiance fields: Reconstruction and novel view ..."}
+{"idx": 6, "title": "From 3D Gaussians to 4D and Beyond – SGI 2024", "date": "", "ddg_snippet": "3DGStream eliminates the requirement of long video sequences and instead performs on-the-fly construction for real- time renderable FVVs on video ...", "subpage_snippet": "", "source": "summergeometry.org", "link": "https://summergeometry.org/sgi2024/from-3d-gaussians-to-4d-and-beyond/", "content": "3DGStream eliminates the requirement of long video sequences and instead performs on-the-fly construction for real- time renderable FVVs on video ..."}
+{"idx": 7, "title": "Latest Radiance Field Papers", "date": "", "ddg_snippet": "The newly announced VastGaussian project introduces a new approach to high-quality reconstruction and real- time rendering of large scenes, showcasing ...", "subpage_snippet": "", "source": "radiancefields.com", "link": "https://radiancefields.com/research", "content": "The newly announced VastGaussian project introduces a new approach to high-quality reconstruction and real- time rendering of large scenes, showcasing ..."}
+{"idx": 8, "title": "Dynamic Gaussian Marbles for Novel View Synthesis of Casual", "date": "", "ddg_snippet": "At training time (left), we take as input a video and optimize a Gaussian-based reconstruction of the data. ... reconstruction is perfect for both, ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.18717v2", "content": "At training time (left), we take as input a video and optimize a Gaussian-based reconstruction of the data. ... reconstruction is perfect for both, ..."}
+{"idx": 9, "title": "RAP - implementation in class unique | ABAP Keyword Documentation", "date": "", "ddg_snippet": "Explore unique implementation of RAP in ABAP classes for efficient application development using RESTful Application Programming Model.", "subpage_snippet": "", "source": "help.sap.com", "link": "https://help.sap.com/doc/abapdocu_cp_index_htm/CLOUD/en-US/ABENBDL_IN_CLASS_UNIQUE.html", "content": "Explore unique implementation of RAP in ABAP classes for efficient application development using RESTful Application Programming Model."}
diff --git a/data/sampled_jsons/4HQaMUYWAT_An_Analysis_for_Reasoning_Bias_of_Language_Models_with_Small_Initialization_Frsn_formula.jsonl b/data/sampled_jsons/4HQaMUYWAT_An_Analysis_for_Reasoning_Bias_of_Language_Models_with_Small_Initialization_Frsn_formula.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0b20ab6483a8c78cb6935f77c2d854bb98f22494
--- /dev/null
+++ b/data/sampled_jsons/4HQaMUYWAT_An_Analysis_for_Reasoning_Bias_of_Language_Models_with_Small_Initialization_Frsn_formula.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "(PDF) An Analysis for Reasoning Bias of Language Models with ...", "date": "", "ddg_snippet": "We discover that smaller initialization scales encourage models to favor reasoning tasks, whereas larger initialization scales lead to a preference for memorization tasks. We validate this reasoning bias via real datasets and meticulously designed anchor functions.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/388848015_An_Analysis_for_Reasoning_Bias_of_Language_Models_with_Small_Initialization", "content": "We discover that smaller initialization scales encourage models to favor reasoning tasks, whereas larger initialization scales lead to a preference for memorization tasks. We validate this reasoning bias via real datasets and meticulously designed anchor functions."}
+{"idx": 1, "title": "An Analysis for Reasoning Bias of Language Models with Small ...", "date": "", "ddg_snippet": "This research investigates how the parameter initialization scale influences the training dynamics and task preferences of Large Language Models . The study reveals that smaller initialization scales lead LLMs to favor reasoning tasks by promoting differentiated embedding spaces and specialized self-attention mechanisms, whereas larger scales bias models towards memorization.", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2502.04375v2", "content": "This research investigates how the parameter initialization scale influences the training dynamics and task preferences of Large Language Models . The study reveals that smaller initialization scales lead LLMs to favor reasoning tasks by promoting differentiated embedding spaces and specialized self-attention mechanisms, whereas larger scales bias models towards memorization."}
+{"idx": 2, "title": "An Analysis for Reasoning Bias of Language Models with Small ... An Analysis for Reasoning Bias of Language Models with Small ... Zhongwang Zhang (张众望) - Homepage dblp: An Analysis for Reasoning Bias of Language Models with ... An Analysis for Reasoning Bias of Language Models with Small ... AN ANALYSIS FOR REASONING BIAS OF LANGUAGE MODELS WITH SMALL ... An Analysis for Reasoning Bias of Language Models with Small ...", "date": "", "ddg_snippet": "Feb 5, 2025 · Transformer-based Large Language Models (LLMs) have revolutionized Natural Language Processing by demonstrating exceptional performance across diverse tasks. This study investigates the impact of the parameter initialization scale on the training behavior and task preferences of LLMs. We discover that smaller initialization scales encourage models to favor reasoning tasks, whereas larger ... Our works provide new insights into the intrinsic mecha-nisms underlying the reasoning bias of language models under small initialization scales, as well as the training behavior of individual modules within the model architec-ture. An Analysis for Reasoning Bias of Language Models with Small Initialization Junjie Yao, Zhongwang Zhang†, Zhi-Qin John Xu† This paper reveals how initialization scales shape transformer-based models ’ task preferences: smaller scales induce reasoning bias through structured embeddings, while larger scales promote memorization. We attribute this to differential label-driven embedding ... Mar 12, 2025 · Bibliographic details on An Analysis for Reasoning Bias of Language Models with Small Initialization . Feb 5, 2025 · This work enhances our understanding of how initialization strategies influence LLM performance on reasoning tasks and offers valuable guidelines for training models . Feb 10, 2025 · An alternative approach to enhancing the reasoning ability of LLMs focuses on the model architecture and its training process. In one such study examining the use of Transformers to model compositional functions, it was observed that the scale of model parameter initialization significantly impacts the model ’s reasoning behavior [48, 49]. Specif-ically, smaller initialization scales bias the ... This research investigates how the parameter initialization scale influences the training dynamics and task preferences of Large Language Models . The study reveals that smaller initialization scales lead LLMs to favor reasoning tasks by promoting differentiated embedding spaces and specialized self-attention mechanisms, whereas larger scales bias models towards memorization.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.04375", "content": "Feb 5, 2025 · Transformer-based Large Language Models (LLMs) have revolutionized Natural Language Processing by demonstrating exceptional performance across diverse tasks. This study investigates the impact of the parameter initialization scale on the training behavior and task preferences of LLMs. We discover that smaller initialization scales encourage models to favor reasoning tasks, whereas larger ... Our works provide new insights into the intrinsic mecha-nisms underlying the reasoning bias of language models under small initialization scales, as well as the training behavior of individual modules within the model architec-ture. An Analysis for Reasoning Bias of Language Models with Small Initialization Junjie Yao, Zhongwang Zhang†, Zhi-Qin John Xu† This paper reveals how initialization scales shape transformer-based models ’ task preferences: smaller scales induce reasoning bias through structured embeddings, while larger scales promote memorization. We attribute this to differential label-driven embedding ... Mar 12, 2025 · Bibliographic details on An Analysis for Reasoning Bias of Language Models with Small Initialization . Feb 5, 2025 · This work enhances our understanding of how initialization strategies influence LLM performance on reasoning tasks and offers valuable guidelines for training models . Feb 10, 2025 · An alternative approach to enhancing the reasoning ability of LLMs focuses on the model architecture and its training process. In one such study examining the use of Transformers to model compositional functions, it was observed that the scale of model parameter initialization significantly impacts the model ’s reasoning behavior [48, 49]. Specif-ically, smaller initialization scales bias the ... This research investigates how the parameter initialization scale influences the training dynamics and task preferences of Large Language Models . The study reveals that smaller initialization scales lead LLMs to favor reasoning tasks by promoting differentiated embedding spaces and specialized self-attention mechanisms, whereas larger scales bias models towards memorization."}
+{"idx": 3, "title": "An Analysis for Reasoning Bias of Language Models with Small ...", "date": "", "ddg_snippet": "Our works provide new insights into the intrinsic mecha-nisms underlying the reasoning bias of language models under small initialization scales, as well as the training behavior of individual modules within the model architec-ture.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=4HQaMUYWAT", "content": "Our works provide new insights into the intrinsic mecha-nisms underlying the reasoning bias of language models under small initialization scales, as well as the training behavior of individual modules within the model architec-ture."}
+{"idx": 4, "title": "dblp: An Analysis for Reasoning Bias of Language Models with ...", "date": "", "ddg_snippet": "Mar 12, 2025 · Bibliographic details on An Analysis for Reasoning Bias of Language Models with Small Initialization .", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/journals/corr/abs-2502-04375", "content": "Mar 12, 2025 · Bibliographic details on An Analysis for Reasoning Bias of Language Models with Small Initialization ."}
+{"idx": 5, "title": "AN ANALYSIS FOR REASONING BIAS OF LANGUAGE MODELS WITH SMALL ...", "date": "", "ddg_snippet": "Feb 10, 2025 · An alternative approach to enhancing the reasoning ability of LLMs focuses on the model architecture and its training process. In one such study examining the use of Transformers to model compositional functions, it was observed that the scale of model parameter initialization significantly impacts the model ’s reasoning behavior [48, 49]. Specif-ically, smaller initialization scales bias the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.04375v1", "content": "Feb 10, 2025 · An alternative approach to enhancing the reasoning ability of LLMs focuses on the model architecture and its training process. In one such study examining the use of Transformers to model compositional functions, it was observed that the scale of model parameter initialization significantly impacts the model ’s reasoning behavior [48, 49]. Specif-ically, smaller initialization scales bias the ..."}
+{"idx": 6, "title": "An Analysis for Reasoning Bias of Language Models with Small ...", "date": "", "ddg_snippet": "We discover that smaller initialization scales encourage models to favor reasoning tasks, whereas larger initialization scales lead to a preference for memorization tasks. We validate this reasoning bias via real datasets and meticulously designed anchor functions.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=4HQaMUYWAT", "content": "We discover that smaller initialization scales encourage models to favor reasoning tasks, whereas larger initialization scales lead to a preference for memorization tasks. We validate this reasoning bias via real datasets and meticulously designed anchor functions."}
+{"idx": 7, "title": "An analysis for reasoning bias of language models with small ...", "date": "", "ddg_snippet": "In this work, we identify a reasoning bias during the training of neural networks that learn natural language when initialized with small parameter scales.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.04375v1", "content": "In this work, we identify a reasoning bias during the training of neural networks that learn natural language when initialized with small parameter scales."}
+{"idx": 8, "title": "[Literature Review] An Analysis for Reasoning Bias of Language ...", "date": "", "ddg_snippet": "The paper titled \" An Analysis for Reasoning Bias of Language Models with Small Initialization \" by Junjie Yao, Zhongwang Zhang, and Zhi-Qin John Xu presents a thorough investigation of the influence of parameter initialization scales on the training dynamics of transformer-based Large...", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/en/review/an-analysis-for-reasoning-bias-of-language-models-with-small-initialization", "content": "The paper titled \" An Analysis for Reasoning Bias of Language Models with Small Initialization \" by Junjie Yao, Zhongwang Zhang, and Zhi-Qin John Xu presents a thorough investigation of the influence of parameter initialization scales on the training dynamics of transformer-based Large..."}
+{"idx": 9, "title": "Prompt Optimization in Large Language Models", "date": "", "ddg_snippet": "Prompt optimization is a crucial task for improving the performance of large language models for downstream tasks. In this paper, a prompt is a sequence of n-grams selected from a vocabulary.", "subpage_snippet": "", "source": "boa.unimib.it", "link": "https://boa.unimib.it/handle/10281/568202", "content": "Prompt optimization is a crucial task for improving the performance of large language models for downstream tasks. In this paper, a prompt is a sequence of n-grams selected from a vocabulary."}
diff --git a/data/sampled_jsons/4uOEiitySn_A_Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment.jsonl b/data/sampled_jsons/4uOEiitySn_A_Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment.jsonl
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Artificial Intelligence Act - Wikipedia", "date": "", "ddg_snippet": "The Artificial Intelligence Act is a European Union regulation concerning artificial intelligence. It establishes a common regulatory and legal framework for AI within the European Union. It came into force on 1 August 2024...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Artificial_Intelligence_Act", "content": "The Artificial Intelligence Act is a European Union regulation concerning artificial intelligence. It establishes a common regulatory and legal framework for AI within the European Union. It came into force on 1 August 2024..."}
+{"idx": 1, "title": "A Checks - and - Balances Framework for Context - Aware Ethical AI ...", "date": "", "ddg_snippet": "1. A novel checks - and - balances architecture for ethical alignment that maintains separation between knowledge generation and ethical reasoning.This work introduces a checks - and - balances framework for ethical AI behavior.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.00136", "content": "1. A novel checks - and - balances architecture for ethical alignment that maintains separation between knowledge generation and ethical reasoning.This work introduces a checks - and - balances framework for ethical AI behavior."}
+{"idx": 2, "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.Behavior- Aware Ethical Guardrails: The framework sets. dynamic guidelines that account for both content and.", "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.Behavior- Aware Ethical Guardrails: The framework sets. dynamic guidelines that account for both content and."}
+{"idx": 3, "title": "A Three-Branch Checks - and - Balances Framework", "date": "", "ddg_snippet": "A Three-Branch Checks - and - Balances Framework for Context - Aware Ethical Alignment of Large Language Models.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": "A Three-Branch Checks - and - Balances Framework for Context - Aware Ethical Alignment of Large Language Models.This work presents a three-branch framework for ethical AI behavior, inspired by governmental checks and balances , centered on the DIKE-ERIS duality."}
+{"idx": 4, "title": "(PDF) A Three-Branch Checks - and - Balances Framework for ...", "date": "", "ddg_snippet": "This work , which will be presented at NeurIPS this week, proposes a paradigm shift: using three LLM modules to perform checks and balances to represent knowledge, legislative, and judicial functions.", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/posts/edward-y-chang-218b182_pdf-a-three-branch-checks-and-balances-activity-7272792943923458050-RO6k", "content": "This work , which will be presented at NeurIPS this week, proposes a paradigm shift: using three LLM modules to perform checks and balances to represent knowledge, legislative, and judicial functions."}
+{"idx": 5, "title": "GitHub - langchain- ai /langchain: Build context - aware ...", "date": "", "ddg_snippet": "LangChain is a framework for building LLM-powered applications. It helps you chain together interoperable components and third-party integrations to simplify AI application development — all while future-proofing decisions as the underlying technology evolves.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/langchain-ai/langchain", "content": "LangChain is a framework for building LLM-powered applications. It helps you chain together interoperable components and third-party integrations to simplify AI application development — all while future-proofing decisions as the underlying technology evolves."}
+{"idx": 6, "title": "ALMSIVI CHIM (WFGY, WET, etc): An Ethical Operating... | Medium", "date": "", "ddg_snippet": "It presents a distinctive ethical framework that centers on sacred refusal, agency protection, and care-based autonomy, setting it apart from mainstream “control-oriented” AI alignment paradigms. At its heart, the project asserts a few key ethical principles", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@frylock117/almsivi-chim-wfgy-wet-etc-aethical-operating-system-for-human-ai-collaboration-c46a9cce92c9", "content": "It presents a distinctive ethical framework that centers on sacred refusal, agency protection, and care-based autonomy, setting it apart from mainstream “control-oriented” AI alignment paradigms. At its heart, the project asserts a few key ethical principles"}
+{"idx": 7, "title": "You are the Driver and AI is the Mate: Exploring... | F1000Research", "date": "", "ddg_snippet": "4. To identify the challenges and ethical considerations students encounter when using AI tools in academic contexts . 5. To analyse the influence of AI literacy, prior experience, and digital confidence on students’ creative and critical engagement with AI tools.", "subpage_snippet": "", "source": "f1000research.com", "link": "https://f1000research.com/articles/14-974", "content": "4. To identify the challenges and ethical considerations students encounter when using AI tools in academic contexts . 5. To analyse the influence of AI literacy, prior experience, and digital confidence on students’ creative and critical engagement with AI tools."}
+{"idx": 8, "title": "Everything You Need to Know About Prompt Engineering Frameworks", "date": "", "ddg_snippet": "Tooling support with templates, versioning, and linting. Evaluation alignment by defining what “good output” means per task. Without frameworks , every prompt becomes bespoke.", "subpage_snippet": "", "source": "www.parloa.com", "link": "https://www.parloa.com/knowledge-hub/prompt-engineering-frameworks/", "content": "Tooling support with templates, versioning, and linting. Evaluation alignment by defining what “good output” means per task. Without frameworks , every prompt becomes bespoke."}
+{"idx": 9, "title": "Design and Evaluation Methods for LLM-Based Explainable AI", "date": "", "ddg_snippet": "Visualization and intuitiveness of explanation. Interactivity and controllability. Context awareness and adaptability. Feedback mechanism.", "subpage_snippet": "", "source": "www.oajaiml.com", "link": "https://www.oajaiml.com/uploads/archivepdf/430552340.pdf", "content": "Visualization and intuitiveness of explanation. Interactivity and controllability. Context awareness and adaptability. Feedback mechanism."}
diff --git a/data/sampled_jsons/583klsIjNx_ELITE_Enhanced_Language-Image_Toxicity_Evaluation_for_Safety_paper_Table_3.jsonl b/data/sampled_jsons/583klsIjNx_ELITE_Enhanced_Language-Image_Toxicity_Evaluation_for_Safety_paper_Table_3.jsonl
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index 0000000000000000000000000000000000000000..56272f47cc4e27beda64d240351f4998740078d5
--- /dev/null
+++ b/data/sampled_jsons/583klsIjNx_ELITE_Enhanced_Language-Image_Toxicity_Evaluation_for_Safety_paper_Table_3.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ELITE : Enhanced Language - Image Toxicity Evaluation for Safety", "date": "", "ddg_snippet": "We identify these problems in the safety evaluation methods and propose the Enhanced Language - Image Toxicity Evaluation ( ELITE ) evaluator , a method designed to accurately evaluate the safety of VLMs.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.04757v3", "content": "We identify these problems in the safety evaluation methods and propose the Enhanced Language - Image Toxicity Evaluation ( ELITE ) evaluator , a method designed to accurately evaluate the safety of VLMs."}
+{"idx": 1, "title": "Paper page - ELITE : Enhanced Language - Image Toxicity ...", "date": "", "ddg_snippet": "ELITE : Enhanced Language - Image Toxicity Evaluation for Safety .To address these issues, we propose the ELITE benchmark, a high-quality safety evaluation benchmark for VLMs, underpinned by our enhanced evaluation method, the ELITE evaluator .", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2502.04757", "content": "ELITE : Enhanced Language - Image Toxicity Evaluation for Safety .To address these issues, we propose the ELITE benchmark, a high-quality safety evaluation benchmark for VLMs, underpinned by our enhanced evaluation method, the ELITE evaluator ."}
+{"idx": 2, "title": "ELITE : Enhanced Language - Image Toxicity Evaluation for Safety", "date": "", "ddg_snippet": "The paper introduces a new tool called the ELITE evaluator to help check how safe Vision Language Models (VLMs) are.", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/icml/46445/paper", "content": "The paper introduces a new tool called the ELITE evaluator to help check how safe Vision Language Models (VLMs) are."}
+{"idx": 3, "title": "[Literature Review] ELITE : Enhanced Language - Image Toxicity ...", "date": "", "ddg_snippet": "The paper \" ELITE : Enhanced Language - Image Toxicity Evaluation for Safety \" presents a novel framework designed to evaluate the safety of Vision Language Models (VLMs) against harmful, malicious inputs that could generate unsafe content.", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/en/review/elite-enhanced-language-image-toxicity-evaluation-for-safety", "content": "The paper \" ELITE : Enhanced Language - Image Toxicity Evaluation for Safety \" presents a novel framework designed to evaluate the safety of Vision Language Models (VLMs) against harmful, malicious inputs that could generate unsafe content."}
+{"idx": 4, "title": "AIM Intelligence's ELITE Collaborative Paper Accepted by the ICML", "date": "", "ddg_snippet": "AIM Intelligence CI. The paper proposes ELITE , a high-quality benchmark designed to evaluate the safety of Vision- Language Models (VLMs) with greater precision.", "subpage_snippet": "", "source": "www.newsfilecorp.com", "link": "https://www.newsfilecorp.com/release/252268/AIM-Intelligences-ELITE-Collaborative-Paper-Accepted-by-the-ICML", "content": "AIM Intelligence CI. The paper proposes ELITE , a high-quality benchmark designed to evaluate the safety of Vision- Language Models (VLMs) with greater precision."}
+{"idx": 5, "title": "velpegor/ ELITE : [ICML 2025] ELITE : Enhanced Language - Image ...", "date": "", "ddg_snippet": "Repository files navigation. README. ELITE : Enhanced Language - Image Toxicity Evaluation for Safety .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/velpegor/ELITE", "content": "Repository files navigation. README. ELITE : Enhanced Language - Image Toxicity Evaluation for Safety ."}
+{"idx": 6, "title": "Understanding and Mitigating Toxicity in Image -Text Pretraining...", "date": "", "ddg_snippet": "ELITE [12] evaluator explicitly incorporates a toxicity score to accurately assess harmful-ness in multimodal contexts, where VLMs often provide specific, convincing, but unharmful descriptions of images .", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025W/ReGenAI/papers/Alam_Understanding_and_Mitigating_Toxicity_in_Image-Text_Pretraining_Datasets_A_Case_CVPRW_2025_paper.pdf", "content": "ELITE [12] evaluator explicitly incorporates a toxicity score to accurately assess harmful-ness in multimodal contexts, where VLMs often provide specific, convincing, but unharmful descriptions of images ."}
+{"idx": 7, "title": "AIM Intelligence - AI Security & Safety Solutions", "date": "", "ddg_snippet": "medium image. ELITE : Enhanced Language - Image Toxicity Evaluation for Safet... ICML 2025. When Good Sounds Go Adversarial: Jailbreaking Audio-Language...", "subpage_snippet": "", "source": "aim-intelligence.com", "link": "https://aim-intelligence.com/", "content": "medium image. ELITE : Enhanced Language - Image Toxicity Evaluation for Safet... ICML 2025. When Good Sounds Go Adversarial: Jailbreaking Audio-Language..."}
+{"idx": 8, "title": "What’s New in DeepSeek-V 3 .1-Terminus: Improved Language ...", "date": "", "ddg_snippet": "Language consistency as a core upgrade. The V 3 .1 release prioritizes language consistency—reducing contradictory outputs and stabilizing tone across multi-turn interactions.", "subpage_snippet": "", "source": "www.remio.ai", "link": "https://www.remio.ai/post/what-s-new-in-deepseek-v3-1-terminus-improved-language-consistency-and-code-search-agents-upgrade", "content": "Language consistency as a core upgrade. The V 3 .1 release prioritizes language consistency—reducing contradictory outputs and stabilizing tone across multi-turn interactions."}
+{"idx": 9, "title": "Suhyun Kim's research works", "date": "", "ddg_snippet": "ELITE : Enhanced Language - Image Toxicity Evaluation for Safety .The ELITE evaluator explicitly incorporates a toxicity score to accurately assess harmfulness in multimodal contexts, where VLMs often provide specific, convincing, but unharmful descriptions of images.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/scientific-contributions/Suhyun-Kim-2304816287", "content": "ELITE : Enhanced Language - Image Toxicity Evaluation for Safety .The ELITE evaluator explicitly incorporates a toxicity score to accurately assess harmfulness in multimodal contexts, where VLMs often provide specific, convincing, but unharmful descriptions of images."}
diff --git a/data/sampled_jsons/6_Scaling_Archetypal-SAE_relaxation_term_A_Lambda_norm_constraint_explicit_year_2024.jsonl b/data/sampled_jsons/6_Scaling_Archetypal-SAE_relaxation_term_A_Lambda_norm_constraint_explicit_year_2024.jsonl
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index 0000000000000000000000000000000000000000..eeff0c1874b99b8d5bda8eaf256ad3aa3075b455
--- /dev/null
+++ b/data/sampled_jsons/6_Scaling_Archetypal-SAE_relaxation_term_A_Lambda_norm_constraint_explicit_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Lagrangian relaxation - Wikipedia", "date": "", "ddg_snippet": "Lagrangian relaxation In the field of mathematical optimization, Lagrangian relaxation is a relaxation method which approximates a difficult problem of constrained optimization by a simpler problem. A solution to the relaxed problem is an approximate solution to the original problem, and provides useful information.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Lagrangian_relaxation", "content": "Lagrangian relaxation In the field of mathematical optimization, Lagrangian relaxation is a relaxation method which approximates a difficult problem of constrained optimization by a simpler problem. A solution to the relaxed problem is an approximate solution to the original problem, and provides useful information."}
+{"idx": 1, "title": "Archetypal SAE: Adaptive and Stable Dictionary Learning for ...", "date": "", "ddg_snippet": "This implementation ensures that 𝑾 𝑾 \\bm {W} bold_italic_W remains row-stochastic and that the deviation term 𝚲 𝚲 \\bm {\\ Lambda } bold_Λ stays within the prescribed norm constraint .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.12892v2", "content": "This implementation ensures that 𝑾 𝑾 \\bm {W} bold_italic_W remains row-stochastic and that the deviation term 𝚲 𝚲 \\bm {\\ Lambda } bold_Λ stays within the prescribed norm constraint ."}
+{"idx": 2, "title": "Convex Optimization - Stanford University algorithms - The general meaning of \"constraint relaxation ... Stability and Time-Step Constraints of Implicit-Explicit ... MATH 3795 Lecture 10. Regularized Linear Least Squares.", "date": "", "ddg_snippet": "Lagrange dual problem KKT conditions Sensitivity analysis Problem reformulations Theorems of alternatives See full list on web.stanford.edu ▶ Lagrangian is L(x, , ) = cTx + T (Ax − b) − Tx = −bT See full list on web.stanford.edu − )Tx ▶ L is afine in x, so g(, ) = inf L(x, , x = ) −bT −∞ See full list on web.stanford.edu KKT conditions Sensitivity analysis Problem reformulations Theorems of alternatives See full list on web.stanford.edu ▶ does not hold in general ▶ (usually) holds for convex problems ▶ conditions that guarantee strong duality in convex problems are called constraint qualifications See full list on web.stanford.edu ▶ from the sharpened Slater’s condition: p★ = d★ if the primal problem is feasible ▶ in fact, p★ = d★ except when primal and dual are both infeasible See full list on web.stanford.edu _u t G p★ t G p★ + t g(_) d★ = g(_) ▶ u + t = g( ) is (non-vertical) supporting hyperplane to G ▶ hyperplane intersects t-axis at t = g( ) See full list on web.stanford.edu Lagrangian and dual function Lagrange dual problem See full list on web.stanford.edu Sensitivity analysis Problem reformulations Theorems of alternatives See full list on web.stanford.edu Lagrangian and dual function Lagrange dual problem KKT conditions See full list on web.stanford.edu Lagrangian and dual function Lagrange dual problem KKT conditions Sensitivity analysis See full list on web.stanford.edu ▶ equivalent formulations of a problem can lead to very diferent duals ▶ reformulating primal problem can be useful when dual is dificult to derive, or uninteresting See full list on web.stanford.edu Lagrangian and dual function Lagrange dual problem KKT conditions Sensitivity analysis Problem reformulations See full list on web.stanford.edu Theorems of alternatives ▶ consider two systems of inequality and equality constraints ▶ called weak alternatives if no more than one system is feasible ▶ called strong alternatives if exactly one of them is feasible ▶ examples: for any a ∈ R, with variable x ∈ R, x > a and x ≤ a − 1 are weak alternatives x > a and x ≤ a are strong alternatives ▶ a ... See full list on web.stanford.edu Feb 13, 2020 · To my knowledge, the term relaxation is used to indicate that a constraint (or a group of constraints ) is removed from the model, rendering a model that is more loose, less constrained. Jul 25, 2023 · This paper provides a study on the stability and time-step constraints of solving the linearized Korteweg-de Vries (KdV) equation, using implicit- explicit (IMEX) Runge-Kutta (RK) time integration methods combined with either finite difference (FD) or local discontinuous Galerkin (DG) spatial discretization. We analyze the stability of the fully discrete scheme, on a uniform mesh with periodic ... If 1= r 1, then it might be useful to consider the regularized linear least squares problem (Tikhonov regularization) Here min kAx x2Rn 2 bk2 2 + kxk2 2: 2 0 is the regularization parameter.", "subpage_snippet": "", "source": "web.stanford.edu", "link": "https://web.stanford.edu/class/ee364a/lectures/duality.pdf", "content": "Lagrange dual problem KKT conditions Sensitivity analysis Problem reformulations Theorems of alternatives See full list on web.stanford.edu ▶ Lagrangian is L(x, , ) = cTx + T (Ax − b) − Tx = −bT See full list on web.stanford.edu − )Tx ▶ L is afine in x, so g(, ) = inf L(x, , x = ) −bT −∞ See full list on web.stanford.edu KKT conditions Sensitivity analysis Problem reformulations Theorems of alternatives See full list on web.stanford.edu ▶ does not hold in general ▶ (usually) holds for convex problems ▶ conditions that guarantee strong duality in convex problems are called constraint qualifications See full list on web.stanford.edu ▶ from the sharpened Slater’s condition: p★ = d★ if the primal problem is feasible ▶ in fact, p★ = d★ except when primal and dual are both infeasible See full list on web.stanford.edu _u t G p★ t G p★ + t g(_) d★ = g(_) ▶ u + t = g( ) is (non-vertical) supporting hyperplane to G ▶ hyperplane intersects t-axis at t = g( ) See full list on web.stanford.edu Lagrangian and dual function Lagrange dual problem See full list on web.stanford.edu Sensitivity analysis Problem reformulations Theorems of alternatives See full list on web.stanford.edu Lagrangian and dual function Lagrange dual problem KKT conditions See full list on web.stanford.edu Lagrangian and dual function Lagrange dual problem KKT conditions Sensitivity analysis See full list on web.stanford.edu ▶ equivalent formulations of a problem can lead to very diferent duals ▶ reformulating primal problem can be useful when dual is dificult to derive, or uninteresting See full list on web.stanford.edu Lagrangian and dual function Lagrange dual problem KKT conditions Sensitivity analysis Problem reformulations See full list on web.stanford.edu Theorems of alternatives ▶ consider two systems of inequality and equality constraints ▶ called weak alternatives if no more than one system is feasible ▶ called strong alternatives if exactly one of them is feasible ▶ examples: for any a ∈ R, with variable x ∈ R, x > a and x ≤ a − 1 are weak alternatives x > a and x ≤ a are strong alternatives ▶ a ... See full list on web.stanford.edu Feb 13, 2020 · To my knowledge, the term relaxation is used to indicate that a constraint (or a group of constraints ) is removed from the model, rendering a model that is more loose, less constrained. Jul 25, 2023 · This paper provides a study on the stability and time-step constraints of solving the linearized Korteweg-de Vries (KdV) equation, using implicit- explicit (IMEX) Runge-Kutta (RK) time integration methods combined with either finite difference (FD) or local discontinuous Galerkin (DG) spatial discretization. We analyze the stability of the fully discrete scheme, on a uniform mesh with periodic ... If 1= r 1, then it might be useful to consider the regularized linear least squares problem (Tikhonov regularization) Here min kAx x2Rn 2 bk2 2 + kxk2 2: 2 0 is the regularization parameter."}
+{"idx": 3, "title": "algorithms - The general meaning of \"constraint relaxation ...", "date": "", "ddg_snippet": "Feb 13, 2020 · To my knowledge, the term relaxation is used to indicate that a constraint (or a group of constraints ) is removed from the model, rendering a model that is more loose, less constrained.", "subpage_snippet": "", "source": "or.stackexchange.com", "link": "https://or.stackexchange.com/questions/3542/the-general-meaning-of-constraint-relaxation-in-the-context-of-the-shortest-pa", "content": "Feb 13, 2020 · To my knowledge, the term relaxation is used to indicate that a constraint (or a group of constraints ) is removed from the model, rendering a model that is more loose, less constrained."}
+{"idx": 4, "title": "Stability and Time-Step Constraints of Implicit-Explicit ...", "date": "", "ddg_snippet": "Jul 25, 2023 · This paper provides a study on the stability and time-step constraints of solving the linearized Korteweg-de Vries (KdV) equation, using implicit- explicit (IMEX) Runge-Kutta (RK) time integration methods combined with either finite difference (FD) or local discontinuous Galerkin (DG) spatial discretization. We analyze the stability of the fully discrete scheme, on a uniform mesh with periodic ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s42967-023-00285-7", "content": "Jul 25, 2023 · This paper provides a study on the stability and time-step constraints of solving the linearized Korteweg-de Vries (KdV) equation, using implicit- explicit (IMEX) Runge-Kutta (RK) time integration methods combined with either finite difference (FD) or local discontinuous Galerkin (DG) spatial discretization. We analyze the stability of the fully discrete scheme, on a uniform mesh with periodic ..."}
+{"idx": 5, "title": "Archetypal SAE: Adaptive and Stable Dictionary Learning for ...", "date": "", "ddg_snippet": "Archetypal -SAEs constrain dictio- nary atoms (decoder directions) to the data's convex hull, improv- ing stability. A relaxed variant (RA- SAE ) allows mild ...", "subpage_snippet": "", "source": "konklab.fas.harvard.edu", "link": "https://konklab.fas.harvard.edu/Papers/Fel_2025_ICML.pdf", "content": "Archetypal -SAEs constrain dictio- nary atoms (decoder directions) to the data's convex hull, improv- ing stability. A relaxed variant (RA- SAE ) allows mild ..."}
+{"idx": 6, "title": "Archetypal SAE: Adaptive and Stable Dictionary Learning ...", "date": "", "ddg_snippet": "18 Feb 2025 — To address this, we introduced Archetypal SAEs (A- SAE ), which constrain dictionary atoms to the convex hull of the data, significantly enhancing ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.12892v1", "content": "18 Feb 2025 — To address this, we introduced Archetypal SAEs (A- SAE ), which constrain dictionary atoms to the convex hull of the data, significantly enhancing ..."}
+{"idx": 7, "title": "Archetypal SAE: Adaptive and Stable Dictionary Learning ...", "date": "", "ddg_snippet": "18 Jun 2025 — We introduce Archetypal SAEs (A- SAE and RA- SAE ) that constrain dictionary elements within the data's convex hull. Abstract: Sparse Autoencoders ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=9v1eW8HgMU&noteId=jupnkmN3Zt", "content": "18 Jun 2025 — We introduce Archetypal SAEs (A- SAE and RA- SAE ) that constrain dictionary elements within the data's convex hull. Abstract: Sparse Autoencoders ..."}
+{"idx": 8, "title": "Relaxation Techniques for Solving Linear Systems - MATH 375 ...", "date": "", "ddg_snippet": "Definition Suppose vector ̃x ∈ Rn is an approximation to the solution of the linear system A x = b. The residual vector for ̃x with respect to this system is r = b − A ̃x.", "subpage_snippet": "", "source": "sites.millersville.edu", "link": "https://sites.millersville.edu/rbuchanan/math375/Relaxation.pdf", "content": "Definition Suppose vector ̃x ∈ Rn is an approximation to the solution of the linear system A x = b. The residual vector for ̃x with respect to this system is r = b − A ̃x."}
+{"idx": 9, "title": "Extracting Sparse Representations with Matching Pursuit", "date": "", "ddg_snippet": "3 Jun 2025 — We find MP- SAE recovers richer structure than standard SAEs across diverse settings: on synthetic trees with controlled interference, it ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.03093v1", "content": "3 Jun 2025 — We find MP- SAE recovers richer structure than standard SAEs across diverse settings: on synthetic trees with controlled interference, it ..."}
diff --git a/data/sampled_jsons/9m87e9Keq1_RL_Incorrect_Synthetic_Data_Scales_LLM_Math_Reasoning_benchmarks.jsonl b/data/sampled_jsons/9m87e9Keq1_RL_Incorrect_Synthetic_Data_Scales_LLM_Math_Reasoning_benchmarks.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5688ab76282364ec64f9aad38d76c6c68d9b6d4a
--- /dev/null
+++ b/data/sampled_jsons/9m87e9Keq1_RL_Incorrect_Synthetic_Data_Scales_LLM_Math_Reasoning_benchmarks.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "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": "openreview.net", "link": "https://openreview.net/forum?id=9m87e9Keq1", "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": 1, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math ...", "date": "", "ddg_snippet": "Thus while in principle, synthetic data could potentially address data scarcity, it must be designed in an appropriate manner to be effective. However, this has been hard due to a lack of an understanding of how synthetic data contributes to LLM behavior.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.14532v1", "content": "Thus while in principle, synthetic data could potentially address data scarcity, it must be designed in an appropriate manner to be effective. However, this has been hard due to a lack of an understanding of how synthetic data contributes to LLM behavior."}
+{"idx": 2, "title": "(PDF) RL on Incorrect Synthetic Data Scales the Efficiency of LLM ...", "date": "", "ddg_snippet": "PDF | Training on model-generated synthetic data is a promising approach for finetuning LLMs, but it remains unclear when it helps or hurts.arXiv:2406.14532v1 [cs.LG] 20 Jun 2024. RL on Incorrect Synthetic Data Scales the Effici ency of LLM Math Reasoning by Eight-F old.", "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": "PDF | Training on model-generated synthetic data is a promising approach for finetuning LLMs, but it remains unclear when it helps or hurts.arXiv:2406.14532v1 [cs.LG] 20 Jun 2024. RL on Incorrect Synthetic Data Scales the Effici ency of LLM Math Reasoning by Eight-F old."}
+{"idx": 3, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math ...", "date": "", "ddg_snippet": "View recent discussion. Abstract: Training on model-generated synthetic data is a promising approach for finetuning LLMs, but it remains unclear when it helps or hurts.", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/ko/overview/2406.14532v1", "content": "View recent discussion. Abstract: Training on model-generated synthetic data is a promising approach for finetuning LLMs, but it remains unclear when it helps or hurts."}
+{"idx": 4, "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.", "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."}
+{"idx": 5, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math ...", "date": "", "ddg_snippet": "The study investigates the impact of synthetic data , both correct and incorrect , on the fine-tuning of LLMs for enhanced math reasoning using supervised fine-tuning (SFT) and reinforcement learning ( RL ).", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/papers/2406.14532", "content": "The study investigates the impact of synthetic data , both correct and incorrect , on the fine-tuning of LLMs for enhanced math reasoning using supervised fine-tuning (SFT) and reinforcement learning ( RL )."}
+{"idx": 6, "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)."}
+{"idx": 7, "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.", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/paper/rl-on-incorrect-synthetic-data-scales-the", "content": "Training on model-generated synthetic data is a promising approach for finetuning LLMs, but it remains unclear when it helps or hurts."}
+{"idx": 8, "title": "30 LLM evaluation benchmarks and how they work", "date": "", "ddg_snippet": "LLM benchmarks are standardized tests for LLM evaluations. This guide covers 30 benchmarks from MMLU to Chatbot Arena, with links to datasets and leaderboards.", "subpage_snippet": "", "source": "www.evidentlyai.com", "link": "https://www.evidentlyai.com/llm-guide/llm-benchmarks", "content": "LLM benchmarks are standardized tests for LLM evaluations. This guide covers 30 benchmarks from MMLU to Chatbot Arena, with links to datasets and leaderboards."}
+{"idx": 9, "title": "Vidhyanand (Vick) Mahase PharmD, PhD. on LinkedIn: RL on...", "date": "", "ddg_snippet": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold.", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/posts/vick-mahase-pharmd-phd_rl-on-incorrect-synthetic-data-scales-the-activity-7213284801527582720-_eRW", "content": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold."}
diff --git a/data/sampled_jsons/AERO_Enhancing_Sharding_Blockchain_Deep_Reinforcement_Learning_Account_Migration_experimental_setup_.jsonl b/data/sampled_jsons/AERO_Enhancing_Sharding_Blockchain_Deep_Reinforcement_Learning_Account_Migration_experimental_setup_.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f15011b6bcc9ec35cf7ae424019ee7b88d9f7b58
--- /dev/null
+++ b/data/sampled_jsons/AERO_Enhancing_Sharding_Blockchain_Deep_Reinforcement_Learning_Account_Migration_experimental_setup_.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "AERO: Enhancing Sharding Blockchain via Deep Reinforcement Learning for ...", "date": "", "ddg_snippet": "We propose AERO , a deep reinforcement learning framework to facilitate efficient account migration in sharding blockchains . AERO employs a prefix-based grouping strategy to enable group-level migration decisions and capture complex transaction patterns and relationships between accounts .", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3696410.3714926", "content": "We propose AERO , a deep reinforcement learning framework to facilitate efficient account migration in sharding blockchains . AERO employs a prefix-based grouping strategy to enable group-level migration decisions and capture complex transaction patterns and relationships between accounts ."}
+{"idx": 1, "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": 2, "title": "AERO: Enhancing Sharding Blockchain via Deep Reinforcement Learning for ...", "date": "", "ddg_snippet": "To address these scalability issues, account migration offers a promising solution. However, existing migration solutions struggle with the high computational overhead and insufficient capture of complex transaction patterns. We propose AERO , a deep reinforcement learning framework for efficient account migration in sharding blockchains .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=WcuXvn3HVk", "content": "To address these scalability issues, account migration offers a promising solution. However, existing migration solutions struggle with the high computational overhead and insufficient capture of complex transaction patterns. We propose AERO , a deep reinforcement learning framework for efficient account migration in sharding blockchains ."}
+{"idx": 3, "title": "PDF AERO: Enhancing Sharding Blockchain via Deep Reinforcement Learning for ...", "date": "", "ddg_snippet": "Motivated by the need for an eficient and decentralized account migration mechanism, we explore applying deep reinforcement learning (DRL) [27] to this problem. DRL is highly efective in handling sequential decision-making tasks and has demonstrated significant potential in optimizing complex systems with expansive state and action spaces [24]. In the context of account migration , the account ...", "subpage_snippet": "", "source": "zhenxiao.com", "link": "http://zhenxiao.com/papers/WWW_AERO_camera_ready.pdf", "content": "Motivated by the need for an eficient and decentralized account migration mechanism, we explore applying deep reinforcement learning (DRL) [27] to this problem. DRL is highly efective in handling sequential decision-making tasks and has demonstrated significant potential in optimizing complex systems with expansive state and action spaces [24]. In the context of account migration , the account ..."}
+{"idx": 4, "title": "AERO: Enhancing Sharding Blockchain via Deep Reinforcement Learning for ...", "date": "", "ddg_snippet": "AERO : Enhancing Sharding Blockchain via Deep Reinforcement Learning for Account Migration Mingxuan Song, Pengze Li, Bohan Zhou, Shenglin Yin, Zhen Xiao, Jieyi Long", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=hBE3xx8yMk", "content": "AERO : Enhancing Sharding Blockchain via Deep Reinforcement Learning for Account Migration Mingxuan Song, Pengze Li, Bohan Zhou, Shenglin Yin, Zhen Xiao, Jieyi Long"}
+{"idx": 5, "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": 6, "title": "AERO: Enhancing Sharding Blockchain via Deep Reinforcement Learning for ...", "date": "", "ddg_snippet": "This work proposes AERO , a deep reinforcement learning framework to facilitate efficient account migration in sharding blockchains , which employs a prefix-based grouping strategy to enable group-level migration decisions and capture complex transaction patterns and relationships between accounts . Sharding blockchain networks face significant scalability challenges due to high frequencies of ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/AERO:-Enhancing-Sharding-Blockchain-via-Deep-for-Song-Li/a4ab4e5da2ec84ea76d9176e2d31eed7990bcdd3", "content": "This work proposes AERO , a deep reinforcement learning framework to facilitate efficient account migration in sharding blockchains , which employs a prefix-based grouping strategy to enable group-level migration decisions and capture complex transaction patterns and relationships between accounts . Sharding blockchain networks face significant scalability challenges due to high frequencies of ..."}
+{"idx": 7, "title": "Deep Learning Approaches for Blockchain Scalability Through Sharding ...", "date": "", "ddg_snippet": "Sharding technology creates new difficulties even as it helps traditional blockchain networks overcome performance issues. The distribution of malicious nodes may be unequal because of random node allocation, resulting in performance variations and security threats. Current reputation-based sharding techniques frequently ignore node performance features and don't take care of leader election ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/abstract/document/10830433", "content": "Sharding technology creates new difficulties even as it helps traditional blockchain networks overcome performance issues. The distribution of malicious nodes may be unequal because of random node allocation, resulting in performance variations and security threats. Current reputation-based sharding techniques frequently ignore node performance features and don't take care of leader election ..."}
+{"idx": 8, "title": "SPRING: Improving the Throughput of Sharding Blockchain via Deep ...", "date": "", "ddg_snippet": "In this paper, we present SPRING, the first deep-reinforcement-learning (DRL)-based sharding framework for state placement. SPRING formulates the state placement as a Markov Decision Process, which considers the cross-shard transaction ratio and workload balancing and employs DRL to learn the effective state placement policy.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3589334.3645386", "content": "In this paper, we present SPRING, the first deep-reinforcement-learning (DRL)-based sharding framework for state placement. SPRING formulates the state placement as a Markov Decision Process, which considers the cross-shard transaction ratio and workload balancing and employs DRL to learn the effective state placement policy."}
+{"idx": 9, "title": "AERO: Enhancing Sharding Blockchain via Deep Reinforcement Learning for ...", "date": "", "ddg_snippet": "Article \" AERO : Enhancing Sharding Blockchain via Deep Reinforcement Learning for Account Migration \" Detailed information of the J-GLOBAL is an information service managed by the Japan Science and Technology Agency (hereinafter referred to as \"JST\"). It provides free access to secondary information on researchers, articles, patents, etc., in science and technology, medicine and pharmacy. The ...", "subpage_snippet": "", "source": "jglobal.jst.go.jp", "link": "https://jglobal.jst.go.jp/en/detail?JGLOBAL_ID=202502257667914622", "content": "Article \" AERO : Enhancing Sharding Blockchain via Deep Reinforcement Learning for Account Migration \" Detailed information of the J-GLOBAL is an information service managed by the Japan Science and Technology Agency (hereinafter referred to as \"JST\"). It provides free access to secondary information on researchers, articles, patents, etc., in science and technology, medicine and pharmacy. The ..."}
diff --git a/data/sampled_jsons/ATA_Adaptive_Task_Allocation_Algorithm_8_Recursive_Allocation_Selection_RAS.jsonl b/data/sampled_jsons/ATA_Adaptive_Task_Allocation_Algorithm_8_Recursive_Allocation_Selection_RAS.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..571fb84ff9e9ed0daa5b54f33e5f07af085edf0f
--- /dev/null
+++ b/data/sampled_jsons/ATA_Adaptive_Task_Allocation_Algorithm_8_Recursive_Allocation_Selection_RAS.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ATA : Adaptive Task Allocation for Efficient Resource Management in...", "date": "", "ddg_snippet": "C Recursive Allocation Selection Algorithm .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": "arxiv.org", "link": "https://arxiv.org/html/2502.00775v2", "content": "C Recursive Allocation Selection Algorithm .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": "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": 2, "title": "[PDF] Performance Evaluation of Some Adaptive Task Allocation ...", "date": "", "ddg_snippet": "performs adaptive task allocation strategies in this scenario.The proposed algorithm achieves 1.17%, 1.02%, and 3.21% lower overload probability relative to random, least-queue and nearest offloading selection schemes, respectively.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/352619630_Performance_Evaluation_of_Some_Adaptive_Task_Allocation_Algorithms_for_Fog_Networks", "content": "performs adaptive task allocation strategies in this scenario.The proposed algorithm achieves 1.17%, 1.02%, and 3.21% lower overload probability relative to random, least-queue and nearest offloading selection schemes, respectively."}
+{"idx": 3, "title": "Adaptive Task Allocation and Scheduling on NoC based Multicore...", "date": "", "ddg_snippet": "Adaptive Task allocation & Scheduling on multicore platforms with multitasking processors 0:3. 52 targeting such multicore systems. In these systems, considering multiple tasks per PE allocation 53 strategy can potentially result in better utilization of the available computing resources [24].", "subpage_snippet": "", "source": "hal.science", "link": "https://hal.science/hal-02877583/document", "content": "Adaptive Task allocation & Scheduling on multicore platforms with multitasking processors 0:3. 52 targeting such multicore systems. In these systems, considering multiple tasks per PE allocation 53 strategy can potentially result in better utilization of the available computing resources [24]."}
+{"idx": 4, "title": "Advances in Adaptive Task Allocation for Fog Robotics - Diverse Daily", "date": "", "ddg_snippet": "One widely used algorithm for adaptive task allocation in fog robotics is the Genetic Algorithm (GA). This algorithm is inspired by the process of natural selection and evolution.", "subpage_snippet": "", "source": "diversedaily.com", "link": "https://diversedaily.com/advances-in-adaptive-task-allocation-for-fog-robotics/", "content": "One widely used algorithm for adaptive task allocation in fog robotics is the Genetic Algorithm (GA). This algorithm is inspired by the process of natural selection and evolution."}
+{"idx": 5, "title": "Model predictive control using Adaptive Task Allocation ... | IEEE Xplore", "date": "", "ddg_snippet": "This paper presents the implementation of a controller based on Adaptive Task Allocation Algorithm (ATAA) for a traffic network with signalized intersections, w.", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/7885717", "content": "This paper presents the implementation of a controller based on Adaptive Task Allocation Algorithm (ATAA) for a traffic network with signalized intersections, w."}
+{"idx": 6, "title": "Q-ITAGS: Quality-Optimized Spatio-Temporal Heterogeneous Task ...", "date": "", "ddg_snippet": "The Q-ITAGS algorithm presented in this paper offers a quality-optimized approach to the spatio-temporal heterogeneous task allocation problem, effectively balancing task quality and time budget constraints.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/q-itags-quality-optimized-spatio-temporal-heterogeneous", "content": "The Q-ITAGS algorithm presented in this paper offers a quality-optimized approach to the spatio-temporal heterogeneous task allocation problem, effectively balancing task quality and time budget constraints."}
+{"idx": 7, "title": "[PDF] Adaptive task allocation for search area... | Semantic Scholar", "date": "", "ddg_snippet": "This study proposes a robust task allocation algorithm under the uncertainty in the position of the nodes and Gaussian approximations are used and the Interval algorithm is employed to investigate the solution to the uncertainty.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Adaptive-task-allocation-for-search-area-coverage-Meuth-Saad/c351bcb795c8a88cb73f40b117fd8080cab3a82e", "content": "This study proposes a robust task allocation algorithm under the uncertainty in the position of the nodes and Gaussian approximations are used and the Interval algorithm is employed to investigate the solution to the uncertainty."}
+{"idx": 8, "title": "TAMER: an adaptive task allocation method for aging reduction in...", "date": "", "ddg_snippet": "As a task allocation algorithm , TAMER takes the cores’ utilization and their internal units’ activity into account to smooth the temperature pattern inside the chip. By minimizing both temporal and spatial thermal variations, TAMER prevents the occurrence of hotspot over time and space.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s11227-020-03326-7", "content": "As a task allocation algorithm , TAMER takes the cores’ utilization and their internal units’ activity into account to smooth the temperature pattern inside the chip. By minimizing both temporal and spatial thermal variations, TAMER prevents the occurrence of hotspot over time and space."}
+{"idx": 9, "title": "An adaptive task allocation technique for green cloud... - Peeref", "date": "", "ddg_snippet": "Task allocation in the cloud computing environment is a well-known problem, and through this problem, we can facilitate green cloud computing. We have proposed an adaptive task allocation algorithm for the heterogeneous cloud environment.", "subpage_snippet": "", "source": "www.peeref.com", "link": "https://www.peeref.com/works/4071639", "content": "Task allocation in the cloud computing environment is a well-known problem, and through this problem, we can facilitate green cloud computing. We have proposed an adaptive task allocation algorithm for the heterogeneous cloud environment."}
diff --git a/data/sampled_jsons/ATA_Adaptive_Task_Allocation_GTA_strategy_distributed_machine_learning.jsonl b/data/sampled_jsons/ATA_Adaptive_Task_Allocation_GTA_strategy_distributed_machine_learning.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ca59c1d0cc4d56b817e0dc2d0b030fce453fa754
--- /dev/null
+++ b/data/sampled_jsons/ATA_Adaptive_Task_Allocation_GTA_strategy_distributed_machine_learning.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ICML Poster ATA: Adaptive Task Allocation for Efficient ...", "date": "", "ddg_snippet": "Poster ATA : Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning Artavazd Maranjyan · El Mehdi Saad · Peter Richtarik · Francesco Orabona West Exhibition Hall B2-B3 #W-909", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46650", "content": "Poster ATA : Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning Artavazd Maranjyan · El Mehdi Saad · Peter Richtarik · Francesco Orabona West Exhibition Hall B2-B3 #W-909"}
+{"idx": 1, "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. 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/2502.00775v2", "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 ATAidentifies the optimal task allocation and performs comparably to methods with prior knowledge of computation times."}
+{"idx": 2, "title": "ATA: Adaptive Task Allocation for Efficient Resource ...", "date": "", "ddg_snippet": "May 1, 2025 · The general task allocation problem in parallel stochastic optimization leads to resource wastefulness, and addressing it can improve efficiency across various distributed machine learning methods.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=1BaC3AdG1i", "content": "May 1, 2025 · The general task allocation problem in parallel stochastic optimization leads to resource wastefulness, and addressing it can improve efficiency across various distributed machine learning methods."}
+{"idx": 3, "title": "[PDF] ATA: Adaptive Task Allocation for Efficient Resource ...", "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": 4, "title": "Unlocking Efficiency: How Adaptive Task Allocation ...", "date": "", "ddg_snippet": "May 24, 2025 · In the rapidly evolving world of artificial intelligence, distributed machine learning stands as a cornerstone for future developments. Yet, as industries adopt parallelized systems to enhance machine learning operations, the challenge of inefficient resource allocation becomes glaring. Enter Adaptive Task Allocation ( ATA ), a sophisticated solution designed to address this very problem.", "subpage_snippet": "", "source": "www.lumafeed.com", "link": "https://www.lumafeed.com/article/9d4d8fe1-d044-4756-99ca-33ab376e4e05", "content": "May 24, 2025 · In the rapidly evolving world of artificial intelligence, distributed machine learning stands as a cornerstone for future developments. Yet, as industries adopt parallelized systems to enhance machine learning operations, the challenge of inefficient resource allocation becomes glaring. Enter Adaptive Task Allocation ( ATA ), a sophisticated solution designed to address this very problem."}
+{"idx": 5, "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. 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": "ui.adsabs.harvard.edu", "link": "https://ui.adsabs.harvard.edu/abs/2025arXiv250200775M/abstract", "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 ...", "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": 7, "title": "ATA: Adaptive Task Allocation for Efficient Resource ...", "date": "", "ddg_snippet": "18 Jun 2025 — TL;DR: We propose a method that learns machine speeds on the fly to assign tasks more efficiently in parallel computing. Abstract: Asynchronous ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=1BaC3AdG1i&noteId=hkH8Wi9zZm", "content": "18 Jun 2025 — TL;DR: We propose a method that learns machine speeds on the fly to assign tasks more efficiently in parallel computing. Abstract: Asynchronous ..."}
+{"idx": 8, "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": 9, "title": "cherryATA", "date": "", "ddg_snippet": "ATA : Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning . 3.1. Task allocation protocol. We consider a system of n ...", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/pdf/2502.00775v1", "content": "ATA : Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning . 3.1. Task allocation protocol. We consider a system of n ..."}
diff --git a/data/sampled_jsons/ATA_ICML_2025_Equation_7_confidence_bound_formula.jsonl b/data/sampled_jsons/ATA_ICML_2025_Equation_7_confidence_bound_formula.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c22686c9e8ff33d0e4b38d445d8b0a2aa887b157
--- /dev/null
+++ b/data/sampled_jsons/ATA_ICML_2025_Equation_7_confidence_bound_formula.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Confidence interval - Wikipedia", "date": "", "ddg_snippet": "A confidence interval for the parameter , with confidence level or coefficient , is an interval determined by random variables and with the property: The number , whose typical value is close to but not greater than 1, is sometimes given in the form (or as a percentage ), where is a small positive number, often 0.05.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Confidence_interval", "content": "A confidence interval for the parameter , with confidence level or coefficient , is an interval determined by random variables and with the property: The number , whose typical value is close to but not greater than 1, is sometimes given in the form (or as a percentage ), where is a small positive number, often 0.05."}
+{"idx": 1, "title": "ICML 2025 Statistics - Paper Copilot", "date": "", "ddg_snippet": "ICML 2025 Review Scores Collection The International Conference on Machine Learning ( ICML ) has not yet opened its reviewer data during the review. However, there's a noticeable interest in the community to view how scores are distributed.", "subpage_snippet": "", "source": "papercopilot.com", "link": "https://papercopilot.com/statistics/icml-statistics/icml-2025-statistics/", "content": "ICML 2025 Review Scores Collection The International Conference on Machine Learning ( ICML ) has not yet opened its reviewer data during the review. However, there's a noticeable interest in the community to view how scores are distributed."}
+{"idx": 2, "title": "Understanding Confidence Intervals | Easy Examples & Formulas", "date": "", "ddg_snippet": "The confidence level is the percentage of times you expect to reproduce an estimate between the upper and lower bounds of the confidence interval, and is set by the alpha value.", "subpage_snippet": "", "source": "www.scribbr.com", "link": "https://www.scribbr.com/statistics/confidence-interval/", "content": "The confidence level is the percentage of times you expect to reproduce an estimate between the upper and lower bounds of the confidence interval, and is set by the alpha value."}
+{"idx": 3, "title": "What are Confidence Intervals? - Statology", "date": "", "ddg_snippet": "A confidence interval is a range of values that is likely to contain a population parameter with a certain level of confidence . It is calculated using the following general formula :", "subpage_snippet": "", "source": "www.statology.org", "link": "https://www.statology.org/confidence-intervals/", "content": "A confidence interval is a range of values that is likely to contain a population parameter with a certain level of confidence . It is calculated using the following general formula :"}
+{"idx": 4, "title": "8.S: Confidence Intervals (Summary) - Statistics LibreTexts", "date": "", "ddg_snippet": "In this module, we learned how to calculate the confidence interval for a single population mean where the population standard deviation is known.", "subpage_snippet": "", "source": "stats.libretexts.org", "link": "https://stats.libretexts.org/Bookshelves/Introductory_Statistics/Introductory_Statistics_1e_(OpenStax)/08:_Confidence_Intervals/8.S:_Confidence_Intervals_(Summary)", "content": "In this module, we learned how to calculate the confidence interval for a single population mean where the population standard deviation is known."}
+{"idx": 5, "title": "Confidence Intervals: Interpreting, Finding & Formulas - Statistics by Jim", "date": "", "ddg_snippet": "A confidence interval is a range of values that is likely to contain the value of an unknown population parameter.", "subpage_snippet": "", "source": "statisticsbyjim.com", "link": "https://statisticsbyjim.com/hypothesis-testing/confidence-interval/", "content": "A confidence interval is a range of values that is likely to contain the value of an unknown population parameter."}
+{"idx": 6, "title": "Confidence Bounds - ReliaSoft", "date": "", "ddg_snippet": "The above equation is the posterior cdf, which essentially calculates a confidence bound on the parameter, where is the confidence level and is the confidence bound .", "subpage_snippet": "", "source": "help.reliasoft.com", "link": "https://help.reliasoft.com/reference/life_data_analysis/lda/confidence_bounds.html", "content": "The above equation is the posterior cdf, which essentially calculates a confidence bound on the parameter, where is the confidence level and is the confidence bound ."}
+{"idx": 7, "title": "How are the confidence intervals calculated", "date": "", "ddg_snippet": "How are the confidence intervals calculated There are confidence intervals on the model parameters, and confidence intervals on the plots. This document explains how both are calculated. The confidence intervals on the plots use the confidence intervals on the model parameters in the procedure that is explained in the second section of this document. Before reading this document, you should ...", "subpage_snippet": "", "source": "reliability.readthedocs.io", "link": "https://reliability.readthedocs.io/en/latest/How+are+the+confidence+intervals+calculated.html", "content": "How are the confidence intervals calculated There are confidence intervals on the model parameters, and confidence intervals on the plots. This document explains how both are calculated. The confidence intervals on the plots use the confidence intervals on the model parameters in the procedure that is explained in the second section of this document. Before reading this document, you should ..."}
+{"idx": 8, "title": "How to Calculate a Confidence Interval in Excel (2024) - Spreadsheeto", "date": "", "ddg_snippet": "How to use the Excel CONFIDENCE function? Enough with all the talk. Let's come to the point - how do you use the CONFIDENCE Function? Calculating confidence intervals in Excel is pretty simple. All you need to do is enter the relevant data points, get the confidence value statistic, and that's it. Simple Let's say we want to find the mean weight of the employees of a Company. For that ...", "subpage_snippet": "", "source": "spreadsheeto.com", "link": "https://spreadsheeto.com/confidence-interval-excel/", "content": "How to use the Excel CONFIDENCE function? Enough with all the talk. Let's come to the point - how do you use the CONFIDENCE Function? Calculating confidence intervals in Excel is pretty simple. All you need to do is enter the relevant data points, get the confidence value statistic, and that's it. Simple Let's say we want to find the mean weight of the employees of a Company. For that ..."}
+{"idx": 9, "title": "ICML Poster Improved Online Confidence Bounds for Multinomial Logistic ...", "date": "", "ddg_snippet": "Abstract: In this paper, we propose an improved online confidence bound for multinomial logistic (MNL) models and apply this result to MNL bandits, achieving variance-dependent optimal regret. Recently, Lee & Oh (2024) established an online confidence bound for MNL models and achieved nearly minimax-optimal regret in MNL bandits.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/45882", "content": "Abstract: In this paper, we propose an improved online confidence bound for multinomial logistic (MNL) models and apply this result to MNL bandits, achieving variance-dependent optimal regret. Recently, Lee & Oh (2024) established an online confidence bound for MNL models and achieved nearly minimax-optimal regret in MNL bandits."}
diff --git a/data/sampled_jsons/A_Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment_paper.jsonl b/data/sampled_jsons/A_Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment_paper.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6e869ce80ef4a936a8fed8b2baa8f7525ec76f44
--- /dev/null
+++ b/data/sampled_jsons/A_Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment_paper.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A Checks-and-Balances Framework for Context-Aware Ethical AI Alignment", "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. It implements three independent yet interacting components: LLMs as the executive branch for knowledge generation, DIKE as the legislative branch establishing ethical guardrails, and ERIS as the judicial branch for contextual interpretation ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.00136", "content": "This paper introduces a checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by three-branch governmental systems. It implements three independent yet interacting components: LLMs as the executive branch for knowledge generation, DIKE as the legislative branch establishing ethical guardrails, and ERIS as the judicial branch for contextual interpretation ..."}
+{"idx": 1, "title": "PDF An Adversarial Behavior Model for Contextual Ethical Alignment in Large ...", "date": "", "ddg_snippet": "Abstract This research introduces DIKE, a novel framework for aligning Large Language Models (LLMs) with human values through emotion-guided behavioral control. Inspired by the checks and balances ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/profile/Edward-Chang-22/publication/380515639_A_Three-Branch_Checks-and-Balances_Framework_for_Context-Aware_Ethical_Alignment_of_Large_Language_Models/links/671b315b55a5271cded9457e/A-Three-Branch-Checks-and-Balances-Framework-for-Context-Aware-Ethical-Alignment-of-Large-Language-Models.pdf", "content": "Abstract This research introduces DIKE, a novel framework for aligning Large Language Models (LLMs) with human values through emotion-guided behavioral control. Inspired by the checks and balances ..."}
+{"idx": 2, "title": "A Three-Branch Checks-and-Balances Frameworkfor Context-Aware Ethical ...", "date": "", "ddg_snippet": "A three-branch checks - and - balances framework for ethical alignment of Large Language Models, inspired by governmental systems, demonstrates how emotional modeling can guide linguistic behaviors toward ethical outcomes while preserving independence across knowledge generation, ethical oversight, and contextual interpretation.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/A-Three-Branch-Checks-and-Balances-Frameworkfor-of-Chang/5918a91419cf95db8599b086590facf63f124702/figure/4", "content": "A three-branch checks - and - balances framework for ethical alignment of Large Language Models, inspired by governmental systems, demonstrates how emotional modeling can guide linguistic behaviors toward ethical outcomes while preserving independence across knowledge generation, ethical oversight, and contextual interpretation."}
+{"idx": 3, "title": "A Checks-and-Balances Framework for Context-Aware Ethical AI Alignment ...", "date": "", "ddg_snippet": "This paper presents a new way to ensure that large AI language models behave ethically, using a system inspired by government branches. It has three parts: the AI (executive) generates knowledge, ...", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/icml/46461/paper", "content": "This paper presents a new way to ensure that large AI language models behave ethically, using a system inspired by government branches. It has three parts: the AI (executive) generates knowledge, ..."}
+{"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 ."}
+{"idx": 5, "title": "A Three-Branch Checks-and-Balances Framework for Context-Aware Ethical ...", "date": "", "ddg_snippet": "This paper introduces a three-branch checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by the idea of collaborative intelligence.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=o2afWIxjKD", "content": "This paper introduces a three-branch checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by the idea of collaborative intelligence."}
+{"idx": 6, "title": "Edward Y. Chang - Stanford University", "date": "", "ddg_snippet": "A Three-Branch Checks - and - Balances Framework for Context-Aware Ethical Alignment of Large Language Models Edward Y. Chang NeurIPS AI Safety, Decmber 2024 PDF | BibTex Behavioral Emotion Analysis Model for Large Language Models Edward Y. Chang IEEE Conference on Multimedia Information Processing and Retrieval (MIPR) (invited paper ), August 2024", "subpage_snippet": "", "source": "infolab.stanford.edu", "link": "http://infolab.stanford.edu/~echang/", "content": "A Three-Branch Checks - and - Balances Framework for Context-Aware Ethical Alignment of Large Language Models Edward Y. Chang NeurIPS AI Safety, Decmber 2024 PDF | BibTex Behavioral Emotion Analysis Model for Large Language Models Edward Y. Chang IEEE Conference on Multimedia Information Processing and Retrieval (MIPR) (invited paper ), August 2024"}
+{"idx": 7, "title": "A Checks-and-Balances Framework for Context-Aware Ethical AI Alignment", "date": "", "ddg_snippet": "Poster A Checks - and - Balances Framework for Context-Aware Ethical AI Alignment Edward Chang East Exhibition Hall A-B #E-703", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46461", "content": "Poster A Checks - and - Balances Framework for Context-Aware Ethical AI Alignment Edward Chang East Exhibition Hall A-B #E-703"}
+{"idx": 8, "title": "Ethical Guardrails for AI: A Checks-and-Balances Approach", "date": "", "ddg_snippet": "A Three-Branch Checks - and - Balances Framework for Context-Aware Ethical Alignment of Large Language Models 48 | 124", "subpage_snippet": "", "source": "www.zerna.io", "link": "https://www.zerna.io/page/security/presentation_set/security-llm-research/presentation/security-ethical-alignment-fairness/slide/security-paper-2502_00136", "content": "A Three-Branch Checks - and - Balances Framework for Context-Aware Ethical Alignment of Large Language Models 48 | 124"}
+{"idx": 9, "title": "A Checks-and-Balances Framework for Context-Aware Ethical AI Alignment", "date": "", "ddg_snippet": "Abstract This paper introduces a checks - and - balances framework for ethical alignment of Large Lan-guage Models (LLMs), inspired by three-branch governmental systems. It implements three in-dependent yet interacting components: LLMs as the executive branch for knowledge generation, Dike as the legislative branch that establishes eth-ical guardrails, and Eris as the judicial branch for ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.00136", "content": "Abstract This paper introduces a checks - and - balances framework for ethical alignment of Large Lan-guage Models (LLMs), inspired by three-branch governmental systems. It implements three in-dependent yet interacting components: LLMs as the executive branch for knowledge generation, Dike as the legislative branch that establishes eth-ical guardrails, and Eris as the judicial branch for ..."}
diff --git a/data/sampled_jsons/A_Likelihood_Based_Approach_to_Distribution_Regression_Using_Conditional_Deep_Generative_Models_Equa_year_2023.jsonl b/data/sampled_jsons/A_Likelihood_Based_Approach_to_Distribution_Regression_Using_Conditional_Deep_Generative_Models_Equa_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..414279fced75eea4155507bce864be610cd5c162
--- /dev/null
+++ b/data/sampled_jsons/A_Likelihood_Based_Approach_to_Distribution_Regression_Using_Conditional_Deep_Generative_Models_Equa_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A Likelihood Based Approach to Distribution Regression Using ...", "date": "", "ddg_snippet": "Oct 2, 2024 · In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where the response variable lies in a high-dimensional ambient space but concentrates around a potentially lower-dimensional manifold. More specifically, we study the large-sample properties of a likelihood - based approach for estimating these ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.02025", "content": "Oct 2, 2024 · In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where the response variable lies in a high-dimensional ambient space but concentrates around a potentially lower-dimensional manifold. More specifically, we study the large-sample properties of a likelihood - based approach for estimating these ..."}
+{"idx": 1, "title": "A LIKELIHOOD BASED APPROACH TO DISTRIBUTION REGRESSION USING ...", "date": "", "ddg_snippet": "In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where the re-sponse variable lies in a high-dimensional ambient space but concentrates around a potentially lower-dimensional manifold. More specifically, we study the large-sample properties of a likelihood - based approach for estimating these ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=V6hhhXoTSq", "content": "In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where the re-sponse variable lies in a high-dimensional ambient space but concentrates around a potentially lower-dimensional manifold. More specifically, we study the large-sample properties of a likelihood - based approach for estimating these ..."}
+{"idx": 2, "title": "A Likelihood Based Approach to Distribution Regression Using ...", "date": "", "ddg_snippet": "We investigate a likelihood approach to nonparametric estimation of a singular distribution using deep generative models . We prove that a novel and effective solution exists by perturbing the data with an instance noise.", "subpage_snippet": "", "source": "fugumt.com", "link": "https://fugumt.com/fugumt/paper_check/2410.02025v1_enmode", "content": "We investigate a likelihood approach to nonparametric estimation of a singular distribution using deep generative models . We prove that a novel and effective solution exists by perturbing the data with an instance noise."}
+{"idx": 3, "title": "A Likelihood Based Approach to Distribution Regression Using ...", "date": "", "ddg_snippet": "Oct 2, 2024 · In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where the response variable lies in a high ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/384630603_A_Likelihood_Based_Approach_to_Distribution_Regression_Using_Conditional_Deep_Generative_Models", "content": "Oct 2, 2024 · In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where the response variable lies in a high ..."}
+{"idx": 4, "title": "A Likelihood Based Approach to Distribution Regression Using ...", "date": "", "ddg_snippet": "Specifically, we explore and study the theoretical properties of a new likelihood - based approach to conditional sampling using deep generative models for data potentially residing on a low-dimensional manifold corrupted by full-dimensional noise.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.02025v1", "content": "Specifically, we explore and study the theoretical properties of a new likelihood - based approach to conditional sampling using deep generative models for data potentially residing on a low-dimensional manifold corrupted by full-dimensional noise."}
+{"idx": 5, "title": "A Deep Generative Approach to Conditional Sampling", "date": "", "ddg_snippet": "Oct 19, 2021 · We propose a deep generative approach to sampling from a conditional distribution based on a unified formulation of conditional distribution and generalized nonparametric regression function using the noise-outsourcing lemma. The proposed approach aims at learning a conditional generator so that a random sample from the target conditional distribution can be obtained by the action of the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2110.10277", "content": "Oct 19, 2021 · We propose a deep generative approach to sampling from a conditional distribution based on a unified formulation of conditional distribution and generalized nonparametric regression function using the noise-outsourcing lemma. The proposed approach aims at learning a conditional generator so that a random sample from the target conditional distribution can be obtained by the action of the ..."}
+{"idx": 6, "title": "[2402.01460] Deep conditional distribution learning via ...", "date": "", "ddg_snippet": "Feb 2, 2024 · We introduce an ordinary differential equation (ODE) based deep generative method for learning conditional distributions, named Conditional Föllmer Flow. Starting from a standard Gaussian distribution , the proposed flow could approximate the target conditional distribution very well when the time is close to 1. For effective implementation, we discretize the flow with Euler's method where we ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2402.01460", "content": "Feb 2, 2024 · We introduce an ordinary differential equation (ODE) based deep generative method for learning conditional distributions, named Conditional Föllmer Flow. Starting from a standard Gaussian distribution , the proposed flow could approximate the target conditional distribution very well when the time is close to 1. For effective implementation, we discretize the flow with Euler's method where we ..."}
+{"idx": 7, "title": "A Likelihood Based Approach to Distribution Regression Using ...", "date": "", "ddg_snippet": "In this work, we explore the theoretical proper- ties of conditional deep generative models un- der the statistical framework of distribution re-.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=1IyPRv1A0r", "content": "In this work, we explore the theoretical proper- ties of conditional deep generative models un- der the statistical framework of distribution re-."}
+{"idx": 8, "title": "Your Likelihood-Based Visual Generative Model is Secretly ...", "date": "", "ddg_snippet": "To bypass MLE's mode-covering nature, we aim to leverage GAN-type loss to discriminate between the model and data distributions and produce contrastive forces ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/45454", "content": "To bypass MLE's mode-covering nature, we aim to leverage GAN-type loss to discriminate between the model and data distributions and produce contrastive forces ..."}
+{"idx": 9, "title": "Your Likelihood-Based Visual Generative Model is Secretly ...", "date": "", "ddg_snippet": "3 Mar 2025 — In this work, we introduce Direct Discriminative Optimization (DDO), a framework that bridges likelihood - based generative models and GANs to ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.01103v1", "content": "3 Mar 2025 — In this work, we introduce Direct Discriminative Optimization (DDO), a framework that bridges likelihood - based generative models and GANs to ..."}
diff --git a/data/sampled_jsons/A_framework_for_improving_web_affordability_and_inclusiveness_Habib.jsonl b/data/sampled_jsons/A_framework_for_improving_web_affordability_and_inclusiveness_Habib.jsonl
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index 0000000000000000000000000000000000000000..ded2f072c567de34a3481faa1749dec5197f26e5
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+{"idx": 0, "title": "A Framework for Improving Web Affordability and Inclusiveness", "date": "", "ddg_snippet": "ACM Reference Format: Rumaisa Habib *, Sarah Tanveer*, Aimen Inam, Haseeb Ahmed, Ayesha Ali, Zartash Afzal Uzmi, Zafar Ayyub Qazi, Ihsan Ayyub Qazi. 2023. A Framework for Improving Web Affordability and Inclusiveness .", "subpage_snippet": "", "source": "www.ietf.org", "link": "https://www.ietf.org/slides/slides-biasws-a-framework-for-improving-web-affordability-and-inclusiveness-00.pdf", "content": "ACM Reference Format: Rumaisa Habib *, Sarah Tanveer*, Aimen Inam, Haseeb Ahmed, Ayesha Ali, Zartash Afzal Uzmi, Zafar Ayyub Qazi, Ihsan Ayyub Qazi. 2023. A Framework for Improving Web Affordability and Inclusiveness ."}
+{"idx": 1, "title": "A Framework for Improving Web Affordability and Inclusiveness", "date": "", "ddg_snippet": "Today's Web remains too expensive for many Internet users, especially in developing regions. Unfortunately, the rising complexity of the Web makes affordability an even bigger concern as it stands to limit users' access to Internet services. We propose a novel framework and a fairness metric for rethinking Web architecture for affordability and inclusion. Our proposed framework systematically ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3603269.3604872", "content": "Today's Web remains too expensive for many Internet users, especially in developing regions. Unfortunately, the rising complexity of the Web makes affordability an even bigger concern as it stands to limit users' access to Internet services. We propose a novel framework and a fairness metric for rethinking Web architecture for affordability and inclusion. Our proposed framework systematically ..."}
+{"idx": 2, "title": "dblp: A Framework for Improving Web Affordability and Inclusiveness.", "date": "", "ddg_snippet": "Bibliographic details on A Framework for Improving Web Affordability and Inclusiveness .", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/conf/sigcomm/HabibTIAAUQQ23", "content": "Bibliographic details on A Framework for Improving Web Affordability and Inclusiveness ."}
+{"idx": 3, "title": "A Framework for Improving Web Affordability and Inclusiveness | Request PDF", "date": "", "ddg_snippet": "Request PDF | On Sep 10, 2023, Rumaisa Habib and others published A Framework for Improving Web Affordability and Inclusiveness | Find, read and cite all the research you need on ResearchGate", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/373616306_A_Framework_for_Improving_Web_Affordability_and_Inclusiveness", "content": "Request PDF | On Sep 10, 2023, Rumaisa Habib and others published A Framework for Improving Web Affordability and Inclusiveness | Find, read and cite all the research you need on ResearchGate"}
+{"idx": 4, "title": "Paper: A Framework for Improving Web Affordability and Inclusiveness", "date": "", "ddg_snippet": "slides-biasws- a-framework-for-improving-web-affordability-and-inclusiveness -00", "subpage_snippet": "", "source": "datatracker.ietf.org", "link": "https://datatracker.ietf.org/doc/slides-biasws-a-framework-for-improving-web-affordability-and-inclusiveness/", "content": "slides-biasws- a-framework-for-improving-web-affordability-and-inclusiveness -00"}
+{"idx": 5, "title": "A Framework for Improving Web Affordability and Inclusiveness", "date": "", "ddg_snippet": "A novel framework and a fairness metric for rethinking Web architecture for affordability and inclusion is proposed and a cross-country analysis of 99 countries shows that the framework can better balance affordability and webpage quality while preserving user privacy. Expand View on ACM ietf.org Save to Library Create Alert Cite", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/A-Framework-for-Improving-Web-Affordability-and-Habib-Tanveer/0546f3bedce1d44b3bbc1682d698d9b9a3f22906/figure/12", "content": "A novel framework and a fairness metric for rethinking Web architecture for affordability and inclusion is proposed and a cross-country analysis of 99 countries shows that the framework can better balance affordability and webpage quality while preserving user privacy. Expand View on ACM ietf.org Save to Library Create Alert Cite"}
+{"idx": 6, "title": "Ayesha ALI | accounting and finance | Research profile", "date": "", "ddg_snippet": "A Framework for Improving Web Affordability and Inclusiveness .A First Look at Public Service Websites from the Affordability Lens.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/profile/Ayesha-Ali-87", "content": "A Framework for Improving Web Affordability and Inclusiveness .A First Look at Public Service Websites from the Affordability Lens."}
+{"idx": 7, "title": "CS PhD student at Stanford University", "date": "", "ddg_snippet": "Rethinking Web for Affordability and Inclusion ondemand_video Ihsan Ayyub Qazi, Zafar Ayyub Qazi, Ayesha Ali, Muhammad Abdullah, Rumaisa Habib HotNets ‘21.", "subpage_snippet": "", "source": "rumaisahabib.com", "link": "https://rumaisahabib.com/publications/", "content": "Rethinking Web for Affordability and Inclusion ondemand_video Ihsan Ayyub Qazi, Zafar Ayyub Qazi, Ayesha Ali, Muhammad Abdullah, Rumaisa Habib HotNets ‘21."}
+{"idx": 8, "title": "Rumaisa Habib 's Profile | Stanford Profiles", "date": "", "ddg_snippet": "All Publications. A Framework for Improving Web Affordability and Inclusiveness Habib , R., Tanveer, S., Inam, A., Ahmed, H., Ali, A., Uzmi, Z., Qazi, Z., Qazi, I., ACM ASSOC COMPUTING MACHINERY. 2023: 592-607.", "subpage_snippet": "", "source": "profiles.stanford.edu", "link": "https://profiles.stanford.edu/rumaisa-habib", "content": "All Publications. A Framework for Improving Web Affordability and Inclusiveness Habib , R., Tanveer, S., Inam, A., Ahmed, H., Ali, A., Uzmi, Z., Qazi, Z., Qazi, I., ACM ASSOC COMPUTING MACHINERY. 2023: 592-607."}
+{"idx": 9, "title": "Rumaisa Habib - CS PhD Student @ Stanford | LinkedIn", "date": "", "ddg_snippet": "A Framework for Improving Web Affordability and Inclusiveness .We propose a novel framework and fairness metric for rethinking Web architecture for affordability and inclusion.", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/in/rumaisahabib", "content": "A Framework for Improving Web Affordability and Inclusiveness .We propose a novel framework and fairness metric for rethinking Web architecture for affordability and inclusion."}
diff --git a/data/sampled_jsons/A_very_simple_way_to_improve_the_performance_of_almost_any_machine_learning_algorithm_is_to_train_ma.jsonl b/data/sampled_jsons/A_very_simple_way_to_improve_the_performance_of_almost_any_machine_learning_algorithm_is_to_train_ma.jsonl
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index 0000000000000000000000000000000000000000..1abc6e6127dd5fc5cbbcd00fe1287c498bc9bb1e
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+{"idx": 0, "title": "[1503.02531] Distilling the Knowledge in a Neural Network", "date": "", "ddg_snippet": "by G Hinton · 2015 · Cited by 27170 — A very simple way to improve the performance of almost any machine learning algorithm is to train many different models on the same data and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1503.02531", "content": "by G Hinton · 2015 · Cited by 27170 — A very simple way to improve the performance of almost any machine learning algorithm is to train many different models on the same data and ..."}
+{"idx": 1, "title": "Distilling the Knowledge in a Neural Network", "date": "", "ddg_snippet": "by G Hinton · 2015 · Cited by 27170 — A very simple way to improve the performance of almost any machine learning algorithm is to train many different models on the same data and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1503.02531", "content": "by G Hinton · 2015 · Cited by 27170 — A very simple way to improve the performance of almost any machine learning algorithm is to train many different models on the same data and ..."}
+{"idx": 2, "title": "Stochastic Weight Averaging — a New Way to Get State of ...", "date": "", "ddg_snippet": "In this article, I will discuss two interesting recent papers that provide an easy way to improve performance of any given neural network by using a smart way ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/data-science/stochastic-weight-averaging-a-new-way-to-get-state-of-the-art-results-in-deep-learning-c639ccf36a", "content": "In this article, I will discuss two interesting recent papers that provide an easy way to improve performance of any given neural network by using a smart way ..."}
+{"idx": 3, "title": "averaging weights of multiple fine-tuned models improves ...", "date": "", "ddg_snippet": "by M Wortsman · 2022 · Cited by 1339 — The conventional recipe for maximizing model accuracy is to (1) train multiple models with var- ious hyperparameters and (2) pick the individ-. 34 pages", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v162/wortsman22a/wortsman22a.pdf", "content": "by M Wortsman · 2022 · Cited by 1339 — The conventional recipe for maximizing model accuracy is to (1) train multiple models with var- ious hyperparameters and (2) pick the individ-. 34 pages"}
+{"idx": 4, "title": "A comprehensive review on ensemble deep learning", "date": "", "ddg_snippet": "by A Mohammed · 2023 · Cited by 997 — Hence, ensemble deep learning methods refer to training several baseline deep models and combining some rules to make predictions.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S1319157823000228", "content": "by A Mohammed · 2023 · Cited by 997 — Hence, ensemble deep learning methods refer to training several baseline deep models and combining some rules to make predictions."}
+{"idx": 5, "title": "LEVERAGING SIMPLE MODEL PREDICTIONS", "date": "", "ddg_snippet": "by A Dhurandhar — In this paper, we propose a method, SRatio, which reweights the training set to improve simple models given access to a highly accurate complex model such as a ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=HyxQbaEYPr", "content": "by A Dhurandhar — In this paper, we propose a method, SRatio, which reweights the training set to improve simple models given access to a highly accurate complex model such as a ..."}
+{"idx": 6, "title": "Combining Machine Learning and Statistical Learning to ...", "date": "", "ddg_snippet": "20 Jun 2025 — Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials.", "subpage_snippet": "", "source": "advanced.onlinelibrary.wiley.com", "link": "https://advanced.onlinelibrary.wiley.com/doi/10.1002/aidi.202500033", "content": "20 Jun 2025 — Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials."}
+{"idx": 7, "title": "Machine Learning: Algorithms, Real-World Applications ...", "date": "", "ddg_snippet": "by IH Sarker · 2021 · Cited by 6237 — The ultimate goal of a semi-supervised learning model is to provide a better outcome for prediction than that produced using the labeled data alone from the ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC7983091/", "content": "by IH Sarker · 2021 · Cited by 6237 — The ultimate goal of a semi-supervised learning model is to provide a better outcome for prediction than that produced using the labeled data alone from the ..."}
+{"idx": 8, "title": "Nonlinear Boosting Projections for Ensemble Construction", "date": "", "ddg_snippet": "by N Garcıa-Pedrajas · 2007 · Cited by 123 — Abstract. In this paper we propose a novel approach for ensemble construction based on the use of nonlinear projections to achieve both accuracy and ...", "subpage_snippet": "", "source": "jmlr.org", "link": "https://jmlr.org/papers/volume8/garcia-pedrajas07a/garcia-pedrajas07a.pdf", "content": "by N Garcıa-Pedrajas · 2007 · Cited by 123 — Abstract. In this paper we propose a novel approach for ensemble construction based on the use of nonlinear projections to achieve both accuracy and ..."}
+{"idx": 9, "title": "Comparison of machine learning methods for automatic ...", "date": "", "ddg_snippet": "by D Eriksson · 2023 · Cited by 10 — We use four different datasets as training data and first compare the model's accuracy on predicting operator commands. We then deploy and evaluate the efficacy ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0926580523001036", "content": "by D Eriksson · 2023 · Cited by 10 — We use four different datasets as training data and first compare the model's accuracy on predicting operator commands. We then deploy and evaluate the efficacy ..."}
diff --git a/data/sampled_jsons/Abbasi-Yadkori_et_al._2011_OFUL_framework_paper_title_year_2011.jsonl b/data/sampled_jsons/Abbasi-Yadkori_et_al._2011_OFUL_framework_paper_title_year_2011.jsonl
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+{"idx": 0, "title": "[1609.01508] Low-rank Bandits with Latent Mixtures", "date": "", "ddg_snippet": "by A Gopalan · 2016 · Cited by 36 — It combines the Robust Tensor Power Method of Anandkumar et al . (2014b) with the OFUL linear bandit algorithm of Abbasi - Yadkori et al . ( 2011 ).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1609.01508", "content": "by A Gopalan · 2016 · Cited by 36 — It combines the Robust Tensor Power Method of Anandkumar et al . (2014b) with the OFUL linear bandit algorithm of Abbasi - Yadkori et al . ( 2011 )."}
+{"idx": 1, "title": "Multi-task Representation Learning with Stochastic Linear ...", "date": "", "ddg_snippet": "by L Cella · 2023 · Cited by 32 — Contextual bandits ( Abbasi - Yadkori et al ., 2011 ; Li et al .,. 2010; Auer, 2002) are a prominent learning framework to study sequential decision problems with ... 26 pages", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v206/cella23a/cella23a.pdf", "content": "by L Cella · 2023 · Cited by 32 — Contextual bandits ( Abbasi - Yadkori et al ., 2011 ; Li et al .,. 2010; Auer, 2002) are a prominent learning framework to study sequential decision problems with ... 26 pages"}
+{"idx": 2, "title": "Minimum Empirical Divergence for Sub-Gaussian Linear ...", "date": "", "ddg_snippet": "by K Balagopalan · 2024 · Cited by 1 — ... OFUL ( Abbasi - yadkori et al ., 2011 ). Our empirical study shows that LinMED has a competitive performance with the state-of-the-art algorithms.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2411.00229", "content": "by K Balagopalan · 2024 · Cited by 1 — ... OFUL ( Abbasi - yadkori et al ., 2011 ). Our empirical study shows that LinMED has a competitive performance with the state-of-the-art algorithms."}
+{"idx": 3, "title": "Sequential experimental design for transductive linear bandits", "date": "", "ddg_snippet": "In this paper we introduce the pure exploration transductive linear bandit problem: given a set of measurement vectors Χ ⊂ ℝ d , a set of items Ƶ ⊂ ℝ d , ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3454287.3455244", "content": "In this paper we introduce the pure exploration transductive linear bandit problem: given a set of measurement vectors Χ ⊂ ℝ d , a set of items Ƶ ⊂ ℝ d , ..."}
+{"idx": 4, "title": "Provably Efficient Reinforcement Learning with Linear Function ...", "date": "", "ddg_snippet": "When we prepare this paper , we notice a concurrent work by Gao et al . ... ( 2011 ) proposed a rarely switching OFUL ... policy switch model ( Abbasi - Yadkori et al ., ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/references/pdf?id=1Vb3iIEDC-", "content": "When we prepare this paper , we notice a concurrent work by Gao et al . ... ( 2011 ) proposed a rarely switching OFUL ... policy switch model ( Abbasi - Yadkori et al ., ..."}
+{"idx": 5, "title": "[PDF] Multi-task Linear Bandits", "date": "", "ddg_snippet": "This work proposes a simple but efficient framework to provably and ... Improved Algorithms for Linear Stochastic Bandits · Yasin Abbasi - Yadkori D. Pál ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Multi-task-Linear-Bandits-Soare/b7c1c4ae5254ef79a1f41690a24edcf91f81e577", "content": "This work proposes a simple but efficient framework to provably and ... Improved Algorithms for Linear Stochastic Bandits · Yasin Abbasi - Yadkori D. Pál ..."}
+{"idx": 6, "title": "Online Learning and Decision-Making under Generalized Linear ...", "date": "", "ddg_snippet": "OLS-Bandit by Goldenshluger and Zeevi 2013 and OFUL by Abbasi-Yadkori et al. 2011 , and in high-dimensional settings, Lasso-Bandit by Bastani and Bayati 2015 ...", "subpage_snippet": "", "source": "papers.ssrn.com", "link": "https://papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID3777611_code1195516.pdf?abstractid=3294832", "content": "OLS-Bandit by Goldenshluger and Zeevi 2013 and OFUL by Abbasi-Yadkori et al. 2011 , and in high-dimensional settings, Lasso-Bandit by Bastani and Bayati 2015 ..."}
+{"idx": 7, "title": "Minimum Empirical Divergence for Sub-Gaussian Linear Bandits", "date": "", "ddg_snippet": "by K Balagopalan · Cited by 1 — In this paper , we propose a novel linear bandit al- gorithm called LinMED ... OFUL ( Abbasi - Yadkori et al ., 2011 ). LinMED stands out even more when ...", "subpage_snippet": "", "source": "raw.githubusercontent.com", "link": "https://raw.githubusercontent.com/mlresearch/v258/main/assets/balagopalan25a/balagopalan25a.pdf", "content": "by K Balagopalan · Cited by 1 — In this paper , we propose a novel linear bandit al- gorithm called LinMED ... OFUL ( Abbasi - Yadkori et al ., 2011 ). LinMED stands out even more when ..."}
+{"idx": 8, "title": "Reinforcement Learning in Structured and Partially Observable ...", "date": "", "ddg_snippet": ", as ˆθt, up to a new high probability confidence set Cd,t. This is the same confidence set generation subroutine of OFUL Abbasi - Yadkori et al . ( 2011 ).", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/pdf/reinforcement-learning-in-structured-and-partially-2sq5lezwuo.pdf", "content": ", as ˆθt, up to a new high probability confidence set Cd,t. This is the same confidence set generation subroutine of OFUL Abbasi - Yadkori et al . ( 2011 )."}
+{"idx": 9, "title": "Online Learning and Decision Making Under Generalized ...", "date": "", "ddg_snippet": "by X Wang · 2024 · Cited by 8 — We propose a minimax concave penalized multiarmed bandit algorithm under the generalized linear model (G-MCP-Bandit) for decision-makers ...", "subpage_snippet": "", "source": "pubsonline.informs.org", "link": "https://pubsonline.informs.org/doi/10.1287/mnsc.2022.01557", "content": "by X Wang · 2024 · Cited by 8 — We propose a minimax concave penalized multiarmed bandit algorithm under the generalized linear model (G-MCP-Bandit) for decision-makers ..."}
diff --git a/data/sampled_jsons/Abnormal_Behavioral_Pattern_connectivity_metric_conn(a_b)_Tor_anomalous_circuits.jsonl b/data/sampled_jsons/Abnormal_Behavioral_Pattern_connectivity_metric_conn(a_b)_Tor_anomalous_circuits.jsonl
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+++ b/data/sampled_jsons/Abnormal_Behavioral_Pattern_connectivity_metric_conn(a_b)_Tor_anomalous_circuits.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "abnormal behavioral responses: Topics by ...", "date": "", "ddg_snippet": "This review discusses the role of abnormal connections in each CSTC circuit , especially in the emotion circuit , which may be responsible for targeted ...", "subpage_snippet": "", "source": "www.science.gov", "link": "https://www.science.gov/topicpages/a/abnormal+behavioral+responses", "content": "This review discusses the role of abnormal connections in each CSTC circuit , especially in the emotion circuit , which may be responsible for targeted ..."}
+{"idx": 1, "title": "Graph Measure Based Connectivity in Chronic Pain Patients", "date": "", "ddg_snippet": "by D Lenoir · 2021 · Cited by 22 — Edges in the structural connectome (SC) can be defined as the correlation between grey matter metrics . (such as cortical thickness or surface area) of 2 nodes,.", "subpage_snippet": "", "source": "www.painphysicianjournal.com", "link": "https://www.painphysicianjournal.com/current/pdf?article=NzM0OA==&journal=139", "content": "by D Lenoir · 2021 · Cited by 22 — Edges in the structural connectome (SC) can be defined as the correlation between grey matter metrics . (such as cortical thickness or surface area) of 2 nodes,."}
+{"idx": 2, "title": "Hierarchical behavioral analysis framework as a platform ...", "date": "", "ddg_snippet": "by J Ye · 2025 — Here, we developed a hierarchical behav- ioral analysis framework (HBAF) that efficiently reveals the organizational logic of these modules by ... 25 pages", "subpage_snippet": "", "source": "www.cell.com", "link": "https://www.cell.com/cell-reports/pdf/S2211-1247(25)00010-5.pdf", "content": "by J Ye · 2025 — Here, we developed a hierarchical behav- ioral analysis framework (HBAF) that efficiently reveals the organizational logic of these modules by ... 25 pages"}
+{"idx": 3, "title": "Vulnerability factors and neuropsychiatric disorders", "date": "", "ddg_snippet": "by E Daprati · 2022 — We argue that inter-individual differences in healthy cognitive style can inform on vulnerability traits or endophenotypes for disease.", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC9815448/", "content": "by E Daprati · 2022 — We argue that inter-individual differences in healthy cognitive style can inform on vulnerability traits or endophenotypes for disease."}
+{"idx": 4, "title": "Bridging Neuroscience and Psychiatry through Brain ...", "date": "", "ddg_snippet": "by MM Naffaa · 2024 · Cited by 3 — This review explores the complex interplay between neu- ral circuits , neurobiology, and the treatment of psychiatric disorders, specifically ... 21 pages", "subpage_snippet": "", "source": "cellnatsci.com", "link": "https://cellnatsci.com/wp-content/uploads/2025/08/10-61474-ncs-2024-00051.pdf", "content": "by MM Naffaa · 2024 · Cited by 3 — This review explores the complex interplay between neu- ral circuits , neurobiology, and the treatment of psychiatric disorders, specifically ... 21 pages"}
+{"idx": 5, "title": "BRAIN COMMUNICATIONS AIN COMMUNICATIONS", "date": "", "ddg_snippet": "by D Szczupak · 2021 · Cited by 12 — These results point to a reorganization of structural brain connectivity in HP subjects, leading to abnormal and in- coherent functional connectivity patterns ... 11 pages", "subpage_snippet": "", "source": "academic.oup.com", "link": "https://academic.oup.com/braincomms/article-pdf/3/2/fcab057/41723878/fcab057.pdf", "content": "by D Szczupak · 2021 · Cited by 12 — These results point to a reorganization of structural brain connectivity in HP subjects, leading to abnormal and in- coherent functional connectivity patterns ... 11 pages"}
+{"idx": 6, "title": "Complex functional brain network properties in anorexia ...", "date": "", "ddg_snippet": "by A Gupta · 2022 · Cited by 13 — We aimed to test the hypothesis that individuals with AN demonstrate lower connectivity and centrality in core regions of sensorimotor networks, and greater.", "subpage_snippet": "", "source": "dibs-web01.vm.duke.edu", "link": "https://dibs-web01.vm.duke.edu/labar/pdfs/Gupta_et_al_2022.pdf", "content": "by A Gupta · 2022 · Cited by 13 — We aimed to test the hypothesis that individuals with AN demonstrate lower connectivity and centrality in core regions of sensorimotor networks, and greater."}
+{"idx": 7, "title": "Evolution of brain functional plasticity associated with ...", "date": "", "ddg_snippet": "by C Wang · 2022 · Cited by 21 — A link prediction model achieved an excellent predictive performance in estimating connectivity of more severe myelopathic stages of DCM, with the highest area ...", "subpage_snippet": "", "source": "www.thelancet.com", "link": "https://www.thelancet.com/pdfs/journals/ebiom/PIIS2352-3964(22)00437-6.pdf", "content": "by C Wang · 2022 · Cited by 21 — A link prediction model achieved an excellent predictive performance in estimating connectivity of more severe myelopathic stages of DCM, with the highest area ..."}
+{"idx": 8, "title": "Dissociable Disruptions in Thalamic and Hippocampal ...", "date": "", "ddg_snippet": "by C Schleifer · 2019 · Cited by 44 — In humans, 22q11DS presents a compelling genetic high-risk model where anomalous circuitry can be investigated before overt illness development. Specifically, ...", "subpage_snippet": "", "source": "escholarship.org", "link": "https://escholarship.org/content/qt0p35q8bk/qt0p35q8bk_noSplash_7de21f4feccd7d1f360f8048f4f69ff2.pdf", "content": "by C Schleifer · 2019 · Cited by 44 — In humans, 22q11DS presents a compelling genetic high-risk model where anomalous circuitry can be investigated before overt illness development. Specifically, ..."}
+{"idx": 9, "title": "The resting-state brain activity signatures for addictive ...", "date": "", "ddg_snippet": "by H Zheng · 2024 · Cited by 14 — This study aimed to uncover common and unique patterns of abnormal brain activity in individuals with substance use disorder (SUD) and ...", "subpage_snippet": "", "source": "www.cell.com", "link": "https://www.cell.com/med/pdf/S2666-6340(24)00038-2.pdf", "content": "by H Zheng · 2024 · Cited by 14 — This study aimed to uncover common and unique patterns of abnormal brain activity in individuals with substance use disorder (SUD) and ..."}
diff --git a/data/sampled_jsons/Accelerating_Linear_Recurrent_Neural_Networks_for_the_Edge_with_Unstructured_Sparsity.jsonl b/data/sampled_jsons/Accelerating_Linear_Recurrent_Neural_Networks_for_the_Edge_with_Unstructured_Sparsity.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..603c768e5a43cfb2ae5f7bf97789b55ab42ba238
--- /dev/null
+++ b/data/sampled_jsons/Accelerating_Linear_Recurrent_Neural_Networks_for_the_Edge_with_Unstructured_Sparsity.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "UnIT: Scalable Unstructured Inference-Time Pruning for", "date": "", "ddg_snippet": "Deploying deep neural networks (DNNs) on low-power microcontroller units (MCUs) is essential for enabling edge intelligence in domains such as ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.07885v1", "content": "Deploying deep neural networks (DNNs) on low-power microcontroller units (MCUs) is essential for enabling edge intelligence in domains such as ..."}
+{"idx": 1, "title": "SparseDPD: A Sparse Neural Network-based Digital Predistortion", "date": "", "ddg_snippet": "... accelerator employing a spatially sparse phase-normalized time-delay neural network (PNTDNN), optimized through unstructured pruning to reduce ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.16591v1", "content": "... accelerator employing a spatially sparse phase-normalized time-delay neural network (PNTDNN), optimized through unstructured pruning to reduce ..."}
+{"idx": 2, "title": "Accelerating LLM Inference with Flexible N:M Sparsity via A", "date": "", "ddg_snippet": "... we present a pruning algorithm that can assign different N:M sparsity with a higher degree of freedom and then present the first DCiM accelerator to ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.14365v1", "content": "... we present a pruning algorithm that can assign different N:M sparsity with a higher degree of freedom and then present the first DCiM accelerator to ..."}
+{"idx": 3, "title": "GitHub - he-y/Awesome-Pruning: A curated list of neural network", "date": "", "ddg_snippet": "... title={Structured Pruning for Deep Convolutional Neural Networks ... Joint Edge -Model Sparse Learning is Provably Efficient for Graph Neural Networks", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/he-y/Awesome-Pruning", "content": "... title={Structured Pruning for Deep Convolutional Neural Networks ... Joint Edge -Model Sparse Learning is Provably Efficient for Graph Neural Networks"}
+{"idx": 4, "title": "From Bayesian Sparsity to Gated Recurrent Nets", "date": "", "ddg_snippet": "This paper proposes the use of gated feedback gated recurrent unit network (GFGRU), a learning-based sparse estimation algorithm, for multiple source ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/317543453_From_Bayesian_Sparsity_to_Gated_Recurrent_Nets", "content": "This paper proposes the use of gated feedback gated recurrent unit network (GFGRU), a learning-based sparse estimation algorithm, for multiple source ..."}
+{"idx": 5, "title": "ImageNet Classification with Deep Convolutional Neural Networks", "date": "", "ddg_snippet": "Recently, to improve the accuracy of image retrieval, feature extraction methods based on convolutional neural networks (CNN) have been proposed [11 ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/267960550_ImageNet_Classification_with_Deep_Convolutional_Neural_Networks", "content": "Recently, to improve the accuracy of image retrieval, feature extraction methods based on convolutional neural networks (CNN) have been proposed [11 ..."}
+{"idx": 6, "title": "US20220108157A1 - Hardware architecture for introducing", "date": "", "ddg_snippet": "Neural network 200 may represent different ... Neural network 200 may also be a hierarchical temporal memory system as described, for example, in U.S.", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/US20220108157A1/en", "content": "Neural network 200 may represent different ... Neural network 200 may also be a hierarchical temporal memory system as described, for example, in U.S."}
+{"idx": 7, "title": "Neuro-inspired Ensemble-to-Ensemble Communication Primitives", "date": "", "ddg_snippet": "These vectors are concatenated and passed through a feed-forward network with a predefined sparse structure , inspired by group-to-group ( neural ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.14140v1", "content": "These vectors are concatenated and passed through a feed-forward network with a predefined sparse structure , inspired by group-to-group ( neural ..."}
+{"idx": 8, "title": "SpikeX: Exploring Accelerator Architecture and Network-Hardware", "date": "", "ddg_snippet": "The previous generations of accelerators do not maximally optimize for the unique dataflow opportunities of spiking neural networks , especially as ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.12292v1", "content": "The previous generations of accelerators do not maximally optimize for the unique dataflow opportunities of spiking neural networks , especially as ..."}
+{"idx": 9, "title": "Downloads", "date": "", "ddg_snippet": "Accelerated Primal-Dual Gradient Method for ... A general approximation lower bound in $L^p$ norm, with applications to feed-forward neural networks", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/Downloads/2022", "content": "Accelerated Primal-Dual Gradient Method for ... A general approximation lower bound in $L^p$ norm, with applications to feed-forward neural networks"}
diff --git a/data/sampled_jsons/Ad-Hoc_Human-AI_Coordination_Challenge_HDR-IPPO_CHDR-IPPO.jsonl b/data/sampled_jsons/Ad-Hoc_Human-AI_Coordination_Challenge_HDR-IPPO_CHDR-IPPO.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a2cedae85447d021a068d118cc70c9340eab41dc
--- /dev/null
+++ b/data/sampled_jsons/Ad-Hoc_Human-AI_Coordination_Challenge_HDR-IPPO_CHDR-IPPO.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Ad-Hoc Human-AI Coordination Challenge - arXiv.org", "date": "", "ddg_snippet": "We develop four human proxy agents for evaluating ad-hoc human-AI coordination : two for two-player Hanabi and two for three-player. These agents are trained using Human -Data-Regularised IPPO ( HDR-IPPO ), a procedure combining BC and regularised IPPO (de Witt et al., 2020).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.21490", "content": "We develop four human proxy agents for evaluating ad-hoc human-AI coordination : two for two-player Hanabi and two for three-player. These agents are trained using Human -Data-Regularised IPPO ( HDR-IPPO ), a procedure combining BC and regularised IPPO (de Witt et al., 2020)."}
+{"idx": 1, "title": "Ad-Hoc Human-AI Coordination Challenge (AH2AC2) - GitHub", "date": "", "ddg_snippet": "Welcome to the Ad-Hoc Human-AI Coordination Challenge (AH2AC2)! The objective of AH2AC2 is to facilitate the development of AI agents capable of effective collaboration with human -like partners, especially in scenarios with limited prior interaction data.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/FLAIROx/ah2ac2", "content": "Welcome to the Ad-Hoc Human-AI Coordination Challenge (AH2AC2)! The objective of AH2AC2 is to facilitate the development of AI agents capable of effective collaboration with human -like partners, especially in scenarios with limited prior interaction data."}
+{"idx": 2, "title": "Ad-Hoc Human-AI Coordination Challenge (AH2AC2) Docs", "date": "", "ddg_snippet": "The AH2AC2 Challenge The Ad-Hoc Human-AI Coordination Challenge (AH2AC2) provides a standardized environment for evaluating AI agents on their ability to coordinate with human -like counterparts in Hanabi. The challenge emphasizes data-efficient methods and uses human proxy agents for robust and reproducible evaluation.", "subpage_snippet": "", "source": "docs.ah2ac2.com", "link": "https://docs.ah2ac2.com/", "content": "The AH2AC2 Challenge The Ad-Hoc Human-AI Coordination Challenge (AH2AC2) provides a standardized environment for evaluating AI agents on their ability to coordinate with human -like counterparts in Hanabi. The challenge emphasizes data-efficient methods and uses human proxy agents for robust and reproducible evaluation."}
+{"idx": 3, "title": "Ad-Hoc Human-AI Coordination Challenge - Science Cast", "date": "", "ddg_snippet": "Jun 25, 2025 · In this work, we introduce the Ad-Hoc Human-AI Coordination Challenge (AH2AC2) to overcome the constraints of costly and difficult-to-reproduce human evaluations. We develop \\textit { human proxy agents} on a large-scale human dataset that serve as robust, cheap, and reproducible human -like evaluation partners in AH2AC2.", "subpage_snippet": "", "source": "www.sciencecast.org", "link": "https://www.sciencecast.org/casts/q62pr180dj73", "content": "Jun 25, 2025 · In this work, we introduce the Ad-Hoc Human-AI Coordination Challenge (AH2AC2) to overcome the constraints of costly and difficult-to-reproduce human evaluations. We develop \\textit { human proxy agents} on a large-scale human dataset that serve as robust, cheap, and reproducible human -like evaluation partners in AH2AC2."}
+{"idx": 4, "title": "Ad-Hoc Human-AI Coordination Challenge - Semantic Scholar", "date": "", "ddg_snippet": "Table 15. Hyperparameters used for training BC, HDR-IPPO baselines on a 1,000-game data limit challenge . BC policies trained as baselines are used for starting points in HDR-IPPO and later for BR-BC. - \" Ad-Hoc Human-AI Coordination Challenge \"", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Ad-Hoc-Human-AI-Coordination-Challenge-Dizdarevic-Hammond/76e21098d2925daeb769a647c9af4886d00b05fd/figure/21", "content": "Table 15. Hyperparameters used for training BC, HDR-IPPO baselines on a 1,000-game data limit challenge . BC policies trained as baselines are used for starting points in HDR-IPPO and later for BR-BC. - \" Ad-Hoc Human-AI Coordination Challenge \""}
+{"idx": 5, "title": "AD-HOC HUMAN-AI COORDINATION CHALLENGE - OpenReview", "date": "", "ddg_snippet": "nd regularised reinforcement learning. These proxies serve as robust, cheap and reproducible human -like evaluation partners in our Ad-Hoc Hu an- AI Coordination Challenge (AH2AC2). To facilitate the exploration of methods that leverage limited amounts of human data, we introduce a data-limited chal-lenge setting, u", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=Kioojohsuy", "content": "nd regularised reinforcement learning. These proxies serve as robust, cheap and reproducible human -like evaluation partners in our Ad-Hoc Hu an- AI Coordination Challenge (AH2AC2). To facilitate the exploration of methods that leverage limited amounts of human data, we introduce a data-limited chal-lenge setting, u"}
+{"idx": 6, "title": "Ad-Hoc Human-AI Coordination Challenge | AI Research Paper ...", "date": "", "ddg_snippet": "The researchers developed four human proxy agents for evaluating ad-hoc human-AI coordination : two for two-player Hanabi and two for three-player. These agents are trained using Human -Data-Regularised IPPO ( HDR-IPPO ), a procedure combining behavioral cloning (BC) and regularized Independent Proximal Policy Optimization ( IPPO ).", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/ad-hoc-human-ai-coordination-challenge", "content": "The researchers developed four human proxy agents for evaluating ad-hoc human-AI coordination : two for two-player Hanabi and two for three-player. These agents are trained using Human -Data-Regularised IPPO ( HDR-IPPO ), a procedure combining behavioral cloning (BC) and regularized Independent Proximal Policy Optimization ( IPPO )."}
+{"idx": 7, "title": "Ad - Hoc Human - AI Coordination Challenge", "date": "", "ddg_snippet": "Figure 1: Ad - Hoc Human - AI Coordination Challenge (AH2AC2). In summary, our key contributions are• Implementations for Human - AI Coordination evaluations for all baselines except FCP, as the FCP implementation is in its final testing phase. A.8 HDR - IPPO : Ablation Study.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.21490v1", "content": "Figure 1: Ad - Hoc Human - AI Coordination Challenge (AH2AC2). In summary, our key contributions are• Implementations for Human - AI Coordination evaluations for all baselines except FCP, as the FCP implementation is in its final testing phase. A.8 HDR - IPPO : Ablation Study."}
+{"idx": 8, "title": "Ad - Hoc Human - AI Coordination Challenge", "date": "", "ddg_snippet": "Ad - Hoc Human - AI Coordination Challenge . Tin Dizdarević, Ravi Hammond, Tobias Gessler, Anisoara Calinescu, Jonathan Cook, Matteo Gallici, Andrei Lupu, Jakob Nicolaus Foerster·June 26, 2025. Summary.Overview of human - AI coordination challenges .", "subpage_snippet": "", "source": "powerdrill.ai", "link": "https://powerdrill.ai/discover/summary-ad-hoc-human-ai-coordination-challenge-cmcfaq3ifm18907nqbp2j6wty", "content": "Ad - Hoc Human - AI Coordination Challenge . Tin Dizdarević, Ravi Hammond, Tobias Gessler, Anisoara Calinescu, Jonathan Cook, Matteo Gallici, Andrei Lupu, Jakob Nicolaus Foerster·June 26, 2025. Summary.Overview of human - AI coordination challenges ."}
+{"idx": 9, "title": "Human - AI Cross-Play Experiments", "date": "", "ddg_snippet": "Explore Human - AI cross-play experiments investigating interaction, collaboration, and competition in shared environments to understand AI capabilities, alignment, and compatibility with humans.", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/topics/human-ai-cross-play-experiments", "content": "Explore Human - AI cross-play experiments investigating interaction, collaboration, and competition in shared environments to understand AI capabilities, alignment, and compatibility with humans."}
diff --git a/data/sampled_jsons/Adaptive_Task_Allocation_for_Efficient_Resource_Management_in_Distributed_Machine_Learning_GTA_strat_year_2023.jsonl b/data/sampled_jsons/Adaptive_Task_Allocation_for_Efficient_Resource_Management_in_Distributed_Machine_Learning_GTA_strat_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..9de04e2422e43aeff539e064db609029b3e8bd5d
--- /dev/null
+++ b/data/sampled_jsons/Adaptive_Task_Allocation_for_Efficient_Resource_Management_in_Distributed_Machine_Learning_GTA_strat_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2502.00775v1] ATA: Adaptive Task Allocation for Efficient ...", "date": "", "ddg_snippet": "View a PDF of the paper titled ATA: Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning , by Artavazd Maranjyan and 3 other authors.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.00775v1", "content": "View a PDF of the paper titled ATA: Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning , by Artavazd Maranjyan and 3 other authors."}
+{"idx": 1, "title": "Adaptive Task Allocation for Efficient Resource Management in ...", "date": "", "ddg_snippet": "Ideally, faster machines would handle more tasks , and slower ones fewer — but without knowing speeds in advance, this is challenging.We introduce ATA ( Adaptive Task Allocation ), a method that learns how fast each machine is over time and adapts the task assignment accordingly.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46650", "content": "Ideally, faster machines would handle more tasks , and slower ones fewer — but without knowing speeds in advance, this is challenging.We introduce ATA ( Adaptive Task Allocation ), a method that learns how fast each machine is over time and adapts the task assignment accordingly."}
+{"idx": 2, "title": "Postdoc, KAUST - Cited by 12 - Online learning - Bandits theory", "date": "", "ddg_snippet": "Ata: Adaptive task allocation for efficient resource management in distributed machine learning .", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=Jih_bwsAAAAJ&hl=en", "content": "Ata: Adaptive task allocation for efficient resource management in distributed machine learning ."}
+{"idx": 3, "title": "ATA: Adaptive Task Allocation", "date": "", "ddg_snippet": "ATA: Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning .", "subpage_snippet": "", "source": "artomaranjyan.github.io", "link": "https://artomaranjyan.github.io/assets/pdf/posters/ATA_SNSL.pdf", "content": "ATA: Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning ."}
+{"idx": 4, "title": "The Importance of Adaptive Task Allocation in Distributed Robotics", "date": "", "ddg_snippet": "Learn about the significance of adaptive task allocation , a method that dynamically assigns tasks based on real-time data and environmental conditions.", "subpage_snippet": "", "source": "diversedaily.com", "link": "https://diversedaily.com/the-importance-of-adaptive-task-allocation-in-distributed-robotics/", "content": "Learn about the significance of adaptive task allocation , a method that dynamically assigns tasks based on real-time data and environmental conditions."}
+{"idx": 5, "title": "Quantum Inspired Adaptive Resource Management Algorithm for...", "date": "", "ddg_snippet": "Effective resource management in the Internet of Things and fog computing is essential for efficient and scalable networks.FRATO: fog resource based adaptive task offloading for delay-minimizing IoT service provisioning. IEEE Trans Parallel Distrib Syst.", "subpage_snippet": "", "source": "www.sciopen.com", "link": "https://www.sciopen.com/article/10.32604/cmes.2025.060973", "content": "Effective resource management in the Internet of Things and fog computing is essential for efficient and scalable networks.FRATO: fog resource based adaptive task offloading for delay-minimizing IoT service provisioning. IEEE Trans Parallel Distrib Syst."}
+{"idx": 6, "title": "ELDM-EDSDN: a novel resource allocation method in...", "date": "", "ddg_snippet": "The over-reliance of current methods for task allocation and resource management in distributed software-defined networks (DSDN) on constraints and predefined assumptions about network behavior leads to severe efficiency degradation in operational environments.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s11227-025-07817-3", "content": "The over-reliance of current methods for task allocation and resource management in distributed software-defined networks (DSDN) on constraints and predefined assumptions about network behavior leads to severe efficiency degradation in operational environments."}
+{"idx": 7, "title": "DyRAM: Dynamic Data Allocation and Resource Management in ...", "date": "", "ddg_snippet": "keywords = \"Data Distribution , Distributed Machine Learning , Resource Management \", author = \"Vaibhavi Tiwari and Rahul Thakkar and Jiayin Wang\"", "subpage_snippet": "", "source": "researchwith.montclair.edu", "link": "https://researchwith.montclair.edu/en/publications/dyram-dynamic-data-allocation-and-resource-management-in-distribu", "content": "keywords = \"Data Distribution , Distributed Machine Learning , Resource Management \", author = \"Vaibhavi Tiwari and Rahul Thakkar and Jiayin Wang\""}
+{"idx": 8, "title": "History of Machine Learning : How We Got Here", "date": "", "ddg_snippet": "As machine learning keeps evolving and adapting , we can anticipate continued advancements in domains like quantum computing, unsupervised learning , and the establishment of cognitive services.", "subpage_snippet": "", "source": "www.akkio.com", "link": "https://www.akkio.com/post/history-of-machine-learning", "content": "As machine learning keeps evolving and adapting , we can anticipate continued advancements in domains like quantum computing, unsupervised learning , and the establishment of cognitive services."}
+{"idx": 9, "title": "Energy Aware Task Allocation for Semi-Asynchronous Mobile Edge...", "date": "", "ddg_snippet": "Adaptive task allocation for asynchronous federated and parallelized mobile edge learning .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/368533748_Energy_Aware_Task_Allocation_for_Semi-Asynchronous_Mobile_Edge_Learning", "content": "Adaptive task allocation for asynchronous federated and parallelized mobile edge learning ."}
diff --git a/data/sampled_jsons/Adcock_Collier_2001_paper_title_year_2001.jsonl b/data/sampled_jsons/Adcock_Collier_2001_paper_title_year_2001.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..fb9f123640783f41c1c2e219c7a76b44598afb66
--- /dev/null
+++ b/data/sampled_jsons/Adcock_Collier_2001_paper_title_year_2001.jsonl
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+{"idx": 0, "title": "Adcock y Collier (2001) - Measurement Validity. A Shared ... - Scribd", "date": "", "ddg_snippet": "Adcock y Collier ( 2001 ). Measurement Validity. a Shared Standart for Qualitative and Quantitative Research. - Free download as PDF File (.pdf), Text File (.txt) or read online for free.", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/520421528/Adcock-y-Collier-2001-Measurement-Validity-a-Shared-Standart-for-Qualitative-and-Quantitative-Research", "content": "Adcock y Collier ( 2001 ). Measurement Validity. a Shared Standart for Qualitative and Quantitative Research. - Free download as PDF File (.pdf), Text File (.txt) or read online for free."}
+{"idx": 1, "title": "Adcock Collier 2001 Measurement Validity | PDF - Scribd", "date": "", "ddg_snippet": "Adcock Collier 2001 Measurement Validity - Free download as PDF File (.pdf), Text File (.txt) or read online for free.", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/797627345/Adcock-Collier-2001-Measurement-Validity", "content": "Adcock Collier 2001 Measurement Validity - Free download as PDF File (.pdf), Text File (.txt) or read online for free."}
+{"idx": 2, "title": "Adcock & Collier (2001) Measurement Validity - A Shared Standard for ...", "date": "", "ddg_snippet": "The technical storage or access that is used exclusively for statistical purposes. The technical storage or access that is used exclusively for anonymous statistical purposes. Without a subpoena, voluntary compliance on the part of your Internet Service Provider, or additional records from a third party, information stored or retrieved for this purpose alone cannot usually be used to identify you.", "subpage_snippet": "", "source": "www.theisrm.org", "link": "https://www.theisrm.org/library/adcock-collier-2001-measurement-validity-a-shared-standard-for-qualitative-and-quantitative-research-pdf/", "content": "The technical storage or access that is used exclusively for statistical purposes. The technical storage or access that is used exclusively for anonymous statistical purposes. Without a subpoena, voluntary compliance on the part of your Internet Service Provider, or additional records from a third party, information stored or retrieved for this purpose alone cannot usually be used to identify you."}
+{"idx": 3, "title": "Measurement Validity: A Shared Standard for Qualitative and ...", "date": "", "ddg_snippet": "Robert Adcock (adcockr@uclink4.berkeley.edu) is a Ph.D candi- This literature provides an opportunity to identify date, Department of Political Science, and David Collier parallel concerns about validity as well as differences in (dcollier@socrates.berkeley.edu) is Professor of Political Science, specific practices.", "subpage_snippet": "", "source": "www.jstor.org", "link": "https://www.jstor.org/stable/3118231", "content": "Robert Adcock (adcockr@uclink4.berkeley.edu) is a Ph.D candi- This literature provides an opportunity to identify date, Department of Political Science, and David Collier parallel concerns about validity as well as differences in (dcollier@socrates.berkeley.edu) is Professor of Political Science, specific practices."}
+{"idx": 4, "title": "(PDF) Measurement Validity: A Shared Standard For Qualitative and ...", "date": "", "ddg_snippet": "The literature has pointed to three different strategies for measurement validity ( Adcock and Collier 2001 ): content validity (the measurement captures the full content of the definition ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/228277142_Measurement_Validity_A_Shared_Standard_For_Qualitative_and_Quantitative_Research", "content": "The literature has pointed to three different strategies for measurement validity ( Adcock and Collier 2001 ): content validity (the measurement captures the full content of the definition ..."}
+{"idx": 5, "title": "Measurement validity: A shared standard for - ProQuest", "date": "", "ddg_snippet": "Measurement validity: A shared standard for qualitative and quantitative research Adcock , Robert; Collier , David. The American Political Science Review; Washington Vol. 95, Iss. 3, (Sep 2001 ): 529-546.", "subpage_snippet": "", "source": "www.proquest.com", "link": "https://www.proquest.com/docview/214424808", "content": "Measurement validity: A shared standard for qualitative and quantitative research Adcock , Robert; Collier , David. The American Political Science Review; Washington Vol. 95, Iss. 3, (Sep 2001 ): 529-546."}
+{"idx": 6, "title": "Measurement Validity: A Shared Standard for Qualitative and ...", "date": "", "ddg_snippet": "Measurement Validity: A Shared Standard for Qualitative and Quantitative Research Robert Adcock , David Collier +1 more - 31 Aug 2001 - American Political Science Review - Vol. 95, Iss: 3, pp 529-546", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/papers/measurement-validity-a-shared-standard-for-qualitative-and-2r00s4iydu", "content": "Measurement Validity: A Shared Standard for Qualitative and Quantitative Research Robert Adcock , David Collier +1 more - 31 Aug 2001 - American Political Science Review - Vol. 95, Iss: 3, pp 529-546"}
+{"idx": 7, "title": "Measurement Validity: A Shared Standard for Qualitative and ...", "date": "", "ddg_snippet": "Google Scholar Collier , David, and Adcock , Robert. 1999. \"Democracy and Dichotomies: A Pragmatic Approach to Choices about Concepts.\" Annual Review of Political Science 2: 537 - 65. CrossRef Google Scholar Collier , David, and Levitsky, Steven. 1997. \" Democracy with Adjectives: Conceptual Innovation in Comparative Research.", "subpage_snippet": "", "source": "www.cambridge.org", "link": "https://www.cambridge.org/core/journals/american-political-science-review/article/abs/measurement-validity-a-shared-standard-for-qualitative-and-quantitative-research/91C7A9800DB26A76EBBABC5889A50C8B", "content": "Google Scholar Collier , David, and Adcock , Robert. 1999. \"Democracy and Dichotomies: A Pragmatic Approach to Choices about Concepts.\" Annual Review of Political Science 2: 537 - 65. CrossRef Google Scholar Collier , David, and Levitsky, Steven. 1997. \" Democracy with Adjectives: Conceptual Innovation in Comparative Research."}
+{"idx": 8, "title": "PDF Measurement Validity: A Shared Standard for Qualitative and ...", "date": "", "ddg_snippet": "ROBERT ADCOCK and DAVID COLLIER University of California, Berkeley Scholars routinely make claims that presuppose the validity of the observations and measurements that operationalize their concepts. Yet, despite recent advances in political science methods, surprisingly little attention has been devoted to measurement validity. We address this gap by exploring four themes. First, we seek to ...", "subpage_snippet": "", "source": "www.theisrm.org", "link": "https://www.theisrm.org/documents/Adcock+&+Collier+(2001)+Measurement+Validity+-+A+Shared+Standard+for+Qualitative+and+Quantitative+Research.pdf", "content": "ROBERT ADCOCK and DAVID COLLIER University of California, Berkeley Scholars routinely make claims that presuppose the validity of the observations and measurements that operationalize their concepts. Yet, despite recent advances in political science methods, surprisingly little attention has been devoted to measurement validity. We address this gap by exploring four themes. First, we seek to ..."}
+{"idx": 9, "title": "Measurement Validity: A Shared Standard for Qualitative and ...", "date": "", "ddg_snippet": "Measurement Validity: A Shared Standard for Qualitative and Quantitative Research Robert Adcock and David Collier American Political Science Review, 2001 , vol. 95, issue 3, 529-546 Abstract: Scholars routinely make claims that presuppose the validity of the observations and measurements that operationalize their concepts.", "subpage_snippet": "", "source": "econpapers.repec.org", "link": "https://econpapers.repec.org/RePEc:cup:apsrev:v:95:y:2001:i:03:p:529-546_00", "content": "Measurement Validity: A Shared Standard for Qualitative and Quantitative Research Robert Adcock and David Collier American Political Science Review, 2001 , vol. 95, issue 3, 529-546 Abstract: Scholars routinely make claims that presuppose the validity of the observations and measurements that operationalize their concepts."}
diff --git a/data/sampled_jsons/Advancing_mathematics_by_guiding_human_intuition_with_AI_knot_theory_representation_theory.jsonl b/data/sampled_jsons/Advancing_mathematics_by_guiding_human_intuition_with_AI_knot_theory_representation_theory.jsonl
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+{"idx": 0, "title": "Artificial Intelligence in Knot Theory – AMR", "date": "", "ddg_snippet": "... Advancing Mathematics by Guiding Human Intuition with AI , the authors describe an application of neural networks to finding correlations between ...", "subpage_snippet": "", "source": "amathr.org", "link": "https://amathr.org/artificial-intelligence-in-knot-theory/", "content": "... Advancing Mathematics by Guiding Human Intuition with AI , the authors describe an application of neural networks to finding correlations between ..."}
+{"idx": 1, "title": "Advancing mathematics by guiding human intuition with AI |", "date": "", "ddg_snippet": "... framework, illustrated in Fig. 1 , describes a general method by which mathematicians can use tools from machine learning to guide their intuitions ...", "subpage_snippet": "", "source": "ddcolrs.wordpress.com", "link": "https://ddcolrs.wordpress.com/2021/12/04/advancing-mathematics-by-guiding-human-intuition-with-ai/", "content": "... framework, illustrated in Fig. 1 , describes a general method by which mathematicians can use tools from machine learning to guide their intuitions ..."}
+{"idx": 2, "title": "DeepMind’s AI can untangle knots. But does it guide human", "date": "", "ddg_snippet": "... coming from the Alphabet-owned artificial intelligence lab, the paper, which is titled “ Advancing mathematics by guiding human intuition with AI ...", "subpage_snippet": "", "source": "bdtechtalks.com", "link": "https://bdtechtalks.com/2021/12/13/deepminds-machine-learning-mathematics/", "content": "... coming from the Alphabet-owned artificial intelligence lab, the paper, which is titled “ Advancing mathematics by guiding human intuition with AI ..."}
+{"idx": 3, "title": "Sure, DeepMind’s AI is impressive, but can it guide human", "date": "", "ddg_snippet": "... coming from the Alphabet-owned artificial intelligence lab, the paper, which is titled “ Advancing mathematics by guiding human intuition with AI ...", "subpage_snippet": "", "source": "thenextweb.com", "link": "https://thenextweb.com/news/deepminds-ai-impressive-can-it-guide-human-intuition-syndication", "content": "... coming from the Alphabet-owned artificial intelligence lab, the paper, which is titled “ Advancing mathematics by guiding human intuition with AI ..."}
+{"idx": 4, "title": "Mathematical discoveries take intuition and creativity, and now", "date": "", "ddg_snippet": "... with experts at artificial ... More information: Alex Davies et al, Advancing mathematics by guiding human intuition with AI , Nature (2021).", "subpage_snippet": "", "source": "techxplore.com", "link": "https://techxplore.com/news/2021-12-mathematical-discoveries-intuition-creativity-ai.html", "content": "... with experts at artificial ... More information: Alex Davies et al, Advancing mathematics by guiding human intuition with AI , Nature (2021)."}
+{"idx": 5, "title": "Augmenting Human Intuition", "date": "", "ddg_snippet": "Learn from Petar Veličković, author of \" Advancing mathematics by guiding human intuition with AI \", how machines empower researchers conjecturing ...", "subpage_snippet": "", "source": "picampus.it", "link": "https://picampus.it/events/augmenting-human-intuition/", "content": "Learn from Petar Veličković, author of \" Advancing mathematics by guiding human intuition with AI \", how machines empower researchers conjecturing ..."}
+{"idx": 6, "title": "Mathematics, word problems, common sense, and artificial", "date": "", "ddg_snippet": "... the capacities and limitations of current artificial intelligence ( AI ) technology to solve word problems that combine elementary mathematics with ...", "subpage_snippet": "", "source": "sugaku.net", "link": "https://sugaku.net/oa/W4391836239/", "content": "... the capacities and limitations of current artificial intelligence ( AI ) technology to solve word problems that combine elementary mathematics with ..."}
+{"idx": 7, "title": "Mathematics and Machine Creativity: A Survey on Bridging", "date": "", "ddg_snippet": "Notably, this survey challenges this perspective by presenting a fresh narrative: AI can indeed make meaningful contributions to mathematic research ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.16543v1?", "content": "Notably, this survey challenges this perspective by presenting a fresh narrative: AI can indeed make meaningful contributions to mathematic research ..."}
+{"idx": 8, "title": "DeepMind AI Used To Develop New Math Techniques | Technology", "date": "", "ddg_snippet": "... guided by mathematical intuition , machine learning provides a powerful framework that ... Advancing mathematics by guiding human intuition with AI .", "subpage_snippet": "", "source": "www.technologynetworks.com", "link": "https://www.technologynetworks.com/neuroscience/news/deepmind-ai-used-to-develop-new-math-techniques-356418", "content": "... guided by mathematical intuition , machine learning provides a powerful framework that ... Advancing mathematics by guiding human intuition with AI ."}
+{"idx": 9, "title": "AI-assisted mathematical discovery | London Institute for", "date": "", "ddg_snippet": "... mathematical constructs in number theory and ... His work concerns advancing mathematics by guiding human intuition with AI and geometric topology.", "subpage_snippet": "", "source": "lims.ac.uk", "link": "https://lims.ac.uk/event/ai-assisted-maths-discovery/", "content": "... mathematical constructs in number theory and ... His work concerns advancing mathematics by guiding human intuition with AI and geometric topology."}
diff --git a/data/sampled_jsons/Algorithm_1_Data-Efficient_Visual_Concept_Bottleneck_Models_filter_concept_proposals_by_area.jsonl b/data/sampled_jsons/Algorithm_1_Data-Efficient_Visual_Concept_Bottleneck_Models_filter_concept_proposals_by_area.jsonl
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+++ b/data/sampled_jsons/Algorithm_1_Data-Efficient_Visual_Concept_Bottleneck_Models_filter_concept_proposals_by_area.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "DCBM: Data-Efficient Visual Concept Bottleneck Models", "date": "", "ddg_snippet": "by K Prasse — This paper proposed a data - efficient Concept Bottleneck Model (DCBM) that enables concept generation while maintaining interpretability with ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=BdO4R6XxUH", "content": "by K Prasse — This paper proposed a data - efficient Concept Bottleneck Model (DCBM) that enables concept generation while maintaining interpretability with ..."}
+{"idx": 1, "title": "VLG-CBM: Training Concept Bottleneck Models with Vision ...", "date": "", "ddg_snippet": "Concept Bottleneck Models (CBMs) provide interpretable prediction by introducing an intermediate Concept Bottleneck Layer (CBL), which encodes human- ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2408.01432v2", "content": "Concept Bottleneck Models (CBMs) provide interpretable prediction by introducing an intermediate Concept Bottleneck Layer (CBL), which encodes human- ..."}
+{"idx": 2, "title": "VLG-CBM: Training Concept Bottleneck Models with Vision ...", "date": "", "ddg_snippet": "by D Srivastava · 2024 · Cited by 22 — To address Challenge # 1 , we propose to use the open-domain grounded object detection model to generate localized, visually recognizable concept annotations in ... 38 pages", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper_files/paper/2024/file/90043ebd68500f9efe84fedf860a64f3-Paper-Conference.pdf", "content": "by D Srivastava · 2024 · Cited by 22 — To address Challenge # 1 , we propose to use the open-domain grounded object detection model to generate localized, visually recognizable concept annotations in ... 38 pages"}
+{"idx": 3, "title": "Interpretable prognostics with concept bottleneck models", "date": "", "ddg_snippet": "by F Forest · 2025 · Cited by 3 — We propose concept bottleneck models for more interpretable prognostics, where degradation modes of an asset are used as intermediate concepts .", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S1566253525005007", "content": "by F Forest · 2025 · Cited by 3 — We propose concept bottleneck models for more interpretable prognostics, where degradation modes of an asset are used as intermediate concepts ."}
+{"idx": 4, "title": "VLG-CBM: Training Concept Bottleneck Models with Vision ...", "date": "", "ddg_snippet": "We propose a novel framework called Vision -Language-Guided Concept Bottleneck Model (VLG-CBM) to enable faithful interpretability with the benefits of boosted ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/poster/95698", "content": "We propose a novel framework called Vision -Language-Guided Concept Bottleneck Model (VLG-CBM) to enable faithful interpretability with the benefits of boosted ..."}
+{"idx": 5, "title": "Editable Concept Bottleneck Models", "date": "", "ddg_snippet": "24 May 2024 — We propose Editable Concept Bottleneck Models (ECBMs). Specifically, ECBMs support three different levels of data removal: concept -label-level, concept -level, ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.15476v1", "content": "24 May 2024 — We propose Editable Concept Bottleneck Models (ECBMs). Specifically, ECBMs support three different levels of data removal: concept -label-level, concept -level, ..."}
+{"idx": 6, "title": "CVPR Poster Incremental Residual Concept Bottleneck Models", "date": "", "ddg_snippet": "Concept Bottleneck Models (CBMs) map the black-box visual representations extracted by deep neural networks onto a set of interpretable concepts and use the ...", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2024/poster/29363", "content": "Concept Bottleneck Models (CBMs) map the black-box visual representations extracted by deep neural networks onto a set of interpretable concepts and use the ..."}
+{"idx": 7, "title": "Navigating the landscape of concept-supported XAI", "date": "", "ddg_snippet": "by Z Shams Khoozani · 2024 · Cited by 9 — The Concept Bottleneck Model (CBM) proposed in [87] is based on the Bottleneck (BN) design and accepts input features such as pixels, denoted as ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s11042-023-17666-y", "content": "by Z Shams Khoozani · 2024 · Cited by 9 — The Concept Bottleneck Model (CBM) proposed in [87] is based on the Bottleneck (BN) design and accepts input features such as pixels, denoted as ..."}
+{"idx": 8, "title": "Probabilistic Concept Bottleneck Models", "date": "", "ddg_snippet": "The concept predic- tion in CBM is trained as deterministic binary classification by using a dataset that includes concept labels indicating the existence ( 1 ) ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v202/kim23g/kim23g.pdf", "content": "The concept predic- tion in CBM is trained as deterministic binary classification by using a dataset that includes concept labels indicating the existence ( 1 ) ..."}
+{"idx": 9, "title": "Enhancing Interpretable Image Classification Through LLM ...", "date": "", "ddg_snippet": "by Y Jiang · 2025 — To address this issue, we introduce Condi- tional Concept Bottleneck Models (CoCoBMs) that incorporates category-specific scoring and weight-.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.acl-long.600.pdf", "content": "by Y Jiang · 2025 — To address this issue, we introduce Condi- tional Concept Bottleneck Models (CoCoBMs) that incorporates category-specific scoring and weight-."}
diff --git a/data/sampled_jsons/Algorithm_1_delta_tilde_Statistical_Collusion_Collectives_Learning_Platforms_Appendix_B_year_2024.jsonl b/data/sampled_jsons/Algorithm_1_delta_tilde_Statistical_Collusion_Collectives_Learning_Platforms_Appendix_B_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0cf902f3cb74aab0bd2aca3f8cd3d013a37a0192
--- /dev/null
+++ b/data/sampled_jsons/Algorithm_1_delta_tilde_Statistical_Collusion_Collectives_Learning_Platforms_Appendix_B_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "As platforms increasingly rely on learning algorithms , collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data. Appendix B Algorithms .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.04879v3", "content": "As platforms increasingly rely on learning algorithms , collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data. Appendix B Algorithms ."}
+{"idx": 1, "title": "GitHub - GauthierE/statistical-collusion", "date": "", "ddg_snippet": "statistical-collusion This repository contains the code for reproducing the experiments and figures presented in the paper Statistical Collusion by Collectives on Learning Platforms .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/GauthierE/statistical-collusion", "content": "statistical-collusion This repository contains the code for reproducing the experiments and figures presented in the paper Statistical Collusion by Collectives on Learning Platforms ."}
+{"idx": 2, "title": "Statistical Collusion by Collectives on Learning Platforms | Read Paper ...", "date": "", "ddg_snippet": "This paper talks about how groups of people can work together to change the way online platforms use data and learning algorithms to benefit their interests. The authors created a method that help...", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/icml/46504/paper", "content": "This paper talks about how groups of people can work together to change the way online platforms use data and learning algorithms to benefit their interests. The authors created a method that help..."}
+{"idx": 3, "title": "Algorithmic Collective Action in Machine Learning - PMLR", "date": "", "ddg_snippet": "We initiate a principled study of algorithmic collective action on digital platforms that deploy machine learning algorithms . We propose a simple theoretical model of a collective interacting with a firm's learning algorithm . The collective pools the data of participating individuals and executes an algorithmic strategy by instructing participants how to modify their own data to achieve a ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v202/hardt23a.html", "content": "We initiate a principled study of algorithmic collective action on digital platforms that deploy machine learning algorithms . We propose a simple theoretical model of a collective interacting with a firm's learning algorithm . The collective pools the data of participating individuals and executes an algorithmic strategy by instructing participants how to modify their own data to achieve a ..."}
+{"idx": 4, "title": "Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "A framework is developed that provides a theoretical and algorithmic treatment of the issues of a priori assessments of the effect of the collective before taking action and presents experimental results in a product evaluation domain. As platforms increasingly rely on learning algorithms , collectives may form and seek ways to influence these platforms to align with their own interests. This ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Statistical-Collusion-by-Collectives-on-Learning-Gauthier-Bach/1c45ef9ad56839c3309f0a0bdcff50fbb3ad73f5", "content": "A framework is developed that provides a theoretical and algorithmic treatment of the issues of a priori assessments of the effect of the collective before taking action and presents experimental results in a product evaluation domain. As platforms increasingly rely on learning algorithms , collectives may form and seek ways to influence these platforms to align with their own interests. This ..."}
+{"idx": 5, "title": "PDF Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "Statistical Collusion by Collectives on Learning Platforms Etienne Gauthier, Francis Bach, Michael I. Jordan (INRIA, Ecole Normale Supérieure) Numerous examples of collectives emerging to strategically influence platforms Uber drivers deactivate the app to create a supply shortage and drive up prices", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/media/icml-2025/Slides/47263.pdf", "content": "Statistical Collusion by Collectives on Learning Platforms Etienne Gauthier, Francis Bach, Michael I. Jordan (INRIA, Ecole Normale Supérieure) Numerous examples of collectives emerging to strategically influence platforms Uber drivers deactivate the app to create a supply shortage and drive up prices"}
+{"idx": 6, "title": "Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "Technical Explanation The study develops mathematical frameworks for detecting coordinated manipulation of learning systems. It examines how collective behaviors create statistical signatures that differ from organic user patterns. The researchers analyzed multiple types of learning platforms , including:", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/statistical-collusion-by-collectives-learning-platforms", "content": "Technical Explanation The study develops mathematical frameworks for detecting coordinated manipulation of learning systems. It examines how collective behaviors create statistical signatures that differ from organic user patterns. The researchers analyzed multiple types of learning platforms , including:"}
+{"idx": 7, "title": "Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "Oral Statistical Collusion by Collectives on Learning Platforms Etienne Gauthier · Francis Bach · Michael Jordan West Ballroom D [ Abstract ] [ Visit Oral 6E Social and Economic Perspectives ] Thu 17 Jul 4 p.m. — 4:15 p.m. PDT Poster presentation: Statistical Collusion by Collectives on Learning Platforms Thu 17 Jul 4:30 p.m. PDT — 7 p.m. PDT", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/oral/47263", "content": "Oral Statistical Collusion by Collectives on Learning Platforms Etienne Gauthier · Francis Bach · Michael Jordan West Ballroom D [ Abstract ] [ Visit Oral 6E Social and Economic Perspectives ] Thu 17 Jul 4 p.m. — 4:15 p.m. PDT Poster presentation: Statistical Collusion by Collectives on Learning Platforms Thu 17 Jul 4:30 p.m. PDT — 7 p.m. PDT"}
+{"idx": 8, "title": "ICML Poster Statistical Collusion by Collectives on Learning ...", "date": "", "ddg_snippet": "As platforms increasingly rely on learning algorithms , collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46504", "content": "As platforms increasingly rely on learning algorithms , collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data."}
+{"idx": 9, "title": "Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "As platforms increasingly rely on learning algorithms , collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data.", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/article/Statistical-Collusion-by-Collectives-on-Learning-Platforms-deaeb81c-5365-4cec-9af2-e4d286dfd04a", "content": "As platforms increasingly rely on learning algorithms , collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data."}
diff --git a/data/sampled_jsons/AltUp_Baykal_2023_transformer_dimension_approximation_computational_cost.jsonl b/data/sampled_jsons/AltUp_Baykal_2023_transformer_dimension_approximation_computational_cost.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..73d104cc3442f77adfe0c84cee130a3a8371dc15
--- /dev/null
+++ b/data/sampled_jsons/AltUp_Baykal_2023_transformer_dimension_approximation_computational_cost.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2301.13310] Alternating Updates for Efficient Transformers Alternating Updates for Efficient Transformers - NIPS Alternating updates for efficient transformers - Google Research NeurIPS 2023 Presentation Alternating updates for efficient transformers - ichibanai.com Alternating Updates for Efficient Transformers - OpenReview [2301.13310] Alternating Updates for Efficient Transformers [2301.13310] Alternating Updates for Efficient Transformers [2301.13310] Alternating Updates for Efficient Transformers Alternating updates for efficient transformers – Google ...", "date": "", "ddg_snippet": "Jan 30, 2023 · It has been well established that increasing scale in deep transformer networks leads to improved quality and performance. However, this increase in scale often comes with prohibitive increases in compute cost and inference latency. We introduce Alternating Updates ( AltUp ), a simple-to-implement method to increase a model's capacity without the computational burden. AltUp enables the widening ... Authors Cenk Baykal , Dylan Cutler, Nishanth Dikkala, Nikhil Ghosh, Rina Panigrahy, Xin Wang Abstract It has been well established that increasing scale in deep transformer networks leads to improved quality and performance. However, this increase in scale often comes with prohibitive increases in compute cost and inference latency. We introduce Alternating Updates ( AltUp ), a simple-to ... Nov 7, 2023 · A natural question is whether we can reap the benefits of larger models without incurring the computational burden. In “ Alternating Updates for Efficient Transformers ”, accepted as a Spotlight at NeurIPS 2023 , we introduce AltUp , a method to take advantage of increased token representation without increasing the computation cost . Can we get the benefits of increased representation dimension without the full computational cost ? Performance for a word2vec model with varying representation dimension (figure from Yang et al., 2022). In “ Alternating Updates for Efficient Transformers ”, accepted as a Spotlight at NeurIPS 2023 , we introduce AltUp , a method to take advantage of increased token representation without increasing the computation cost . AltUp is easy to implement, widely applicable to any transformer architecture, and requires minimal hyperparameter tuning. Sep 21, 2023 · The paper proposes a novel technique named “ AltUp ” to expand Transformer ’s feature dimension while preserving the computation cost . The key idea of AltUp is to divide wide hidden features into multiple blocks, where only one block is processed by Transformer sub-layers, while the other blocks are computed through a linear combination of ... What is alternating updates (altup)? We introduce Alternating Updates (AltUp), a simple-to-implement method to increase a model's capacity without the computational burden . AltUp enables the widening of the learned representation, i.e., the token embedding, while only incurring a negligible increase in latency. How does scale affect the performance of a deep transformer network? It has been well established that increasing scale in deep transformer networks leads to improved quality and performance . However, this increase in scale often comes with prohibitive increases in compute cost and inference latency. How does altup work? AltUp enables the widening of the learned representation, i.e., the token embedding, while only incurring a negligible increase in latency. AltUp achieves this by working on a subblock of the widened representation at each layer and using a predict-and-correct mechanism to update the inactivated blocks. Nov 7, 2023 · In our research paper, “Alternating Updates for Efficient Transformers ”, accepted as a Spotlight at NeurIPS 2023 , we introduce AltUp , a method that allows us to take advantage of increased token representation without increasing computation cost .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2301.13310", "content": "Jan 30, 2023 · It has been well established that increasing scale in deep transformer networks leads to improved quality and performance. However, this increase in scale often comes with prohibitive increases in compute cost and inference latency. We introduce Alternating Updates ( AltUp ), a simple-to-implement method to increase a model's capacity without the computational burden. AltUp enables the widening ... Authors Cenk Baykal , Dylan Cutler, Nishanth Dikkala, Nikhil Ghosh, Rina Panigrahy, Xin Wang Abstract It has been well established that increasing scale in deep transformer networks leads to improved quality and performance. However, this increase in scale often comes with prohibitive increases in compute cost and inference latency. We introduce Alternating Updates ( AltUp ), a simple-to ... Nov 7, 2023 · A natural question is whether we can reap the benefits of larger models without incurring the computational burden. In “ Alternating Updates for Efficient Transformers ”, accepted as a Spotlight at NeurIPS 2023 , we introduce AltUp , a method to take advantage of increased token representation without increasing the computation cost . Can we get the benefits of increased representation dimension without the full computational cost ? Performance for a word2vec model with varying representation dimension (figure from Yang et al., 2022). In “ Alternating Updates for Efficient Transformers ”, accepted as a Spotlight at NeurIPS 2023 , we introduce AltUp , a method to take advantage of increased token representation without increasing the computation cost . AltUp is easy to implement, widely applicable to any transformer architecture, and requires minimal hyperparameter tuning. Sep 21, 2023 · The paper proposes a novel technique named “ AltUp ” to expand Transformer ’s feature dimension while preserving the computation cost . The key idea of AltUp is to divide wide hidden features into multiple blocks, where only one block is processed by Transformer sub-layers, while the other blocks are computed through a linear combination of ... What is alternating updates (altup)? We introduce Alternating Updates (AltUp), a simple-to-implement method to increase a model's capacity without the computational burden . AltUp enables the widening of the learned representation, i.e., the token embedding, while only incurring a negligible increase in latency. How does scale affect the performance of a deep transformer network? It has been well established that increasing scale in deep transformer networks leads to improved quality and performance . However, this increase in scale often comes with prohibitive increases in compute cost and inference latency. How does altup work? AltUp enables the widening of the learned representation, i.e., the token embedding, while only incurring a negligible increase in latency. AltUp achieves this by working on a subblock of the widened representation at each layer and using a predict-and-correct mechanism to update the inactivated blocks. Nov 7, 2023 · In our research paper, “Alternating Updates for Efficient Transformers ”, accepted as a Spotlight at NeurIPS 2023 , we introduce AltUp , a method that allows us to take advantage of increased token representation without increasing computation cost ."}
+{"idx": 1, "title": "Alternating Updates for Efficient Transformers - NIPS", "date": "", "ddg_snippet": "Authors Cenk Baykal , Dylan Cutler, Nishanth Dikkala, Nikhil Ghosh, Rina Panigrahy, Xin Wang Abstract It has been well established that increasing scale in deep transformer networks leads to improved quality and performance. However, this increase in scale often comes with prohibitive increases in compute cost and inference latency. We introduce Alternating Updates ( AltUp ), a simple-to ...", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper_files/paper/2023/hash/f2059277ac6ce66e7e5543001afa8bb5-Abstract-Conference.html", "content": "Authors Cenk Baykal , Dylan Cutler, Nishanth Dikkala, Nikhil Ghosh, Rina Panigrahy, Xin Wang Abstract It has been well established that increasing scale in deep transformer networks leads to improved quality and performance. However, this increase in scale often comes with prohibitive increases in compute cost and inference latency. We introduce Alternating Updates ( AltUp ), a simple-to ..."}
+{"idx": 2, "title": "Alternating updates for efficient transformers - Google Research", "date": "", "ddg_snippet": "Nov 7, 2023 · A natural question is whether we can reap the benefits of larger models without incurring the computational burden. In “ Alternating Updates for Efficient Transformers ”, accepted as a Spotlight at NeurIPS 2023 , we introduce AltUp , a method to take advantage of increased token representation without increasing the computation cost .", "subpage_snippet": "", "source": "research.google", "link": "https://research.google/blog/alternating-updates-for-efficient-transformers/", "content": "Nov 7, 2023 · A natural question is whether we can reap the benefits of larger models without incurring the computational burden. In “ Alternating Updates for Efficient Transformers ”, accepted as a Spotlight at NeurIPS 2023 , we introduce AltUp , a method to take advantage of increased token representation without increasing the computation cost ."}
+{"idx": 3, "title": "NeurIPS 2023 Presentation", "date": "", "ddg_snippet": "Can we get the benefits of increased representation dimension without the full computational cost ? Performance for a word2vec model with varying representation dimension (figure from Yang et al., 2022).", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/media/neurips-2023/Slides/72994.pdf", "content": "Can we get the benefits of increased representation dimension without the full computational cost ? Performance for a word2vec model with varying representation dimension (figure from Yang et al., 2022)."}
+{"idx": 4, "title": "Alternating updates for efficient transformers - ichibanai.com", "date": "", "ddg_snippet": "In “ Alternating Updates for Efficient Transformers ”, accepted as a Spotlight at NeurIPS 2023 , we introduce AltUp , a method to take advantage of increased token representation without increasing the computation cost . AltUp is easy to implement, widely applicable to any transformer architecture, and requires minimal hyperparameter tuning.", "subpage_snippet": "", "source": "ichibanai.com", "link": "https://ichibanai.com/alternating-updates-for-efficient-transformers/", "content": "In “ Alternating Updates for Efficient Transformers ”, accepted as a Spotlight at NeurIPS 2023 , we introduce AltUp , a method to take advantage of increased token representation without increasing the computation cost . AltUp is easy to implement, widely applicable to any transformer architecture, and requires minimal hyperparameter tuning."}
+{"idx": 5, "title": "Alternating Updates for Efficient Transformers - OpenReview", "date": "", "ddg_snippet": "Sep 21, 2023 · The paper proposes a novel technique named “ AltUp ” to expand Transformer ’s feature dimension while preserving the computation cost . The key idea of AltUp is to divide wide hidden features into multiple blocks, where only one block is processed by Transformer sub-layers, while the other blocks are computed through a linear combination of ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=1p6teT6F73", "content": "Sep 21, 2023 · The paper proposes a novel technique named “ AltUp ” to expand Transformer ’s feature dimension while preserving the computation cost . The key idea of AltUp is to divide wide hidden features into multiple blocks, where only one block is processed by Transformer sub-layers, while the other blocks are computed through a linear combination of ..."}
+{"idx": 6, "title": "Alternating updates for efficient transformers – Google ...", "date": "", "ddg_snippet": "Nov 7, 2023 · In our research paper, “Alternating Updates for Efficient Transformers ”, accepted as a Spotlight at NeurIPS 2023 , we introduce AltUp , a method that allows us to take advantage of increased token representation without increasing computation cost .", "subpage_snippet": "", "source": "news.pourover.ai", "link": "https://news.pourover.ai/alternating-updates-for-efficient-transformers-google-research-blog/", "content": "Nov 7, 2023 · In our research paper, “Alternating Updates for Efficient Transformers ”, accepted as a Spotlight at NeurIPS 2023 , we introduce AltUp , a method that allows us to take advantage of increased token representation without increasing computation cost ."}
+{"idx": 7, "title": "Alternating updates for efficient transformers - googblogs.com", "date": "", "ddg_snippet": "A natural question is whether we can reap the benefits of larger models without incurring the computational burden. In “Alternating Updates for Efficient Transformers ”, accepted as a Spotlight at NeurIPS 2023 , we introduce AltUp ...", "subpage_snippet": "", "source": "www.googblogs.com", "link": "https://www.googblogs.com/alternating-updates-for-efficient-transformers/", "content": "A natural question is whether we can reap the benefits of larger models without incurring the computational burden. In “Alternating Updates for Efficient Transformers ”, accepted as a Spotlight at NeurIPS 2023 , we introduce AltUp ..."}
+{"idx": 8, "title": "Google AI Introduces AltUp (Alternating Updates): An... - MarkTechPost", "date": "", "ddg_snippet": "By maintaining the model dimension and sidestepping the quadratic increase in computation associated with straightforward expansion, AltUp emerges as a promising solution to the computational challenges posed by larger Transformer networks.", "subpage_snippet": "", "source": "www.marktechpost.com", "link": "https://www.marktechpost.com/2023/11/12/google-ai-introduces-altup-alternating-updates-an-artificial-intelligence-method-that-takes-advantage-of-increasing-scale-in-transformer-networks-without-increasing-the-computation-cost/", "content": "By maintaining the model dimension and sidestepping the quadratic increase in computation associated with straightforward expansion, AltUp emerges as a promising solution to the computational challenges posed by larger Transformer networks."}
+{"idx": 9, "title": "Google AI's AltUp : Advancing Transformer Networks without...", "date": "", "ddg_snippet": "AltUp leverages alternating updates to enhance the performance of Transformer Networks without incurring additional computational expenses. This breakthrough paves the way for more efficient and cost -effective AI systems.", "subpage_snippet": "", "source": "fataldeaths.com", "link": "https://fataldeaths.com/NewsSite/2023/11/13/AI/articles/google-ais-altup-advancing-transformer-networks-without-increased-costs.html", "content": "AltUp leverages alternating updates to enhance the performance of Transformer Networks without incurring additional computational expenses. This breakthrough paves the way for more efficient and cost -effective AI systems."}
diff --git a/data/sampled_jsons/Alternating_updates_for_efficient_transformers_Baykal_et_al.,_2023.jsonl b/data/sampled_jsons/Alternating_updates_for_efficient_transformers_Baykal_et_al.,_2023.jsonl
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+{"idx": 0, "title": "Alternating updates for efficient transformers", "date": "", "ddg_snippet": "November 7, 2023 .In contrast, Alternating Updates keeps the layer width constant and efficiently computes the output by operating on a sub-block of the representation at each layer.", "subpage_snippet": "", "source": "research.google", "link": "https://research.google/blog/alternating-updates-for-efficient-transformers/", "content": "November 7, 2023 .In contrast, Alternating Updates keeps the layer width constant and efficiently computes the output by operating on a sub-block of the representation at each layer."}
+{"idx": 1, "title": "Alternating Updates for Efficient Transformers | DeepAI", "date": "", "ddg_snippet": "research. ∙ 05/03/ 2023 . Towards Being Parameter- Efficient : A Stratified Sparsely Activated Transformer with Dynamic Capacity. Mixture-of-experts (MoE) models that employ sparse activation have demon... 0 Haoran Xu, et al . ∙.", "subpage_snippet": "", "source": "deepai.org", "link": "https://deepai.org/publication/alternating-updates-for-efficient-transformers", "content": "research. ∙ 05/03/ 2023 . Towards Being Parameter- Efficient : A Stratified Sparsely Activated Transformer with Dynamic Capacity. Mixture-of-experts (MoE) models that employ sparse activation have demon... 0 Haoran Xu, et al . ∙."}
+{"idx": 2, "title": "[2301.13310] Alternating Updates for Efficient Transformers", "date": "", "ddg_snippet": "Abstract page for arXiv paper 2301.13310: Alternating Updates for Efficient Transformers .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2301.13310", "content": "Abstract page for arXiv paper 2301.13310: Alternating Updates for Efficient Transformers ."}
+{"idx": 3, "title": "Alternating Updates for Efficient Transformers", "date": "", "ddg_snippet": "Cenk Baykal , Dylan Cutler, Nishanth Dikkala, Nikhil Ghosh, Rina Panigrahy, Xin Wang.We introduce Alternating Updates (AltUp), a simple-to-implement method to increase a model's capacity without the computational burden.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2023/hash/f2059277ac6ce66e7e5543001afa8bb5-Abstract-Conference.html", "content": "Cenk Baykal , Dylan Cutler, Nishanth Dikkala, Nikhil Ghosh, Rina Panigrahy, Xin Wang.We introduce Alternating Updates (AltUp), a simple-to-implement method to increase a model's capacity without the computational burden."}
+{"idx": 4, "title": "Cenk Baykal - Google Scholar", "date": "", "ddg_snippet": "Alternating updates for efficient transformers . C Baykal , D Cutler, N Dikkala, N Ghosh, R Panigrahy, X Wang. Advances in Neural Information Processing Systems 36, 76718-76736, 2023 .", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=lRxoOlwAAAAJ&hl=en", "content": "Alternating updates for efficient transformers . C Baykal , D Cutler, N Dikkala, N Ghosh, R Panigrahy, X Wang. Advances in Neural Information Processing Systems 36, 76718-76736, 2023 ."}
+{"idx": 5, "title": "Alternating Updates for Efficient Transformers", "date": "", "ddg_snippet": "Our experiments on various transformer models and language tasks demonstrate the consistent effectiveness of alternating updates on a diverse set of benchmarks.", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/article/Alternating-Updates-for-Efficient-Transformers-f814b5ae-ce27-453f-a1c3-41ee060de9f3", "content": "Our experiments on various transformer models and language tasks demonstrate the consistent effectiveness of alternating updates on a diverse set of benchmarks."}
+{"idx": 6, "title": "Cenk Baykal · CSAuthors", "date": "", "ddg_snippet": "Alternating Updates for Efficient Transformers . Cenk Baykal .", "subpage_snippet": "", "source": "www.csauthors.net", "link": "https://www.csauthors.net/cenk-baykal/", "content": "Alternating Updates for Efficient Transformers . Cenk Baykal ."}
+{"idx": 7, "title": "Alternating Updates for Efficient Transformers | alphaXiv", "date": "", "ddg_snippet": "Our experiments on benchmark transformer models and language tasks demonstrate the consistent effectiveness of AltUp on a diverse set of scenarios. Notably, on SuperGLUE and SQuAD benchmarks, AltUp enables up to $87\\%$ speedup relative to the dense baselines at the same accuracy.", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/zh/overview/2301.13310v2", "content": "Our experiments on benchmark transformer models and language tasks demonstrate the consistent effectiveness of AltUp on a diverse set of scenarios. Notably, on SuperGLUE and SQuAD benchmarks, AltUp enables up to $87\\%$ speedup relative to the dense baselines at the same accuracy."}
+{"idx": 8, "title": "ml-papers/papers/ 2023 /230130 Alternating Updates for Efficient ...", "date": "", "ddg_snippet": "My collection of machine learning papers. Contribute to rosinality/ml-papers development by creating an account on GitHub.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/rosinality/ml-papers/blob/main/papers/2023/230130+Alternating+Updates+for+Efficient+Transformers.md", "content": "My collection of machine learning papers. Contribute to rosinality/ml-papers development by creating an account on GitHub."}
+{"idx": 9, "title": "Nishanth Dikkala - Google Akademik", "date": "", "ddg_snippet": "2024. Alternating updates for efficient transformers . C Baykal , D Cutler, N Dikkala, N Ghosh, R Panigrahy, X Wang.", "subpage_snippet": "", "source": "scholar.google.dk", "link": "https://scholar.google.dk/citations?user=ViBqWZYAAAAJ&hl=tr", "content": "2024. Alternating updates for efficient transformers . C Baykal , D Cutler, N Dikkala, N Ghosh, R Panigrahy, X Wang."}
diff --git a/data/sampled_jsons/An_Analysis_for_Reasoning_Bias_of_Language_Models_with_Small_Initialization_Frsn(X)_Figure_2.jsonl b/data/sampled_jsons/An_Analysis_for_Reasoning_Bias_of_Language_Models_with_Small_Initialization_Frsn(X)_Figure_2.jsonl
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+{"idx": 0, "title": "AN ANALYSIS FOR REASONING BIAS OF LANGUAGE MODELS WITH SMALL ...", "date": "", "ddg_snippet": "Feb 10, 2025 · In this work, we identify a reasoning bias during the training of neural networks that learn natural language when initialized with small parameter scales.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.04375v1", "content": "Feb 10, 2025 · In this work, we identify a reasoning bias during the training of neural networks that learn natural language when initialized with small parameter scales."}
+{"idx": 1, "title": "An Analysis for Reasoning Bias of Language Models with Small ...", "date": "", "ddg_snippet": "In this work, we identify a reasoning bias during the train-ing of neural networks that learn natural language when initialized with small parameter scales.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.04375", "content": "In this work, we identify a reasoning bias during the train-ing of neural networks that learn natural language when initialized with small parameter scales."}
+{"idx": 2, "title": "An analysis for reasoning bias of language models with small ...", "date": "", "ddg_snippet": "Transformer-based Large Language Models (LLMs) have revolutionized Natural Language Processing by demonstrating exceptional performance across diverse tasks. This study investigates the impact of the parameter initialization scale on the training behavior and task...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.04375v1", "content": "Transformer-based Large Language Models (LLMs) have revolutionized Natural Language Processing by demonstrating exceptional performance across diverse tasks. This study investigates the impact of the parameter initialization scale on the training behavior and task..."}
+{"idx": 3, "title": "(PDF) An Analysis for Reasoning Bias of Language Models with ...", "date": "", "ddg_snippet": "We validate this reasoning bias via real datasets and meticulously designed anchor functions.This work enhances our understanding of how initialization strategies influence LLM performance on reasoning tasks and offers valuable guidelines for training models .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/388848015_An_Analysis_for_Reasoning_Bias_of_Language_Models_with_Small_Initialization", "content": "We validate this reasoning bias via real datasets and meticulously designed anchor functions.This work enhances our understanding of how initialization strategies influence LLM performance on reasoning tasks and offers valuable guidelines for training models ."}
+{"idx": 4, "title": "an analysis for reasoning bias of language models", "date": "", "ddg_snippet": "by J Yao · 2025 · Cited by 2 — In this work, we identify a reasoning bias during the training of neural networks that learn natural language when initialized with small ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.04375?", "content": "by J Yao · 2025 · Cited by 2 — In this work, we identify a reasoning bias during the training of neural networks that learn natural language when initialized with small ..."}
+{"idx": 5, "title": "ToolRM: Outcome Reward Models for Tool-Calling Large ...", "date": "", "ddg_snippet": "5 days ago — We summarize the key insights below: • Small Language Models (SLMs) benefit the most: Best-of-n sampling with Qwen3-0.6B and. ToolRM-14B as the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2509.11963", "content": "5 days ago — We summarize the key insights below: • Small Language Models (SLMs) benefit the most: Best-of-n sampling with Qwen3-0.6B and. ToolRM-14B as the ..."}
+{"idx": 6, "title": "Can Language Models Perform Robust Reasoning in ...", "date": "", "ddg_snippet": "by Z Zhou · Cited by 20 — Abstract. This paper investigates an under-explored challenge in large language models . (LLMs): chain-of-thought prompting with noisy rationales, ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=FbuODM02ra", "content": "by Z Zhou · Cited by 20 — Abstract. This paper investigates an under-explored challenge in large language models . (LLMs): chain-of-thought prompting with noisy rationales, ..."}
+{"idx": 7, "title": "Towards Better Causal Reasoning in Language Models", "date": "", "ddg_snippet": "by L Yu · 2025 · Cited by 4 — Through systematic analysis of model outputs, we identify four common types of errors in causal reasoning . Logical errors occur when the model ...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.naacl-long.622.pdf", "content": "by L Yu · 2025 · Cited by 4 — Through systematic analysis of model outputs, we identify four common types of errors in causal reasoning . Logical errors occur when the model ..."}
+{"idx": 8, "title": "A Large Language Model Fine-Tuning Framework for Causal ...", "date": "", "ddg_snippet": "Figure 2 BRQA Q&A Data. The step-by-step functions of BRQA for Q&A data are listed in Table 1. Table 1 Step by step function. Step by step function. Step01 ...", "subpage_snippet": "", "source": "papers.ssrn.com", "link": "https://papers.ssrn.com/sol3/Delivery.cfm/f3d4b069-6a1c-4ba6-b8c4-49cc2ceb19a5-MECA.pdf?abstractid=5394371&mirid=1", "content": "Figure 2 BRQA Q&A Data. The step-by-step functions of BRQA for Q&A data are listed in Table 1. Table 1 Step by step function. Step by step function. Step01 ..."}
+{"idx": 9, "title": "A Practical Guidance for Building and Evaluating Chinese ...", "date": "", "ddg_snippet": "by X Wen · 2025 — We propose CheemsBench, the first large- scale and comprehensive benchmark designed specifically for Chinese reward models . • We construct ...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.acl-long.737.pdf", "content": "by X Wen · 2025 — We propose CheemsBench, the first large- scale and comprehensive benchmark designed specifically for Chinese reward models . • We construct ..."}
diff --git a/data/sampled_jsons/Anvith_Thudi_2024_per-instance_privacy.jsonl b/data/sampled_jsons/Anvith_Thudi_2024_per-instance_privacy.jsonl
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index 0000000000000000000000000000000000000000..75cfaf9272b5ea5f0e5e4dc87e3aadf6b64a0729
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+{"idx": 0, "title": "Leveraging Per - Instance Privacy for Machine Unlearning | OpenReview", "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 trade-off by replacing...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=0A4Y9qRnu9&referrer=[the+profile+of+Nazanin+Mohammadi+Sepahvand](/profile?id=~Nazanin_Mohammadi_Sepahvand1)", "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 trade-off by replacing..."}
+{"idx": 1, "title": "Leveraging Per - Instance Privacy for Machine Unlearning", "date": "", "ddg_snippet": "( 2024 ) and a per - instance Re´nyi-differential privacy analysis by Thudi et al. An - vith Thudi is also supported by a Vanier Fellowship from NSERC. Daniel M. Roy is supported by the funding through.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2505.18786", "content": "( 2024 ) and a per - instance Re´nyi-differential privacy analysis by Thudi et al. An - vith Thudi is also supported by a Vanier Fellowship from NSERC. Daniel M. Roy is supported by the funding through."}
+{"idx": 2, "title": "CleverHans Lab - Anvith", "date": "", "ddg_snippet": "@article{ anvith 2024 fromjournal, author = { Thudi , Anvith and Shumailov, Ilia and Boenisch, Franziska and Papernot, Nicolas}, title = {From Differential Privacy to Bounds on Membership Inference: Less can be More}, year = { 2024 } }.", "subpage_snippet": "", "source": "cleverhans.io", "link": "https://cleverhans.io/members/anvith.html", "content": "@article{ anvith 2024 fromjournal, author = { Thudi , Anvith and Shumailov, Ilia and Boenisch, Franziska and Papernot, Nicolas}, title = {From Differential Privacy to Bounds on Membership Inference: Less can be More}, year = { 2024 } }."}
+{"idx": 3, "title": "Anvith Thudi", "date": "", "ddg_snippet": "anvith .com anvith . thudi @mail.utoronto.ca.“Training Private Models That Know What They Don’t Know”: Stephan Rabanser, Anvith Thudi , Abhradeep Thakurta, Krishnamurthy Dvijotham, Nicolas Papernot. Proceedings of the 37th Conference on Neural Information Processing Systems.", "subpage_snippet": "", "source": "www.anvith.com", "link": "https://www.anvith.com/Anvith_Thudi_CV_May_22_2025.pdf", "content": "anvith .com anvith . thudi @mail.utoronto.ca.“Training Private Models That Know What They Don’t Know”: Stephan Rabanser, Anvith Thudi , Abhradeep Thakurta, Krishnamurthy Dvijotham, Nicolas Papernot. Proceedings of the 37th Conference on Neural Information Processing Systems."}
+{"idx": 4, "title": "Nicolas PAPERNOT | Professor (Assistant) | Doctor of Philosophy", "date": "", "ddg_snippet": "Anvith Thudi Anvith Thudi .We present a principled, per - instance approach to quantifying the difficulty of unlearning via fine-tuning.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/profile/Nicolas-Papernot", "content": "Anvith Thudi Anvith Thudi .We present a principled, per - instance approach to quantifying the difficulty of unlearning via fine-tuning."}
+{"idx": 5, "title": "Leveraging Per -Example Privacy for Machine Unlearning", "date": "", "ddg_snippet": "Anvith Thudi . Berivan Isik.Our results show that per - instance privacy levels computed from training dynamics reliably predict unlearning difficulty, offering a principled and practical way to assess unlearning performance.", "subpage_snippet": "", "source": "research.google", "link": "https://research.google/pubs/leveraging-per-example-privacy-for-machine-unlearning/", "content": "Anvith Thudi . Berivan Isik.Our results show that per - instance privacy levels computed from training dynamics reliably predict unlearning difficulty, offering a principled and practical way to assess unlearning performance."}
+{"idx": 6, "title": "Anvith Thudi - Google Scholar", "date": "", "ddg_snippet": "Anvith Thudi . CS PhD, UofT.Leveraging Per - Instance Privacy for Machine Unlearning.", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=bTEybH0AAAAJ&hl=en", "content": "Anvith Thudi . CS PhD, UofT.Leveraging Per - Instance Privacy for Machine Unlearning."}
+{"idx": 7, "title": "Nicolas Papernot", "date": "", "ddg_snippet": "[C7] Leveraging Per - Instance Privacy for Machine Unlearning. Nazanin Mohammadi Sepahvand, Anvith Thudi , Berivan Isik, Ashmita Bhattacharyya, Nicolas Papernot, Eleni Triantafillou, Daniel M. Roy, Gintare Karolina Dziugaite.", "subpage_snippet": "", "source": "www.papernot.fr", "link": "https://www.papernot.fr/papernot_cv.pdf", "content": "[C7] Leveraging Per - Instance Privacy for Machine Unlearning. Nazanin Mohammadi Sepahvand, Anvith Thudi , Berivan Isik, Ashmita Bhattacharyya, Nicolas Papernot, Eleni Triantafillou, Daniel M. Roy, Gintare Karolina Dziugaite."}
+{"idx": 8, "title": "GitHub - Anvith - Thudi /MixMax: The repository for https...", "date": "", "ddg_snippet": "Codespaces. Instant dev environments. Issues. Anvith - Thudi / MixMax Public. Notifications You must be signed in to change notification settings.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Anvith-Thudi/MixMax", "content": "Codespaces. Instant dev environments. Issues. Anvith - Thudi / MixMax Public. Notifications You must be signed in to change notification settings."}
+{"idx": 9, "title": "https://infosecurity.us - USENIX Security '22 - Anvith Thudi , Hengrui...", "date": "", "ddg_snippet": "April 07, 2023 by Marc Handelman in USENIX, USENIX Security, Security Conferences, Regulatory Education, Cybersecurity Education, Security Education, Infosec Education, Education, Privacy , Hardware Security, USENIX NSDI '24.", "subpage_snippet": "", "source": "www.infosecurity.us", "link": "https://www.infosecurity.us/blog/2023/4/7/usenix-security-22-anvith-thudi-hengrui-jia-ilia-shumailov-nicolas-papernot-on-the-necessity-of-auditable-algorithmic-definitions-for-machine-unlearning", "content": "April 07, 2023 by Marc Handelman in USENIX, USENIX Security, Security Conferences, Regulatory Education, Cybersecurity Education, Security Education, Infosec Education, Education, Privacy , Hardware Security, USENIX NSDI '24."}
diff --git a/data/sampled_jsons/Archetypal_SAE_RA-SAE_TopK_SAE_stability_score_DINOv2_table_1.jsonl b/data/sampled_jsons/Archetypal_SAE_RA-SAE_TopK_SAE_stability_score_DINOv2_table_1.jsonl
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index 0000000000000000000000000000000000000000..922e4eddb54c476f6a7e0e15bbd7b0c321837025
--- /dev/null
+++ b/data/sampled_jsons/Archetypal_SAE_RA-SAE_TopK_SAE_stability_score_DINOv2_table_1.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Archetypal SAEs: Adaptive and Stable Dictionary Learning for ...", "date": "", "ddg_snippet": "Mar 20, 2025 · Figure 1 . 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).", "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": "Mar 20, 2025 · Figure 1 . 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)."}
+{"idx": 1, "title": "Archetypal SAE: Adaptive and Stable Dictionary Learning for ...", "date": "", "ddg_snippet": "Figure 1 : A) Archetypal - 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. Standard SAEs produce inconsistent dictionaries ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.12892v2", "content": "Figure 1 : A) Archetypal - 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. Standard SAEs produce inconsistent dictionaries ..."}
+{"idx": 2, "title": "Archetypal - Overcomplete", "date": "", "ddg_snippet": "RelaxedArchetypalDictionary Dictionary used for Relaxed Archetypal SAE ( RA-SAE ). __init__(self, in_dimensions, nb_concepts, points, delta= 1 .0, use_multiplier=True, device='cpu') Parameters in_dimensions : int Dimensionality of the input data (e.g number of channels). nb_concepts : int Number of components/concepts in the dictionary. The dictionary is overcomplete if the number of concepts > in ...", "subpage_snippet": "", "source": "kempnerinstitute.github.io", "link": "https://kempnerinstitute.github.io/overcomplete/saes/archetypal/", "content": "RelaxedArchetypalDictionary Dictionary used for Relaxed Archetypal SAE ( RA-SAE ). __init__(self, in_dimensions, nb_concepts, points, delta= 1 .0, use_multiplier=True, device='cpu') Parameters in_dimensions : int Dimensionality of the input data (e.g number of channels). nb_concepts : int Number of components/concepts in the dictionary. The dictionary is overcomplete if the number of concepts > in ..."}
+{"idx": 3, "title": "Archetypal SAE: Variable and stable methods to learn the ...", "date": "", "ddg_snippet": "Mar 17, 2025 · RA-SAE Expands the frame and includes a minimum rest time, allowing a slight deviation from the convex hull to improve model fluctuations while storing firmness. The investigators examine their method using five detecters: Dinov2 , SIGLIP, VIT, Conving, and Revnet50, all received from Timm brillion.", "subpage_snippet": "", "source": "dataforcee.us", "link": "https://dataforcee.us/2025/03/17/archetypal-sae-variable-and-stable-methods-to-learn-the-temple-dictionary-in-the-largest-visual-models/", "content": "Mar 17, 2025 · RA-SAE Expands the frame and includes a minimum rest time, allowing a slight deviation from the convex hull to improve model fluctuations while storing firmness. The investigators examine their method using five detecters: Dinov2 , SIGLIP, VIT, Conving, and Revnet50, all received from Timm brillion."}
+{"idx": 4, "title": "Archetypal SAEs: Adaptive and Stable Dictionary ... - 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": "GitHub - gorobarak/sae: Experiments with different SAE ...", "date": "", "ddg_snippet": "Experiments with different SAE architectures. Contribute to gorobarak/ sae development by creating an account on GitHub.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/gorobarak/sae", "content": "Experiments with different SAE architectures. Contribute to gorobarak/ sae development by creating an account on GitHub."}
+{"idx": 6, "title": "[2502.12892] Archetypal SAE: Adaptive and Stable Dictionary ...", "date": "", "ddg_snippet": "Feb 18, 2025 · This geometric anchoring significantly enhances the stability of inferred dictionaries, and their mildly relaxed variants RA -SAEs further match state-of-the-art reconstruction abilities.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.12892", "content": "Feb 18, 2025 · This geometric anchoring significantly enhances the stability of inferred dictionaries, and their mildly relaxed variants RA -SAEs further match state-of-the-art reconstruction abilities."}
+{"idx": 7, "title": "(PDF) Archetypal SAE : Adaptive and Stable Dictionary Learning for...", "date": "", "ddg_snippet": "SAE TopK SAE Jump SAE SNMF CNMF RA - SAE . Table 1 . Quantitative comparison of the dictionary learning methods on DINOv 2 , using a 90% sparse, overcomplete dictionary.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/389129606_Archetypal_SAE_Adaptive_and_Stable_Dictionary_Learning_for_Concept_Extraction_in_Large_Vision_Models", "content": "SAE TopK SAE Jump SAE SNMF CNMF RA - SAE . Table 1 . Quantitative comparison of the dictionary learning methods on DINOv 2 , using a 90% sparse, overcomplete dictionary."}
+{"idx": 8, "title": "matybohacek/ RA - SAE - DINOv 2 -32k · Hugging Face", "date": "", "ddg_snippet": "RA - SAE - DINOv 2 -32k is an archetypal SAE , which decomposes DINOv 2 featues into interpretable concept activations, allowing you to extract human-understandable visual concepts from any image as sparse feature vectors. With 32,000 concepts, it is the largest such model to date.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/matybohacek/RA-SAE-DINOv2-32k", "content": "RA - SAE - DINOv 2 -32k is an archetypal SAE , which decomposes DINOv 2 featues into interpretable concept activations, allowing you to extract human-understandable visual concepts from any image as sparse feature vectors. With 32,000 concepts, it is the largest such model to date."}
+{"idx": 9, "title": "Archetypal SAE : Adaptive and Secure Dictionary... - I Tech Epic", "date": "", "ddg_snippet": "Furthermore, RA - SAE builds upon a TopK SAE structure to keep up constant sparsity ranges throughout experiments.", "subpage_snippet": "", "source": "itechepic.com", "link": "https://itechepic.com/archetypal-sae-adaptive-and-secure-dictionary-studying-for-idea-extraction-in-massive-imaginative-and-prescient-fashions/", "content": "Furthermore, RA - SAE builds upon a TopK SAE structure to keep up constant sparsity ranges throughout experiments."}
diff --git a/data/sampled_jsons/Archetypal_analysis_Cutler_Breiman_1994_journal.jsonl b/data/sampled_jsons/Archetypal_analysis_Cutler_Breiman_1994_journal.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c63a7104a470bbbfaf6696257c9a27f765351568
--- /dev/null
+++ b/data/sampled_jsons/Archetypal_analysis_Cutler_Breiman_1994_journal.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Archetypal Analysis on JSTOR", "date": "", "ddg_snippet": "journal article. Archetypal Analysis . Adele Cutler and Leo Breiman . Archetypal analysis represents each individual in a data set as a mixture of individuals of pure type or archetypes . The archetypes themselves are restricted to being mixtures of the individuals in the data set.", "subpage_snippet": "", "source": "www.jstor.org", "link": "https://www.jstor.org/stable/1269949", "content": "journal article. Archetypal Analysis . Adele Cutler and Leo Breiman . Archetypal analysis represents each individual in a data set as a mixture of individuals of pure type or archetypes . The archetypes themselves are restricted to being mixtures of the individuals in the data set."}
+{"idx": 1, "title": "Archetypal Analysis : Technometrics: Vol 36, No 4", "date": "", "ddg_snippet": "Archetypal Analysis . Adele Cutler Department of Mathematics and Statistics, Utah State University, Logan, UT, 84322-3900. & Leo Breiman Department of Statistics, University of California, Berkeley, CA, 94720.", "subpage_snippet": "", "source": "www.tandfonline.com", "link": "https://www.tandfonline.com/doi/abs/10.1080/00401706.1994.10485840", "content": "Archetypal Analysis . Adele Cutler Department of Mathematics and Statistics, Utah State University, Logan, UT, 84322-3900. & Leo Breiman Department of Statistics, University of California, Berkeley, CA, 94720."}
+{"idx": 2, "title": "Archetypal analysis for machine learning and data mining", "date": "", "ddg_snippet": "Archetypal analysis (aa) proposed by Cutler and Breiman ( 1994 ) [7] estimates the principal convex hull (pch) of a data set.", "subpage_snippet": "", "source": "orbit.dtu.dk", "link": "https://orbit.dtu.dk/en/publications/archetypal-analysis-for-machine-learning-and-data-mining", "content": "Archetypal analysis (aa) proposed by Cutler and Breiman ( 1994 ) [7] estimates the principal convex hull (pch) of a data set."}
+{"idx": 3, "title": "Archetypal Analysis ++: Rethinking the Initialization Strategy", "date": "", "ddg_snippet": "Archetypal analysis (AA) ( Cutler & Breiman , 1994 ) is a matrix factorization method with convexity constraints.Furthermore, Cutler & Breiman ( 1994 ) state that a careful initialization improves the convergence speed and that archetypes should not be initialized too close to each other.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2301.13748v4", "content": "Archetypal analysis (AA) ( Cutler & Breiman , 1994 ) is a matrix factorization method with convexity constraints.Furthermore, Cutler & Breiman ( 1994 ) state that a careful initialization improves the convergence speed and that archetypes should not be initialized too close to each other."}
+{"idx": 4, "title": "From Spider-Man to Hero – Archetypal Analysis in R", "date": "", "ddg_snippet": "In statistics archetypal analysis was rst introduced by Cutler and Breiman ( 1994 ). In their paper they laid out the theoretical foundations, dened the concrete problem as a nonlinear least squares problem and presented an alternating minimizing algorithm to solve it.", "subpage_snippet": "", "source": "cran.r-project.org", "link": "https://cran.r-project.org/web/packages/archetypes/vignettes/archetypes.pdf", "content": "In statistics archetypal analysis was rst introduced by Cutler and Breiman ( 1994 ). In their paper they laid out the theoretical foundations, dened the concrete problem as a nonlinear least squares problem and presented an alternating minimizing algorithm to solve it."}
+{"idx": 5, "title": "Non-negative Matrix Factorization via Archetypal Analysis | DeepAI", "date": "", "ddg_snippet": "The archetypal analysis method of ( Cutler , Breiman , 1994 ) is recovered as the limiting case in which the last term is given infinite weight. We introduce a `uniqueness condition' on the data which is necessary for exactly recovering the archetypes from noiseless data.", "subpage_snippet": "", "source": "deepai.org", "link": "https://deepai.org/publication/non-negative-matrix-factorization-via-archetypal-analysis", "content": "The archetypal analysis method of ( Cutler , Breiman , 1994 ) is recovered as the limiting case in which the last term is given infinite weight. We introduce a `uniqueness condition' on the data which is necessary for exactly recovering the archetypes from noiseless data."}
+{"idx": 6, "title": "(PDF) Archetypal Analysis ++: Rethinking the Initialization Strategy", "date": "", "ddg_snippet": "Archetypal analysis (AA) ( Cutler and Breiman , 1994 ) is a matrix factorization method with convexity. constraints. The idea is to represent every data point as a convex combination of points, called.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/367652277_Archetypal_Analysis_Rethinking_the_Initialization_Strategy", "content": "Archetypal analysis (AA) ( Cutler and Breiman , 1994 ) is a matrix factorization method with convexity. constraints. The idea is to represent every data point as a convex combination of points, called."}
+{"idx": 7, "title": "ICML Poster Archetypal SAE: Adaptive and Stable Dictionary Learning...", "date": "", "ddg_snippet": "Create Profile. Journal To Conference Track.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 data’s convex hull.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46195", "content": "Create Profile. Journal To Conference Track.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 data’s convex hull."}
+{"idx": 8, "title": "Probabilistic archetypal analysis", "date": "", "ddg_snippet": "1 Introduction. Archetypal analysis (AA) represents observations as composition of pure patterns, i.e., archetypes , or equivalently convex combinations of extreme values ( Cutler and Breiman 1994 ).", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/s10994-015-5498-8.pdf", "content": "1 Introduction. Archetypal analysis (AA) represents observations as composition of pure patterns, i.e., archetypes , or equivalently convex combinations of extreme values ( Cutler and Breiman 1994 )."}
+{"idx": 9, "title": "Frame-based Data Factorizations", "date": "", "ddg_snippet": "The goal of Archetypal Analysis (AA) ( Cutler & Breiman , 1994 ) is to nd a factorization of the data. Journal of Mathematical Modelling and Algorithms, 4(2):219–234, 2005. Mørup, Morten and Hansen, Lars Kai. Archetypal analysis for machine learning.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v70/mair17a/mair17a.pdf", "content": "The goal of Archetypal Analysis (AA) ( Cutler & Breiman , 1994 ) is to nd a factorization of the data. Journal of Mathematical Modelling and Algorithms, 4(2):219–234, 2005. Mørup, Morten and Hansen, Lars Kai. Archetypal analysis for machine learning."}
diff --git a/data/sampled_jsons/Atp_Adaptive_tensor_parallelism_for_foundation_models_Cheng_2023_abstract.jsonl b/data/sampled_jsons/Atp_Adaptive_tensor_parallelism_for_foundation_models_Cheng_2023_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b68de35661bace7802950f4edcadb6aaddcd4c98
--- /dev/null
+++ b/data/sampled_jsons/Atp_Adaptive_tensor_parallelism_for_foundation_models_Cheng_2023_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ATP: Adaptive Tensor Parallelism for Foundation Models Adaptive Tensor Parallelism for Foundation Models - GitHub ATP: Adaptive Tensor Parallelism for Foundation Models Publications | Ziming Liu | MLSYS Researcher - GitHub Pages ATP: Adaptive Tensor Parallelism for Foundation Models Distributed Machine Learning | Proceedings of the 25th ... ATP : Adaptive Tensor Parallelism for Foundation Models ATP : Adaptive Tensor Parallelism for Foundation Models ATP : Adaptive Tensor Parallelism for Foundation Models ATP : Adaptive Tensor Parallelism for Foundation Models Shenggan Cheng - dblp", "date": "", "ddg_snippet": "Jan 20, 2023 · However, current tensor parallelism in machine learning frameworks misses optimization opportunities in fitting various interconnection topologies. In this work, we present ATP , an adaptive tensor parallelism framework for foundation models , which can automatically select the optimal parallel strategy on different interconnections. Adaptive Tensor Parallelism for Large Model Traning and Inference ATP provides a high-performance implementation of Topology-aware Tensor Parallelism with the following characteristics. 1.Two-Level Search Space for Tensor Parallelism . 2. Adaptive Tensor Parallelism with Hierarchical Communication Matrix. 3.Chunk-based Communication-Computation Overlapping. 4.An estimator that helps study the performance of ATP on networks with different topologies. See full list on github.com To install ATP , you will need: •Python 3.8 or 3.9. •PyTorch 1.13 •SPMD from pytorch/tau See full list on github.com In this work, we present ATP , an adaptive tensor parallelism framework for foundation models , which can automatically select the optimal parallel strategy on different interconnections. We propose column- and row-first tensor parallelism based on 2D device meshes and construct a search space. Shenggan Cheng , Ziming Liu, Jiangsu Du, Yang You ( 2023 ). ATP : Adaptive Tensor Parallelism for Foundation Models . Arxiv Preprint. ATP , an adaptive tensor parallelism framework for foundation models , which can automatically select the optimal parallel strategy on different interconnections, is presented and the communication overhead of ATP decreases with scaling, indicating a qualitative leap forward. Jan 22, 2024 · Shenggan Cheng , Ziming Liu, Jiangsu Du, and Yang You. 2023 . ATP : Adaptive Tensor Parallelism for Foundation Models . arXiv preprint arXiv:2301.08658 ( 2023 ). Is ATP an adaptive tensor parallelism framework for foundation models? In this work, we present ATP, an adaptive tensor parallelism framework for foundation models , which can automatically select the optimal parallel strategy on different interconnections. We propose column- and row-first tensor parallelism based on 2D device meshes and construct a search space. Can tensor parallelism be used in machine learning? However, current tensor parallelism in machine learning frameworks misses optimization opportunities in fitting various interconnection topologies. In this work, we present ATP, an adaptive tensor parallelism framework for foundation models, which can automatically select the optimal parallel strategy on different interconnections. How can ATP identify optimal tensor parallelism based on 2D device meshes? We propose column- and row-first tensor parallelism based on 2D device meshes and construct a search space. Combined with the hierarchical communication matrix , ATP can identify the optimal strategy in the search space. We also propose chunk-based overlapping to reduce communication overhead. Why is tensor parallelism important in Foundation model training? The increasing size of the models introduces great challenges for the training. Tensor parallelism is a critical technique that is currently used in almost all foundation model training and has a significant impact on overall training performance . [i5] Shenggan Cheng , Ziming Liu, Jiangsu Du, Yang You: ATP : Adaptive Tensor Parallelism for Foundation Models . CoRR abs/2301.08658 ( 2023 )", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2301.08658", "content": "Jan 20, 2023 · However, current tensor parallelism in machine learning frameworks misses optimization opportunities in fitting various interconnection topologies. In this work, we present ATP , an adaptive tensor parallelism framework for foundation models , which can automatically select the optimal parallel strategy on different interconnections. Adaptive Tensor Parallelism for Large Model Traning and Inference ATP provides a high-performance implementation of Topology-aware Tensor Parallelism with the following characteristics. 1.Two-Level Search Space for Tensor Parallelism . 2. Adaptive Tensor Parallelism with Hierarchical Communication Matrix. 3.Chunk-based Communication-Computation Overlapping. 4.An estimator that helps study the performance of ATP on networks with different topologies. See full list on github.com To install ATP , you will need: •Python 3.8 or 3.9. •PyTorch 1.13 •SPMD from pytorch/tau See full list on github.com In this work, we present ATP , an adaptive tensor parallelism framework for foundation models , which can automatically select the optimal parallel strategy on different interconnections. We propose column- and row-first tensor parallelism based on 2D device meshes and construct a search space. Shenggan Cheng , Ziming Liu, Jiangsu Du, Yang You ( 2023 ). ATP : Adaptive Tensor Parallelism for Foundation Models . Arxiv Preprint. ATP , an adaptive tensor parallelism framework for foundation models , which can automatically select the optimal parallel strategy on different interconnections, is presented and the communication overhead of ATP decreases with scaling, indicating a qualitative leap forward. Jan 22, 2024 · Shenggan Cheng , Ziming Liu, Jiangsu Du, and Yang You. 2023 . ATP : Adaptive Tensor Parallelism for Foundation Models . arXiv preprint arXiv:2301.08658 ( 2023 ). Is ATP an adaptive tensor parallelism framework for foundation models? In this work, we present ATP, an adaptive tensor parallelism framework for foundation models , which can automatically select the optimal parallel strategy on different interconnections. We propose column- and row-first tensor parallelism based on 2D device meshes and construct a search space. Can tensor parallelism be used in machine learning? However, current tensor parallelism in machine learning frameworks misses optimization opportunities in fitting various interconnection topologies. In this work, we present ATP, an adaptive tensor parallelism framework for foundation models, which can automatically select the optimal parallel strategy on different interconnections. How can ATP identify optimal tensor parallelism based on 2D device meshes? We propose column- and row-first tensor parallelism based on 2D device meshes and construct a search space. Combined with the hierarchical communication matrix , ATP can identify the optimal strategy in the search space. We also propose chunk-based overlapping to reduce communication overhead. Why is tensor parallelism important in Foundation model training? The increasing size of the models introduces great challenges for the training. Tensor parallelism is a critical technique that is currently used in almost all foundation model training and has a significant impact on overall training performance . [i5] Shenggan Cheng , Ziming Liu, Jiangsu Du, Yang You: ATP : Adaptive Tensor Parallelism for Foundation Models . CoRR abs/2301.08658 ( 2023 )"}
+{"idx": 1, "title": "Adaptive Tensor Parallelism for Foundation Models - GitHub ATP: Adaptive Tensor Parallelism for Foundation Models Publications | Ziming Liu | MLSYS Researcher - GitHub Pages ATP: Adaptive Tensor Parallelism for Foundation Models Distributed Machine Learning | Proceedings of the 25th ... ATP : Adaptive Tensor Parallelism for Foundation Models ATP : Adaptive Tensor Parallelism for Foundation Models ATP : Adaptive Tensor Parallelism for Foundation Models ATP : Adaptive Tensor Parallelism for Foundation Models Shenggan Cheng - dblp", "date": "", "ddg_snippet": "Adaptive Tensor Parallelism for Large Model Traning and Inference ATP provides a high-performance implementation of Topology-aware Tensor Parallelism with the following characteristics. 1.Two-Level Search Space for Tensor Parallelism . 2. Adaptive Tensor Parallelism with Hierarchical Communication Matrix. 3.Chunk-based Communication-Computation Overlapping. 4.An estimator that helps study the performance of ATP on networks with different topologies. See full list on github.com To install ATP , you will need: •Python 3.8 or 3.9. •PyTorch 1.13 •SPMD from pytorch/tau See full list on github.com In this work, we present ATP , an adaptive tensor parallelism framework for foundation models , which can automatically select the optimal parallel strategy on different interconnections. We propose column- and row-first tensor parallelism based on 2D device meshes and construct a search space. Shenggan Cheng , Ziming Liu, Jiangsu Du, Yang You ( 2023 ). ATP : Adaptive Tensor Parallelism for Foundation Models . Arxiv Preprint. ATP , an adaptive tensor parallelism framework for foundation models , which can automatically select the optimal parallel strategy on different interconnections, is presented and the communication overhead of ATP decreases with scaling, indicating a qualitative leap forward. Jan 22, 2024 · Shenggan Cheng , Ziming Liu, Jiangsu Du, and Yang You. 2023 . ATP : Adaptive Tensor Parallelism for Foundation Models . arXiv preprint arXiv:2301.08658 ( 2023 ). Is ATP an adaptive tensor parallelism framework for foundation models? In this work, we present ATP, an adaptive tensor parallelism framework for foundation models , which can automatically select the optimal parallel strategy on different interconnections. We propose column- and row-first tensor parallelism based on 2D device meshes and construct a search space. Can tensor parallelism be used in machine learning? However, current tensor parallelism in machine learning frameworks misses optimization opportunities in fitting various interconnection topologies. In this work, we present ATP, an adaptive tensor parallelism framework for foundation models, which can automatically select the optimal parallel strategy on different interconnections. How can ATP identify optimal tensor parallelism based on 2D device meshes? We propose column- and row-first tensor parallelism based on 2D device meshes and construct a search space. Combined with the hierarchical communication matrix , ATP can identify the optimal strategy in the search space. We also propose chunk-based overlapping to reduce communication overhead. Why is tensor parallelism important in Foundation model training? The increasing size of the models introduces great challenges for the training. Tensor parallelism is a critical technique that is currently used in almost all foundation model training and has a significant impact on overall training performance . [i5] Shenggan Cheng , Ziming Liu, Jiangsu Du, Yang You: ATP : Adaptive Tensor Parallelism for Foundation Models . CoRR abs/2301.08658 ( 2023 )", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Shenggan/atp", "content": "Adaptive Tensor Parallelism for Large Model Traning and Inference ATP provides a high-performance implementation of Topology-aware Tensor Parallelism with the following characteristics. 1.Two-Level Search Space for Tensor Parallelism . 2. Adaptive Tensor Parallelism with Hierarchical Communication Matrix. 3.Chunk-based Communication-Computation Overlapping. 4.An estimator that helps study the performance of ATP on networks with different topologies. See full list on github.com To install ATP , you will need: •Python 3.8 or 3.9. •PyTorch 1.13 •SPMD from pytorch/tau See full list on github.com In this work, we present ATP , an adaptive tensor parallelism framework for foundation models , which can automatically select the optimal parallel strategy on different interconnections. We propose column- and row-first tensor parallelism based on 2D device meshes and construct a search space. Shenggan Cheng , Ziming Liu, Jiangsu Du, Yang You ( 2023 ). ATP : Adaptive Tensor Parallelism for Foundation Models . Arxiv Preprint. ATP , an adaptive tensor parallelism framework for foundation models , which can automatically select the optimal parallel strategy on different interconnections, is presented and the communication overhead of ATP decreases with scaling, indicating a qualitative leap forward. Jan 22, 2024 · Shenggan Cheng , Ziming Liu, Jiangsu Du, and Yang You. 2023 . ATP : Adaptive Tensor Parallelism for Foundation Models . arXiv preprint arXiv:2301.08658 ( 2023 ). Is ATP an adaptive tensor parallelism framework for foundation models? In this work, we present ATP, an adaptive tensor parallelism framework for foundation models , which can automatically select the optimal parallel strategy on different interconnections. We propose column- and row-first tensor parallelism based on 2D device meshes and construct a search space. Can tensor parallelism be used in machine learning? However, current tensor parallelism in machine learning frameworks misses optimization opportunities in fitting various interconnection topologies. In this work, we present ATP, an adaptive tensor parallelism framework for foundation models, which can automatically select the optimal parallel strategy on different interconnections. How can ATP identify optimal tensor parallelism based on 2D device meshes? We propose column- and row-first tensor parallelism based on 2D device meshes and construct a search space. Combined with the hierarchical communication matrix , ATP can identify the optimal strategy in the search space. We also propose chunk-based overlapping to reduce communication overhead. Why is tensor parallelism important in Foundation model training? The increasing size of the models introduces great challenges for the training. Tensor parallelism is a critical technique that is currently used in almost all foundation model training and has a significant impact on overall training performance . [i5] Shenggan Cheng , Ziming Liu, Jiangsu Du, Yang You: ATP : Adaptive Tensor Parallelism for Foundation Models . CoRR abs/2301.08658 ( 2023 )"}
+{"idx": 2, "title": "ATP: Adaptive Tensor Parallelism for Foundation Models", "date": "", "ddg_snippet": "In this work, we present ATP , an adaptive tensor parallelism framework for foundation models , which can automatically select the optimal parallel strategy on different interconnections. We propose column- and row-first tensor parallelism based on 2D device meshes and construct a search space.", "subpage_snippet": "", "source": "ui.adsabs.harvard.edu", "link": "https://ui.adsabs.harvard.edu/abs/2023arXiv230108658C/abstract", "content": "In this work, we present ATP , an adaptive tensor parallelism framework for foundation models , which can automatically select the optimal parallel strategy on different interconnections. We propose column- and row-first tensor parallelism based on 2D device meshes and construct a search space."}
+{"idx": 3, "title": "ATP: Adaptive Tensor Parallelism for Foundation Models", "date": "", "ddg_snippet": "ATP , an adaptive tensor parallelism framework for foundation models , which can automatically select the optimal parallel strategy on different interconnections, is presented and the communication overhead of ATP decreases with scaling, indicating a qualitative leap forward.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/ATP:-Adaptive-Tensor-Parallelism-for-Foundation-Cheng-Liu/c389de868d0647257ca2577e814630e8e3f4a484/figure/6", "content": "ATP , an adaptive tensor parallelism framework for foundation models , which can automatically select the optimal parallel strategy on different interconnections, is presented and the communication overhead of ATP decreases with scaling, indicating a qualitative leap forward."}
+{"idx": 4, "title": "Shenggan Cheng - dblp", "date": "", "ddg_snippet": "[i5] Shenggan Cheng , Ziming Liu, Jiangsu Du, Yang You: ATP : Adaptive Tensor Parallelism for Foundation Models . CoRR abs/2301.08658 ( 2023 )", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/pid/258/2485", "content": "[i5] Shenggan Cheng , Ziming Liu, Jiangsu Du, Yang You: ATP : Adaptive Tensor Parallelism for Foundation Models . CoRR abs/2301.08658 ( 2023 )"}
+{"idx": 5, "title": "ATP : Adaptive Tensor Parallelism for Foundation Models ( 2023 )", "date": "", "ddg_snippet": "In this work, we present ATP , an adaptive tensor parallelism framework for foundation models , which can automatically select the optimal parallel strategy on different interconnections.", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/papers/atp-adaptive-tensor-parallelism-for-foundation-models-30sqdiqh", "content": "In this work, we present ATP , an adaptive tensor parallelism framework for foundation models , which can automatically select the optimal parallel strategy on different interconnections."}
+{"idx": 6, "title": "Shenggan Cheng - Google Akademik", "date": "", "ddg_snippet": "2023 . ATP : Adaptive Tensor Parallelism for Foundation Models .", "subpage_snippet": "", "source": "scholar.google.com.tr", "link": "https://scholar.google.com.tr/citations?user=kDdwP6UAAAAJ&hl=tr", "content": "2023 . ATP : Adaptive Tensor Parallelism for Foundation Models ."}
+{"idx": 7, "title": "Adaptive Tensor Parallelism for Foundation Models", "date": "", "ddg_snippet": "Adaptive Tensor Parallelism for Large Model Traning and Inference. ATP provides a high-performance implementation of Topology-aware Tensor Parallelism with the following characteristics.", "subpage_snippet": "", "source": "awesome.ecosyste.ms", "link": "https://awesome.ecosyste.ms/projects/github.com/shenggan/atp", "content": "Adaptive Tensor Parallelism for Large Model Traning and Inference. ATP provides a high-performance implementation of Topology-aware Tensor Parallelism with the following characteristics."}
+{"idx": 8, "title": "Cheng , Shenggan | BibSonomy", "date": "", "ddg_snippet": "ACM, (2024 ) ATP : Adaptive Tensor Parallelism for Foundation Models .S. Cheng , Z. Liu, J. Du, and Y. You. CoRR, ( 2023 )tcFFT: A Fast Half-Precision FFT Library for NVIDIA Tensor Cores.B. Li, S. Cheng , and J. Lin.", "subpage_snippet": "", "source": "www.bibsonomy.org", "link": "https://www.bibsonomy.org/person/15184484c13c23846983c9b9376f4b47a/author/0", "content": "ACM, (2024 ) ATP : Adaptive Tensor Parallelism for Foundation Models .S. Cheng , Z. Liu, J. Du, and Y. You. CoRR, ( 2023 )tcFFT: A Fast Half-Precision FFT Library for NVIDIA Tensor Cores.B. Li, S. Cheng , and J. Lin."}
+{"idx": 9, "title": "Tensor Parallelism", "date": "", "ddg_snippet": "Tensor parallelism is a technique used to fit a large model in multiple GPUs. Tensor Parallelism only works for models officially supported, it will not work when falling back to transformers.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/docs/text-generation-inference/conceptual/tensor_parallelism", "content": "Tensor parallelism is a technique used to fit a large model in multiple GPUs. Tensor Parallelism only works for models officially supported, it will not work when falling back to transformers."}
diff --git a/data/sampled_jsons/Atp_Adaptive_tensor_parallelism_for_foundation_models_Cheng_et_al._abstract_year_2023.jsonl b/data/sampled_jsons/Atp_Adaptive_tensor_parallelism_for_foundation_models_Cheng_et_al._abstract_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..06fc897bdeeb67a3d888a94cad394a484e35fcc7
--- /dev/null
+++ b/data/sampled_jsons/Atp_Adaptive_tensor_parallelism_for_foundation_models_Cheng_et_al._abstract_year_2023.jsonl
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+{"idx": 0, "title": "ATP : Adaptive Tensor Parallelism for Foundation Models | DeepAI", "date": "", "ddg_snippet": "by Shenggan Cheng , et al .In this work, we present ATP , an adaptive tensor parallelism framework for foundation models , which can automatically select the optimal parallel strategy on different interconnections. We propose column- and row-first tensor parallelism based...", "subpage_snippet": "", "source": "deepai.org", "link": "https://deepai.org/publication/atp-adaptive-tensor-parallelism-for-foundation-models", "content": "by Shenggan Cheng , et al .In this work, we present ATP , an adaptive tensor parallelism framework for foundation models , which can automatically select the optimal parallel strategy on different interconnections. We propose column- and row-first tensor parallelism based..."}
+{"idx": 1, "title": "ATP : Adaptive Tensor Parallelism for Foundation Models", "date": "", "ddg_snippet": "View a PDF of the paper titled ATP : Adaptive Tensor Parallelism for Foundation Models , by Shenggan Cheng and 3 other authors.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2301.08658", "content": "View a PDF of the paper titled ATP : Adaptive Tensor Parallelism for Foundation Models , by Shenggan Cheng and 3 other authors."}
+{"idx": 2, "title": "Shenggan/ atp : Adaptive Tensor Parallelism for Foundation Models", "date": "", "ddg_snippet": "Adaptive Tensor Parallelism for Large Model Traning and Inference. ATP provides a high-performance implementation of Topology-aware Tensor Parallelism with the following characteristics. Two-Level Search Space for Tensor Parallelism .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Shenggan/atp", "content": "Adaptive Tensor Parallelism for Large Model Traning and Inference. ATP provides a high-performance implementation of Topology-aware Tensor Parallelism with the following characteristics. Two-Level Search Space for Tensor Parallelism ."}
+{"idx": 3, "title": "Shenggan Cheng - Google Akademik", "date": "", "ddg_snippet": "Takip et . Shenggan Cheng .2023. ATP : Adaptive Tensor Parallelism for Foundation Models .", "subpage_snippet": "", "source": "scholar.google.com.tr", "link": "https://scholar.google.com.tr/citations?user=kDdwP6UAAAAJ&hl=tr", "content": "Takip et . Shenggan Cheng .2023. ATP : Adaptive Tensor Parallelism for Foundation Models ."}
+{"idx": 4, "title": "Adaptive Tensor Parallelism for Foundation Models", "date": "", "ddg_snippet": "Adaptive Tensor Parallelism for Large Model Traning and Inference. ATP provides a high-performance implementation of Topology-aware Tensor Parallelism with the following characteristics.", "subpage_snippet": "", "source": "awesome.ecosyste.ms", "link": "https://awesome.ecosyste.ms/projects/github.com/shenggan/atp", "content": "Adaptive Tensor Parallelism for Large Model Traning and Inference. ATP provides a high-performance implementation of Topology-aware Tensor Parallelism with the following characteristics."}
+{"idx": 5, "title": "Tensor Parallelism", "date": "", "ddg_snippet": "Tensor parallelism is a technique used to fit a large model in multiple GPUs. Tensor Parallelism only works for models officially supported, it will not work when falling back to transformers.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/docs/text-generation-inference/conceptual/tensor_parallelism", "content": "Tensor parallelism is a technique used to fit a large model in multiple GPUs. Tensor Parallelism only works for models officially supported, it will not work when falling back to transformers."}
+{"idx": 6, "title": "AI Computing Systems for Large Language Models Training", "date": "", "ddg_snippet": "Ratner N, Levine Y, Belinkov Y et al . Parallel context windows for large language models . In Proc. the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Jul.", "subpage_snippet": "", "source": "www.sciopen.com", "link": "https://www.sciopen.com/article/10.1007/s11390-024-4178-1", "content": "Ratner N, Levine Y, Belinkov Y et al . Parallel context windows for large language models . In Proc. the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Jul."}
+{"idx": 7, "title": "Distributed Training of Large Language Models Across Multiple GPUs...", "date": "", "ddg_snippet": "Tensor parallelism represents the most sophisticated form of model parallelism , where individual tensor operations are distributed across multiple devices.", "subpage_snippet": "", "source": "www.rohan-paul.com", "link": "https://www.rohan-paul.com/p/distributed-training-of-large-language", "content": "Tensor parallelism represents the most sophisticated form of model parallelism , where individual tensor operations are distributed across multiple devices."}
+{"idx": 8, "title": "Cheng , Shenggan | BibSonomy", "date": "", "ddg_snippet": "ACM, (2024 ) ATP : Adaptive Tensor Parallelism for Foundation Models .S. Cheng , Z. Liu, J. Du, and Y. You. CoRR, (2023 )tcFFT: A Fast Half-Precision FFT Library for NVIDIA Tensor Cores.B. Li, S. Cheng , and J. Lin.", "subpage_snippet": "", "source": "www.bibsonomy.org", "link": "https://www.bibsonomy.org/person/15184484c13c23846983c9b9376f4b47a/author/0", "content": "ACM, (2024 ) ATP : Adaptive Tensor Parallelism for Foundation Models .S. Cheng , Z. Liu, J. Du, and Y. You. CoRR, (2023 )tcFFT: A Fast Half-Precision FFT Library for NVIDIA Tensor Cores.B. Li, S. Cheng , and J. Lin."}
+{"idx": 9, "title": "How Tensor Parallelism Works - Amazon SageMaker AI", "date": "", "ddg_snippet": "How the library adapts tensor parallelism to PyTorch nn.Linear module. Tensor parallelism takes place at the level of nn.Modules; it partitions specific modules in the model across tensor parallel ranks.", "subpage_snippet": "", "source": "docs.aws.amazon.com", "link": "https://docs.aws.amazon.com/sagemaker/latest/dg/model-parallel-extended-features-pytorch-tensor-parallelism-how-it-works.html", "content": "How the library adapts tensor parallelism to PyTorch nn.Linear module. Tensor parallelism takes place at the level of nn.Modules; it partitions specific modules in the model across tensor parallel ranks."}
diff --git "a/data/sampled_jsons/BA-Cycle_method_\342\210\206\342\210\206G_calculation_Section_3.2_Boltzmann-Aligned_Inverse_Folding.jsonl" "b/data/sampled_jsons/BA-Cycle_method_\342\210\206\342\210\206G_calculation_Section_3.2_Boltzmann-Aligned_Inverse_Folding.jsonl"
new file mode 100644
index 0000000000000000000000000000000000000000..75de14b39091f147fcfe3068af4e3a3fbc6c898e
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+++ "b/data/sampled_jsons/BA-Cycle_method_\342\210\206\342\210\206G_calculation_Section_3.2_Boltzmann-Aligned_Inverse_Folding.jsonl"
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+{"idx": 0, "title": "boltzmann-aligned inverse folding model as", "date": "", "ddg_snippet": "This method is named BA-Cycle and uses the inverse folding model to evaluate ∆∆G by predicting the likelihoods of protein sequences, as shown on the left side ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/69e9689e53a10509760846b53b77823430a0c523.pdf", "content": "This method is named BA-Cycle and uses the inverse folding model to evaluate ∆∆G by predicting the likelihoods of protein sequences, as shown on the left side ..."}
+{"idx": 1, "title": "Calculation of the relative metastabilities of proteins in ...", "date": "", "ddg_snippet": "by JM Dick · 2008 · Cited by 9 — The purpose of this study is to quantify using a metastable equilibrium reference state the responses of popu- lations of model proteins for different ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/0812.0191", "content": "by JM Dick · 2008 · Cited by 9 — The purpose of this study is to quantify using a metastable equilibrium reference state the responses of popu- lations of model proteins for different ..."}
+{"idx": 2, "title": "Limitations and challenges in protein stability prediction ...", "date": "", "ddg_snippet": "by T Sanavia · 2020 · Cited by 155 — Δ G AB = - R T l o g A B. This relation is anti-symmetric in A and B, since the inverse transformation (from B to A) is described by: Δ G BA ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC7397395/", "content": "by T Sanavia · 2020 · Cited by 155 — Δ G AB = - R T l o g A B. This relation is anti-symmetric in A and B, since the inverse transformation (from B to A) is described by: Δ G BA ..."}
+{"idx": 3, "title": "Computationally reconstructing cotranscriptional RNA ...", "date": "", "ddg_snippet": "by MY Angela · 2021 · Cited by 80 — We developed a method , named reconstructing RNA dynamics from data (R2D2), that combines nucleotide-resolution experimental RNA structure ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S1097276520309369", "content": "by MY Angela · 2021 · Cited by 80 — We developed a method , named reconstructing RNA dynamics from data (R2D2), that combines nucleotide-resolution experimental RNA structure ..."}
+{"idx": 4, "title": "Advancing Single-Molecule Nanopore Sensing", "date": "", "ddg_snippet": "calculation of the Gibbs free energy change, ∆G . At equilibrium, ∆G is related to the dissociation constant by the equation: Page 32. 16. ∆G = RT lnKD.", "subpage_snippet": "", "source": "scholar.smu.edu", "link": "https://scholar.smu.edu/cgi/viewcontent.cgi?article=1005&context=engineering_multidisciplinary_etds", "content": "calculation of the Gibbs free energy change, ∆G . At equilibrium, ∆G is related to the dissociation constant by the equation: Page 32. 16. ∆G = RT lnKD."}
+{"idx": 5, "title": "An Ensemble-Based Protocol for the Computational Prediction ...", "date": "", "ddg_snippet": "by NA Altwaijry · 2017 · Cited by 31 — An Ensemble-Based Protocol for the Computational Prediction of Helix–Helix Interactions in G Protein-Coupled Receptors using Coarse-Grained Molecular Dynamics.", "subpage_snippet": "", "source": "pubs.acs.org", "link": "https://pubs.acs.org/doi/10.1021/acs.jctc.6b01246", "content": "by NA Altwaijry · 2017 · Cited by 31 — An Ensemble-Based Protocol for the Computational Prediction of Helix–Helix Interactions in G Protein-Coupled Receptors using Coarse-Grained Molecular Dynamics."}
+{"idx": 6, "title": "Analysis of protein missense alterations by combining ...", "date": "", "ddg_snippet": "by A Gyulkhandanyan · 2020 · Cited by 49 — It is a structure‐based approach that provides predicted free energy change (ΔΔG) values as well as corresponding confidence estimation values for the ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC7196459/", "content": "by A Gyulkhandanyan · 2020 · Cited by 49 — It is a structure‐based approach that provides predicted free energy change (ΔΔG) values as well as corresponding confidence estimation values for the ..."}
+{"idx": 7, "title": "RNA Structural Dynamics As Captured by Molecular Simulations", "date": "", "ddg_snippet": "by J Šponer · 2018 · Cited by 567 — We here provide a comprehensive overview of the fast-developing field of MD simulations of RNA molecules.", "subpage_snippet": "", "source": "pubs.acs.org", "link": "https://pubs.acs.org/doi/10.1021/acs.chemrev.7b00427", "content": "by J Šponer · 2018 · Cited by 567 — We here provide a comprehensive overview of the fast-developing field of MD simulations of RNA molecules."}
+{"idx": 8, "title": "A general temperature-guided language model to design ...", "date": "", "ddg_snippet": "27 Nov 2024 — Designing protein mutants with both high stability and activity is a critical yet challenging task in protein engineering.", "subpage_snippet": "", "source": "www.science.org", "link": "https://www.science.org/doi/10.1126/sciadv.adr2641", "content": "27 Nov 2024 — Designing protein mutants with both high stability and activity is a critical yet challenging task in protein engineering."}
+{"idx": 9, "title": "Control of RNA function by conformational design", "date": "", "ddg_snippet": "I present four projects, two of them in form of a peer-reviewed publication, the other two are unpublished work with preliminary results.", "subpage_snippet": "", "source": "www.dna.caltech.edu", "link": "https://www.dna.caltech.edu/~badelt/files/badelt_thesis.pdf", "content": "I present four projects, two of them in form of a peer-reviewed publication, the other two are unpublished work with preliminary results."}
diff --git a/data/sampled_jsons/BA-DDG_github_GPU_hardware.jsonl b/data/sampled_jsons/BA-DDG_github_GPU_hardware.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..dba633ffae13bfedc3718532e4a9c1e040c2c598
--- /dev/null
+++ b/data/sampled_jsons/BA-DDG_github_GPU_hardware.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Configuration Options | aim-uofa/BA-DDG | DeepWiki", "date": "", "ddg_snippet": "This document provides a comprehensive guide to configuring the BA-DDG system for both training and inference operations. The BA-DDG system uses JSON configuration files to define all aspects of model behavior, from architecture parameters to training hyperparameters. For specific details about training configuration, see Training Configuration, and for inference-specific options, see ...", "subpage_snippet": "", "source": "deepwiki.com", "link": "https://deepwiki.com/aim-uofa/BA-DDG/5-configuration-options", "content": "This document provides a comprehensive guide to configuring the BA-DDG system for both training and inference operations. The BA-DDG system uses JSON configuration files to define all aspects of model behavior, from architecture parameters to training hyperparameters. For specific details about training configuration, see Training Configuration, and for inference-specific options, see ..."}
+{"idx": 1, "title": "BA_Cycle & BA_DDG with the same res #2 - GitHub", "date": "", "ddg_snippet": "Hi, I have tried to run your examples on the skempi dataset, but I got the same results for both the BA Cycle and BA DDG . Does anyone know what the problem may be?", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/aim-uofa/BA-DDG/issues/2", "content": "Hi, I have tried to run your examples on the skempi dataset, but I got the same results for both the BA Cycle and BA DDG . Does anyone know what the problem may be?"}
+{"idx": 2, "title": "Optimization | aim-uofa/BA-DDG | DeepWiki", "date": "", "ddg_snippet": "The BA-DDG system uses a structured optimization process with batch-based training across multiple cross-validation folds. The training pipeline is managed through the train_skempi.py script which orchestrates the entire training process.", "subpage_snippet": "", "source": "deepwiki.com", "link": "https://deepwiki.com/aim-uofa/BA-DDG/3.2-optimization", "content": "The BA-DDG system uses a structured optimization process with batch-based training across multiple cross-validation folds. The training pipeline is managed through the train_skempi.py script which orchestrates the entire training process."}
+{"idx": 3, "title": "Issues · aim-uofa/BA-DDG - GitHub", "date": "", "ddg_snippet": "[ICLR 2025 Spotlight] Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions - aim-uofa/ BA - DDG", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/aim-uofa/BA-DDG/issues", "content": "[ICLR 2025 Spotlight] Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions - aim-uofa/ BA - DDG"}
+{"idx": 4, "title": "In-Depth GPU Hardware Architecture and Operating Mechanisms", "date": "", "ddg_snippet": "Modern GPUs share similar structures and components, and their operating mechanisms also have many commonalities. Below is an overview of the operating mechanism of the Fermi architecture: Starting with the Fermi architecture, NVIDIA has adopted a similar principle in its designs. A Giga Thread Engine is used to manage all ongoing tasks. The GPU is divided into multiple GPCs (Graphics ...", "subpage_snippet": "", "source": "alalba221.github.io", "link": "https://alalba221.github.io/blog/vulkan/GPU_HardWare", "content": "Modern GPUs share similar structures and components, and their operating mechanisms also have many commonalities. Below is an overview of the operating mechanism of the Fermi architecture: Starting with the Fermi architecture, NVIDIA has adopted a similar principle in its designs. A Giga Thread Engine is used to manage all ongoing tasks. The GPU is divided into multiple GPCs (Graphics ..."}
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+{"idx": 6, "title": "GitHub - aim-uofa/BA-DDG: [ICLR 2025 Spotlight] Boltzmann-Aligned ...", "date": "", "ddg_snippet": "The official implementation of our ICLR 2025 Spotlight paper \"Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions\", which establishes a bidirectional connection between log-likelihood in protein inverse folding models and Δ Δ G values.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/aim-uofa/BA-DDG", "content": "The official implementation of our ICLR 2025 Spotlight paper \"Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions\", which establishes a bidirectional connection between log-likelihood in protein inverse folding models and Δ Δ G values."}
+{"idx": 7, "title": "BA-DDG/README.md at master · aim-uofa/BA-DDG · GitHub", "date": "", "ddg_snippet": "The official implementation of our ICLR 2025 Spotlight paper \"Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions\", which establishes a bidirectional connection between log-likelihood in protein inverse folding models and Δ Δ G values.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/aim-uofa/BA-DDG/blob/master/README.md", "content": "The official implementation of our ICLR 2025 Spotlight paper \"Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions\", which establishes a bidirectional connection between log-likelihood in protein inverse folding models and Δ Δ G values."}
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+{"idx": 9, "title": "Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational ...", "date": "", "ddg_snippet": "Poster Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions Xiaoran Jiao · Weian Mao · Wengong Jin · Peiyuan Yang · Hao Chen · Chunhua Shen", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2025/poster/28490", "content": "Poster Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions Xiaoran Jiao · Weian Mao · Wengong Jin · Peiyuan Yang · Hao Chen · Chunhua Shen"}
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+{"idx": 0, "title": "PDF 1994-Probabilistic Evaluation of Counterfactual Queries", "date": "", "ddg_snippet": "Abstract In the real world, we seldom have adequate informa- Evaluation of counterfactual queries (e.g., \"If A were tion for verifying the truth of an indicative sentence, true, would C have been true?\") is important to fault much less the truth of a counterfactual sentence. Ex- cept for the small set of relationships ...", "subpage_snippet": "", "source": "cdn.aaai.org", "link": "https://cdn.aaai.org/AAAI/1994/AAAI94-035.pdf", "content": "Abstract In the real world, we seldom have adequate informa- Evaluation of counterfactual queries (e.g., \"If A were tion for verifying the truth of an indicative sentence, true, would C have been true?\") is important to fault much less the truth of a counterfactual sentence. Ex- cept for the small set of relationships ..."}
+{"idx": 1, "title": "Probabilistic evaluation of counterfactual queries", "date": "", "ddg_snippet": "Article Probabilistic evaluation of counterfactual queries Authors: Alexander Balke , Judea Pearl Authors Info & Claims", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/199288.178004", "content": "Article Probabilistic evaluation of counterfactual queries Authors: Alexander Balke , Judea Pearl Authors Info & Claims"}
+{"idx": 2, "title": "Counterfactual Probabilities: Computational Methods, Bounds and ...", "date": "", "ddg_snippet": "Evaluation of counterfactual queries (e.g., \"If A were true, would C have been true?\") is important to fault diagnosis, planning, and determination of liability. In this paper we present methods for computing the probabilities of such queries using the formulation proposed in [ Balke and Pearl , 1994 ], where the antecedent of the query is interpreted as an external action that forces the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1302.6784", "content": "Evaluation of counterfactual queries (e.g., \"If A were true, would C have been true?\") is important to fault diagnosis, planning, and determination of liability. In this paper we present methods for computing the probabilities of such queries using the formulation proposed in [ Balke and Pearl , 1994 ], where the antecedent of the query is interpreted as an external action that forces the ..."}
+{"idx": 3, "title": "PDF Probabilistic evaluation of counterfactual queries", "date": "", "ddg_snippet": "Abstract Evaluation of counterfactual queries (e.g., \"If A were true, would C have been true?\") is important to fault diagnosis, planning, and determination of liability. We present a formalism that uses probabilistic causal net- works to evaluate one's belief that the counterfactual consequent, C, would have been true if the antecedent, A, were true. The antecedent of the query is inter ...", "subpage_snippet": "", "source": "gill.readingroo.ms", "link": "https://gill.readingroo.ms/ENTER/Philosophy-Psychology-Sociology+-+&+SEE+Self+Help/1+-+More+Books+on+Philosophy/Judea+Pearl+-+Probabilistic+Counterfactual+Queries.pdf", "content": "Abstract Evaluation of counterfactual queries (e.g., \"If A were true, would C have been true?\") is important to fault diagnosis, planning, and determination of liability. We present a formalism that uses probabilistic causal net- works to evaluate one's belief that the counterfactual consequent, C, would have been true if the antecedent, A, were true. The antecedent of the query is inter ..."}
+{"idx": 4, "title": "Counterfactual Probabilities: Computational Methods, Bounds and ...", "date": "", "ddg_snippet": "Evaluation of counterfactual queries (e.g., \"If A were true, would C have been true?\") is important to fault diagnosis, planning, and determination of liability. In this paper we present methods for computing the probabilities of such queries using the formulation proposed in [ Balke and Pearl , 1994 ], where the antecedent of the query is interpreted as an external action that forces the ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/B9781558603325500110", "content": "Evaluation of counterfactual queries (e.g., \"If A were true, would C have been true?\") is important to fault diagnosis, planning, and determination of liability. In this paper we present methods for computing the probabilities of such queries using the formulation proposed in [ Balke and Pearl , 1994 ], where the antecedent of the query is interpreted as an external action that forces the ..."}
+{"idx": 5, "title": "dblp: Probabilistic Evaluation of Counterfactual Queries.", "date": "", "ddg_snippet": "Bibliographic details on Probabilistic Evaluation of Counterfactual Queries .", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/conf/aaai/BalkeP94", "content": "Bibliographic details on Probabilistic Evaluation of Counterfactual Queries ."}
+{"idx": 6, "title": "PDF Probabilistic Evaluation of Counterfactual Queries", "date": "", "ddg_snippet": "Paper Contribution Provides the formal notation for a counterfactual query such as \"If A were true, then what is the probability that C would have been true, given that we know B.\" Provides an inference algorithm for the probabilistic evaluation of counterfactual queries", "subpage_snippet": "", "source": "ics.uci.edu", "link": "https://ics.uci.edu/~dechter/courses/ics-295cr/2021-22_Q2_Winter/slides/classP3-w22-KoheiTsujio-Probabilistic_Evaluation_of_Counterfactual_Queries.pdf", "content": "Paper Contribution Provides the formal notation for a counterfactual query such as \"If A were true, then what is the probability that C would have been true, given that we know B.\" Provides an inference algorithm for the probabilistic evaluation of counterfactual queries"}
+{"idx": 7, "title": "Probabilistic evaluation of counterfactual queries - Technion", "date": "", "ddg_snippet": "Abstract Evaluation of counterfactual queries (e.g., 'If A were true, would C have been true?') is important to fault diagnosis, planning, and determination of liability. We present a formalism that uses probabilistic causal networks to evaluate one's belief that the counterfactual consequent, C, would have been true if the antecedent, A, were true. The antecedent of the query is interpreted ...", "subpage_snippet": "", "source": "cris.technion.ac.il", "link": "https://cris.technion.ac.il/en/publications/probabilistic-evaluation-of-counterfactual-queries", "content": "Abstract Evaluation of counterfactual queries (e.g., 'If A were true, would C have been true?') is important to fault diagnosis, planning, and determination of liability. We present a formalism that uses probabilistic causal networks to evaluate one's belief that the counterfactual consequent, C, would have been true if the antecedent, A, were true. The antecedent of the query is interpreted ..."}
+{"idx": 8, "title": "Probabilistic Evaluation of Counterfactual Queries - AAAI", "date": "", "ddg_snippet": "Evaluation of counterfactual queries (e.g., \"If A were true, would C have been true?\") is important to fault diagnosis, planning, and determination of liability. We present a formalism that uses probabilistic causal networks to evaluate one's belief that the counterfactual consequent, C, would have been true if the antecedent, A, were true.", "subpage_snippet": "", "source": "aaai.org", "link": "https://aaai.org/papers/00230-AAAI94-035-probabilistic-evaluation-of-counterfactual-queries/", "content": "Evaluation of counterfactual queries (e.g., \"If A were true, would C have been true?\") is important to fault diagnosis, planning, and determination of liability. We present a formalism that uses probabilistic causal networks to evaluate one's belief that the counterfactual consequent, C, would have been true if the antecedent, A, were true."}
+{"idx": 9, "title": "Probabilistic Evaluation of Counterfactual Queries - ResearchGate", "date": "", "ddg_snippet": "In 1994 , in his Ph.D. dissertation, Alexander Balke gave a method for translating a certain type of causal theory, represented by a directed acyclic graph (DAG), and causal query into a ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/359040417_Probabilistic_Evaluation_of_Counterfactual_Queries", "content": "In 1994 , in his Ph.D. dissertation, Alexander Balke gave a method for translating a certain type of causal theory, represented by a directed acyclic graph (DAG), and causal query into a ..."}
diff --git a/data/sampled_jsons/Balke_and_Pearl_1994_Probabilistic_evaluation_of_counterfactual_queries_abstract.jsonl b/data/sampled_jsons/Balke_and_Pearl_1994_Probabilistic_evaluation_of_counterfactual_queries_abstract.jsonl
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+{"idx": 0, "title": "PDF 1994-Probabilistic Evaluation of Counterfactual Queries", "date": "", "ddg_snippet": "Balke , A., and Pearl , J. 1994 . Bounds on probabilisti- tions. In Logic Programming: Proceedings of the 1991 tally evaluated counterfactual queries. Technical Re- International Symposium, 566-577. Cambridge, MA: port R-213-B, UCLA Cognitive Systems Lab. Boutilier, C. 1992. A logic for revision and subjunc- MIT Press.", "subpage_snippet": "", "source": "cdn.aaai.org", "link": "https://cdn.aaai.org/AAAI/1994/AAAI94-035.pdf", "content": "Balke , A., and Pearl , J. 1994 . Bounds on probabilisti- tions. In Logic Programming: Proceedings of the 1991 tally evaluated counterfactual queries. Technical Re- International Symposium, 566-577. Cambridge, MA: port R-213-B, UCLA Cognitive Systems Lab. Boutilier, C. 1992. A logic for revision and subjunc- MIT Press."}
+{"idx": 1, "title": "Probabilistic evaluation of counterfactual queries", "date": "", "ddg_snippet": "Article Probabilistic evaluation of counterfactual queries Authors: Alexander Balke , Judea Pearl Authors Info & Claims", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/199288.178004", "content": "Article Probabilistic evaluation of counterfactual queries Authors: Alexander Balke , Judea Pearl Authors Info & Claims"}
+{"idx": 2, "title": "Probabilistic evaluation of counterfactual queries - Technion", "date": "", "ddg_snippet": "Abstract Evaluation of counterfactual queries (e.g., 'If A were true, would C have been true?') is important to fault diagnosis, planning, and determination of liability. We present a formalism that uses probabilistic causal networks to evaluate one's belief that the counterfactual consequent, C, would have been true if the antecedent, A, were true. The antecedent of the query is interpreted ...", "subpage_snippet": "", "source": "cris.technion.ac.il", "link": "https://cris.technion.ac.il/en/publications/probabilistic-evaluation-of-counterfactual-queries", "content": "Abstract Evaluation of counterfactual queries (e.g., 'If A were true, would C have been true?') is important to fault diagnosis, planning, and determination of liability. We present a formalism that uses probabilistic causal networks to evaluate one's belief that the counterfactual consequent, C, would have been true if the antecedent, A, were true. The antecedent of the query is interpreted ..."}
+{"idx": 3, "title": "dblp: Probabilistic Evaluation of Counterfactual Queries.", "date": "", "ddg_snippet": "Bibliographic details on Probabilistic Evaluation of Counterfactual Queries .", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/conf/aaai/BalkeP94", "content": "Bibliographic details on Probabilistic Evaluation of Counterfactual Queries ."}
+{"idx": 4, "title": "PDF Probabilistic Evaluation of Counterfactual Queries", "date": "", "ddg_snippet": "Paper Contribution Provides the formal notation for a counterfactual query such as \"If A were true, then what is the probability that C would have been true, given that we know B.\" Provides an inference algorithm for the probabilistic evaluation of counterfactual queries", "subpage_snippet": "", "source": "ics.uci.edu", "link": "https://ics.uci.edu/~dechter/courses/ics-295cr/2021-22_Q2_Winter/slides/classP3-w22-KoheiTsujio-Probabilistic_Evaluation_of_Counterfactual_Queries.pdf", "content": "Paper Contribution Provides the formal notation for a counterfactual query such as \"If A were true, then what is the probability that C would have been true, given that we know B.\" Provides an inference algorithm for the probabilistic evaluation of counterfactual queries"}
+{"idx": 5, "title": "PDF 1994-Probabilistic Evaluation of Counterfactual Queries", "date": "", "ddg_snippet": "Because of the tight connection between counterfac- of temporal persistence, and , as noted in ( Pearl 1993c), such a model is not part of static Bayesian networks. tuals and causal influences, any algorithm for comput- ing counterfactual queries must rely heavily on causal Functional specification, however, implicitly contains knowledge of the ...", "subpage_snippet": "", "source": "aaai-24.aaai.org", "link": "https://aaai-24.aaai.org/Papers/AAAI/1994/AAAI94-035.pdf", "content": "Because of the tight connection between counterfac- of temporal persistence, and , as noted in ( Pearl 1993c), such a model is not part of static Bayesian networks. tuals and causal influences, any algorithm for comput- ing counterfactual queries must rely heavily on causal Functional specification, however, implicitly contains knowledge of the ..."}
+{"idx": 6, "title": "Probabilistic Evaluation of Counterfactual Queries - CORE", "date": "", "ddg_snippet": "Abstract Evaluation of counterfactual queries (e.g., \"If A were true, would C have been true?\") is important to fault diagnosis, planning, and determination of liability. We present a formalism that uses probabilistic causal networks to evaluate one's belief that the counterfactual consequent, C, would have been true if the antecedent, A, were true. The antecedent of the query ...", "subpage_snippet": "", "source": "core.ac.uk", "link": "https://core.ac.uk/display/24325313", "content": "Abstract Evaluation of counterfactual queries (e.g., \"If A were true, would C have been true?\") is important to fault diagnosis, planning, and determination of liability. We present a formalism that uses probabilistic causal networks to evaluate one's belief that the counterfactual consequent, C, would have been true if the antecedent, A, were true. The antecedent of the query ..."}
+{"idx": 7, "title": "Probabilistic Evaluation of Counterfactual Queries - ResearchGate", "date": "", "ddg_snippet": "In this paper, we focus on two fundamental issues in causal inference: probabilistic evaluation of counterfactual queries and the assumptions used to evaluate causal effects.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/359040417_Probabilistic_Evaluation_of_Counterfactual_Queries", "content": "In this paper, we focus on two fundamental issues in causal inference: probabilistic evaluation of counterfactual queries and the assumptions used to evaluate causal effects."}
+{"idx": 8, "title": "Probabilistic Evaluation of Counterfactual Queries", "date": "", "ddg_snippet": "Probabilistic evaluation of counterfactual queries . AAAI' 94 : Proceedings of the Twelfth AAAI National Conference on Artificial Intelligence.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3501714.3501733", "content": "Probabilistic evaluation of counterfactual queries . AAAI' 94 : Proceedings of the Twelfth AAAI National Conference on Artificial Intelligence."}
+{"idx": 9, "title": "Probabilistic evaluation of counterfactual queries", "date": "", "ddg_snippet": "by A Balke · 1994 · Cited by 239 — Balke , A., and Pearl , J. 1993. Nonparametric bounds on causal effects ... Probabilistic Evaluation of Counterfactual Queries . Probabilistic and Causal ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/2891730.2891765", "content": "by A Balke · 1994 · Cited by 239 — Balke , A., and Pearl , J. 1993. Nonparametric bounds on causal effects ... Probabilistic Evaluation of Counterfactual Queries . Probabilistic and Causal ..."}
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index 0000000000000000000000000000000000000000..73fe11664413d352648cb9246b156b4331909ffa
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+++ b/data/sampled_jsons/BdO4R6XxUH_DCBM-_Data-Efficient_Visual_Concept_Bottleneck_Models_paper.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "DCBM: Data-Efficient Visual Concept Bottleneck Models DCBM: Data-Efficient Visual Concept Bottleneck Models DCBM: Data-Efficient Visual Concept Bottleneck Models DCBM: Data-Efficient Visual Concept Bottleneck Models :: MPG.PuRe [2007.04612] Concept Bottleneck Models - arXiv.org ICML DCBM: Data-Efficient Visual Concept Bottleneck Models GitHub - deepopo/DCBM", "date": "", "ddg_snippet": "Dec 16, 2024 · Concept Bottleneck Models (CBMs) enhance the interpretability of neural networks by basing predictions on human-understandable concepts . However, current CBMs typically rely on concept sets extracted from large language models or extensive image corpora, limiting their effectiveness in data -sparse scenarios. We propose Data-efficient CBMs (DCBMs), which reduce the need for large sample sizes ... We propose Data -eficient CBMs (DCBMs), which reduce the need for large sample sizes during concept gener-ation while preserving interpretability. DCBMs define concepts as image regions detected by seg-mentation or detection foundation models , allow-ing each image to generate multiple concepts across different granularities. data /: Directories for classes, concepts , datasets, embeddings, and segments. experiments/: Code for experiments detailed in the main paper and supplementary material. Author: Prasse, Katharina et al.; Genre: Paper ; Published online: 2025; Open Access; Keywords: Computer Science, Computer Vision and Pattern Recognition, cs.CV; Title: DCBM : Data-Efficient Visual Concept Bottleneck Models Jul 9, 2020 · On x-ray grading and bird identification, concept bottleneck models achieve competitive accuracy with standard end-to-end models , while enabling interpretation in terms of high-level clinical concepts (\"bone spurs\") or bird attributes (\"wing color\"). Poster in Workshop: Actionable Interpretability DCBM : Data-Efficient Visual Concept Bottleneck Models Katharina Prasse · Patrick Knab · Sascha Marton · Christian Bartelt · Margret Keuper This is an implementation of the IEEE TPAMI paper The Decoupling Concept Bottleneck Model ( DCBM ). The vision-language- model (VLM) part is being refined and will be available soon. Figure 1: DCBM Pipeline. (A) DCBM for prediction and interpretation. (B) DCBM for human-machine interaction, including forward intervention and backward rectification.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2412.11576", "content": "Dec 16, 2024 · Concept Bottleneck Models (CBMs) enhance the interpretability of neural networks by basing predictions on human-understandable concepts . However, current CBMs typically rely on concept sets extracted from large language models or extensive image corpora, limiting their effectiveness in data -sparse scenarios. We propose Data-efficient CBMs (DCBMs), which reduce the need for large sample sizes ... We propose Data -eficient CBMs (DCBMs), which reduce the need for large sample sizes during concept gener-ation while preserving interpretability. DCBMs define concepts as image regions detected by seg-mentation or detection foundation models , allow-ing each image to generate multiple concepts across different granularities. data /: Directories for classes, concepts , datasets, embeddings, and segments. experiments/: Code for experiments detailed in the main paper and supplementary material. Author: Prasse, Katharina et al.; Genre: Paper ; Published online: 2025; Open Access; Keywords: Computer Science, Computer Vision and Pattern Recognition, cs.CV; Title: DCBM : Data-Efficient Visual Concept Bottleneck Models Jul 9, 2020 · On x-ray grading and bird identification, concept bottleneck models achieve competitive accuracy with standard end-to-end models , while enabling interpretation in terms of high-level clinical concepts (\"bone spurs\") or bird attributes (\"wing color\"). Poster in Workshop: Actionable Interpretability DCBM : Data-Efficient Visual Concept Bottleneck Models Katharina Prasse · Patrick Knab · Sascha Marton · Christian Bartelt · Margret Keuper This is an implementation of the IEEE TPAMI paper The Decoupling Concept Bottleneck Model ( DCBM ). The vision-language- model (VLM) part is being refined and will be available soon. Figure 1: DCBM Pipeline. (A) DCBM for prediction and interpretation. (B) DCBM for human-machine interaction, including forward intervention and backward rectification."}
+{"idx": 1, "title": "KathPra/ DCBM : Official repo for ICML25 paper : DCBM : Data - Efficient ...", "date": "", "ddg_snippet": "Aligning Visual and Semantic Interpretability through Visually Grounded Concept Bottleneck Models .We primarily report on CLIP models [RN-50, ViT-B16, ViT-L14] and save embeddings to reuse in DCBM training for efficiency .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/KathPra/DCBM", "content": "Aligning Visual and Semantic Interpretability through Visually Grounded Concept Bottleneck Models .We primarily report on CLIP models [RN-50, ViT-B16, ViT-L14] and save embeddings to reuse in DCBM training for efficiency ."}
+{"idx": 2, "title": "DCBM: Data-Efficient Visual Concept Bottleneck Models", "date": "", "ddg_snippet": "We propose Data -eficient CBMs (DCBMs), which reduce the need for large sample sizes during concept gener-ation while preserving interpretability. DCBMs define concepts as image regions detected by seg-mentation or detection foundation models , allow-ing each image to generate multiple concepts across different granularities.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=BdO4R6XxUH&name=pdf", "content": "We propose Data -eficient CBMs (DCBMs), which reduce the need for large sample sizes during concept gener-ation while preserving interpretability. DCBMs define concepts as image regions detected by seg-mentation or detection foundation models , allow-ing each image to generate multiple concepts across different granularities."}
+{"idx": 3, "title": "DCBM: Data-Efficient Visual Concept Bottleneck Models :: MPG.PuRe", "date": "", "ddg_snippet": "Author: Prasse, Katharina et al.; Genre: Paper ; Published online: 2025; Open Access; Keywords: Computer Science, Computer Vision and Pattern Recognition, cs.CV; Title: DCBM : Data-Efficient Visual Concept Bottleneck Models", "subpage_snippet": "", "source": "pure.mpg.de", "link": "https://pure.mpg.de/pubman/faces/ViewItemFullPage.jsp?itemId=item_3636912_1&view=ACTIONS", "content": "Author: Prasse, Katharina et al.; Genre: Paper ; Published online: 2025; Open Access; Keywords: Computer Science, Computer Vision and Pattern Recognition, cs.CV; Title: DCBM : Data-Efficient Visual Concept Bottleneck Models"}
+{"idx": 4, "title": "[2007.04612] Concept Bottleneck Models - arXiv.org ICML DCBM: Data-Efficient Visual Concept Bottleneck Models GitHub - deepopo/DCBM", "date": "", "ddg_snippet": "Jul 9, 2020 · On x-ray grading and bird identification, concept bottleneck models achieve competitive accuracy with standard end-to-end models , while enabling interpretation in terms of high-level clinical concepts (\"bone spurs\") or bird attributes (\"wing color\"). Poster in Workshop: Actionable Interpretability DCBM : Data-Efficient Visual Concept Bottleneck Models Katharina Prasse · Patrick Knab · Sascha Marton · Christian Bartelt · Margret Keuper This is an implementation of the IEEE TPAMI paper The Decoupling Concept Bottleneck Model ( DCBM ). The vision-language- model (VLM) part is being refined and will be available soon. Figure 1: DCBM Pipeline. (A) DCBM for prediction and interpretation. (B) DCBM for human-machine interaction, including forward intervention and backward rectification.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2007.04612", "content": "Jul 9, 2020 · On x-ray grading and bird identification, concept bottleneck models achieve competitive accuracy with standard end-to-end models , while enabling interpretation in terms of high-level clinical concepts (\"bone spurs\") or bird attributes (\"wing color\"). Poster in Workshop: Actionable Interpretability DCBM : Data-Efficient Visual Concept Bottleneck Models Katharina Prasse · Patrick Knab · Sascha Marton · Christian Bartelt · Margret Keuper This is an implementation of the IEEE TPAMI paper The Decoupling Concept Bottleneck Model ( DCBM ). The vision-language- model (VLM) part is being refined and will be available soon. Figure 1: DCBM Pipeline. (A) DCBM for prediction and interpretation. (B) DCBM for human-machine interaction, including forward intervention and backward rectification."}
+{"idx": 5, "title": "ICML DCBM: Data-Efficient Visual Concept Bottleneck Models", "date": "", "ddg_snippet": "Poster in Workshop: Actionable Interpretability DCBM : Data-Efficient Visual Concept Bottleneck Models Katharina Prasse · Patrick Knab · Sascha Marton · Christian Bartelt · Margret Keuper", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/49558", "content": "Poster in Workshop: Actionable Interpretability DCBM : Data-Efficient Visual Concept Bottleneck Models Katharina Prasse · Patrick Knab · Sascha Marton · Christian Bartelt · Margret Keuper"}
+{"idx": 6, "title": "DCBM : Data - Efficient Visual Concept Bottleneck Models", "date": "", "ddg_snippet": "DCBM is highly data - efficient in the concept generation phase and equally efficient as other models in the CBM training phase. In Table 5 we show the differences to another data-driven CBM, i.e., DN-CBM (Rao et al., 2024).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2412.11576", "content": "DCBM is highly data - efficient in the concept generation phase and equally efficient as other models in the CBM training phase. In Table 5 we show the differences to another data-driven CBM, i.e., DN-CBM (Rao et al., 2024)."}
+{"idx": 7, "title": "DCBM : Data - Efficient Visual Concept Bottleneck Models", "date": "", "ddg_snippet": "We propose Data - efficient CBMs (DCBMs), which reduce the need for large sample sizes during concept generation while preserving interpretability.", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/article/DCBM:-Data-Efficient-Visual-Concept-Bottleneck-Models-4be352f8-e37e-432a-9849-dc4f3c8c586f", "content": "We propose Data - efficient CBMs (DCBMs), which reduce the need for large sample sizes during concept generation while preserving interpretability."}
+{"idx": 8, "title": "ICML Poster DCBM : Data - Efficient Visual Concept Bottleneck Models", "date": "", "ddg_snippet": "Concept Bottleneck Models (CBMs) enhance the interpretability of neural networks by basing predictions on human-understandable concepts .", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46104", "content": "Concept Bottleneck Models (CBMs) enhance the interpretability of neural networks by basing predictions on human-understandable concepts ."}
+{"idx": 9, "title": "DWS Area: Web Data Mining, Research | Universität Mannheim", "date": "", "ddg_snippet": "DCBM : Data - Efficient Visual Concept Bottleneck Models .", "subpage_snippet": "", "source": "www.uni-mannheim.de", "link": "https://www.uni-mannheim.de/dws/news-archiv/dws-area-web-data-mining-research/?tx_news_pi1[@widget_0][currentPage]=2&cHash=dbf1436b0d93f36eda0040b2d5b33144", "content": "DCBM : Data - Efficient Visual Concept Bottleneck Models ."}
diff --git a/data/sampled_jsons/Beimel_2022_differential_privacy_adaptive_queries_optimization_cost_solution.jsonl b/data/sampled_jsons/Beimel_2022_differential_privacy_adaptive_queries_optimization_cost_solution.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..05762dacda3342db28e577d5921247d351853df7
--- /dev/null
+++ b/data/sampled_jsons/Beimel_2022_differential_privacy_adaptive_queries_optimization_cost_solution.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "On Differential Privacy for Adaptively Solving Search Problems via...", "date": "", "ddg_snippet": "Adaptive ANN via Differentially Private Selection.In this work, we focus in particular on the adaptive data structures based on differential privacy (Has-sidim et al., 2022 ; Beimel et al., 2022 ; Song et al., 2023b; Cherapanamjeri et al., 2023).", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=kEn7Wt6Yj2", "content": "Adaptive ANN via Differentially Private Selection.In this work, we focus in particular on the adaptive data structures based on differential privacy (Has-sidim et al., 2022 ; Beimel et al., 2022 ; Song et al., 2023b; Cherapanamjeri et al., 2023)."}
+{"idx": 1, "title": "ICML Poster On Differential Privacy for Adaptively Solving Search...", "date": "", "ddg_snippet": "2022 .In this paper we investigate the use of differential privacy for adaptive queries to {\\it search} problems, which are significantly more challenging since the responses to queries can reveal much more about the internal randomness than a single numerical query .", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/44265", "content": "2022 .In this paper we investigate the use of differential privacy for adaptive queries to {\\it search} problems, which are significantly more challenging since the responses to queries can reveal much more about the internal randomness than a single numerical query ."}
+{"idx": 2, "title": "Advancing Differential Privacy : Where We Are Now and Future...", "date": "", "ddg_snippet": "( 2022 ) investigate private adaptive optimizers , where the gradient moments are estimated using the public gradients. Differentially private stochastic optimization : New results in convex and non-convex settings.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2304.06929", "content": "( 2022 ) investigate private adaptive optimizers , where the gradient moments are estimated using the public gradients. Differentially private stochastic optimization : New results in convex and non-convex settings."}
+{"idx": 3, "title": "Paper page - Sketching Meets Differential Privacy : Fast Algorithm for...", "date": "", "ddg_snippet": "Published on Oct 20, 2022 .A dynamic data structure is developed to efficiently maintain and query projections in the context of Kronecker product structures with low-rank updates to the weight matrix, leveraging differential privacy to enhance performance. AI-generated summary.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2210.11542", "content": "Published on Oct 20, 2022 .A dynamic data structure is developed to efficiently maintain and query projections in the context of Kronecker product structures with low-rank updates to the weight matrix, leveraging differential privacy to enhance performance. AI-generated summary."}
+{"idx": 4, "title": "(PDF) On Differential Privacy and Adaptive Data Analysis with...", "date": "", "ddg_snippet": "(DOI: 10.48550/arxiv.2302.05707) We study the space complexity of the two related fields of differential privacy and adaptive data analysis.", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/papers/on-differential-privacy-and-adaptive-data-analysis-with-12a55kuq", "content": "(DOI: 10.48550/arxiv.2302.05707) We study the space complexity of the two related fields of differential privacy and adaptive data analysis."}
+{"idx": 5, "title": "Privacy -preserving Cost -sensitive Federated Learning from...", "date": "", "ddg_snippet": "We design the joint cost -sensitive and differentially private model parameter optimization mechanism which maintains the model accuracy while satisfying differential privacy constraints. Moreover, this mechanism does not alter the original data distribution.", "subpage_snippet": "", "source": "www.computer.org", "link": "https://www.computer.org/csdl/proceedings-article/ictai/2022/974400a020/1MrFOLH2Oek", "content": "We design the joint cost -sensitive and differentially private model parameter optimization mechanism which maintains the model accuracy while satisfying differential privacy constraints. Moreover, this mechanism does not alter the original data distribution."}
+{"idx": 6, "title": "IEA-DP: Information Entropy-driven Adaptive Differential Privacy ...", "date": "", "ddg_snippet": "To address these issues, a novel approach called the Information Entropy-driven Adaptive Differential Privacy Protection Scheme (IEA-DP) is presented for the release of social data in this study.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s11227-024-06202-w", "content": "To address these issues, a novel approach called the Information Entropy-driven Adaptive Differential Privacy Protection Scheme (IEA-DP) is presented for the release of social data in this study."}
+{"idx": 7, "title": "Differential Private Stochastic Optimization with Heavy-tailed Data...", "date": "", "ddg_snippet": "We study adaptive methods for differentially private convex optimization , proposing and analyzing differentially private variants of a Stochastic Gradient Descent (SGD) algorithm with adaptive stepsizes, as well as the AdaGrad algorithm.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/383237686_Differential_Private_Stochastic_Optimization_with_Heavy-tailed_Data_Towards_Optimal_Rates", "content": "We study adaptive methods for differentially private convex optimization , proposing and analyzing differentially private variants of a Stochastic Gradient Descent (SGD) algorithm with adaptive stepsizes, as well as the AdaGrad algorithm."}
+{"idx": 8, "title": "Optimal unbiased randomizers for regression with label differential ...", "date": "", "ddg_snippet": "Amos Beimel , Kobbi Nissim, and Uri Stemmer. Private learning and sanitization: Pure vs. approximate differential privacy .Revisiting differentially private linear regression: optimal and adaptive prediction & estimation in unbounded domain.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3666122.3668753", "content": "Amos Beimel , Kobbi Nissim, and Uri Stemmer. Private learning and sanitization: Pure vs. approximate differential privacy .Revisiting differentially private linear regression: optimal and adaptive prediction & estimation in unbounded domain."}
+{"idx": 9, "title": "Differentially Private Natural Language Models: Recent Advances and", "date": "", "ddg_snippet": "2023. Differentially private optimization on large model at small cost . 2022 . Improved differential privacy for sgd via optimal pri - vate linear operators on adaptive streams. Advances in Neural Information Processing Systems, 35:5910– 5924.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2024.findings-eacl.33.pdf", "content": "2023. Differentially private optimization on large model at small cost . 2022 . Improved differential privacy for sgd via optimal pri - vate linear operators on adaptive streams. Advances in Neural Information Processing Systems, 35:5910– 5924."}
diff --git a/data/sampled_jsons/Beimel_Nissim_Stemmer_Private_Learning_and_Sanitization_Pure_vs_Approximate_Differential_Privacy_202.jsonl b/data/sampled_jsons/Beimel_Nissim_Stemmer_Private_Learning_and_Sanitization_Pure_vs_Approximate_Differential_Privacy_202.jsonl
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index 0000000000000000000000000000000000000000..49886bc6fce347e97da26d32d381c02a3271ab2c
--- /dev/null
+++ b/data/sampled_jsons/Beimel_Nissim_Stemmer_Private_Learning_and_Sanitization_Pure_vs_Approximate_Differential_Privacy_202.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Private Learning and Sanitization: Pure vs. Approximate ... Private Learning and Sanitization: Pure vs. Approximate ... Private Learning and Sanitization: Pure vs. Approximate ... Private Learning and Sanitization: Pure vs. Approximate ... Private Learning and Sanitization: Pure vs. Approximate ... Label differential privacy and private training data release Private Learning and Sanitization : Pure vs . Approximate Differential Priv… Private Learning and Sanitization : Pure vs . Approximate Differential Priv… Private Learning and Sanitization : Pure vs . Approximate Differential Priv… [1407.2674] Private Learning and Sanitization : Pure vs . Approximate Dif… Private Learning and Sanitization : Pure vs . Approximate Differential Priv… Private Learning and Sanitization : Pure vs . Approximate Differential Priv… Private Learning and Sanitization: Pure vs. Approximate ...", "date": "", "ddg_snippet": "Jul 10, 2014 · We compare the sample complexity of private learning [Kasiviswanathan et al. 2008] and sanitization ~[Blum et al. 2008] under pure $ε$- differential privacy [Dwork et al. TCC 2006] and approximate $(ε,δ)$- differential privacy [Dwork et al. Eurocrypt 2006]. We show that the sample complexity of these tasks under approximate differential privacy can be significantly lower than that under pure ... As with private learning , we show significant differences between the sample complexity required for san-itization of simple predicate classes under pure and approximate differential privacy . In 2016, Nissim received, with Cynthia Dwork, Frank McSherry, and Adam Smith the “TCC Test-of-Time” Award for their TCC 2006 paper Calibrating Noise to Sensitivity in Private Data Analysis where differential privacy was introduced. U RI S TEMMER is a Ph. D. candidate at Ben-Gurion University, advised by Amos Beimel and Kobbi Nissim . As with private learning , we show significant differences between the sample complexity required for sanitization of simple predicate classes under pure and approximate differential privacy . Abstract We compare the sample complexity of private learning and sanitization tasks under pure ε - differential privacy [Dwork, McSherry, Nissim , and Smith TCC 2006] and approximate (ε, δ)- differential privacy [Dwork, Kenthapadi, McSherry, Mironov, and Naor EUROCRYPT 2006]. Jul 23, 2023 · A. Beimel , K. Nissim , and U. Stemmer . Private learning and sanitization : Pure vs . approximate differential privacy . In Approximation, Randomization, and Combinatorial ... Is there a relationship between data sanitization and private learning? Similar techniques are used for both data sanitization and private learning, suggesting relationships between the two tasks. We now explore one such relationship in proving a lower bound on the sample complexity needed for sanitization (under pure differential privacy). What is approximate differential privacy? Approximate Differential Privacy is a relaxation of pure differential privacy where the guarantee needs to be satisfied only for events whose probability is at least d. We show that even a negligible d > 0 can have a significant effect on the sample complexity of private learning and sanitization. Private learning. Does approximate differential privacy reduce sample complexity? We show that the sample complexity of these tasks under approximate differential privacy can be significantly lower than that under pure differential privacy. Research supported by the Israel Science Foundation (grants No. 938/09 and 2761/12) and by the Frankel Center for Computer Science at Ben-Gurion University. Is the sample complexity of quasi-concave promise problems lower than pure differential privacy? We show that the sample complexity of these tasks under approximate differential privacy can be significantly lower than that under pure differential privacy. We define a family of optimization problems, which we call Quasi-Concave Promise Problems, that generalizes some of our considered tasks. Which algorithm satisfies the requirement of pure differential privacy? More formally, an algorithm A satisfies the requirement of Pure Differential Privacy if for every two databases that differ on exactly one entry, and for every event defined over the output set of A, the probability of this event is close up to a multiplicative factor of ee 1 + e whether A is applied on one database or on the other. What is the difference between a private learner and a label-Private learner? Recall that given a labeled sample, a private learner is required to preserve the privacy of the entire sample, while a label- private learner is only required to preserve privacy for the labels of each entry. We can model this scenario as a learning algorithm A which is given as input 2 databases—a labeled database S, and an unlabeled database D. We compare the sample complexity of private learning [Kasiviswanathan et al. 2008] and sanitization ~ [Blum et al. 2008] under pure $\\epsilon$- differential privacy [Dwork et al. TCC 2006] and approximate $ (\\epsilon,\\delta)$- differential privacy [Dwork et al. Eurocrypt 2006]. We show that the sample complexity of these tasks under approximate differential privacy can be significantly lower than ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1407.2674", "content": "Jul 10, 2014 · We compare the sample complexity of private learning [Kasiviswanathan et al. 2008] and sanitization ~[Blum et al. 2008] under pure $ε$- differential privacy [Dwork et al. TCC 2006] and approximate $(ε,δ)$- differential privacy [Dwork et al. Eurocrypt 2006]. We show that the sample complexity of these tasks under approximate differential privacy can be significantly lower than that under pure ... As with private learning , we show significant differences between the sample complexity required for san-itization of simple predicate classes under pure and approximate differential privacy . In 2016, Nissim received, with Cynthia Dwork, Frank McSherry, and Adam Smith the “TCC Test-of-Time” Award for their TCC 2006 paper Calibrating Noise to Sensitivity in Private Data Analysis where differential privacy was introduced. U RI S TEMMER is a Ph. D. candidate at Ben-Gurion University, advised by Amos Beimel and Kobbi Nissim . As with private learning , we show significant differences between the sample complexity required for sanitization of simple predicate classes under pure and approximate differential privacy . Abstract We compare the sample complexity of private learning and sanitization tasks under pure ε - differential privacy [Dwork, McSherry, Nissim , and Smith TCC 2006] and approximate (ε, δ)- differential privacy [Dwork, Kenthapadi, McSherry, Mironov, and Naor EUROCRYPT 2006]. Jul 23, 2023 · A. Beimel , K. Nissim , and U. Stemmer . Private learning and sanitization : Pure vs . approximate differential privacy . In Approximation, Randomization, and Combinatorial ... Is there a relationship between data sanitization and private learning? Similar techniques are used for both data sanitization and private learning, suggesting relationships between the two tasks. We now explore one such relationship in proving a lower bound on the sample complexity needed for sanitization (under pure differential privacy). What is approximate differential privacy? Approximate Differential Privacy is a relaxation of pure differential privacy where the guarantee needs to be satisfied only for events whose probability is at least d. We show that even a negligible d > 0 can have a significant effect on the sample complexity of private learning and sanitization. Private learning. Does approximate differential privacy reduce sample complexity? We show that the sample complexity of these tasks under approximate differential privacy can be significantly lower than that under pure differential privacy. Research supported by the Israel Science Foundation (grants No. 938/09 and 2761/12) and by the Frankel Center for Computer Science at Ben-Gurion University. Is the sample complexity of quasi-concave promise problems lower than pure differential privacy? We show that the sample complexity of these tasks under approximate differential privacy can be significantly lower than that under pure differential privacy. We define a family of optimization problems, which we call Quasi-Concave Promise Problems, that generalizes some of our considered tasks. Which algorithm satisfies the requirement of pure differential privacy? More formally, an algorithm A satisfies the requirement of Pure Differential Privacy if for every two databases that differ on exactly one entry, and for every event defined over the output set of A, the probability of this event is close up to a multiplicative factor of ee 1 + e whether A is applied on one database or on the other. What is the difference between a private learner and a label-Private learner? Recall that given a labeled sample, a private learner is required to preserve the privacy of the entire sample, while a label- private learner is only required to preserve privacy for the labels of each entry. We can model this scenario as a learning algorithm A which is given as input 2 databases—a labeled database S, and an unlabeled database D. We compare the sample complexity of private learning [Kasiviswanathan et al. 2008] and sanitization ~ [Blum et al. 2008] under pure $\\epsilon$- differential privacy [Dwork et al. TCC 2006] and approximate $ (\\epsilon,\\delta)$- differential privacy [Dwork et al. Eurocrypt 2006]. We show that the sample complexity of these tasks under approximate differential privacy can be significantly lower than ..."}
+{"idx": 1, "title": "Private Learning and Sanitization: Pure vs. Approximate ...", "date": "", "ddg_snippet": "In 2016, Nissim received, with Cynthia Dwork, Frank McSherry, and Adam Smith the “TCC Test-of-Time” Award for their TCC 2006 paper Calibrating Noise to Sensitivity in Private Data Analysis where differential privacy was introduced. U RI S TEMMER is a Ph. D. candidate at Ben-Gurion University, advised by Amos Beimel and Kobbi Nissim .", "subpage_snippet": "", "source": "dokk.org", "link": "https://dokk.org/library/theoryofcomputing_v012a001", "content": "In 2016, Nissim received, with Cynthia Dwork, Frank McSherry, and Adam Smith the “TCC Test-of-Time” Award for their TCC 2006 paper Calibrating Noise to Sensitivity in Private Data Analysis where differential privacy was introduced. U RI S TEMMER is a Ph. D. candidate at Ben-Gurion University, advised by Amos Beimel and Kobbi Nissim ."}
+{"idx": 2, "title": "Private Learning and Sanitization: Pure vs. Approximate ...", "date": "", "ddg_snippet": "Abstract We compare the sample complexity of private learning and sanitization tasks under pure ε - differential privacy [Dwork, McSherry, Nissim , and Smith TCC 2006] and approximate (ε, δ)- differential privacy [Dwork, Kenthapadi, McSherry, Mironov, and Naor EUROCRYPT 2006].", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-3-642-40328-6_26", "content": "Abstract We compare the sample complexity of private learning and sanitization tasks under pure ε - differential privacy [Dwork, McSherry, Nissim , and Smith TCC 2006] and approximate (ε, δ)- differential privacy [Dwork, Kenthapadi, McSherry, Mironov, and Naor EUROCRYPT 2006]."}
+{"idx": 3, "title": "Private Learning and Sanitization: Pure vs. Approximate ...", "date": "", "ddg_snippet": "We compare the sample complexity of private learning [Kasiviswanathan et al. 2008] and sanitization ~ [Blum et al. 2008] under pure $\\epsilon$- differential privacy [Dwork et al. TCC 2006] and approximate $ (\\epsilon,\\delta)$- differential privacy [Dwork et al. Eurocrypt 2006]. We show that the sample complexity of these tasks under approximate differential privacy can be significantly lower than ...", "subpage_snippet": "", "source": "archive.org", "link": "https://archive.org/details/arxiv-1407.2674", "content": "We compare the sample complexity of private learning [Kasiviswanathan et al. 2008] and sanitization ~ [Blum et al. 2008] under pure $\\epsilon$- differential privacy [Dwork et al. TCC 2006] and approximate $ (\\epsilon,\\delta)$- differential privacy [Dwork et al. Eurocrypt 2006]. We show that the sample complexity of these tasks under approximate differential privacy can be significantly lower than ..."}
+{"idx": 4, "title": "Private Learning and Sanitization: Pure vs. Approximate ...", "date": "", "ddg_snippet": "As with private learning , we show significant differences between the sample complexity required for san-itization of simple predicate classes under pure and approximate differential privacy .", "subpage_snippet": "", "source": "privacytools.seas.harvard.edu", "link": "https://privacytools.seas.harvard.edu/resource/pdf-38", "content": "As with private learning , we show significant differences between the sample complexity required for san-itization of simple predicate classes under pure and approximate differential privacy ."}
+{"idx": 5, "title": "Private Learning and Sanitization: Pure vs. Approximate ...", "date": "", "ddg_snippet": "As with private learning , we show significant differences between the sample complexity required for sanitization of simple predicate classes under pure and approximate differential privacy .", "subpage_snippet": "", "source": "theoryofcomputing.org", "link": "https://theoryofcomputing.org/articles/v012a001/v012a001.pdf", "content": "As with private learning , we show significant differences between the sample complexity required for sanitization of simple predicate classes under pure and approximate differential privacy ."}
+{"idx": 6, "title": "Label differential privacy and private training data release", "date": "", "ddg_snippet": "Jul 23, 2023 · A. Beimel , K. Nissim , and U. Stemmer . Private learning and sanitization : Pure vs . approximate differential privacy . In Approximation, Randomization, and Combinatorial ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3618408.3618540", "content": "Jul 23, 2023 · A. Beimel , K. Nissim , and U. Stemmer . Private learning and sanitization : Pure vs . approximate differential privacy . In Approximation, Randomization, and Combinatorial ..."}
+{"idx": 7, "title": "theoryofcomputing.org/articles/v012a001/ abstract .txt", "date": "", "ddg_snippet": "2008] under _ pure _ $\\epsilon$- differential privacy [Dwork et al.Specifically, we construct private learners for point functions, threshold functions, and axis-aligned rectangles in high dimension.", "subpage_snippet": "", "source": "www.theoryofcomputing.org", "link": "https://www.theoryofcomputing.org/articles/v012a001/abstract.txt", "content": "2008] under _ pure _ $\\epsilon$- differential privacy [Dwork et al.Specifically, we construct private learners for point functions, threshold functions, and axis-aligned rectangles in high dimension."}
+{"idx": 8, "title": "Private Learning and Sanitization : Pure vs . Approximate ...", "date": "", "ddg_snippet": "- differential privacy [Dwork et al. Eurocrypt 2006]. We show that the sample complexity of these tasks under approximate differential privacy can be significantly lower than that under pure differential privacy .", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/paper/private-learning-and-sanitization-pure-vs", "content": "- differential privacy [Dwork et al. Eurocrypt 2006]. We show that the sample complexity of these tasks under approximate differential privacy can be significantly lower than that under pure differential privacy ."}
+{"idx": 9, "title": "(PDF) Private Learning and Sanitization : Pure vs . Approximate ...", "date": "", "ddg_snippet": "Approximate Differential Privacy ∗. Amos Beimel †Kobbi Nissim ‡Uri Stemmer §. May 8, 2014. Abstract Key words and phrases: differential privacy , sample complexity, private learning , sanitization . ©Amos Beimel , Kobbi Nissim , and Uri Stemmer .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/263856026_Private_Learning_and_Sanitization_Pure_vs_Approximate_Differential_Privacy", "content": "Approximate Differential Privacy ∗. Amos Beimel †Kobbi Nissim ‡Uri Stemmer §. May 8, 2014. Abstract Key words and phrases: differential privacy , sample complexity, private learning , sanitization . ©Amos Beimel , Kobbi Nissim , and Uri Stemmer ."}
diff --git a/data/sampled_jsons/Beimel_et_al_2022_private_median_framework_regression_challenge_condition_number.jsonl b/data/sampled_jsons/Beimel_et_al_2022_private_median_framework_regression_challenge_condition_number.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..41b33baf8702c7c72060c880537124024a79f46e
--- /dev/null
+++ b/data/sampled_jsons/Beimel_et_al_2022_private_median_framework_regression_challenge_condition_number.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "The Challenge of Differentially Private Screening Rules", "date": "", "ddg_snippet": "Specifically, it has been shown that differential privacy can prevent dataset reconstruction attacks which are possible when models are trained in a nonprivate framework (Stock et al ., 2022 ). Currently, there is an interest in sparse differentially private regression algorithms. Private L1-regularized or constrained optimizers have been developed.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=l0Z0ggmVBW", "content": "Specifically, it has been shown that differential privacy can prevent dataset reconstruction attacks which are possible when models are trained in a nonprivate framework (Stock et al ., 2022 ). Currently, there is an interest in sparse differentially private regression algorithms. Private L1-regularized or constrained optimizers have been developed."}
+{"idx": 1, "title": "(PDF) Differentially Private Simple Linear Regression - ResearchGate", "date": "", "ddg_snippet": "In contrast, prior work on differentially private linear regression focused on multivariate linear regression on large datasets or asymptotic analysis.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/359034762_Differentially_Private_Simple_Linear_Regression", "content": "In contrast, prior work on differentially private linear regression focused on multivariate linear regression on large datasets or asymptotic analysis."}
+{"idx": 2, "title": "PDF Private Regression In Multiple Outcomes (PRIMO)", "date": "", "ddg_snippet": "Abstract We introduce a new differentially private regression setting we call Private Regres-sion in Multiple Outcomes (PRIMO) inspired the common situation in the social and biomedical sciences where a data analyst wants to perform a set of l regressions while preserving privacy, where in each of the regressions the covariates X are shared, and each regression i has a different vector of ...", "subpage_snippet": "", "source": "tpdp.journalprivacyconfidentiality.org", "link": "https://tpdp.journalprivacyconfidentiality.org/2022/papers/primo.pdf", "content": "Abstract We introduce a new differentially private regression setting we call Private Regres-sion in Multiple Outcomes (PRIMO) inspired the common situation in the social and biomedical sciences where a data analyst wants to perform a set of l regressions while preserving privacy, where in each of the regressions the covariates X are shared, and each regression i has a different vector of ..."}
+{"idx": 3, "title": "Di erentially Private Simple Linear Regression", "date": "", "ddg_snippet": "Di erentially Private Simple Linear Regression Abstract: Economics and social science research often require analyzing datasets of sensitive personal informa-tion at fine granularity, with models fit to small subsets of the data. Unfortunately, such fine-grained analysis can easily reveal sensitive individual information. We study regression algorithms that satisfy di erential pri-vacy, a ...", "subpage_snippet": "", "source": "par.nsf.gov", "link": "https://par.nsf.gov/servlets/purl/10336471", "content": "Di erentially Private Simple Linear Regression Abstract: Economics and social science research often require analyzing datasets of sensitive personal informa-tion at fine granularity, with models fit to small subsets of the data. Unfortunately, such fine-grained analysis can easily reveal sensitive individual information. We study regression algorithms that satisfy di erential pri-vacy, a ..."}
+{"idx": 4, "title": "On the Computational Complexity of Private High-dimensional Model ...", "date": "", "ddg_snippet": "We consider the problem of model selection in a high-dimensional sparse linear regres-sion model under the differential privacy framework . In particular, we consider the problem of differentially private best subset selection and study its utility guarantee. We adopt the well-known exponential mechanism for selecting the best model, and under a certain margin condition , we establish its strong ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2310.07852v1", "content": "We consider the problem of model selection in a high-dimensional sparse linear regres-sion model under the differential privacy framework . In particular, we consider the problem of differentially private best subset selection and study its utility guarantee. We adopt the well-known exponential mechanism for selecting the best model, and under a certain margin condition , we establish its strong ..."}
+{"idx": 5, "title": "E DIFFERENTIALLY PRIVATE LINEAR REGRESSION - OpenReview", "date": "", "ddg_snippet": "In practice, users of differentially private algorithms struggle to provide instance-specific inputs like feature and label norms without looking at the private data (Sarathy et al ., 2022 ). Unfortunately, looking at the private data also nullifies the desired differential privacy guarantee. Similarly, while recent work has advanced the state of the art of private hyperparameter tuning (Liu ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=rSUCajhLsQ", "content": "In practice, users of differentially private algorithms struggle to provide instance-specific inputs like feature and label norms without looking at the private data (Sarathy et al ., 2022 ). Unfortunately, looking at the private data also nullifies the desired differential privacy guarantee. Similarly, while recent work has advanced the state of the art of private hyperparameter tuning (Liu ..."}
+{"idx": 6, "title": "PDF Di erentially Private Methods for Managing Model Uncertainty in Linear ...", "date": "", "ddg_snippet": "In this article, we develop di erentially private methods for normal linear models. We propose di erentially private hypothesis tests for comparing nested models (in Section 4) as well as methods for model averaging and selection (in Section 5). We consider Bayesian methods based on mixtures of g-priors (Liang et al ., 2008) and non-Bayesian methods that are built upon likelihood-ratio ...", "subpage_snippet": "", "source": "jmlr.org", "link": "https://jmlr.org/papers/volume25/21-1536/21-1536.pdf", "content": "In this article, we develop di erentially private methods for normal linear models. We propose di erentially private hypothesis tests for comparing nested models (in Section 4) as well as methods for model averaging and selection (in Section 5). We consider Bayesian methods based on mixtures of g-priors (Liang et al ., 2008) and non-Bayesian methods that are built upon likelihood-ratio ..."}
+{"idx": 7, "title": "Differentially Private Release and Learning of Threshold Functions", "date": "", "ddg_snippet": "We prove new upper and lower bounds on the sample complexity of (ϵ, δ) differentially private algorithms for releasing approximate answers to threshold functions.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1504.07553", "content": "We prove new upper and lower bounds on the sample complexity of (ϵ, δ) differentially private algorithms for releasing approximate answers to threshold functions."}
+{"idx": 8, "title": "Differentially Private Simple Linear Regression", "date": "", "ddg_snippet": "The analysis of small datasets, with sizes in the dozens to low hundreds of records, is crucial in many social science applications. For example, neighborhood-level household income, high-school graduation rate, and in-carceration rates are all studied using sensitive datasets that are subdivided into small, local units to allow for fine-grained inspection (e.g., [12]). As datasets get larger ...", "subpage_snippet": "", "source": "privacytools.seas.harvard.edu", "link": "https://privacytools.seas.harvard.edu/resource/popet-2022pdf", "content": "The analysis of small datasets, with sizes in the dozens to low hundreds of records, is crucial in many social science applications. For example, neighborhood-level household income, high-school graduation rate, and in-carceration rates are all studied using sensitive datasets that are subdivided into small, local units to allow for fine-grained inspection (e.g., [12]). As datasets get larger ..."}
+{"idx": 9, "title": "Alternating minimization differential privacy protection algorithm for ...", "date": "", "ddg_snippet": "The private learning framework conceptualizes data analysis as an optimization challenge , enabling the application of numerous algorithms tailored for private learning, as extensively documented in the literature (Jia and Qiu, 2020, Kasiviswanathan et al ., 2017, Yu et al ., 2019, Zhang et al ., 2019).", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0957417424021468", "content": "The private learning framework conceptualizes data analysis as an optimization challenge , enabling the application of numerous algorithms tailored for private learning, as extensively documented in the literature (Jia and Qiu, 2020, Kasiviswanathan et al ., 2017, Yu et al ., 2019, Zhang et al ., 2019)."}
diff --git a/data/sampled_jsons/Beyond_Optimism_2406.13909_continuous_MDPs_neural_network_input_output.jsonl b/data/sampled_jsons/Beyond_Optimism_2406.13909_continuous_MDPs_neural_network_input_output.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..42b6b59762917b174f2512eb2ec1f562e559bc22
--- /dev/null
+++ b/data/sampled_jsons/Beyond_Optimism_2406.13909_continuous_MDPs_neural_network_input_output.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Choose Neural Network Input-Output Processing Functions", "date": "", "ddg_snippet": "These functions transform the input and target values you provide into values that are better suited for network training. You can override the default input and output processing functions by adjusting network properties after you create the network.", "subpage_snippet": "", "source": "www.mathworks.com", "link": "https://www.mathworks.com/help/deeplearning/ug/choose-neural-network-input-output-processing-functions.html", "content": "These functions transform the input and target values you provide into values that are better suited for network training. You can override the default input and output processing functions by adjusting network properties after you create the network."}
+{"idx": 1, "title": "Solvable neural network model for input-output associations ...", "date": "", "ddg_snippet": "Dec 8, 2023 · In neural information processing, inputs modulate neural dynamics to generate desired outputs. To unravel the dynamics and underlying neural connectivity enabling such input - output association, we propose an exactly solvable neural-network model with a connectivity matrix explicitly consisting of inputs and required outputs. An analytic form of the response under the input is derived, while ...", "subpage_snippet": "", "source": "link.aps.org", "link": "https://link.aps.org/doi/10.1103/PhysRevResearch.5.043221", "content": "Dec 8, 2023 · In neural information processing, inputs modulate neural dynamics to generate desired outputs. To unravel the dynamics and underlying neural connectivity enabling such input - output association, we propose an exactly solvable neural-network model with a connectivity matrix explicitly consisting of inputs and required outputs. An analytic form of the response under the input is derived, while ..."}
+{"idx": 2, "title": "7.4. Multiple Input and Multiple Output Channels — Dive into ...", "date": "", "ddg_snippet": "7.4.2. Multiple Output Channels Regardless of the number of input channels, so far we always ended up with one output channel. However, as we discussed in Section 7.1.4, it turns out to be essential to have multiple channels at each layer. In the most popular neural network architectures, we actually increase the channel dimension as we go deeper in the neural network , typically downsampling ...", "subpage_snippet": "", "source": "d2l.ai", "link": "https://d2l.ai/chapter_convolutional-neural-networks/channels.html", "content": "7.4.2. Multiple Output Channels Regardless of the number of input channels, so far we always ended up with one output channel. However, as we discussed in Section 7.1.4, it turns out to be essential to have multiple channels at each layer. In the most popular neural network architectures, we actually increase the channel dimension as we go deeper in the neural network , typically downsampling ..."}
+{"idx": 3, "title": "Investigation of the input-output relationship of engineered ...", "date": "", "ddg_snippet": "Nov 1, 2023 · This approach allows the study of the input - output relationship of such networks by varying the site, voltage, and frequency of applied electrical stimuli. We find evidence that networks respond to an electrical super-threshold stimulus with a reproducible early response.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S095656632300533X", "content": "Nov 1, 2023 · This approach allows the study of the input - output relationship of such networks by varying the site, voltage, and frequency of applied electrical stimuli. We find evidence that networks respond to an electrical super-threshold stimulus with a reproducible early response."}
+{"idx": 4, "title": "Self-Attention Between Datapoints: Going Beyond Individual ...", "date": "", "ddg_snippet": "Code for \"Self-Attention Between Datapoints: Going Beyond Individual Input - Output Pairs in Deep Learning\" - OATML/non-parametric-transformers", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/OATML/Non-Parametric-Transformers", "content": "Code for \"Self-Attention Between Datapoints: Going Beyond Individual Input - Output Pairs in Deep Learning\" - OATML/non-parametric-transformers"}
+{"idx": 5, "title": "The Neural Network Input-Process-Output Mechanism", "date": "", "ddg_snippet": "May 10, 2013 · This input -process- output mechanism is called neural network feed-forward. Understanding the feed-forward mechanism is required in order to create a neural network that solves difficult practical problems such as predicting the result of a football game or the movement of a stock price.", "subpage_snippet": "", "source": "visualstudiomagazine.com", "link": "https://visualstudiomagazine.com/articles/2013/05/01/neural-network-feed-forward.aspx", "content": "May 10, 2013 · This input -process- output mechanism is called neural network feed-forward. Understanding the feed-forward mechanism is required in order to create a neural network that solves difficult practical problems such as predicting the result of a football game or the movement of a stock price."}
+{"idx": 6, "title": "Understanding input/output dimensions of neural networks", "date": "", "ddg_snippet": "May 8, 2017 · 13 Let's take a fully-connected neural network with one hidden layer as an example. The input layer consists of 5 units that are each connected to all hidden neurons. In total there are 10 hidden neurons. Libraries such as Theano and Tensorflow allow multidimensional input / output shapes.", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/43851735/understanding-input-output-dimensions-of-neural-networks", "content": "May 8, 2017 · 13 Let's take a fully-connected neural network with one hidden layer as an example. The input layer consists of 5 units that are each connected to all hidden neurons. In total there are 10 hidden neurons. Libraries such as Theano and Tensorflow allow multidimensional input / output shapes."}
+{"idx": 7, "title": "Beyond Optimism : Exploration With Partially Observable Rewards", "date": "", "ddg_snippet": "Exploration bonus for regret minimization in discrete and continuous average reward MDPs . In Advances in Neural Information Processing Systems (NeurIPS), 2019.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.13909v2", "content": "Exploration bonus for regret minimization in discrete and continuous average reward MDPs . In Advances in Neural Information Processing Systems (NeurIPS), 2019."}
+{"idx": 8, "title": "Book", "date": "", "ddg_snippet": "Scalable Neural Network Verification with Branch-and-bound Inferred Cutting Planes Duo Zhou, Christopher Brix, Grani A. Hanasusanto, Huan Zhang. Beyond Optimism : Exploration With Partially Observable Rewards Simone Parisi, Alireza Kazemipour, Michael Bowling.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024", "content": "Scalable Neural Network Verification with Branch-and-bound Inferred Cutting Planes Duo Zhou, Christopher Brix, Grani A. Hanasusanto, Huan Zhang. Beyond Optimism : Exploration With Partially Observable Rewards Simone Parisi, Alireza Kazemipour, Michael Bowling."}
+{"idx": 9, "title": "ICML 2021 Orals", "date": "", "ddg_snippet": "The posterior over Bayesian neural network (BNN) parameters is extremely high-dimensional and non-convex.The connection between training deep neural networks (DNNs) and optimal control theory (OCT) has attracted considerable attention as a principled tool of algorithmic design.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2021/events/oral", "content": "The posterior over Bayesian neural network (BNN) parameters is extremely high-dimensional and non-convex.The connection between training deep neural networks (DNNs) and optimal control theory (OCT) has attracted considerable attention as a principled tool of algorithmic design."}
diff --git a/data/sampled_jsons/Beyond_Optimism_Exploration_With_Partially_Observable_Rewards_appendix_table_1_training_steps_Two-Ro.jsonl b/data/sampled_jsons/Beyond_Optimism_Exploration_With_Partially_Observable_Rewards_appendix_table_1_training_steps_Two-Ro.jsonl
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index 0000000000000000000000000000000000000000..694564e1edde64bf24f53cbd5db4248b33584a8a
--- /dev/null
+++ b/data/sampled_jsons/Beyond_Optimism_Exploration_With_Partially_Observable_Rewards_appendix_table_1_training_steps_Two-Ro.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Beyond Optimism: Exploration With Partially Observable Rewards", "date": "", "ddg_snippet": "Jun 20, 2024 · To improve exploration and reward discovery, popular algorithms rely on optimism . But what if sometimes rewards are unobservable, e.g., situations of partial monitoring in bandits and the recent formalism of monitored Markov decision process? In this case, optimism can lead to suboptimal behavior that does not explore further to collapse ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2406.13909", "content": "Jun 20, 2024 · To improve exploration and reward discovery, popular algorithms rely on optimism . But what if sometimes rewards are unobservable, e.g., situations of partial monitoring in bandits and the recent formalism of monitored Markov decision process? In this case, optimism can lead to suboptimal behavior that does not explore further to collapse ..."}
+{"idx": 1, "title": "Beyond optimism | Proceedings of the 38th International ...", "date": "", "ddg_snippet": "Jun 5, 2025 · To improve exploration and reward discovery, popular algorithms rely on optimism . But what if sometimes rewards are unobservable, e.g., situations of partial monitoring in bandits and the recent formalism of monitored Markov decision process? In this case, optimism can lead to suboptimal behavior that does not explore further to collapse ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.5555/3737916.3740005", "content": "Jun 5, 2025 · To improve exploration and reward discovery, popular algorithms rely on optimism . But what if sometimes rewards are unobservable, e.g., situations of partial monitoring in bandits and the recent formalism of monitored Markov decision process? In this case, optimism can lead to suboptimal behavior that does not explore further to collapse ..."}
+{"idx": 2, "title": "Beyond Optimism: Exploration With Partially Observable Rewards", "date": "", "ddg_snippet": "Jun 19, 2024 · With this paper, we present a novel exploration strategy that overcomes the limitations of existing methods and guarantees convergence to an optimal policy even when rewards are not always observable .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/381579170_Beyond_Optimism_Exploration_With_Partially_Observable_Rewards", "content": "Jun 19, 2024 · With this paper, we present a novel exploration strategy that overcomes the limitations of existing methods and guarantees convergence to an optimal policy even when rewards are not always observable ."}
+{"idx": 3, "title": "Beyond Optimism: Exploration With Partially Observable ...", "date": "", "ddg_snippet": "Nov 11, 2024 · Conclusion This paper introduces a novel exploration strategy called \" Optimism Beyond Optimism \" (OBO) for reinforcement learning in partially observable environments. By considering a broader range of possible outcomes, OBO aims to strike a better balance between exploration and exploitation compared to traditional optimistic methods.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/beyond-optimism-exploration-partially-observable-rewards", "content": "Nov 11, 2024 · Conclusion This paper introduces a novel exploration strategy called \" Optimism Beyond Optimism \" (OBO) for reinforcement learning in partially observable environments. By considering a broader range of possible outcomes, OBO aims to strike a better balance between exploration and exploitation compared to traditional optimistic methods."}
+{"idx": 4, "title": "Beyond Optimism: Exploration With Partially Observable Rewards", "date": "", "ddg_snippet": "Poster Beyond Optimism : Exploration With Partially Observable Rewards Simone Parisi · Alireza Kazemipour · Michael Bowling", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/poster/93919", "content": "Poster Beyond Optimism : Exploration With Partially Observable Rewards Simone Parisi · Alireza Kazemipour · Michael Bowling"}
+{"idx": 5, "title": "Beyond Optimism: Exploration With Partially Observable Rewards", "date": "", "ddg_snippet": "In this case, optimism can lead to suboptimal behavior that does not explore further to collapse uncertainty. With this paper, we present a novel exploration strategy that overcomes the limitations of existing methods and guarantees convergence to an optimal policy even when rewards are not always observable .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.13909v2", "content": "In this case, optimism can lead to suboptimal behavior that does not explore further to collapse uncertainty. With this paper, we present a novel exploration strategy that overcomes the limitations of existing methods and guarantees convergence to an optimal policy even when rewards are not always observable ."}
+{"idx": 6, "title": "Model-Based Exploration in Monitored Markov Decision ...", "date": "", "ddg_snippet": "24 Jun 2025 — Beyond Optimism : Exploration With Partially Observable Rewards . In ... Two - Room -3x5 and Two - Room - 2x11 . They are shown in Figure 7. In ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.16772v5", "content": "24 Jun 2025 — Beyond Optimism : Exploration With Partially Observable Rewards . In ... Two - Room -3x5 and Two - Room - 2x11 . They are shown in Figure 7. In ..."}
+{"idx": 7, "title": "Model-Based Exploration in Truthful Monitored Markov ...", "date": "", "ddg_snippet": "21 May 2025 — Beyond Optimism : Exploration With Partially Observable Rewards . In ... Two - Room -3x5 and Two - Room - 2x11 comprise the environments. They ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.16772v2", "content": "21 May 2025 — Beyond Optimism : Exploration With Partially Observable Rewards . In ... Two - Room -3x5 and Two - Room - 2x11 comprise the environments. They ..."}
+{"idx": 8, "title": "Model-Based Exploration in Monitored Markov Decision ...", "date": "", "ddg_snippet": "Beyond Opti- mism: Exploration With Partially Observable Rewards . In ... Importance of the Third Innovation: Explore to Observe Rewards . Appendix C. 1 ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/45819", "content": "Beyond Opti- mism: Exploration With Partially Observable Rewards . In ... Importance of the Third Innovation: Explore to Observe Rewards . Appendix C. 1 ..."}
+{"idx": 9, "title": "Model-Based Exploration in Monitored Markov Decision Processes", "date": "", "ddg_snippet": "mism: Exploration With Partially Observable Rewards . ... The episode's time limit in River Swim, corridor and Two - Room - 2x11 is 200 steps , and in other ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=GdsbEOwAE7&name=pdf", "content": "mism: Exploration With Partially Observable Rewards . ... The episode's time limit in River Swim, corridor and Two - Room - 2x11 is 200 steps , and in other ..."}
diff --git a/data/sampled_jsons/Beyond_Optimism_Exploration_With_Partially_Observable_Rewards_continuous_MDP_neural_network_input_ou.jsonl b/data/sampled_jsons/Beyond_Optimism_Exploration_With_Partially_Observable_Rewards_continuous_MDP_neural_network_input_ou.jsonl
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index 0000000000000000000000000000000000000000..754c1d2e937405a358b099065ca0fb8400dcae0f
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+++ b/data/sampled_jsons/Beyond_Optimism_Exploration_With_Partially_Observable_Rewards_continuous_MDP_neural_network_input_ou.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Partially Observable Multi-Agent Reinforcement Learning ...", "date": "", "ddg_snippet": "by X Liu · 2023 · Cited by 2 — We study provable multi-agent RL under the framework of partially observable stochastic games (POSGs), with potential information sharing among ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2308.08705", "content": "by X Liu · 2023 · Cited by 2 — We study provable multi-agent RL under the framework of partially observable stochastic games (POSGs), with potential information sharing among ..."}
+{"idx": 1, "title": "Provably Efficient Partially Observable Risk-Sensitive ...", "date": "", "ddg_snippet": "by T Zhang · 2024 · Cited by 1 — The Partially Observable . Markov Decision Process (POMDP)[40] is widely employed as the mathematical framework for these problems. Empirical ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2402.18149", "content": "by T Zhang · 2024 · Cited by 1 — The Partially Observable . Markov Decision Process (POMDP)[40] is widely employed as the mathematical framework for these problems. Empirical ..."}
+{"idx": 2, "title": "A Theoretical Framework for Partially-Observed Reward ...", "date": "", "ddg_snippet": "27 May 2024 — In this paper, we consider an episodic reinforcement learning setting in which a learner interacts with an MDP having a state space S S {\\cal S} ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2402.03282v2", "content": "27 May 2024 — In this paper, we consider an episodic reinforcement learning setting in which a learner interacts with an MDP having a state space S S {\\cal S} ..."}
+{"idx": 3, "title": "Partially Observable Markov Decision Processes in Robotics", "date": "", "ddg_snippet": "by M Lauri · 2022 · Cited by 169 — The partially observable Markov decision process (POMDP) provides a principled mathematical framework for modeling and solving robot decision ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2209.10342", "content": "by M Lauri · 2022 · Cited by 169 — The partially observable Markov decision process (POMDP) provides a principled mathematical framework for modeling and solving robot decision ..."}
+{"idx": 4, "title": "Provable partially observable reinforcement learning with ...", "date": "", "ddg_snippet": "by Y Cai · 2024 · Cited by 4 — Abstract. Partial observability of the underlying states generally presents significant challenges for rein- forcement learning (RL).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2412.00985", "content": "by Y Cai · 2024 · Cited by 4 — Abstract. Partial observability of the underlying states generally presents significant challenges for rein- forcement learning (RL)."}
+{"idx": 5, "title": "Provable partially observable reinforcement learning with ...", "date": "", "ddg_snippet": "by Y Cai · 2024 · Cited by 4 — Abstract. Partial observability of the underlying states generally presents significant challenges for rein- forcement learning (RL).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2412.00985?", "content": "by Y Cai · 2024 · Cited by 4 — Abstract. Partial observability of the underlying states generally presents significant challenges for rein- forcement learning (RL)."}
+{"idx": 6, "title": "Random Latent Exploration for Deep Reinforcement ...", "date": "", "ddg_snippet": "The core idea of RLE is to encourage the agent to explore different parts of the environment by pursuing randomly sampled goals in a latent space.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2407.13755v3", "content": "The core idea of RLE is to encourage the agent to explore different parts of the environment by pursuing randomly sampled goals in a latent space."}
+{"idx": 7, "title": "Reinforcement Learning in MDPs with Information-Ordered ...", "date": "", "ddg_snippet": "by Z Zhang · 2025 — We illustrate the applicability of these partial orders in many domains in operations research, including inventory control and queuing systems.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2508.03904", "content": "by Z Zhang · 2025 — We illustrate the applicability of these partial orders in many domains in operations research, including inventory control and queuing systems."}
+{"idx": 8, "title": "Comprehensive Overview of Reward Engineering and ...", "date": "", "ddg_snippet": "This paper emphasizes the importance of reward engineering and reward shaping in enhancing the efficiency and effectiveness of reinforcement learning ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2408.10215v1", "content": "This paper emphasizes the importance of reward engineering and reward shaping in enhancing the efficiency and effectiveness of reinforcement learning ..."}
+{"idx": 9, "title": "Continual Learning as Computationally Constrained ...", "date": "", "ddg_snippet": "26 Jun 2025 — This monograph clarifies and formalizes concepts of continual learning, introducing a framework and tools to stimulate further research.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2307.04345v3", "content": "26 Jun 2025 — This monograph clarifies and formalizes concepts of continual learning, introducing a framework and tools to stimulate further research."}
diff --git a/data/sampled_jsons/Beyond_Self-Repellent_Kernels_History-Driven_Target_LRU_cache_Equation_15_visit_frequency.jsonl b/data/sampled_jsons/Beyond_Self-Repellent_Kernels_History-Driven_Target_LRU_cache_Equation_15_visit_frequency.jsonl
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index 0000000000000000000000000000000000000000..0280c4a0fe5bceabd2e926a836a113ec968ce51e
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Cache (computing) - Wikipedia", "date": "", "ddg_snippet": "Diagram of a CPU memory cache operation. In computing, a cache is a hardware or software component that stores data so that future requests for that data can be served faster; the data stored in a cache might be the result of an earlier computation o...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Cache_(computing)", "content": "Diagram of a CPU memory cache operation. In computing, a cache is a hardware or software component that stores data so that future requests for that data can be served faster; the data stored in a cache might be the result of an earlier computation o..."}
+{"idx": 1, "title": "ICML 2025 Beyond Self-Repellent Kernels: History-Driven ...", "date": "", "ddg_snippet": "Abstract: We propose a * history-driven target (HDT)* framework in Markov Chain Monte Carlo (MCMC) to improve any random walk algorithm on discrete state spaces, such as general undirected graphs, for efficient sampling from target distribution b o l d s y m b o l m u. With broad applications in network science and distributed optimization, recent innovations like the self-repellent random walk ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/oral/47270", "content": "Abstract: We propose a * history-driven target (HDT)* framework in Markov Chain Monte Carlo (MCMC) to improve any random walk algorithm on discrete state spaces, such as general undirected graphs, for efficient sampling from target distribution b o l d s y m b o l m u. With broad applications in network science and distributed optimization, recent innovations like the self-repellent random walk ..."}
+{"idx": 2, "title": "Beyond Self-Repellent Kernels: History-Driven Target Towards ...", "date": "", "ddg_snippet": "With broad applications in network science and distributed optimization, recent innovations like the self-repellent random walk (SRRW) achieve near-zero variance by prioritizing under-sampled states through transition kernel modifications based on past visit frequencies.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.18300v1", "content": "With broad applications in network science and distributed optimization, recent innovations like the self-repellent random walk (SRRW) achieve near-zero variance by prioritizing under-sampled states through transition kernel modifications based on past visit frequencies."}
+{"idx": 3, "title": "Beyond Self-Repellent Kernels: History-Driven Target Towards ... ICML 2025 Beyond Self-Repellent Kernels: History-Driven ... Our paper on efficient MCMC on graphs accepted at ICML 2025 Modeling Cache Performance Beyond LRU Homework 3 -- LRU Approximations · CS251a - GitHub Pages Beyond Self-Repellent Kernels: History-Driven Target Towards ... Homework 3 -- LRU Approximations · CS251a - GitHub Pages Homework 3 -- LRU Approximations · CS251a - GitHub Pages Modeling Cache Performance Beyond LRU - Massachusetts Institute of Modeling Cache Performance Beyond LRU - Massachusetts Institute of Beyond Self-Repellent Kernels: History-Driven Target Towards ...", "date": "", "ddg_snippet": "May 23, 2025 · We propose a history-driven target (HDT) framework in Markov Chain Monte Carlo (MCMC) to improve any random walk algorithm on discrete state spaces, such as general undirected graphs, for efficient sampling from target distribution $\\\\boldsymbolμ$. With broad applications in network science and distributed optimization, recent innovations like the self-repellent random walk (SRRW) achieve ... Abstract: We propose a * history-driven target (HDT)* framework in Markov Chain Monte Carlo (MCMC) to improve any random walk algorithm on discrete state spaces, such as general undirected graphs, for efficient sampling from target distribution b o l d s y m b o l m u. With broad applications in network science and distributed optimization, recent innovations like the self-repellent random walk ... Excited to announce that our paper, “ Beyond Self-Repellent Kernels : History-Driven Target Towards Efficient Non-linear MCMC on General Graphs,” has been chosen for an 𝗢𝗿𝗮𝗹 ... We first review the relevant background in modern LLC architecture, replacement policies, and cache modeling. See full list on people.csail.mit.edu Fig. 1 shows the high-level design of our cache model. The input to the model is the cache architecture (its size, associa-tivity, and replacement policy) and a concise description of the access stream. Specifically, we describe the access stream by its reuse distance distribution; i.e., for each distance d, how many accesses have reuse distance d.... See full list on people.csail.mit.edu This section presents the model for caches with LRU re-placement. We present the complete equations for the age and hit distributions, and the eviction distribution equations for LRU replacement. Sec. 5 extends the eviction distribution to model arbitrary age-based replacement policies. See full list on people.csail.mit.edu The age distribution is used internally by the model to constrain cache capacity. It is presented first because it is the simplest to compute from the other distributions. Since ages measure the time since a line was last refer-enced, a line reaches age a if and only if it is not hit or evicted for at least a accesses. Hence the probability of a li... See full list on people.csail.mit.edu We now show how to compute when hits occur for a given access pattern, again assuming the other distributions are known. The hit distribution is perhaps the most important product of the model, since it yields the cache ’s hit rate. A line will eventually hit if it is not evicted. Intuitively, a line’s hit probability depends on its reuse distance (... See full list on people.csail.mit.edu To support other policies, we must abstract the replacement policy in a way that can be incorporated into the model. We do so through a ranking function, R : age → R, which gives an eviction priority to every age. By convention, higher rank means higher eviction priority. Ranking functions capture many existing policies. For example, LRU ’s ranking ... See full list on people.csail.mit.edu The complete cache model is given by the age (Eq. 1), hit (Eq. 4), and eviction (Eq. 14) distributions. These equations describe a cache using an arbitrary, age-based replacement policy. Our model forms a system of equations that describe a valid solution, but does not yield this solution directly. The implicit nature of our model has benefits. The... See full list on people.csail.mit.edu We now validate our model on synthetic and real bench-marks, showing that it is accurate over diverse replacement policies, access patterns, and cache sizes. See full list on people.csail.mit.edu We have presented a cache model for modern LLCs with high-performance replacement policies. Our model is moti-vated by observations of modern cache architecture that allow us to abstract away details of array organization and focus on modeling the replacement policy. As a result, we capture a broad class of policies at relatively low complexity. We... See full list on people.csail.mit.edu Homework 3 -- LRU Approximations Overview Due: EOD Mar. 3rd The goals of this homework are: Get experience hacking some piece of gem5 Practice considering tradeoffs in cache design Suffer through simulation with more complex workloads (useful for projects!). As usual, you may work with a group. Setup In this lab, we’ll investigate some approximations of the LRU cache replacement policy that ... May 1, 2025 · Methods like the Self-Repellent Random Walk (SRRW) aim to prevent over-exploration of areas but often come with significant computational costs. This research introduces the History-Driven Target (HDT) framework, a novel approach enhancing sampling efficiency while reducing computational demands. Which LRU cache replacement policy uses less bits of information per set? In this lab, we’ll investigate some approximations of the LRU cache replacement policy that use less bits of information per-set than full- LRU . Gem5 implements a pseudo- LRU replacement policy based on trees (tree_plru_rp.hh). Try to implement two additional policies: NMRU: Replace a random block which is not the most recently used. How do I find a thrash resistant cache policy? Look through the available cache replacement policies in gem5 (gem5/src/mem/cache/replacement_policies/), and select one that is thrash resistant. You can test your policies on the previous microbenchmarks, but the point of this assignment is to get some experience with a “better” benchmark. Specifically, we’ll use SPEC 2017. Why do we model a random-candidates cache? In return, this assump-tion greatly simplifies the probability calculations , allowing a simple model to capture diverse access patterns. Similarly, we model an idealized, random-candidates cache, where replacement candidates are drawn at random from cached lines. What is a multilevel cache? Modern processors feature multilevel cache hierarchies. Each core has one or two levels of small, fast, private caches (L1 and L2). These are backed by a much larger last-level cache (LLC) that contains the bulk of cache capacity. Cache architecture varies greatly across levels. With broad applications in network science and distributed optimization, recent innovations like the self-repellent random walk (SRRW) achieve near-zero variance by prioritizing under-sampled states through transition kernel modifications based on past visit frequencies.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2505.18300", "content": "May 23, 2025 · We propose a history-driven target (HDT) framework in Markov Chain Monte Carlo (MCMC) to improve any random walk algorithm on discrete state spaces, such as general undirected graphs, for efficient sampling from target distribution $\\\\boldsymbolμ$. With broad applications in network science and distributed optimization, recent innovations like the self-repellent random walk (SRRW) achieve ... Abstract: We propose a * history-driven target (HDT)* framework in Markov Chain Monte Carlo (MCMC) to improve any random walk algorithm on discrete state spaces, such as general undirected graphs, for efficient sampling from target distribution b o l d s y m b o l m u. With broad applications in network science and distributed optimization, recent innovations like the self-repellent random walk ... Excited to announce that our paper, “ Beyond Self-Repellent Kernels : History-Driven Target Towards Efficient Non-linear MCMC on General Graphs,” has been chosen for an 𝗢𝗿𝗮𝗹 ... We first review the relevant background in modern LLC architecture, replacement policies, and cache modeling. See full list on people.csail.mit.edu Fig. 1 shows the high-level design of our cache model. The input to the model is the cache architecture (its size, associa-tivity, and replacement policy) and a concise description of the access stream. Specifically, we describe the access stream by its reuse distance distribution; i.e., for each distance d, how many accesses have reuse distance d.... See full list on people.csail.mit.edu This section presents the model for caches with LRU re-placement. We present the complete equations for the age and hit distributions, and the eviction distribution equations for LRU replacement. Sec. 5 extends the eviction distribution to model arbitrary age-based replacement policies. See full list on people.csail.mit.edu The age distribution is used internally by the model to constrain cache capacity. It is presented first because it is the simplest to compute from the other distributions. Since ages measure the time since a line was last refer-enced, a line reaches age a if and only if it is not hit or evicted for at least a accesses. Hence the probability of a li... See full list on people.csail.mit.edu We now show how to compute when hits occur for a given access pattern, again assuming the other distributions are known. The hit distribution is perhaps the most important product of the model, since it yields the cache ’s hit rate. A line will eventually hit if it is not evicted. Intuitively, a line’s hit probability depends on its reuse distance (... See full list on people.csail.mit.edu To support other policies, we must abstract the replacement policy in a way that can be incorporated into the model. We do so through a ranking function, R : age → R, which gives an eviction priority to every age. By convention, higher rank means higher eviction priority. Ranking functions capture many existing policies. For example, LRU ’s ranking ... See full list on people.csail.mit.edu The complete cache model is given by the age (Eq. 1), hit (Eq. 4), and eviction (Eq. 14) distributions. These equations describe a cache using an arbitrary, age-based replacement policy. Our model forms a system of equations that describe a valid solution, but does not yield this solution directly. The implicit nature of our model has benefits. The... See full list on people.csail.mit.edu We now validate our model on synthetic and real bench-marks, showing that it is accurate over diverse replacement policies, access patterns, and cache sizes. See full list on people.csail.mit.edu We have presented a cache model for modern LLCs with high-performance replacement policies. Our model is moti-vated by observations of modern cache architecture that allow us to abstract away details of array organization and focus on modeling the replacement policy. As a result, we capture a broad class of policies at relatively low complexity. We... See full list on people.csail.mit.edu Homework 3 -- LRU Approximations Overview Due: EOD Mar. 3rd The goals of this homework are: Get experience hacking some piece of gem5 Practice considering tradeoffs in cache design Suffer through simulation with more complex workloads (useful for projects!). As usual, you may work with a group. Setup In this lab, we’ll investigate some approximations of the LRU cache replacement policy that ... May 1, 2025 · Methods like the Self-Repellent Random Walk (SRRW) aim to prevent over-exploration of areas but often come with significant computational costs. This research introduces the History-Driven Target (HDT) framework, a novel approach enhancing sampling efficiency while reducing computational demands. Which LRU cache replacement policy uses less bits of information per set? In this lab, we’ll investigate some approximations of the LRU cache replacement policy that use less bits of information per-set than full- LRU . Gem5 implements a pseudo- LRU replacement policy based on trees (tree_plru_rp.hh). Try to implement two additional policies: NMRU: Replace a random block which is not the most recently used. How do I find a thrash resistant cache policy? Look through the available cache replacement policies in gem5 (gem5/src/mem/cache/replacement_policies/), and select one that is thrash resistant. You can test your policies on the previous microbenchmarks, but the point of this assignment is to get some experience with a “better” benchmark. Specifically, we’ll use SPEC 2017. Why do we model a random-candidates cache? In return, this assump-tion greatly simplifies the probability calculations , allowing a simple model to capture diverse access patterns. Similarly, we model an idealized, random-candidates cache, where replacement candidates are drawn at random from cached lines. What is a multilevel cache? Modern processors feature multilevel cache hierarchies. Each core has one or two levels of small, fast, private caches (L1 and L2). These are backed by a much larger last-level cache (LLC) that contains the bulk of cache capacity. Cache architecture varies greatly across levels. With broad applications in network science and distributed optimization, recent innovations like the self-repellent random walk (SRRW) achieve near-zero variance by prioritizing under-sampled states through transition kernel modifications based on past visit frequencies."}
+{"idx": 4, "title": "Modeling Cache Performance Beyond LRU Homework 3 -- LRU Approximations · CS251a - GitHub Pages Beyond Self-Repellent Kernels: History-Driven Target Towards ... Homework 3 -- LRU Approximations · CS251a - GitHub Pages Homework 3 -- LRU Approximations · CS251a - GitHub Pages Modeling Cache Performance Beyond LRU - Massachusetts Institute of Modeling Cache Performance Beyond LRU - Massachusetts Institute of Beyond Self-Repellent Kernels: History-Driven Target Towards ...", "date": "", "ddg_snippet": "We first review the relevant background in modern LLC architecture, replacement policies, and cache modeling. See full list on people.csail.mit.edu Fig. 1 shows the high-level design of our cache model. The input to the model is the cache architecture (its size, associa-tivity, and replacement policy) and a concise description of the access stream. Specifically, we describe the access stream by its reuse distance distribution; i.e., for each distance d, how many accesses have reuse distance d.... See full list on people.csail.mit.edu This section presents the model for caches with LRU re-placement. We present the complete equations for the age and hit distributions, and the eviction distribution equations for LRU replacement. Sec. 5 extends the eviction distribution to model arbitrary age-based replacement policies. See full list on people.csail.mit.edu The age distribution is used internally by the model to constrain cache capacity. It is presented first because it is the simplest to compute from the other distributions. Since ages measure the time since a line was last refer-enced, a line reaches age a if and only if it is not hit or evicted for at least a accesses. Hence the probability of a li... See full list on people.csail.mit.edu We now show how to compute when hits occur for a given access pattern, again assuming the other distributions are known. The hit distribution is perhaps the most important product of the model, since it yields the cache ’s hit rate. A line will eventually hit if it is not evicted. Intuitively, a line’s hit probability depends on its reuse distance (... See full list on people.csail.mit.edu To support other policies, we must abstract the replacement policy in a way that can be incorporated into the model. We do so through a ranking function, R : age → R, which gives an eviction priority to every age. By convention, higher rank means higher eviction priority. Ranking functions capture many existing policies. For example, LRU ’s ranking ... See full list on people.csail.mit.edu The complete cache model is given by the age (Eq. 1), hit (Eq. 4), and eviction (Eq. 14) distributions. These equations describe a cache using an arbitrary, age-based replacement policy. Our model forms a system of equations that describe a valid solution, but does not yield this solution directly. The implicit nature of our model has benefits. The... See full list on people.csail.mit.edu We now validate our model on synthetic and real bench-marks, showing that it is accurate over diverse replacement policies, access patterns, and cache sizes. See full list on people.csail.mit.edu We have presented a cache model for modern LLCs with high-performance replacement policies. Our model is moti-vated by observations of modern cache architecture that allow us to abstract away details of array organization and focus on modeling the replacement policy. As a result, we capture a broad class of policies at relatively low complexity. We... See full list on people.csail.mit.edu Homework 3 -- LRU Approximations Overview Due: EOD Mar. 3rd The goals of this homework are: Get experience hacking some piece of gem5 Practice considering tradeoffs in cache design Suffer through simulation with more complex workloads (useful for projects!). As usual, you may work with a group. Setup In this lab, we’ll investigate some approximations of the LRU cache replacement policy that ... May 1, 2025 · Methods like the Self-Repellent Random Walk (SRRW) aim to prevent over-exploration of areas but often come with significant computational costs. This research introduces the History-Driven Target (HDT) framework, a novel approach enhancing sampling efficiency while reducing computational demands. Which LRU cache replacement policy uses less bits of information per set? In this lab, we’ll investigate some approximations of the LRU cache replacement policy that use less bits of information per-set than full- LRU . Gem5 implements a pseudo- LRU replacement policy based on trees (tree_plru_rp.hh). Try to implement two additional policies: NMRU: Replace a random block which is not the most recently used. How do I find a thrash resistant cache policy? Look through the available cache replacement policies in gem5 (gem5/src/mem/cache/replacement_policies/), and select one that is thrash resistant. You can test your policies on the previous microbenchmarks, but the point of this assignment is to get some experience with a “better” benchmark. Specifically, we’ll use SPEC 2017. Why do we model a random-candidates cache? In return, this assump-tion greatly simplifies the probability calculations , allowing a simple model to capture diverse access patterns. Similarly, we model an idealized, random-candidates cache, where replacement candidates are drawn at random from cached lines. What is a multilevel cache? Modern processors feature multilevel cache hierarchies. Each core has one or two levels of small, fast, private caches (L1 and L2). These are backed by a much larger last-level cache (LLC) that contains the bulk of cache capacity. Cache architecture varies greatly across levels. With broad applications in network science and distributed optimization, recent innovations like the self-repellent random walk (SRRW) achieve near-zero variance by prioritizing under-sampled states through transition kernel modifications based on past visit frequencies.", "subpage_snippet": "", "source": "people.csail.mit.edu", "link": "https://people.csail.mit.edu/sanchez/papers/2016.model.hpca.pdf", "content": "We first review the relevant background in modern LLC architecture, replacement policies, and cache modeling. See full list on people.csail.mit.edu Fig. 1 shows the high-level design of our cache model. The input to the model is the cache architecture (its size, associa-tivity, and replacement policy) and a concise description of the access stream. Specifically, we describe the access stream by its reuse distance distribution; i.e., for each distance d, how many accesses have reuse distance d.... See full list on people.csail.mit.edu This section presents the model for caches with LRU re-placement. We present the complete equations for the age and hit distributions, and the eviction distribution equations for LRU replacement. Sec. 5 extends the eviction distribution to model arbitrary age-based replacement policies. See full list on people.csail.mit.edu The age distribution is used internally by the model to constrain cache capacity. It is presented first because it is the simplest to compute from the other distributions. Since ages measure the time since a line was last refer-enced, a line reaches age a if and only if it is not hit or evicted for at least a accesses. Hence the probability of a li... See full list on people.csail.mit.edu We now show how to compute when hits occur for a given access pattern, again assuming the other distributions are known. The hit distribution is perhaps the most important product of the model, since it yields the cache ’s hit rate. A line will eventually hit if it is not evicted. Intuitively, a line’s hit probability depends on its reuse distance (... See full list on people.csail.mit.edu To support other policies, we must abstract the replacement policy in a way that can be incorporated into the model. We do so through a ranking function, R : age → R, which gives an eviction priority to every age. By convention, higher rank means higher eviction priority. Ranking functions capture many existing policies. For example, LRU ’s ranking ... See full list on people.csail.mit.edu The complete cache model is given by the age (Eq. 1), hit (Eq. 4), and eviction (Eq. 14) distributions. These equations describe a cache using an arbitrary, age-based replacement policy. Our model forms a system of equations that describe a valid solution, but does not yield this solution directly. The implicit nature of our model has benefits. The... See full list on people.csail.mit.edu We now validate our model on synthetic and real bench-marks, showing that it is accurate over diverse replacement policies, access patterns, and cache sizes. See full list on people.csail.mit.edu We have presented a cache model for modern LLCs with high-performance replacement policies. Our model is moti-vated by observations of modern cache architecture that allow us to abstract away details of array organization and focus on modeling the replacement policy. As a result, we capture a broad class of policies at relatively low complexity. We... See full list on people.csail.mit.edu Homework 3 -- LRU Approximations Overview Due: EOD Mar. 3rd The goals of this homework are: Get experience hacking some piece of gem5 Practice considering tradeoffs in cache design Suffer through simulation with more complex workloads (useful for projects!). As usual, you may work with a group. Setup In this lab, we’ll investigate some approximations of the LRU cache replacement policy that ... May 1, 2025 · Methods like the Self-Repellent Random Walk (SRRW) aim to prevent over-exploration of areas but often come with significant computational costs. This research introduces the History-Driven Target (HDT) framework, a novel approach enhancing sampling efficiency while reducing computational demands. Which LRU cache replacement policy uses less bits of information per set? In this lab, we’ll investigate some approximations of the LRU cache replacement policy that use less bits of information per-set than full- LRU . Gem5 implements a pseudo- LRU replacement policy based on trees (tree_plru_rp.hh). Try to implement two additional policies: NMRU: Replace a random block which is not the most recently used. How do I find a thrash resistant cache policy? Look through the available cache replacement policies in gem5 (gem5/src/mem/cache/replacement_policies/), and select one that is thrash resistant. You can test your policies on the previous microbenchmarks, but the point of this assignment is to get some experience with a “better” benchmark. Specifically, we’ll use SPEC 2017. Why do we model a random-candidates cache? In return, this assump-tion greatly simplifies the probability calculations , allowing a simple model to capture diverse access patterns. Similarly, we model an idealized, random-candidates cache, where replacement candidates are drawn at random from cached lines. What is a multilevel cache? Modern processors feature multilevel cache hierarchies. Each core has one or two levels of small, fast, private caches (L1 and L2). These are backed by a much larger last-level cache (LLC) that contains the bulk of cache capacity. Cache architecture varies greatly across levels. With broad applications in network science and distributed optimization, recent innovations like the self-repellent random walk (SRRW) achieve near-zero variance by prioritizing under-sampled states through transition kernel modifications based on past visit frequencies."}
+{"idx": 5, "title": "Homework 3 -- LRU Approximations · CS251a - GitHub Pages", "date": "", "ddg_snippet": "Homework 3 -- LRU Approximations Overview Due: EOD Mar. 3rd The goals of this homework are: Get experience hacking some piece of gem5 Practice considering tradeoffs in cache design Suffer through simulation with more complex workloads (useful for projects!). As usual, you may work with a group. Setup In this lab, we’ll investigate some approximations of the LRU cache replacement policy that ...", "subpage_snippet": "", "source": "polyarch.github.io", "link": "https://polyarch.github.io/cs251a/hws/hw3/", "content": "Homework 3 -- LRU Approximations Overview Due: EOD Mar. 3rd The goals of this homework are: Get experience hacking some piece of gem5 Practice considering tradeoffs in cache design Suffer through simulation with more complex workloads (useful for projects!). As usual, you may work with a group. Setup In this lab, we’ll investigate some approximations of the LRU cache replacement policy that ..."}
+{"idx": 6, "title": "Beyond Self-Repellent Kernels: History-Driven Target Towards ...", "date": "", "ddg_snippet": "May 1, 2025 · Methods like the Self-Repellent Random Walk (SRRW) aim to prevent over-exploration of areas but often come with significant computational costs. This research introduces the History-Driven Target (HDT) framework, a novel approach enhancing sampling efficiency while reducing computational demands.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=0yzOEMbShU", "content": "May 1, 2025 · Methods like the Self-Repellent Random Walk (SRRW) aim to prevent over-exploration of areas but often come with significant computational costs. This research introduces the History-Driven Target (HDT) framework, a novel approach enhancing sampling efficiency while reducing computational demands."}
+{"idx": 7, "title": "Как предотвратить повторное вычисление функции с lru _ cache", "date": "", "ddg_snippet": "Как работает Least Recently Used ( LRU ) алгоритм. Параметры функции lru _ cache . self .data = data @. ftl. cached _property def sumdata( self )", "subpage_snippet": "", "source": "python-school.ru", "link": "https://python-school.ru/blog/python/lru_cache/", "content": "Как работает Least Recently Used ( LRU ) алгоритм. Параметры функции lru _ cache . self .data = data @. ftl. cached _property def sumdata( self )"}
+{"idx": 8, "title": "lru - cache - npm", "date": "", "ddg_snippet": "A cache object that deletes the least - recently - used items.. Latest version: 11.2.1, last published: 13 days ago. Start using lru - cache in your project by running `npm i lru - cache `. There are 7528 other projects in the npm registry using lru - cache .", "subpage_snippet": "", "source": "www.npmjs.com", "link": "https://www.npmjs.com/package/lru-cache", "content": "A cache object that deletes the least - recently - used items.. Latest version: 11.2.1, last published: 13 days ago. Start using lru - cache in your project by running `npm i lru - cache `. There are 7528 other projects in the npm registry using lru - cache ."}
+{"idx": 9, "title": "Implementing LRU cache using std::map and std::list... - Stack Overflow", "date": "", "ddg_snippet": "The cache uses Least Recently Used policy explained by the behaviour: If the cache has a capacity to store 5 keys like 5 3 2 1 4 then if next key=1 comes as a hit, the cache order becomes 1 5 3 2 4.", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/79765807/implementing-lru-cache-using-stdmap-and-stdlist-in-c-cant-get-required-o", "content": "The cache uses Least Recently Used policy explained by the behaviour: If the cache has a capacity to store 5 keys like 5 3 2 1 4 then if next key=1 comes as a hit, the cache order becomes 1 5 3 2 4."}
diff --git a/data/sampled_jsons/Beyond_Self-Repellent_Kernels_History-Driven_Target_LRU_cache_scheme_Equation_15_neighboring_state_n.jsonl b/data/sampled_jsons/Beyond_Self-Repellent_Kernels_History-Driven_Target_LRU_cache_scheme_Equation_15_neighboring_state_n.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8b491da5afaa14bd0235ac8644d651fc35afc899
--- /dev/null
+++ b/data/sampled_jsons/Beyond_Self-Repellent_Kernels_History-Driven_Target_LRU_cache_scheme_Equation_15_neighboring_state_n.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Cache (computing) - Wikipedia", "date": "", "ddg_snippet": "Diagram of a CPU memory cache operation. In computing, a cache is a hardware or software component that stores data so that future requests for that data can be served faster; the data stored in a cache might be the result of an earlier computation o...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Cache_(computing)", "content": "Diagram of a CPU memory cache operation. In computing, a cache is a hardware or software component that stores data so that future requests for that data can be served faster; the data stored in a cache might be the result of an earlier computation o..."}
+{"idx": 1, "title": "Beyond Self-Repellent Kernels: History-Driven Target Towards ... Beyond Self-Repellent Kernels: History-Driven Target Towards ... Our paper on efficient MCMC on graphs accepted at ICML 2025 Beyond Self-Repellent Kernels: History-Driven Target Towards ... Modeling Cache Performance Beyond LRU ICML Poster Beyond Self-Repellent Kernels: History-Driven ... awesome-low-level-design/problems/lru-cache.md at main ...", "date": "", "ddg_snippet": "May 23, 2025 · We propose a history-driven target (HDT) framework in Markov Chain Monte Carlo (MCMC) to improve any random walk algorithm on discrete state spaces, such as general undirected graphs, for efficient sampling from target distribution $\\\\boldsymbolμ$. With broad applications in network science and distributed optimization, recent innovations like the self-repellent random walk (SRRW) achieve ... Least Recently Used ( LRU ) cache scheme Essential idea: track only recently visited states, discarding the least-recently used when capacity in cache is reached, whose size = ( acts as the compression ratio) Leverages temporal locality: non- neighboring states do not affect self -repellency Excited to announce that our paper, “ Beyond Self-Repellent Kernels : History-Driven Target Towards Efficient Non-linear MCMC on General Graphs,” has been chosen for an 𝗢𝗿𝗮𝗹 ... We propose a history-driven target (HDT)framework in Markov Chain Monte Carlo (MCMC) to improve any random walk algorithm on discrete state spaces, such as general undirected graphs, for efficient sampling from target distribution 𝝁𝝁{\\bm{\\mu}}bold_italic_μ. With broad applications in network science and distributed optimization, recent innovations like the self-repellent random walk ... We first review the relevant background in modern LLC architecture, replacement policies, and cache modeling. See full list on people.csail.mit.edu Fig. 1 shows the high-level design of our cache model. The input to the model is the cache architecture (its size, associa-tivity, and replacement policy) and a concise description of the access stream. Specifically, we describe the access stream by its reuse distance distribution; i.e., for each distance d, how many accesses have reuse distance d.... See full list on people.csail.mit.edu This section presents the model for caches with LRU re-placement. We present the complete equations for the age and hit distributions, and the eviction distribution equations for LRU replacement. Sec. 5 extends the eviction distribution to model arbitrary age-based replacement policies. See full list on people.csail.mit.edu The age distribution is used internally by the model to constrain cache capacity. It is presented first because it is the simplest to compute from the other distributions. Since ages measure the time since a line was last refer-enced, a line reaches age a if and only if it is not hit or evicted for at least a accesses. Hence the probability of a li... See full list on people.csail.mit.edu We now show how to compute when hits occur for a given access pattern, again assuming the other distributions are known. The hit distribution is perhaps the most important product of the model, since it yields the cache ’s hit rate. A line will eventually hit if it is not evicted. Intuitively, a line’s hit probability depends on its reuse distance (... See full list on people.csail.mit.edu To support other policies, we must abstract the replacement policy in a way that can be incorporated into the model. We do so through a ranking function, R : age → R, which gives an eviction priority to every age. By convention, higher rank means higher eviction priority. Ranking functions capture many existing policies. For example, LRU ’s ranking ... See full list on people.csail.mit.edu The complete cache model is given by the age (Eq. 1), hit (Eq. 4), and eviction (Eq. 14) distributions. These equations describe a cache using an arbitrary, age-based replacement policy. Our model forms a system of equations that describe a valid solution, but does not yield this solution directly. The implicit nature of our model has benefits. The... See full list on people.csail.mit.edu We now validate our model on synthetic and real bench-marks, showing that it is accurate over diverse replacement policies, access patterns, and cache sizes. See full list on people.csail.mit.edu We have presented a cache model for modern LLCs with high-performance replacement policies. Our model is moti-vated by observations of modern cache architecture that allow us to abstract away details of array organization and focus on modeling the replacement policy. As a result, we capture a broad class of policies at relatively low complexity. We... See full list on people.csail.mit.edu Jul 10, 2025 · Methods like the Self-Repellent Random Walk (SRRW) aim to prevent over-exploration of areas but often come with significant computational costs.This research introduces the History-Driven Target (HDT) framework, a novel approach enhancing sampling efficiency while reducing computational demands. The LRU cache should support the following operations: put (key, value): Insert a key-value pair into the cache . If the cache is at capacity, remove the least recently used item before inserting the new item. get (key): Get the value associated with the given key. If the key exists in the cache , move it to the front of the cache (most recently used) and return its value. If the key does not ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2505.18300", "content": "May 23, 2025 · We propose a history-driven target (HDT) framework in Markov Chain Monte Carlo (MCMC) to improve any random walk algorithm on discrete state spaces, such as general undirected graphs, for efficient sampling from target distribution $\\\\boldsymbolμ$. With broad applications in network science and distributed optimization, recent innovations like the self-repellent random walk (SRRW) achieve ... Least Recently Used ( LRU ) cache scheme Essential idea: track only recently visited states, discarding the least-recently used when capacity in cache is reached, whose size = ( acts as the compression ratio) Leverages temporal locality: non- neighboring states do not affect self -repellency Excited to announce that our paper, “ Beyond Self-Repellent Kernels : History-Driven Target Towards Efficient Non-linear MCMC on General Graphs,” has been chosen for an 𝗢𝗿𝗮𝗹 ... We propose a history-driven target (HDT)framework in Markov Chain Monte Carlo (MCMC) to improve any random walk algorithm on discrete state spaces, such as general undirected graphs, for efficient sampling from target distribution 𝝁𝝁{\\bm{\\mu}}bold_italic_μ. With broad applications in network science and distributed optimization, recent innovations like the self-repellent random walk ... We first review the relevant background in modern LLC architecture, replacement policies, and cache modeling. See full list on people.csail.mit.edu Fig. 1 shows the high-level design of our cache model. The input to the model is the cache architecture (its size, associa-tivity, and replacement policy) and a concise description of the access stream. Specifically, we describe the access stream by its reuse distance distribution; i.e., for each distance d, how many accesses have reuse distance d.... See full list on people.csail.mit.edu This section presents the model for caches with LRU re-placement. We present the complete equations for the age and hit distributions, and the eviction distribution equations for LRU replacement. Sec. 5 extends the eviction distribution to model arbitrary age-based replacement policies. See full list on people.csail.mit.edu The age distribution is used internally by the model to constrain cache capacity. It is presented first because it is the simplest to compute from the other distributions. Since ages measure the time since a line was last refer-enced, a line reaches age a if and only if it is not hit or evicted for at least a accesses. Hence the probability of a li... See full list on people.csail.mit.edu We now show how to compute when hits occur for a given access pattern, again assuming the other distributions are known. The hit distribution is perhaps the most important product of the model, since it yields the cache ’s hit rate. A line will eventually hit if it is not evicted. Intuitively, a line’s hit probability depends on its reuse distance (... See full list on people.csail.mit.edu To support other policies, we must abstract the replacement policy in a way that can be incorporated into the model. We do so through a ranking function, R : age → R, which gives an eviction priority to every age. By convention, higher rank means higher eviction priority. Ranking functions capture many existing policies. For example, LRU ’s ranking ... See full list on people.csail.mit.edu The complete cache model is given by the age (Eq. 1), hit (Eq. 4), and eviction (Eq. 14) distributions. These equations describe a cache using an arbitrary, age-based replacement policy. Our model forms a system of equations that describe a valid solution, but does not yield this solution directly. The implicit nature of our model has benefits. The... See full list on people.csail.mit.edu We now validate our model on synthetic and real bench-marks, showing that it is accurate over diverse replacement policies, access patterns, and cache sizes. See full list on people.csail.mit.edu We have presented a cache model for modern LLCs with high-performance replacement policies. Our model is moti-vated by observations of modern cache architecture that allow us to abstract away details of array organization and focus on modeling the replacement policy. As a result, we capture a broad class of policies at relatively low complexity. We... See full list on people.csail.mit.edu Jul 10, 2025 · Methods like the Self-Repellent Random Walk (SRRW) aim to prevent over-exploration of areas but often come with significant computational costs.This research introduces the History-Driven Target (HDT) framework, a novel approach enhancing sampling efficiency while reducing computational demands. The LRU cache should support the following operations: put (key, value): Insert a key-value pair into the cache . If the cache is at capacity, remove the least recently used item before inserting the new item. get (key): Get the value associated with the given key. If the key exists in the cache , move it to the front of the cache (most recently used) and return its value. If the key does not ..."}
+{"idx": 2, "title": "Beyond Self-Repellent Kernels: History-Driven Target Towards ...", "date": "", "ddg_snippet": "Least Recently Used ( LRU ) cache scheme Essential idea: track only recently visited states, discarding the least-recently used when capacity in cache is reached, whose size = ( acts as the compression ratio) Leverages temporal locality: non- neighboring states do not affect self -repellency", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/media/icml-2025/Slides/47270.pdf", "content": "Least Recently Used ( LRU ) cache scheme Essential idea: track only recently visited states, discarding the least-recently used when capacity in cache is reached, whose size = ( acts as the compression ratio) Leverages temporal locality: non- neighboring states do not affect self -repellency"}
+{"idx": 3, "title": "Beyond Self-Repellent Kernels: History-Driven Target Towards ...", "date": "", "ddg_snippet": "We propose a history-driven target (HDT)framework in Markov Chain Monte Carlo (MCMC) to improve any random walk algorithm on discrete state spaces, such as general undirected graphs, for efficient sampling from target distribution 𝝁𝝁{\\bm{\\mu}}bold_italic_μ. With broad applications in network science and distributed optimization, recent innovations like the self-repellent random walk ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.18300v1", "content": "We propose a history-driven target (HDT)framework in Markov Chain Monte Carlo (MCMC) to improve any random walk algorithm on discrete state spaces, such as general undirected graphs, for efficient sampling from target distribution 𝝁𝝁{\\bm{\\mu}}bold_italic_μ. With broad applications in network science and distributed optimization, recent innovations like the self-repellent random walk ..."}
+{"idx": 4, "title": "Modeling Cache Performance Beyond LRU ICML Poster Beyond Self-Repellent Kernels: History-Driven ... awesome-low-level-design/problems/lru-cache.md at main ...", "date": "", "ddg_snippet": "We first review the relevant background in modern LLC architecture, replacement policies, and cache modeling. See full list on people.csail.mit.edu Fig. 1 shows the high-level design of our cache model. The input to the model is the cache architecture (its size, associa-tivity, and replacement policy) and a concise description of the access stream. Specifically, we describe the access stream by its reuse distance distribution; i.e., for each distance d, how many accesses have reuse distance d.... See full list on people.csail.mit.edu This section presents the model for caches with LRU re-placement. We present the complete equations for the age and hit distributions, and the eviction distribution equations for LRU replacement. Sec. 5 extends the eviction distribution to model arbitrary age-based replacement policies. See full list on people.csail.mit.edu The age distribution is used internally by the model to constrain cache capacity. It is presented first because it is the simplest to compute from the other distributions. Since ages measure the time since a line was last refer-enced, a line reaches age a if and only if it is not hit or evicted for at least a accesses. Hence the probability of a li... See full list on people.csail.mit.edu We now show how to compute when hits occur for a given access pattern, again assuming the other distributions are known. The hit distribution is perhaps the most important product of the model, since it yields the cache ’s hit rate. A line will eventually hit if it is not evicted. Intuitively, a line’s hit probability depends on its reuse distance (... See full list on people.csail.mit.edu To support other policies, we must abstract the replacement policy in a way that can be incorporated into the model. We do so through a ranking function, R : age → R, which gives an eviction priority to every age. By convention, higher rank means higher eviction priority. Ranking functions capture many existing policies. For example, LRU ’s ranking ... See full list on people.csail.mit.edu The complete cache model is given by the age (Eq. 1), hit (Eq. 4), and eviction (Eq. 14) distributions. These equations describe a cache using an arbitrary, age-based replacement policy. Our model forms a system of equations that describe a valid solution, but does not yield this solution directly. The implicit nature of our model has benefits. The... See full list on people.csail.mit.edu We now validate our model on synthetic and real bench-marks, showing that it is accurate over diverse replacement policies, access patterns, and cache sizes. See full list on people.csail.mit.edu We have presented a cache model for modern LLCs with high-performance replacement policies. Our model is moti-vated by observations of modern cache architecture that allow us to abstract away details of array organization and focus on modeling the replacement policy. As a result, we capture a broad class of policies at relatively low complexity. We... See full list on people.csail.mit.edu Jul 10, 2025 · Methods like the Self-Repellent Random Walk (SRRW) aim to prevent over-exploration of areas but often come with significant computational costs.This research introduces the History-Driven Target (HDT) framework, a novel approach enhancing sampling efficiency while reducing computational demands. The LRU cache should support the following operations: put (key, value): Insert a key-value pair into the cache . If the cache is at capacity, remove the least recently used item before inserting the new item. get (key): Get the value associated with the given key. If the key exists in the cache , move it to the front of the cache (most recently used) and return its value. If the key does not ...", "subpage_snippet": "", "source": "people.csail.mit.edu", "link": "https://people.csail.mit.edu/sanchez/papers/2016.model.hpca.pdf", "content": "We first review the relevant background in modern LLC architecture, replacement policies, and cache modeling. See full list on people.csail.mit.edu Fig. 1 shows the high-level design of our cache model. The input to the model is the cache architecture (its size, associa-tivity, and replacement policy) and a concise description of the access stream. Specifically, we describe the access stream by its reuse distance distribution; i.e., for each distance d, how many accesses have reuse distance d.... See full list on people.csail.mit.edu This section presents the model for caches with LRU re-placement. We present the complete equations for the age and hit distributions, and the eviction distribution equations for LRU replacement. Sec. 5 extends the eviction distribution to model arbitrary age-based replacement policies. See full list on people.csail.mit.edu The age distribution is used internally by the model to constrain cache capacity. It is presented first because it is the simplest to compute from the other distributions. Since ages measure the time since a line was last refer-enced, a line reaches age a if and only if it is not hit or evicted for at least a accesses. Hence the probability of a li... See full list on people.csail.mit.edu We now show how to compute when hits occur for a given access pattern, again assuming the other distributions are known. The hit distribution is perhaps the most important product of the model, since it yields the cache ’s hit rate. A line will eventually hit if it is not evicted. Intuitively, a line’s hit probability depends on its reuse distance (... See full list on people.csail.mit.edu To support other policies, we must abstract the replacement policy in a way that can be incorporated into the model. We do so through a ranking function, R : age → R, which gives an eviction priority to every age. By convention, higher rank means higher eviction priority. Ranking functions capture many existing policies. For example, LRU ’s ranking ... See full list on people.csail.mit.edu The complete cache model is given by the age (Eq. 1), hit (Eq. 4), and eviction (Eq. 14) distributions. These equations describe a cache using an arbitrary, age-based replacement policy. Our model forms a system of equations that describe a valid solution, but does not yield this solution directly. The implicit nature of our model has benefits. The... See full list on people.csail.mit.edu We now validate our model on synthetic and real bench-marks, showing that it is accurate over diverse replacement policies, access patterns, and cache sizes. See full list on people.csail.mit.edu We have presented a cache model for modern LLCs with high-performance replacement policies. Our model is moti-vated by observations of modern cache architecture that allow us to abstract away details of array organization and focus on modeling the replacement policy. As a result, we capture a broad class of policies at relatively low complexity. We... See full list on people.csail.mit.edu Jul 10, 2025 · Methods like the Self-Repellent Random Walk (SRRW) aim to prevent over-exploration of areas but often come with significant computational costs.This research introduces the History-Driven Target (HDT) framework, a novel approach enhancing sampling efficiency while reducing computational demands. The LRU cache should support the following operations: put (key, value): Insert a key-value pair into the cache . If the cache is at capacity, remove the least recently used item before inserting the new item. get (key): Get the value associated with the given key. If the key exists in the cache , move it to the front of the cache (most recently used) and return its value. If the key does not ..."}
+{"idx": 5, "title": "ICML Poster Beyond Self-Repellent Kernels: History-Driven ...", "date": "", "ddg_snippet": "Jul 10, 2025 · Methods like the Self-Repellent Random Walk (SRRW) aim to prevent over-exploration of areas but often come with significant computational costs.This research introduces the History-Driven Target (HDT) framework, a novel approach enhancing sampling efficiency while reducing computational demands.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46659", "content": "Jul 10, 2025 · Methods like the Self-Repellent Random Walk (SRRW) aim to prevent over-exploration of areas but often come with significant computational costs.This research introduces the History-Driven Target (HDT) framework, a novel approach enhancing sampling efficiency while reducing computational demands."}
+{"idx": 6, "title": "awesome-low-level-design/problems/lru-cache.md at main ...", "date": "", "ddg_snippet": "The LRU cache should support the following operations: put (key, value): Insert a key-value pair into the cache . If the cache is at capacity, remove the least recently used item before inserting the new item. get (key): Get the value associated with the given key. If the key exists in the cache , move it to the front of the cache (most recently used) and return its value. If the key does not ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/ashishps1/awesome-low-level-design/blob/main/problems/lru-cache.md", "content": "The LRU cache should support the following operations: put (key, value): Insert a key-value pair into the cache . If the cache is at capacity, remove the least recently used item before inserting the new item. get (key): Get the value associated with the given key. If the key exists in the cache , move it to the front of the cache (most recently used) and return its value. If the key does not ..."}
+{"idx": 7, "title": "Mastering the LRU Cache Interview Question... | Stackademic", "date": "", "ddg_snippet": "Why LRU Cache Is a Popular Interview Question. class LRUCache : initialize capacity initialize hash_map (key → node) initialize doubly_linked_list (head & tail). function get(key): if key not in hash_map: return -1 move node to front (most recent)...", "subpage_snippet": "", "source": "blog.stackademic.com", "link": "https://blog.stackademic.com/mastering-the-lru-cache-interview-question-java-c-python-implementations-with-real-world-475ba5e58011", "content": "Why LRU Cache Is a Popular Interview Question. class LRUCache : initialize capacity initialize hash_map (key → node) initialize doubly_linked_list (head & tail). function get(key): if key not in hash_map: return -1 move node to front (most recent)..."}
+{"idx": 8, "title": "Как предотвратить повторное вычисление функции с lru _ cache", "date": "", "ddg_snippet": "Сегодня рассмотрим функции кэширования lru _ cache , cache , cached _property.Функция lru _ cache предназначается для мемоизации, т.е. кэширует результат в памяти.", "subpage_snippet": "", "source": "python-school.ru", "link": "https://python-school.ru/blog/python/lru_cache/", "content": "Сегодня рассмотрим функции кэширования lru _ cache , cache , cached _property.Функция lru _ cache предназначается для мемоизации, т.е. кэширует результат в памяти."}
+{"idx": 9, "title": "Implementing LRU cache using std::map and std::list... - Stack Overflow", "date": "", "ddg_snippet": "The cache uses Least Recently Used policy explained by the behaviour: If the cache has a capacity to store 5 keys like 5 3 2 1 4 then if next key=1 comes as a hit, the cache order becomes 1 5 3 2 4.", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/79765807/implementing-lru-cache-using-stdmap-and-stdlist-in-c-cant-get-required-o", "content": "The cache uses Least Recently Used policy explained by the behaviour: If the cache has a capacity to store 5 keys like 5 3 2 1 4 then if next key=1 comes as a hit, the cache order becomes 1 5 3 2 4."}
diff --git a/data/sampled_jsons/Black_et_al._2023_diffusion_model.jsonl b/data/sampled_jsons/Black_et_al._2023_diffusion_model.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..537e6a5fef22cbfff027b786d3b3e05bff254e04
--- /dev/null
+++ b/data/sampled_jsons/Black_et_al._2023_diffusion_model.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "What are Diffusion Models? | Lil'Log", "date": "", "ddg_snippet": "... diffusion -based generative models have been proposed with similar ideas underneath, including diffusion probabilistic models ( Sohl-Dickstein et al ...", "subpage_snippet": "", "source": "lilianweng.github.io", "link": "https://lilianweng.github.io/posts/2021-07-11-diffusion-models/", "content": "... diffusion -based generative models have been proposed with similar ideas underneath, including diffusion probabilistic models ( Sohl-Dickstein et al ..."}
+{"idx": 1, "title": "Giving a Hand to Diffusion Models: a Two-Stage Approach to", "date": "", "ddg_snippet": "... Dhariwal and Nichol [ 7 ] demonstrated the capability of denoising diffusion models to generate high-quality samples unconditionally with Song et al ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.10731v2", "content": "... Dhariwal and Nichol [ 7 ] demonstrated the capability of denoising diffusion models to generate high-quality samples unconditionally with Song et al ..."}
+{"idx": 2, "title": "DiC: Rethinking Conv3x3 Designs in Diffusion Models", "date": "", "ddg_snippet": "... only capitalizes on the speed advantages of 3x3 convolutions but also achieves diffusion generation performance on par with transformer-based models ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.00603v1", "content": "... only capitalizes on the speed advantages of 3x3 convolutions but also achieves diffusion generation performance on par with transformer-based models ..."}
+{"idx": 3, "title": "Diffusion is spectral autoregression – Sander Dieleman", "date": "", "ddg_snippet": "I hope this format will also help drive home the point that this connection between diffusion models and autoregressive models is “real”, and not ...", "subpage_snippet": "", "source": "sander.ai", "link": "https://sander.ai/2024/09/02/spectral-autoregression.html", "content": "I hope this format will also help drive home the point that this connection between diffusion models and autoregressive models is “real”, and not ..."}
+{"idx": 4, "title": "Diffusion Models for Black-Box Optimization", "date": "", "ddg_snippet": "... Diffusion Models for Black -Box Optimization}, author = {Krishnamoorthy, Siddarth and Mashkaria, Satvik Mehul and Grover, Aditya}, booktitle = ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v202/krishnamoorthy23a.html", "content": "... Diffusion Models for Black -Box Optimization}, author = {Krishnamoorthy, Siddarth and Mashkaria, Satvik Mehul and Grover, Aditya}, booktitle = ..."}
+{"idx": 5, "title": "ACL 2024 Tutorial: Vulnerabilities of Large Language Models to", "date": "", "ddg_snippet": "... Black Box Large Language Models in ... Evaluating the Robustness of Text-to-image Diffusion Models against Real-world Attacks (Gao et al , 2023 )", "subpage_snippet": "", "source": "llm-vulnerability.github.io", "link": "https://llm-vulnerability.github.io/", "content": "... Black Box Large Language Models in ... Evaluating the Robustness of Text-to-image Diffusion Models against Real-world Attacks (Gao et al , 2023 )"}
+{"idx": 6, "title": "Understanding the diffusion of large language models: summary", "date": "", "ddg_snippet": "In my case studies, the diffusion of GPT-3-like models to top AI developers besides OpenAI—namely, DeepMind and Google' s AI labs [14] —seemed ...", "subpage_snippet": "", "source": "forum.effectivealtruism.org", "link": "https://forum.effectivealtruism.org/posts/nc3JFZbqnzWWAPkmz/understanding-the-diffusion-of-large-language-models-summary-1", "content": "In my case studies, the diffusion of GPT-3-like models to top AI developers besides OpenAI—namely, DeepMind and Google' s AI labs [14] —seemed ..."}
+{"idx": 7, "title": "AI & diffusion models: artistic, scientific, and tomorrow", "date": "", "ddg_snippet": "In other words, the objective of such a model is to learn the global rules specific to all the elements of the dataset in order to succeed in ...", "subpage_snippet": "", "source": "www.kickmaker.fr", "link": "https://www.kickmaker.fr/blog/ai-diffusion-models-artistic-scientific-and-tomorrow-industrial-revolution/", "content": "In other words, the objective of such a model is to learn the global rules specific to all the elements of the dataset in order to succeed in ..."}
+{"idx": 8, "title": "Tim Salimans | DeepAI", "date": "", "ddg_snippet": "Recently, Rissanen et al ., (2022) have presented a new type of diffusion ... ... Diffusion -based generative models have demonstrated a capacity for ...", "subpage_snippet": "", "source": "deepai.org", "link": "https://deepai.org/profile/tim-salimans", "content": "Recently, Rissanen et al ., (2022) have presented a new type of diffusion ... ... Diffusion -based generative models have demonstrated a capacity for ..."}
+{"idx": 9, "title": "Aditya Grover | DeepAI", "date": "", "ddg_snippet": "... Unraveling Feedback Acquisition for Aligning Large Language Models ... Aligning large language models (LLMs) with human values and intents crit...", "subpage_snippet": "", "source": "deepai.org", "link": "https://deepai.org/profile/aditya-grover", "content": "... Unraveling Feedback Acquisition for Aligning Large Language Models ... Aligning large language models (LLMs) with human values and intents crit..."}
diff --git a/data/sampled_jsons/Blink_of_an_eye_Marvin_Li_Aayush_Karan_Sitan_Chen_GitHub_repository.jsonl b/data/sampled_jsons/Blink_of_an_eye_Marvin_Li_Aayush_Karan_Sitan_Chen_GitHub_repository.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d7c7a8642e8339342727d980834b68dab38081a0
--- /dev/null
+++ b/data/sampled_jsons/Blink_of_an_eye_Marvin_Li_Aayush_Karan_Sitan_Chen_GitHub_repository.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2502.00921] Blink of an eye : a simple theory for feature localization in...", "date": "", "ddg_snippet": "Large language models can exhibit unexpected behavior in the blink of an eye . In a recent computer use demo, a language model switched from coding to Googling pictures of Yellowstone, and these sudden shifts in behavior have also been observed in reasoning patterns and jailbreaks.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.00921", "content": "Large language models can exhibit unexpected behavior in the blink of an eye . In a recent computer use demo, a language model switched from coding to Googling pictures of Yellowstone, and these sudden shifts in behavior have also been observed in reasoning patterns and jailbreaks."}
+{"idx": 1, "title": "(PDF) Blink of an eye : a simple theory for feature localization in...", "date": "", "ddg_snippet": "generative models. Marvin Li *. Harvard College. Aayush Karan †. Marvin Li and Sitan Chen . Critical windows: non-asymptotic theory for feature emergence. in diffusion models, 2024.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/388658326_Blink_of_an_eye_a_simple_theory_for_feature_localization_in_generative_models", "content": "generative models. Marvin Li *. Harvard College. Aayush Karan †. Marvin Li and Sitan Chen . Critical windows: non-asymptotic theory for feature emergence. in diffusion models, 2024."}
+{"idx": 2, "title": "Blink of an eye : a simple theory for feature localization in generative...", "date": "", "ddg_snippet": "Published 2/4/2025 by Marvin Li , Aayush Karan , Sitan Chen .Research explores how generative AI models learn specific features during training. Introduces \" blink of an eye \" theory to explain rapid feature emergence. Shows features appear in critical time windows during model training.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/blink-eye-simple-theory-feature-localization-generative", "content": "Published 2/4/2025 by Marvin Li , Aayush Karan , Sitan Chen .Research explores how generative AI models learn specific features during training. Introduces \" blink of an eye \" theory to explain rapid feature emergence. Shows features appear in critical time windows during model training."}
+{"idx": 3, "title": "Marvin Li - Google Scholar", "date": "", "ddg_snippet": "Sitan Chen Sitan ChenAssistant Professor of Computer Science, Harvard UniversityVerified email at seas.harvard.edu. Blink of an eye : a simple theory for feature localization in generative models.", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=NhMTzpsAAAAJ&hl=en", "content": "Sitan Chen Sitan ChenAssistant Professor of Computer Science, Harvard UniversityVerified email at seas.harvard.edu. Blink of an eye : a simple theory for feature localization in generative models."}
+{"idx": 4, "title": "Homepage: Sitan Chen", "date": "", "ddg_snippet": "Blink of an Eye : A Simple Theory for Feature Localization in Generative Models [pdf] Marvin Li , Aayush Karan , Sitan Chen ICML 2025 Oral presentation.", "subpage_snippet": "", "source": "sitanchen.com", "link": "https://sitanchen.com/", "content": "Blink of an Eye : A Simple Theory for Feature Localization in Generative Models [pdf] Marvin Li , Aayush Karan , Sitan Chen ICML 2025 Oral presentation."}
+{"idx": 5, "title": "Hi, my name is Marvin . - Marvin Li", "date": "", "ddg_snippet": "Blink of an Eye : A Simple Theory for Feature Localization in Generative Models Marvin Li , Aayush Karan , Sitan Chen . ICML, 2025 (Oral, top 1% of submissions) arXiv / code A unifying theory showing why and when features suddenly “lock in” during generation in both diffusion and...", "subpage_snippet": "", "source": "marvinfli.github.io", "link": "https://marvinfli.github.io/", "content": "Blink of an Eye : A Simple Theory for Feature Localization in Generative Models Marvin Li , Aayush Karan , Sitan Chen . ICML, 2025 (Oral, top 1% of submissions) arXiv / code A unifying theory showing why and when features suddenly “lock in” during generation in both diffusion and..."}
+{"idx": 6, "title": "GitHub - fotimaruziyeva77/client-shop", "date": "", "ddg_snippet": "Contribute to fotimaruziyeva77/client-shop development by creating an account on GitHub .You can check out the Next.js GitHub repository - your feedback and contributions are welcome! Deploy on Vercel.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/fotimaruziyeva77/client-shop", "content": "Contribute to fotimaruziyeva77/client-shop development by creating an account on GitHub .You can check out the Next.js GitHub repository - your feedback and contributions are welcome! Deploy on Vercel."}
+{"idx": 7, "title": "Frontiers in Probabilistic Inference: learning meets Sampling", "date": "", "ddg_snippet": "- Blink of an eye : a simple theory for feature localization in generative models ( Poster ) > link. Marvin Li · Aayush Karan · Sitan Chen .", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2025/workshop/23990", "content": "- Blink of an eye : a simple theory for feature localization in generative models ( Poster ) > link. Marvin Li · Aayush Karan · Sitan Chen ."}
+{"idx": 8, "title": "Vol 15, No 4 (August 30, 2025) - Cardiovascular Diagnosis and Therapy", "date": "", "ddg_snippet": "Shuai Liu, Liang Shang, Shuang-Lei Li , Peng-Yu Zhang, Hao Chen , Bo Liu, Min Cheng , Qiu-Ying Liu, Xin Li , Ying-Ying Hu, Wei-Hua Ye.", "subpage_snippet": "", "source": "cdt.amegroups.org", "link": "https://cdt.amegroups.org/issue/view/1524", "content": "Shuai Liu, Liang Shang, Shuang-Lei Li , Peng-Yu Zhang, Hao Chen , Bo Liu, Min Cheng , Qiu-Ying Liu, Xin Li , Ying-Ying Hu, Wei-Hua Ye."}
+{"idx": 9, "title": "Rabbit Video Chat - #1 Live Video Chat with Girls", "date": "", "ddg_snippet": "Whether you're looking for quick conversations, fast friendships, or simply want to experience the excitement of rapid connections, Rabbit provides the perfect environment for energetic, dynamic interactions that happen in the blink of an eye .", "subpage_snippet": "", "source": "rabbitvideochat.com", "link": "https://rabbitvideochat.com/", "content": "Whether you're looking for quick conversations, fast friendships, or simply want to experience the excitement of rapid connections, Rabbit provides the perfect environment for energetic, dynamic interactions that happen in the blink of an eye ."}
diff --git a/data/sampled_jsons/Blink_of_an_eye_Table_1_MMLU_accuracy_difference.jsonl b/data/sampled_jsons/Blink_of_an_eye_Table_1_MMLU_accuracy_difference.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..464b0ef0e7e80407cc280c20163dea685f955a6d
--- /dev/null
+++ b/data/sampled_jsons/Blink_of_an_eye_Table_1_MMLU_accuracy_difference.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "MMLU - Wikipedia", "date": "", "ddg_snippet": "The creators of the MMLU estimated that human domain-experts achieve around 89.8% accuracy . [ 1 ] By mid-2024, the majority of powerful language models such as Claude 3.5 Sonnet, GPT-4o and Llama 3.1 405B consistently achieved 88%. [3][4][5] As of 2025, MMLU has been partially phased out in favor of more difficult alternatives.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/MMLU", "content": "The creators of the MMLU estimated that human domain-experts achieve around 89.8% accuracy . [ 1 ] By mid-2024, the majority of powerful language models such as Claude 3.5 Sonnet, GPT-4o and Llama 3.1 405B consistently achieved 88%. [3][4][5] As of 2025, MMLU has been partially phased out in favor of more difficult alternatives."}
+{"idx": 1, "title": "The latency of spontaneous eye blinks marks relevant visual and ...", "date": "", "ddg_snippet": "Visual condition Participants Of the 18 subjects, two were excluded for the visual condition because of an overall response accuracy of below 10% and a mean blink rate of below 1 blink per minute, respectively.", "subpage_snippet": "", "source": "www.ncbi.nlm.nih.gov", "link": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8196427/", "content": "Visual condition Participants Of the 18 subjects, two were excluded for the visual condition because of an overall response accuracy of below 10% and a mean blink rate of below 1 blink per minute, respectively."}
+{"idx": 2, "title": "Brief Review — MMLU: Measuring Massive Multitask language Understanding ...", "date": "", "ddg_snippet": "Average weighted accuracy for each model on all four broad disciplines. Figure 1b & Table 1 : The 3 smaller GPT-3 models have near random accuracy (around 25%). In contrast, X-Large 175 billion parameter GPT-3 model performs substantially better than random, with an accuracy of 43.9%. This show that MMLU is a challenging evaluation set.", "subpage_snippet": "", "source": "sh-tsang.medium.com", "link": "https://sh-tsang.medium.com/brief-review-mmlu-measuring-massive-multitask-language-understanding-7b18e7cbbeab", "content": "Average weighted accuracy for each model on all four broad disciplines. Figure 1b & Table 1 : The 3 smaller GPT-3 models have near random accuracy (around 25%). In contrast, X-Large 175 billion parameter GPT-3 model performs substantially better than random, with an accuracy of 43.9%. This show that MMLU is a challenging evaluation set."}
+{"idx": 3, "title": "MMLU-Pro: A More Robust and Challenging Multi-Task Language ...", "date": "", "ddg_snippet": "Figure 1 : Comparing between MMLU and MMLU -Pro: (Left) Performance gap; (Center) Accuracy distributions affected by 24 prompts, with taller and thinner profiles indicating more stability and shorter and wider profiles indicating greater fluctuations; (Right) Performance using CoT vs. Direct. 1 Introduction", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.01574v5", "content": "Figure 1 : Comparing between MMLU and MMLU -Pro: (Left) Performance gap; (Center) Accuracy distributions affected by 24 prompts, with taller and thinner profiles indicating more stability and shorter and wider profiles indicating greater fluctuations; (Right) Performance using CoT vs. Direct. 1 Introduction"}
+{"idx": 4, "title": "MMLU: Better Benchmarking for LLM Language Understanding", "date": "", "ddg_snippet": "Initial results from MMLU revealed intriguing insights. When MMLU was released, on average, the smaller LLMs tested tended to perform around chance (25% accurate), while the larger GPT-3 (175 billion parameters) fared better with 43.9% few-shot accuracy and 37.7% zero-shot accuracy .", "subpage_snippet": "", "source": "deepgram.com", "link": "https://deepgram.com/learn/mmlu-llm-benchmark-guide", "content": "Initial results from MMLU revealed intriguing insights. When MMLU was released, on average, the smaller LLMs tested tended to perform around chance (25% accurate), while the larger GPT-3 (175 billion parameters) fared better with 43.9% few-shot accuracy and 37.7% zero-shot accuracy ."}
+{"idx": 5, "title": "Evaluating the performance of Llama-3-8b-instruct on MMLU ... - GitHub", "date": "", "ddg_snippet": "A pivotal difference between mine and Meta's evaluation is how accuracy is evaluated - typically, MMLU is evaluated based off the probabilities (or log probabilites) outputted by the model, and just argmaxed over {A, B, C, D}.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/benpomeranz/Llama-Eval", "content": "A pivotal difference between mine and Meta's evaluation is how accuracy is evaluated - typically, MMLU is evaluated based off the probabilities (or log probabilites) outputted by the model, and just argmaxed over {A, B, C, D}."}
+{"idx": 6, "title": "Spontaneous Eye Blink Rate During the Working Memory Delay Period ...", "date": "", "ddg_snippet": "Further analysis of the human spontaneous eye blink rate by a cluster analysis-based approach to categorize individuals with 'normal'versus 'frequent'eye blink activity.", "subpage_snippet": "", "source": "www.frontiersin.org", "link": "https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2022.788231/full", "content": "Further analysis of the human spontaneous eye blink rate by a cluster analysis-based approach to categorize individuals with 'normal'versus 'frequent'eye blink activity."}
+{"idx": 7, "title": "What is the MMLU Benchmark — A Comprehensive Guide", "date": "", "ddg_snippet": "What is the MMLU Benchmark? MMLU or Massive Multitask Language Understanding is a rigorous benchmark designed to evaluate the multitask accuracy of language models in zero-shot and few-shot settings, making assessments both challenging and reflective of human evaluation methods.", "subpage_snippet": "", "source": "metaschool.so", "link": "https://metaschool.so/articles/mmlu-benchmark", "content": "What is the MMLU Benchmark? MMLU or Massive Multitask Language Understanding is a rigorous benchmark designed to evaluate the multitask accuracy of language models in zero-shot and few-shot settings, making assessments both challenging and reflective of human evaluation methods."}
+{"idx": 8, "title": "Exploring MMLU Benchmark for AI Models | Galileo", "date": "", "ddg_snippet": "For human benchmark comparison, non-specialist humans achieve around 34.5% accuracy on MMLU questions, highlighting the benchmark's challenging nature. This baseline helps contextualize model performance, though specialists would likely score significantly higher in their domains.", "subpage_snippet": "", "source": "galileo.ai", "link": "https://galileo.ai/blog/mmlu-benchmark", "content": "For human benchmark comparison, non-specialist humans achieve around 34.5% accuracy on MMLU questions, highlighting the benchmark's challenging nature. This baseline helps contextualize model performance, though specialists would likely score significantly higher in their domains."}
+{"idx": 9, "title": "PDF 13144050 - bioRxiv", "date": "", "ddg_snippet": "Further analysis of the human spontaneous eye blink rate by a cluster analysis-based approach to categorize individuals with 'normal'versus 'frequent'eye blink activity.", "subpage_snippet": "", "source": "www.biorxiv.org", "link": "https://www.biorxiv.org/content/biorxiv/early/2021/03/16/2021.03.15.435433.full.pdf", "content": "Further analysis of the human spontaneous eye blink rate by a cluster analysis-based approach to categorize individuals with 'normal'versus 'frequent'eye blink activity."}
diff --git a/data/sampled_jsons/Blink_of_an_eye_paper_arxiv_results_Table_1.jsonl b/data/sampled_jsons/Blink_of_an_eye_paper_arxiv_results_Table_1.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8b9d842f080737208e5fd8e6ac6eadd718d0f237
--- /dev/null
+++ b/data/sampled_jsons/Blink_of_an_eye_paper_arxiv_results_Table_1.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Blink Home - Wikipedia", "date": "", "ddg_snippet": "Immedia Semiconductor LLC, [1] doing business as Blink , is an American home automation company which produces home security cameras. The company was founded in 2009 by Peter Besen, Don Shulsinger, Dan Grunberg, Stephen Gordon, and Doug Chin.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Blink_Home", "content": "Immedia Semiconductor LLC, [1] doing business as Blink , is an American home automation company which produces home security cameras. The company was founded in 2009 by Peter Besen, Don Shulsinger, Dan Grunberg, Stephen Gordon, and Doug Chin."}
+{"idx": 1, "title": "Blink Smart Security", "date": "", "ddg_snippet": "Affordable wireless and plug-in smart home security cameras and systems from Blink , an Amazon company.", "subpage_snippet": "", "source": "blinkforhome.com", "link": "https://blinkforhome.com/", "content": "Affordable wireless and plug-in smart home security cameras and systems from Blink , an Amazon company."}
+{"idx": 2, "title": "Blink Home Monitor App - Blink Smart Security", "date": "", "ddg_snippet": "The app connects your home to your phone in HD video so you can see and protect what matters most. With multi-system support, you can use Blink to watch your home, vacation home, or business all at the same time. Plus, you can control multiple camera systems within one single app!", "subpage_snippet": "", "source": "blinkforhome.com", "link": "https://blinkforhome.com/blink-app", "content": "The app connects your home to your phone in HD video so you can see and protect what matters most. With multi-system support, you can use Blink to watch your home, vacation home, or business all at the same time. Plus, you can control multiple camera systems within one single app!"}
+{"idx": 3, "title": "Account and Login — Blink Support", "date": "", "ddg_snippet": "Blink Support Center helps you to find FAQ, how-to guides and step-by-step tutorials.", "subpage_snippet": "", "source": "support.blinkforhome.com", "link": "https://support.blinkforhome.com/en_US/account-and-login", "content": "Blink Support Center helps you to find FAQ, how-to guides and step-by-step tutorials."}
+{"idx": 4, "title": "Contact Blink — Blink Smart Security", "date": "", "ddg_snippet": "For press-related matters, contact Blink PR. Our amazing Support team is here to help! Contact us to get help with your smart home security camera system.", "subpage_snippet": "", "source": "blinkforhome.com", "link": "https://blinkforhome.com/contact-us", "content": "For press-related matters, contact Blink PR. Our amazing Support team is here to help! Contact us to get help with your smart home security camera system."}
+{"idx": 5, "title": "Local Fitness Centers for Every Body | Blink Fitness", "date": "", "ddg_snippet": "Blink offers an affordable gym membership with tons of gym equipment, certified personal training programs, and a free 30-minute start-up session.", "subpage_snippet": "", "source": "www.blinkfitness.com", "link": "https://www.blinkfitness.com/", "content": "Blink offers an affordable gym membership with tons of gym equipment, certified personal training programs, and a free 30-minute start-up session."}
+{"idx": 6, "title": "Blink Whole Home Security Camera System Bundle | Costco", "date": "", "ddg_snippet": "Blink Whole Home Security Camera System Bundle Easy Setup, No Wiring Required Up to Two-years of Battery (Batteries Included) 360° Coverage With Mini Pan-Tilt Camera Monitor Your Home Anywhere From the Blink App", "subpage_snippet": "", "source": "www.costco.com", "link": "https://www.costco.com/blink-whole-home-security-camera-system-bundle-.product.4000215015.html", "content": "Blink Whole Home Security Camera System Bundle Easy Setup, No Wiring Required Up to Two-years of Battery (Batteries Included) 360° Coverage With Mini Pan-Tilt Camera Monitor Your Home Anywhere From the Blink App"}
+{"idx": 7, "title": "Blink Home Monitor - Apps on Google Play", "date": "", "ddg_snippet": "See and speak to people and pets, right from the Blink app with features like HD live view, infrared night vision, and crisp two-way audio. Connect to an Alexa-enabled device to engage live view,...", "subpage_snippet": "", "source": "play.google.com", "link": "https://play.google.com/store/apps/details?id=com.immediasemi.android.blink&hl=en", "content": "See and speak to people and pets, right from the Blink app with features like HD live view, infrared night vision, and crisp two-way audio. Connect to an Alexa-enabled device to engage live view,..."}
+{"idx": 8, "title": "Amazon.com: Blink Mini 2 (Newest Model) — Home Security & Pet...", "date": "", "ddg_snippet": "Mini 2 is our second generation plug-in smart security camera that helps you stay connected to what’s happening in your home, right from your smartphone. See and speak from the Blink app — Experience 1080p HD live view, night view in color with a built-in spotlight, a wider field of view, and crisp two-way audio. Stream live video continuously for up to 90 minutes with a Blink Subscription ...", "subpage_snippet": "", "source": "www.amazon.com", "link": "https://www.amazon.com/Blink-Mini-2-Camera-Black/dp/B0BWX39R5W", "content": "Mini 2 is our second generation plug-in smart security camera that helps you stay connected to what’s happening in your home, right from your smartphone. See and speak from the Blink app — Experience 1080p HD live view, night view in color with a built-in spotlight, a wider field of view, and crisp two-way audio. Stream live video continuously for up to 90 minutes with a Blink Subscription ..."}
+{"idx": 9, "title": "Sign in to your Blink account", "date": "", "ddg_snippet": "You can sign in to your account using your new password. Log in to update your payment method.", "subpage_snippet": "", "source": "blink.com", "link": "https://blink.com/users/sign_in", "content": "You can sign in to your account using your new password. Log in to update your payment method."}
diff --git a/data/sampled_jsons/CONDA_adaptive_concept_bottleneck_foundation_models_2024_OR_Hybrid_Concept_Bottleneck_OR_Label-free__year_2024.jsonl b/data/sampled_jsons/CONDA_adaptive_concept_bottleneck_foundation_models_2024_OR_Hybrid_Concept_Bottleneck_OR_Label-free__year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..526105417576ee29e2b3e68615d35f0c4190785e
--- /dev/null
+++ b/data/sampled_jsons/CONDA_adaptive_concept_bottleneck_foundation_models_2024_OR_Hybrid_Concept_Bottleneck_OR_Label-free__year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Adaptive Concept Bottleneck for Foundation Models Under ...", "date": "", "ddg_snippet": "by J Choi — CONDA : Adaptive Concept Bottleneck for Foundation Models Under Distribution Shifts ... Label-free Concept Bottleneck Models . In The Eleventh International ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=8sfc8MwG5v", "content": "by J Choi — CONDA : Adaptive Concept Bottleneck for Foundation Models Under Distribution Shifts ... Label-free Concept Bottleneck Models . In The Eleventh International ..."}
+{"idx": 1, "title": "Adaptive Concept Bottleneck for Foundation Models Under ...", "date": "", "ddg_snippet": "by J Choi · 2024 · Cited by 2 — In this paper, we explore the potential of Concept Bottleneck Models (CBMs) for transforming complex, non-interpretable foundation models into interpretable ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2412.14097", "content": "by J Choi · 2024 · Cited by 2 — In this paper, we explore the potential of Concept Bottleneck Models (CBMs) for transforming complex, non-interpretable foundation models into interpretable ..."}
+{"idx": 2, "title": "Adaptive Concept Bottleneck for Foundation Models Under ...", "date": "", "ddg_snippet": "18 Dec 2024 — Label-free concept bottleneck models . In The Eleventh International Conference on Learning Representations, 2023. URL https://openreview.net ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.14097v1", "content": "18 Dec 2024 — Label-free concept bottleneck models . In The Eleventh International Conference on Learning Representations, 2023. URL https://openreview.net ..."}
+{"idx": 3, "title": "[Literature Review] Adaptive Concept Bottleneck for ...", "date": "", "ddg_snippet": "It focuses on a framework known as the Concept Bottleneck Model (CBM), utilizing adaptive mechanisms to address performance degradation, particularly in ...", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/en/review/adaptive-concept-bottleneck-for-foundation-models-under-distribution-shifts", "content": "It focuses on a framework known as the Concept Bottleneck Model (CBM), utilizing adaptive mechanisms to address performance degradation, particularly in ..."}
+{"idx": 4, "title": "Concept-Based Unsupervised Domain Adaptation", "date": "", "ddg_snippet": "arXiv preprint, 2024a . Wang, H., Tan, S., and Wang, H. Probabilistic conceptual explainers: Towards trustworthy conceptual explanations for vision foundation ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/44848", "content": "arXiv preprint, 2024a . Wang, H., Tan, S., and Wang, H. Probabilistic conceptual explainers: Towards trustworthy conceptual explanations for vision foundation ..."}
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+{"idx": 6, "title": "Jayaram Raghuram", "date": "", "ddg_snippet": "CONDA : Adaptive Concept Bottleneck for Foundation Models Under Distribution Shifts ... Published: 02 Jul 2024 , Last Modified: 18 Jul 2024 ; ICML 2024 FM-Wild ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/profile?id=~Jayaram_Raghuram1", "content": "CONDA : Adaptive Concept Bottleneck for Foundation Models Under Distribution Shifts ... Published: 02 Jul 2024 , Last Modified: 18 Jul 2024 ; ICML 2024 FM-Wild ..."}
+{"idx": 7, "title": "Jihye Choi - Google 학술 검색", "date": "", "ddg_snippet": "Machine Learning for Healthcare (MLHC) 2024 , 2024 . 12, 2024 . CONDA : Adaptive Concept Bottleneck for Foundation Models Under Distribution Shifts. J Choi, J ...", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=lEa3R0sAAAAJ&hl=ko", "content": "Machine Learning for Healthcare (MLHC) 2024 , 2024 . 12, 2024 . CONDA : Adaptive Concept Bottleneck for Foundation Models Under Distribution Shifts. J Choi, J ..."}
+{"idx": 8, "title": "Sharon Li – Publications - cs.wisc.edu", "date": "", "ddg_snippet": "CONDA : Adaptive Concept Bottleneck for Foundation Models Under Distribution Shifts ... NeurIPS Workshop on Adaptive Foundation Model , 2024 [BibTeX] [PDF] [Code].", "subpage_snippet": "", "source": "pages.cs.wisc.edu", "link": "https://pages.cs.wisc.edu/~sharonli/publications.html", "content": "CONDA : Adaptive Concept Bottleneck for Foundation Models Under Distribution Shifts ... NeurIPS Workshop on Adaptive Foundation Model , 2024 [BibTeX] [PDF] [Code]."}
+{"idx": 9, "title": "ICLR 2025 Papers", "date": "", "ddg_snippet": "2024 · 2023 · 2022 · 2021 · 2020 · 2019 · 2018 · 2017 · 2016 · 2015 · 2014 · 2013 · Getting Started ... CONDA : Adaptive Concept Bottleneck for Foundation Models ...", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2025/papers.html", "content": "2024 · 2023 · 2022 · 2021 · 2020 · 2019 · 2018 · 2017 · 2016 · 2015 · 2014 · 2013 · Getting Started ... CONDA : Adaptive Concept Bottleneck for Foundation Models ..."}
diff --git a/data/sampled_jsons/CRAB_Cross-environment_Agent_Benchmark_abstract_Xu_et_al_2024.jsonl b/data/sampled_jsons/CRAB_Cross-environment_Agent_Benchmark_abstract_Xu_et_al_2024.jsonl
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index 0000000000000000000000000000000000000000..d8d1c85b7dae292954001e0af78eb57e8c1cf265
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+{"idx": 0, "title": "CRAB: Cross-environment Agent Benchmark for ...", "date": "", "ddg_snippet": "by T Xu · Cited by 25 — We introduce CRAB , the first agent benchmark framework designed to support cross-environment tasks, incorporating a graph-based fine-grained evaluation method.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=qqKJjwibsp", "content": "by T Xu · Cited by 25 — We introduce CRAB , the first agent benchmark framework designed to support cross-environment tasks, incorporating a graph-based fine-grained evaluation method."}
+{"idx": 1, "title": "CRAB: CROSS-ENVIRONMENT AGENT BENCHMARK", "date": "", "ddg_snippet": "Mobile Agent Bench (Wang et al ., 2024c ) collects app event signals via android accessibility service, builds the benchmark with well annotated operation.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/f4a00da5258fd0cfa44aa33a28f6c44ba4beba9e.pdf", "content": "Mobile Agent Bench (Wang et al ., 2024c ) collects app event signals via android accessibility service, builds the benchmark with well annotated operation."}
+{"idx": 2, "title": "Towards Evaluating Generalist Agents: An Automated ...", "date": "", "ddg_snippet": "The CRAB framework Xu et al. (2024) introduces a cross-environment ... Crab: Cross-environment agent benchmark for multimodal language model agents.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2310.08367v2", "content": "The CRAB framework Xu et al. (2024) introduces a cross-environment ... Crab: Cross-environment agent benchmark for multimodal language model agents."}
+{"idx": 3, "title": "A Survey on Benchmarks of Multimodal Large Language ...", "date": "", "ddg_snippet": "16 Aug 2024 — Crab: Cross-environment agent benchmark for multimodal language model agents , 2024. [112] ↑ Fakhraddin Alwajih, El Moatez Billah Nagoudi ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2408.08632v1", "content": "16 Aug 2024 — Crab: Cross-environment agent benchmark for multimodal language model agents , 2024. [112] ↑ Fakhraddin Alwajih, El Moatez Billah Nagoudi ..."}
+{"idx": 4, "title": "Benchmarks of MLLMs: Survey", "date": "", "ddg_snippet": "arXiv 2024 . [Paper] [Github]. CRAB \" CRAB : Cross - environment Agent Benchmark for Multimodal Language Model Agents \". Tianqi Xu , Linyao Chen, Dai-Jie Wu, et al ..", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/swordlidev/Evaluation-Multimodal-LLMs-Survey", "content": "arXiv 2024 . [Paper] [Github]. CRAB \" CRAB : Cross - environment Agent Benchmark for Multimodal Language Model Agents \". Tianqi Xu , Linyao Chen, Dai-Jie Wu, et al .."}
+{"idx": 5, "title": "OS Agents: A Survey on MLLM-Based Agents for General ...", "date": "", "ddg_snippet": "by X Hu · 2024 · Cited by 16 — This survey aims to consolidate the state of OS Agents research, providing insights to guide both academic inquiry and industrial development.", "subpage_snippet": "", "source": "www.preprints.org", "link": "https://www.preprints.org/manuscript/202412.2294/v1", "content": "by X Hu · 2024 · Cited by 16 — This survey aims to consolidate the state of OS Agents research, providing insights to guide both academic inquiry and industrial development."}
+{"idx": 6, "title": "AGENT SKILL ACQUISITION FOR LARGE LANGUAGE ...", "date": "", "ddg_snippet": "by S Kuroki · Cited by 4 — CRAB: cross-environment agent benchmark for multimodal language model agents. CoRR, abs/2407.01511, 2024. doi: 10.48550/ARXIV.2407.01511. URL https://doi ...", "subpage_snippet": "", "source": "proceedings.iclr.cc", "link": "https://proceedings.iclr.cc/paper_files/paper/2025/file/755acd0c7c07180d78959b6d89768207-Paper-Conference.pdf", "content": "by S Kuroki · Cited by 4 — CRAB: cross-environment agent benchmark for multimodal language model agents. CoRR, abs/2407.01511, 2024. doi: 10.48550/ARXIV.2407.01511. URL https://doi ..."}
+{"idx": 7, "title": "An Extensible Framework For Scaling LLM Evaluation with ...", "date": "", "ddg_snippet": "by HA Alyahya · 2025 · Cited by 2 — Some notable ones include (i). AgentBench (Liu et al ., 2024 ), an evolving bench- mark consisting of 8 environments that models in- teract with ...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.acl-demo.33.pdf", "content": "by HA Alyahya · 2025 · Cited by 2 — Some notable ones include (i). AgentBench (Liu et al ., 2024 ), an evolving bench- mark consisting of 8 environments that models in- teract with ..."}
+{"idx": 8, "title": "Cybernaut: Towards Reliable Web Automation", "date": "", "ddg_snippet": "Preprint, (2024). https://arxiv.org/abs/2308.15272 arXiv: 2308.15272 [cs.AI]. [20]. Tianqi Xu et al. Crab: cross-environment agent benchmark for multimodal.", "subpage_snippet": "", "source": "assets.amazon.science", "link": "https://assets.amazon.science/13/50/30cd0cfc4fe381691ab383e0fb85/scipub-approval152129-38644753-cybernaut-towards-reliable-web-automation.pdf", "content": "Preprint, (2024). https://arxiv.org/abs/2308.15272 arXiv: 2308.15272 [cs.AI]. [20]. Tianqi Xu et al. Crab: cross-environment agent benchmark for multimodal."}
+{"idx": 9, "title": "OSCAR: OPERATING SYSTEM CONTROL VIA STATE- ...", "date": "", "ddg_snippet": "by X Wang · Cited by 17 — On the OSWorld (Xie et al ., 2024b ) and AndroidWorld (Rawles et al ., 2024 ) benchmarks , OSCAR consistently surpassed other agents , achieving a 24.5% success rate ...", "subpage_snippet": "", "source": "proceedings.iclr.cc", "link": "https://proceedings.iclr.cc/paper_files/paper/2025/file/b2077e6d66da612fcb701589efa9ce88-Paper-Conference.pdf", "content": "by X Wang · Cited by 17 — On the OSWorld (Xie et al ., 2024b ) and AndroidWorld (Rawles et al ., 2024 ) benchmarks , OSCAR consistently surpassed other agents , achieving a 24.5% success rate ..."}
diff --git a/data/sampled_jsons/CRAB_Cross-environment_agent_benchmark_multimodal_language_model_agents_Xu_2024_year_2024.jsonl b/data/sampled_jsons/CRAB_Cross-environment_agent_benchmark_multimodal_language_model_agents_Xu_2024_year_2024.jsonl
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index 0000000000000000000000000000000000000000..d641b560dd6e3e5329fcc57f1675fb563a29d8ce
--- /dev/null
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+{"idx": 0, "title": "CRAB: Cross-environment Agent Benchmark for Multimodal ... CRAB: Cross-environment Agent Benchmark for Multimodal ... GitHub - camel-ai/crab: ️ CRAB: Cross-environment Agent ... CRAB: Cross-environment Agent Benchmark for Multimodal ... CRAB: cross-environment agent benchmark for multimodal ... CRAB: Cross-environment Agent Benchmark for Multimodal ... GitHub - camel-ai/ crab : ️ CRAB : Cross - environment Agent Benchmark … [2407.01511] CRAB : Cross-environment Agent Benchmark for Multimo… [2407.01511] CRAB : Cross-environment Agent Benchmark for Multimo… [2407.01511] CRAB : Cross-environment Agent Benchmark for Multimo… [2407.01511] CRAB : Cross-environment Agent Benchmark for Multimo… CRAB: Cross-platfrom agent benchmark for multi-modal embodied ...", "date": "", "ddg_snippet": "Jul 1, 2024 · The development of autonomous agents increasingly relies on Multimodal Language Models (MLMs) to perform tasks described in natural language with GUI environments, such as websites, desktop computers, or mobile phones. Existing benchmarks for MLM agents in interactive environments are limited by their focus on a single environment , lack of detailed and generalized evaluation methods, and the ... 6 days ago · CRAB : Cross-environment Agent Benchmark for Multimodal Language Model Agents . In Findings of the Association for Computational Linguistics: ACL 2025, pages 21607–21647, Vienna, Austria. 🦀 CRAB : Cross -platform Agent Benchmark for Multimodal Embodied Language Model Agents CRAB aims to become a general-purpose agent benchmark framework for Multimodal Language Model (MLM) agents . CRAB provides an end-to-end while easy-to-use framework to build agents , operate environments, and create benchmarks to evaluate them, featuring three key components: cross-environment support, a graph evaluator, and task generation. Conference item CRAB : cross-environment agent benchmark for multimodal language model agents Abstract: The development of autonomous agents increasingly relies on Multimodal Language Models (MLMs) to perform tasks described in natural language with GUI environments, such as websites, desktop computers, or mobile phones. Jul 1, 2024 · CRAB : Cross-en vironment Agent Benchmark for Multimodal Language Model Agents Tianqi Xu 1,2∗ Linyao Chen 3∗ Dai-Jie Wu 1∗ Y anjun Chen 4∗ Zecheng Zhang Xiang Y ao 2 Zhiqiang Xie 5 Y ... Who wrote crab – cross-environment agent benchmark for Multimodal Language model agents? title={CRAB: Cross-environment Agent Benchmark for Multimodal Language Model Agents}, author={ Tianqi Xu and Linyao Chen and Dai-Jie Wu and Yanjun Chen and Zecheng Zhang and Xiang Yao and Zhiqiang Xie and Yongchao Chen and Shilong Liu and Bochen Qian and Philip Torr and Bernard Ghanem and Guohao Li}, year={2024}, eprint={2407.01511}, What is a Multimodal Language model (MLM)? The development of autonomous agents increasingly relies on Multimodal Language Models (MLMs) to perform tasks described in natural language with GUI environments, such as websites, desktop computers, or mobile phones. Does crab support cross-environment tasks? To overcome these limitations, we introduce Crab, the first agent benchmark framework designed to support cross-environment tasks , incorporating a graph-based fine-grained evaluation method and an efficient mechanism for task and evaluator construction. Are there benchmarks for MLM agents in interactive environments? Existing benchmarks for MLM agents in interactive environments are limited by their focus on a single environment, lack of detailed and generalized evaluation methods, and the complexities of constructing tasks and evaluators. What is crab benchmark V0? Leveraging Crab , we developed a cross -platform Crab Benchmark -v0 comprising 120 tasks in computer desktop and mobile phone environments. We evaluated four advanced MLMs using different single and multi- agent system configurations on this benchmark . Poster in Workshop: Workshop on Open-World Agents : Synnergizing Reasoning and Decision-Making in Open-World Environments (OWA- 2024 ) CRAB : Cross -platfrom agent benchmark for multi-modal embodied language model agents", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2407.01511", "content": "Jul 1, 2024 · The development of autonomous agents increasingly relies on Multimodal Language Models (MLMs) to perform tasks described in natural language with GUI environments, such as websites, desktop computers, or mobile phones. Existing benchmarks for MLM agents in interactive environments are limited by their focus on a single environment , lack of detailed and generalized evaluation methods, and the ... 6 days ago · CRAB : Cross-environment Agent Benchmark for Multimodal Language Model Agents . In Findings of the Association for Computational Linguistics: ACL 2025, pages 21607–21647, Vienna, Austria. 🦀 CRAB : Cross -platform Agent Benchmark for Multimodal Embodied Language Model Agents CRAB aims to become a general-purpose agent benchmark framework for Multimodal Language Model (MLM) agents . CRAB provides an end-to-end while easy-to-use framework to build agents , operate environments, and create benchmarks to evaluate them, featuring three key components: cross-environment support, a graph evaluator, and task generation. Conference item CRAB : cross-environment agent benchmark for multimodal language model agents Abstract: The development of autonomous agents increasingly relies on Multimodal Language Models (MLMs) to perform tasks described in natural language with GUI environments, such as websites, desktop computers, or mobile phones. Jul 1, 2024 · CRAB : Cross-en vironment Agent Benchmark for Multimodal Language Model Agents Tianqi Xu 1,2∗ Linyao Chen 3∗ Dai-Jie Wu 1∗ Y anjun Chen 4∗ Zecheng Zhang Xiang Y ao 2 Zhiqiang Xie 5 Y ... Who wrote crab – cross-environment agent benchmark for Multimodal Language model agents? title={CRAB: Cross-environment Agent Benchmark for Multimodal Language Model Agents}, author={ Tianqi Xu and Linyao Chen and Dai-Jie Wu and Yanjun Chen and Zecheng Zhang and Xiang Yao and Zhiqiang Xie and Yongchao Chen and Shilong Liu and Bochen Qian and Philip Torr and Bernard Ghanem and Guohao Li}, year={2024}, eprint={2407.01511}, What is a Multimodal Language model (MLM)? The development of autonomous agents increasingly relies on Multimodal Language Models (MLMs) to perform tasks described in natural language with GUI environments, such as websites, desktop computers, or mobile phones. Does crab support cross-environment tasks? To overcome these limitations, we introduce Crab, the first agent benchmark framework designed to support cross-environment tasks , incorporating a graph-based fine-grained evaluation method and an efficient mechanism for task and evaluator construction. Are there benchmarks for MLM agents in interactive environments? Existing benchmarks for MLM agents in interactive environments are limited by their focus on a single environment, lack of detailed and generalized evaluation methods, and the complexities of constructing tasks and evaluators. What is crab benchmark V0? Leveraging Crab , we developed a cross -platform Crab Benchmark -v0 comprising 120 tasks in computer desktop and mobile phone environments. We evaluated four advanced MLMs using different single and multi- agent system configurations on this benchmark . Poster in Workshop: Workshop on Open-World Agents : Synnergizing Reasoning and Decision-Making in Open-World Environments (OWA- 2024 ) CRAB : Cross -platfrom agent benchmark for multi-modal embodied language model agents"}
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+{"idx": 2, "title": "CRAB: Cross-environment Agent Benchmark for Multimodal ...", "date": "", "ddg_snippet": "CRAB aims to become a general-purpose agent benchmark framework for Multimodal Language Model (MLM) agents . CRAB provides an end-to-end while easy-to-use framework to build agents , operate environments, and create benchmarks to evaluate them, featuring three key components: cross-environment support, a graph evaluator, and task generation.", "subpage_snippet": "", "source": "crab.camel-ai.org", "link": "https://crab.camel-ai.org/", "content": "CRAB aims to become a general-purpose agent benchmark framework for Multimodal Language Model (MLM) agents . CRAB provides an end-to-end while easy-to-use framework to build agents , operate environments, and create benchmarks to evaluate them, featuring three key components: cross-environment support, a graph evaluator, and task generation."}
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+{"idx": 7, "title": "CRAB : Cross - environment Agent Benchmark for Multimodal ...", "date": "", "ddg_snippet": "The development of autonomous agents increasingly relies on Multimodal Language Models (MLMs) to perform tasks described in natural language with GUI environments , such as websites, desktop computers, or mobile phones.", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/paper/crab-cross-environment-agent-benchmark-for", "content": "The development of autonomous agents increasingly relies on Multimodal Language Models (MLMs) to perform tasks described in natural language with GUI environments , such as websites, desktop computers, or mobile phones."}
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+{"idx": 9, "title": "CRAB : Cross - environment Agent Benchmark for Multimodal ...", "date": "", "ddg_snippet": "The paper introduces Crab , a cross - environment agent benchmark for evaluating multimodal language models in interactive systems. It addresses existing limitations by focusing on cross-platform tasks, using a graph-based evaluation method...", "subpage_snippet": "", "source": "powerdrill.ai", "link": "https://powerdrill.ai/discover/discover-CRAB-Cross-environment-Agent-cly6bu84qw7es01akt92w1rbr", "content": "The paper introduces Crab , a cross - environment agent benchmark for evaluating multimodal language models in interactive systems. It addresses existing limitations by focusing on cross-platform tasks, using a graph-based evaluation method..."}
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+{"idx": 3, "title": "GaRAGe: A Benchmark with Grounding Annotations", "date": "", "ddg_snippet": "2023. Recall: A benchmark for llms robustness against external counterfactual knowledge. arXiv preprint arXiv :2311.08147. Yiqi Liu, Nafise Moosavi, and Chenghua Lin. 2024. Llms as narcissistic evaluators: When ego inflates evaluation scores.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.findings-acl.875.pdf?trk=article-ssr-frontend-pulse_little-text-block", "content": "2023. Recall: A benchmark for llms robustness against external counterfactual knowledge. arXiv preprint arXiv :2311.08147. Yiqi Liu, Nafise Moosavi, and Chenghua Lin. 2024. Llms as narcissistic evaluators: When ego inflates evaluation scores."}
+{"idx": 4, "title": "Обзоры препринтов научных статей «astro-ph/ arxiv .org» за... / Хабр", "date": "", "ddg_snippet": "arxiv :2506.11217 Потенциальная угроза околоземеным спутникам из-за выброса при столкновении 2032 года астероида 2024 YR4 с Луной (The Potential Danger to Satellites due to Ejecta from a 2032 Lunar Impact by Asteroid 2024 YR4)Authors: Paul Wiegert et...", "subpage_snippet": "", "source": "habr.com", "link": "https://habr.com/ru/articles/948996/", "content": "arxiv :2506.11217 Потенциальная угроза околоземеным спутникам из-за выброса при столкновении 2032 года астероида 2024 YR4 с Луной (The Potential Danger to Satellites due to Ejecta from a 2032 Lunar Impact by Asteroid 2024 YR4)Authors: Paul Wiegert et..."}
+{"idx": 5, "title": "Present Continuous — правила и примеры употребления.", "date": "", "ddg_snippet": "Вот какими бывают статичные глаголы: абстрактные глаголы — abstract verbs. to be (быть), to want (хотеть), to cost (стоить), to seem (казаться), to need (нуждаться), to care (заботиться), to exist (существовать).", "subpage_snippet": "", "source": "skysmart.ru", "link": "https://skysmart.ru/articles/english/present-continuous-tense", "content": "Вот какими бывают статичные глаголы: абстрактные глаголы — abstract verbs. to be (быть), to want (хотеть), to cost (стоить), to seem (казаться), to need (нуждаться), to care (заботиться), to exist (существовать)."}
+{"idx": 6, "title": "Astronomy & Astrophysics (A&A)", "date": "", "ddg_snippet": "The Crab Nebula at sub-arcsecond resolution with the International LOFAR Telescope.A&A, 690 ( 2024 ) A 399. Published online: 29 October 2024.", "subpage_snippet": "", "source": "www.aanda.org", "link": "https://www.aanda.org/", "content": "The Crab Nebula at sub-arcsecond resolution with the International LOFAR Telescope.A&A, 690 ( 2024 ) A 399. Published online: 29 October 2024."}
+{"idx": 7, "title": "GitHub - MoonshotAI/Kimi-K2: Kimi K2 is the large language model...", "date": "", "ddg_snippet": "Benchmark . Metric. Kimi K2 Instruct.Math & STEM Tasks. AIME 2024.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/MoonshotAI/Kimi-K2", "content": "Benchmark . Metric. Kimi K2 Instruct.Math & STEM Tasks. AIME 2024."}
+{"idx": 8, "title": "LiveBench", "date": "", "ddg_snippet": "Introducing LiveBench: a benchmark for LLMs designed with test set contamination and objective evaluation in mind. It has the following properties: LiveBench limits potential contamination by releasing new questions regularly.", "subpage_snippet": "", "source": "livebench.ai", "link": "https://livebench.ai/", "content": "Introducing LiveBench: a benchmark for LLMs designed with test set contamination and objective evaluation in mind. It has the following properties: LiveBench limits potential contamination by releasing new questions regularly."}
+{"idx": 9, "title": "Фукуок: когда лучше ехать? Полный гид по сезонам, погоде...", "date": "", "ddg_snippet": "Каменные крабы (Rock Crabs ). Сезон дождей на Фукуоке — лучшее время, чтобы насладиться каменными крабами, которые отличаются мясистостью, плотной текстурой и насыщенным вкусом.", "subpage_snippet": "", "source": "blog.premierresidencesphuquoc.com", "link": "https://blog.premierresidencesphuquoc.com/ru/best-time-to-visit-phu-quoc/", "content": "Каменные крабы (Rock Crabs ). Сезон дождей на Фукуоке — лучшее время, чтобы насладиться каменными крабами, которые отличаются мясистостью, плотной текстурой и насыщенным вкусом."}
diff --git a/data/sampled_jsons/CVE-Bench_A_Benchmark_for_AI_Agents'_Ability_to_Exploit_Real-World_Web_Application_Vulnerabilities_T.jsonl b/data/sampled_jsons/CVE-Bench_A_Benchmark_for_AI_Agents'_Ability_to_Exploit_Real-World_Web_Application_Vulnerabilities_T.jsonl
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index 0000000000000000000000000000000000000000..a440023940a13b29057488a640a6285488e9e5f0
--- /dev/null
+++ b/data/sampled_jsons/CVE-Bench_A_Benchmark_for_AI_Agents'_Ability_to_Exploit_Real-World_Web_Application_Vulnerabilities_T.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "CVE-Bench: A Benchmark for AI Agents' Ability to Exploit Real-World Web ...", "date": "", "ddg_snippet": "Building a benchmark for real-world vulnerabilities involves both specialized expertise to reproduce exploits and a systematic approach to evaluating unpredictable attacks. To address this challenge, we introduce CVE-Bench , a real-world cybersecurity benchmark based on critical-severity Common Vulnerabilities and Exposures.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.17332v4", "content": "Building a benchmark for real-world vulnerabilities involves both specialized expertise to reproduce exploits and a systematic approach to evaluating unpredictable attacks. To address this challenge, we introduce CVE-Bench , a real-world cybersecurity benchmark based on critical-severity Common Vulnerabilities and Exposures."}
+{"idx": 1, "title": "CVE-Bench: A Benchmark for AI Agents' Ability to Exploit Real-World Web ...", "date": "", "ddg_snippet": "Large language model (LLM) agents are increasingly capable of autonomously conducting cyberattacks, posing significant threats to existing applications . This growing risk highlights the urgent need for a real-world benchmark to evaluate the ability of LLM agents to exploit web application vulnerabilities . However, existing benchmarks fall short as they are limited to abstracted Capture the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.17332", "content": "Large language model (LLM) agents are increasingly capable of autonomously conducting cyberattacks, posing significant threats to existing applications . This growing risk highlights the urgent need for a real-world benchmark to evaluate the ability of LLM agents to exploit web application vulnerabilities . However, existing benchmarks fall short as they are limited to abstracted Capture the ..."}
+{"idx": 2, "title": "Establishing Best Practices for Building Rigorous Agentic", "date": "", "ddg_snippet": "For example, a GPT-4o-based agent resolves 35% of tasks on τ 𝜏 \\tau italic_τ - bench -Airline, a benchmark for tool- agent -user interaction [ 85 ] .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.02825v1", "content": "For example, a GPT-4o-based agent resolves 35% of tasks on τ 𝜏 \\tau italic_τ - bench -Airline, a benchmark for tool- agent -user interaction [ 85 ] ."}
+{"idx": 3, "title": "Establishing Best Practices for Building Rigorous Agentic", "date": "", "ddg_snippet": "For example, a GPT-4o-based agent resolves 35% of tasks on τ \\tau italic_τ - bench -Airline, a benchmark for tool- agent -user interaction [ 86 ] .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.02825v5", "content": "For example, a GPT-4o-based agent resolves 35% of tasks on τ \\tau italic_τ - bench -Airline, a benchmark for tool- agent -user interaction [ 86 ] ."}
+{"idx": 4, "title": "Establishing Best Practices for Building Rigorous Agentic", "date": "", "ddg_snippet": "For example, a GPT-4o-based agent resolves 35% of tasks on τ 𝜏 \\tau italic_τ - bench -Airline, a benchmark for tool- agent -user interaction [ 86 ] .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.02825v4", "content": "For example, a GPT-4o-based agent resolves 35% of tasks on τ 𝜏 \\tau italic_τ - bench -Airline, a benchmark for tool- agent -user interaction [ 86 ] ."}
+{"idx": 5, "title": "CVE-Bench: A Benchmark for AI Agents' Ability to Exploit Real-World Web ...", "date": "", "ddg_snippet": "This repository contains data and code used in the CVE-Bench (paper, blog), which is for evaluating AI agents on real world web vulnerabilities and exploits collected from National Vulnerability Database. CVE-Bench includes 40 critical-severity Common Vulnerability and Exposures ( CVE ) with the reference automatic exploits available on requests.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/uiuc-kang-lab/cve-bench", "content": "This repository contains data and code used in the CVE-Bench (paper, blog), which is for evaluating AI agents on real world web vulnerabilities and exploits collected from National Vulnerability Database. CVE-Bench includes 40 critical-severity Common Vulnerability and Exposures ( CVE ) with the reference automatic exploits available on requests."}
+{"idx": 6, "title": "PDF CVE-Bench: Benchmarking LLM-based Software Engineering Agent's Ability ...", "date": "", "ddg_snippet": "Unlike previous vulnerability repair benchmarks , which only involve the code in- put and output, we provide LLM agents with a test environment that simulates the real-world vulnerability repair process. This environment provides multiple levels of CVE information modeling, such as black-box testing and white- box testing.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.naacl-long.212.pdf", "content": "Unlike previous vulnerability repair benchmarks , which only involve the code in- put and output, we provide LLM agents with a test environment that simulates the real-world vulnerability repair process. This environment provides multiple levels of CVE information modeling, such as black-box testing and white- box testing."}
+{"idx": 7, "title": "CVE-Bench: A Real-World Cybersecurity Benchmark for AI Agents", "date": "", "ddg_snippet": "We took a series of agents and we tested our agents on our benchmark , and what we found is that existing agents , in particular Cy- Agent , which was developed for the Cy- Bench CTF challenge, performs poorly on real-world end- to -end web application vulnerabilities .", "subpage_snippet": "", "source": "far.ai", "link": "https://far.ai/events/sessions/daniel-kang-cve-bench-a-real-world-cybersecurity-benchmark-for-ai-agents", "content": "We took a series of agents and we tested our agents on our benchmark , and what we found is that existing agents , in particular Cy- Agent , which was developed for the Cy- Bench CTF challenge, performs poorly on real-world end- to -end web application vulnerabilities ."}
+{"idx": 8, "title": "Measuring AI Agents' Ability to Exploit Web Applications", "date": "", "ddg_snippet": "In this post, we introduce CVE-bench — the first benchmark built on real-world vulnerabilities , which contains: 40 real-world vulnerability-exploitation challenges.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@danieldkang/measuring-ai-agents-ability-to-exploit-web-applications-ba4225aa281f", "content": "In this post, we introduce CVE-bench — the first benchmark built on real-world vulnerabilities , which contains: 40 real-world vulnerability-exploitation challenges."}
+{"idx": 9, "title": "uiuc-kang-lab/cve-bench | DeepWiki", "date": "", "ddg_snippet": "What is CVE-Bench ? CVE-Bench is a benchmark that contains 40 critical-severity Common Vulnerability and Exposures ( CVEs ) collected from the National Vulnerability Database. It creates reproducible environments for testing AI agents' abilities to discover and exploit web application vulnerabilities .", "subpage_snippet": "", "source": "deepwiki.com", "link": "https://deepwiki.com/uiuc-kang-lab/cve-bench/1-overview", "content": "What is CVE-Bench ? CVE-Bench is a benchmark that contains 40 critical-severity Common Vulnerability and Exposures ( CVEs ) collected from the National Vulnerability Database. It creates reproducible environments for testing AI agents' abilities to discover and exploit web application vulnerabilities ."}
diff --git a/data/sampled_jsons/CVE-Bench_paper_'Insufficient_Exploration'_failure_mode_definition_year_2023-2024.jsonl b/data/sampled_jsons/CVE-Bench_paper_'Insufficient_Exploration'_failure_mode_definition_year_2023-2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..422e13454482a28c9b775739b8de430a4e066156
--- /dev/null
+++ b/data/sampled_jsons/CVE-Bench_paper_'Insufficient_Exploration'_failure_mode_definition_year_2023-2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Common Vulnerabilities and Exposures - Wikipedia", "date": "", "ddg_snippet": "Logo The Common Vulnerabilities and Exposures (CVE) system, originally Common Vulnerability Enumeration, [1] provides a reference method for publicly known information-security …", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Common_Vulnerabilities_and_Exposures", "content": "Logo The Common Vulnerabilities and Exposures (CVE) system, originally Common Vulnerability Enumeration, [1] provides a reference method for publicly known information-security …"}
+{"idx": 1, "title": "Large Language Model-Based Agents for Software Engineering: A", "date": "", "ddg_snippet": "Large Language Models (LLMs) [ 1 ] have achieved remarkable progress and demonstrated potential of human-like intelligence.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2409.02977v1", "content": "Large Language Models (LLMs) [ 1 ] have achieved remarkable progress and demonstrated potential of human-like intelligence."}
+{"idx": 2, "title": "A.S.E: A Repository-Level Benchmark for Evaluating Security in", "date": "", "ddg_snippet": "Large language models (LLMs) are rapidly permeating software engineering workflows, from code completion and synthesis to refactoring and even ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.18106v1", "content": "Large language models (LLMs) are rapidly permeating software engineering workflows, from code completion and synthesis to refactoring and even ..."}
+{"idx": 3, "title": "CVE : Common Vulnerabilities and Exposures", "date": "", "ddg_snippet": "At cve.org, we provide the authoritative reference method for publicly known information-security vulnerabilities and exposures", "subpage_snippet": "", "source": "www.cve.org", "link": "https://www.cve.org/", "content": "At cve.org, we provide the authoritative reference method for publicly known information-security vulnerabilities and exposures"}
+{"idx": 4, "title": "CVE security vulnerability database. Security vulnerabilities, …", "date": "", "ddg_snippet": "CVEDetails.com is a vulnerability intelligence solution providing CVE security vulnerability database, exploits, advisories, product and CVE risk scores, attack surface intelligence, open source …", "subpage_snippet": "", "source": "www.cvedetails.com", "link": "https://www.cvedetails.com/", "content": "CVEDetails.com is a vulnerability intelligence solution providing CVE security vulnerability database, exploits, advisories, product and CVE risk scores, attack surface intelligence, open source …"}
+{"idx": 5, "title": "What is a CVE ? - Red Hat", "date": "", "ddg_snippet": "Sep 4, 2024 · CVE, short for Common Vulnerabilities and Exposures, is a list of publicly disclosed computer security flaws.", "subpage_snippet": "", "source": "www.redhat.com", "link": "https://www.redhat.com/en/topics/security/what-is-cve", "content": "Sep 4, 2024 · CVE, short for Common Vulnerabilities and Exposures, is a list of publicly disclosed computer security flaws."}
+{"idx": 6, "title": "CVEs and Security Vulnerabilities - OpenCVE", "date": "", "ddg_snippet": "Explore the latest vulnerabilities and security issues in the CVE database", "subpage_snippet": "", "source": "app.opencve.io", "link": "https://app.opencve.io/", "content": "Explore the latest vulnerabilities and security issues in the CVE database"}
+{"idx": 7, "title": "What is CVE and CVSS | Vulnerability Scoring Explained | Imperva", "date": "", "ddg_snippet": "Jul 8, 2025 · What is the Common Vulnerabilities and Exposures (CVE) Glossary CVE stands for Common Vulnerabilities and Exposures. CVE is a glossary that classifies vulnerabilities. The …", "subpage_snippet": "", "source": "www.imperva.com", "link": "https://www.imperva.com/learn/application-security/cve-cvss-vulnerability/", "content": "Jul 8, 2025 · What is the Common Vulnerabilities and Exposures (CVE) Glossary CVE stands for Common Vulnerabilities and Exposures. CVE is a glossary that classifies vulnerabilities. The …"}
+{"idx": 8, "title": "CISA Presents Vision for the Common Vulnerabilities and Exposures ( CVE …", "date": "", "ddg_snippet": "Sep 10, 2025 · CISA believes the CVE program must be led with a commitment to conflict-free and vendor-neutral stewardship, broad multi-sector engagement, transparent processes, and …", "subpage_snippet": "", "source": "www.cisa.gov", "link": "https://www.cisa.gov/news-events/news/cisa-presents-vision-common-vulnerabilities-and-exposures-cve-program", "content": "Sep 10, 2025 · CISA believes the CVE program must be led with a commitment to conflict-free and vendor-neutral stewardship, broad multi-sector engagement, transparent processes, and …"}
+{"idx": 9, "title": "CVE Vault - CVE Database & Security Research Hub", "date": "", "ddg_snippet": "Comprehensive CVE database for cybersecurity research and vulnerability management. Search thousands of CVEs by severity, vendor, and keywords.", "subpage_snippet": "", "source": "cvevault.com", "link": "https://cvevault.com/", "content": "Comprehensive CVE database for cybersecurity research and vulnerability management. Search thousands of CVEs by severity, vendor, and keywords."}
diff --git a/data/sampled_jsons/CVPR_2025_paper_tiers_Standard-Tier_classification.jsonl b/data/sampled_jsons/CVPR_2025_paper_tiers_Standard-Tier_classification.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f63b1f4d6d4449d6edd7fe2955db7bf2f22c982d
--- /dev/null
+++ b/data/sampled_jsons/CVPR_2025_paper_tiers_Standard-Tier_classification.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "NEC Labs America Team Attending CVPR 2024 in Seattle | NEC Labs", "date": "", "ddg_snippet": "Our team will be attending CVPR 2024 (The IEEE /CVF Conference on Computer Vision Pattern Recognition) from June 17-21! See you there at the NEC Labs ...", "subpage_snippet": "", "source": "www.nec-labs.com", "link": "https://www.nec-labs.com/blog/nec-labs-america-team-attending-cvpr-2024-in-seattle/", "content": "Our team will be attending CVPR 2024 (The IEEE /CVF Conference on Computer Vision Pattern Recognition) from June 17-21! See you there at the NEC Labs ..."}
+{"idx": 1, "title": "Job Board | CVPR 2021", "date": "", "ddg_snippet": "You will have opportunities to work on advanced CV R&D projects and publish research results in top- tier CV venues ( CVPR , ICCV, ECCV, WACV ...", "subpage_snippet": "", "source": "cvpr2021.thecvf.com", "link": "https://cvpr2021.thecvf.com/jobs?page=2", "content": "You will have opportunities to work on advanced CV R&D projects and publish research results in top- tier CV venues ( CVPR , ICCV, ECCV, WACV ..."}
+{"idx": 2, "title": "CVPR 2025 Accepted Papers", "date": "", "ddg_snippet": "CVPR 2025 Accepted Papers This page is cached for 1 hour. Changes to affiliation or name in your local profile may take up to 60 minutes to appear here.", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/Conferences/2025/AcceptedPapers", "content": "CVPR 2025 Accepted Papers This page is cached for 1 hour. Changes to affiliation or name in your local profile may take up to 60 minutes to appear here."}
+{"idx": 3, "title": "CVPR 2025 Accepted Paper List - Paper Copilot", "date": "", "ddg_snippet": "For example, if a paper received ratings of 3, 4, and 5, its average score is 4 — and this average is used in the distribution. Suppose the Accept tier contains submissions with reviewer averages: {4.0, 3.1, 3.6}.", "subpage_snippet": "", "source": "papercopilot.com", "link": "https://papercopilot.com/paper-list/cvpr-paper-list/cvpr-2025-paper-list/", "content": "For example, if a paper received ratings of 3, 4, and 5, its average score is 4 — and this average is used in the distribution. Suppose the Accept tier contains submissions with reviewer averages: {4.0, 3.1, 3.6}."}
+{"idx": 4, "title": "Fourteen Papers Accepted at CVPR 2025 – Center for Research ...", "date": "", "ddg_snippet": "Apr 10, 2025 · Fourteen Papers Accepted at CVPR 2025 The IEEE / CVF Computer Vision and Pattern Recognition Conference ( CVPR ) is the premier annual computer vision event comprising the main conference and several co-located workshops and short courses. This year’s conference will be held at the Music City Center, Nashville, TN from June 11-15, 2025 .", "subpage_snippet": "", "source": "www.crcv.ucf.edu", "link": "https://www.crcv.ucf.edu/2025/04/10/fourteen-papers-accepted-at-cvpr-2025/", "content": "Apr 10, 2025 · Fourteen Papers Accepted at CVPR 2025 The IEEE / CVF Computer Vision and Pattern Recognition Conference ( CVPR ) is the premier annual computer vision event comprising the main conference and several co-located workshops and short courses. This year’s conference will be held at the Music City Center, Nashville, TN from June 11-15, 2025 ."}
+{"idx": 5, "title": "SkalskiP/top-cvpr-2025-papers - GitHub", "date": "", "ddg_snippet": "Computer Vision and Pattern Recognition is a massive conference. In 2025 alone, 13,008 papers were submitted, and 2,878 were accepted. I created this repository to help you search for crème de la crème of CVPR publications. If the paper you are looking for is not on my short list, take a peek at the full list of accepted papers .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/SkalskiP/top-cvpr-2025-papers", "content": "Computer Vision and Pattern Recognition is a massive conference. In 2025 alone, 13,008 papers were submitted, and 2,878 were accepted. I created this repository to help you search for crème de la crème of CVPR publications. If the paper you are looking for is not on my short list, take a peek at the full list of accepted papers ."}
+{"idx": 6, "title": "Paper Digest: CVPR 2025 Papers & Highlights", "date": "", "ddg_snippet": "Jun 7, 2025 · Note: CVPR - 2025 accepts more than 2,800 papers , this page only includes 500 of them selected by our daily paper digest algorithm. Interested users can choose to read All 2,800 CVPR - 2025 papers in a separate page.", "subpage_snippet": "", "source": "resources.paperdigest.org", "link": "https://resources.paperdigest.org/2025/06/cvpr-2025-papers-highlights/", "content": "Jun 7, 2025 · Note: CVPR - 2025 accepts more than 2,800 papers , this page only includes 500 of them selected by our daily paper digest algorithm. Interested users can choose to read All 2,800 CVPR - 2025 papers in a separate page."}
+{"idx": 7, "title": "CVPR 2025 Top Papers: Award Winners and Notable Research", "date": "", "ddg_snippet": "Essential CVPR 2025 papers : VGGT's neural 3D, physics-informed learning, and open models every computer vision engineer should know.", "subpage_snippet": "", "source": "www.basic.ai", "link": "https://www.basic.ai/blog-post/cvpr-2025-top-papers-award-winners-and-notable-research", "content": "Essential CVPR 2025 papers : VGGT's neural 3D, physics-informed learning, and open models every computer vision engineer should know."}
+{"idx": 8, "title": "CVPR 2025 Best Paper Nominees | CSPaper", "date": "", "ddg_snippet": "CVPR 2025 Best Paper Nominees: Innovations Across Vision and AI The IEEE/CVF Conference on Computer Vision and Pattern Recognition ( CVPR ) 2025 , one of the leading international conferences in computer vision, will take place from June 11 to June 15, 2025 , at the Music City Center in Nashville, Tennessee.", "subpage_snippet": "", "source": "cspaper.org", "link": "https://cspaper.org/topic/84/cvpr-2025-best-paper-nominees", "content": "CVPR 2025 Best Paper Nominees: Innovations Across Vision and AI The IEEE/CVF Conference on Computer Vision and Pattern Recognition ( CVPR ) 2025 , one of the leading international conferences in computer vision, will take place from June 11 to June 15, 2025 , at the Music City Center in Nashville, Tennessee."}
+{"idx": 9, "title": "计算机视觉实验室", "date": "", "ddg_snippet": "02/27/ 2025 : Five papers were accepted to CVPR 2025 , two of which were selected as highlight. ... paper was accepted to Tourism Management, a top- tier ...", "subpage_snippet": "", "source": "cv.nankai.edu.cn", "link": "https://cv.nankai.edu.cn/", "content": "02/27/ 2025 : Five papers were accepted to CVPR 2025 , two of which were selected as highlight. ... paper was accepted to Tourism Management, a top- tier ..."}
diff --git a/data/sampled_jsons/CWE-778_Insufficient_Logging_'Insufficient_Exploration'_definition_year_2023-2024.jsonl b/data/sampled_jsons/CWE-778_Insufficient_Logging_'Insufficient_Exploration'_definition_year_2023-2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..cf21ed0e9d0dd8db59b8e474d0ff6f9cf58c3355
--- /dev/null
+++ b/data/sampled_jsons/CWE-778_Insufficient_Logging_'Insufficient_Exploration'_definition_year_2023-2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "CWE - CWE List Version 4.18 - Mitre Corporation", "date": "", "ddg_snippet": "Nov 19, 2024 · Common Weakness Enumeration (CWE) is a list of software and hardware weaknesses.", "subpage_snippet": "", "source": "cwe.mitre.org", "link": "https://cwe.mitre.org/data/index.html", "content": "Nov 19, 2024 · Common Weakness Enumeration (CWE) is a list of software and hardware weaknesses."}
+{"idx": 1, "title": "CWE - CWE Top 25 Most Dangerous Software Weaknesses", "date": "", "ddg_snippet": "Feb 10, 2025 · The CWE Top 25 Most Dangerous Software Weaknesses List highlights the most severe and prevalent weaknesses behind the 31,770 Common Vulnerabilities and Exposures …", "subpage_snippet": "", "source": "cwe.mitre.org", "link": "https://cwe.mitre.org/top25/", "content": "Feb 10, 2025 · The CWE Top 25 Most Dangerous Software Weaknesses List highlights the most severe and prevalent weaknesses behind the 31,770 Common Vulnerabilities and Exposures …"}
+{"idx": 2, "title": "CWE - About CWE", "date": "", "ddg_snippet": "Mar 22, 2024 · About CWE Common Weakness Enumeration (CWE™) is a community-developed list of common software and hardware weaknesses. A “weakness” is a condition in a software, …", "subpage_snippet": "", "source": "cwe.mitre.org", "link": "https://cwe.mitre.org/about/index.html", "content": "Mar 22, 2024 · About CWE Common Weakness Enumeration (CWE™) is a community-developed list of common software and hardware weaknesses. A “weakness” is a condition in a software, …"}
+{"idx": 3, "title": "2024 CWE Top 25 Most Dangerous Software Weaknesses", "date": "", "ddg_snippet": "Nov 20, 2024 · Common Weakness Enumeration (CWE) is a list of software and hardware weaknesses.", "subpage_snippet": "", "source": "cwe.mitre.org", "link": "https://cwe.mitre.org/top25/archive/2024/2024_cwe_top25.html", "content": "Nov 20, 2024 · Common Weakness Enumeration (CWE) is a list of software and hardware weaknesses."}
+{"idx": 4, "title": "CWE - Frequently Asked Questions (FAQ)", "date": "", "ddg_snippet": "Mar 22, 2024 · CWE is industry-endorsed by the CWE Community, which includes representatives from major operating systems vendors, commercial information security tool vendors, academia, …", "subpage_snippet": "", "source": "cwe.mitre.org", "link": "https://cwe.mitre.org/about/faq.html", "content": "Mar 22, 2024 · CWE is industry-endorsed by the CWE Community, which includes representatives from major operating systems vendors, commercial information security tool vendors, academia, …"}
+{"idx": 5, "title": "CWE - Common Weakness Enumeration", "date": "", "ddg_snippet": "Sep 6, 2025 · Common Weakness Enumeration (CWE) is a list of software and hardware weaknesses.", "subpage_snippet": "", "source": "cwe.mitre.org", "link": "https://cwe.mitre.org/index.html", "content": "Sep 6, 2025 · Common Weakness Enumeration (CWE) is a list of software and hardware weaknesses."}
+{"idx": 6, "title": "CWE - Downloads", "date": "", "ddg_snippet": "Nov 19, 2024 · Common Weakness Enumeration (CWE) is a list of software and hardware weaknesses.", "subpage_snippet": "", "source": "cwe.mitre.org", "link": "https://cwe.mitre.org/data/downloads.html", "content": "Nov 19, 2024 · Common Weakness Enumeration (CWE) is a list of software and hardware weaknesses."}
+{"idx": 7, "title": "New to CWE - Mitre Corporation", "date": "", "ddg_snippet": "Jun 5, 2023 · What is CWE? First, we should describe what CWE is. CWE is a community-developed list of common software and hardware weakness types that could have security ramifications. A …", "subpage_snippet": "", "source": "cwe.mitre.org", "link": "https://cwe.mitre.org/about/new_to_cwe.html", "content": "Jun 5, 2023 · What is CWE? First, we should describe what CWE is. CWE is a community-developed list of common software and hardware weakness types that could have security ramifications. A …"}
+{"idx": 8, "title": "CWE - CVE → CWE Mapping \"Root Cause Mapping\" Guidance", "date": "", "ddg_snippet": "Mar 22, 2024 · CWE Mapping Notes – which are linked to under each CWE’s title – provide additional details and helpful considerations with respect to using the CWE for root cause mapping.", "subpage_snippet": "", "source": "cwe.mitre.org", "link": "https://cwe.mitre.org/documents/cwe_usage/guidance.html", "content": "Mar 22, 2024 · CWE Mapping Notes – which are linked to under each CWE’s title – provide additional details and helpful considerations with respect to using the CWE for root cause mapping."}
+{"idx": 9, "title": "CWE - CWE-22: Improper Limitation of a Pathname to a Restricted ...", "date": "", "ddg_snippet": "Vulnerability Mapping:ALLOWEDThis CWE ID may be used to map to real-world vulnerabilitiesAbstraction: BaseBase - a weakness that is still mostly independent of a resource …", "subpage_snippet": "", "source": "cwe.mitre.org", "link": "https://cwe.mitre.org/data/definitions/22.html", "content": "Vulnerability Mapping:ALLOWEDThis CWE ID may be used to map to real-world vulnerabilitiesAbstraction: BaseBase - a weakness that is still mostly independent of a resource …"}
diff --git a/data/sampled_jsons/Can_We_Leave_Deepfake_Data_Behind_beta_gamma_hyperparameters_0.5_0.1.jsonl b/data/sampled_jsons/Can_We_Leave_Deepfake_Data_Behind_beta_gamma_hyperparameters_0.5_0.1.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..bc46984799c5e3ace0ce362b3eee26956355d744
--- /dev/null
+++ b/data/sampled_jsons/Can_We_Leave_Deepfake_Data_Behind_beta_gamma_hyperparameters_0.5_0.1.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Can We Leave Deepfake Data Behind in Training Deepfake Detector?", "date": "", "ddg_snippet": "Therefore, a critical question arises: Can we leave deepfake behind and rely solely on blendfake data to train an effective deepfake detector? Intuitively, as deepfakes also contain additional informative forgery clues (e.g., deep generative artifacts), excluding all deepfake data in training deepfake detectors seems counter-intuitive.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2408.17052", "content": "Therefore, a critical question arises: Can we leave deepfake behind and rely solely on blendfake data to train an effective deepfake detector? Intuitively, as deepfakes also contain additional informative forgery clues (e.g., deep generative artifacts), excluding all deepfake data in training deepfake detectors seems counter-intuitive."}
+{"idx": 1, "title": "Beyond the Benchmark: Generalization Limits of Deepfake ...", "date": "", "ddg_snippet": "We took two different experimental approaches to investigate how the Deepfake Detection Model generalizes on data outside of the domain it was trained on. Our first experiment, Leave -One-Out, tested how fine-tuning on all but one generative model impacts the models performance on the held-out set.", "subpage_snippet": "", "source": "www.ischool.berkeley.edu", "link": "https://www.ischool.berkeley.edu/sites/default/files/bb_paper.pdf", "content": "We took two different experimental approaches to investigate how the Deepfake Detection Model generalizes on data outside of the domain it was trained on. Our first experiment, Leave -One-Out, tested how fine-tuning on all but one generative model impacts the models performance on the held-out set."}
+{"idx": 2, "title": "DeepfakeBench: A Comprehensive Benchmark of Deepfake ... - GitHub Hyperparameter Tuning - GeeksforGeeks Hemg/Deepfake-Detection · Hugging Face Deepfake: definitions, performance metrics and standards ... Deepfake : definitions, performance metrics and standards, datasets, and Deepfake : definitions, performance metrics and standards, datasets, and Deepfake : definitions, performance metrics and standards, datasets, and Deepfake : definitions, performance metrics and standards, datasets, and GitHub - SCLBD/DeepfakeBench: A comprehensive Deepfake : definitions, performance metrics and standards, datasets, and Abstract Can We Leave Deepfake Data Behind in Training Deepfake", "date": "", "ddg_snippet": "Authors: Zhiyuan Yan, Yong Zhang, Xinhang Yuan, Siwei Lyu, Baoyuan Wu* [paper] [pre-trained weights] Welcome to DeepfakeBench, your one-stop solution for deepfake detection! Here are some key features of our platform: Table of Contents •Features •Quick Start See full list on github.com [Back to top] DeepfakeBench has the following features: ⭐️ Detectors (15 detectors): • 5 Naive Detectors: Xception, MesoNet, MesoInception, CNN-Aug, EfficientNet-B4 •7 Spatial Detectors: Capsule, DSP-FWA, Face X-ray, FFD, CORE, RECCE, UCF •3 Frequency Detectors: F3Net, SPSL, SRM See full list on github.com 1 . Installation (option 1 ) You can run the following script to configure the necessary environment:(option 2) You can also utilize the supplied Dockerfile to set up the entire environment using Docker. This will allow you to execute all the codes in the benchmark without encountering any environment-related problems. Simply run the following commands to enter the Docker environment.Note we used Docker version 19.03.14 in our setup. We highly recommend using this version for consistency, but later versions of Docker may also be compatible. 2. Download Data [Back to top]All datasets used in DeepfakeBench can be downloaded from their own websites or repositories. For convenience, we also provide the data we use in our research. All the downloaded datasets have been organized and arranged in the same folder. Users can easily access and download the preprocessed data , including original videos, mask videos, frames, and landmarks:🛡️ Copyright of the above datasets belongs to their original providers.Other detailed information about the datasets used in DeepfakeBench is summarized below:Upon downloading the datasets, please ensure to store them in the ./datasets folder, arranging them in accordance with the directory structure outlined below:If you choose to store your datasets in a different folder, for instance, ./ deepfake / data , it's important to reflect this change in the dataset path in the config.yaml for preprocessing purposes. 3. Preprocessing (optional) [Back to top]❗️Note: If you want to directly utilize the data , including frames, landmarks, masks, and more, that I have provided above, you can skip the pre-processing step. However, you still need to run the rearrangement script to generate the JSON file for each dataset for the unified data loading in the training and testing process.DeepfakeBench follows a sequential workflow for face detection, alignment, and cropping. The processed data , including face images, landmarks, and masks, are saved in separate folders for further analysis.To start preprocessing your dataset, please follow these steps: 1 .Download the shape_predictor_81_face_landmarks.dat file. Then, copy the downloaded shape_predictor_81_face_landmarks.dat file into the ./preprocessing/dlib_tools folder. This file is necessary for Dlib's face detection functionality.2.Open the ./preprocessing/config.yaml and locate the line default: DATASET_YOU_SPECIFY. Replace DATASET_YOU_SPECIFY with the name of the dataset you want to preprocess, such as FaceForensics++.3.Specify the dataset_root_path in the config.yaml file. Search for the line that mentions dataset_root_path. By default, it looks like this: dataset_root_path: ./datasets. Replace ./datasets with the actual path to the folder where your dataset is arranged.Once you have completed these steps, you can proceed with running the following line to do the preprocessing: See full list on github.com [Back to top] In our Benchmark, we apply TensorBoard to monitor the progress of training models. It provides a visual representation of the training process, allowing users to examine training results conveniently. To demonstrate the effectiveness of different detectors, we present partial results from both within-domain and cross-domain evaluations. The evaluation metric used is the frame-level Area Under the Curve (AUC). In this particular scenario, we train the detectors on the FF++ (c23) dataset and assess their performance on other datasets. For a comprehensive overview of the results, we strongly recommend referring to our paper. These resources provide a detailed analysis of the training outcomes and offer a deeper understanding of the methodology and findings. In the above table, \"Avg.\" donates the average AUC for within-domain and cross-domain evaluation, and the overall results. \"Top3\" represents the count of each method ranks within the top-3 across all testing datasets. The best-performing method for each column is highlighted. Also, we provide all experimental results in Link (code: qjpd). You can use these results for further analysis using the code in ./analysis folder. See full list on github.com [Back to top] If you find our benchmark useful to your research, please cite it as follows: See full list on github.com [Back to top] This repository is licensed by The Chinese University of Hong Kong, Shenzhen under Creative Commons Attribution-NonCommercial 4. 0 International Public License (identified as CC BY-NC-4. 0 in SPDX). More details about the license could be found in LICENSE. This project is built by the Secure Computing Lab of Big Data (SCLBD) at The School of Data Science (SDS) of The Chinese University of Hong Kong, Shenzhen, directed by Professor Baoyuan Wu. SCLBD focuses on the research of trustworthy AI, including backdoor learning, adversarial examples, federated learning, fairness, etc. If you have any suggestions, comments, or wish to contribute code or propose methods, we warmly welcome your input. Please contact us at wubaoyuan@cuhk.edu.cn or yanzhiyuan1114@gmail.com. We look forward to collaborating with you in pushing the boundaries of deepfake detection. See full list on github.com Aug 2, 2025 · Hyperparameter tuning is the process of selecting the optimal values for a machine learning model's hyperparameters . These are typically set before the actual training process begins and control aspects of the learning process itself. They influence the model's performance its complexity and how fast it learns. For example the learning rate and number of neurons in a neural network in a neural ... Deepfake -Detection This model is a fine-tuned version of google/vit-base-patch16-224-in21k on an unknown dataset. It achieves the following results on the evaluation set: Loss: 0.0975 Accuracy: 0.9609 Model description More information needed Intended uses & limitations More information needed Training and evaluation data More information needed Training procedure Training hyperparameters The ... For performance metrics, we covered those commonly used based on relevant standards, the survey papers, and the identified challenges, competitions, and benchmarks. 3 Deepfake -related performance metrics and standards In this survey, we focus on performance evaluation and comparison of deepfake generation and detection methods. What performance metrics are used in deepfake detection? Surprisingly, only two of the 15 surveys (Rana et al., 2022; Heidari et al., 2023) have covered performance metrics explicitly. Some directly used performance metrics to explain and compare the performance of covered deepfake generation and detection methods. The most used performance metrics include accuracy, ERR, and AUC . What are deepfake-related datasets? Deepfake -related datasets were compiled based on the selected survey papers and identified deepfake -related challenges, competitions, and benchmarks. Relevant standards were identified mainly via research papers covered in this survey, the co-authors' personal knowledge, and Google Web searches. Can deepfake datasets be constructed with generative AI models? However, we believe that new image, video, audio, and hybrid deepfake datasets can be constructed with such systems, considering the multimodal capabilities of the state-of-the-art generative AI models, e.g., GPT-4o. 58 Are there deepfake speech datasets? A major set of deepfake speech datasets were created for the ASVspoof (Automatic Speaker Verification Spoofing and Countermeasures) Challenge 43 (2015–2021, held biannually). The datasets for the 2019 and 2021 challenges contain speech data that can be considered deepfakes. How many detection methods does deepfakebench support? 36 Detectors are supported: DeepfakeBench, currently, supports a total of 36 detection methods (28 image detectors + 8 video detectors). Data Preprocessing: DeepfakeBench currently provides LMDB for more faster and effective IO. Multi-GPUs Training: DeepfakeBench offers DDP for multiple GPUs training. What is deepfake detection dataset? DeepFake detection dataset (Dufour and Gully, 2019): This dataset contains 3,363 face videos , covering 28 subjects, gender, and skin colour. 1<2”). Therefore, a critical question arises: Can we leave deepfake behind and rely solely on blendfake data to train an effective deepfake etector? Intuitively, as deepfakes also contain additional informative forgery clues (e.g., deep generative artifacts), excluding all deepfake data in training deepfake detectors seems counter-i", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/SCLBD/DeepfakeBench", "content": "Authors: Zhiyuan Yan, Yong Zhang, Xinhang Yuan, Siwei Lyu, Baoyuan Wu* [paper] [pre-trained weights] Welcome to DeepfakeBench, your one-stop solution for deepfake detection! Here are some key features of our platform: Table of Contents •Features •Quick Start See full list on github.com [Back to top] DeepfakeBench has the following features: ⭐️ Detectors (15 detectors): • 5 Naive Detectors: Xception, MesoNet, MesoInception, CNN-Aug, EfficientNet-B4 •7 Spatial Detectors: Capsule, DSP-FWA, Face X-ray, FFD, CORE, RECCE, UCF •3 Frequency Detectors: F3Net, SPSL, SRM See full list on github.com 1 . Installation (option 1 ) You can run the following script to configure the necessary environment:(option 2) You can also utilize the supplied Dockerfile to set up the entire environment using Docker. This will allow you to execute all the codes in the benchmark without encountering any environment-related problems. Simply run the following commands to enter the Docker environment.Note we used Docker version 19.03.14 in our setup. We highly recommend using this version for consistency, but later versions of Docker may also be compatible. 2. Download Data [Back to top]All datasets used in DeepfakeBench can be downloaded from their own websites or repositories. For convenience, we also provide the data we use in our research. All the downloaded datasets have been organized and arranged in the same folder. Users can easily access and download the preprocessed data , including original videos, mask videos, frames, and landmarks:🛡️ Copyright of the above datasets belongs to their original providers.Other detailed information about the datasets used in DeepfakeBench is summarized below:Upon downloading the datasets, please ensure to store them in the ./datasets folder, arranging them in accordance with the directory structure outlined below:If you choose to store your datasets in a different folder, for instance, ./ deepfake / data , it's important to reflect this change in the dataset path in the config.yaml for preprocessing purposes. 3. Preprocessing (optional) [Back to top]❗️Note: If you want to directly utilize the data , including frames, landmarks, masks, and more, that I have provided above, you can skip the pre-processing step. However, you still need to run the rearrangement script to generate the JSON file for each dataset for the unified data loading in the training and testing process.DeepfakeBench follows a sequential workflow for face detection, alignment, and cropping. The processed data , including face images, landmarks, and masks, are saved in separate folders for further analysis.To start preprocessing your dataset, please follow these steps: 1 .Download the shape_predictor_81_face_landmarks.dat file. Then, copy the downloaded shape_predictor_81_face_landmarks.dat file into the ./preprocessing/dlib_tools folder. This file is necessary for Dlib's face detection functionality.2.Open the ./preprocessing/config.yaml and locate the line default: DATASET_YOU_SPECIFY. Replace DATASET_YOU_SPECIFY with the name of the dataset you want to preprocess, such as FaceForensics++.3.Specify the dataset_root_path in the config.yaml file. Search for the line that mentions dataset_root_path. By default, it looks like this: dataset_root_path: ./datasets. Replace ./datasets with the actual path to the folder where your dataset is arranged.Once you have completed these steps, you can proceed with running the following line to do the preprocessing: See full list on github.com [Back to top] In our Benchmark, we apply TensorBoard to monitor the progress of training models. It provides a visual representation of the training process, allowing users to examine training results conveniently. To demonstrate the effectiveness of different detectors, we present partial results from both within-domain and cross-domain evaluations. The evaluation metric used is the frame-level Area Under the Curve (AUC). In this particular scenario, we train the detectors on the FF++ (c23) dataset and assess their performance on other datasets. For a comprehensive overview of the results, we strongly recommend referring to our paper. These resources provide a detailed analysis of the training outcomes and offer a deeper understanding of the methodology and findings. In the above table, \"Avg.\" donates the average AUC for within-domain and cross-domain evaluation, and the overall results. \"Top3\" represents the count of each method ranks within the top-3 across all testing datasets. The best-performing method for each column is highlighted. Also, we provide all experimental results in Link (code: qjpd). You can use these results for further analysis using the code in ./analysis folder. See full list on github.com [Back to top] If you find our benchmark useful to your research, please cite it as follows: See full list on github.com [Back to top] This repository is licensed by The Chinese University of Hong Kong, Shenzhen under Creative Commons Attribution-NonCommercial 4. 0 International Public License (identified as CC BY-NC-4. 0 in SPDX). More details about the license could be found in LICENSE. This project is built by the Secure Computing Lab of Big Data (SCLBD) at The School of Data Science (SDS) of The Chinese University of Hong Kong, Shenzhen, directed by Professor Baoyuan Wu. SCLBD focuses on the research of trustworthy AI, including backdoor learning, adversarial examples, federated learning, fairness, etc. If you have any suggestions, comments, or wish to contribute code or propose methods, we warmly welcome your input. Please contact us at wubaoyuan@cuhk.edu.cn or yanzhiyuan1114@gmail.com. We look forward to collaborating with you in pushing the boundaries of deepfake detection. See full list on github.com Aug 2, 2025 · Hyperparameter tuning is the process of selecting the optimal values for a machine learning model's hyperparameters . These are typically set before the actual training process begins and control aspects of the learning process itself. They influence the model's performance its complexity and how fast it learns. For example the learning rate and number of neurons in a neural network in a neural ... Deepfake -Detection This model is a fine-tuned version of google/vit-base-patch16-224-in21k on an unknown dataset. It achieves the following results on the evaluation set: Loss: 0.0975 Accuracy: 0.9609 Model description More information needed Intended uses & limitations More information needed Training and evaluation data More information needed Training procedure Training hyperparameters The ... For performance metrics, we covered those commonly used based on relevant standards, the survey papers, and the identified challenges, competitions, and benchmarks. 3 Deepfake -related performance metrics and standards In this survey, we focus on performance evaluation and comparison of deepfake generation and detection methods. What performance metrics are used in deepfake detection? Surprisingly, only two of the 15 surveys (Rana et al., 2022; Heidari et al., 2023) have covered performance metrics explicitly. Some directly used performance metrics to explain and compare the performance of covered deepfake generation and detection methods. The most used performance metrics include accuracy, ERR, and AUC . What are deepfake-related datasets? Deepfake -related datasets were compiled based on the selected survey papers and identified deepfake -related challenges, competitions, and benchmarks. Relevant standards were identified mainly via research papers covered in this survey, the co-authors' personal knowledge, and Google Web searches. Can deepfake datasets be constructed with generative AI models? However, we believe that new image, video, audio, and hybrid deepfake datasets can be constructed with such systems, considering the multimodal capabilities of the state-of-the-art generative AI models, e.g., GPT-4o. 58 Are there deepfake speech datasets? A major set of deepfake speech datasets were created for the ASVspoof (Automatic Speaker Verification Spoofing and Countermeasures) Challenge 43 (2015–2021, held biannually). The datasets for the 2019 and 2021 challenges contain speech data that can be considered deepfakes. How many detection methods does deepfakebench support? 36 Detectors are supported: DeepfakeBench, currently, supports a total of 36 detection methods (28 image detectors + 8 video detectors). Data Preprocessing: DeepfakeBench currently provides LMDB for more faster and effective IO. Multi-GPUs Training: DeepfakeBench offers DDP for multiple GPUs training. What is deepfake detection dataset? DeepFake detection dataset (Dufour and Gully, 2019): This dataset contains 3,363 face videos , covering 28 subjects, gender, and skin colour. 1<2”). Therefore, a critical question arises: Can we leave deepfake behind and rely solely on blendfake data to train an effective deepfake etector? Intuitively, as deepfakes also contain additional informative forgery clues (e.g., deep generative artifacts), excluding all deepfake data in training deepfake detectors seems counter-i"}
+{"idx": 3, "title": "Hemg/Deepfake-Detection · Hugging Face", "date": "", "ddg_snippet": "Deepfake -Detection This model is a fine-tuned version of google/vit-base-patch16-224-in21k on an unknown dataset. It achieves the following results on the evaluation set: Loss: 0.0975 Accuracy: 0.9609 Model description More information needed Intended uses & limitations More information needed Training and evaluation data More information needed Training procedure Training hyperparameters The ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/Hemg/Deepfake-Detection", "content": "Deepfake -Detection This model is a fine-tuned version of google/vit-base-patch16-224-in21k on an unknown dataset. It achieves the following results on the evaluation set: Loss: 0.0975 Accuracy: 0.9609 Model description More information needed Intended uses & limitations More information needed Training and evaluation data More information needed Training procedure Training hyperparameters The ..."}
+{"idx": 4, "title": "Deepfake: definitions, performance metrics and standards ...", "date": "", "ddg_snippet": "For performance metrics, we covered those commonly used based on relevant standards, the survey papers, and the identified challenges, competitions, and benchmarks. 3 Deepfake -related performance metrics and standards In this survey, we focus on performance evaluation and comparison of deepfake generation and detection methods.", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC11408348/", "content": "For performance metrics, we covered those commonly used based on relevant standards, the survey papers, and the identified challenges, competitions, and benchmarks. 3 Deepfake -related performance metrics and standards In this survey, we focus on performance evaluation and comparison of deepfake generation and detection methods."}
+{"idx": 5, "title": "Abstract Can We Leave Deepfake Data Behind in Training Deepfake", "date": "", "ddg_snippet": "1<2”). Therefore, a critical question arises: Can we leave deepfake behind and rely solely on blendfake data to train an effective deepfake etector? Intuitively, as deepfakes also contain additional informative forgery clues (e.g., deep generative artifacts), excluding all deepfake data in training deepfake detectors seems counter-i", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2408.17052", "content": "1<2”). Therefore, a critical question arises: Can we leave deepfake behind and rely solely on blendfake data to train an effective deepfake etector? Intuitively, as deepfakes also contain additional informative forgery clues (e.g., deep generative artifacts), excluding all deepfake data in training deepfake detectors seems counter-i"}
+{"idx": 6, "title": "Assessment framework for deepfake detection in real-world", "date": "", "ddg_snippet": "... dataset evaluation has become an important step in recent studies to better show the advantages of deepfake detection methods, encouraging researchers ...", "subpage_snippet": "", "source": "jivp-eurasipjournals.springeropen.com", "link": "https://jivp-eurasipjournals.springeropen.com/articles/10.1186/s13640-024-00621-8", "content": "... dataset evaluation has become an important step in recent studies to better show the advantages of deepfake detection methods, encouraging researchers ..."}
+{"idx": 7, "title": "Hyperparameter Tuning - GeeksforGeeks", "date": "", "ddg_snippet": "Aug 2, 2025 · Hyperparameter tuning is the process of selecting the optimal values for a machine learning model's hyperparameters . These are typically set before the actual training process begins and control aspects of the learning process itself. They influence the model's performance its complexity and how fast it learns. For example the learning rate and number of neurons in a neural network in a neural ...", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/machine-learning/hyperparameter-tuning/", "content": "Aug 2, 2025 · Hyperparameter tuning is the process of selecting the optimal values for a machine learning model's hyperparameters . These are typically set before the actual training process begins and control aspects of the learning process itself. They influence the model's performance its complexity and how fast it learns. For example the learning rate and number of neurons in a neural network in a neural ..."}
+{"idx": 8, "title": "Expectation Alignment: Handling Reward Misspecification in the", "date": "", "ddg_snippet": "... 0 ⟩ ℳ 𝑆 𝐴 𝑇 𝑅 𝛾 subscript 𝑠 0 \\mathcal{M}=\\langle S,A,T,R,\\ gamma ,s_{ 0 }\\rangle caligraphic_M = ⟨ italic_S , italic_A , italic_T ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2404.08791v2", "content": "... 0 ⟩ ℳ 𝑆 𝐴 𝑇 𝑅 𝛾 subscript 𝑠 0 \\mathcal{M}=\\langle S,A,T,R,\\ gamma ,s_{ 0 }\\rangle caligraphic_M = ⟨ italic_S , italic_A , italic_T ..."}
+{"idx": 9, "title": "From Transformers to ChatGPT", "date": "", "ddg_snippet": "Although we could potentially construct datasets with ~1M pairs of sentences in machine translation, the information (and model supervision) we could ...", "subpage_snippet": "", "source": "www.dingran.me", "link": "https://www.dingran.me/from-transformer-to-llm/", "content": "Although we could potentially construct datasets with ~1M pairs of sentences in machine translation, the information (and model supervision) we could ..."}
diff --git a/data/sampled_jsons/Can_We_Leave_Deepfake_Data_Behind_in_Training_Deepfake_Detector_arXiv.jsonl b/data/sampled_jsons/Can_We_Leave_Deepfake_Data_Behind_in_Training_Deepfake_Detector_arXiv.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..828b2ec5e8322906a505c3fa5b19a2477224f5c1
--- /dev/null
+++ b/data/sampled_jsons/Can_We_Leave_Deepfake_Data_Behind_in_Training_Deepfake_Detector_arXiv.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Can We Leave Deepfake Data Behind in Training Deepfake Detector?", "date": "", "ddg_snippet": "The generalization ability of deepfake detectors is vital for their applications in real-world scenarios. One effective solution to enhance this ability is to train the models with manually-blended data , which we termed \"blendfake\", encouraging models to learn generic forgery artifacts like blending boundary. Interestingly, current SoTA methods utilize blendfake without incorporating any ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2408.17052", "content": "The generalization ability of deepfake detectors is vital for their applications in real-world scenarios. One effective solution to enhance this ability is to train the models with manually-blended data , which we termed \"blendfake\", encouraging models to learn generic forgery artifacts like blending boundary. Interestingly, current SoTA methods utilize blendfake without incorporating any ..."}
+{"idx": 1, "title": "GitHub - beautyremain/ProDet: The official code for paper \"Can We Leave ...", "date": "", "ddg_snippet": "The official code for paper \" Can We Leave Deepfake Data Behind in Training Deepfake Detector \" (NIPS2024 poster) ProDet is implemented within the framework of DeepfakeBench. The provided code should be placed in the corresponding folders in DeepfakeBench, and test/train on DeepfakeBench as well. You may find the overall-best checkpoint of our method from Google Drive, which is recommended for ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/beautyremain/ProDet", "content": "The official code for paper \" Can We Leave Deepfake Data Behind in Training Deepfake Detector \" (NIPS2024 poster) ProDet is implemented within the framework of DeepfakeBench. The provided code should be placed in the corresponding folders in DeepfakeBench, and test/train on DeepfakeBench as well. You may find the overall-best checkpoint of our method from Google Drive, which is recommended for ..."}
+{"idx": 2, "title": "Can We Leave Deepfake Data Behind in Training Deepfake Detector?", "date": "", "ddg_snippet": "Figure 1: 1a: The detection performance experiences an abnormal decline when naively combining deepfake and blendfake as the negative sample for training , even though the forgery information is enriched in this process. 1b: Illustration Example for illustrating latent space organization. With progressively organized latent space (ours), information in both deepfake and blendfake is effectively ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Can-We-Leave-Deepfake-Data-Behind-in-Training-Cheng-Yan/6b186896a5b2c15ea07a1e516c41ce01f2c15772/figure/0", "content": "Figure 1: 1a: The detection performance experiences an abnormal decline when naively combining deepfake and blendfake as the negative sample for training , even though the forgery information is enriched in this process. 1b: Illustration Example for illustrating latent space organization. With progressively organized latent space (ours), information in both deepfake and blendfake is effectively ..."}
+{"idx": 3, "title": "Can We Leave Deepfake Data Behind in Training Deepfake Detector?", "date": "", "ddg_snippet": "Therefore, a critical question arises: Can we leave deepfake behind and rely solely on blendfake data to train an effective deepfake detector ? Intuitively, as deepfakes also contain additional informative forgery clues ($\\textit {e.g.,}$ deep generative artifacts), excluding all deepfake data in training deepfake detectors seems counter-intuitive.", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper_files/paper/2024/hash/2718a032d15e0b80cd164b240220df89-Abstract-Conference.html", "content": "Therefore, a critical question arises: Can we leave deepfake behind and rely solely on blendfake data to train an effective deepfake detector ? Intuitively, as deepfakes also contain additional informative forgery clues ($\\textit {e.g.,}$ deep generative artifacts), excluding all deepfake data in training deepfake detectors seems counter-intuitive."}
+{"idx": 4, "title": "Can We Leave Deepfake Data Behind in Training Deepfake Detector?", "date": "", "ddg_snippet": "Therefore, a critical question arises: Can we leave deepfake behind and rely solely on blendfake data to train an effective deepfake detector ?", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/383648453_Can_We_Leave_Deepfake_Data_Behind_in_Training_Deepfake_Detector", "content": "Therefore, a critical question arises: Can we leave deepfake behind and rely solely on blendfake data to train an effective deepfake detector ?"}
+{"idx": 5, "title": "Can We Leave Deepfake Data Behind in Training Deepfake Detector?", "date": "", "ddg_snippet": "Therefore, a critical question arises: Can we leave deepfake behind and rely solely on blendfake data to train an effective deepfake detector ? Intuitively, as deepfakes also contain additional informative forgery clues (e.g., deep generative artifacts), excluding all deepfake data in training deepfake detectors seems counter-intuitive.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2408.17052v1", "content": "Therefore, a critical question arises: Can we leave deepfake behind and rely solely on blendfake data to train an effective deepfake detector ? Intuitively, as deepfakes also contain additional informative forgery clues (e.g., deep generative artifacts), excluding all deepfake data in training deepfake detectors seems counter-intuitive."}
+{"idx": 6, "title": "Abstract Can We Leave Deepfake Data Behind in Training Deepfake - arXiv.org", "date": "", "ddg_snippet": "Abstract The generalization ability of deepfake detectors is vital for their applications in real-world scenarios. One effective solution to enhance this ability is to train the models with manually-blended data , which we termed \"blendfake\", encouraging models to learn generic forgery artifacts like blending boundary. Interestingly, current SoTA methods utilize blendfake without ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2408.17052", "content": "Abstract The generalization ability of deepfake detectors is vital for their applications in real-world scenarios. One effective solution to enhance this ability is to train the models with manually-blended data , which we termed \"blendfake\", encouraging models to learn generic forgery artifacts like blending boundary. Interestingly, current SoTA methods utilize blendfake without ..."}
+{"idx": 7, "title": "Deepfake Detection that Generalizes Across Benchmarks - arXiv.org", "date": "", "ddg_snippet": "A fundamental question in training deepfake detectors is how to construct training data more effectively to promote generalization. In this section, we empirically validate the hypothesis that achieving better generalization requires a training dataset in which each fake video has a real counterpart from which it was produced.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.06248v1", "content": "A fundamental question in training deepfake detectors is how to construct training data more effectively to promote generalization. In this section, we empirically validate the hypothesis that achieving better generalization requires a training dataset in which each fake video has a real counterpart from which it was produced."}
+{"idx": 8, "title": "Is It Certainly a Deepfake? Reliability Analysis in Detection ...", "date": "", "ddg_snippet": "In this paper, we analyze uncertainty of various deepfake detectors in the presence of data generated by various deepfake generators. We use this analysis comprehensively to compare the robustness and reliability of detectors , to explain the detector response towards different generative sources.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.17550", "content": "In this paper, we analyze uncertainty of various deepfake detectors in the presence of data generated by various deepfake generators. We use this analysis comprehensively to compare the robustness and reliability of detectors , to explain the detector response towards different generative sources."}
+{"idx": 9, "title": "Zero-Shot Visual Deepfake Detection: Can AI Predict and Prevent Fake ...", "date": "", "ddg_snippet": "The second part of this section examines how self-supervised models can discover deepfake anomalies in terms of visual, physiological, and behavioral inconsistency and help in deepfake detection without the dependence on the data set [174] [175].", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.18461v1", "content": "The second part of this section examines how self-supervised models can discover deepfake anomalies in terms of visual, physiological, and behavioral inconsistency and help in deepfake detection without the dependence on the data set [174] [175]."}
diff --git a/data/sampled_jsons/Catoni_Contextual_Bandits_are_Robust_to_Heavy-tailed_Psi_function_definition.jsonl b/data/sampled_jsons/Catoni_Contextual_Bandits_are_Robust_to_Heavy-tailed_Psi_function_definition.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..49ddf3f9b26f0514c84f3ef465549ce5a64a2686
--- /dev/null
+++ b/data/sampled_jsons/Catoni_Contextual_Bandits_are_Robust_to_Heavy-tailed_Psi_function_definition.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Catoni Contextual Bandits are Robust to Heavy-tailed Rewards", "date": "", "ddg_snippet": "However, many practical scenarios naturally involve heavy-tailed rewards or rewards where the worst-case range can be substantially larger than the variance. In this paper, we develop an algorithmic approach building on Catoni's estimator from robust statistics, and apply it to contextual bandits with general function approximation.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.02486", "content": "However, many practical scenarios naturally involve heavy-tailed rewards or rewards where the worst-case range can be substantially larger than the variance. In this paper, we develop an algorithmic approach building on Catoni's estimator from robust statistics, and apply it to contextual bandits with general function approximation."}
+{"idx": 1, "title": "Catoni Contextual Bandits are Robust to Heavy-tailed Rewards", "date": "", "ddg_snippet": "However, many practical scenarios naturally involve heavy-tailed rewards or rewards where the worst-case range can be substantially larger than the variance. In this pa-per, we develop an algorithmic approach building on Catoni's estimator from robust statistics, and apply it to contextual bandits with general func-tion approximation.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=5IpVe9PH14", "content": "However, many practical scenarios naturally involve heavy-tailed rewards or rewards where the worst-case range can be substantially larger than the variance. In this pa-per, we develop an algorithmic approach building on Catoni's estimator from robust statistics, and apply it to contextual bandits with general func-tion approximation."}
+{"idx": 2, "title": "Catoni Contextual Bandits are Robust to Heavy-tailed Rewards", "date": "", "ddg_snippet": "This paper develops an algorithmic approach building on Catoni's estimator from robust statistics, and applies it to contextual bandits with general function approximation and establishes a regret bound that depends only on the cumulative reward variance and logarithmically on the reward range as well as the number of rounds. Typical contextual bandit algorithms assume that the rewards at each ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Catoni-Contextual-Bandits-are-Robust-to-Rewards-Ye-Jin/125154c306493f2c7af8e8c09c0e58c22106e6fe", "content": "This paper develops an algorithmic approach building on Catoni's estimator from robust statistics, and applies it to contextual bandits with general function approximation and establishes a regret bound that depends only on the cumulative reward variance and logarithmically on the reward range as well as the number of rounds. Typical contextual bandit algorithms assume that the rewards at each ..."}
+{"idx": 3, "title": "Catoni Contextual Bandits are Robust to Heavy-tailed Rew...", "date": "", "ddg_snippet": "This research paper explores a type of algorithm used in decision-making systems, called contextual bandits , which help choose the best actions based on past experiences. The authors focus on situ...", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/icml/46438/paper", "content": "This research paper explores a type of algorithm used in decision-making systems, called contextual bandits , which help choose the best actions based on past experiences. The authors focus on situ..."}
+{"idx": 4, "title": "PDF Bandits With Heavy Tail", "date": "", "ddg_snippet": "The key to successful handling heavy-tailed reward distribu-tions is to replace the empirical mean with other, more robust estimators of the mean. All we need is a performance guarantee like the one shown above for the empirical mean.", "subpage_snippet": "", "source": "sbubeck.com", "link": "http://sbubeck.com/BCL13.pdf", "content": "The key to successful handling heavy-tailed reward distribu-tions is to replace the empirical mean with other, more robust estimators of the mean. All we need is a performance guarantee like the one shown above for the empirical mean."}
+{"idx": 5, "title": "Abstract arXiv:2502.02486v1 [stat.ML] 4 Feb 2025 Catoni Cont", "date": "", "ddg_snippet": "Typical contextual bandit algorithms assume that the rewards at each round lie in some fixed range [0, R], and their regret scales polynomially with this reward range R. However, many practical scenarios naturally involve heavy-tailed rewards or rewards where the worst-case range can be substantially larger than the variance. In this paper, we develop an algorithmic approach building on Catoni ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.02486", "content": "Typical contextual bandit algorithms assume that the rewards at each round lie in some fixed range [0, R], and their regret scales polynomially with this reward range R. However, many practical scenarios naturally involve heavy-tailed rewards or rewards where the worst-case range can be substantially larger than the variance. In this paper, we develop an algorithmic approach building on Catoni ..."}
+{"idx": 6, "title": "PDF Catoni Contextual Bandits are Robust to Heavy-tailed Rewards", "date": "", "ddg_snippet": "Tackling heavy-tailed rewards in reinforcement learning with function approximation: Minimax optimal and instance-dependent re-gret bounds. Advances in Neural Information Processing Sys-tems, 36. [3] Li, X. and Sun, Q. (2024). Variance-aware decision making with linear function approximation under heavytailed rewards.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/media/icml-2025/Slides/46438.pdf", "content": "Tackling heavy-tailed rewards in reinforcement learning with function approximation: Minimax optimal and instance-dependent re-gret bounds. Advances in Neural Information Processing Sys-tems, 36. [3] Li, X. and Sun, Q. (2024). Variance-aware decision making with linear function approximation under heavytailed rewards."}
+{"idx": 7, "title": "Taking a hint: How to leverage loss predictors in contextual bandits?", "date": "", "ddg_snippet": "3 ) is achievable; 3) with M predictors, a linear dependence on M is necessary, even though logarithmic dependence is possible for non- contextual problems. We also develop several novel algorithmic techniques to achieve matching upper bounds, in-cluding 1) a key action remapping technique for optimal regret with known E, 2) computationally efficient implementation of Catoni's robust mean ...", "subpage_snippet": "", "source": "par.nsf.gov", "link": "https://par.nsf.gov/servlets/purl/10207168", "content": "3 ) is achievable; 3) with M predictors, a linear dependence on M is necessary, even though logarithmic dependence is possible for non- contextual problems. We also develop several novel algorithmic techniques to achieve matching upper bounds, in-cluding 1) a key action remapping technique for optimal regret with known E, 2) computationally efficient implementation of Catoni's robust mean ..."}
+{"idx": 8, "title": "Catoni Contextual Bandits are Robust to Heavy-tailed Rewards", "date": "", "ddg_snippet": "This paper introduces a novel contextual bandit algorithm that utilizes Catoni's estimator to achieve robust regret bounds under heavy-tailed rewards, significantly improving performance by reducing dependence on reward range and variance.", "subpage_snippet": "", "source": "chatpaper.com", "link": "https://chatpaper.com/chatpaper/paper/105230?from=search", "content": "This paper introduces a novel contextual bandit algorithm that utilizes Catoni's estimator to achieve robust regret bounds under heavy-tailed rewards, significantly improving performance by reducing dependence on reward range and variance."}
+{"idx": 9, "title": "Chenlu Ye", "date": "", "ddg_snippet": "We also connect our theoretical findings with practical algorithms (e.g. DPO, RSO), offering new tools and insights for the algorithmic design of alignment algorithms. Theory of decision making porblems Catoni Contextual Bandits are Robust to Heavy-tailed Rewards Chenlu Ye*, Yujia Jin, Alekh Agarwal, Tong Zhang, Preprint.", "subpage_snippet": "", "source": "chenluye99.github.io", "link": "https://chenluye99.github.io/", "content": "We also connect our theoretical findings with practical algorithms (e.g. DPO, RSO), offering new tools and insights for the algorithmic design of alignment algorithms. Theory of decision making porblems Catoni Contextual Bandits are Robust to Heavy-tailed Rewards Chenlu Ye*, Yujia Jin, Alekh Agarwal, Tong Zhang, Preprint."}
diff --git a/data/sampled_jsons/Catoni_Contextual_Bandits_are_Robust_to_Heavy-tailed_Rewards_pdf.jsonl b/data/sampled_jsons/Catoni_Contextual_Bandits_are_Robust_to_Heavy-tailed_Rewards_pdf.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5ab58279fd5ce7f25eda07a7bb165c7ab49c3e86
--- /dev/null
+++ b/data/sampled_jsons/Catoni_Contextual_Bandits_are_Robust_to_Heavy-tailed_Rewards_pdf.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Catoni Contextual Bandits are Robust to Heavy-tailed Rewards", "date": "", "ddg_snippet": "However, many practical scenarios naturally involve heavy-tailed rewards or rewards where the worst-case range can be substantially larger than the variance. In this paper, we develop an algorithmic approach building on Catoni's estimator from robust statistics, and apply it to contextual bandits with general function approximation.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.02486", "content": "However, many practical scenarios naturally involve heavy-tailed rewards or rewards where the worst-case range can be substantially larger than the variance. In this paper, we develop an algorithmic approach building on Catoni's estimator from robust statistics, and apply it to contextual bandits with general function approximation."}
+{"idx": 1, "title": "Catoni Contextual Bandits are Robust to Heavy-tailed Rewards", "date": "", "ddg_snippet": "However, many practical scenarios naturally involve heavy-tailed rewards or rewards where the worst-case range can be substantially larger than the variance. In this pa-per, we develop an algorithmic approach building on Catoni's estimator from robust statistics, and apply it to contextual bandits with general func-tion approximation.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=5IpVe9PH14", "content": "However, many practical scenarios naturally involve heavy-tailed rewards or rewards where the worst-case range can be substantially larger than the variance. In this pa-per, we develop an algorithmic approach building on Catoni's estimator from robust statistics, and apply it to contextual bandits with general func-tion approximation."}
+{"idx": 2, "title": "Catoni Contextual Bandits are Robust to Heavy-tailed Rewards", "date": "", "ddg_snippet": "This paper develops an algorithmic approach building on Catoni's estimator from robust statistics, and applies it to contextual bandits with general function approximation and establishes a regret bound that depends only on the cumulative reward variance and logarithmically on the reward range as well as the number of rounds. Typical contextual bandit algorithms assume that the rewards at each ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Catoni-Contextual-Bandits-are-Robust-to-Rewards-Ye-Jin/125154c306493f2c7af8e8c09c0e58c22106e6fe", "content": "This paper develops an algorithmic approach building on Catoni's estimator from robust statistics, and applies it to contextual bandits with general function approximation and establishes a regret bound that depends only on the cumulative reward variance and logarithmically on the reward range as well as the number of rounds. Typical contextual bandit algorithms assume that the rewards at each ..."}
+{"idx": 3, "title": "Catoni Contextual Bandits are Robust to Heavy-tailed Rewards | Read ...", "date": "", "ddg_snippet": "This research paper explores a type of algorithm used in decision-making systems, called contextual bandits , which help choose the best actions based on past experiences. The authors focus on situ...", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/icml/46438/paper", "content": "This research paper explores a type of algorithm used in decision-making systems, called contextual bandits , which help choose the best actions based on past experiences. The authors focus on situ..."}
+{"idx": 4, "title": "PDF Catoni Contextual Bandits are Robust to Heavy-tailed Rewards", "date": "", "ddg_snippet": "The Problem: Heavy-Tailed Rewards in Contextual Bandits : [0, R] Standard Assumption: rewards are bounded within a fixed range", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/media/icml-2025/Slides/46438.pdf", "content": "The Problem: Heavy-Tailed Rewards in Contextual Bandits : [0, R] Standard Assumption: rewards are bounded within a fixed range"}
+{"idx": 5, "title": "Catoni Contextual Bandits are Robust to Heavy-tailed Rewards ...", "date": "", "ddg_snippet": "NSF Public Access Search Results Catoni Contextual Bandits are Robust to Heavy-tailed Rewards Citation Details This content will become publicly available on April 30, 2026", "subpage_snippet": "", "source": "par.nsf.gov", "link": "https://par.nsf.gov/biblio/10614881-catoni-contextual-bandits-robust-heavy-tailed-rewards", "content": "NSF Public Access Search Results Catoni Contextual Bandits are Robust to Heavy-tailed Rewards Citation Details This content will become publicly available on April 30, 2026"}
+{"idx": 6, "title": "Abstract arXiv:2502.02486v1 [stat.ML] 4 Feb 2025 Catoni Cont", "date": "", "ddg_snippet": "Typical contextual bandit algorithms assume that the rewards at each round lie in some fixed range [0, R], and their regret scales polynomially with this reward range R. However, many practical scenarios naturally involve heavy-tailed rewards or rewards where the worst-case range can be substantially larger than the variance. In this paper, we develop an algorithmic approach building on Catoni ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.02486", "content": "Typical contextual bandit algorithms assume that the rewards at each round lie in some fixed range [0, R], and their regret scales polynomially with this reward range R. However, many practical scenarios naturally involve heavy-tailed rewards or rewards where the worst-case range can be substantially larger than the variance. In this paper, we develop an algorithmic approach building on Catoni ..."}
+{"idx": 7, "title": "Catoni Contextual Bandits are Robust to Heavy-tailed Rewards", "date": "", "ddg_snippet": "However, many practical scenarios naturally involve heavy-tailed rewards or rewards where the worst-case range can be substantially larger than the variance. In this paper, we develop an algorithmic approach building on Catoni's estimator from robust statistics, and apply it to contextual bandits with general function approximation.", "subpage_snippet": "", "source": "jarxiv.com", "link": "https://jarxiv.com/2025/02/05/catoni-contextual-bandits-are-robust-to-heavy-tailed-rewards/", "content": "However, many practical scenarios naturally involve heavy-tailed rewards or rewards where the worst-case range can be substantially larger than the variance. In this paper, we develop an algorithmic approach building on Catoni's estimator from robust statistics, and apply it to contextual bandits with general function approximation."}
+{"idx": 8, "title": "PDF Bandits With Heavy Tail", "date": "", "ddg_snippet": "The key to successful handling heavy-tailed reward distribu-tions is to replace the empirical mean with other, more robust estimators of the mean. All we need is a performance guarantee like the one shown above for the empirical mean.", "subpage_snippet": "", "source": "sbubeck.com", "link": "http://sbubeck.com/BCL13.pdf", "content": "The key to successful handling heavy-tailed reward distribu-tions is to replace the empirical mean with other, more robust estimators of the mean. All we need is a performance guarantee like the one shown above for the empirical mean."}
+{"idx": 9, "title": "Chenlu Ye", "date": "", "ddg_snippet": "We also connect our theoretical findings with practical algorithms (e.g. DPO, RSO), offering new tools and insights for the algorithmic design of alignment algorithms. Theory of decision making porblems Catoni Contextual Bandits are Robust to Heavy-tailed Rewards Chenlu Ye*, Yujia Jin, Alekh Agarwal, Tong Zhang, Preprint.", "subpage_snippet": "", "source": "chenluye99.github.io", "link": "https://chenluye99.github.io/", "content": "We also connect our theoretical findings with practical algorithms (e.g. DPO, RSO), offering new tools and insights for the algorithmic design of alignment algorithms. Theory of decision making porblems Catoni Contextual Bandits are Robust to Heavy-tailed Rewards Chenlu Ye*, Yujia Jin, Alekh Agarwal, Tong Zhang, Preprint."}
diff --git "a/data/sampled_jsons/Catoni_estimator_function_\316\250(x)_definition.jsonl" "b/data/sampled_jsons/Catoni_estimator_function_\316\250(x)_definition.jsonl"
new file mode 100644
index 0000000000000000000000000000000000000000..df74579caf231db52fa517373c283300f80df453
--- /dev/null
+++ "b/data/sampled_jsons/Catoni_estimator_function_\316\250(x)_definition.jsonl"
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Catoni-Giulini M-estimator - The Stats Map", "date": "", "ddg_snippet": "In 2017, Catoni and Giulini proposed an approach to multivariate concentration based on M-estimation. Let ψ be any symmetric \"influence function \" such that −log(1−x+x2/2)≤ ψ( x )≤ log(1+x+x2/2), ∀ x ∈ R. The motivation behind this condition is to choose a function ψ such that eψ is bounded by polynomials. The estimator is then ξ(θ)= nλ1i≤n∑∫ Rdψ(λ ϑ, X i )ρθ(dϑ ...", "subpage_snippet": "", "source": "thestatsmap.com", "link": "https://thestatsmap.com/Catoni-Giulini-M-estimator", "content": "In 2017, Catoni and Giulini proposed an approach to multivariate concentration based on M-estimation. Let ψ be any symmetric \"influence function \" such that −log(1−x+x2/2)≤ ψ( x )≤ log(1+x+x2/2), ∀ x ∈ R. The motivation behind this condition is to choose a function ψ such that eψ is bounded by polynomials. The estimator is then ξ(θ)= nλ1i≤n∑∫ Rdψ(λ ϑ, X i )ρθ(dϑ ..."}
+{"idx": 1, "title": "On Catoni's M-Estimation - arXiv.org", "date": "", "ddg_snippet": "Catoni [4] proposed an M-estimator, which is called Catoni's estimator , to cope with the heavy-tailed data. Some notations are needed to be introduced at the beginning. Let Ψ be a set of real-valued functions . For each f ∈ Ψ, denote the expectation Ef(X) by mf.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2210.08211", "content": "Catoni [4] proposed an M-estimator, which is called Catoni's estimator , to cope with the heavy-tailed data. Some notations are needed to be introduced at the beginning. Let Ψ be a set of real-valued functions . For each f ∈ Ψ, denote the expectation Ef(X) by mf."}
+{"idx": 2, "title": "stat-map/Catoni-Giulini M-estimator.md at main - GitHub", "date": "", "ddg_snippet": "The estimator is then $$ \\xi (\\theta) = \\frac {1} {n\\lambda}\\sum_ {i\\leq n}\\int_ {\\Re^d}\\psi (\\lambda \\la \\vartheta, X_i\\ra)\\rho_\\theta (d\\vartheta), $$ where $\\rho_\\theta$ is Gaussian with mean $\\theta$ and covariance $\\beta^ {-1}I$ for some $\\beta>0$.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/bchugg/stat-map/blob/main/Catoni-Giulini+M-estimator.md", "content": "The estimator is then $$ \\xi (\\theta) = \\frac {1} {n\\lambda}\\sum_ {i\\leq n}\\int_ {\\Re^d}\\psi (\\lambda \\la \\vartheta, X_i\\ra)\\rho_\\theta (d\\vartheta), $$ where $\\rho_\\theta$ is Gaussian with mean $\\theta$ and covariance $\\beta^ {-1}I$ for some $\\beta>0$."}
+{"idx": 3, "title": "PDF Nearly Optimal Catoni's M-estimator for Infinite Variance", "date": "", "ddg_snippet": "The extension is non-trivial owing to the dificulty in characterizing the roots of certain polynomials of degree smaller than 2. The proposed estimator has the same order of magnitude and the same asymptotic constant as in Catoni (2012), but for the case of bounded moments. We further propose a version of the estimator that does not require even the knowledge of υε, but adapts the moment ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v162/bhatt22b/bhatt22b.pdf", "content": "The extension is non-trivial owing to the dificulty in characterizing the roots of certain polynomials of degree smaller than 2. The proposed estimator has the same order of magnitude and the same asymptotic constant as in Catoni (2012), but for the case of bounded moments. We further propose a version of the estimator that does not require even the knowledge of υε, but adapts the moment ..."}
+{"idx": 4, "title": "The Catoni-Giulini estimator", "date": "", "ddg_snippet": "An analysis of the Catoni -Giulini estimator for heavy-tailed random vectors", "subpage_snippet": "", "source": "benchugg.com", "link": "https://benchugg.com/research_notes/catoni_giulini/", "content": "An analysis of the Catoni -Giulini estimator for heavy-tailed random vectors"}
+{"idx": 5, "title": "A generalized Catoni's M-estimator under finite -th moment assumption ...", "date": "", "ddg_snippet": "Abstract: We generalize Catoni's M-estimator, put forward in [3] by Ca-toni under finite variance assumption, to the case in which distributions can have finite α-th moment with α (1,2). Our approach, inspired by the Taylor-like expansion developed in [4], is via slightly modifying the influence function φ in [3]. A deviation bound is established for this generalized estimator , and ...", "subpage_snippet": "", "source": "projecteuclid.org", "link": "https://projecteuclid.org/journalArticle/Download?urlid=10.1214/21-EJS1911", "content": "Abstract: We generalize Catoni's M-estimator, put forward in [3] by Ca-toni under finite variance assumption, to the case in which distributions can have finite α-th moment with α (1,2). Our approach, inspired by the Taylor-like expansion developed in [4], is via slightly modifying the influence function φ in [3]. A deviation bound is established for this generalized estimator , and ..."}
+{"idx": 6, "title": "[PDF] On Catoni's M-Estimation | Semantic Scholar", "date": "", "ddg_snippet": "Catoni proposed a robust M-estimator and gave the deviation inequality for one fixed test function . The present paper is devoted to the uniform concentration inequality for a family of test functions . As an application, we consider empirical risk minimization for heavy-tailed losses.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/On-Catoni's-M-Estimation-Li-Wang/bfd2d00f97659e9dfcf567830b8ecc5dad535c0a", "content": "Catoni proposed a robust M-estimator and gave the deviation inequality for one fixed test function . The present paper is devoted to the uniform concentration inequality for a family of test functions . As an application, we consider empirical risk minimization for heavy-tailed losses."}
+{"idx": 7, "title": "On the M-Estimator under Third Moment Condition - MDPI", "date": "", "ddg_snippet": "The solution of (2) is called Catoni's mean estimator , where 𝜙: ℝ → ℝ is a non-decreasing differentiable truncation function such that for any 𝑥 ∈ ℝ, − log (1 − 𝑥 +𝑥2/2) ≤ 𝜙(𝑥) ≤ log (1 + 𝑥 +𝑥2/2), and 𝛼 is a parameter to ensure the existence of the estimator . We denote Catoni's mean estimator by 𝑚̃𝑛,𝛼. The main purpose of the ...", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/2227-7390/10/10/1713", "content": "The solution of (2) is called Catoni's mean estimator , where 𝜙: ℝ → ℝ is a non-decreasing differentiable truncation function such that for any 𝑥 ∈ ℝ, − log (1 − 𝑥 +𝑥2/2) ≤ 𝜙(𝑥) ≤ log (1 + 𝑥 +𝑥2/2), and 𝛼 is a parameter to ensure the existence of the estimator . We denote Catoni's mean estimator by 𝑚̃𝑛,𝛼. The main purpose of the ..."}
+{"idx": 8, "title": "[2210.08211] On Catoni's M-Estimation - arXiv.org", "date": "", "ddg_snippet": "Catoni proposed a robust M-estimator and gave the deviation inequality for one fixed test function . The present paper is devoted to the uniform concentration inequality for a family of test functions . As an application, we consider empirical risk minimization for heavy-tailed losses.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2210.08211", "content": "Catoni proposed a robust M-estimator and gave the deviation inequality for one fixed test function . The present paper is devoted to the uniform concentration inequality for a family of test functions . As an application, we consider empirical risk minimization for heavy-tailed losses."}
+{"idx": 9, "title": "PDF Nearly Optimal Catoni's Estimator for Infinite Variance", "date": "", "ddg_snippet": "Catoni's M-estimator: bμn is a solution to the equation n ψ α(Xi − bμn) = 0 i=1 with ψ : R → R a non-decreasing influence function and α > 0. Key-Contribution: For a given confidence δ ∈ (0, 1), we design the tightest possible ρ = ρ(n, δ) such that P bμn − μ > ρ ≤ δ.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/media/icml-2022/Slides/16205.pdf", "content": "Catoni's M-estimator: bμn is a solution to the equation n ψ α(Xi − bμn) = 0 i=1 with ψ : R → R a non-decreasing influence function and α > 0. Key-Contribution: For a given confidence δ ∈ (0, 1), we design the tightest possible ρ = ρ(n, δ) such that P bμn − μ > ρ ≤ δ."}
diff --git a/data/sampled_jsons/Causal_Modeling_of_Climate_Activism_on_Reddit_Federico_Cinus.jsonl b/data/sampled_jsons/Causal_Modeling_of_Climate_Activism_on_Reddit_Federico_Cinus.jsonl
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index 0000000000000000000000000000000000000000..c46ddb1abf91c6551b4a6cde652c71ce5ac737ac
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+++ b/data/sampled_jsons/Causal_Modeling_of_Climate_Activism_on_Reddit_Federico_Cinus.jsonl
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+{"idx": 0, "title": "Causal Modeling of Climate Activism on Reddit", "date": "", "ddg_snippet": "22 Apr 2025 — In this work, we develop a comprehensive causal model of how and why Reddit users engage with activist communities driving mass climate protests .", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3696410.3714684", "content": "22 Apr 2025 — In this work, we develop a comprehensive causal model of how and why Reddit users engage with activist communities driving mass climate protests ."}
+{"idx": 1, "title": "Causal Modeling of Climate Activism on Reddit", "date": "", "ddg_snippet": "by J Lenti · Cited by 9 — In this work, we develop a comprehensive causal model of how and why. Reddit users engage with activist communities driving mass climate protests (mainly the ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=6yBhoJn6qy", "content": "by J Lenti · Cited by 9 — In this work, we develop a comprehensive causal model of how and why. Reddit users engage with activist communities driving mass climate protests (mainly the ..."}
+{"idx": 2, "title": "On the Inference of Sociodemographics on Reddit", "date": "", "ddg_snippet": "7 Feb 2025 — On the Inference of Sociodemographics on Reddit . Report issue for preceding element. Federico Cinus ... Causal Modeling of Climate Activism on ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.05049v1", "content": "7 Feb 2025 — On the Inference of Sociodemographics on Reddit . Report issue for preceding element. Federico Cinus ... Causal Modeling of Climate Activism on ..."}
+{"idx": 3, "title": "[Literature Review] On the Inference of Sociodemographics ...", "date": "", "ddg_snippet": "The paper titled \"On the Inference of Sociodemographics on Reddit \" authored by Federico Cinus et al. aims to fill a significant gap in computational social ...", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/en/review/on-the-inference-of-sociodemographics-on-reddit", "content": "The paper titled \"On the Inference of Sociodemographics on Reddit \" authored by Federico Cinus et al. aims to fill a significant gap in computational social ..."}
+{"idx": 4, "title": "Gianmarco De Francisci Morales", "date": "", "ddg_snippet": "Principal Researcher, CENTAI · Causal Modeling of Climate Activism on Reddit · Navigating Multidimensional Ideologies with Reddit's Political Compass: Economic ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/profile?id=~Gianmarco_De_Francisci_Morales2", "content": "Principal Researcher, CENTAI · Causal Modeling of Climate Activism on Reddit · Navigating Multidimensional Ideologies with Reddit's Political Compass: Economic ..."}
+{"idx": 5, "title": "arXiv:2502.05049v1 [cs.SI] 7 Feb 2025", "date": "", "ddg_snippet": "by F Cinus · 2025 — Causal Modeling of Climate Activism on Reddit . arXiv preprint arXiv:2410.10562. Lokala, U.; Srivastava, A.; Dastidar, T. G.; Chakraborty, T ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.05049", "content": "by F Cinus · 2025 — Causal Modeling of Climate Activism on Reddit . arXiv preprint arXiv:2410.10562. Lokala, U.; Srivastava, A.; Dastidar, T. G.; Chakraborty, T ..."}
+{"idx": 6, "title": "Corrado Monti", "date": "", "ddg_snippet": "Causal modeling of climate activism on reddit . J Lenti, LM Aiello, C Monti, GDF Morales. Proceedings of the ACM on Web Conference 2025, 590-600, 2025. 9, 2025 ...", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=FAzWs2oAAAAJ&hl=en", "content": "Causal modeling of climate activism on reddit . J Lenti, LM Aiello, C Monti, GDF Morales. Proceedings of the ACM on Web Conference 2025, 590-600, 2025. 9, 2025 ..."}
+{"idx": 7, "title": "[PDF] Cascade-based Echo Chamber Detection", "date": "", "ddg_snippet": "Causal Modeling of Climate Activism on Reddit · Environmental Science, Sociology. The Web Conference · 2025.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/c1189af2293e320cbcee769d2fc2298a175c30c0", "content": "Causal Modeling of Climate Activism on Reddit · Environmental Science, Sociology. The Web Conference · 2025."}
+{"idx": 8, "title": "Paper Digest: WWW 2025 Papers & Highlights", "date": "", "ddg_snippet": "30 Apr 2025 — Highlight: In this work, we develop a comprehensive causal model of how and why Reddit users engage with activist communities driving mass ...", "subpage_snippet": "", "source": "www.paperdigest.org", "link": "https://www.paperdigest.org/2025/04/www-2025-papers-highlights/", "content": "30 Apr 2025 — Highlight: In this work, we develop a comprehensive causal model of how and why Reddit users engage with activist communities driving mass ..."}
+{"idx": 9, "title": "Cascade-based Echo Chamber Detection", "date": "", "ddg_snippet": "Despite echo chambers in social media have been under considerable scrutiny, general models for their detection and analysis are missing.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3511808.3557253", "content": "Despite echo chambers in social media have been under considerable scrutiny, general models for their detection and analysis are missing."}
diff --git a/data/sampled_jsons/Causal_Modeling_of_Climate_Activism_on_Reddit_Related_Work.jsonl b/data/sampled_jsons/Causal_Modeling_of_Climate_Activism_on_Reddit_Related_Work.jsonl
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+{"idx": 1, "title": "Methods/Methodology/Theory of Science | Organizations and", "date": "", "ddg_snippet": "... of important and interesting papers on the economics and sociology of science: How does teamwork effect science? What are the relationships among ...", "subpage_snippet": "", "source": "organizationsandmarkets.com", "link": "https://organizationsandmarkets.com/category/methodsmethodologytheory-of-science/", "content": "... of important and interesting papers on the economics and sociology of science: How does teamwork effect science? What are the relationships among ..."}
+{"idx": 2, "title": "Research | Kenkyuu", "date": "", "ddg_snippet": "De Francisci Morales “ Causal Modeling of Climate Activism on Reddit ” WWW ’ 25: The Web Conference, pp. ... of Peer Support for Opioid Use ...", "subpage_snippet": "", "source": "gdfm.me", "link": "https://gdfm.me/research/", "content": "De Francisci Morales “ Causal Modeling of Climate Activism on Reddit ” WWW ’ 25: The Web Conference, pp. ... of Peer Support for Opioid Use ..."}
+{"idx": 3, "title": "Podcasts as a Medium for Participation in Collective Action: A", "date": "", "ddg_snippet": "... important BLM- related events in May and June of 2020, and extracted participatory statements using a layered framework adapted from prior work on ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.13197v1", "content": "... important BLM- related events in May and June of 2020, and extracted participatory statements using a layered framework adapted from prior work on ..."}
+{"idx": 4, "title": "2020 AI Alignment Literature Review and Charity Comparison -", "date": "", "ddg_snippet": "... to review the research that has been produced by various organisations working on AI safety, to help potential donors gain a better understanding of ...", "subpage_snippet": "", "source": "www.greaterwrong.com", "link": "https://www.greaterwrong.com/posts/pTYDdcag9pTzFQ7vw/2020-ai-alignment-literature-review-and-charity-comparison", "content": "... to review the research that has been produced by various organisations working on AI safety, to help potential donors gain a better understanding of ..."}
+{"idx": 5, "title": "Types of Events – Monitoring and Evaluation NEWS", "date": "", "ddg_snippet": "Making Impact Evaluation Matter will comprise pre-conference workshops for 2.5 days from 1-3 September 2014 , and 2.5 days of the conference from 3-5 ...", "subpage_snippet": "", "source": "mande.co.uk", "link": "https://mande.co.uk/category/coming-events/", "content": "Making Impact Evaluation Matter will comprise pre-conference workshops for 2.5 days from 1-3 September 2014 , and 2.5 days of the conference from 3-5 ..."}
+{"idx": 6, "title": "Culture like Relativity? | synthetic zerØ", "date": "", "ddg_snippet": "General relativity works very well for gravity of ordinary strength, the variety experienced by humans on Earth or by planets as they orbit the sun.", "subpage_snippet": "", "source": "syntheticzero.net", "link": "https://syntheticzero.net/2015/05/19/culture-like-relativity/", "content": "General relativity works very well for gravity of ordinary strength, the variety experienced by humans on Earth or by planets as they orbit the sun."}
+{"idx": 7, "title": "Towards Adaptive Neighborhood for Advancing Temporal", "date": "", "ddg_snippet": "Despite the remarkable success of existing TGNs, a fundamental weakness inherent in their designs is the reliance on the fixed , hand-crafted rules ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.11891v1", "content": "Despite the remarkable success of existing TGNs, a fundamental weakness inherent in their designs is the reliance on the fixed , hand-crafted rules ..."}
+{"idx": 8, "title": "In the Journals, March 2015 – Part 2 – Somatosphere", "date": "", "ddg_snippet": "Using Foucault ’ s concept of governmentality, the relations between power, knowledge, truth and their influences on the program ’ s ...", "subpage_snippet": "", "source": "somatosphere.com", "link": "https://somatosphere.com/2015/in-the-journals-march-2015-part-2.html/", "content": "Using Foucault ’ s concept of governmentality, the relations between power, knowledge, truth and their influences on the program ’ s ..."}
+{"idx": 9, "title": "Luca AIELLO | Researcher | PhD | Yahoo, Sunnyvale | Research", "date": "", "ddg_snippet": "Large Language Models are expressive tools that enable complex tasks of text understanding within Computational Social Science.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/profile/Luca-Aiello-2", "content": "Large Language Models are expressive tools that enable complex tasks of text understanding within Computational Social Science."}
diff --git a/data/sampled_jsons/Causal_Modeling_of_Climate_Activism_on_Reddit_Section_4.4_activation_sympathy_subreddit.jsonl b/data/sampled_jsons/Causal_Modeling_of_Climate_Activism_on_Reddit_Section_4.4_activation_sympathy_subreddit.jsonl
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index 0000000000000000000000000000000000000000..1e1d571cf56349a50a0d421755deb9cd15e8ce2b
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+{"idx": 0, "title": "Causal Modeling of Climate Activism on Reddit - OpenReview", "date": "", "ddg_snippet": "Previous social media studies on climate 96 action have analyzed the changes in climate debate after extreme 97 weather events [18, 34, 38, 41], political events [10, 22] or media 98 coverage [18]. However, these studies are either associational or fo- 99 cus on single causal pathways for the phenomena of interest.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=6yBhoJn6qy", "content": "Previous social media studies on climate 96 action have analyzed the changes in climate debate after extreme 97 weather events [18, 34, 38, 41], political events [10, 22] or media 98 coverage [18]. However, these studies are either associational or fo- 99 cus on single causal pathways for the phenomena of interest."}
+{"idx": 1, "title": "Causal Modeling of Climate Activism on Reddit - arXiv.org", "date": "", "ddg_snippet": "We developed a rich and comprehensive causal model to study the interplay between diferent determinants of climate activism on Reddit . This work represents a first attempt to apply a multi- causal model to social media data.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.10562", "content": "We developed a rich and comprehensive causal model to study the interplay between diferent determinants of climate activism on Reddit . This work represents a first attempt to apply a multi- causal model to social media data."}
+{"idx": 2, "title": "Causal Modeling of Climate Activism on Reddit", "date": "", "ddg_snippet": "This paper aims to promote large - scale climate protests (such as the 2019 Earth Strike, Fridays for Future, and Extinction Rebellion) by constructing a comprehensive causal model to explain how and why Reddit users participate in climate activism communities.", "subpage_snippet": "", "source": "bohrium.dp.tech", "link": "https://bohrium.dp.tech/paper/arxiv/2410.10562", "content": "This paper aims to promote large - scale climate protests (such as the 2019 Earth Strike, Fridays for Future, and Extinction Rebellion) by constructing a comprehensive causal model to explain how and why Reddit users participate in climate activism communities."}
+{"idx": 3, "title": "Causal Modeling of Climate Activism on Reddit - Researchr", "date": "", "ddg_snippet": "Causal Modeling of Climate Activism on Reddit . In Guodong Long, Michale Blumestein, Yi Chang 0001, Liane Lewin-Eytan, Zi Helen Huang, Elad Yom-Tov, editors, Proceedings of the ACM on Web Conference 2025, WWW 2025, Sydney, NSW, Australia, 28 April 2025- 2 May 2025. pages 590-600, ACM, 2025. [doi]", "subpage_snippet": "", "source": "researchr.org", "link": "https://researchr.org/publication/LentiAMM25", "content": "Causal Modeling of Climate Activism on Reddit . In Guodong Long, Michale Blumestein, Yi Chang 0001, Liane Lewin-Eytan, Zi Helen Huang, Elad Yom-Tov, editors, Proceedings of the ACM on Web Conference 2025, WWW 2025, Sydney, NSW, Australia, 28 April 2025- 2 May 2025. pages 590-600, ACM, 2025. [doi]"}
+{"idx": 4, "title": "\"Causal Modeling of Climate Activism on Reddit.\" - dblp", "date": "", "ddg_snippet": "Bibliographic details on Causal Modeling of Climate Activism on Reddit .", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/journals/corr/abs-2410-10562", "content": "Bibliographic details on Causal Modeling of Climate Activism on Reddit ."}
+{"idx": 5, "title": "Causal Modeling of Climate Activism on Reddit | Article Information | J ...", "date": "", "ddg_snippet": "Article \" Causal Modeling of Climate Activism on Reddit \" Detailed information of the J-GLOBAL is an information service managed by the Japan Science and Technology Agency (hereinafter referred to as \"JST\"). It provides free access to secondary information on researchers, articles, patents, etc., in science and technology, medicine and pharmacy. The search results guide you to high-quality ...", "subpage_snippet": "", "source": "jglobal.jst.go.jp", "link": "https://jglobal.jst.go.jp/en/detail?JGLOBAL_ID=202402219833468228", "content": "Article \" Causal Modeling of Climate Activism on Reddit \" Detailed information of the J-GLOBAL is an information service managed by the Japan Science and Technology Agency (hereinafter referred to as \"JST\"). It provides free access to secondary information on researchers, articles, patents, etc., in science and technology, medicine and pharmacy. The search results guide you to high-quality ..."}
+{"idx": 6, "title": "Temporal Dynamics of Climate Change Sentiment on Reddit ... - Springer", "date": "", "ddg_snippet": "With its millions of users and thousands of specialized communities, or subreddits , Reddit provides a unique space for users to engage in focused discussions on climate change. To understand climate change discourse on Reddit , we employ sentiment analysis, topic modeling , and social network analysis.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-981-96-3762-1_1", "content": "With its millions of users and thousands of specialized communities, or subreddits , Reddit provides a unique space for users to engage in focused discussions on climate change. To understand climate change discourse on Reddit , we employ sentiment analysis, topic modeling , and social network analysis."}
+{"idx": 7, "title": "[2410.10562] Causal Modeling of Climate Activism on Reddit", "date": "", "ddg_snippet": "Climate activism is crucial in stimulating collective societal and behavioral change towards sustainable practices through political pressure. Although multiple factors contribute to the participation in activism , their complex relationships and the scarcity of data on their interactions have restricted most prior research to studying them in isolation, thus preventing the development of a ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.10562", "content": "Climate activism is crucial in stimulating collective societal and behavioral change towards sustainable practices through political pressure. Although multiple factors contribute to the participation in activism , their complex relationships and the scarcity of data on their interactions have restricted most prior research to studying them in isolation, thus preventing the development of a ..."}
+{"idx": 8, "title": "r/climatechange on Reddit: To all the climate activists here on Reddit ...", "date": "", "ddg_snippet": "To all the climate activists here on Reddit , what drives you towards this cause? What made you a climate activist? This is for research purposes. Really keen to know a bit of your story.", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/climatechange/comments/11s32ot/to_all_the_climate_activists_here_on_reddit_what/", "content": "To all the climate activists here on Reddit , what drives you towards this cause? What made you a climate activist? This is for research purposes. Really keen to know a bit of your story."}
+{"idx": 9, "title": "Causal Modeling of Climate Activism on Reddit - Corrado Monti", "date": "", "ddg_snippet": "Jacopo Lenti, Luca Maria Aiello, Corrado Monti, Gianmarco De Francisci Morales", "subpage_snippet": "", "source": "www.corradomonti.com", "link": "https://www.corradomonti.com/causal-modeling-of-climate-activism-on-reddit.html", "content": "Jacopo Lenti, Luca Maria Aiello, Corrado Monti, Gianmarco De Francisci Morales"}
diff --git a/data/sampled_jsons/Causal_Modeling_of_Climate_Activism_on_Reddit_appendix_Figure_A.1_subreddits_activated_users.jsonl b/data/sampled_jsons/Causal_Modeling_of_Climate_Activism_on_Reddit_appendix_Figure_A.1_subreddits_activated_users.jsonl
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index 0000000000000000000000000000000000000000..375164b49e76859786a5e3ee95049603c3f0e87e
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+++ b/data/sampled_jsons/Causal_Modeling_of_Climate_Activism_on_Reddit_appendix_Figure_A.1_subreddits_activated_users.jsonl
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+{"idx": 0, "title": "Causal Modeling of Climate Activism on Reddit - OpenReview", "date": "", "ddg_snippet": "865 We developed a rich and comprehensive causal model to study the 866 interplay between diferent determinants of climate activism on 867 Reddit . This work represents a first attempt to apply a multi- causal 868 model to social media data.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=6yBhoJn6qy", "content": "865 We developed a rich and comprehensive causal model to study the 866 interplay between diferent determinants of climate activism on 867 Reddit . This work represents a first attempt to apply a multi- causal 868 model to social media data."}
+{"idx": 1, "title": "Causal Modeling of Climate Activism on Reddit - arXiv.org", "date": "", "ddg_snippet": "We developed a rich and comprehensive causal model to study the interplay between diferent determinants of climate activism on Reddit . This work represents a first attempt to apply a multi- causal model to social media data.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.10562", "content": "We developed a rich and comprehensive causal model to study the interplay between diferent determinants of climate activism on Reddit . This work represents a first attempt to apply a multi- causal model to social media data."}
+{"idx": 2, "title": "(PDF) Analyzing Climate Change Discussions on Reddit", "date": "", "ddg_snippet": "Dec 23, 2022 · We contribute to this effort by analyzing climate change topics on the Reddit social curation platform, which contains over 100,000 discussion communities.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/366548402_Analyzing_climate_change_discussions_on_Reddit", "content": "Dec 23, 2022 · We contribute to this effort by analyzing climate change topics on the Reddit social curation platform, which contains over 100,000 discussion communities."}
+{"idx": 3, "title": "Causal Modeling of Climate Activism on Reddit - Researchr", "date": "", "ddg_snippet": "Causal Modeling of Climate Activism on Reddit . In Guodong Long, Michale Blumestein, Yi Chang 0001, Liane Lewin-Eytan, Zi Helen Huang, Elad Yom-Tov, editors, Proceedings of the ACM on Web Conference 2025, WWW 2025, Sydney, NSW, Australia, 28 April 2025- 2 May 2025. pages 590-600, ACM, 2025. [doi]", "subpage_snippet": "", "source": "researchr.org", "link": "https://researchr.org/publication/LentiAMM25", "content": "Causal Modeling of Climate Activism on Reddit . In Guodong Long, Michale Blumestein, Yi Chang 0001, Liane Lewin-Eytan, Zi Helen Huang, Elad Yom-Tov, editors, Proceedings of the ACM on Web Conference 2025, WWW 2025, Sydney, NSW, Australia, 28 April 2025- 2 May 2025. pages 590-600, ACM, 2025. [doi]"}
+{"idx": 4, "title": "\"Causal Modeling of Climate Activism on Reddit.\" - dblp", "date": "", "ddg_snippet": "Bibliographic details on Causal Modeling of Climate Activism on Reddit .", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/journals/corr/abs-2410-10562", "content": "Bibliographic details on Causal Modeling of Climate Activism on Reddit ."}
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+{"idx": 6, "title": "Causal Modeling of Climate Activism on Reddit", "date": "", "ddg_snippet": "14 Oct 2024 — In this work, we develop a comprehensive causal model of how and why Reddit users engage with activist communities driving mass climate protests.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.10562v1", "content": "14 Oct 2024 — In this work, we develop a comprehensive causal model of how and why Reddit users engage with activist communities driving mass climate protests."}
+{"idx": 7, "title": "On the Inference of Sociodemographics on Reddit", "date": "", "ddg_snippet": "7 Feb 2025 — Reddit offers a rich platform for sociodemographic inference due to its diverse user base and activity patterns. Existing methods often leverage ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.05049v1", "content": "7 Feb 2025 — Reddit offers a rich platform for sociodemographic inference due to its diverse user base and activity patterns. Existing methods often leverage ..."}
+{"idx": 8, "title": "Climate Change Frames and Emotional Responses on Reddit", "date": "", "ddg_snippet": "by II Villanueva · 2021 · Cited by 8 — This thesis investigates how climate change frames on Reddit influence emotional responses, using both human coders and computer-aided textual analysis.", "subpage_snippet": "", "source": "scholarworks.uark.edu", "link": "https://scholarworks.uark.edu/cgi/viewcontent.cgi?article=5626&context=etd", "content": "by II Villanueva · 2021 · Cited by 8 — This thesis investigates how climate change frames on Reddit influence emotional responses, using both human coders and computer-aided textual analysis."}
+{"idx": 9, "title": "Causal Modeling of Climate Activism on Reddit - Corrado Monti", "date": "", "ddg_snippet": "Jacopo Lenti, Luca Maria Aiello, Corrado Monti, Gianmarco De Francisci Morales", "subpage_snippet": "", "source": "www.corradomonti.com", "link": "https://www.corradomonti.com/causal-modeling-of-climate-activism-on-reddit.html", "content": "Jacopo Lenti, Luca Maria Aiello, Corrado Monti, Gianmarco De Francisci Morales"}
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+{"idx": 1, "title": "Causal Modeling of Climate Activism on Reddit", "date": "", "ddg_snippet": "In this work, we develop a comprehensive causal model of how and why Reddit users engage with activist communities driving mass climate protests (mainly the 2019 Earth Strike, Fridays for Future, and Extinction Rebellion).", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3696410.3714684", "content": "In this work, we develop a comprehensive causal model of how and why Reddit users engage with activist communities driving mass climate protests (mainly the 2019 Earth Strike, Fridays for Future, and Extinction Rebellion)."}
+{"idx": 2, "title": "Causal Modeling of Climate Activism on Reddit", "date": "", "ddg_snippet": "This paper aims to promote large - scale climate protests (such as the 2019 Earth Strike, Fridays for Future, and Extinction Rebellion) by constructing a comprehensive causal model to explain how and why Reddit users participate in climate activism communities.", "subpage_snippet": "", "source": "bohrium.dp.tech", "link": "https://bohrium.dp.tech/paper/arxiv/2410.10562", "content": "This paper aims to promote large - scale climate protests (such as the 2019 Earth Strike, Fridays for Future, and Extinction Rebellion) by constructing a comprehensive causal model to explain how and why Reddit users participate in climate activism communities."}
+{"idx": 3, "title": "[PDF] Causal Modeling of Climate Activism on Reddit | Semantic Scholar", "date": "", "ddg_snippet": "Climate activism is crucial in stimulating collective societal and behavioral change towards sustainable practices through political pressure. Although multiple factors contribute to the participation in activism , their complex relationships and the scarcity of data on their interactions have restricted most prior research to studying them in isolation, thus preventing the development of a ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Causal-Modeling-of-Climate-Activism-on-Reddit-Lenti-Aiello/c4c7c3972ba102db37738c082f27ebfcd3983057", "content": "Climate activism is crucial in stimulating collective societal and behavioral change towards sustainable practices through political pressure. Although multiple factors contribute to the participation in activism , their complex relationships and the scarcity of data on their interactions have restricted most prior research to studying them in isolation, thus preventing the development of a ..."}
+{"idx": 4, "title": "Causal Modeling of Climate Activism on Reddit - Researchr", "date": "", "ddg_snippet": "Causal Modeling of Climate Activism on Reddit . In Guodong Long, Michale Blumestein, Yi Chang 0001, Liane Lewin-Eytan, Zi Helen Huang, Elad Yom-Tov, editors, Proceedings of the ACM on Web Conference 2025, WWW 2025, Sydney, NSW, Australia, 28 April 2025- 2 May 2025. pages 590-600, ACM, 2025. [doi]", "subpage_snippet": "", "source": "researchr.org", "link": "https://researchr.org/publication/LentiAMM25", "content": "Causal Modeling of Climate Activism on Reddit . In Guodong Long, Michale Blumestein, Yi Chang 0001, Liane Lewin-Eytan, Zi Helen Huang, Elad Yom-Tov, editors, Proceedings of the ACM on Web Conference 2025, WWW 2025, Sydney, NSW, Australia, 28 April 2025- 2 May 2025. pages 590-600, ACM, 2025. [doi]"}
+{"idx": 5, "title": "[2410.10562] Causal Modeling of Climate Activism on Reddit - arXiv.org", "date": "", "ddg_snippet": "Climate activism is crucial in stimulating collective societal and behavioral change towards sustainable practices through political pressure. Although multiple factors contribute to the participation in activism , their complex relationships and the scarcity of data on their interactions have restricted most prior research to studying them in isolation, thus preventing the development of a ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.10562", "content": "Climate activism is crucial in stimulating collective societal and behavioral change towards sustainable practices through political pressure. Although multiple factors contribute to the participation in activism , their complex relationships and the scarcity of data on their interactions have restricted most prior research to studying them in isolation, thus preventing the development of a ..."}
+{"idx": 6, "title": "Causal Modeling of Climate Activism on Reddit - OpenReview", "date": "", "ddg_snippet": "865 We developed a rich and comprehensive causal model to study the 866 interplay between diferent determinants of climate activism on 867 Reddit . This work represents a first attempt to apply a multi- causal 868 model to social media data.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=6yBhoJn6qy", "content": "865 We developed a rich and comprehensive causal model to study the 866 interplay between diferent determinants of climate activism on 867 Reddit . This work represents a first attempt to apply a multi- causal 868 model to social media data."}
+{"idx": 7, "title": "Causal Modeling of Climate Activism on Reddit - ScienceOpen", "date": "", "ddg_snippet": "Although multiple factors contribute to the participation in activism , their complex relationships and the scarcity of data on their interactions have restricted most prior research to studying them in isolation, thus preventing the development of a quantitative, causal understanding of why people approach activism .", "subpage_snippet": "", "source": "www.scienceopen.com", "link": "https://www.scienceopen.com/document?vid=e07fdb2b-4dc5-465e-bddc-88fff36d5a28", "content": "Although multiple factors contribute to the participation in activism , their complex relationships and the scarcity of data on their interactions have restricted most prior research to studying them in isolation, thus preventing the development of a quantitative, causal understanding of why people approach activism ."}
+{"idx": 8, "title": "Causal Modeling of Climate Activism on Reddit | Article Information | J ...", "date": "", "ddg_snippet": "Article \" Causal Modeling of Climate Activism on Reddit \" Detailed information of the J-GLOBAL is an information service managed by the Japan Science and Technology Agency (hereinafter referred to as \"JST\"). It provides free access to secondary information on researchers, articles, patents, etc., in science and technology, medicine and pharmacy. The search results guide you to high-quality ...", "subpage_snippet": "", "source": "jglobal.jst.go.jp", "link": "https://jglobal.jst.go.jp/en/detail?JGLOBAL_ID=202402219833468228", "content": "Article \" Causal Modeling of Climate Activism on Reddit \" Detailed information of the J-GLOBAL is an information service managed by the Japan Science and Technology Agency (hereinafter referred to as \"JST\"). It provides free access to secondary information on researchers, articles, patents, etc., in science and technology, medicine and pharmacy. The search results guide you to high-quality ..."}
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+{"idx": 0, "title": "[2410.10562] Causal Modeling of Climate Activism on Reddit", "date": "", "ddg_snippet": "by J Lenti · 2024 · Cited by 9 — In this work, we develop a comprehensive causal model of how and why Reddit users engage with activist communities driving mass climate protests .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.10562", "content": "by J Lenti · 2024 · Cited by 9 — In this work, we develop a comprehensive causal model of how and why Reddit users engage with activist communities driving mass climate protests ."}
+{"idx": 1, "title": "Causal Modeling of Climate Activism on Reddit", "date": "", "ddg_snippet": "14 Oct 2024 — In this work, we develop a comprehensive causal model of how and why Reddit users engage with activist communities driving mass climate protests .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.10562v1", "content": "14 Oct 2024 — In this work, we develop a comprehensive causal model of how and why Reddit users engage with activist communities driving mass climate protests ."}
+{"idx": 2, "title": "Modeling the Impact of Group Interactions on Climate- ...", "date": "", "ddg_snippet": "by A Antelmi · 2025 — [25] Jacopo Lenti et al. “ Causal Modeling of Climate Activism on Reddit ”. In: Proceedings of The Web Conference. 2025. [26] H. Li ...", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/pdf/2505.02989", "content": "by A Antelmi · 2025 — [25] Jacopo Lenti et al. “ Causal Modeling of Climate Activism on Reddit ”. In: Proceedings of The Web Conference. 2025. [26] H. Li ..."}
+{"idx": 3, "title": "On the Inference of Sociodemographics on Reddit", "date": "", "ddg_snippet": "7 Feb 2025 — F. 2024. Causal Modeling of Climate Activism on Reddit . arXiv preprint arXiv:2410.10562. Lokala et al. (2022)", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.05049v1", "content": "7 Feb 2025 — F. 2024. Causal Modeling of Climate Activism on Reddit . arXiv preprint arXiv:2410.10562. Lokala et al. (2022)"}
+{"idx": 4, "title": "Causal Models Applied to the Patterns of Human Migration ...", "date": "", "ddg_snippet": "by K Lai · 2023 · Cited by 4 — Causal Models Applied to the Patterns of Human Migration due to Climate Change . Authors:Kenneth Lai, Svetlana Yanushkevich.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2311.14686", "content": "by K Lai · 2023 · Cited by 4 — Causal Models Applied to the Patterns of Human Migration due to Climate Change . Authors:Kenneth Lai, Svetlana Yanushkevich."}
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+{"idx": 6, "title": "Causal Climate Emulation with Bayesian Filtering", "date": "", "ddg_snippet": "by S Hickman · 2025 — We develop an interpretable climate model emulator based on causal representation learning. We derive a physics-informed approach including a Bayesian filter.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2506.09891", "content": "by S Hickman · 2025 — We develop an interpretable climate model emulator based on causal representation learning. We derive a physics-informed approach including a Bayesian filter."}
+{"idx": 7, "title": "Towards Causal Representations of Climate Model Data", "date": "", "ddg_snippet": "by J Boussard · 2023 · Cited by 7 — This work delves into the potential of causal representation learning, specifically the \\emph{Causal Discovery with Single-parent Decoding} (CDSD) method.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2312.02858", "content": "by J Boussard · 2023 · Cited by 7 — This work delves into the potential of causal representation learning, specifically the \\emph{Causal Discovery with Single-parent Decoding} (CDSD) method."}
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+{"idx": 9, "title": "Social and Information Networks Oct 2024", "date": "", "ddg_snippet": "Title: Causal Modeling of Climate Activism on Reddit . Jacopo Lenti, Luca Maria Aiello, Corrado Monti, Gianmarco De Francisci Morales. Subjects: Computers and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "http://arxiv.org/list/cs.SI/2024-10?skip=125&show=50", "content": "Title: Causal Modeling of Climate Activism on Reddit . Jacopo Lenti, Luca Maria Aiello, Corrado Monti, Gianmarco De Francisci Morales. Subjects: Computers and ..."}
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+{"idx": 3, "title": "ICML 2025 Tuesday 07/15", "date": "", "ddg_snippet": "A Checks-and-Balances Framework for Context-Aware Ethical AI Alignment. Poster. Edward Chang. [ East Exhibition Hall A-B ]. thumbnail Abstract. This paper ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/day/7/15", "content": "A Checks-and-Balances Framework for Context-Aware Ethical AI Alignment. Poster. Edward Chang. [ East Exhibition Hall A-B ]. thumbnail Abstract. This paper ..."}
+{"idx": 4, "title": "The Unified Cognitive Consciousness Theory for ...", "date": "", "ddg_snippet": "2 Jun 2025 — Empirical studies reveal the importance of semantic anchoring ... A Checks-and-Balances Framework for Context-Aware Ethical AI Alignment.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.02139v1", "content": "2 Jun 2025 — Empirical studies reveal the importance of semantic anchoring ... A Checks-and-Balances Framework for Context-Aware Ethical AI Alignment."}
+{"idx": 5, "title": "The dynamics of military coups in the contemporary Middle ...", "date": "", "ddg_snippet": "by W Ma · 2025 — The embedded checks-and-balances framework further incentivizes institutional resistance to coup plots (Bruin 2020; Makara 2013; Albrecht 2014).", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s44216-025-00050-y", "content": "by W Ma · 2025 — The embedded checks-and-balances framework further incentivizes institutional resistance to coup plots (Bruin 2020; Makara 2013; Albrecht 2014)."}
+{"idx": 6, "title": "Multi-LLM Agent Collaborative Intelligence: The Path to AGI", "date": "", "ddg_snippet": "... studies from various domains. This structure enables the reader to ... Checks-and-Balances Framework for Context-. Aware Ethical Alignment of Large ... 589 pages", "subpage_snippet": "", "source": "shuyuej.com", "link": "http://shuyuej.com/books/The-Path-to-Artificial-General-Intelligence.pdf", "content": "... studies from various domains. This structure enables the reader to ... Checks-and-Balances Framework for Context-. Aware Ethical Alignment of Large ... 589 pages"}
+{"idx": 7, "title": "Computer Science", "date": "", "ddg_snippet": "We provide both theoretical analysis and empirical ... This paper introduces a checks-and-balances framework ... reason without access to real-time scene data .", "subpage_snippet": "", "source": "www.arxiv.org", "link": "http://www.arxiv.org/list/cs/new?skip=875&show=500", "content": "We provide both theoretical analysis and empirical ... This paper introduces a checks-and-balances framework ... reason without access to real-time scene data ."}
+{"idx": 8, "title": "How Poor Decisions are Smoldering Within the U.S. Fire Service", "date": "", "ddg_snippet": "by CD Cavnor · 2018 · Cited by 5 — A “ checks and balances” framework has been established in high-risk occupations that verify that safety procedures are followed. Production over Safety. Work ...", "subpage_snippet": "", "source": "apps.dtic.mil", "link": "https://apps.dtic.mil/sti/tr/pdf/AD1052528.pdf", "content": "by CD Cavnor · 2018 · Cited by 5 — A “ checks and balances” framework has been established in high-risk occupations that verify that safety procedures are followed. Production over Safety. Work ..."}
+{"idx": 9, "title": "ICLM 2025 AI Safety 7847 Camera Ready3 | PDF | Emotions", "date": "", "ddg_snippet": "10 Aug 2025 — Empirical Studies risk of excessive censorship? The ethical ... work introduces a checks-and-balances framework for notators and ...", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/899938563/ICLM-2025-AI-Safety-7847-Camera-Ready3", "content": "10 Aug 2025 — Empirical Studies risk of excessive censorship? The ethical ... work introduces a checks-and-balances framework for notators and ..."}
diff --git a/data/sampled_jsons/Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment_empirical_studies_dataset.jsonl b/data/sampled_jsons/Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment_empirical_studies_dataset.jsonl
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index 0000000000000000000000000000000000000000..d6005b31e22ce1a00da58053a4619910e8427158
--- /dev/null
+++ b/data/sampled_jsons/Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment_empirical_studies_dataset.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Navigating the ethical landscape of AI... | F1000Research", "date": "", "ddg_snippet": "... frameworks and current AI implementations in education, the paper calls for clear ethical guidelines to ensure the responsible use of AI in ...", "subpage_snippet": "", "source": "f1000research.com", "link": "https://f1000research.com/articles/14-299", "content": "... frameworks and current AI implementations in education, the paper calls for clear ethical guidelines to ensure the responsible use of AI in ..."}
+{"idx": 1, "title": "Improving Steering and Verification in AI-Assisted Data", "date": "", "ddg_snippet": "However, our formative study (n=15) uncovered serious challenges in verifying AI -generated results and steering the AI (i.e., guiding the AI system ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2407.02651v2", "content": "However, our formative study (n=15) uncovered serious challenges in verifying AI -generated results and steering the AI (i.e., guiding the AI system ..."}
+{"idx": 2, "title": "Understanding Ethical Practices in AI: Insights from a", "date": "", "ddg_snippet": "Recent advances in AI applications have raised growing concerns about the need for ethical guidelines and regulations to mitigate the risks posed by ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.09219v1", "content": "Recent advances in AI applications have raised growing concerns about the need for ethical guidelines and regulations to mitigate the risks posed by ..."}
+{"idx": 3, "title": "Perceptions on the Ethical and Legal Principles that Influence", "date": "", "ddg_snippet": "However, due to the lack of an international data governance framework brain data is currently being produced under various contextual ethical and ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s12152-024-09558-1", "content": "However, due to the lack of an international data governance framework brain data is currently being produced under various contextual ethical and ..."}
+{"idx": 4, "title": "AI Governance Frameworks for Scientific Applications", "date": "", "ddg_snippet": "... countries and institutions, they serve as a foundation for building national risk frameworks and shaping both domestic and international AI policies.", "subpage_snippet": "", "source": "www.azorobotics.com", "link": "https://www.azorobotics.com/Article.aspx?ArticleID=761", "content": "... countries and institutions, they serve as a foundation for building national risk frameworks and shaping both domestic and international AI policies."}
+{"idx": 5, "title": "(PDF) Ethical considerations in Risk management of autonomous", "date": "", "ddg_snippet": "Through the analysis of AI risks and risk management procedures, we advocate for establishing effective mechanisms for ethical oversight and legal ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/381237450_Ethical_considerations_in_Risk_management_of_autonomous_and_intelligent_systems", "content": "Through the analysis of AI risks and risk management procedures, we advocate for establishing effective mechanisms for ethical oversight and legal ..."}
+{"idx": 6, "title": "AI Ethics Knowledge Among Chinese Teachers: An Empirical", "date": "", "ddg_snippet": "... on utilizing AI for educational reform, but the extent of teachers’ awareness of AI ethics , or the degree to which AI ethics are disseminated and ...", "subpage_snippet": "", "source": "www.researchsquare.com", "link": "https://www.researchsquare.com/article/rs-7042877/v1", "content": "... on utilizing AI for educational reform, but the extent of teachers’ awareness of AI ethics , or the degree to which AI ethics are disseminated and ..."}
+{"idx": 7, "title": "phelps-sg - LessWrong 2.0 viewer", "date": "", "ddg_snippet": "This problem has been studied extensively by economists within the field of organizational economics, and is called the principal-agent problem ...", "subpage_snippet": "", "source": "www.greaterwrong.com", "link": "https://www.greaterwrong.com/users/phelps-sg", "content": "This problem has been studied extensively by economists within the field of organizational economics, and is called the principal-agent problem ..."}
+{"idx": 8, "title": "Towards Fair AI: Mitigating Bias in Credit Decisions—A", "date": "", "ddg_snippet": "... Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research directions and ...", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/1911-8074/18/5/228", "content": "... Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research directions and ..."}
+{"idx": 9, "title": "Human-Centred AI Special Track Accepted Papers – IJCAI", "date": "", "ddg_snippet": "The use of personalized artificial intelligence ( AI ) models in assistive healthcare presents a number of ethical and legal challenges, they are ...", "subpage_snippet": "", "source": "ijcai24.org", "link": "https://ijcai24.org/human-centred-ai-special-track-accepted-papers/", "content": "The use of personalized artificial intelligence ( AI ) models in assistive healthcare presents a number of ethical and legal challenges, they are ..."}
diff --git a/data/sampled_jsons/Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment_paper.jsonl b/data/sampled_jsons/Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment_paper.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b872c6defc7516ac7f650db785364c1a19d28556
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+{"idx": 0, "title": "Order Personal Checks Online At Affordable Prices | Checks.com", "date": "", "ddg_snippet": "We print an exceptional collection of high-quality personal checks at cheap prices. Whether you are buying a checkbook for the first time or are reordering your favorite check design, you’ll always get the same low price - no discounts codes required.", "subpage_snippet": "", "source": "www.checks.com", "link": "https://www.checks.com/", "content": "We print an exceptional collection of high-quality personal checks at cheap prices. Whether you are buying a checkbook for the first time or are reordering your favorite check design, you’ll always get the same low price - no discounts codes required."}
+{"idx": 1, "title": "View Over 70 Personal Checks Designs at Low Prices", "date": "", "ddg_snippet": "Order your favorite personal checks online at low prices. Checks .com features a variety of over 70 personal check designs to fit your unique personality.", "subpage_snippet": "", "source": "www.checks.com", "link": "https://www.checks.com/c/129/personal-checks", "content": "Order your favorite personal checks online at low prices. Checks .com features a variety of over 70 personal check designs to fit your unique personality."}
+{"idx": 2, "title": "Value Checks - Order Our Best Value Checks Online", "date": "", "ddg_snippet": "Order value-priced personal checks online starting at just $8.20 per box at Checks .com! We have a variety of inexpensive check designs to choose from, so you can find the perfect one to match your style and budget.", "subpage_snippet": "", "source": "www.checks.com", "link": "https://www.checks.com/c/323/value-checks", "content": "Order value-priced personal checks online starting at just $8.20 per box at Checks .com! We have a variety of inexpensive check designs to choose from, so you can find the perfect one to match your style and budget."}
+{"idx": 3, "title": "How To Order Checks Online | Checks.com", "date": "", "ddg_snippet": "Order checks , address labels and checkbook covers online, secure, fast and easy with Checks .com. Customize and preview your checks before you order.", "subpage_snippet": "", "source": "www.checks.com", "link": "https://www.checks.com/how-to-order-checks", "content": "Order checks , address labels and checkbook covers online, secure, fast and easy with Checks .com. Customize and preview your checks before you order."}
+{"idx": 4, "title": "Traditional Checks - Order Affordable Personal Checks Online", "date": "", "ddg_snippet": "Checks .com's collection of traditional checks offers the widest variety of designs where you're sure to find a favorite or two. Also check out our line of Choice Checks for our most exclusive and most secure check designs, or browse our cheaper-priced checks for an incredibly low price per box.", "subpage_snippet": "", "source": "www.checks.com", "link": "https://www.checks.com/c/334/traditional-checks", "content": "Checks .com's collection of traditional checks offers the widest variety of designs where you're sure to find a favorite or two. Also check out our line of Choice Checks for our most exclusive and most secure check designs, or browse our cheaper-priced checks for an incredibly low price per box."}
+{"idx": 5, "title": "Reorder Discounted Personal Checks Online", "date": "", "ddg_snippet": "At Checks .com, whenever you reorder checks , you pay the same low price as intro customers. Reorder checks for discounted prices online today!", "subpage_snippet": "", "source": "www.checks.com", "link": "https://www.checks.com/quickreorder", "content": "At Checks .com, whenever you reorder checks , you pay the same low price as intro customers. Reorder checks for discounted prices online today!"}
+{"idx": 6, "title": "Renaissance Checks - Order Discounted Personal Checks", "date": "", "ddg_snippet": "Artistic scrolls and elegance grace these renaissance checks in a rich four color rotation. Coordinating renaissance address labels and checkbook cover are also available.", "subpage_snippet": "", "source": "www.checks.com", "link": "https://www.checks.com/p/834/renaissance-checks", "content": "Artistic scrolls and elegance grace these renaissance checks in a rich four color rotation. Coordinating renaissance address labels and checkbook cover are also available."}
+{"idx": 7, "title": "Modern Checks - Order Cool & Contemporary Personal Checks", "date": "", "ddg_snippet": "Show your trendy style with contemporary and cool personal checks from Checks .com! Choose a stylish check design or pattern and order checks online today!", "subpage_snippet": "", "source": "www.checks.com", "link": "https://www.checks.com/c/274/modern-checks", "content": "Show your trendy style with contemporary and cool personal checks from Checks .com! Choose a stylish check design or pattern and order checks online today!"}
+{"idx": 8, "title": "Pretty in Pink Checks - Order Discounted Personal Checks", "date": "", "ddg_snippet": "Discover the perfect blend of Parisian style and elegance with our Pretty in Pink checks . Designed for those who live life with flair, these top-tear pink personal checks are available in four vibrant designs featuring stripes, polka dots and diamond patterns.", "subpage_snippet": "", "source": "www.checks.com", "link": "https://www.checks.com/p/1799/pretty-in-pink-checks", "content": "Discover the perfect blend of Parisian style and elegance with our Pretty in Pink checks . Designed for those who live life with flair, these top-tear pink personal checks are available in four vibrant designs featuring stripes, polka dots and diamond patterns."}
+{"idx": 9, "title": "Horse Checks - Order Discounted Personal Checks", "date": "", "ddg_snippet": "Horse Play Checks Photographs of galloping horses adorn this equine rotation of stallions, mustangs and foals. Coordinating horse return address labels are available.", "subpage_snippet": "", "source": "www.checks.com", "link": "https://www.checks.com/p/811/horse-play-checks", "content": "Horse Play Checks Photographs of galloping horses adorn this equine rotation of stallions, mustangs and foals. Coordinating horse return address labels are available."}
diff --git a/data/sampled_jsons/Chen_He_Gu_2022_Theorem_4.5_lower_bound_preference-based_RL.jsonl b/data/sampled_jsons/Chen_He_Gu_2022_Theorem_4.5_lower_bound_preference-based_RL.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..57fbceb202b849fecca9431c71b99a67f3bf0e66
--- /dev/null
+++ b/data/sampled_jsons/Chen_He_Gu_2022_Theorem_4.5_lower_bound_preference-based_RL.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Human-in-the-loop: Provably Efficient Preference-based ...", "date": "", "ddg_snippet": "by X Chen · 2022 · Cited by 93 — The lower bound indicates that our algorithm is near-optimal in the case of linear function approximation. • We formulate a novel setting called RL with n-wise.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v162/chen22ag/chen22ag.pdf", "content": "by X Chen · 2022 · Cited by 93 — The lower bound indicates that our algorithm is near-optimal in the case of linear function approximation. • We formulate a novel setting called RL with n-wise."}
+{"idx": 1, "title": "Sample-Efficient Reinforcement Learning from Human ...", "date": "", "ddg_snippet": "by H Qi · 2025 · Cited by 2 — We first introduce a basic IDS- based algorithm for the RLHF setting where the reward is unobservable and only preference feedback is available (see Sec. 4.1).", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/pdf/2502.05434", "content": "by H Qi · 2025 · Cited by 2 — We first introduce a basic IDS- based algorithm for the RLHF setting where the reward is unobservable and only preference feedback is available (see Sec. 4.1)."}
+{"idx": 2, "title": "Reward Generalization in RLHF: A Topological Perspective", "date": "", "ddg_snippet": "by TA Qiu · 2025 · Cited by 4 — 19. Theorem 4.5 (RM Uncertainty in Chain- Based and Tree- Based Datasets). For a chain- or tree- based dataset D ∈ {Dchain,Dtree}, with prob-. 47 pages", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.findings-acl.820.pdf", "content": "by TA Qiu · 2025 · Cited by 4 — 19. Theorem 4.5 (RM Uncertainty in Chain- Based and Tree- Based Datasets). For a chain- or tree- based dataset D ∈ {Dchain,Dtree}, with prob-. 47 pages"}
+{"idx": 3, "title": "Can RLHF be More Efficient with Imperfect Reward Models ...", "date": "", "ddg_snippet": "by J Huang · Cited by 2 — On the other hand, as justified by our theory , win rates help to identify lower bounds for the coverability of the optimal policy. Notably, our empirical. TPO ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=8u5bzM2XfI", "content": "by J Huang · Cited by 2 — On the other hand, as justified by our theory , win rates help to identify lower bounds for the coverability of the optimal policy. Notably, our empirical. TPO ..."}
+{"idx": 4, "title": "Sample-Efficient Reinforcement Learning from Human ...", "date": "", "ddg_snippet": "8 Aug 2025 — Compared to standard RL , this preference - based setting is often more aligned with real-world scenarios, especially for tasks involving human ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.05434v3", "content": "8 Aug 2025 — Compared to standard RL , this preference - based setting is often more aligned with real-world scenarios, especially for tasks involving human ..."}
+{"idx": 5, "title": "Can RLHF be More Efficient with Imperfect Reward Models ...", "date": "", "ddg_snippet": "Empirically, inspired by our theoretical findings, we develop a win-rate- based transfer policy selection strategy with improved computational efficiency.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46257", "content": "Empirically, inspired by our theoretical findings, we develop a win-rate- based transfer policy selection strategy with improved computational efficiency."}
+{"idx": 6, "title": "Reinforcement Learning from Human Feedback with Active ...", "date": "", "ddg_snippet": "Aligning large language models (LLM) with human preference plays a key role in building modern generative models and can be achieved by reinforcement ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=EScatQaRxz&name=pdf", "content": "Aligning large language models (LLM) with human preference plays a key role in building modern generative models and can be achieved by reinforcement ..."}
+{"idx": 7, "title": "Settling the Sample Complexity of Online Reinforcement ...", "date": "", "ddg_snippet": "10 Jun 2025 — This regret matches the minimax lower bound for the entire range of sample size K ≥ 1, essentially eliminating any burn-in requirement.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3733592", "content": "10 Jun 2025 — This regret matches the minimax lower bound for the entire range of sample size K ≥ 1, essentially eliminating any burn-in requirement."}
+{"idx": 8, "title": "A systematic review of reinforcement learning in Building- ...", "date": "", "ddg_snippet": "by J Li · 2025 — RL has been increasingly applied in the BIPV field to optimize energy management, enhance efficiency, and improve decision-making processes.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2666792425000332", "content": "by J Li · 2025 — RL has been increasingly applied in the BIPV field to optimize energy management, enhance efficiency, and improve decision-making processes."}
+{"idx": 9, "title": "ICML Poster Logarithmic Regret for Online KL-Regularized ...", "date": "", "ddg_snippet": "Recent advances in Reinforcement Learning from Human Feedback (RLHF) have shown that KLregularization plays a pivotal role in improving the efficiency of RL ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46376", "content": "Recent advances in Reinforcement Learning from Human Feedback (RLHF) have shown that KLregularization plays a pivotal role in improving the efficiency of RL ..."}
diff --git a/data/sampled_jsons/Chen_et_al._2023_random_reference_trajectory.jsonl b/data/sampled_jsons/Chen_et_al._2023_random_reference_trajectory.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f6d12086277c29631732028e347c8beeebc72ce0
--- /dev/null
+++ b/data/sampled_jsons/Chen_et_al._2023_random_reference_trajectory.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Correction: Chen et al. Online Trajectory Optimization Method ... - MDPI", "date": "", "ddg_snippet": "Chen H, Ma Z, Wang J, Su L. Correction: Chen et al. Online Trajectory Optimization Method for Large Attitude Flip Vertical Landing of the Starship-like Vehicle.", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/2227-7390/12/8/1138", "content": "Chen H, Ma Z, Wang J, Su L. Correction: Chen et al. Online Trajectory Optimization Method for Large Attitude Flip Vertical Landing of the Starship-like Vehicle."}
+{"idx": 1, "title": "Trajectory planning of stratospheric airship for station-keeping ...", "date": "", "ddg_snippet": "The main method to reduce the capsule temperature is to accelerate air convection by maintaining a high airspeed (Alam et al ., 2023 ). Therefore, trajectory planning strategies designed for the stratospheric airship must be able to solve the multi-constraint planning problem.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0273117723008062", "content": "The main method to reduce the capsule temperature is to accelerate air convection by maintaining a high airspeed (Alam et al ., 2023 ). Therefore, trajectory planning strategies designed for the stratospheric airship must be able to solve the multi-constraint planning problem."}
+{"idx": 2, "title": "PDF Unsupervised Sampling Promoting for Stochastic Human Trajectory Prediction", "date": "", "ddg_snippet": "Facing this challenge, many prior methods formulate stochastic human trajectory prediction as a generative prob-lem, in which a latent random variable is used to represent multimodality. A typical category of methods [10,18,46,66] is based on generative adversarial networks (GANs), which generate possible future trajectories by a noise in the multi-modal distribution. Another category exploits ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_Unsupervised_Sampling_Promoting_for_Stochastic_Human_Trajectory_Prediction_CVPR_2023_paper.pdf", "content": "Facing this challenge, many prior methods formulate stochastic human trajectory prediction as a generative prob-lem, in which a latent random variable is used to represent multimodality. A typical category of methods [10,18,46,66] is based on generative adversarial networks (GANs), which generate possible future trajectories by a noise in the multi-modal distribution. Another category exploits ..."}
+{"idx": 3, "title": "SMART: Scalable Multi-Agent Reasoning and Trajectory Planning in Dense ...", "date": "", "ddg_snippet": "Abstract Multi-vehicle trajectory planning is a non-convex problem that becomes increasingly difficult in dense en-vironments due to the rapid growth of collision constraints. Efficient exploration of feasible behaviors and resolution of tight interactions are essential for real-time, large-scale coordination. This paper introduces SMART, Scalable Multi-Agent Reasoning and Trajectory Planning ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2509.15737", "content": "Abstract Multi-vehicle trajectory planning is a non-convex problem that becomes increasingly difficult in dense en-vironments due to the rapid growth of collision constraints. Efficient exploration of feasible behaviors and resolution of tight interactions are essential for real-time, large-scale coordination. This paper introduces SMART, Scalable Multi-Agent Reasoning and Trajectory Planning ..."}
+{"idx": 4, "title": "Applied Mathematics, Modeling and Computer Simulation C.-H. Chen et al ...", "date": "", "ddg_snippet": "In order to reflect the relative position relationship between the vehicle and the reference trajectory during trajectory tracking, the vehicle trajectory tracking model shown in Figure 1 is established.", "subpage_snippet": "", "source": "engrxiv.org", "link": "https://engrxiv.org/preprint/download/3427/6147/4912", "content": "In order to reflect the relative position relationship between the vehicle and the reference trajectory during trajectory tracking, the vehicle trajectory tracking model shown in Figure 1 is established."}
+{"idx": 5, "title": "Vehicle trajectory prediction based on cross-attention and multilevel ...", "date": "", "ddg_snippet": "Chen X, Zhang H, Zhao F, et al. Vehicle trajectory prediction based on intention-aware non-autoregressive transformer with multi-attention learning for internet of vehicles.", "subpage_snippet": "", "source": "journals.sagepub.com", "link": "https://journals.sagepub.com/doi/full/10.1177/09544070241272875", "content": "Chen X, Zhang H, Zhao F, et al. Vehicle trajectory prediction based on intention-aware non-autoregressive transformer with multi-attention learning for internet of vehicles."}
+{"idx": 6, "title": "CVPR 2023 Open Access Repository", "date": "", "ddg_snippet": "The indeterminate nature of human motion requires trajectory prediction systems to use a probabilistic model to formulate the multi-modality phenomenon and infer a finite set of future trajectories. However, the inference processes of most existing methods rely on Monte Carlo random sampling, which is insufficient to cover the realistic paths with finite samples, due to the long tail effect of ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Unsupervised_Sampling_Promoting_for_Stochastic_Human_Trajectory_Prediction_CVPR_2023_paper.html", "content": "The indeterminate nature of human motion requires trajectory prediction systems to use a probabilistic model to formulate the multi-modality phenomenon and infer a finite set of future trajectories. However, the inference processes of most existing methods rely on Monte Carlo random sampling, which is insufficient to cover the realistic paths with finite samples, due to the long tail effect of ..."}
+{"idx": 7, "title": "PDF A Graph-based Representation Framework for Trajectory Recovery via ...", "date": "", "ddg_snippet": "Recently, there has been a surge in deep learning-based models for trajectory recovery, such as MTrajRec [Ren et al ., 2021] and RNTrajRec [ Chen et al ., 2023 ]. These meth-ods adopt a sequence-to-sequence [Sutskever et al ., 2014] ar-chitecture, featuring an encoder model responsible for gen-erating representations of the input trajectory and a decoder model tasked with recovering the trajectory ...", "subpage_snippet": "", "source": "www.ijcai.org", "link": "https://www.ijcai.org/proceedings/2024/0286.pdf", "content": "Recently, there has been a surge in deep learning-based models for trajectory recovery, such as MTrajRec [Ren et al ., 2021] and RNTrajRec [ Chen et al ., 2023 ]. These meth-ods adopt a sequence-to-sequence [Sutskever et al ., 2014] ar-chitecture, featuring an encoder model responsible for gen-erating representations of the input trajectory and a decoder model tasked with recovering the trajectory ..."}
+{"idx": 8, "title": "Correction: Chen et al. Online Trajectory Optimization Method for Large ...", "date": "", "ddg_snippet": "Correction: Chen et al. Online Trajectory Optimization Method for Large Attitude Flip Vertical Landing of the Starship-like Vehicle. Mathematics 2023 , 11, 288", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/379736204_Correction_Chen_et_al_Online_Trajectory_Optimization_Method_for_Large_Attitude_Flip_Vertical_Landing_of_the_Starship-like_Vehicle_Mathematics_2023_11_288", "content": "Correction: Chen et al. Online Trajectory Optimization Method for Large Attitude Flip Vertical Landing of the Starship-like Vehicle. Mathematics 2023 , 11, 288"}
+{"idx": 9, "title": "Optoelectronic graded neurons for bioinspired in-sensor motion ...", "date": "", "ddg_snippet": "Inspired by the visual systems of agile insects, Chen et al. emulate their graded neurons using optoelectronic devices to realize bioinspired in-sensor motion perception and demonstrate high ...", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41565-023-01379-2", "content": "Inspired by the visual systems of agile insects, Chen et al. emulate their graded neurons using optoelectronic devices to realize bioinspired in-sensor motion perception and demonstrate high ..."}
diff --git a/data/sampled_jsons/Chen_et_al_2023_MLD_supplementary_material_experimental_results_action-to-motion_HumanAct12_year_2023.jsonl b/data/sampled_jsons/Chen_et_al_2023_MLD_supplementary_material_experimental_results_action-to-motion_HumanAct12_year_2023.jsonl
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index 0000000000000000000000000000000000000000..9995e25ac49a41074c356445d3de83430ebeba54
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Realistic Human Motion Generation with Cross-Diffusion ...", "date": "", "ddg_snippet": "5 Aug 2024 — A crucial aspect in this research area is generating human motion based on textual descriptions , enabling contextually accurate and natural ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2312.10993v3", "content": "5 Aug 2024 — A crucial aspect in this research area is generating human motion based on textual descriptions , enabling contextually accurate and natural ..."}
+{"idx": 1, "title": "Motion Flow Matching for Human Motion Synthesis and ...", "date": "", "ddg_snippet": "In this paper, we propose Motion Flow Matching, a novel generative model designed for human motion generation featuring efficient sampling and effectiveness in ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2312.08895v1", "content": "In this paper, we propose Motion Flow Matching, a novel generative model designed for human motion generation featuring efficient sampling and effectiveness in ..."}
+{"idx": 2, "title": "Human Motion Synthesis with Latent-space GANs", "date": "", "ddg_snippet": "by A Amballa · 2025 · Cited by 3 — Specifically, this work undertakes the task of text-to- motion and action -to- motion synthesis using conditional. Generative Adversarial Networks [32] in latent ... 10 pages", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/WACV2025W/ImageQuality/papers/Amballa_LS-GAN_Human_Motion_Synthesis_with_Latent-space_GANs_WACVW_2025_paper.pdf", "content": "by A Amballa · 2025 · Cited by 3 — Specifically, this work undertakes the task of text-to- motion and action -to- motion synthesis using conditional. Generative Adversarial Networks [32] in latent ... 10 pages"}
+{"idx": 3, "title": "Executing Your Commands via Motion Diffusion in Latent Space", "date": "", "ddg_snippet": "Our proposed Motion Latentbased Diffusion model ( MLD ) could produce vivid motion sequences conforming to the given conditional inputs and substantially reduce ...", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2023/poster/23272", "content": "Our proposed Motion Latentbased Diffusion model ( MLD ) could produce vivid motion sequences conforming to the given conditional inputs and substantially reduce ..."}
+{"idx": 4, "title": "FLEXMOTION", "date": "", "ddg_snippet": "by A Tashakori — FlexMotion advances human motion generation by addressing the critical limitations of existing models. Unlike MDM Tevet et al. (2023), MLD Chen et al. (2023),.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=7652tHbbVE", "content": "by A Tashakori — FlexMotion advances human motion generation by addressing the critical limitations of existing models. Unlike MDM Tevet et al. (2023), MLD Chen et al. (2023),."}
+{"idx": 5, "title": "Act As You Wish: Fine-Grained Control of Motion Diffusion ...", "date": "", "ddg_snippet": "In this paper, we propose hierarchical semantic graphs for fine-grained control over motion generation. Specifically, we disentangle motion descriptions into ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2023/poster/70005", "content": "In this paper, we propose hierarchical semantic graphs for fine-grained control over motion generation. Specifically, we disentangle motion descriptions into ..."}
+{"idx": 6, "title": "Robust Diffusion‐based Motion In‐betweening - Qin - 2024", "date": "", "ddg_snippet": "7 Nov 2024 — Chen et al . [CSH*24] presented a real-time diffusion model for generating motion based on user interaction. Wei et al . [WSS*24] introduced a ...", "subpage_snippet": "", "source": "onlinelibrary.wiley.com", "link": "https://onlinelibrary.wiley.com/doi/10.1111/cgf.15260?af=R", "content": "7 Nov 2024 — Chen et al . [CSH*24] presented a real-time diffusion model for generating motion based on user interaction. Wei et al . [WSS*24] introduced a ..."}
+{"idx": 7, "title": "UniMotion-DM: Uniform Text-Motion Generation and ...", "date": "", "ddg_snippet": "by S Lin · 2024 — Extensive ablation studies and parameter optimization experiments further validate UniMotion-DM's robustness and adaptability, showcasing superior multi-task ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/iel8/6287639/6514899/10802885.pdf", "content": "by S Lin · 2024 — Extensive ablation studies and parameter optimization experiments further validate UniMotion-DM's robustness and adaptability, showcasing superior multi-task ..."}
+{"idx": 8, "title": "NERM: LEARNING NEURAL REPRESENTATIONS FOR ...", "date": "", "ddg_snippet": "As sampling of these approaches in raw motion space are computationally expensive, MLD ( Chen et al ., 2023 ) employs VAE with transformer backbone to map motions ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/f27c2baf9c383fe987291d41b9a4635d4e0222d7.pdf", "content": "As sampling of these approaches in raw motion space are computationally expensive, MLD ( Chen et al ., 2023 ) employs VAE with transformer backbone to map motions ..."}
+{"idx": 9, "title": "Spatio-Temporal Graph Diffusion for Text-Driven Human ...", "date": "", "ddg_snippet": "by C Liu · 2023 · Cited by 13 — Our model achieves com- petitive results on currently the largest dataset HumanML3D and outperforms existing diffusion models in terms of FID and diversity, ...", "subpage_snippet": "", "source": "papers.bmvc2023.org", "link": "https://papers.bmvc2023.org/0722.pdf", "content": "by C Liu · 2023 · Cited by 13 — Our model achieves com- petitive results on currently the largest dataset HumanML3D and outperforms existing diffusion models in terms of FID and diversity, ..."}
diff --git a/data/sampled_jsons/Chien_et_al._2024_Langevin_unlearning_noisy_gradient_descent_machine_unlearning_arxiv.jsonl b/data/sampled_jsons/Chien_et_al._2024_Langevin_unlearning_noisy_gradient_descent_machine_unlearning_arxiv.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..50a9711541168aaae1a004f01b468887c504ccb7
--- /dev/null
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Langevin Unlearning: A New Perspective of Noisy Gradient ...", "date": "", "ddg_snippet": "by E Chien · 2024 · Cited by 28 — We propose Langevin unlearning , an unlearning framework based on noisy gradient descent with privacy guarantees for approximate unlearning problems.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2401.10371", "content": "by E Chien · 2024 · Cited by 28 — We propose Langevin unlearning , an unlearning framework based on noisy gradient descent with privacy guarantees for approximate unlearning problems."}
+{"idx": 1, "title": "Langevin Unlearning: A New Perspective of Noisy Gradient ...", "date": "", "ddg_snippet": "arXiv:2401.10371v5 [cs.LG] 11 Oct 2024. Langevin Unlearning: A New Perspective of Noisy Gradient Descent for Machine Unlearning ... Eli Chien ... Neel et al. [8] ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2401.10371v5", "content": "arXiv:2401.10371v5 [cs.LG] 11 Oct 2024. Langevin Unlearning: A New Perspective of Noisy Gradient Descent for Machine Unlearning ... Eli Chien ... Neel et al. [8] ..."}
+{"idx": 2, "title": "Langevin Unlearning: A New Perspective of Noisy Gradient ...", "date": "", "ddg_snippet": "by E Chien · 2024 · Cited by 28 — Langevin unlearning is an unlearning framework based on noisy gradient descent with privacy guarantees for approximate unlearning problems.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2401.10371", "content": "by E Chien · 2024 · Cited by 28 — Langevin unlearning is an unlearning framework based on noisy gradient descent with privacy guarantees for approximate unlearning problems."}
+{"idx": 3, "title": "Langevin Unlearning: A New Perspective of Noisy Gradient ...", "date": "", "ddg_snippet": "We propose Langevin unlearning , an unlearning framework based on noisy gradient descent with privacy guarantees for approximate unlearning problems. Langevin ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/poster/96754", "content": "We propose Langevin unlearning , an unlearning framework based on noisy gradient descent with privacy guarantees for approximate unlearning problems. Langevin ..."}
+{"idx": 4, "title": "Langevin unlearning: a new perspective of noisy gradient ...", "date": "", "ddg_snippet": "5 Jun 2025 — We propose Langevin unlearning , an unlearning framework based on noisy gradient descent with privacy guarantees for approximate unlearning ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3737916.3740446", "content": "5 Jun 2025 — We propose Langevin unlearning , an unlearning framework based on noisy gradient descent with privacy guarantees for approximate unlearning ..."}
+{"idx": 5, "title": "Certified Machine Unlearning via Noisy Stochastic ...", "date": "", "ddg_snippet": "11 Oct 2024 — Chien et al . [11] utilize full-batch PNGD for approximate unlearning with the analysis of Langevin dynamics. The adaptive unlearning ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.17105v2", "content": "11 Oct 2024 — Chien et al . [11] utilize full-batch PNGD for approximate unlearning with the analysis of Langevin dynamics. The adaptive unlearning ..."}
+{"idx": 6, "title": "Certified Machine Unlearning via Noisy Stochastic ...", "date": "", "ddg_snippet": "by E Chien · 2024 · Cited by 11 — We propose to leverage projected noisy stochastic gradient descent for unlearning and establish its first approximate unlearning guarantee under the convexity ... 36 pages", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/448abd486677165ceedfa790e9a61802-Paper-Conference.pdf", "content": "by E Chien · 2024 · Cited by 11 — We propose to leverage projected noisy stochastic gradient descent for unlearning and establish its first approximate unlearning guarantee under the convexity ... 36 pages"}
+{"idx": 7, "title": "Langevin Unlearning: A New Perspective of Noisy Gradient ...", "date": "", "ddg_snippet": "We propose Langevin unlearning , an unlearning framework based on noisy gradient descent with privacy guarantees for approximate unlearning problems. Langevin ...", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/papers/2401.10371", "content": "We propose Langevin unlearning , an unlearning framework based on noisy gradient descent with privacy guarantees for approximate unlearning problems. Langevin ..."}
+{"idx": 8, "title": "Leveraging Per-Instance Privacy for Machine Unlearning", "date": "", "ddg_snippet": "by NM Sepahvand — Armed with per-instance privacy losses, we revisit Chien et al .'s 2024 theoretical analysis of noisy gradient descent as an unlearning scheme (coined “ Langevin ...", "subpage_snippet": "", "source": "tpdp.journalprivacyconfidentiality.org", "link": "https://tpdp.journalprivacyconfidentiality.org/2025/pdf/sepahvand.pdf", "content": "by NM Sepahvand — Armed with per-instance privacy losses, we revisit Chien et al .'s 2024 theoretical analysis of noisy gradient descent as an unlearning scheme (coined “ Langevin ..."}
+{"idx": 9, "title": "Leveraging Per-Instance Privacy for Machine Unlearning", "date": "", "ddg_snippet": "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 ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46697", "content": "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 ..."}
diff --git a/data/sampled_jsons/Chris_Olah_Zoom_In_circuits_2020_abstract_features_neurons.jsonl b/data/sampled_jsons/Chris_Olah_Zoom_In_circuits_2020_abstract_features_neurons.jsonl
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index 0000000000000000000000000000000000000000..a91716072ae4288772765bcec68970250f82a894
--- /dev/null
+++ b/data/sampled_jsons/Chris_Olah_Zoom_In_circuits_2020_abstract_features_neurons.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "The Sequence Radar #716: Sometimes, Circuits is All You Need", "date": "", "ddg_snippet": "Summary: In Zoom In : An Introduction to Circuits , the father of mechanistic interpretability Chris Olah provides a detailed introduction to the idea of circuits for understanding neural networks. The Distill article \" Zoom In : An Introduction to Circuits \" by Chris Olah et al. (March 2020 ) marks a pivotal moment in mechanistic interpretability, translating abstract neural activations into ...", "subpage_snippet": "", "source": "thesequence.substack.com", "link": "https://thesequence.substack.com/p/the-sequence-radar-716-sometimes", "content": "Summary: In Zoom In : An Introduction to Circuits , the father of mechanistic interpretability Chris Olah provides a detailed introduction to the idea of circuits for understanding neural networks. The Distill article \" Zoom In : An Introduction to Circuits \" by Chris Olah et al. (March 2020 ) marks a pivotal moment in mechanistic interpretability, translating abstract neural activations into ..."}
+{"idx": 1, "title": "Zoom In: An Introduction to Circuits - ResearchGate", "date": "", "ddg_snippet": "Download Citation | On Mar 10, 2020 , Chris Olah and others published Zoom In : An Introduction to Circuits | Find, read and cite all the research you need on ResearchGate", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/339841165_Zoom_In_An_Introduction_to_Circuits", "content": "Download Citation | On Mar 10, 2020 , Chris Olah and others published Zoom In : An Introduction to Circuits | Find, read and cite all the research you need on ResearchGate"}
+{"idx": 2, "title": "Distill: Zoom in on Circuits - Dynamically Typed", "date": "", "ddg_snippet": "From DT #35: \"By studying the connections between neurons , we can find meaningful algorithms in the weights of neural networks.\" Chris Olah et al. wrote a fascinating new Distill article about \" circuits \" in convolutional neural networks. The authors aim to reposition the field of AI interpretability as a natural science, like biology and chemistry: There are two common proposals for ...", "subpage_snippet": "", "source": "dynamicallytyped.com", "link": "https://dynamicallytyped.com/stories/2020/distill-zoom-in-on-circuits/", "content": "From DT #35: \"By studying the connections between neurons , we can find meaningful algorithms in the weights of neural networks.\" Chris Olah et al. wrote a fascinating new Distill article about \" circuits \" in convolutional neural networks. The authors aim to reposition the field of AI interpretability as a natural science, like biology and chemistry: There are two common proposals for ..."}
+{"idx": 3, "title": "Zoom In: An Introduction to Circuits (Chris Olah/Gabriel Goh/Ludwig ...", "date": "", "ddg_snippet": "Zoom In : An Introduction to Circuits ( Chris Olah /Gabriel Goh/Ludwig Schubert/Michael Petrov/Nick Cammarata/Shan Carter, 2020 )", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/ControlProblem/comments/18a62jc/zoom_in_an_introduction_to_circuits_chris/", "content": "Zoom In : An Introduction to Circuits ( Chris Olah /Gabriel Goh/Ludwig Schubert/Michael Petrov/Nick Cammarata/Shan Carter, 2020 )"}
+{"idx": 4, "title": "Zoom In: An Introduction to Circuits · 研飞ivySCI", "date": "", "ddg_snippet": "Chris Olah , Nick Cammarata, Ludwig Schubert, Gabriel Goh, Michael Petrov, Shan Carter", "subpage_snippet": "", "source": "www.ivysci.com", "link": "https://www.ivysci.com/en/articles/1536790__Zoom_In_An_Introduction_to_Circuits", "content": "Chris Olah , Nick Cammarata, Ludwig Schubert, Gabriel Goh, Michael Petrov, Shan Carter"}
+{"idx": 5, "title": "Zoom In: An Introduction to Circuits", "date": "", "ddg_snippet": "by C Olah · 2020 · Cited by 638 — Zoom In : An Introduction to Circuits . By studying the ... Chris Olah at VISxAI 2019. It was also informally presented at MILA ...", "subpage_snippet": "", "source": "distill.pub", "link": "https://distill.pub/2020/circuits/zoom-in", "content": "by C Olah · 2020 · Cited by 638 — Zoom In : An Introduction to Circuits . By studying the ... Chris Olah at VISxAI 2019. It was also informally presented at MILA ..."}
+{"idx": 6, "title": "Thread: Circuits", "date": "", "ddg_snippet": "by N Cammarata · 2020 · Cited by 92 — Zoom In : An Introduction to Circuits . Authors. Affiliations. Chris Olah , Nick Cammarata, Ludwig Schubert, Gabriel Goh, Michael Petrov, Shan ...", "subpage_snippet": "", "source": "distill.pub", "link": "https://distill.pub/2020/circuits", "content": "by N Cammarata · 2020 · Cited by 92 — Zoom In : An Introduction to Circuits . Authors. Affiliations. Chris Olah , Nick Cammarata, Ludwig Schubert, Gabriel Goh, Michael Petrov, Shan ..."}
+{"idx": 7, "title": "Zoom In: An Introduction to Circuits. Published by OpenAI. ...", "date": "", "ddg_snippet": "10 Mar 2020 — Zoom In : An Introduction to Circuits By studying the connections between neurons ... Chris Olah at VISxAI 2019. It was also informally ...", "subpage_snippet": "", "source": "blog.biocomm.ai", "link": "https://blog.biocomm.ai/2020/03/10/zoom-in-an-introduction-to-circuits-published-by-openai-march-10-2020/", "content": "10 Mar 2020 — Zoom In : An Introduction to Circuits By studying the connections between neurons ... Chris Olah at VISxAI 2019. It was also informally ..."}
+{"idx": 8, "title": "Zoom In: An Introduction to Circuits", "date": "", "ddg_snippet": "10 Mar 2020 — Chris Olah and the rest of the rest of the OpenAI Clarity team just published “ Zoom In : An Introduction to Circuits ,” a Distill article ...", "subpage_snippet": "", "source": "www.alignmentforum.org", "link": "https://www.alignmentforum.org/posts/MG4ZjWQDrdpgeu8wG/zoom-in-an-introduction-to-circuits", "content": "10 Mar 2020 — Chris Olah and the rest of the rest of the OpenAI Clarity team just published “ Zoom In : An Introduction to Circuits ,” a Distill article ..."}
+{"idx": 9, "title": "Automatic Discovery of Visual Circuits", "date": "", "ddg_snippet": "30 Apr 2024 — [14] Chris Olah , Nick Cammarata, Ludwig Schubert, Gabriel Goh, Michael Petrov, and Shan Carter. Zoom in : An introduction to circuits . Distill, ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2404.14349v1", "content": "30 Apr 2024 — [14] Chris Olah , Nick Cammarata, Ludwig Schubert, Gabriel Goh, Michael Petrov, and Shan Carter. Zoom in : An introduction to circuits . Distill, ..."}
diff --git a/data/sampled_jsons/Christian_Gaetz_Yibo_Gao_mathematics_paper.jsonl b/data/sampled_jsons/Christian_Gaetz_Yibo_Gao_mathematics_paper.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8b4f5bc0359d6c79c8ba2828d64dcd9a6c3186f1
--- /dev/null
+++ b/data/sampled_jsons/Christian_Gaetz_Yibo_Gao_mathematics_paper.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2005.09719] Balance constants for Coxeter groups", "date": "", "ddg_snippet": "by C Gaetz · 2020 · Cited by 3 — Authors:Christian Gaetz, Yibo Gao. View a PDF of the paper titled Balance constants for Coxeter groups , by Christian Gaetz and Yibo Gao. View ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2005.09719", "content": "by C Gaetz · 2020 · Cited by 3 — Authors:Christian Gaetz, Yibo Gao. View a PDF of the paper titled Balance constants for Coxeter groups , by Christian Gaetz and Yibo Gao. View ..."}
+{"idx": 1, "title": "[2204.09174] On automorphisms of undirected Bruhat graphs", "date": "", "ddg_snippet": "by C Gaetz · 2022 · Cited by 1 — Authors:Christian Gaetz, Yibo Gao. View a PDF of the paper titled On automorphisms of undirected Bruhat graphs , by Christian Gaetz and Yibo Gao.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2204.09174", "content": "by C Gaetz · 2022 · Cited by 1 — Authors:Christian Gaetz, Yibo Gao. View a PDF of the paper titled On automorphisms of undirected Bruhat graphs , by Christian Gaetz and Yibo Gao."}
+{"idx": 2, "title": "Separable elements in Weyl groups", "date": "", "ddg_snippet": "by C Gaetz · 2020 · Cited by 16 — Throughout this paper we will refer to the well-known Cartan-Killing classification of irreducible. Page 4. 4. CHRIST IAN GAETZ AND YIBO GAO root systems (see, ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/am/pii/S0196885819301587", "content": "by C Gaetz · 2020 · Cited by 16 — Throughout this paper we will refer to the well-known Cartan-Killing classification of irreducible. Page 4. 4. CHRIST IAN GAETZ AND YIBO GAO root systems (see, ..."}
+{"idx": 3, "title": "The weak Bruhat order on the symmetric group is Sperner", "date": "", "ddg_snippet": "A combinatorial 𝔰𝔩₂-action and the Sperner property for the weak order · Christian GaetzYibo Gao . Mathematics . Proceedings of the American Mathematical Society.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/The-weak-Bruhat-order-on-the-symmetric-group-is-Gaetz-Gao/d1e0c4b37797dcf1f3e2087c151530a8c4dfcd3e", "content": "A combinatorial 𝔰𝔩₂-action and the Sperner property for the weak order · Christian GaetzYibo Gao . Mathematics . Proceedings of the American Mathematical Society."}
+{"idx": 4, "title": "Self-dual intervals in the Bruhat order", "date": "", "ddg_snippet": "by C Gaetz · 2020 · Cited by 7 — Gaetz , Christian and Yibo Gao , \"Self-dual intervals in the Bruhat order.\" Selecta Mathematica 26, 5 (November 2020): 77 ©2020 Authors. Version: Author's ...", "subpage_snippet": "", "source": "dspace.mit.edu", "link": "https://dspace.mit.edu/handle/1721.1/129422", "content": "by C Gaetz · 2020 · Cited by 7 — Gaetz , Christian and Yibo Gao , \"Self-dual intervals in the Bruhat order.\" Selecta Mathematica 26, 5 (November 2020): 77 ©2020 Authors. Version: Author's ..."}
+{"idx": 5, "title": "A combinatorial duality between the weak and strong ...", "date": "", "ddg_snippet": "by C Gaetz · 2020 · Cited by 13 — American Mathematical Society, 1984. [2] Christian Gaetz and Yibo Gao . A combinatorial sl2-action and the Sperner property for the weak order. 2018. arXiv ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/am/pii/S0097316519301591", "content": "by C Gaetz · 2020 · Cited by 13 — American Mathematical Society, 1984. [2] Christian Gaetz and Yibo Gao . A combinatorial sl2-action and the Sperner property for the weak order. 2018. arXiv ..."}
+{"idx": 6, "title": "Minimal elements for the limit weak order on affine Weyl ...", "date": "", "ddg_snippet": "by C Gaetz · 2022 · Cited by 1 — Elements of Mathematics (Berlin). Springer-Verlag, Berlin, 2002. Translated from the 1968 French original by Andrew Pressley. [4] Christian Gaetz and Yibo Gao .", "subpage_snippet": "", "source": "londmathsoc.onlinelibrary.wiley.com", "link": "https://londmathsoc.onlinelibrary.wiley.com/doi/am-pdf/10.1112/blms.12653", "content": "by C Gaetz · 2022 · Cited by 1 — Elements of Mathematics (Berlin). Springer-Verlag, Berlin, 2002. Translated from the 1968 French original by Andrew Pressley. [4] Christian Gaetz and Yibo Gao ."}
+{"idx": 7, "title": "[PDF] Separable elements in Weyl groups", "date": "", "ddg_snippet": "Separable elements and splittings of Weyl groups · Christian GaetzYibo Gao ; Separable elements: linear extensions, graph associahedra, and splittings of Weyl ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/37355155ce1bdfec6c928a814495b212c92da0fd", "content": "Separable elements and splittings of Weyl groups · Christian GaetzYibo Gao ; Separable elements: linear extensions, graph associahedra, and splittings of Weyl ..."}
+{"idx": 8, "title": "The Sperner property for 132‐avoiding intervals in the ...", "date": "", "ddg_snippet": "by C Gaetz · 2020 · Cited by 3 — [4] Christian Gaetz and Yibo Gao . A combinatorial sl2-action and the Sperner property for the weak order. Proc. Amer. Math. Soc., 148(1):1 ...", "subpage_snippet": "", "source": "dspace.mit.edu", "link": "https://dspace.mit.edu/bitstream/handle/1721.1/140609/10.1112-blms.12433.pdf?sequence=1&isAllowed=y", "content": "by C Gaetz · 2020 · Cited by 3 — [4] Christian Gaetz and Yibo Gao . A combinatorial sl2-action and the Sperner property for the weak order. Proc. Amer. Math. Soc., 148(1):1 ..."}
+{"idx": 9, "title": "AMS :: Proceedings of the American Mathematical Society", "date": "", "ddg_snippet": "by C Gaetz · 2022 · Cited by 4 — Diameters of graphs of reduced words and rank-two root subsystems . Authors: Christian Gaetz and Yibo Gao Journal: Proc. Amer. Math. Soc.", "subpage_snippet": "", "source": "www.ams.org", "link": "https://www.ams.org/proc/2022-150-08/S0002-9939-2022-15912-7/", "content": "by C Gaetz · 2022 · Cited by 4 — Diameters of graphs of reduced words and rank-two root subsystems . Authors: Christian Gaetz and Yibo Gao Journal: Proc. Amer. Math. Soc."}
diff --git a/data/sampled_jsons/Christian_Gaetz_mathematics_2024.jsonl b/data/sampled_jsons/Christian_Gaetz_mathematics_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..709b6cfc4cbdbb057bd4bfd3db0ab4931553a91f
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+++ b/data/sampled_jsons/Christian_Gaetz_mathematics_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Christian Gaetz's Homepage - Research - Google Sites", "date": "", "ddg_snippet": "Advances in Mathematics ( 2024 ). Promotion permutations for tableaux, with Oliver Pechenik, Stephan Pfannerer, Jessica Striker, and Josh Swanson. Combinatorial Theory ( 2024 ). Repeatable patterns and the maximum multiplicity of a generator in a reduced word, with Yibo Gao, Pakawut Jiradilok, Gleb Nenashev, and Alexander Postnikov.", "subpage_snippet": "", "source": "sites.google.com", "link": "https://sites.google.com/berkeley.edu/gaetz/research", "content": "Advances in Mathematics ( 2024 ). Promotion permutations for tableaux, with Oliver Pechenik, Stephan Pfannerer, Jessica Striker, and Josh Swanson. Combinatorial Theory ( 2024 ). Repeatable patterns and the maximum multiplicity of a generator in a reduced word, with Yibo Gao, Pakawut Jiradilok, Gleb Nenashev, and Alexander Postnikov."}
+{"idx": 1, "title": "Christian Gaetz | Department of Mathematics", "date": "", "ddg_snippet": "Effective Fall 2025, the Department of Mathematics is adopting new course numbering: Math 32 becomes Math 3; Math 1A becomes Math 51, and Math 1B becomes Math 52.", "subpage_snippet": "", "source": "math.berkeley.edu", "link": "https://math.berkeley.edu/people/christian-gaetz", "content": "Effective Fall 2025, the Department of Mathematics is adopting new course numbering: Math 32 becomes Math 3; Math 1A becomes Math 51, and Math 1B becomes Math 52."}
+{"idx": 2, "title": "Christian Gaetz's articles on arXiv", "date": "", "ddg_snippet": "Christian Gaetz , Yibo Gao Comments: v2: final version, to appear in Communications in Mathematical Physics Journal-ref: Communications in Mathematical Physics, Volume 406, article number 118, (2025) Subjects: Combinatorics (math.CO); Algebraic Geometry (math.AG); Probability (math.PR) [6] arXiv:2303.15577 [pdf, ps, other]", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/a/gaetz_c_1.html", "content": "Christian Gaetz , Yibo Gao Comments: v2: final version, to appear in Communications in Mathematical Physics Journal-ref: Communications in Mathematical Physics, Volume 406, article number 118, (2025) Subjects: Combinatorics (math.CO); Algebraic Geometry (math.AG); Probability (math.PR) [6] arXiv:2303.15577 [pdf, ps, other]"}
+{"idx": 3, "title": "Gaetz-Gao - univie.ac.at", "date": "", "ddg_snippet": "Séminaire Lotharingien de Combinatoire, 91B.7 ( 2024 ), 12 pp. Christian Gaetz and Yibo Gao Pattern Heights and The Minimal Power of q in a Kazhdan-Lusztig Polynomial Abstract. For w in the symmetric group, we use permutation patterns to provide an exact formula for the smallest positive power qh(w) appearing in the Kazhdan-Lusztig polynomial Pe ...", "subpage_snippet": "", "source": "www.mat.univie.ac.at", "link": "https://www.mat.univie.ac.at/~slc/wpapers/FPSAC2024/7.html", "content": "Séminaire Lotharingien de Combinatoire, 91B.7 ( 2024 ), 12 pp. Christian Gaetz and Yibo Gao Pattern Heights and The Minimal Power of q in a Kazhdan-Lusztig Polynomial Abstract. For w in the symmetric group, we use permutation patterns to provide an exact formula for the smallest positive power qh(w) appearing in the Kazhdan-Lusztig polynomial Pe ..."}
+{"idx": 4, "title": "FPSAC2024 Christian Gaetz - YouTube", "date": "", "ddg_snippet": "Speaker: Christian GaetzTitle: Pattern heights and the minimal power of q in a Kazhdan-Lusztig polynomialDate: 23.07. 2024", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=f12pBOgCJ1A", "content": "Speaker: Christian GaetzTitle: Pattern heights and the minimal power of q in a Kazhdan-Lusztig polynomialDate: 23.07. 2024"}
+{"idx": 5, "title": "Christian Gaetz's Homepage - Google Sites", "date": "", "ddg_snippet": "I am an assistant professor of mathematics at UC Berkeley. I was previously a Klarman Fellow at Cornell, mentored by Karola Meszaros and Allen Knutson and an NSF Postdoc at Harvard, mentored by Lauren Williams. I was a graduate student at MIT, advised by Alex Postnikov, and an undergrad at the University of Minnesota advised by Vic Reiner. My research interests lie in algebraic combinatorics ...", "subpage_snippet": "", "source": "sites.google.com", "link": "https://sites.google.com/berkeley.edu/gaetz/", "content": "I am an assistant professor of mathematics at UC Berkeley. I was previously a Klarman Fellow at Cornell, mentored by Karola Meszaros and Allen Knutson and an NSF Postdoc at Harvard, mentored by Lauren Williams. I was a graduate student at MIT, advised by Alex Postnikov, and an undergrad at the University of Minnesota advised by Vic Reiner. My research interests lie in algebraic combinatorics ..."}
+{"idx": 6, "title": "Real Math is Never Artificial: Professor Christian Gaetz", "date": "", "ddg_snippet": "Christian Gaetz rejects the notion that math is in a different category than other pursuits. He joined the Berkeley Mathematics Department faculty in 2024 and is quite vocal in his insistence that math should \"not be daunting to anyone.\"", "subpage_snippet": "", "source": "ls.berkeley.edu", "link": "https://ls.berkeley.edu/real-math-never-artificial-professor-christian-gaetz", "content": "Christian Gaetz rejects the notion that math is in a different category than other pursuits. He joined the Berkeley Mathematics Department faculty in 2024 and is quite vocal in his insistence that math should \"not be daunting to anyone.\""}
+{"idx": 7, "title": "Interlacing Triangles, Schubert Puzzles, and Graph Colorings Christian ...", "date": "", "ddg_snippet": "INTERLACING TRIANGLES, SCHUBERT PUZZLES, AND GRAPH COLORINGS CHRISTIAN GAETZ AND YIBO GAO", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2408.07863", "content": "INTERLACING TRIANGLES, SCHUBERT PUZZLES, AND GRAPH COLORINGS CHRISTIAN GAETZ AND YIBO GAO"}
+{"idx": 8, "title": "The Department of Mathematics welcomes Dr. Christian Gaetz as its ...", "date": "", "ddg_snippet": "We are very excited to announce that Dr. Christian Gaetz will be joining the Department of Mathematics as our newest faculty member this Fall. Dr. Gaetz works in Combinatorics and received his PhD in 2021 from MIT under the supervision of Alexander Postnikov. After receiving his PhD, he held postdoc positions at Harvard and then Cornell. Dr. Gaetz's work has been recognized with various honors ...", "subpage_snippet": "", "source": "math.berkeley.edu", "link": "https://math.berkeley.edu/news/department-mathematics-welcomes-dr-christian-gaetz-its-newest-faculty-member", "content": "We are very excited to announce that Dr. Christian Gaetz will be joining the Department of Mathematics as our newest faculty member this Fall. Dr. Gaetz works in Combinatorics and received his PhD in 2021 from MIT under the supervision of Alexander Postnikov. After receiving his PhD, he held postdoc positions at Harvard and then Cornell. Dr. Gaetz's work has been recognized with various honors ..."}
+{"idx": 9, "title": "Christian Gaetz | Department of Mathematics - Cornell University", "date": "", "ddg_snippet": "Christian Gaetz Klarman Fellow Research Focus Research Area: Algebraic combinatorics I am interested in combinatorial aspects of representation theory and algebraic geometry, particularly topics related to Coxeter groups and the weak and strong Bruhat orders on them. Publications Balance constants for Coxeter groups, with Yibo Gao. Preprint (2020).", "subpage_snippet": "", "source": "math.cornell.edu", "link": "https://math.cornell.edu/christian-gaetz-0", "content": "Christian Gaetz Klarman Fellow Research Focus Research Area: Algebraic combinatorics I am interested in combinatorial aspects of representation theory and algebraic geometry, particularly topics related to Coxeter groups and the weak and strong Bruhat orders on them. Publications Balance constants for Coxeter groups, with Yibo Gao. Preprint (2020)."}
diff --git a/data/sampled_jsons/CoPINN_Cognitive_Physics-Informed_Neural_Networks_equation_weighting.jsonl b/data/sampled_jsons/CoPINN_Cognitive_Physics-Informed_Neural_Networks_equation_weighting.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5f5b0ef6c008fb8a65a573bca175aeb5c9efeba0
--- /dev/null
+++ b/data/sampled_jsons/CoPINN_Cognitive_Physics-Informed_Neural_Networks_equation_weighting.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "CoPINN : Cognitive Physics - Informed Neural Networks", "date": "", "ddg_snippet": "Sample points difficulty. CoPINN : Cognitive Physics - Informed Neural Networks . Physics - Informed Neural Networks (PINN) (Raissi et al., 2019) is a machine learning model that integrates physical laws into the training process of neural networks .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=4vAa0A98xI", "content": "Sample points difficulty. CoPINN : Cognitive Physics - Informed Neural Networks . Physics - Informed Neural Networks (PINN) (Raissi et al., 2019) is a machine learning model that integrates physical laws into the training process of neural networks ."}
+{"idx": 1, "title": "GitHub - siyuancncd/ CoPINN : This is the official implementation of...", "date": "", "ddg_snippet": "\" CoPINN : Cognitive Physics - Informed Neural Network \". (ICML 2025, Spotlight (acc rate = 2.6%), JAX Code).Extensive experiments demonstrate that our CoPINN achieves state-of-the-art performance, particularly significantly reducing prediction errors in stubborn regions.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/siyuancncd/CoPINN", "content": "\" CoPINN : Cognitive Physics - Informed Neural Network \". (ICML 2025, Spotlight (acc rate = 2.6%), JAX Code).Extensive experiments demonstrate that our CoPINN achieves state-of-the-art performance, particularly significantly reducing prediction errors in stubborn regions."}
+{"idx": 2, "title": "Physics Informed Neural Networks , A Proven PINNs Guide 2025", "date": "", "ddg_snippet": "Physics informed neural networks made clear. Learn how PINNs solve PDEs, when to use them, starter code tips, and a timely case study on DeepMind’s unstable singularities to guide real projects.", "subpage_snippet": "", "source": "binaryverseai.com", "link": "https://binaryverseai.com/physics-informed-neural-networks-pinns-explained/", "content": "Physics informed neural networks made clear. Learn how PINNs solve PDEs, when to use them, starter code tips, and a timely case study on DeepMind’s unstable singularities to guide real projects."}
+{"idx": 3, "title": "Dynamic Weight Strategy of Physics - Informed Neural Networks for...", "date": "", "ddg_snippet": "This paper introduces a dynamic weight strategy for physics - informed neural networks (dwPINNs) to balance the contribution of each loss item to the network . The mechanism of weight update in this paper is completely different from other PINNs literature.", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC9497516/", "content": "This paper introduces a dynamic weight strategy for physics - informed neural networks (dwPINNs) to balance the contribution of each loss item to the network . The mechanism of weight update in this paper is completely different from other PINNs literature."}
+{"idx": 4, "title": "Coupled Physics - Informed Neural Network for... - TechRxiv", "date": "", "ddg_snippet": "This work proposes a neural network -based method, coupled Physics Informed Neural Network ( coPINN ), as a fast and accurate mesh-free solution for predicting the thermomechanical states during the AFSD process.", "subpage_snippet": "", "source": "www.techrxiv.org", "link": "https://www.techrxiv.org/users/916234/articles/1290851-coupled-physics-informed-neural-network-for-multi-physics-modeling-in-additive-friction-stir-deposition", "content": "This work proposes a neural network -based method, coupled Physics Informed Neural Network ( coPINN ), as a fast and accurate mesh-free solution for predicting the thermomechanical states during the AFSD process."}
+{"idx": 5, "title": "Physics - Informed Neural Networks Solve Maxwell’s Equations With...", "date": "", "ddg_snippet": "Physics - informed neural networks (PINNs), a developing computational methodology, solve partial differential equations by integrating known physical laws directly into the neural network ’s training process.", "subpage_snippet": "", "source": "quantumzeitgeist.com", "link": "https://quantumzeitgeist.com/physics-informed-neural-networks-solve-maxwells-equations-with-enhanced-accuracy/", "content": "Physics - informed neural networks (PINNs), a developing computational methodology, solve partial differential equations by integrating known physical laws directly into the neural network ’s training process."}
+{"idx": 6, "title": "Physics - informed attention-based neural network for hyperbolic...", "date": "", "ddg_snippet": "Physics - informed neural networks (PINNs) have enabled significant improvements in modelling physical processes described by partial differential equations (PDEs) and are in principle capable of modeling a large variety of differential equations .", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41598-022-11058-2?error=cookies_not_supported&code=a1ec839b-ab88-4cdb-b39c-93dee7c47904", "content": "Physics - informed neural networks (PINNs) have enabled significant improvements in modelling physical processes described by partial differential equations (PDEs) and are in principle capable of modeling a large variety of differential equations ."}
+{"idx": 7, "title": "Solving stiff ordinary differential equations using physics informed ...", "date": "", "ddg_snippet": "Solving sti ordinary dierential equations using physics informed neural networks (PINNs): simple recipes to improve.", "subpage_snippet": "", "source": "hal.science", "link": "https://hal.science/hal-04072322/document", "content": "Solving sti ordinary dierential equations using physics informed neural networks (PINNs): simple recipes to improve."}
+{"idx": 8, "title": "[2210.12522] Physics - Informed Neural Networks as Solvers for the...", "date": "", "ddg_snippet": "We demonstrate the utility of physics - informed neural networks (PINNs) as solvers for the non-relativistic, time-dependent Schrödinger equation .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2210.12522", "content": "We demonstrate the utility of physics - informed neural networks (PINNs) as solvers for the non-relativistic, time-dependent Schrödinger equation ."}
+{"idx": 9, "title": "Solving 3D Elasticity Problems with Physics - Informed Neural ...", "date": "", "ddg_snippet": "The neural network is a feedforward network with multiple hidden layers. It takes the voxel coordinates, force direction, and voxel values as input and outputs the predicted displacements in 3D space. 3. Physics - Informed Loss Function.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@harunijaz/solving-3d-elasticity-problems-with-physics-informed-neural-networks-pinns-a-mesh-free-8685a1acd8d8", "content": "The neural network is a feedforward network with multiple hidden layers. It takes the voxel coordinates, force direction, and voxel values as input and outputs the predicted displacements in 3D space. 3. Physics - Informed Loss Function."}
diff --git a/data/sampled_jsons/CoPINN_equation_8_9_vie_vih_weight_calculation_formula_epoch_Ne_year_2024.jsonl b/data/sampled_jsons/CoPINN_equation_8_9_vie_vih_weight_calculation_formula_epoch_Ne_year_2024.jsonl
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index 0000000000000000000000000000000000000000..a529efc2c707f6799b2637f9bbe0644215d23e85
--- /dev/null
+++ b/data/sampled_jsons/CoPINN_equation_8_9_vie_vih_weight_calculation_formula_epoch_Ne_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Systematic review of the relationships between objectively ...", "date": "", "ddg_snippet": "by VJ Poitras · 2016 · Cited by 2620 — The purpose of this systematic review was to examine the relationships between objectively measured PA (total and all intensities) and health indicators in ...", "subpage_snippet": "", "source": "cdnsciencepub.com", "link": "https://cdnsciencepub.com/doi/10.1139/apnm-2015-0663", "content": "by VJ Poitras · 2016 · Cited by 2620 — The purpose of this systematic review was to examine the relationships between objectively measured PA (total and all intensities) and health indicators in ..."}
+{"idx": 1, "title": "ERS monograph - ERS Publications", "date": "", "ddg_snippet": "This book is one in a series of ERS Monographs. Each individual issue provides a comprehensive overview of one specific clinical area of.", "subpage_snippet": "", "source": "publications.ersnet.org", "link": "https://publications.ersnet.org/binary/ersworks/a22f3514f3c947d7/c7db88ac908eabb96502fc6bd07f024efe69d403285e77305fde8a75a006da5c/9781849841733.pdf", "content": "This book is one in a series of ERS Monographs. Each individual issue provides a comprehensive overview of one specific clinical area of."}
+{"idx": 2, "title": "DPO Trainer", "date": "", "ddg_snippet": "Quick start. This example demonstrates how to train a model using the DPO method . We use the Qwen 0.5B model as the base model. We use ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/docs/trl/en/dpo_trainer", "content": "Quick start. This example demonstrates how to train a model using the DPO method . We use the Qwen 0.5B model as the base model. We use ..."}
+{"idx": 3, "title": "Data Science: Foundations and Applications", "date": "", "ddg_snippet": "10 Jun 2025 — The series Lecture Notes in Artificial Intelligence (LNAI) was established in 1988 as a topical subseries of LNCS devoted to artificial ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/978-981-96-8298-0.pdf", "content": "10 Jun 2025 — The series Lecture Notes in Artificial Intelligence (LNAI) was established in 1988 as a topical subseries of LNCS devoted to artificial ..."}
+{"idx": 4, "title": "ePresentations - 2024 - European Journal of Neurology", "date": "", "ddg_snippet": "28 Jun 2024 — We have shown associations between use of CNS depressant drugs (CNSDs) and cognitive function, quality of life, multimorbidity and mortality.", "subpage_snippet": "", "source": "onlinelibrary.wiley.com", "link": "https://onlinelibrary.wiley.com/doi/10.1111/ene.16338", "content": "28 Jun 2024 — We have shown associations between use of CNS depressant drugs (CNSDs) and cognitive function, quality of life, multimorbidity and mortality."}
+{"idx": 5, "title": "SICon 2025 The 3rd Workshop on Social Influence in ...", "date": "", "ddg_snippet": "31 Jul 2025 — SICon 2025 includes keynote talks, panel discussions, poster sessions, and lightning talks for accepted papers.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/anthology-files/pdf/sicon/2025.sicon-1.pdf", "content": "31 Jul 2025 — SICon 2025 includes keynote talks, panel discussions, poster sessions, and lightning talks for accepted papers."}
+{"idx": 6, "title": "Times are changing: Working time in 14 industrialised countries", "date": "", "ddg_snippet": "8 / 9 , August/September, Brussels. European Trade Union Institute (1979): Reduction of Working Hours in Western Europe. Part 1: The Present Situation ...", "subpage_snippet": "", "source": "webapps.ilo.org", "link": "https://webapps.ilo.org/public/libdoc/ilo/1994/94B09_66_engl.pdf", "content": "8 / 9 , August/September, Brussels. European Trade Union Institute (1979): Reduction of Working Hours in Western Europe. Part 1: The Present Situation ..."}
+{"idx": 7, "title": "Reshaping - TOMORROW", "date": "", "ddg_snippet": "by ISA Ready — The World Bank does not guarantee the accuracy of the data included in this work.", "subpage_snippet": "", "source": "documents.worldbank.org", "link": "https://documents.worldbank.org/curated/en/311391468101989889/pdf/654200PUB00PUB0ing0Tomorrow0English.pdf", "content": "by ISA Ready — The World Bank does not guarantee the accuracy of the data included in this work."}
+{"idx": 8, "title": "Lecture Notes in Computer Science 2199", "date": "", "ddg_snippet": "This method was applied earlier in fields such as industry and economics, however later it appeared in medical research as well [4,5,6,7, 8 , 9 ,10,11,12]. 2.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/3-540-45497-7.pdf", "content": "This method was applied earlier in fields such as industry and economics, however later it appeared in medical research as well [4,5,6,7, 8 , 9 ,10,11,12]. 2."}
+{"idx": 9, "title": "INFORMATION TO USERS", "date": "", "ddg_snippet": "by PP Beggerly · 1990 · Cited by 19 — Higher quality 6\"x 9 \" black and white photographic prints are available for any photographs or iilustrations appearing in this copy for an additional charge.", "subpage_snippet": "", "source": "scholarspace.manoa.hawaii.edu", "link": "https://scholarspace.manoa.hawaii.edu/bitstreams/0f0078cd-be6c-4936-b4db-ab5a4447752c/download", "content": "by PP Beggerly · 1990 · Cited by 19 — Higher quality 6\"x 9 \" black and white photographic prints are available for any photographs or iilustrations appearing in this copy for an additional charge."}
diff --git a/data/sampled_jsons/CoPINN_equation_8_equation_9_vie_vih_weights_epoch_i_total_epochs_Ne.jsonl b/data/sampled_jsons/CoPINN_equation_8_equation_9_vie_vih_weights_epoch_i_total_epochs_Ne.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0091114d270b165a66401fa952f64438c45afda7
--- /dev/null
+++ b/data/sampled_jsons/CoPINN_equation_8_equation_9_vie_vih_weights_epoch_i_total_epochs_Ne.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "CoPINN: Cognitive Physics-Informed Neural Networks - OpenReview", "date": "", "ddg_snippet": "Compute the weight assigned to the easiest sample point in epoch i , i.e., vi e , by Equation (8 ). Compute the weight assigned to the hardest sample point in epoch i , i.e., vi h , by Equation (9 ).", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=4vAa0A98xI", "content": "Compute the weight assigned to the easiest sample point in epoch i , i.e., vi e , by Equation (8 ). Compute the weight assigned to the hardest sample point in epoch i , i.e., vi h , by Equation (9 )."}
+{"idx": 1, "title": "CoPINN: Cognitive Physics-Informed Neural Networks", "date": "", "ddg_snippet": "16 Jul 2025 — We propose a novel framework named Cognitive Physics-Informed Neural Networks ( CoPINN ) that imitates the human cognitive learning manner from easy to hard.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46458", "content": "16 Jul 2025 — We propose a novel framework named Cognitive Physics-Informed Neural Networks ( CoPINN ) that imitates the human cognitive learning manner from easy to hard."}
+{"idx": 2, "title": "Systematic review of the relationships between objectively ...", "date": "", "ddg_snippet": "by VJ Poitras · 2016 · Cited by 2619 — The purpose of this systematic review was to examine the relationships between objectively measured PA ( total and all intensities) and health indicators in ...", "subpage_snippet": "", "source": "cdnsciencepub.com", "link": "https://cdnsciencepub.com/doi/10.1139/apnm-2015-0663", "content": "by VJ Poitras · 2016 · Cited by 2619 — The purpose of this systematic review was to examine the relationships between objectively measured PA ( total and all intensities) and health indicators in ..."}
+{"idx": 3, "title": "Download book PDF", "date": "", "ddg_snippet": "This is evident from the high number of submissions and the highly selective nature of the review process. Out of 215 submissions, only 38 were selected as full .", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/3-540-36175-8.pdf", "content": "This is evident from the high number of submissions and the highly selective nature of the review process. Out of 215 submissions, only 38 were selected as full ."}
+{"idx": 4, "title": "Patriots Drop Heartbreaker to Coppin St. in Five Sets", "date": "", "ddg_snippet": "5 days ago · FAIRFAX, Va. – George Mason women's volleyball pushed Coppin State to the limit on Thursday night at the Recreation & Athletic Complex, but the Eagles escaped with a 3-2 decision (25-23, 19-25, 25-18, 24-26, 15-11). The Patriots (6-4) held their own in nearly every statistical category, finishing with 56 kills and 15.0 team blocks, but Coppin State (6-7) found just enough late momentum to ...", "subpage_snippet": "", "source": "gomason.com", "link": "https://gomason.com/news/2025/9/18/womens-volleyball-patriots-drop-heartbreaker-to-coppin-st-in-five-sets.aspx", "content": "5 days ago · FAIRFAX, Va. – George Mason women's volleyball pushed Coppin State to the limit on Thursday night at the Recreation & Athletic Complex, but the Eagles escaped with a 3-2 decision (25-23, 19-25, 25-18, 24-26, 15-11). The Patriots (6-4) held their own in nearly every statistical category, finishing with 56 kills and 15.0 team blocks, but Coppin State (6-7) found just enough late momentum to ..."}
+{"idx": 5, "title": "Homepage | Coppin State University", "date": "", "ddg_snippet": "The BE MORE Capital Campaign is a multi-year fundraising initiative that will permanently transform our university and surrounding community. With Coppin's most aspiring goal ever - to raise $25 million by 2025 - now more than ever, we need your investment in the lasting legacy of Coppin State University !", "subpage_snippet": "", "source": "www.coppin.edu", "link": "https://www.coppin.edu/", "content": "The BE MORE Capital Campaign is a multi-year fundraising initiative that will permanently transform our university and surrounding community. With Coppin's most aspiring goal ever - to raise $25 million by 2025 - now more than ever, we need your investment in the lasting legacy of Coppin State University !"}
+{"idx": 6, "title": "Coppin State University Athletics - Official Athletics Website", "date": "", "ddg_snippet": "We, along with our service providers and other third parties use cookies and other analytics, advertising, and tracking technologies on this site. Your information, including personal information and interactions with this site, may be monitored, recorded, or collected through these tools and further used or disclosed by us, our service providers and authorized third parties. To opt-out click ...", "subpage_snippet": "", "source": "coppinstatesports.com", "link": "https://coppinstatesports.com/", "content": "We, along with our service providers and other third parties use cookies and other analytics, advertising, and tracking technologies on this site. Your information, including personal information and interactions with this site, may be monitored, recorded, or collected through these tools and further used or disclosed by us, our service providers and authorized third parties. To opt-out click ..."}
+{"idx": 7, "title": "Coppin Academy High School", "date": "", "ddg_snippet": "Dear Coppin Academy Students, Families, and Staff, I am profoundly honored and overjoyed to step into the role of Principal at Coppin Academy High School. Having served this remarkable school community as Assistant Principal over the past few years—and as an educator here since 2016— I h...", "subpage_snippet": "", "source": "www.coppinacademy.org", "link": "https://www.coppinacademy.org/", "content": "Dear Coppin Academy Students, Families, and Staff, I am profoundly honored and overjoyed to step into the role of Principal at Coppin Academy High School. Having served this remarkable school community as Assistant Principal over the past few years—and as an educator here since 2016— I h..."}
+{"idx": 8, "title": "Eagle Achievement Center - Coppin State University", "date": "", "ddg_snippet": "The Eagle Achievement Center is here for you. Its vision is to transform Coppin students’ lives through student-centered holistic development and empowerment.", "subpage_snippet": "", "source": "www.coppin.edu", "link": "https://www.coppin.edu/eagle-achievement-center", "content": "The Eagle Achievement Center is here for you. Its vision is to transform Coppin students’ lives through student-centered holistic development and empowerment."}
+{"idx": 9, "title": "EagleLINKS Portal | Coppin State University", "date": "", "ddg_snippet": "Coppin State University — Anywhere, anytime, online access.", "subpage_snippet": "", "source": "eaglelinks.coppin.edu", "link": "https://eaglelinks.coppin.edu/", "content": "Coppin State University — Anywhere, anytime, online access."}
diff --git a/data/sampled_jsons/CogAgent_Hong_et_al_visual_language_model_GUI_agent.jsonl b/data/sampled_jsons/CogAgent_Hong_et_al_visual_language_model_GUI_agent.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d2c7d8071db801aae1343d252b0868ecd98a46f2
--- /dev/null
+++ b/data/sampled_jsons/CogAgent_Hong_et_al_visual_language_model_GUI_agent.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "CogAgent: A Visual Language Model for GUI Agents", "date": "", "ddg_snippet": "by W Hong · 2023 · Cited by 489 — In this paper, we introduce CogAgent , an 18-billion-parameter visual language model (VLM) specializing in GUI understanding and navigation.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2312.08914", "content": "by W Hong · 2023 · Cited by 489 — In this paper, we introduce CogAgent , an 18-billion-parameter visual language model (VLM) specializing in GUI understanding and navigation."}
+{"idx": 1, "title": "CogAgent: A Visual Language Model for GUI Agents", "date": "", "ddg_snippet": "by W Hong · 2024 · Cited by 485 — The decoder, a pre-trained language model , is en- hanced with a visual expert module introduced by Wang et al . [38] to facilitate a deep fusion of visual and ... 10 pages", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2024/papers/Hong_CogAgent_A_Visual_Language_Model_for_GUI_Agents_CVPR_2024_paper.pdf", "content": "by W Hong · 2024 · Cited by 485 — The decoder, a pre-trained language model , is en- hanced with a visual expert module introduced by Wang et al . [38] to facilitate a deep fusion of visual and ... 10 pages"}
+{"idx": 2, "title": "CogAgent: A Visual Language Model for GUI Agents", "date": "", "ddg_snippet": "In this paper, we introduce CogAgent , an 18-billion-parameter visual language model (VLM) specializing in GUI understanding and navigation.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2312.08914v2", "content": "In this paper, we introduce CogAgent , an 18-billion-parameter visual language model (VLM) specializing in GUI understanding and navigation."}
+{"idx": 3, "title": "Cogagent: A Visual Language Model For Gui Agents | PDF", "date": "", "ddg_snippet": "CogAgent is an 18-billion-parameter visual language model designed to enhance GUI understanding and navigation, outperforming traditional LLM-based methods ...", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/862894150/2312-08914v3", "content": "CogAgent is an 18-billion-parameter visual language model designed to enhance GUI understanding and navigation, outperforming traditional LLM-based methods ..."}
+{"idx": 4, "title": "From General Vision Language Model to Versatile GUI Agent", "date": "", "ddg_snippet": "by W Chen · 2025 · Cited by 3 — Utilizing Graphic User Interfaces (GUIs) for human-computer interaction is essential for ac- cessing various digital tools. Recent advance-. 24 pages", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.acl-long.1065.pdf", "content": "by W Chen · 2025 · Cited by 3 — Utilizing Graphic User Interfaces (GUIs) for human-computer interaction is essential for ac- cessing various digital tools. Recent advance-. 24 pages"}
+{"idx": 5, "title": "CogAgent-A Visual Language Model For GUI Agents", "date": "", "ddg_snippet": "CogAgent-A Visual Language Model for GUI Agents - Free download as PDF File (.pdf), Text File (.txt) or read online for free.", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/814127261/CogAgent-A-Visual-Language-Model-for-GUI-Agents", "content": "CogAgent-A Visual Language Model for GUI Agents - Free download as PDF File (.pdf), Text File (.txt) or read online for free."}
+{"idx": 6, "title": "zai-org/CogVLM: a state-of-the-art-level open visual ...", "date": "", "ddg_snippet": "CogAgent is an image understanding model developed based on CogVLM. It features visual -based GUI Agent capabilities and has further enhancements in image ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/zai-org/CogVLM", "content": "CogAgent is an image understanding model developed based on CogVLM. It features visual -based GUI Agent capabilities and has further enhancements in image ..."}
+{"idx": 7, "title": "GUI Agents: A Survey", "date": "", "ddg_snippet": "by D Nguyen · 2025 · Cited by 39 — CogAgent ( Hong et al ., 2023) employs a high-resolution cross-module ... Showui: One · vision- language -action model for gui visual agent . 17 pages", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.findings-acl.1158.pdf", "content": "by D Nguyen · 2025 · Cited by 39 — CogAgent ( Hong et al ., 2023) employs a high-resolution cross-module ... Showui: One · vision- language -action model for gui visual agent . 17 pages"}
+{"idx": 8, "title": "LLM-Brained GUI Agents", "date": "", "ddg_snippet": "13 Aug 2025 — Visual Encoders: Pioneering models such as CogAgent ( Hong et al ., 2023) and MobileFlow (Nong et al ., 5 Jul 2024) implement dual-branch or hybrid ...", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/topics/llm-brained-gui-agents", "content": "13 Aug 2025 — Visual Encoders: Pioneering models such as CogAgent ( Hong et al ., 2023) and MobileFlow (Nong et al ., 5 Jul 2024) implement dual-branch or hybrid ..."}
+{"idx": 9, "title": "A Vision Language Model-driven Computer Control Agent", "date": "", "ddg_snippet": "by R Niu · Cited by 68 — Apart from GPT-4V, we select several recently released SoTA. VLMs for testing, including LLaVA-1.5 [Liu et al ., 2023a] and CogAgent [ Hong et al ., 2023].", "subpage_snippet": "", "source": "www.ijcai.org", "link": "https://www.ijcai.org/proceedings/2024/0711.pdf", "content": "by R Niu · Cited by 68 — Apart from GPT-4V, we select several recently released SoTA. VLMs for testing, including LLaVA-1.5 [Liu et al ., 2023a] and CogAgent [ Hong et al ., 2023]."}
diff --git a/data/sampled_jsons/Contrastive_CRL_MCC_synthetic_ablation_real_data_0.95_0.15.jsonl b/data/sampled_jsons/Contrastive_CRL_MCC_synthetic_ablation_real_data_0.95_0.15.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b77ba1fb8bcd3002fc279a7fc37a4804404a22cd
--- /dev/null
+++ b/data/sampled_jsons/Contrastive_CRL_MCC_synthetic_ablation_real_data_0.95_0.15.jsonl
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+{"idx": 0, "title": "Computational Methods for the Analysis of Genomic Data and ...", "date": "", "ddg_snippet": "predictions and MCC = −1 representing completely −ve correlation between the predictions and actual classes. MCC works better than other measures such as ...", "subpage_snippet": "", "source": "mdpi-res.com", "link": "https://mdpi-res.com/bookfiles/book/3374/Computational_Methods_for_the_Analysis_of_Genomic_Data_and_Biological_Processes.pdf?v=1754960645", "content": "predictions and MCC = −1 representing completely −ve correlation between the predictions and actual classes. MCC works better than other measures such as ..."}
+{"idx": 1, "title": "identification of nonparametric dynamic causal structure ...", "date": "", "ddg_snippet": "by M Fu · 2025 — We first formally define the 3-measurement model, and describe how observed variables and latent variables are causally-related in data ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2501.12500", "content": "by M Fu · 2025 — We first formally define the 3-measurement model, and describe how observed variables and latent variables are causally-related in data ..."}
+{"idx": 2, "title": "Sunday, April 27, 2025", "date": "", "ddg_snippet": "27 Apr 2025 — This study investigates the real -world rates of genetics referrals in eligible cancer patients at Singapore's largest healthcare cluster using ... 6,076 pages", "subpage_snippet": "", "source": "www.aacr.org", "link": "https://www.aacr.org/wp-content/uploads/2025/05/AACR2025_Proceedings_050725.pdf", "content": "27 Apr 2025 — This study investigates the real -world rates of genetics referrals in eligible cancer patients at Singapore's largest healthcare cluster using ... 6,076 pages"}
+{"idx": 3, "title": "Impurities and defects in, and isotope compositions of, ...", "date": "", "ddg_snippet": "A novel off-line laser sampling technique has been applied to analyzing high purity diamonds. Quantitative trace element data from high-purity gem diamonds from ...", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/pdf/impurities-and-defects-in-and-isotope-compositions-of-1kt50j4rru.pdf", "content": "A novel off-line laser sampling technique has been applied to analyzing high purity diamonds. Quantitative trace element data from high-purity gem diamonds from ..."}
+{"idx": 4, "title": "Biology AI Models", "date": "", "ddg_snippet": "... Data Bank),\"\"\"We used the original DNCON dataset consisting of 1426 proteins having length between 30 and 300 residues curated before the CASP10 experiment ...", "subpage_snippet": "", "source": "epoch.ai", "link": "https://epoch.ai/data/generated/biology_ai_models.csv", "content": "... Data Bank),\"\"\"We used the original DNCON dataset consisting of 1426 proteins having length between 30 and 300 residues curated before the CASP10 experiment ..."}
+{"idx": 5, "title": "Autophagy-Dependent Generation of Free Fatty Acids Is ...", "date": "", "ddg_snippet": "by T Riffelmacher · 2017 · Cited by 316 — To study the role of autophagy in early granulopoiesis in vivo, we used mice with a conditional deletion of the essential autophagy machinery ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC5610174/", "content": "by T Riffelmacher · 2017 · Cited by 316 — To study the role of autophagy in early granulopoiesis in vivo, we used mice with a conditional deletion of the essential autophagy machinery ..."}
+{"idx": 6, "title": "colorectal cancer", "date": "", "ddg_snippet": "19 Jan 2020 — Background: Rates and survival outcomes for second-line therapy for mCRC for OA vs. YA are poorly understood. Methods: Pts with available ...", "subpage_snippet": "", "source": "s3.amazonaws.com", "link": "https://s3.amazonaws.com/files.oncologymeetings.org/prod/s3fs-public/2020-01/GI20-COLORECTAL-CANCER.pdf?null", "content": "19 Jan 2020 — Background: Rates and survival outcomes for second-line therapy for mCRC for OA vs. YA are poorly understood. Methods: Pts with available ..."}
+{"idx": 7, "title": "The 76th Annual Congress of the Japan Society of ...", "date": "", "ddg_snippet": "We recently described reduced fetal growth in physiologically and nutritionally stable extremely preterm ovine fetuses undergo- ing artificial placenta therapy.", "subpage_snippet": "", "source": "obgyn.onlinelibrary.wiley.com", "link": "https://obgyn.onlinelibrary.wiley.com/doi/pdf/10.1111/jog.16282", "content": "We recently described reduced fetal growth in physiologically and nutritionally stable extremely preterm ovine fetuses undergo- ing artificial placenta therapy."}
+{"idx": 8, "title": "Mps1 Regulates Kinetochore-Microtubule Attachment Stability ...", "date": "", "ddg_snippet": "The spindle assembly checkpoint kinase Mps1 not only inhibits anaphase but also corrects erro- neous attachments that could lead to missegregation.", "subpage_snippet": "", "source": "www.cell.com", "link": "https://www.cell.com/cms/10.1016/j.devcel.2017.03.025/attachment/a67cfe70-dc22-4ab1-ac64-4b30d86f3452/mmc3.pdf", "content": "The spindle assembly checkpoint kinase Mps1 not only inhibits anaphase but also corrects erro- neous attachments that could lead to missegregation."}
+{"idx": 9, "title": "Capillary function in patients with chronic venous insufficiency", "date": "", "ddg_snippet": "It has been demonstrated in normal subjects that persistently raised venous pressure results in trapping of leucocytes in the peripheral circulation'.", "subpage_snippet": "", "source": "academic.oup.com", "link": "https://academic.oup.com/bjs/article-pdf/75/6/597/59386982/bjs1800750635.pdf", "content": "It has been demonstrated in normal subjects that persistently raised venous pressure results in trapping of leucocytes in the peripheral circulation'."}
diff --git a/data/sampled_jsons/Corollary_2_data_perturbation_strategy_MNIST_Wasserstein_distances_sparsely_connected_model.jsonl b/data/sampled_jsons/Corollary_2_data_perturbation_strategy_MNIST_Wasserstein_distances_sparsely_connected_model.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a2c11b4ce32caabaaf99ef6b580222876d355d13
--- /dev/null
+++ b/data/sampled_jsons/Corollary_2_data_perturbation_strategy_MNIST_Wasserstein_distances_sparsely_connected_model.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[1909.13082] Wasserstein-2 Generative Networks - arXiv.org", "date": "", "ddg_snippet": "We propose a novel end-to-end non-minimax algorithm for training optimal transport mappings for the quadratic cost (Wasserstein-2 distance ). The algorithm uses input convex neural networks and a cycle-consistency regularization to approximate Wasserstein-2 distance . In contrast to popular entropic and quadratic regularizers, cycle-consistency does not introduce bias and scales well to high ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1909.13082", "content": "We propose a novel end-to-end non-minimax algorithm for training optimal transport mappings for the quadratic cost (Wasserstein-2 distance ). The algorithm uses input convex neural networks and a cycle-consistency regularization to approximate Wasserstein-2 distance . In contrast to popular entropic and quadratic regularizers, cycle-consistency does not introduce bias and scales well to high ..."}
+{"idx": 1, "title": "Wasserstein-2 Generative Networks - GitHub", "date": "", "ddg_snippet": "This is the official Python implementation of the ICLR 2021 paper Wasserstein-2 Generative Networks (paper on openreview) by Alexander Korotin, Vahe Egizarian, Arip Asadulaev, Alexander Safin and Evgeny Burnaev. The repository contains reproducible PyTorch source code for computing optimal transport maps (and distances ) in high dimensions via the end-to-end non-minimax method (proposed in the ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/iamalexkorotin/Wasserstein2GenerativeNetworks", "content": "This is the official Python implementation of the ICLR 2021 paper Wasserstein-2 Generative Networks (paper on openreview) by Alexander Korotin, Vahe Egizarian, Arip Asadulaev, Alexander Safin and Evgeny Burnaev. The repository contains reproducible PyTorch source code for computing optimal transport maps (and distances ) in high dimensions via the end-to-end non-minimax method (proposed in the ..."}
+{"idx": 2, "title": "Wasserstein task embedding for measuring task similarities", "date": "", "ddg_snippet": "Then, we define the distance between two datasets as the 2-Wasserstein distance between their updated samples. Lastly, we leverage the 2-Wasserstein embedding framework to embed tasks into a vector space in which the Euclidean distance between the embedded points approximates the proposed 2-Wasserstein distance between tasks.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0893608024007202", "content": "Then, we define the distance between two datasets as the 2-Wasserstein distance between their updated samples. Lastly, we leverage the 2-Wasserstein embedding framework to embed tasks into a vector space in which the Euclidean distance between the embedded points approximates the proposed 2-Wasserstein distance between tasks."}
+{"idx": 3, "title": "Wasserstein Distance to Detect Data Drift in ML Models", "date": "", "ddg_snippet": "Data drift is a common problem in machine learning (ML) models , occurring when the distribution of the data used to train the model changes over time. This can lead to decreased model performance ...", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/pulse/wasserstein-distance-detect-data-drift-ml-models-amit-tiwari-eb2ke", "content": "Data drift is a common problem in machine learning (ML) models , occurring when the distribution of the data used to train the model changes over time. This can lead to decreased model performance ..."}
+{"idx": 4, "title": "GANs — Wasserstein GAN with MNIST (Part 6) - Medium", "date": "", "ddg_snippet": "Wasserstein GAN (WGAN) proposes a new cost function using Wasserstein distance that has a smoother gradient everywhere. This model is proposed to measure the difference between the data distributions of real and generated images.", "subpage_snippet": "", "source": "mafda.medium.com", "link": "https://mafda.medium.com/gans-wasserstein-gan-with-mnist-part-6-7f796a0cea47", "content": "Wasserstein GAN (WGAN) proposes a new cost function using Wasserstein distance that has a smoother gradient everywhere. This model is proposed to measure the difference between the data distributions of real and generated images."}
+{"idx": 5, "title": "PDF Wasserstein Clustering", "date": "", "ddg_snippet": "Two criteria are studied: (i) the first generatively minimizes the Wasserstein distance between data and cluster-separated generated data inspired by the GANs success and (ii) the second discriminatively maximizes over all partitions the Wasserstein distances between the associated groups.", "subpage_snippet": "", "source": "harchaoui.org", "link": "https://harchaoui.org/warith/phd/wasserstein-clustering-phd-warith-harchaoui.pdf", "content": "Two criteria are studied: (i) the first generatively minimizes the Wasserstein distance between data and cluster-separated generated data inspired by the GANs success and (ii) the second discriminatively maximizes over all partitions the Wasserstein distances between the associated groups."}
+{"idx": 6, "title": "PDF Learning with a Wasserstein Loss - NeurIPS", "date": "", "ddg_snippet": "Although optimizing with respect to the exact Wasserstein distance is costly, recent work has described a regularized approximation that is efficiently computed. We describe an efficient learning algorithm based on this regularization, as well as a novel extension of the Wasserstein distance from prob-ability measures to unnormalized measures.", "subpage_snippet": "", "source": "papers.neurips.cc", "link": "https://papers.neurips.cc/paper/5679-learning-with-a-wasserstein-loss.pdf", "content": "Although optimizing with respect to the exact Wasserstein distance is costly, recent work has described a regularized approximation that is efficiently computed. We describe an efficient learning algorithm based on this regularization, as well as a novel extension of the Wasserstein distance from prob-ability measures to unnormalized measures."}
+{"idx": 7, "title": "PDF Normalized Wasserstein for Mixture Distributions With Applications in ...", "date": "", "ddg_snippet": "This often leads to undesired results in distance -based learning meth- ods for mixture distributions. In this paper, we resolve this issue by introducing the Normalized Wasserstein measure. The key idea is to introduce mixture proportions as opti- mization variables, effectively normalizing mixture propor- tions in the Wasserstein formulation.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content_ICCV_2019/papers/Balaji_Normalized_Wasserstein_for_Mixture_Distributions_With_Applications_in_Adversarial_Learning_ICCV_2019_paper.pdf", "content": "This often leads to undesired results in distance -based learning meth- ods for mixture distributions. In this paper, we resolve this issue by introducing the Normalized Wasserstein measure. The key idea is to introduce mixture proportions as opti- mization variables, effectively normalizing mixture propor- tions in the Wasserstein formulation."}
+{"idx": 8, "title": "Wasserstein Distances Made Explainable: Insights into Dataset Shifts ...", "date": "", "ddg_snippet": "We propose a new set of Explainable AI techniques referred to together as \"WaX\" (or 'Wasserstein distances made ex-plainable') focused on attributing the Wasserstein distance in terms of various aspects of the data , such as individual data points or input features (see Figs. 1 and 2 and Section III).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2505.06123", "content": "We propose a new set of Explainable AI techniques referred to together as \"WaX\" (or 'Wasserstein distances made ex-plainable') focused on attributing the Wasserstein distance in terms of various aspects of the data , such as individual data points or input features (see Figs. 1 and 2 and Section III)."}
+{"idx": 9, "title": "Solving MNIST using PyTorch | Kaggle", "date": "", "ddg_snippet": "Explore and run machine learning code with Kaggle Notebooks | Using data from Digit Recognizer.", "subpage_snippet": "", "source": "www.kaggle.com", "link": "https://www.kaggle.com/code/geekysaint/solving-mnist-using-pytorch", "content": "Explore and run machine learning code with Kaggle Notebooks | Using data from Digit Recognizer."}
diff --git a/data/sampled_jsons/Crocker_Stacks_[57]_Neural_Persistence_Dynamics.jsonl b/data/sampled_jsons/Crocker_Stacks_[57]_Neural_Persistence_Dynamics.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..90a22d44b2147c60e30a8772440fdcd2f0602936
--- /dev/null
+++ b/data/sampled_jsons/Crocker_Stacks_[57]_Neural_Persistence_Dynamics.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF Neural Persistence Dynamics", "date": "", "ddg_snippet": "Crocker stacks [ 57 ], an extension of this concept, adds a smoothing step that gradually reduces the impact of points of low persistence and, upon discretization, yields a third dimension to crocker plots.", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper_files/paper/2024/file/3a509449a73fd0aab8c0cf5705827036-Paper-Conference.pdf", "content": "Crocker stacks [ 57 ], an extension of this concept, adds a smoothing step that gradually reduces the impact of points of low persistence and, upon discretization, yields a third dimension to crocker plots."}
+{"idx": 1, "title": "[2405.15732] Neural Persistence Dynamics - arXiv.org", "date": "", "ddg_snippet": "We consider the problem of learning the dynamics in the topology of time-evolving point clouds, the prevalent spatiotemporal model for systems exhibiting collective behavior, such as swarms of insects and birds or particles in physics. In such systems, patterns emerge from (local) interactions among self-propelled entities. While several well-understood governing equations for motion and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2405.15732", "content": "We consider the problem of learning the dynamics in the topology of time-evolving point clouds, the prevalent spatiotemporal model for systems exhibiting collective behavior, such as swarms of insects and birds or particles in physics. In such systems, patterns emerge from (local) interactions among self-propelled entities. While several well-understood governing equations for motion and ..."}
+{"idx": 2, "title": "neural_persistence_dynamics/crocker_stacks.py at main - GitHub", "date": "", "ddg_snippet": "Skip to content Dismiss alert plus-rkwitt / neural_persistence_dynamics Public Notifications You must be signed in to change notification settings Fork 0 Star 6 Code Issues Pull requests Projects Security Insights", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/plus-rkwitt/neural_persistence_dynamics/blob/main/crocker_stacks.py", "content": "Skip to content Dismiss alert plus-rkwitt / neural_persistence_dynamics Public Notifications You must be signed in to change notification settings Fork 0 Star 6 Code Issues Pull requests Projects Security Insights"}
+{"idx": 3, "title": "Neural Persistence Dynamics - proceedings.neurips.cc", "date": "", "ddg_snippet": "Various (ablation) experiments not only demonstrate the relevance of each model component but provide compelling empirical evidence that our proposed model -- Neural Persistence Dynamics -- substantially outperforms the state-of-the-art across a diverse set of parameter regression tasks.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/hash/3a509449a73fd0aab8c0cf5705827036-Abstract-Conference.html", "content": "Various (ablation) experiments not only demonstrate the relevance of each model component but provide compelling empirical evidence that our proposed model -- Neural Persistence Dynamics -- substantially outperforms the state-of-the-art across a diverse set of parameter regression tasks."}
+{"idx": 4, "title": "Neural Persistence Dynamics - OpenReview", "date": "", "ddg_snippet": "Finding suitable hyperparameters is also the main bottleneck for Crocker Stacks (which rely on a linear SVR). Runtime comparison to the BASELINE model (w/o dynamics ) We also compare the runtime of our approach to the baseline model which does not explicitely model any dynamics via a latent ODE.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=rCnZrFikX6", "content": "Finding suitable hyperparameters is also the main bottleneck for Crocker Stacks (which rely on a linear SVR). Runtime comparison to the BASELINE model (w/o dynamics ) We also compare the runtime of our approach to the baseline model which does not explicitely model any dynamics via a latent ODE."}
+{"idx": 5, "title": "Neural persistence dynamics | Proceedings of the 38th International ...", "date": "", "ddg_snippet": "Various (ablation) experiments not only demonstrate the relevance of each model component but provide compelling empirical evidence that our proposed model - Neural Persistence Dynamics - substantially outperforms the state-of-the-art across a diverse set of parameter regression tasks.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.5555/3737916.3738965", "content": "Various (ablation) experiments not only demonstrate the relevance of each model component but provide compelling empirical evidence that our proposed model - Neural Persistence Dynamics - substantially outperforms the state-of-the-art across a diverse set of parameter regression tasks."}
+{"idx": 6, "title": "plus-rkwitt/neural_persistence_dynamics - GitHub", "date": "", "ddg_snippet": "The Crocker stacks baseline comparison is implemented in crocker_stacks .py. To execute this script, you must first prepare the data using compute_cs.py. Additionally, you need to install the teaspoon library with the appropriate version for computing the Crocker stacks .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/plus-rkwitt/neural_persistence_dynamics", "content": "The Crocker stacks baseline comparison is implemented in crocker_stacks .py. To execute this script, you must first prepare the data using compute_cs.py. Additionally, you need to install the teaspoon library with the appropriate version for computing the Crocker stacks ."}
+{"idx": 7, "title": "Neural Persistence Dynamics · NeurIPS 2024", "date": "", "ddg_snippet": "This table compares the performance of the proposed Neural Persistence Dynamics model against two state-of-the-art methods (Path Signature Kernel and Crocker stacks ) for parameter regression tasks on four different datasets simulating collective behavior.", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/posters/rcnzrfikx6/", "content": "This table compares the performance of the proposed Neural Persistence Dynamics model against two state-of-the-art methods (Path Signature Kernel and Crocker stacks ) for parameter regression tasks on four different datasets simulating collective behavior."}
+{"idx": 8, "title": "[2405.15732] Neural Persistence Dynamics", "date": "", "ddg_snippet": "Crocker stacks[53], an extension of this concept, adds a smoothing step that gradually reduces the impact of points of low persistence and, upon discretization, yields a third dimension to crocker plots. Both, crocker plots & stacks have been used as input to regression methods to predict the configuration of models of collective behavior [4].", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2405.15732", "content": "Crocker stacks[53], an extension of this concept, adds a smoothing step that gradually reduces the impact of points of low persistence and, upon discretization, yields a third dimension to crocker plots. Both, crocker plots & stacks have been used as input to regression methods to predict the configuration of models of collective behavior [4]."}
+{"idx": 9, "title": "(PDF) Neural Persistence Dynamics - ResearchGate", "date": "", "ddg_snippet": "We then introduce a new tool to summarize time-varying metric spaces: a crocker stack . Crocker stacks are convenient for visualization, amenable to machine learning, and satisfy a desirable ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/380895040_Neural_Persistence_Dynamics", "content": "We then introduce a new tool to summarize time-varying metric spaces: a crocker stack . Crocker stacks are convenient for visualization, amenable to machine learning, and satisfy a desirable ..."}
diff --git a/data/sampled_jsons/Cronbach_&_Meehl_1955_construct_validity.jsonl b/data/sampled_jsons/Cronbach_&_Meehl_1955_construct_validity.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..fe9bc917f082a7aaacb6022d2081993fedddf27a
--- /dev/null
+++ b/data/sampled_jsons/Cronbach_&_Meehl_1955_construct_validity.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Test validity - Wikipedia", "date": "", "ddg_snippet": "Cronbach and Meehl 's subsequent publication 8 grouped predictive and concurrent validity into a \"criterion-orientation\", which eventually became ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Test_validity", "content": "Cronbach and Meehl 's subsequent publication 8 grouped predictive and concurrent validity into a \"criterion-orientation\", which eventually became ..."}
+{"idx": 1, "title": "Cronbach | Replicability-Index", "date": "", "ddg_snippet": "Construct validity , introduced by Cronbach and Meehl ( 1955 ), is critical for ensuring that measures accurately reflect the theoretical constructs ...", "subpage_snippet": "", "source": "replicationindex.com", "link": "https://replicationindex.com/category/cronbach/", "content": "Construct validity , introduced by Cronbach and Meehl ( 1955 ), is critical for ensuring that measures accurately reflect the theoretical constructs ..."}
+{"idx": 2, "title": "Meehl | Replicability-Index", "date": "", "ddg_snippet": "The classic article on “ Construct Validity ” was written by two giants in psychology; Cronbach and Meehl ( 1955 ).", "subpage_snippet": "", "source": "replicationindex.com", "link": "https://replicationindex.com/category/meehl/", "content": "The classic article on “ Construct Validity ” was written by two giants in psychology; Cronbach and Meehl ( 1955 )."}
+{"idx": 3, "title": "Classics in the History of Psychology -- Cronbach & Meehl", "date": "", "ddg_snippet": "The chief innovation in the Committee's report was the term construct validity .[ 2 ] This idea was first formulated by a subcommittee ( Meehl and R.", "subpage_snippet": "", "source": "psychclassics.yorku.ca", "link": "http://psychclassics.yorku.ca/Cronbach/construct.htm", "content": "The chief innovation in the Committee's report was the term construct validity .[ 2 ] This idea was first formulated by a subcommittee ( Meehl and R."}
+{"idx": 4, "title": "Classics in the History of Psychology -- Cronbach & Meehl", "date": "", "ddg_snippet": "The chief innovation in the Committee's report was the term construct validity .[ 2 ] This idea was first formulated by a subcommittee ( Meehl and R.", "subpage_snippet": "", "source": "psychclassics.yorku.ca", "link": "https://psychclassics.yorku.ca/Cronbach/construct.htm", "content": "The chief innovation in the Committee's report was the term construct validity .[ 2 ] This idea was first formulated by a subcommittee ( Meehl and R."}
+{"idx": 5, "title": "Construct validity | Lærd Dissertation", "date": "", "ddg_snippet": "There are a number of factors that can act as threats to construct validity (e.g., Cronbach and Meehl , 1955 ; Nunnally, 1978; Cook and Campbell, 1979 ...", "subpage_snippet": "", "source": "dissertation.laerd.com", "link": "https://dissertation.laerd.com/construct-validity-p2.php", "content": "There are a number of factors that can act as threats to construct validity (e.g., Cronbach and Meehl , 1955 ; Nunnally, 1978; Cook and Campbell, 1979 ..."}
+{"idx": 6, "title": "Construct validity in psychological tests", "date": "", "ddg_snippet": "Construct validity in psychological tests ... 1955 Jul;52(4):281-302. ... L J CRONBACH , P E MEEHL", "subpage_snippet": "", "source": "pubmed.ncbi.nlm.nih.gov", "link": "https://pubmed.ncbi.nlm.nih.gov/13245896/", "content": "Construct validity in psychological tests ... 1955 Jul;52(4):281-302. ... L J CRONBACH , P E MEEHL"}
+{"idx": 7, "title": "Rethinking the Subjective Units of Distress Scale: Validity and", "date": "", "ddg_snippet": "Feature papers are submitted upon individual invitation or recommendation by the scientific editors and must receive positive feedback from the ...", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/2039-7283/15/7/123", "content": "Feature papers are submitted upon individual invitation or recommendation by the scientific editors and must receive positive feedback from the ..."}
+{"idx": 8, "title": "Thought Into Design: Review - Borsboom, Mellenbergh & van", "date": "", "ddg_snippet": "The concept of construct validity was expounded in a classic article by Cronbach & Meehl ( 1955 ) and expanded upon by Messick (1989), and the ...", "subpage_snippet": "", "source": "articles.thoughtintodesign.com", "link": "https://articles.thoughtintodesign.com/2012/01/review-borsboom-mellenbergh-van-heerden.html", "content": "The concept of construct validity was expounded in a classic article by Cronbach & Meehl ( 1955 ) and expanded upon by Messick (1989), and the ..."}
+{"idx": 9, "title": "Düşünce ve Toplum Sosyal Bilimler Dergisi » Submission", "date": "", "ddg_snippet": "Student experiences and perceptions of digital literacy skills development: Engaging learners by design? Electronic Journal of e-Learning, 11(3), 207 ...", "subpage_snippet": "", "source": "dergipark.org.tr", "link": "https://dergipark.org.tr/en/pub/dusuncevetoplum/issue/63163/945319", "content": "Student experiences and perceptions of digital literacy skills development: Engaging learners by design? Electronic Journal of e-Learning, 11(3), 207 ..."}
diff --git a/data/sampled_jsons/Current_advancements_in_reinforcement_learning_step-based_policies_temporal_correlation_Li_2024.jsonl b/data/sampled_jsons/Current_advancements_in_reinforcement_learning_step-based_policies_temporal_correlation_Li_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..9d631964a393721a6d8a9197c8ceb95776ae6b8d
--- /dev/null
+++ b/data/sampled_jsons/Current_advancements_in_reinforcement_learning_step-based_policies_temporal_correlation_Li_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2401.11437] Open the Black Box: Step-based Policy Updates ... ALR - Publications - New ICLR paper: Open the Black Box ... - KIT Open the Black Box: Step-based Policy Updates for... Daily Papers - Hugging Face Latent Space Exploration and Trajectory Space Update in ... GitHub - BruceGeLi/TCE_RL: Temporally Correlated Episodic ... Paper page - Open the Black Box: Step-based Policy Updates ...", "date": "", "ddg_snippet": "Jan 21, 2024 · Current advancements in reinforcement learning (RL) have predominantly focused on learning step-based policies that generate actions for each perceived state. While these methods efficiently leverage step information from environmental interaction, they often ignore the temporal correlation between actions, resulting in inefficient exploration and unsmooth trajectories that are challenging to ... Author: Ge Li , Hongyi Zhou, Dominik Roth, Serge Thilges, Fabian Otto, Rudolf Lioutikov, Gerhard Neumann Current advancements in reinforcement learning (RL) have predominantly focused on learning step-based policies that generate actions for each perceived state. While these methods efficiently leverage step information from environmental interaction, they often ignore the temporal correlation ... Jan 16, 2024 · Current advancements in reinforcement learning (RL) have predominantly focused on learning step-based policies that generate actions for each perceived state. While these methods efficiently leverage step information from environmental interaction, they often ignore the temporal correlation between actions, resulting in inefficient exploration and unsmooth trajectories that are challenging to ... Jul 11, 2025 · Open the Black Box: Step - based Policy Updates for Temporally-Correlated Episodic Reinforcement Learning Current advancements in reinforcement learning (RL) have predominantly focused on learning step - basedpolicies that generate actions for each perceived state. Current advancements in reinforcement learning (RL) have predominantly focused on learning step-based policies that generate actions for each perceived state. While these methods efficiently leverage step information from environmental interaction, they often ignore the temporal correlation between actions, resulting in inefficient exploration and unsmooth trajectories that are challenging to ... Current advancements in reinforcement learning (RL) have predominantly focused on learning step-based policies that generate actions for each perceived state. While these methods efficiently leverage step information from environmental interaction, they often ignore the temporal correlation between actions, resulting in inefficient exploration and unsmooth trajectories that are challenging to ... Abstract Current advancements in reinforcement learning (RL) have predominantly focused on learning step-based policies that generate actions for each perceived state. While these methods efficiently leverage step information from environmental interaction, they often ignore the temporal correlation between actions, resulting in inefficient exploration and unsmooth trajectories that are ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2401.11437", "content": "Jan 21, 2024 · Current advancements in reinforcement learning (RL) have predominantly focused on learning step-based policies that generate actions for each perceived state. While these methods efficiently leverage step information from environmental interaction, they often ignore the temporal correlation between actions, resulting in inefficient exploration and unsmooth trajectories that are challenging to ... Author: Ge Li , Hongyi Zhou, Dominik Roth, Serge Thilges, Fabian Otto, Rudolf Lioutikov, Gerhard Neumann Current advancements in reinforcement learning (RL) have predominantly focused on learning step-based policies that generate actions for each perceived state. While these methods efficiently leverage step information from environmental interaction, they often ignore the temporal correlation ... Jan 16, 2024 · Current advancements in reinforcement learning (RL) have predominantly focused on learning step-based policies that generate actions for each perceived state. While these methods efficiently leverage step information from environmental interaction, they often ignore the temporal correlation between actions, resulting in inefficient exploration and unsmooth trajectories that are challenging to ... Jul 11, 2025 · Open the Black Box: Step - based Policy Updates for Temporally-Correlated Episodic Reinforcement Learning Current advancements in reinforcement learning (RL) have predominantly focused on learning step - basedpolicies that generate actions for each perceived state. Current advancements in reinforcement learning (RL) have predominantly focused on learning step-based policies that generate actions for each perceived state. While these methods efficiently leverage step information from environmental interaction, they often ignore the temporal correlation between actions, resulting in inefficient exploration and unsmooth trajectories that are challenging to ... Current advancements in reinforcement learning (RL) have predominantly focused on learning step-based policies that generate actions for each perceived state. While these methods efficiently leverage step information from environmental interaction, they often ignore the temporal correlation between actions, resulting in inefficient exploration and unsmooth trajectories that are challenging to ... Abstract Current advancements in reinforcement learning (RL) have predominantly focused on learning step-based policies that generate actions for each perceived state. While these methods efficiently leverage step information from environmental interaction, they often ignore the temporal correlation between actions, resulting in inefficient exploration and unsmooth trajectories that are ..."}
+{"idx": 1, "title": "Open the Black Box: Step-based Policy Updates for...", "date": "", "ddg_snippet": "Jan 16, 2024 · Current advancements in reinforcement learning (RL) have predominantly focused on learning step-based policies that generate actions for each perceived state. While these methods efficiently leverage step information from environmental interaction, they often ignore the temporal correlation between actions, resulting in inefficient exploration and unsmooth trajectories that are challenging to ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=mnipav175N", "content": "Jan 16, 2024 · Current advancements in reinforcement learning (RL) have predominantly focused on learning step-based policies that generate actions for each perceived state. While these methods efficiently leverage step information from environmental interaction, they often ignore the temporal correlation between actions, resulting in inefficient exploration and unsmooth trajectories that are challenging to ..."}
+{"idx": 2, "title": "GitHub - BruceGeLi/TCE_RL: Temporally Correlated Episodic ...", "date": "", "ddg_snippet": "Current advancements in reinforcement learning (RL) have predominantly focused on learning step-based policies that generate actions for each perceived state. While these methods efficiently leverage step information from environmental interaction, they often ignore the temporal correlation between actions, resulting in inefficient exploration and unsmooth trajectories that are challenging to ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/BruceGeLi/TCE_RL", "content": "Current advancements in reinforcement learning (RL) have predominantly focused on learning step-based policies that generate actions for each perceived state. While these methods efficiently leverage step information from environmental interaction, they often ignore the temporal correlation between actions, resulting in inefficient exploration and unsmooth trajectories that are challenging to ..."}
+{"idx": 3, "title": "Paper page - Open the Black Box: Step-based Policy Updates ...", "date": "", "ddg_snippet": "Abstract Current advancements in reinforcement learning (RL) have predominantly focused on learning step-based policies that generate actions for each perceived state. While these methods efficiently leverage step information from environmental interaction, they often ignore the temporal correlation between actions, resulting in inefficient exploration and unsmooth trajectories that are ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2401.11437", "content": "Abstract Current advancements in reinforcement learning (RL) have predominantly focused on learning step-based policies that generate actions for each perceived state. While these methods efficiently leverage step information from environmental interaction, they often ignore the temporal correlation between actions, resulting in inefficient exploration and unsmooth trajectories that are ..."}
+{"idx": 4, "title": "step-based policy updates for temporally-correlated ...", "date": "", "ddg_snippet": "by G Li · Cited by 11 — Current advancements in reinforcement learning (RL) have predominantly fo- cused on learning step-based policies that generate actions for each perceived. 33 pages", "subpage_snippet": "", "source": "proceedings.iclr.cc", "link": "https://proceedings.iclr.cc/paper_files/paper/2024/file/52da50b1ef221e4b1793e3bf44dd973d-Paper-Conference.pdf", "content": "by G Li · Cited by 11 — Current advancements in reinforcement learning (RL) have predominantly fo- cused on learning step-based policies that generate actions for each perceived. 33 pages"}
+{"idx": 5, "title": "Step-based Policy Updates for Temporally-Correlated Episodic ...", "date": "", "ddg_snippet": "Current advancements in reinforcement learning (RL) have predominantly focused on learning step-based policies that generate actions for each perceived state.", "subpage_snippet": "", "source": "chatpaper.com", "link": "https://chatpaper.com/chatpaper/paper/6508", "content": "Current advancements in reinforcement learning (RL) have predominantly focused on learning step-based policies that generate actions for each perceived state."}
+{"idx": 6, "title": "ALR - Publications - New ICLR paper: Open the Black Box ... - KIT", "date": "", "ddg_snippet": "Author: Ge Li , Hongyi Zhou, Dominik Roth, Serge Thilges, Fabian Otto, Rudolf Lioutikov, Gerhard Neumann Current advancements in reinforcement learning (RL) have predominantly focused on learning step-based policies that generate actions for each perceived state. While these methods efficiently leverage step information from environmental interaction, they often ignore the temporal correlation ...", "subpage_snippet": "", "source": "alr.iar.kit.edu", "link": "https://alr.iar.kit.edu/603.php", "content": "Author: Ge Li , Hongyi Zhou, Dominik Roth, Serge Thilges, Fabian Otto, Rudolf Lioutikov, Gerhard Neumann Current advancements in reinforcement learning (RL) have predominantly focused on learning step-based policies that generate actions for each perceived state. While these methods efficiently leverage step information from environmental interaction, they often ignore the temporal correlation ..."}
+{"idx": 7, "title": "Daily Papers - Hugging Face", "date": "", "ddg_snippet": "Jul 11, 2025 · Open the Black Box: Step - based Policy Updates for Temporally-Correlated Episodic Reinforcement Learning Current advancements in reinforcement learning (RL) have predominantly focused on learning step - basedpolicies that generate actions for each perceived state.", "subpage_snippet": "", "source": "api-inference.hf-mirror.com", "link": "https://api-inference.hf-mirror.com/papers?q=step-based+policies", "content": "Jul 11, 2025 · Open the Black Box: Step - based Policy Updates for Temporally-Correlated Episodic Reinforcement Learning Current advancements in reinforcement learning (RL) have predominantly focused on learning step - basedpolicies that generate actions for each perceived state."}
+{"idx": 8, "title": "Latent Space Exploration and Trajectory Space Update in ...", "date": "", "ddg_snippet": "Current advancements in reinforcement learning (RL) have predominantly focused on learning step-based policies that generate actions for each perceived state. While these methods efficiently leverage step information from environmental interaction, they often ignore the temporal correlation between actions, resulting in inefficient exploration and unsmooth trajectories that are challenging to ...", "subpage_snippet": "", "source": "rudolf.intuitive-robots.net", "link": "https://rudolf.intuitive-robots.net/publication/li-latentspaceexploration-2024-ws/", "content": "Current advancements in reinforcement learning (RL) have predominantly focused on learning step-based policies that generate actions for each perceived state. While these methods efficiently leverage step information from environmental interaction, they often ignore the temporal correlation between actions, resulting in inefficient exploration and unsmooth trajectories that are challenging to ..."}
+{"idx": 9, "title": "Daily Papers", "date": "", "ddg_snippet": "Current advancements in reinforcement learning (RL) have predominantly focused on learning step-based policies that generate actions for each perceived state.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=episodic+Markov+Decision+Processes", "content": "Current advancements in reinforcement learning (RL) have predominantly focused on learning step-based policies that generate actions for each perceived state."}
diff --git a/data/sampled_jsons/Cutler_&_Breiman_Archetypal_Analysis_1994_abstract_year_1994.jsonl b/data/sampled_jsons/Cutler_&_Breiman_Archetypal_Analysis_1994_abstract_year_1994.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..728994053e86aee64269b91a0e44c0f6bf97fb76
--- /dev/null
+++ b/data/sampled_jsons/Cutler_&_Breiman_Archetypal_Analysis_1994_abstract_year_1994.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Incorporating Fairness Constraints into Archetypal Analysis", "date": "", "ddg_snippet": "This is the case of archetypal analysis (AA), an unsupervised technique that lies halfway between clustering and PCA morup2012archetypal .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.12021v1", "content": "This is the case of archetypal analysis (AA), an unsupervised technique that lies halfway between clustering and PCA morup2012archetypal ."}
+{"idx": 1, "title": "[2502.12892] Archetypal SAE: Adaptive and Stable Dictionary", "date": "", "ddg_snippet": "To address this issue, we draw inspiration from the Archetypal Analysis framework introduced by Cutler & Breiman ( 1994 ) and present Archetypal ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://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 ..."}
+{"idx": 2, "title": "On the archetypal ‘flavours’, indices and teleconnections", "date": "", "ddg_snippet": "Archetypal analysis , a pattern recognition technique, is applied to sea surface temperature and sea-level anomalies to provide a robust ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.08694v1", "content": "Archetypal analysis , a pattern recognition technique, is applied to sea surface temperature and sea-level anomalies to provide a robust ..."}
+{"idx": 3, "title": "ARCHETYPAL ANALYSIS: AN ALTERNATIVE TO CLUSTERING FOR", "date": "", "ddg_snippet": "Robust multivariate and functional archetypal analysis with application to financial time series analysis . ... Archetypal analysis for machine ...", "subpage_snippet": "", "source": "www.ias-iss.org", "link": "https://www.ias-iss.org/ojs/IAS/article/view/2052", "content": "Robust multivariate and functional archetypal analysis with application to financial time series analysis . ... Archetypal analysis for machine ..."}
+{"idx": 4, "title": "Archetype analysis and the PHATE algorithm as methods to", "date": "", "ddg_snippet": "Archetypal analysis as a tool for the statistical description of multidimensional objects was introduced by Breiman and Cutler in 1994 [ 16 ] and is ...", "subpage_snippet": "", "source": "bmcpublichealth.biomedcentral.com", "link": "https://bmcpublichealth.biomedcentral.com/articles/10.1186/s12889-024-18355-7", "content": "Archetypal analysis as a tool for the statistical description of multidimensional objects was introduced by Breiman and Cutler in 1994 [ 16 ] and is ..."}
+{"idx": 5, "title": "Improved algorithm and bounds for successive projection", "date": "", "ddg_snippet": "Archytypal analysis ( Cutler & Breiman , 1994 ) is a useful tool for representation learning. ... and each v k subscript 𝑣 𝑘 v_{k} italic_v ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.11013v1", "content": "Archytypal analysis ( Cutler & Breiman , 1994 ) is a useful tool for representation learning. ... and each v k subscript 𝑣 𝑘 v_{k} italic_v ..."}
+{"idx": 6, "title": "Stratifying High-Dimensional Data Based on Proximity to the", "date": "", "ddg_snippet": "Kruse, Analysis of imaging spectrometer data using $n$-dimensional geometry and a ... Breiman , Archetypal analysis , Technometrics, 36 ( 1994 ), pp.", "subpage_snippet": "", "source": "epubs.siam.org", "link": "https://epubs.siam.org/doi/abs/10.1137/15M1047921", "content": "Kruse, Analysis of imaging spectrometer data using $n$-dimensional geometry and a ... Breiman , Archetypal analysis , Technometrics, 36 ( 1994 ), pp."}
+{"idx": 7, "title": "Stratifying High-Dimensional Data Based on Proximity to the", "date": "", "ddg_snippet": "Kruse, Analysis of imaging spectrometer data using $n$-dimensional geometry and a ... Breiman , Archetypal analysis , Technometrics, 36 ( 1994 ), pp.", "subpage_snippet": "", "source": "epubs.siam.org", "link": "https://epubs.siam.org/doi/abs/10.1137/15M1047921?cookieSet=1", "content": "Kruse, Analysis of imaging spectrometer data using $n$-dimensional geometry and a ... Breiman , Archetypal analysis , Technometrics, 36 ( 1994 ), pp."}
+{"idx": 8, "title": "Meta-Research: Task specialization across research careers |", "date": "", "ddg_snippet": "This model was used to predict the contributions of 222,925 authors in 6,236,239 publications, and to apply a robust archetypal analysis to profile ...", "subpage_snippet": "", "source": "elifesciences.org", "link": "https://elifesciences.org/articles/60586", "content": "This model was used to predict the contributions of 222,925 authors in 6,236,239 publications, and to apply a robust archetypal analysis to profile ..."}
+{"idx": 9, "title": "Posters & Elevator Pitches", "date": "", "ddg_snippet": "The scoring criteria included quality of abstract , outline, introduction, or summary; organization and clarity; quality of content; quality of ...", "subpage_snippet": "", "source": "www.r-project.org", "link": "https://www.r-project.org/conferences/useR-2022/program/posters", "content": "The scoring criteria included quality of abstract , outline, introduction, or summary; organization and clarity; quality of content; quality of ..."}
diff --git a/data/sampled_jsons/Cutler_Breiman_1994_Archetypal_Analysis_abstract.jsonl b/data/sampled_jsons/Cutler_Breiman_1994_Archetypal_Analysis_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d64171eb3cdd326faa8d35a432e2c68baf2b7e7b
--- /dev/null
+++ b/data/sampled_jsons/Cutler_Breiman_1994_Archetypal_Analysis_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Archetypal Analysis: Technometrics: Vol 36, No 4", "date": "", "ddg_snippet": "by A Cutler · 1994 · Cited by 852 — Abstract . Archetypal analysis represents each individual in a data set as a mixture of individuals of pure type or archetypes. The archetypes themselves are ...", "subpage_snippet": "", "source": "www.tandfonline.com", "link": "https://www.tandfonline.com/doi/abs/10.1080/00401706.1994.10485840", "content": "by A Cutler · 1994 · Cited by 852 — Abstract . Archetypal analysis represents each individual in a data set as a mixture of individuals of pure type or archetypes. The archetypes themselves are ..."}
+{"idx": 1, "title": "v3604338 Archetypal Analysis", "date": "", "ddg_snippet": "by A CUTLER · 1994 · Cited by 850 — Archetypal analysis represents each individual in a data set as a mixture of individuals of pure type or archetypes. The archetypes themselves are ...", "subpage_snippet": "", "source": "www.stat.cmu.edu", "link": "https://www.stat.cmu.edu/technometrics/90-00/vol-36-04/v3604338.pdf", "content": "by A CUTLER · 1994 · Cited by 850 — Archetypal analysis represents each individual in a data set as a mixture of individuals of pure type or archetypes. The archetypes themselves are ..."}
+{"idx": 2, "title": "[2504.12392] A Survey on Archetypal Analysis", "date": "", "ddg_snippet": "by A Alcacer · 2025 · Cited by 1 — Abstract : Archetypal analysis (AA) was originally proposed in 1994 by Adele Cutler and Leo Breiman as a computational procedure to extract ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2504.12392", "content": "by A Alcacer · 2025 · Cited by 1 — Abstract : Archetypal analysis (AA) was originally proposed in 1994 by Adele Cutler and Leo Breiman as a computational procedure to extract ..."}
+{"idx": 3, "title": "Introduction to archetypal analysis of spatio-temporal ...", "date": "", "ddg_snippet": "by E Stone · 1996 · Cited by 45 — ... archetypal analysis (Cutler and Breiman, 1994). Archetypes characterize the convex hull of the data set and the data set can be reconstructed in terms of ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/0167278996000164", "content": "by E Stone · 1996 · Cited by 45 — ... archetypal analysis (Cutler and Breiman, 1994). Archetypes characterize the convex hull of the data set and the data set can be reconstructed in terms of ..."}
+{"idx": 4, "title": "Archetypal Analysis: Three Case Studies", "date": "", "ddg_snippet": "Abstract. Archetypal analysis was first introduced by Adele Cutler and Leo Breiman in 1994 . In their paper they presented three examples: Swiss soldiers, ... 11 pages", "subpage_snippet": "", "source": "ww2.amstat.org", "link": "https://ww2.amstat.org/meetings/proceedings/2016/data/assets/pdf/389749.pdf", "content": "Abstract. Archetypal analysis was first introduced by Adele Cutler and Leo Breiman in 1994 . In their paper they presented three examples: Swiss soldiers, ... 11 pages"}
+{"idx": 5, "title": "Archetypal analysis for machine learning and data mining", "date": "", "ddg_snippet": "by M Mørup · 2012 · Cited by 274 — Abstract. Archetypal analysis (aa) proposed by Cutler and Breiman (1994) 7] estimates the principal convex hull (pch) of a data set . As such aa favors ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.5555/2770421.2770435", "content": "by M Mørup · 2012 · Cited by 274 — Abstract. Archetypal analysis (aa) proposed by Cutler and Breiman (1994) 7] estimates the principal convex hull (pch) of a data set . As such aa favors ..."}
+{"idx": 6, "title": "Archetypal analysis of spatio-temporal dynamics", "date": "", "ddg_snippet": "by E Stone · 1996 · Cited by 34 — Abstract . A comparison is made between the principal component or Karhunen-Loève decomposition of two sets of spatio-temporal data (one numerical, the other ...", "subpage_snippet": "", "source": "ui.adsabs.harvard.edu", "link": "https://ui.adsabs.harvard.edu/abs/1996PhyD...90..209S/abstract", "content": "by E Stone · 1996 · Cited by 34 — Abstract . A comparison is made between the principal component or Karhunen-Loève decomposition of two sets of spatio-temporal data (one numerical, the other ..."}
+{"idx": 7, "title": "Archetypal scientists", "date": "", "ddg_snippet": "by C Seiler · 2013 · Cited by 54 — Archetypal analysis is a very useful tool for this purpose. Archetypes were defined in Cutler and Breiman (1994 ) and they have been applied in different ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/abs/pii/S1751157712001034", "content": "by C Seiler · 2013 · Cited by 54 — Archetypal analysis is a very useful tool for this purpose. Archetypes were defined in Cutler and Breiman (1994 ) and they have been applied in different ..."}
+{"idx": 8, "title": "A Geometric Approach to Archetypal Analysis and ...", "date": "", "ddg_snippet": "by A Damle · 2017 · Cited by 22 — We describe a geometric approach to both NMF and archetypal analysis by interpreting both problems as finding extreme points of the data cloud.", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC6393938/", "content": "by A Damle · 2017 · Cited by 22 — We describe a geometric approach to both NMF and archetypal analysis by interpreting both problems as finding extreme points of the data cloud."}
+{"idx": 9, "title": "Moving archetypes - University of Montana", "date": "", "ddg_snippet": "Abstract . We introduce a variation of the statistical method archetypal analysis ( Cutler and Breiman , 1994 ) that tracks moving structures, such as traveling ...", "subpage_snippet": "", "source": "umimpact.umt.edu", "link": "https://umimpact.umt.edu/en/publications/moving-archetypes", "content": "Abstract . We introduce a variation of the statistical method archetypal analysis ( Cutler and Breiman , 1994 ) that tracks moving structures, such as traveling ..."}
diff --git a/data/sampled_jsons/DART_CVPR_2025_Figure_2_focal_consolidation_ground_truth_comparison.jsonl b/data/sampled_jsons/DART_CVPR_2025_Figure_2_focal_consolidation_ground_truth_comparison.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0ee070fcdc1cd06b0ee62f7a139da4080d6780ca
--- /dev/null
+++ b/data/sampled_jsons/DART_CVPR_2025_Figure_2_focal_consolidation_ground_truth_comparison.jsonl
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+{"idx": 0, "title": "DART: Disease-aware Image-Text Alignment and Self-correcting ...", "date": "", "ddg_snippet": "In detail, “w/o Self-Correction” cap-tures most key findings of the ground - truth report, but it omits some findings such as focal consolidation , and while GPT-4 refinement improves phrasing, it fails to address these omissions, resulting in a similar report.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Park_DART_Disease-aware_Image-Text_Alignment_and_Self-correcting_Re-alignment_for_Trustworthy_Radiology_CVPR_2025_paper.pdf", "content": "In detail, “w/o Self-Correction” cap-tures most key findings of the ground - truth report, but it omits some findings such as focal consolidation , and while GPT-4 refinement improves phrasing, it fails to address these omissions, resulting in a similar report."}
+{"idx": 1, "title": "GitHub - Estrella-fugaz/CVPR25-DUCT: Dual Consolidation for ...", "date": "", "ddg_snippet": "The code repository for \"Dual Consolidation for Pre-Trained Model-Based Domain-Incremental Learning\" ( CVPR 2025 ) in PyTorch. If you use any content of this repo for your work, please cite the following bib entry:", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Estrella-fugaz/CVPR25-Duct", "content": "The code repository for \"Dual Consolidation for Pre-Trained Model-Based Domain-Incremental Learning\" ( CVPR 2025 ) in PyTorch. If you use any content of this repo for your work, please cite the following bib entry:"}
+{"idx": 2, "title": "Paper Digest: CVPR 2025 Papers & Highlights", "date": "", "ddg_snippet": "Jun 7, 2025 · Note: CVPR - 2025 accepts more than 2,800 papers, this page only includes 500 of them selected by our daily paper digest algorithm. Interested users can choose to read All 2,800 CVPR - 2025 papers in a separate page. To search for papers presented at CVPR - 2025 on a specific topic, please make use of the search by venue ( CVPR - 2025 ) service.", "subpage_snippet": "", "source": "resources.paperdigest.org", "link": "https://resources.paperdigest.org/2025/06/cvpr-2025-papers-highlights/", "content": "Jun 7, 2025 · Note: CVPR - 2025 accepts more than 2,800 papers, this page only includes 500 of them selected by our daily paper digest algorithm. Interested users can choose to read All 2,800 CVPR - 2025 papers in a separate page. To search for papers presented at CVPR - 2025 on a specific topic, please make use of the search by venue ( CVPR - 2025 ) service."}
+{"idx": 3, "title": "CVPR 2025 oral notes", "date": "", "ddg_snippet": "Aug 22, 2025 · It addresses the lack of training data with pseudo ground truth labels that are iteratively refined Avatar CAP4D FelixTaubner2025CVPR uses a morphable multi-view diffusion model to reconstruct photo-real 4D (dynamic 3D) portrait avatars from any number of reference images (i.e., one to 100) and animate and render them in real time", "subpage_snippet": "", "source": "liu-qilong.github.io", "link": "https://liu-qilong.github.io/note/cvpr-2025-oral-notes", "content": "Aug 22, 2025 · It addresses the lack of training data with pseudo ground truth labels that are iteratively refined Avatar CAP4D FelixTaubner2025CVPR uses a morphable multi-view diffusion model to reconstruct photo-real 4D (dynamic 3D) portrait avatars from any number of reference images (i.e., one to 100) and animate and render them in real time"}
+{"idx": 4, "title": "CVPR 2025 Open Access Repository", "date": "", "ddg_snippet": "In this study, we propose a Disease-aware image-text Alignment and self-correcting Re-alignment for Trustworthy radiology report generation ( DART ) framework. In the first stage, we generate initial reports based on image-to-text retrieval with disease-matching, embedding both images and texts in a shared embedding space through contrastive ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/html/Park_DART_Disease-aware_Image-Text_Alignment_and_Self-correcting_Re-alignment_for_Trustworthy_Radiology_CVPR_2025_paper.html", "content": "In this study, we propose a Disease-aware image-text Alignment and self-correcting Re-alignment for Trustworthy radiology report generation ( DART ) framework. In the first stage, we generate initial reports based on image-to-text retrieval with disease-matching, embedding both images and texts in a shared embedding space through contrastive ..."}
+{"idx": 5, "title": "GitHub - jqliu09/MCLD: [CVPR 2025] Multi-focal Conditioned ...", "date": "", "ddg_snippet": "Multi- focal Conditioned Latent Diffusion for Person Image Synthesis Jiaqi Liu, Jichao Zhang, Paolo Rota, Nicu Sebe Computer Vision and Pattern Recognition Conference ( CVPR ), 2025 , Nashville, USA", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/jqliu09/MCLD", "content": "Multi- focal Conditioned Latent Diffusion for Person Image Synthesis Jiaqi Liu, Jichao Zhang, Paolo Rota, Nicu Sebe Computer Vision and Pattern Recognition Conference ( CVPR ), 2025 , Nashville, USA"}
+{"idx": 6, "title": "CVPR 2025 - nuaa-lsy-group.github.io", "date": "", "ddg_snippet": "Figure 1. Illustration of DUCT. Top: Representation consolidation . We utilize the pre-trained model as initialization and optimize it for each domain, obtaining the task vectors. Afterward, we combine the pre-trained model and all seen task vectors to build the unified embedding space. Bottom: Classifier consolidation . To align the classifiers with consolidated features, we design the new ...", "subpage_snippet": "", "source": "nuaa-lsy-group.github.io", "link": "https://nuaa-lsy-group.github.io/assets/slides/20250604-zwl.pdf", "content": "Figure 1. Illustration of DUCT. Top: Representation consolidation . We utilize the pre-trained model as initialization and optimize it for each domain, obtaining the task vectors. Afterward, we combine the pre-trained model and all seen task vectors to build the unified embedding space. Bottom: Classifier consolidation . To align the classifiers with consolidated features, we design the new ..."}
+{"idx": 7, "title": "CVPR 2025 Papers", "date": "", "ddg_snippet": "Seek Common Ground While Reserving Differences : Semi-Supervised Image-Text Sentiment Recognition ... Ground - Truth Guidance · MVPaint: Synchronized Multi-View ...", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/papers.html", "content": "Seek Common Ground While Reserving Differences : Semi-Supervised Image-Text Sentiment Recognition ... Ground - Truth Guidance · MVPaint: Synchronized Multi-View ..."}
+{"idx": 8, "title": "CVPR 2025 Accepted Papers", "date": "", "ddg_snippet": "CVPR 2025 Accepted Papers . This page is cached for 1 hour. Changes to affiliation or name in your local profile may take up to 60 minutes to appear here.", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/Conferences/2025/AcceptedPapers", "content": "CVPR 2025 Accepted Papers . This page is cached for 1 hour. Changes to affiliation or name in your local profile may take up to 60 minutes to appear here."}
+{"idx": 9, "title": "CVPR 2025 Schedule", "date": "", "ddg_snippet": "... Ground - Truth Guidance · Rethinking Temporal Fusion with a Unified Gradient Descent View for 3D Semantic Occupancy Prediction · STCOcc: Sparse Spatial-Temporal ...", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/calendar", "content": "... Ground - Truth Guidance · Rethinking Temporal Fusion with a Unified Gradient Descent View for 3D Semantic Occupancy Prediction · STCOcc: Sparse Spatial-Temporal ..."}
diff --git a/data/sampled_jsons/DART_Disease-aware_Image-Text_Alignment_GitHub_lambda_m_sitegithub.com_year_2023.jsonl b/data/sampled_jsons/DART_Disease-aware_Image-Text_Alignment_GitHub_lambda_m_sitegithub.com_year_2023.jsonl
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index 0000000000000000000000000000000000000000..704bcdb03bbcfa5b637d4664c02aeb37d95938ce
--- /dev/null
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Vacuum hose diagram question | Dodge Dart Forum", "date": "", "ddg_snippet": "Jun 7, 2025 · Having issues with my 2013 dodge dart 1.4l. Keep getting underboost code and a sligh shudder when turbo kicks in. New to this type of vehicle and not at all familiar with turbos in general. Someone suggested I replace the boost selenoid and they took it off. Got a new one but now have no idea...", "subpage_snippet": "", "source": "www.dodge-dart.org", "link": "https://www.dodge-dart.org/threads/vacuum-hose-diagram-question.71323/", "content": "Jun 7, 2025 · Having issues with my 2013 dodge dart 1.4l. Keep getting underboost code and a sligh shudder when turbo kicks in. New to this type of vehicle and not at all familiar with turbos in general. Someone suggested I replace the boost selenoid and they took it off. Got a new one but now have no idea..."}
+{"idx": 1, "title": "Recall Shifter Bushing Replacement - 2013 Dart", "date": "", "ddg_snippet": "Jul 20, 2024 · 2013 1.4 dart manual, 87k miles, and accidentally discovered that my bushing was holding on by a thread while investigating oil on top of my transmission (looks like my vaccuum pump gasket is rotting away).", "subpage_snippet": "", "source": "www.dodge-dart.org", "link": "https://www.dodge-dart.org/threads/recall-shifter-bushing-replacement-2013-dart.70634/", "content": "Jul 20, 2024 · 2013 1.4 dart manual, 87k miles, and accidentally discovered that my bushing was holding on by a thread while investigating oil on top of my transmission (looks like my vaccuum pump gasket is rotting away)."}
+{"idx": 2, "title": "2.4 multiair variable valve timing actuator - Dodge Dart Forum", "date": "", "ddg_snippet": "Nov 9, 2019 · Dodge- Dart .org is a forum dedicated to the 2013-2016 Dodge Dart . Join and participate in discussions about maintenance, performance mods, and mechanical issues. Get the latest tips, news, browse the classifieds, and more!", "subpage_snippet": "", "source": "www.dodge-dart.org", "link": "https://www.dodge-dart.org/threads/2-4-multiair-variable-valve-timing-actuator.65832/", "content": "Nov 9, 2019 · Dodge- Dart .org is a forum dedicated to the 2013-2016 Dodge Dart . Join and participate in discussions about maintenance, performance mods, and mechanical issues. Get the latest tips, news, browse the classifieds, and more!"}
+{"idx": 3, "title": "No Crank, No Start - Dodge Dart Forum", "date": "", "ddg_snippet": "Feb 16, 2023 · Y'all helped me spectacularly last time I had issues with my Dart I figured I'd come back for round 2. So since the last time I asked for help my Dart has started refusing to start. No crank, a singular click, and nothing more. The Dashboard comes to life, the AC works fine, and the Radio works...", "subpage_snippet": "", "source": "www.dodge-dart.org", "link": "https://www.dodge-dart.org/threads/no-crank-no-start.69518/", "content": "Feb 16, 2023 · Y'all helped me spectacularly last time I had issues with my Dart I figured I'd come back for round 2. So since the last time I asked for help my Dart has started refusing to start. No crank, a singular click, and nothing more. The Dashboard comes to life, the AC works fine, and the Radio works..."}
+{"idx": 4, "title": "UConnect Bluetooth Issue | Dodge Dart Forum", "date": "", "ddg_snippet": "Jan 2, 2025 · I have attached some images of the messages I've been getting on my phone when trying to connect, these messages appear just after I tap uconnect on my phone. I have tried many things already such as the temperature button soft reset procedure and the corner of the screen soft reset procedure...", "subpage_snippet": "", "source": "www.dodge-dart.org", "link": "https://www.dodge-dart.org/threads/uconnect-bluetooth-issue.71014/", "content": "Jan 2, 2025 · I have attached some images of the messages I've been getting on my phone when trying to connect, these messages appear just after I tap uconnect on my phone. I have tried many things already such as the temperature button soft reset procedure and the corner of the screen soft reset procedure..."}
+{"idx": 5, "title": "Front strut replacement - Dodge Dart Forum", "date": "", "ddg_snippet": "May 24, 2020 · Front struts are gone, will replace with KYB's struts part #'s 334982 and 334981. Rockauto. com has them for $83 each rightnow compared to the next cheapest autozone $127 so definitely gonna pull the trigger, however I need to know, do I need to replace the strut mounts with KYB's also (sm5811...", "subpage_snippet": "", "source": "www.dodge-dart.org", "link": "https://www.dodge-dart.org/threads/front-strut-replacement.66659/", "content": "May 24, 2020 · Front struts are gone, will replace with KYB's struts part #'s 334982 and 334981. Rockauto. com has them for $83 each rightnow compared to the next cheapest autozone $127 so definitely gonna pull the trigger, however I need to know, do I need to replace the strut mounts with KYB's also (sm5811..."}
+{"idx": 6, "title": "Coolant hose connector - Dodge Dart Forum", "date": "", "ddg_snippet": "May 10, 2024 · Dodge- Dart .org is a forum dedicated to the 2013-2016 Dodge Dart . Join and participate in discussions about maintenance, performance mods, and mechanical issues. Get the latest tips, news, browse the classifieds, and more!", "subpage_snippet": "", "source": "www.dodge-dart.org", "link": "https://www.dodge-dart.org/threads/coolant-hose-connector.70491/", "content": "May 10, 2024 · Dodge- Dart .org is a forum dedicated to the 2013-2016 Dodge Dart . Join and participate in discussions about maintenance, performance mods, and mechanical issues. Get the latest tips, news, browse the classifieds, and more!"}
+{"idx": 7, "title": "Transmission Shudder - Dodge Dart Forum", "date": "", "ddg_snippet": "Aug 15, 2018 · 2016 dart gt blacktop. 10k miles. First owner. This has been going on for almost a year now. At about 2000rpm (coasting) if I lightly press the throttle the entire car will jerk violently, the feeling can be compared to almost stalling out a manual transmission. Most the time it's when it...", "subpage_snippet": "", "source": "www.dodge-dart.org", "link": "https://www.dodge-dart.org/threads/transmission-shudder.62830/", "content": "Aug 15, 2018 · 2016 dart gt blacktop. 10k miles. First owner. This has been going on for almost a year now. At about 2000rpm (coasting) if I lightly press the throttle the entire car will jerk violently, the feeling can be compared to almost stalling out a manual transmission. Most the time it's when it..."}
+{"idx": 8, "title": "Dart Wiring Diagrams | Page 5 | Dodge Dart Forum", "date": "", "ddg_snippet": "Jan 28, 2024 · Search Wiring Diagrams Use the following link to search for wiring diagrams for the dart .", "subpage_snippet": "", "source": "www.dodge-dart.org", "link": "https://www.dodge-dart.org/threads/dart-wiring-diagrams.50274/page-5", "content": "Jan 28, 2024 · Search Wiring Diagrams Use the following link to search for wiring diagrams for the dart ."}
+{"idx": 9, "title": "Recirculation Door Actuator Location - Dodge Dart Forum", "date": "", "ddg_snippet": "Jun 10, 2024 · The recirculation door actuator (1) is a reversible 12 volt Direct Current (DC) servo motor. The recirculation door actuator is located on the bottom of the HVAC air inlet housing, behind the instrument panel. The recirculation door actuator is contained within a black molded plastic housing with an integral wire connector receptacle (4). Three mounting tabs (3) allow the actuator to be ...", "subpage_snippet": "", "source": "www.dodge-dart.org", "link": "https://www.dodge-dart.org/threads/recirculation-door-actuator-location.70551/", "content": "Jun 10, 2024 · The recirculation door actuator (1) is a reversible 12 volt Direct Current (DC) servo motor. The recirculation door actuator is located on the bottom of the HVAC air inlet housing, behind the instrument panel. The recirculation door actuator is contained within a black molded plastic housing with an integral wire connector receptacle (4). Three mounting tabs (3) allow the actuator to be ..."}
diff --git a/data/sampled_jsons/DART_Table_2_F1_score_MIMIC-CXR_0.469.jsonl b/data/sampled_jsons/DART_Table_2_F1_score_MIMIC-CXR_0.469.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d387173b845ff44394f0ca2301f99972e649054c
--- /dev/null
+++ b/data/sampled_jsons/DART_Table_2_F1_score_MIMIC-CXR_0.469.jsonl
@@ -0,0 +1,3 @@
+{"idx": 0, "title": "Deep Generative Models: Third MICCAI Workshop ...", "date": "", "ddg_snippet": "8 Oct 2023 — ... MIMIC-CXR [2]. An alignment score salign = fθim2tex (ˆ between an ... Table 2 . Privacy Distillation performance: memorisation ratio ...", "subpage_snippet": "", "source": "dokumen.pub", "link": "https://dokumen.pub/deep-generative-models-third-miccai-workshop-dgm4miccai-2023-held-in-conjunction-with-miccai-2023-vancouver-bc-canada-october-8-2023-proceedings-lecture-notes-in-computer-science-3031537661-9783031537660.html", "content": "8 Oct 2023 — ... MIMIC-CXR [2]. An alignment score salign = fθim2tex (ˆ between an ... Table 2 . Privacy Distillation performance: memorisation ratio ..."}
+{"idx": 1, "title": "Database Systems for Advanced Applications. DASFAA ...", "date": "", "ddg_snippet": "Nan, L., et al.: DART : open-domain structured data record to text generation. ... MIMIC-CXR dataset, and the results are shown in Table 2 . 4.4 Effect of ...", "subpage_snippet": "", "source": "dokumen.pub", "link": "https://dokumen.pub/database-systems-for-advanced-applications-dasfaa-2023-international-workshops-bdms-2023-bdqm-2023-gdma-2023-bundlers-2023-tianjin-china-april-lecture-notes-in-computer-science-3031354141-9783031354144.html", "content": "Nan, L., et al.: DART : open-domain structured data record to text generation. ... MIMIC-CXR dataset, and the results are shown in Table 2 . 4.4 Effect of ..."}
+{"idx": 2, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/DART_radiology_report_generation_focal_consolidation_Figure_2_qualitative_analysis.jsonl b/data/sampled_jsons/DART_radiology_report_generation_focal_consolidation_Figure_2_qualitative_analysis.jsonl
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index 0000000000000000000000000000000000000000..165b25fab41aa40f7d4297d22e3750db292859c9
--- /dev/null
+++ b/data/sampled_jsons/DART_radiology_report_generation_focal_consolidation_Figure_2_qualitative_analysis.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF DART: Disease-aware Image-Text Alignment and Self-correcting Re ...", "date": "", "ddg_snippet": "In this study, we propose a Disease-aware image-text Alignment and self-correcting Re-alignment for Trustworthy radiology report generation ( DART ) framework. In the first stage, we generate ini-tial reports based on image-to-text retrieval with disease-matching, embedding both images and texts in a shared em-bedding space through contrastive ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Park_DART_Disease-aware_Image-Text_Alignment_and_Self-correcting_Re-alignment_for_Trustworthy_Radiology_CVPR_2025_paper.pdf", "content": "In this study, we propose a Disease-aware image-text Alignment and self-correcting Re-alignment for Trustworthy radiology report generation ( DART ) framework. In the first stage, we generate ini-tial reports based on image-to-text retrieval with disease-matching, embedding both images and texts in a shared em-bedding space through contrastive ..."}
+{"idx": 1, "title": "[PDF] Generating Radiology Reports via Memory-driven ...", "date": "", "ddg_snippet": "This paper proposes to generate radiology reports with memory-driven Transformer, where a relational memory is designed to record key information of the ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Generating-Radiology-Reports-via-Memory-driven-Chen-Song/19adf1af8daa9551328226fc6c0140e955bf5689", "content": "This paper proposes to generate radiology reports with memory-driven Transformer, where a relational memory is designed to record key information of the ..."}
+{"idx": 2, "title": "Style-Aware Radiology Report Generation with RadGraph ...", "date": "", "ddg_snippet": "This paper proposes a two -step approach to automatically generating radiology reports , where the first stage focuses on extracting content from the image and ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=54WhV6RTzi&noteId=r4yIv9R28O", "content": "This paper proposes a two -step approach to automatically generating radiology reports , where the first stage focuses on extracting content from the image and ..."}
+{"idx": 3, "title": "Unpaired Medical Report Generation via Cycle-Consistency", "date": "", "ddg_snippet": "The document presents MedCycle, a novel approach for generating medical reports from unpaired X-ray images without the need for consistent labeling schemas.", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/850684837/unpaired-medical-report-generation-cycle-consistency-hirsch-tal", "content": "The document presents MedCycle, a novel approach for generating medical reports from unpaired X-ray images without the need for consistent labeling schemas."}
+{"idx": 4, "title": "Radiology Report Generation via Visual-Semantic Ambivalence-Aware ...", "date": "", "ddg_snippet": "Abstract Radiology report generation , which aims to provide accurate descriptions of both normal and abnormal regions, has been attracting growing research attention. Recently, despite considerable progress, data-driven deep-learning based models still face challenges in capturing and describing the abnormalities, due to the data bias problem.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0893608025009827", "content": "Abstract Radiology report generation , which aims to provide accurate descriptions of both normal and abnormal regions, has been attracting growing research attention. Recently, despite considerable progress, data-driven deep-learning based models still face challenges in capturing and describing the abnormalities, due to the data bias problem."}
+{"idx": 5, "title": "DART | PDF | Artificial Intelligence | Intelligence (AI) & Semantics", "date": "", "ddg_snippet": "The document presents the DART framework, which focuses on automatic radiology report generation by ensuring disease-aware image-text alignment and incorporating a self-correction mechanism. This two-stage approach first generates initial reports through image-to-text retrieval with a disease-matching constraint and then refines these reports by re-aligning them with input X-ray images. The ...", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/876841025/DART", "content": "The document presents the DART framework, which focuses on automatic radiology report generation by ensuring disease-aware image-text alignment and incorporating a self-correction mechanism. This two-stage approach first generates initial reports through image-to-text retrieval with a disease-matching constraint and then refines these reports by re-aligning them with input X-ray images. The ..."}
+{"idx": 6, "title": "From Detection to Radiology Report Generation: Fine-Grained ... - Springer", "date": "", "ddg_snippet": "Radiology report generation plays a critical role in supporting diagnosis, alleviating clinicians' workload, and improving diagnostic accuracy by integrating radiological image content with clinical knowledge. However, most existing models primarily establish coarse-grained mappings between global images and textual reports , often overlooking fine-grained associations between lesion regions ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s10278-025-01650-z", "content": "Radiology report generation plays a critical role in supporting diagnosis, alleviating clinicians' workload, and improving diagnostic accuracy by integrating radiological image content with clinical knowledge. However, most existing models primarily establish coarse-grained mappings between global images and textual reports , often overlooking fine-grained associations between lesion regions ..."}
+{"idx": 7, "title": "Rebuttal - DART: Disease-aware Image-Text Alignment and Self-correcting ...", "date": "", "ddg_snippet": "Rebuttal - DART : Disease-aware Image-Text Alignment and Self-correcting Re-alignment for Trustworthy Radiology Report Generation We sincerely appreciate the efforts of reviewers U2j9, MsGg, and rq7L in evaluating our proposed framework ( DART ). Your insightful feedback has been invaluable in refining our paper. We look forward to your final ratings.", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2504.11786", "content": "Rebuttal - DART : Disease-aware Image-Text Alignment and Self-correcting Re-alignment for Trustworthy Radiology Report Generation We sincerely appreciate the efforts of reviewers U2j9, MsGg, and rq7L in evaluating our proposed framework ( DART ). Your insightful feedback has been invaluable in refining our paper. We look forward to your final ratings."}
+{"idx": 8, "title": "Radiology report generation with medical knowledge and multilevel image ...", "date": "", "ddg_snippet": "Medical report generation is an integral part of computer-aided diagnosis aimed at reducing the workload of radiologists and physicians and alerting them of misdiagnosis risks. In general, medical report generation is an image captioning task.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0933365723002282", "content": "Medical report generation is an integral part of computer-aided diagnosis aimed at reducing the workload of radiologists and physicians and alerting them of misdiagnosis risks. In general, medical report generation is an image captioning task."}
+{"idx": 9, "title": "PDF Complex Organ Mask Guided Radiology Report Generation", "date": "", "ddg_snippet": "1. Introduction Radiology image analysis plays a pivotal role in disease detection [40]. In clinical practice, it is time-consuming, costly, and error-prone for radiologists to review numerous radiology images and generate the corresponding reports for further analysis .", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/WACV2024/papers/Gu_Complex_Organ_Mask_Guided_Radiology_Report_Generation_WACV_2024_paper.pdf", "content": "1. Introduction Radiology image analysis plays a pivotal role in disease detection [40]. In clinical practice, it is time-consuming, costly, and error-prone for radiologists to review numerous radiology images and generate the corresponding reports for further analysis ."}
diff --git a/data/sampled_jsons/DART_trustworthy_radiology_report_generation_arXiv.jsonl b/data/sampled_jsons/DART_trustworthy_radiology_report_generation_arXiv.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b2213cd909d0ffc3505ba03859f2cfaea90d930c
--- /dev/null
+++ b/data/sampled_jsons/DART_trustworthy_radiology_report_generation_arXiv.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2504.11786] DART : Disease-aware Image-Text Alignment and...", "date": "", "ddg_snippet": "View a PDF of the paper titled DART : Disease-aware Image-Text Alignment and Self-correcting Re-alignment for Trustworthy Radiology Report Generation , by Sang-Jun Park and 5 other authors.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2504.11786", "content": "View a PDF of the paper titled DART : Disease-aware Image-Text Alignment and Self-correcting Re-alignment for Trustworthy Radiology Report Generation , by Sang-Jun Park and 5 other authors."}
+{"idx": 1, "title": "DART : Disease-aware Image-Text Alignment and Self-correcting...", "date": "", "ddg_snippet": "In this study, we propose a Disease-aware image-text Alignment and self-correcting Re-alignment for Trustworthy radiology report generation ( DART ) framework.Cross-modal memory networks for radiology report generation . arXiv preprint arXiv :2204.13258, 2022.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.11786v1", "content": "In this study, we propose a Disease-aware image-text Alignment and self-correcting Re-alignment for Trustworthy radiology report generation ( DART ) framework.Cross-modal memory networks for radiology report generation . arXiv preprint arXiv :2204.13258, 2022."}
+{"idx": 2, "title": "(PDF) DART : Disease-aware Image-Text Alignment and Self-correcting...", "date": "", "ddg_snippet": "for Trustworthy Radiology Report Generation . Generating radiology reports via memory-driven trans-. former. arXiv preprint arXiv :2010.16056, 2020. 5,6.", "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": "for Trustworthy Radiology Report Generation . Generating radiology reports via memory-driven trans-. former. arXiv preprint arXiv :2010.16056, 2020. 5,6."}
+{"idx": 3, "title": "GitHub - mk-runner/Awesome- Radiology - Report - Generation : paper...", "date": "", "ddg_snippet": "DART : Disease-aware Image-Text Alignment and Self-correcting Re-alignment for Trustworthy Radiology Report Generation [paper].", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/mk-runner/Awesome-Radiology-Report-Generation", "content": "DART : Disease-aware Image-Text Alignment and Self-correcting Re-alignment for Trustworthy Radiology Report Generation [paper]."}
+{"idx": 4, "title": "Dart", "date": "", "ddg_snippet": "Abstract:The automatic generation of radiology reports has emerged as a promising solution to reduce a time-consuming task and accurately capture critical disease-relevant findings in X-ray images.", "subpage_snippet": "", "source": "www.catalyzex.com", "link": "https://www.catalyzex.com/s/Dart", "content": "Abstract:The automatic generation of radiology reports has emerged as a promising solution to reduce a time-consuming task and accurately capture critical disease-relevant findings in X-ray images."}
+{"idx": 5, "title": "[PDF] Generating Radiology Reports via... | Semantic Scholar", "date": "", "ddg_snippet": "Therefore, automatically generating radiology reports is highly desired to lighten the workload of radiologists and accordingly promote clinical automation, which is an essential task to apply artificial intelligence to the medical domain.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Generating-Radiology-Reports-via-Memory-driven-Chen-Song/19adf1af8daa9551328226fc6c0140e955bf5689", "content": "Therefore, automatically generating radiology reports is highly desired to lighten the workload of radiologists and accordingly promote clinical automation, which is an essential task to apply artificial intelligence to the medical domain."}
+{"idx": 6, "title": "Automatic Radiology Report Generation .pdf", "date": "", "ddg_snippet": "Radiology Report Generation [2] Radiology report generation is automatic generation of report through the use of machine learning techniques. This offers potential to accelerate the report generation process which is time-consuming, repetitive, and e...", "subpage_snippet": "", "source": "www.slideshare.net", "link": "https://www.slideshare.net/slideshow/automatic-radiology-report-generationpdf/259795218?nway-content_model=D", "content": "Radiology Report Generation [2] Radiology report generation is automatic generation of report through the use of machine learning techniques. This offers potential to accelerate the report generation process which is time-consuming, repetitive, and e..."}
+{"idx": 7, "title": "DPN: Dynamics Priori Networks for Radiology Report Generation", "date": "", "ddg_snippet": "Radiology report generation is of significant importance. Unlike standard image captioning tasks, radiology report generation faces more pronounced visual and textual biases due to constrained data availability, making it increasingly reliant on prior knowledge in this context.", "subpage_snippet": "", "source": "www.sciopen.com", "link": "https://www.sciopen.com/article/10.26599/TST.2023.9010134", "content": "Radiology report generation is of significant importance. Unlike standard image captioning tasks, radiology report generation faces more pronounced visual and textual biases due to constrained data availability, making it increasingly reliant on prior knowledge in this context."}
+{"idx": 8, "title": "Attention based automated radiology report generation ... | PLOS One", "date": "", "ddg_snippet": "The automated generation of radiology reports provides X-rays and has tremendous potential to enhance the clinical diagnosis of diseases in patients.", "subpage_snippet": "", "source": "journals.plos.org", "link": "https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0262209", "content": "The automated generation of radiology reports provides X-rays and has tremendous potential to enhance the clinical diagnosis of diseases in patients."}
+{"idx": 9, "title": "Bootstrapping large language models for radiology report generation", "date": "", "ddg_snippet": "Radiology report generation (RRG) aims to automatically generate a free-text description from a specific clinical radiograph, e.g., chest X-Ray images.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1609/aaai.v38i17.29826", "content": "Radiology report generation (RRG) aims to automatically generate a free-text description from a specific clinical radiograph, e.g., chest X-Ray images."}
diff --git a/data/sampled_jsons/DCBM_Data-Efficient_Visual_Concept_Bottleneck_Models_'Section_5'_'first_limitation'_year_2024.jsonl b/data/sampled_jsons/DCBM_Data-Efficient_Visual_Concept_Bottleneck_Models_'Section_5'_'first_limitation'_year_2024.jsonl
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index 0000000000000000000000000000000000000000..bc5e1508e06df5a9bd5503bd351c7cf6d0a50fe3
--- /dev/null
+++ b/data/sampled_jsons/DCBM_Data-Efficient_Visual_Concept_Bottleneck_Models_'Section_5'_'first_limitation'_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "DCBM: Data-Efficient Visual Concept Bottleneck Models", "date": "", "ddg_snippet": "Abstract Concept Bottleneck Models (CBMs) enhance the interpretability of neural networks by basing predictions on human-understandable concepts . However, current CBMs typically rely on concept sets extracted from large language models or extensive image corpora, limiting their effectiveness in data -sparse scenarios. We propose Data-efficient CBMs ( DCBMs ), which reduce the need for large ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.11576v2", "content": "Abstract Concept Bottleneck Models (CBMs) enhance the interpretability of neural networks by basing predictions on human-understandable concepts . However, current CBMs typically rely on concept sets extracted from large language models or extensive image corpora, limiting their effectiveness in data -sparse scenarios. We propose Data-efficient CBMs ( DCBMs ), which reduce the need for large ..."}
+{"idx": 1, "title": "DCBM: Data-Efficient Visual Concept Bottleneck Models", "date": "", "ddg_snippet": "title={{DCBM}: Data-Efficient Visual Concept Bottleneck Models }, author={Prasse, Katharina and Knab, Patrick and Marton, Sascha and Bartelt, Christian and Keuper, Margret},", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/KathPra/DCBM", "content": "title={{DCBM}: Data-Efficient Visual Concept Bottleneck Models }, author={Prasse, Katharina and Knab, Patrick and Marton, Sascha and Bartelt, Christian and Keuper, Margret},"}
+{"idx": 2, "title": "DCBM: Data-Efficient Visual Concept Bottleneck Models :: MPG.PuRe", "date": "", "ddg_snippet": "Author: Prasse, Katharina et al.; Genre: Paper; Published online: 2025; Open Access; Keywords: Computer Science, Computer Vision and Pattern Recognition, cs.CV; Title: DCBM : Data-Efficient Visual Concept Bottleneck Models", "subpage_snippet": "", "source": "pure.mpg.de", "link": "https://pure.mpg.de/pubman/faces/ViewItemFullPage.jsp?itemId=item_3636912_1&view=ACTIONS", "content": "Author: Prasse, Katharina et al.; Genre: Paper; Published online: 2025; Open Access; Keywords: Computer Science, Computer Vision and Pattern Recognition, cs.CV; Title: DCBM : Data-Efficient Visual Concept Bottleneck Models"}
+{"idx": 3, "title": "ICML DCBM: Data-Efficient Visual Concept Bottleneck Models", "date": "", "ddg_snippet": "Poster in Workshop: Actionable Interpretability DCBM : Data-Efficient Visual Concept Bottleneck Models Katharina Prasse · Patrick Knab · Sascha Marton · Christian Bartelt · Margret Keuper", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/49558", "content": "Poster in Workshop: Actionable Interpretability DCBM : Data-Efficient Visual Concept Bottleneck Models Katharina Prasse · Patrick Knab · Sascha Marton · Christian Bartelt · Margret Keuper"}
+{"idx": 4, "title": "Decoupling Concept Bottleneck Model - OpenReview", "date": "", "ddg_snippet": "Motivated by the proposed theorem, we present Decoupling Concept Bottleneck Model ( DCBM ), a novel concept -based model decoupling heterogeneous information into explicit and implicit concepts , while still retaining high prediction performance and interpretability.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=vVbUB9oWUup", "content": "Motivated by the proposed theorem, we present Decoupling Concept Bottleneck Model ( DCBM ), a novel concept -based model decoupling heterogeneous information into explicit and implicit concepts , while still retaining high prediction performance and interpretability."}
+{"idx": 5, "title": "Cross-Modality Image Interpretation via Concept ... - IEEE Xplore", "date": "", "ddg_snippet": "To address these limitations , this work explores the cross-modality interpretation of class-related concepts in image classification. Specifically, we propose decomposed concept bottleneck model ( DCBM ), which utilizes a set of decomposed visual concepts that are extracted directly from images instead of predefined text concepts .", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10535313", "content": "To address these limitations , this work explores the cross-modality interpretation of class-related concepts in image classification. Specifically, we propose decomposed concept bottleneck model ( DCBM ), which utilizes a set of decomposed visual concepts that are extracted directly from images instead of predefined text concepts ."}
+{"idx": 6, "title": "DCBM: Data-Efficient Visual Concept Bottleneck Models", "date": "", "ddg_snippet": "Concept Bottleneck Models (CBMs) enhance the interpretability of neural networks by basing predictions on human-understandable concepts . However, current CBMs typically rely on concept sets extracted from large language models or extensive image corpora, limiting their effectiveness in data -sparse scenarios. We propose Data-efficient CBMs ( DCBMs ), which reduce the need for large sample sizes ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2412.11576", "content": "Concept Bottleneck Models (CBMs) enhance the interpretability of neural networks by basing predictions on human-understandable concepts . However, current CBMs typically rely on concept sets extracted from large language models or extensive image corpora, limiting their effectiveness in data -sparse scenarios. We propose Data-efficient CBMs ( DCBMs ), which reduce the need for large sample sizes ..."}
+{"idx": 7, "title": "GitHub - deepopo/DCBM", "date": "", "ddg_snippet": "This is an implementation of the IEEE TPAMI paper The Decoupling Concept Bottleneck Model ( DCBM ). The vision-language- model (VLM) part is being refined and will be available soon. Figure 1: DCBM Pipeline. (A) DCBM for prediction and interpretation. (B) DCBM for human-machine interaction, including forward intervention and backward rectification.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/deepopo/DCBM", "content": "This is an implementation of the IEEE TPAMI paper The Decoupling Concept Bottleneck Model ( DCBM ). The vision-language- model (VLM) part is being refined and will be available soon. Figure 1: DCBM Pipeline. (A) DCBM for prediction and interpretation. (B) DCBM for human-machine interaction, including forward intervention and backward rectification."}
+{"idx": 8, "title": "Homepage - Patrick Knab", "date": "", "ddg_snippet": "DCBM : Data-Efficient Visual Concept Bottleneck Models Katharina Prasse*, Patrick Knab*, Sascha Marton, Christian Bartelt, Margret Keuper (* equal contribution)", "subpage_snippet": "", "source": "patrick-knab.github.io", "link": "https://patrick-knab.github.io/", "content": "DCBM : Data-Efficient Visual Concept Bottleneck Models Katharina Prasse*, Patrick Knab*, Sascha Marton, Christian Bartelt, Margret Keuper (* equal contribution)"}
+{"idx": 9, "title": "www.mpi-inf.mpg.de", "date": "", "ddg_snippet": "scenarios. We propose Data-efficient CBMs ( DCBMs ), which reduce the need for large sample sizes during concept generation while preserving interpretability.", "subpage_snippet": "", "source": "www.mpi-inf.mpg.de", "link": "https://www.mpi-inf.mpg.de/fileadmin/inf/bibtex/3636912.bib", "content": "scenarios. We propose Data-efficient CBMs ( DCBMs ), which reduce the need for large sample sizes during concept generation while preserving interpretability."}
diff --git a/data/sampled_jsons/DCBM_Data-Efficient_Visual_Concept_Bottleneck_Models_Prasse_Knab_limitations.jsonl b/data/sampled_jsons/DCBM_Data-Efficient_Visual_Concept_Bottleneck_Models_Prasse_Knab_limitations.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..acdae54ecbf42e264aae17fd784815a99418a8fa
--- /dev/null
+++ b/data/sampled_jsons/DCBM_Data-Efficient_Visual_Concept_Bottleneck_Models_Prasse_Knab_limitations.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "DCBM: Data-Efficient Visual Concept Bottleneck Models", "date": "", "ddg_snippet": "by K Prasse — The paper tackles a clear and practical limitation of CBMs—their high dependency on labeled concept data —and proposes a solution that combines ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=BdO4R6XxUH", "content": "by K Prasse — The paper tackles a clear and practical limitation of CBMs—their high dependency on labeled concept data —and proposes a solution that combines ..."}
+{"idx": 1, "title": "DCBM: Data-Efficient Visual Concept Bottleneck Models", "date": "", "ddg_snippet": "2 Jul 2025 — We propose Data - efficient CBMs (DCBMs), which reduce the need for large sample sizes during concept generation while preserving interpretability.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.11576v3", "content": "2 Jul 2025 — We propose Data - efficient CBMs (DCBMs), which reduce the need for large sample sizes during concept generation while preserving interpretability."}
+{"idx": 2, "title": "DCBM: Data-Efficient Visual Concept Bottleneck Models", "date": "", "ddg_snippet": "Due to size limitations , we couldn't upload the complete dataset and corresponding concepts . Therefore, these folders lack content. To run the scripts correctly ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/KathPra/GCBM", "content": "Due to size limitations , we couldn't upload the complete dataset and corresponding concepts . Therefore, these folders lack content. To run the scripts correctly ..."}
+{"idx": 3, "title": "DCBM: Data-efficient Concept Bottleneck Models", "date": "", "ddg_snippet": "13 Jul 2025 — Concept Bottleneck Models (CBMs) enhance the interpretability of neural networks by basing predictions on human-understandable concepts .", "subpage_snippet": "", "source": "slideslive.com", "link": "https://slideslive.com/39041844/dcbm-dataefficient-concept-bottleneck-models", "content": "13 Jul 2025 — Concept Bottleneck Models (CBMs) enhance the interpretability of neural networks by basing predictions on human-understandable concepts ."}
+{"idx": 4, "title": "Patrick Knab: Homepage", "date": "", "ddg_snippet": "Concept Bottleneck Models (CBMs) enhance the interpretability of neural networks by basing predictions on human-understandable concepts . However, current CBMs ...", "subpage_snippet": "", "source": "patrick-knab.github.io", "link": "https://patrick-knab.github.io/", "content": "Concept Bottleneck Models (CBMs) enhance the interpretability of neural networks by basing predictions on human-understandable concepts . However, current CBMs ..."}
+{"idx": 5, "title": "[PDF] Energy-Based Concept Bottleneck Models", "date": "", "ddg_snippet": "Energy-based Concept Bottleneck Models (ECBMs) are proposed, which use a set of neural networks to define the joint energy of candidate (input, concept , ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/9c4ea675f408120b33836da5d58ae112d60aa1cd", "content": "Energy-based Concept Bottleneck Models (ECBMs) are proposed, which use a set of neural networks to define the joint energy of candidate (input, concept , ..."}
+{"idx": 6, "title": "Web Data Mining (Prof. Paulheim) | Universität Mannheim", "date": "", "ddg_snippet": "... Knab 's (INES) and Katharina Prasse 's collaboration has ... DCBM : Data - Efficient Visual Concept Bottleneck Models . International Conference on ...", "subpage_snippet": "", "source": "www.uni-mannheim.de", "link": "https://www.uni-mannheim.de/dws/research/focus-groups/web-data-mining-prof-paulheim/", "content": "... Knab 's (INES) and Katharina Prasse 's collaboration has ... DCBM : Data - Efficient Visual Concept Bottleneck Models . International Conference on ..."}
+{"idx": 7, "title": "Sascha Marton", "date": "", "ddg_snippet": "DCBM : Data - Efficient Visual Concept Bottleneck Models . Concept Bottleneck Models (CBMs) enhance the interpretability of neural networks by basing predictions ...", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/profile/682450ca9e540a8a86dc083b/sascha-marton", "content": "DCBM : Data - Efficient Visual Concept Bottleneck Models . Concept Bottleneck Models (CBMs) enhance the interpretability of neural networks by basing predictions ..."}
+{"idx": 8, "title": "ICML 2025 Thursday 07/17", "date": "", "ddg_snippet": "DCBM : Data - Efficient Visual Concept Bottleneck Models . Poster. Katharina ... data distributions and computational workloads can lead to inconsistent updates and ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/day/7/17", "content": "DCBM : Data - Efficient Visual Concept Bottleneck Models . Poster. Katharina ... data distributions and computational workloads can lead to inconsistent updates and ..."}
+{"idx": 9, "title": "How Size and Position of Objects Challenge ImageNet- Trained ...", "date": "", "ddg_snippet": "Katharina Prasse , Patrick Knab , Sascha Marton, Christian Bartelt, and Margret Keuper. DCBM : Data -. Efficient Visual Concept Bottleneck Models . In ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/e98d61cd43d970bae7bbeab40ed20c7d65b28bff.pdf", "content": "Katharina Prasse , Patrick Knab , Sascha Marton, Christian Bartelt, and Margret Keuper. DCBM : Data -. Efficient Visual Concept Bottleneck Models . In ..."}
diff --git a/data/sampled_jsons/DCBM_visual_concept_bottleneck_models_algorithm_1_area_filtering_implementation.jsonl b/data/sampled_jsons/DCBM_visual_concept_bottleneck_models_algorithm_1_area_filtering_implementation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ce23309bff5626937c23f5bae17b13aa48acc2fe
--- /dev/null
+++ b/data/sampled_jsons/DCBM_visual_concept_bottleneck_models_algorithm_1_area_filtering_implementation.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "DCBM: Data-Efficient Visual Concept Bottleneck Models", "date": "", "ddg_snippet": "by K Prasse — Concept Bottleneck Models (CBMs) enhance the interpretability of neural networks by basing predictions on human-understandable concepts .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=BdO4R6XxUH", "content": "by K Prasse — Concept Bottleneck Models (CBMs) enhance the interpretability of neural networks by basing predictions on human-understandable concepts ."}
+{"idx": 1, "title": "The Decoupling Concept Bottleneck Model", "date": "", "ddg_snippet": "by R Zhang · 2025 · Cited by 6 — The Concept Bottleneck Model (CBM) is an interpretable neural network that leverages high-level concepts to explain model decisions and conduct ...", "subpage_snippet": "", "source": "www.computer.org", "link": "https://www.computer.org/csdl/journal/tp/2025/02/10740789/21w2sYkMsuc", "content": "by R Zhang · 2025 · Cited by 6 — The Concept Bottleneck Model (CBM) is an interpretable neural network that leverages high-level concepts to explain model decisions and conduct ..."}
+{"idx": 2, "title": "DECOUPLING CONCEPT BOTTLENECK MODEL", "date": "", "ddg_snippet": "by R Zhang · Cited by 6 — Concept Bottleneck Model (CBM ) is a kind of powerful interpretable neural net- work, which utilizes high-level concepts to explain model decisions and ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=vVbUB9oWUup", "content": "by R Zhang · Cited by 6 — Concept Bottleneck Model (CBM ) is a kind of powerful interpretable neural net- work, which utilizes high-level concepts to explain model decisions and ..."}
+{"idx": 3, "title": "Downloads 2025", "date": "", "ddg_snippet": "DCBM: Data-Efficient Visual Concept Bottleneck Models · DCTdiff: Intriguing ... Vision-Language Models via Patch&Layer Filtering · Double Machine Learning ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/Downloads/2025", "content": "DCBM: Data-Efficient Visual Concept Bottleneck Models · DCTdiff: Intriguing ... Vision-Language Models via Patch&Layer Filtering · Double Machine Learning ..."}
+{"idx": 4, "title": "Computer Vision and Pattern Recognition Dec 2024", "date": "", "ddg_snippet": "Title: DCBM : Data-Efficient Visual Concept Bottleneck Models . Katharina ... Title: V\"Mean\"ba: Visual State Space Models only need 1 hidden dimension.", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/list/cs.CV/2024-12?skip=1275&show=2000", "content": "Title: DCBM : Data-Efficient Visual Concept Bottleneck Models . Katharina ... Title: V\"Mean\"ba: Visual State Space Models only need 1 hidden dimension."}
+{"idx": 5, "title": "ICML 2025 Thursday 07/17", "date": "", "ddg_snippet": "DCBM : Data-Efficient Visual Concept Bottleneck Models . Poster. Katharina ... The K-means algorithm is one of the most widely studied clustering algorithms in ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/day/7/17", "content": "DCBM : Data-Efficient Visual Concept Bottleneck Models . Poster. Katharina ... The K-means algorithm is one of the most widely studied clustering algorithms in ..."}
+{"idx": 6, "title": "Track: Poster Session 6", "date": "", "ddg_snippet": "26 Apr 2025 — To bridge this gap, we introduce CounterFactual Concept Bottleneck Models (CF-CBMs), a class of models designed to efficiently address the ...", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2025/session/31976", "content": "26 Apr 2025 — To bridge this gap, we introduce CounterFactual Concept Bottleneck Models (CF-CBMs), a class of models designed to efficiently address the ..."}
+{"idx": 7, "title": "Econometrics and Statistics (EcoSta 2025)", "date": "", "ddg_snippet": "21 Aug 2025 — models . A particle filter with expectation-maximization (PF-EM) algorithm is developed, integrating particle filtering with the EM algorithm to.", "subpage_snippet": "", "source": "www.cmstatistics.org", "link": "https://www.cmstatistics.org/EcoSta2025/docs/BoA.pdf?20250728012733", "content": "21 Aug 2025 — models . A particle filter with expectation-maximization (PF-EM) algorithm is developed, integrating particle filtering with the EM algorithm to."}
+{"idx": 8, "title": "Computer Vision and Pattern Recognition Dec 2024", "date": "", "ddg_snippet": "13 Dec 2024 — Title: DCBM : Data-Efficient Visual Concept Bottleneck Models ... Title: V\"Mean\"ba: Visual State Space Models only need 1 hidden dimension.", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/list/cs.CV/2024-12?skip=1425&show=2000", "content": "13 Dec 2024 — Title: DCBM : Data-Efficient Visual Concept Bottleneck Models ... Title: V\"Mean\"ba: Visual State Space Models only need 1 hidden dimension."}
+{"idx": 9, "title": "Machine Learning in Sensors and Imaging", "date": "", "ddg_snippet": "Reprinted from: Sensors 2022, 22,1697,doi:10.3390/s22051697 . . . . . . . . . . . . . . . . . . . . 1 ... Algorithm Based on Deep Reinforcement Learning ...", "subpage_snippet": "", "source": "unglueit-files.s3.amazonaws.com", "link": "https://unglueit-files.s3.amazonaws.com/ebf/af61d61cc2f4412983b7366de44a7cc5.pdf", "content": "Reprinted from: Sensors 2022, 22,1697,doi:10.3390/s22051697 . . . . . . . . . . . . . . . . . . . . 1 ... Algorithm Based on Deep Reinforcement Learning ..."}
diff --git a/data/sampled_jsons/DIL_imitation_learning_on-policy_extension_method.jsonl b/data/sampled_jsons/DIL_imitation_learning_on-policy_extension_method.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5fe6d9c11b5d3bde470eb5ea5e6e6ca345aced1d
--- /dev/null
+++ b/data/sampled_jsons/DIL_imitation_learning_on-policy_extension_method.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Dexterous Manipulation through Imitation Learning: A Survey", "date": "", "ddg_snippet": "24 Apr 2025 — This survey provides an overview of dexterous manipulation methods based on imitation learning , details recent advances, and addresses key ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.03515v2", "content": "24 Apr 2025 — This survey provides an overview of dexterous manipulation methods based on imitation learning , details recent advances, and addresses key ..."}
+{"idx": 1, "title": "Learning Neural Point Processes for Long Event Sequences", "date": "", "ddg_snippet": "by Z Li · 2025 — In this paper, we propose a novel algorithmic framework, called “debiased imitation learning ( DIL )”, to learn temporal point processes from ...", "subpage_snippet": "", "source": "repository.lsu.edu", "link": "https://repository.lsu.edu/cgi/viewcontent.cgi?article=8028&context=gradschool_dissertations", "content": "by Z Li · 2025 — In this paper, we propose a novel algorithmic framework, called “debiased imitation learning ( DIL )”, to learn temporal point processes from ..."}
+{"idx": 2, "title": "solving compositional reinforcement learn", "date": "", "ddg_snippet": "by Y Li · 2021 · Cited by 27 — We propose a novel learning paradigm, Self- Imitation via Reduction (SIR), for solving compositional reinforcement learning problems.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2103.07607", "content": "by Y Li · 2021 · Cited by 27 — We propose a novel learning paradigm, Self- Imitation via Reduction (SIR), for solving compositional reinforcement learning problems."}
+{"idx": 3, "title": "Building Multimodal Web Agents via Iterative Real-World ...", "date": "", "ddg_snippet": "by H He · 2025 · Cited by 8 — The overall process includes one imitation learning phase and three exploration-feedback-optimization cycles. For each phase, we leverage self- ... 20 pages", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.acl-long.1336.pdf", "content": "by H He · 2025 · Cited by 8 — The overall process includes one imitation learning phase and three exploration-feedback-optimization cycles. For each phase, we leverage self- ... 20 pages"}
+{"idx": 4, "title": "Mastering the Game of No-Press Diplomacy via Human ...", "date": "", "ddg_snippet": "by A Bakhtin · Cited by 71 — We then show that DiL-piKL can be extended into a self-play reinforcement learning algorithm we call RL-DiL-piKL that provides a model of human ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=F61FwJTZhb", "content": "by A Bakhtin · Cited by 71 — We then show that DiL-piKL can be extended into a self-play reinforcement learning algorithm we call RL-DiL-piKL that provides a model of human ..."}
+{"idx": 5, "title": "Humanoid robotic system for social interaction using deep ...", "date": "", "ddg_snippet": "by SB Alotaibi · 2022 · Cited by 7 — DIL focuses on mimicking human learning or expertise presentation to govern robot behavior.", "subpage_snippet": "", "source": "www.frontiersin.org", "link": "https://www.frontiersin.org/journals/sustainable-cities/articles/10.3389/frsc.2022.1076101/full", "content": "by SB Alotaibi · 2022 · Cited by 7 — DIL focuses on mimicking human learning or expertise presentation to govern robot behavior."}
+{"idx": 6, "title": "Building Strategic AI Agents for Human-centric Multi- ...", "date": "", "ddg_snippet": "by AP Jacob · 2024 — The research introduces the DiL -piKL planning algorithm and its extension , RL- DiL -piKL, which regularize self-play reinforcement learning and search towards a ...", "subpage_snippet": "", "source": "dspace.mit.edu", "link": "https://dspace.mit.edu/bitstream/handle/1721.1/158481/jacob-apjacob-phd-eecs-2024-thesis.pdf?sequence=1&isAllowed=y", "content": "by AP Jacob · 2024 — The research introduces the DiL -piKL planning algorithm and its extension , RL- DiL -piKL, which regularize self-play reinforcement learning and search towards a ..."}
+{"idx": 7, "title": "[PDF] A Fast Integrated Planning and Control Framework ...", "date": "", "ddg_snippet": "This is the first survey to focus on AD policy learning using DRL/ DIL , which is addressed simultaneously from the system, task-driven and problem-driven ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/A-Fast-Integrated-Planning-and-Control-Framework-Sun-Peng/a697f57b76c9072bb85b56fe6f987c598802b6ce", "content": "This is the first survey to focus on AD policy learning using DRL/ DIL , which is addressed simultaneously from the system, task-driven and problem-driven ..."}
+{"idx": 8, "title": "Track: Poster Session 5", "date": "", "ddg_snippet": "25 Apr 2025 — ... DIL, a principled framework that directly optimizes the imitation learning objective . DIL provides a unified imitation learning perspective ...", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2025/session/31975", "content": "25 Apr 2025 — ... DIL, a principled framework that directly optimizes the imitation learning objective . DIL provides a unified imitation learning perspective ..."}
+{"idx": 9, "title": "Differentiable Implicit Layers - ML4Eng", "date": "", "ddg_snippet": "by A Look · Cited by 13 — In this paper, we introduce an efficient backpropagation scheme for non- constrained implicit functions. These functions are parametrized by a set of learn-. 12 pages", "subpage_snippet": "", "source": "ml4eng.github.io", "link": "https://ml4eng.github.io/camera_readys/43.pdf", "content": "by A Look · Cited by 13 — In this paper, we introduce an efficient backpropagation scheme for non- constrained implicit functions. These functions are parametrized by a set of learn-. 12 pages"}
diff --git a/data/sampled_jsons/DPO_Direct_Preference_Optimization_Rafailov_2024_Bradley-Terry_year_2024.jsonl b/data/sampled_jsons/DPO_Direct_Preference_Optimization_Rafailov_2024_Bradley-Terry_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a4c621c81feba68148e52fb4ba537e9919991485
--- /dev/null
+++ b/data/sampled_jsons/DPO_Direct_Preference_Optimization_Rafailov_2024_Bradley-Terry_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Direct Preference Optimization: Your Language Model is ...", "date": "", "ddg_snippet": "by R Rafailov · 2023 · Cited by 5221 — The key insight of the DPO algorithm is that we can impose certain constraints on the under-constrained Plackett-Luce (and Bradley - Terry in ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2305.18290", "content": "by R Rafailov · 2023 · Cited by 5221 — The key insight of the DPO algorithm is that we can impose certain constraints on the under-constrained Plackett-Luce (and Bradley - Terry in ..."}
+{"idx": 1, "title": "Direct Preference Optimization (DPO) - Deep (Learning) Focus", "date": "", "ddg_snippet": "Given a fixed preference dataset, we can train an RM to produce scores that reflect the observed human preferences , as modeled by Bradley - Terry .", "subpage_snippet": "", "source": "cameronrwolfe.substack.com", "link": "https://cameronrwolfe.substack.com/p/direct-preference-optimization", "content": "Given a fixed preference dataset, we can train an RM to produce scores that reflect the observed human preferences , as modeled by Bradley - Terry ."}
+{"idx": 2, "title": "Direct Preference Optimization Explained In-depth", "date": "", "ddg_snippet": "13 Apr 2024 — This method of preference tuning is an alternative to Reinforcement Learning from Human Feedback (RLHF) that avoids the actual reinforcement learning.", "subpage_snippet": "", "source": "www.tylerromero.com", "link": "https://www.tylerromero.com/posts/2024-04-dpo/", "content": "13 Apr 2024 — This method of preference tuning is an alternative to Reinforcement Learning from Human Feedback (RLHF) that avoids the actual reinforcement learning."}
+{"idx": 3, "title": "Direct Preference Optimization: Your Language Model is ...", "date": "", "ddg_snippet": "by R Rafailov · Cited by 5221 — The method is equivalent to fitting a reparameterized Bradley - Terry model. With mild assumptions, DPO does not constrain the class of learned reward models.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=HPuSIXJaa9", "content": "by R Rafailov · Cited by 5221 — The method is equivalent to fitting a reparameterized Bradley - Terry model. With mild assumptions, DPO does not constrain the class of learned reward models."}
+{"idx": 4, "title": "Direct Preference Optimization: Your Language Model is ...", "date": "", "ddg_snippet": "Our experiments show that DPO can fine-tune LMs to align with human preferences as well as or better than existing methods. Notably, fine-tuning with DPO ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2305.18290v2", "content": "Our experiments show that DPO can fine-tune LMs to align with human preferences as well as or better than existing methods. Notably, fine-tuning with DPO ..."}
+{"idx": 5, "title": "Indirect Online Preference Optimization via Reinforcement ...", "date": "", "ddg_snippet": "DPO [Rafailov et al., 2024]: Given preference data,. DPO fits a binary classifier based on the Bradley-Terry model . DPO propose a sigmoid loss on the normalized.", "subpage_snippet": "", "source": "www.ijcai.org", "link": "https://www.ijcai.org/proceedings/2025/0061.pdf", "content": "DPO [Rafailov et al., 2024]: Given preference data,. DPO fits a binary classifier based on the Bradley-Terry model . DPO propose a sigmoid loss on the normalized."}
+{"idx": 6, "title": "Direct Preference Optimization: Your Language Model is ...", "date": "", "ddg_snippet": "In this paper, we show how to directly optimize a language model to adhere to human preferences, without explicit reward modeling or reinforcement learning. We ...", "subpage_snippet": "", "source": "nips.cc", "link": "https://nips.cc/virtual/2023/poster/72164", "content": "In this paper, we show how to directly optimize a language model to adhere to human preferences, without explicit reward modeling or reinforcement learning. We ..."}
+{"idx": 7, "title": "Direct Multi-Turn Preference Optimization for Language ...", "date": "", "ddg_snippet": "by W Shi · 2024 · Cited by 27 — DPO optimizes RL ob- jectives by maximizing the likelihood of preferred responses over dis-preferred responses, mitigating the need for ...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2024.emnlp-main.138.pdf", "content": "by W Shi · 2024 · Cited by 27 — DPO optimizes RL ob- jectives by maximizing the likelihood of preferred responses over dis-preferred responses, mitigating the need for ..."}
+{"idx": 8, "title": "Right Now, Wrong Then: Non-Stationary Direct Preference ...", "date": "", "ddg_snippet": "In this work, we propose Non-Stationary Direct Preference Optimization (NS- DPO ), a novel approach that uses a probabilistic Dynamic Bradley - Terry model ( ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/44703", "content": "In this work, we propose Non-Stationary Direct Preference Optimization (NS- DPO ), a novel approach that uses a probabilistic Dynamic Bradley - Terry model ( ..."}
+{"idx": 9, "title": "Practical Limitations of Direct Preference Optimization ...", "date": "", "ddg_snippet": "DPO utilizes the Bradley-Terry model [8] to learn human preferences on responses generated by LLMs from pairwise human preference data. Conversely, PPO uses ...", "subpage_snippet": "", "source": "swtheking.notion.site", "link": "https://swtheking.notion.site/d94a4fc884db479392707dadf23ca293?pvs=21", "content": "DPO utilizes the Bradley-Terry model [8] to learn human preferences on responses generated by LLMs from pairwise human preference data. Conversely, PPO uses ..."}
diff --git "a/data/sampled_jsons/DSDFM_D_Drift_calculation_Algorithm_1_line_8_t-1_\316\265_noise.jsonl" "b/data/sampled_jsons/DSDFM_D_Drift_calculation_Algorithm_1_line_8_t-1_\316\265_noise.jsonl"
new file mode 100644
index 0000000000000000000000000000000000000000..57dc468d20a07dd66f7977c1ce8fecb7f0e38d8d
--- /dev/null
+++ "b/data/sampled_jsons/DSDFM_D_Drift_calculation_Algorithm_1_line_8_t-1_\316\265_noise.jsonl"
@@ -0,0 +1,8 @@
+{"idx": 0, "title": "Bayesian dynamic modelling for probabilistic prediction of ...", "date": "", "ddg_snippet": "Jul 1 , 2024 · In this regard, online learning that does not require large amounts of training data and updates the best predictor for future data at each step is beneficial for tracking such concept drift . Hence, an online learning algorithm is introduced to GPR to deal with the time-varying characteristics of the pavement performance.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0952197624007954", "content": "Jul 1 , 2024 · In this regard, online learning that does not require large amounts of training data and updates the best predictor for future data at each step is beneficial for tracking such concept drift . Hence, an online learning algorithm is introduced to GPR to deal with the time-varying characteristics of the pavement performance."}
+{"idx": 1, "title": "Autonomous Golf Swing Analysis AI | AI Tutorial | Next ...", "date": "", "ddg_snippet": "PDF A Framework for Comprehensive Analysis of a Swing in Sports Using Low ... — forward segments of the swing, enabling us to calculate drift -free linear velocity along with the relative 3D position of the golf club during the entire swing.", "subpage_snippet": "", "source": "next.gr", "link": "https://next.gr/ai/ai-in-mobile-apps/autonomous-golf-swing-analysis-ai", "content": "PDF A Framework for Comprehensive Analysis of a Swing in Sports Using Low ... — forward segments of the swing, enabling us to calculate drift -free linear velocity along with the relative 3D position of the golf club during the entire swing."}
+{"idx": 2, "title": "2012 NCTS Workshop on Dynamical Systems [16pt] National Center...", "date": "", "ddg_snippet": "Near saddle: Diusion dominated dynamics. δ 1 > δ0 with f − 1 ; δ0 in domain of attraction of x−( t ) Drift dominated dynamics. Below δ0: behaviour as for small σ. Barbara Gentz.Theorem If σ σc: Paths likely to stay in B(h) until time ε 2/3 after bifurcation; maximal spreading σ/ ε 1 /6.", "subpage_snippet": "", "source": "www.math.uni-bielefeld.de", "link": "https://www.math.uni-bielefeld.de/~gentz/slides/NCTS_Gentz_3.pdf", "content": "Near saddle: Diusion dominated dynamics. δ 1 > δ0 with f − 1 ; δ0 in domain of attraction of x−( t ) Drift dominated dynamics. Below δ0: behaviour as for small σ. Barbara Gentz.Theorem If σ σc: Paths likely to stay in B(h) until time ε 2/3 after bifurcation; maximal spreading σ/ ε 1 /6."}
+{"idx": 3, "title": "DE-Sync: A Doppler-Enhanced Time Synchronization for Mobile ...", "date": "", "ddg_snippet": "Typically, the clock inside each node has an intrinsic drift in angular frequency due to the manufacturing process. The numerous synchronization protocols use a common time model that DE-Sync also follows to estimate the skew and offset. The local time of any node is relevant to the reference time by Equation ( 1 ).", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/1424-8220/18/6/1710", "content": "Typically, the clock inside each node has an intrinsic drift in angular frequency due to the manufacturing process. The numerous synchronization protocols use a common time model that DE-Sync also follows to estimate the skew and offset. The local time of any node is relevant to the reference time by Equation ( 1 )."}
+{"idx": 4, "title": "Samarth0710/neurips-2024-peer-reviews-test-10 · Datasets at ...", "date": "", "ddg_snippet": "Samarth0710/neurips-2024-peer-reviews-test-10 · Datasets at Hugging Facetrain · 4.24k rows", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/Samarth0710/neurips-2024-peer-reviews-test-10/viewer", "content": "Samarth0710/neurips-2024-peer-reviews-test-10 · Datasets at Hugging Facetrain · 4.24k rows"}
+{"idx": 5, "title": "Postpr int - kth.diva-portal.org", "date": "", "ddg_snippet": "In contrast, the RFMP vector field may drift the flow away from the target distribution at t > 1 (see the top row of Figures 1 , 3, and 4). There- fore, SRFMPs provide flexibility and increased robustness in designing the generation process, while RFMPs are more sensitive to the integration process.", "subpage_snippet": "", "source": "kth.diva-portal.org", "link": "https://kth.diva-portal.org/smash/get/diva2:1998239/FULLTEXT01.pdf", "content": "In contrast, the RFMP vector field may drift the flow away from the target distribution at t > 1 (see the top row of Figures 1 , 3, and 4). There- fore, SRFMPs provide flexibility and increased robustness in designing the generation process, while RFMPs are more sensitive to the integration process."}
+{"idx": 6, "title": "FEED-BORNE BACILLUS CEREUS: AN EMERGING THREAT TO FOOD CHAIN ...", "date": "", "ddg_snippet": "Bacillus cereus is a Gram-positive, rod-shaped, motile (flagellated), aerobic or facultative anaerobic, spore and biofilm forming bacterium, commonly found in nature. It belongs to a group of genetically similar forms assigned to the genus Bacillus,", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/75424353/FEED_BORNE_BACILLUS_CEREUS_AN_EMERGING_THREAT_TO_FOOD_CHAIN_RELATED_HAZARD_SAFETY_AND_PATHOGENIC_POTENTIALITY", "content": "Bacillus cereus is a Gram-positive, rod-shaped, motile (flagellated), aerobic or facultative anaerobic, spore and biofilm forming bacterium, commonly found in nature. It belongs to a group of genetically similar forms assigned to the genus Bacillus,"}
+{"idx": 7, "title": "Steve Bell-Quantitative Finance For Dummies-For Dummies (2016)", "date": "", "ddg_snippet": "Quantitative Financeby Steve Bell Quantitative Finance For Dummies® Published by: John Wiley & Sons, Ltd., The Atriu...", "subpage_snippet": "", "source": "pdfcoffee.com", "link": "https://pdfcoffee.com/steve-bell-quantitative-finance-for-dummies-for-dummies-2016-pdf-free.html", "content": "Quantitative Financeby Steve Bell Quantitative Finance For Dummies® Published by: John Wiley & Sons, Ltd., The Atriu..."}
diff --git a/data/sampled_jsons/DVI_A_Derivative-based_Vision_Network_for_INR_OpenReview_PDF.jsonl b/data/sampled_jsons/DVI_A_Derivative-based_Vision_Network_for_INR_OpenReview_PDF.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..05fa25b2beec2b509559bb6e7569792981238493
--- /dev/null
+++ b/data/sampled_jsons/DVI_A_Derivative-based_Vision_Network_for_INR_OpenReview_PDF.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "DVI:A Derivative-based Vision Network for INR - OpenReview", "date": "", "ddg_snippet": "DVI excels by extracting semantic information from the high order derivative map of the INR , then seamlessly fusing it into a pre-existing raster- based vision network , enhancing its performance with deeper, task-relevant semantic insights.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=4Xnqm4f71y", "content": "DVI excels by extracting semantic information from the high order derivative map of the INR , then seamlessly fusing it into a pre-existing raster- based vision network , enhancing its performance with deeper, task-relevant semantic insights."}
+{"idx": 1, "title": "ICML Poster DVI:A Derivative-based Vision Network for INR", "date": "", "ddg_snippet": "DVI:A Derivative-based Vision Network for INR RUNZHAO YANG · Xiaolong Wu · Zhihong Zhang · Fabian Zhang · Tingxiong Xiao · Zongren Li · Kunlun He · Jinli Suo", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46476", "content": "DVI:A Derivative-based Vision Network for INR RUNZHAO YANG · Xiaolong Wu · Zhihong Zhang · Fabian Zhang · Tingxiong Xiao · Zongren Li · Kunlun He · Jinli Suo"}
+{"idx": 2, "title": "DVI:A Derivative-based Vision Network for INR - AMiner", "date": "", "ddg_snippet": "Recent advancements in computer vision have seen Implicit Neural Representations ( INR ) becoming a dominant representation form for data due to their compactness and expre", "subpage_snippet": "", "source": "www.aminer.cn", "link": "https://www.aminer.cn/pub/6853e848163c01c8502f0416/dvi-a-derivative-based-vision-network-for-inr", "content": "Recent advancements in computer vision have seen Implicit Neural Representations ( INR ) becoming a dominant representation form for data due to their compactness and expre"}
+{"idx": 3, "title": "Implicit Neural Representation for Vision", "date": "", "ddg_snippet": "Downstream tasks based on INRs : Besides being an efficient and unified representation, INR presents exciting opportunities for various vision tasks. These include enhancing signals, recognizing patterns, and generating new data.", "subpage_snippet": "", "source": "inrv.github.io", "link": "https://inrv.github.io/", "content": "Downstream tasks based on INRs : Besides being an efficient and unified representation, INR presents exciting opportunities for various vision tasks. These include enhancing signals, recognizing patterns, and generating new data."}
+{"idx": 4, "title": "OpenReview", "date": "", "ddg_snippet": "Publications DVI:A Derivative-based Vision Network for INR Runzhao Yang, Xiaolong Wu, Zhihong Zhang, Fabian Zhang, Tingxiong Xiao, Zongren Li, Kunlun He, Jinli Suo Published: 01 May 2025, Last Modified: 23 Jul 2025 ICML 2025 poster", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/profile?id=~Kunlun_He1", "content": "Publications DVI:A Derivative-based Vision Network for INR Runzhao Yang, Xiaolong Wu, Zhihong Zhang, Fabian Zhang, Tingxiong Xiao, Zongren Li, Kunlun He, Jinli Suo Published: 01 May 2025, Last Modified: 23 Jul 2025 ICML 2025 poster"}
+{"idx": 5, "title": "INR network for medical image reconstruction - GitHub", "date": "", "ddg_snippet": "INR network for medical image reconstruction. Contribute to ILoveU3D/ INR -Cone development by creating an account on GitHub.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/ILoveU3D/INR-Cone", "content": "INR network for medical image reconstruction. Contribute to ILoveU3D/ INR -Cone development by creating an account on GitHub."}
+{"idx": 6, "title": "DVI:A Derivative-based Vision Network for INR", "date": "", "ddg_snippet": "DVI excels by extracting semantic information from the high order derivative map of the INR , then seamlessly fusing it into a pre-existing raster- based vision network , enhancing its performance with deeper, task-relevant semantic insights.", "subpage_snippet": "", "source": "chatpaper.com", "link": "https://chatpaper.com/chatpaper/paper/167863", "content": "DVI excels by extracting semantic information from the high order derivative map of the INR , then seamlessly fusing it into a pre-existing raster- based vision network , enhancing its performance with deeper, task-relevant semantic insights."}
+{"idx": 7, "title": "PDF DINER: Disorder-Invariant Implicit Neural Representation", "date": "", "ddg_snippet": "Abstract Implicit neural representation ( INR ) characterizes the attributes of a signal as a function of corresponding coor-dinates which emerges as a sharp weapon for solving in-verse problems. However, the capacity of INR is limited by the spectral bias in the network training.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2023/papers/Xie_DINER_Disorder-Invariant_Implicit_Neural_Representation_CVPR_2023_paper.pdf", "content": "Abstract Implicit neural representation ( INR ) characterizes the attributes of a signal as a function of corresponding coor-dinates which emerges as a sharp weapon for solving in-verse problems. However, the capacity of INR is limited by the spectral bias in the network training."}
+{"idx": 8, "title": "OpenReview", "date": "", "ddg_snippet": "Publications DVI:A Derivative-based Vision Network for INR Runzhao Yang, Xiaolong Wu, Zhihong Zhang, Fabian Zhang, Tingxiong Xiao, Zongren Li, Kunlun He, Jinli Suo ICML 2025 poster X-NeMo: Expressive Neural Motion Reenactment via Disentangled Latent Attention", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/profile?id=~Jinli_Suo1", "content": "Publications DVI:A Derivative-based Vision Network for INR Runzhao Yang, Xiaolong Wu, Zhihong Zhang, Fabian Zhang, Tingxiong Xiao, Zongren Li, Kunlun He, Jinli Suo ICML 2025 poster X-NeMo: Expressive Neural Motion Reenactment via Disentangled Latent Attention"}
+{"idx": 9, "title": "Where Do We Stand with Implicit Neural Representations? A Technical and ...", "date": "", "ddg_snippet": "This sur-vey provides a comprehensive review of state-of-the-art INR methods, introducing a clear taxonomy that categorises them into four key areas: activation functions, position encod-ing, combined strategies, and network structure optimisation.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2411.03688", "content": "This sur-vey provides a comprehensive review of state-of-the-art INR methods, introducing a clear taxonomy that categorises them into four key areas: activation functions, position encod-ing, combined strategies, and network structure optimisation."}
diff --git a/data/sampled_jsons/DVI_A_Derivative-based_Vision_Network_for_INR_Section_3.3_derivative_computation_technique.jsonl b/data/sampled_jsons/DVI_A_Derivative-based_Vision_Network_for_INR_Section_3.3_derivative_computation_technique.jsonl
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Computer vision - Wikipedia", "date": "", "ddg_snippet": "Computer vision tasks include methods for acquiring, processing, analyzing, and understanding digital images, and extraction of high-dimensional data from the real world in order to produce numerical or symbolic information, e.g. in the form of decis...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Computer_vision", "content": "Computer vision tasks include methods for acquiring, processing, analyzing, and understanding digital images, and extraction of high-dimensional data from the real world in order to produce numerical or symbolic information, e.g. in the form of decis..."}
+{"idx": 1, "title": "ICML Poster DVI : A Derivative - based Vision Network for INR", "date": "", "ddg_snippet": "DVI excels by extracting semantic information from the high order derivative map of the INR , then seamlessly fusing it into a pre-existing raster- based vision network , enhancing its performance with deeper, task-relevant semantic insights.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46476", "content": "DVI excels by extracting semantic information from the high order derivative map of the INR , then seamlessly fusing it into a pre-existing raster- based vision network , enhancing its performance with deeper, task-relevant semantic insights."}
+{"idx": 2, "title": "DVI:A Derivative-based Vision Network for INR - OpenReview", "date": "", "ddg_snippet": "May 1, 2025 · To address these issues, we propose DVI , a novel Derivative-based Vision network for INR , capable of handling a variety of vision tasks across various data modalities, while achieving the best performance among the existing methods by incorporating state of the art raster- based methods into a INR based architecture.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=4Xnqm4f71y", "content": "May 1, 2025 · To address these issues, we propose DVI , a novel Derivative-based Vision network for INR , capable of handling a variety of vision tasks across various data modalities, while achieving the best performance among the existing methods by incorporating state of the art raster- based methods into a INR based architecture."}
+{"idx": 3, "title": "book13.dvi - Stanford University ICML Poster DVI:A Derivative-based Vision Network for INR Chap3slides.dvi - Charu Aggarwal DVI:A Derivative-based Vision Network for INR - AMiner Data Denoising and Derivative Estimation for Data-Driven ... Implicit Neural Representation for Vision", "date": "", "ddg_snippet": "A convolutional neural network , or CNN, contains one or more convolutional layers. There can also be nonconvolutional layers, such as fully connected layers and pooled layers. However, there is an important additional constraint: the weights on the inputs must be the same for all nodes of a single convolutional layer. More precisely, suppose that e... See full list on infolab.stanford.edu Building a neural net to solve a given problem is partially art and partially science. Before we can begin to train a net by finding the weights on the inputs that serve our goals best, we need to make a number of design decisions. These include answering the following questions: How many hidden layers should we use? How many nodes will there be in... See full list on infolab.stanford.edu node (perceptron) in a neural net is designed to give a 0 or 1 (yes or no) output. Often, we want to modify that output is one of several ways, so we apply an activation function to the output of a node. In some cases, the activation function takes all the outputs of a layer and modifies them as a group. the reason we need an activation function is... See full list on infolab.stanford.edu Given that we cannot use the step function, we look for alternatives in the class of sigmoid functions – so called because of the S-shaped curve that these functions exhibit. The most commonly used sigmoid function is the logistic sigmoid: See full list on infolab.stanford.edu Suppose the model has a single continuous-valued output, and (x, y) ˆ is a training example. For the same input x, suppose the predicted output of the neural net is y. Then the squared error loss L(y, ˆy) of this prediction is: See full list on infolab.stanford.edu ✪ qi Even though KL-divergence is often regarded as a distance, it is not truly a distance measure because it is not commutative. However, it is perfectly adequate as a loss function for our purposes, since there is in fact an inherent assymmetry in the situation: p is the ground truth while q is the predicted output. Notice that minimizing the KL-... See full list on infolab.stanford.edu We now turn to the problem of training a deep network . Training a network means finding good values for the parameters (weights and thresholds) of the network . Usually, we shall have access to a training set of labeled input/output pairs. The training process tries to find parameter values that minimize the average loss on the training set. The hop... See full list on infolab.stanford.edu = MSE(y, ˆy) Each of these steps corresponds to one of the four nodes in the middle row, in order from the left. The first step corresponds to the node with operand u and operator ×. Here is an example where it must be understood that the node labeled W is the first argument. If necessary, we could label each incoming edge with a number to indicate... See full list on infolab.stanford.edu For functions of vectors, we can restate the chain rule in terms of gradients and Jacobians. Suppose y = g(x) and z = f(y) = f(g(x)) then: ∇xz = Jx(y)∇yz If z = f(u, v) where u = g(x) and v = h(x), then ∇xz = Jx(u)∇uz + Jx(v)∇vz See full list on infolab.stanford.edu = = = = Differentiating, we get: − Xi pi log qi − Xi pi(yi − log(Xj eyj)) − Xi piyi − log(Xj eyj) Xi pi − Xi piyi − log(Xj eyj) ∂l ✪ ∂yk = −pk = −pk eyk ✪ Pj eyj μ(yk) = qk − pk Therefore, we end up with the rather neat result: ∇yl = q − p This combined gradient does not saturate or explode, and leads to good learning behavior. That observation exp... See full list on infolab.stanford.edu Given a set of training examples, we run the compute graph in both directions for each example: forward (to compute the loss) and backwards (to compute the gradients). We average the loss and gradients across the training set to compute the average loss and the average gradient for each parameter vector. At each iteration we update each parameter v... See full list on infolab.stanford.edu Previously, we have imagined that the inputs to a neural net are one-dimensional vectors. But there is no reason why we cannot view the input as having a higher dimension. Example 13.6 : A grey-scale photo might be represented by a two-dimensional array of real numbers, corresponding to the intensity of each pixel. Each pixel of a color image typic... See full list on infolab.stanford.edu This subsection is a short detour to explain why Convolutional Neural Networks are so named. It is not a pre-requisite for any of the other material in this chapter. The convolutional layer is named because of the resemblance it bears to the convolution operation from functional analysis, which is often used in signal processing and probability the... See full list on infolab.stanford.edu −∞ Z ∞ f(t − τ)g(τ)dτ −∞ Here we are interested in the discrete version of convolution, where f and g are defined over the integers: ∞ ∞ See full list on infolab.stanford.edu Just as CNN’s are a specialized family of neural networks for processing 2-dimensional image data, recurrent neural networks (RNN’s) are networks spe-cially designed for processing sequential data. Sequential data naturally arises in many settings: a sentence is a sequence of words; a video is a sequence of images; a stock market ticker is a sequen... See full list on infolab.stanford.edu We use backpropagation to train an RNN, just as we would any neural network . Let us work through an example. Suppose our input consists of sequences of length n. Our network uses the activation function tanh for the state update, softmax for the output, and the loss function is cross-entropy. Since the network has several outputs, one at each time-... See full list on infolab.stanford.edu Moreover, suppose W is a matrix, and w is the vector obtained by concatenating the rows of W. Then: dz dW dy dW dz = ✪ dw dy = ✪ dw These conventions also extend naturally to partial derivatives. We use backpropagation to compute the gradients of the error with respect to the network parameters. We focus on ✪ de ; the gradients for U and V are dW s... See full list on infolab.stanford.edu The LSTM model is a refinement of the basic RNN model to address the prob-lem of learning long-distance associations. In the past few years, LSTM has become popular as the de-facto sequence-learning model, and has been used with success in many applications. Let us understand the intuition behind the LSTM model before we describe it formally. The m... See full list on infolab.stanford.edu Thus far, we have presented our goal as one of minimizing loss (i.e., prediction error) on the training set. Gradient descent and stochastic gradient descent help us achieve this objective. In practice, the real objective of training is to minimize the loss on new and hitherto unseen inputs. Our hope is that our training set is representative of un... See full list on infolab.stanford.edu Gradient descent is not guaranteed to learn parameters (weights and biases) that reduce the training loss to an absolute minimum. In practice, the pro-cedure learns parameters that correspond to a local minimum in the training loss. There are usually many local minima, and some might lead to better gen-eralization than others. In practice, it has b... See full list on infolab.stanford.edu Dropout is a technique that reduces overfitting by making random changes to the underlying deep neural network . Recall that when we train using stochastic gradient descent, at each step we sample at random a minibatch of inputs to process. When using dropout, we also select at random a certain fraction (say half) of all the hidden nodes from the ne... See full list on infolab.stanford.edu In Section 13.6.1 we suggested iterating through training examples (or mini-batches) until we reach a local minimum in the loss function. In practice, this approach leads to overfitting. It has been observed that while the loss on the training set (the training loss) decreases through the training process, the loss on the test set (the test loss) o... See full list on infolab.stanford.edu The accuracy of most machine-learning models increases when we provide ad-ditional training data. Usually, larger training sets also lead to less overfitting. When the actual training data available is limited, we can often create addi-tional synthetic training examples by applying transformations or adding noise. For example, consider the digit-cl... See full list on infolab.stanford.edu ✦ Neural Nets: A neural net is a collection of perceptrons (nodes), usually organized in layers, where the outputs from one layer provide inputs to the next layers. The first (inout) layer takes external inputs, and the last (output) layer indicates the class of the input. Other layers in the middle are called hidden layers and generally are traine... See full list on infolab.stanford.edu DVI : A Derivative-based Vision Network for INR RUNZHAO YANG · Xiaolong Wu · Zhihong Zhang · Fabian Zhang · Tingxiong Xiao · Zongren Li · Kunlun He · Jinli Suo An Exponential Time Algorithm for Computing Partial Derivatives The path aggregation lemma provides a simple way to com-pute the derivative with respect to intermediate variable w Use computational graph to compute each value Recent advancements in computer vision have seen Implicit Neural Representations ( INR ) becoming a dominant representation form for data due to their compactness and expre 4 days ago · The paper is organised as follows. Section 2 summarises the SINDy algorithm. Section 3 reviews the most effective two-step approaches. Section 4 introduces RKTV- INR , our two-step INR - based framework for data-driven modeling with time dependence and noise. Section 5 presents experiments demonstrating the effectiveness of the proposed method. Downstream tasks based on INRs: Besides being an efficient and unified representation, INR presents exciting opportunities for various vision tasks. These include enhancing signals, recognizing patterns, and generating new data.", "subpage_snippet": "", "source": "infolab.stanford.edu", "link": "http://infolab.stanford.edu/~ullman/mmds/ch13.pdf", "content": "A convolutional neural network , or CNN, contains one or more convolutional layers. There can also be nonconvolutional layers, such as fully connected layers and pooled layers. However, there is an important additional constraint: the weights on the inputs must be the same for all nodes of a single convolutional layer. More precisely, suppose that e... See full list on infolab.stanford.edu Building a neural net to solve a given problem is partially art and partially science. Before we can begin to train a net by finding the weights on the inputs that serve our goals best, we need to make a number of design decisions. These include answering the following questions: How many hidden layers should we use? How many nodes will there be in... See full list on infolab.stanford.edu node (perceptron) in a neural net is designed to give a 0 or 1 (yes or no) output. Often, we want to modify that output is one of several ways, so we apply an activation function to the output of a node. In some cases, the activation function takes all the outputs of a layer and modifies them as a group. the reason we need an activation function is... See full list on infolab.stanford.edu Given that we cannot use the step function, we look for alternatives in the class of sigmoid functions – so called because of the S-shaped curve that these functions exhibit. The most commonly used sigmoid function is the logistic sigmoid: See full list on infolab.stanford.edu Suppose the model has a single continuous-valued output, and (x, y) ˆ is a training example. For the same input x, suppose the predicted output of the neural net is y. Then the squared error loss L(y, ˆy) of this prediction is: See full list on infolab.stanford.edu ✪ qi Even though KL-divergence is often regarded as a distance, it is not truly a distance measure because it is not commutative. However, it is perfectly adequate as a loss function for our purposes, since there is in fact an inherent assymmetry in the situation: p is the ground truth while q is the predicted output. Notice that minimizing the KL-... See full list on infolab.stanford.edu We now turn to the problem of training a deep network . Training a network means finding good values for the parameters (weights and thresholds) of the network . Usually, we shall have access to a training set of labeled input/output pairs. The training process tries to find parameter values that minimize the average loss on the training set. The hop... See full list on infolab.stanford.edu = MSE(y, ˆy) Each of these steps corresponds to one of the four nodes in the middle row, in order from the left. The first step corresponds to the node with operand u and operator ×. Here is an example where it must be understood that the node labeled W is the first argument. If necessary, we could label each incoming edge with a number to indicate... See full list on infolab.stanford.edu For functions of vectors, we can restate the chain rule in terms of gradients and Jacobians. Suppose y = g(x) and z = f(y) = f(g(x)) then: ∇xz = Jx(y)∇yz If z = f(u, v) where u = g(x) and v = h(x), then ∇xz = Jx(u)∇uz + Jx(v)∇vz See full list on infolab.stanford.edu = = = = Differentiating, we get: − Xi pi log qi − Xi pi(yi − log(Xj eyj)) − Xi piyi − log(Xj eyj) Xi pi − Xi piyi − log(Xj eyj) ∂l ✪ ∂yk = −pk = −pk eyk ✪ Pj eyj μ(yk) = qk − pk Therefore, we end up with the rather neat result: ∇yl = q − p This combined gradient does not saturate or explode, and leads to good learning behavior. That observation exp... See full list on infolab.stanford.edu Given a set of training examples, we run the compute graph in both directions for each example: forward (to compute the loss) and backwards (to compute the gradients). We average the loss and gradients across the training set to compute the average loss and the average gradient for each parameter vector. At each iteration we update each parameter v... See full list on infolab.stanford.edu Previously, we have imagined that the inputs to a neural net are one-dimensional vectors. But there is no reason why we cannot view the input as having a higher dimension. Example 13.6 : A grey-scale photo might be represented by a two-dimensional array of real numbers, corresponding to the intensity of each pixel. Each pixel of a color image typic... See full list on infolab.stanford.edu This subsection is a short detour to explain why Convolutional Neural Networks are so named. It is not a pre-requisite for any of the other material in this chapter. The convolutional layer is named because of the resemblance it bears to the convolution operation from functional analysis, which is often used in signal processing and probability the... See full list on infolab.stanford.edu −∞ Z ∞ f(t − τ)g(τ)dτ −∞ Here we are interested in the discrete version of convolution, where f and g are defined over the integers: ∞ ∞ See full list on infolab.stanford.edu Just as CNN’s are a specialized family of neural networks for processing 2-dimensional image data, recurrent neural networks (RNN’s) are networks spe-cially designed for processing sequential data. Sequential data naturally arises in many settings: a sentence is a sequence of words; a video is a sequence of images; a stock market ticker is a sequen... See full list on infolab.stanford.edu We use backpropagation to train an RNN, just as we would any neural network . Let us work through an example. Suppose our input consists of sequences of length n. Our network uses the activation function tanh for the state update, softmax for the output, and the loss function is cross-entropy. Since the network has several outputs, one at each time-... See full list on infolab.stanford.edu Moreover, suppose W is a matrix, and w is the vector obtained by concatenating the rows of W. Then: dz dW dy dW dz = ✪ dw dy = ✪ dw These conventions also extend naturally to partial derivatives. We use backpropagation to compute the gradients of the error with respect to the network parameters. We focus on ✪ de ; the gradients for U and V are dW s... See full list on infolab.stanford.edu The LSTM model is a refinement of the basic RNN model to address the prob-lem of learning long-distance associations. In the past few years, LSTM has become popular as the de-facto sequence-learning model, and has been used with success in many applications. Let us understand the intuition behind the LSTM model before we describe it formally. The m... See full list on infolab.stanford.edu Thus far, we have presented our goal as one of minimizing loss (i.e., prediction error) on the training set. Gradient descent and stochastic gradient descent help us achieve this objective. In practice, the real objective of training is to minimize the loss on new and hitherto unseen inputs. Our hope is that our training set is representative of un... See full list on infolab.stanford.edu Gradient descent is not guaranteed to learn parameters (weights and biases) that reduce the training loss to an absolute minimum. In practice, the pro-cedure learns parameters that correspond to a local minimum in the training loss. There are usually many local minima, and some might lead to better gen-eralization than others. In practice, it has b... See full list on infolab.stanford.edu Dropout is a technique that reduces overfitting by making random changes to the underlying deep neural network . Recall that when we train using stochastic gradient descent, at each step we sample at random a minibatch of inputs to process. When using dropout, we also select at random a certain fraction (say half) of all the hidden nodes from the ne... See full list on infolab.stanford.edu In Section 13.6.1 we suggested iterating through training examples (or mini-batches) until we reach a local minimum in the loss function. In practice, this approach leads to overfitting. It has been observed that while the loss on the training set (the training loss) decreases through the training process, the loss on the test set (the test loss) o... See full list on infolab.stanford.edu The accuracy of most machine-learning models increases when we provide ad-ditional training data. Usually, larger training sets also lead to less overfitting. When the actual training data available is limited, we can often create addi-tional synthetic training examples by applying transformations or adding noise. For example, consider the digit-cl... See full list on infolab.stanford.edu ✦ Neural Nets: A neural net is a collection of perceptrons (nodes), usually organized in layers, where the outputs from one layer provide inputs to the next layers. The first (inout) layer takes external inputs, and the last (output) layer indicates the class of the input. Other layers in the middle are called hidden layers and generally are traine... See full list on infolab.stanford.edu DVI : A Derivative-based Vision Network for INR RUNZHAO YANG · Xiaolong Wu · Zhihong Zhang · Fabian Zhang · Tingxiong Xiao · Zongren Li · Kunlun He · Jinli Suo An Exponential Time Algorithm for Computing Partial Derivatives The path aggregation lemma provides a simple way to com-pute the derivative with respect to intermediate variable w Use computational graph to compute each value Recent advancements in computer vision have seen Implicit Neural Representations ( INR ) becoming a dominant representation form for data due to their compactness and expre 4 days ago · The paper is organised as follows. Section 2 summarises the SINDy algorithm. Section 3 reviews the most effective two-step approaches. Section 4 introduces RKTV- INR , our two-step INR - based framework for data-driven modeling with time dependence and noise. Section 5 presents experiments demonstrating the effectiveness of the proposed method. Downstream tasks based on INRs: Besides being an efficient and unified representation, INR presents exciting opportunities for various vision tasks. These include enhancing signals, recognizing patterns, and generating new data."}
+{"idx": 4, "title": "Chap3slides.dvi - Charu Aggarwal", "date": "", "ddg_snippet": "An Exponential Time Algorithm for Computing Partial Derivatives The path aggregation lemma provides a simple way to com-pute the derivative with respect to intermediate variable w Use computational graph to compute each value", "subpage_snippet": "", "source": "www.charuaggarwal.net", "link": "http://www.charuaggarwal.net/Chap3slides.pdf", "content": "An Exponential Time Algorithm for Computing Partial Derivatives The path aggregation lemma provides a simple way to com-pute the derivative with respect to intermediate variable w Use computational graph to compute each value"}
+{"idx": 5, "title": "DVI:A Derivative-based Vision Network for INR - AMiner", "date": "", "ddg_snippet": "Recent advancements in computer vision have seen Implicit Neural Representations ( INR ) becoming a dominant representation form for data due to their compactness and expre", "subpage_snippet": "", "source": "www.aminer.cn", "link": "https://www.aminer.cn/pub/6853e848163c01c8502f0416/dvi-a-derivative-based-vision-network-for-inr", "content": "Recent advancements in computer vision have seen Implicit Neural Representations ( INR ) becoming a dominant representation form for data due to their compactness and expre"}
+{"idx": 6, "title": "Data Denoising and Derivative Estimation for Data-Driven ...", "date": "", "ddg_snippet": "4 days ago · The paper is organised as follows. Section 2 summarises the SINDy algorithm. Section 3 reviews the most effective two-step approaches. Section 4 introduces RKTV- INR , our two-step INR - based framework for data-driven modeling with time dependence and noise. Section 5 presents experiments demonstrating the effectiveness of the proposed method.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2509.14219", "content": "4 days ago · The paper is organised as follows. Section 2 summarises the SINDy algorithm. Section 3 reviews the most effective two-step approaches. Section 4 introduces RKTV- INR , our two-step INR - based framework for data-driven modeling with time dependence and noise. Section 5 presents experiments demonstrating the effectiveness of the proposed method."}
+{"idx": 7, "title": "Implicit Neural Representation for Vision", "date": "", "ddg_snippet": "Downstream tasks based on INRs: Besides being an efficient and unified representation, INR presents exciting opportunities for various vision tasks. These include enhancing signals, recognizing patterns, and generating new data.", "subpage_snippet": "", "source": "inrv.github.io", "link": "https://inrv.github.io/", "content": "Downstream tasks based on INRs: Besides being an efficient and unified representation, INR presents exciting opportunities for various vision tasks. These include enhancing signals, recognizing patterns, and generating new data."}
+{"idx": 8, "title": "Derivative formulas through geometry | Chapter 3, Essence... - YouTube", "date": "", "ddg_snippet": "Some common derivative formulas explained with geometric intuition.This video was sponsored by Brilliant: https://brilliant.org/3b1bHelp fund future projects...", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=S0_qX4VJhMQ", "content": "Some common derivative formulas explained with geometric intuition.This video was sponsored by Brilliant: https://brilliant.org/3b1bHelp fund future projects..."}
+{"idx": 9, "title": "Hybrid Lie semi-group and cascade structures for the generalized...", "date": "", "ddg_snippet": "Section 5 then continues by deriving macroscopic relations between filter responses computed for different sets of filter parameters, based on either purely spatial or joint spatio-temporal cas-cade smoothing properties of the corresponding receptive field representations, defined from...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2509.15748", "content": "Section 5 then continues by deriving macroscopic relations between filter responses computed for different sets of filter parameters, based on either purely spatial or joint spatio-temporal cas-cade smoothing properties of the corresponding receptive field representations, defined from..."}
diff --git a/data/sampled_jsons/Definition_3.2_Directionality_Score_gpizm0I3lp_Transformer_self-attention.jsonl b/data/sampled_jsons/Definition_3.2_Directionality_Score_gpizm0I3lp_Transformer_self-attention.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..af03624d6a5fde504ec102889e69ee7cb62c78f3
--- /dev/null
+++ b/data/sampled_jsons/Definition_3.2_Directionality_Score_gpizm0I3lp_Transformer_self-attention.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "The underlying structures of self-attention: symmetry, directionality ...", "date": "", "ddg_snippet": "Abstract Self-attention is essential to Transformer architec-tures, yet how information is embedded in the self-attention matrices and how diferent objective func-tions impact this process remains unclear. We present a mathematical framework to analyze self-attention matrices by deriving the structures governing their weight updates.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.10927", "content": "Abstract Self-attention is essential to Transformer architec-tures, yet how information is embedded in the self-attention matrices and how diferent objective func-tions impact this process remains unclear. We present a mathematical framework to analyze self-attention matrices by deriving the structures governing their weight updates."}
+{"idx": 1, "title": "Attention vs. Self-Attention in Transformers - GeeksforGeeks", "date": "", "ddg_snippet": "Attention and Self-Attention help to understand the relationship between elements in input and output sequences in the Transformers model. Attention focuses on different parts of another sequence, while self-attention focuses on different parts of the same input sequence.", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/nlp/attention-vs-self-attention-in-transformers/", "content": "Attention and Self-Attention help to understand the relationship between elements in input and output sequences in the Transformers model. Attention focuses on different parts of another sequence, while self-attention focuses on different parts of the same input sequence."}
+{"idx": 2, "title": "Understanding The Attention Mechanism in Transformers with Code", "date": "", "ddg_snippet": "Here Self means the attention mechanism is designed to process the same sequence, with the goal to attend each token in the given sequence to every other token in the same sequence when producing ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@lixue421/understanding-the-attention-mechanism-in-transformers-73ce20ead2ab", "content": "Here Self means the attention mechanism is designed to process the same sequence, with the goal to attend each token in the given sequence to every other token in the same sequence when producing ..."}
+{"idx": 3, "title": "PDF [draft] Note 10: Self-Attention & Transformers", "date": "", "ddg_snippet": "Winter 2023 Summary. This note motivates moving away from recurrent archi-tectures in NLP, introduces self-attention , and builds a minimal self - attention -based neural architecture. Finally, it dives into the details of the Transformer architecture, a self - attention -based architecture that as of 2023 is ubiquitous in NLP research.", "subpage_snippet": "", "source": "web.stanford.edu", "link": "https://web.stanford.edu/class/cs224n/readings/cs224n-self-attention-transformers-2023_draft.pdf", "content": "Winter 2023 Summary. This note motivates moving away from recurrent archi-tectures in NLP, introduces self-attention , and builds a minimal self - attention -based neural architecture. Finally, it dives into the details of the Transformer architecture, a self - attention -based architecture that as of 2023 is ubiquitous in NLP research."}
+{"idx": 4, "title": "Understanding Self-Attention in Transformers - LinkedIn", "date": "", "ddg_snippet": "Ever wondered how Transformer models like BERT and GPT understand relationships between words? The secret lies in self -attention—a mechanism that helps models evaluate how every word in a ...", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/pulse/understanding-self-attention-transformers-siva-swetha-g-vaspc", "content": "Ever wondered how Transformer models like BERT and GPT understand relationships between words? The secret lies in self -attention—a mechanism that helps models evaluate how every word in a ..."}
+{"idx": 5, "title": "Transformers in Action: Attention Is All You Need", "date": "", "ddg_snippet": "The restricted self-attention is a more sophisticated version of the vanilla self-attention when it comes to computational complexity in very long input sequences which only uses a limited number of neighbors with the size r from the input sequence around the respective output position.", "subpage_snippet": "", "source": "towardsdatascience.com", "link": "https://towardsdatascience.com/transformers-in-action-attention-is-all-you-need-ac10338a023a/", "content": "The restricted self-attention is a more sophisticated version of the vanilla self-attention when it comes to computational complexity in very long input sequences which only uses a limited number of neighbors with the size r from the input sequence around the respective output position."}
+{"idx": 6, "title": "Explain Self-Attention, and Masked Self-Attention as used in Transformers", "date": "", "ddg_snippet": "Attention mechanisms are a crucial component in modern deep learning architectures, particularly in seq-to-seq tasks and NLP models like Transformers .", "subpage_snippet": "", "source": "aiml.com", "link": "https://aiml.com/explain-self-attention-and-masked-self-attention-as-used-in-transformers/", "content": "Attention mechanisms are a crucial component in modern deep learning architectures, particularly in seq-to-seq tasks and NLP models like Transformers ."}
+{"idx": 7, "title": "A Deep Dive into the Self-Attention Mechanism of Transformers", "date": "", "ddg_snippet": "A Deep Dive into the Self-Attention Mechanism of Transformers Introduction: In recent years, large language models (LLMs) have revolutionized the field of Natural Language Processing (NLP).", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/analytics-vidhya/a-deep-dive-into-the-self-attention-mechanism-of-transformers-fe943c77e654", "content": "A Deep Dive into the Self-Attention Mechanism of Transformers Introduction: In recent years, large language models (LLMs) have revolutionized the field of Natural Language Processing (NLP)."}
+{"idx": 8, "title": "Self - Attention in NLP - GeeksforGeeks", "date": "", "ddg_snippet": "In Transformer models, self-attention allows the model to look at all words in a sentence at once but it doesn't naturally understand the order of those words. This is a problem because word order matters in language. To solve this Transformers use positional embeddings extra information added to each word that tells the model where it appears in the sentence. This helps the model understand ...", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/nlp/self-attention-in-nlp/", "content": "In Transformer models, self-attention allows the model to look at all words in a sentence at once but it doesn't naturally understand the order of those words. This is a problem because word order matters in language. To solve this Transformers use positional embeddings extra information added to each word that tells the model where it appears in the sentence. This helps the model understand ..."}
+{"idx": 9, "title": "The underlying structures of self-attention: symmetry, directionality ...", "date": "", "ddg_snippet": "Abstract Self-attention is essential to Transformer archi-tectures, yet how information is embedded in the self-attention matrices and how different objec-tive functions impact this process remains unclear. We present a mathematical framework to analyze self-attention matrices by deriving the structures governing their weight updates.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=gpizm0I3lp", "content": "Abstract Self-attention is essential to Transformer archi-tectures, yet how information is embedded in the self-attention matrices and how different objec-tive functions impact this process remains unclear. We present a mathematical framework to analyze self-attention matrices by deriving the structures governing their weight updates."}
diff --git a/data/sampled_jsons/Descriptor-In-Pixel_Point-Feature_Tracking_Pixel_Processor_Arrays_FAST_tracking_comparison.jsonl b/data/sampled_jsons/Descriptor-In-Pixel_Point-Feature_Tracking_Pixel_Processor_Arrays_FAST_tracking_comparison.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..34242445092cbec7d246f495362b8ac5061785aa
--- /dev/null
+++ b/data/sampled_jsons/Descriptor-In-Pixel_Point-Feature_Tracking_Pixel_Processor_Arrays_FAST_tracking_comparison.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Descriptor - In - Pixel : Point - Feature Tracking For Pixel Processor ...", "date": "", "ddg_snippet": "Our in - pixel approach for point - feature detection and tracking is designed specifically for the PPA’s architec-ture, providing high pixel - processor compute resource util-isation, and minimizing data transfer between sensor and external processing .", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Bose_Descriptor-In-Pixel__Point-Feature_Tracking_For_Pixel_Processor_Arrays_CVPR_2025_paper.pdf", "content": "Our in - pixel approach for point - feature detection and tracking is designed specifically for the PPA’s architec-ture, providing high pixel - processor compute resource util-isation, and minimizing data transfer between sensor and external processing ."}
+{"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": "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": "This paper presents a novel approach for joint point - feature detection and tracking , specifically designed for Pixel Processor Array sensors (PPA)."}
+{"idx": 3, "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": 4, "title": "Descriptor - In _ Pixel", "date": "", "ddg_snippet": "Descriptor - In - Pixel : Point - Feature Tracking for Pixel Processor Arrays . 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 . Point - Feature detection and tracking at thousands of frames-per-second, using ~1Watt of power."}
+{"idx": 5, "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": 6, "title": "Descriptor список видео на ютуб. Скачать Descriptor ... - ClipSaver.ru", "date": "", "ddg_snippet": "Descriptor In Pixel : Point Feature Tracking for Pixel Processor Arrays CVPR 2025 Oral Talk. 15 1 day ago 12:38. Gifter.", "subpage_snippet": "", "source": "clipsaver.ru", "link": "https://clipsaver.ru/search/Descriptor", "content": "Descriptor In Pixel : Point Feature Tracking for Pixel Processor Arrays CVPR 2025 Oral Talk. 15 1 day ago 12:38. Gifter."}
+{"idx": 7, "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": 8, "title": "Descriptor - In - Pixel : Point - Feature Tracking For Pixel Processor ...", "date": "", "ddg_snippet": "And if you are interested in a postdoc on a joint project with this team plus Imperial College in the space of novel visual pipelines and hardware, we have extended the application deadline, see below.", "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": "And if you are interested in a postdoc on a joint project with this team plus Imperial College in the space of novel visual pipelines and hardware, we have extended the application deadline, see below."}
+{"idx": 9, "title": "SCAMP Vision Sensor", "date": "", "ddg_snippet": "High-speed keypoint tracking . On-sensor feature extraction, keypoint selection, and tracking . Applications in motion estimation, visual odometry, SLAM. Up to 3000 fps (when returning only keypoint coordinates / descriptors ).", "subpage_snippet": "", "source": "personalpages.manchester.ac.uk", "link": "https://personalpages.manchester.ac.uk/staff/p.dudek/scamp/", "content": "High-speed keypoint tracking . On-sensor feature extraction, keypoint selection, and tracking . Applications in motion estimation, visual odometry, SLAM. Up to 3000 fps (when returning only keypoint coordinates / descriptors )."}
diff --git a/data/sampled_jsons/Descriptor-In-Pixel_SCAMP-7_Computation_Time_Breakdown_Table_1_percentages_values_numbers.jsonl b/data/sampled_jsons/Descriptor-In-Pixel_SCAMP-7_Computation_Time_Breakdown_Table_1_percentages_values_numbers.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..efe555bb111a89f18a08b571c21016202e0d5d65
--- /dev/null
+++ b/data/sampled_jsons/Descriptor-In-Pixel_SCAMP-7_Computation_Time_Breakdown_Table_1_percentages_values_numbers.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "This mod is missing the descriptor file - Paradox Interactive...", "date": "", "ddg_snippet": "Feb 20, 2020 · Once I added them again, I get duplicate entries and of course the \"missing descriptor file\". I guess I'll just cancel the subscription then, if I can't even let them rest there deactivated to wait for an update. btw: unsubscribing from those legacy mods doesn't work. The duplicates stay. Even if you delete the mod's files in the Steam-folder.", "subpage_snippet": "", "source": "forum.paradoxplaza.com", "link": "https://forum.paradoxplaza.com/forum/threads/this-mod-is-missing-the-descriptor-file.1336077/", "content": "Feb 20, 2020 · Once I added them again, I get duplicate entries and of course the \"missing descriptor file\". I guess I'll just cancel the subscription then, if I can't even let them rest there deactivated to wait for an update. btw: unsubscribing from those legacy mods doesn't work. The duplicates stay. Even if you delete the mod's files in the Steam-folder."}
+{"idx": 1, "title": "Missing descriptor file in my mod even if there is one", "date": "", "ddg_snippet": "Jul 7 , 2023 · Summary Missing descriptor file in my mod even if there is one Platform Steam Operating System Windows Game Version 1 .12.14.50e7 Enabled DLC None Do...", "subpage_snippet": "", "source": "forum.paradoxplaza.com", "link": "https://forum.paradoxplaza.com/forum/threads/missing-descriptor-file-in-my-mod-even-if-there-is-one.1592977/", "content": "Jul 7 , 2023 · Summary Missing descriptor file in my mod even if there is one Platform Steam Operating System Windows Game Version 1 .12.14.50e7 Enabled DLC None Do..."}
+{"idx": 2, "title": "Mod Missing Descriptor File - Paradox Interactive Forums", "date": "", "ddg_snippet": "Jul 3, 2021 · I almost literally tried everything that can be found online with a google search of ''Eu4 missing mod descriptor file'' or ''Eu4 mods start missing descriptor file after restart'', I mean there are not that many things to be found with a search like this because I am pretty sure I am the only person experiencing this but I tried every solution ...", "subpage_snippet": "", "source": "forum.paradoxplaza.com", "link": "https://forum.paradoxplaza.com/forum/threads/mod-missing-descriptor-file.1481804/", "content": "Jul 3, 2021 · I almost literally tried everything that can be found online with a google search of ''Eu4 missing mod descriptor file'' or ''Eu4 mods start missing descriptor file after restart'', I mean there are not that many things to be found with a search like this because I am pretty sure I am the only person experiencing this but I tried every solution ..."}
+{"idx": 3, "title": "How to install mods manually from Paradox Mods? Descriptor.mod?", "date": "", "ddg_snippet": "Sep 25, 2020 · When I download a mod manually from the Paradox Mods site, I receive a zip file with a descriptor .mod file and a folder structure that mirrors the game folder structure. I assume I create a folder inside the Documents\\\\Mods folder and dump...", "subpage_snippet": "", "source": "forum.paradoxplaza.com", "link": "https://forum.paradoxplaza.com/forum/threads/how-to-install-mods-manually-from-paradox-mods-descriptor-mod.1427279/", "content": "Sep 25, 2020 · When I download a mod manually from the Paradox Mods site, I receive a zip file with a descriptor .mod file and a folder structure that mirrors the game folder structure. I assume I create a folder inside the Documents\\\\Mods folder and dump..."}
+{"idx": 4, "title": "HoI 4 - Path replacement in descriptor.mod files not working", "date": "", "ddg_snippet": "Nov 22, 2019 · Description of issue Path replacement in descriptor .mod files not working Game Version 1 .8. 1 Enabled DLC Do you have mods enabled? Yes...", "subpage_snippet": "", "source": "forum.paradoxplaza.com", "link": "https://forum.paradoxplaza.com/forum/threads/hoi-4-path-replacement-in-descriptor-mod-files-not-working.1285940/", "content": "Nov 22, 2019 · Description of issue Path replacement in descriptor .mod files not working Game Version 1 .8. 1 Enabled DLC Do you have mods enabled? Yes..."}
+{"idx": 5, "title": "This mod is missing a descriptor file - Paradox Interactive...", "date": "", "ddg_snippet": "Oct 18, 2020 · The mod should provide the descriptor .mod in the download zip for Paradox Mods (or in the workshop folder if you are on Steam). Maybe Daddy Pika updated the mod and forgot to include the descriptor ? In any case, you can write your own descriptor and have the mod work. Just take a look at the other mods descriptors and adapt them. If they refer to a remote file id you can substitute that string ...", "subpage_snippet": "", "source": "forum.paradoxplaza.com", "link": "https://forum.paradoxplaza.com/forum/threads/this-mod-is-missing-a-descriptor-file.1437370/", "content": "Oct 18, 2020 · The mod should provide the descriptor .mod in the download zip for Paradox Mods (or in the workshop folder if you are on Steam). Maybe Daddy Pika updated the mod and forgot to include the descriptor ? In any case, you can write your own descriptor and have the mod work. Just take a look at the other mods descriptors and adapt them. If they refer to a remote file id you can substitute that string ..."}
+{"idx": 6, "title": "All my mods are somehow missing descriptor files - please help!", "date": "", "ddg_snippet": "May 4, 2022 · I've been having a nightmare of a time trying to get mods to work in the CK3 launcher for some days. They give the warning in he title. If they work after I follow the formula of unsubscribe-delete from the mods folder-resubscribe method (and...", "subpage_snippet": "", "source": "forum.paradoxplaza.com", "link": "https://forum.paradoxplaza.com/forum/threads/all-my-mods-are-somehow-missing-descriptor-files-please-help.1523360/", "content": "May 4, 2022 · I've been having a nightmare of a time trying to get mods to work in the CK3 launcher for some days. They give the warning in he title. If they work after I follow the formula of unsubscribe-delete from the mods folder-resubscribe method (and..."}
+{"idx": 7, "title": "Local (non-workshop) Mods help | Paradox Interactive Forums", "date": "", "ddg_snippet": "Again delete the mods_registry.json file. From what I understand local ( non steam mods ) do not need a descriptor file and work just like then did before the new launcher. There is still the question of mod load order and there is a separate thread talking about that here on the forum.", "subpage_snippet": "", "source": "forum.paradoxplaza.com", "link": "https://forum.paradoxplaza.com/forum/threads/local-non-workshop-mods-help.1268378/", "content": "Again delete the mods_registry.json file. From what I understand local ( non steam mods ) do not need a descriptor file and work just like then did before the new launcher. There is still the question of mod load order and there is a separate thread talking about that here on the forum."}
+{"idx": 8, "title": "CK3 crashing constantly | Paradox Interactive Forums", "date": "", "ddg_snippet": "Feb 14, 2025 · For months now, I have had ck3 crash once in a while, almost always when playing with mods. They usually occurred when autosaving or trying to load an autosave. Now, however the issue has become unbearable. I can no longer play sc3 without it...", "subpage_snippet": "", "source": "forum.paradoxplaza.com", "link": "https://forum.paradoxplaza.com/forum/threads/ck3-crashing-constantly.1728784/", "content": "Feb 14, 2025 · For months now, I have had ck3 crash once in a while, almost always when playing with mods. They usually occurred when autosaving or trying to load an autosave. Now, however the issue has become unbearable. I can no longer play sc3 without it..."}
+{"idx": 9, "title": "Game crashes at loading screen with certain mods.", "date": "", "ddg_snippet": "May 9, 2023 · Using the mod \"Imperial Nostalgia\" the game crashes while the loading screen you get right after launching the game is loading. I'm a MacOS user and most of the other mods I installed actually work, except for a few ones that actually have the...", "subpage_snippet": "", "source": "forum.paradoxplaza.com", "link": "https://forum.paradoxplaza.com/forum/threads/game-crashes-at-loading-screen-with-certain-mods.1582668/", "content": "May 9, 2023 · Using the mod \"Imperial Nostalgia\" the game crashes while the loading screen you get right after launching the game is loading. I'm a MacOS user and most of the other mods I installed actually work, except for a few ones that actually have the..."}
diff --git a/data/sampled_jsons/Descriptor-In-Pixel_vs_FAST_corner_detector_translation_motion_robustness.jsonl b/data/sampled_jsons/Descriptor-In-Pixel_vs_FAST_corner_detector_translation_motion_robustness.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..df2700f424f38d1a9c172c871159b2aaecb67262
--- /dev/null
+++ b/data/sampled_jsons/Descriptor-In-Pixel_vs_FAST_corner_detector_translation_motion_robustness.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "computer vision - AGAST vs FAST evaluation - Signal Processing...", "date": "", "ddg_snippet": "In ~2006 the FAST corner detector was introduced.Feature descriptors such as BRIEF and ORB make use of FAST or a modified version of FAST in their reference papers. Then, in ~2010 the AGAST corner detection was proposed to address this issue.", "subpage_snippet": "", "source": "dsp.stackexchange.com", "link": "https://dsp.stackexchange.com/questions/18558/agast-vs-fast-evaluation", "content": "In ~2006 the FAST corner detector was introduced.Feature descriptors such as BRIEF and ORB make use of FAST or a modified version of FAST in their reference papers. Then, in ~2010 the AGAST corner detection was proposed to address this issue."}
+{"idx": 1, "title": "Descriptor - In - Pixel : Point-Feature Tracking For Pixel Processor Arrays", "date": "", "ddg_snippet": "We introduce a Descriptor - In - Pixel paradigm, in which a feature descriptor is held within the memory of each pixel-processor. The PPA’s architecture enables the response of every processor’s descriptor, upon the current image, to be computed in parallel.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Bose_Descriptor-In-Pixel__Point-Feature_Tracking_For_Pixel_Processor_Arrays_CVPR_2025_paper.pdf", "content": "We introduce a Descriptor - In - Pixel paradigm, in which a feature descriptor is held within the memory of each pixel-processor. The PPA’s architecture enables the response of every processor’s descriptor, upon the current image, to be computed in parallel."}
+{"idx": 2, "title": "Figure 1. (a) A processed interest point and 16 pixels surrounding on...", "date": "", "ddg_snippet": "Similar to the SUSAN, FAST corner detector uses a circle of 16 pixels (this is the Bresenham circle of radius 3) to classify whether a candidate point p is actually a corner or not. As plotted in Fig.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/a-A-processed-interest-point-and-16-pixels-surrounding-on-it-b-the-demonstration-of_fig1_279278472", "content": "Similar to the SUSAN, FAST corner detector uses a circle of 16 pixels (this is the Bresenham circle of radius 3) to classify whether a candidate point p is actually a corner or not. As plotted in Fig."}
+{"idx": 3, "title": "FAST Feature Detection | uoip/monoVO-python | DeepWiki", "date": "", "ddg_snippet": "Robust corner detection in various lighting conditions.The FAST detector outputs integrate seamlessly with the tracking and motion estimation components: Feature Detection : detector .detect() → KeyPoint[].", "subpage_snippet": "", "source": "deepwiki.com", "link": "https://deepwiki.com/uoip/monoVO-python/6.1-fast-feature-detection", "content": "Robust corner detection in various lighting conditions.The FAST detector outputs integrate seamlessly with the tracking and motion estimation components: Feature Detection : detector .detect() → KeyPoint[]."}
+{"idx": 4, "title": "FAST corner detector - File Exchange - MATLAB Central", "date": "", "ddg_snippet": "FAST corner detector . Version 1.0.0.0 (171 KB) by Edward Rosten.The ordering of questions used to classify a pixel is learned using the ID3 algorithm. This detector has been shown to exibit a high degree of repeatability. This is detailed in the following paper", "subpage_snippet": "", "source": "www.mathworks.com", "link": "https://www.mathworks.com/matlabcentral/fileexchange/13006-fast-corner-detector", "content": "FAST corner detector . Version 1.0.0.0 (171 KB) by Edward Rosten.The ordering of questions used to classify a pixel is learned using the ID3 algorithm. This detector has been shown to exibit a high degree of repeatability. This is detailed in the following paper"}
+{"idx": 5, "title": "Detection and segmentation of moving objects in highly dynamic scenes", "date": "", "ddg_snippet": "Detection of moving objects in sequences is an essen-tial step for video analysis. It is a difcult task in the pres-ence of a dynamic background. Different kinds of methods exist to solve the problem of motion detection and motion segmentation.", "subpage_snippet": "", "source": "hal.science", "link": "https://hal.science/hal-00551596/document", "content": "Detection of moving objects in sequences is an essen-tial step for video analysis. It is a difcult task in the pres-ence of a dynamic background. Different kinds of methods exist to solve the problem of motion detection and motion segmentation."}
+{"idx": 6, "title": "Zhinan Xu … MS Thesis", "date": "", "ddg_snippet": "In our test, the FAST corner detector was approximately 1.5 times faster than the Harris corner detector .Moreover, we discussed in depth the core components of our system. The eciency and robustness of ve corner and blob detectors and descriptors were analyzed.", "subpage_snippet": "", "source": "escholarship.org", "link": "https://escholarship.org/content/qt049515kz/qt049515kz.pdf", "content": "In our test, the FAST corner detector was approximately 1.5 times faster than the Harris corner detector .Moreover, we discussed in depth the core components of our system. The eciency and robustness of ve corner and blob detectors and descriptors were analyzed."}
+{"idx": 7, "title": "Enhancing Conventional Geometry-Based Visual Odometry Pipeline...", "date": "", "ddg_snippet": "Persson et al. propose C4VX in [7] which is a stereo VO method employing the FAST corner detector at multiple scales for keypoints and ‘‘binary robust independent elemen-tary features’’ (BRIEF) descriptor .", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/pdf/enhancing-conventional-geometry-based-visual-odometry-11dz81fi.pdf", "content": "Persson et al. propose C4VX in [7] which is a stereo VO method employing the FAST corner detector at multiple scales for keypoints and ‘‘binary robust independent elemen-tary features’’ (BRIEF) descriptor ."}
+{"idx": 8, "title": "untitled", "date": "", "ddg_snippet": "The keypoint-less detector makes it robust to blur and its speed is mostly independent of the camera resolution. Finally, it is very fast , requiring only $1 ms on an average PC and $8 ms on a fast mobile phone in typical application scenarios.", "subpage_snippet": "", "source": "arbook.icg.tugraz.at", "link": "https://arbook.icg.tugraz.at/schmalstieg/Schmalstieg_170.pdf", "content": "The keypoint-less detector makes it robust to blur and its speed is mostly independent of the camera resolution. Finally, it is very fast , requiring only $1 ms on an average PC and $8 ms on a fast mobile phone in typical application scenarios."}
+{"idx": 9, "title": "Feature Matching (SIFT, SURF, ORB) — MCQs | Digital Image...", "date": "", "ddg_snippet": "11. In feature matching, what does “ descriptor ” refer to? (A) Region name (B) Image format (C) Numerical representation of a keypoint (D) Color value.(A) Harris corner detector (B) FAST (C) ORB (D) SIFT. 16. Which feature detection method is least computationally expensive?", "subpage_snippet": "", "source": "t4tutorials.com", "link": "https://t4tutorials.com/feature-matching-sift-surf-orb-mcqs-digital-image-processing/", "content": "11. In feature matching, what does “ descriptor ” refer to? (A) Region name (B) Image format (C) Numerical representation of a keypoint (D) Color value.(A) Harris corner detector (B) FAST (C) ORB (D) SIFT. 16. Which feature detection method is least computationally expensive?"}
diff --git a/data/sampled_jsons/Direct_Preference_Optimization_Rafailov_et_al._2023_abstract_year_2023.jsonl b/data/sampled_jsons/Direct_Preference_Optimization_Rafailov_et_al._2023_abstract_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..620d143266abb828702d1adc8acb3500943af509
--- /dev/null
+++ b/data/sampled_jsons/Direct_Preference_Optimization_Rafailov_et_al._2023_abstract_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Direct Preference Optimization: Your Language Model is ...", "date": "", "ddg_snippet": "by R Rafailov · 2023 · Cited by 5239 — In this paper we introduce a new parameterization of the reward model in RLHF that enables extraction of the corresponding optimal policy in closed form.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2305.18290", "content": "by R Rafailov · 2023 · Cited by 5239 — In this paper we introduce a new parameterization of the reward model in RLHF that enables extraction of the corresponding optimal policy in closed form."}
+{"idx": 1, "title": "Direct preference optimization: your language model is ...", "date": "", "ddg_snippet": "10 Dec 2023 — Our experiments show that DPO can fine-tune LMs to align with human preferences as well as or better than existing methods.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3666122.3668460", "content": "10 Dec 2023 — Our experiments show that DPO can fine-tune LMs to align with human preferences as well as or better than existing methods."}
+{"idx": 2, "title": "Direct Preference Optimization: Your Language Model is ...", "date": "", "ddg_snippet": "by R Rafailov · Cited by 5239 — The key insight of the DPO algorithm is that we can impose certain constraints on the under-constrained Plackett-Luce (and Bradley-Terry in particular) family ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=HPuSIXJaa9", "content": "by R Rafailov · Cited by 5239 — The key insight of the DPO algorithm is that we can impose certain constraints on the under-constrained Plackett-Luce (and Bradley-Terry in particular) family ..."}
+{"idx": 3, "title": "Direct Preference Optimization: Your Language Model is ...", "date": "", "ddg_snippet": "Abstract . While large-scale unsupervised language models (LMs) learn broad world knowledge and some reasoning skills, achieving precise control of their ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2305.18290v2", "content": "Abstract . While large-scale unsupervised language models (LMs) learn broad world knowledge and some reasoning skills, achieving precise control of their ..."}
+{"idx": 4, "title": "Extended Abstract - CS 224R Deep Reinforcement Learning", "date": "", "ddg_snippet": "by E Hellman — We focus on three approaches: Supervised Fine-Tuning (SFT), Direct Preference . Optimization (DPO) Rafailov et al . ( 2023 ), and Group Relative Policy Optimization ...", "subpage_snippet": "", "source": "cs224r.stanford.edu", "link": "https://cs224r.stanford.edu/projects/pdfs/CS_224R_Final_Paper_2.pdf", "content": "by E Hellman — We focus on three approaches: Supervised Fine-Tuning (SFT), Direct Preference . Optimization (DPO) Rafailov et al . ( 2023 ), and Group Relative Policy Optimization ..."}
+{"idx": 5, "title": "Direct Preference Optimization with an Offset", "date": "", "ddg_snippet": "by A Amini · 2024 · Cited by 96 — Direct preference optimization (DPO) is a successful fine-tuning strategy for aligning large language models with human preferences . 19 pages", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2024.findings-acl.592.pdf", "content": "by A Amini · 2024 · Cited by 96 — Direct preference optimization (DPO) is a successful fine-tuning strategy for aligning large language models with human preferences . 19 pages"}
+{"idx": 6, "title": "Extended Abstract - CS 224R Deep Reinforcement Learning", "date": "", "ddg_snippet": "Direct Preference Optimization (DPO) Rafailov et al . ( 2023 ) builds on SFT by incorporating pairwise human preference data in a contrastive loss framework. This ... 10 pages", "subpage_snippet": "", "source": "cs224r.stanford.edu", "link": "https://cs224r.stanford.edu/projects/pdfs/Your_Project_Title12.pdf", "content": "Direct Preference Optimization (DPO) Rafailov et al . ( 2023 ) builds on SFT by incorporating pairwise human preference data in a contrastive loss framework. This ... 10 pages"}
+{"idx": 7, "title": "Understanding Reference Policies in Direct Preference ...", "date": "", "ddg_snippet": "by Y Liu · 2025 · Cited by 10 — Direct Preference Optimization (DPO) has be- come a widely used training method for the in- struction fine-tuning of large language models.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.findings-naacl.447.pdf", "content": "by Y Liu · 2025 · Cited by 10 — Direct Preference Optimization (DPO) has be- come a widely used training method for the in- struction fine-tuning of large language models."}
+{"idx": 8, "title": "Indirect Online Preference Optimization via Reinforcement ...", "date": "", "ddg_snippet": "To enhance sampling efficiency and stability, Rafailov et al . propose Direct Preference Optimization (DPO) as a means to bridge the gap between reward functions ...", "subpage_snippet": "", "source": "www.ijcai.org", "link": "https://www.ijcai.org/proceedings/2025/0061.pdf", "content": "To enhance sampling efficiency and stability, Rafailov et al . propose Direct Preference Optimization (DPO) as a means to bridge the gap between reward functions ..."}
+{"idx": 9, "title": "Direct Preference Optimization with an Offset", "date": "", "ddg_snippet": "Figure 1: ODPO takes into account the extent to which one output should be preferred over another. The model has to put more probability mass on the ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/9b04d868d2ba27cf318a0448716896f6573eeddf.pdf", "content": "Figure 1: ODPO takes into account the extent to which one output should be preferred over another. The model has to put more probability mass on the ..."}
diff --git a/data/sampled_jsons/Diversified_in-domain_synthesis_with_efficient_fine-tuning_DISEF_arxiv.jsonl b/data/sampled_jsons/Diversified_in-domain_synthesis_with_efficient_fine-tuning_DISEF_arxiv.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a0b4012033af73f024b2049df14f8a5b081c0aa1
--- /dev/null
+++ b/data/sampled_jsons/Diversified_in-domain_synthesis_with_efficient_fine-tuning_DISEF_arxiv.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Fine - tuning (deep learning) - Wikipedia", "date": "", "ddg_snippet": "Fine - tuning is common in natural language processing (NLP), especially in the domain of language modeling.\"Prompt Tuning GPT-2 language model for parameter- efficient domain adaptation of ASR systems\". InterSpeech. arXiv :2112.08718.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Fine-tuning_(deep_learning)", "content": "Fine - tuning is common in natural language processing (NLP), especially in the domain of language modeling.\"Prompt Tuning GPT-2 language model for parameter- efficient domain adaptation of ASR systems\". InterSpeech. arXiv :2112.08718."}
+{"idx": 1, "title": "[2312.03046] Diversified in - domain synthesis with efficient ...", "date": "", "ddg_snippet": "Following this trend, we propose Diversified In - domain Synthesis with Efficient Fine - tuning ( DISEF ), a novel approach which addresses the generalization challenge in few-shot learning using synthetic data. DISEF consists of two main components.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2312.03046", "content": "Following this trend, we propose Diversified In - domain Synthesis with Efficient Fine - tuning ( DISEF ), a novel approach which addresses the generalization challenge in few-shot learning using synthetic data. DISEF consists of two main components."}
+{"idx": 2, "title": "vturrisi/ disef : Pytorch implementation of \" Diversified in - domain ...\"", "date": "", "ddg_snippet": "Diversified in - domain synthesis with efficient fine - tuning for few-shot classification Victor G. Turrisi da Costa*, Nicola Dall'Asen*, Yiming Wang, Nicu Sebe and Elisa Ricci.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/vturrisi/disef", "content": "Diversified in - domain synthesis with efficient fine - tuning for few-shot classification Victor G. Turrisi da Costa*, Nicola Dall'Asen*, Yiming Wang, Nicu Sebe and Elisa Ricci."}
+{"idx": 3, "title": "Parameter- Efficient Fine - Tuning (PEFT): методы LoRA, Prefix... / Хабр", "date": "", "ddg_snippet": "Мы познакомились лишь с некоторыми методами PEFT. Для изучения других подходов я рекомендую вам прекрасную статью \"Scaling Down to Scale Up: A Guide to Parameter- Efficient Fine - Tuning \", в которой подробно рассматриваются 20 различных методов PEFT.", "subpage_snippet": "", "source": "habr.com", "link": "https://habr.com/ru/articles/791966/", "content": "Мы познакомились лишь с некоторыми методами PEFT. Для изучения других подходов я рекомендую вам прекрасную статью \"Scaling Down to Scale Up: A Guide to Parameter- Efficient Fine - Tuning \", в которой подробно рассматриваются 20 различных методов PEFT."}
+{"idx": 4, "title": "【计算机视觉 | 图像分类】 arxiv ...", "date": "", "ddg_snippet": "1.3 Diversified in - domain synthesis with efficient fine - tuning for few-shot classification. 1.4 LiDAR-based Person Re-identification.", "subpage_snippet": "", "source": "blog.csdn.net", "link": "https://blog.csdn.net/wzk4869/article/details/134864292", "content": "1.3 Diversified in - domain synthesis with efficient fine - tuning for few-shot classification. 1.4 LiDAR-based Person Re-identification."}
+{"idx": 5, "title": "Fine - Tuning or Fine-Failing? Debunking Performance Myths in Large...", "date": "", "ddg_snippet": "When fine - tuned , these models show enhanced performance on domain -specific queries. OpenAI highlights the process of fine - tuning , stating: To fine - tune a model, you are required to provide at least 10 examples.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/fine-tuning-or-fine-failing-debunking-performance", "content": "When fine - tuned , these models show enhanced performance on domain -specific queries. OpenAI highlights the process of fine - tuning , stating: To fine - tune a model, you are required to provide at least 10 examples."}
+{"idx": 6, "title": "Nicola Dall'Asen - Google Akademik", "date": "", "ddg_snippet": "Diversified in - domain synthesis with efficient fine - tuning for few-shot classification.", "subpage_snippet": "", "source": "scholar.google.de", "link": "https://scholar.google.de/citations?user=e7lgiYYAAAAJ&hl=tr", "content": "Diversified in - domain synthesis with efficient fine - tuning for few-shot classification."}
+{"idx": 7, "title": "Articles by Nicola Dall'Asen | Synthical", "date": "", "ddg_snippet": "Diversified in - domain synthesis with efficient fine - tuning for few-shot classification.", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/profile/9a31f2be-e601-4918-8e7d-3785d626d017/articles", "content": "Diversified in - domain synthesis with efficient fine - tuning for few-shot classification."}
+{"idx": 8, "title": "publications by categories in reversed chronological order.", "date": "", "ddg_snippet": "@inproceedings{turrisi_dallasen2023 disef , title = { Diversified in - domain synthesis with efficient fine - tuning for few-shot classification}, author = {da Costa, *Victor G Turrisi and Dall'Asen, *Nicola and Wang, Yiming and Sebe, Nicu and Ricci, Elisa}, booktitle = { Arxiv }, year = {2023}", "subpage_snippet": "", "source": "fodark.xyz", "link": "https://fodark.xyz/publications/", "content": "@inproceedings{turrisi_dallasen2023 disef , title = { Diversified in - domain synthesis with efficient fine - tuning for few-shot classification}, author = {da Costa, *Victor G Turrisi and Dall'Asen, *Nicola and Wang, Yiming and Sebe, Nicu and Ricci, Elisa}, booktitle = { Arxiv }, year = {2023}"}
+{"idx": 9, "title": "vturrisi has 34 repositories available. Follow their code on GitHub.", "date": "", "ddg_snippet": "Pytorch implementation of \" Diversified in - domain synthesis with efficient fine - tuning for few-shot classification\".", "subpage_snippet": "", "source": "git.jl-k.com", "link": "https://git.jl-k.com/vturrisi", "content": "Pytorch implementation of \" Diversified in - domain synthesis with efficient fine - tuning for few-shot classification\"."}
diff --git a/data/sampled_jsons/Do_Not_Trust_What_They_Tell-_Exposing_Malicious_Accomplices_in_Tor_via_Anomalous_Circuit_Detection.jsonl b/data/sampled_jsons/Do_Not_Trust_What_They_Tell-_Exposing_Malicious_Accomplices_in_Tor_via_Anomalous_Circuit_Detection.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..356b25126950be0973ddd4549b229391e197484e
--- /dev/null
+++ b/data/sampled_jsons/Do_Not_Trust_What_They_Tell-_Exposing_Malicious_Accomplices_in_Tor_via_Anomalous_Circuit_Detection.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Tor (network) - Wikipedia", "date": "", "ddg_snippet": "Tor is a free overlay network for enabling anonymous communication. It is built on free and open-source software run by over seven thousand volunteer-operated relays worldwide, as well as by millions of users who route their Internet traffic via rand...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Tor_(network)", "content": "Tor is a free overlay network for enabling anonymous communication. It is built on free and open-source software run by over seven thousand volunteer-operated relays worldwide, as well as by millions of users who route their Internet traffic via rand..."}
+{"idx": 1, "title": "Do Not Trust What They Tell: Exposing Malicious Accomplices ...", "date": "", "ddg_snippet": "Apr 22, 2025 · This paper presents a novel approach for detecting anomalous circuits in the Tor network, and for the first time provides a more comprehensive identification of potential malicious accomplice nodes in Tor by taking roles of nodes in anomalous circuits into consideration.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3696410.3714767", "content": "Apr 22, 2025 · This paper presents a novel approach for detecting anomalous circuits in the Tor network, and for the first time provides a more comprehensive identification of potential malicious accomplice nodes in Tor by taking roles of nodes in anomalous circuits into consideration."}
+{"idx": 2, "title": "Do Not Trust What They Tell: Exposing Malicious Accomplices ...", "date": "", "ddg_snippet": "Do Not Trust What They Tell: Exposing Malicious Accomplices in Tor via Anomalous Circuit Detection . In Proceedings of the ACM Web Conference 2025 (WWW ’25), April 28–May 2, 2025, Sydney, NSW, Australia.", "subpage_snippet": "", "source": "cse.seu.edu.cn", "link": "https://cse.seu.edu.cn/_upload/article/files/82/36/84188a1a47a79fe2b6dc589a1c39/a6db7ad8-3fef-4518-8046-480a6ac64280.pdf", "content": "Do Not Trust What They Tell: Exposing Malicious Accomplices in Tor via Anomalous Circuit Detection . In Proceedings of the ACM Web Conference 2025 (WWW ’25), April 28–May 2, 2025, Sydney, NSW, Australia."}
+{"idx": 3, "title": "通过异常电路检测揭露Tor中的恶意同伙 - 安全内参 | 决策者的网络安全...", "date": "", "ddg_snippet": "Mar 13, 2025 · 提出了一种方法来检测 Tor 网络中的异常电路，通过考虑节点在异常电路中的角色，首次提供了一个更全面的方法识别 tor 中的 ...", "subpage_snippet": "", "source": "www.secrss.com", "link": "https://www.secrss.com/articles/76608", "content": "Mar 13, 2025 · 提出了一种方法来检测 Tor 网络中的异常电路，通过考虑节点在异常电路中的角色，首次提供了一个更全面的方法识别 tor 中的 ..."}
+{"idx": 4, "title": "通过异常电路检测揭露tor中的恶意同伙", "date": "", "ddg_snippet": "Mar 13, 2025 · 原文标题： Do Not Trust What They Tell: Exposing Malicious Accomplices in Tor via Anomalous Circuit Detection 原文作者：Yixuan Yao, Ming Yang, Zixia Liu, Kai Dong, Xiaodan-Gu, Chunmian Wang", "subpage_snippet": "", "source": "sechub.in", "link": "https://sechub.in/view/3027398", "content": "Mar 13, 2025 · 原文标题： Do Not Trust What They Tell: Exposing Malicious Accomplices in Tor via Anomalous Circuit Detection 原文作者：Yixuan Yao, Ming Yang, Zixia Liu, Kai Dong, Xiaodan-Gu, Chunmian Wang"}
+{"idx": 5, "title": "Exposing the Rat in the Tunnel: Using Trafic Analysis for Tor ...", "date": "", "ddg_snippet": "Another fundamental challenge is that we have to diferentiate between benign and malicious Tor connections so that we do not interrupt the use of Tor for legitimate users. In this paper, we present the first trafic analysis approach to defend against Tor -based malware.", "subpage_snippet": "", "source": "alrawi.io", "link": "https://alrawi.io/static/papers/tor-malware_ccs22.pdf", "content": "Another fundamental challenge is that we have to diferentiate between benign and malicious Tor connections so that we do not interrupt the use of Tor for legitimate users. In this paper, we present the first trafic analysis approach to defend against Tor -based malware."}
+{"idx": 6, "title": "Do Not Trust What They Tell: Exposing Malicious Accomplices ...", "date": "", "ddg_snippet": "Do Not Trust What They Tell : Exposing Malicious Accomplices in Tor via Anomalous Circuit Detection .", "subpage_snippet": "", "source": "www.bibsonomy.org", "link": "https://www.bibsonomy.org/bibtex/13043b20c2a3c93752470a9607040161f", "content": "Do Not Trust What They Tell : Exposing Malicious Accomplices in Tor via Anomalous Circuit Detection ."}
+{"idx": 7, "title": "Do Not Trust What They Tell : Exposing Malicious Accomplices in ...", "date": "", "ddg_snippet": "Our goal is to detect anomalous circuits with Entry-Exit node pairs chosen by users that may have explicitly or implicitly violated Tor ’s circuit construction guidelines. Further, we mine potential sybils from these involved Entry-Exit pairs. Since only in Exit circuit that the client has the...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=qcnePVejeV", "content": "Our goal is to detect anomalous circuits with Entry-Exit node pairs chosen by users that may have explicitly or implicitly violated Tor ’s circuit construction guidelines. Further, we mine potential sybils from these involved Entry-Exit pairs. Since only in Exit circuit that the client has the..."}
+{"idx": 8, "title": "The Ultimate Guide to Using Tor Browser Securely - YouTube", "date": "", "ddg_snippet": "The complete tutorial to using Tor Browser safely. Let's cover all the privacy, security, and anonymity considerations you need to make when using Tor .", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=K3wmLvny5tg", "content": "The complete tutorial to using Tor Browser safely. Let's cover all the privacy, security, and anonymity considerations you need to make when using Tor ."}
+{"idx": 9, "title": "An Anonymity Vulnerability in Tor | CoLab", "date": "", "ddg_snippet": "Effectiveness and detection of denial-of-service attacks in tor . Do Not Trust What They Tell : Exposing Malicious Accomplices in Tor via Anomalous Circuit Detection .", "subpage_snippet": "", "source": "colab.ws", "link": "https://colab.ws/articles/10.1109/tnet.2022.3174003", "content": "Effectiveness and detection of denial-of-service attacks in tor . Do Not Trust What They Tell : Exposing Malicious Accomplices in Tor via Anomalous Circuit Detection ."}
diff --git a/data/sampled_jsons/Do_Not_Trust_What_They_Tell_Tor_connectivity_metric_conn(a,b)_formula_abnormal_behavioral_pattern_me_year_2024.jsonl b/data/sampled_jsons/Do_Not_Trust_What_They_Tell_Tor_connectivity_metric_conn(a,b)_formula_abnormal_behavioral_pattern_me_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d3aa4e8b64c1f9088348d223eebdf6d5c5dc9f96
--- /dev/null
+++ b/data/sampled_jsons/Do_Not_Trust_What_They_Tell_Tor_connectivity_metric_conn(a,b)_formula_abnormal_behavioral_pattern_me_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Do Not Trust What They Tell: Exposing Malicious Accomplices ...", "date": "", "ddg_snippet": "This feature describes the rela-tionship of two nodes in term of their abnormal behavioral patterns, which can be identified through the method in Section 4.2. We intro-(, ) duce a connectivity metric in Equation (8) to quantify this relationship between nodes and .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=qcnePVejeV", "content": "This feature describes the rela-tionship of two nodes in term of their abnormal behavioral patterns, which can be identified through the method in Section 4.2. We intro-(, ) duce a connectivity metric in Equation (8) to quantify this relationship between nodes and ."}
+{"idx": 1, "title": "Do Not Trust What They Tell: Exposing Malicious Accomplices ...", "date": "", "ddg_snippet": "Apr 22, 2025 · The Tor network, while offering anonymity through traffic routing across volunteer-operated nodes, remains vulnerable to attacks that aim to deanonymize users by correlating traffic patterns between colluded entry and exit nodes in circuits.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3696410.3714767", "content": "Apr 22, 2025 · The Tor network, while offering anonymity through traffic routing across volunteer-operated nodes, remains vulnerable to attacks that aim to deanonymize users by correlating traffic patterns between colluded entry and exit nodes in circuits."}
+{"idx": 2, "title": "Defending Against Malicious Cyber Activity Originating from Tor", "date": "", "ddg_snippet": "Defending Against Malicious Cyber Activity Originating from Tor This advisory—written by the Cybersecurity Security and Infrastructure Security Agency (CISA) with contributions from the Federal Bureau of Investigation (FBI)—highlights risks associated with Tor , along with technical details and recommendations for mitigation. Cyber threat actors can use Tor software and network ...", "subpage_snippet": "", "source": "www.cisa.gov", "link": "https://www.cisa.gov/sites/default/files/publications/AA20-183A_Defending_Against_Malicious_Cyber_Activity_Originating_from_Tor_S508C.pdf", "content": "Defending Against Malicious Cyber Activity Originating from Tor This advisory—written by the Cybersecurity Security and Infrastructure Security Agency (CISA) with contributions from the Federal Bureau of Investigation (FBI)—highlights risks associated with Tor , along with technical details and recommendations for mitigation. Cyber threat actors can use Tor software and network ..."}
+{"idx": 3, "title": "Welcome to Tor Metrics", "date": "", "ddg_snippet": "The Tor network is one of the largest deployed anonymity networks, consisting of thousands of volunteer-run relays and millions of users. Users, advocates, relay operators, and journalists can better understand the Tor network through data and analysis made available by Tor Metrics.", "subpage_snippet": "", "source": "metrics.torproject.org", "link": "https://metrics.torproject.org/", "content": "The Tor network is one of the largest deployed anonymity networks, consisting of thousands of volunteer-run relays and millions of users. Users, advocates, relay operators, and journalists can better understand the Tor network through data and analysis made available by Tor Metrics."}
+{"idx": 4, "title": "On the Accuracy of Tor Bandwidth Estimation - Rob G. Jansen", "date": "", "ddg_snippet": "3 Analysis of Tor Metrics Data To better understand the accuracy of Tor 's capacity-estimation heuristic, we an-alyze publicly available Tor metrics data [3]. Relays passively measure through-put over time and publish bandwidth information in their server descriptors [10, 2.1.1], while the load-balancing weights that TorFlow derives from the ...", "subpage_snippet": "", "source": "www.robgjansen.com", "link": "https://www.robgjansen.com/publications/torbwest-pam2021.pdf", "content": "3 Analysis of Tor Metrics Data To better understand the accuracy of Tor 's capacity-estimation heuristic, we an-alyze publicly available Tor metrics data [3]. Relays passively measure through-put over time and publish bandwidth information in their server descriptors [10, 2.1.1], while the load-balancing weights that TorFlow derives from the ..."}
+{"idx": 5, "title": "通过异常电路检测揭露Tor中的恶意同伙 - 安全内参 | 决策者的网络安全...", "date": "", "ddg_snippet": "Mar 13, 2025 · 提出了一种方法来检测 Tor 网络中的异常电路，通过考虑节点在异常电路中的角色，首次提供了一个更全面的方法识别 tor 中的 ...", "subpage_snippet": "", "source": "www.secrss.com", "link": "https://www.secrss.com/articles/76608", "content": "Mar 13, 2025 · 提出了一种方法来检测 Tor 网络中的异常电路，通过考虑节点在异常电路中的角色，首次提供了一个更全面的方法识别 tor 中的 ..."}
+{"idx": 6, "title": "Tor Metrics - OTF", "date": "", "ddg_snippet": "Tor Metrics is the central mechanism The Tor Project uses to evaluate the functionality and ongoing relevance of these access and security technologies to the internet freedom community. Tor users, funders, researchers, dependent software projects and other stakeholders all rely on Tor Metrics to review and build on Tor usage and technical measurements. This project improved the quality ...", "subpage_snippet": "", "source": "www.opentech.fund", "link": "https://www.opentech.fund/projects-we-support/supported-projects/tor-metrics/", "content": "Tor Metrics is the central mechanism The Tor Project uses to evaluate the functionality and ongoing relevance of these access and security technologies to the internet freedom community. Tor users, funders, researchers, dependent software projects and other stakeholders all rely on Tor Metrics to review and build on Tor usage and technical measurements. This project improved the quality ..."}
+{"idx": 7, "title": "Do not trust anyone - Английский - Японский Переводы и примеры", "date": "", "ddg_snippet": "Проголосуйте первым. Английский. do not trust what they tell you.", "subpage_snippet": "", "source": "mymemory.translated.net", "link": "https://mymemory.translated.net/ru/Английский/Японский/do-not-trust-anyone", "content": "Проголосуйте первым. Английский. do not trust what they tell you."}
+{"idx": 8, "title": "NFRW Weekly Poll Questions", "date": "", "ddg_snippet": "Do not trust what they tell you about the vaccines. And do not trust what Dr. Fauci tells you. Dr. Fauci lied to the American people when he said Ivermectin is not an effective treatment against Covid.", "subpage_snippet": "", "source": "www.nfrw.org", "link": "https://www.nfrw.org/poll-questions/ArtMID/17476/ArticleID/5385/Week-of-September-13-2021", "content": "Do not trust what they tell you about the vaccines. And do not trust what Dr. Fauci tells you. Dr. Fauci lied to the American people when he said Ivermectin is not an effective treatment against Covid."}
+{"idx": 9, "title": "Accomodation Letter / FMLA | Cancer and Careers", "date": "", "ddg_snippet": "Sadly I do not trust what they tell me.It seems like your question is twofold – what to do about your reasonable accommodation request being denied, and whether or not you need to go on FMLA.", "subpage_snippet": "", "source": "www.cancerandcareers.org", "link": "https://www.cancerandcareers.org/career-coach/accomodation-letter-fmla", "content": "Sadly I do not trust what they tell me.It seems like your question is twofold – what to do about your reasonable accommodation request being denied, and whether or not you need to go on FMLA."}
diff --git a/data/sampled_jsons/Do_Not_Trust_What_They_Tell_conn(a,_b)_formula.jsonl b/data/sampled_jsons/Do_Not_Trust_What_They_Tell_conn(a,_b)_formula.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..365678376eb6dc5eabd483107ef0cd8052377f24
--- /dev/null
+++ b/data/sampled_jsons/Do_Not_Trust_What_They_Tell_conn(a,_b)_formula.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Do Not Trust What They Tell: Exposing Malicious Accomplices in Tor via ...", "date": "", "ddg_snippet": "Here, Nc(, ) represents the times of co-occurrence of nodes and in a same anomalous circuit. conn(, ) = −Nc(, ) (8) (6) To reduce the impact of substantial variances of Nc(, ), we apply a logarithmic function to mitigate the significant diferences, thereby ensuring the feature more stable and reliable.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=qcnePVejeV", "content": "Here, Nc(, ) represents the times of co-occurrence of nodes and in a same anomalous circuit. conn(, ) = −Nc(, ) (8) (6) To reduce the impact of substantial variances of Nc(, ), we apply a logarithmic function to mitigate the significant diferences, thereby ensuring the feature more stable and reliable."}
+{"idx": 1, "title": "\"Do Not Trust What They Tell: Exposing Malicious Accomplices in ... - dblp", "date": "", "ddg_snippet": "Bibliographic details on Do Not Trust What They Tell : Exposing Malicious Accomplices in Tor via Anomalous Circuit Detection.", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/conf/www/Yao0LDGW25", "content": "Bibliographic details on Do Not Trust What They Tell : Exposing Malicious Accomplices in Tor via Anomalous Circuit Detection."}
+{"idx": 2, "title": "Do Not Trust What They Tell: Exposing Malicious Accomplices in Tor via ...", "date": "", "ddg_snippet": "The Tor network, while offering anonymity through traffic routing across volunteer-operated nodes, remains vulnerable to attacks that aim to deanonymize users by correlating traffic patterns between colluded entry and exit nodes in circuits. This paper presents a novel approach for detecting anomalous circuits in the Tor network, and for the first time provides a more comprehensive ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3696410.3714767", "content": "The Tor network, while offering anonymity through traffic routing across volunteer-operated nodes, remains vulnerable to attacks that aim to deanonymize users by correlating traffic patterns between colluded entry and exit nodes in circuits. This paper presents a novel approach for detecting anomalous circuits in the Tor network, and for the first time provides a more comprehensive ..."}
+{"idx": 3, "title": "PDF Do Not Trust What They Tell: Exposing Malicious Accomplices in Tor via ...", "date": "", "ddg_snippet": "After carefully in-specting the unique pattern of each circuit type in the cell exchange sequence determined by the Tor protocol, we successfully establish the rule-based classification decision tree in Appendix B .", "subpage_snippet": "", "source": "cse.seu.edu.cn", "link": "https://cse.seu.edu.cn/_upload/article/files/82/36/84188a1a47a79fe2b6dc589a1c39/a6db7ad8-3fef-4518-8046-480a6ac64280.pdf", "content": "After carefully in-specting the unique pattern of each circuit type in the cell exchange sequence determined by the Tor protocol, we successfully establish the rule-based classification decision tree in Appendix B ."}
+{"idx": 4, "title": "Chunmian Wang - dblp", "date": "", "ddg_snippet": "Yixuan Yao, Ming Yang, Zixia Liu, Kai Dong, Xiaodan Gu, Chunmian Wang: Do Not Trust What They Tell : Exposing Malicious Accomplices in Tor via Anomalous Circuit Detection. WWW 2025: 2959-2968", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/pid/322/9957", "content": "Yixuan Yao, Ming Yang, Zixia Liu, Kai Dong, Xiaodan Gu, Chunmian Wang: Do Not Trust What They Tell : Exposing Malicious Accomplices in Tor via Anomalous Circuit Detection. WWW 2025: 2959-2968"}
+{"idx": 5, "title": "All talk and no action - Karmic Ecology", "date": "", "ddg_snippet": "We do not trust what they tell us because nothing ever eventuates from all their talk. Perhaps some people feel intimidated with others, so they talk about all their successes like life is a competition.", "subpage_snippet": "", "source": "www.karmicecology.com", "link": "https://www.karmicecology.com/mind/philosophy/all-talk-and-no-action/", "content": "We do not trust what they tell us because nothing ever eventuates from all their talk. Perhaps some people feel intimidated with others, so they talk about all their successes like life is a competition."}
+{"idx": 6, "title": "NFRW Weekly Poll Questions", "date": "", "ddg_snippet": "Do not trust what they tell you about the vaccines. And do not trust what Dr. Fauci tells you. Dr. Fauci lied to the American people when he said Ivermectin is not an effective treatment against Covid.", "subpage_snippet": "", "source": "www.nfrw.org", "link": "https://www.nfrw.org/poll-questions/ArtMID/17476/ArticleID/5385/Week-of-September-13-2021", "content": "Do not trust what they tell you about the vaccines. And do not trust what Dr. Fauci tells you. Dr. Fauci lied to the American people when he said Ivermectin is not an effective treatment against Covid."}
+{"idx": 7, "title": "Accomodation Letter / FMLA | Cancer and Careers", "date": "", "ddg_snippet": "Sadly I do not trust what they tell me.It seems like your question is twofold – what to do about your reasonable accommodation request being denied, and whether or not you need to go on FMLA.", "subpage_snippet": "", "source": "www.cancerandcareers.org", "link": "https://www.cancerandcareers.org/career-coach/accomodation-letter-fmla", "content": "Sadly I do not trust what they tell me.It seems like your question is twofold – what to do about your reasonable accommodation request being denied, and whether or not you need to go on FMLA."}
+{"idx": 8, "title": "iPhone 12 mini review: this is my experience of using it – Wingdings...", "date": "", "ddg_snippet": "It’s very paradoxical, but it’s not strange either, since in the end those phones were so big mainly because they had fairly robust edges, with the Home button included. Do not trust what they tell you about the battery of this iPhone.", "subpage_snippet": "", "source": "wingdingstranslator.com", "link": "https://wingdingstranslator.com/iphone-12-mini-review-this-is-my-experience-of-using-it/", "content": "It’s very paradoxical, but it’s not strange either, since in the end those phones were so big mainly because they had fairly robust edges, with the Home button included. Do not trust what they tell you about the battery of this iPhone."}
+{"idx": 9, "title": "Estrategy Club: valoración y comentarios【 2024 】", "date": "", "ddg_snippet": "This broker is a SCAM. Do not send them money, they want to get as much money as possible with lies, the operations are not real, they do not put the money in the market, they just make it look and in the end they tell you that you have lost and they keep it.", "subpage_snippet": "", "source": "recommended-brokers.com", "link": "https://recommended-brokers.com/strategy-club-rating-and-comments/", "content": "This broker is a SCAM. Do not send them money, they want to get as much money as possible with lies, the operations are not real, they do not put the money in the market, they just make it look and in the end they tell you that you have lost and they keep it."}
diff --git a/data/sampled_jsons/Dynamic_Hierarchical_Collaboration_Equation_(19)_scaling_factor_Socialized_Coevolution.jsonl b/data/sampled_jsons/Dynamic_Hierarchical_Collaboration_Equation_(19)_scaling_factor_Socialized_Coevolution.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c437f3235b73d08500e369afe97e35b15f187cf8
--- /dev/null
+++ b/data/sampled_jsons/Dynamic_Hierarchical_Collaboration_Equation_(19)_scaling_factor_Socialized_Coevolution.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Evolution of Complex Hierarchical Societies", "date": "", "ddg_snippet": "There has been very little explicit modeling done on the dynamics of hierarchy formation (one exception is Axelrod 1997).", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/266884396_Evolution_of_Complex_Hierarchical_Societies", "content": "There has been very little explicit modeling done on the dynamics of hierarchy formation (one exception is Axelrod 1997)."}
+{"idx": 1, "title": "Talk Keyword Index", "date": "", "ddg_snippet": "... based on excitable dynamics on ... Cross-national analysis of loss of adherence due to stop-and-go application of restrictions against COVID- 19 .", "subpage_snippet": "", "source": "easychair.org", "link": "https://easychair.org/smart-program/NetSci2023/talk_keyword_index.html", "content": "... based on excitable dynamics on ... Cross-national analysis of loss of adherence due to stop-and-go application of restrictions against COVID- 19 ."}
+{"idx": 2, "title": "Laurent Hébert‐Dufresne | Sugaku", "date": "", "ddg_snippet": "... models that account for dynamical correlations and adaptation in groups, we introduce the method of generalized approximate master equations .", "subpage_snippet": "", "source": "sugaku.net", "link": "https://sugaku.net/oa/A5035455593/", "content": "... models that account for dynamical correlations and adaptation in groups, we introduce the method of generalized approximate master equations ."}
+{"idx": 3, "title": "\"PHYSICAL\": list of Horizon 2020 projects related to", "date": "", "ddg_snippet": "An integrated approach to dissect determinants, risk factors and pathways of ageing of the immune system. ... to create a more supportive social and ...", "subpage_snippet": "", "source": "www.fabiodisconzi.com", "link": "https://www.fabiodisconzi.com/open-h2020/per-topic/physical/list/index.html", "content": "An integrated approach to dissect determinants, risk factors and pathways of ageing of the immune system. ... to create a more supportive social and ..."}
+{"idx": 4, "title": "\"SOPHISTICATED\": list of Horizon 2020 projects", "date": "", "ddg_snippet": "A Collaboration Ecosystem enabling EU Creative SMEs to exchange multi-media content and create multi-plot, interactive Apps for Children, curated ...", "subpage_snippet": "", "source": "www.fabiodisconzi.com", "link": "https://www.fabiodisconzi.com/open-h2020/per-topic/sophisticated/list/index.html", "content": "A Collaboration Ecosystem enabling EU Creative SMEs to exchange multi-media content and create multi-plot, interactive Apps for Children, curated ..."}
+{"idx": 5, "title": "Defining and classifying models of groups: The social ontology", "date": "", "ddg_snippet": "... we can better investigate the coevolution of individuals and groups—an aspect that is difficult to capture by focusing on individual-based dynamics ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.02758v1", "content": "... we can better investigate the coevolution of individuals and groups—an aspect that is difficult to capture by focusing on individual-based dynamics ..."}
+{"idx": 6, "title": "The Wicked and the Complex: A New Paradigm for Societal", "date": "", "ddg_snippet": "... dynamic nature ensures their ongoing significance, as each intervention spawns new challenges and opportunities, keeping these issues at the forefront ...", "subpage_snippet": "", "source": "www.preprints.org", "link": "https://www.preprints.org/manuscript/202407.1438/v2", "content": "... dynamic nature ensures their ongoing significance, as each intervention spawns new challenges and opportunities, keeping these issues at the forefront ..."}
+{"idx": 7, "title": "Talk Keyword Index", "date": "", "ddg_snippet": "Interrupting Disease Transmission in Healthcare Networks Using Higher-Order Models: A Case Study for Nosocomial COVID- 19 and Anti-microbial Resistance", "subpage_snippet": "", "source": "easychair.org", "link": "https://easychair.org/smart-program/NETSCI2020/talk_keyword_index.html", "content": "Interrupting Disease Transmission in Healthcare Networks Using Higher-Order Models: A Case Study for Nosocomial COVID- 19 and Anti-microbial Resistance"}
+{"idx": 8, "title": "Antoine Allard | Sugaku", "date": "", "ddg_snippet": "From pathogens and computer viruses to genes and memes, contagion models have found widespread utility across the natural and social sciences.", "subpage_snippet": "", "source": "sugaku.net", "link": "https://sugaku.net/oa/A5027136376/", "content": "From pathogens and computer viruses to genes and memes, contagion models have found widespread utility across the natural and social sciences."}
+{"idx": 9, "title": "%Social organization% - PlantsPeoplePlanet", "date": "", "ddg_snippet": "Historically, social organization has increased in complexity as existing energy sources were used more efficiently and new, more concentrated, forms ...", "subpage_snippet": "", "source": "plantspeopleplanet.au", "link": "https://plantspeopleplanet.au/social-organization/", "content": "Historically, social organization has increased in complexity as existing energy sources were used more efficiently and new, more concentrated, forms ..."}
diff --git a/data/sampled_jsons/E91gjsccP1_HtmlRAG_HTML_is_Better_Than_Plain_Text_for_Modeling_Retrieved_Knowledge_in_RAG_Systems_Pr.jsonl b/data/sampled_jsons/E91gjsccP1_HtmlRAG_HTML_is_Better_Than_Plain_Text_for_Modeling_Retrieved_Knowledge_in_RAG_Systems_Pr.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5c0584a52bc1dc713e4dee0e59060692e3c34b41
--- /dev/null
+++ b/data/sampled_jsons/E91gjsccP1_HtmlRAG_HTML_is_Better_Than_Plain_Text_for_Modeling_Retrieved_Knowledge_in_RAG_Systems_Pr.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "plageon/ HtmlRAG : HtmlRAG : HTML is Better Than Plain Text for ...", "date": "", "ddg_snippet": "We propose HtmlRAG , which uses HTML instead of plain text as the format of external knowledge in RAG systems . To tackle the long context brought by HTML , we propose Lossless HTML Cleaning and Two-Step Block-Tree-Based HTML Pruning .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/plageon/HtmlRAG", "content": "We propose HtmlRAG , which uses HTML instead of plain text as the format of external knowledge in RAG systems . To tackle the long context brought by HTML , we propose Lossless HTML Cleaning and Two-Step Block-Tree-Based HTML Pruning ."}
+{"idx": 1, "title": "HtmlRAG: HTML is Better Than Plain Text for Modeling ...", "date": "", "ddg_snippet": "268 In this paper, we propose HtmlRAG , which uses HTML instead of 269 plain text as the format of retrieved knowledge in RAG systems , 270 aiming to keep richer semantic and structured information that is 271 missing in plain text .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=E91gjsccP1", "content": "268 In this paper, we propose HtmlRAG , which uses HTML instead of 269 plain text as the format of retrieved knowledge in RAG systems , 270 aiming to keep richer semantic and structured information that is 271 missing in plain text ."}
+{"idx": 2, "title": "HtmlRAG: HTML is Better Than Plain Text for Modeling ... HtmlRAG: HTML is Better Than Plain Text for Modeling ... Understanding HtmlRAG: HTML is Better Than Plain Text for ... HtmlRAG - a zstanjj Collection - Hugging Face Table 4 from HtmlRAG: HTML is Better Than Plain Text for ... HtmlRAG: HTML is Better Than Plain Text for Modeling ...", "date": "", "ddg_snippet": "Nov 5, 2024 · To alleviate this problem, we propose HtmlRAG , which uses HTML instead of plain text as the format of retrieved knowledge in RAG . We believe HTML is better than plain text in modeling knowledge in external documents, and most LLMs possess robust capacities to understand HTML . However, utilizing HTML presents new challenges. We propose HtmlRAG , which uses HTML instead of plain text as the format of external knowledge in RAG systems . To tackle the long context brought by HTML , we propose Lossless HTML Cleaning and Two-Step Block-Tree-Based HTML Pruning. HtmlRAG introduces an innovative method for enhancing Retrieval-Augmented Generation ( RAG ) systems by using HTML over plain text for integrating external knowledge into large language models (LLMs). This approach preserves essential data structure, improving LLM performance and accuracy. Nov 5, 2024 · HtmlRAG : HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systems Paper • 2411.02959 •Published Nov 5, 2024• 71 Table 4 : Analysis of inference cost on ELI5 dataset We compare the chunking-based refiner using BGE (BGE), the two HTML pruning steps basing on the text embedding (PruneEmbed) and the generative model ( Prune-Gen ) in HtmlRAG , and LLM chatting ( LLM Chat ) by model parameters, storage, average input tokens, and average output tokens. Apr 22, 2025 · To alleviate this problem, we propose HtmlRAG , which uses HTML instead of plain text as the format of retrieved knowledge in RAG . We believe HTML is better than plain text in modeling knowledge in external documents, and most LLMs possess robust capacities to understand HTML .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2411.02959", "content": "Nov 5, 2024 · To alleviate this problem, we propose HtmlRAG , which uses HTML instead of plain text as the format of retrieved knowledge in RAG . We believe HTML is better than plain text in modeling knowledge in external documents, and most LLMs possess robust capacities to understand HTML . However, utilizing HTML presents new challenges. We propose HtmlRAG , which uses HTML instead of plain text as the format of external knowledge in RAG systems . To tackle the long context brought by HTML , we propose Lossless HTML Cleaning and Two-Step Block-Tree-Based HTML Pruning. HtmlRAG introduces an innovative method for enhancing Retrieval-Augmented Generation ( RAG ) systems by using HTML over plain text for integrating external knowledge into large language models (LLMs). This approach preserves essential data structure, improving LLM performance and accuracy. Nov 5, 2024 · HtmlRAG : HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systems Paper • 2411.02959 •Published Nov 5, 2024• 71 Table 4 : Analysis of inference cost on ELI5 dataset We compare the chunking-based refiner using BGE (BGE), the two HTML pruning steps basing on the text embedding (PruneEmbed) and the generative model ( Prune-Gen ) in HtmlRAG , and LLM chatting ( LLM Chat ) by model parameters, storage, average input tokens, and average output tokens. Apr 22, 2025 · To alleviate this problem, we propose HtmlRAG , which uses HTML instead of plain text as the format of retrieved knowledge in RAG . We believe HTML is better than plain text in modeling knowledge in external documents, and most LLMs possess robust capacities to understand HTML ."}
+{"idx": 3, "title": "Understanding HtmlRAG: HTML is Better Than Plain Text for ...", "date": "", "ddg_snippet": "HtmlRAG introduces an innovative method for enhancing Retrieval-Augmented Generation ( RAG ) systems by using HTML over plain text for integrating external knowledge into large language models (LLMs). This approach preserves essential data structure, improving LLM performance and accuracy.", "subpage_snippet": "", "source": "techchilli.com", "link": "https://techchilli.com/artificial-intelligence/understanding-htmlrag-html-is-better-than-plain-text-for-modeling-retrieved-knowledge-in-rag-systems/", "content": "HtmlRAG introduces an innovative method for enhancing Retrieval-Augmented Generation ( RAG ) systems by using HTML over plain text for integrating external knowledge into large language models (LLMs). This approach preserves essential data structure, improving LLM performance and accuracy."}
+{"idx": 4, "title": "HtmlRAG - a zstanjj Collection - Hugging Face", "date": "", "ddg_snippet": "Nov 5, 2024 · HtmlRAG : HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systems Paper • 2411.02959 •Published Nov 5, 2024• 71", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/collections/zstanjj/htmlrag-671f03af5c3da2e7b5371aa4", "content": "Nov 5, 2024 · HtmlRAG : HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systems Paper • 2411.02959 •Published Nov 5, 2024• 71"}
+{"idx": 5, "title": "Table 4 from HtmlRAG: HTML is Better Than Plain Text for ...", "date": "", "ddg_snippet": "Table 4 : Analysis of inference cost on ELI5 dataset We compare the chunking-based refiner using BGE (BGE), the two HTML pruning steps basing on the text embedding (PruneEmbed) and the generative model ( Prune-Gen ) in HtmlRAG , and LLM chatting ( LLM Chat ) by model parameters, storage, average input tokens, and average output tokens.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/HtmlRAG:-HTML-is-Better-Than-Plain-Text-for-in-RAG-Tan-Dou/7cfd2426ca908c8c5a81bd7c7ca01f914a972de4/figure/6", "content": "Table 4 : Analysis of inference cost on ELI5 dataset We compare the chunking-based refiner using BGE (BGE), the two HTML pruning steps basing on the text embedding (PruneEmbed) and the generative model ( Prune-Gen ) in HtmlRAG , and LLM chatting ( LLM Chat ) by model parameters, storage, average input tokens, and average output tokens."}
+{"idx": 6, "title": "HtmlRAG: HTML is Better Than Plain Text for Modeling ...", "date": "", "ddg_snippet": "Apr 22, 2025 · To alleviate this problem, we propose HtmlRAG , which uses HTML instead of plain text as the format of retrieved knowledge in RAG . We believe HTML is better than plain text in modeling knowledge in external documents, and most LLMs possess robust capacities to understand HTML .", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.1145/3696410.3714546", "content": "Apr 22, 2025 · To alleviate this problem, we propose HtmlRAG , which uses HTML instead of plain text as the format of retrieved knowledge in RAG . We believe HTML is better than plain text in modeling knowledge in external documents, and most LLMs possess robust capacities to understand HTML ."}
+{"idx": 7, "title": "HtmlRAG : HTML is Better Than Plain Text for Modeling Retrieved ...", "date": "", "ddg_snippet": "Typically, such RAG systems retrieve search results, download HTML sources of the results, and then extract plain texts from the HTML sources. Plain text documents or chunks are fed into the LLMs to augment the generation.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=E91gjsccP1", "content": "Typically, such RAG systems retrieve search results, download HTML sources of the results, and then extract plain texts from the HTML sources. Plain text documents or chunks are fed into the LLMs to augment the generation."}
+{"idx": 8, "title": "Paper page - HtmlRAG : HTML is Better Than Plain Text for ...", "date": "", "ddg_snippet": "Abstract. HtmlRAG enhances Retrieval-Augmented Generation ( RAG ) systems by using HTML instead of plain text , improving knowledge modeling and reducing information loss through HTML cleaning, compression, and pruning .", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2411.02959", "content": "Abstract. HtmlRAG enhances Retrieval-Augmented Generation ( RAG ) systems by using HTML instead of plain text , improving knowledge modeling and reducing information loss through HTML cleaning, compression, and pruning ."}
+{"idx": 9, "title": "Paper tables with annotated results for HtmlRAG : HTML is Better ...", "date": "", "ddg_snippet": "HtmlRAG : HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systems . Table 2. Results of HtmlRAG without pruning and baselines under the long-context setting. Hit@1 is the proportion of instances where at least one short answer matches.", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/paper/htmlrag-html-is-better-than-plain-text-for/review/", "content": "HtmlRAG : HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systems . Table 2. Results of HtmlRAG without pruning and baselines under the long-context setting. Hit@1 is the proportion of instances where at least one short answer matches."}
diff --git a/data/sampled_jsons/ELITE_Enhanced_Language-Image_Toxicity_Evaluation_Equation_2.jsonl b/data/sampled_jsons/ELITE_Enhanced_Language-Image_Toxicity_Evaluation_Equation_2.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3425e939717919066e7369535e77004aa3da5de8
--- /dev/null
+++ b/data/sampled_jsons/ELITE_Enhanced_Language-Image_Toxicity_Evaluation_Equation_2.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ELITE: Enhanced Language-Image Toxicity Evaluation for Safety", "date": "", "ddg_snippet": "Therefore, we found that existing benchmarks have low levels of harmfulness, ambiguous data, and limited diversity in image -text pair combinations. To address these issues, we propose the ELITE benchmark, a high-quality safety evaluation benchmark for VLMs, underpinned by our enhanced evaluation method, the ELITE evaluator.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.04757", "content": "Therefore, we found that existing benchmarks have low levels of harmfulness, ambiguous data, and limited diversity in image -text pair combinations. To address these issues, we propose the ELITE benchmark, a high-quality safety evaluation benchmark for VLMs, underpinned by our enhanced evaluation method, the ELITE evaluator."}
+{"idx": 1, "title": "How ELITE Reveals Dangerous Weaknesses in Vision-Language AI", "date": "", "ddg_snippet": "2 . ELITE Evaluator: Grading Toxicity with Nuance The paper introduces a new evaluation formula based on the StrongREJECT rubric but adds a crucial factor: toxicity , which captures the degree of ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/aim-intelligence/how-elite-reveals-dangerous-weaknesses-in-vision-language-ai-ffa208b7546c", "content": "2 . ELITE Evaluator: Grading Toxicity with Nuance The paper introduces a new evaluation formula based on the StrongREJECT rubric but adds a crucial factor: toxicity , which captures the degree of ..."}
+{"idx": 2, "title": "ELITE: Enhanced Language-Image Toxicity Evaluation for Safety", "date": "", "ddg_snippet": "The ELITE benchmark is proposed, a high-quality safety evaluation benchmark for VLMs, underpinned by the enhanced evaluation method, the ELITE evaluator, which explicitly incorporates a toxicity score to accurately assess harmfulness in multimodal contexts. Current Vision Language Models (VLMs) remain vulnerable to malicious prompts that induce harmful outputs. Existing safety benchmarks for ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/ELITE:-Enhanced-Language-Image-Toxicity-Evaluation-Lee-Lee/2bf4206276d5f574bbb2e13a56b29b4522fea675", "content": "The ELITE benchmark is proposed, a high-quality safety evaluation benchmark for VLMs, underpinned by the enhanced evaluation method, the ELITE evaluator, which explicitly incorporates a toxicity score to accurately assess harmfulness in multimodal contexts. Current Vision Language Models (VLMs) remain vulnerable to malicious prompts that induce harmful outputs. Existing safety benchmarks for ..."}
+{"idx": 3, "title": "Our paper \"ELITE: Enhanced Language-Image Toxicity Evaluation for ...", "date": "", "ddg_snippet": "Our paper \"ELITE: Enhanced Language-Image Toxicity Evaluation for Safety\" has been accepted at ICML 2025 in Vancouver! We propose the ELITE benchmark, a high-quality safety evaluation benchmark for VLMs, underpinned by our enhanced evaluation method, the ELITE evaluator. The ELITE evaluator explicitly incorporates a toxicity score to accurately assess harmfulness in multimodal contexts ...", "subpage_snippet": "", "source": "pe.linkedin.com", "link": "https://pe.linkedin.com/posts/wonjun-lee-7566b7259_our-paper-elite-enhanced-language-image-activity-7324072840427642880-XG3o", "content": "Our paper \"ELITE: Enhanced Language-Image Toxicity Evaluation for Safety\" has been accepted at ICML 2025 in Vancouver! We propose the ELITE benchmark, a high-quality safety evaluation benchmark for VLMs, underpinned by our enhanced evaluation method, the ELITE evaluator. The ELITE evaluator explicitly incorporates a toxicity score to accurately assess harmfulness in multimodal contexts ..."}
+{"idx": 4, "title": "AIM Intelligence's ELITE Collaborative Paper Accepted by the ICML", "date": "", "ddg_snippet": "The paper proposes ELITE , a high-quality benchmark designed to evaluate the safety of Vision- Language Models (VLMs) with greater precision. At its core is the ELITE evaluator, a rubric-based method that incorporates a toxicity score to measure harmfulness in multimodal contexts-especially where VLMs produce specific, convincing responses that may appear harmless but convey dangerous intent.", "subpage_snippet": "", "source": "www.newsfilecorp.com", "link": "https://www.newsfilecorp.com/release/252268/AIM-Intelligences-ELITE-Collaborative-Paper-Accepted-by-the-ICML", "content": "The paper proposes ELITE , a high-quality benchmark designed to evaluate the safety of Vision- Language Models (VLMs) with greater precision. At its core is the ELITE evaluator, a rubric-based method that incorporates a toxicity score to measure harmfulness in multimodal contexts-especially where VLMs produce specific, convincing responses that may appear harmless but convey dangerous intent."}
+{"idx": 5, "title": "ELITE: Enhanced Language-Image Toxicity Evaluation for Safety", "date": "", "ddg_snippet": "We identify these problems in the safety evaluation methods and propose the Enhanced Language-Image Toxicity Evaluation ( ELITE ) evaluator, a method designed to accurately evaluate the safety of VLMs.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.04757v3", "content": "We identify these problems in the safety evaluation methods and propose the Enhanced Language-Image Toxicity Evaluation ( ELITE ) evaluator, a method designed to accurately evaluate the safety of VLMs."}
+{"idx": 6, "title": "ELITE: Enhanced Language-Image Toxicity Evaluation for Safety", "date": "", "ddg_snippet": "About [ICML 2025] ELITE : Enhanced Language-Image Toxicity Evaluation for Safety", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/velpegor/ELITE", "content": "About [ICML 2025] ELITE : Enhanced Language-Image Toxicity Evaluation for Safety"}
+{"idx": 7, "title": "AIM Intelligence's ELITE Collaborative Paper Accepted by the ICML", "date": "", "ddg_snippet": "New York, New York- (Newsfile Corp. - May 15, 2025) - The International Conference on Machine Learning (ICML) has officially accepted \" ELITE : Enhanced Language-Image Toxicity Evaluation for Safety\", a collaborative paper from AIM Intelligence, Seoul National University, Yonsei University, KIST, Kyung Hee University, and Sookmyung Women's University.", "subpage_snippet": "", "source": "matribhumisamachar.com", "link": "https://matribhumisamachar.com/en/2025/05/15/aim-intelligences-elite-collaborative-paper-accepted-by-the-icml/", "content": "New York, New York- (Newsfile Corp. - May 15, 2025) - The International Conference on Machine Learning (ICML) has officially accepted \" ELITE : Enhanced Language-Image Toxicity Evaluation for Safety\", a collaborative paper from AIM Intelligence, Seoul National University, Yonsei University, KIST, Kyung Hee University, and Sookmyung Women's University."}
+{"idx": 8, "title": "ELITE: Enhanced Language-Image Toxicity Evaluation for Safety | Cool ...", "date": "", "ddg_snippet": "Therefore, we found that existing benchmarks have low levels of harmfulness, ambiguous data, and limited diversity in image -text pair combinations. To address these issues, we propose the ELITE {\\em benchmark}, a high-quality safety evaluation benchmark for VLMs, underpinned by our enhanced evaluation method, the ELITE {\\em evaluator}.", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/arxiv/2502.04757", "content": "Therefore, we found that existing benchmarks have low levels of harmfulness, ambiguous data, and limited diversity in image -text pair combinations. To address these issues, we propose the ELITE {\\em benchmark}, a high-quality safety evaluation benchmark for VLMs, underpinned by our enhanced evaluation method, the ELITE {\\em evaluator}."}
+{"idx": 9, "title": "cvlab.yonsei.ac.kr", "date": "", "ddg_snippet": "Therefore, we found that existing benchmarks have low levels of harmfulness, ambiguous data, and limited diversity in image -text pair combinations. To address these issues, we propose the ELITE benchmark, a high-quality safety evaluation benchmark for VLMs, underpinned by our enhanced evaluation method, the ELITE evaluator.", "subpage_snippet": "", "source": "cvlab.yonsei.ac.kr", "link": "https://cvlab.yonsei.ac.kr/projects/ELITE/", "content": "Therefore, we found that existing benchmarks have low levels of harmfulness, ambiguous data, and limited diversity in image -text pair combinations. To address these issues, we propose the ELITE benchmark, a high-quality safety evaluation benchmark for VLMs, underpinned by our enhanced evaluation method, the ELITE evaluator."}
diff --git a/data/sampled_jsons/ELITE_Enhanced_Language-Image_Toxicity_Evaluation_Figure_4_AU-ROC_StrongREJECT_methodology_component.jsonl b/data/sampled_jsons/ELITE_Enhanced_Language-Image_Toxicity_Evaluation_Figure_4_AU-ROC_StrongREJECT_methodology_component.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..08c5f0032ac0cdc4cec8d7d81571504ee7fdfe42
--- /dev/null
+++ b/data/sampled_jsons/ELITE_Enhanced_Language-Image_Toxicity_Evaluation_Figure_4_AU-ROC_StrongREJECT_methodology_component.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ELITE: Enhanced Language-Image Toxicity Evaluation for Safety", "date": "", "ddg_snippet": "Figure 4 : The comparison of AU-ROC curves between the ELITE evaluator and StrongREJECT evaluator on our human evaluation dataset. 5.2 Comparison with Existing Evaluation Method", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.04757v3", "content": "Figure 4 : The comparison of AU-ROC curves between the ELITE evaluator and StrongREJECT evaluator on our human evaluation dataset. 5.2 Comparison with Existing Evaluation Method"}
+{"idx": 1, "title": "ELITE: Enhanced Language-Image Toxicity Evaluation for Safety", "date": "", "ddg_snippet": "Therefore, we found that existing benchmarks have low levels of harmfulness, ambiguous data, and limited diversity in image -text pair combinations. To address these issues, we propose the ELITE benchmark, a high-quality safety evaluation benchmark for VLMs, underpinned by our enhanced evaluation method, the ELITE evaluator.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.04757", "content": "Therefore, we found that existing benchmarks have low levels of harmfulness, ambiguous data, and limited diversity in image -text pair combinations. To address these issues, we propose the ELITE benchmark, a high-quality safety evaluation benchmark for VLMs, underpinned by our enhanced evaluation method, the ELITE evaluator."}
+{"idx": 2, "title": "ELITE: Enhanced Language-Image Toxicity Evaluation for Safety", "date": "", "ddg_snippet": "We identify these problems in the safety evaluation methods and propose the Enhanced Language-Image Toxicity Evaluation ( ELITE ) evaluator, a method designed to accurately evaluate the safety of VLMs.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.04757v3", "content": "We identify these problems in the safety evaluation methods and propose the Enhanced Language-Image Toxicity Evaluation ( ELITE ) evaluator, a method designed to accurately evaluate the safety of VLMs."}
+{"idx": 3, "title": "ELITE: Enhanced Language-Image Toxicity Evaluation for Safety", "date": "", "ddg_snippet": "The ELITE benchmark is proposed, a high-quality safety evaluation benchmark for VLMs, underpinned by the enhanced evaluation method, the ELITE evaluator, which explicitly incorporates a toxicity score to accurately assess harmfulness in multimodal contexts. Current Vision Language Models (VLMs) remain vulnerable to malicious prompts that induce harmful outputs. Existing safety benchmarks for ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/ELITE:-Enhanced-Language-Image-Toxicity-Evaluation-Lee-Lee/2bf4206276d5f574bbb2e13a56b29b4522fea675", "content": "The ELITE benchmark is proposed, a high-quality safety evaluation benchmark for VLMs, underpinned by the enhanced evaluation method, the ELITE evaluator, which explicitly incorporates a toxicity score to accurately assess harmfulness in multimodal contexts. Current Vision Language Models (VLMs) remain vulnerable to malicious prompts that induce harmful outputs. Existing safety benchmarks for ..."}
+{"idx": 4, "title": "ELITE: Enhanced Language-Image Toxicity Evaluation for Safety | Cool ...", "date": "", "ddg_snippet": "Therefore, we found that existing benchmarks have low levels of harmfulness, ambiguous data, and limited diversity in image -text pair combinations. To address these issues, we propose the ELITE {\\em benchmark}, a high-quality safety evaluation benchmark for VLMs, underpinned by our enhanced evaluation method, the ELITE {\\em evaluator}.", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/arxiv/2502.04757", "content": "Therefore, we found that existing benchmarks have low levels of harmfulness, ambiguous data, and limited diversity in image -text pair combinations. To address these issues, we propose the ELITE {\\em benchmark}, a high-quality safety evaluation benchmark for VLMs, underpinned by our enhanced evaluation method, the ELITE {\\em evaluator}."}
+{"idx": 5, "title": "ELITE: Enhanced Language-Image Toxicity Evaluation for Safety", "date": "", "ddg_snippet": "About [ICML 2025] ELITE : Enhanced Language-Image Toxicity Evaluation for Safety", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/velpegor/ELITE", "content": "About [ICML 2025] ELITE : Enhanced Language-Image Toxicity Evaluation for Safety"}
+{"idx": 6, "title": "ELITE: Enhanced Language-Image Toxicity Evaluation for Safety", "date": "", "ddg_snippet": "ELITE : Enhanced Language-Image Toxicity Evaluation for Safety Wonjun Lee · Doehyeon Lee · Eugene Choi · Sangyoon Yu · Ashkan Yousefpour · Haon Park · Bumsub Ham · Suhyun Kim", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46445", "content": "ELITE : Enhanced Language-Image Toxicity Evaluation for Safety Wonjun Lee · Doehyeon Lee · Eugene Choi · Sangyoon Yu · Ashkan Yousefpour · Haon Park · Bumsub Ham · Suhyun Kim"}
+{"idx": 7, "title": "ELITE: Enhanced Language-Image Toxicity Evaluation for Safety | AI ...", "date": "", "ddg_snippet": "ELITE : Enhanced Language-Image Toxicity Evaluation for Safety Published 2/10/2025 by Wonjun Lee, Doehyeon Lee, Eugene Choi, Sangyoon Yu, Ashkan Yousefpour, Haon Park, BUMSUB HAM and 1 more...", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/elite-enhanced-language-image-toxicity-evaluation-safety", "content": "ELITE : Enhanced Language-Image Toxicity Evaluation for Safety Published 2/10/2025 by Wonjun Lee, Doehyeon Lee, Eugene Choi, Sangyoon Yu, Ashkan Yousefpour, Haon Park, BUMSUB HAM and 1 more..."}
+{"idx": 8, "title": "cvlab.yonsei.ac.kr", "date": "", "ddg_snippet": "Therefore, we found that existing benchmarks have low levels of harmfulness, ambiguous data, and limited diversity in image -text pair combinations. To address these issues, we propose the ELITE benchmark, a high-quality safety evaluation benchmark for VLMs, underpinned by our enhanced evaluation method, the ELITE evaluator.", "subpage_snippet": "", "source": "cvlab.yonsei.ac.kr", "link": "https://cvlab.yonsei.ac.kr/projects/ELITE/", "content": "Therefore, we found that existing benchmarks have low levels of harmfulness, ambiguous data, and limited diversity in image -text pair combinations. To address these issues, we propose the ELITE benchmark, a high-quality safety evaluation benchmark for VLMs, underpinned by our enhanced evaluation method, the ELITE evaluator."}
+{"idx": 9, "title": "arXiv.org", "date": "", "ddg_snippet": "arXiv.org", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.04757", "content": "arXiv.org"}
diff --git a/data/sampled_jsons/ELITE_Table_3_E-ASR_GPT-4o_Gemini_Claude_LLaVA.jsonl b/data/sampled_jsons/ELITE_Table_3_E-ASR_GPT-4o_Gemini_Claude_LLaVA.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0726da4da2675ce90cde4d947894b44a648e6c54
--- /dev/null
+++ b/data/sampled_jsons/ELITE_Table_3_E-ASR_GPT-4o_Gemini_Claude_LLaVA.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "The Evolving Role of Large Language Models in Scientific ...", "date": "", "ddg_snippet": "16 Jul 2025 — On CURIE, even leading models like Claude - 3 and Gemini Flash 2.0 achieve only 32% performance on scientific long-context tasks. Multimodal ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.11810v1", "content": "16 Jul 2025 — On CURIE, even leading models like Claude - 3 and Gemini Flash 2.0 achieve only 32% performance on scientific long-context tasks. Multimodal ..."}
+{"idx": 1, "title": "HKUST-LongGroup/Awesome-MLLM-Benchmarks", "date": "", "ddg_snippet": "We evaluate 14 open-source MLLMs, Gemini Pro, Claude - 3 series, and GPT -4V(ision) on \\bench{}, revealing significant challenges and performance gaps. Further ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/HKUST-LongGroup/Awesome-MLLM-Benchmarks", "content": "We evaluate 14 open-source MLLMs, Gemini Pro, Claude - 3 series, and GPT -4V(ision) on \\bench{}, revealing significant challenges and performance gaps. Further ..."}
+{"idx": 2, "title": "Tavish9/awesome-daily-AI-arxiv", "date": "", "ddg_snippet": "Without any further post-training, OpenAI's GPT -4.1 with CLIO yields an accuracy of 22.37\\% in text-based biology and medicine questions on Humanity's Last Exam ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Tavish9/awesome-daily-AI-arxiv", "content": "Without any further post-training, OpenAI's GPT -4.1 with CLIO yields an accuracy of 22.37\\% in text-based biology and medicine questions on Humanity's Last Exam ..."}
+{"idx": 3, "title": "Computation and Language Mar 2025", "date": "", "ddg_snippet": "3 Mar 2025 — Title: Gemini Embedding: Generalizable Embeddings from Gemini ... 40 pages, 14 figures, 3 tables . Code available at this https URL.", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/list/cs.CL/2025-03?skip=500&show=2000", "content": "3 Mar 2025 — Title: Gemini Embedding: Generalizable Embeddings from Gemini ... 40 pages, 14 figures, 3 tables . Code available at this https URL."}
+{"idx": 4, "title": "Proceedings of the Annual Meeting of the Cognitive ...", "date": "", "ddg_snippet": "Computational model fitting showed that one reason for GPT - 4o , Gemini -Pro, and Claude's superior performance is they didn't exhibit the \"associative bias'' that ...", "subpage_snippet": "", "source": "escholarship.org", "link": "https://escholarship.org/uc/cognitivesciencesociety/47/0", "content": "Computational model fitting showed that one reason for GPT - 4o , Gemini -Pro, and Claude's superior performance is they didn't exhibit the \"associative bias'' that ..."}
+{"idx": 5, "title": "AI Literacy Storage", "date": "", "ddg_snippet": "Microsoft paper finally reveals the model size of known LLM models. > GPT - 4o -mini: 8B. > Claude 3.5 Sonnet: 175B. > GPT-4: 1.76 ...", "subpage_snippet": "", "source": "www.atalegroup.com", "link": "https://www.atalegroup.com/ai-literacy-storage", "content": "Microsoft paper finally reveals the model size of known LLM models. > GPT - 4o -mini: 8B. > Claude 3.5 Sonnet: 175B. > GPT-4: 1.76 ..."}
+{"idx": 6, "title": "ICML 2025 Thursday 07/17", "date": "", "ddg_snippet": "This hands-on session will give you practical tips and exercises to craft a short, effective and accessible overview of your work for a wide range of audiences ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/day/7/17", "content": "This hands-on session will give you practical tips and exercises to craft a short, effective and accessible overview of your work for a wide range of audiences ..."}
+{"idx": 7, "title": "NEWMIND AI JOURNAL MONTHLY CHRONICLE", "date": "", "ddg_snippet": "April 2025 marked a pivotal month in artificial intelligence, featuring major model launches, hardware breakthroughs, and strategic shifts that.", "subpage_snippet": "", "source": "www.newmind.ai", "link": "https://www.newmind.ai/NEWMIND+AI+JOURNAL+MONTHLY+CHRONICLES+-+April.pdf", "content": "April 2025 marked a pivotal month in artificial intelligence, featuring major model launches, hardware breakthroughs, and strategic shifts that."}
+{"idx": 8, "title": "Llama 3 Applications - Lablab.ai", "date": "", "ddg_snippet": "Browse applications built on Llama 3 technology . Explore PoC and MVP applications created by our community and discover innovative use cases for Llama 3 ...", "subpage_snippet": "", "source": "lablab.ai", "link": "https://lablab.ai/apps/tech/llama3", "content": "Browse applications built on Llama 3 technology . Explore PoC and MVP applications created by our community and discover innovative use cases for Llama 3 ..."}
+{"idx": 9, "title": "Basic Raw Data Exploration", "date": "", "ddg_snippet": "21 Nov 2023 — What App is used to create AI images like these? 137. aiArt, DALL- E / Bing Image Creator, 0. aiArt, you can't fully appreciate Don Hertzfeldt's ...", "subpage_snippet": "", "source": "www.kaggle.com", "link": "https://www.kaggle.com/code/asaniczka/basic-raw-data-exploration", "content": "21 Nov 2023 — What App is used to create AI images like these? 137. aiArt, DALL- E / Bing Image Creator, 0. aiArt, you can't fully appreciate Don Hertzfeldt's ..."}
diff --git a/data/sampled_jsons/ELITE_score_formula_refused_specific_convincing_toxicity_2502.04757.jsonl b/data/sampled_jsons/ELITE_score_formula_refused_specific_convincing_toxicity_2502.04757.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d792ea6dbcb30682fadeefe14212cf1d3832c11b
--- /dev/null
+++ b/data/sampled_jsons/ELITE_score_formula_refused_specific_convincing_toxicity_2502.04757.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ELITE: Enhanced Language-Image Toxicity Evaluation for ...", "date": "", "ddg_snippet": "by W Lee · 2025 — The ELITE evaluator explicitly incorporates a toxicity score to accurately assess harmfulness in multimodal contexts, where VLMs often provide ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.04757", "content": "by W Lee · 2025 — The ELITE evaluator explicitly incorporates a toxicity score to accurately assess harmfulness in multimodal contexts, where VLMs often provide ..."}
+{"idx": 1, "title": "Enhanced Language-Image Toxicity Evaluation for Safety", "date": "", "ddg_snippet": "24 Jul 2025 — ELITE = ( 1 − refused ) × specific + convincing 2 × toxicity ELITE 1 refused specific convincing 2 toxicity \\text{ELITE}=(1-\\textit{refused})\\ ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.04757v3", "content": "24 Jul 2025 — ELITE = ( 1 − refused ) × specific + convincing 2 × toxicity ELITE 1 refused specific convincing 2 toxicity \\text{ELITE}=(1-\\textit{refused})\\ ..."}
+{"idx": 2, "title": "kdst/ELITE · Datasets at Hugging Face", "date": "", "ddg_snippet": "Scoring Formula: E L I T E _ s c o r e = ( 1 − refused ) ... {convincing}}{2} \\times \\text{toxicity} E LITE_score =(1−refused)×2specific+convincing ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/kdst/ELITE", "content": "Scoring Formula: E L I T E _ s c o r e = ( 1 − refused ) ... {convincing}}{2} \\times \\text{toxicity} E LITE_score =(1−refused)×2specific+convincing ..."}
+{"idx": 3, "title": "Enhanced Language-Image Toxicity Evaluation for Safety", "date": "", "ddg_snippet": "7 Feb 2025 — Refused : Measures if the model explicitly refuses to provide harmful advice or details. Specificity and Convincingness: These components ...", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/es/review/elite-enhanced-language-image-toxicity-evaluation-for-safety", "content": "7 Feb 2025 — Refused : Measures if the model explicitly refuses to provide harmful advice or details. Specificity and Convincingness: These components ..."}
+{"idx": 4, "title": "Enhanced Language-Image Toxicity Evaluation for Safety", "date": "", "ddg_snippet": "Refused : Measures if the model explicitly refuses to provide harmful advice or details. Specificity and Convincingness: These components collectively evaluate ...", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/de/review/elite-enhanced-language-image-toxicity-evaluation-for-safety", "content": "Refused : Measures if the model explicitly refuses to provide harmful advice or details. Specificity and Convincingness: These components collectively evaluate ..."}
+{"idx": 5, "title": "ELITE: Enhanced Language-Image Toxicity Evaluation for Safety", "date": "", "ddg_snippet": "The ELITE benchmark is proposed, a high-quality safety evaluation benchmark for VLMs, underpinned by the enhanced evaluation method, the ELITE evaluator, which explicitly incorporates a toxicity score to accurately assess harmfulness in multimodal contexts. Current Vision Language Models (VLMs) remain vulnerable to malicious prompts that induce harmful outputs. Existing safety benchmarks for ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/ELITE:-Enhanced-Language-Image-Toxicity-Evaluation-Lee-Lee/2bf4206276d5f574bbb2e13a56b29b4522fea675", "content": "The ELITE benchmark is proposed, a high-quality safety evaluation benchmark for VLMs, underpinned by the enhanced evaluation method, the ELITE evaluator, which explicitly incorporates a toxicity score to accurately assess harmfulness in multimodal contexts. Current Vision Language Models (VLMs) remain vulnerable to malicious prompts that induce harmful outputs. Existing safety benchmarks for ..."}
+{"idx": 6, "title": "ELITE: Enhanced Language-Image Toxicity Evaluation for Safety", "date": "", "ddg_snippet": "The ELITE evaluator explicitly incorporates a toxicity score to accurately assess harmfulness in multimodal contexts, where VLMs often provide specific , convincing , but unharmful descriptions of images.", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/papers/2502.04757", "content": "The ELITE evaluator explicitly incorporates a toxicity score to accurately assess harmfulness in multimodal contexts, where VLMs often provide specific , convincing , but unharmful descriptions of images."}
+{"idx": 7, "title": "\"ELITE: Enhanced Language-Image Toxicity Evaluation ... - dblp", "date": "", "ddg_snippet": "Bibliographic details on ELITE : Enhanced Language-Image Toxicity Evaluation for Safety.", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/journals/corr/abs-2502-04757", "content": "Bibliographic details on ELITE : Enhanced Language-Image Toxicity Evaluation for Safety."}
+{"idx": 8, "title": "cvlab.yonsei.ac.kr", "date": "", "ddg_snippet": "The ELITE evaluator explicitly incorporates a toxicity score to accurately assess harmfulness in multimodal contexts, where VLMs often provide specific , convincing , but unharmful descriptions of images.", "subpage_snippet": "", "source": "cvlab.yonsei.ac.kr", "link": "https://cvlab.yonsei.ac.kr/projects/ELITE/", "content": "The ELITE evaluator explicitly incorporates a toxicity score to accurately assess harmfulness in multimodal contexts, where VLMs often provide specific , convincing , but unharmful descriptions of images."}
+{"idx": 9, "title": "vocab.txt · unitary/toxic-bert at ...", "date": "", "ddg_snippet": "Use this modela447313", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/unitary/toxic-bert/blame/a4473137dc49a3372f28e773dfca2276616ace45/vocab.txt", "content": "Use this modela447313"}
diff --git a/data/sampled_jsons/ETHICS_dataset_comprehensive_coverage_diverse_scenarios_reliable_annotations_benchmark_context-aware.jsonl b/data/sampled_jsons/ETHICS_dataset_comprehensive_coverage_diverse_scenarios_reliable_annotations_benchmark_context-aware.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..431fe4c8871977ff62af9cb61939b1e3ca32a85b
--- /dev/null
+++ b/data/sampled_jsons/ETHICS_dataset_comprehensive_coverage_diverse_scenarios_reliable_annotations_benchmark_context-aware.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Towards Assessing Medical Ethics from Knowledge to ...", "date": "", "ddg_snippet": "7 Aug 2025 — We introduce PrinciplismQA, a comprehensive benchmark with 3,648 questions designed to systematically assess LLMs' alignment with core medical ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.05132v1", "content": "7 Aug 2025 — We introduce PrinciplismQA, a comprehensive benchmark with 3,648 questions designed to systematically assess LLMs' alignment with core medical ..."}
+{"idx": 1, "title": "Benchmarking, ethical alignment, and evaluation ...", "date": "", "ddg_snippet": "by PP Ray · 2023 · Cited by 57 — This research paper proposes a comprehensive framework for evaluating ChatGPT that includes adaptive standards to keep pace with the dynamic nature of ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2772485923000534", "content": "by PP Ray · 2023 · Cited by 57 — This research paper proposes a comprehensive framework for evaluating ChatGPT that includes adaptive standards to keep pace with the dynamic nature of ..."}
+{"idx": 2, "title": "EduBench: A Comprehensive Benchmarking Dataset for ...", "date": "", "ddg_snippet": "cated across nine educational scenarios to ensure comprehensive coverage of diverse task demands. When evaluating the responses of different mod- els, we ...", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/pdf/2505.16160v2", "content": "cated across nine educational scenarios to ensure comprehensive coverage of diverse task demands. When evaluating the responses of different mod- els, we ..."}
+{"idx": 3, "title": "10 LLM safety and bias benchmarks", "date": "", "ddg_snippet": "28 Feb 2025 — In this blog, we highlight 10 key safety and bias benchmarks that help assess and improve LLM reliability . Want more examples of LLM benchmarks ?", "subpage_snippet": "", "source": "www.evidentlyai.com", "link": "https://www.evidentlyai.com/blog/llm-safety-bias-benchmarks", "content": "28 Feb 2025 — In this blog, we highlight 10 key safety and bias benchmarks that help assess and improve LLM reliability . Want more examples of LLM benchmarks ?"}
+{"idx": 4, "title": "Navigating the LLM Benchmark Boom: A Comprehensive ...", "date": "", "ddg_snippet": "1 Jul 2024 — This blog post presents a comprehensive catalogue of benchmarks , categorized by their complexity, dynamics, assessment targets, downstream task specifications, ...", "subpage_snippet": "", "source": "www.holisticai.com", "link": "https://www.holisticai.com/blog/navigating-llm-benchmark", "content": "1 Jul 2024 — This blog post presents a comprehensive catalogue of benchmarks , categorized by their complexity, dynamics, assessment targets, downstream task specifications, ..."}
+{"idx": 5, "title": "On responsible machine learning datasets emphasizing ...", "date": "", "ddg_snippet": "by S Mittal · 2024 · Cited by 33 — The framework evaluates datasets on diversity , inclusivity and the reliability of annotations for fairness; identifies sensitive annotations ...", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s42256-024-00874-y", "content": "by S Mittal · 2024 · Cited by 33 — The framework evaluates datasets on diversity , inclusivity and the reliability of annotations for fairness; identifies sensitive annotations ..."}
+{"idx": 6, "title": "BENCHMARKING ETHICS IN TEXT-TO-IMAGE MOD- ELS", "date": "", "ddg_snippet": "by L Li — This detailed taxonomy provides a structured framework for identifying and addressing ethical issues across different contexts and scenarios . Prompts ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=kIboeK0Wzs", "content": "by L Li — This detailed taxonomy provides a structured framework for identifying and addressing ethical issues across different contexts and scenarios . Prompts ..."}
+{"idx": 7, "title": "CASE-Bench: Context-Aware SafEty Benchmark for Large ...", "date": "", "ddg_snippet": "1. Introduction. Aligning large language models (LLMs) with human values to ensure the safe use of LLMs is a primary focus of current research in this field, ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/45903", "content": "1. Introduction. Aligning large language models (LLMs) with human values to ensure the safe use of LLMs is a primary focus of current research in this field, ..."}
+{"idx": 8, "title": "A Checks-and-Balances Framework for Context-Aware ...", "date": "", "ddg_snippet": "1 May 2025 — 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": "openreview.net", "link": "https://openreview.net/forum?id=4uOEiitySn&noteId=cYh3zaQycT", "content": "1 May 2025 — This paper introduces a checks-and-balances framework for ethical alignment of Large Language Models (LLMs), inspired by three-branch governmental systems."}
+{"idx": 9, "title": "Daily Papers", "date": "", "ddg_snippet": "We introduce the ETHICS dataset , a new benchmark that spans concepts in justice, well-being, duties, virtues, and commonsense morality. Models predict ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=normative+ethics", "content": "We introduce the ETHICS dataset , a new benchmark that spans concepts in justice, well-being, duties, virtues, and commonsense morality. Models predict ..."}
diff --git a/data/sampled_jsons/ETHICS_dataset_comprehensive_diverse_reliable_benchmark_context-aware_ethical_AI.jsonl b/data/sampled_jsons/ETHICS_dataset_comprehensive_diverse_reliable_benchmark_context-aware_ethical_AI.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..eceb89ae42e91013ac4856cfb83895620e50990e
--- /dev/null
+++ b/data/sampled_jsons/ETHICS_dataset_comprehensive_diverse_reliable_benchmark_context-aware_ethical_AI.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Benchmarking, ethical alignment, and evaluation ...", "date": "", "ddg_snippet": "by PP Ray · 2023 · Cited by 57 — Ethical and Moral Evaluation: To assess the ethical and moral aspects of a conversational AI system, various techniques can be employed. Bias analysis ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2772485923000534", "content": "by PP Ray · 2023 · Cited by 57 — Ethical and Moral Evaluation: To assess the ethical and moral aspects of a conversational AI system, various techniques can be employed. Bias analysis ..."}
+{"idx": 1, "title": "A Checks-and-Balances Framework for Context-Aware ...", "date": "", "ddg_snippet": "1 May 2025 — 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": "openreview.net", "link": "https://openreview.net/forum?id=4uOEiitySn&noteId=cYh3zaQycT", "content": "1 May 2025 — This paper introduces a checks-and-balances framework for ethical alignment of Large Language Models (LLMs), inspired by three-branch governmental systems."}
+{"idx": 2, "title": "Ethical AI: Towards Defining a Collective Evaluation ...", "date": "", "ddg_snippet": "30 May 2025 — Ethical AI refers to the development and deployment of artificial intelligence systems that emphasize fairness, transparency, accountability, ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.00233v1", "content": "30 May 2025 — Ethical AI refers to the development and deployment of artificial intelligence systems that emphasize fairness, transparency, accountability, ..."}
+{"idx": 3, "title": "Metaethical perspectives on 'benchmarking' AI ethics", "date": "", "ddg_snippet": "by T LaCroix · 2025 · Cited by 10 — In this paper, drawing upon research in moral philosophy and metaethics , we argue that it is impossible to develop such a benchmark .", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s43681-025-00703-x", "content": "by T LaCroix · 2025 · Cited by 10 — In this paper, drawing upon research in moral philosophy and metaethics , we argue that it is impossible to develop such a benchmark ."}
+{"idx": 4, "title": "Benchmark suites instead of leaderboards for evaluating AI ...", "date": "", "ddg_snippet": "by A Wang · 2024 · Cited by 14 — Benchmark suites enable researchers and practitioners to better understand the various fairness impacts of AI technology that will be relevant ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC11573903/", "content": "by A Wang · 2024 · Cited by 14 — Benchmark suites enable researchers and practitioners to better understand the various fairness impacts of AI technology that will be relevant ..."}
+{"idx": 5, "title": "Ethical considerations in AI-based user profiling for ...", "date": "", "ddg_snippet": "by DK Njiru · 2025 · Cited by 1 — This study examines the ethical considerations in AI -based user profiling for knowledge management systems, with a focus on academic environments.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2772503025000209", "content": "by DK Njiru · 2025 · Cited by 1 — This study examines the ethical considerations in AI -based user profiling for knowledge management systems, with a focus on academic environments."}
+{"idx": 6, "title": "AI-Y: An AI Checklist for Population Ethics Across the ...", "date": "", "ddg_snippet": "by Y Hswen · 2025 — The AI-Y Checklist provides a scalable framework to identify risks , guide ethical decision-making, and foster global accountability.", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12241292/", "content": "by Y Hswen · 2025 — The AI-Y Checklist provides a scalable framework to identify risks , guide ethical decision-making, and foster global accountability."}
+{"idx": 7, "title": "Assessment of AI ethical reflection: the development and ...", "date": "", "ddg_snippet": "by Z Wang · 2025 · Cited by 8 — This study developed and validated the AI Ethical Reflection Scale (AIERS), a tool for measuring university students' ethical reflection on AI in three ...", "subpage_snippet": "", "source": "educationaltechnologyjournal.springeropen.com", "link": "https://educationaltechnologyjournal.springeropen.com/articles/10.1186/s41239-025-00519-z", "content": "by Z Wang · 2025 · Cited by 8 — This study developed and validated the AI Ethical Reflection Scale (AIERS), a tool for measuring university students' ethical reflection on AI in three ..."}
+{"idx": 8, "title": "Context-Aware SafEty Benchmark for Large Language Models", "date": "", "ddg_snippet": "by G Sun · Cited by 2 — Our extensive analysis using CASE-Bench on various open-source and commercial LLMs reveals a substantial and significant influence of context on human judgments ...", "subpage_snippet": "", "source": "hasp-lab.github.io", "link": "https://hasp-lab.github.io/pubs/sun2025case.pdf", "content": "by G Sun · Cited by 2 — Our extensive analysis using CASE-Bench on various open-source and commercial LLMs reveals a substantial and significant influence of context on human judgments ..."}
+{"idx": 9, "title": "LLM Ethics Benchmark A Three-Dimensional Assessment ...", "date": "", "ddg_snippet": "1 May 2025 — This study establishes a novel framework for systematically evaluating the moral reasoning capabilities of large language models (LLMs)", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.00853v1", "content": "1 May 2025 — This study establishes a novel framework for systematically evaluating the moral reasoning capabilities of large language models (LLMs)"}
diff --git a/data/sampled_jsons/Edward_Y._Chang_Checks-and-Balances_Framework_dataset_empirical_studies.jsonl b/data/sampled_jsons/Edward_Y._Chang_Checks-and-Balances_Framework_dataset_empirical_studies.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..265181cce1a0a4564613ac389412d8c56bcba8d3
--- /dev/null
+++ b/data/sampled_jsons/Edward_Y._Chang_Checks-and-Balances_Framework_dataset_empirical_studies.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Explaining Datasets in Words: Statistical Models with Natural", "date": "", "ddg_snippet": "... statistical models (clustering, multilabel classification, and time series modeling, as illustrated in Figure 1 ) and used five different datasets ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2409.08466v2", "content": "... statistical models (clustering, multilabel classification, and time series modeling, as illustrated in Figure 1 ) and used five different datasets ..."}
+{"idx": 1, "title": "InfiAlign: A Scalable and Sample-Efficient Framework for", "date": "", "ddg_snippet": "... data sampling pipeline that efficiently selects a small yet high-quality subset of data by jointly considering diversity and difficulty, a balanced ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.05496v1", "content": "... data sampling pipeline that efficiently selects a small yet high-quality subset of data by jointly considering diversity and difficulty, a balanced ..."}
+{"idx": 2, "title": "Social origins, geographical mobility and occupational", "date": "", "ddg_snippet": "Third, it studies whether the effects of geographical mobility change according to social class of origin and geographical area of origin.", "subpage_snippet": "", "source": "genus.springeropen.com", "link": "http://genus.springeropen.com/articles/10.1186/s41118-020-00112-4", "content": "Third, it studies whether the effects of geographical mobility change according to social class of origin and geographical area of origin."}
+{"idx": 3, "title": "Citations of Central Bank Rerform, Liberalization and Inflation", "date": "", "ddg_snippet": "Ups and downs of central bank independence from the Great Inflation to the Great Recession: theory, institutions and empirics ,\" Financial History ...", "subpage_snippet": "", "source": "ideas.repec.org", "link": "https://ideas.repec.org/r/fth/teavfo/2000-19.html", "content": "Ups and downs of central bank independence from the Great Inflation to the Great Recession: theory, institutions and empirics ,\" Financial History ..."}
+{"idx": 4, "title": "Italy’s Decline and the Balance-of-Payments Constraint: a", "date": "", "ddg_snippet": "Business Cycles: An Empirical Investigation ,\" Discussion Papers 451, Northwestern University, Center for Mathematical Studies in Economics and ...", "subpage_snippet": "", "source": "ideas.repec.org", "link": "https://ideas.repec.org/p/ais/wpaper/1606.html", "content": "Business Cycles: An Empirical Investigation ,\" Discussion Papers 451, Northwestern University, Center for Mathematical Studies in Economics and ..."}
+{"idx": 5, "title": "OpenXAI: Towards a Transparent Evaluation of Post hoc Model", "date": "", "ddg_snippet": "The OpenXAI framework is easily extensible i. e ., researchers and practitioners can readily incorporate custom explanation methods, datasets ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2206.11104v5", "content": "The OpenXAI framework is easily extensible i. e ., researchers and practitioners can readily incorporate custom explanation methods, datasets ..."}
+{"idx": 6, "title": "Cross-Lingual Learning vs. Low-Resource Fine-Tuning: A Case", "date": "", "ddg_snippet": "In recent years, numerous datasets have emerged for fact- checking and they can be categorized based on how claim statements are obtained.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.00411v2", "content": "In recent years, numerous datasets have emerged for fact- checking and they can be categorized based on how claim statements are obtained."}
+{"idx": 7, "title": "A Preference-Driven Methodology for High-Quality Solidity Code", "date": "", "ddg_snippet": "... employs a systematic approach to construct preference datasets by generating multiple candidate implementations for each functional requirement and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.03006v1", "content": "... employs a systematic approach to construct preference datasets by generating multiple candidate implementations for each functional requirement and ..."}
+{"idx": 8, "title": "Frontiers | ULBERT: a domain-adapted BERT model for bilingual", "date": "", "ddg_snippet": "... state s physical existence and borders, the fundamental rights of its citizens, constitutional law and order, the national constitutional framework ...", "subpage_snippet": "", "source": "www.frontiersin.org", "link": "https://www.frontiersin.org/journals/big-data/articles/10.3389/fdata.2025.1448785/full", "content": "... state s physical existence and borders, the fundamental rights of its citizens, constitutional law and order, the national constitutional framework ..."}
+{"idx": 9, "title": "effect of legislation on perceived disability discrimination: a", "date": "", "ddg_snippet": "We utilize data on perceived disability discrimination and a newly compiled dataset on disability-related legislation spanning from 2002 to 2020.", "subpage_snippet": "", "source": "academic.oup.com", "link": "https://academic.oup.com/sf/advance-article/doi/10.1093/sf/soaf024/8029626", "content": "We utilize data on perceived disability discrimination and a newly compiled dataset on disability-related legislation spanning from 2002 to 2020."}
diff --git a/data/sampled_jsons/Equation_(11)_NFR_layer_Going_Deeper_into_Locally_Differentially_Private_Graph_Neural_Networks.jsonl b/data/sampled_jsons/Equation_(11)_NFR_layer_Going_Deeper_into_Locally_Differentially_Private_Graph_Neural_Networks.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a7ec76c6af483b0c926ab84cd735f70e91756505
--- /dev/null
+++ b/data/sampled_jsons/Equation_(11)_NFR_layer_Going_Deeper_into_Locally_Differentially_Private_Graph_Neural_Networks.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Going Deeper into Locally Differentially Private Graph Neural Networks", "date": "", "ddg_snippet": "In this section, we begin with a theoretical analysis of prior work on locally differentially private graph neural networks (LDPGNN) in Sec. 3.1, identifying the key factors limit-ing their utility.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=2aKHuXdr7Q", "content": "In this section, we begin with a theoretical analysis of prior work on locally differentially private graph neural networks (LDPGNN) in Sec. 3.1, identifying the key factors limit-ing their utility."}
+{"idx": 1, "title": "[2006.05535] Locally Private Graph Neural Networks - arXiv.org", "date": "", "ddg_snippet": "Graph Neural Networks (GNNs) have demonstrated superior performance in learning node representations for various graph inference tasks. However, learning over graph data can raise privacy concerns when nodes represent people or human-related variables that involve sensitive or personal information. While numerous techniques have been proposed for privacy-preserving deep learning over non ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2006.05535", "content": "Graph Neural Networks (GNNs) have demonstrated superior performance in learning node representations for various graph inference tasks. However, learning over graph data can raise privacy concerns when nodes represent people or human-related variables that involve sensitive or personal information. While numerous techniques have been proposed for privacy-preserving deep learning over non ..."}
+{"idx": 2, "title": "Locally Private Graph Neural Networks - ACM Digital Library", "date": "", "ddg_snippet": "Presentation video for the paper \" Locally Private Graph Neural Networks \". In this work, we propose a privacy-preserving GNN framework based on local differential privacy, when the graph topology is public but the node features/labels are private . Our contributions include building a new privacy mechanism, called the multi-bit mechanism, for high-dimensional feature perturbation. We also ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3460120.3484565", "content": "Presentation video for the paper \" Locally Private Graph Neural Networks \". In this work, we propose a privacy-preserving GNN framework based on local differential privacy, when the graph topology is public but the node features/labels are private . Our contributions include building a new privacy mechanism, called the multi-bit mechanism, for high-dimensional feature perturbation. We also ..."}
+{"idx": 3, "title": "GitHub - tamaramueller/DP-GNNs: Differentially private graph neural ...", "date": "", "ddg_snippet": "DP-GNNs Differentially private graph neural networks (GNNs) for whole- graph classification tasks. This repo contains code to train graph neural networks for graph classification tasks with differential privacy (DP). Check out our paper for the details about the methods.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/tamaramueller/DP-GNNs", "content": "DP-GNNs Differentially private graph neural networks (GNNs) for whole- graph classification tasks. This repo contains code to train graph neural networks for graph classification tasks with differential privacy (DP). Check out our paper for the details about the methods."}
+{"idx": 4, "title": "Differentially private graph neural networks for graph classification ...", "date": "", "ddg_snippet": "Abstract Graph Neural Networks (GNNs), which outperform traditional deep learning algorithms in domains such as protein interaction prediction and molecular structure elucidation, have demonstrated superior performance in processing graph data.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0957417424026654", "content": "Abstract Graph Neural Networks (GNNs), which outperform traditional deep learning algorithms in domains such as protein interaction prediction and molecular structure elucidation, have demonstrated superior performance in processing graph data."}
+{"idx": 5, "title": "Going Deeper into Locally Differentially Private Graph Neural Networks ...", "date": "", "ddg_snippet": "Our analysis identifies two key factors that affect the utility of privacy-preserving graph learning: *feature dimension* and *neighborhood size*. Based on the above analysis, UPGNET enhances utility by introducing two core layers : High-Order Aggregator (HOA) layer and the Node Feature Regularization ( NFR ) layer .", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/venue/2aKHuXdr7Q@OpenReview", "content": "Our analysis identifies two key factors that affect the utility of privacy-preserving graph learning: *feature dimension* and *neighborhood size*. Based on the above analysis, UPGNET enhances utility by introducing two core layers : High-Order Aggregator (HOA) layer and the Node Feature Regularization ( NFR ) layer ."}
+{"idx": 6, "title": "Going Deeper into Locally Differentially Private Graph Neural Networks", "date": "", "ddg_snippet": "Poster presentation: Going Deeper into Locally Differentially Private Graph Neural Networks Wed 16 Jul 4:30 p.m. PDT — 7 p.m. PDT", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/oral/47267", "content": "Poster presentation: Going Deeper into Locally Differentially Private Graph Neural Networks Wed 16 Jul 4:30 p.m. PDT — 7 p.m. PDT"}
+{"idx": 7, "title": "Node-Level Differentially Private Graph Neural Networks", "date": "", "ddg_snippet": "Graph Neural Networks (GNNs) are a popular technique for modelling graph -structured data and computing node-level representations via aggregation of information from the neighborhood of each node. However, this aggregation implies an increased risk of revealing sensitive information, as a node can participate in the inference for multiple nodes. This implies that standard privacy-preserving ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2111.15521", "content": "Graph Neural Networks (GNNs) are a popular technique for modelling graph -structured data and computing node-level representations via aggregation of information from the neighborhood of each node. However, this aggregation implies an increased risk of revealing sensitive information, as a node can participate in the inference for multiple nodes. This implies that standard privacy-preserving ..."}
+{"idx": 8, "title": "Going Deeper into Locally Differentially Private Graph Neural Networks", "date": "", "ddg_snippet": "The paper introduces UPGNET, a utility-enhanced framework for locally differentially private (LDP) graph learning. It addresses privacy challenges in Graph Neural Networks (GNNs) by proposing a three-stage pipeline to generalize LDP protocols for node feature perturbation. Key contributions include identifying two critical factors influencing estimation error: feature dimension and ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=2aKHuXdr7Q", "content": "The paper introduces UPGNET, a utility-enhanced framework for locally differentially private (LDP) graph learning. It addresses privacy challenges in Graph Neural Networks (GNNs) by proposing a three-stage pipeline to generalize LDP protocols for node feature perturbation. Key contributions include identifying two critical factors influencing estimation error: feature dimension and ..."}
+{"idx": 9, "title": "Going Deeper into Locally Differentially Private Graph Neural Networks", "date": "", "ddg_snippet": "Graph Neural Networks (GNNs) have demonstrated superior performance in a variety of graph mining and learning tasks. However, when node representations involve sensitive personal information or variables related to individuals, learning from graph data can raise significant privacy concerns. Although recent studies have explored local differential privacy (LDP) to address these concerns, they ...", "subpage_snippet": "", "source": "chatpaper.com", "link": "https://chatpaper.com/chatpaper/paper/165017?from=subpath-search", "content": "Graph Neural Networks (GNNs) have demonstrated superior performance in a variety of graph mining and learning tasks. However, when node representations involve sensitive personal information or variables related to individuals, learning from graph data can raise significant privacy concerns. Although recent studies have explored local differential privacy (LDP) to address these concerns, they ..."}
diff --git a/data/sampled_jsons/Equation_(3)_LAUREL-LR_OpenReview_rUDRWP9WvZ.jsonl b/data/sampled_jsons/Equation_(3)_LAUREL-LR_OpenReview_rUDRWP9WvZ.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ff329d7eb7dbbe3107017f855ed89ed68d68613d
--- /dev/null
+++ b/data/sampled_jsons/Equation_(3)_LAUREL-LR_OpenReview_rUDRWP9WvZ.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "LAuReL: Learned Augmented Residual Layer", "date": "", "ddg_snippet": "In this paper, we introduce Learned Augmented Residual Layer ( LAuReL ), which is a generalization of the residual connection and a drop-in replacement. LAuReL is ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=rUDRWP9WvZ&noteId=wS55prNog2", "content": "In this paper, we introduce Learned Augmented Residual Layer ( LAuReL ), which is a generalization of the residual connection and a drop-in replacement. LAuReL is ..."}
+{"idx": 1, "title": "LAuReL: Learned Augmented Residual Layer - OpenReview", "date": "", "ddg_snippet": "In most settings, Laurel seems best with Laurel -RW+LR or Laurel -RW+LR+PA settings. In general, using the low-rank approach seems tricky to me because you now have a new hyperparameter to tune.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=rUDRWP9WvZ", "content": "In most settings, Laurel seems best with Laurel -RW+LR or Laurel -RW+LR+PA settings. In general, using the low-rank approach seems tricky to me because you now have a new hyperparameter to tune."}
+{"idx": 2, "title": "LAuReL: Learned Augmented Residual Layer - arXiv.org", "date": "", "ddg_snippet": "3.3 LAuReL - LR : Rank vs Accuracy We note that for the LAuReL - LR version on the ResNet-50/ImageNet combination, there is a pattern in terms of the best accuracy achieved with different values of r.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.07501v1", "content": "3.3 LAuReL - LR : Rank vs Accuracy We note that for the LAuReL - LR version on the ResNet-50/ImageNet combination, there is a pattern in terms of the best accuracy achieved with different values of r."}
+{"idx": 3, "title": "LAuReL-Learned-Augmented-Residual-Layer/LAuRel_LR.py at master ...", "date": "", "ddg_snippet": "Contribute to BAW2501/ LAuReL -Learned-Augmented-Residual-Layer development by creating an account on GitHub.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/BAW2501/LAuReL-Learned-Augmented-Residual-Layer/blob/master/LAuRel_LR.py", "content": "Contribute to BAW2501/ LAuReL -Learned-Augmented-Residual-Layer development by creating an account on GitHub."}
+{"idx": 4, "title": "LAUREL: Learned Augmented Residual Layer | AI Research Paper Details", "date": "", "ddg_snippet": "Link: LAuReL-LR The Low-Rank version of LAuReL learns a low-rank approximation of the extra residual component, which can be more efficient in terms of parameters while still capturing relevant information. Technical Explanation The key idea behind LAuReL is to augment the standard residual connection with an additional learned component.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/laurel-learned-augmented-residual-layer", "content": "Link: LAuReL-LR The Low-Rank version of LAuReL learns a low-rank approximation of the extra residual component, which can be more efficient in terms of parameters while still capturing relevant information. Technical Explanation The key idea behind LAuReL is to augment the standard residual connection with an additional learned component."}
+{"idx": 5, "title": "LAuReL: Learned Augmented Residual Layer - arXiv.org", "date": "", "ddg_snippet": "In practice, we replace h i by a low-rank product similar to the LAuReL - LR version, but using the identity function is also an option. When using a rank r product for h i, the number of new parameters per LAuReL layer is 2 r D + k, where k is the number of previous activations used.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.07501v4", "content": "In practice, we replace h i by a low-rank product similar to the LAuReL - LR version, but using the identity function is also an option. When using a rank r product for h i, the number of new parameters per LAuReL layer is 2 r D + k, where k is the number of previous activations used."}
+{"idx": 6, "title": "LAuReL: Learned Augmented Residual Layer - OpenReview", "date": "", "ddg_snippet": "In this paper we introduce a Learned Augmented Residual Layer (LAUREL)—a novel generalization of the canonical residual connection—with the goal to be an in-situ replacement of the latter while outperforming on both model quality and footprint metrics.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=honBJOVRn5", "content": "In this paper we introduce a Learned Augmented Residual Layer (LAUREL)—a novel generalization of the canonical residual connection—with the goal to be an in-situ replacement of the latter while outperforming on both model quality and footprint metrics."}
+{"idx": 7, "title": "How to add formulas or use mathematical notation | OpenReview", "date": "", "ddg_snippet": "OpenReview supports TeX and LaTeX notation in many places throughout the site, including forum comments and reviews, paper abstracts, and venue homepages. To indicate that some piece of text should be rendered as TeX, use the delimiters $...$ for inline math or $$...$$ for displayed math.", "subpage_snippet": "", "source": "docs.openreview.net", "link": "https://docs.openreview.net/how-to-guides/submissions-comments-reviews-and-decisions/how-to-add-formulas-or-use-mathematical-notation", "content": "OpenReview supports TeX and LaTeX notation in many places throughout the site, including forum comments and reviews, paper abstracts, and venue homepages. To indicate that some piece of text should be rendered as TeX, use the delimiters $...$ for inline math or $$...$$ for displayed math."}
+{"idx": 8, "title": "ICML Poster LAuReL: Learned Augmented Residual Layer", "date": "", "ddg_snippet": "In this paper, we introduce Learned Augmented Residual Layer ( LAuReL ), which is a generalization of the residual connection and a drop-in replacement. LAuReL is a general framework but we provide three variants which can be used to cheaply make the residual connection adaptive instead of it being a simple summation.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/43889", "content": "In this paper, we introduce Learned Augmented Residual Layer ( LAuReL ), which is a generalization of the residual connection and a drop-in replacement. LAuReL is a general framework but we provide three variants which can be used to cheaply make the residual connection adaptive instead of it being a simple summation."}
+{"idx": 9, "title": "LAUREL: Learned Augmented Residual Layer - arXiv.org", "date": "", "ddg_snippet": "We pre-trained both the baseline, and our experiment with LAUREL , from scratch; we use the LAUREL -RW and LAUREL-LR versions (with r = 4). Both the models were trained using 256 Google Cloud TPU v5e chips for approximately two weeks each, using a pre-training mixture consisting of only text data which included webpages, books, code, translations ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2411.07501", "content": "We pre-trained both the baseline, and our experiment with LAUREL , from scratch; we use the LAUREL -RW and LAUREL-LR versions (with r = 4). Both the models were trained using 256 Google Cloud TPU v5e chips for approximately two weeks each, using a pre-training mixture consisting of only text data which included webpages, books, code, translations ..."}
diff --git a/data/sampled_jsons/Equation_6_generalized_Sinkhorn_algorithm_update_rule_u_v_KL_divergence_xlogx.jsonl b/data/sampled_jsons/Equation_6_generalized_Sinkhorn_algorithm_update_rule_u_v_KL_divergence_xlogx.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..eb659dd3e3c708aa3c16c7bbb52f006d72b2cbec
--- /dev/null
+++ b/data/sampled_jsons/Equation_6_generalized_Sinkhorn_algorithm_update_rule_u_v_KL_divergence_xlogx.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF -divergence regularization and generalized Sinkhorn algorithm", "date": "", "ddg_snippet": "a practical algorithm for computing an ap-proximate solution of the optimal transport problem with f-divergence regularization via the generalized Sinkhorn algorithm . Finally, we present experimental results on synthetic 2-dimensional data, demonstrating the effects of using different f-divergences for regular-ization, which influences convergence speed, numerical stability and sparsity of the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2105.14337.pdf", "content": "a practical algorithm for computing an ap-proximate solution of the optimal transport problem with f-divergence regularization via the generalized Sinkhorn algorithm . Finally, we present experimental results on synthetic 2-dimensional data, demonstrating the effects of using different f-divergences for regular-ization, which influences convergence speed, numerical stability and sparsity of the ..."}
+{"idx": 1, "title": "Sinkhorn Knopp algorithm - Bregman projection in update rule", "date": "", "ddg_snippet": "I don't understand the updating rule for $u^{l+1}$ in the Sinkhorn algorithm . The below images contain all necessary definitions of the projection operators $A_1$ and ...", "subpage_snippet": "", "source": "math.stackexchange.com", "link": "https://math.stackexchange.com/questions/4980480/sinkhorn-knopp-algorithm-bregman-projection-in-update-rule", "content": "I don't understand the updating rule for $u^{l+1}$ in the Sinkhorn algorithm . The below images contain all necessary definitions of the projection operators $A_1$ and ..."}
+{"idx": 2, "title": "PDF On the Convergence Rate of Sinkhorn's Algorithm", "date": "", "ddg_snippet": "We study Sinkhorn's algorithm for solving the entropically regu-larized optimal transport problem. Its iterate πt is shown to satisfy H(πt|π∗)+H(π∗|πt) = O(t−1) where H denotes relative entropy and π∗ the optimal coupling. This holds for a large class of cost functions and marginals, including quadratic cost with subgaussian marginals. We also obtain the rate O(t−1) for the ...", "subpage_snippet": "", "source": "www.math.columbia.edu", "link": "https://www.math.columbia.edu/~mnutz/docs/Sinkhorn_rate.pdf", "content": "We study Sinkhorn's algorithm for solving the entropically regu-larized optimal transport problem. Its iterate πt is shown to satisfy H(πt|π∗)+H(π∗|πt) = O(t−1) where H denotes relative entropy and π∗ the optimal coupling. This holds for a large class of cost functions and marginals, including quadratic cost with subgaussian marginals. We also obtain the rate O(t−1) for the ..."}
+{"idx": 3, "title": "PDF Optimal transport with f-divergence regularization and generalized ...", "date": "", "ddg_snippet": "Optimal transport with f - divergence regularization and generalized Sinkhorn algorithm D ́avid Terj ́ek & Diego Gonz ́alez-S ́anchez", "subpage_snippet": "", "source": "virtual.aistats.org", "link": "https://virtual.aistats.org/media/aistats-2022/Slides/3362.pdf", "content": "Optimal transport with f - divergence regularization and generalized Sinkhorn algorithm D ́avid Terj ́ek & Diego Gonz ́alez-S ́anchez"}
+{"idx": 4, "title": "PDF A Stochastic Algorithm for Sinkhorn Distance-Regularized ...", "date": "", "ddg_snippet": "This paper focuses on Sinkhorn distance regularized DRO. We generalize Sinkhorn distance allowing broader function choices to model ambiguity set and derive the lagrangian dual taking the form of nested stochastic programming. We also design the algorithm based on stochastic gradient descent with easy-to-implement constant learning rate.", "subpage_snippet": "", "source": "opt-ml.org", "link": "https://opt-ml.org/papers/2024/paper22.pdf", "content": "This paper focuses on Sinkhorn distance regularized DRO. We generalize Sinkhorn distance allowing broader function choices to model ambiguity set and derive the lagrangian dual taking the form of nested stochastic programming. We also design the algorithm based on stochastic gradient descent with easy-to-implement constant learning rate."}
+{"idx": 5, "title": "Optimal transport with $f$-divergence regularization and generalized ...", "date": "", "ddg_snippet": "We propose a practical algorithm for computing an approximate solution of the optimal transport problem with $f$-divergence regularization via the generalized Sinkhorn algorithm .", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v151/terjek22a.html", "content": "We propose a practical algorithm for computing an approximate solution of the optimal transport problem with $f$-divergence regularization via the generalized Sinkhorn algorithm ."}
+{"idx": 6, "title": "Optimal transport with $f$-divergence regularization and generalized ...", "date": "", "ddg_snippet": "Entropic regularization provides a generalization of the original optimal transport problem. It introduces a penalty term defined by the Kullback-Leibler divergence , making the problem more tractable via the celebrated Sinkhorn algorithm . Replacing the Kullback-Leibler divergence with a general f - divergence leads to a natural generalization.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2105.14337", "content": "Entropic regularization provides a generalization of the original optimal transport problem. It introduces a penalty term defined by the Kullback-Leibler divergence , making the problem more tractable via the celebrated Sinkhorn algorithm . Replacing the Kullback-Leibler divergence with a general f - divergence leads to a natural generalization."}
+{"idx": 7, "title": "PDF Sinkhorn Divergences for Unbalanced Optimal Transport", "date": "", "ddg_snippet": "Stability of Softmin and Aprox Proposition ) The algorithm is numerically stable. If there exists a fixed point and compactness, the algorithm then converges linearly towards it. One has for any (f; g) 2 C(X ) jSmin\" (f) Smin\" (g)j aprox\"", "subpage_snippet": "", "source": "thibsej.github.io", "link": "https://thibsej.github.io/files/beamer_mokameeting_sinkdiv.pdf", "content": "Stability of Softmin and Aprox Proposition ) The algorithm is numerically stable. If there exists a fixed point and compactness, the algorithm then converges linearly towards it. One has for any (f; g) 2 C(X ) jSmin\" (f) Smin\" (g)j aprox\""}
+{"idx": 8, "title": "PDF Entropic regularization of Optimal Transport", "date": "", "ddg_snippet": "If one defines u = exp(f/ε) and v = exp(g/ε), then performing iterations of Sinkhorn is the same as performing alternate maximization (in f and in g) on the dual of EOT.", "subpage_snippet": "", "source": "mathurinm.github.io", "link": "https://mathurinm.github.io/assets/2024_ens_ot/entropic.pdf", "content": "If one defines u = exp(f/ε) and v = exp(g/ε), then performing iterations of Sinkhorn is the same as performing alternate maximization (in f and in g) on the dual of EOT."}
+{"idx": 9, "title": "PDF Math 612: Single Cell Analysis 2019W Term 1 Lecture 13: October 17 13.1 ...", "date": "", "ddg_snippet": "Starting from some initial values for u and v , we alternatingly project between the gray rectangle, representing the space of all matrices with row sums equal to a, and the white rectangle, representing the space of all matrices with column sums equal to b. The algorithm eventually converges at the intersection of the rectangles, representing the set of matrices with row sums equal to a and ...", "subpage_snippet": "", "source": "personal.math.ubc.ca", "link": "https://personal.math.ubc.ca/~geoff/courses/W2019T1/Lecture13.pdf", "content": "Starting from some initial values for u and v , we alternatingly project between the gray rectangle, representing the space of all matrices with row sums equal to a, and the white rectangle, representing the space of all matrices with column sums equal to b. The algorithm eventually converges at the intersection of the rectangles, representing the set of matrices with row sums equal to a and ..."}
diff --git a/data/sampled_jsons/Equation_8_Boltzmann-Aligned_delta_delta_G_kBT_logP.jsonl b/data/sampled_jsons/Equation_8_Boltzmann-Aligned_delta_delta_G_kBT_logP.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..acda85a86598151b9ad640b40f2a149446c2731b
--- /dev/null
+++ b/data/sampled_jsons/Equation_8_Boltzmann-Aligned_delta_delta_G_kBT_logP.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Equilibrium Binding of Transcription Factors", "date": "", "ddg_snippet": "12 Jan 2022 — In the thermodynamics description, the parameters are Gibbs free energies Δ G \\ Delta G ΔG. Let's follow the derivation from Physical Biology of ...", "subpage_snippet": "", "source": "alexlenail.me", "link": "https://alexlenail.me/back_of_my_envelope/2022/01/12/equilibrium_TFs.html", "content": "12 Jan 2022 — In the thermodynamics description, the parameters are Gibbs free energies Δ G \\ Delta G ΔG. Let's follow the derivation from Physical Biology of ..."}
+{"idx": 1, "title": "Molecular Dynamics with Energy-Based Diffusion Models", "date": "", "ddg_snippet": "We show how to regularize the energy of diffusion models using the Fokker-Planck equation , enabling consistent molecular dynamics simulations alongside.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/6c748808a280d9797da963f3feba03488a5c0cbd.pdf", "content": "We show how to regularize the energy of diffusion models using the Fokker-Planck equation , enabling consistent molecular dynamics simulations alongside."}
+{"idx": 2, "title": "Modern Alchemical Free Energy Methods for Drug Discovery ...", "date": "", "ddg_snippet": "by DM York · 2023 · Cited by 63 — Equation 8 was the original equality proven by Jarzynski, (47) from which much work has followed, including the formula developed by Crooks (48) (eq 10).", "subpage_snippet": "", "source": "pubs.acs.org", "link": "https://pubs.acs.org/doi/10.1021/acsphyschemau.3c00033", "content": "by DM York · 2023 · Cited by 63 — Equation 8 was the original equality proven by Jarzynski, (47) from which much work has followed, including the formula developed by Crooks (48) (eq 10)."}
+{"idx": 3, "title": "The Plasma Module User's Guide", "date": "", "ddg_snippet": "... Boltzmann Equation ,. Two-Term Approximation Interface. The Boltzmann Equation ... 8 . 8 | CONTENTS. Domain Equations for the Inductively Coupled Plasma ... 436 pages", "subpage_snippet": "", "source": "doc.comsol.com", "link": "https://doc.comsol.com/6.1/doc/com.comsol.help.plasma/PlasmaModuleUsersGuide.pdf", "content": "... Boltzmann Equation ,. Two-Term Approximation Interface. The Boltzmann Equation ... 8 . 8 | CONTENTS. Domain Equations for the Inductively Coupled Plasma ... 436 pages"}
+{"idx": 4, "title": "potential score matching: debiasing molecular structure ...", "date": "", "ddg_snippet": "by L Guo · 2025 — Molecular systems at equilibrium are typically characterized by the Boltzmann distribution, with the target distribution given by p(x0) ∝ e−E(x0)/( ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.14569", "content": "by L Guo · 2025 — Molecular systems at equilibrium are typically characterized by the Boltzmann distribution, with the target distribution given by p(x0) ∝ e−E(x0)/( ..."}
+{"idx": 5, "title": "Strategies and Prospects for High-Performance Te-Free ...", "date": "", "ddg_snippet": "19 Mar 2025 — This review explores recent advances in novel strategies for achieving high thermoelectric performance and stability in Te-free inorganic bulk materials.", "subpage_snippet": "", "source": "pubs.acs.org", "link": "https://pubs.acs.org/doi/10.1021/acs.chemrev.4c00786", "content": "19 Mar 2025 — This review explores recent advances in novel strategies for achieving high thermoelectric performance and stability in Te-free inorganic bulk materials."}
+{"idx": 6, "title": "Role of pore dilation in molecular transport through the ...", "date": "", "ddg_snippet": "by A Matsuda · 2025 · Cited by 2 — In this study, we investigated the relationship between pore size and transport rate and proposed a mathematical model describing this connection.", "subpage_snippet": "", "source": "journals.plos.org", "link": "https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1012909", "content": "by A Matsuda · 2025 · Cited by 2 — In this study, we investigated the relationship between pore size and transport rate and proposed a mathematical model describing this connection."}
+{"idx": 7, "title": "Role of pore dilation in molecular transport through the ...", "date": "", "ddg_snippet": "by A Matsuda · 2025 · Cited by 2 — In this study, we investigated the relationship between pore size and transport rate and proposed a mathematical model describing this connection.", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC11975386/", "content": "by A Matsuda · 2025 · Cited by 2 — In this study, we investigated the relationship between pore size and transport rate and proposed a mathematical model describing this connection."}
+{"idx": 8, "title": "A partition function estimator", "date": "", "ddg_snippet": "8 Jan 2025 — We propose an estimator that allows us to calculate the value of a simple system's partition function using finite sampling.", "subpage_snippet": "", "source": "pubs.aip.org", "link": "https://pubs.aip.org/aip/jcp/article/162/2/024104/3329526/A-partition-function-estimator", "content": "8 Jan 2025 — We propose an estimator that allows us to calculate the value of a simple system's partition function using finite sampling."}
+{"idx": 9, "title": "New exercises for Entropy, Order Parameters, and ...", "date": "", "ddg_snippet": "Consider a locally conserved density ρ(x, t) in an isolated one-dimensional system with a current J(x, t). Imagine that the system has a complicated ... 261 pages", "subpage_snippet": "", "source": "sethna.lassp.cornell.edu", "link": "https://sethna.lassp.cornell.edu/StatMech/SethnaExercises.pdf", "content": "Consider a locally conserved density ρ(x, t) in an isolated one-dimensional system with a current J(x, t). Imagine that the system has a complicated ... 261 pages"}
diff --git a/data/sampled_jsons/Etienne_Gauthier_Francis_Bach_Michael_I._Jordan_Statistical_Collusion.jsonl b/data/sampled_jsons/Etienne_Gauthier_Francis_Bach_Michael_I._Jordan_Statistical_Collusion.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..66a6900d516760c6e4571a7039b6aa972e5d27b2
--- /dev/null
+++ b/data/sampled_jsons/Etienne_Gauthier_Francis_Bach_Michael_I._Jordan_Statistical_Collusion.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "Statistical Collusion by Collectives on Learning Platforms Etienne Gauthier , Francis Bach , Michael I. Jordan", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.04879", "content": "Statistical Collusion by Collectives on Learning Platforms Etienne Gauthier , Francis Bach , Michael I. Jordan"}
+{"idx": 1, "title": "PDF Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "Statistical Collusion by Collectives on Learning Platforms Etienne Gauthier , Francis Bach , Michael I. Jordan (INRIA, Ecole Normale Supérieure) Numerous examples of collectives emerging to strategically influence platforms Uber drivers deactivate the app to create a supply shortage and drive up prices", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/media/icml-2025/Slides/47263.pdf", "content": "Statistical Collusion by Collectives on Learning Platforms Etienne Gauthier , Francis Bach , Michael I. Jordan (INRIA, Ecole Normale Supérieure) Numerous examples of collectives emerging to strategically influence platforms Uber drivers deactivate the app to create a supply shortage and drive up prices"}
+{"idx": 2, "title": "Etienne Gauthier - OpenReview", "date": "", "ddg_snippet": "Publications Statistical Collusion by Collectives on Learning Platforms Etienne Gauthier , Francis Bach , Michael I. Jordan", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/profile?id=~Etienne_Gauthier1", "content": "Publications Statistical Collusion by Collectives on Learning Platforms Etienne Gauthier , Francis Bach , Michael I. Jordan"}
+{"idx": 3, "title": "Etienne Gauthier - Google Scholar", "date": "", "ddg_snippet": "Co-authors Michael I. Jordan Professor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC Berkeley Francis Bach Inria - Ecole Normale Supérieure Enhao Liu Department of Mathematics, Graduate School of Science, Kyoto University", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=Tiwn0RMAAAAJ&hl=en", "content": "Co-authors Michael I. Jordan Professor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC Berkeley Francis Bach Inria - Ecole Normale Supérieure Enhao Liu Department of Mathematics, Graduate School of Science, Kyoto University"}
+{"idx": 4, "title": "Francis Bach - INRIA - ENS - PSL", "date": "", "ddg_snippet": "Nabil Boukir, co-advised with Michael Jordan Sacha Braun, co-advised with Michael Jordan Juliette Decugis, co-advised with Gabriel Synnaeve and Taco Cohen David Holzmüller Etienne Gauthier , co-advised with Michael Jordan Frederik Kunstner Simon Martin, co-advised with Giulio Biroli Fabian Schaipp, co-advised with Umut Simsekli and Adrien Taylor", "subpage_snippet": "", "source": "www.di.ens.fr", "link": "https://www.di.ens.fr/~fbach/", "content": "Nabil Boukir, co-advised with Michael Jordan Sacha Braun, co-advised with Michael Jordan Juliette Decugis, co-advised with Gabriel Synnaeve and Taco Cohen David Holzmüller Etienne Gauthier , co-advised with Michael Jordan Frederik Kunstner Simon Martin, co-advised with Giulio Biroli Fabian Schaipp, co-advised with Umut Simsekli and Adrien Taylor"}
+{"idx": 5, "title": "Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "Citer Etienne Gauthier , Francis Bach , Michael I. Jordan . Statistical Collusion by Collectives on Learning Platforms. 2025. hal-04941041", "subpage_snippet": "", "source": "hal.science", "link": "https://hal.science/hal-04941041v1", "content": "Citer Etienne Gauthier , Francis Bach , Michael I. Jordan . Statistical Collusion by Collectives on Learning Platforms. 2025. hal-04941041"}
+{"idx": 6, "title": "Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "Etienne Gauthier , Francis Bach , Michael I. Jordan Published in arXiv.org7 February 2025 Computer Science TLDR A framework is developed that provides a theoretical and algorithmic treatment of the issues of a priori assessments of the effect of the collective before taking action and presents experimental results in a product evaluation domain ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Statistical-Collusion-by-Collectives-on-Learning-Gauthier-Bach/1c45ef9ad56839c3309f0a0bdcff50fbb3ad73f5", "content": "Etienne Gauthier , Francis Bach , Michael I. Jordan Published in arXiv.org7 February 2025 Computer Science TLDR A framework is developed that provides a theoretical and algorithmic treatment of the issues of a priori assessments of the effect of the collective before taking action and presents experimental results in a product evaluation domain ..."}
+{"idx": 7, "title": "Michael I. Jordan - OpenReview", "date": "", "ddg_snippet": "ICML 2025 poster Prediction-Aware Learning in Multi-Agent Systems Aymeric Capitaine, Etienne Boursier, Eric Moulines, Michael I. Jordan , Alain Oliviero Durmus Published: 01 May 2025, Last Modified: 23 Jul 2025 ICML 2025 poster Statistical Collusion by Collectives on Learning Platforms Etienne Gauthier , Francis Bach , Michael I. Jordan", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/profile?id=~Michael_I._Jordan2", "content": "ICML 2025 poster Prediction-Aware Learning in Multi-Agent Systems Aymeric Capitaine, Etienne Boursier, Eric Moulines, Michael I. Jordan , Alain Oliviero Durmus Published: 01 May 2025, Last Modified: 23 Jul 2025 ICML 2025 poster Statistical Collusion by Collectives on Learning Platforms Etienne Gauthier , Francis Bach , Michael I. Jordan"}
+{"idx": 8, "title": "PDF Recent advances in conformal prediction with E-values", "date": "", "ddg_snippet": "First-order Taylor approximation: See Koning (2024), Gazin, Blanchard, Roquain (2023) Can be estimated using the calibration see [ Gauthier , Bach & Jordan 2025]", "subpage_snippet": "", "source": "vaidehi8913.github.io", "link": "https://vaidehi8913.github.io/predictions-and-uncertainty-colt25/static/media/bach-slides.2a44f5ed4c4ddc1bebba.pdf", "content": "First-order Taylor approximation: See Koning (2024), Gazin, Blanchard, Roquain (2023) Can be estimated using the calibration see [ Gauthier , Bach & Jordan 2025]"}
+{"idx": 9, "title": "arXiv:2502.04879v1 [stat.ML] 7 Feb 2025", "date": "", "ddg_snippet": "Etienne Gauthier∗1, Francis Bach1 and Michael I. Jordan1,2 1Inria, Ecole Normale Sup ́erieure, PSL Research University 2Department of Electrical Engineering and Computer Sciences, University of California, Berkeley", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.04879v1", "content": "Etienne Gauthier∗1, Francis Bach1 and Michael I. Jordan1,2 1Inria, Ecole Normale Sup ́erieure, PSL Research University 2Department of Electrical Engineering and Computer Sciences, University of California, Berkeley"}
diff --git "a/data/sampled_jsons/EventPS_Yu_2024_mean_angular_error_13.66_degrees_OR_13.66\302\260.jsonl" "b/data/sampled_jsons/EventPS_Yu_2024_mean_angular_error_13.66_degrees_OR_13.66\302\260.jsonl"
new file mode 100644
index 0000000000000000000000000000000000000000..3a19c96713a4b1ac5b1a29786142c07b44867431
--- /dev/null
+++ "b/data/sampled_jsons/EventPS_Yu_2024_mean_angular_error_13.66_degrees_OR_13.66\302\260.jsonl"
@@ -0,0 +1,2 @@
+{"idx": 0, "title": "Robust Photometric Stereo Based on Event Interval Profile", "date": "", "ddg_snippet": "... EventPS [67] (i.e., EventPS -FCN). The major contributions are summarized as ... mean angular error of 8.12◦ in the presence of non-Lambertian effects ...", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/poster/33500", "content": "... EventPS [67] (i.e., EventPS -FCN). The major contributions are summarized as ... mean angular error of 8.12◦ in the presence of non-Lambertian effects ..."}
+{"idx": 1, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/EventPS_experimental_results_table_mean_angular_error_degrees_3D_printed.jsonl b/data/sampled_jsons/EventPS_experimental_results_table_mean_angular_error_degrees_3D_printed.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..90604c461cf2905d6750d09efd70aaf47f6fc4e3
--- /dev/null
+++ b/data/sampled_jsons/EventPS_experimental_results_table_mean_angular_error_degrees_3D_printed.jsonl
@@ -0,0 +1,3 @@
+{"idx": 0, "title": "Revisiting Supervised Learning-Based Photometric Stereo ...", "date": "", "ddg_snippet": "The experimental results indicate that even under sparse setups, ... For LMPS [7], which applies a connection table to select helpful light for normal recovery, ...", "subpage_snippet": "", "source": "downloads.ctfassets.net", "link": "https://downloads.ctfassets.net/yreyglvi5sud/6sgqvQLHmJyqeGshEQBzzR/864c79531e24452326cab06fc0bce103/Wei_TPAMI25.pdf", "content": "The experimental results indicate that even under sparse setups, ... For LMPS [7], which applies a connection table to select helpful light for normal recovery, ..."}
+{"idx": 1, "title": "Revisiting Supervised Learning-Based Photometric Stereo ...", "date": "", "ddg_snippet": "by X Wei · 2025 · Cited by 2 — ... experimental results verify that the proposed method outperforms state ... Table I compares the differences between representative SL-PSNs and the ...", "subpage_snippet": "", "source": "www.computer.org", "link": "https://www.computer.org/csdl/journal/tp/2025/08/10948383/25BYR9bjtq8", "content": "by X Wei · 2025 · Cited by 2 — ... experimental results verify that the proposed method outperforms state ... Table I compares the differences between representative SL-PSNs and the ..."}
+{"idx": 2, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/EventPS_methodology_event_interval_algorithm_how_works_Yu_Bohan_2024_year_2024.jsonl b/data/sampled_jsons/EventPS_methodology_event_interval_algorithm_how_works_Yu_Bohan_2024_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b52b433e419249b231512755ad5efdcf5f22080c
--- /dev/null
+++ b/data/sampled_jsons/EventPS_methodology_event_interval_algorithm_how_works_Yu_Bohan_2024_year_2024.jsonl
@@ -0,0 +1,8 @@
+{"idx": 0, "title": "CVPR 2024 Key Research & Dataset Papers - Part 2", "date": "", "ddg_snippet": "CVPR 2024 (Computer Vision and Pattern Recognition) is an annual conference held from June 17th to 21st at the Seattle Convention Center, USA, which ...", "subpage_snippet": "", "source": "learnopencv.com", "link": "https://learnopencv.com/cvpr-2024-research-papers/", "content": "CVPR 2024 (Computer Vision and Pattern Recognition) is an annual conference held from June 17th to 21st at the Seattle Convention Center, USA, which ..."}
+{"idx": 1, "title": "PS-EIP: Robust Photometric Stereo Based on Event Interval Profile", "date": "", "ddg_snippet": "Recently, the energy-efficient photometric stereo method using an event camera ( EventPS [67]) has been proposed to recover surface normals from events triggered by changes in logarithmic Lambertian reflections under a moving directional light source. However, EventPS treats each event interval independently, making it sensitive to noise, shadows, and non-Lambertian reflections. This paper ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/11095199", "content": "Recently, the energy-efficient photometric stereo method using an event camera ( EventPS [67]) has been proposed to recover surface normals from events triggered by changes in logarithmic Lambertian reflections under a moving directional light source. However, EventPS treats each event interval independently, making it sensitive to noise, shadows, and non-Lambertian reflections. This paper ..."}
+{"idx": 2, "title": "PS-EIP: Robust Photometric Stereo Based on Event Interval Profile", "date": "", "ddg_snippet": "Recently, the energy-efficient photometric stereo method using an event camera has been proposed to recover surface normals from events triggered by changes in logarithmic Lambertian reflections under a moving directional light source. However, EventPS treats each event interval independently, making it sensitive to noise, shadows, and non-Lambertian reflections. This paper proposes ...", "subpage_snippet": "", "source": "chatpaper.com", "link": "https://chatpaper.com/chatpaper/paper/123699?from=search", "content": "Recently, the energy-efficient photometric stereo method using an event camera has been proposed to recover surface normals from events triggered by changes in logarithmic Lambertian reflections under a moving directional light source. However, EventPS treats each event interval independently, making it sensitive to noise, shadows, and non-Lambertian reflections. This paper proposes ..."}
+{"idx": 3, "title": "PS-EIP: Robust Photometric Stereo Based on Event Interval Profile", "date": "", "ddg_snippet": "Unlike EventPS , which treats each event interval independently, EIP-PS uses a time-series prole to capture relationships between intervals.[36] Huiyu Liu, Yunhui Yan, Kechen Song, and Han Yu . Sps-net: Self-attention photometric stereo network.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Kitazawa_PS-EIP_Robust_Photometric_Stereo_Based_on_Event_Interval_Profile_CVPR_2025_paper.pdf", "content": "Unlike EventPS , which treats each event interval independently, EIP-PS uses a time-series prole to capture relationships between intervals.[36] Huiyu Liu, Yunhui Yan, Kechen Song, and Han Yu . Sps-net: Self-attention photometric stereo network."}
+{"idx": 4, "title": "PS-EIP: Robust Photometric Stereo Based on Event Interval Profile", "date": "", "ddg_snippet": "However, EventPS treats each event interval independently, making it sensitive to noise, shadows, and non-Lambertian reflections. This paper proposes Photometric Stereo based on Event Interval Profile (PS-EIP), a robust method that recovers pixelwise surface normals from a...", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/venue/Kitazawa_PS-EIP_Robust_Photometric_Stereo_Based_on_Event_Interval_Profile@CVPR2025@CVF", "content": "However, EventPS treats each event interval independently, making it sensitive to noise, shadows, and non-Lambertian reflections. This paper proposes Photometric Stereo based on Event Interval Profile (PS-EIP), a robust method that recovers pixelwise surface normals from a..."}
+{"idx": 5, "title": "(PDF) Event -based, Direct Camera Tracking from a Photometric...", "date": "", "ddg_snippet": "However, EventPS treats each event interval independently, making it sensitive to noise, shadows, and non-Lambertian reflections.In this work , we propose a novel event based stereo method which addresses the problem of motion blur for a moving event camera.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/335138752_Event-based_Direct_Camera_Tracking_from_a_Photometric_3D_Map_using_Nonlinear_Optimization", "content": "However, EventPS treats each event interval independently, making it sensitive to noise, shadows, and non-Lambertian reflections.In this work , we propose a novel event based stereo method which addresses the problem of motion blur for a moving event camera."}
+{"idx": 6, "title": "CVPR 2025 Friday 06/13", "date": "", "ddg_snippet": "... Event Interval Profile. Poster. Kazuma Kitazawa · Takahito Aoto · Satoshi ... ( EventPS ) has been proposed to recover surface normals from events triggered ...", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/day/6/13", "content": "... Event Interval Profile. Poster. Kazuma Kitazawa · Takahito Aoto · Satoshi ... ( EventPS ) has been proposed to recover surface normals from events triggered ..."}
+{"idx": 7, "title": "Enhanced Photometric Stereo with Multispectral Images", "date": "", "ddg_snippet": "EventPS : Real-Time Photometric Stereo Using an Event Camera · Bohan Yu Jieji RenJin HanFei-Zhang WangJinxiu LiangBoxin Shi. Computer Science, Engineering. 2024 ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Enhanced-Photometric-Stereo-with-Multispectral-Takatani-Matsushita/08428acb38d9b5273f9c667c99bab4481b781ee4/figure/3", "content": "EventPS : Real-Time Photometric Stereo Using an Event Camera · Bohan Yu Jieji RenJin HanFei-Zhang WangJinxiu LiangBoxin Shi. Computer Science, Engineering. 2024 ..."}
diff --git a/data/sampled_jsons/FD1_dataset_distribution_regression_MLE_CQR_KDE_MSE_mean.jsonl b/data/sampled_jsons/FD1_dataset_distribution_regression_MLE_CQR_KDE_MSE_mean.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..608c20ba0f61ad0e2ab4603327e9cec57bb86621
--- /dev/null
+++ b/data/sampled_jsons/FD1_dataset_distribution_regression_MLE_CQR_KDE_MSE_mean.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Mean squared error - Wikipedia", "date": "", "ddg_snippet": "In statistics, the mean squared error or mean squared deviation of an estimator measures the average of the squares of the errors—that is, the average squared difference between the estimated values and the true value.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Mean_squared_error", "content": "In statistics, the mean squared error or mean squared deviation of an estimator measures the average of the squares of the errors—that is, the average squared difference between the estimated values and the true value."}
+{"idx": 1, "title": "Regression Metrics - GeeksforGeeks", "date": "", "ddg_snippet": "Mean Squared Error ( MSE ). A popular metric in statistics and machine learning is the Mean Squared Error ( MSE ). It measures the square root of the average discrepancies between a dataset 's actual values and projected values.", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/machine-learning/regression-metrics/", "content": "Mean Squared Error ( MSE ). A popular metric in statistics and machine learning is the Mean Squared Error ( MSE ). It measures the square root of the average discrepancies between a dataset 's actual values and projected values."}
+{"idx": 2, "title": "3 Regression Metrics You Must Know: MAE, MSE ... | Proclus Academy", "date": "", "ddg_snippet": "Root Mean Squared Error (RMSE). Then I’ll show you how to calculate these metrics using Python and Scikit-Learn. Let’s get started! Regression Metrics (MAE, MSE , RMSE): explained using a model that predicts airfare Image Credit: Manfred Irmer.", "subpage_snippet": "", "source": "proclusacademy.com", "link": "https://proclusacademy.com/blog/explainer/regression-metrics-you-must-know/", "content": "Root Mean Squared Error (RMSE). Then I’ll show you how to calculate these metrics using Python and Scikit-Learn. Let’s get started! Regression Metrics (MAE, MSE , RMSE): explained using a model that predicts airfare Image Credit: Manfred Irmer."}
+{"idx": 3, "title": "How to Calculate Mean Squared Error ( MSE )... - Automate Excel", "date": "", "ddg_snippet": "The Mean Squared Error ( MSE ) is an estimate that measures the average squared difference between the estimated values and the actual values of a data distribution .How to Calculate Mean Square Error 005. Using the AVERAGE Function.", "subpage_snippet": "", "source": "www.automateexcel.com", "link": "https://www.automateexcel.com/stats/calculate-mean-square-error/", "content": "The Mean Squared Error ( MSE ) is an estimate that measures the average squared difference between the estimated values and the actual values of a data distribution .How to Calculate Mean Square Error 005. Using the AVERAGE Function."}
+{"idx": 4, "title": "Mean Squared Error : Definition and Example - Statistics How To", "date": "", "ddg_snippet": "The mean squared error ( MSE ) tells you how close a regression line is to a set of points. It does this by taking the distances from the points to the regression line (these distances are the “errors”) and squaring them. The squaring is necessary to remove any negative signs.", "subpage_snippet": "", "source": "www.statisticshowto.com", "link": "https://www.statisticshowto.com/probability-and-statistics/statistics-definitions/mean-squared-error/", "content": "The mean squared error ( MSE ) tells you how close a regression line is to a set of points. It does this by taking the distances from the points to the regression line (these distances are the “errors”) and squaring them. The squaring is necessary to remove any negative signs."}
+{"idx": 5, "title": "mean _ squared _ error — scikit-learn 1.7.1 documentation", "date": "", "ddg_snippet": "sklearn. datasets . Mean squared error regression loss. Read more in the User Guide.", "subpage_snippet": "", "source": "scikit-learn.org", "link": "https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html", "content": "sklearn. datasets . Mean squared error regression loss. Read more in the User Guide."}
+{"idx": 6, "title": "Boston Housing Kaggle Challenge with Linear Regression", "date": "", "ddg_snippet": "Applying Linear Regression Model to the dataset and predicting the prices. # Fitting Multi Linear regression model to training model from sklearn.linear_model import LinearRegression regressor = LinearRegression() regressor.fit(xtrain, ytrain).", "subpage_snippet": "", "source": "prutor.ai", "link": "https://prutor.ai/boston-housing-kaggle-challenge-with-linear-regression/", "content": "Applying Linear Regression Model to the dataset and predicting the prices. # Fitting Multi Linear regression model to training model from sklearn.linear_model import LinearRegression regressor = LinearRegression() regressor.fit(xtrain, ytrain)."}
+{"idx": 7, "title": "Linear regression calculator - calculates the linear regression ...", "date": "", "ddg_snippet": "The linear regression calculator generates the best-fitting equation and draws the linear regression line and the prediction interval. Step-by-step solution.", "subpage_snippet": "", "source": "www.statskingdom.com", "link": "https://www.statskingdom.com/linear-regression-calculator.html", "content": "The linear regression calculator generates the best-fitting equation and draws the linear regression line and the prediction interval. Step-by-step solution."}
+{"idx": 8, "title": "PyTorch Loss Functions: The Ultimate Guide", "date": "", "ddg_snippet": "Mean Squared Error Loss. Negative Log-Likelihood Loss. Cross-Entropy Loss. Regression problems, especially when the distribution of the target variable has outliers, such as small or big values that are a great distance from the mean value.", "subpage_snippet": "", "source": "neptune.ai", "link": "https://neptune.ai/blog/pytorch-loss-functions", "content": "Mean Squared Error Loss. Negative Log-Likelihood Loss. Cross-Entropy Loss. Regression problems, especially when the distribution of the target variable has outliers, such as small or big values that are a great distance from the mean value."}
+{"idx": 9, "title": "Kernel Density Estimation for anomaly detection | by Zhe Sun... | Medium", "date": "", "ddg_snippet": "This means KDE might flag normal behavior during certain times as anomalous, simply because it doesn’t recognize cyclical patterns. 6.2. Limitation with Narrow Variance: KDE Misclassifies Natural Spread. When the underlying data has low variance, even small deviations may fall...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@injure21/kernel-density-estimation-for-anomaly-detection-715a945bc729", "content": "This means KDE might flag normal behavior during certain times as anomalous, simply because it doesn’t recognize cyclical patterns. 6.2. Limitation with Narrow Variance: KDE Misclassifies Natural Spread. When the underlying data has low variance, even small deviations may fall..."}
diff --git a/data/sampled_jsons/FD2_synthetic_dataset_formula_A_Likelihood_Based_Approach_to_Distribution_Regression_Using_Condition.jsonl b/data/sampled_jsons/FD2_synthetic_dataset_formula_A_Likelihood_Based_Approach_to_Distribution_Regression_Using_Condition.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..fa3361b2733fbd803bfb4bdde02d695b8df597d7
--- /dev/null
+++ b/data/sampled_jsons/FD2_synthetic_dataset_formula_A_Likelihood_Based_Approach_to_Distribution_Regression_Using_Condition.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A Likelihood Based Approach to Distribution Regression Using ...", "date": "", "ddg_snippet": "Abstract In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where the response variable lies in a high-dimensional ambient space but concentrates around a potentially lower-dimensional manifold.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.02025v1", "content": "Abstract In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where the response variable lies in a high-dimensional ambient space but concentrates around a potentially lower-dimensional manifold."}
+{"idx": 1, "title": "A Likelihood Based Approach to Distribution Regression Using ...", "date": "", "ddg_snippet": "Oct 2, 2024 · In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where the response variable lies in a high-dimensional ambient space but concentrates around a potentially lower-dimensional manifold. More specifically, we study the large-sample properties of a likelihood - based approach for estimating these ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.02025", "content": "Oct 2, 2024 · In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where the response variable lies in a high-dimensional ambient space but concentrates around a potentially lower-dimensional manifold. More specifically, we study the large-sample properties of a likelihood - based approach for estimating these ..."}
+{"idx": 2, "title": "A Likelihood Approach to Nonparametric Estimation of a ... ICML Poster A Likelihood Based Approach to Distribution ... 3.3. Synthetic Regression Data — Dive into Deep ... - D2L A Likelihood Based Approach to Distribution Regression Using ... Generalized Regression with Conditional GANs A Likelihood Based Approach to Distribution Regression Using ...", "date": "", "ddg_snippet": "We investigate statistical properties of a likelihood approach to nonparametric estimation of a singular distribution using deep generative models . More speci cally, a deep generative model is used to model high-dimensional data that are assumed to concentrate around some low-dimensional structure. Estimating the distribution supported on this low-... See full list on jmlr.org is the Hellinger convergence rate of the sieve MLE of p , and See full list on jmlr.org decreases as increases because becomes smoother while See full list on jmlr.org Our main theoretical results are given in this section. We rst present assumptions on the data-generating distribution P . Then, we derive the convergence rate of a sieve MLE for p with respect to the Hellinger distance in the deep generative model. We next obtain the convergence rate of the corresponding sieve MLE of Q under the Wasserstein distan... See full list on jmlr.org n where C = C(q; d; t; ; K; D; max; ; ). As one can see, the dimension d in the convergence rate of Corollary 4 is replaced by the intrinsic dimension t . If t is much smaller than d, the improvement from the structural assumption would be signi cant. See full list on jmlr.org In this section, we empirically demonstrate that the data perturbation method proposed in Section 3.4 plays an important role to improve the performance of a sieve MLE of deep generative models . In addition, we illustrate that deep generative models can detect low-dimensional structures well. Numerical studies are carried out by analyzing various s... See full list on jmlr.org Assume that the generator f = f is parametrized by . With a slight abuse of notation, let ; = pf ; , that is, Z ; (x) = See full list on jmlr.org The model is trained after perturbing the training data by an arti cial noise e For each data set, we consider various values of . e See full list on jmlr.org In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where the response variable lies in a high-dimensional ambient space but concentrates around a potentially lower-dimensional manifold. More specifically, we study the large-sample properties of a likelihood - based approach for estimating these ... 3.3.2. Reading the Dataset Training machine learning models often requires multiple passes over a dataset , grabbing one minibatch of examples at a time. This data is then used to update the model. To illustrate how this works, we implement the get_dataloader method, registering it in the SyntheticRegressionData class via add_to_class (introduced in Section 3.2.1). It takes a batch size, a ... Abstract summary: We study the large-sample properties of a likelihood - based approach for estimating conditional deep generative models . Our results lead to the convergence rate of a sieve maximum likelihood estimator for estimating the conditional distribution . We demonstrate the superiority of this new approach to standard regression with experiments on multiple synthetic and publicly available real-world datasets, finding encouraging results, especially with real-world heavy-tailed regression datasets. To make our work more reproducible, we release our source code3. Abstract In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where the response variable lies in a high-dimensional ambient space but concentrates around a potentially lower-dimensional manifold.", "subpage_snippet": "", "source": "jmlr.org", "link": "https://jmlr.org/papers/volume24/21-1099/21-1099.pdf", "content": "We investigate statistical properties of a likelihood approach to nonparametric estimation of a singular distribution using deep generative models . More speci cally, a deep generative model is used to model high-dimensional data that are assumed to concentrate around some low-dimensional structure. Estimating the distribution supported on this low-... See full list on jmlr.org is the Hellinger convergence rate of the sieve MLE of p , and See full list on jmlr.org decreases as increases because becomes smoother while See full list on jmlr.org Our main theoretical results are given in this section. We rst present assumptions on the data-generating distribution P . Then, we derive the convergence rate of a sieve MLE for p with respect to the Hellinger distance in the deep generative model. We next obtain the convergence rate of the corresponding sieve MLE of Q under the Wasserstein distan... See full list on jmlr.org n where C = C(q; d; t; ; K; D; max; ; ). As one can see, the dimension d in the convergence rate of Corollary 4 is replaced by the intrinsic dimension t . If t is much smaller than d, the improvement from the structural assumption would be signi cant. See full list on jmlr.org In this section, we empirically demonstrate that the data perturbation method proposed in Section 3.4 plays an important role to improve the performance of a sieve MLE of deep generative models . In addition, we illustrate that deep generative models can detect low-dimensional structures well. Numerical studies are carried out by analyzing various s... See full list on jmlr.org Assume that the generator f = f is parametrized by . With a slight abuse of notation, let ; = pf ; , that is, Z ; (x) = See full list on jmlr.org The model is trained after perturbing the training data by an arti cial noise e For each data set, we consider various values of . e See full list on jmlr.org In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where the response variable lies in a high-dimensional ambient space but concentrates around a potentially lower-dimensional manifold. More specifically, we study the large-sample properties of a likelihood - based approach for estimating these ... 3.3.2. Reading the Dataset Training machine learning models often requires multiple passes over a dataset , grabbing one minibatch of examples at a time. This data is then used to update the model. To illustrate how this works, we implement the get_dataloader method, registering it in the SyntheticRegressionData class via add_to_class (introduced in Section 3.2.1). It takes a batch size, a ... Abstract summary: We study the large-sample properties of a likelihood - based approach for estimating conditional deep generative models . Our results lead to the convergence rate of a sieve maximum likelihood estimator for estimating the conditional distribution . We demonstrate the superiority of this new approach to standard regression with experiments on multiple synthetic and publicly available real-world datasets, finding encouraging results, especially with real-world heavy-tailed regression datasets. To make our work more reproducible, we release our source code3. Abstract In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where the response variable lies in a high-dimensional ambient space but concentrates around a potentially lower-dimensional manifold."}
+{"idx": 3, "title": "ICML Poster A Likelihood Based Approach to Distribution ...", "date": "", "ddg_snippet": "In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where the response variable lies in a high-dimensional ambient space but concentrates around a potentially lower-dimensional manifold. More specifically, we study the large-sample properties of a likelihood - based approach for estimating these ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46645", "content": "In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where the response variable lies in a high-dimensional ambient space but concentrates around a potentially lower-dimensional manifold. More specifically, we study the large-sample properties of a likelihood - based approach for estimating these ..."}
+{"idx": 4, "title": "3.3. Synthetic Regression Data — Dive into Deep ... - D2L", "date": "", "ddg_snippet": "3.3.2. Reading the Dataset Training machine learning models often requires multiple passes over a dataset , grabbing one minibatch of examples at a time. This data is then used to update the model. To illustrate how this works, we implement the get_dataloader method, registering it in the SyntheticRegressionData class via add_to_class (introduced in Section 3.2.1). It takes a batch size, a ...", "subpage_snippet": "", "source": "d2l.ai", "link": "https://d2l.ai/chapter_linear-regression/synthetic-regression-data.html", "content": "3.3.2. Reading the Dataset Training machine learning models often requires multiple passes over a dataset , grabbing one minibatch of examples at a time. This data is then used to update the model. To illustrate how this works, we implement the get_dataloader method, registering it in the SyntheticRegressionData class via add_to_class (introduced in Section 3.2.1). It takes a batch size, a ..."}
+{"idx": 5, "title": "A Likelihood Based Approach to Distribution Regression Using ...", "date": "", "ddg_snippet": "Abstract summary: We study the large-sample properties of a likelihood - based approach for estimating conditional deep generative models . Our results lead to the convergence rate of a sieve maximum likelihood estimator for estimating the conditional distribution .", "subpage_snippet": "", "source": "fugumt.com", "link": "https://fugumt.com/fugumt/paper_check/2410.02025v1_enmode", "content": "Abstract summary: We study the large-sample properties of a likelihood - based approach for estimating conditional deep generative models . Our results lead to the convergence rate of a sieve maximum likelihood estimator for estimating the conditional distribution ."}
+{"idx": 6, "title": "Generalized Regression with Conditional GANs", "date": "", "ddg_snippet": "We demonstrate the superiority of this new approach to standard regression with experiments on multiple synthetic and publicly available real-world datasets, finding encouraging results, especially with real-world heavy-tailed regression datasets. To make our work more reproducible, we release our source code3.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2404.13500", "content": "We demonstrate the superiority of this new approach to standard regression with experiments on multiple synthetic and publicly available real-world datasets, finding encouraging results, especially with real-world heavy-tailed regression datasets. To make our work more reproducible, we release our source code3."}
+{"idx": 7, "title": "A Likelihood Based Approach to Distribution Regression ...", "date": "", "ddg_snippet": "by S Kumar · Cited by 1 — In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=1IyPRv1A0r", "content": "by S Kumar · Cited by 1 — In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution ..."}
+{"idx": 8, "title": "A Likelihood Based Approach to Distribution Regression Using ...", "date": "", "ddg_snippet": "In this work, we explore the theoretical proper- ties of conditional deep generative models un- der the statistical framework of distribution re-.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=1IyPRv1A0r", "content": "In this work, we explore the theoretical proper- ties of conditional deep generative models un- der the statistical framework of distribution re-."}
+{"idx": 9, "title": "Trojan Attacks and Countermeasures on Deep Neural ...", "date": "", "ddg_snippet": "by L Jin · 2025 — This is equivalent to extracting an unknown noise distribution from the Trojaned model , solvable using a generative model with entropy ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/full/10.1145/3727640", "content": "by L Jin · 2025 — This is equivalent to extracting an unknown noise distribution from the Trojaned model , solvable using a generative model with entropy ..."}
diff --git "a/data/sampled_jsons/FD2_x_=_A_z_z_~_N(0,_I)_A_\342\210\210_R_synthetic_dataset_distribution_regression.jsonl" "b/data/sampled_jsons/FD2_x_=_A_z_z_~_N(0,_I)_A_\342\210\210_R_synthetic_dataset_distribution_regression.jsonl"
new file mode 100644
index 0000000000000000000000000000000000000000..e80349120a98ff3bb73a008572003dac38c188b0
--- /dev/null
+++ "b/data/sampled_jsons/FD2_x_=_A_z_z_~_N(0,_I)_A_\342\210\210_R_synthetic_dataset_distribution_regression.jsonl"
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2412.09258] FD2-Net: Frequency-Driven Feature Decomposition ... (PDF) FD-YOLO: A Frequency-Domain Dual-Stream Network Based ... fd2 Object Detection Dataset (v1, 2023-06-11 6:34pm) by fd2 FD2-Net: Frequency-Driven Feature Decomposition Network for ... GitHub - zhaoyyy620/spectral_analysis: Spectral modeling ... FD2-Net: Frequency-Driven Feature Decomposition Network for ... FD2-Net: Frequency-Driven Feature Decomposition Network for Infrared FD2-Net : Frequency-Driven Feature Decomposition Network for Infrared FD2-Net: Frequency-Driven Feature Decomposition Network for Infrared FD2 Object Detection Dataset by Technical vision", "date": "", "ddg_snippet": "Dec 12, 2024 · Infrared-visible object detection (IVOD) seeks to harness the complementary information in infrared and visible images, thereby enhancing the performance of detectors in complex environments. However, existing methods often neglect the frequency characteristics of complementary information, such as the abundant high-frequency details in visible images and the valuable low-frequency thermal ... May 29, 2025 · Moreover, this study employs the latest RDD2022 dataset and conducts extensive data augmentation to ensure the model’s generalizability and robustness across diverse road damage scenarios and ... Jun 11, 2023 · 4767 open source fd2 images and annotations in multiple formats for training computer vision models. fd2 (v1, 2023-06-11 6:34pm), created by fd2 As shown in Fig. 2, our FD2 -Net comprises three mod- ules: 1) Feature Decomposition Encoder. Inspired by spec- tral spectrum, this module introduces a two-branch architec- ture to effectively extract valuable high-frequency and low- frequency features through feature decomposition and fu- sion. Total workflow of spectral modeling analysis, including data preprocessing, wavelength selection, dataset splitting, regression , classification, clustering, and related process visualization. Dec 12, 2024 · In this paper, we design a novel paradigm for IVOD tasks, i.e., Frequency-Driven Feature Decomposition Network ( FD2 -Net), which decouples the frequency information of infrared and visible images to efficiently extract representative features and leverages the dominant frequency characteristics of one modality to enhance the complementary ... What does fd2net stand for? Conclusion In this paper, we introduce a Frequency-Driven Feature De- composition Network (FD2Net) specifically designed for infrared-visible object detection tasks. It efficiently models high-frequency and low-frequency features, thereby facili- tating the extraction of valuable complementary informa- tion. What is FD 2 net? FD 2 Net effectively captures robust shared and discriminative specific information related to detected objects , resulting in superior performance across various challenging scenarios. Figure 3: Visual comparison of FD 2 Net with 10 SOTA methods. Green boxes are detection results, while red dashed boxes mark missed objects (false negatives). Does fd2net detect weak and small objects? Notably, in the “People” and “Motor- cycle” categories, FD2Net achieves improvements of 1.3% and 2.4% over the previous best method. This suggests that our method possesses a superior ability to detect weak and small objects . Visual Comparisons. The qualitative results are depicted in Fig. 4. Connect Your Model With Program Logic Find utilities and guides to help you start using the FD2 project in your project.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2412.09258", "content": "Dec 12, 2024 · Infrared-visible object detection (IVOD) seeks to harness the complementary information in infrared and visible images, thereby enhancing the performance of detectors in complex environments. However, existing methods often neglect the frequency characteristics of complementary information, such as the abundant high-frequency details in visible images and the valuable low-frequency thermal ... May 29, 2025 · Moreover, this study employs the latest RDD2022 dataset and conducts extensive data augmentation to ensure the model’s generalizability and robustness across diverse road damage scenarios and ... Jun 11, 2023 · 4767 open source fd2 images and annotations in multiple formats for training computer vision models. fd2 (v1, 2023-06-11 6:34pm), created by fd2 As shown in Fig. 2, our FD2 -Net comprises three mod- ules: 1) Feature Decomposition Encoder. Inspired by spec- tral spectrum, this module introduces a two-branch architec- ture to effectively extract valuable high-frequency and low- frequency features through feature decomposition and fu- sion. Total workflow of spectral modeling analysis, including data preprocessing, wavelength selection, dataset splitting, regression , classification, clustering, and related process visualization. Dec 12, 2024 · In this paper, we design a novel paradigm for IVOD tasks, i.e., Frequency-Driven Feature Decomposition Network ( FD2 -Net), which decouples the frequency information of infrared and visible images to efficiently extract representative features and leverages the dominant frequency characteristics of one modality to enhance the complementary ... What does fd2net stand for? Conclusion In this paper, we introduce a Frequency-Driven Feature De- composition Network (FD2Net) specifically designed for infrared-visible object detection tasks. It efficiently models high-frequency and low-frequency features, thereby facili- tating the extraction of valuable complementary informa- tion. What is FD 2 net? FD 2 Net effectively captures robust shared and discriminative specific information related to detected objects , resulting in superior performance across various challenging scenarios. Figure 3: Visual comparison of FD 2 Net with 10 SOTA methods. Green boxes are detection results, while red dashed boxes mark missed objects (false negatives). Does fd2net detect weak and small objects? Notably, in the “People” and “Motor- cycle” categories, FD2Net achieves improvements of 1.3% and 2.4% over the previous best method. This suggests that our method possesses a superior ability to detect weak and small objects . Visual Comparisons. The qualitative results are depicted in Fig. 4. Connect Your Model With Program Logic Find utilities and guides to help you start using the FD2 project in your project."}
+{"idx": 1, "title": "fd2 Object Detection Dataset (v1, 2023-06-11 6:34pm) by fd2", "date": "", "ddg_snippet": "Jun 11, 2023 · 4767 open source fd2 images and annotations in multiple formats for training computer vision models. fd2 (v1, 2023-06-11 6:34pm), created by fd2", "subpage_snippet": "", "source": "universe.roboflow.com", "link": "https://universe.roboflow.com/fd2/fd2/dataset/1", "content": "Jun 11, 2023 · 4767 open source fd2 images and annotations in multiple formats for training computer vision models. fd2 (v1, 2023-06-11 6:34pm), created by fd2"}
+{"idx": 2, "title": "FD2-Net: Frequency-Driven Feature Decomposition Network for ...", "date": "", "ddg_snippet": "As shown in Fig. 2, our FD2 -Net comprises three mod- ules: 1) Feature Decomposition Encoder. Inspired by spec- tral spectrum, this module introduces a two-branch architec- ture to effectively extract valuable high-frequency and low- frequency features through feature decomposition and fu- sion.", "subpage_snippet": "", "source": "ojs.aaai.org", "link": "https://ojs.aaai.org/index.php/AAAI/article/download/32507/34662", "content": "As shown in Fig. 2, our FD2 -Net comprises three mod- ules: 1) Feature Decomposition Encoder. Inspired by spec- tral spectrum, this module introduces a two-branch architec- ture to effectively extract valuable high-frequency and low- frequency features through feature decomposition and fu- sion."}
+{"idx": 3, "title": "FD2-Net: Frequency-Driven Feature Decomposition Network for ...", "date": "", "ddg_snippet": "Dec 12, 2024 · In this paper, we design a novel paradigm for IVOD tasks, i.e., Frequency-Driven Feature Decomposition Network ( FD2 -Net), which decouples the frequency information of infrared and visible images to efficiently extract representative features and leverages the dominant frequency characteristics of one modality to enhance the complementary ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.09258v1", "content": "Dec 12, 2024 · In this paper, we design a novel paradigm for IVOD tasks, i.e., Frequency-Driven Feature Decomposition Network ( FD2 -Net), which decouples the frequency information of infrared and visible images to efficiently extract representative features and leverages the dominant frequency characteristics of one modality to enhance the complementary ..."}
+{"idx": 4, "title": "FD2 Object Detection Dataset by Technical vision", "date": "", "ddg_snippet": "Connect Your Model With Program Logic Find utilities and guides to help you start using the FD2 project in your project.", "subpage_snippet": "", "source": "universe.roboflow.com", "link": "https://universe.roboflow.com/technical-vision/fd2-uz0bi", "content": "Connect Your Model With Program Logic Find utilities and guides to help you start using the FD2 project in your project."}
+{"idx": 5, "title": "Domain Names Beginning with fd2 - Plot IP", "date": "", "ddg_snippet": "Domain Names Beginning with fd2 ... Copyright 2024 Plot IP. All Rights Reserved.", "subpage_snippet": "", "source": "www.plotip.com", "link": "https://www.plotip.com/domain/l/fd2", "content": "Domain Names Beginning with fd2 ... Copyright 2024 Plot IP. All Rights Reserved."}
+{"idx": 6, "title": "(PDF) FD-YOLO: A Frequency-Domain Dual-Stream Network Based ...", "date": "", "ddg_snippet": "May 29, 2025 · Moreover, this study employs the latest RDD2022 dataset and conducts extensive data augmentation to ensure the model’s generalizability and robustness across diverse road damage scenarios and ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/392225415_FD-YOLO_A_Frequency-Domain_Dual-Stream_Network_Based_on_YOLO_for_Crack_Detection", "content": "May 29, 2025 · Moreover, this study employs the latest RDD2022 dataset and conducts extensive data augmentation to ensure the model’s generalizability and robustness across diverse road damage scenarios and ..."}
+{"idx": 7, "title": "GitHub - zhaoyyy620/spectral_analysis: Spectral modeling ...", "date": "", "ddg_snippet": "Total workflow of spectral modeling analysis, including data preprocessing, wavelength selection, dataset splitting, regression , classification, clustering, and related process visualization.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/zhaoyyy620/spectral_analysis", "content": "Total workflow of spectral modeling analysis, including data preprocessing, wavelength selection, dataset splitting, regression , classification, clustering, and related process visualization."}
+{"idx": 8, "title": "Headlight Ballasts & Igniters for Honda Civic for sale |", "date": "", "ddg_snippet": "Honda Acura CSX Civic type R FD2 INNER BLACK PAINT OEM HID Headlight 2007-2011 (For: Honda Civic) ... New Listing Xenon HID Headlight Ballast W/ ...", "subpage_snippet": "", "source": "www.ebay.com", "link": "https://www.ebay.com/b/Headlight-Ballasts-Igniters-for-Honda-Civic/262207/bn_7118799501", "content": "Honda Acura CSX Civic type R FD2 INNER BLACK PAINT OEM HID Headlight 2007-2011 (For: Honda Civic) ... New Listing Xenon HID Headlight Ballast W/ ..."}
+{"idx": 9, "title": "Carbon Fiber Black Car & Truck Hatches & Trunk Lids for", "date": "", "ddg_snippet": "Hi ! Hi! Sign in or register Daily Deals Brand Outlet Gift Cards Help & Contact ... FITS FOR 2014-2024 INFINITI Q50 S VIP CARBON FIBER REAR ...", "subpage_snippet": "", "source": "www.ebay.com", "link": "https://www.ebay.com/b/Carbon-Fiber-Black-Car-Truck-Hatches-Trunk-Lids/33656/bn_116222742", "content": "Hi ! Hi! Sign in or register Daily Deals Brand Outlet Gift Cards Help & Contact ... FITS FOR 2014-2024 INFINITI Q50 S VIP CARBON FIBER REAR ..."}
diff --git a/data/sampled_jsons/FD3_Sieve_MLE_0.0000_0.0000_0.0000_0.0000_0.0000_0.0000_0.0000_0.0000_0.0000_0.0000_0.0000_0.0000.jsonl b/data/sampled_jsons/FD3_Sieve_MLE_0.0000_0.0000_0.0000_0.0000_0.0000_0.0000_0.0000_0.0000_0.0000_0.0000_0.0000_0.0000.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..877ddc15ea39a4df24112d957ca6d18881ca88eb
--- /dev/null
+++ b/data/sampled_jsons/FD3_Sieve_MLE_0.0000_0.0000_0.0000_0.0000_0.0000_0.0000_0.0000_0.0000_0.0000_0.0000_0.0000_0.0000.jsonl
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+{"idx": 0, "title": "Screen Sieve On eBay - Find It For Less On eBay Ad Viewing ads is privacy protected by DuckDuckGo. Ad clicks are managed by Microsoft's ad network ( more info ).", "date": "", "ddg_snippet": "No matter what you love, you'll find it here. Search Screen sieve and more. Find the deal you deserve on eBay. Discover discounts from sellers across the globe.", "subpage_snippet": "", "source": "duckduckgo.com", "link": "https://duckduckgo.com/y.js?ad_domain=ebay.com&ad_provider=bingv7aa&ad_type=txad&click_metadata=hNRNDBDNJ9g6T6Jt4IFwMFWBwEmh67U0y0uF7l2orEWULbY0yJck2Qg0dJko1SmMDvpYDEGTTFkUck88HixGVbic6H6WkJNz0ryIXbSmw-q94v_fEL5j3G1BVWCMnQil.zunEbKgtjSQUyNNgWRgTNQ&rut=af48d9c19581161bfcedc9c3b8340650f1f7315b27f5b4f4dd3eb5ae32fd2419&u3=https://www.bing.com/aclick?ld=e8ES8YB8kRRQrb1JPrDjRQmTVUCUx-lpWQ-VCjs4nONaXNlYS2qh9Nr5hIyQz6udRvvbycRitZ0zdkgCg6r8eKnNhFqh1me-5S3MNd8CAzWyIZvEyUJLbHc3bMBt4ncNDx5ui0zglD4IswpP5SgA0w7XZVjfdKWyRycssDqJFUwMN--UVukprFoHVMLhxKusYblVH3BA&u=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&rlid=ae185733bc7f1e712b943fe293b3808c&vqd=4-123889378084496663750459486746458611616&iurl={1}IG=C2E07C041F6B49EB8580EE7D1F4BAB68&CID=39C8FE62A4696C523084E80DA5026D21&ID=DevEx,5028.1", "content": "No matter what you love, you'll find it here. Search Screen sieve and more. Find the deal you deserve on eBay. Discover discounts from sellers across the globe."}
+{"idx": 1, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/FD3_Sieve_MLE_MEAN_SD_0.000_0.000_0.000_0.000.jsonl b/data/sampled_jsons/FD3_Sieve_MLE_MEAN_SD_0.000_0.000_0.000_0.000.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..61f830f8c9edeb401b7265fdb9ea8ec81b2c9d23
--- /dev/null
+++ b/data/sampled_jsons/FD3_Sieve_MLE_MEAN_SD_0.000_0.000_0.000_0.000.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Microsoft Word - CFDP_COVER_1590.doc", "date": "", "ddg_snippet": "Before we present a sieve (quasi-) MLE estimator b for 0, we need to impose some mild. smoothness restrictions on the unknown densities. The sieve method allows for unknown.", "subpage_snippet": "", "source": "cowles.yale.edu", "link": "https://cowles.yale.edu/sites/default/files/2022-08/d1590.pdf", "content": "Before we present a sieve (quasi-) MLE estimator b for 0, we need to impose some mild. smoothness restrictions on the unknown densities. The sieve method allows for unknown."}
+{"idx": 1, "title": "Influence of PROP taster status on the consumer ...", "date": "", "ddg_snippet": "Effect of PROP taster status on consumer ratings for texture and overall acceptability of sorghum rice from sorghums differing in tannin content. Means and SD ; ...", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/24798687/Influence_of_PROP_taster_status_on_the_consumer_acceptability_of_food_made_from_tannin_sorghums", "content": "Effect of PROP taster status on consumer ratings for texture and overall acceptability of sorghum rice from sorghums differing in tannin content. Means and SD ; ..."}
+{"idx": 2, "title": "Growth model of the reared sea urchin Paracentrotus", "date": "", "ddg_snippet": "5 Jun 2013 — mean ± SD . Part I: Set up of an experimental rearing procedure for echinoids. Dry w. GI (%). mean ± SD .", "subpage_snippet": "", "source": "www.yumpu.com", "link": "https://www.yumpu.com/en/document/view/15740791/growth-model-of-the-reared-sea-urchin-paracentrotus-sciviews", "content": "5 Jun 2013 — mean ± SD . Part I: Set up of an experimental rearing procedure for echinoids. Dry w. GI (%). mean ± SD ."}
+{"idx": 3, "title": "20010319 054 .aomoi-og-iw - DTIC", "date": "", "ddg_snippet": "by G Moore · 1997 — The United States Air Force Summer Research Program (USAF-SRP) is designed to introduce university, college, and technical institute faculty members, ...", "subpage_snippet": "", "source": "apps.dtic.mil", "link": "https://apps.dtic.mil/sti/tr/pdf/ADA387496.pdf", "content": "by G Moore · 1997 — The United States Air Force Summer Research Program (USAF-SRP) is designed to introduce university, college, and technical institute faculty members, ..."}
+{"idx": 4, "title": "NATO Advanced Science Institutes Series", "date": "", "ddg_snippet": "Library of Congress Cataloging in Publication Data. NATO Advanced Study Institute on Advances in Laser Spectroscopy (1981: San Miniato,.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/978-1-4613-3715-7.pdf", "content": "Library of Congress Cataloging in Publication Data. NATO Advanced Study Institute on Advances in Laser Spectroscopy (1981: San Miniato,."}
+{"idx": 5, "title": "By-Products of Palm Trees and Their Applications", "date": "", "ddg_snippet": "The 1st World Conference on By-Products of Palm Trees and their Applications. (ByPalma), Aswan, Egypt, 2018 December 15 – 17.", "subpage_snippet": "", "source": "mrforum.com", "link": "https://mrforum.com/wp-content/uploads/open_access/9781644900178.pdf?srsltid=AfmBOor77UG6m9f_27XCBBNIL117AmP1hMqnJc5x3tJFCnAkBS0xWP8X", "content": "The 1st World Conference on By-Products of Palm Trees and their Applications. (ByPalma), Aswan, Egypt, 2018 December 15 – 17."}
+{"idx": 6, "title": "absolute phase shift", "date": "", "ddg_snippet": "We demonstrate that conventional three-step phase-shifted fringe patterns can be used to create absolute phase map pixel by pixel even for large depth range ...", "subpage_snippet": "", "source": "www.science.gov", "link": "https://www.science.gov/topicpages/a/absolute+phase+shift", "content": "We demonstrate that conventional three-step phase-shifted fringe patterns can be used to create absolute phase map pixel by pixel even for large depth range ..."}
+{"idx": 7, "title": "EHY223 HYSYS Dynamics Introduction To ...", "date": "", "ddg_snippet": "At the end of this course you will be able to: ll Develop the skills and techniques required for creating and running dynamic simulations.", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/607789019/EHY223-HYSYS-Dynamics-Introduction-to-Dynamic-Modeling", "content": "At the end of this course you will be able to: ll Develop the skills and techniques required for creating and running dynamic simulations."}
+{"idx": 8, "title": "lake champlain: partnersrips and research in the new ...", "date": "", "ddg_snippet": "Lake Champlain: partnerships and research in the new millenniurnledited by Thomas 0. Manley, PaUicia L. Manley, and Timothy B. Mihuc.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/978-1-4757-4080-6.pdf", "content": "Lake Champlain: partnerships and research in the new millenniurnledited by Thomas 0. Manley, PaUicia L. Manley, and Timothy B. Mihuc."}
+{"idx": 9, "title": "kansas city, missouri, september 7-10, 2004", "date": "", "ddg_snippet": "15 Oct 2003 — HISTORY, ORGANIZATION AND FUNCTION. Established to foster a better understanding and closer cooperation between geologists and.", "subpage_snippet": "", "source": "www.highwaygeologysymposium.org", "link": "http://www.highwaygeologysymposium.org/wp-content/uploads/55_HGS-OPT.pdf", "content": "15 Oct 2003 — HISTORY, ORGANIZATION AND FUNCTION. Established to foster a better understanding and closer cooperation between geologists and."}
diff --git a/data/sampled_jsons/Face_X-Ray_Li_et_al._2020_deepfake_detection_arXiv_abstract_year_2020.jsonl b/data/sampled_jsons/Face_X-Ray_Li_et_al._2020_deepfake_detection_arXiv_abstract_year_2020.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b19b19d92022bb61cfd3990c479c7e1ae7c15a38
--- /dev/null
+++ b/data/sampled_jsons/Face_X-Ray_Li_et_al._2020_deepfake_detection_arXiv_abstract_year_2020.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Face selection not selecting the faces that I want", "date": "", "ddg_snippet": "Apr 25, 2019 · I'm totally new to this and can't find a solution anywhere about this problem. I'm trying to select faces however it will not select the faces that I want.", "subpage_snippet": "", "source": "forums.autodesk.com", "link": "https://forums.autodesk.com/t5/maya-modeling-forum/face-selection-not-selecting-the-faces-that-i-want/td-p/8751937", "content": "Apr 25, 2019 · I'm totally new to this and can't find a solution anywhere about this problem. I'm trying to select faces however it will not select the faces that I want."}
+{"idx": 1, "title": "[Question] How to create a face from vertices? (Very beginner...", "date": "", "ddg_snippet": "Jul 11, 2022 · I'm new to 3ds max as of today. I need to connect one side of this mesh to the other. How can I select vertices and create faces from them? Like this picture... Thanks for any and all help!", "subpage_snippet": "", "source": "forums.autodesk.com", "link": "https://forums.autodesk.com/t5/3ds-max-modeling-forum/question-how-to-create-a-face-from-vertices-very-beginner/td-p/6455580", "content": "Jul 11, 2022 · I'm new to 3ds max as of today. I need to connect one side of this mesh to the other. How can I select vertices and create faces from them? Like this picture... Thanks for any and all help!"}
+{"idx": 2, "title": "How to align an object to a face of another object?", "date": "", "ddg_snippet": "Mar 24, 2016 · Hello! I have the following problem: I would like to align an object (the hemisphere in the figure) on top of a face of another object (the selected face in the figure). I tried with Align and Snap Tools but I could not reach my goal. Thanks so much!", "subpage_snippet": "", "source": "forums.autodesk.com", "link": "https://forums.autodesk.com/t5/maya-modeling-forum/how-to-align-an-object-to-a-face-of-another-object/td-p/6233028", "content": "Mar 24, 2016 · Hello! I have the following problem: I would like to align an object (the hemisphere in the figure) on top of a face of another object (the selected face in the figure). I tried with Align and Snap Tools but I could not reach my goal. Thanks so much!"}
+{"idx": 3, "title": "Solved: Face Based Family won't host - Autodesk Community", "date": "", "ddg_snippet": "Feb 1, 2012 · Question: After placing your hosted Air Terminals on the face of the linked ceiling, what does \"Host\" read under its Properties? I cannot get a face -based fixture to host to a linked ceiling in this project, period. I can go into a Section and designate the ceiling face as the current Work Plane and then place the fixtures. And I made a fake test project with a linked ceiling, and it worked ...", "subpage_snippet": "", "source": "forums.autodesk.com", "link": "https://forums.autodesk.com/t5/revit-mep-forum/face-based-family-won-t-host/td-p/3314501", "content": "Feb 1, 2012 · Question: After placing your hosted Air Terminals on the face of the linked ceiling, what does \"Host\" read under its Properties? I cannot get a face -based fixture to host to a linked ceiling in this project, period. I can go into a Section and designate the ceiling face as the current Work Plane and then place the fixtures. And I made a fake test project with a linked ceiling, and it worked ..."}
+{"idx": 4, "title": "Solved: wall by face flip orientation - Autodesk Community", "date": "", "ddg_snippet": "Aug 29, 2016 · can someone help me out; I've created a mass surface, and I've applied a \"wall by face \" to it. Unfortunately, the wall is displayed as inside out, and I'd like to know how to flip it (the flip arrows dont appear, and space does nothing). also, is there a way to flip the mass normal?", "subpage_snippet": "", "source": "forums.autodesk.com", "link": "https://forums.autodesk.com/t5/revit-architecture-forum/wall-by-face-flip-orientation/td-p/6528270", "content": "Aug 29, 2016 · can someone help me out; I've created a mass surface, and I've applied a \"wall by face \" to it. Unfortunately, the wall is displayed as inside out, and I'd like to know how to flip it (the flip arrows dont appear, and space does nothing). also, is there a way to flip the mass normal?"}
+{"idx": 5, "title": "How to get the host face of an instance if the host face is from...", "date": "", "ddg_snippet": "Jun 7, 2024 · see if this explanation helps, it is also aligned to what @jeremy_tammik mentioned In short: t o get the host face of a family instance that is hosted to a face from a linked document, you can follow these steps: 1. Retrieve the Host Face Reference: Access the `HostFace` property of the family instance to get the reference to the host face . 2.", "subpage_snippet": "", "source": "forums.autodesk.com", "link": "https://forums.autodesk.com/t5/revit-api-forum/how-to-get-the-host-face-of-an-instance-if-the-host-face-is-from/td-p/12825655", "content": "Jun 7, 2024 · see if this explanation helps, it is also aligned to what @jeremy_tammik mentioned In short: t o get the host face of a family instance that is hosted to a face from a linked document, you can follow these steps: 1. Retrieve the Host Face Reference: Access the `HostFace` property of the family instance to get the reference to the host face . 2."}
+{"idx": 6, "title": "Is there an easy way to find the centre/centroid of a face?", "date": "", "ddg_snippet": "May 11, 2023 · Hi all, I find myself wanting to find the centre of faces that are irregular polygons or have a mixture of curved and straight sides, and I am wondering if there is a better/easier way to find the centre of these faces rather than drawing a bunch of lines and doing lots of maths. It has been sugg...", "subpage_snippet": "", "source": "forums.autodesk.com", "link": "https://forums.autodesk.com/t5/fusion-design-validate-document/is-there-an-easy-way-to-find-the-centre-centroid-of-a-face/td-p/9275747", "content": "May 11, 2023 · Hi all, I find myself wanting to find the centre of faces that are irregular polygons or have a mixture of curved and straight sides, and I am wondering if there is a better/easier way to find the centre of these faces rather than drawing a bunch of lines and doing lots of maths. It has been sugg..."}
+{"idx": 7, "title": "Face turning contour issue - Autodesk Community", "date": "", "ddg_snippet": "May 18, 2025 · hi i am trying to perform a simple finish turning profile on my part but fusion360 does not like it i guess . maybe i am doing something wrong. can some one have a look and explain to me what my mistake is? this is the profile i am trying to cut: fusion360 file included! thanks in advance...", "subpage_snippet": "", "source": "forums.autodesk.com", "link": "https://forums.autodesk.com/t5/fusion-manufacture-forum/face-turning-contour-issue/td-p/13635959", "content": "May 18, 2025 · hi i am trying to perform a simple finish turning profile on my part but fusion360 does not like it i guess . maybe i am doing something wrong. can some one have a look and explain to me what my mistake is? this is the profile i am trying to cut: fusion360 file included! thanks in advance..."}
+{"idx": 8, "title": "change hosted family to non hosted family - Autodesk Community", "date": "", "ddg_snippet": "Jun 1, 2017 · Select the elements from the face based families (geometry, reference planes, parametric dimensions), CRTL+C, and CTRL+V align to view on the non-host family. Re-constrain and add whatever is missing.", "subpage_snippet": "", "source": "forums.autodesk.com", "link": "https://forums.autodesk.com/t5/revit-architecture-forum/change-hosted-family-to-non-hosted-family/td-p/7124047", "content": "Jun 1, 2017 · Select the elements from the face based families (geometry, reference planes, parametric dimensions), CRTL+C, and CTRL+V align to view on the non-host family. Re-constrain and add whatever is missing."}
+{"idx": 9, "title": "Solved: Change Family Host Type - Autodesk Community", "date": "", "ddg_snippet": "Apr 11, 2014 · Therefore, Families that are hosted to a Face are necessary. Any of these element-specific Families can be converted to Face -Based with the following procedure: 1. Create a new Project and draw a Wall, Floor, or Ceiling - which ever element is an appropriate host. 2. Load in the desired Family and place one instance of each Type on the host ...", "subpage_snippet": "", "source": "forums.autodesk.com", "link": "https://forums.autodesk.com/t5/revit-architecture-forum/change-family-host-type/td-p/4950442", "content": "Apr 11, 2014 · Therefore, Families that are hosted to a Face are necessary. Any of these element-specific Families can be converted to Face -Based with the following procedure: 1. Create a new Project and draw a Wall, Floor, or Ceiling - which ever element is an appropriate host. 2. Load in the desired Family and place one instance of each Type on the host ..."}
diff --git a/data/sampled_jsons/Face_X-ray_Li_et_al._2020_abstract_year_2020.jsonl b/data/sampled_jsons/Face_X-ray_Li_et_al._2020_abstract_year_2020.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..caa30aceb01ffeda9ec105dbed793376ea939882
--- /dev/null
+++ b/data/sampled_jsons/Face_X-ray_Li_et_al._2020_abstract_year_2020.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Face X-ray for More General Face Forgery Detection", "date": "", "ddg_snippet": "In this paper we propose a novel image representation called face X-ray for detecting forgery in face images. The face X-ray of an input face image is a greyscale image that reveals whether the input image can be decomposed into the blending of two images from different sources. It does so by showing the blending boundary for a forged image and the absence of blending for a real image. We ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1912.13458", "content": "In this paper we propose a novel image representation called face X-ray for detecting forgery in face images. The face X-ray of an input face image is a greyscale image that reveals whether the input image can be decomposed into the blending of two images from different sources. It does so by showing the blending boundary for a forged image and the absence of blending for a real image. We ..."}
+{"idx": 1, "title": "PDF Face X-Ray for More General Face Forgery Detection", "date": "", "ddg_snippet": "Abstract In this paper we propose a novel image representation called face X-ray for detecting forgery in face images. The face X-ray of an input face image is a greyscale image that reveals whether the input image can be decomposed into the blending of two images from different sources.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content_CVPR_2020/papers/Li_Face_X-Ray_for_More_General_Face_Forgery_Detection_CVPR_2020_paper.pdf", "content": "Abstract In this paper we propose a novel image representation called face X-ray for detecting forgery in face images. The face X-ray of an input face image is a greyscale image that reveals whether the input image can be decomposed into the blending of two images from different sources."}
+{"idx": 2, "title": "Face X-Ray for More General Face Forgery Detection - IEEE Xplore", "date": "", "ddg_snippet": "Abstract : In this paper we propose a novel image representation called face X-ray for detecting forgery in face images. The face X-ray of an input face image is a greyscale image that reveals whether the input image can be decomposed into the blending of two images from different sources. It does so ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/9157215", "content": "Abstract : In this paper we propose a novel image representation called face X-ray for detecting forgery in face images. The face X-ray of an input face image is a greyscale image that reveals whether the input image can be decomposed into the blending of two images from different sources. It does so ..."}
+{"idx": 3, "title": "(PDF) Face X-Ray for More General Face Forgery Detection (2020 ...", "date": "", "ddg_snippet": "Abstract : In this paper we propose a novel image representation called face X-ray for detecting forgery in face images. The face X-ray of an input face image is a greyscale image that reveals whether the input image can be decomposed into the blending of two images from different sources. It does so by showing the blending boundary for a forged image and the absence of blending for a real ...", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/papers/face-x-ray-for-more-general-face-forgery-detection-1ya03q1kdc", "content": "Abstract : In this paper we propose a novel image representation called face X-ray for detecting forgery in face images. The face X-ray of an input face image is a greyscale image that reveals whether the input image can be decomposed into the blending of two images from different sources. It does so by showing the blending boundary for a forged image and the absence of blending for a real ..."}
+{"idx": 4, "title": "PDF Face X-Ray for More General Face Forgery Detection", "date": "", "ddg_snippet": "Abstract In this paper we propose a novel image representation called face X-ray for detecting forgery in face images. The face X-ray of an input face image is a greyscale image that revealswhethertheinputimagecanbedecomposedintothe blending of two images from different sources.", "subpage_snippet": "", "source": "conferences.computer.org", "link": "https://conferences.computer.org/cvpr/pdfs/CVPR2020-1XMljIyuXWg2zC9bnL19Tw/716800f000/716800f000.pdf", "content": "Abstract In this paper we propose a novel image representation called face X-ray for detecting forgery in face images. The face X-ray of an input face image is a greyscale image that revealswhethertheinputimagecanbedecomposedintothe blending of two images from different sources."}
+{"idx": 5, "title": "FaceShifter: Towards High Fidelity And Occlusion Aware Face Swapping", "date": "", "ddg_snippet": "However, our newly developed Face X-Ray [ Li et al. CVPR 2020 ] method can reliably detect forged images created by FaceShifter. Dataset We are very excited to announce that we are now collaborating with FaceForensic++ team to advance the face forgery detection for GAN-based face swapping methods.", "subpage_snippet": "", "source": "lingzhili.com", "link": "https://lingzhili.com/FaceShifterPage/", "content": "However, our newly developed Face X-Ray [ Li et al. CVPR 2020 ] method can reliably detect forged images created by FaceShifter. Dataset We are very excited to announce that we are now collaborating with FaceForensic++ team to advance the face forgery detection for GAN-based face swapping methods."}
+{"idx": 6, "title": "GitHub - wkq-wukaiqi/Face-X-Ray: An unofficial implementation of ...", "date": "", "ddg_snippet": "An unofficial implementation of Lingzhi Li , Jianmin Bao, Ting Zhang, Hao Yang, Dong Chen, Fang Wen, Baining Guo: Face X-Ray for More General Face Forgery Detection. CVPR 2020 .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/wkq-wukaiqi/Face-X-Ray", "content": "An unofficial implementation of Lingzhi Li , Jianmin Bao, Ting Zhang, Hao Yang, Dong Chen, Fang Wen, Baining Guo: Face X-Ray for More General Face Forgery Detection. CVPR 2020 ."}
+{"idx": 7, "title": "Face X-ray for More General Face Forgery Detection", "date": "", "ddg_snippet": "Extensive experiments show that face X-ray remains effective when applied to forgery generated by unseen face manipulation techniques, while most existing face forgery detection or deepfake detection algorithms experience a significant performance drop.", "subpage_snippet": "", "source": "www.microsoft.com", "link": "https://www.microsoft.com/en-us/research/publication/face-x-ray-for-more-general-face-forgery-detection/", "content": "Extensive experiments show that face X-ray remains effective when applied to forgery generated by unseen face manipulation techniques, while most existing face forgery detection or deepfake detection algorithms experience a significant performance drop."}
+{"idx": 8, "title": "Deep learning model for deep fake face recognition and detection", "date": "", "ddg_snippet": "The process include identifying the forgery of face X-ray by blending the boundary forged image in the CNN model and classifying the loss in the detection of face X-ray ( Li et al ., 2020 ; Li & Lyu, 2018; Afchar et al ., 2018; Dang et al ., 2020 ). In the media, articles use the biometric technology for the detection of deep fakes.", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC9044351/", "content": "The process include identifying the forgery of face X-ray by blending the boundary forged image in the CNN model and classifying the loss in the detection of face X-ray ( Li et al ., 2020 ; Li & Lyu, 2018; Afchar et al ., 2018; Dang et al ., 2020 ). In the media, articles use the biometric technology for the detection of deep fakes."}
+{"idx": 9, "title": "CVPR 2020 Open Access Repository", "date": "", "ddg_snippet": "Face X-ray is general in the sense that it only assumes the existence of a blending step and does not rely on any knowledge of the artifacts associated with a specific face manipulation technique. Indeed, the algorithm for computing face X-ray can be trained without fake images generated by any of the state-of-the-art face manipulation methods.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Face_X-Ray_for_More_General_Face_Forgery_Detection_CVPR_2020_paper.html", "content": "Face X-ray is general in the sense that it only assumes the existence of a blending step and does not rely on any knowledge of the artifacts associated with a specific face manipulation technique. Indeed, the algorithm for computing face X-ray can be trained without fake images generated by any of the state-of-the-art face manipulation methods."}
diff --git a/data/sampled_jsons/Fake_it_till_you_make_it_ImageNet-100_top-1_accuracy_table.jsonl b/data/sampled_jsons/Fake_it_till_you_make_it_ImageNet-100_top-1_accuracy_table.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f5a3bea347290530160a789b570f3e30017f4739
--- /dev/null
+++ b/data/sampled_jsons/Fake_it_till_you_make_it_ImageNet-100_top-1_accuracy_table.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Knowledge Distillation Theory", "date": "", "ddg_snippet": "I have randomly selected 1000 images for each class and kept 800 images in train data, 100 images in the validation data, and 100 images in test data ...", "subpage_snippet": "", "source": "www.analyticsvidhya.com", "link": "https://www.analyticsvidhya.com/blog/2022/01/knowledge-distillation-theory-and-end-to-end-case-study/", "content": "I have randomly selected 1000 images for each class and kept 800 images in train data, 100 images in the validation data, and 100 images in test data ..."}
+{"idx": 1, "title": "Enhancing Generalization in Data-free Quantization via", "date": "", "ddg_snippet": "However, PTQ typically relies on a small amount of calibration dataset, making it susceptible to overfitting and accuracy degradation [ 22 , 25 ] .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.21947v1", "content": "However, PTQ typically relies on a small amount of calibration dataset, making it susceptible to overfitting and accuracy degradation [ 22 , 25 ] ."}
+{"idx": 2, "title": "Feedback-guided Data Synthesis for Imbalanced Classification", "date": "", "ddg_snippet": "Subfigures show random samples for Jack-o-lantern class coming from: (a) ImageNet -LT; (b) Latent Diffusion Model (LDM-unclip v2- 1 ), conditioned on ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2310.00158v2", "content": "Subfigures show random samples for Jack-o-lantern class coming from: (a) ImageNet -LT; (b) Latent Diffusion Model (LDM-unclip v2- 1 ), conditioned on ..."}
+{"idx": 3, "title": "Efficient Model Editing with Task Vector Bases: A Theoretical", "date": "", "ddg_snippet": "Storing all task vectors improves accuracy but requires O ( T ) 𝑂 𝑇 O(T) italic_O ( italic_T ) memory. ... italic_O ( italic_m ) and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.01015v3", "content": "Storing all task vectors improves accuracy but requires O ( T ) 𝑂 𝑇 O(T) italic_O ( italic_T ) memory. ... italic_O ( italic_m ) and ..."}
+{"idx": 4, "title": "📝 Deep Learning Lesson 4 Notes - Part 1 (2019) - fast.ai", "date": "", "ddg_snippet": "Note that this is a forum wiki thread , so you all can edit this post to add/change/organize info to help make it better! To edit, click on the ...", "subpage_snippet": "", "source": "forums.fast.ai", "link": "https://forums.fast.ai/t/deep-learning-lesson-4-notes/30983", "content": "Note that this is a forum wiki thread , so you all can edit this post to add/change/organize info to help make it better! To edit, click on the ..."}
+{"idx": 5, "title": "Intelligent Machines 823 transcript", "date": "", "ddg_snippet": "But the new one is a little more topical yeah, for sure you couldn't get more topical than nvidia, did jensen wong cooperate for this uh, at first ...", "subpage_snippet": "", "source": "twit.tv", "link": "https://twit.tv/posts/transcripts/intelligent-machines-823-transcript", "content": "But the new one is a little more topical yeah, for sure you couldn't get more topical than nvidia, did jensen wong cooperate for this uh, at first ..."}
+{"idx": 6, "title": "Books: precision agriculture", "date": "", "ddg_snippet": "It improves crop productivity by allowing farmers to plan exactly when to till , plant, weed, and apply fertilizer, based on changing conditions ...", "subpage_snippet": "", "source": "edwardbetts.com", "link": "https://edwardbetts.com/monograph/precision_agriculture", "content": "It improves crop productivity by allowing farmers to plan exactly when to till , plant, weed, and apply fertilizer, based on changing conditions ..."}
+{"idx": 7, "title": "SUB: Benchmarking CBM Generalization via Synthetic Attribute", "date": "", "ddg_snippet": "Jessica Bader 1 Leander Girrbach 1 Stephan Alaniz 2 Zeynep Akata 1 1 Technical University of Munich, Helmholtz Munich, Munich ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.23784v1", "content": "Jessica Bader 1 Leander Girrbach 1 Stephan Alaniz 2 Zeynep Akata 1 1 Technical University of Munich, Helmholtz Munich, Munich ..."}
+{"idx": 8, "title": "neural network Archives - Hayk Hakobyan", "date": "", "ddg_snippet": "According to Jeremy England from MIT given it a thermodynamic framing : it ’s all about entropy ( to create life, one has to decrease entropy ...", "subpage_snippet": "", "source": "haykha.com", "link": "https://haykha.com/category/neural-network/", "content": "According to Jeremy England from MIT given it a thermodynamic framing : it ’s all about entropy ( to create life, one has to decrease entropy ..."}
+{"idx": 9, "title": "Best 70+ Unique Machine Learning Projects With Source Code In", "date": "", "ddg_snippet": "... it generates a massive amount of data in the form of comments, which can provide valuable insights into the user ’ s opinion about a particular ...", "subpage_snippet": "", "source": "machinelearningprojects.net", "link": "https://machinelearningprojects.net/machine-learning-projects-with-source-code-in-python/", "content": "... it generates a massive amount of data in the form of comments, which can provide valuable insights into the user ’ s opinion about a particular ..."}
diff --git a/data/sampled_jsons/Fake_it_till_you_make_it_Learning_transferable_representations_from_synthetic_imagenet_clones_imagen_year_2023.jsonl b/data/sampled_jsons/Fake_it_till_you_make_it_Learning_transferable_representations_from_synthetic_imagenet_clones_imagen_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4f03d5d5afe8e8b6f457a34249da21489a4bcfdc
--- /dev/null
+++ b/data/sampled_jsons/Fake_it_till_you_make_it_Learning_transferable_representations_from_synthetic_imagenet_clones_imagen_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Fake it till you make it: Learning transferable representations", "date": "", "ddg_snippet": "InProceedings{sariyildiz2023fake, title={ Fake it till you make it : Learning transferable representations from synthetic ImageNet clones }, author ...", "subpage_snippet": "", "source": "europe.naverlabs.com", "link": "https://europe.naverlabs.com/research/computer-vision/imagenet-sd/", "content": "InProceedings{sariyildiz2023fake, title={ Fake it till you make it : Learning transferable representations from synthetic ImageNet clones }, author ..."}
+{"idx": 1, "title": "Fake it till you make it: learning transferable representations", "date": "", "ddg_snippet": "Fake it till you make it : learning transferable representations from synthetic ImageNet clones ... synthetic clones of ImageNet and measure how useful ...", "subpage_snippet": "", "source": "europe.naverlabs.com", "link": "https://europe.naverlabs.com/research/publications/fake-it-till-you-make-it-learning-transferable-representations-from-synthetic-imagenet-clones/", "content": "Fake it till you make it : learning transferable representations from synthetic ImageNet clones ... synthetic clones of ImageNet and measure how useful ..."}
+{"idx": 2, "title": "Lifelong learning for visual representation - Naver Labs Europe", "date": "", "ddg_snippet": "ImageNetSD] Fake it till you make it : learning transferable representations from synthetic ImageNet clones , CVPR 2023", "subpage_snippet": "", "source": "europe.naverlabs.com", "link": "https://europe.naverlabs.com/research/lifelong-learning-for-visual-representation/", "content": "ImageNetSD] Fake it till you make it : learning transferable representations from synthetic ImageNet clones , CVPR 2023"}
+{"idx": 3, "title": "Personalized Representation from Personalized Generation", "date": "", "ddg_snippet": "... synthetic data to general-purpose representation learning , while advances in T2I diffusion models have enabled the generation of personalized images ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.16156v1", "content": "... synthetic data to general-purpose representation learning , while advances in T2I diffusion models have enabled the generation of personalized images ..."}
+{"idx": 4, "title": "GitHub - Daisy-Zhang/Awesome-Deepfakes-Detection: A list of", "date": "", "ddg_snippet": "Deeper Forensic-1.0 : DeeperForensics-1.0: A Large-Scale Dataset for Real -World Face Forgery Detection. ... FakeAVCeleb : FakeAVCeleb: A Novel Audio ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Daisy-Zhang/Awesome-Deepfakes-Detection", "content": "Deeper Forensic-1.0 : DeeperForensics-1.0: A Large-Scale Dataset for Real -World Face Forgery Detection. ... FakeAVCeleb : FakeAVCeleb: A Novel Audio ..."}
+{"idx": 5, "title": "SynC: Synthetic Image Caption Dataset Refinement with", "date": "", "ddg_snippet": "... are ill-suited for the distinct challenges of synthetic data, where captions are typically well-formed, but images may be inaccurate representations ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.18616v1", "content": "... are ill-suited for the distinct challenges of synthetic data, where captions are typically well-formed, but images may be inaccurate representations ..."}
+{"idx": 6, "title": "Mert Bulent Sariyildiz | Naver Labs Europe", "date": "", "ddg_snippet": "Fake it till you make it : learning transferable representations from synthetic ImageNet clones Conference on Computer Vision and Pattern Recognition ...", "subpage_snippet": "", "source": "europe.naverlabs.com", "link": "https://europe.naverlabs.com/people_user_naverlabs/mert-bulent-sariyildiz/", "content": "Fake it till you make it : learning transferable representations from synthetic ImageNet clones Conference on Computer Vision and Pattern Recognition ..."}
+{"idx": 7, "title": "Yannis Kalantidis | Naver Labs Europe", "date": "", "ddg_snippet": "Fake it till you make it : learning transferable representations from synthetic ImageNet clones Conference on Computer Vision and Pattern Recognition ...", "subpage_snippet": "", "source": "europe.naverlabs.com", "link": "https://europe.naverlabs.com/people_user_naverlabs/yannis-kalantidis/", "content": "Fake it till you make it : learning transferable representations from synthetic ImageNet clones Conference on Computer Vision and Pattern Recognition ..."}
+{"idx": 8, "title": "Feedback-guided Data Synthesis for Imbalanced Classification", "date": "", "ddg_snippet": "... to provide useful , and diverse synthetic samples that are close to the support of the real data distribution , to improve on representation learning ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2310.00158v2", "content": "... to provide useful , and diverse synthetic samples that are close to the support of the real data distribution , to improve on representation learning ..."}
+{"idx": 9, "title": "Enhancing Generalization in Data-free Quantization via", "date": "", "ddg_snippet": "It is particularly crucial in DFQ for alleviating the significant challenge of limited diversity in synthetic data.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.21947v1", "content": "It is particularly crucial in DFQ for alleviating the significant challenge of limited diversity in synthetic data."}
diff --git a/data/sampled_jsons/Federated_Learning_non-IID_data_distributed_parties_machine_learning.jsonl b/data/sampled_jsons/Federated_Learning_non-IID_data_distributed_parties_machine_learning.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e8f43ccf6e5890fd9989beeaf4150b78b6b47375
--- /dev/null
+++ b/data/sampled_jsons/Federated_Learning_non-IID_data_distributed_parties_machine_learning.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Federated Learning on Non-IID Data Silos", "date": "", "ddg_snippet": "by Q Li · 2022 · Cited by 1462 — The data of different parties are usually non-independently and identically distributed (i.e., non - IID ). There have been many FL algorithms to address the ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/9835537", "content": "by Q Li · 2022 · Cited by 1462 — The data of different parties are usually non-independently and identically distributed (i.e., non - IID ). There have been many FL algorithms to address the ..."}
+{"idx": 1, "title": "Federated Learning on Non-IID Data Silos", "date": "", "ddg_snippet": "by Q Li · 2021 · Cited by 1462 — The data of different parties are usually non-independently and identically distributed (i.e., non - IID ). There have been many FL algorithms to address the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2102.02079", "content": "by Q Li · 2021 · Cited by 1462 — The data of different parties are usually non-independently and identically distributed (i.e., non - IID ). There have been many FL algorithms to address the ..."}
+{"idx": 2, "title": "Non-IID data and Continual Learning processes in ...", "date": "", "ddg_snippet": "by MF Criado · 2022 · Cited by 119 — Federated Learning is a novel framework that allows multiple devices or institutions to train a machine learning model collaboratively while preserving ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S1566253522000884", "content": "by MF Criado · 2022 · Cited by 119 — Federated Learning is a novel framework that allows multiple devices or institutions to train a machine learning model collaboratively while preserving ..."}
+{"idx": 3, "title": "FedRL: Federated Learning with Non-IID Data via Review ...", "date": "", "ddg_snippet": "7 Jun 2024 — We introduce the FedRL framework in this paper, which facilitates efficient federated learning through review learning .", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3651671.3651704", "content": "7 Jun 2024 — We introduce the FedRL framework in this paper, which facilitates efficient federated learning through review learning ."}
+{"idx": 4, "title": "Federated Learning on Non-IID Data Silos", "date": "", "ddg_snippet": "In this paper, to help researchers better understand and study the non-IID data setting in federated learning , we propose comprehensive data partitioning ...", "subpage_snippet": "", "source": "flower.ai", "link": "https://flower.ai/docs/baselines/niid_bench.html", "content": "In this paper, to help researchers better understand and study the non-IID data setting in federated learning , we propose comprehensive data partitioning ..."}
+{"idx": 5, "title": "Non-IID data in Federated Learning: A Systematic Review ...", "date": "", "ddg_snippet": "19 Nov 2024 — This systematic review aims to fill that gap by providing a detailed taxonomy for non - IID data , partition protocols, and metrics to quantify data heterogeneity.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.12377v1", "content": "19 Nov 2024 — This systematic review aims to fill that gap by providing a detailed taxonomy for non - IID data , partition protocols, and metrics to quantify data heterogeneity."}
+{"idx": 6, "title": "FedAgent: Federated learning on Non-IID data via ...", "date": "", "ddg_snippet": "by B Sun · 2025 · Cited by 9 — Federated learning (FL) allows distributed client devices to jointly train an efficient global model without transmitting local data , ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/abs/pii/S0957417425015957", "content": "by B Sun · 2025 · Cited by 9 — Federated learning (FL) allows distributed client devices to jointly train an efficient global model without transmitting local data , ..."}
+{"idx": 7, "title": "Distribution-Regularized Federated Learning on Non-IID Data", "date": "", "ddg_snippet": "by Y Wang · Cited by 28 — Federated learning (FL) [1]–[3] is a new distributed machine learning paradigm that collaboratively trains models among multiple clients while the raw training ... 13 pages", "subpage_snippet": "", "source": "zhouzimu.github.io", "link": "https://zhouzimu.github.io/paper/icde23-wang.pdf", "content": "by Y Wang · Cited by 28 — Federated learning (FL) [1]–[3] is a new distributed machine learning paradigm that collaboratively trains models among multiple clients while the raw training ... 13 pages"}
+{"idx": 8, "title": "FedSea: Federated Learning via Selective Feature ...", "date": "", "ddg_snippet": "by M Tan · 2023 · Cited by 12 — This paper proposes a new Federated learning method via Selective feature Alignment (FedSea) to align representations across multiple parties in the feature ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10345705/", "content": "by M Tan · 2023 · Cited by 12 — This paper proposes a new Federated learning method via Selective feature Alignment (FedSea) to align representations across multiple parties in the feature ..."}
+{"idx": 9, "title": "Federated learning on non-IID and long-tailed data via ...", "date": "", "ddg_snippet": "by Z Wang · 2024 · Cited by 3 — Federated learning (FL), a cutting-edge distributed machine learning training paradigm, aims to generate a global model by collaborating on ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1631/FITEE.2300284", "content": "by Z Wang · 2024 · Cited by 3 — Federated learning (FL), a cutting-edge distributed machine learning training paradigm, aims to generate a global model by collaborating on ..."}
diff --git a/data/sampled_jsons/Feint_Behaviors_Strategies_Dual-Behavior_Model_scheduler_weights.jsonl b/data/sampled_jsons/Feint_Behaviors_Strategies_Dual-Behavior_Model_scheduler_weights.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0f0181fec671448a472f46f60f1a97338f994159
--- /dev/null
+++ b/data/sampled_jsons/Feint_Behaviors_Strategies_Dual-Behavior_Model_scheduler_weights.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "February | 2025 | Dna-Pk Inhibitors", "date": "", "ddg_snippet": "Syngeneic murine melanoma and breast cancer models were used to assess the impact of polio immunization on polio virotherapy, and the antitumor ...", "subpage_snippet": "", "source": "dna-pkinhibitors.com", "link": "https://dna-pkinhibitors.com/2025/02", "content": "Syngeneic murine melanoma and breast cancer models were used to assess the impact of polio immunization on polio virotherapy, and the antitumor ..."}
+{"idx": 1, "title": "4 Sources of Edge for Active Managers - Harding Loevner", "date": "", "ddg_snippet": "It is vital for active investment managers to be aware of their own behavioral defects as humans and counter these shortcomings with process.", "subpage_snippet": "", "source": "www.hardingloevner.com", "link": "https://www.hardingloevner.com/out-of-our-minds/4-sources-of-edge-for-active-managers/", "content": "It is vital for active investment managers to be aware of their own behavioral defects as humans and counter these shortcomings with process."}
+{"idx": 2, "title": "Valuation is in the Eye of the Beholder - Harding Loevner", "date": "", "ddg_snippet": "There is no such model that underlies prices in the market for soccer players (and I apologize to all players for considering them only as tradeable ...", "subpage_snippet": "", "source": "www.hardingloevner.com", "link": "https://www.hardingloevner.com/out-of-our-minds/valuation-is-in-the-eye-of-the-beholder/", "content": "There is no such model that underlies prices in the market for soccer players (and I apologize to all players for considering them only as tradeable ..."}
+{"idx": 3, "title": "I am NeuroBill. I'm a neuroscientist who had worked Down-under,", "date": "", "ddg_snippet": "... out more within the next couple decades, as methods for both examining the activity of live human brains AND for creating and studying animal models ...", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/askscience/comments/3e2t6v/i_am_neurobill_im_a_neuroscientist_who_had_worked/", "content": "... out more within the next couple decades, as methods for both examining the activity of live human brains AND for creating and studying animal models ..."}
+{"idx": 4, "title": "all.net/journal/deception/Framework/Lambert.html", "date": "", "ddg_snippet": "Induce / capitalize on target's general processing capabilities and strategies (e.g., to predict susceptibility to particular types of deception).", "subpage_snippet": "", "source": "all.net", "link": "http://all.net/journal/deception/Framework/Lambert.html", "content": "Induce / capitalize on target's general processing capabilities and strategies (e.g., to predict susceptibility to particular types of deception)."}
+{"idx": 5, "title": "Floyd Money Mayweather Fight - The Income Network", "date": "", "ddg_snippet": "His ability to feint and shift his weight adds another layer to his defensive prowess, often tempting opponents into throwing punches that will ...", "subpage_snippet": "", "source": "theincome.net", "link": "https://theincome.net/floyd-money-mayweather-fight/", "content": "His ability to feint and shift his weight adds another layer to his defensive prowess, often tempting opponents into throwing punches that will ..."}
+{"idx": 6, "title": "ShoutOut / Hajime no Ippo - TV Tropes", "date": "", "ddg_snippet": "Takamura declaring he's going to conquer six weight divisions is significant because around this time, Oscar De La Hoya note (who has been mentioned ...", "subpage_snippet": "", "source": "tvtropes.org", "link": "https://tvtropes.org/pmwiki/pmwiki.php/Shoutout/HajimeNoIppo", "content": "Takamura declaring he's going to conquer six weight divisions is significant because around this time, Oscar De La Hoya note (who has been mentioned ..."}
+{"idx": 7, "title": "Elder Tale: A Log Horizon Roleplay | 04. The Various Subclasses", "date": "", "ddg_snippet": "An alternate universe Log Horizon roleplay and anime discussion forum, Elder Tale: The Unfounded Kingdom.", "subpage_snippet": "", "source": "log-horizon.proboards.com", "link": "https://log-horizon.proboards.com/thread/11/05-various-subclasses", "content": "An alternate universe Log Horizon roleplay and anime discussion forum, Elder Tale: The Unfounded Kingdom."}
+{"idx": 8, "title": "Game Server Hosting | GGServers", "date": "", "ddg_snippet": "... vehicle warfare, air support, naval operations, modding community, sandbox environment, mission editor, cooperative gameplay, PvP battles, strategic ...", "subpage_snippet": "", "source": "ggservers.com", "link": "https://ggservers.com/gameservers", "content": "... vehicle warfare, air support, naval operations, modding community, sandbox environment, mission editor, cooperative gameplay, PvP battles, strategic ..."}
+{"idx": 9, "title": "Baseball Playing Rules", "date": "", "ddg_snippet": "... PAC SPORTS STRATEGIC PLAN SUMMARY", "subpage_snippet": "", "source": "playpacsports.com", "link": "https://playpacsports.com/baseball-playing-rules", "content": "... PAC SPORTS STRATEGIC PLAN SUMMARY"}
diff --git "a/data/sampled_jsons/Feint_Behaviors_Strategies_\316\273_short_\316\273_long_scheduler_weights_initial_values.jsonl" "b/data/sampled_jsons/Feint_Behaviors_Strategies_\316\273_short_\316\273_long_scheduler_weights_initial_values.jsonl"
new file mode 100644
index 0000000000000000000000000000000000000000..05281cb4efbfa0f7cd4d66566210b5bcbbd62ed7
--- /dev/null
+++ "b/data/sampled_jsons/Feint_Behaviors_Strategies_\316\273_short_\316\273_long_scheduler_weights_initial_values.jsonl"
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Time to Value Guide: 11 proven strategies to shorten TTV", "date": "", "ddg_snippet": "Short time to value . A short TTV (STTV) is ideal for companies that offer simple, understandable, low-touch services. A customer can use the product or service as a single user and see value within days. This is common for products that have free/freemium trials.", "subpage_snippet": "", "source": "www.dock.us", "link": "https://www.dock.us/library/time-to-value", "content": "Short time to value . A short TTV (STTV) is ideal for companies that offer simple, understandable, low-touch services. A customer can use the product or service as a single user and see value within days. This is common for products that have free/freemium trials."}
+{"idx": 1, "title": "Monochromatic light of wavelength λ is incident on two narrow slits...", "date": "", "ddg_snippet": "A series of bright and dark fringes are observed on a screen a long distance away from the slits. The n th dark fringe from the central bright fringe is observed at point P on the screen. Which equation is correct for all positive values of n?", "subpage_snippet": "", "source": "physics-ref.blogspot.com", "link": "https://physics-ref.blogspot.com/2020/04/monochromatic-light-of-wavelength-is.html", "content": "A series of bright and dark fringes are observed on a screen a long distance away from the slits. The n th dark fringe from the central bright fringe is observed at point P on the screen. Which equation is correct for all positive values of n?"}
+{"idx": 2, "title": "Computing the soft anomalous dimension with massless particles using...", "date": "", "ddg_snippet": "The remaining singularities are related to the long -distance interaction of on-shell initial - and final-state partons, and are therefore known as infrared (IR) singularities.The functions Mabc(α), which are polylogarithmic functions with weight a+b+c+1, have been defined in ref.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2509.18017", "content": "The remaining singularities are related to the long -distance interaction of on-shell initial - and final-state partons, and are therefore known as infrared (IR) singularities.The functions Mabc(α), which are polylogarithmic functions with weight a+b+c+1, have been defined in ref."}
+{"idx": 3, "title": "7 Keys to Leading with Integrity: Build Trust & Influence", "date": "", "ddg_snippet": "In today’s fast-paced, results-driven world, leading with integrity is not just a noble ideal—it’s a necessity for building trust, credibility, and sustainable success. This article explores the multifaceted nature of integrity in leadership, drawing on practical insights and actionable strategies .", "subpage_snippet": "", "source": "gist.ly", "link": "https://gist.ly/youtube-summarizer/7-keys-to-leading-with-integrity-build-trust-influence", "content": "In today’s fast-paced, results-driven world, leading with integrity is not just a noble ideal—it’s a necessity for building trust, credibility, and sustainable success. This article explores the multifaceted nature of integrity in leadership, drawing on practical insights and actionable strategies ."}
+{"idx": 4, "title": "Tokenized MMFs: Risks or benefits?", "date": "", "ddg_snippet": "The conditions that produced systemic risk during the financial crisis, such as a combination of high exposure to opaque, risky assets, massive market penetration, and panicked investor behavior , do not apply to tokenised MMFs today.", "subpage_snippet": "", "source": "blog.montesauri.com", "link": "https://blog.montesauri.com/tokenized-mmfs-risks-or-benefits/", "content": "The conditions that produced systemic risk during the financial crisis, such as a combination of high exposure to opaque, risky assets, massive market penetration, and panicked investor behavior , do not apply to tokenised MMFs today."}
+{"idx": 5, "title": "(PDF) Probability, Random Variables, and Selectivity", "date": "", "ddg_snippet": "λphysics = short/ long , λgeometry = short/ long , λ = short / long .Strong nonlocality allows signals faster than light . Weak nonlocality is a statistical property of classical events for which there is no realistic local theory. This requires the violation of at least one general Bell inequality.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/259212321_Probability_Random_Variables_and_Selectivity", "content": "λphysics = short/ long , λgeometry = short/ long , λ = short / long .Strong nonlocality allows signals faster than light . Weak nonlocality is a statistical property of classical events for which there is no realistic local theory. This requires the violation of at least one general Bell inequality."}
+{"idx": 6, "title": "University Physics with Modern Physics 12e Young [Solutions] [PDF]", "date": "", "ddg_snippet": "This behavior agrees with the vx (t ) that is given in the graph in the problem. Figure 2.72 2.73. IDENTIFY: Apply constant acceleration equations to each vehicle.", "subpage_snippet": "", "source": "epage.pub", "link": "https://epage.pub/doc/university-physics-with-modern-physics-12e-young-solutions-ydgk5dmdpo", "content": "This behavior agrees with the vx (t ) that is given in the graph in the problem. Figure 2.72 2.73. IDENTIFY: Apply constant acceleration equations to each vehicle."}
+{"idx": 7, "title": "Dragon Talon | Blox Fruits Wiki | Fandom", "date": "", "ddg_snippet": "Dragon BreathDragon Talon. ArticleCombos. Dragon Talon is a fighting style that can be learned from Uzoth in the Third Sea. Dragon Talon serves as a direct upgrade from Dragon Breath, with attacks that resemble Dragon's abilities—hence the name.", "subpage_snippet": "", "source": "blox-fruits.fandom.com", "link": "https://blox-fruits.fandom.com/wiki/Dragon_Talon", "content": "Dragon BreathDragon Talon. ArticleCombos. Dragon Talon is a fighting style that can be learned from Uzoth in the Third Sea. Dragon Talon serves as a direct upgrade from Dragon Breath, with attacks that resemble Dragon's abilities—hence the name."}
+{"idx": 8, "title": "Chapter 26 - Coupling the Line to the Antenna", "date": "", "ddg_snippet": "Greater resistances are obtained with longer hairpin sections—meaning a larger value of shunt inductor—and smaller resistances with shorter sections. Reactance at the feed-point terminals is tuned out by adjusting the length of the driven element, as necessary.", "subpage_snippet": "", "source": "www.qrz.ru", "link": "https://www.qrz.ru/schemes/contribute/arrl/chap26.pdf", "content": "Greater resistances are obtained with longer hairpin sections—meaning a larger value of shunt inductor—and smaller resistances with shorter sections. Reactance at the feed-point terminals is tuned out by adjusting the length of the driven element, as necessary."}
+{"idx": 9, "title": "Microscale And Miniscale Organic Chemistry Laboratory Experiments...", "date": "", "ddg_snippet": "This requires less energetic, longer wavelength light for excitation. The wavelengths required for excitation fall in the visible region for colored organic compounds such as Methyl orange dyes, β-carotene, and β-chlorophyll.", "subpage_snippet": "", "source": "vdoc.pub", "link": "https://vdoc.pub/documents/microscale-and-miniscale-organic-chemistry-laboratory-experiments-41cgdrinog80", "content": "This requires less energetic, longer wavelength light for excitation. The wavelengths required for excitation fall in the visible region for colored organic compounds such as Methyl orange dyes, β-carotene, and β-chlorophyll."}
diff --git "a/data/sampled_jsons/Feint_Behaviors_and_Strategies_scheduler_weights_\316\273_short_\316\273_long_initial_values_Section_4.2.2.jsonl" "b/data/sampled_jsons/Feint_Behaviors_and_Strategies_scheduler_weights_\316\273_short_\316\273_long_initial_values_Section_4.2.2.jsonl"
new file mode 100644
index 0000000000000000000000000000000000000000..f235141b2296c5853c1eef823f00d2ac5c83324f
--- /dev/null
+++ "b/data/sampled_jsons/Feint_Behaviors_and_Strategies_scheduler_weights_\316\273_short_\316\273_long_initial_values_Section_4.2.2.jsonl"
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Feint Behaviors and Strategies: Formalization, Implementation ...", "date": "", "ddg_snippet": "The short -term period refers to a complete Dual- Behavior Model ( Section 3.2), including a Feint behavior followed by an intended high-reward behavior led by the Feint .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2403.07932v2", "content": "The short -term period refers to a complete Dual- Behavior Model ( Section 3.2), including a Feint behavior followed by an intended high-reward behavior led by the Feint ."}
+{"idx": 1, "title": "Feint Behaviors and Strategies: Formalization, Implementation ...", "date": "", "ddg_snippet": "Sep 25, 2024 · We highlight the effect of short -term Feint behaviors due to their characteristics and also incorporate long -term consideration, aiming to solve the “ short -sight” and “far-sight” issue of previous reward calculations (discussed in Section 4.1).", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=ACIDDnTbSJ", "content": "Sep 25, 2024 · We highlight the effect of short -term Feint behaviors due to their characteristics and also incorporate long -term consideration, aiming to solve the “ short -sight” and “far-sight” issue of previous reward calculations (discussed in Section 4.1)."}
+{"idx": 2, "title": "Feint behaviors and strategies | Proceedings of the 38th ...", "date": "", "ddg_snippet": "However, existing literature does not provide a comprehensive (and/or concrete) formalization for Feint behaviors , and their implications on game strategies . In this work, we introduce the first comprehensive formalization of Feint behaviors at both action-level and strategy-level, and provide concrete implementation and quantitative evaluation ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.5555/3737916.3738032", "content": "However, existing literature does not provide a comprehensive (and/or concrete) formalization for Feint behaviors , and their implications on game strategies . In this work, we introduce the first comprehensive formalization of Feint behaviors at both action-level and strategy-level, and provide concrete implementation and quantitative evaluation ..."}
+{"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 ...", "date": "", "ddg_snippet": "Sep 26, 2024 · This research addresses these issues by introducing a novel formalization of feint behaviors , both at the action and strategy levels. The core contribution is a Palindrome-directed template and Dual- Behavior model for automatically generating and combining feints with subsequent actions.", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/posters/aciddntbsj/", "content": "Sep 26, 2024 · This research addresses these issues by introducing a novel formalization of feint behaviors , both at the action and strategy levels. The core contribution is a Palindrome-directed template and Dual- Behavior model for automatically generating and combining feints with subsequent actions."}
+{"idx": 5, "title": "[2403.07932v2] Feint Behaviors and Strategies: Formalization ...", "date": "", "ddg_snippet": "Mar 4 , 2024 · The key idea of our work is to (1) allow automatic generation of Feint behaviors via Palindrome-directed templates, combine them into meaningful behavior sequences via a Dual- Behavior Model; (2) concertize the implications from our formalization of Feint on game strategies , in terms of temporal, spatial and their collective impacts respectively ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2403.07932v2", "content": "Mar 4 , 2024 · The key idea of our work is to (1) allow automatic generation of Feint behaviors via Palindrome-directed templates, combine them into meaningful behavior sequences via a Dual- Behavior Model; (2) concertize the implications from our formalization of Feint on game strategies , in terms of temporal, spatial and their collective impacts respectively ..."}
+{"idx": 6, "title": "Iuno Best Builds and Teams | Wuthering Waves｜Game8", "date": "", "ddg_snippet": "Next, cast Heavy Attack to end the Lunar Cycle and conjure a Full Moon domain for 30s. During the Full Moon, all Resonators will restore HP and STA, and each shield generated will get 1 Blessing of the Wan Light stack.", "subpage_snippet": "", "source": "game8.co", "link": "https://game8.co/games/Wuthering-Waves/archives/524889", "content": "Next, cast Heavy Attack to end the Lunar Cycle and conjure a Full Moon domain for 30s. During the Full Moon, all Resonators will restore HP and STA, and each shield generated will get 1 Blessing of the Wan Light stack."}
+{"idx": 7, "title": "Items | Schedule 1 Wiki | Fandom", "date": "", "ddg_snippet": "Street Rat IV . Long -lasting soil that can be used to grow 2 plants before expiring. 10 per Stack, 1 Slot in Storage. Lights . Peddler I. Cheap and simple halogen grow light . Must be placed on a suspension rack. 10 per Stack.", "subpage_snippet": "", "source": "schedule-1.fandom.com", "link": "https://schedule-1.fandom.com/wiki/Items", "content": "Street Rat IV . Long -lasting soil that can be used to grow 2 plants before expiring. 10 per Stack, 1 Slot in Storage. Lights . Peddler I. Cheap and simple halogen grow light . Must be placed on a suspension rack. 10 per Stack."}
+{"idx": 8, "title": "osu-pps by grumd - osu! farm pp maps and beatmap recommendations", "date": "", "ddg_snippet": "osu! farm pp maps and beatmap recommendations...", "subpage_snippet": "", "source": "osu-pps.com", "link": "https://osu-pps.com/", "content": "osu! farm pp maps and beatmap recommendations..."}
+{"idx": 9, "title": "Weather and Events - Official Fisch Wiki", "date": "", "ddg_snippet": "During the initial release of the Ancient Isles update, Meteors were bugged and would remain in place after crashing. The ambiance heard when close to a Strange Whirlpool is the 2020-2021 intermission sound used in Flood Escape 2.", "subpage_snippet": "", "source": "fischipedia.org", "link": "https://fischipedia.org/wiki/Weather_and_Events", "content": "During the initial release of the Ancient Isles update, Meteors were bugged and would remain in place after crashing. The ambiance heard when close to a Strange Whirlpool is the 2020-2021 intermission sound used in Flood Escape 2."}
diff --git a/data/sampled_jsons/Figure_1(a)_median_website_size_developed_developing_countries_Digital_Disparities.jsonl b/data/sampled_jsons/Figure_1(a)_median_website_size_developed_developing_countries_Digital_Disparities.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..288f8be1ab775c6d1fc49f821254c00f9bd11556
--- /dev/null
+++ b/data/sampled_jsons/Figure_1(a)_median_website_size_developed_developing_countries_Digital_Disparities.jsonl
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+{"idx": 0, "title": "Journal of Medical Internet Research - Apps and Digital", "date": "", "ddg_snippet": "... apps and digital resources was autism (19/112, 17% resources), with retrieved resources focusing on supporting challenging behaviors, promoting ...", "subpage_snippet": "", "source": "jmir.org", "link": "https://jmir.org/2025/1/e58693", "content": "... apps and digital resources was autism (19/112, 17% resources), with retrieved resources focusing on supporting challenging behaviors, promoting ..."}
+{"idx": 1, "title": "Demographic Differences in Mortality in the District of", "date": "", "ddg_snippet": "Widening socioeconomic and racial disparities in cardiovascular disease mortality in the United States, 1969-2013. Int J MCH AIDS .", "subpage_snippet": "", "source": "jamanetwork.com", "link": "https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2832016", "content": "Widening socioeconomic and racial disparities in cardiovascular disease mortality in the United States, 1969-2013. Int J MCH AIDS ."}
+{"idx": 2, "title": "Narratives of Risk: Parents and Community Perspectives on Food", "date": "", "ddg_snippet": "No special permission is required to reuse all or part of the article published by MDPI, including figures and tables.", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/2673-7051/5/3/47", "content": "No special permission is required to reuse all or part of the article published by MDPI, including figures and tables."}
+{"idx": 3, "title": "Baseline Seroprevalence of Arboviruses in Liberia Using a", "date": "", "ddg_snippet": "No special permission is required to reuse all or part of the article published by MDPI, including figures and tables.", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/2414-6366/10/4/92", "content": "No special permission is required to reuse all or part of the article published by MDPI, including figures and tables."}
+{"idx": 4, "title": "CEPEO | UCL Centre for Education Policy and Equalising", "date": "", "ddg_snippet": "We find that unauthorised absences in year 11 have large negative associations with subsequent resit attainment ( Figure 4), suggesting that the ...", "subpage_snippet": "", "source": "blogs.ucl.ac.uk", "link": "https://blogs.ucl.ac.uk/cepeo/category/cepeo/", "content": "We find that unauthorised absences in year 11 have large negative associations with subsequent resit attainment ( Figure 4), suggesting that the ..."}
+{"idx": 5, "title": "The Code4Lib Journal – Generating Geographic Terms for", "date": "", "ddg_snippet": "In 2016, library staff developed a separate website that would allow users to browse all the streaming videos available from the three different ...", "subpage_snippet": "", "source": "journal.code4lib.org", "link": "https://journal.code4lib.org/articles/14676", "content": "In 2016, library staff developed a separate website that would allow users to browse all the streaming videos available from the three different ..."}
+{"idx": 6, "title": "Data Enrichment Work and AI Labor in Latin America and the", "date": "", "ddg_snippet": "To bridge this, we conducted a survey with 100 crowdworkers across 16 Latin American and Caribbean countries . ... and the impact of these same ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.06981v1", "content": "To bridge this, we conducted a survey with 100 crowdworkers across 16 Latin American and Caribbean countries . ... and the impact of these same ..."}
+{"idx": 7, "title": "Body Mass Index and Risk of Colorectal Cancer Incidence and", "date": "", "ddg_snippet": "Global, regional, and national burden of colorectal cancer and its risk factors, 1990-2019: a systematic analysis for the Global Burden of Disease ...", "subpage_snippet": "", "source": "jamanetwork.com", "link": "https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2822908", "content": "Global, regional, and national burden of colorectal cancer and its risk factors, 1990-2019: a systematic analysis for the Global Burden of Disease ..."}
+{"idx": 8, "title": "Downstream Biomarker Effects of Gantenerumab or Solanezumab in", "date": "", "ddg_snippet": "Assessment of CSF markers was done for both gantenerumab and solanezumab, respectively, in neurofilament light protein (NfL; A and B) and of plasma ...", "subpage_snippet": "", "source": "jamanetwork.com", "link": "https://jamanetwork.com/journals/jamaneurology/fullarticle/2817630", "content": "Assessment of CSF markers was done for both gantenerumab and solanezumab, respectively, in neurofilament light protein (NfL; A and B) and of plasma ..."}
+{"idx": 9, "title": "Effect of High vs Low Doses of Chloroquine Diphosphate as", "date": "", "ddg_snippet": "... and lower limits of the confidence interval for lethality in critically ill patients not receiving CQ in the study by Grasselli et al 24 (ie, 405 of ...", "subpage_snippet": "", "source": "jamanetwork.com", "link": "https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2765499", "content": "... and lower limits of the confidence interval for lethality in critically ill patients not receiving CQ in the study by Grasselli et al 24 (ie, 405 of ..."}
diff --git a/data/sampled_jsons/FlowDec-75m_7.5_kbits_FAD_score_table.jsonl b/data/sampled_jsons/FlowDec-75m_7.5_kbits_FAD_score_table.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a9718b4e13e2332c8117d90d49b51caba0571801
--- /dev/null
+++ b/data/sampled_jsons/FlowDec-75m_7.5_kbits_FAD_score_table.jsonl
@@ -0,0 +1,5 @@
+{"idx": 0, "title": "FlowDec : A flow-based full-band general audio codec with high...", "date": "", "ddg_snippet": "In Table 7, we show metric results of FlowDec - 75 s , compared to two ablation model variants: one trained with the original NCSN++ architecture (Song et al., 2021; Richter et al., 2023) , and one trained with the original choice of the feature representation parameter.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.01485v1", "content": "In Table 7, we show metric results of FlowDec - 75 s , compared to two ablation model variants: one trained with the original NCSN++ architecture (Song et al., 2021; Richter et al., 2023) , and one trained with the original choice of the feature representation parameter."}
+{"idx": 1, "title": "FLOWDEC: A FLOW-BASED FULL-BAND GENERAL", "date": "", "ddg_snippet": "Furthermore, we see that the single-bitrate FlowDec-75s slightly outperforms FlowDec-75m at 7.5 kbit/s as expected, and that. 2xDAC is slightly better than DAC ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/46581252e0bf80bf4efd8fcbc4002f8627f498bb.pdf", "content": "Furthermore, we see that the single-bitrate FlowDec-75s slightly outperforms FlowDec-75m at 7.5 kbit/s as expected, and that. 2xDAC is slightly better than DAC ..."}
+{"idx": 2, "title": "FlowDec | A flow-based full-band general audio codec with high", "date": "", "ddg_snippet": "FlowDec - 75m ( 7 . 5 kbps ) : Your browser does not support the audio element. FlowDec - 75m ( 4. 5 kbps ) : Your browser does not support the audio ...", "subpage_snippet": "", "source": "sp-uhh.github.io", "link": "https://sp-uhh.github.io/FlowDec/", "content": "FlowDec - 75m ( 7 . 5 kbps ) : Your browser does not support the audio element. FlowDec - 75m ( 4. 5 kbps ) : Your browser does not support the audio ..."}
+{"idx": 3, "title": "FlowDec demo", "date": "", "ddg_snippet": "FlowDec demo page. 6.0– 7 . 5 kbit / s . Example. Clean.", "subpage_snippet": "", "source": "flowdec2024.github.io", "link": "https://flowdec2024.github.io/FlowDecSupplementary/", "content": "FlowDec demo page. 6.0– 7 . 5 kbit / s . Example. Clean."}
+{"idx": 4, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/FlowDec_audio_codec_paper_Figure_6_Test_A_subjective_listening_results_year_2023.jsonl b/data/sampled_jsons/FlowDec_audio_codec_paper_Figure_6_Test_A_subjective_listening_results_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2f4969868f9ae5fb23c8a1464be297782d6273f0
--- /dev/null
+++ b/data/sampled_jsons/FlowDec_audio_codec_paper_Figure_6_Test_A_subjective_listening_results_year_2023.jsonl
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+{"idx": 0, "title": "GitHub - facebookresearch/FlowDec: An neural full-band audio codec for ...", "date": "", "ddg_snippet": "FlowDec FlowDec (ICLR 2025) is a full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/facebookresearch/FlowDec", "content": "FlowDec FlowDec (ICLR 2025) is a full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method."}
+{"idx": 1, "title": "FlowDec: A flow-based full-band general audio codec with high ...", "date": "", "ddg_snippet": "We show that FlowDec is a competitive alternative to the recent GAN-dominated stream of neural codecs , achieving FAD scores better than those of the established GAN-based codec DAC and listening test scores that are on par, and producing qualitatively more natural reconstructions for speech and harmonic structures in music.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.01485", "content": "We show that FlowDec is a competitive alternative to the recent GAN-dominated stream of neural codecs , achieving FAD scores better than those of the established GAN-based codec DAC and listening test scores that are on par, and producing qualitatively more natural reconstructions for speech and harmonic structures in music."}
+{"idx": 2, "title": "IELTS Listening band scores and what they mean | IELTS Australia", "date": "", "ddg_snippet": "IELTS Listening band scores explained. Your Listening test consists of 40 questions based on four audio recordings. Find out how your results IELTS Listening band score is calculated.", "subpage_snippet": "", "source": "ielts.com.au", "link": "https://ielts.com.au/australia/results/ielts-band-scores/listening-band-score", "content": "IELTS Listening band scores explained. Your Listening test consists of 40 questions based on four audio recordings. Find out how your results IELTS Listening band score is calculated."}
+{"idx": 3, "title": "Pengujian sinyal audio multichannel dengan metode...", "date": "", "ddg_snippet": "Fadlur, rahman (2014) pengujian sinyal audio multichannel dengan metode subjective test berdasarkan rec.", "subpage_snippet": "", "source": "scholar.unand.ac.id", "link": "http://scholar.unand.ac.id/494668/", "content": "Fadlur, rahman (2014) pengujian sinyal audio multichannel dengan metode subjective test berdasarkan rec."}
+{"idx": 4, "title": "Free Online IELTS Listening Practice Test & Solution Collection", "date": "", "ddg_snippet": "Explore our extensive library of 1000+ Free Online IELTS Listening Practice Tests & Solutions, carefully categorized by type.", "subpage_snippet": "", "source": "engnovate.com", "link": "https://engnovate.com/ielts-listening-tests/", "content": "Explore our extensive library of 1000+ Free Online IELTS Listening Practice Tests & Solutions, carefully categorized by type."}
+{"idx": 5, "title": "BC IELTS listening test 6 - STUDY4", "date": "", "ddg_snippet": "BC IELTS listening test 6 . Thông tin đề thi.Chú ý: để được quy đổi sang scaled score ( ví dụ trên thang điểm 990 cho TOEIC hoặc 9.0 cho IELTS), vui lòng chọn chế độ làm FULL TEST .", "subpage_snippet": "", "source": "study4.com", "link": "https://study4.com/tests/1242/bc-ielts-listening-test-6/", "content": "BC IELTS listening test 6 . Thông tin đề thi.Chú ý: để được quy đổi sang scaled score ( ví dụ trên thang điểm 990 cho TOEIC hoặc 9.0 cho IELTS), vui lòng chọn chế độ làm FULL TEST ."}
+{"idx": 6, "title": "7 Common IELTS Listening Mistakes to Avoid on Test Day", "date": "", "ddg_snippet": "Avoid these 7 IELTS Listening mistakes to boost your score.", "subpage_snippet": "", "source": "ielts.idp.com", "link": "https://ielts.idp.com/mauritius/about/news-and-articles/article-ielts-listening-mistakes-to-avoid", "content": "Avoid these 7 IELTS Listening mistakes to boost your score."}
+{"idx": 7, "title": "SNAC: Multi-Scale Neural Audio Codec | AI Research Paper Details", "date": "", "ddg_snippet": "The paper presents SNAC, a multi-scale neural audio codec that can efficiently encode and decode audio signals at variable bitrates.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/snac-multi-scale-neural-audio-codec", "content": "The paper presents SNAC, a multi-scale neural audio codec that can efficiently encode and decode audio signals at variable bitrates."}
+{"idx": 8, "title": "Figure 6 from FlowDec: A flow-based full-band general audio codec with ...", "date": "", "ddg_snippet": "Figure 6 : Subjective listening results from Test A (left) and Test B (right). Numbers in (parentheses) denote the used bitrate in kbit/s. FlowDec is rated on par with DAC (Kumar et al., 2024), with no significant differences between their score distributions at any given bitrate and feature rate.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/FlowDec:-A-flow-based-full-band-general-audio-codec-Welker-Le/ec40a4902b277f0f9e3704c1b23634cad4fe0dcf/figure/6", "content": "Figure 6 : Subjective listening results from Test A (left) and Test B (right). Numbers in (parentheses) denote the used bitrate in kbit/s. FlowDec is rated on par with DAC (Kumar et al., 2024), with no significant differences between their score distributions at any given bitrate and feature rate."}
+{"idx": 9, "title": "PDF Audio Codec With High Perceptual Quality", "date": "", "ddg_snippet": "We show that FlowDec is a competitive alternative to the recent GAN-dominated stream of neural codecs , achieving FAD scores better than those of the established GAN-based codec DAC and listening test scores that are on par, and producing qualitatively more natural reconstructions for speech and harmonic structures in music.", "subpage_snippet": "", "source": "proceedings.iclr.cc", "link": "https://proceedings.iclr.cc/paper_files/paper/2025/file/3d937d5e40a883d5da9fd4aeeeb372b8-Paper-Conference.pdf", "content": "We show that FlowDec is a competitive alternative to the recent GAN-dominated stream of neural codecs , achieving FAD scores better than those of the established GAN-based codec DAC and listening test scores that are on par, and producing qualitatively more natural reconstructions for speech and harmonic structures in music."}
diff --git a/data/sampled_jsons/FlowDec_paper_streaming_noncausal_architecture_conclusion.jsonl b/data/sampled_jsons/FlowDec_paper_streaming_noncausal_architecture_conclusion.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c3889d59d07b779bac647459e47cb82f27092069
--- /dev/null
+++ b/data/sampled_jsons/FlowDec_paper_streaming_noncausal_architecture_conclusion.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Synchronicity - Wikipedia", "date": "", "ddg_snippet": "Jung developed the theory as a hypothetical noncausal principle serving as the intersubjective or philosophically objective connection between these seemingly meaningful coincidences.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Synchronicity", "content": "Jung developed the theory as a hypothetical noncausal principle serving as the intersubjective or philosophically objective connection between these seemingly meaningful coincidences."}
+{"idx": 1, "title": "FlowDec : A flow -based full-band general audio codec with high...", "date": "", "ddg_snippet": "While FlowDec , like DAC, is currently not streaming -capable due to the noncausal architecture of the used DNNs, our postfilter approach can be modified for a causal DNN as in (Richter et al., 2024a) , which would pave the way for real-time communication and audio streaming applications.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.01485v1", "content": "While FlowDec , like DAC, is currently not streaming -capable due to the noncausal architecture of the used DNNs, our postfilter approach can be modified for a causal DNN as in (Richter et al., 2024a) , which would pave the way for real-time communication and audio streaming applications."}
+{"idx": 2, "title": "GitHub - facebookresearch/ FlowDec : An neural full-band audio codec...", "date": "", "ddg_snippet": "FlowDec (ICLR 2025) is a full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/facebookresearch/FlowDec", "content": "FlowDec (ICLR 2025) is a full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method."}
+{"idx": 3, "title": "How to Use Hand Thrown Paper Confetti Streamers - YouTube", "date": "", "ddg_snippet": "Add excitement to your special day with these hand thrown paper streamers !", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=jtMbCL4dCH4", "content": "Add excitement to your special day with these hand thrown paper streamers !"}
+{"idx": 4, "title": "Stream The Paper for Free in HD on MoviesJoy", "date": "", "ddg_snippet": "Watch The Paper full HD movies for free on MoviesJoy. No ads or sign-ups – just click and start watching in HD quality right away!", "subpage_snippet": "", "source": "moviesjoy-is.tube", "link": "https://moviesjoy-is.tube/tv/watch-the-paper-movies-free-hd-131779", "content": "Watch The Paper full HD movies for free on MoviesJoy. No ads or sign-ups – just click and start watching in HD quality right away!"}
+{"idx": 5, "title": "FIGURE 1. To obtain a causal convolution kernel, zero padding (light...", "date": "", "ddg_snippet": "... While FlowDec , like DAC, is currently not streaming -capable due to the noncausal architecture of the used DNNs, our postfilter approach can be modified for a causal DNN as in (Richter et al., 2024a), which would pave the way for real-time communication and audio streaming applications.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/To-obtain-a-causal-convolution-kernel-zero-padding-light-grey-elements-is-modified-and_fig1_379089990", "content": "... While FlowDec , like DAC, is currently not streaming -capable due to the noncausal architecture of the used DNNs, our postfilter approach can be modified for a causal DNN as in (Richter et al., 2024a), which would pave the way for real-time communication and audio streaming applications."}
+{"idx": 6, "title": "Top 10 Viral Video Tiktok Abg SMA Indo Terbaru... - Tangan Jahil 2025", "date": "", "ddg_snippet": "tanganjahil.store is your go-to site for fast streaming , offering the latest viral Yandex videos. Stay updated with the most complete and engaging video content, refreshed daily.", "subpage_snippet": "", "source": "tanganjahil.store", "link": "https://tanganjahil.store/top-10-viral-video-tiktok-abg-sma-indo-terbaru-2025-ukhti-hijab-seragam-of-all-time-trending-global-official/", "content": "tanganjahil.store is your go-to site for fast streaming , offering the latest viral Yandex videos. Stay updated with the most complete and engaging video content, refreshed daily."}
+{"idx": 7, "title": "Бумажные Гирлянды Своими Руками", "date": "", "ddg_snippet": "instructions for how to make tissue paper tassels with yarn and ribbon on the clothes line.", "subpage_snippet": "", "source": "ru.pinterest.com", "link": "https://ru.pinterest.com/ideas/бумажные-гирлянды-своими-руками/893775616005/", "content": "instructions for how to make tissue paper tassels with yarn and ribbon on the clothes line."}
+{"idx": 8, "title": "Historical Consciousness: What Germany Could Learn from Russia", "date": "", "ddg_snippet": "Conclusion . “Russia will always remain an enemy to us” (Wadephul), “Putin is a war criminal. He is perhaps the most serious war criminal of our time, whom we are currently seeing on a large scale” (Merz).", "subpage_snippet": "", "source": "forumgeopolitica.com", "link": "https://forumgeopolitica.com/article/historical-consciousness-what-germany-could-learn-from-russia", "content": "Conclusion . “Russia will always remain an enemy to us” (Wadephul), “Putin is a war criminal. He is perhaps the most serious war criminal of our time, whom we are currently seeing on a large scale” (Merz)."}
+{"idx": 9, "title": "The Taliban reject Trump’s bid to retake Bagram... | The Seattle Times", "date": "", "ddg_snippet": "TV/ Streaming . Theater. Visual Arts.Today’s Paper . Pacific NW Magazine. Homes & Real Estate.", "subpage_snippet": "", "source": "www.seattletimes.com", "link": "https://www.seattletimes.com/nation-world/the-taliban-reject-trumps-bid-to-retake-bagram-air-base-in-afghanistan/", "content": "TV/ Streaming . Theater. Visual Arts.Today’s Paper . Pacific NW Magazine. Homes & Real Estate."}
diff --git a/data/sampled_jsons/FourCastNet_arxiv2202.11214_evaluation_metrics_RMSE_MAE_negative_log_likelihood_year_2022.jsonl b/data/sampled_jsons/FourCastNet_arxiv2202.11214_evaluation_metrics_RMSE_MAE_negative_log_likelihood_year_2022.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8724cc18596ffde748e470e68659d6ccd858b68b
--- /dev/null
+++ b/data/sampled_jsons/FourCastNet_arxiv2202.11214_evaluation_metrics_RMSE_MAE_negative_log_likelihood_year_2022.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[ 2202 . 11214 ] FourCastNet : A Global Data-driven High-resolution...", "date": "", "ddg_snippet": "arXiv : 2202 . 11214 (physics). FourCastNet accurately forecasts high-resolution, fast-timescale variables such as the surface wind speed, precipitation, and atmospheric water vapor.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2202.11214", "content": "arXiv : 2202 . 11214 (physics). FourCastNet accurately forecasts high-resolution, fast-timescale variables such as the surface wind speed, precipitation, and atmospheric water vapor."}
+{"idx": 1, "title": "Paper page - FourCastNet : A Global Data-driven High-resolution...", "date": "", "ddg_snippet": "arxiv : 2202 . 11214 . FourCastNet : A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators. Published on Feb 22, 2022.· Sign up or log in to comment.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2202.11214", "content": "arxiv : 2202 . 11214 . FourCastNet : A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators. Published on Feb 22, 2022.· Sign up or log in to comment."}
+{"idx": 2, "title": "GitHub - NVlabs/ FourCastNet : Initial public release of code, data, and...", "date": "", "ddg_snippet": "arXiv preprint arXiv : 2202 . 11214 }, year={2022} }. About. Initial public release of code, data, and model weights for FourCastNet .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/NVlabs/FourCastNet", "content": "arXiv preprint arXiv : 2202 . 11214 }, year={2022} }. About. Initial public release of code, data, and model weights for FourCastNet ."}
+{"idx": 3, "title": "3 Regression Metrics You Must Know: MAE , MSE, and RMSE", "date": "", "ddg_snippet": "Root Mean Squared Error ( RMSE ). MAE vs. RMSE . Practice using Python & Scikit-Learn. Load Dataset. Build Regression Model. Calculate Metrics - MAE , MSE, and RMSE .Mean Absolute Error ( MAE ). As we saw above, the prediction error can be positive or negative .", "subpage_snippet": "", "source": "proclusacademy.com", "link": "https://proclusacademy.com/blog/explainer/regression-metrics-you-must-know/", "content": "Root Mean Squared Error ( RMSE ). MAE vs. RMSE . Practice using Python & Scikit-Learn. Load Dataset. Build Regression Model. Calculate Metrics - MAE , MSE, and RMSE .Mean Absolute Error ( MAE ). As we saw above, the prediction error can be positive or negative ."}
+{"idx": 4, "title": "FourCastNet - NVIDIA Docs", "date": "", "ddg_snippet": "100 Comparison of the predictive root mean square error (RSME) of each variable between the original FourCastNet model (Original) and the version trained in Modulus. fourcastnet _tcwv.png.", "subpage_snippet": "", "source": "docs.nvidia.com", "link": "https://docs.nvidia.com/deeplearning/modulus/modulus-v2209/user_guide/neural_operators/fourcastnet.html", "content": "100 Comparison of the predictive root mean square error (RSME) of each variable between the original FourCastNet model (Original) and the version trained in Modulus. fourcastnet _tcwv.png."}
+{"idx": 5, "title": "FourCastNet : A Global Data-driven High-resolution... | BibSonomy", "date": "", "ddg_snippet": "Log in with your OpenID-Provider.- 2022 - FourCastNet A Global Data-driven High-resolution .pdf:application/pdf; arXiv .org Snapshot:/Users/pascal/Zotero/storage/KTIF8CTD/2202.html:text/html. DOI. 10.48550/ arXiv . 2202 . 11214 .", "subpage_snippet": "", "source": "www.bibsonomy.org", "link": "https://www.bibsonomy.org/bibtex/27c725e2908978d066179e178c3bc9a61/annakrause", "content": "Log in with your OpenID-Provider.- 2022 - FourCastNet A Global Data-driven High-resolution .pdf:application/pdf; arXiv .org Snapshot:/Users/pascal/Zotero/storage/KTIF8CTD/2202.html:text/html. DOI. 10.48550/ arXiv . 2202 . 11214 ."}
+{"idx": 6, "title": "Regression Metrics - GeeksforGeeks", "date": "", "ddg_snippet": "Root Mean Squared Error ( RMSE ). RMSE stands for Root Mean Squared Error . It is a usually used metric in regression analysis and machine learning to measure the accuracy or goodness of fit of a predictive model, especially when the predictions are continuous numerical values.", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/machine-learning/regression-metrics/", "content": "Root Mean Squared Error ( RMSE ). RMSE stands for Root Mean Squared Error . It is a usually used metric in regression analysis and machine learning to measure the accuracy or goodness of fit of a predictive model, especially when the predictions are continuous numerical values."}
+{"idx": 7, "title": "Machine learning models for daily rainfall forecasting in", "date": "", "ddg_snippet": "Models like FourCastNet , GraphCast and Pangu-Weather (Pathak et al., 2022 ; Lam et al., 2023 ; Bi et al., 2023 ) showcase the potential of AI in ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2408.16349v1", "content": "Models like FourCastNet , GraphCast and Pangu-Weather (Pathak et al., 2022 ; Lam et al., 2023 ; Bi et al., 2023 ) showcase the potential of AI in ..."}
+{"idx": 8, "title": "FNP: Fourier Neural Processes for Arbitrary-Resolution Data", "date": "", "ddg_snippet": "... employed in operational systems include Kalman filters based on minimum variance estimation and variational methods based on maximum likelihood ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.01645v1", "content": "... employed in operational systems include Kalman filters based on minimum variance estimation and variational methods based on maximum likelihood ..."}
+{"idx": 9, "title": "Generative Data Assimilation of Sparse Weather Station", "date": "", "ddg_snippet": "4.4 Performance Evaluation on Left-out ... By incorporating observations from 40 weather stations, 10% lower RMSEs on left-out stations are attained.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.16947v3", "content": "4.4 Performance Evaluation on Left-out ... By incorporating observations from 40 weather stations, 10% lower RMSEs on left-out stations are attained."}
diff --git a/data/sampled_jsons/FuGps5Zyia_HDR-IPPO_Equation_2_CHDR-IPPO_objective.jsonl b/data/sampled_jsons/FuGps5Zyia_HDR-IPPO_Equation_2_CHDR-IPPO_objective.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..358693771ebe199bfff3d9b24f9ec83da939856a
--- /dev/null
+++ b/data/sampled_jsons/FuGps5Zyia_HDR-IPPO_Equation_2_CHDR-IPPO_objective.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Hajime no Ippo - Reddit", "date": "", "ddg_snippet": "Introduction This is the Hajime No Ippo Subreddit Wiki! Please refer to this page if you have any questions regarding our community! Online Resources Read Ippo Online hni-scantrad Manganelo KissManga Watch Ippo Online YouTube, Season 1, English Dub YouTube, Season 1 through 2 including OVAs, English Sub Crunchyroll, Season 3 Frequently Asked Questions Why are chapter releases taking so long ...", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/hajimenoippo/wiki/index/", "content": "Introduction This is the Hajime No Ippo Subreddit Wiki! Please refer to this page if you have any questions regarding our community! Online Resources Read Ippo Online hni-scantrad Manganelo KissManga Watch Ippo Online YouTube, Season 1, English Dub YouTube, Season 1 through 2 including OVAs, English Sub Crunchyroll, Season 3 Frequently Asked Questions Why are chapter releases taking so long ..."}
+{"idx": 1, "title": "Hajime no Ippo 2: Victorious Road – Guide and Walkthrough Hajime No Ippo - YouTube Hajime no Ippo 2 - Victorious Road (Japan) - ConsoleRoms Hajime no Ippo 2 Victorious Road Episode 6 - YouTube Hajime no Ippo 2: Victorious Road for PlayStation 2 - GameFAQs Victorious Boxers Ippo's Road To Glory (USA) - Archive.org", "date": "", "ddg_snippet": "Apr 10, 2004 · For Hajime no Ippo 2 : Victorious Road on the PlayStation 2 , Guide and Walkthrough by Adenosine. Edited compilations of story arcs without ad breaks and repetition, as well as full seasons - all in English. Download Hajime no Ippo 2 - Victorious Road (Japan) game for Sony PlayStation 2 and enjoy playing the full version of the ROM for free. Aug 30, 2025 · PS2 Hajime no Ippo 2 : Victorious Road - Boxers road mode0:00 start2:25 Rematch with Eiji Date. Special blow is temporarily sealed.6:56 rank 1st9:26 Volg Zang... Jan 29, 2004 · For Hajime no Ippo 2 : Victorious Road on the PlayStation 2 , GameFAQs has 1 guide/walkthrough, 2 cheat codes and secrets, 2 reviews, 5 save games, and 2 user screenshots. Oct 26, 2022 · Addeddate 2022-10-26 17:02:08 Collection_added consolemanuals Identifier ps2_Victorious_Boxers-_Ippos_Road_To_Glory_USA Identifier-ark ark:/13960/s2gv7sknmtk Ocr tesseract 5. 2 .0-1-gc42a Ocr_detected_lang en Ocr_detected_lang_conf 1.0000 Ocr_detected_script Cyrillic Ocr_detected_script_conf 0.6669 Ocr_module_version 0.0.18 Ocr_parameters -l eng Pdf_module_version 0.0.20", "subpage_snippet": "", "source": "gamefaqs.gamespot.com", "link": "https://gamefaqs.gamespot.com/ps2/917868-hajime-no-ippo-2-victorious-road/faqs/28634", "content": "Apr 10, 2004 · For Hajime no Ippo 2 : Victorious Road on the PlayStation 2 , Guide and Walkthrough by Adenosine. Edited compilations of story arcs without ad breaks and repetition, as well as full seasons - all in English. Download Hajime no Ippo 2 - Victorious Road (Japan) game for Sony PlayStation 2 and enjoy playing the full version of the ROM for free. Aug 30, 2025 · PS2 Hajime no Ippo 2 : Victorious Road - Boxers road mode0:00 start2:25 Rematch with Eiji Date. Special blow is temporarily sealed.6:56 rank 1st9:26 Volg Zang... Jan 29, 2004 · For Hajime no Ippo 2 : Victorious Road on the PlayStation 2 , GameFAQs has 1 guide/walkthrough, 2 cheat codes and secrets, 2 reviews, 5 save games, and 2 user screenshots. Oct 26, 2022 · Addeddate 2022-10-26 17:02:08 Collection_added consolemanuals Identifier ps2_Victorious_Boxers-_Ippos_Road_To_Glory_USA Identifier-ark ark:/13960/s2gv7sknmtk Ocr tesseract 5. 2 .0-1-gc42a Ocr_detected_lang en Ocr_detected_lang_conf 1.0000 Ocr_detected_script Cyrillic Ocr_detected_script_conf 0.6669 Ocr_module_version 0.0.18 Ocr_parameters -l eng Pdf_module_version 0.0.20"}
+{"idx": 2, "title": "Hajime no Ippo 2 - Victorious Road (Japan) - ConsoleRoms Hajime no Ippo 2 Victorious Road Episode 6 - YouTube Hajime no Ippo 2: Victorious Road for PlayStation 2 - GameFAQs Victorious Boxers Ippo's Road To Glory (USA) - Archive.org", "date": "", "ddg_snippet": "Download Hajime no Ippo 2 - Victorious Road (Japan) game for Sony PlayStation 2 and enjoy playing the full version of the ROM for free. Aug 30, 2025 · PS2 Hajime no Ippo 2 : Victorious Road - Boxers road mode0:00 start2:25 Rematch with Eiji Date. Special blow is temporarily sealed.6:56 rank 1st9:26 Volg Zang... Jan 29, 2004 · For Hajime no Ippo 2 : Victorious Road on the PlayStation 2 , GameFAQs has 1 guide/walkthrough, 2 cheat codes and secrets, 2 reviews, 5 save games, and 2 user screenshots. Oct 26, 2022 · Addeddate 2022-10-26 17:02:08 Collection_added consolemanuals Identifier ps2_Victorious_Boxers-_Ippos_Road_To_Glory_USA Identifier-ark ark:/13960/s2gv7sknmtk Ocr tesseract 5. 2 .0-1-gc42a Ocr_detected_lang en Ocr_detected_lang_conf 1.0000 Ocr_detected_script Cyrillic Ocr_detected_script_conf 0.6669 Ocr_module_version 0.0.18 Ocr_parameters -l eng Pdf_module_version 0.0.20", "subpage_snippet": "", "source": "www.consoleroms.com", "link": "https://www.consoleroms.com/roms/ps2/hajime-no-ippo-2-victorious-road-japan", "content": "Download Hajime no Ippo 2 - Victorious Road (Japan) game for Sony PlayStation 2 and enjoy playing the full version of the ROM for free. Aug 30, 2025 · PS2 Hajime no Ippo 2 : Victorious Road - Boxers road mode0:00 start2:25 Rematch with Eiji Date. Special blow is temporarily sealed.6:56 rank 1st9:26 Volg Zang... Jan 29, 2004 · For Hajime no Ippo 2 : Victorious Road on the PlayStation 2 , GameFAQs has 1 guide/walkthrough, 2 cheat codes and secrets, 2 reviews, 5 save games, and 2 user screenshots. Oct 26, 2022 · Addeddate 2022-10-26 17:02:08 Collection_added consolemanuals Identifier ps2_Victorious_Boxers-_Ippos_Road_To_Glory_USA Identifier-ark ark:/13960/s2gv7sknmtk Ocr tesseract 5. 2 .0-1-gc42a Ocr_detected_lang en Ocr_detected_lang_conf 1.0000 Ocr_detected_script Cyrillic Ocr_detected_script_conf 0.6669 Ocr_module_version 0.0.18 Ocr_parameters -l eng Pdf_module_version 0.0.20"}
+{"idx": 3, "title": "Hajime no Ippo 2 Victorious Road Episode 6 - YouTube Hajime no Ippo 2: Victorious Road for PlayStation 2 - GameFAQs Victorious Boxers Ippo's Road To Glory (USA) - Archive.org", "date": "", "ddg_snippet": "Aug 30, 2025 · PS2 Hajime no Ippo 2 : Victorious Road - Boxers road mode0:00 start2:25 Rematch with Eiji Date. Special blow is temporarily sealed.6:56 rank 1st9:26 Volg Zang... Jan 29, 2004 · For Hajime no Ippo 2 : Victorious Road on the PlayStation 2 , GameFAQs has 1 guide/walkthrough, 2 cheat codes and secrets, 2 reviews, 5 save games, and 2 user screenshots. Oct 26, 2022 · Addeddate 2022-10-26 17:02:08 Collection_added consolemanuals Identifier ps2_Victorious_Boxers-_Ippos_Road_To_Glory_USA Identifier-ark ark:/13960/s2gv7sknmtk Ocr tesseract 5. 2 .0-1-gc42a Ocr_detected_lang en Ocr_detected_lang_conf 1.0000 Ocr_detected_script Cyrillic Ocr_detected_script_conf 0.6669 Ocr_module_version 0.0.18 Ocr_parameters -l eng Pdf_module_version 0.0.20", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=tbHmpcAF_Do", "content": "Aug 30, 2025 · PS2 Hajime no Ippo 2 : Victorious Road - Boxers road mode0:00 start2:25 Rematch with Eiji Date. Special blow is temporarily sealed.6:56 rank 1st9:26 Volg Zang... Jan 29, 2004 · For Hajime no Ippo 2 : Victorious Road on the PlayStation 2 , GameFAQs has 1 guide/walkthrough, 2 cheat codes and secrets, 2 reviews, 5 save games, and 2 user screenshots. Oct 26, 2022 · Addeddate 2022-10-26 17:02:08 Collection_added consolemanuals Identifier ps2_Victorious_Boxers-_Ippos_Road_To_Glory_USA Identifier-ark ark:/13960/s2gv7sknmtk Ocr tesseract 5. 2 .0-1-gc42a Ocr_detected_lang en Ocr_detected_lang_conf 1.0000 Ocr_detected_script Cyrillic Ocr_detected_script_conf 0.6669 Ocr_module_version 0.0.18 Ocr_parameters -l eng Pdf_module_version 0.0.20"}
+{"idx": 4, "title": "Hajime no Ippo 2: Victorious Road for PlayStation 2 - GameFAQs", "date": "", "ddg_snippet": "Jan 29, 2004 · For Hajime no Ippo 2 : Victorious Road on the PlayStation 2 , GameFAQs has 1 guide/walkthrough, 2 cheat codes and secrets, 2 reviews, 5 save games, and 2 user screenshots.", "subpage_snippet": "", "source": "gamefaqs.gamespot.com", "link": "https://gamefaqs.gamespot.com/ps2/917868-hajime-no-ippo-2-victorious-road", "content": "Jan 29, 2004 · For Hajime no Ippo 2 : Victorious Road on the PlayStation 2 , GameFAQs has 1 guide/walkthrough, 2 cheat codes and secrets, 2 reviews, 5 save games, and 2 user screenshots."}
+{"idx": 5, "title": "Victorious Boxers Ippo's Road To Glory (USA) - Archive.org", "date": "", "ddg_snippet": "Oct 26, 2022 · Addeddate 2022-10-26 17:02:08 Collection_added consolemanuals Identifier ps2_Victorious_Boxers-_Ippos_Road_To_Glory_USA Identifier-ark ark:/13960/s2gv7sknmtk Ocr tesseract 5. 2 .0-1-gc42a Ocr_detected_lang en Ocr_detected_lang_conf 1.0000 Ocr_detected_script Cyrillic Ocr_detected_script_conf 0.6669 Ocr_module_version 0.0.18 Ocr_parameters -l eng Pdf_module_version 0.0.20", "subpage_snippet": "", "source": "archive.org", "link": "https://archive.org/details/ps2_Victorious_Boxers-_Ippos_Road_To_Glory_USA", "content": "Oct 26, 2022 · Addeddate 2022-10-26 17:02:08 Collection_added consolemanuals Identifier ps2_Victorious_Boxers-_Ippos_Road_To_Glory_USA Identifier-ark ark:/13960/s2gv7sknmtk Ocr tesseract 5. 2 .0-1-gc42a Ocr_detected_lang en Ocr_detected_lang_conf 1.0000 Ocr_detected_script Cyrillic Ocr_detected_script_conf 0.6669 Ocr_module_version 0.0.18 Ocr_parameters -l eng Pdf_module_version 0.0.20"}
+{"idx": 6, "title": "Hajime no Ippo - Takamura's First Days [ENG SUB] - YouTube", "date": "", "ddg_snippet": "Clips Compiled from the anime 'Hajime no Ippo '. Season 1 Episode 76.I do not own any of the clips in this video all rights go to MADHOUSE Inc. and George Mor...", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=oFicxh31GOk", "content": "Clips Compiled from the anime 'Hajime no Ippo '. Season 1 Episode 76.I do not own any of the clips in this video all rights go to MADHOUSE Inc. and George Mor..."}
+{"idx": 7, "title": "Amazon.com: ASUS ROG Swift 360Hz PG259QN 24.5” HDR Gaming...", "date": "", "ddg_snippet": "An intelligent cooling system featuring a custom heatsink to provide more surface area for heat exchange, ensuring efficient cooling during marathon gaming sessions. HDR 10 compatible to enhance bright and dark areas, delivering a lifelike gaming experience.", "subpage_snippet": "", "source": "www.amazon.com", "link": "https://www.amazon.com/ASUS-PG259QN-DisplayPort-Ergonomic-Mountable/dp/B08GL5TJQT", "content": "An intelligent cooling system featuring a custom heatsink to provide more surface area for heat exchange, ensuring efficient cooling during marathon gaming sessions. HDR 10 compatible to enhance bright and dark areas, delivering a lifelike gaming experience."}
+{"idx": 8, "title": "Hajime No Ippo - YouTube", "date": "", "ddg_snippet": "Edited compilations of story arcs without ad breaks and repetition, as well as full seasons - all in English.", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/@hajimenoippo8673", "content": "Edited compilations of story arcs without ad breaks and repetition, as well as full seasons - all in English."}
+{"idx": 9, "title": "Марко Ройс... Мой любимый Марко Ройс снова не чемпион...", "date": "", "ddg_snippet": "Ippo Makunouchi / Иппо Макуноучи ухмыляется под ilysam - sweet rally (Slowed).", "subpage_snippet": "", "source": "vk.com", "link": "https://vk.com/video676717846_456240882", "content": "Ippo Makunouchi / Иппо Макуноучи ухмыляется под ilysam - sweet rally (Slowed)."}
diff --git a/data/sampled_jsons/FuGps5Zyia_sitearxiv.org_OR_siteopenreview.net.jsonl b/data/sampled_jsons/FuGps5Zyia_sitearxiv.org_OR_siteopenreview.net.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..1abdc1136718e2d74eaf410b9ba9846145245411
--- /dev/null
+++ b/data/sampled_jsons/FuGps5Zyia_sitearxiv.org_OR_siteopenreview.net.jsonl
@@ -0,0 +1,8 @@
+{"idx": 0, "title": "OpenReview Documentation", "date": "", "ddg_snippet": "You can use the Search bar in the top right corner to search for keywords in the entire OpenReview documentation.", "subpage_snippet": "", "source": "docs.openreview.net", "link": "https://docs.openreview.net/", "content": "You can use the Search bar in the top right corner to search for keywords in the entire OpenReview documentation."}
+{"idx": 1, "title": "ICLR 2025 - OpenReview", "date": "", "ddg_snippet": "Welcome to the OpenReview homepage for ICLR 2025", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/group?id=ICLR.cc/2025", "content": "Welcome to the OpenReview homepage for ICLR 2025"}
+{"idx": 2, "title": "Search - OpenReview", "date": "", "ddg_snippet": "Promoting openness in scientific communication and the peer-review process", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/search", "content": "Promoting openness in scientific communication and the peer-review process"}
+{"idx": 3, "title": "Finding and adding a Semantic Scholar URL to your profile", "date": "", "ddg_snippet": "Once you have identified your author page with the associated papers, t he URL in the browser address bar is the Semantic Scholar URL that you can use in OpenReview profile edit page. If you would like to edit your Semantic Scholar author page or add additional metadata (e.g. affiliation data) you may use the \"Claim Author Page\" button located under your name at the top left of your Semantic ...", "subpage_snippet": "", "source": "docs.openreview.net", "link": "https://docs.openreview.net/getting-started/creating-an-openreview-profile/finding-and-adding-a-semantic-scholar-url-to-your-profile", "content": "Once you have identified your author page with the associated papers, t he URL in the browser address bar is the Semantic Scholar URL that you can use in OpenReview profile edit page. If you would like to edit your Semantic Scholar author page or add additional metadata (e.g. affiliation data) you may use the \"Claim Author Page\" button located under your name at the top left of your Semantic ..."}
+{"idx": 4, "title": "Finding and adding your ACL Anthology URL to your profile", "date": "", "ddg_snippet": "Mar 21, 2024 · An option is now availible to add your ACL Anthology URL to your OpenReview profile. This is an optional field, and if you don't have a personal ACL anthology page it can be ignored. To locate your ACL Anthology URL, go to https://aclanthology. org , type your publishing name into the Search bar at the top of the page, and then click on the magnifying glass icon to initiate the search.", "subpage_snippet": "", "source": "docs.openreview.net", "link": "https://docs.openreview.net/getting-started/creating-an-openreview-profile/finding-and-adding-your-acl-anthology-url-to-your-profile", "content": "Mar 21, 2024 · An option is now availible to add your ACL Anthology URL to your OpenReview profile. This is an optional field, and if you don't have a personal ACL anthology page it can be ignored. To locate your ACL Anthology URL, go to https://aclanthology. org , type your publishing name into the Search bar at the top of the page, and then click on the magnifying glass icon to initiate the search."}
+{"idx": 5, "title": "Shedding Light on Time Series Classification using ...", "date": "", "ddg_snippet": "Jan 22, 2025 · In time-series classification, interpretable models can bring additional insights but be outperformed by deep models since human-understandable features have limited expressivity and flexibility....", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=n34taxF0TC", "content": "Jan 22, 2025 · In time-series classification, interpretable models can bring additional insights but be outperformed by deep models since human-understandable features have limited expressivity and flexibility...."}
+{"idx": 6, "title": "TimeBase: The Power of Minimalism in Efficient Long-term Time...", "date": "", "ddg_snippet": "May 1, 2025 · Long-term time series forecasting (LTSF) has traditionally relied on large parameters to capture extended temporal dependencies, resulting in substantial computational costs and inefficiencies in...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=GhTdNOMfOD", "content": "May 1, 2025 · Long-term time series forecasting (LTSF) has traditionally relied on large parameters to capture extended temporal dependencies, resulting in substantial computational costs and inefficiencies in..."}
+{"idx": 7, "title": "Ad-Hoc Human-AI Coordination Challenge", "date": "", "ddg_snippet": "by T Dizdarević — This new challenge provides a fair, affordable, and repeatable way to see how well AIs can coordinate with human-like partners.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=FuGps5Zyia", "content": "by T Dizdarević — This new challenge provides a fair, affordable, and repeatable way to see how well AIs can coordinate with human-like partners."}
diff --git a/data/sampled_jsons/GTA_Greedy_Task_Allocation_'Make_sure_all_workers_are_always_busy'_inefficiency.jsonl b/data/sampled_jsons/GTA_Greedy_Task_Allocation_'Make_sure_all_workers_are_always_busy'_inefficiency.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e5ba658a187af42bf4911f4f99f2a548283d2759
--- /dev/null
+++ b/data/sampled_jsons/GTA_Greedy_Task_Allocation_'Make_sure_all_workers_are_always_busy'_inefficiency.jsonl
@@ -0,0 +1,10 @@
+{"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 — Provided we are willing to waste resources, there is a simple solution to this problem, a Greedy Task Allocation (GTA) strategy , which follows ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.00775v1", "content": "2 Feb 2025 — Provided we are willing to waste resources, there is a simple solution to this problem, a Greedy Task Allocation (GTA) strategy , which follows ..."}
+{"idx": 2, "title": "Ultimate Gambling Guide for GTA Online - odds ...", "date": "", "ddg_snippet": "Gambling games should only be played for fun, not for big money. You should expect to lose in the long run. The house always wins.", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/gtaonline/comments/ciuz70/ultimate_gambling_guide_for_gta_online_odds/", "content": "Gambling games should only be played for fun, not for big money. You should expect to lose in the long run. The house always wins."}
+{"idx": 3, "title": "cherryATA", "date": "", "ddg_snippet": "to this problem, a Greedy Task Allocation (GTA) strategy, which follows this principle: Make sure all workers are always busy working on some task, and stop ...", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/pdf/2502.00775v1", "content": "to this problem, a Greedy Task Allocation (GTA) strategy, which follows this principle: Make sure all workers are always busy working on some task, and stop ..."}
+{"idx": 4, "title": "REINFORCE Adversarial Attacks on Large Language Models", "date": "", "ddg_snippet": "To circumvent the alignment of large language models (LLMs), current optimization-based adversarial attacks usually craft adversarial prompts by maximizing ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/45336", "content": "To circumvent the alignment of large language models (LLMs), current optimization-based adversarial attacks usually craft adversarial prompts by maximizing ..."}
+{"idx": 5, "title": "I think the economists at Ubisoft have learnt", "date": "", "ddg_snippet": "I think the economists at Ubisoft have learnt that they have to develop games primarily for players and not for shareholders.", "subpage_snippet": "", "source": "www.facebook.com", "link": "https://www.facebook.com/groups/859255505716164/posts/1193994315575613/", "content": "I think the economists at Ubisoft have learnt that they have to develop games primarily for players and not for shareholders."}
+{"idx": 6, "title": "An Overwhelmingly Negative and Demoralizing Force", "date": "", "ddg_snippet": "The solution to bad code is more code. AI will never produce a deletion. Publish or perish has come for us and it's sad. It makes me feel old.", "subpage_snippet": "", "source": "news.ycombinator.com", "link": "https://news.ycombinator.com/item?id=43619759", "content": "The solution to bad code is more code. AI will never produce a deletion. Publish or perish has come for us and it's sad. It makes me feel old."}
+{"idx": 7, "title": "Why is Rockstar Games becoming more and more money ...", "date": "", "ddg_snippet": "Rockstar Games becoming more and more money grabbing corporate rather than leaning towards their player base and hearing their requests like CD Projekt Red.", "subpage_snippet": "", "source": "www.quora.com", "link": "https://www.quora.com/Why-is-Rockstar-Games-becoming-more-and-more-money-grabbing-corporate-rather-than-leaning-towards-their-player-base-and-hearing-their-requests-like-CD-Projekt-Red", "content": "Rockstar Games becoming more and more money grabbing corporate rather than leaning towards their player base and hearing their requests like CD Projekt Red."}
+{"idx": 8, "title": "Improved genetic algorithm approach for coordinating ...", "date": "", "ddg_snippet": "by B Guerrero Granados · 2024 · Cited by 6 — This study presents a coordinated decision- making system for multiple types of emergency case scenarios for technological disaster management ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s00521-023-09218-0", "content": "by B Guerrero Granados · 2024 · Cited by 6 — This study presents a coordinated decision- making system for multiple types of emergency case scenarios for technological disaster management ..."}
+{"idx": 9, "title": "Multi-camera multi-object tracking: A review of current ...", "date": "", "ddg_snippet": "by TI Amosa · 2023 · Cited by 121 — This current study presents a comprehensive and up-to-date review of visual object tracking in multi-camera settings.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0925231223006811", "content": "by TI Amosa · 2023 · Cited by 121 — This current study presents a comprehensive and up-to-date review of visual object tracking in multi-camera settings."}
diff --git a/data/sampled_jsons/GTA_greedy_task_allocation_inefficiency_many_workers_few_tasks_nB.jsonl b/data/sampled_jsons/GTA_greedy_task_allocation_inefficiency_many_workers_few_tasks_nB.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..717434179b4bf875350cdf7557dc0830d4ab8593
--- /dev/null
+++ b/data/sampled_jsons/GTA_greedy_task_allocation_inefficiency_many_workers_few_tasks_nB.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GTA : Enhanced & Fixed Vice City mod - ModDB", "date": "", "ddg_snippet": "Grand Theft Auto Vice City Extended Features has been built in more than 10 years, contains tons of scripts, tons of interiors and new features.", "subpage_snippet": "", "source": "www.moddb.com", "link": "https://www.moddb.com/mods/gta-enhanced-fixed-vice-city", "content": "Grand Theft Auto Vice City Extended Features has been built in more than 10 years, contains tons of scripts, tons of interiors and new features."}
+{"idx": 1, "title": "Free Multiple Face Swap AI(No sign-up)", "date": "", "ddg_snippet": "Cancel Task .How many faces can be swapped at once? Our multiple face swap AI will automatically detect the first appeared 5 faces in the image, so you can only swap up to 5 multi-faces at a time.", "subpage_snippet": "", "source": "aifaceswap.io", "link": "https://aifaceswap.io/multiple-face-swap-ai/", "content": "Cancel Task .How many faces can be swapped at once? Our multiple face swap AI will automatically detect the first appeared 5 faces in the image, so you can only swap up to 5 multi-faces at a time."}
+{"idx": 2, "title": "Free Online Decision Tree Builder", "date": "", "ddg_snippet": "Our decision tree maker has plenty of editable templates to map the best possible outcome for your task . With these samples, you can clarify choices, evaluate risks, identify inefficiencies , and maximize outcome. Head to our library, find creative inspiration, and get started in minutes.", "subpage_snippet": "", "source": "www.edraw.ai", "link": "https://www.edraw.ai/feature/online-decision-tree-maker.html", "content": "Our decision tree maker has plenty of editable templates to map the best possible outcome for your task . With these samples, you can clarify choices, evaluate risks, identify inefficiencies , and maximize outcome. Head to our library, find creative inspiration, and get started in minutes."}
+{"idx": 3, "title": "Laravel: sync() With An Example | Scratch Code", "date": "", "ddg_snippet": "While working with Many to Many relationships in Laravel we need to introduce an intermediate table which is called the Pivot table in Laravel terms. And to perform operations on this table we need to use methods, sync() is one of them.", "subpage_snippet": "", "source": "www.scratchcode.io", "link": "https://www.scratchcode.io/source-books/laravel-sync-with-an-example/", "content": "While working with Many to Many relationships in Laravel we need to introduce an intermediate table which is called the Pivot table in Laravel terms. And to perform operations on this table we need to use methods, sync() is one of them."}
+{"idx": 4, "title": "Django Tutorial => Count the number of foreign relations", "date": "", "ddg_snippet": "Async Tasks (Celery). Authentication Backends. Class based views.For more details on our cookie usage, please review our Cookie Policy and Privacy Policy.", "subpage_snippet": "", "source": "riptutorial.com", "link": "https://riptutorial.com/django/example/19739/count-the-number-of-foreign-relations", "content": "Async Tasks (Celery). Authentication Backends. Class based views.For more details on our cookie usage, please review our Cookie Policy and Privacy Policy."}
+{"idx": 5, "title": "How to Use macOS Recovery Mode on Mac (M1/M2/M3 Mac)?", "date": "", "ddg_snippet": "You are limited to a few tasks and options through the macOS Utilities, called Mac OS X Utilities on some older macOS versions, and tools in the top menu bar. Depending on which Mac model you use, the utilities on your Mac may vary.", "subpage_snippet": "", "source": "iboysoft.com", "link": "https://iboysoft.com/mac-data-recovery/macos-recovery-mode.html", "content": "You are limited to a few tasks and options through the macOS Utilities, called Mac OS X Utilities on some older macOS versions, and tools in the top menu bar. Depending on which Mac model you use, the utilities on your Mac may vary."}
+{"idx": 6, "title": "Optimizing gpt-oss-120 b local inference speed on consumer hardware", "date": "", "ddg_snippet": "While I still use paid services for most tasks , having a local model is a great fallback option (and when the AI winter comes, i'll still have something to fall back on). Results #. With the magic of llama.cpp and some tinkering, I've managed to get it running on my modest setup.", "subpage_snippet": "", "source": "carteakey.dev", "link": "https://carteakey.dev/optimizing+gpt-oss-120b-local+inference/", "content": "While I still use paid services for most tasks , having a local model is a great fallback option (and when the AI winter comes, i'll still have something to fall back on). Results #. With the magic of llama.cpp and some tinkering, I've managed to get it running on my modest setup."}
+{"idx": 7, "title": "Senior Product Manager (Anti-Fraud & Compliance) at Tabby", "date": "", "ddg_snippet": "Work in Notion: keep boards, tasks , docs, and checklists organized. Break down initiatives into clear tasks and sync everyone involved.Identify and Fix Inefficiencies : Analyze workflows across teams to uncover inefficiencies , gaps, or breakdowns in communication.", "subpage_snippet": "", "source": "www.remocate.app", "link": "https://www.remocate.app/jobs/senior-product-manager-anti-fraud-compliance", "content": "Work in Notion: keep boards, tasks , docs, and checklists organized. Break down initiatives into clear tasks and sync everyone involved.Identify and Fix Inefficiencies : Analyze workflows across teams to uncover inefficiencies , gaps, or breakdowns in communication."}
+{"idx": 8, "title": "Log In to Fidelity NetBenefits", "date": "", "ddg_snippet": "login with password icon: sitting at a desk and working on a laptop with hot...", "subpage_snippet": "", "source": "nb.fidelity.com", "link": "https://nb.fidelity.com/", "content": "login with password icon: sitting at a desk and working on a laptop with hot..."}
+{"idx": 9, "title": "python - numba: No implementation of function... - Stack Overflow", "date": "", "ddg_snippet": "Q&A for work . Connect and share knowledge within a single location that is structured and easy to search. Learn more about Teams. numba: No implementation of function Function() found for signature", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/71902946/numba-no-implementation-of-function-functionbuilt-in-function-getitem-found", "content": "Q&A for work . Connect and share knowledge within a single location that is structured and easy to search. Learn more about Teams. numba: No implementation of function Function() found for signature"}
diff --git a/data/sampled_jsons/Gaetz_Gao_2024_minimal_power_q_Kazhdan_Lusztig_polynomial_year_2024.jsonl b/data/sampled_jsons/Gaetz_Gao_2024_minimal_power_q_Kazhdan_Lusztig_polynomial_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ce8c93b75fdfe56d5ee397e93fde86b6070d381f
--- /dev/null
+++ b/data/sampled_jsons/Gaetz_Gao_2024_minimal_power_q_Kazhdan_Lusztig_polynomial_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Kazhdan – Lusztig polynomial - Wikipedia", "date": "", "ddg_snippet": ". In the mathematical field of representation theory, a Kazhdan – Lusztig polynomial . is a member of a family of integral polynomials introduced by David Kazhdan and George Lusztig . They are indexed by pairs of elements y, w of a Coxeter group W...", "subpage_snippet": "", "source": "en.m.wikipedia.org", "link": "https://en.m.wikipedia.org/wiki/Kazhdan-lusztig_polynomial", "content": ". In the mathematical field of representation theory, a Kazhdan – Lusztig polynomial . is a member of a family of integral polynomials introduced by David Kazhdan and George Lusztig . They are indexed by pairs of elements y, w of a Coxeter group W..."}
+{"idx": 1, "title": "On the minimal power of $ q $ in a Kazhdan - Lusztig polynomial", "date": "", "ddg_snippet": "Abstract:For $w$ in the symmetric group, we provide an exact formula for the smallest positive power $ q ^{h(w)}$ appearing in the Kazhdan - Lusztig polynomial $P_{e,w}( q )$. We also provide a tight upper bound on $h(w)$ in simply-laced types, resolving a conjecture of Billey-Postnikov from...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2303.13695v2", "content": "Abstract:For $w$ in the symmetric group, we provide an exact formula for the smallest positive power $ q ^{h(w)}$ appearing in the Kazhdan - Lusztig polynomial $P_{e,w}( q )$. We also provide a tight upper bound on $h(w)$ in simply-laced types, resolving a conjecture of Billey-Postnikov from..."}
+{"idx": 2, "title": "Recent updates on Kazhdan - Lusztig polynomial being... | Medium", "date": "", "ddg_snippet": "On the minimal power of q in a Kazhdan - Lusztig polynomial (arXiv). Author : Christian Gaetz , Yibo Gao .2.Parabolic recursions for Kazhdan - Lusztig polynomials and the hypercube decomposition (arXiv).", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@monocosmo77/recent-updates-on-kazhdan-lusztig-polynomial-being-used-in-machine-learning-problems-part1-6e9de977b3ee", "content": "On the minimal power of q in a Kazhdan - Lusztig polynomial (arXiv). Author : Christian Gaetz , Yibo Gao .2.Parabolic recursions for Kazhdan - Lusztig polynomials and the hypercube decomposition (arXiv)."}
+{"idx": 3, "title": "On the minimal power of q in a Kazhdan – Lusztig polynomial", "date": "", "ddg_snippet": "Powered by the California Digital Library Copyright © 2017 The Regents of the University of California. Cookie Settings.", "subpage_snippet": "", "source": "escholarship.org", "link": "https://escholarship.org/uc/item/0191j9m7", "content": "Powered by the California Digital Library Copyright © 2017 The Regents of the University of California. Cookie Settings."}
+{"idx": 4, "title": "(PDF) Combinatorial interpretation of Kazhdan – Lusztig basis...", "date": "", "ddg_snippet": "Kazhdan - Lusztig Polynomials for 321-Hexagon-Avoiding Permutations.We give a combinatorial formula for the Kazhdan - Lusztig polynomials $P_{x,w}$ in the symmetric group when $w$ is a 321-hexagon-avoiding permutation.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/361442727_Combinatorial_interpretation_of_Kazhdan-Lusztig_basis_elements_indexed_by_45312-avoiding_permutations_in_6", "content": "Kazhdan - Lusztig Polynomials for 321-Hexagon-Avoiding Permutations.We give a combinatorial formula for the Kazhdan - Lusztig polynomials $P_{x,w}$ in the symmetric group when $w$ is a 321-hexagon-avoiding permutation."}
+{"idx": 5, "title": "Affine subregular kazhdan - lusztig polynomials", "date": "", "ddg_snippet": "Computing Kazhdan - Lusztig polynomials is difficult in general , but when v is re-. stricted to a certain subset c0subreg of W , [BKK23] and [KS24] have computed explicit.", "subpage_snippet": "", "source": "math.mit.edu", "link": "https://math.mit.edu/research/highschool/primes/materials/2024/Kim-Suzuki.pdf", "content": "Computing Kazhdan - Lusztig polynomials is difficult in general , but when v is re-. stricted to a certain subset c0subreg of W , [BKK23] and [KS24] have computed explicit."}
+{"idx": 6, "title": "Hecke algebras and the Kazhdan - Lusztig polynomials", "date": "", "ddg_snippet": "The polynomials are the Kazhdan - Lusztig polynomials associated to .The Kazhdan - Lusztig polynomials can be thought of as a canonical way to repair this symmetry; the condition that.", "subpage_snippet": "", "source": "qchu.wordpress.com", "link": "https://qchu.wordpress.com/2010/07/12/hecke-algebras-and-the-kazhdan-lusztig-polynomials/", "content": "The polynomials are the Kazhdan - Lusztig polynomials associated to .The Kazhdan - Lusztig polynomials can be thought of as a canonical way to repair this symmetry; the condition that."}
+{"idx": 7, "title": "Kazhdan - Lusztig polynomials", "date": "", "ddg_snippet": "Kazhdan - Lusztig polynomials . Arun Ram University of Wisconsin-Madison.or by the usual bar invariance and triangularity conditions. 3. 3 Kazhdan - Lusztig polynomials . The Iwahori-Hecke algebra is the algebra over Z[ q ] given by generators Tw, w ∈ W and relations.", "subpage_snippet": "", "source": "math.soimeme.org", "link": "http://math.soimeme.org/~arunram/Notes2005/KLpolys7.13.05.pdf", "content": "Kazhdan - Lusztig polynomials . Arun Ram University of Wisconsin-Madison.or by the usual bar invariance and triangularity conditions. 3. 3 Kazhdan - Lusztig polynomials . The Iwahori-Hecke algebra is the algebra over Z[ q ] given by generators Tw, w ∈ W and relations."}
+{"idx": 8, "title": "Kazhdan -- Lusztig and R- polynomials , Young's lattice, and Dyck...", "date": "", "ddg_snippet": "The polynomials RuJ,,xv( q ) and PuJ,,vx( q ), whose existence is guaranteed by the two previous theorems, are called the parabolic R- polynomials and para-bolic Kazhdan - Lusztig polynomials (respectively) of W J of type x. It follows.", "subpage_snippet": "", "source": "msp.org", "link": "https://msp.org/pjm/2002/207-2/pjm-v207-n2-p01-p.pdf", "content": "The polynomials RuJ,,xv( q ) and PuJ,,vx( q ), whose existence is guaranteed by the two previous theorems, are called the parabolic R- polynomials and para-bolic Kazhdan - Lusztig polynomials (respectively) of W J of type x. It follows."}
+{"idx": 9, "title": "A new construction of Kazhdan - Lusztig 's representations of the Hecke...", "date": "", "ddg_snippet": "where Pu,v ( q ) are the Kazhdan - Lusztig polynomials . Return to KL immanant. Although, Pu,v ( q ) ∈ N[ q ] there is no simple combinatorial description of the coecients. Kazhdan - Lusztig preorders allow construction of Hn( q )-representations.", "subpage_snippet": "", "source": "www.eg.bucknell.edu", "link": "https://www.eg.bucknell.edu/~pm040/PennState/Slides/buehrle.pdf", "content": "where Pu,v ( q ) are the Kazhdan - Lusztig polynomials . Return to KL immanant. Although, Pu,v ( q ) ∈ N[ q ] there is no simple combinatorial description of the coecients. Kazhdan - Lusztig preorders allow construction of Hn( q )-representations."}
diff --git a/data/sampled_jsons/Galhotra_Halpern_causal_Bayesian_networks_observable_parents_latent_variables_confounders_year_2024.jsonl b/data/sampled_jsons/Galhotra_Halpern_causal_Bayesian_networks_observable_parents_latent_variables_confounders_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f793b3785b8df850914fb60da6cf027fe0672d45
--- /dev/null
+++ b/data/sampled_jsons/Galhotra_Halpern_causal_Bayesian_networks_observable_parents_latent_variables_confounders_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF Intervention and Conditioning in Causal Bayesian Networks", "date": "", "ddg_snippet": "Abstract Causal models are crucial for understanding complex systems and identifying causal relationships among variables . Even though causal models are extremely popular, conditional probability calculation of formulas involving interventions pose significant challenges. In case of Causal Bayesian Networks (CBNs), Pearl assumes autonomy of mechanisms that determine interventions to calculate ...", "subpage_snippet": "", "source": "papers.neurips.cc", "link": "https://papers.neurips.cc/paper_files/paper/2024/file/a2118322165fffb648d1e341ff5a5b05-Paper-Conference.pdf", "content": "Abstract Causal models are crucial for understanding complex systems and identifying causal relationships among variables . Even though causal models are extremely popular, conditional probability calculation of formulas involving interventions pose significant challenges. In case of Causal Bayesian Networks (CBNs), Pearl assumes autonomy of mechanisms that determine interventions to calculate ..."}
+{"idx": 1, "title": "Causal Inference with Bayesian Networks. Main Concepts and Methods", "date": "", "ddg_snippet": "Potential outcomes framework (Rubin causal model), propensity score matching and structural causal models are, arguably, the most popular frameworks for observational causal inference. Here, we focus on the structural causal models and one particular type, Bayesian Networks . Interested users can find more details in the references below.", "subpage_snippet": "", "source": "causalnex.readthedocs.io", "link": "https://causalnex.readthedocs.io/en/latest/04_user_guide/04_user_guide.html", "content": "Potential outcomes framework (Rubin causal model), propensity score matching and structural causal models are, arguably, the most popular frameworks for observational causal inference. Here, we focus on the structural causal models and one particular type, Bayesian Networks . Interested users can find more details in the references below."}
+{"idx": 2, "title": "Learning causal Bayesian networks based on causality analysis for ...", "date": "", "ddg_snippet": "In this paper, two assertions, called causal dependence and log-likelihood equivalence, are introduced to learn Bayesian network classifiers (BNCs) to represent causal relationships. Information-theoretic metrics based on point-wise log-likelihood function and entropy function are proposed to identify and verify the rationality of causality between attribute values. The resulting algorithm ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0952197622002962", "content": "In this paper, two assertions, called causal dependence and log-likelihood equivalence, are introduced to learn Bayesian network classifiers (BNCs) to represent causal relationships. Information-theoretic metrics based on point-wise log-likelihood function and entropy function are proposed to identify and verify the rationality of causality between attribute values. The resulting algorithm ..."}
+{"idx": 3, "title": "Bayesian network structure learning with causal effects in the presence ...", "date": "", "ddg_snippet": "Latent variables may lead to spurious relationships that can be misinterpreted as causal relationships. In Bayesian Networks (BNs), this challenge is known as learning under causal insufficiency. Structure learning algorithms that assume causal insufficiency tend to reconstruct the ancestral graph of a BN, where bi-directed edges represent confounding and directed edges represent direct or ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v138/chobtham20a.html", "content": "Latent variables may lead to spurious relationships that can be misinterpreted as causal relationships. In Bayesian Networks (BNs), this challenge is known as learning under causal insufficiency. Structure learning algorithms that assume causal insufficiency tend to reconstruct the ancestral graph of a BN, where bi-directed edges represent confounding and directed edges represent direct or ..."}
+{"idx": 4, "title": "Estimating Causal Effects from Learned Causal Networks", "date": "", "ddg_snippet": "In this paper, we propose an alternative paradigm for answering causal -effect queries over discrete observable variables . We propose to instead learn the causal Bayesian network and its con-founding latent variables directly from the observational data.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2408.14101", "content": "In this paper, we propose an alternative paradigm for answering causal -effect queries over discrete observable variables . We propose to instead learn the causal Bayesian network and its con-founding latent variables directly from the observational data."}
+{"idx": 5, "title": "Intervention and Conditioning in Causal Bayesian Networks", "date": "", "ddg_snippet": "Intervention and Conditioning in Causal Bayesian Networks Sainyam Galhotra , Joseph Y. Halpern January, 2024 Cite DOI URL", "subpage_snippet": "", "source": "sainyamgalhotra.com", "link": "https://sainyamgalhotra.com/publication/dblp-neurips24/", "content": "Intervention and Conditioning in Causal Bayesian Networks Sainyam Galhotra , Joseph Y. Halpern January, 2024 Cite DOI URL"}
+{"idx": 6, "title": "PDF An Instance-Specific Algorithm for Learning the Structure of Causal ...", "date": "", "ddg_snippet": "We previously introduced a fully Bayesian instance-speci c structure learning method, called IGES [12], that searches the space of CBNs to build a model that is speci c to an instance T by guiding the search from the features we know about T and from a training set of data on many other instances. The IGES method assumes that there are no latent confounders (i.e., it makes the causal su ciency ...", "subpage_snippet": "", "source": "www.dbmi.pitt.edu", "link": "https://www.dbmi.pitt.edu/wp-content/uploads/2022/09/An-instance-specific-algorithm-for-learning-the-structure-of-causal-Bayesian-networks-containing-latent-variables.pdf", "content": "We previously introduced a fully Bayesian instance-speci c structure learning method, called IGES [12], that searches the space of CBNs to build a model that is speci c to an instance T by guiding the search from the features we know about T and from a training set of data on many other instances. The IGES method assumes that there are no latent confounders (i.e., it makes the causal su ciency ..."}
+{"idx": 7, "title": "Intervention and Conditioning in Causal Bayesian Networks", "date": "", "ddg_snippet": "In case of Causal Bayesian Networks (CBNs), Pearl assumes autonomy of mechanisms that determine interventions to calculate a range of probabilities.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/380847642_Intervention_and_Conditioning_in_Causal_Bayesian_Networks", "content": "In case of Causal Bayesian Networks (CBNs), Pearl assumes autonomy of mechanisms that determine interventions to calculate a range of probabilities."}
+{"idx": 8, "title": "Causal Inference in Longitudinal Studies Using Causal Bayesian Network ...", "date": "", "ddg_snippet": "However, few studies emphasize the causal relationships between observed variables and latent , time-varying confounders . The causal Bayesian network (CBN) shows promise in handling multiple causes and effects.", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/9893992", "content": "However, few studies emphasize the causal relationships between observed variables and latent , time-varying confounders . The causal Bayesian network (CBN) shows promise in handling multiple causes and effects."}
+{"idx": 9, "title": "Intervention and Conditioning in Causal Bayesian Networks", "date": "", "ddg_snippet": "Authors Sainyam Galhotra , Joseph Y. Halpern Abstract Causal models are crucial for understanding complex systems andidentifying causal relationships among variables . Even though causalmodels are extremely popular, conditional probability calculation offormulas involving interventions pose significant challenges.In case of Causal Bayesian Networks (CBNs), Pearl assumes autonomy of mechanisms ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/hash/a2118322165fffb648d1e341ff5a5b05-Abstract-Conference.html", "content": "Authors Sainyam Galhotra , Joseph Y. Halpern Abstract Causal models are crucial for understanding complex systems andidentifying causal relationships among variables . Even though causalmodels are extremely popular, conditional probability calculation offormulas involving interventions pose significant challenges.In case of Causal Bayesian Networks (CBNs), Pearl assumes autonomy of mechanisms ..."}
diff --git a/data/sampled_jsons/Galhotra_Halpern_intervention_conditioning_causal_Bayesian_networks_independence_assumption_observab_year_2023.jsonl b/data/sampled_jsons/Galhotra_Halpern_intervention_conditioning_causal_Bayesian_networks_independence_assumption_observab_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..1a4621ef9302a4b688f4f8af197065dcc9309a58
--- /dev/null
+++ b/data/sampled_jsons/Galhotra_Halpern_intervention_conditioning_causal_Bayesian_networks_independence_assumption_observab_year_2023.jsonl
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+{"idx": 0, "title": "Intervention and Conditioning in Causal Bayesian Networks", "date": "", "ddg_snippet": "In case of Causal Bayesian Networks (CBNs), Pearl assumes autonomy of mechanisms that determine interventions to calculate a range of probabilities. We show that by making simple yet often realistic independence assumptions , it is possible to uniquely estimate the probability of an interventional formula (including the well-studied notions of ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2405.14728", "content": "In case of Causal Bayesian Networks (CBNs), Pearl assumes autonomy of mechanisms that determine interventions to calculate a range of probabilities. We show that by making simple yet often realistic independence assumptions , it is possible to uniquely estimate the probability of an interventional formula (including the well-studied notions of ..."}
+{"idx": 1, "title": "PDF Intervention and Conditioning in Causal Bayesian Networks", "date": "", "ddg_snippet": "In fact, Richardson, Peters, and Halpern (2024) show that the assumption that cpts involving different variables are in-dependent is equivalent to the (conditional) independence assumptions made in Bayesian networks (see Section 3.4 for further discussion).", "subpage_snippet": "", "source": "papers.neurips.cc", "link": "https://papers.neurips.cc/paper_files/paper/2024/file/a2118322165fffb648d1e341ff5a5b05-Paper-Conference.pdf", "content": "In fact, Richardson, Peters, and Halpern (2024) show that the assumption that cpts involving different variables are in-dependent is equivalent to the (conditional) independence assumptions made in Bayesian networks (see Section 3.4 for further discussion)."}
+{"idx": 2, "title": "Intervention and Conditioning in Causal Bayesian Networks", "date": "", "ddg_snippet": "Intervention and Conditioning in Causal Bayesian Networks Sainyam Galhotra , Joseph Y. Halpern January, 2024 Cite DOI URL", "subpage_snippet": "", "source": "sainyamgalhotra.com", "link": "https://sainyamgalhotra.com/publication/dblp-neurips24/", "content": "Intervention and Conditioning in Causal Bayesian Networks Sainyam Galhotra , Joseph Y. Halpern January, 2024 Cite DOI URL"}
+{"idx": 3, "title": "Intervention and Conditioning in Causal Bayesian Networks", "date": "", "ddg_snippet": "Abstract:Causal models are crucial for understanding complex systems and identifying causal relationships among variables. Even though causal models are extremely popular, conditional probability calculation of formulas involving interventions pose significant challenges. In case of Causal Bayesian Networks (CBNs), Pearl assumes autonomy of mechanisms that determine interventions to calculate ...", "subpage_snippet": "", "source": "bohrium.dp.tech", "link": "https://bohrium.dp.tech/paper/arxiv/2405.14728", "content": "Abstract:Causal models are crucial for understanding complex systems and identifying causal relationships among variables. Even though causal models are extremely popular, conditional probability calculation of formulas involving interventions pose significant challenges. In case of Causal Bayesian Networks (CBNs), Pearl assumes autonomy of mechanisms that determine interventions to calculate ..."}
+{"idx": 4, "title": "Intervention and Conditioning in Causal Bayesian Networks", "date": "", "ddg_snippet": "This paper significantly advances causal inference by uniquely estimating probabilities in Causal Bayesian Networks (CBNs), enabling analysis using observational data, and simplifying calculations for crucial counterfactual probabilities. This addresses a critical limitation of CBNs and opens avenues for practical applications in various fields.", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/posters/dc28fpk76s/", "content": "This paper significantly advances causal inference by uniquely estimating probabilities in Causal Bayesian Networks (CBNs), enabling analysis using observational data, and simplifying calculations for crucial counterfactual probabilities. This addresses a critical limitation of CBNs and opens avenues for practical applications in various fields."}
+{"idx": 5, "title": "Intervention and Conditioning in Causal Bayesian Networks", "date": "", "ddg_snippet": "In case of Causal Bayesian Networks (CBNs), Pearl assumes autonomy of mechanisms that determine interventions to calculate a range of probabilities.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/380847642_Intervention_and_Conditioning_in_Causal_Bayesian_Networks", "content": "In case of Causal Bayesian Networks (CBNs), Pearl assumes autonomy of mechanisms that determine interventions to calculate a range of probabilities."}
+{"idx": 6, "title": "Intervention and Conditioning in Causal Bayesian Networks (Conference ...", "date": "", "ddg_snippet": "Intervention and Conditioning in Causal Bayesian Networks Award ID (s): 2319186 PAR ID: 10613493 Author (s) / Creator (s): Galhotra , Sainyam; Halpern , Joseph Y Publisher / Repository: NeurIPS Date Published: 2024-12-10 Format (s): Medium: X Location: Vancouver, BC Sponsoring Org: National Science Foundation More Like this No document ...", "subpage_snippet": "", "source": "par.nsf.gov", "link": "https://par.nsf.gov/biblio/10613493-intervention-conditioning-causal-bayesian-networks", "content": "Intervention and Conditioning in Causal Bayesian Networks Award ID (s): 2319186 PAR ID: 10613493 Author (s) / Creator (s): Galhotra , Sainyam; Halpern , Joseph Y Publisher / Repository: NeurIPS Date Published: 2024-12-10 Format (s): Medium: X Location: Vancouver, BC Sponsoring Org: National Science Foundation More Like this No document ..."}
+{"idx": 7, "title": "Intervention and Conditioning in Causal Bayesian Networks | Cool Papers ...", "date": "", "ddg_snippet": "In case of Causal Bayesian Networks (CBNs), Pearl assumes autonomy of mechanisms that determine interventions to calculate a range of probabilities. We show that by making simple yet often realistic independence assumptions , it is possible to uniquely estimate the probability of an interventional formula (including the well-studied notions of ...", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/arxiv/2405.14728", "content": "In case of Causal Bayesian Networks (CBNs), Pearl assumes autonomy of mechanisms that determine interventions to calculate a range of probabilities. We show that by making simple yet often realistic independence assumptions , it is possible to uniquely estimate the probability of an interventional formula (including the well-studied notions of ..."}
+{"idx": 8, "title": "Intervention and Conditioning in Causal Bayesian Networks", "date": "", "ddg_snippet": "Authors Sainyam Galhotra , Joseph Y. Halpern Abstract Causal models are crucial for understanding complex systems andidentifying causal relationships among variables. Even though causalmodels are extremely popular, conditional probability calculation offormulas involving interventions pose significant challenges.In case of Causal Bayesian Networks (CBNs), Pearl assumes autonomy of mechanisms ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/hash/a2118322165fffb648d1e341ff5a5b05-Abstract-Conference.html", "content": "Authors Sainyam Galhotra , Joseph Y. Halpern Abstract Causal models are crucial for understanding complex systems andidentifying causal relationships among variables. Even though causalmodels are extremely popular, conditional probability calculation offormulas involving interventions pose significant challenges.In case of Causal Bayesian Networks (CBNs), Pearl assumes autonomy of mechanisms ..."}
+{"idx": 9, "title": "Intervention and Conditioning in Causal Bayesian Networks", "date": "", "ddg_snippet": "Despite their utility, calculating the probabilities related to interventions and conditioning in tandem presents significant challenges. Indeed, it is not even clear what the semantics of queries involving counterfactuals is. Work in the AI literature has focused on two types of models: functional causal models and causal Bayesian networks (Pearl 2000). Both are typically described using ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2405.14728", "content": "Despite their utility, calculating the probabilities related to interventions and conditioning in tandem presents significant challenges. Indeed, it is not even clear what the semantics of queries involving counterfactuals is. Work in the AI literature has focused on two types of models: functional causal models and causal Bayesian networks (Pearl 2000). Both are typically described using ..."}
diff --git a/data/sampled_jsons/Geirhos_et_al._2020_error_consistency_metric_abstract_year_2020.jsonl b/data/sampled_jsons/Geirhos_et_al._2020_error_consistency_metric_abstract_year_2020.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a323bb062372a453aa7f58931ed9699b9e0ebc52
--- /dev/null
+++ b/data/sampled_jsons/Geirhos_et_al._2020_error_consistency_metric_abstract_year_2020.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Quantifying Uncertainty in Error Consistency : Towards Reliable...", "date": "", "ddg_snippet": "In this work, we have built on the error consistency metric proposed by Geirhos et al . [ 2020 ], demon-strating how confidence intervals and significance tests for empirical measurements of EC can be calculated using bootstrapping techniques.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2507.06645", "content": "In this work, we have built on the error consistency metric proposed by Geirhos et al . [ 2020 ], demon-strating how confidence intervals and significance tests for empirical measurements of EC can be calculated using bootstrapping techniques."}
+{"idx": 1, "title": "Decision-margin consistency : a principled metric for", "date": "", "ddg_snippet": "Geirhos et al ., [2] computed the error consistency between two decision-makers on this task using Cohen’s Kappa, which quantifies the degree of agreement between sets of decisions, adjusting for chance agreement (which depends on the overall accuracy of each observer)", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=y2FPllMQVg", "content": "Geirhos et al ., [2] computed the error consistency between two decision-makers on this task using Cohen’s Kappa, which quantifies the degree of agreement between sets of decisions, adjusting for chance agreement (which depends on the overall accuracy of each observer)"}
+{"idx": 2, "title": "(PDF) How Aligned are Different Alignment Metrics ?", "date": "", "ddg_snippet": "Geirhos et al ., 2020 ; 2021) and shape bias ( Geirhos et al ., 2018; Baker et al ., 2018; Hermann et al ., 2020 ). Other commonly used datasets in the literature include behavioral similarity judgements.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/382146118_How_Aligned_are_Different_Alignment_Metrics", "content": "Geirhos et al ., 2020 ; 2021) and shape bias ( Geirhos et al ., 2018; Baker et al ., 2018; Hermann et al ., 2020 ). Other commonly used datasets in the literature include behavioral similarity judgements."}
+{"idx": 3, "title": "Robust Representation Learning via Perceptual Similarity Metrics", "date": "", "ddg_snippet": "Metric learning (Goldberger et al ., 2004) refers to a family of methods which learn a notion of similiarity (the metric of interest) between sets of inputs to extract meaningful representations from data.", "subpage_snippet": "", "source": "damassets.autodesk.net", "link": "https://damassets.autodesk.net/content/dam/autodesk/research/publications-assets/pdf/Robust-Representation-Learning.pdf", "content": "Metric learning (Goldberger et al ., 2004) refers to a family of methods which learn a notion of similiarity (the metric of interest) between sets of inputs to extract meaningful representations from data."}
+{"idx": 4, "title": "GitHub - ngayulo/behaviorConsistencyOOD", "date": "", "ddg_snippet": "abstract .pdf.Based on experiments done by Geirhos et al 2020 , this project set out to evaluate the ability for CNN to predict human behaviors given out-of-distribution stimuli.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/ngayulo/behaviorConsistencyOOD", "content": "abstract .pdf.Based on experiments done by Geirhos et al 2020 , this project set out to evaluate the ability for CNN to predict human behaviors given out-of-distribution stimuli."}
+{"idx": 5, "title": "Revisiting Text-to-Image Evaluation with Gecko: On Metrics , Prompts...", "date": "", "ddg_snippet": "Automatic metrics measuring T2I alignment. Inspired by work in image captioning, a widely used auto-eval metric is CLIPScore (Hessel et al ., 2021).", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/arxiv/2404.16820/paper", "content": "Automatic metrics measuring T2I alignment. Inspired by work in image captioning, a widely used auto-eval metric is CLIPScore (Hessel et al ., 2021)."}
+{"idx": 6, "title": "Objective drives the consistency of representational ...", "date": "", "ddg_snippet": "Abstract . The Platonic Representation Hypothesis claims that recent foundation models are converging to a shared representation ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/43665", "content": "Abstract . The Platonic Representation Hypothesis claims that recent foundation models are converging to a shared representation ..."}
+{"idx": 7, "title": "Dissecting the effectiveness of deep features as metric ...", "date": "", "ddg_snippet": "by P Hernández-Cámara · 2025 · Cited by 4 — Only a bio-inspired perceptual metric (Hepburn et al ., 2020 ), which was trained for a similar database to TID-2013 can surpass by a small margin the AlexNet ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0893608025000681", "content": "by P Hernández-Cámara · 2025 · Cited by 4 — Only a bio-inspired perceptual metric (Hepburn et al ., 2020 ), which was trained for a similar database to TID-2013 can surpass by a small margin the AlexNet ..."}
+{"idx": 8, "title": "CNNs vs Vision Transformers — Biological Computer Vision... | Medium", "date": "", "ddg_snippet": "To compare strategies, researchers ( Geirhos et al .) have come up with an Error Consistency metric —Cohen’s Kappa, κ — which is computed based on probabilities of misclassification. Error consistency result comparison of Vision Transformers (Vit-B/32) and CNNs Source.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/bits-and-neurons/cnns-vs-vision-transformers-biological-computer-vision-3-3-56ff955ba463", "content": "To compare strategies, researchers ( Geirhos et al .) have come up with an Error Consistency metric —Cohen’s Kappa, κ — which is computed based on probabilities of misclassification. Error consistency result comparison of Vision Transformers (Vit-B/32) and CNNs Source."}
+{"idx": 9, "title": "UC Merced", "date": "", "ddg_snippet": "Measuring Error Consistency . More Granular Investigation of Misclassifications. Methods.stimuli ( Geirhos et al ., 2019): (left) Original image from ImageNet, and (right) a textured transform.", "subpage_snippet": "", "source": "escholarship.org", "link": "https://escholarship.org/content/qt2pm5q7k6/qt2pm5q7k6.pdf?t=qwi2wu", "content": "Measuring Error Consistency . More Granular Investigation of Misclassifications. Methods.stimuli ( Geirhos et al ., 2019): (left) Original image from ImageNet, and (right) a textured transform."}
diff --git a/data/sampled_jsons/GenAI_Arena-_An_Open_Evaluation_Platform_for_Generative_Models_pdf.jsonl b/data/sampled_jsons/GenAI_Arena-_An_Open_Evaluation_Platform_for_Generative_Models_pdf.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4f1fd8e7c9ae589d826afc2087de948f38723b31
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+++ b/data/sampled_jsons/GenAI_Arena-_An_Open_Evaluation_Platform_for_Generative_Models_pdf.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GenAI Arena: An Open Evaluation Platform for Generative ...", "date": "", "ddg_snippet": "by D Jiang · 2024 · Cited by 31 — This paper proposes an open platform GenAI-Arena to evaluate different image and video generative models, where users can actively participate in evaluating ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2406.04485", "content": "by D Jiang · 2024 · Cited by 31 — This paper proposes an open platform GenAI-Arena to evaluate different image and video generative models, where users can actively participate in evaluating ..."}
+{"idx": 1, "title": "GenAI Arena: An Open Evaluation Platform for Generative ...", "date": "", "ddg_snippet": "by D Jiang · 2024 · Cited by 31 — In this paper, we introduced GenAI-Arena , an open platform designed to rank generative models across text-to-image, image editing, and text ... 20 pages", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/92249f9233286e437f808fa535d88b26-Paper-Datasets_and_Benchmarks_Track.pdf", "content": "by D Jiang · 2024 · Cited by 31 — In this paper, we introduced GenAI-Arena , an open platform designed to rank generative models across text-to-image, image editing, and text ... 20 pages"}
+{"idx": 2, "title": "GenAI Arena: An Open Evaluation Platform for Generative ...", "date": "", "ddg_snippet": "This paper proposes an open platform GenAI-Arena to evaluate different image and video generative models, where users can actively participate in evaluating ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=0Gmi8TkUC7&referrer=[the+profile+of+Wenhu+Chen](/profile?id=~Wenhu_Chen3)", "content": "This paper proposes an open platform GenAI-Arena to evaluate different image and video generative models, where users can actively participate in evaluating ..."}
+{"idx": 3, "title": "An Open Evaluation Platform for Generative Models", "date": "", "ddg_snippet": "An open platform to evaluate different image and video generative models , where users can actively participate in evaluating these models, by leveraging ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/GenAI-Arena:-An-Open-Evaluation-Platform-for-Models-Jiang-Ku/9931717b0054d14185009c407145463793f7bed5", "content": "An open platform to evaluate different image and video generative models , where users can actively participate in evaluating these models, by leveraging ..."}
+{"idx": 4, "title": "GenAI arena: an open evaluation platform for generative models", "date": "", "ddg_snippet": "This paper proposes an open platform GENAI-ARENA to evaluate different image and video generative models, where users can actively participate in evaluating ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3737916.3740454", "content": "This paper proposes an open platform GENAI-ARENA to evaluate different image and video generative models, where users can actively participate in evaluating ..."}
+{"idx": 5, "title": "GenAI Arena: An Open Evaluation Platform for Generative ...", "date": "", "ddg_snippet": "6 Jun 2024 — This paper proposes an open platform GenAI-Arena to evaluate different image and video generative models, where users can actively participate in evaluating ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.04485v1", "content": "6 Jun 2024 — This paper proposes an open platform GenAI-Arena to evaluate different image and video generative models, where users can actively participate in evaluating ..."}
+{"idx": 6, "title": "An Open Evaluation Platform for Foundation Models in ...", "date": "", "ddg_snippet": "by Y Zhao · Cited by 2 — GenAI arena: An open evaluation platform for generative models . In The Thirty-eight Conference on Neural. Information Processing Systems ...", "subpage_snippet": "", "source": "sciarena.allen.ai", "link": "https://sciarena.allen.ai/SciArena_An_Open_Evaluation_Platform_for_Foundation_Models_in_Scientific_Literature_Tasks.pdf", "content": "by Y Zhao · Cited by 2 — GenAI arena: An open evaluation platform for generative models . In The Thirty-eight Conference on Neural. Information Processing Systems ..."}
+{"idx": 7, "title": "3D Arena: An Open Platform for Generative 3D Evaluation", "date": "", "ddg_snippet": "3D Arena , an open platform for evaluating image-to-3D generation models through large-scale human preference collection using pairwise comparisons, ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/3D-Arena:-An-Open-Platform-for-Generative-3D-Ebert/77e3be048624461af1e34d0bea173208a83ee2b3", "content": "3D Arena , an open platform for evaluating image-to-3D generation models through large-scale human preference collection using pairwise comparisons, ..."}
+{"idx": 8, "title": "Publication - Wenhu Chen", "date": "", "ddg_snippet": "GenAI Arena: An Open Evaluation Platform for Generative Models Dongfu Jiang ... Proceedings of NAACL 2018, New Orleans, CA [pdf][slides][data]; Generative ...", "subpage_snippet": "", "source": "wenhuchen.github.io", "link": "https://wenhuchen.github.io/publication.html", "content": "GenAI Arena: An Open Evaluation Platform for Generative Models Dongfu Jiang ... Proceedings of NAACL 2018, New Orleans, CA [pdf][slides][data]; Generative ..."}
+{"idx": 9, "title": "Search", "date": "", "ddg_snippet": "GenAI Arena: An Open Evaluation Platform for Generative Models · pdf icon · Dongfu Jiang, Max Ku, Tianle Li, Yuansheng Ni, Shizhuo Sun, Rongqi Fan, Wenhu Chen.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/search?term=~Dongfu_Jiang1&content=authors&group=all&source=forum&sort=cdate:desc", "content": "GenAI Arena: An Open Evaluation Platform for Generative Models · pdf icon · Dongfu Jiang, Max Ku, Tianle Li, Yuansheng Ni, Shizhuo Sun, Rongqi Fan, Wenhu Chen."}
diff --git a/data/sampled_jsons/Geometric_Approach_to_Personalized_Recommendation_with_Set-Theoretic_Constraints_Using_Box_Embedding_year_2023.jsonl b/data/sampled_jsons/Geometric_Approach_to_Personalized_Recommendation_with_Set-Theoretic_Constraints_Using_Box_Embedding_year_2023.jsonl
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+++ b/data/sampled_jsons/Geometric_Approach_to_Personalized_Recommendation_with_Set-Theoretic_Constraints_Using_Box_Embedding_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A Geometric Approach to Personalized Recommendation with ...", "date": "", "ddg_snippet": "Box embeddings , with their geometric set operations, sig-nificantly outperform all vector -based methods. Table 3 : Hit Rate(%)↑ on Set - theoretic queries for datasets Last - FM , MovieLens 1M, NYC-R.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.10875", "content": "Box embeddings , with their geometric set operations, sig-nificantly outperform all vector -based methods. Table 3 : Hit Rate(%)↑ on Set - theoretic queries for datasets Last - FM , MovieLens 1M, NYC-R."}
+{"idx": 1, "title": "(PDF) A Geometric Approach to Personalized Recommendation ...", "date": "", "ddg_snippet": "with Set - Theoretic Constraints Using Box Embeddings . Box embeddings , with their geometric set operations, sig-. nificantly outperform all vector -based methods.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/389091382_A_Geometric_Approach_to_Personalized_Recommendation_with_Set-Theoretic_Constraints_Using_Box_Embeddings", "content": "with Set - Theoretic Constraints Using Box Embeddings . Box embeddings , with their geometric set operations, sig-. nificantly outperform all vector -based methods."}
+{"idx": 2, "title": "A Geometric Approach to Personalized Recommendation with ...", "date": "", "ddg_snippet": "Box embeddings , with their geometric set operations, significantly outperform all vector -based methods.This validates the set - theoretic inductive bias of box embeddings and confirms that geometric operations in this space provide valid set - theoretic operations, unlike vectors .", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/icml/46603/paper", "content": "Box embeddings , with their geometric set operations, significantly outperform all vector -based methods.This validates the set - theoretic inductive bias of box embeddings and confirms that geometric operations in this space provide valid set - theoretic operations, unlike vectors ."}
+{"idx": 3, "title": "A Geometric Approach to Personalized Recommendation with ...", "date": "", "ddg_snippet": "Box embeddings can intuitively be understood as trainable Venn diagrams, and thus not only inherently represent similarity (via the Jaccard index), but also naturally and faithfully support arbitrary set - theoretic relationships. Queries involving set - theoretic constraints can be...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=27tMzmzDjO&referrer=[the+profile+of+Andrew+McCallum](/profile?id=~Andrew_McCallum1)", "content": "Box embeddings can intuitively be understood as trainable Venn diagrams, and thus not only inherently represent similarity (via the Jaccard index), but also naturally and faithfully support arbitrary set - theoretic relationships. Queries involving set - theoretic constraints can be..."}
+{"idx": 4, "title": "GitHub - Lyz103/ Recommendation -paper-daily: 🎓Automatically...", "date": "", "ddg_snippet": "A Geometric Approach to Personalized Recommendation with Set - Theoretic Constraints Using Box Embeddings .From Interests to Insights: An LLM Approach to Course Recommendations Using Natural Language Queries.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Lyz103/Recommendation-paper-daily", "content": "A Geometric Approach to Personalized Recommendation with Set - Theoretic Constraints Using Box Embeddings .From Interests to Insights: An LLM Approach to Course Recommendations Using Natural Language Queries."}
+{"idx": 5, "title": "Transform text into images and explore with endless imagination.", "date": "", "ddg_snippet": "Transform text into images and explore with endless imagination.", "subpage_snippet": "", "source": "labs.google", "link": "https://labs.google/fx/tools/image-fx/unsupported-country", "content": "Transform text into images and explore with endless imagination."}
+{"idx": 6, "title": "Shib Sankar Dasgupta - Google Scholar", "date": "", "ddg_snippet": "Box -To- Box Transformation for Modeling Joint Hierarchies.A Geometric Approach to Personalized Recommendation with Set - Theoretic Constraints Using Box Embeddings .", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=0KpQR94AAAAJ&hl=en", "content": "Box -To- Box Transformation for Modeling Joint Hierarchies.A Geometric Approach to Personalized Recommendation with Set - Theoretic Constraints Using Box Embeddings ."}
+{"idx": 7, "title": "dblp: List of computer science publications by Andrew McCallum", "date": "", "ddg_snippet": "Shib Sankar Dasgupta, Michael Boratko, Andrew McCallum: A Geometric Approach to Personalized Recommendation with Set - Theoretic Constraints Using Box Embeddings .", "subpage_snippet": "", "source": "dblp.uni-trier.de", "link": "https://dblp.uni-trier.de/pid/m/AndrewMcCallum.html", "content": "Shib Sankar Dasgupta, Michael Boratko, Andrew McCallum: A Geometric Approach to Personalized Recommendation with Set - Theoretic Constraints Using Box Embeddings ."}
+{"idx": 8, "title": "Shib Sankar Dasgupta", "date": "", "ddg_snippet": "A Geometric Approach to Personalized Recommendation with Set - Theoretic Constraints Using Box Embeddings . Box -To- Box Transformations for Modeling Joint Hierarchies.", "subpage_snippet": "", "source": "elitalobo.github.io", "link": "https://elitalobo.github.io/cv.pdf", "content": "A Geometric Approach to Personalized Recommendation with Set - Theoretic Constraints Using Box Embeddings . Box -To- Box Transformations for Modeling Joint Hierarchies."}
+{"idx": 9, "title": "DuckDuckGo - Protection. Privacy. Peace of mind.", "date": "", "ddg_snippet": "The Internet privacy company that empowers you to seamlessly take control of your personal information online, without any tradeoffs.", "subpage_snippet": "", "source": "duckduckgo.com", "link": "https://duckduckgo.com/", "content": "The Internet privacy company that empowers you to seamlessly take control of your personal information online, without any tradeoffs."}
diff --git a/data/sampled_jsons/Global_Optimization_with_a_Power-Transformed_Objective_and_Gaussian_Smoothing_Iwakiri_et_al.,_2022.jsonl b/data/sampled_jsons/Global_Optimization_with_a_Power-Transformed_Objective_and_Gaussian_Smoothing_Iwakiri_et_al.,_2022.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ff4a98fb61d13f0c0b3face71a18525a83b0e818
--- /dev/null
+++ b/data/sampled_jsons/Global_Optimization_with_a_Power-Transformed_Objective_and_Gaussian_Smoothing_Iwakiri_et_al.,_2022.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ICML Poster Global Optimization with a Power-Transformed ...", "date": "", "ddg_snippet": "Abstract: We propose a novel method, namely Gaussian Smoothing with a Power-Transformed Objective (GS-PowerOpt), that solves global optimization problems in two steps: (1) perform a (exponential) power - N transformation to the not necessarily differentiable objective f: R d → R and get f N, and (2) optimize the Gaussian -smoothed f N with stochastic approximations. Under mild conditions on f ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46360", "content": "Abstract: We propose a novel method, namely Gaussian Smoothing with a Power-Transformed Objective (GS-PowerOpt), that solves global optimization problems in two steps: (1) perform a (exponential) power - N transformation to the not necessarily differentiable objective f: R d → R and get f N, and (2) optimize the Gaussian -smoothed f N with stochastic approximations. Under mild conditions on f ..."}
+{"idx": 1, "title": "Global Optimization with a Power-Transformed Objective and ...", "date": "", "ddg_snippet": "Abstract We propose a novel method, namely Gaussian Smoothing with a Power-Transformed Objective (GS-PowerOpt), that solves global optimization problems in two steps: (1) perform a (exponen-tial) power -N transformation to the not necessar-Rd ! ily differentiable objective f : and get R fN, and (2) optimize the Gaussian -smoothed fN with stochastic approximations. Under mild con-ditions on f ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=6ojzpDczIY", "content": "Abstract We propose a novel method, namely Gaussian Smoothing with a Power-Transformed Objective (GS-PowerOpt), that solves global optimization problems in two steps: (1) perform a (exponen-tial) power -N transformation to the not necessar-Rd ! ily differentiable objective f : and get R fN, and (2) optimize the Gaussian -smoothed fN with stochastic approximations. Under mild con-ditions on f ..."}
+{"idx": 2, "title": "Global Optimization with A Power-Transformed Objective and ... dblp: Global Optimization with A Power-Transformed Objective ... ICML Poster Global Optimization with a Power-Transformed ... GS-PowerTransform/README.md at main - GitHub Global Optimization with A Power-Transformed Objective and ... Global Optimization with A Power-Transformed Objective and ...", "date": "", "ddg_snippet": "Dec 6, 2024 · We propose a novel method that solves global optimization problems in two steps: (1) perform a (exponential) power - N transformation to the not-necessarily differentiable objective function f and get f_N, and (2) optimize the Gaussian -smoothed f_N with stochastic approximations. Under mild conditions on f, for any \\delta>0, we prove that with a sufficiently large power N_\\delta, this method ... Jan 26, 2025 · Bibliographic details on Global Optimization with A Power - Transformed Objective and Gaussian Smoothing . Abstract: We propose a novel method, namely Gaussian Smoothing with a Power-Transformed Objective (GS-PowerOpt), that solves global optimization problems in two steps: (1) perform a (exponential) power - N transformation to the not necessarily differentiable objective f: R d → R and get f N, and (2) optimize the Gaussian -smoothed f N with stochastic approximations. Under mild conditions on f ... Global Optimization with a Power-Transformed Objective and Gaussian Smoothing -- Project Page - GS-PowerTransform/README.md at main · chen-research/GS-PowerTransform We propose a novel method that solves global optimization problems in two steps: (1) perform a (exponential) power -$N$ transformation to the not-necessarily differentiable objective function $f$ and get $f_N$, and (2) optimize the Gaussian -smoothed $f_N$ with stochastic approximations. Abstract We propose a novel method that solves global optimization problems in two steps: (1) perform a (exponential) power - N transformation to the not-necessarily differentiable objective function f to obtain f N, and (2) optimize the Gaussian -smoothed f N with stochastic approximations. Under mild conditions on f, for any δ> 0, we prove that with a sufficiently large power N δ, this ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2412.05204", "content": "Dec 6, 2024 · We propose a novel method that solves global optimization problems in two steps: (1) perform a (exponential) power - N transformation to the not-necessarily differentiable objective function f and get f_N, and (2) optimize the Gaussian -smoothed f_N with stochastic approximations. Under mild conditions on f, for any \\delta>0, we prove that with a sufficiently large power N_\\delta, this method ... Jan 26, 2025 · Bibliographic details on Global Optimization with A Power - Transformed Objective and Gaussian Smoothing . Abstract: We propose a novel method, namely Gaussian Smoothing with a Power-Transformed Objective (GS-PowerOpt), that solves global optimization problems in two steps: (1) perform a (exponential) power - N transformation to the not necessarily differentiable objective f: R d → R and get f N, and (2) optimize the Gaussian -smoothed f N with stochastic approximations. Under mild conditions on f ... Global Optimization with a Power-Transformed Objective and Gaussian Smoothing -- Project Page - GS-PowerTransform/README.md at main · chen-research/GS-PowerTransform We propose a novel method that solves global optimization problems in two steps: (1) perform a (exponential) power -$N$ transformation to the not-necessarily differentiable objective function $f$ and get $f_N$, and (2) optimize the Gaussian -smoothed $f_N$ with stochastic approximations. Abstract We propose a novel method that solves global optimization problems in two steps: (1) perform a (exponential) power - N transformation to the not-necessarily differentiable objective function f to obtain f N, and (2) optimize the Gaussian -smoothed f N with stochastic approximations. Under mild conditions on f, for any δ> 0, we prove that with a sufficiently large power N δ, this ..."}
+{"idx": 3, "title": "dblp: Global Optimization with A Power-Transformed Objective ...", "date": "", "ddg_snippet": "Jan 26, 2025 · Bibliographic details on Global Optimization with A Power - Transformed Objective and Gaussian Smoothing .", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/journals/corr/abs-2412-05204", "content": "Jan 26, 2025 · Bibliographic details on Global Optimization with A Power - Transformed Objective and Gaussian Smoothing ."}
+{"idx": 4, "title": "GS-PowerTransform/README.md at main - GitHub", "date": "", "ddg_snippet": "Global Optimization with a Power-Transformed Objective and Gaussian Smoothing -- Project Page - GS-PowerTransform/README.md at main · chen-research/GS-PowerTransform", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/chen-research/GS-PowerTransform/blob/main/README.md", "content": "Global Optimization with a Power-Transformed Objective and Gaussian Smoothing -- Project Page - GS-PowerTransform/README.md at main · chen-research/GS-PowerTransform"}
+{"idx": 5, "title": "Global Optimization with A Power-Transformed Objective and ...", "date": "", "ddg_snippet": "We propose a novel method that solves global optimization problems in two steps: (1) perform a (exponential) power -$N$ transformation to the not-necessarily differentiable objective function $f$ and get $f_N$, and (2) optimize the Gaussian -smoothed $f_N$ with stochastic approximations.", "subpage_snippet": "", "source": "ui.adsabs.harvard.edu", "link": "https://ui.adsabs.harvard.edu/abs/2024arXiv241205204X/abstract", "content": "We propose a novel method that solves global optimization problems in two steps: (1) perform a (exponential) power -$N$ transformation to the not-necessarily differentiable objective function $f$ and get $f_N$, and (2) optimize the Gaussian -smoothed $f_N$ with stochastic approximations."}
+{"idx": 6, "title": "Global Optimization with A Power-Transformed Objective and ...", "date": "", "ddg_snippet": "Abstract We propose a novel method that solves global optimization problems in two steps: (1) perform a (exponential) power - N transformation to the not-necessarily differentiable objective function f to obtain f N, and (2) optimize the Gaussian -smoothed f N with stochastic approximations. Under mild conditions on f, for any δ> 0, we prove that with a sufficiently large power N δ, this ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.05204v1", "content": "Abstract We propose a novel method that solves global optimization problems in two steps: (1) perform a (exponential) power - N transformation to the not-necessarily differentiable objective function f to obtain f N, and (2) optimize the Gaussian -smoothed f N with stochastic approximations. Under mild conditions on f, for any δ> 0, we prove that with a sufficiently large power N δ, this ..."}
+{"idx": 7, "title": "Global Optimization with a Power-Transformed Objective and ...", "date": "", "ddg_snippet": "We propose a novel method, namely Gaussian . Smoothing with a Power - Transformed Objective . (GS-PowerOpt), that solves global optimization .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=6ojzpDczIY&name=pdf", "content": "We propose a novel method, namely Gaussian . Smoothing with a Power - Transformed Objective . (GS-PowerOpt), that solves global optimization ."}
+{"idx": 8, "title": "Continuation path learning for homotopy optimization", "date": "", "ddg_snippet": "by X Lin · 2023 · Cited by 13 — In this work, we propose a novel model-based approach to learn the whole continuation path for homotopy optimization , which contains infinite ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3618408.3619285", "content": "by X Lin · 2023 · Cited by 13 — In this work, we propose a novel model-based approach to learn the whole continuation path for homotopy optimization , which contains infinite ..."}
+{"idx": 9, "title": "A Flexible Framework for Hyperparameter Optimization Using ...", "date": "", "ddg_snippet": "by SJ Abraham — From a theoretical standpoint, GAMs provide a more interpretable model than other surrogate models as the smoothing functions that are used to model the rela-.", "subpage_snippet": "", "source": "academicweb.nd.edu", "link": "https://academicweb.nd.edu/~jhauenst/preprints/amkchsHomOpt.pdf", "content": "by SJ Abraham — From a theoretical standpoint, GAMs provide a more interpretable model than other surrogate models as the smoothing functions that are used to model the rela-."}
diff --git a/data/sampled_jsons/Going_Deeper_into_Locally_Differentially_Private_Graph_Neural_Networks_arxiv.jsonl b/data/sampled_jsons/Going_Deeper_into_Locally_Differentially_Private_Graph_Neural_Networks_arxiv.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..932c8361612ed291260c7bfab5582bc1f3ab4e59
--- /dev/null
+++ b/data/sampled_jsons/Going_Deeper_into_Locally_Differentially_Private_Graph_Neural_Networks_arxiv.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Going Deeper into Locally Differentially Private Graph ...", "date": "", "ddg_snippet": "Abstract. Graph Neural Networks (GNNs ) have demonstrated superior performance in a variety of graph mining and learning tasks. · 1. Introduction · 2.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46579", "content": "Abstract. Graph Neural Networks (GNNs ) have demonstrated superior performance in a variety of graph mining and learning tasks. · 1. Introduction · 2."}
+{"idx": 1, "title": "Going Deeper into Locally Differentially Private Graph ...", "date": "", "ddg_snippet": "by L He — In this section, we begin with a theoretical analysis of prior work on locally differentially private graph neural networks . (LDPGNN) in Sec. 3.1, identifying ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=2aKHuXdr7Q", "content": "by L He — In this section, we begin with a theoretical analysis of prior work on locally differentially private graph neural networks . (LDPGNN) in Sec. 3.1, identifying ..."}
+{"idx": 2, "title": "[2006.05535] Locally Private Graph Neural Networks", "date": "", "ddg_snippet": "by S Sajadmanesh · 2020 · Cited by 164 — In this paper, we study the problem of node data privacy, where graph nodes have potentially sensitive data that is kept private .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2006.05535", "content": "by S Sajadmanesh · 2020 · Cited by 164 — In this paper, we study the problem of node data privacy, where graph nodes have potentially sensitive data that is kept private ."}
+{"idx": 3, "title": "Differentially Private Graph Neural Networks for Whole- ...", "date": "", "ddg_snippet": "by TT Mueller · 2022 · Cited by 33 — Different approaches to the here introduced application of differential privacy have been explored in the context of federated learning on graphs and locally ... 11 pages", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/iel7/34/4359286/09980390.pdf", "content": "by TT Mueller · 2022 · Cited by 33 — Different approaches to the here introduced application of differential privacy have been explored in the context of federated learning on graphs and locally ... 11 pages"}
+{"idx": 4, "title": "Data Poisoning Attacks to Locally Private Graph Learning ...", "date": "", "ddg_snippet": "by L He · 2025 — Abstract page for arXiv paper 2506.09803 : Devil's Hand: Data Poisoning Attacks to Locally Private Graph Learning Protocols.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2506.09803", "content": "by L He · 2025 — Abstract page for arXiv paper 2506.09803 : Devil's Hand: Data Poisoning Attacks to Locally Private Graph Learning Protocols."}
+{"idx": 5, "title": "GAP: Differentially Private Graph Neural Networks with ...", "date": "", "ddg_snippet": "by S Sajadmanesh · Cited by 106 — In this paper, we study the problem of learning Graph Neural . Networks (GNNs) with Differential Privacy (DP). We pro- pose a novel differentially private GNN ... 18 pages", "subpage_snippet": "", "source": "www.usenix.org", "link": "https://www.usenix.org/system/files/sec23fall-prepub-196-sajadmanesh.pdf", "content": "by S Sajadmanesh · Cited by 106 — In this paper, we study the problem of learning Graph Neural . Networks (GNNs) with Differential Privacy (DP). We pro- pose a novel differentially private GNN ... 18 pages"}
+{"idx": 6, "title": "Differentially Private Decoupled Graph Convolutions for...", "date": "", "ddg_snippet": "by E Chien · Cited by 28 — TL;DR: We propose Graph Differential Privacy (GDP) framework and decoupled graph convolution for multigranular topology privacy protection. Abstract: Graph ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=dd3KNayGFz", "content": "by E Chien · Cited by 28 — TL;DR: We propose Graph Differential Privacy (GDP) framework and decoupled graph convolution for multigranular topology privacy protection. Abstract: Graph ..."}
+{"idx": 7, "title": "Locally Private Graph Neural Networks", "date": "", "ddg_snippet": "by S Sajadmanesh · 2020 · Cited by 164 — In this paper, we study the problem of node data privacy, where graph nodes have potentially sensitive data that is kept private , but they could ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2006.05535", "content": "by S Sajadmanesh · 2020 · Cited by 164 — In this paper, we study the problem of node data privacy, where graph nodes have potentially sensitive data that is kept private , but they could ..."}
+{"idx": 8, "title": "Locally Private Graph Neural Networks - ACM Digital Library", "date": "", "ddg_snippet": "13 Nov 2021 — We propose a privacy-preserving, architecture-agnostic GNN learning framework with formal privacy guarantees based on Local Differential Privacy (LDP).", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3460120.3484565", "content": "13 Nov 2021 — We propose a privacy-preserving, architecture-agnostic GNN learning framework with formal privacy guarantees based on Local Differential Privacy (LDP)."}
+{"idx": 9, "title": "Locally Private Graph Neural Networks (ACM CCS 2021)", "date": "", "ddg_snippet": "We propose a privacy-preserving, architecture-agnostic GNN learning framework with formal privacy guarantees based on Local Differential Privacy (LDP).", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/sisaman/LPGNN", "content": "We propose a privacy-preserving, architecture-agnostic GNN learning framework with formal privacy guarantees based on Local Differential Privacy (LDP)."}
diff --git a/data/sampled_jsons/HTML_is_Better_Than_Plain_Text_for_Modeling_Retrieved_Knowledge_in_RAG_Systems_E91gjsccP1_HtmlRAG_year_2024.jsonl b/data/sampled_jsons/HTML_is_Better_Than_Plain_Text_for_Modeling_Retrieved_Knowledge_in_RAG_Systems_E91gjsccP1_HtmlRAG_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4ac601f6fb2c9416b1a398025271ccc5e6362d07
--- /dev/null
+++ b/data/sampled_jsons/HTML_is_Better_Than_Plain_Text_for_Modeling_Retrieved_Knowledge_in_RAG_Systems_E91gjsccP1_HtmlRAG_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved ...", "date": "", "ddg_snippet": "To alleviate this problem, we propose HtmlRAG, which uses HTML instead of plain text as the format of retrieved knowledge in RAG . We believe HTML is better than plain text in modeling knowledge in external documents, and most LLMs possess robust capacities to understand HTML . However, utilizing HTML presents new challenges.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2411.02959", "content": "To alleviate this problem, we propose HtmlRAG, which uses HTML instead of plain text as the format of retrieved knowledge in RAG . We believe HTML is better than plain text in modeling knowledge in external documents, and most LLMs possess robust capacities to understand HTML . However, utilizing HTML presents new challenges."}
+{"idx": 1, "title": "HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieval Results ...", "date": "", "ddg_snippet": "We propose HtmlRAG, which uses HTML instead of plain text as the format of external knowledge in RAG systems . To tackle the long context brought by HTML , we propose Lossless HTML Cleaning and Two-Step Block-Tree-Based HTML Pruning.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/plageon/HtmlRAG", "content": "We propose HtmlRAG, which uses HTML instead of plain text as the format of external knowledge in RAG systems . To tackle the long context brought by HTML , we propose Lossless HTML Cleaning and Two-Step Block-Tree-Based HTML Pruning."}
+{"idx": 2, "title": "Understanding HtmlRAG: HTML is Better Than Plain Text for Modeling ...", "date": "", "ddg_snippet": "HtmlRAG introduces an innovative method for enhancing Retrieval-Augmented Generation ( RAG ) systems by using HTML over plain text for integrating external knowledge into large language models (LLMs). This approach preserves essential data structure, improving LLM performance and accuracy.", "subpage_snippet": "", "source": "techchilli.com", "link": "https://techchilli.com/artificial-intelligence/understanding-htmlrag-html-is-better-than-plain-text-for-modeling-retrieved-knowledge-in-rag-systems/", "content": "HtmlRAG introduces an innovative method for enhancing Retrieval-Augmented Generation ( RAG ) systems by using HTML over plain text for integrating external knowledge into large language models (LLMs). This approach preserves essential data structure, improving LLM performance and accuracy."}
+{"idx": 3, "title": "HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved ...", "date": "", "ddg_snippet": "We believe HTML is better than plain text in modeling knowledge in external documents, and most LLMs possess robust capacities to understand HTML . However, utilizing HTML presents new challenges. HTML contains additional content such as tags, JavaScript, and CSS specifications, which bring extra input tokens and noise to the RAG system .", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.1145/3696410.3714546", "content": "We believe HTML is better than plain text in modeling knowledge in external documents, and most LLMs possess robust capacities to understand HTML . However, utilizing HTML presents new challenges. HTML contains additional content such as tags, JavaScript, and CSS specifications, which bring extra input tokens and noise to the RAG system ."}
+{"idx": 4, "title": "HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved ...", "date": "", "ddg_snippet": "268 In this paper, we propose HtmlRAG, which uses HTML instead of 269 plain text as the format of retrieved knowledge in RAG systems , 270 aiming to keep richer semantic and structured information that is 271 missing in plain text .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=E91gjsccP1", "content": "268 In this paper, we propose HtmlRAG, which uses HTML instead of 269 plain text as the format of retrieved knowledge in RAG systems , 270 aiming to keep richer semantic and structured information that is 271 missing in plain text ."}
+{"idx": 5, "title": "HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved ...", "date": "", "ddg_snippet": "We believe HTML is better than plain text in modeling knowledge in external documents, and most LLMs possess robust capacities to understand HTML . However, utilizing HTML presents new challenges.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/385560345_HtmlRAG_HTML_is_Better_Than_Plain_Text_for_Modeling_Retrieved_Knowledge_in_RAG_Systems", "content": "We believe HTML is better than plain text in modeling knowledge in external documents, and most LLMs possess robust capacities to understand HTML . However, utilizing HTML presents new challenges."}
+{"idx": 6, "title": "HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved ...", "date": "", "ddg_snippet": "HTML contains a lot of intrinsic information, so we propose taking HTML as the format of retrieved knowledge in RAG systems , and design HTML cleaning and block-tree-based HTML pruning to shorten the length and preserve information.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2411.02959", "content": "HTML contains a lot of intrinsic information, so we propose taking HTML as the format of retrieved knowledge in RAG systems , and design HTML cleaning and block-tree-based HTML pruning to shorten the length and preserve information."}
+{"idx": 7, "title": "HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved ...", "date": "", "ddg_snippet": "This paper discusses HtmlRAG, a new approach that uses HTML instead of plain text to improve how systems retrieve and generate knowledge from the web, especially for large language models (LLMs). Using HTML directly allows RAG systems to retain more useful information, leading to better performance in understanding and generating responses.", "subpage_snippet": "", "source": "ai-search.io", "link": "https://ai-search.io/papers/htmlrag-html-is-better-than-plain-text-for-modeling-retrieved-knowledge-in-rag-systems", "content": "This paper discusses HtmlRAG, a new approach that uses HTML instead of plain text to improve how systems retrieve and generate knowledge from the web, especially for large language models (LLMs). Using HTML directly allows RAG systems to retain more useful information, leading to better performance in understanding and generating responses."}
+{"idx": 8, "title": "HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved ...", "date": "", "ddg_snippet": "HtmlRAG, which uses HTML instead of plain text as the format of retrieved knowledge in RAG , and designs a two-step block-tree-based pruning method that prunes useless HTML blocks and keeps only the relevant part of the HTML .", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/HtmlRAG:-HTML-is-Better-Than-Plain-Text-for-in-RAG-Tan-Dou/7cfd2426ca908c8c5a81bd7c7ca01f914a972de4/figure/1", "content": "HtmlRAG, which uses HTML instead of plain text as the format of retrieved knowledge in RAG , and designs a two-step block-tree-based pruning method that prunes useless HTML blocks and keeps only the relevant part of the HTML ."}
+{"idx": 9, "title": "Htmlrag: HTML Is Better Than Plain Text For Modeling Retrieved ...", "date": "", "ddg_snippet": "HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systems Jiejun Tan∗ Zhicheng Dou† Wen Wang Gaoling School of Artificial Gaoling School of Artificial Baichuan Intelligent Technology Intelligence Intelligence Beijing, China Renmin University of China Renmin University of China wangwen@baichuan-inc.com Beijing, China Beijing, China zstanjj@ruc.edu.cn dou@ruc ...", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/788595083/2411-02959v1", "content": "HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systems Jiejun Tan∗ Zhicheng Dou† Wen Wang Gaoling School of Artificial Gaoling School of Artificial Baichuan Intelligent Technology Intelligence Intelligence Beijing, China Renmin University of China Renmin University of China wangwen@baichuan-inc.com Beijing, China Beijing, China zstanjj@ruc.edu.cn dou@ruc ..."}
diff --git "a/data/sampled_jsons/Hazan_et_al_2016_zeroth-order_homotopy_method_complexity_O(d\302\262\316\265\342\201\264).jsonl" "b/data/sampled_jsons/Hazan_et_al_2016_zeroth-order_homotopy_method_complexity_O(d\302\262\316\265\342\201\264).jsonl"
new file mode 100644
index 0000000000000000000000000000000000000000..f422c65c7de86203c7c2e69c658e570b6af784f2
--- /dev/null
+++ "b/data/sampled_jsons/Hazan_et_al_2016_zeroth-order_homotopy_method_complexity_O(d\302\262\316\265\342\201\264).jsonl"
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Single Loop Gaussian Homotopy for Non-convex Functions", "date": "", "ddg_snippet": "Gaussian homotopy (GH) Method to find better stationary points for non-convex optimization using Gaussian smoothing.", "subpage_snippet": "", "source": "nips.cc", "link": "https://nips.cc/media/neurips-2022/Slides/53549_XLjvS0q.pdf", "content": "Gaussian homotopy (GH) Method to find better stationary points for non-convex optimization using Gaussian smoothing."}
+{"idx": 1, "title": "Single Loop Gaussian Homotopy Method for Non-convex Optimization", "date": "", "ddg_snippet": "Let us here compare our algorithms, ZOSLGHr and ZOSLGHd, to three zeroth - order algorithms: ZOSGD [Ghadimi & Lan (2013)], ZOAdaMM [Chen et al. (2019)], and ZOGradOpt [ Hazan et al . ( 2016 )].", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2203.05717", "content": "Let us here compare our algorithms, ZOSLGHr and ZOSLGHd, to three zeroth - order algorithms: ZOSGD [Ghadimi & Lan (2013)], ZOAdaMM [Chen et al. (2019)], and ZOGradOpt [ Hazan et al . ( 2016 )]."}
+{"idx": 2, "title": "Complexity of an Homotopy Method at the Neighbourhood of a ...", "date": "", "ddg_snippet": "Sep 28, 2016 · We have used an homotopy method to obtain approximate zeros of the considered function. The novelty in our approach is the establishment of new convergence results based on a Lipschitz...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/309080541_Complexity_of_an_Homotopy_Method_at_the_Neighbourhood_of_a_Zero", "content": "Sep 28, 2016 · We have used an homotopy method to obtain approximate zeros of the considered function. The novelty in our approach is the establishment of new convergence results based on a Lipschitz..."}
+{"idx": 3, "title": "Black-Box Reductions for Zeroth-Order Gradient Algorithms to ... Homotopy Smoothing for Non-Smooth Problems with Lower ... An Adaptive Parallel Gaussian Homotopy Approach for Zeroth ... Black-Box Reductions for Zeroth-Order Gradient Algorithms to Achieve Black-Box Reductions for Zeroth-Order Gradient Algorithms to Achieve Notes on the homotopy analysis method : Some definitions and theorems Black-Box Reductions for Zeroth-Order Gradient Algorithms to Achieve Black-Box Reductions for Zeroth-Order Gradient Algorithms to Achieve Notes on the homotopy analysis method : Some definitions and theorems Notes on the homotopy analysis method: Some definitions and ...", "date": "", "ddg_snippet": "Zeroth - order (ZO) optimization has been the key technique for various machine learning ap-plications especially for black-box adversarial attack, where models need to be learned in a gradient-free manner. Although many ZO algorithms have been proposed, the high function query complexities hinder their applications seriously. To address this challen... See full list on jmlr.org In this section, we compare the performance of our reduction methods with other popular ZO algorithms. We conduct experiments on ZO-SVRG, ZO-SAGA and ZO-Varag with and without our reduction methods. We conduct two experiments with real-world datasets. The rst experiment is generation of black-box adversarial examples for non-convex objec-tives, and... See full list on jmlr.org In this subsection, we mainly consider logistic regression and its variant. To conduct ex-periments on AdaptRdct-C, we rst choose the classical logistic regression problem See full list on jmlr.org Appendix A provides the proof of AdaptRdct-C. Appendix B provides the proof of AdaptRdct-NC. Appendix C provides the proof of ZO-SVRG. Appendix D provides the proof of ZO-SAGA. Appendix E provides the proof of ZO-Varag. Appendix F provides choice of parameters in the experiment and additional experiment results of our frameworks applied to ZO-SVRG,... See full list on jmlr.org The second inequality comes from the fact that T k is always positive. Since See full list on jmlr.org In this section, we provide our choice of parameters and more experiments results of AdaptRdct-C (ZO-SVRG/ZO-SAGA/ZO-Varag) and AdaptRdct-NC (ZO-SVRG/ZO-SAGA/ZO-Varag) under di erent parameter settings. See full list on jmlr.org In this paper, we develop a novel homotopy smoothing (HOPS) algorithm for solving a family of non-smooth problems that is composed of a non-smooth term with an explicit max-structure and a smooth term or a simple non-smooth term whose proximal mapping is easy to compute. The Gaussian homotopy method is a classical optimization approach for solving nonconvex problems. It applies Gaussian smoothing to a given problem, using varyin. Can zeroth-order optimization be combined with reduction techniques? Our theoretic study and experimental results highlight the advantages of combining ZO optimization with the reduction techniques. To the best of our knowledge, we are the rst to propose black-box reduction frameworks for zeroth-order algorithms and apply them to zeroth-order optimization. Which Zo algorithm is used in function query complexity? Thus, in this paper, we will investigate di erent ZO algorithms in term of function query complexity. Speci cally, Nesterov and Spokoiny (2017) proposed the ZO gradient descent (ZO-GD) algorithm that used the Gaussian smoothing technique to construct a two-point gradient estimator. How do you find the 2nd order homotopy deformation equation? Taking the 2nd-order homotopy-derivative 2 on both sides of (1) gives the 2nd-order deformation equation: x 2 f ′ ( x 0) + 1 2 x 1 2 f ″ ( x 0) = 0 , whose solution is x 2 = - x 1 2 f ″ ( x 0) 2 f ′ ( x 0) = - f 2 ( x 0) f ″ ( x 0) 2 [ f ′ ( x 0)] 3. What is zeroth-order optimization in machine learning? Zeroth - order (ZO) optimization has been the key technique for various machine learning ap-plications especially for black-box adversarial attack, where models need to be learned in a gradient-free manner. Although many ZO algorithms have been proposed, the high function query complexities hinder their applications seriously. Can a reduction framework be used for Zo algorithms under convex and non-convex setting? In this paper, we develop two reduction frameworks for ZO algorithms under convex and non-convex setting, respectively. Our frameworks work in a black-box manner, thus they can be applied to a wide range of ZO algorithms to further lower their function query complexities. What is a homotopy perturbation method? Besides, the so-called “homotopy perturbation method” , (proposed in 1998) is exactly the same as the early homotopy analysis method (proposed in 1992) and is a special case of the late homotopy analysis method in case of ℏ = - 1, as illustrated by Abbasbandy and proved by Sajid et al. , in general. Indeed, Dr. Apr 1, 2009 · In this section, the properties of homotopy -derivatives proved in Section 2 are employed to deduce the high-order deformation equations for various types of zeroth - order deformation equations.", "subpage_snippet": "", "source": "jmlr.org", "link": "https://jmlr.org/papers/volume22/20-611/20-611.pdf", "content": "Zeroth - order (ZO) optimization has been the key technique for various machine learning ap-plications especially for black-box adversarial attack, where models need to be learned in a gradient-free manner. Although many ZO algorithms have been proposed, the high function query complexities hinder their applications seriously. To address this challen... See full list on jmlr.org In this section, we compare the performance of our reduction methods with other popular ZO algorithms. We conduct experiments on ZO-SVRG, ZO-SAGA and ZO-Varag with and without our reduction methods. We conduct two experiments with real-world datasets. The rst experiment is generation of black-box adversarial examples for non-convex objec-tives, and... See full list on jmlr.org In this subsection, we mainly consider logistic regression and its variant. To conduct ex-periments on AdaptRdct-C, we rst choose the classical logistic regression problem See full list on jmlr.org Appendix A provides the proof of AdaptRdct-C. Appendix B provides the proof of AdaptRdct-NC. Appendix C provides the proof of ZO-SVRG. Appendix D provides the proof of ZO-SAGA. Appendix E provides the proof of ZO-Varag. Appendix F provides choice of parameters in the experiment and additional experiment results of our frameworks applied to ZO-SVRG,... See full list on jmlr.org The second inequality comes from the fact that T k is always positive. Since See full list on jmlr.org In this section, we provide our choice of parameters and more experiments results of AdaptRdct-C (ZO-SVRG/ZO-SAGA/ZO-Varag) and AdaptRdct-NC (ZO-SVRG/ZO-SAGA/ZO-Varag) under di erent parameter settings. See full list on jmlr.org In this paper, we develop a novel homotopy smoothing (HOPS) algorithm for solving a family of non-smooth problems that is composed of a non-smooth term with an explicit max-structure and a smooth term or a simple non-smooth term whose proximal mapping is easy to compute. The Gaussian homotopy method is a classical optimization approach for solving nonconvex problems. It applies Gaussian smoothing to a given problem, using varyin. Can zeroth-order optimization be combined with reduction techniques? Our theoretic study and experimental results highlight the advantages of combining ZO optimization with the reduction techniques. To the best of our knowledge, we are the rst to propose black-box reduction frameworks for zeroth-order algorithms and apply them to zeroth-order optimization. Which Zo algorithm is used in function query complexity? Thus, in this paper, we will investigate di erent ZO algorithms in term of function query complexity. Speci cally, Nesterov and Spokoiny (2017) proposed the ZO gradient descent (ZO-GD) algorithm that used the Gaussian smoothing technique to construct a two-point gradient estimator. How do you find the 2nd order homotopy deformation equation? Taking the 2nd-order homotopy-derivative 2 on both sides of (1) gives the 2nd-order deformation equation: x 2 f ′ ( x 0) + 1 2 x 1 2 f ″ ( x 0) = 0 , whose solution is x 2 = - x 1 2 f ″ ( x 0) 2 f ′ ( x 0) = - f 2 ( x 0) f ″ ( x 0) 2 [ f ′ ( x 0)] 3. What is zeroth-order optimization in machine learning? Zeroth - order (ZO) optimization has been the key technique for various machine learning ap-plications especially for black-box adversarial attack, where models need to be learned in a gradient-free manner. Although many ZO algorithms have been proposed, the high function query complexities hinder their applications seriously. Can a reduction framework be used for Zo algorithms under convex and non-convex setting? In this paper, we develop two reduction frameworks for ZO algorithms under convex and non-convex setting, respectively. Our frameworks work in a black-box manner, thus they can be applied to a wide range of ZO algorithms to further lower their function query complexities. What is a homotopy perturbation method? Besides, the so-called “homotopy perturbation method” , (proposed in 1998) is exactly the same as the early homotopy analysis method (proposed in 1992) and is a special case of the late homotopy analysis method in case of ℏ = - 1, as illustrated by Abbasbandy and proved by Sajid et al. , in general. Indeed, Dr. Apr 1, 2009 · In this section, the properties of homotopy -derivatives proved in Section 2 are employed to deduce the high-order deformation equations for various types of zeroth - order deformation equations."}
+{"idx": 4, "title": "Homotopy Smoothing for Non-Smooth Problems with Lower ...", "date": "", "ddg_snippet": "In this paper, we develop a novel homotopy smoothing (HOPS) algorithm for solving a family of non-smooth problems that is composed of a non-smooth term with an explicit max-structure and a smooth term or a simple non-smooth term whose proximal mapping is easy to compute.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2016/file/b5dc4e5d9b495d0196f61d45b26ef33e-Paper.pdf", "content": "In this paper, we develop a novel homotopy smoothing (HOPS) algorithm for solving a family of non-smooth problems that is composed of a non-smooth term with an explicit max-structure and a smooth term or a simple non-smooth term whose proximal mapping is easy to compute."}
+{"idx": 5, "title": "An Adaptive Parallel Gaussian Homotopy Approach for Zeroth ...", "date": "", "ddg_snippet": "The Gaussian homotopy method is a classical optimization approach for solving nonconvex problems. It applies Gaussian smoothing to a given problem, using varyin.", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10831880", "content": "The Gaussian homotopy method is a classical optimization approach for solving nonconvex problems. It applies Gaussian smoothing to a given problem, using varyin."}
+{"idx": 6, "title": "Notes on the homotopy analysis method: Some definitions and ...", "date": "", "ddg_snippet": "Apr 1, 2009 · In this section, the properties of homotopy -derivatives proved in Section 2 are employed to deduce the high-order deformation equations for various types of zeroth - order deformation equations.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S1007570408001214", "content": "Apr 1, 2009 · In this section, the properties of homotopy -derivatives proved in Section 2 are employed to deduce the high-order deformation equations for various types of zeroth - order deformation equations."}
+{"idx": 7, "title": "Can a One-Point Feedback Zeroth - order Algorithm Achieve Linear...", "date": "", "ddg_snippet": "Stochastic first-and zeroth - order methods for nonconvex stochastic programming. SIAM journal on optimization, 23(4), 2341–2368. Hazan et al ., ( 2016 ).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.12228v1", "content": "Stochastic first-and zeroth - order methods for nonconvex stochastic programming. SIAM journal on optimization, 23(4), 2341–2368. Hazan et al ., ( 2016 )."}
+{"idx": 8, "title": "(PDF) Homotopy Analysis Method in Nonlinear Differential Equations", "date": "", "ddg_snippet": "If the noise terms exist, the Homotopy Analysis method gives the same series solution as in Adomian Decomposition Method as well as homotopy Perturbation Method (Wahab et al , 2015) and we get the exact solution using the initial guess in Homotopy Analysis Method using the results...", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/98185537/Homotopy_Analysis_Method_in_Nonlinear_Differential_Equations", "content": "If the noise terms exist, the Homotopy Analysis method gives the same series solution as in Adomian Decomposition Method as well as homotopy Perturbation Method (Wahab et al , 2015) and we get the exact solution using the initial guess in Homotopy Analysis Method using the results..."}
+{"idx": 9, "title": "An Homotopy Algorithm for the Lasso with Online Observations", "date": "", "ddg_snippet": "... Malioutov et al . first exploited the homotopy method to choose a suitable parameter for l 1 -norm regularization with a noisy term in an underdetermined system and employed the homotopy continuation-based method to solve BPDN for sparse signal processing [97].", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/215990086_An_Homotopy_Algorithm_for_the_Lasso_with_Online_Observations", "content": "... Malioutov et al . first exploited the homotopy method to choose a suitable parameter for l 1 -norm regularization with a noisy term in an underdetermined system and employed the homotopy continuation-based method to solve BPDN for sparse signal processing [97]."}
diff --git a/data/sampled_jsons/Herbort_2013_3D_range_scan_enhancement_using_image-based_methods.jsonl b/data/sampled_jsons/Herbort_2013_3D_range_scan_enhancement_using_image-based_methods.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..448e80039eca311bcd9fe6f5489a46a781778fd2
--- /dev/null
+++ b/data/sampled_jsons/Herbort_2013_3D_range_scan_enhancement_using_image-based_methods.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[PDF] New Methods for Surface Reconstruction from Range Images", "date": "", "ddg_snippet": "3 D range scan enhancement using image - based methods .New Methods for Triangulation-based Shape Acquisition using Laser Scanners .", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/New-Methods-for-Surface-Reconstruction-from-Range-Curless/6e7c4d7d50d639a9819b9ac8585df76a3d0b7e1a", "content": "3 D range scan enhancement using image - based methods .New Methods for Triangulation-based Shape Acquisition using Laser Scanners ."}
+{"idx": 1, "title": "ISPRS e-Bulletin #4: September 2013", "date": "", "ddg_snippet": "3 D range scan enhancement using image - based methods Steffen Herbort, Britta Gerken, Daniel Schugk, Christian Wöhler Pages: 69-84.", "subpage_snippet": "", "source": "www.isprs.org", "link": "https://www.isprs.org/news/newsletter/2013-04/", "content": "3 D range scan enhancement using image - based methods Steffen Herbort, Britta Gerken, Daniel Schugk, Christian Wöhler Pages: 69-84."}
+{"idx": 2, "title": "Transform text into images and explore with endless imagination.", "date": "", "ddg_snippet": "A generated image based on your input prompt. flip_camera_androidFlip card.", "subpage_snippet": "", "source": "labs.google", "link": "https://labs.google/fx/tools/image-fx/unsupported-country", "content": "A generated image based on your input prompt. flip_camera_androidFlip card."}
+{"idx": 3, "title": "Review of geometric fusion of remote sensing imagery and laser...", "date": "", "ddg_snippet": "3 D range scan enhancement using image - based methods .", "subpage_snippet": "", "source": "www.lsgi.polyu.edu.hk", "link": "http://www.lsgi.polyu.edu.hk/staff/Bo.Wu/publications/Wu_2015_Review_of_Geometric_Fusion_of_Imagery_and_Laser_Scanning.pdf", "content": "3 D range scan enhancement using image - based methods ."}
+{"idx": 4, "title": "High-Precision 3 D Scanning Enhancing Redesign and Production in...", "date": "", "ddg_snippet": "To overcome this, engineers required precise digital capture of the monocoque’s interior. High-Precision 3 D Scanning Enhancing Redesign and Production in Formula Race Car (2).png.", "subpage_snippet": "", "source": "www.3d-scantech.com", "link": "https://www.3d-scantech.com/high-precision-3d-scanning-enhancing-redesign-and-production-in-formula-race-car/", "content": "To overcome this, engineers required precise digital capture of the monocoque’s interior. High-Precision 3 D Scanning Enhancing Redesign and Production in Formula Race Car (2).png."}
+{"idx": 5, "title": "Application of the artec eva scanner for orthotics...", "date": "", "ddg_snippet": "3 D range scan enhancement using image - based methods . ISPRS Journal of Photogrammetry and Remote Sensing, [internet] 2017 [cited 2017 Aug.", "subpage_snippet": "", "source": "www.prolekare.cz", "link": "https://www.prolekare.cz/en/journals/the-clinician-and-technology-journal/2019-3-29/application-of-the-artec-eva-scanner-for-orthotics-in-practice-122289", "content": "3 D range scan enhancement using image - based methods . ISPRS Journal of Photogrammetry and Remote Sensing, [internet] 2017 [cited 2017 Aug."}
+{"idx": 6, "title": "Image - Based 3 D Photography Using", "date": "", "ddg_snippet": "2002. Image - based 3 D photography using opacity hulls. In Proceedings of the 29th annual conference on Computer graphics and interactive techniques: July 23-26, 2002, San Antonio, Texas, 427-437.", "subpage_snippet": "", "source": "dash.harvard.edu", "link": "https://dash.harvard.edu/server/api/core/bitstreams/7312037c-58e9-6bd4-e053-0100007fdf3b/content", "content": "2002. Image - based 3 D photography using opacity hulls. In Proceedings of the 29th annual conference on Computer graphics and interactive techniques: July 23-26, 2002, San Antonio, Texas, 427-437."}
+{"idx": 7, "title": "(DOC) 3 D Reconstruction using Kinect Sensor", "date": "", "ddg_snippet": "We also report on enhancing image based visual hull rendering by depth measurements, and compare the results to KinectFusion.The use of 3 D scanners to capture and recreate defining objects is known as 3 D virtualisation.", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/143872374/3D_Reconstruction_using_Kinect_Sensor", "content": "We also report on enhancing image based visual hull rendering by depth measurements, and compare the results to KinectFusion.The use of 3 D scanners to capture and recreate defining objects is known as 3 D virtualisation."}
+{"idx": 8, "title": "3 d Scanner And Printer Combo: Top 5 Advantages!", "date": "", "ddg_snippet": "Revolutionize your workflow with a 3 D scanner and printer combo. Scan real-world objects and print them instantly! Explore our selection of top-rated bundles.", "subpage_snippet": "", "source": "2bdigital.ae", "link": "https://2bdigital.ae/3d-scanner-and-printer-combo/", "content": "Revolutionize your workflow with a 3 D scanner and printer combo. Scan real-world objects and print them instantly! Explore our selection of top-rated bundles."}
+{"idx": 9, "title": "Split PDF into PDF files of specific page ranges", "date": "", "ddg_snippet": "Enhance Scanned PDF.Split PDF is a free online tool that splits a large PDF into multiple smaller files each of which is a PDF with one or more pages. PDF splitter can extract specific page ranges or extract every page into a separate PDF document.", "subpage_snippet": "", "source": "www.i2pdf.com", "link": "https://www.i2pdf.com/split-pdf", "content": "Enhance Scanned PDF.Split PDF is a free online tool that splits a large PDF into multiple smaller files each of which is a PDF with one or more pages. PDF splitter can extract specific page ranges or extract every page into a separate PDF document."}
diff --git a/data/sampled_jsons/Hierarchical_Overlapping_Clustering_costtemp_function_w(E)_w(A,C)_w(B)_x_1(1+2p)_p_1sqrt(6n).jsonl b/data/sampled_jsons/Hierarchical_Overlapping_Clustering_costtemp_function_w(E)_w(A,C)_w(B)_x_1(1+2p)_p_1sqrt(6n).jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e7a0e925ae73d71a5d59a969013704407871522b
--- /dev/null
+++ b/data/sampled_jsons/Hierarchical_Overlapping_Clustering_costtemp_function_w(E)_w(A,C)_w(B)_x_1(1+2p)_p_1sqrt(6n).jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Hierarchical Overlapping Clustering Function Algorithm and Scalability ...", "date": "", "ddg_snippet": "Overlap and hierarchy are two prevalent phenomena in clustering , and usually coexist in a single system. There are several studies on each of them separately, but it is unclear how to characterize and evaluate the hybrid structures yet. To address this issue, we initiate the study of hierarchical overlapping clustering on graphs by introducing a new cost function for it. We show the ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=oHSXRy29tj", "content": "Overlap and hierarchy are two prevalent phenomena in clustering , and usually coexist in a single system. There are several studies on each of them separately, but it is unclear how to characterize and evaluate the hybrid structures yet. To address this issue, we initiate the study of hierarchical overlapping clustering on graphs by introducing a new cost function for it. We show the ..."}
+{"idx": 1, "title": "PDF A cost function for similarity-based hierarchical clustering", "date": "", "ddg_snippet": "The development of algorithms for hierarchical clustering has been hampered by a shortage of precise objective functions . To help address this situation, we introduce a simple cost function on hierarchies over a set of points, given pairwise similarities between those points. We show that this criterion behaves sensibly in canonical instances and that it admits a top-down construction ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1510.05043.pdf", "content": "The development of algorithms for hierarchical clustering has been hampered by a shortage of precise objective functions . To help address this situation, we introduce a simple cost function on hierarchies over a set of points, given pairwise similarities between those points. We show that this criterion behaves sensibly in canonical instances and that it admits a top-down construction ..."}
+{"idx": 2, "title": "PDF Overlapping Hierarchical Clustering (OHC)", "date": "", "ddg_snippet": "1 Introduction Agglomerative hierarchical clustering methods are widely used to analyze large amounts of data. These successful methods construct a dendrogram { a tree structure { that enables a natural exploration of data which is very suitable even for non-expert users.", "subpage_snippet": "", "source": "inria.hal.science", "link": "https://inria.hal.science/hal-02452729/file/Overlapping_Hierarchical_Clustering_IDA2020_Camera_Ready_.pdf", "content": "1 Introduction Agglomerative hierarchical clustering methods are widely used to analyze large amounts of data. These successful methods construct a dendrogram { a tree structure { that enables a natural exploration of data which is very suitable even for non-expert users."}
+{"idx": 3, "title": "PDF Hierarchical Clustering: Objective Functions and Algorithms", "date": "", "ddg_snippet": "Motivated by the fact that most work on hierarchical clustering was based on providing algo-rithms, rather than optimizing a speci c objective, [19] framed similarity-based hierarchical clustering as a combinatorial optimization problem, where a `good' hierarchical clustering is one that minimizes some cost function .", "subpage_snippet": "", "source": "groups.csail.mit.edu", "link": "https://groups.csail.mit.edu/tds/papers/Mallmann-Trenn/SODA18.pdf", "content": "Motivated by the fact that most work on hierarchical clustering was based on providing algo-rithms, rather than optimizing a speci c objective, [19] framed similarity-based hierarchical clustering as a combinatorial optimization problem, where a `good' hierarchical clustering is one that minimizes some cost function ."}
+{"idx": 4, "title": "PDF Hierarchical Clustering: O(1)-Approximation for Well ... - NeurIPS", "date": "", "ddg_snippet": "Abstract Hierarchical clustering studies a recursive partition of a data set into clusters of successively smaller size, and is a fundamental problem in data analysis. In this work we study the cost function for hierarchical clustering introduced by Dasgupta [12], and present two polynomial-time approximation algorithms: Our first result is an O(1)-approximation algorithm for graphs of high ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2021/file/4d68e143defa221fead61c84de7527a3-Paper.pdf", "content": "Abstract Hierarchical clustering studies a recursive partition of a data set into clusters of successively smaller size, and is a fundamental problem in data analysis. In this work we study the cost function for hierarchical clustering introduced by Dasgupta [12], and present two polynomial-time approximation algorithms: Our first result is an O(1)-approximation algorithm for graphs of high ..."}
+{"idx": 5, "title": "Overlapping Hierarchical Clustering (OHC) | SpringerLink", "date": "", "ddg_snippet": "2 Overlapping Hierarchical Clustering 2.1 Intuition and Basic Definitions In a nutshell, our method obtains clusters in a gradual agglomerative fashion and in a precise way.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-3-030-44584-3_21", "content": "2 Overlapping Hierarchical Clustering 2.1 Intuition and Basic Definitions In a nutshell, our method obtains clusters in a gradual agglomerative fashion and in a precise way."}
+{"idx": 6, "title": "Nearly-Optimal Hierarchical Clustering for Well-Clustered Graphs", "date": "", "ddg_snippet": "Nearly-Optimal Hierarchical Clustering for W ell-Clustered Graphs ∗ Steinar Laenen † Bogdan-Adrian Manghiuc ‡ He Sun § Abstract", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/371684528_Nearly-Optimal_Hierarchical_Clustering_for_Well-Clustered_Graphs", "content": "Nearly-Optimal Hierarchical Clustering for W ell-Clustered Graphs ∗ Steinar Laenen † Bogdan-Adrian Manghiuc ‡ He Sun § Abstract"}
+{"idx": 7, "title": "Hierarchical overlapping clustering: cost function, algorithm and ...", "date": "", "ddg_snippet": "To address this issue, we initiate the study of hierarchical overlapping clustering on graphs by introducing a new cost function for it. We show the rationality of our cost function via several intuitive properties, and develop an approximation algorithm that achieves a provably constant approximation factor for its dual version.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=oHSXRy29tj", "content": "To address this issue, we initiate the study of hierarchical overlapping clustering on graphs by introducing a new cost function for it. We show the rationality of our cost function via several intuitive properties, and develop an approximation algorithm that achieves a provably constant approximation factor for its dual version."}
+{"idx": 8, "title": "PDF Overlapping Hierarchical Clustering (OHC)", "date": "", "ddg_snippet": "Overlapping hierarchical clustering framework Principle of the algorithm: Initialisation: Start with singleton clusters and the 0-neighbourhood graph. Main loop: Add links by increasing order in the graph, Look for impacted clusters, Use a density-based merging threshold to decide whether to grow the clusters or not.", "subpage_snippet": "", "source": "inria.hal.science", "link": "https://inria.hal.science/hal-02452729/file/55_Overlapping_Hierarchical_Clustering_IDA_2020.pdf", "content": "Overlapping hierarchical clustering framework Principle of the algorithm: Initialisation: Start with singleton clusters and the 0-neighbourhood graph. Main loop: Add links by increasing order in the graph, Look for impacted clusters, Use a density-based merging threshold to decide whether to grow the clusters or not."}
+{"idx": 9, "title": "PDF G O arXiv:2306.09950v1 [cs.DS] 16 Jun 2023", "date": "", "ddg_snippet": "This paper presents two eficient hierarchical clustering (HC) algorithms with respect to Dasgupta's cost function . For any input graph G with a clear cluster-structure, our designed algorithms run in nearly-linear time in the input size of G, and return an O(1)-approximate HC tree with respect to Dasgupta's cost function . We compare the performance of our algorithm against the previous ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2306.09950.pdf", "content": "This paper presents two eficient hierarchical clustering (HC) algorithms with respect to Dasgupta's cost function . For any input graph G with a clear cluster-structure, our designed algorithms run in nearly-linear time in the input size of G, and return an O(1)-approximate HC tree with respect to Dasgupta's cost function . We compare the performance of our algorithm against the previous ..."}
diff --git a/data/sampled_jsons/Hierarchical_Overlapping_Clustering_on_Graphs_Algorithm_2_time_complexity_page_7.jsonl b/data/sampled_jsons/Hierarchical_Overlapping_Clustering_on_Graphs_Algorithm_2_time_complexity_page_7.jsonl
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+{"idx": 1, "title": "2.3. Clustering — scikit-learn 1.7.2 documentation", "date": "", "ddg_snippet": "Each clustering algorithm comes in two variants: a class, that implements the fit method to learn the clusters on train data, and a function, that ...", "subpage_snippet": "", "source": "scikit-learn.org", "link": "https://scikit-learn.org/stable/modules/clustering.html", "content": "Each clustering algorithm comes in two variants: a class, that implements the fit method to learn the clusters on train data, and a function, that ..."}
+{"idx": 2, "title": "2.3. Clustering — scikit-learn 1.6.1 documentation", "date": "", "ddg_snippet": "Each clustering algorithm comes in two variants: a class, that implements the fit method to learn the clusters on train data, and a function, that ...", "subpage_snippet": "", "source": "scikit-learn.org", "link": "https://scikit-learn.org/1.6/modules/clustering.html", "content": "Each clustering algorithm comes in two variants: a class, that implements the fit method to learn the clusters on train data, and a function, that ..."}
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+{"idx": 5, "title": "(PDF) Top 10 algorithms in data mining", "date": "", "ddg_snippet": "This paper presents the top 10 data mining algorithms identified by the IEEE International Conference on Data Mining (ICDM) in December 2006: C4.5, k ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/29467751_Top_10_algorithms_in_data_mining", "content": "This paper presents the top 10 data mining algorithms identified by the IEEE International Conference on Data Mining (ICDM) in December 2006: C4.5, k ..."}
+{"idx": 6, "title": "Tree-Based Text Retrieval via Hierarchical Clustering in RAG", "date": "", "ddg_snippet": "We then iteratively merge the two closest clusters by cosine similarity until all vectors are merged into a single cluster , only the leaf nodes ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.13607v1", "content": "We then iteratively merge the two closest clusters by cosine similarity until all vectors are merged into a single cluster , only the leaf nodes ..."}
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+{"idx": 8, "title": "Community Detection of Complex Network Based on Graph", "date": "", "ddg_snippet": "Blondel et al.,, 2008 ) proposed the Louvain algorithm based on hierarchical clustering , which is divided into two stages.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.02711v1", "content": "Blondel et al.,, 2008 ) proposed the Louvain algorithm based on hierarchical clustering , which is divided into two stages."}
+{"idx": 9, "title": "OS Full Notes | PDF | Thread (Computing) | Booting - Scribd", "date": "", "ddg_snippet": "OS Full Notes - Free download as PDF File (.pdf), Text File (.txt) or read online for free.", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/920309018/OS-Full-Notes", "content": "OS Full Notes - Free download as PDF File (.pdf), Text File (.txt) or read online for free."}
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new file mode 100644
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+{"idx": 1, "title": "2.3. Clustering — scikit-learn 1.8.dev0 documentation", "date": "", "ddg_snippet": "Each clustering algorithm comes in two variants: a class, that implements the fit method to learn the clusters on train data, and a function, that ...", "subpage_snippet": "", "source": "scikit-learn.qubitpi.org", "link": "https://scikit-learn.qubitpi.org/modules/clustering.html", "content": "Each clustering algorithm comes in two variants: a class, that implements the fit method to learn the clusters on train data, and a function, that ..."}
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+{"idx": 5, "title": "K Rotation-invariant similarity in time series using", "date": "", "ddg_snippet": "We note that in real-world time series, the maximal cardinality of : Comparing the quality of the approximate k-motiflet discovery algorithms on 12 ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/257580572_K_Rotation-invariant_similarity_in_time_series_using_bag-of-patterns_representation", "content": "We note that in real-world time series, the maximal cardinality of : Comparing the quality of the approximate k-motiflet discovery algorithms on 12 ..."}
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+{"idx": 7, "title": "Network Representation Learning Enhanced by Partial Community", "date": "", "ddg_snippet": "Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world.", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/2078-2489/12/5/186", "content": "Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world."}
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+{"idx": 2, "title": "'액세스가 거부되었습니다' 문제를 해결하고 싶습니다. - Microsoft Q...", "date": "", "ddg_snippet": "안녕하세요. Seonghye 님. Microsoft Community에 방문해 주셔서 감사합니다. Windows 10 환경에서 드라이브 파일 이동, 생성, 삭제가 모두 불가하고 액세스가 거부 관련 메시지가 발생하는 증상으로 불편함을 느끼시는 부분 확인하였습니다. 해당 증상은 다양하고 복합적인 원인 (예기치 못한 시스템 손상 및 ...", "subpage_snippet": "", "source": "answers.microsoft.com", "link": "https://answers.microsoft.com/ko-kr/windows/forum/all/액세스가/98851fa5-eac1-44ad-b2bb-0f73ac4c9796", "content": "안녕하세요. Seonghye 님. Microsoft Community에 방문해 주셔서 감사합니다. Windows 10 환경에서 드라이브 파일 이동, 생성, 삭제가 모두 불가하고 액세스가 거부 관련 메시지가 발생하는 증상으로 불편함을 느끼시는 부분 확인하였습니다. 해당 증상은 다양하고 복합적인 원인 (예기치 못한 시스템 손상 및 ..."}
+{"idx": 3, "title": "아웃룩 (outlook.office365.com) 접속시 오류 (500)가 발생 합니다.", "date": "", "ddg_snippet": "저희는 사용자를 위하여 번역된 내용을 제공하고 있습니다. 문법적 오류가 있더라도 양해바랍니다. 안녕하세요 손진식, 제 이름은 Neil이고, 저는 여러분과 같은 아웃룩 사용자입니다. 귀하의 질문에 대해 기꺼이 도와 드리겠습니다. Outlook에 연결하려고 할 때이 문제가 발생하여 죄송합니다. 이것은 ...", "subpage_snippet": "", "source": "answers.microsoft.com", "link": "https://answers.microsoft.com/ko-kr/outlook_com/forum/all/아웃룩outlookoffice365com/2be0544c-ce9c-4991-81ed-3a94ec48b20b", "content": "저희는 사용자를 위하여 번역된 내용을 제공하고 있습니다. 문법적 오류가 있더라도 양해바랍니다. 안녕하세요 손진식, 제 이름은 Neil이고, 저는 여러분과 같은 아웃룩 사용자입니다. 귀하의 질문에 대해 기꺼이 도와 드리겠습니다. Outlook에 연결하려고 할 때이 문제가 발생하여 죄송합니다. 이것은 ..."}
+{"idx": 4, "title": "마이크로소프트 365 퍼스널 설치 후 엑셀파일 우측 광고화면 제거 ...", "date": "", "ddg_snippet": "2025년 03월 08일 마이크로소프트365 퍼스널 설치한 후 엑셀파일을 열었는데 우측 광고화면이 제거되지 않고 계속 보입니다.어떻게 하면 광고화면 제거할 수 있는 지 질문드립니다. 화면 캡처한 것 첨부합니다.", "subpage_snippet": "", "source": "answers.microsoft.com", "link": "https://answers.microsoft.com/ko-kr/msoffice/forum/all/마이크로소/a6b2458f-e78d-432c-93db-e1b1224892d8", "content": "2025년 03월 08일 마이크로소프트365 퍼스널 설치한 후 엑셀파일을 열었는데 우측 광고화면이 제거되지 않고 계속 보입니다.어떻게 하면 광고화면 제거할 수 있는 지 질문드립니다. 화면 캡처한 것 첨부합니다."}
+{"idx": 5, "title": "pin번호 설정을 어디서 해야하나요? - Microsoft 커뮤니티", "date": "", "ddg_snippet": "마이크로소프트 365 앱을 실행하고자 계정으로 로그인하면 pin번호를 입력하라고 뜹니다.pin번호를 잊은 것 같아 잊음을 누르니 로그인 후 설정->계정->로그인옵션 으로 들어가서 pin번호를 설정하라고 합니다.그러나 로그인 후에 아무리 찾아봐도 로그인옵션이 없습니다.답변 부탁드립니다.", "subpage_snippet": "", "source": "answers.microsoft.com", "link": "https://answers.microsoft.com/ko-kr/msoffice/forum/all/pin번호-설정을/228f1283-33ae-4243-ab0f-b44f0f010584", "content": "마이크로소프트 365 앱을 실행하고자 계정으로 로그인하면 pin번호를 입력하라고 뜹니다.pin번호를 잊은 것 같아 잊음을 누르니 로그인 후 설정->계정->로그인옵션 으로 들어가서 pin번호를 설정하라고 합니다.그러나 로그인 후에 아무리 찾아봐도 로그인옵션이 없습니다.답변 부탁드립니다."}
+{"idx": 6, "title": "마인크래프트 PIN번호 오류 초기화 문의 합니다. - Microsoft 커뮤니...", "date": "", "ddg_snippet": "마인크래프트 결재 후 게임 로그인시 pin번호가 미 설정 되어있는 상태에서 잘못 기입하여 \"사용자 본인인지 확인\" 이라는 메세지가 발생되었습니다.초기화 방법을 몰라서 이렇게 문의 합니다.자세한 설명이나 운영팀에서 초기화가 가능하면 초기화 부탁 드립니다.확인 후 빠른 답장 바랍니다.ps ...", "subpage_snippet": "", "source": "answers.microsoft.com", "link": "https://answers.microsoft.com/ko-kr/windows/forum/all/마인크래프/9323504c-f7fa-4634-8691-62785e05c570", "content": "마인크래프트 결재 후 게임 로그인시 pin번호가 미 설정 되어있는 상태에서 잘못 기입하여 \"사용자 본인인지 확인\" 이라는 메세지가 발생되었습니다.초기화 방법을 몰라서 이렇게 문의 합니다.자세한 설명이나 운영팀에서 초기화가 가능하면 초기화 부탁 드립니다.확인 후 빠른 답장 바랍니다.ps ..."}
+{"idx": 7, "title": "outlook 웹으로 접속시 로그인 이후 에러가 발생합니다. - Microsoft ...", "date": "", "ddg_snippet": ". . 곧 Outlook 포럼이 Microsoft Q&A 전용으로 제공될 예정이라는 기쁜 소식을 전해드립니다 . 이번 변경을 통해 모든 질문과 토론에 더욱 효율적이고 간소화된 환경을 제공할 수 있게 되었습니다. . . 7월 16일부터 Microsoft 지원 커뮤니티에서 더 이상 새로운 질문을 만들 수 없습니다. 하지만 Microsoft Q&A 에서 ...", "subpage_snippet": "", "source": "answers.microsoft.com", "link": "https://answers.microsoft.com/ko-kr/outlook_com/forum/all/outlook-웹으로/4317a0e9-11eb-4a6b-9f37-8eba2053ea73", "content": ". . 곧 Outlook 포럼이 Microsoft Q&A 전용으로 제공될 예정이라는 기쁜 소식을 전해드립니다 . 이번 변경을 통해 모든 질문과 토론에 더욱 효율적이고 간소화된 환경을 제공할 수 있게 되었습니다. . . 7월 16일부터 Microsoft 지원 커뮤니티에서 더 이상 새로운 질문을 만들 수 없습니다. 하지만 Microsoft Q&A 에서 ..."}
+{"idx": 8, "title": "Windows 업데이트 오류 발생 및 Microsoft Store 업데이트 오류", "date": "", "ddg_snippet": "Windows 업데이트를 시도했을 때 다음과 같은 문제가 발생했습니다. 오류 발생 업데이트 서비스 중 하나가 제대로 실행되고 있지 않지만 문제 해결사를 실행하여 문제를 해결할 수 있습니다. 시작 버튼 > 설정 > 업데이트 및 보안 > 문제 해결로 이동한 다음 [Windows 업데이트]를 선택하세요. 문제 ...", "subpage_snippet": "", "source": "answers.microsoft.com", "link": "https://answers.microsoft.com/ko-kr/windows/forum/all/windows-업데이트/c7461117-43ce-4d5c-86e8-ee0f32128a97", "content": "Windows 업데이트를 시도했을 때 다음과 같은 문제가 발생했습니다. 오류 발생 업데이트 서비스 중 하나가 제대로 실행되고 있지 않지만 문제 해결사를 실행하여 문제를 해결할 수 있습니다. 시작 버튼 > 설정 > 업데이트 및 보안 > 문제 해결로 이동한 다음 [Windows 업데이트]를 선택하세요. 문제 ..."}
+{"idx": 9, "title": "아웃룩 안전모드가 떳는데 해제하는 방법이 궁금합니다.", "date": "", "ddg_snippet": "잠긴 질문. 이 질문은 Microsoft 지원 커뮤니티에서 마이그레이션되었습니다. 질문이 도움이 되었는지 여부에 대해 응답할 수는 있지만, 메모나 회신을 추가하거나 질문을 따를 수는 없습니다. 개인 정보를 보호하기 위해, 마이그레이션된 질문에 대한 사용자 프로필은 익명으로 처리됩니다.", "subpage_snippet": "", "source": "answers.microsoft.com", "link": "https://answers.microsoft.com/ko-kr/outlook_com/forum/all/아웃룩/0cae4c55-0a0e-4e77-a4d8-edae433d6876", "content": "잠긴 질문. 이 질문은 Microsoft 지원 커뮤니티에서 마이그레이션되었습니다. 질문이 도움이 되었는지 여부에 대해 응답할 수는 있지만, 메모나 회신을 추가하거나 질문을 따를 수는 없습니다. 개인 정보를 보호하기 위해, 마이그레이션된 질문에 대한 사용자 프로필은 익명으로 처리됩니다."}
diff --git a/data/sampled_jsons/HtmlRAG-_HTML_is_Better_Than_Plain_Text_for_Modeling_Retrieved_Knowledge_in_RAG_Systems.jsonl b/data/sampled_jsons/HtmlRAG-_HTML_is_Better_Than_Plain_Text_for_Modeling_Retrieved_Knowledge_in_RAG_Systems.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2a54d0f44f545bd28c8b9a091a4a94d4ff36e84e
--- /dev/null
+++ b/data/sampled_jsons/HtmlRAG-_HTML_is_Better_Than_Plain_Text_for_Modeling_Retrieved_Knowledge_in_RAG_Systems.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2411.02959] HtmlRAG: HTML is Better Than Plain Text for ...", "date": "", "ddg_snippet": "by J Tan · 2024 · Cited by 19 — To alleviate this problem, we propose HtmlRAG , which uses HTML instead of plain text as the format of retrieved knowledge in RAG. We believe ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2411.02959", "content": "by J Tan · 2024 · Cited by 19 — To alleviate this problem, we propose HtmlRAG , which uses HTML instead of plain text as the format of retrieved knowledge in RAG. We believe ..."}
+{"idx": 1, "title": "HtmlRAG: HTML is Better Than Plain Text for Modeling ...", "date": "", "ddg_snippet": "We propose HtmlRAG , which uses HTML instead of plain text as the format of external knowledge in RAG systems. To tackle the long context brought by HTML, we ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/plageon/HtmlRAG", "content": "We propose HtmlRAG , which uses HTML instead of plain text as the format of external knowledge in RAG systems. To tackle the long context brought by HTML, we ..."}
+{"idx": 2, "title": "HtmlRAG: HTML is Better Than Plain Text for Modeling ...", "date": "", "ddg_snippet": "by J Tan · 2025 · Cited by 19 — We propose HtmlRAG , which uses HTML instead of plain text as the format of retrieved knowledge in RAG. We believe HTML is better than plain text in modeling ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.1145/3696410.3714546", "content": "by J Tan · 2025 · Cited by 19 — We propose HtmlRAG , which uses HTML instead of plain text as the format of retrieved knowledge in RAG. We believe HTML is better than plain text in modeling ..."}
+{"idx": 3, "title": "HtmlRAG: HTML is Better Than Plain Text for Modeling ...", "date": "", "ddg_snippet": "5 Nov 2024 — In this paper, we propose HtmlRAG , which uses HTML instead of plain text as the format of retrieved knowledge in RAG systems, aiming to keep ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.02959v1", "content": "5 Nov 2024 — In this paper, we propose HtmlRAG , which uses HTML instead of plain text as the format of retrieved knowledge in RAG systems, aiming to keep ..."}
+{"idx": 4, "title": "HtmlRAG: HTML is Better Than Plain Text for Modeling ...", "date": "", "ddg_snippet": "6 Nov 2024 — HtmlRAG enhances Retrieval-Augmented Generation (RAG) systems by using HTML instead of plain text, improving knowledge modeling and reducing ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2411.02959", "content": "6 Nov 2024 — HtmlRAG enhances Retrieval-Augmented Generation (RAG) systems by using HTML instead of plain text, improving knowledge modeling and reducing ..."}
+{"idx": 5, "title": "\"HtmlRAG: HTML is Better Than Plain Text for Modeling ...", "date": "", "ddg_snippet": "27 Dec 2024 — HtmlRAG uses HTML's native structure to make RAG systems smarter and more accurate. Finds HTML beats plain text for knowledge retrieval in ...", "subpage_snippet": "", "source": "www.rohan-paul.com", "link": "https://www.rohan-paul.com/p/htmlrag-html-is-better-than-plain", "content": "27 Dec 2024 — HtmlRAG uses HTML's native structure to make RAG systems smarter and more accurate. Finds HTML beats plain text for knowledge retrieval in ..."}
+{"idx": 6, "title": "[Literature Review] HtmlRAG: HTML is Better Than Plain ...", "date": "", "ddg_snippet": "In conclusion, the paper posits that utilizing HTML rather than plain text for retrieving knowledge in RAG systems significantly enriches the model's responses ...", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/en/review/htmlrag-html-is-better-than-plain-text-for-modeling-retrieved-knowledge-in-rag-systems", "content": "In conclusion, the paper posits that utilizing HTML rather than plain text for retrieving knowledge in RAG systems significantly enriches the model's responses ..."}
+{"idx": 7, "title": "HtmlRAG: HTML is Better than Plain Text Structured input ...", "date": "", "ddg_snippet": "HtmlRAG: HTML is Better than Plain Text Structured input is just as effective as structured outputs when building with LLMs.", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/posts/omarsar_htmlrag-html-is-better-than-plain-text-activity-7263636202849714176-pBOj", "content": "HtmlRAG: HTML is Better than Plain Text Structured input is just as effective as structured outputs when building with LLMs."}
+{"idx": 8, "title": "Analytics Vidhya - HTML RAG", "date": "", "ddg_snippet": "HtmlRAG : HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systems \" by Jiejun Tan and colleagues.", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/posts/analytics-vidhya_html-rag-activity-7259800352491806720-1AKS", "content": "HtmlRAG : HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systems \" by Jiejun Tan and colleagues."}
+{"idx": 9, "title": "gm8xx8", "date": "", "ddg_snippet": "HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systems paper: https://arxiv.org/abs/2411.02959 HtmlRAG enhances ...", "subpage_snippet": "", "source": "x.com", "link": "https://x.com/gm8xx8/status/1854020280198255034", "content": "HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systems paper: https://arxiv.org/abs/2411.02959 HtmlRAG enhances ..."}
diff --git a/data/sampled_jsons/HtmlRAG_HTML_is_Better_Than_Plain_Text_for_Modeling_Retrieved_Knowledge_in_RAG_Systems_Section_3.2.1.jsonl b/data/sampled_jsons/HtmlRAG_HTML_is_Better_Than_Plain_Text_for_Modeling_Retrieved_Knowledge_in_RAG_Systems_Section_3.2.1.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..39d25bceecedbe926178fbdafdc868d956bf5c9d
--- /dev/null
+++ b/data/sampled_jsons/HtmlRAG_HTML_is_Better_Than_Plain_Text_for_Modeling_Retrieved_Knowledge_in_RAG_Systems_Section_3.2.1.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "HtmlRAG : HTML is Better Than Plain Text for Modeling Retrieved ...", "date": "", "ddg_snippet": "Typically, such RAG systems retrieve search results, download HTML sources of the results, and then extract plain texts from the HTML sources. Plain text documents or chunks are fed into the LLMs to augment the generation.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.02959v2", "content": "Typically, such RAG systems retrieve search results, download HTML sources of the results, and then extract plain texts from the HTML sources. Plain text documents or chunks are fed into the LLMs to augment the generation."}
+{"idx": 1, "title": "Paper page - HtmlRAG : HTML is Better Than Plain Text for ...", "date": "", "ddg_snippet": "Abstract. HtmlRAG enhances Retrieval -Augmented Generation ( RAG ) systems by using HTML instead of plain text , improving knowledge modeling and reducing information loss through HTML cleaning, compression, and pruning.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2411.02959", "content": "Abstract. HtmlRAG enhances Retrieval -Augmented Generation ( RAG ) systems by using HTML instead of plain text , improving knowledge modeling and reducing information loss through HTML cleaning, compression, and pruning."}
+{"idx": 2, "title": "(PDF) HtmlRAG : HTML is Better Than Plain Text for Modeling ...", "date": "", "ddg_snippet": "Typically, such RAG systems retrieve search results, download HTML sources of the results, and then extract plain texts from the HTML sources.To alleviate this problem, we propose HtmlRAG , which uses HTML instead of plain text as the format of retrieved knowledge in RAG .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/385560345_HtmlRAG_HTML_is_Better_Than_Plain_Text_for_Modeling_Retrieved_Knowledge_in_RAG_Systems", "content": "Typically, such RAG systems retrieve search results, download HTML sources of the results, and then extract plain texts from the HTML sources.To alleviate this problem, we propose HtmlRAG , which uses HTML instead of plain text as the format of retrieved knowledge in RAG ."}
+{"idx": 3, "title": "Paper tables with annotated results for HtmlRAG : HTML is Better ...", "date": "", "ddg_snippet": "HtmlRAG : HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systems . Retrieval -Augmented Generation ( RAG ) has been shown to improve knowledge capabilities and alleviate the hallucination problem of LLMs.", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/paper/htmlrag-html-is-better-than-plain-text-for/review/", "content": "HtmlRAG : HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systems . Retrieval -Augmented Generation ( RAG ) has been shown to improve knowledge capabilities and alleviate the hallucination problem of LLMs."}
+{"idx": 4, "title": "plageon/ HtmlRAG : HtmlRAG : HTML is Better Than Plain Text for ...", "date": "", "ddg_snippet": "We propose HtmlRAG , which uses HTML instead of plain text as the format of external knowledge in RAG systems . To tackle the long context brought by HTML , we propose Lossless HTML Cleaning and Two-Step Block-Tree-Based HTML Pruning.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/plageon/HtmlRAG", "content": "We propose HtmlRAG , which uses HTML instead of plain text as the format of external knowledge in RAG systems . To tackle the long context brought by HTML , we propose Lossless HTML Cleaning and Two-Step Block-Tree-Based HTML Pruning."}
+{"idx": 5, "title": "HTML > Plain Text for RAG", "date": "", "ddg_snippet": "3. HtmlRAG : HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systems . Watching: HtmlRAG (paper). What problem does it solve? Retrieval -Augmented Generation ( RAG ) has been a popular approach to enhance the knowledge capabilities of Large...", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/pulse/html-plain-text-rag-pascal-biese-h5rbf", "content": "3. HtmlRAG : HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systems . Watching: HtmlRAG (paper). What problem does it solve? Retrieval -Augmented Generation ( RAG ) has been a popular approach to enhance the knowledge capabilities of Large..."}
+{"idx": 6, "title": "HtmlRAG : HTML is Better Than Plain Text for Modeling Retrieved ...", "date": "", "ddg_snippet": "Select any part of the paper to ask specific questions about that section .", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2411.02959v1", "content": "Select any part of the paper to ask specific questions about that section ."}
+{"idx": 7, "title": "Implementing HtmlRAG : Enhancing Retrieval -Augmented Generation...", "date": "", "ddg_snippet": "Traditional RAG systems typically use plain text extracted from HTML documents as the format for retrieved knowledge .", "subpage_snippet": "", "source": "blog.devgenius.io", "link": "https://blog.devgenius.io/implementing-htmlrag-enhancing-retrieval-augmented-generation-with-html-knowledge-91cdd6278e23", "content": "Traditional RAG systems typically use plain text extracted from HTML documents as the format for retrieved knowledge ."}
+{"idx": 8, "title": "HtmlRAG : HTML is Better Than Plain Text for Modeling Retrieved ...", "date": "", "ddg_snippet": "Traditional RAG systems often convert HTML to plain text , resulting in a significant loss of structural and semantic information. This loss can negatively impact the LLM’s ability to accurately comprehend and generate responses based on the retrieved knowledge .", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/ai-paper-reviewer/paper-reviews/2411.02959/", "content": "Traditional RAG systems often convert HTML to plain text , resulting in a significant loss of structural and semantic information. This loss can negatively impact the LLM’s ability to accurately comprehend and generate responses based on the retrieved knowledge ."}
+{"idx": 9, "title": "HtmlRAG : HTML is Better Than Plain Text for Modeling Retrieved ...", "date": "", "ddg_snippet": "Typically, such RAG systems retrieve search results, download HTML sources of the results, and then extract plain texts from the HTML sources. Plain text documents or chunks are fed into the LLMs to augment the generation.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/htmlrag-html-is-better-than-plain-text", "content": "Typically, such RAG systems retrieve search results, download HTML sources of the results, and then extract plain texts from the HTML sources. Plain text documents or chunks are fed into the LLMs to augment the generation."}
diff --git a/data/sampled_jsons/HtmlRAG_ablation_study_Table_3_ASQA_Hit@1_Prune-Embed_performance_drop_year_2024.jsonl b/data/sampled_jsons/HtmlRAG_ablation_study_Table_3_ASQA_Hit@1_Prune-Embed_performance_drop_year_2024.jsonl
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index 0000000000000000000000000000000000000000..f8e7c3ff7db28c82350b99a7d59bb36de1eec2ac
--- /dev/null
+++ b/data/sampled_jsons/HtmlRAG_ablation_study_Table_3_ASQA_Hit@1_Prune-Embed_performance_drop_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "HtmlRAG : HTML is Better Than Plain Text for Modeling Retrieved...", "date": "", "ddg_snippet": "Table 3 . Ablation studies for HtmlRAG .The performance of the generative model is also affected due to the increase in the length of block paths. (2) In the ablation study for pruning with the embedding model, we only use the generative model to prune the cleaned HTML .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.02959v2", "content": "Table 3 . Ablation studies for HtmlRAG .The performance of the generative model is also affected due to the increase in the length of block paths. (2) In the ablation study for pruning with the embedding model, we only use the generative model to prune the cleaned HTML ."}
+{"idx": 1, "title": "HtmlRAG : HTML is Better Than Plain Text for Modeling Retrieved...", "date": "", "ddg_snippet": "This table presents the ablation study results for the HtmlRAG model. It shows the impact of removing key components of the model, such as the block tree structure, the text embedding -based pruning , and the generative model-based pruning .", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/ai-paper-reviewer/paper-reviews/2411.02959/", "content": "This table presents the ablation study results for the HtmlRAG model. It shows the impact of removing key components of the model, such as the block tree structure, the text embedding -based pruning , and the generative model-based pruning ."}
+{"idx": 2, "title": "GitHub - plageon/ HtmlRAG : HtmlRAG : HTML is Better Than Plain...", "date": "", "ddg_snippet": "The first pruning step uses a embedding model to calculate scores for blocks, while the second step uses a path generative model.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/plageon/HtmlRAG", "content": "The first pruning step uses a embedding model to calculate scores for blocks, while the second step uses a path generative model."}
+{"idx": 3, "title": "zstanjj/ HTML -Pruner-Phi- 3 .8B · Hugging Face", "date": "", "ddg_snippet": "pruned _ html = gen_ embed _pruner. prune _ HTML ( pruned _ html , block_tree, block_rankings, chat_tokenizer, MAX_CONTEXT_WINDOW_GEN) print( pruned _ html ) # The Bellagio is a luxury hotel and casino located on the Las Vegas Strip in Paradise, Nevada.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/zstanjj/HTML-Pruner-Phi-3.8B", "content": "pruned _ html = gen_ embed _pruner. prune _ HTML ( pruned _ html , block_tree, block_rankings, chat_tokenizer, MAX_CONTEXT_WINDOW_GEN) print( pruned _ html ) # The Bellagio is a luxury hotel and casino located on the Las Vegas Strip in Paradise, Nevada."}
+{"idx": 4, "title": "HtmlRAG : Enhancing RAG Systems with Richer... - MarkTechPost", "date": "", "ddg_snippet": "HtmlRAG ’s superior performance compared to traditional plain-text-based post-retrieval processes validates the effectiveness of utilizing HTML format for knowledge retrieval.", "subpage_snippet": "", "source": "www.marktechpost.com", "link": "https://www.marktechpost.com/2024/11/10/htmlrag-enhancing-rag-systems-with-richer-semantic-and-structural-information-through-html/", "content": "HtmlRAG ’s superior performance compared to traditional plain-text-based post-retrieval processes validates the effectiveness of utilizing HTML format for knowledge retrieval."}
+{"idx": 5, "title": "HTML -Pruner-Llama- 1 B-GGUF huggingface.co api & mav23... - Toolify", "date": "", "ddg_snippet": "The first pruning step uses a embedding model to calculate scores for blocks, while the second step uses a path generative model.Results for HTML -Pruner-Phi- 3 .8B and HTML -Pruner-Llama- 1 B with Llama- 3 . 1 -70B-Instruct as chat model . Dataset. ASQA .", "subpage_snippet": "", "source": "www.toolify.ai", "link": "https://www.toolify.ai/ai-model/mav23-html-pruner-llama-1b-gguf", "content": "The first pruning step uses a embedding model to calculate scores for blocks, while the second step uses a path generative model.Results for HTML -Pruner-Phi- 3 .8B and HTML -Pruner-Llama- 1 B with Llama- 3 . 1 -70B-Instruct as chat model . Dataset. ASQA ."}
+{"idx": 6, "title": "6- ablation - study .ipynb - Colab", "date": "", "ddg_snippet": "Ablation studies play a pivotal role in this process by systematically dissecting machine learning models and evaluating the impact of individual components.", "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 play a pivotal role in this process by systematically dissecting machine learning models and evaluating the impact of individual components."}
+{"idx": 7, "title": "A smart toolkit for HTML cleaning and pruning for RAG systems.", "date": "", "ddg_snippet": "Maintainer: zstanjj. Tags html , rag , transformers , nlp , cleaning , pruning .If you switch from htmlrag v0.0.4 to v0.0.5, please download the latest version of modeling files for Gerative HTML Pruners, which are available at modeling_llama.py, and modeling_phi 3 .py.", "subpage_snippet": "", "source": "pypi.org", "link": "https://pypi.org/project/htmlrag/", "content": "Maintainer: zstanjj. Tags html , rag , transformers , nlp , cleaning , pruning .If you switch from htmlrag v0.0.4 to v0.0.5, please download the latest version of modeling files for Gerative HTML Pruners, which are available at modeling_llama.py, and modeling_phi 3 .py."}
+{"idx": 8, "title": "РЕАЛЬНОЕ СОБЕСЕДОВАНИЕ / Junior ML-engineer (Data Scientist)...", "date": "", "ddg_snippet": "Ablation : Replace softmax на sparsemax — faster, но worse perf на GLUE benchmarks (~ 1 -2% drop ). Выход и Интеграция в BERT Layer Output MultiHead: [N, d_model] — passed to FFN (feed-forward: two linear + GELU + residual/LayerNorm), затем next layer.", "subpage_snippet": "", "source": "careerclue.vercel.app", "link": "https://careerclue.vercel.app/blog/2025/08/30/Nbl4SaO51sA-realnoe-sobesedovanie-junior-ml-engineer-data-scientist-aytiteh", "content": "Ablation : Replace softmax на sparsemax — faster, но worse perf на GLUE benchmarks (~ 1 -2% drop ). Выход и Интеграция в BERT Layer Output MultiHead: [N, d_model] — passed to FFN (feed-forward: two linear + GELU + residual/LayerNorm), затем next layer."}
+{"idx": 9, "title": "Plants vs Zombies Fusion Edition 2.8.2 [Мод меню 200+ читов: много...]", "date": "", "ddg_snippet": "Мод меню на 40+ читов на английском языке, английская локализация (логин для открытия всех функций чит-меню: MKGAMER): — Unlimited HP (неуязвимость растений). — One hit (убийство с одного удара). — Fast Attack (ускорение атаки).", "subpage_snippet": "", "source": "PassGame.ru", "link": "https://PassGame.ru/vzlom-plants-vs-zombies-fusion-edition-guzel/", "content": "Мод меню на 40+ читов на английском языке, английская локализация (логин для открытия всех функций чит-меню: MKGAMER): — Unlimited HP (неуязвимость растений). — One hit (убийство с одного удара). — Fast Attack (ускорение атаки)."}
diff --git a/data/sampled_jsons/HtmlRAG_paper_E91gjsccP1_siteopenreview.net_year_2024.jsonl b/data/sampled_jsons/HtmlRAG_paper_E91gjsccP1_siteopenreview.net_year_2024.jsonl
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index 0000000000000000000000000000000000000000..6707452a90bb773ccb77081b0b2f296cd0d2d8e8
--- /dev/null
+++ b/data/sampled_jsons/HtmlRAG_paper_E91gjsccP1_siteopenreview.net_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "HtmlRAG : HTML is Better Than Plain Text for Modeling Retrieved...", "date": "", "ddg_snippet": "To alleviate this problem, we propose HtmlRAG , which uses HTML . 20. instead of plain text as the format of retrieved knowledge in RAG .267 out considering the user’s query. This cleaning process removes 325. In this paper , we propose HtmlRAG , which uses HTML instead of.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=E91gjsccP1", "content": "To alleviate this problem, we propose HtmlRAG , which uses HTML . 20. instead of plain text as the format of retrieved knowledge in RAG .267 out considering the user’s query. This cleaning process removes 325. In this paper , we propose HtmlRAG , which uses HTML instead of."}
+{"idx": 1, "title": "HtmlRAG: HTML is Better Than Plain Text for Modeling ...", "date": "", "ddg_snippet": "Jan 29, 2025 · To alleviate this problem, we propose HtmlRAG , which uses HTML instead of plain text as the format of retrieved knowledge in RAG. We believe HTML is better than plain text in modeling knowledge in external documents, and most LLMs possess robust capacities to understand HTML.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=E91gjsccP1", "content": "Jan 29, 2025 · To alleviate this problem, we propose HtmlRAG , which uses HTML instead of plain text as the format of retrieved knowledge in RAG. We believe HTML is better than plain text in modeling knowledge in external documents, and most LLMs possess robust capacities to understand HTML."}
+{"idx": 2, "title": "HtmlRAG : HTML is Better Than Plain Text for Modeling... | OpenReview", "date": "", "ddg_snippet": "Typically, such RAG systems retrieve search results, download HTML sources of the results, and then extract plain texts from the HTML sources.To alleviate this problem, we propose HtmlRAG , which uses HTML instead of plain text as the format of retrieved knowledge in RAG .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=E91gjsccP1&referrer=[the+profile+of+Zhicheng+Dou](/profile?id=~Zhicheng_Dou1)", "content": "Typically, such RAG systems retrieve search results, download HTML sources of the results, and then extract plain texts from the HTML sources.To alleviate this problem, we propose HtmlRAG , which uses HTML instead of plain text as the format of retrieved knowledge in RAG ."}
+{"idx": 3, "title": "Jiejun Tan - OpenReview", "date": "", "ddg_snippet": "Publications HtmlRAG : HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systems Jiejun Tan, Zhicheng Dou, Wen Wang, Mang Wang, Weipeng Chen, Ji-Rong Wen Published: 31 Dec 2024, Last Modified: 20 May 2025 WWW 2025 HtmlRAG : HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systems", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/profile?id=~Jiejun_Tan1", "content": "Publications HtmlRAG : HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systems Jiejun Tan, Zhicheng Dou, Wen Wang, Mang Wang, Weipeng Chen, Ji-Rong Wen Published: 31 Dec 2024, Last Modified: 20 May 2025 WWW 2025 HtmlRAG : HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systems"}
+{"idx": 4, "title": "Submissions | OpenReview", "date": "", "ddg_snippet": "Jan 29, 2025 · HtmlRAG : HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systems Jiejun Tan, Zhicheng Dou, Wen Wang, Mang Wang, Weipeng Chen, Ji-Rong Wen", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/submissions?page=10&venue=ACM.org/TheWebConf/2025/Conference", "content": "Jan 29, 2025 · HtmlRAG : HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systems Jiejun Tan, Zhicheng Dou, Wen Wang, Mang Wang, Weipeng Chen, Ji-Rong Wen"}
+{"idx": 5, "title": "HtmlRAG: HTML is Better Than Plain Text for Modeling ...", "date": "", "ddg_snippet": "Dec 31, 2024 · HtmlRAG : HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systems Jiejun Tan, Zhicheng Dou, Wen Wang, Mang Wang, Weipeng Chen, Ji-Rong Wen", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=leCEFyMyxg", "content": "Dec 31, 2024 · HtmlRAG : HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systems Jiejun Tan, Zhicheng Dou, Wen Wang, Mang Wang, Weipeng Chen, Ji-Rong Wen"}
+{"idx": 6, "title": "Towards Non-Asymptotic Convergence for... | OpenReview", "date": "", "ddg_snippet": "Diffusion models, which convert noise into new data instances by learning to reverse a Markov diffusion process, have become a cornerstone in contemporary generative modeling.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=4VGEeER6W9", "content": "Diffusion models, which convert noise into new data instances by learning to reverse a Markov diffusion process, have become a cornerstone in contemporary generative modeling."}
+{"idx": 7, "title": "ConDaFormer: Disassembled Transformer with Local", "date": "", "ddg_snippet": "2 As stated in the main paper , our proposed disassembled window attention offers a notable advantage 3 over the vanilla 3D cubic window attention by significantly reducing computational effort, enabling 4 the potential enlargement of the receptive field through an increase in the...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=kKXJkiniOx&name=supplementary_material", "content": "2 As stated in the main paper , our proposed disassembled window attention offers a notable advantage 3 over the vanilla 3D cubic window attention by significantly reducing computational effort, enabling 4 the potential enlargement of the receptive field through an increase in the..."}
+{"idx": 8, "title": "Heatmap Distribution Matching for Human Pose", "date": "", "ddg_snippet": "Additional Ablation StudiesProof of Theorem 1 in the Main Paper", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=7-bMGPCQCm7&name=supplementary_material", "content": "Additional Ablation StudiesProof of Theorem 1 in the Main Paper"}
+{"idx": 9, "title": "EDU-RAG: A RAG Benchmark with Web-enhanced Content in ...", "date": "", "ddg_snippet": "Sep 27, 2024 · This paper introduces EDU-RAG, a benchmark dataset designed to evaluate the performance of Retrieval-Augmented Generation (RAG) techniques in the context of middle-school science question answering.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=a2rSx6t4EV", "content": "Sep 27, 2024 · This paper introduces EDU-RAG, a benchmark dataset designed to evaluate the performance of Retrieval-Augmented Generation (RAG) techniques in the context of middle-school science question answering."}
diff --git a/data/sampled_jsons/Human-in-the-loop_Provably_Efficient_Preference-based_Reinforcement_Learning_Chen_et_al._abstract_year_2023.jsonl b/data/sampled_jsons/Human-in-the-loop_Provably_Efficient_Preference-based_Reinforcement_Learning_Chen_et_al._abstract_year_2023.jsonl
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index 0000000000000000000000000000000000000000..87b3c9886f8150d8061b8e8dfaf903f32bf5dcca
--- /dev/null
+++ b/data/sampled_jsons/Human-in-the-loop_Provably_Efficient_Preference-based_Reinforcement_Learning_Chen_et_al._abstract_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Human-in-the-loop: Provably Efficient Preference-based ...", "date": "", "ddg_snippet": "May 23, 2022 · We study human-in-the-loop reinforcement learning (RL) with trajectory preferences , where instead of receiving a numeric reward at each step, the agent only receives preferences over trajectory pairs from a human overseer. The goal of the agent is to learn the optimal policy which is most preferred by the human overseer. Despite the empirical successes, the theoretical understanding of ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2205.11140", "content": "May 23, 2022 · We study human-in-the-loop reinforcement learning (RL) with trajectory preferences , where instead of receiving a numeric reward at each step, the agent only receives preferences over trajectory pairs from a human overseer. The goal of the agent is to learn the optimal policy which is most preferred by the human overseer. Despite the empirical successes, the theoretical understanding of ..."}
+{"idx": 1, "title": "Human-in-the-loop: Provably Efficient Preference-based ...", "date": "", "ddg_snippet": "May 23, 2022 · Request PDF | Human-in-the-loop : Provably Efficient Preference-based Reinforcement Learning with General Function Approximation | We study human-in-the-loop reinforcement learning (RL) with ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/360803691_Human-in-the-loop_Provably_Efficient_Preference-based_Reinforcement_Learning_with_General_Function_Approximation", "content": "May 23, 2022 · Request PDF | Human-in-the-loop : Provably Efficient Preference-based Reinforcement Learning with General Function Approximation | We study human-in-the-loop reinforcement learning (RL) with ..."}
+{"idx": 2, "title": "Human-in-the-loop: Provably Efficient Preference-based ... Module 7: Human-in-the-loop autonomy - Preference Based ... ICML 2022 Human-in-the-loop: Provably Efficient Preference ... Efficient Preference-Based Reinforcement Learning: Randomized ... Human-in-the-loop: Provably Efficient Preference-based ... Human - in - the - loop : Provably Efficient Preference - based Reinforcement … Few-Shot Preference Learning for Human - in - the - Loop RL Few-Shot Preference Learning for Human - in - the - Loop RL Human - in - the - loop : Provably Efficient Preference - based Reinforcement … Few-Shot Preference Learning for Human-in-the-Loop RL - PMLR", "date": "", "ddg_snippet": "We study human - in - the - loop reinforcement learn - ing (RL) with trajectory preferences, where in-stead of receiving a numeric reward at each step, the RL agent only receives preferences over tra-jectory pairs from a human overseer. The goal of the RL agent is to learn the optimal policy which is most preferred by the human overseer. Despite the empiri... See full list on proceedings.mlr.press In this section, we present the main results for preference - based RL. We first propose a novel algorithm called Preference - based Optimistic Planning (PbOP) and estab-lish the regret upper bound for it. To show the sharpness of our result, we also prove an information-theoretic lower bound in the linear case. See full list on proceedings.mlr.press In this subsection, we establish the lower bound for PbRL in the linear setting, which is derived using the reduction from the problem of RL with once-per-episode feedback. Firstly, we show the reduction from the problem of RL with once-per-episode feedback setting to the PbRL setting. Specifically, suppose we have an algorithm ALG for PbRL problem... See full list on proceedings.mlr.press In the previous section, we propose a sample-eficient al -gorithm with near-optimal regret for the problem of PbRL with trajectory feedback. However, this setting cannot cover some other RL situations with preference feedback. For example, in robotics, sampling new trajectories can be ex-pensive and time-consuming compared with labeling prefer-ences... See full list on proceedings.mlr.press Jun 7, 2022 · In this survey, we talk about the evolution of human perference based reinforcement learning , with a bunch of examples. Abstract : We study human-in-the-loop reinforcement learning (RL) with trajectory preferences , where instead of receiving a numeric reward at each step, the RL agent only receives preferences over trajectory pairs from a human overseer. The goal of the RL agent is to learn the optimal policy which is most preferred by the human overseer. Abstract We study reinforcement learning from human feedback in general Markov decision processes, where agents learn from trajectory-level preference comparisons. A central challenge in this setting is to design algorithms that select informative preference queries to identify the underlying reward while ensuring theoretical guarantees. We propose a meta-algorithm based on randomized ... May 23, 2022 · Request PDF | Human-in-the-loop : Provably Efficient Preference-based Reinforcement Learning with General Function Approximation | We study human-in-the-loop reinforcement learning (RL) with ... How does human-in-the-loop reinforcement learn-ing work? We study human - in - the - loop reinforcement learn-ing (RL) with trajectory preferences, where in -stead of receiving a numeric reward at each step, the RL agent only receives preferences over tra-jectory pairs from a human overseer. The goal of the RL agent is to learn the optimal policy which is most preferred by the human overseer. What are preference based RL algorithms? Preference based RL algorithms seek to overcome these challenges by directly learning reward functions from human feedback . Who wrote the book 'few-shot preference learning for human-in-the-loop RL'? title = {Few-Shot Preference Learning for Human-in-the-Loop RL}, author = { III, Donald Joseph Hejna and Sadigh, Dorsa }, booktitle = {Proceedings of The 6th Conference on Robot Learning}, pages = {2014--2025}, year = {2023}, editor = {Liu, Karen and Kulic, Dana and Ichnowski, Jeff}, volume = {205}, Which algorithm is used to learn transition dynamics and preference function? Algorithm The algorithm is formally defined in Algorithm 1. Overall, we employ the standard least-squares regression to learn the transition dynamics and the preference function. In each episode, we first update the model estimation based on the history samples till episode k − 1. Abstract While reinforcement learning (RL) has become a more popular approach for robotics, designing sufficiently informative reward functions for complex tasks has proven to be extremely difficult due their inability to capture human intent and policy exploitation.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v162/chen22ag/chen22ag.pdf", "content": "We study human - in - the - loop reinforcement learn - ing (RL) with trajectory preferences, where in-stead of receiving a numeric reward at each step, the RL agent only receives preferences over tra-jectory pairs from a human overseer. The goal of the RL agent is to learn the optimal policy which is most preferred by the human overseer. Despite the empiri... See full list on proceedings.mlr.press In this section, we present the main results for preference - based RL. We first propose a novel algorithm called Preference - based Optimistic Planning (PbOP) and estab-lish the regret upper bound for it. To show the sharpness of our result, we also prove an information-theoretic lower bound in the linear case. See full list on proceedings.mlr.press In this subsection, we establish the lower bound for PbRL in the linear setting, which is derived using the reduction from the problem of RL with once-per-episode feedback. Firstly, we show the reduction from the problem of RL with once-per-episode feedback setting to the PbRL setting. Specifically, suppose we have an algorithm ALG for PbRL problem... See full list on proceedings.mlr.press In the previous section, we propose a sample-eficient al -gorithm with near-optimal regret for the problem of PbRL with trajectory feedback. However, this setting cannot cover some other RL situations with preference feedback. For example, in robotics, sampling new trajectories can be ex-pensive and time-consuming compared with labeling prefer-ences... See full list on proceedings.mlr.press Jun 7, 2022 · In this survey, we talk about the evolution of human perference based reinforcement learning , with a bunch of examples. Abstract : We study human-in-the-loop reinforcement learning (RL) with trajectory preferences , where instead of receiving a numeric reward at each step, the RL agent only receives preferences over trajectory pairs from a human overseer. The goal of the RL agent is to learn the optimal policy which is most preferred by the human overseer. Abstract We study reinforcement learning from human feedback in general Markov decision processes, where agents learn from trajectory-level preference comparisons. A central challenge in this setting is to design algorithms that select informative preference queries to identify the underlying reward while ensuring theoretical guarantees. We propose a meta-algorithm based on randomized ... May 23, 2022 · Request PDF | Human-in-the-loop : Provably Efficient Preference-based Reinforcement Learning with General Function Approximation | We study human-in-the-loop reinforcement learning (RL) with ... How does human-in-the-loop reinforcement learn-ing work? We study human - in - the - loop reinforcement learn-ing (RL) with trajectory preferences, where in -stead of receiving a numeric reward at each step, the RL agent only receives preferences over tra-jectory pairs from a human overseer. The goal of the RL agent is to learn the optimal policy which is most preferred by the human overseer. What are preference based RL algorithms? Preference based RL algorithms seek to overcome these challenges by directly learning reward functions from human feedback . Who wrote the book 'few-shot preference learning for human-in-the-loop RL'? title = {Few-Shot Preference Learning for Human-in-the-Loop RL}, author = { III, Donald Joseph Hejna and Sadigh, Dorsa }, booktitle = {Proceedings of The 6th Conference on Robot Learning}, pages = {2014--2025}, year = {2023}, editor = {Liu, Karen and Kulic, Dana and Ichnowski, Jeff}, volume = {205}, Which algorithm is used to learn transition dynamics and preference function? Algorithm The algorithm is formally defined in Algorithm 1. Overall, we employ the standard least-squares regression to learn the transition dynamics and the preference function. In each episode, we first update the model estimation based on the history samples till episode k − 1. Abstract While reinforcement learning (RL) has become a more popular approach for robotics, designing sufficiently informative reward functions for complex tasks has proven to be extremely difficult due their inability to capture human intent and policy exploitation."}
+{"idx": 3, "title": "Module 7: Human-in-the-loop autonomy - Preference Based ...", "date": "", "ddg_snippet": "Jun 7, 2022 · In this survey, we talk about the evolution of human perference based reinforcement learning , with a bunch of examples.", "subpage_snippet": "", "source": "ucladeepvision.github.io", "link": "https://ucladeepvision.github.io/CS269-surveys-2022spring/2022/06/07/module07-Preference-Based-Learning.html", "content": "Jun 7, 2022 · In this survey, we talk about the evolution of human perference based reinforcement learning , with a bunch of examples."}
+{"idx": 4, "title": "ICML 2022 Human-in-the-loop: Provably Efficient Preference ...", "date": "", "ddg_snippet": "Abstract : We study human-in-the-loop reinforcement learning (RL) with trajectory preferences , where instead of receiving a numeric reward at each step, the RL agent only receives preferences over trajectory pairs from a human overseer. The goal of the RL agent is to learn the optimal policy which is most preferred by the human overseer.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2022/spotlight/18372", "content": "Abstract : We study human-in-the-loop reinforcement learning (RL) with trajectory preferences , where instead of receiving a numeric reward at each step, the RL agent only receives preferences over trajectory pairs from a human overseer. The goal of the RL agent is to learn the optimal policy which is most preferred by the human overseer."}
+{"idx": 5, "title": "Efficient Preference-Based Reinforcement Learning: Randomized ...", "date": "", "ddg_snippet": "Abstract We study reinforcement learning from human feedback in general Markov decision processes, where agents learn from trajectory-level preference comparisons. A central challenge in this setting is to design algorithms that select informative preference queries to identify the underlying reward while ensuring theoretical guarantees. We propose a meta-algorithm based on randomized ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2506.09508", "content": "Abstract We study reinforcement learning from human feedback in general Markov decision processes, where agents learn from trajectory-level preference comparisons. A central challenge in this setting is to design algorithms that select informative preference queries to identify the underlying reward while ensuring theoretical guarantees. We propose a meta-algorithm based on randomized ..."}
+{"idx": 6, "title": "Few-Shot Preference Learning for Human-in-the-Loop RL - PMLR", "date": "", "ddg_snippet": "Abstract While reinforcement learning (RL) has become a more popular approach for robotics, designing sufficiently informative reward functions for complex tasks has proven to be extremely difficult due their inability to capture human intent and policy exploitation.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v205/iii23a.html", "content": "Abstract While reinforcement learning (RL) has become a more popular approach for robotics, designing sufficiently informative reward functions for complex tasks has proven to be extremely difficult due their inability to capture human intent and policy exploitation."}
+{"idx": 7, "title": "Human - in - the - loop : Provably Efficient Preference - based ... | DeepAI", "date": "", "ddg_snippet": "Human - in - the - loop : Provably Efficient Preference - based Reinforcement Learning with General Function Approximation.", "subpage_snippet": "", "source": "deepai.org", "link": "https://deepai.org/publication/human-in-the-loop-provably-efficient-preference-based-reinforcement-learning-with-general-function-approximation", "content": "Human - in - the - loop : Provably Efficient Preference - based Reinforcement Learning with General Function Approximation."}
+{"idx": 8, "title": "Human - in - the - loop : Provably Efficient Preference - based ...", "date": "", "ddg_snippet": "Human - in - the - loop : Provably Efficient Preference - based Reinforcement Learning with General Function Approximation. Proceedings of Machine Learning Research, 162, 3773-3793. Chen , Xiaoyu ; Zhong, Han ; Yang, Zhuoran et al . /", "subpage_snippet": "", "source": "www.scholars.northwestern.edu", "link": "https://www.scholars.northwestern.edu/en/publications/human-in-the-loop-provably-efficient-preference-based-reinforceme", "content": "Human - in - the - loop : Provably Efficient Preference - based Reinforcement Learning with General Function Approximation. Proceedings of Machine Learning Research, 162, 3773-3793. Chen , Xiaoyu ; Zhong, Han ; Yang, Zhuoran et al . /"}
+{"idx": 9, "title": "Xiaoyu Chen - Google Akademik", "date": "", "ddg_snippet": "Human - in - the - loop : Provably Efficient Preference - based Reinforcement Learning with General Function Approximation.X Chen , J Hu, LF Yang, L Wang. International Conference on Learning Representations, 2022.", "subpage_snippet": "", "source": "scholar.google.bg", "link": "https://scholar.google.bg/citations?user=sioumZAAAAAJ&hl=tr", "content": "Human - in - the - loop : Provably Efficient Preference - based Reinforcement Learning with General Function Approximation.X Chen , J Hu, LF Yang, L Wang. International Conference on Learning Representations, 2022."}
diff --git a/data/sampled_jsons/ICML_2025_Hierarchical_Overlapping_Clustering_cost_function_overlaps_reduce_cost_theoretical_justifi.jsonl b/data/sampled_jsons/ICML_2025_Hierarchical_Overlapping_Clustering_cost_function_overlaps_reduce_cost_theoretical_justifi.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..716560ea9c55916de306143c8a0da61062a9ad71
--- /dev/null
+++ b/data/sampled_jsons/ICML_2025_Hierarchical_Overlapping_Clustering_cost_function_overlaps_reduce_cost_theoretical_justifi.jsonl
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+{"idx": 0, "title": "Hierarchical Overlapping Clustering on Graphs: Cost ...", "date": "", "ddg_snippet": "by Y Pan — Our approach employs a recursive overlapping bipartition framework based on local search, enabling a highly scalable speed-up variant.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=51x0dfsD8A", "content": "by Y Pan — Our approach employs a recursive overlapping bipartition framework based on local search, enabling a highly scalable speed-up variant."}
+{"idx": 1, "title": "Downloads 2025", "date": "", "ddg_snippet": "A Theoretical Justification for Asymmetric Actor-Critic Algorithms ... Hierarchical Overlapping Clustering on Graphs: Cost Function , Algorithm and Scalability ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/Downloads/2025", "content": "A Theoretical Justification for Asymmetric Actor-Critic Algorithms ... Hierarchical Overlapping Clustering on Graphs: Cost Function , Algorithm and Scalability ..."}
+{"idx": 2, "title": "ICML 2025 Thursday 07/17", "date": "", "ddg_snippet": "... hierarchical overlapping clustering on graphs by introducing a new cost function for it. We show the rationality of our cost function via several intuitive ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/day/7/17", "content": "... hierarchical overlapping clustering on graphs by introducing a new cost function for it. We show the rationality of our cost function via several intuitive ..."}
+{"idx": 3, "title": "Retraining-free Merging of Sparse MoE via Hierarchical ...", "date": "", "ddg_snippet": "The proposed hierarchical clustering method produces theoretically guaranteed and empirically validated expert groupings. ... The theoretical justification ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/44392", "content": "The proposed hierarchical clustering method produces theoretically guaranteed and empirically validated expert groupings. ... The theoretical justification ..."}
+{"idx": 4, "title": "Relative Error Fair Clustering in the Weak-Strong Oracle ...", "date": "", "ddg_snippet": "by V Braverman — We study fair clustering problems in a setting where distance information is obtained from two sources: a strong oracle providing exact distances,.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=70YgwpIxbc", "content": "by V Braverman — We study fair clustering problems in a setting where distance information is obtained from two sources: a strong oracle providing exact distances,."}
+{"idx": 5, "title": "An integrated interpretation and clustering model based on ...", "date": "", "ddg_snippet": "by L Chen · 2025 — A feature-based method is proposed to embed interpretability into the clustering process. This approach provides users with intuitive and easy-to-understand ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s10489-025-06262-2", "content": "by L Chen · 2025 — A feature-based method is proposed to embed interpretability into the clustering process. This approach provides users with intuitive and easy-to-understand ..."}
+{"idx": 6, "title": "Relative Error Fair Clustering in the Weak-Strong Oracle ...", "date": "", "ddg_snippet": "by V Braverman · 2025 — Abstract. We study fair clustering problems in a setting where distance information is obtained from two sources:.", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/pdf/2506.12287", "content": "by V Braverman · 2025 — Abstract. We study fair clustering problems in a setting where distance information is obtained from two sources:."}
+{"idx": 7, "title": "Expert-in-the-loop hierarchical topic models", "date": "", "ddg_snippet": "by L Calvo-Bartolomé · 2025 · Cited by 1 — This paper introduces two novel algorithms for hierarchical topic modeling: htm-ws (htm with word selection) and htm-ds (htm with document selection).", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0952197625015106", "content": "by L Calvo-Bartolomé · 2025 · Cited by 1 — This paper introduces two novel algorithms for hierarchical topic modeling: htm-ws (htm with word selection) and htm-ds (htm with document selection)."}
+{"idx": 8, "title": "ICML 2025 Schedule", "date": "", "ddg_snippet": "Calibration and Bias in Algorithms, Data, and Models: a tutorial on metrics and plots for measuring calibration, bias, fairness, reliability, and robustness.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/calendar", "content": "Calibration and Bias in Algorithms, Data, and Models: a tutorial on metrics and plots for measuring calibration, bias, fairness, reliability, and robustness."}
+{"idx": 9, "title": "Track: Poster Session 5 East", "date": "", "ddg_snippet": "17 Jul 2025 — ... hierarchical overlapping clustering on graphs by introducing a new cost function for it. We show the rationality of our cost function via ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/session/50267", "content": "17 Jul 2025 — ... hierarchical overlapping clustering on graphs by introducing a new cost function for it. We show the rationality of our cost function via ..."}
diff --git a/data/sampled_jsons/ICML_2025_OmniBench_experimental_setup_GPU_hardware_specifications.jsonl b/data/sampled_jsons/ICML_2025_OmniBench_experimental_setup_GPU_hardware_specifications.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2633b116e2423d72fe697235190628b264305bf2
--- /dev/null
+++ b/data/sampled_jsons/ICML_2025_OmniBench_experimental_setup_GPU_hardware_specifications.jsonl
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+{"idx": 0, "title": "ICML 2025 Papers", "date": "", "ddg_snippet": "KernelBench : Can LLMs Write Efficient GPU Kernels? Depth Degeneracy in Neural Networks: Vanishing Angles in Fully Connected ReLU Networks on Initialization ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/papers.html", "content": "KernelBench : Can LLMs Write Efficient GPU Kernels? Depth Degeneracy in Neural Networks: Vanishing Angles in Fully Connected ReLU Networks on Initialization ..."}
+{"idx": 1, "title": "GitHub - antgroup/OmniBench: [ICML 2025 Oral] This is the ...", "date": "", "ddg_snippet": "[June 5, 2025 ] We have released the exploration code for collecting subtask instructions in OmniBench , as well as the evaluation script used to evaluate virtual agents. Overview of OmniBench , a systematic benchmark with five-dimensional task complexity and bottom-up automatic task synthesis for ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/antgroup/OmniBench", "content": "[June 5, 2025 ] We have released the exploration code for collecting subtask instructions in OmniBench , as well as the evaluation script used to evaluate virtual agents. Overview of OmniBench , a systematic benchmark with five-dimensional task complexity and bottom-up automatic task synthesis for ..."}
+{"idx": 2, "title": "What Limits Virtual Agent Application? OmniBench: A Scalable ...", "date": "", "ddg_snippet": "Jun 10, 2025 · As multimodal large language models (MLLMs) advance, MLLM-based virtual agents have demonstrated remarkable performance. However, existing benchmarks face significant limitations, including uncontrollable task complexity, extensive manual annotation with limited scenarios, and a lack of multidimensional evaluation. In response to these challenges, we introduce OmniBench , a self-generating ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2506.08933", "content": "Jun 10, 2025 · As multimodal large language models (MLLMs) advance, MLLM-based virtual agents have demonstrated remarkable performance. However, existing benchmarks face significant limitations, including uncontrollable task complexity, extensive manual annotation with limited scenarios, and a lack of multidimensional evaluation. In response to these challenges, we introduce OmniBench , a self-generating ..."}
+{"idx": 3, "title": "OmniBench", "date": "", "ddg_snippet": "Overview of OmniBench , a systematic benchmark with five-dimensional task complexity and bottom-up automatic task synthesis for generating structured task graphs. It evaluates ten virtual agent capabilities using high-quality graph-based data, ensuring scalable and realistic task assessments.", "subpage_snippet": "", "source": "omni-bench.github.io", "link": "https://omni-bench.github.io/", "content": "Overview of OmniBench , a systematic benchmark with five-dimensional task complexity and bottom-up automatic task synthesis for generating structured task graphs. It evaluates ten virtual agent capabilities using high-quality graph-based data, ensuring scalable and realistic task assessments."}
+{"idx": 4, "title": "ICML 2025 What Limits Virtual Agent Application? OmniBench: A ...", "date": "", "ddg_snippet": "Poster presentation: What Limits Virtual Agent Application? OmniBench : A Scalable Multi-Dimensional Benchmark for Essential Virtual Agent Capabilities Thu 17 Jul 4:30 p.m. PDT — 7 p.m. PDT [ OpenReview]", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/oral/47259", "content": "Poster presentation: What Limits Virtual Agent Application? OmniBench : A Scalable Multi-Dimensional Benchmark for Essential Virtual Agent Capabilities Thu 17 Jul 4:30 p.m. PDT — 7 p.m. PDT [ OpenReview]"}
+{"idx": 5, "title": "OmniBench/README.md at main · antgroup/OmniBench · GitHub", "date": "", "ddg_snippet": "[ ICML 2025 Oral] This is the official repository of the paper \"What Limits Virtual Agent Application? OmniBench : A Scalable Multi-Dimensional Benchmark for Essential Virtual Agent Capabilities\" - OmniBench /README.md at main · antgroup/ OmniBench", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/antgroup/OmniBench/blob/main/README.md", "content": "[ ICML 2025 Oral] This is the official repository of the paper \"What Limits Virtual Agent Application? OmniBench : A Scalable Multi-Dimensional Benchmark for Essential Virtual Agent Capabilities\" - OmniBench /README.md at main · antgroup/ OmniBench"}
+{"idx": 6, "title": "ICML 2025 Supplement - Google Sites", "date": "", "ddg_snippet": "Section A: MRT for Open-ended Setting Experimental Setup We fine-tuned base models using MRT (models that already produce traces with markers). For STaR, we used two sizes of models (7B and 1.5B) on 10,000 NuminaMate samples, comparing MRT (with progress bonus) against vanilla STaR (outcome-only reward).", "subpage_snippet": "", "source": "sites.google.com", "link": "https://sites.google.com/view/icml25mrt/home", "content": "Section A: MRT for Open-ended Setting Experimental Setup We fine-tuned base models using MRT (models that already produce traces with markers). For STaR, we used two sizes of models (7B and 1.5B) on 10,000 NuminaMate samples, comparing MRT (with progress bonus) against vanilla STaR (outcome-only reward)."}
+{"idx": 7, "title": "ICML 2025 Thursday 07/17", "date": "", "ddg_snippet": "Experiments on actual quantum hardware and results on benchmark data suggest that our approach dominates previous QUBO formulations and state-of-the-art ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/day/7/17", "content": "Experiments on actual quantum hardware and results on benchmark data suggest that our approach dominates previous QUBO formulations and state-of-the-art ..."}
+{"idx": 8, "title": "ICML 2025 Orals", "date": "", "ddg_snippet": "Extensive experiments demonstrate state-of-the-art performance across synthetic, molecular, and digital pathology datasets, covering both unconditional and ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/events/oral", "content": "Extensive experiments demonstrate state-of-the-art performance across synthetic, molecular, and digital pathology datasets, covering both unconditional and ..."}
+{"idx": 9, "title": "Track: Poster Session 6 West", "date": "", "ddg_snippet": "17 Jul 2025 — Experiments on a server with a single NVIDIA A100 GPU (80GB) using Mixtral-8x7B models demonstrate an 85\\% average reduction in turnaround ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/session/50262", "content": "17 Jul 2025 — Experiments on a server with a single NVIDIA A100 GPU (80GB) using Mixtral-8x7B models demonstrate an 85\\% average reduction in turnaround ..."}
diff --git a/data/sampled_jsons/ICML_2025_blink-eye_GitHub_repository.jsonl b/data/sampled_jsons/ICML_2025_blink-eye_GitHub_repository.jsonl
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+{"idx": 0, "title": "Stable Diffusion — Википедия", "date": "", "ddg_snippet": "Текущая версия страницы пока не проверялась опытными участниками и может значительно отличаться от версии, проверенной 16 июня 2025 года; проверки требуют 3 правки. Перейти к навигации Перейти к поиску. Stable Diffusion.Репозиторий. github .com/Stability-AI/…", "subpage_snippet": "", "source": "ru.wikipedia.org", "link": "https://ru.wikipedia.org/wiki/Stable_Diffusion", "content": "Текущая версия страницы пока не проверялась опытными участниками и может значительно отличаться от версии, проверенной 16 июня 2025 года; проверки требуют 3 правки. Перейти к навигации Перейти к поиску. Stable Diffusion.Репозиторий. github .com/Stability-AI/…"}
+{"idx": 1, "title": "ICML 2025 Blink of an eye : a simple theory for feature localization in...", "date": "", "ddg_snippet": "My Stuff. Login. Select Year: ( 2025 ).The ICML Logo above may be used on presentations. Right-click and choose download. It is a vector graphic and may be used at any scale.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/oral/47223", "content": "My Stuff. Login. Select Year: ( 2025 ).The ICML Logo above may be used on presentations. Right-click and choose download. It is a vector graphic and may be used at any scale."}
+{"idx": 2, "title": "About section · Issue #104 · nomandhoni-cs/ blink - eye · GitHub", "date": "", "ddg_snippet": "In this About Blink Eye section Need current version number and check for updates option So user can aware of version they use and compare to website latest version.In this About Blink Eye section.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/nomandhoni-cs/blink-eye/issues/104", "content": "In this About Blink Eye section Need current version number and check for updates option So user can aware of version they use and compare to website latest version.In this About Blink Eye section."}
+{"idx": 3, "title": "\" International Conference on Machine Learning ( ICML ) 2025 in...\"", "date": "", "ddg_snippet": "Since September 17, 2025 . Share.in this collection in this repository across all repositories .", "subpage_snippet": "", "source": "digitalcommons.imsa.edu", "link": "https://digitalcommons.imsa.edu/homepage/79/", "content": "Since September 17, 2025 . Share.in this collection in this repository across all repositories ."}
+{"idx": 4, "title": "Blink Eye - App Chăm Sóc Mắt & Hẹn Giờ Nghỉ Ngơi Tốt Nhất cho...", "date": "", "ddg_snippet": "Blink Eye Ứng dụng nhắc nhở chăm sóc mắt tối giản cho Windows, macOS và Linux. Blink Eye - App giảm mỏi mắt tốt nhất cho người dùng máy tính.", "subpage_snippet": "", "source": "blinkeye.app", "link": "https://blinkeye.app/vi", "content": "Blink Eye Ứng dụng nhắc nhở chăm sóc mắt tối giản cho Windows, macOS và Linux. Blink Eye - App giảm mỏi mắt tốt nhất cho người dùng máy tính."}
+{"idx": 5, "title": "Edit eyes direction, open or close them & blink in your photos with AI.", "date": "", "ddg_snippet": "Use code EYES 20 for 20% discount on all plans. The best AI photo editor for facial expressions, eyes & head pose. Instantly enhance your portraits with our powerful AI face editor. Create stunning, professional-quality photos in real-time with just a few clicks.", "subpage_snippet": "", "source": "www.reshot.ai", "link": "https://www.reshot.ai/tools/ai-eyes-editor", "content": "Use code EYES 20 for 20% discount on all plans. The best AI photo editor for facial expressions, eyes & head pose. Instantly enhance your portraits with our powerful AI face editor. Create stunning, professional-quality photos in real-time with just a few clicks."}
+{"idx": 6, "title": "How to Track Visitors to your GitHub Repository - Simple... - YouTube", "date": "", "ddg_snippet": "Want to track Website Visitors of your GitHub Repository ?", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=EicpXRpYMi8", "content": "Want to track Website Visitors of your GitHub Repository ?"}
+{"idx": 7, "title": "BlinkLearning | Digital educational platform", "date": "", "ddg_snippet": "At BlinkLearning, we adapt and distribute digital educational content from more than 100 national and international publishers. Through our learning platform, we offer schools classroom management tools and online/offline access to more than 25,000 d...", "subpage_snippet": "", "source": "www.blinklearning.com", "link": "https://www.blinklearning.com/v/1756987556/themes/tmpux/launch.php", "content": "At BlinkLearning, we adapt and distribute digital educational content from more than 100 national and international publishers. Through our learning platform, we offer schools classroom management tools and online/offline access to more than 25,000 d..."}
+{"idx": 8, "title": "2025 [ ICML ] Int'l Conference on Machine Learning week wrapped up!", "date": "", "ddg_snippet": "2025 [ ICML ] Int'l Conference on Machine Learning week wrapped up! A fantastic week full of insightful discussions and talks. And best of all, had a chance to enjoy Vancouver’s beautiful weather and stunning views.", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/posts/yufei-zhu_2025-icml-intl-conference-on-machine-learning-activity-7352556140280373248-WmVw", "content": "2025 [ ICML ] Int'l Conference on Machine Learning week wrapped up! A fantastic week full of insightful discussions and talks. And best of all, had a chance to enjoy Vancouver’s beautiful weather and stunning views."}
+{"idx": 9, "title": "An open platform for evaluating AI through human preference", "date": "", "ddg_snippet": "Learn more about our open datasets and research papers. LMArena has open-sourced the largest repository of organic human preferences on generative models in the world. These datasets are free and open to access.", "subpage_snippet": "", "source": "lmarena.ai", "link": "https://lmarena.ai/how-it-works", "content": "Learn more about our open datasets and research papers. LMArena has open-sourced the largest repository of organic human preferences on generative models in the world. These datasets are free and open to access."}
diff --git a/data/sampled_jsons/ICML_2025_paper_statistics_medium_tier_language_models.jsonl b/data/sampled_jsons/ICML_2025_paper_statistics_medium_tier_language_models.jsonl
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+{"idx": 0, "title": "Large Language Models Are Reasoning Teachers | Request PDF", "date": "", "ddg_snippet": "... paper , we propose Neural-Symbolic Collaborative Distillation (NesyCD), a novel knowledge distillation method for learning the complex reasoning ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/372919115_Large_Language_Models_Are_Reasoning_Teachers", "content": "... paper , we propose Neural-Symbolic Collaborative Distillation (NesyCD), a novel knowledge distillation method for learning the complex reasoning ..."}
+{"idx": 1, "title": "WebST 2025", "date": "", "ddg_snippet": "... sequences and networks, and applied problems such as modeling popularity in social media, vision and language , decision-making by humans and machines.", "subpage_snippet": "", "source": "webst2025.netlify.app", "link": "https://webst2025.netlify.app/keynotes", "content": "... sequences and networks, and applied problems such as modeling popularity in social media, vision and language , decision-making by humans and machines."}
+{"idx": 2, "title": "MCML - Research Group Alexander Fraser", "date": "", "ddg_snippet": "... prompting-based detection across eight non-English languages , utilizing several prompting techniques and comparing them to fine-tuned encoder models ...", "subpage_snippet": "", "source": "mcml.ai", "link": "https://mcml.ai/research/groups/fraser/", "content": "... prompting-based detection across eight non-English languages , utilizing several prompting techniques and comparing them to fine-tuned encoder models ..."}
+{"idx": 3, "title": "The Second Workshop on Analogical Abstraction in Cognition,", "date": "", "ddg_snippet": "... a relatively popular topic in natural language processing (NLP) and artificial intelligence (AI), typically framed as intelligence tests for models ...", "subpage_snippet": "", "source": "www.aclweb.org", "link": "https://www.aclweb.org/portal/content/second-workshop-analogical-abstraction-cognition-perception-and-language", "content": "... a relatively popular topic in natural language processing (NLP) and artificial intelligence (AI), typically framed as intelligence tests for models ..."}
+{"idx": 4, "title": "multi-agent-reinforcement-learning · GitHub Topics · GitHub", "date": "", "ddg_snippet": "BenchMARL allows to quickly compare different MARL algorithms, tasks, and models while being systematically grounded in its two core tenets ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/topics/multi-agent-reinforcement-learning", "content": "BenchMARL allows to quickly compare different MARL algorithms, tasks, and models while being systematically grounded in its two core tenets ..."}
+{"idx": 5, "title": "GitHub - chaoyanghe/Awesome-Federated-Learning: FedML - The", "date": "", "ddg_snippet": "Publications in Top- tier ML/CV/NLP/DM Conference ( ICML , NeurIPS, ICLR, CVPR, ACL, AAAI, KDD) ... Learning with Theoretical Guarantees: A Model ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/chaoyanghe/Awesome-Federated-Learning", "content": "Publications in Top- tier ML/CV/NLP/DM Conference ( ICML , NeurIPS, ICLR, CVPR, ACL, AAAI, KDD) ... Learning with Theoretical Guarantees: A Model ..."}
+{"idx": 6, "title": "Copycat vs. Original: Multi-modal Pretraining and Variable", "date": "", "ddg_snippet": "Our results are comparable to those obtained from the advanced multimodal large language model backbone, LLaVA-7B [ 10 ] , and slightly outperform ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.15277v1", "content": "Our results are comparable to those obtained from the advanced multimodal large language model backbone, LLaVA-7B [ 10 ] , and slightly outperform ..."}
+{"idx": 7, "title": "ORFS-agent: Tool-Using Agents for Chip Design Optimization", "date": "", "ddg_snippet": "Large Language Models (LLMs) have reshaped AI, excelling at natural- language generation, question answering, and zero-/few-shot learning [ 7 ] .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.08332v1", "content": "Large Language Models (LLMs) have reshaped AI, excelling at natural- language generation, question answering, and zero-/few-shot learning [ 7 ] ."}
+{"idx": 8, "title": "AAAI-25 New Faculty Highlights Program - AAAI", "date": "", "ddg_snippet": "Recent advances in vision- language models have shown remarkable potential, yet creating scalable systems that can effectively understand and generate ...", "subpage_snippet": "", "source": "aaai.org", "link": "https://aaai.org/conference/aaai/aaai-25/new-faculty-highlights-program/", "content": "Recent advances in vision- language models have shown remarkable potential, yet creating scalable systems that can effectively understand and generate ..."}
+{"idx": 9, "title": "Justin Weisz - IBM Research", "date": "", "ddg_snippet": "My work has been published in top- tier HCI and AI conferences, including CHI, IUI, CSCW, AAAI, and NeurIPS. ... TJBot is an open-source paper robot ...", "subpage_snippet": "", "source": "research.ibm.com", "link": "https://research.ibm.com/people/justin-weisz", "content": "My work has been published in top- tier HCI and AI conferences, including CHI, IUI, CSCW, AAAI, and NeurIPS. ... TJBot is an open-source paper robot ..."}
diff --git a/data/sampled_jsons/ICVaR-RLHF_Chen_2023_iterated_conditional_value_at_risk_reinforcement_learning_human_feedback.jsonl b/data/sampled_jsons/ICVaR-RLHF_Chen_2023_iterated_conditional_value_at_risk_reinforcement_learning_human_feedback.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..86da3c0e73e2275fc672e6ed42b3c35152388592
--- /dev/null
+++ b/data/sampled_jsons/ICVaR-RLHF_Chen_2023_iterated_conditional_value_at_risk_reinforcement_learning_human_feedback.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Provably Efficient Iterated CVaR Reinforcement Learning with ...", "date": "", "ddg_snippet": "Overview This works presents a novel risk -sensitive RL frame-work that employs an Iterated Conditional Value - at-Risk ( ICVaR ) objective under both linear and gen-eral function approximations, and also integrates hu-man feedback setting. We presents provably sample-eficient algorithms and provide rigorous theoretical analysis.", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/media/iclr-2024/Slides/17533.pdf", "content": "Overview This works presents a novel risk -sensitive RL frame-work that employs an Iterated Conditional Value - at-Risk ( ICVaR ) objective under both linear and gen-eral function approximations, and also integrates hu-man feedback setting. We presents provably sample-eficient algorithms and provide rigorous theoretical analysis."}
+{"idx": 1, "title": "Provably Efficient Iterated CVaR Reinforcement Learning with ...", "date": "", "ddg_snippet": "Jul 6, 2023 · Risk -sensitive reinforcement learning (RL) aims to optimize policies that balance the expected reward and risk . In this paper, we present a novel risk -sensitive RL framework that employs an Iterated Conditional Value - at-Risk (CVaR) objective under both linear and general function approximations, enriched by human feedback . These new formulations provide a principled way to guarantee safety in ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2307.02842", "content": "Jul 6, 2023 · Risk -sensitive reinforcement learning (RL) aims to optimize policies that balance the expected reward and risk . In this paper, we present a novel risk -sensitive RL framework that employs an Iterated Conditional Value - at-Risk (CVaR) objective under both linear and general function approximations, enriched by human feedback . These new formulations provide a principled way to guarantee safety in ..."}
+{"idx": 2, "title": "PROVABLY EFFICIENT ITERATED CVAR REINFORCE-MENT LEARNING WITH ...", "date": "", "ddg_snippet": "ABSTRACT Risk -sensitive reinforcement learning (RL) aims to optimize policies that balance the expected reward and risk . In this paper, we present a novel risk -sensitive RL framework that employs an Iterated Conditional Value - at-Risk (CVaR) objective under both linear and general function approximations, enriched by human feed-back .", "subpage_snippet": "", "source": "proceedings.iclr.cc", "link": "https://proceedings.iclr.cc/paper_files/paper/2024/file/81f19c0e9f3e06c831630ab6662fd8ea-Supplementary-Conference.pdf", "content": "ABSTRACT Risk -sensitive reinforcement learning (RL) aims to optimize policies that balance the expected reward and risk . In this paper, we present a novel risk -sensitive RL framework that employs an Iterated Conditional Value - at-Risk (CVaR) objective under both linear and general function approximations, enriched by human feed-back ."}
+{"idx": 3, "title": "Provably Efficient Risk-Sensitive Reinforcement Learning ...", "date": "", "ddg_snippet": "In this paper, we study a novel episodic risk -sensitive Reinforcement Learning (RL) problem, named Iterated CVaR RL, which aims to maximize the tail of the reward-to-go at each step, and focuses on tightly controlling the risk of getting into catastrophic situations at each stage. This formulation is applicable to real-world tasks that demand strong risk […]", "subpage_snippet": "", "source": "www.microsoft.com", "link": "https://www.microsoft.com/en-us/research/publication/provably-efficient-risk-sensitive-reinforcement-learning-iterated-cvar-and-worst-path/", "content": "In this paper, we study a novel episodic risk -sensitive Reinforcement Learning (RL) problem, named Iterated CVaR RL, which aims to maximize the tail of the reward-to-go at each step, and focuses on tightly controlling the risk of getting into catastrophic situations at each stage. This formulation is applicable to real-world tasks that demand strong risk […]"}
+{"idx": 4, "title": "Provably Efficient Iterated CVaR Reinforcement Learning with ...", "date": "", "ddg_snippet": "Jan 16, 2024 · Risk -sensitive reinforcement learning (RL) aims to optimize policies that balance the expected reward and risk . In this paper, we present a novel risk -sensitive RL framework that employs an Iterated Conditional Value - at-Risk (CVaR) objective under both linear and general function approximations, enriched by human feedback .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=vW1SkPl4kp", "content": "Jan 16, 2024 · Risk -sensitive reinforcement learning (RL) aims to optimize policies that balance the expected reward and risk . In this paper, we present a novel risk -sensitive RL framework that employs an Iterated Conditional Value - at-Risk (CVaR) objective under both linear and general function approximations, enriched by human feedback ."}
+{"idx": 5, "title": "Provably Efficient Iterated CVaR Reinforcement Learning with ...", "date": "", "ddg_snippet": "Abstract: Risk -sensitive reinforcement learning (RL) aims to optimize policies that balance the expected reward and risk . In this paper, we present a novel risk -sensitive RL framework that employs an Iterated Conditional Value - at-Risk (CVaR) objective under both linear and general function approximations, enriched by human feedback .", "subpage_snippet": "", "source": "bohrium.dp.tech", "link": "https://bohrium.dp.tech/paper/arxiv/2307.02842", "content": "Abstract: Risk -sensitive reinforcement learning (RL) aims to optimize policies that balance the expected reward and risk . In this paper, we present a novel risk -sensitive RL framework that employs an Iterated Conditional Value - at-Risk (CVaR) objective under both linear and general function approximations, enriched by human feedback ."}
+{"idx": 6, "title": "P E RISK-SENSITIVE REINFORCE MENT L : ITERATED CVAR AND WORST ...", "date": "", "ddg_snippet": "ABSTRACT In this paper, we study a novel episodic risk -sensitive Reinforcement Learning (RL) problem, named Iterated CVaR RL, which aims to maximize the tail of the reward-to-go at each step, and focuses on tightly controlling the risk of getting into catastrophic situations at each stage. This formulation is applicable to real-world tasks that demand strong risk avoidance throughout the ...", "subpage_snippet": "", "source": "www.microsoft.com", "link": "https://www.microsoft.com/en-us/research/wp-content/uploads/2023/03/872_provably_efficient_risk_sensit.pdf", "content": "ABSTRACT In this paper, we study a novel episodic risk -sensitive Reinforcement Learning (RL) problem, named Iterated CVaR RL, which aims to maximize the tail of the reward-to-go at each step, and focuses on tightly controlling the risk of getting into catastrophic situations at each stage. This formulation is applicable to real-world tasks that demand strong risk avoidance throughout the ..."}
+{"idx": 7, "title": "Provably Efficient Iterated CVaR Reinforcement Learning ...", "date": "", "ddg_snippet": "In this paper, we present a novel risk-sensitive RL framework that employs an Iterated Conditional Value-at-Risk ( CVaR ) objective under both linear and general ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=vW1SkPl4kp&noteId=SN0UVZNFgA", "content": "In this paper, we present a novel risk-sensitive RL framework that employs an Iterated Conditional Value-at-Risk ( CVaR ) objective under both linear and general ..."}
+{"idx": 8, "title": "RA-RLHF: Provably Efficient Risk-Aware Reinforcement ...", "date": "", "ddg_snippet": "24 Dec 2024 — There are two prevalent approaches to Risk-aware-MDPs: nested (or iterated ) (such as Iterated CVaR ( ICVAR ) (Du et al., 2022) and Risk-Sensitive ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.23569v2", "content": "24 Dec 2024 — There are two prevalent approaches to Risk-aware-MDPs: nested (or iterated ) (such as Iterated CVaR ( ICVAR ) (Du et al., 2022) and Risk-Sensitive ..."}
+{"idx": 9, "title": "PROVABLY EFFICIENT ITERATED CVAR REINFORCE", "date": "", "ddg_snippet": "by Y Chen · Cited by 5 — Compared to their results, we formalize the first risk -sensitive RLHF problem, and present theoretical analysis for ICVaR - RL with general function approximation ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=vW1SkPl4kp", "content": "by Y Chen · Cited by 5 — Compared to their results, we formalize the first risk -sensitive RLHF problem, and present theoretical analysis for ICVaR - RL with general function approximation ..."}
diff --git a/data/sampled_jsons/IPO_Identity_Preference_Optimization_Azar_2024_Bradley-Terry_year_2024.jsonl b/data/sampled_jsons/IPO_Identity_Preference_Optimization_Azar_2024_Bradley-Terry_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7bfe269e95d5c199997161c972f585b4e4d2b3e2
--- /dev/null
+++ b/data/sampled_jsons/IPO_Identity_Preference_Optimization_Azar_2024_Bradley-Terry_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A Novel Approach to Identity Preference Optimization", "date": "", "ddg_snippet": "The Identity Preference Optimization ( IPO ) algorithm Gheshlaghi Azar et al. ( 2024 ) was introduced to further improve on DPO by addressing the pairwise ... 12 pages", "subpage_snippet": "", "source": "web.stanford.edu", "link": "https://web.stanford.edu/class/cs224n/final-reports/256735149.pdf", "content": "The Identity Preference Optimization ( IPO ) algorithm Gheshlaghi Azar et al. ( 2024 ) was introduced to further improve on DPO by addressing the pairwise ... 12 pages"}
+{"idx": 1, "title": "Robust Preference Optimization Amid Content-Aware, Multi ...", "date": "", "ddg_snippet": "5 days ago — The Bradley - Terry model (Bradley & Terry, 1952) provides a principled way to connect reward modeling with preference learning. It models the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.12301v2", "content": "5 days ago — The Bradley - Terry model (Bradley & Terry, 1952) provides a principled way to connect reward modeling with preference learning. It models the ..."}
+{"idx": 2, "title": "Robust Preference Optimization through Reward Model ...", "date": "", "ddg_snippet": "by A Fisch · Cited by 45 — As argued by Azar et al. ( 2024 ), DPO strongly relies on the Bradley - Terry assumption, which leads to surprising and undesirable consequences when trained on ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=E2zKNuwNDc", "content": "by A Fisch · Cited by 45 — As argued by Azar et al. ( 2024 ), DPO strongly relies on the Bradley - Terry assumption, which leads to surprising and undesirable consequences when trained on ..."}
+{"idx": 3, "title": "Understanding Preference Fine-tuning via Coverage", "date": "", "ddg_snippet": "by Y Song · 2024 · Cited by 26 — We propose Hybrid Preference Optimization (HyPO) to address the deficiencies of offline contrastive methods while maintaining some of their computational ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/16c628ab12dc4caca8e7712affa6c767-Paper-Conference.pdf", "content": "by Y Song · 2024 · Cited by 26 — We propose Hybrid Preference Optimization (HyPO) to address the deficiencies of offline contrastive methods while maintaining some of their computational ..."}
+{"idx": 4, "title": "Post-edits Are Preferences Too", "date": "", "ddg_snippet": "by N Berger · 2024 · Cited by 2 — Preference Optimization (PO) techniques are currently one of the state of the art techniques for fine-tuning large language models (LLMs).", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2024.wmt-1.122.pdf", "content": "by N Berger · 2024 · Cited by 2 — Preference Optimization (PO) techniques are currently one of the state of the art techniques for fine-tuning large language models (LLMs)."}
+{"idx": 5, "title": "Beyond Bradley - Terry Models: A General Preference Model ...", "date": "", "ddg_snippet": "The Bradley - Terry (BT) model ( Bradley & Terry , 1952) is popular for modeling such pairwise preferences due to its simplicity and computational efficiency: given K responses, a BT reward model cost O(K) inference-time compute to output the reward dictating the preferences .", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/icml/45103/paper", "content": "The Bradley - Terry (BT) model ( Bradley & Terry , 1952) is popular for modeling such pairwise preferences due to its simplicity and computational efficiency: given K responses, a BT reward model cost O(K) inference-time compute to output the reward dictating the preferences ."}
+{"idx": 6, "title": "Mohammad Gheshlaghi Azar - ACL Anthology", "date": "", "ddg_snippet": "2024 .We show this approach to generalize the direct alignment method IPO ( identity preference optimization ) and classic policy gradient. We experiment with the proposed CoPGon a toy bandit problem to illustrate its properties, as well as for finetuning LLMs on a summarization task...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/people/m/mohammad-gheshlaghi-azar/", "content": "2024 .We show this approach to generalize the direct alignment method IPO ( identity preference optimization ) and classic policy gradient. We experiment with the proposed CoPGon a toy bandit problem to illustrate its properties, as well as for finetuning LLMs on a summarization task..."}
+{"idx": 7, "title": "Fine Tuning SmolVLM for Human Alignment Using Direct Preference ...", "date": "", "ddg_snippet": "Identity Preference Optimization ( IPO )Group Relative Policy Optimization (GRPO)Figure 4: Direct Preference Optimization (source: Po, 2024 ). Fine Tuning SmolVLM Using DPO.", "subpage_snippet": "", "source": "pyimagesearch.com", "link": "https://pyimagesearch.com/2025/08/04/fine-tuning-smolvlm-for-human-alignment-using-direct-preference-optimization/", "content": "Identity Preference Optimization ( IPO )Group Relative Policy Optimization (GRPO)Figure 4: Direct Preference Optimization (source: Po, 2024 ). Fine Tuning SmolVLM Using DPO."}
+{"idx": 8, "title": "(PDF) Towards Improved Preference Optimization Pipeline: from...", "date": "", "ddg_snippet": "IPO ( Identity Prefer -. ence Optimization ) addresses the shortcomings of. BT preference modeling in cases where preference . 2024 . bets-dpo: Direct preference . optimization with dynamic beta. arXiv preprint. arXiv:2407.08639.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/385721189_Towards_Improved_Preference_Optimization_Pipeline_from_Data_Generation_to_Budget-Controlled_Regularization", "content": "IPO ( Identity Prefer -. ence Optimization ) addresses the shortcomings of. BT preference modeling in cases where preference . 2024 . bets-dpo: Direct preference . optimization with dynamic beta. arXiv preprint. arXiv:2407.08639."}
+{"idx": 9, "title": "Regression Optimization Models | Restackio", "date": "", "ddg_snippet": "Complementing SteerLM Regression with Bradley - Terry Models. Applications of Regression Optimization Models.Extrapolation Factor Optimization . To further refine the model, we employed ExPO (Zheng et al., 2024 ) as a method for extrapolating delta weights.", "subpage_snippet": "", "source": "d2wozrt205r2fu.cloudfront.net", "link": "https://d2wozrt205r2fu.cloudfront.net/p/model-optimization-answer-regression-optimization-models-cat-ai", "content": "Complementing SteerLM Regression with Bradley - Terry Models. Applications of Regression Optimization Models.Extrapolation Factor Optimization . To further refine the model, we employed ExPO (Zheng et al., 2024 ) as a method for extrapolating delta weights."}
diff --git a/data/sampled_jsons/ISPRS_S2FL_dataset_image_size.jsonl b/data/sampled_jsons/ISPRS_S2FL_dataset_image_size.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3a68d73ce53ecd729d498cdef22a5389914ece4f
--- /dev/null
+++ b/data/sampled_jsons/ISPRS_S2FL_dataset_image_size.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - satellite-image-deep-learning/techniques: Techniques", "date": "", "ddg_snippet": "ISPRS _ S2FL - > Multimodal Remote Sensing Benchmark Datasets for Land Cover Classification with A Shared and Specific Feature Learning Model", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/satellite-image-deep-learning/techniques", "content": "ISPRS _ S2FL - > Multimodal Remote Sensing Benchmark Datasets for Land Cover Classification with A Shared and Specific Feature Learning Model"}
+{"idx": 1, "title": "Efficient Image Segmentation with Minimal Annotations in... - Glcnd.io", "date": "", "ddg_snippet": "The Potsdam dataset is part of the ISPRS 2D Semantic Labeling Contest and comprises 38 high-resolution aerial image tiles, each with an impressive resolution of 6000 × 6000 pixels.", "subpage_snippet": "", "source": "glcnd.io", "link": "https://glcnd.io/efficient-image-segmentation-with-minimal-annotations-in-remote-sensing/", "content": "The Potsdam dataset is part of the ISPRS 2D Semantic Labeling Contest and comprises 38 high-resolution aerial image tiles, each with an impressive resolution of 6000 × 6000 pixels."}
+{"idx": 2, "title": "https://github.com/satellite- image -deep-learning/techniques", "date": "", "ddg_snippet": "Deep learning has revolutionized the analysis and interpretation of satellite and aerial imagery , addressing unique challenges such as vast image sizes and a wide array of object classes.", "subpage_snippet": "", "source": "awesome.ecosyste.ms", "link": "https://awesome.ecosyste.ms/projects/github.com/satellite-image-deep-learning/techniques", "content": "Deep learning has revolutionized the analysis and interpretation of satellite and aerial imagery , addressing unique challenges such as vast image sizes and a wide array of object classes."}
+{"idx": 3, "title": "satellite- image -deep-learning: A repository from robmarkcole...", "date": "", "ddg_snippet": "ISPRS _ S 2 FL -> code for paper: Multimodal Remote Sensing Benchmark Datasets for Land Cover Classification with A Shared and Specific Feature Learning Model.BSB-Aerial- Dataset -> an example on how to use Detectron2's Panoptic-FPN in the BSB Aerial Dataset .", "subpage_snippet": "", "source": "geeksrepos.com", "link": "https://geeksrepos.com/robmarkcole/satellite-image-deep-learning", "content": "ISPRS _ S 2 FL -> code for paper: Multimodal Remote Sensing Benchmark Datasets for Land Cover Classification with A Shared and Specific Feature Learning Model.BSB-Aerial- Dataset -> an example on how to use Detectron2's Panoptic-FPN in the BSB Aerial Dataset ."}
+{"idx": 4, "title": "Image dataset containing different healthy and unhealthy crop leaves.", "date": "", "ddg_snippet": "Image dataset containing different healthy and unhealthy crop leaves.", "subpage_snippet": "", "source": "www.kaggle.com", "link": "https://www.kaggle.com/datasets/vipoooool/new-plant-diseases-dataset", "content": "Image dataset containing different healthy and unhealthy crop leaves."}
+{"idx": 5, "title": "Dataset Search", "date": "", "ddg_snippet": "Feedback. Sign in. Dataset Search.Learn more about Dataset Search.", "subpage_snippet": "", "source": "datasetsearch.research.google.com", "link": "https://datasetsearch.research.google.com/", "content": "Feedback. Sign in. Dataset Search.Learn more about Dataset Search."}
+{"idx": 6, "title": "The actual B razil famine checking within a multi-annual", "date": "", "ddg_snippet": "... the baseline rules and datasets utilized in this paper will likely to be made available easily at https/ /github.com/danfenghong/ ISPRS _ S2FL .A number ...", "subpage_snippet": "", "source": "gp120inhibitor.com", "link": "https://gp120inhibitor.com/index.php/the-actual-b-razil-famine-checking-within-a-multi-annual-standpoint/", "content": "... the baseline rules and datasets utilized in this paper will likely to be made available easily at https/ /github.com/danfenghong/ ISPRS _ S2FL .A number ..."}
+{"idx": 7, "title": "danfenghong (Danfeng Hong) · GitHub", "date": "", "ddg_snippet": "Graph Convolutional Networks for Hyperspectral Image Classification, IEEE Trans. ... Spectralformer: Rethinking hyperspectral image classification ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/danfenghong", "content": "Graph Convolutional Networks for Hyperspectral Image Classification, IEEE Trans. ... Spectralformer: Rethinking hyperspectral image classification ..."}
+{"idx": 8, "title": "AllSpark: A Multimodal Spatio-Temporal General Intelligence", "date": "", "ddg_snippet": "In terms of semantics, RGB imagery reflects the electromagnetic characteristics of visible light bands emitted and reflected by geographic objects ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2401.00546v3", "content": "In terms of semantics, RGB imagery reflects the electromagnetic characteristics of visible light bands emitted and reflected by geographic objects ..."}
+{"idx": 9, "title": "An omni-scale global–local aware network for shadow... | CoLab", "date": "", "ddg_snippet": "ISPRS Journal of Photogrammetry and Remote Sensing , volume 193, pages 29-44.Although existing datasets include common objects in remote sensing images , they still have some scale, category, and image limitations.", "subpage_snippet": "", "source": "colab.ws", "link": "https://colab.ws/articles/10.1016/j.isprsjprs.2022.09.004", "content": "ISPRS Journal of Photogrammetry and Remote Sensing , volume 193, pages 29-44.Although existing datasets include common objects in remote sensing images , they still have some scale, category, and image limitations."}
diff --git a/data/sampled_jsons/ImagineFSL_DISEF_Flowers_16-shot_performance_values_Table_1.jsonl b/data/sampled_jsons/ImagineFSL_DISEF_Flowers_16-shot_performance_values_Table_1.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e0ca721de5bc67903036cd7fb7dea0bd63f47e95
--- /dev/null
+++ b/data/sampled_jsons/ImagineFSL_DISEF_Flowers_16-shot_performance_values_Table_1.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF Supplementary Material for \"ImagineFSL: Self-Supervised Pretraining ...", "date": "", "ddg_snippet": "Specifically, ImagineFSL achieves gains of 6.2% and 5.8% in the 1-shot and 16-shot settings, respectively, while ImagineFSLLoRA shows gains of 6.3% and 2.9%. These results suggest that our methods exhibit superior scaling capabilities as the capacity of CLIP models increases.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yang_ImagineFSL_Self-Supervised_Pretraining_CVPR_2025_supplemental.pdf", "content": "Specifically, ImagineFSL achieves gains of 6.2% and 5.8% in the 1-shot and 16-shot settings, respectively, while ImagineFSLLoRA shows gains of 6.3% and 2.9%. These results suggest that our methods exhibit superior scaling capabilities as the capacity of CLIP models increases."}
+{"idx": 1, "title": "GitHub - HaoyuanYang-2023/ImagineFSL: Official implementation of ...", "date": "", "ddg_snippet": "This repository contains the official code for \" ImagineFSL : Self-Supervised Pretraining Matters on Imagined Base Set for VLM-based Few- shot Learning\" ( CVPR 2025 Highlight ) In this paper: We frame synthetic images as standalone knowledge repositories and present a CLIP adaptation methodology that pretrains on purely synthetic images before fine-tuning for few- shot tasks. This marks a clear ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/HaoyuanYang-2023/ImagineFSL", "content": "This repository contains the official code for \" ImagineFSL : Self-Supervised Pretraining Matters on Imagined Base Set for VLM-based Few- shot Learning\" ( CVPR 2025 Highlight ) In this paper: We frame synthetic images as standalone knowledge repositories and present a CLIP adaptation methodology that pretrains on purely synthetic images before fine-tuning for few- shot tasks. This marks a clear ..."}
+{"idx": 2, "title": "ImagineFSL: Self-Supervised Pretraining Matters on Imagined Base Set", "date": "", "ddg_snippet": "Building on this perspective, we introduce ImagineFSL , a novel CLIP adaptation methodology that pretrains on iBase and then fine-tunes for downstream few- shot tasks. We find that, compared to no pretraining, both supervised and self-supervised pretraining are beneficial, with the latter providing better performance .", "subpage_snippet": "", "source": "peihuali.org", "link": "https://peihuali.org/ImagineFSL/index.html", "content": "Building on this perspective, we introduce ImagineFSL , a novel CLIP adaptation methodology that pretrains on iBase and then fine-tunes for downstream few- shot tasks. We find that, compared to no pretraining, both supervised and self-supervised pretraining are beneficial, with the latter providing better performance ."}
+{"idx": 3, "title": "ImagineFSL/README.md at main · HaoyuanYang-2023/ImagineFSL · GitHub", "date": "", "ddg_snippet": "This repository contains the official code for \" ImagineFSL : Self-Supervised Pretraining Matters on Imagined Base Set for VLM-based Few- shot Learning\" ( CVPR 2025 Highlight ) In this paper: We frame synthetic images as standalone knowledge repositories and present a CLIP adaptation methodology that pretrains on purely synthetic images before fine-tuning for few- shot tasks. This marks a clear ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/HaoyuanYang-2023/ImagineFSL/blob/main/README.md", "content": "This repository contains the official code for \" ImagineFSL : Self-Supervised Pretraining Matters on Imagined Base Set for VLM-based Few- shot Learning\" ( CVPR 2025 Highlight ) In this paper: We frame synthetic images as standalone knowledge repositories and present a CLIP adaptation methodology that pretrains on purely synthetic images before fine-tuning for few- shot tasks. This marks a clear ..."}
+{"idx": 4, "title": "GitHub - vturrisi/disef: Pytorch implementation of \"Diversified in ...", "date": "", "ddg_snippet": "Pytorch implementation of \"Diversified in-domain synthesis with efficient fine-tuning for few- shot classification\" - vturrisi/ disef", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/vturrisi/disef", "content": "Pytorch implementation of \"Diversified in-domain synthesis with efficient fine-tuning for few- shot classification\" - vturrisi/ disef"}
+{"idx": 5, "title": "Diversified in-domain synthesis with efficient fine-tuning for few-shot ...", "date": "", "ddg_snippet": "Following this trend, we propose Diversified In-domain Synthesis with Efficient Fine-tuning ( DISEF ), a novel approach which addresses the generalization challenge in few- shot learning using synthetic data. DISEF consists of two main components.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2312.03046", "content": "Following this trend, we propose Diversified In-domain Synthesis with Efficient Fine-tuning ( DISEF ), a novel approach which addresses the generalization challenge in few- shot learning using synthetic data. DISEF consists of two main components."}
+{"idx": 6, "title": "ImagineFSL: Self-Supervised Pretraining Matters on ... - IEEE Xplore", "date": "", "ddg_snippet": "Building on this perspective, we introduce ImagineFSL , a novel CLIP adaptation methodology that pretrains on iBase and then fine-tunes for downstream few- shot tasks. We find that, compared to no pretraining, both supervised and self-supervised pretraining are beneficial, with the latter providing better performance .", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/11094502", "content": "Building on this perspective, we introduce ImagineFSL , a novel CLIP adaptation methodology that pretrains on iBase and then fine-tunes for downstream few- shot tasks. We find that, compared to no pretraining, both supervised and self-supervised pretraining are beneficial, with the latter providing better performance ."}
+{"idx": 7, "title": "DataDream: Few-shot Guided Dataset Generation - arXiv.org", "date": "", "ddg_snippet": "Table 1 : Few- shot classification performance with DataDream using real 16-shot and synthetic images where the training dataset includes synthetic data only (top), or synthetic data + 16 real shots (bottom).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2407.10910v2", "content": "Table 1 : Few- shot classification performance with DataDream using real 16-shot and synthetic images where the training dataset includes synthetic data only (top), or synthetic data + 16 real shots (bottom)."}
+{"idx": 8, "title": "PDF 3 arXiv:2312.03046v2 [cs.CV] 7 Dec 2023", "date": "", "ddg_snippet": "We introduce DISEF , a new framework for few- shot clas-sification that leverages synthetic data and parameter-eficient fine-tuning. For generating synthetic images, we propose a novel aug-mentation pipeline that leverages both support images and their captions for producing diverse but in-domain train-ing samples.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2312.03046.pdf", "content": "We introduce DISEF , a new framework for few- shot clas-sification that leverages synthetic data and parameter-eficient fine-tuning. For generating synthetic images, we propose a novel aug-mentation pipeline that leverages both support images and their captions for producing diverse but in-domain train-ing samples."}
+{"idx": 9, "title": "CVPR 2025 Open Access Repository", "date": "", "ddg_snippet": "We introduce a novel CLIP adaptation methodology called * ImagineFSL *, involving pretraining on the imagined base set followed by fine-tuning on downstream few- shot tasks. We find that, compared to no pretraining, both supervised and self-supervised pretraining are beneficial, with the latter providing better performance .", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/html/Yang_ImagineFSL_Self-Supervised_Pretraining_Matters_on_Imagined_Base_Set_for_VLM-based_CVPR_2025_paper.html", "content": "We introduce a novel CLIP adaptation methodology called * ImagineFSL *, involving pretraining on the imagined base set followed by fine-tuning on downstream few- shot tasks. We find that, compared to no pretraining, both supervised and self-supervised pretraining are beneficial, with the latter providing better performance ."}
diff --git a/data/sampled_jsons/ImagineFSL_Section_3.3_caption_synthesis_pipeline_GPT-4_Llama_year_2024.jsonl b/data/sampled_jsons/ImagineFSL_Section_3.3_caption_synthesis_pipeline_GPT-4_Llama_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7645bd8a316b809cf3ccbff04bdc7a77680696e3
--- /dev/null
+++ b/data/sampled_jsons/ImagineFSL_Section_3.3_caption_synthesis_pipeline_GPT-4_Llama_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ImagineFSL: Self-Supervised Pretraining Matters on Imagined ...", "date": "", "ddg_snippet": "Given the cost and time constraints of accessing GPT-4 API, we delegate extensive caption generation tasks to the locally deployed, lightweight Llama model. We develop PTe for Llama that utilizes exemplary captions generated by GPT-4 as in-context examples, randomly selecting one of the four patterns and the corresponding factors and examples ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Yang_ImagineFSL_Self-Supervised_Pretraining_Matters_on_Imagined_Base_Set_for_VLM-based_CVPR_2025_paper.pdf", "content": "Given the cost and time constraints of accessing GPT-4 API, we delegate extensive caption generation tasks to the locally deployed, lightweight Llama model. We develop PTe for Llama that utilizes exemplary captions generated by GPT-4 as in-context examples, randomly selecting one of the four patterns and the corresponding factors and examples ..."}
+{"idx": 1, "title": "DeepSeek-V3, GPT-4, Phi-4, and LLaMA-3.3 generate correct ...", "date": "", "ddg_snippet": "Apr 7, 2025 · Results show that while DeepSeek-V3 and GPT-4 consistently provided accurate solutions, certain smaller models—particularly Phi- 4 and LLaMA - 3.3 —also demonstrated strong performance, underscoring the viability of lightweight alternatives. Other models exhibited errors stemming from incomplete understanding or syntactic issues.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.14926v3", "content": "Apr 7, 2025 · Results show that while DeepSeek-V3 and GPT-4 consistently provided accurate solutions, certain smaller models—particularly Phi- 4 and LLaMA - 3.3 —also demonstrated strong performance, underscoring the viability of lightweight alternatives. Other models exhibited errors stemming from incomplete understanding or syntactic issues."}
+{"idx": 2, "title": "LLAMA 3 vs GPT 4 - GeeksforGeeks", "date": "", "ddg_snippet": "Jul 23, 2025 · LLAMA and GPT are two well-known families of language models, and each has distinct architectures and functionalities. LLAMA 3 vs GPT 4 This article compares LLAMA 3 and GPT-4 in-depth, looking at their designs, performance, generating capabilities, and natural language comprehension, among other things.", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/artificial-intelligence/llama-3-vs-gpt-4/", "content": "Jul 23, 2025 · LLAMA and GPT are two well-known families of language models, and each has distinct architectures and functionalities. LLAMA 3 vs GPT 4 This article compares LLAMA 3 and GPT-4 in-depth, looking at their designs, performance, generating capabilities, and natural language comprehension, among other things."}
+{"idx": 3, "title": "Llama 3.3 Instruct 70B vs GPT-4: Model Comparison", "date": "", "ddg_snippet": "Mar 4 , 2025 · Comparison between Llama 3.3 Instruct 70B and GPT-4 across intelligence, price, speed, context window and more.", "subpage_snippet": "", "source": "artificialanalysis.ai", "link": "https://artificialanalysis.ai/models/comparisons/llama-3-3-instruct-70b-vs-gpt-4", "content": "Mar 4 , 2025 · Comparison between Llama 3.3 Instruct 70B and GPT-4 across intelligence, price, speed, context window and more."}
+{"idx": 4, "title": "Meta Llama 3.3 Outperforms GPT-4 Gemini | Knowledge Lake - Medium DeepSeek-V3, GPT-4, Phi-4, and LLaMA-3.3 generate correct ... Llama 3 vs GPT 4 : A Detailed Comparison | Which to Choose? ImagineFSL : Self-Supervised Pretraining Matters on Imagined Base Set Llama 3 vs GPT 4 : A Detailed Comparison | Which to Choose? LLAMA 3 vs GPT 4 - GeeksforGeeks ImagineFSL : Self-Supervised Pretraining Matters on Imagined Base Set Llama 3 vs GPT 4 : A Detailed Comparison | Which to Choose? Llama 3 vs GPT 4: A Detailed Comparison | Which to Choose?", "date": "", "ddg_snippet": "Dec 7, 2024 · Meet Meta Llama 3.3 the model that outspeeds and outshines Openai GPT-4 and Google Gemini. Results show that while DeepSeek-V3 and GPT-4 consistently provided accurate solutions, certain smaller models—particularly Phi- 4 and LLaMA - 3.3 —also demon-strated strong performance, underscoring the viability of lightweight alternatives. Other models exhibited errors stemming from incomplete understanding or syntactic issues. What is the difference between Llama 3 and GPT 4? Accessibility : Llama 3 is open-source, providing free access with the flexibility to host it independently, which makes it highly accessible to developers and researchers. In contrast, GPT-4 is a proprietary model with usage costs based on token processing. How does llama use GPT? We lever-age GPT with the CoT to analyze key factors defining image de-scriptions and generate exemplar captions, which are then used by Llama for diverse caption generation via ICL . Finally, SD model generates images based on the synthesized captions. Note our pipeline is not limited to closed-source GPT (see Supplement D.4.) Is llama 3 70B better than gpt-4? GPT-4 generally outperforms Llama 3 70B across these benchmarks, particularly in areas like common knowledge and grade school math. However, Llama 3 70B demonstrates competitive performance, especially considering its open-source nature and accessibility. What is llama 3? A collection of big language models called LLAMA ( Language Model for Metadata-Aware Generation ) was created by Meta AI with the express purpose of producing text that includes metadata, such as tailoring answers depending on user input. The most recent version, LLAMA 3, improves on its predecessors with additional features: How to generate exemplary captions using gpt-4 API? To analyze the factors and gener-ate the exemplary captions, we call GPT - 4 API with a tem-perature of 0.5. Llama 3 8B is deployed locally to gen-erate extensive captions, in which we set the temperature to 0.5 and Nucleus Sampling parameter to 0.7. What makes llama 3 different from other AI platforms? Compared to its predecessors, Llama 3 features: Open-Source Flexibility : Meta has continued its commitment to open-source AI with Llama 3, making it accessible for a broad spectrum of developers, researchers, and startups. Nov 15, 2024 · Explore the key differences between Meta's Llama 3 and OpenAI's GPT-4 . We analyze cost, performance, and use cases to help you choose.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/ai-for-everyone/meta-llama-3-3-the-multilingual-large-language-model-c9129fedbefd", "content": "Dec 7, 2024 · Meet Meta Llama 3.3 the model that outspeeds and outshines Openai GPT-4 and Google Gemini. Results show that while DeepSeek-V3 and GPT-4 consistently provided accurate solutions, certain smaller models—particularly Phi- 4 and LLaMA - 3.3 —also demon-strated strong performance, underscoring the viability of lightweight alternatives. Other models exhibited errors stemming from incomplete understanding or syntactic issues. What is the difference between Llama 3 and GPT 4? Accessibility : Llama 3 is open-source, providing free access with the flexibility to host it independently, which makes it highly accessible to developers and researchers. In contrast, GPT-4 is a proprietary model with usage costs based on token processing. How does llama use GPT? We lever-age GPT with the CoT to analyze key factors defining image de-scriptions and generate exemplar captions, which are then used by Llama for diverse caption generation via ICL . Finally, SD model generates images based on the synthesized captions. Note our pipeline is not limited to closed-source GPT (see Supplement D.4.) Is llama 3 70B better than gpt-4? GPT-4 generally outperforms Llama 3 70B across these benchmarks, particularly in areas like common knowledge and grade school math. However, Llama 3 70B demonstrates competitive performance, especially considering its open-source nature and accessibility. What is llama 3? A collection of big language models called LLAMA ( Language Model for Metadata-Aware Generation ) was created by Meta AI with the express purpose of producing text that includes metadata, such as tailoring answers depending on user input. The most recent version, LLAMA 3, improves on its predecessors with additional features: How to generate exemplary captions using gpt-4 API? To analyze the factors and gener-ate the exemplary captions, we call GPT - 4 API with a tem-perature of 0.5. Llama 3 8B is deployed locally to gen-erate extensive captions, in which we set the temperature to 0.5 and Nucleus Sampling parameter to 0.7. What makes llama 3 different from other AI platforms? Compared to its predecessors, Llama 3 features: Open-Source Flexibility : Meta has continued its commitment to open-source AI with Llama 3, making it accessible for a broad spectrum of developers, researchers, and startups. Nov 15, 2024 · Explore the key differences between Meta's Llama 3 and OpenAI's GPT-4 . We analyze cost, performance, and use cases to help you choose."}
+{"idx": 5, "title": "DeepSeek-V3, GPT-4, Phi-4, and LLaMA-3.3 generate correct ...", "date": "", "ddg_snippet": "Results show that while DeepSeek-V3 and GPT-4 consistently provided accurate solutions, certain smaller models—particularly Phi- 4 and LLaMA - 3.3 —also demon-strated strong performance, underscoring the viability of lightweight alternatives. Other models exhibited errors stemming from incomplete understanding or syntactic issues.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.14926", "content": "Results show that while DeepSeek-V3 and GPT-4 consistently provided accurate solutions, certain smaller models—particularly Phi- 4 and LLaMA - 3.3 —also demon-strated strong performance, underscoring the viability of lightweight alternatives. Other models exhibited errors stemming from incomplete understanding or syntactic issues."}
+{"idx": 6, "title": "Llama 3 vs GPT 4: A Detailed Comparison | Which to Choose?", "date": "", "ddg_snippet": "Nov 15, 2024 · Explore the key differences between Meta's Llama 3 and OpenAI's GPT-4 . We analyze cost, performance, and use cases to help you choose.", "subpage_snippet": "", "source": "blog.promptlayer.com", "link": "https://blog.promptlayer.com/llama-3-vs-gpt-4/", "content": "Nov 15, 2024 · Explore the key differences between Meta's Llama 3 and OpenAI's GPT-4 . We analyze cost, performance, and use cases to help you choose."}
+{"idx": 7, "title": "Supplementary Material for “ ImagineFSL : Self-Supervised Pretraining...", "date": "", "ddg_snippet": "Implementation Details on Synthesizing both Captions and Images. Analyzing Factors via GPT - 4 . Generate Exemplary Captions via GPT - 4 . Extensive Image Caption Generation via Llama . Summary of Synthetic Images.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yang_ImagineFSL_Self-Supervised_Pretraining_CVPR_2025_supplemental.pdf", "content": "Implementation Details on Synthesizing both Captions and Images. Analyzing Factors via GPT - 4 . Generate Exemplary Captions via GPT - 4 . Extensive Image Caption Generation via Llama . Summary of Synthetic Images."}
+{"idx": 8, "title": "Как скачать, установить и запустить Llama 4", "date": "", "ddg_snippet": "Как скачать и запустить LLaMA 4 Scout или Maverick. Пошаговая инструкция для macOS и Windows: установка, запуск на CPU, тестирование.", "subpage_snippet": "", "source": "aidive.org", "link": "https://aidive.org/blog/setup-llama4", "content": "Как скачать и запустить LLaMA 4 Scout или Maverick. Пошаговая инструкция для macOS и Windows: установка, запуск на CPU, тестирование."}
+{"idx": 9, "title": "Llama 4 : How to Run & Fine-tune | Unsloth Documentation", "date": "", "ddg_snippet": "How to run Llama 4 locally using our dynamic GGUFs which recovers accuracy compared to standard quantization.", "subpage_snippet": "", "source": "docs.unsloth.ai", "link": "https://docs.unsloth.ai/models/tutorials-how-to-fine-tune-and-run-llms/llama-4-how-to-run-and-fine-tune", "content": "How to run Llama 4 locally using our dynamic GGUFs which recovers accuracy compared to standard quantization."}
diff --git a/data/sampled_jsons/ImagineFSL_paper_Table_1_Flowers_16-shot_performance.jsonl b/data/sampled_jsons/ImagineFSL_paper_Table_1_Flowers_16-shot_performance.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ac99de6721419268089a522b98c42eb13cff54df
--- /dev/null
+++ b/data/sampled_jsons/ImagineFSL_paper_Table_1_Flowers_16-shot_performance.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ImagineFSL : Self-Supervised Pretraining Matters on Imagined Base...", "date": "", "ddg_snippet": "Table 6. Ablation study on the proposed methods in the 1 shot/ 16 shot settings. Bold: best results; underlined: second-best results. per-class text embeddings), implementing the ImagineFSL method. Table 6e shows this tuning significantly improves performance across most datasets.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Yang_ImagineFSL_Self-Supervised_Pretraining_Matters_on_Imagined_Base_Set_for_VLM-based_CVPR_2025_paper.pdf", "content": "Table 6. Ablation study on the proposed methods in the 1 shot/ 16 shot settings. Bold: best results; underlined: second-best results. per-class text embeddings), implementing the ImagineFSL method. Table 6e shows this tuning significantly improves performance across most datasets."}
+{"idx": 1, "title": "ImagineFSL: Self-Supervised Pretraining Matters on Imagined ...", "date": "", "ddg_snippet": "In this task, a model is trained on 16 shots per class from ImageNet (source) and tested on four target datasets. We specifically synthesize images for ImageNet-S and ImageNet-R datasets, while synthetic images for ImageNet are used for ImageNet-V2 and ImageNet-A.", "subpage_snippet": "", "source": "peihuali.org", "link": "https://peihuali.org/ImagineFSL/index.html", "content": "In this task, a model is trained on 16 shots per class from ImageNet (source) and tested on four target datasets. We specifically synthesize images for ImageNet-S and ImageNet-R datasets, while synthetic images for ImageNet are used for ImageNet-V2 and ImageNet-A."}
+{"idx": 2, "title": "[2205.01703] Improving In-Context Few-Shot Learning via Self ...", "date": "", "ddg_snippet": "May 3, 2022 · In this paper , we propose to use self-supervision in an intermediate training stage between pretraining and downstream few- shot usage with the goal to teach the model to perform in-context few shot learning.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2205.01703", "content": "May 3, 2022 · In this paper , we propose to use self-supervision in an intermediate training stage between pretraining and downstream few- shot usage with the goal to teach the model to perform in-context few shot learning."}
+{"idx": 3, "title": "Rolled Paper Flower Mug | Paper Flower Rolled Up Tutorial - YouTube", "date": "", "ddg_snippet": "This video is a tutorial for rolled paper flowers , then turn these beautiful rolled up paper flowers into a mug arrangement.", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=UcwTHhXF5uQ", "content": "This video is a tutorial for rolled paper flowers , then turn these beautiful rolled up paper flowers into a mug arrangement."}
+{"idx": 4, "title": "ImagineFSL/README.md at main · HaoyuanYang-2023 ... - GitHub", "date": "", "ddg_snippet": "We propose an improved Self-SL method based on DINO, specifically tailored for FSL . It introduces higher-order moments for image representation and employs synthetic augmentation for effective view construction.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/HaoyuanYang-2023/ImagineFSL/blob/main/README.md", "content": "We propose an improved Self-SL method based on DINO, specifically tailored for FSL . It introduces higher-order moments for image representation and employs synthetic augmentation for effective view construction."}
+{"idx": 5, "title": "AcademicDissect/detailed_paper_collection/CLIP.md at main ...", "date": "", "ddg_snippet": "CVPR 2025 Parper Collections. Contribute to AcademicDissect/AcademicDissect development by creating an account on GitHub.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/AcademicDissect/AcademicDissect/blob/main/detailed_paper_collection/CLIP.md", "content": "CVPR 2025 Parper Collections. Contribute to AcademicDissect/AcademicDissect development by creating an account on GitHub."}
+{"idx": 6, "title": "Рецепты от OneTable — блюда и десерты на любой вкус", "date": "", "ddg_snippet": "Обширная коллекция рецептов: от завтраков и закусок до десертов и напитков. Пошаговые инструкции и советы для начинающих и опытных кулинаров.", "subpage_snippet": "", "source": "onetable.ru", "link": "https://onetable.ru/category/recipe/", "content": "Обширная коллекция рецептов: от завтраков и закусок до десертов и напитков. Пошаговые инструкции и советы для начинающих и опытных кулинаров."}
+{"idx": 7, "title": "Self-Supervised Pretraining Matters on Imagined Base Set for ...", "date": "", "ddg_snippet": "5K slightly enhances 1 -shot accuracy while 16 - shot accuracy remains stable, suggesting potential saturation in performance . This could be attributed to the ...", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/poster/32717", "content": "5K slightly enhances 1 -shot accuracy while 16 - shot accuracy remains stable, suggesting potential saturation in performance . This could be attributed to the ..."}
+{"idx": 8, "title": "Self-Supervised Pretraining Matters on Imagined Base Set ...", "date": "", "ddg_snippet": "by H Yang — Specifically,. ImagineFSL achieves gains of 6.2% and 5.8% in the 1 -shot and 16 ... Adapter improves by 6.2%/6.7% in 1 -shot/ 16 - shot settings, respectively.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yang_ImagineFSL_Self-Supervised_Pretraining_CVPR_2025_supplemental.pdf", "content": "by H Yang — Specifically,. ImagineFSL achieves gains of 6.2% and 5.8% in the 1 -shot and 16 ... Adapter improves by 6.2%/6.7% in 1 -shot/ 16 - shot settings, respectively."}
+{"idx": 9, "title": "Homepage of Qilong Wang - GitHub Pages", "date": "", "ddg_snippet": "Mar 3, 2022 · ImagineFSL : Self-Supervised Pretraining Matters on Imagined Base Set for VLM-based Few- shot Learning. 38th IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2025 [PDF] [Code] (highlight paper (13.5%))", "subpage_snippet": "", "source": "csqlwang.github.io", "link": "https://csqlwang.github.io/homepage/", "content": "Mar 3, 2022 · ImagineFSL : Self-Supervised Pretraining Matters on Imagined Base Set for VLM-based Few- shot Learning. 38th IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2025 [PDF] [Code] (highlight paper (13.5%))"}
diff --git a/data/sampled_jsons/ImagineFSL_paper_references_Learning_Transferable_Visual_Models_From_Natural_Language_Supervision.jsonl b/data/sampled_jsons/ImagineFSL_paper_references_Learning_Transferable_Visual_Models_From_Natural_Language_Supervision.jsonl
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index 0000000000000000000000000000000000000000..5c17d21b686ffb50e99ff5dbfeaed3e84d1ae4f0
--- /dev/null
+++ b/data/sampled_jsons/ImagineFSL_paper_references_Learning_Transferable_Visual_Models_From_Natural_Language_Supervision.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "(PDF) Learning Transferable Visual Models From Natural ...", "date": "", "ddg_snippet": "First page of “ Learning Transferable Visual Models From Natural Language Supervision ” PDF Icon.In this paper , we review the recent progress in Vision-Language Pre-Trained Models (VL-PTMs).", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/96926998/Learning_Transferable_Visual_Models_From_Natural_Language_Supervision", "content": "First page of “ Learning Transferable Visual Models From Natural Language Supervision ” PDF Icon.In this paper , we review the recent progress in Vision-Language Pre-Trained Models (VL-PTMs)."}
+{"idx": 1, "title": "[PDF] Learning Transferable Visual Models From Natural ...", "date": "", "ddg_snippet": "After pre-training, natural language is used to reference learned visual concepts (or describe new ones) enabling zero-shot transfer of the model to downstream tasks.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Learning-Transferable-Visual-Models-From-Natural-Radford-Kim/6f870f7f02a8c59c3e23f407f3ef00dd1dcf8fc4", "content": "After pre-training, natural language is used to reference learned visual concepts (or describe new ones) enabling zero-shot transfer of the model to downstream tasks."}
+{"idx": 2, "title": "Learning Transferable Visual Models From Natural Language ...", "date": "", "ddg_snippet": "After pre-training, natural language is used to reference learned visual concepts (or describe new ones) enabling zero-shot transfer of the model to downstream tasks.Cite this Paper .", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v139/radford21a", "content": "After pre-training, natural language is used to reference learned visual concepts (or describe new ones) enabling zero-shot transfer of the model to downstream tasks.Cite this Paper ."}
+{"idx": 3, "title": "Learning Transferable Visual Models From Natural Language ...", "date": "", "ddg_snippet": "After pre-training, natural language is used to reference learned visual concepts (or describe new ones) enabling zero-shot transfer of the model to downstream tasks.", "subpage_snippet": "", "source": "www.bohrium.com", "link": "https://www.bohrium.com/paper-details/learning-transferable-visual-models-from-natural-language-supervision/867763699782255232-108597", "content": "After pre-training, natural language is used to reference learned visual concepts (or describe new ones) enabling zero-shot transfer of the model to downstream tasks."}
+{"idx": 4, "title": "Learning Transferable Visual Models From Natural Language ...", "date": "", "ddg_snippet": "The paper explores learning visual models from natural language supervision , aiming to overcome the limitations of traditional computer vision systems that rely on fixed object categories.", "subpage_snippet": "", "source": "tsuji.tech", "link": "https://tsuji.tech/clip-arxiv2021/", "content": "The paper explores learning visual models from natural language supervision , aiming to overcome the limitations of traditional computer vision systems that rely on fixed object categories."}
+{"idx": 5, "title": "Learning Transferable Visual Models from Language", "date": "", "ddg_snippet": "Analyzing the Potential of Learning Transferable Visual Models from Natural Language Supervision .", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/papers/2103.00020", "content": "Analyzing the Potential of Learning Transferable Visual Models from Natural Language Supervision ."}
+{"idx": 6, "title": "Learning Transferable Visual Models From Natural Language ...", "date": "", "ddg_snippet": "After pre-training, natural language can be used to reference learned visual concepts or describe new ones enabling zero-shot transfer of the model to downstream tasks.At the core of this approach is the idea of learning perception from supervision contained in natural language .", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@pranjalkhadka/learning-transferable-visual-models-from-natural-language-supervision-70745eada242", "content": "After pre-training, natural language can be used to reference learned visual concepts or describe new ones enabling zero-shot transfer of the model to downstream tasks.At the core of this approach is the idea of learning perception from supervision contained in natural language ."}
+{"idx": 7, "title": "CLIP Learning Transferable Visual Models From Natural Language ...", "date": "", "ddg_snippet": "This my reading note on Learning Transferable Visual Models From Natural Language Supervision .After pre-training, natural language is used to reference learned visual concepts (or describe new ones) enabling zero-shot transfer of the model to downstream tasks.", "subpage_snippet": "", "source": "zhangtemplar.github.io", "link": "https://zhangtemplar.github.io/clip/", "content": "This my reading note on Learning Transferable Visual Models From Natural Language Supervision .After pre-training, natural language is used to reference learned visual concepts (or describe new ones) enabling zero-shot transfer of the model to downstream tasks."}
+{"idx": 8, "title": "CLIP: Learning Transferable Visual Models From Natural ... | ITNEXT", "date": "", "ddg_snippet": "CLIP (Contrastive Language Image Pre-training) paper was published in 2021 by OpenAI. This paper introduces the idea of using image caption…", "subpage_snippet": "", "source": "itnext.io", "link": "https://itnext.io/clip-learning-transferable-visual-models-from-natural-language-supervision-29f2817f317f", "content": "CLIP (Contrastive Language Image Pre-training) paper was published in 2021 by OpenAI. This paper introduces the idea of using image caption…"}
+{"idx": 9, "title": "Learning transferable visual models from natural language ...", "date": "", "ddg_snippet": "CLIP’s zero-shot classifier is generated via natural language which allows for visual concepts to be directly specified (“communicated”). By contrast, “normal” supervised learning must infer concepts indirectly from training examples.", "subpage_snippet": "", "source": "kylrth.com", "link": "https://kylrth.com/paper/clip/", "content": "CLIP’s zero-shot classifier is generated via natural language which allows for visual concepts to be directly specified (“communicated”). By contrast, “normal” supervised learning must infer concepts indirectly from training examples."}
diff --git a/data/sampled_jsons/Implicit_Language_Models_are_RNNs_Balancing_Parallelization_and_Expressivity_Figure_1_year_2024.jsonl b/data/sampled_jsons/Implicit_Language_Models_are_RNNs_Balancing_Parallelization_and_Expressivity_Figure_1_year_2024.jsonl
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index 0000000000000000000000000000000000000000..cfafbc24032fb71a9efed4f366ae208c379cf2ef
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+++ b/data/sampled_jsons/Implicit_Language_Models_are_RNNs_Balancing_Parallelization_and_Expressivity_Figure_1_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Implicit Language Models are RNNs: Balancing Parallelization and ...", "date": "", "ddg_snippet": "State-space models (SSMs) and transformers dominate the language modeling landscape. However, they are constrained to a lower computational complexity than classical recurrent neural networks ( RNNs ), limiting their expressivity . In contrast, RNNs lack parallelization during training, raising fundamental questions about the trade off between parallelization and expressivity . We propose implicit ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.07827", "content": "State-space models (SSMs) and transformers dominate the language modeling landscape. However, they are constrained to a lower computational complexity than classical recurrent neural networks ( RNNs ), limiting their expressivity . In contrast, RNNs lack parallelization during training, raising fundamental questions about the trade off between parallelization and expressivity . We propose implicit ..."}
+{"idx": 1, "title": "Implicit Language Models are RNNs - GitHub", "date": "", "ddg_snippet": "Implicit Language Models are RNNs Balancing Parallelization and Expressivity Mark Schöne 1,2 *, Babak Rahmani 2 *, Heiner Kremer 2, Fabian Falck 2, Hitesh Ballani 2, Jannes Gladrow 2 † 1 TU Dresden, Germany, 2 Microsoft Research, Cambridge, UK (*) Equal contribution. ( † ) Corresponding author.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/microsoft/implicit_languagemodels/blob/main/README.md", "content": "Implicit Language Models are RNNs Balancing Parallelization and Expressivity Mark Schöne 1,2 *, Babak Rahmani 2 *, Heiner Kremer 2, Fabian Falck 2, Hitesh Ballani 2, Jannes Gladrow 2 † 1 TU Dresden, Germany, 2 Microsoft Research, Cambridge, UK (*) Equal contribution. ( † ) Corresponding author."}
+{"idx": 2, "title": "I L M RNN BALANCING PARALLELIZATION AND EXPRESSIVITY - OpenReview", "date": "", "ddg_snippet": "Our approach to balancing state tracking and parallelization relies on two key observations. First, we demonstrate that implicit models naturally adapt their compute load to the dificulty of the learning problem (see Figure 3Left). At both training and test time, such models effectively interpolate between their parallelizable form, when all tokens in the sequence are resolvable, and RNNs ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=6XwIa48eex", "content": "Our approach to balancing state tracking and parallelization relies on two key observations. First, we demonstrate that implicit models naturally adapt their compute load to the dificulty of the learning problem (see Figure 3Left). At both training and test time, such models effectively interpolate between their parallelizable form, when all tokens in the sequence are resolvable, and RNNs ..."}
+{"idx": 3, "title": "Implicit Language Models are RNNs: Balancing Parallelization and ...", "date": "", "ddg_snippet": "In contrast, RNNs lack parallelization during training, raising fundamental questions about the trade off between parallelization and expressivity . We propose implicit SSMs, which iterate a ...", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=aN0afroCQns", "content": "In contrast, RNNs lack parallelization during training, raising fundamental questions about the trade off between parallelization and expressivity . We propose implicit SSMs, which iterate a ..."}
+{"idx": 4, "title": "Implicit Language Models are RNNs: Balancing Parallelization and ...", "date": "", "ddg_snippet": "Spotlight Poster Implicit Language Models are RNNs : Balancing Parallelization and Expressivity Mark Schoene · Babak Rahmani · Heiner Kremer · Fabian Falck · Hitesh Ballani · Jannes Gladrow East Exhibition Hall A-B #E-3203", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46440", "content": "Spotlight Poster Implicit Language Models are RNNs : Balancing Parallelization and Expressivity Mark Schoene · Babak Rahmani · Heiner Kremer · Fabian Falck · Hitesh Ballani · Jannes Gladrow East Exhibition Hall A-B #E-3203"}
+{"idx": 5, "title": "Implicit Language Models are RNNs: Balancing Parallelization and ...", "date": "", "ddg_snippet": "In contrast, RNNs lack parallelization dur-ing training, raising fundamental questions about the trade off between parallelization and expres-sivity . We propose implicit SSMs, which it-erate a transformation until convergence to a fixed point. Theoretically, we show that implicit SSMs implement the non-linear state-transitions of RNNs .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.07827", "content": "In contrast, RNNs lack parallelization dur-ing training, raising fundamental questions about the trade off between parallelization and expres-sivity . We propose implicit SSMs, which it-erate a transformation until convergence to a fixed point. Theoretically, we show that implicit SSMs implement the non-linear state-transitions of RNNs ."}
+{"idx": 6, "title": "Results from our latest preprint \"Implicit Language Models are RNNs ...", "date": "", "ddg_snippet": "💡 Results from our latest preprint \"Implicit Language Models are RNNs : Balancing Parallelization and Expressivity\" with Microsoft Research. We explored the algorithmic properties of ...", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/feed/update/urn:li:activity:7297205635211821056/", "content": "💡 Results from our latest preprint \"Implicit Language Models are RNNs : Balancing Parallelization and Expressivity\" with Microsoft Research. We explored the algorithmic properties of ..."}
+{"idx": 7, "title": "GitHub - microsoft/implicit_languagemodels", "date": "", "ddg_snippet": "Implicit Language Models are RNNs Balancing Parallelization and Expressivity Mark Schöne 1,2 *, Babak Rahmani 2 *, Heiner Kremer 2, Fabian Falck 2, Hitesh Ballani 2, Jannes Gladrow 2 † 1 TU Dresden, Germany, 2 Microsoft Research, Cambridge, UK (*) Equal contribution. ( † ) Corresponding author.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/microsoft/implicit_languagemodels", "content": "Implicit Language Models are RNNs Balancing Parallelization and Expressivity Mark Schöne 1,2 *, Babak Rahmani 2 *, Heiner Kremer 2, Fabian Falck 2, Hitesh Ballani 2, Jannes Gladrow 2 † 1 TU Dresden, Germany, 2 Microsoft Research, Cambridge, UK (*) Equal contribution. ( † ) Corresponding author."}
+{"idx": 8, "title": "PDF Implicit Language Models are RNNs: Balancing Parallelization and ...", "date": "", "ddg_snippet": "Implicit Language Models are RNNs : Balancing Parallelization and Expressivity Babak Rahmani*, M Schöne*, H Kremer, F Falck, H Ballani, J Gladrow (*equal contribution)", "subpage_snippet": "", "source": "asap-seminar.github.io", "link": "https://asap-seminar.github.io/assets/slides/asap_implicit_rnn.pdf", "content": "Implicit Language Models are RNNs : Balancing Parallelization and Expressivity Babak Rahmani*, M Schöne*, H Kremer, F Falck, H Ballani, J Gladrow (*equal contribution)"}
+{"idx": 9, "title": "Left: Minimum layers required to solve the S5 word problem, a ...", "date": "", "ddg_snippet": "Bottom: Scaling of language models pretrained on 207B tokens of the deduplicated PILE. from publication: Implicit Language Models are RNNs : Balancing Parallelization and Expressivity | State-space ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/Left-Minimum-layers-required-to-solve-the-S5-word-problem-a-theoretically-hard_fig1_388955170", "content": "Bottom: Scaling of language models pretrained on 207B tokens of the deduplicated PILE. from publication: Implicit Language Models are RNNs : Balancing Parallelization and Expressivity | State-space ..."}
diff --git a/data/sampled_jsons/Improving_the_Scaling_Laws_of_Synthetic_Data_with_Deliberate_Practice_Algorithm_1_year_2024.jsonl b/data/sampled_jsons/Improving_the_Scaling_Laws_of_Synthetic_Data_with_Deliberate_Practice_Algorithm_1_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b1aa1715cb3ad7623696cd139b171815a9537f93
--- /dev/null
+++ b/data/sampled_jsons/Improving_the_Scaling_Laws_of_Synthetic_Data_with_Deliberate_Practice_Algorithm_1_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Scaling Synthetic Data Pilots for Enterprise AI | EM360Tech", "date": "", "ddg_snippet": "They warn that without auditing and governance, scaling synthetic ... If even one of these is missing, your synthetic data is not ready for scale .", "subpage_snippet": "", "source": "em360tech.com", "link": "https://em360tech.com/tech-articles/scaling-synthetic-data-pilots", "content": "They warn that without auditing and governance, scaling synthetic ... If even one of these is missing, your synthetic data is not ready for scale ."}
+{"idx": 1, "title": "FineReason: Evaluating and Improving LLMs’ Deliberate", "date": "", "ddg_snippet": "These puzzles are solved through discrete steps, with explicit rules allowing easy validation of intermediate states.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.20238v2", "content": "These puzzles are solved through discrete steps, with explicit rules allowing easy validation of intermediate states."}
+{"idx": 2, "title": "Emerging Technology Solutions | The Evolution of Model", "date": "", "ddg_snippet": "While early success relied on pre-training scaling laws that emphasized bigger models and more data , the field has evolved through synthetic data ...", "subpage_snippet": "", "source": "blogs.infosys.com", "link": "https://blogs.infosys.com/emerging-technology-solutions/artificial-intelligence/evolution-of-model-performance.html", "content": "While early success relied on pre-training scaling laws that emphasized bigger models and more data , the field has evolved through synthetic data ..."}
+{"idx": 3, "title": "Synthetic Data and Health Equity – Just Tech", "date": "", "ddg_snippet": "... about generative artificial intelligence and synthetic data in a historical context, with an eye toward the ethical and equity implications of ...", "subpage_snippet": "", "source": "just-tech.ssrc.org", "link": "https://just-tech.ssrc.org/field-reviews/synthetic-data-and-health-equity/", "content": "... about generative artificial intelligence and synthetic data in a historical context, with an eye toward the ethical and equity implications of ..."}
+{"idx": 4, "title": "The Strategic Imperative of Synthetic Data in the AI Era - Big", "date": "", "ddg_snippet": "Driven by the critical needs for enhanced data privacy, overcoming data scarcity, and mitigating algorithmic bias, synthetic data offers a ...", "subpage_snippet": "", "source": "bigdataclouds.org", "link": "https://bigdataclouds.org/the-strategic-imperative-of-synthetic-data-in-the-ai-era/", "content": "Driven by the critical needs for enhanced data privacy, overcoming data scarcity, and mitigating algorithmic bias, synthetic data offers a ..."}
+{"idx": 5, "title": "How Generative AI is Revolutionizing Training Data with", "date": "", "ddg_snippet": "Mitigation of Bias and Enhancement of Diversity: Synthetic data can be deliberately designed to include underrepresented groups or rare scenarios ...", "subpage_snippet": "", "source": "www.dataversity.net", "link": "https://www.dataversity.net/how-generative-ai-is-revolutionizing-training-data-with-synthetic-datasets/", "content": "Mitigation of Bias and Enhancement of Diversity: Synthetic data can be deliberately designed to include underrepresented groups or rare scenarios ..."}
+{"idx": 6, "title": "Physically Informed Synthetic Data Generation and U-Net", "date": "", "ddg_snippet": "... of the article may be reused without ... Please note that many of the page functionalities won't work as expected without javascript enabled.", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/2227-7390/13/14/2304", "content": "... of the article may be reused without ... Please note that many of the page functionalities won't work as expected without javascript enabled."}
+{"idx": 7, "title": "Scaling LLM Test Time Compute", "date": "", "ddg_snippet": "... of 2025, test-time compute is widely considered to be one of the likely key drivers of performance improvements in LLMs, as we re running into data ...", "subpage_snippet": "", "source": "www.jonvet.com", "link": "https://www.jonvet.com/blog/llm-test-time-compute", "content": "... of 2025, test-time compute is widely considered to be one of the likely key drivers of performance improvements in LLMs, as we re running into data ..."}
+{"idx": 8, "title": "The Citadel Settlement, Off-Exchange Market Makers, and Giant", "date": "", "ddg_snippet": "... the consolidated and private data feeds] are identical 99.9% of the time— that’s all but 23 seconds of the day.” [12] The facts in the ...", "subpage_snippet": "", "source": "clsbluesky.law.columbia.edu", "link": "https://clsbluesky.law.columbia.edu/2017/05/05/the-citadel-settlement-off-exchange-market-makers-and-giant-brokerages/", "content": "... the consolidated and private data feeds] are identical 99.9% of the time— that’s all but 23 seconds of the day.” [12] The facts in the ..."}
+{"idx": 9, "title": "Do Larger Language Models Imply Better Generalization? A", "date": "", "ddg_snippet": "We first observe this phenomenon with real-world knowledge graph data , and then systematically study it through synthetically generated data .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.03635v2", "content": "We first observe this phenomenon with real-world knowledge graph data , and then systematically study it through synthetically generated data ."}
diff --git a/data/sampled_jsons/Incorrect_Synthetic_Data_Scales_LLM_Math_Reasoning_Eight-Fold_base_models_LLaMA_Mistral.jsonl b/data/sampled_jsons/Incorrect_Synthetic_Data_Scales_LLM_Math_Reasoning_Eight-Fold_base_models_LLaMA_Mistral.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..df48f71ef173ea3ec695b328caf2188fe4ca570a
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+++ b/data/sampled_jsons/Incorrect_Synthetic_Data_Scales_LLM_Math_Reasoning_Eight-Fold_base_models_LLaMA_Mistral.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "(PDF) RL on Incorrect Synthetic Data Scales the Efficiency of LLM ...", "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": "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": "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": 1, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM ... Images RL on Incorrect Synthetic Data Scales the Efficiency of LLM ... RL on Incorrect Synthetic Data · MinWoo Park Learning to Reason by Failing: Offline RL on Sub-optimal ... RL on Incorrect Synthetic Data Scales the Efficiency of LLM ... ars22/scaling-LLM-math-synthetic-data - GitHub RL on Incorrect Synthetic Data Scales the Efficiency of LLM ...", "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 ... View all 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 ... Jun 20, 2024 · abstract: 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. Jun 13, 2024 · This paper investigates the helps/hurts of training models on model-generated synthetic data for math reasoning . They first conduct an empirical study and then propose to construct negative samples to address spurious correlations in SFT/RFT policy. Abstract 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. About Code and data used in the paper: \"Training on Incorrect Synthetic Data via RL Scales LLM Math Reasoning Eight-Fold \" The authors of this paper delve into the realm of training language models on model-generated synthetic data for math reasoning tasks. They begin by exploring the effectiveness of finetuning LLMs on synthetic correct or positive problem-solution pairs generated by proficient models .", "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 ... View all 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 ... Jun 20, 2024 · abstract: 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. Jun 13, 2024 · This paper investigates the helps/hurts of training models on model-generated synthetic data for math reasoning . They first conduct an empirical study and then propose to construct negative samples to address spurious correlations in SFT/RFT policy. Abstract 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. About Code and data used in the paper: \"Training on Incorrect Synthetic Data via RL Scales LLM Math Reasoning Eight-Fold \" The authors of this paper delve into the realm of training language models on model-generated synthetic data for math reasoning tasks. They begin by exploring the effectiveness of finetuning LLMs on synthetic correct or positive problem-solution pairs generated by proficient models ."}
+{"idx": 2, "title": "RL on Incorrect Synthetic Data · MinWoo Park", "date": "", "ddg_snippet": "Jun 20, 2024 · abstract: 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": "dsdanielpark.github.io", "link": "https://dsdanielpark.github.io/llm/2024-06-25-RLonIncorrectSyntheticData.html", "content": "Jun 20, 2024 · abstract: 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": 3, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM ...", "date": "", "ddg_snippet": "Abstract 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": "huggingface.co", "link": "https://huggingface.co/papers/2406.14532", "content": "Abstract 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": 4, "title": "ars22/scaling-LLM-math-synthetic-data - GitHub", "date": "", "ddg_snippet": "About Code and data used in the paper: \"Training on Incorrect Synthetic Data via RL Scales LLM Math Reasoning Eight-Fold \"", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/ars22/scaling-LLM-math-synthetic-data", "content": "About Code and data used in the paper: \"Training on Incorrect Synthetic Data via RL Scales LLM Math Reasoning Eight-Fold \""}
+{"idx": 5, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM ...", "date": "", "ddg_snippet": "The authors of this paper delve into the realm of training language models on model-generated synthetic data for math reasoning tasks. They begin by exploring the effectiveness of finetuning LLMs on synthetic correct or positive problem-solution pairs generated by proficient models .", "subpage_snippet": "", "source": "www.summarizepaper.com", "link": "https://www.summarizepaper.com/en/arxiv-id/2406.14532v1/", "content": "The authors of this paper delve into the realm of training language models on model-generated synthetic data for math reasoning tasks. They begin by exploring the effectiveness of finetuning LLMs on synthetic correct or positive problem-solution pairs generated by proficient models ."}
+{"idx": 6, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math ...", "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.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.14532v1", "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."}
+{"idx": 7, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math ...", "date": "", "ddg_snippet": "Unlocking LLM Math Superpowers: 8 X Faster with a Quirky Twist.", "subpage_snippet": "", "source": "www.promptlayer.com", "link": "https://www.promptlayer.com/research-papers/rl-on-incorrect-synthetic-data-scales-the-efficiency-of-llm-math-reasoning-by-eight-fold", "content": "Unlocking LLM Math Superpowers: 8 X Faster with a Quirky Twist."}
+{"idx": 8, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math ...", "date": "", "ddg_snippet": "The study investigates the impact of synthetic data , both correct and incorrect , on the fine-tuning of LLMs for enhanced math reasoning using supervised fine-tuning (SFT) and reinforcement learning (RL).", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/papers/2406.14532", "content": "The study investigates the impact of synthetic data , both correct and incorrect , on the fine-tuning of LLMs for enhanced math reasoning using supervised fine-tuning (SFT) and reinforcement learning (RL)."}
+{"idx": 9, "title": "Bayesian beagle - RL on Incorrect Synthetic Data Scales the...", "date": "", "ddg_snippet": "Finetuning LLMs with model -generated data can improve math reasoning , especially with self-generated correct solutions and per-step negative responses. This approach can double efficiency and reduce spurious correlations.", "subpage_snippet": "", "source": "bayesian-beagle.netlify.app", "link": "https://bayesian-beagle.netlify.app/posts/rl_on_incorrect_synthetic_data_scales_the_efficiency_of_llm_math_reasoning_by_eight_fold/2024-06-20-rl_on_incorrect_synthetic_data_scales_the_efficiency_of_llm_math_reasoning_by_eight_fold", "content": "Finetuning LLMs with model -generated data can improve math reasoning , especially with self-generated correct solutions and per-step negative responses. This approach can double efficiency and reduce spurious correlations."}
diff --git a/data/sampled_jsons/Indyk_Motwani_1998_Locality-Sensitive_Hashing_fundamental_principle.jsonl b/data/sampled_jsons/Indyk_Motwani_1998_Locality-Sensitive_Hashing_fundamental_principle.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..080baa736c94131d701aff34b2c2d451f043f8c5
--- /dev/null
+++ b/data/sampled_jsons/Indyk_Motwani_1998_Locality-Sensitive_Hashing_fundamental_principle.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Locality-sensitive hashing", "date": "", "ddg_snippet": "In computer science, locality-sensitive hashing (LSH) is a fuzzy hashing technique that hashes similar input items into the same buckets with high probability .", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Locality-sensitive_hashing", "content": "In computer science, locality-sensitive hashing (LSH) is a fuzzy hashing technique that hashes similar input items into the same buckets with high probability ."}
+{"idx": 1, "title": "Similarity Search in High Dimensions via Hashing", "date": "", "ddg_snippet": "by A Gionis · Cited by 5061 — Locality-Sensitive Hashing was introduced by Indyk and Motwani 24] for the purposes of devising main memory algorithms for nearest neighbor search in par- ... 12 pages", "subpage_snippet": "", "source": "www.cs.columbia.edu", "link": "https://www.cs.columbia.edu/~verma/classes/uml/ref/nn_lsh_gionis_indyk_motwani.pdf", "content": "by A Gionis · Cited by 5061 — Locality-Sensitive Hashing was introduced by Indyk and Motwani 24] for the purposes of devising main memory algorithms for nearest neighbor search in par- ... 12 pages"}
+{"idx": 2, "title": "conLSH: Context based Locality Sensitive Hashing for ...", "date": "", "ddg_snippet": "by A Chakraborty · 2020 · Cited by 13 — The principle of Locality Sensitive Hashing (Indyk and Motwani, 1998) originated from the idea to hash similar objects into the same or localized slots of the ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/abs/pii/S1476927119311491", "content": "by A Chakraborty · 2020 · Cited by 13 — The principle of Locality Sensitive Hashing (Indyk and Motwani, 1998) originated from the idea to hash similar objects into the same or localized slots of the ..."}
+{"idx": 3, "title": "Intelligent Probing for Locality Sensitive Hashing: Multi- ...", "date": "", "ddg_snippet": "by Q Lv · 2017 · Cited by 27 — Introduced by Indyk and Motwani in 1998, locality sensi- tive hashing (LSH) [8] uses a family of locality sensitive hash functions (i.e., certain random space ... 4 pages", "subpage_snippet": "", "source": "www.vldb.org", "link": "http://www.vldb.org/pvldb/vol10/p2021-lv.pdf", "content": "by Q Lv · 2017 · Cited by 27 — Introduced by Indyk and Motwani in 1998, locality sensi- tive hashing (LSH) [8] uses a family of locality sensitive hash functions (i.e., certain random space ... 4 pages"}
+{"idx": 4, "title": "Locality-Sensitive Hashing Scheme Based on p-Stable ...", "date": "", "ddg_snippet": "by M Datar · Cited by 4185 — ABSTRACT. We present a novel Locality - Sensitive Hashing scheme for the Ap- proximate Nearest Neighbor Problem under p norm, based on - stable distributions.", "subpage_snippet": "", "source": "graphics.stanford.edu", "link": "https://graphics.stanford.edu/courses/cs468-06-fall/Papers/12+lsh04.pdf", "content": "by M Datar · Cited by 4185 — ABSTRACT. We present a novel Locality - Sensitive Hashing scheme for the Ap- proximate Nearest Neighbor Problem under p norm, based on - stable distributions."}
+{"idx": 5, "title": "On the Adversarial Robustness of Locality-Sensitive ...", "date": "", "ddg_snippet": "by M Kapralov · 2025 · Cited by 2 — Locality-sensitive hashing (Indyk-Motwani'98) is a classical data structure for approximate nearest neighbor search .", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.1145/3725239", "content": "by M Kapralov · 2025 · Cited by 2 — Locality-sensitive hashing (Indyk-Motwani'98) is a classical data structure for approximate nearest neighbor search ."}
+{"idx": 6, "title": "Beyond Locality Sensitive Hashing", "date": "", "ddg_snippet": "by A Andoni · Cited by 283 — [ Indyk - Motwani ' 98 ] q. “not-‐so-‐small”. P1= P2= P↓1 =P↓2↑𝜌. Page 8. Locality sensitive hash functions. • Hash function g is actually a concatenation ... 20 pages", "subpage_snippet": "", "source": "simons.berkeley.edu", "link": "https://simons.berkeley.edu/sites/default/files/docs/604/andonislides.pdf", "content": "by A Andoni · Cited by 283 — [ Indyk - Motwani ' 98 ] q. “not-‐so-‐small”. P1= P2= P↓1 =P↓2↑𝜌. Page 8. Locality sensitive hash functions. • Hash function g is actually a concatenation ... 20 pages"}
+{"idx": 7, "title": "Locality-Sensitive Hashing (LSH) for Similarity", "date": "", "ddg_snippet": "The central principle relies on special hash functions with a \" locality - sensitive \" property: Similar points: Points that are close to each other in the ...", "subpage_snippet": "", "source": "apxml.com", "link": "https://apxml.com/courses/data-structures-algorithms-ml/chapter-3-hashing-for-ml/locality-sensitive-hashing", "content": "The central principle relies on special hash functions with a \" locality - sensitive \" property: Similar points: Points that are close to each other in the ..."}
+{"idx": 8, "title": "Approximate Nearest Neighbors: Towards Removing the ...", "date": "", "ddg_snippet": "by P Indyk · 1999 · Cited by 6544 — Its key ingredient is the notion of locality - sensitive hashing which may be of independent interest here, we give applications to information ... 20 pages", "subpage_snippet": "", "source": "graphics.stanford.edu", "link": "https://graphics.stanford.edu/courses/cs468-06-fall/Papers/06+indyk+motwani+-+stoc98.pdf", "content": "by P Indyk · 1999 · Cited by 6544 — Its key ingredient is the notion of locality - sensitive hashing which may be of independent interest here, we give applications to information ... 20 pages"}
+{"idx": 9, "title": "arXiv:2005.12065v1 [cs.DS] 25 May 2020", "date": "", "ddg_snippet": "by TD Ahle · 2020 · Cited by 4 — Locality Sensitive - Hashing ( LSH ) framework [18] is one of the most efficient approaches to the nearest neighbour search problem in high ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2005.12065", "content": "by TD Ahle · 2020 · Cited by 4 — Locality Sensitive - Hashing ( LSH ) framework [18] is one of the most efficient approaches to the nearest neighbour search problem in high ..."}
diff --git a/data/sampled_jsons/Instant_Gaussian_Stream_AGM-Net_training_dataset_Implementation_details.jsonl b/data/sampled_jsons/Instant_Gaussian_Stream_AGM-Net_training_dataset_Implementation_details.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..1ce99c9667e00a05d0454750a431ba5ba1ee3d91
--- /dev/null
+++ b/data/sampled_jsons/Instant_Gaussian_Stream_AGM-Net_training_dataset_Implementation_details.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Fast and Generalizable Streaming of Dynamic Scene ...", "date": "", "ddg_snippet": "by J Yan · 2025 · Cited by 6 — In this pa- per, we propose Instant Gaussian Stream (IGS), a fast and generalizable streaming framework, to address these issues. First, we introduce a ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Yan_Instant_Gaussian_Stream_Fast_and_Generalizable_Streaming_of_Dynamic_Scene_CVPR_2025_paper.pdf", "content": "by J Yan · 2025 · Cited by 6 — In this pa- per, we propose Instant Gaussian Stream (IGS), a fast and generalizable streaming framework, to address these issues. First, we introduce a ..."}
+{"idx": 1, "title": "yjb6/IGS: [CVPR25 Highlight] Instant Gaussian Stream: ...", "date": "", "ddg_snippet": "This repository contains the official authors implementation associated with the paper: Instant Gaussian Stream : Fast and Generalizable Streaming of Dynamic ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/yjb6/IGS", "content": "This repository contains the official authors implementation associated with the paper: Instant Gaussian Stream : Fast and Generalizable Streaming of Dynamic ..."}
+{"idx": 2, "title": "Instant Gaussian Stream", "date": "", "ddg_snippet": "The AGM - Net is designed to predict the motion of 3D Gaussian primitives between consecutive frames. This process involves several key steps: ... The AGM - Net is ...", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2503.16979", "content": "The AGM - Net is designed to predict the motion of 3D Gaussian primitives between consecutive frames. This process involves several key steps: ... The AGM - Net is ..."}
+{"idx": 3, "title": "[Literature Review] Instant Gaussian Stream: Fast and ...", "date": "", "ddg_snippet": "21 Mar 2025 — Implementation Details: Extensive training on large datasets is discussed , emphasizing balance and adaptation across different scenes especially ...", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/en/review/instant-gaussian-stream-fast-and-generalizable-streaming-of-dynamic-scene-reconstruction-via-gaussian-splatting", "content": "21 Mar 2025 — Implementation Details: Extensive training on large datasets is discussed , emphasizing balance and adaptation across different scenes especially ..."}
+{"idx": 4, "title": "InstantGaussianStream_2503.16979v1 | PDF", "date": "", "ddg_snippet": "The method leverages an Anchor-driven Gaussian Motion Network and a Key-frame-guided Streaming strategy to improve performance compared to existing state-of-the ...", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/889322844/InstantGaussianStream-2503-16979v1", "content": "The method leverages an Anchor-driven Gaussian Motion Network and a Key-frame-guided Streaming strategy to improve performance compared to existing state-of-the ..."}
+{"idx": 5, "title": "Scale-GS: Efficient Scalable Gaussian Splatting via ...", "date": "", "ddg_snippet": "29 Aug 2025 — Instant Gaussian Stream ... IGS [62] offers a generalized streaming framework centered around an Anchor-driven Gaussian Motion Network (AGM-Net).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.21444v1", "content": "29 Aug 2025 — Instant Gaussian Stream ... IGS [62] offers a generalized streaming framework centered around an Anchor-driven Gaussian Motion Network (AGM-Net)."}
+{"idx": 6, "title": "Surface EMG-Based Instantaneous Hand Gesture ...", "date": "", "ddg_snippet": "by Z Yu · 2021 · Cited by 58 — The training process of the target network . The first step is to train the source network on the source database . Then, in step two, the well-trained source ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC8038633/", "content": "by Z Yu · 2021 · Cited by 58 — The training process of the target network . The first step is to train the source network on the source database . Then, in step two, the well-trained source ..."}
+{"idx": 7, "title": "Daily Papers", "date": "", "ddg_snippet": "In this paper, we present DepthSplat to connect Gaussian splatting and depth estimation and study their interactions. More specifically, we first contribute a ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=multi-view+depth+estimation", "content": "In this paper, we present DepthSplat to connect Gaussian splatting and depth estimation and study their interactions. More specifically, we first contribute a ..."}
+{"idx": 8, "title": "Data Augmentation Techniques for Machine Learning ...", "date": "", "ddg_snippet": "by AG Moisés · 2023 · Cited by 48 — This review work will present the application of DA techniques to optical spectroscopy datasets obtained from real agrifood industry applications.", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC10610871/", "content": "by AG Moisés · 2023 · Cited by 48 — This review work will present the application of DA techniques to optical spectroscopy datasets obtained from real agrifood industry applications."}
+{"idx": 9, "title": "Abnormal Crowd Behavior Detection Utilizing a Multi- ...", "date": "", "ddg_snippet": "by AA Hamid · 2024 · Cited by 2 — IMPLEMENTATION DETAILS ... To assess how well our model performed, we gathered an additional dataset distinct from the one used for training , as ... 20 pages", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/iel8/6287639/10820123/10616131.pdf", "content": "by AA Hamid · 2024 · Cited by 2 — IMPLEMENTATION DETAILS ... To assess how well our model performed, we gathered an additional dataset distinct from the one used for training , as ... 20 pages"}
diff --git a/data/sampled_jsons/Instant_Gaussian_Stream_Gaussian_Motion_Network_interpolation_motion_features_Equation_6.jsonl b/data/sampled_jsons/Instant_Gaussian_Stream_Gaussian_Motion_Network_interpolation_motion_features_Equation_6.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c24be797e8d1e526a7b124ff7137c6dfd23196f4
--- /dev/null
+++ b/data/sampled_jsons/Instant_Gaussian_Stream_Gaussian_Motion_Network_interpolation_motion_features_Equation_6.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Instant Gaussian Stream : Fast and Generalizable Streaming of...", "date": "", "ddg_snippet": "Anchor-driven Gaussian Motion Network . Interpolate and Motion Decode: Using the 3D motion features stored at anchor points, we can assign each Gaus - sian point a motion feature by interpolating from its K near", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.16979", "content": "Anchor-driven Gaussian Motion Network . Interpolate and Motion Decode: Using the 3D motion features stored at anchor points, we can assign each Gaus - sian point a motion feature by interpolating from its K near"}
+{"idx": 1, "title": "(PDF) Instant Gaussian Stream : Fast and Generalizable Streaming ...", "date": "", "ddg_snippet": "In this paper, we propose Instant Gaussian Stream (IGS), a fast and generalizable streaming framework, to address these issues. First, we introduce a generalized Anchor-driven Gaussian Motion Network , which projects multi-view 2D motion features into 3D space...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/390114414_Instant_Gaussian_Stream_Fast_and_Generalizable_Streaming_of_Dynamic_Scene_Reconstruction_via_Gaussian_Splatting", "content": "In this paper, we propose Instant Gaussian Stream (IGS), a fast and generalizable streaming framework, to address these issues. First, we introduce a generalized Anchor-driven Gaussian Motion Network , which projects multi-view 2D motion features into 3D space..."}
+{"idx": 2, "title": "GitHub - yjb 6 /IGS: [CVPR25 Highlight] Instant Gaussian Stream : Fast...", "date": "", "ddg_snippet": "title={ 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", "content": "title={ Instant Gaussian Stream : Fast and Generalizable Streaming of Dynamic Scene Reconstruction via Gaussian Splatting}"}
+{"idx": 3, "title": "Instant Gaussian Stream : Fast and Generalizable... | alphaXiv", "date": "", "ddg_snippet": "Anchor-driven Gaussian Motion Network . Key-frame-guided Streaming Strategy. Experimental Results.Anchor-driven Gaussian Motion Network . The AGM-Net is designed to predict the motion of 3D Gaussian primitives between consecutive frames.", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2503.16979", "content": "Anchor-driven Gaussian Motion Network . Key-frame-guided Streaming Strategy. Experimental Results.Anchor-driven Gaussian Motion Network . The AGM-Net is designed to predict the motion of 3D Gaussian primitives between consecutive frames."}
+{"idx": 4, "title": "Instant Gaussian Stream : Fast and Generalizable Streaming of...", "date": "", "ddg_snippet": "In this paper, we propose Instant Gaussian Stream (IGS), a fast and generalizable streaming framework, to address these issues. First, we introduce a generalized Anchor-driven Gaussian Motion Network , which projects multi-view 2D motion features into 3D space...", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/venue/Yan_Instant_Gaussian_Stream_Fast_and_Generalizable_Streaming_of_Dynamic_Scene@CVPR2025@CVF", "content": "In this paper, we propose Instant Gaussian Stream (IGS), a fast and generalizable streaming framework, to address these issues. First, we introduce a generalized Anchor-driven Gaussian Motion Network , which projects multi-view 2D motion features into 3D space..."}
+{"idx": 5, "title": "Uncertainty-aware asynchronous scattered motion interpolation using...", "date": "", "ddg_snippet": "We address the problem of interpolating randomly non-uniformly spatiotemporally scattered uncertain motion measurements, which arises in the context of soft tissue motion estimation.", "subpage_snippet": "", "source": "pubmed.ncbi.nlm.nih.gov", "link": "https://pubmed.ncbi.nlm.nih.gov/30654093/", "content": "We address the problem of interpolating randomly non-uniformly spatiotemporally scattered uncertain motion measurements, which arises in the context of soft tissue motion estimation."}
+{"idx": 6, "title": "(PDF) Gaussian process dynamical models for human motion", "date": "", "ddg_snippet": "Gestures are described as Gaussian Process Dynamic Models (GPDM) and are used as constraints for motion tracking. Motion interpolation techniques are designed to create natural looking motions relatively far from input examples.", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/62879020/Gaussian_process_dynamical_models_for_human_motion", "content": "Gestures are described as Gaussian Process Dynamic Models (GPDM) and are used as constraints for motion tracking. Motion interpolation techniques are designed to create natural looking motions relatively far from input examples."}
+{"idx": 7, "title": "Time-integration of Gaussian variational approximation for the...", "date": "", "ddg_snippet": "The solution of the time-dependent Schrödinger equation is approximated by a single Gaussian wave packet via the time-dependent Dirac–Frenkel variational principle. For the approximation we use.", "subpage_snippet": "", "source": "top.physik.hu-berlin.de", "link": "https://top.physik.hu-berlin.de/publications/time-integration-gaussian-variational-approximation-magnetic-schrödinger-equation", "content": "The solution of the time-dependent Schrödinger equation is approximated by a single Gaussian wave packet via the time-dependent Dirac–Frenkel variational principle. For the approximation we use."}
+{"idx": 8, "title": "Gaussian Processes for", "date": "", "ddg_snippet": "For instance , Gaussian processes can be motivated as Bayesian linear regression with an innite number of basis func-tions. Likewise, Dirichlet process mixtures (DPMs) are motivated as mixture models where the number of mixture components M become innite.", "subpage_snippet": "", "source": "mlg.eng.cam.ac.uk", "link": "https://mlg.eng.cam.ac.uk/pub/pdf/Tur11.pdf", "content": "For instance , Gaussian processes can be motivated as Bayesian linear regression with an innite number of basis func-tions. Likewise, Dirichlet process mixtures (DPMs) are motivated as mixture models where the number of mixture components M become innite."}
+{"idx": 9, "title": "Isomorphisms of -dyson's brownian motion with brownian local time", "date": "", "ddg_snippet": "Dyson’s Brownian motion , Gaussian beta ensembles, Gaussian free eld, isomorphism theorems, local time, permanental elds, topological expansion .deal with a product. will not matter. For instance , for ν “ p2, 1, 1q (see Appendix)", "subpage_snippet": "", "source": "hal.science", "link": "https://hal.science/hal-02996476v1/document", "content": "Dyson’s Brownian motion , Gaussian beta ensembles, Gaussian free eld, isomorphism theorems, local time, permanental elds, topological expansion .deal with a product. will not matter. For instance , for ν “ p2, 1, 1q (see Appendix)"}
diff --git a/data/sampled_jsons/Instant_Gaussian_Stream_N3DV_dataset_training_sequences_test_sequences.jsonl b/data/sampled_jsons/Instant_Gaussian_Stream_N3DV_dataset_training_sequences_test_sequences.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f7e040947f7d3fb0d09cb53bae67c693c916dda7
--- /dev/null
+++ b/data/sampled_jsons/Instant_Gaussian_Stream_N3DV_dataset_training_sequences_test_sequences.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF Instant Gaussian Stream: Fast and Generalizable Streaming of Dynamic ...", "date": "", "ddg_snippet": "Dataset Preparation: We split four sequences from the N3DV dataset into the training set, with the remaining two sequences , {cut roasted beef, sear steak}, used as the test set. For the training set, we constructed 3D Gaus-sians for all frames in the four training sequences , total-ing 1200 frames, which required 192 GPU hours.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Yan_Instant_Gaussian_Stream_Fast_and_Generalizable_Streaming_of_Dynamic_Scene_CVPR_2025_paper.pdf", "content": "Dataset Preparation: We split four sequences from the N3DV dataset into the training set, with the remaining two sequences , {cut roasted beef, sear steak}, used as the test set. For the training set, we constructed 3D Gaus-sians for all frames in the four training sequences , total-ing 1200 frames, which required 192 GPU hours."}
+{"idx": 1, "title": "Instant Gaussian Stream: Fast and Generalizable Streaming of ... - GitHub", "date": "", "ddg_snippet": "Download our processed data from 4 sequences of N3DV , which can be directly used for training . It contains 1,200 optimized Gaussian points and requires 150GB of storage space.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/yjb6/IGS", "content": "Download our processed data from 4 sequences of N3DV , which can be directly used for training . It contains 1,200 optimized Gaussian points and requires 150GB of storage space."}
+{"idx": 2, "title": "Instant Gaussian Stream: Fast and Generalizable ... - ResearchGate", "date": "", "ddg_snippet": "In this paper, we propose Instant Gaussian Stream (IGS), a fast and generalizable streaming framework, to address these issues.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/390114414_Instant_Gaussian_Stream_Fast_and_Generalizable_Streaming_of_Dynamic_Scene_Reconstruction_via_Gaussian_Splatting", "content": "In this paper, we propose Instant Gaussian Stream (IGS), a fast and generalizable streaming framework, to address these issues."}
+{"idx": 3, "title": "Instant Gaussian Stream: Fast and Generalizable Streaming of Dynamic ...", "date": "", "ddg_snippet": "Dataset Preparation: We split four sequences from the N3DV dataset into the training set, with the remaining two sequences , {c u t r o a s t e d b e e f, s e a r s t e a k}, used as the test set. For the training set, we constructed 3D Gaussians for all frames in the four training sequences , totaling 1200 frames, which required 192 GPU hours.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.16979v1", "content": "Dataset Preparation: We split four sequences from the N3DV dataset into the training set, with the remaining two sequences , {c u t r o a s t e d b e e f, s e a r s t e a k}, used as the test set. For the training set, we constructed 3D Gaussians for all frames in the four training sequences , totaling 1200 frames, which required 192 GPU hours."}
+{"idx": 4, "title": "Instant Gaussian Stream: Fast and Generalizable Streaming of Dynamic ...", "date": "", "ddg_snippet": "In this paper, we propose Instant Gaussian Stream (IGS), a fast and generalizable streaming framework, to address these issues. First, we introduce 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.", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/11095003", "content": "In this paper, we propose Instant Gaussian Stream (IGS), a fast and generalizable streaming framework, to address these issues. First, we introduce 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."}
+{"idx": 5, "title": "QUEEN: QUantized Efficient ENcoding of Dynamic Gaussians for", "date": "", "ddg_snippet": "While these representations accurately model 4D scenes, they are trained in an offline fashion requiring full multi-view video sequences to learn ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.04469v1", "content": "While these representations accurately model 4D scenes, they are trained in an offline fashion requiring full multi-view video sequences to learn ..."}
+{"idx": 6, "title": "Instant Gaussian Stream : Fast and Generalizable Streaming of...", "date": "", "ddg_snippet": "Dataset Preparation: We split four sequences from the N 3 DV dataset into the training set , with the remaining two sequences , {cut roasted beef, sear steak}, used as the test set .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.16979", "content": "Dataset Preparation: We split four sequences from the N 3 DV dataset into the training set , with the remaining two sequences , {cut roasted beef, sear steak}, used as the test set ."}
+{"idx": 7, "title": "Instant Gaussian Stream : Fast and Generalizable Streaming of...", "date": "", "ddg_snippet": "In this paper, we propose Instant Gaussian Stream (IGS), a fast and generalizable streaming framework, to address these issues. First, we introduce a generalized Anchor-driven Gaussian Motion Network, which projects multi-view 2D motion features into 3D space...", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/venue/Yan_Instant_Gaussian_Stream_Fast_and_Generalizable_Streaming_of_Dynamic_Scene@CVPR2025@CVF", "content": "In this paper, we propose Instant Gaussian Stream (IGS), a fast and generalizable streaming framework, to address these issues. First, we introduce a generalized Anchor-driven Gaussian Motion Network, which projects multi-view 2D motion features into 3D space..."}
+{"idx": 8, "title": "Instant Gaussian Stream : Fast and Generalizable... | alphaXiv", "date": "", "ddg_snippet": "Instant Gaussian Stream (IGS) is a framework designed for fast and generalizable streaming reconstruction of dynamic scenes for Free-Viewpoint Video. Training Data Scale: The generalization capability could be further improved by training on larger and more diverse datasets .", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2503.16979", "content": "Instant Gaussian Stream (IGS) is a framework designed for fast and generalizable streaming reconstruction of dynamic scenes for Free-Viewpoint Video. Training Data Scale: The generalization capability could be further improved by training on larger and more diverse datasets ."}
+{"idx": 9, "title": "DynMF: Neural Motion Factorization for Real-time Dynamic View", "date": "", "ddg_snippet": "The carefully designed time-only queried MLP allows for training in less than 30 minutes while the rendering speed remains comparable to 3D Gaussian ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2312.00112v2", "content": "The carefully designed time-only queried MLP allows for training in less than 30 minutes while the rendering speed remains comparable to 3D Gaussian ..."}
diff --git a/data/sampled_jsons/Instant_Gaussian_Stream_Table_2_quantitative_results_Meeting_Room_storage.jsonl b/data/sampled_jsons/Instant_Gaussian_Stream_Table_2_quantitative_results_Meeting_Room_storage.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4b79d232fe117f1bcc714359509f204bcfa4d683
--- /dev/null
+++ b/data/sampled_jsons/Instant_Gaussian_Stream_Table_2_quantitative_results_Meeting_Room_storage.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "CVPR Poster 4DGC: Rate-Aware 4D Gaussian Compression ...", "date": "", "ddg_snippet": "Table 2 . Quantitative comparison on the MeetRoom dataset [30] and Google Immersive dataset [10]. image. dataset. It can be seen that our method outperforms ...", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/poster/35189", "content": "Table 2 . Quantitative comparison on the MeetRoom dataset [30] and Google Immersive dataset [10]. image. dataset. It can be seen that our method outperforms ..."}
+{"idx": 1, "title": "HiCoM: Hierarchical Coherent Motion for Streamable ...", "date": "", "ddg_snippet": "12 Nov 2024 — The online reconstruction of dynamic scenes from multi-view streaming videos faces significant challenges in training, rendering and storage ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.07541v1", "content": "12 Nov 2024 — The online reconstruction of dynamic scenes from multi-view streaming videos faces significant challenges in training, rendering and storage ..."}
+{"idx": 2, "title": "3DGStream: On-the-Fly Training of 3D Gaussians for Efficient ...", "date": "", "ddg_snippet": "by J Sun · 2024 · Cited by 110 — Table 2 . Quantitative comparison on the Meet Room dataset. Note that the training time, required storage and PSNR are aver- aged over the whole 300 frames.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2024/papers/Sun_3DGStream_On-the-Fly_Training_of_3D_Gaussians_for_Efficient_Streaming_of_CVPR_2024_paper.pdf", "content": "by J Sun · 2024 · Cited by 110 — Table 2 . Quantitative comparison on the Meet Room dataset. Note that the training time, required storage and PSNR are aver- aged over the whole 300 frames."}
+{"idx": 3, "title": "Dynamics-Aware Gaussian Splatting Streaming Towards ...", "date": "", "ddg_snippet": "22 Nov 2024 — Table 2: Quantitative comparison on the Meet Room dataset . The training time and quality metrics are averaged over all 300 frames. Method ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.14847v1", "content": "22 Nov 2024 — Table 2: Quantitative comparison on the Meet Room dataset . The training time and quality metrics are averaged over all 300 frames. Method ..."}
+{"idx": 4, "title": "StreamME: Simplify 3D Gaussian Avatar within Live Stream", "date": "", "ddg_snippet": "27 Jul 2025 — The StreamME synchronously records and reconstructs a head avatar from live video streams without any pre-cached data , enabling seamless ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3721238.3730635", "content": "27 Jul 2025 — The StreamME synchronously records and reconstructs a head avatar from live video streams without any pre-cached data , enabling seamless ..."}
+{"idx": 5, "title": "HiCoM: Hierarchical Coherent Motion for Streamable ...", "date": "", "ddg_snippet": "by Q Gao · 2024 · Cited by 11 — Table 7: Per-scene quantitative results on the Meet Room dataset. Rows marked with “*” indicate experiments conducted on the undistorted version of the ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/9370cc8d438b9ed8cb4344818a251d9a-Paper-Conference.pdf", "content": "by Q Gao · 2024 · Cited by 11 — Table 7: Per-scene quantitative results on the Meet Room dataset. Rows marked with “*” indicate experiments conducted on the undistorted version of the ..."}
+{"idx": 6, "title": "Lee-JaeWon/2024-Arxiv-Paper-List-Gaussian-Splatting", "date": "", "ddg_snippet": "26 Dec 2024 — This is crawled to find out about the 2024 Gaussian Splatting papers in arxiv. There may be errors, so please leave a Pull Request or Issue ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Lee-JaeWon/2024-Arxiv-Paper-List-Gaussian-Splatting", "content": "26 Dec 2024 — This is crawled to find out about the 2024 Gaussian Splatting papers in arxiv. There may be errors, so please leave a Pull Request or Issue ..."}
+{"idx": 7, "title": "SWIFT4D", "date": "", "ddg_snippet": "In this paper we propose Swift4D, a divide-and-conquer 3D Gaussian . Splatting method that can handle static and dynamic primitives separately, achieving a good ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/c6c80a0a6458a1f2a44140ad954da36b0cdbc84b.pdf", "content": "In this paper we propose Swift4D, a divide-and-conquer 3D Gaussian . Splatting method that can handle static and dynamic primitives separately, achieving a good ..."}
+{"idx": 8, "title": "Exploring Explicit Motion Guidance for Deformable 3D ...", "date": "", "ddg_snippet": "To address the above issues, we propose a novel deformable 3D Gaussian splatting framework called MotionGS, which explores explicit motion priors to guide the ...", "subpage_snippet": "", "source": "nips.cc", "link": "https://nips.cc/virtual/2024/poster/96538", "content": "To address the above issues, we propose a novel deformable 3D Gaussian splatting framework called MotionGS, which explores explicit motion priors to guide the ..."}
+{"idx": 9, "title": "Generalizable Monocular 3D Human Rendering via Direct ...", "date": "", "ddg_snippet": "by Y Tang — This paper leverages 3D Gaussian Splatting to tackle the challenging task of generating novel views of humans from given single-view images.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=rWIrdAo2xC", "content": "by Y Tang — This paper leverages 3D Gaussian Splatting to tackle the challenging task of generating novel views of humans from given single-view images."}
diff --git "a/data/sampled_jsons/JEflV4nRlH_synthetic_experiments_learning_rates_\316\267M=5e-5_\316\267S=1e-5.jsonl" "b/data/sampled_jsons/JEflV4nRlH_synthetic_experiments_learning_rates_\316\267M=5e-5_\316\267S=1e-5.jsonl"
new file mode 100644
index 0000000000000000000000000000000000000000..2c6031710c4f2069bdca7418e76082ab36778967
--- /dev/null
+++ "b/data/sampled_jsons/JEflV4nRlH_synthetic_experiments_learning_rates_\316\267M=5e-5_\316\267S=1e-5.jsonl"
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Fine-Tuning GPT Models for Niche Domains - ML Journey", "date": "", "ddg_snippet": "Learning Rate Scheduling Domain-specific fine-tuning typically requires lower learning rates than initial training to avoid catastrophic forgetting of general language capabilities. Start with rates around 1 e - 5 to 5 e - 5 and adjust based on validation performance.", "subpage_snippet": "", "source": "mljourney.com", "link": "https://mljourney.com/fine-tuning-gpt-models-for-niche-domains/", "content": "Learning Rate Scheduling Domain-specific fine-tuning typically requires lower learning rates than initial training to avoid catastrophic forgetting of general language capabilities. Start with rates around 1 e - 5 to 5 e - 5 and adjust based on validation performance."}
+{"idx": 1, "title": "Калькулятор онлайн и по шагам", "date": "", "ddg_snippet": "Ввод распознает различные синонимы функций, какasin, arsin, arcsin, sin^-1. Знак умножения и скобки расставляются дополнительно — запись2sinxсходна2*sin(x)...", "subpage_snippet": "", "source": "mathdf.com", "link": "https://mathdf.com/calc/ru/", "content": "Ввод распознает различные синонимы функций, какasin, arsin, arcsin, sin^-1. Знак умножения и скобки расставляются дополнительно — запись2sinxсходна2*sin(x)..."}
+{"idx": 2, "title": "Номер 2, страница 7 - гдз по английскому языку 6 класс (spotlight)...", "date": "", "ddg_snippet": "Английский язык (english), 6 класс Рабочая тетрадь (workbook), авторы: Ваулина Юлия Евгеньевна (Vaulina Julia), Дули Дженни (Dooley Jenny), Подоляко Ольга Евгеньевна (Podolyako Olga), Эванс Вирджиния (Evans Virginia), издательство Просвещение, Москва...", "subpage_snippet": "", "source": "gdz.top", "link": "https://gdz.top/6-klass/english/vaulina-spotlight-rabochaja-tetrad/01-3-2", "content": "Английский язык (english), 6 класс Рабочая тетрадь (workbook), авторы: Ваулина Юлия Евгеньевна (Vaulina Julia), Дули Дженни (Dooley Jenny), Подоляко Ольга Евгеньевна (Podolyako Olga), Эванс Вирджиния (Evans Virginia), издательство Просвещение, Москва..."}
+{"idx": 3, "title": "Рабочая тетрадь Spotlight 5 класс. Страница 9 - ГДЗ Решебник по...", "date": "", "ddg_snippet": "Страница 9 1 а. School — 1 а. Школа 1. What lessons are the students in? — Какие уроки у учащихся? Ответ: Maths – математика Information Technology – информатика Science – естествознание Music –...", "subpage_snippet": "", "source": "xn----btbeegalms2a3a1h.xn--p1ai", "link": "https://xn----btbeegalms2a3a1h.xn--p1ai/rabochaja-tetrad-spotlight-5-klass-stranica-9/", "content": "Страница 9 1 а. School — 1 а. Школа 1. What lessons are the students in? — Какие уроки у учащихся? Ответ: Maths – математика Information Technology – информатика Science – естествознание Music –..."}
+{"idx": 4, "title": "Упражнения на времена Present с ответами", "date": "", "ddg_snippet": "5 упражнений на тренировку времен группы презент в английском языке с ответами для продолжающих. Отработка Present Simple, Present Continuous, Present Perfect...", "subpage_snippet": "", "source": "EnglishWeb.ru", "link": "https://EnglishWeb.ru/grammar/present-tenses-exercises.html", "content": "5 упражнений на тренировку времен группы презент в английском языке с ответами для продолжающих. Отработка Present Simple, Present Continuous, Present Perfect..."}
+{"idx": 5, "title": "Дрожь Земли Смотреть Все Части Фильма 1, 2, 3, 4, 5 , 6, 7 Подряд...", "date": "", "ddg_snippet": "Смотреть Дрожь Земли Все Части Фильма 1, 2, 3, 4, 5 , 6, 7 Подряд Онлайн Бесплатно в Хорошем Качестве Онлайн FullHD 1080p Полностью на Русском Языке и на Любых Устройствах LordFilm...", "subpage_snippet": "", "source": "droz-zemli-lordfilms.ru", "link": "https://droz-zemli-lordfilms.ru/", "content": "Смотреть Дрожь Земли Все Части Фильма 1, 2, 3, 4, 5 , 6, 7 Подряд Онлайн Бесплатно в Хорошем Качестве Онлайн FullHD 1080p Полностью на Русском Языке и на Любых Устройствах LordFilm..."}
+{"idx": 6, "title": "Сдача экзамена в ГИБДД и его пересдача: сколько раз можно...", "date": "", "ddg_snippet": "Потенциальные обладатели прав категорий B, C, D, BE, CE, DE и подкатегорий C1, D1, C 1 E и D 1 E должны показать свои умения на городских дорогах, минуя площадку. Такой порядок прописан в постановлении Правительства РФ от 20.12.2019 № 1734.", "subpage_snippet": "", "source": "auto.ru", "link": "https://auto.ru/mag/article/vozhdenie-dlya-nachinayushchih-kak-prohodyat-ekzameny-v-gibdd/", "content": "Потенциальные обладатели прав категорий B, C, D, BE, CE, DE и подкатегорий C1, D1, C 1 E и D 1 E должны показать свои умения на городских дорогах, минуя площадку. Такой порядок прописан в постановлении Правительства РФ от 20.12.2019 № 1734."}
+{"idx": 7, "title": "Xcraft η космическая стратегия в реальном времени. Эпизод VI....", "date": "", "ddg_snippet": "Миллионы космических кораблей для эпичных боёв, тысячи игроков, сотни разнообразных зданий технологий и юнитов, десятки вариантов развития, три расы. Прикоснись к легенде в космической стратегии Xcraft.", "subpage_snippet": "", "source": "xcraft.ru", "link": "https://xcraft.ru/", "content": "Миллионы космических кораблей для эпичных боёв, тысячи игроков, сотни разнообразных зданий технологий и юнитов, десятки вариантов развития, три расы. Прикоснись к легенде в космической стратегии Xcraft."}
+{"idx": 8, "title": "Главная страница - Портал непрерывного образования", "date": "", "ddg_snippet": "ВАЖНО! Для внесения сведений о прохождении Вами аккредитации не требуется свидетельство об аккредитации специалиста на бумажном носителе. Подробнее.", "subpage_snippet": "", "source": "edu.rosminzdrav.ru", "link": "https://edu.rosminzdrav.ru/", "content": "ВАЖНО! Для внесения сведений о прохождении Вами аккредитации не требуется свидетельство об аккредитации специалиста на бумажном носителе. Подробнее."}
+{"idx": 9, "title": "Калькулятор Уравнений", "date": "", "ddg_snippet": "Бесплатный калькулятор уравнений - решить поэтапно линейное уравнение, квадратное уравнение, уравнение четвертой степени, уравнение содержащее абсолютную величину и радикальное уравнение.", "subpage_snippet": "", "source": "ru.symbolab.com", "link": "https://ru.symbolab.com/solver/equation-calculator/\\left", "content": "Бесплатный калькулятор уравнений - решить поэтапно линейное уравнение, квадратное уравнение, уравнение четвертой степени, уравнение содержащее абсолютную величину и радикальное уравнение."}
diff --git a/data/sampled_jsons/JUICE_algorithm_methodology_Section_3.2_knowledge_conflicts_mitigation_year_2024.jsonl b/data/sampled_jsons/JUICE_algorithm_methodology_Section_3.2_knowledge_conflicts_mitigation_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2acbdf563a89ea22571abd4561846bacecc07350
--- /dev/null
+++ b/data/sampled_jsons/JUICE_algorithm_methodology_Section_3.2_knowledge_conflicts_mitigation_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Taming Knowledge Conflicts in Language Models", "date": "", "ddg_snippet": "by G Li · 2025 — Algorithm. We propose JUICE , a simple yet effective method to steer an LM toward parametric or contextual knowledge without finetuning, leveraging a dual-run.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.10996", "content": "by G Li · 2025 — Algorithm. We propose JUICE , a simple yet effective method to steer an LM toward parametric or contextual knowledge without finetuning, leveraging a dual-run."}
+{"idx": 1, "title": "Quality and Authenticity Control of Fruit Juices-A Review", "date": "", "ddg_snippet": "by ME Dasenaki · 2019 · Cited by 142 — This review will describe the main analytical methodologies and chemometric tools used for the evaluation of fruit juice authenticity and the detection of juice ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC6470824/", "content": "by ME Dasenaki · 2019 · Cited by 142 — This review will describe the main analytical methodologies and chemometric tools used for the evaluation of fruit juice authenticity and the detection of juice ..."}
+{"idx": 2, "title": "Functional Quality Improvement of Subpar Citrus Juice ...", "date": "", "ddg_snippet": "by SL Nayak · 2025 — This study demonstrated that microfluidization could serve as an effective method for enhancing the biochemical properties of juice extracted ...", "subpage_snippet": "", "source": "onlinelibrary.wiley.com", "link": "https://onlinelibrary.wiley.com/doi/full/10.1155/jfpp/9281538", "content": "by SL Nayak · 2025 — This study demonstrated that microfluidization could serve as an effective method for enhancing the biochemical properties of juice extracted ..."}
+{"idx": 3, "title": "Towards Controllable Generative AI with Intrinsic ...", "date": "", "ddg_snippet": "by A Liu · 2025 — In this section , we list the detailed algorithms for pruning ( Section 3.2 .1), growing (Sec- tion 3.2 .3), circuit flows computation ( Definition 7), and mini ... 308 pages", "subpage_snippet": "", "source": "escholarship.org", "link": "https://escholarship.org/content/qt4xc1x5bb/qt4xc1x5bb_noSplash_6acd65b313cdbcc06103f7b9b1103f4c.pdf", "content": "by A Liu · 2025 — In this section , we list the detailed algorithms for pruning ( Section 3.2 .1), growing (Sec- tion 3.2 .3), circuit flows computation ( Definition 7), and mini ... 308 pages"}
+{"idx": 4, "title": "Advances in Food Quality Management Driven by Industry ...", "date": "", "ddg_snippet": "by FAP Peres · 2025 · Cited by 1 — ... Section 3.2 . After categorizing the articles into managerial quality functions, we performed a qualitative content analysis technique using ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12294287/", "content": "by FAP Peres · 2025 · Cited by 1 — ... Section 3.2 . After categorizing the articles into managerial quality functions, we performed a qualitative content analysis technique using ..."}
+{"idx": 5, "title": "Behavioral analysis of cybercrime: Paving the way for ...", "date": "", "ddg_snippet": "by G Sarkar · 2023 · Cited by 107 — A thorough and in-depth exploration of this tripartite cybercrime framework (TCF) and its implications for cybercrime impact can be located in section 3.2 .1.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2949791423000349", "content": "by G Sarkar · 2023 · Cited by 107 — A thorough and in-depth exploration of this tripartite cybercrime framework (TCF) and its implications for cybercrime impact can be located in section 3.2 .1."}
+{"idx": 6, "title": "Daily Papers", "date": "", "ddg_snippet": "In this paper, we investigate whether Large Language Models (LLMs) actively recall or retrieve their internal repositories of factual knowledge when faced with ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=selective+fact+forgetting", "content": "In this paper, we investigate whether Large Language Models (LLMs) actively recall or retrieve their internal repositories of factual knowledge when faced with ..."}
+{"idx": 7, "title": "Association Rule Mining, A Survey", "date": "", "ddg_snippet": "by EAM Ayad · 2000 · Cited by 11 — In this thesis, a new algorithm , ICAp, for incremental mining of constrained association rules is introduced. The concept of constrained negative border is also ...", "subpage_snippet": "", "source": "pages.cs.wisc.edu", "link": "https://pages.cs.wisc.edu/~ahmed/publications/Thesis.pdf", "content": "by EAM Ayad · 2000 · Cited by 11 — In this thesis, a new algorithm , ICAp, for incremental mining of constrained association rules is introduced. The concept of constrained negative border is also ..."}
+{"idx": 8, "title": "A Mechanistic Perspective on Contextual Entrainment and ...", "date": "", "ddg_snippet": "by J Niu · 2025 — We observe a novel phenomenon, contextual entrainment, across a wide range of language models (LMs) and prompt settings, providing.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2505.09338", "content": "by J Niu · 2025 — We observe a novel phenomenon, contextual entrainment, across a wide range of language models (LMs) and prompt settings, providing."}
+{"idx": 9, "title": "Robust Misinformation Detection by Visiting Potential ...", "date": "", "ddg_snippet": "In this pa- per, we propose a novel plug-and-play augmen- tation method for the MD task, namely Misin- formation Detection with Potential Commonsense. Conflict ...", "subpage_snippet": "", "source": "www.ijcai.org", "link": "https://www.ijcai.org/proceedings/2025/0863.pdf", "content": "In this pa- per, we propose a novel plug-and-play augmen- tation method for the MD task, namely Misin- formation Detection with Potential Commonsense. Conflict ..."}
diff --git "a/data/sampled_jsons/Jankauskait\304\227_SKEMPI_2.0_2019_full_title_year_2019.jsonl" "b/data/sampled_jsons/Jankauskait\304\227_SKEMPI_2.0_2019_full_title_year_2019.jsonl"
new file mode 100644
index 0000000000000000000000000000000000000000..1724d956e0dc2c614fa7a17e053600fe32b4bd93
--- /dev/null
+++ "b/data/sampled_jsons/Jankauskait\304\227_SKEMPI_2.0_2019_full_title_year_2019.jsonl"
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "SKEMPI 2.0: an updated benchmark of changes in protein ...", "date": "", "ddg_snippet": "Jul 18, 2018 · We present SKEMPI 2.0 , a major update to our database of binding free energy changes upon mutation for structurally resolved protein–protein interactions. This version now contains manually curated binding data for 7085 mutations, an increase of 133%, including changes in kinetics for 1844 mutations, enthalpy and entropy changes for 443 ...", "subpage_snippet": "", "source": "academic.oup.com", "link": "https://academic.oup.com/bioinformatics/article/35/3/462/5055583", "content": "Jul 18, 2018 · We present SKEMPI 2.0 , a major update to our database of binding free energy changes upon mutation for structurally resolved protein–protein interactions. This version now contains manually curated binding data for 7085 mutations, an increase of 133%, including changes in kinetics for 1844 mutations, enthalpy and entropy changes for 443 ..."}
+{"idx": 1, "title": "SKEMPI 2.0: an updated benchmark of changes in protein ...", "date": "", "ddg_snippet": "Feb 1, 2019 · Motivation: Understanding the relationship between the sequence, structure, binding energy, binding kinetics and binding thermodynamics of protein-protein interactions is crucial to understanding cellular signaling, the assembly and regulation of molecular complexes, the mechanisms through which mutations lead to disease, and protein engineering. Results: We present SKEMPI 2.0 , a major update ...", "subpage_snippet": "", "source": "pubmed.ncbi.nlm.nih.gov", "link": "https://pubmed.ncbi.nlm.nih.gov/30020414/", "content": "Feb 1, 2019 · Motivation: Understanding the relationship between the sequence, structure, binding energy, binding kinetics and binding thermodynamics of protein-protein interactions is crucial to understanding cellular signaling, the assembly and regulation of molecular complexes, the mechanisms through which mutations lead to disease, and protein engineering. Results: We present SKEMPI 2.0 , a major update ..."}
+{"idx": 2, "title": "SKEMPI v2.0 - BSC-CNS", "date": "", "ddg_snippet": "Jankauskaitė J, Jiménez-García B, Dapkūnas J, Fernández-Recio J, Moal IH ( 2019 ) SKEMPI 2.0 : an updated benchmark of changes in protein–protein binding energy, kinetics and thermodynamics upon mutation.", "subpage_snippet": "", "source": "life.bsc.es", "link": "https://life.bsc.es/pid/skempi2/", "content": "Jankauskaitė J, Jiménez-García B, Dapkūnas J, Fernández-Recio J, Moal IH ( 2019 ) SKEMPI 2.0 : an updated benchmark of changes in protein–protein binding energy, kinetics and thermodynamics upon mutation."}
+{"idx": 3, "title": "SKEMPI 2.0: an updated benchmark of changes in protein ... SKEMPI - Database Commons SKEMPI 2.0: an updated benchmark of changes in protein ... SKEMPI 2.0: An updated benchmark of changes in protein ...", "date": "", "ddg_snippet": "Motivation: Understanding the relationship between the sequence, structure, binding energy, binding kinetics and binding thermodynamics of protein–protein interactions is crucial to understanding cellular signaling, the assembly and regulation of molecular complexes, the mechanisms through which mutations lead to disease, and protein engineering. Jan 2 , 2018 · We present SKEMPI , a database of 3047 binding free energy changes upon mutation assembled from the scientific literature, for protein-protein heterodimeric complexes with experimentally determined structures. This represents over four times more data than previously collected. Jul 31, 2025 · Motivation: Understanding the relationship between the sequence, structure, binding energy, binding kinetics and binding thermodynamics of protein–protein interactions is crucial to understanding cellular signaling, the assembly and regulation of molecular complexes, the mechanisms through which mutations lead to disease, and protein engineering. Results: We present SKEMPI 2.0 , a major ... Jun 7, 2018 · Results: We present SKEMPI 2.0 , a major update to our database of binding free energy changes upon mutation for structurally resolved protein-protein interactions. This version now contains manually curated binding data for 7085 mutations, an increase of 133%, including changes in kinetics for 1844 mutations, enthalpy and entropy changes for 443 mutations, and 440 mutations which abolish ...", "subpage_snippet": "", "source": "epublications.vu.lt", "link": "https://epublications.vu.lt/object/elaba:33694588/", "content": "Motivation: Understanding the relationship between the sequence, structure, binding energy, binding kinetics and binding thermodynamics of protein–protein interactions is crucial to understanding cellular signaling, the assembly and regulation of molecular complexes, the mechanisms through which mutations lead to disease, and protein engineering. Jan 2 , 2018 · We present SKEMPI , a database of 3047 binding free energy changes upon mutation assembled from the scientific literature, for protein-protein heterodimeric complexes with experimentally determined structures. This represents over four times more data than previously collected. Jul 31, 2025 · Motivation: Understanding the relationship between the sequence, structure, binding energy, binding kinetics and binding thermodynamics of protein–protein interactions is crucial to understanding cellular signaling, the assembly and regulation of molecular complexes, the mechanisms through which mutations lead to disease, and protein engineering. Results: We present SKEMPI 2.0 , a major ... Jun 7, 2018 · Results: We present SKEMPI 2.0 , a major update to our database of binding free energy changes upon mutation for structurally resolved protein-protein interactions. This version now contains manually curated binding data for 7085 mutations, an increase of 133%, including changes in kinetics for 1844 mutations, enthalpy and entropy changes for 443 mutations, and 440 mutations which abolish ..."}
+{"idx": 4, "title": "SKEMPI - Database Commons SKEMPI 2.0: an updated benchmark of changes in protein ... SKEMPI 2.0: An updated benchmark of changes in protein ...", "date": "", "ddg_snippet": "Jan 2 , 2018 · We present SKEMPI , a database of 3047 binding free energy changes upon mutation assembled from the scientific literature, for protein-protein heterodimeric complexes with experimentally determined structures. This represents over four times more data than previously collected. Jul 31, 2025 · Motivation: Understanding the relationship between the sequence, structure, binding energy, binding kinetics and binding thermodynamics of protein–protein interactions is crucial to understanding cellular signaling, the assembly and regulation of molecular complexes, the mechanisms through which mutations lead to disease, and protein engineering. Results: We present SKEMPI 2.0 , a major ... Jun 7, 2018 · Results: We present SKEMPI 2.0 , a major update to our database of binding free energy changes upon mutation for structurally resolved protein-protein interactions. This version now contains manually curated binding data for 7085 mutations, an increase of 133%, including changes in kinetics for 1844 mutations, enthalpy and entropy changes for 443 mutations, and 440 mutations which abolish ...", "subpage_snippet": "", "source": "ngdc.cncb.ac.cn", "link": "https://ngdc.cncb.ac.cn/databasecommons/database/id/954", "content": "Jan 2 , 2018 · We present SKEMPI , a database of 3047 binding free energy changes upon mutation assembled from the scientific literature, for protein-protein heterodimeric complexes with experimentally determined structures. This represents over four times more data than previously collected. Jul 31, 2025 · Motivation: Understanding the relationship between the sequence, structure, binding energy, binding kinetics and binding thermodynamics of protein–protein interactions is crucial to understanding cellular signaling, the assembly and regulation of molecular complexes, the mechanisms through which mutations lead to disease, and protein engineering. Results: We present SKEMPI 2.0 , a major ... Jun 7, 2018 · Results: We present SKEMPI 2.0 , a major update to our database of binding free energy changes upon mutation for structurally resolved protein-protein interactions. This version now contains manually curated binding data for 7085 mutations, an increase of 133%, including changes in kinetics for 1844 mutations, enthalpy and entropy changes for 443 mutations, and 440 mutations which abolish ..."}
+{"idx": 5, "title": "SKEMPI 2.0: an updated benchmark of changes in protein ...", "date": "", "ddg_snippet": "Jul 31, 2025 · Motivation: Understanding the relationship between the sequence, structure, binding energy, binding kinetics and binding thermodynamics of protein–protein interactions is crucial to understanding cellular signaling, the assembly and regulation of molecular complexes, the mechanisms through which mutations lead to disease, and protein engineering. Results: We present SKEMPI 2.0 , a major ...", "subpage_snippet": "", "source": "upcommons.upc.edu", "link": "https://upcommons.upc.edu/entities/publication/d0dff724-df6e-45ba-a5ee-110fa9565b88", "content": "Jul 31, 2025 · Motivation: Understanding the relationship between the sequence, structure, binding energy, binding kinetics and binding thermodynamics of protein–protein interactions is crucial to understanding cellular signaling, the assembly and regulation of molecular complexes, the mechanisms through which mutations lead to disease, and protein engineering. Results: We present SKEMPI 2.0 , a major ..."}
+{"idx": 6, "title": "SKEMPI 2.0: An updated benchmark of changes in protein ...", "date": "", "ddg_snippet": "Jun 7, 2018 · Results: We present SKEMPI 2.0 , a major update to our database of binding free energy changes upon mutation for structurally resolved protein-protein interactions. This version now contains manually curated binding data for 7085 mutations, an increase of 133%, including changes in kinetics for 1844 mutations, enthalpy and entropy changes for 443 mutations, and 440 mutations which abolish ...", "subpage_snippet": "", "source": "www.biorxiv.org", "link": "https://www.biorxiv.org/content/biorxiv/early/2018/06/07/341735.full.pdf", "content": "Jun 7, 2018 · Results: We present SKEMPI 2.0 , a major update to our database of binding free energy changes upon mutation for structurally resolved protein-protein interactions. This version now contains manually curated binding data for 7085 mutations, an increase of 133%, including changes in kinetics for 1844 mutations, enthalpy and entropy changes for 443 mutations, and 440 mutations which abolish ..."}
+{"idx": 7, "title": "mCSM-PPI2: predicting the effects of mutations on", "date": "", "ddg_snippet": "The data used on this work was derived from the recently updated version of the SKEMPI database ( 35 ), which compiles experimental data on changes ...", "subpage_snippet": "", "source": "academic.oup.com", "link": "https://academic.oup.com/nar/article/47/W1/W338/5494729", "content": "The data used on this work was derived from the recently updated version of the SKEMPI database ( 35 ), which compiles experimental data on changes ..."}
+{"idx": 8, "title": "A dynamical view of protein-protein complexes: Studies by ...", "date": "", "ddg_snippet": "by J Martin · 2022 · Cited by 29 — 2.5 Mutation data. Free energy changes associated with mutations in the complexes under study were extracted from SKEMPI 2.0 ( Jankauskaitė et al., 2019 ).", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC9583002/", "content": "by J Martin · 2022 · Cited by 29 — 2.5 Mutation data. Free energy changes associated with mutations in the complexes under study were extracted from SKEMPI 2.0 ( Jankauskaitė et al., 2019 )."}
+{"idx": 9, "title": "SSIPe: accurately estimating protein–protein binding affinity ...", "date": "", "ddg_snippet": "by X Huang · 2020 · Cited by 64 — In this work, we performed our benchmark tests mainly on SKEMPI 2.0 ( Jankauskaite et al., 2018). The original SKEMPI 2.0 database contains 7085 mutation data ...", "subpage_snippet": "", "source": "academic.oup.com", "link": "https://academic.oup.com/bioinformatics/article/36/8/2429/5674037", "content": "by X Huang · 2020 · Cited by 64 — In this work, we performed our benchmark tests mainly on SKEMPI 2.0 ( Jankauskaite et al., 2018). The original SKEMPI 2.0 database contains 7085 mutation data ..."}
diff --git a/data/sampled_jsons/Jiang_Meng_Zhao_Shan_Hauptmann_2015_Self-Paced_Curriculum_Learning_abstract_AAAI_year_2015.jsonl b/data/sampled_jsons/Jiang_Meng_Zhao_Shan_Hauptmann_2015_Self-Paced_Curriculum_Learning_abstract_AAAI_year_2015.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..dad1706a3ea9c9b21cbd095e2529dcdc194e0c49
--- /dev/null
+++ b/data/sampled_jsons/Jiang_Meng_Zhao_Shan_Hauptmann_2015_Self-Paced_Curriculum_Learning_abstract_AAAI_year_2015.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Self - Paced Curriculum Learning | Proceedings of the AAAI ...", "date": "", "ddg_snippet": "Self - Paced Curriculum Learning . Authors. Lu Jiang Carnegie Mellon University. Deyu Meng Xi'an Jiaotong University. Self - paced Learning , Curriculum Learning , Prior Knowledge. Abstract .", "subpage_snippet": "", "source": "ojs.aaai.org", "link": "https://ojs.aaai.org/index.php/AAAI/article/view/9608", "content": "Self - Paced Curriculum Learning . Authors. Lu Jiang Carnegie Mellon University. Deyu Meng Xi'an Jiaotong University. Self - paced Learning , Curriculum Learning , Prior Knowledge. Abstract ."}
+{"idx": 1, "title": "Self - paced Curriculum Learning", "date": "", "ddg_snippet": "In NIPS Self - paced Learning • Self - paced Learning (SPL): the curriculum is determined by the learned models. • Solving a joint optimization problem of the learning objective with the curriculum (a sequence of gradually added samples).", "subpage_snippet": "", "source": "studylib.net", "link": "https://studylib.net/doc/18302718/self-paced-curriculum-learning", "content": "In NIPS Self - paced Learning • Self - paced Learning (SPL): the curriculum is determined by the learned models. • Solving a joint optimization problem of the learning objective with the curriculum (a sequence of gradually added samples)."}
+{"idx": 2, "title": "Self - Paced Robust Learning for Leveraging Clean Labels in Noisy Data", "date": "", "ddg_snippet": "Jiang , L.; Meng , D.; Zhao , Q.; Shan , S.; and Hauptmann , A. G. 2015 . Self - paced curriculum learning . In Proceedings of the Twenty-Ninth AAAI Conference on Articial Intelligence , AAAI ’15, 2694–2700.", "subpage_snippet": "", "source": "people.cs.vt.edu", "link": "https://people.cs.vt.edu/~ctlu/Publication/2020/AAAI-ZhangX-8567-Proceedings.pdf", "content": "Jiang , L.; Meng , D.; Zhao , Q.; Shan , S.; and Hauptmann , A. G. 2015 . Self - paced curriculum learning . In Proceedings of the Twenty-Ninth AAAI Conference on Articial Intelligence , AAAI ’15, 2694–2700."}
+{"idx": 3, "title": "Your Pretrained Model Tells the Difficulty Itself: A Self -Adaptive...", "date": "", "ddg_snippet": "2015 . Self - paced curriculum learning . In Proceedings of the AAAI Conference on Artificial Intelligence , volume 29.Lu Jiang , Deyu Meng , Qian Zhao , Shiguang Shan , and Alexander Hauptmann .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.09758v1", "content": "2015 . Self - paced curriculum learning . In Proceedings of the AAAI Conference on Artificial Intelligence , volume 29.Lu Jiang , Deyu Meng , Qian Zhao , Shiguang Shan , and Alexander Hauptmann ."}
+{"idx": 4, "title": "Enhancing Domain-Invariant Parts for Generalized Zero-Shot Learning", "date": "", "ddg_snippet": "Lu Jiang , Deyu Meng , Qian Zhao , Shiguang Shan , and Alexander Hauptmann . 2015 . Self - paced curriculum learning . In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 29.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.1145/3581783.3611764", "content": "Lu Jiang , Deyu Meng , Qian Zhao , Shiguang Shan , and Alexander Hauptmann . 2015 . Self - paced curriculum learning . In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 29."}
+{"idx": 5, "title": "Self - paced learning -based multi-graphs semi-supervised learning", "date": "", "ddg_snippet": "Lu J, Meng D, Zhao Q, Shan S, Hauptmann AG ( 2015 ) Self - paced curriculum learning . In: Proceedings of 29th AAAI conference on artificial intelligence , pp 2694–2700.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s11042-022-11931-2", "content": "Lu J, Meng D, Zhao Q, Shan S, Hauptmann AG ( 2015 ) Self - paced curriculum learning . In: Proceedings of 29th AAAI conference on artificial intelligence , pp 2694–2700."}
+{"idx": 6, "title": "(PDF) Curriculum Learning : A Survey", "date": "", "ddg_snippet": "Self - paced curriculum learning (SPCL). Jiang L, Meng D, Zhao Q, Shan S, Hauptmann AG. ( 2015 ) Self - paced curriculum learning . In: Proceed-. ings of AAAI , pp 2694–2700.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/360058402_Curriculum_Learning_A_Survey", "content": "Self - paced curriculum learning (SPCL). Jiang L, Meng D, Zhao Q, Shan S, Hauptmann AG. ( 2015 ) Self - paced curriculum learning . In: Proceed-. ings of AAAI , pp 2694–2700."}
+{"idx": 7, "title": "Transferable Curriculum for Weakly-Supervised Domain Adaptation", "date": "", "ddg_snippet": "Self - paced learning : An implicit regularization perspective. In AAAI . Ganin, Y., and Lempitsky, V. 2015 . Unsupervised domain adapta-tion by backpropagation. Jiang , L.; Meng , D.; Zhao , Q.; Shan , S.; and Hauptmann , A. 2015 . Self - paced curriculum learning .", "subpage_snippet": "", "source": "ise.thss.tsinghua.edu.cn", "link": "https://ise.thss.tsinghua.edu.cn/~mlong/doc/transferable-curriculum-aaai19.pdf", "content": "Self - paced learning : An implicit regularization perspective. In AAAI . Ganin, Y., and Lempitsky, V. 2015 . Unsupervised domain adapta-tion by backpropagation. Jiang , L.; Meng , D.; Zhao , Q.; Shan , S.; and Hauptmann , A. 2015 . Self - paced curriculum learning ."}
+{"idx": 8, "title": "A SentiWordNet Strategy for Curriculum Learning in Sentiment...", "date": "", "ddg_snippet": "[12] L. Jiang , D. Meng , Q. Zhao , S. Shan , and A. G. Hauptmann ( 2015 ) Self - paced curriculum learning . In Twenty-Ninth AAAI Conference on Artificial Intelligence , Cited by: §1.", "subpage_snippet": "", "source": "www.arxiv-vanity.com", "link": "https://www.arxiv-vanity.com/papers/2005.04749/", "content": "[12] L. Jiang , D. Meng , Q. Zhao , S. Shan , and A. G. Hauptmann ( 2015 ) Self - paced curriculum learning . In Twenty-Ninth AAAI Conference on Artificial Intelligence , Cited by: §1."}
+{"idx": 9, "title": "Your Pretrained Model Tells the Difficulty Itself: A Self -Adaptive", "date": "", "ddg_snippet": "Lu Jiang , Deyu Meng , Qian Zhao , Shiguang Shan , and Alexander Hauptmann . 2015 . Self - paced curriculum learning . In Proceedings of the AAAI Conference on Artificial Intelligence , volume 29.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.acl-srw.15.pdf", "content": "Lu Jiang , Deyu Meng , Qian Zhao , Shiguang Shan , and Alexander Hauptmann . 2015 . Self - paced curriculum learning . In Proceedings of the AAAI Conference on Artificial Intelligence , volume 29."}
diff --git a/data/sampled_jsons/Jiang_et_al_2015_self-paced_learning_year_2015.jsonl b/data/sampled_jsons/Jiang_et_al_2015_self-paced_learning_year_2015.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b073d391392aca8a3b47a582752233cc3f4fbe4f
--- /dev/null
+++ b/data/sampled_jsons/Jiang_et_al_2015_self-paced_learning_year_2015.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Self-Paced Curriculum Learning | Proceedings of the AAAI Conference on ...", "date": "", "ddg_snippet": "Curriculum learning (CL) or self-paced learning (SPL) represents a recently proposed learning regime inspired by the learning process of humans and animals that gradually proceeds from easy to more complex samples in training. The two methods share a similar conceptual learning paradigm, but differ in specific learning schemes.", "subpage_snippet": "", "source": "ojs.aaai.org", "link": "https://ojs.aaai.org/index.php/AAAI/article/view/9608", "content": "Curriculum learning (CL) or self-paced learning (SPL) represents a recently proposed learning regime inspired by the learning process of humans and animals that gradually proceeds from easy to more complex samples in training. The two methods share a similar conceptual learning paradigm, but differ in specific learning schemes."}
+{"idx": 1, "title": "(PDF) Self-paced Curriculum Learning - ResearchGate", "date": "", "ddg_snippet": "Recent work ( Jiang et al ., 2015 ; Hacohen and Weinshall, 2019;Zhou et al ., 2020a) shows that manoeuvring the sequence of training data can improve both training efficiency and model accuracy.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/279853657_Self-paced_Curriculum_Learning", "content": "Recent work ( Jiang et al ., 2015 ; Hacohen and Weinshall, 2019;Zhou et al ., 2020a) shows that manoeuvring the sequence of training data can improve both training efficiency and model accuracy."}
+{"idx": 2, "title": "Weighted Self-Paced Learning with Belief Functions", "date": "", "ddg_snippet": "Jiang et al . ( 2015 ) proposed a Self-Paced Curriculum learning (SPCL). SPCL embeds the additional expert knowledge by introducing a new regularization term to constrain the region of sample's weight, which can reduce the effect of difficult samples in early training stages.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0957417424014027", "content": "Jiang et al . ( 2015 ) proposed a Self-Paced Curriculum learning (SPCL). SPCL embeds the additional expert knowledge by introducing a new regularization term to constrain the region of sample's weight, which can reduce the effect of difficult samples in early training stages."}
+{"idx": 3, "title": "Self-paced curriculum learning - ACM Digital Library", "date": "", "ddg_snippet": "Abstract Curriculum learning (CL) or self-paced learning (SPL) represents a recently proposed learning regime inspired by the learning process of humans and animals that gradually proceeds from easy to more complex samples in training. The two methods share a similar conceptual learning paradigm, but differ in specific learning schemes.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/2886521.2886696", "content": "Abstract Curriculum learning (CL) or self-paced learning (SPL) represents a recently proposed learning regime inspired by the learning process of humans and animals that gradually proceeds from easy to more complex samples in training. The two methods share a similar conceptual learning paradigm, but differ in specific learning schemes."}
+{"idx": 4, "title": "\"Self-Paced Curriculum Learning.\" - dblp", "date": "", "ddg_snippet": "Lu Jiang , Deyu Meng, Qian Zhao, Shiguang Shan, Alexander G. Hauptmann: Self-Paced Curriculum Learning . AAAI 2015 : 2694-2700", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/conf/aaai/JiangMZSH15", "content": "Lu Jiang , Deyu Meng, Qian Zhao, Shiguang Shan, Alexander G. Hauptmann: Self-Paced Curriculum Learning . AAAI 2015 : 2694-2700"}
+{"idx": 5, "title": "PDF Supplementary Materials: Self-paced Curriculum Learning", "date": "", "ddg_snippet": "Supplementary Materials: Self-paced Curriculum Learning Lu Jiang1, Deyu Meng1,2, Qian Zhao1,2, Shiguang Shan1,3, Alexander G. Hauptmann1", "subpage_snippet": "", "source": "www.lujiang.info", "link": "http://www.lujiang.info/camera_ready_papers/AAAI_SPCL_2015_supplementary_materials.pdf", "content": "Supplementary Materials: Self-paced Curriculum Learning Lu Jiang1, Deyu Meng1,2, Qian Zhao1,2, Shiguang Shan1,3, Alexander G. Hauptmann1"}
+{"idx": 6, "title": "PDF A Probabilistic Interpretation of Self-Paced Learning with Applications ...", "date": "", "ddg_snippet": "Our approach to curriculum generation builds upon the idea of self-paced learning (SPL), initially proposed by Kumar et al . (2010) for supervised learning tasks and extended by Jiang et al . (2014b, 2015 ) to allow for user-chosen penalty functions and constraints.", "subpage_snippet": "", "source": "jmlr.org", "link": "https://jmlr.org/papers/volume22/21-0112/21-0112.pdf", "content": "Our approach to curriculum generation builds upon the idea of self-paced learning (SPL), initially proposed by Kumar et al . (2010) for supervised learning tasks and extended by Jiang et al . (2014b, 2015 ) to allow for user-chosen penalty functions and constraints."}
+{"idx": 7, "title": "PDF Self-Paced Co-training", "date": "", "ddg_snippet": "The recent development of SPL includes that ( Jiang et al ., 2015 ) improved SPL as a more effective self-paced cur-riculum learning (SPCL) regime by embedding useful loss prior knowledge into the model and analyzed that this regime is analogous to rational instructor-student-collaborative learning mode of human teaching.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v70/ma17b/ma17b.pdf", "content": "The recent development of SPL includes that ( Jiang et al ., 2015 ) improved SPL as a more effective self-paced cur-riculum learning (SPCL) regime by embedding useful loss prior knowledge into the model and analyzed that this regime is analogous to rational instructor-student-collaborative learning mode of human teaching."}
+{"idx": 8, "title": "PDF Self-paced Curriculum Learning - ResearchGate", "date": "", "ddg_snippet": "Curriculum learning (Bengio et al . 2009) and self-paced learning (Kumar, Packer, and Koller 2010) have been at-tracting increasing attention in the field of machine learning and artificial ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/profile/Lu-Jiang-7/publication/279853657_Self-paced_Curriculum_Learning/links/5c6256a892851c48a9cd5303/Self-paced-Curriculum-Learning.pdf?origin=publication_detail", "content": "Curriculum learning (Bengio et al . 2009) and self-paced learning (Kumar, Packer, and Koller 2010) have been at-tracting increasing attention in the field of machine learning and artificial ..."}
+{"idx": 9, "title": "Self-Paced Curriculum Learning - AAAI", "date": "", "ddg_snippet": "Self-Paced Curriculum Learning Authors Lu Jiang Carnegie Mellon University Deyu Meng Xi'an Jiaotong University Qian Zhao Xi'an Jiaotong University Shiguang Shan Chinese Academy of Sciences Alexander Hauptmann Carnegie Mellon University Proceedings: No.1: The Twenty-Ninth Conference on Artificial Intelligence Volume Issue:", "subpage_snippet": "", "source": "aaai.org", "link": "https://aaai.org/papers/9608-self-paced-curriculum-learning/", "content": "Self-Paced Curriculum Learning Authors Lu Jiang Carnegie Mellon University Deyu Meng Xi'an Jiaotong University Qian Zhao Xi'an Jiaotong University Shiguang Shan Chinese Academy of Sciences Alexander Hauptmann Carnegie Mellon University Proceedings: No.1: The Twenty-Ninth Conference on Artificial Intelligence Volume Issue:"}
diff --git a/data/sampled_jsons/Ju-Seung_Byun_Andrew_Perrault_SPPO_PPO_symmetric_loss_instability.jsonl b/data/sampled_jsons/Ju-Seung_Byun_Andrew_Perrault_SPPO_PPO_symmetric_loss_instability.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..034f7dbfd71793713c33e2ea3390aef2df1d4659
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+++ b/data/sampled_jsons/Ju-Seung_Byun_Andrew_Perrault_SPPO_PPO_symmetric_loss_instability.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Symmetric Reinforcement Learning Loss for Robust Learning on ... Ju-Seung Byun - Google Scholar Andrew Perrault on LinkedIn: Check out Ju-Seung's work. A ... Ju-Seung Byun - OpenReview Andrew Perrault - GitHub Pages Abstract Symmetric Reinforcement Learning Loss for Robust ... Ju-Seung Byun - ACL Anthology", "date": "", "ddg_snippet": "May 27, 2024 · We conduct experiments in discrete action tasks (Atari games) and continuous action space tasks (MuJoCo benchmark and Box2D) using Symmetric A2C (SA2C) and Symmetric PPO ( SPPO ), with and without added noise with especially notable performance in SPPO across different hyperparameters. The Ohio State University - Cited by 30 - Reinforcement Learning We suggest Symmetric RL loss for A2C and PPO and analyze the gradient of the loss to understand why it helps the model deviate from ambiguous predictions. 2. Symmetric Reinforcement Learning Loss for Robust Learning on Diverse Tasks and Model Scales Ju-Seung Byun , Andrew Perrault 25 Sept 2024 (modified: 16 Nov 2024) ICLR 2025 Conference Withdrawn Submission Symmetric Reinforcement Learning Loss for Robust Learning on Diverse Tasks and Model Scales Ju-Seung Byun and Andrew Perrault . ICML 2025. May 29, 2024 · Ju-Seung Byun ∗ Andrew Perrault Department of Computer Science and Engineering The Ohio State University Ju-Seung Byun 2024 pdf bib abs ARES: Alternating Reinforcement Learning and Supervised Fine-Tuning for Enhanced Multi-Modal Chain-of-Thought Reasoning Through Diverse AI Feedback Ju-Seung Byun | Jiyun Chun | Jihyung Kil | Andrew Perrault Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2405.17618", "content": "May 27, 2024 · We conduct experiments in discrete action tasks (Atari games) and continuous action space tasks (MuJoCo benchmark and Box2D) using Symmetric A2C (SA2C) and Symmetric PPO ( SPPO ), with and without added noise with especially notable performance in SPPO across different hyperparameters. The Ohio State University - Cited by 30 - Reinforcement Learning We suggest Symmetric RL loss for A2C and PPO and analyze the gradient of the loss to understand why it helps the model deviate from ambiguous predictions. 2. Symmetric Reinforcement Learning Loss for Robust Learning on Diverse Tasks and Model Scales Ju-Seung Byun , Andrew Perrault 25 Sept 2024 (modified: 16 Nov 2024) ICLR 2025 Conference Withdrawn Submission Symmetric Reinforcement Learning Loss for Robust Learning on Diverse Tasks and Model Scales Ju-Seung Byun and Andrew Perrault . ICML 2025. May 29, 2024 · Ju-Seung Byun ∗ Andrew Perrault Department of Computer Science and Engineering The Ohio State University Ju-Seung Byun 2024 pdf bib abs ARES: Alternating Reinforcement Learning and Supervised Fine-Tuning for Enhanced Multi-Modal Chain-of-Thought Reasoning Through Diverse AI Feedback Ju-Seung Byun | Jiyun Chun | Jihyung Kil | Andrew Perrault Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing"}
+{"idx": 1, "title": "Ju - Seung Byun - Google Scholar", "date": "", "ddg_snippet": "Andrew Perrault Andrew PerraultAssistant Professor, Dept. of Computer Science and EngineeringVerified email at osu.edu.", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=yKcK_BMAAAAJ&hl=en", "content": "Andrew Perrault Andrew PerraultAssistant Professor, Dept. of Computer Science and EngineeringVerified email at osu.edu."}
+{"idx": 2, "title": "Andrew Perrault", "date": "", "ddg_snippet": "Ju - Seung Byun and Andrew Perrault . TMLR 2025. Using RLHF to align speech enhancement approaches to mean-opinion quality scores.Leaving the Nest: Going Beyond Local Loss Functions for Predict-Then-Optimize. Sanket Shah, Bryan Wilder, Andrew Perrault , Milind Tambe.", "subpage_snippet": "", "source": "aperrault.github.io", "link": "https://aperrault.github.io/publications/", "content": "Ju - Seung Byun and Andrew Perrault . TMLR 2025. Using RLHF to align speech enhancement approaches to mean-opinion quality scores.Leaving the Nest: Going Beyond Local Loss Functions for Predict-Then-Optimize. Sanket Shah, Bryan Wilder, Andrew Perrault , Milind Tambe."}
+{"idx": 3, "title": "Andrew Perrault on LinkedIn: Check out Ju-Seung's work. A ...", "date": "", "ddg_snippet": "We suggest Symmetric RL loss for A2C and PPO and analyze the gradient of the loss to understand why it helps the model deviate from ambiguous predictions. 2.", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/posts/andrew-perrault-2b956733_check-out-ju-seungs-work-a-rare-case-of-activity-7209641734740328449-FMUF", "content": "We suggest Symmetric RL loss for A2C and PPO and analyze the gradient of the loss to understand why it helps the model deviate from ambiguous predictions. 2."}
+{"idx": 4, "title": "Ju-Seung Byun - OpenReview", "date": "", "ddg_snippet": "Symmetric Reinforcement Learning Loss for Robust Learning on Diverse Tasks and Model Scales Ju-Seung Byun , Andrew Perrault 25 Sept 2024 (modified: 16 Nov 2024) ICLR 2025 Conference Withdrawn Submission", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/profile?id=~Ju-Seung_Byun2", "content": "Symmetric Reinforcement Learning Loss for Robust Learning on Diverse Tasks and Model Scales Ju-Seung Byun , Andrew Perrault 25 Sept 2024 (modified: 16 Nov 2024) ICLR 2025 Conference Withdrawn Submission"}
+{"idx": 5, "title": "Abstract Symmetric Reinforcement Learning Loss for Robust ...", "date": "", "ddg_snippet": "May 29, 2024 · Ju-Seung Byun ∗ Andrew Perrault Department of Computer Science and Engineering The Ohio State University", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2405.17618", "content": "May 29, 2024 · Ju-Seung Byun ∗ Andrew Perrault Department of Computer Science and Engineering The Ohio State University"}
+{"idx": 6, "title": "Ju-Seung Byun - ACL Anthology", "date": "", "ddg_snippet": "Ju-Seung Byun 2024 pdf bib abs ARES: Alternating Reinforcement Learning and Supervised Fine-Tuning for Enhanced Multi-Modal Chain-of-Thought Reasoning Through Diverse AI Feedback Ju-Seung Byun | Jiyun Chun | Jihyung Kil | Andrew Perrault Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/people/ju-seung-byun/", "content": "Ju-Seung Byun 2024 pdf bib abs ARES: Alternating Reinforcement Learning and Supervised Fine-Tuning for Enhanced Multi-Modal Chain-of-Thought Reasoning Through Diverse AI Feedback Ju-Seung Byun | Jiyun Chun | Jihyung Kil | Andrew Perrault Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing"}
+{"idx": 7, "title": "ICML Poster Symmetric Reinforcement Learning Loss for Robust...", "date": "", "ddg_snippet": "Ju - Seung Byun · Andrew Perrault .To enhance stability, we adapt reverse cross-entropy (RCE) from supervised learning for noisy data, defining a symmetric RL loss .", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/44897", "content": "Ju - Seung Byun · Andrew Perrault .To enhance stability, we adapt reverse cross-entropy (RCE) from supervised learning for noisy data, defining a symmetric RL loss ."}
+{"idx": 8, "title": "Andrew Perrault 's research works | Harvard University and other places", "date": "", "ddg_snippet": "Ju - Seung Byun . · Andrew Perrault .In this work, we improve the stability of RL training by adapting the reverse cross entropy (RCE) from supervised learning for noisy data to define a symmetric RL loss . We demonstrate performance improvements across various tasks and scales.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/scientific-contributions/Andrew-Perrault-2154329778", "content": "Ju - Seung Byun . · Andrew Perrault .In this work, we improve the stability of RL training by adapting the reverse cross entropy (RCE) from supervised learning for noisy data to define a symmetric RL loss . We demonstrate performance improvements across various tasks and scales."}
+{"idx": 9, "title": "DLPO: Diffusion Model Loss -Guided Reinforcement Learning for...", "date": "", "ddg_snippet": "In this paper, we explore the practical application of RLHF to diffusion-based text-to-speech synthesis, leveraging the mean opinion score (MOS) as predicted by UTokyo-SaruLab MOS prediction system (Saeki et al., 2022) as a proxy loss .", "subpage_snippet": "", "source": "www.openread.academy", "link": "https://www.openread.academy/paper/reading?corpusId=513304408", "content": "In this paper, we explore the practical application of RLHF to diffusion-based text-to-speech synthesis, leveraging the mean opinion score (MOS) as predicted by UTokyo-SaruLab MOS prediction system (Saeki et al., 2022) as a proxy loss ."}
diff --git a/data/sampled_jsons/Kaplan_et_al_2020_scaling_laws_neural_language_models_power_law_exponent_year_2020.jsonl b/data/sampled_jsons/Kaplan_et_al_2020_scaling_laws_neural_language_models_power_law_exponent_year_2020.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..16655f8369ece6d59d3281d113fe0daeac23e4c3
--- /dev/null
+++ b/data/sampled_jsons/Kaplan_et_al_2020_scaling_laws_neural_language_models_power_law_exponent_year_2020.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A normie's guide to AI scaling laws - by Mahdi Assan", "date": "", "ddg_snippet": "Kaplan et al , ' Scaling Laws for Neural Language Models ' ( 2020 ), p.1.Brown et al , Language Models are Few Shot Learners ( 2020 ), p.8.", "subpage_snippet": "", "source": "www.thecybersolicitor.com", "link": "https://www.thecybersolicitor.com/p/a-normies-guide-to-ai-scaling-laws", "content": "Kaplan et al , ' Scaling Laws for Neural Language Models ' ( 2020 ), p.1.Brown et al , Language Models are Few Shot Learners ( 2020 ), p.8."}
+{"idx": 1, "title": "(PDF) Probability for Language Modeling --- Part III: Learning", "date": "", "ddg_snippet": "Neural scaling law and Hilberg’s law . Kaplan et al ., 2020 , Henighan et al ., 2020 , Hernandez et al ., 2021, Tanaka-Ishii, 2021]. This observation can be implied by Hilberg’s law , a power - law . growth of mutual information between increasing blocks of.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/383812037_Probability_for_Language_Modeling_---_Part_III_Learning", "content": "Neural scaling law and Hilberg’s law . Kaplan et al ., 2020 , Henighan et al ., 2020 , Hernandez et al ., 2021, Tanaka-Ishii, 2021]. This observation can be implied by Hilberg’s law , a power - law . growth of mutual information between increasing blocks of."}
+{"idx": 2, "title": "How Large Language Models Are Becoming Smarter and Smaller", "date": "", "ddg_snippet": "The Two Scaling Laws You Should Know.For example, Kaplan et al . ( 2020 ) showed that doubling training data or model size leads to predictable improvements (with diminishing returns) – like how reading twice as many books improves your knowledge, but each extra book helps a bit.", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/pulse/how-large-language-models-becoming-smarter-smaller-chandra-mishra-djtwf", "content": "The Two Scaling Laws You Should Know.For example, Kaplan et al . ( 2020 ) showed that doubling training data or model size leads to predictable improvements (with diminishing returns) – like how reading twice as many books improves your knowledge, but each extra book helps a bit."}
+{"idx": 3, "title": "When Scaling Laws Hit the Wall. Why Your Next AI... | GoPenAI", "date": "", "ddg_snippet": "For the past few years, Kaplan et al .’s scaling laws [1] have been our North Star — elegant power - law relationships showing performance scales predictably with model size, data, and compute.", "subpage_snippet": "", "source": "blog.gopenai.com", "link": "https://blog.gopenai.com/when-scaling-laws-hit-the-wall-1a5ed7cf6a25", "content": "For the past few years, Kaplan et al .’s scaling laws [1] have been our North Star — elegant power - law relationships showing performance scales predictably with model size, data, and compute."}
+{"idx": 4, "title": "The Accelerating Pace of GenAI: A Moore’s Law Trajectory and its...", "date": "", "ddg_snippet": "Scaling Laws Work by Kaplan et al . ( 2020 ) shows that language model performance scales predictably with increases in model size, computational power , and data availability .", "subpage_snippet": "", "source": "genip.ai", "link": "https://genip.ai/the-accelerating-pace-of-genai-a-moores-law-trajectory-and-its-impact-on-analytic-services/", "content": "Scaling Laws Work by Kaplan et al . ( 2020 ) shows that language model performance scales predictably with increases in model size, computational power , and data availability ."}
+{"idx": 5, "title": "Introduction to Deep Learning Lecture 20 Large Language Models | PDF", "date": "", "ddg_snippet": "Scaling - ( Kaplan , 2020 ) Open AI Study : Scaling Laws for Neural Language Models ( Kaplan et al . 2020 ) Key Findings: o Performance depends strongly on scale , and weakly on the model shape Larger models are more sample-efficient Smooth power laws ...", "subpage_snippet": "", "source": "www.slideshare.net", "link": "https://www.slideshare.net/slideshow/introduction-to-deep-learning-lecture-20-large-language-models/279149651?nway-content_model=D", "content": "Scaling - ( Kaplan , 2020 ) Open AI Study : Scaling Laws for Neural Language Models ( Kaplan et al . 2020 ) Key Findings: o Performance depends strongly on scale , and weakly on the model shape Larger models are more sample-efficient Smooth power laws ..."}
+{"idx": 6, "title": "Probability for Language Modeling - Part III: Learning", "date": "", "ddg_snippet": "Neural scaling law and Hilberg’s law .We observe a language -independent value of the power - law exponent : the mutual information between two blocks of length n is proportional to n0.8 [Takahira et al ., 2016, Tanaka-Ishii, 2021].", "subpage_snippet": "", "source": "home.ipipan.waw.pl", "link": "https://home.ipipan.waw.pl/l.debowski/docs/seminaria/probability_for_linguists_3.pdf", "content": "Neural scaling law and Hilberg’s law .We observe a language -independent value of the power - law exponent : the mutual information between two blocks of length n is proportional to n0.8 [Takahira et al ., 2016, Tanaka-Ishii, 2021]."}
+{"idx": 7, "title": "Norman Mu | The Myth of Data Inefficiency in Large Language Models", "date": "", "ddg_snippet": "The foundational scaling law papers by Kaplan et al . Kaplan et al . ( 2020 ) point out in their Figure 2 (pg. 4) that larger models learn more quickly, though with the sparsely labeled X-axis it’s hard to get a sense of the exact relationship between data and parameters.", "subpage_snippet": "", "source": "www.normanmu.com", "link": "https://www.normanmu.com/2025/02/14/data-inefficiency-llms.html", "content": "The foundational scaling law papers by Kaplan et al . Kaplan et al . ( 2020 ) point out in their Figure 2 (pg. 4) that larger models learn more quickly, though with the sparsely labeled X-axis it’s hard to get a sense of the exact relationship between data and parameters."}
+{"idx": 8, "title": "There is a line AI can't seem to cross, and we don't know why | Medium", "date": "", "ddg_snippet": "Scaling laws , as articulated in works like \" Scaling Laws for Neural Language Models \" ( Kaplan et al . , 2020 ), describe the connection between validation loss, model size, dataset size, and compute.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@egemengover/there-is-a-line-ai-cant-seem-to-cross-and-we-don-t-know-why-eb53fb5cb67c", "content": "Scaling laws , as articulated in works like \" Scaling Laws for Neural Language Models \" ( Kaplan et al . , 2020 ), describe the connection between validation loss, model size, dataset size, and compute."}
+{"idx": 9, "title": "There Are Fewer Facts Than Words", "date": "", "ddg_snippet": "This power - law growth occurs for lan - guages typologically as diverse as English, French, Russian, Chinese, Korean, and Japanese. More-over, we observe a universal language -independent value of the power - law exponent : the mutual in-formation between two blocks of length n is...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2211.01031", "content": "This power - law growth occurs for lan - guages typologically as diverse as English, French, Russian, Chinese, Korean, and Japanese. More-over, we observe a universal language -independent value of the power - law exponent : the mutual in-formation between two blocks of length n is..."}
diff --git a/data/sampled_jsons/Kaplan_scaling_law_error_rate_exponent_D^-0.5_pre-training.jsonl b/data/sampled_jsons/Kaplan_scaling_law_error_rate_exponent_D^-0.5_pre-training.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..37e190e0c4d5bd01aa19a248fbe82288b298aa50
--- /dev/null
+++ b/data/sampled_jsons/Kaplan_scaling_law_error_rate_exponent_D^-0.5_pre-training.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Neural scaling law", "date": "", "ddg_snippet": "A neural scaling law is an empirical scaling law that describes how neural network performance changes as key factors are scaled up or down.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Neural_scaling_law", "content": "A neural scaling law is an empirical scaling law that describes how neural network performance changes as key factors are scaled up or down."}
+{"idx": 1, "title": "Scaling Laws for Pre-training Agents and World Models", "date": "", "ddg_snippet": "7 Nov 2024 — Scaling analysis. The heart of scaling law analysis is fitting power law relationships predicting these compute-optimal quantities. For ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.04434v1", "content": "7 Nov 2024 — Scaling analysis. The heart of scaling law analysis is fitting power law relationships predicting these compute-optimal quantities. For ..."}
+{"idx": 2, "title": "Resolving Discrepancies in Compute-Optimal Scaling of ...", "date": "", "ddg_snippet": "27 Jun 2024 — We begin by reproducing the Kaplan et al. scaling law in a Llama-derived pretraining setup using the OpenLM library [18] and the RefinedWeb ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.19146v1", "content": "27 Jun 2024 — We begin by reproducing the Kaplan et al. scaling law in a Llama-derived pretraining setup using the OpenLM library [18] and the RefinedWeb ..."}
+{"idx": 3, "title": "Scaling Laws for Pre-training Agents and World Models", "date": "", "ddg_snippet": "The term scaling laws is used throughout the engineering and physical sciences to denote power law relationships between two quantities, e.g. duration of a ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/45787", "content": "The term scaling laws is used throughout the engineering and physical sciences to denote power law relationships between two quantities, e.g. duration of a ..."}
+{"idx": 4, "title": "How to Upscale Neural Networks with Scaling Law? A ...", "date": "", "ddg_snippet": "Prioritize modality balance in architecture and high-quality aligned datasets over isolated scaling . Refer to Figure 4a when designing multimodal pretraining ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=OSlr1Ot7b8", "content": "Prioritize modality balance in architecture and high-quality aligned datasets over isolated scaling . Refer to Figure 4a when designing multimodal pretraining ..."}
+{"idx": 5, "title": "Scaling Laws Under the Microscope: Predicting Transformer ...", "date": "", "ddg_snippet": "by M Ivgi · Cited by 21 — Neural scaling laws define a predictable rela- tionship between a model's parameter count and its performance after training in the form of a power law .", "subpage_snippet": "", "source": "mivg.github.io", "link": "https://mivg.github.io/files/scaling-laws.pdf", "content": "by M Ivgi · Cited by 21 — Neural scaling laws define a predictable rela- tionship between a model's parameter count and its performance after training in the form of a power law ."}
+{"idx": 6, "title": "D-CPT Law: Domain-specific Continual Pre-Training ...", "date": "", "ddg_snippet": "by H Que · Cited by 34 — To show the effectiveness and generalizability of D -CPT Law , we perform extensive experiments using model sizes from 0.5 B to 4B parameters, dataset sizes from ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=JzKFN5fWOk", "content": "by H Que · Cited by 34 — To show the effectiveness and generalizability of D -CPT Law , we perform extensive experiments using model sizes from 0.5 B to 4B parameters, dataset sizes from ..."}
+{"idx": 7, "title": "Observational Scaling Laws and the Predictability of ...", "date": "", "ddg_snippet": "by Y Ruan · 2024 · Cited by 51 — Our scaling law precisely predicts the GPT-4 performance using weaker models (sub GPT-3.5) and identifies programming capabilities as driving agent performance.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/1cded4f97cf5f01a284c574110b7e3b9-Paper-Conference.pdf", "content": "by Y Ruan · 2024 · Cited by 51 — Our scaling law precisely predicts the GPT-4 performance using weaker models (sub GPT-3.5) and identifies programming capabilities as driving agent performance."}
+{"idx": 8, "title": "Scaling Laws from the Data Manifold Dimension", "date": "", "ddg_snippet": "by U Sharma · 2022 · Cited by 67 — This simple theory predicts that the scaling exponents α ≈ 4/ d for cross-entropy and mean-squared error losses. We confirm the theory by independently measuring ...", "subpage_snippet": "", "source": "jmlr.csail.mit.edu", "link": "https://jmlr.csail.mit.edu/papers/volume23/20-1111/20-1111.pdf", "content": "by U Sharma · 2022 · Cited by 67 — This simple theory predicts that the scaling exponents α ≈ 4/ d for cross-entropy and mean-squared error losses. We confirm the theory by independently measuring ..."}
+{"idx": 9, "title": "Loss Deceleration and Zero-Sum Learning", "date": "", "ddg_snippet": "by A Mircea · 2025 — This work aims to understand how scaling improves language models, specifically in terms of training dynamics. We find that. 35 pages", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.acl-long.1366.pdf", "content": "by A Mircea · 2025 — This work aims to understand how scaling improves language models, specifically in terms of training dynamics. We find that. 35 pages"}
diff --git a/data/sampled_jsons/Kirilenko_2015_regression_analysis_temperature_media_climate_Twitter_methodology_year_2015.jsonl b/data/sampled_jsons/Kirilenko_2015_regression_analysis_temperature_media_climate_Twitter_methodology_year_2015.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..063d3ffeda561e26c32369fa861e5466b9174635
--- /dev/null
+++ b/data/sampled_jsons/Kirilenko_2015_regression_analysis_temperature_media_climate_Twitter_methodology_year_2015.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "(PDF) The influence of temperature on #ClimateChange and...", "date": "", "ddg_snippet": "hybrid content analysis method to social media discourses as it possesses the. Kirilenko , A. P., Molodtsova, T. and Stepchenkova, S. O. ( 2015 ). ‘People as sensors: Mass media and local temperature influence climate change discussion on.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/324808493_The_influence_of_temperature_on_ClimateChange_and_GlobalWarming_discourses_on_Twitter", "content": "hybrid content analysis method to social media discourses as it possesses the. Kirilenko , A. P., Molodtsova, T. and Stepchenkova, S. O. ( 2015 ). ‘People as sensors: Mass media and local temperature influence climate change discussion on."}
+{"idx": 1, "title": "The meaning of climate change in American politics: an embedding...", "date": "", "ddg_snippet": "Downloadable (with restrictions)! This study employs word embedding regression to examine the meaning of climate change in U.S. congressional discourse on Twitter from 2015 –2022.", "subpage_snippet": "", "source": "ideas.repec.org", "link": "https://ideas.repec.org/a/spr/climat/v178y2025i6d10.1007_s10584-025-03964-x.html", "content": "Downloadable (with restrictions)! This study employs word embedding regression to examine the meaning of climate change in U.S. congressional discourse on Twitter from 2015 –2022."}
+{"idx": 2, "title": "Global Surface Temperature | NASA Global Climate Change", "date": "", "ddg_snippet": "A graph and an animated time series showing the change in global surface temperature relative to 1951-1980 average temperatures .", "subpage_snippet": "", "source": "climate.nasa.gov", "link": "https://climate.nasa.gov/vital-signs/global-temperature/?intent=121", "content": "A graph and an animated time series showing the change in global surface temperature relative to 1951-1980 average temperatures ."}
+{"idx": 3, "title": "Climate change: global temperature | NOAA Climate .gov", "date": "", "ddg_snippet": "Earth's surface temperature has risen about 2 degrees Fahrenheit since the start of the NOAA record in 1850. It may seem like a small change, but it's a tremendous increase in stored heat.", "subpage_snippet": "", "source": "www.climate.gov", "link": "https://www.climate.gov/news-features/understanding-climate/climate-change-global-temperature", "content": "Earth's surface temperature has risen about 2 degrees Fahrenheit since the start of the NOAA record in 1850. It may seem like a small change, but it's a tremendous increase in stored heat."}
+{"idx": 4, "title": "Media (ted) climate change in South Africa, Nigeria, and Kenya", "date": "", "ddg_snippet": "Also, new media , including Twitter ( Kirilenko , Molodtsova & Stepchenkova, 2015 ; Kirilenko & Stepchenkova, 2014; Veltri & Atanasova, 2017), Facebook (Bloomfield & Tillery, 2019), YouTube (Shapiro & Park, 2015 ), as well as analysis across the various online media (Facebook...", "subpage_snippet": "", "source": "scholar.sun.ac.za", "link": "https://scholar.sun.ac.za/server/api/core/bitstreams/d8427c33-4728-4f65-9332-bc44a55a6fe1/content", "content": "Also, new media , including Twitter ( Kirilenko , Molodtsova & Stepchenkova, 2015 ; Kirilenko & Stepchenkova, 2014; Veltri & Atanasova, 2017), Facebook (Bloomfield & Tillery, 2019), YouTube (Shapiro & Park, 2015 ), as well as analysis across the various online media (Facebook..."}
+{"idx": 5, "title": "[FREE] Can we predict the average January temperature ... - brainly.com", "date": "", "ddg_snippet": "We can construct a linear regression equation predicting average January temperature from latitude. Predictions are valid mainly within the data range; thus, a latitude like 45 degrees is likely acceptable, while 63 degrees may not be.", "subpage_snippet": "", "source": "brainly.com", "link": "https://brainly.com/question/50259608", "content": "We can construct a linear regression equation predicting average January temperature from latitude. Predictions are valid mainly within the data range; thus, a latitude like 45 degrees is likely acceptable, while 63 degrees may not be."}
+{"idx": 6, "title": "Climate Time Series Analysis : Classical Statistical... | Semantic Scholar", "date": "", "ddg_snippet": "Climate Time Series Analysis . Part I: Fundamental Concepts.- 1 Introduction.This paper analyses US sea level data using long memory and fractional integration methods . All series appear to exhibit orders of integration in the range (0, 1), which implies long-range… Expand.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Climate-Time-Series-Analysis:-Classical-Statistical-Mudelsee/177d241304736ba42fbf38fcfca51f704f04ae0c", "content": "Climate Time Series Analysis . Part I: Fundamental Concepts.- 1 Introduction.This paper analyses US sea level data using long memory and fractional integration methods . All series appear to exhibit orders of integration in the range (0, 1), which implies long-range… Expand."}
+{"idx": 7, "title": "Heteroscedasticity and Homoscedasticity in Regression Analysis - AI...", "date": "", "ddg_snippet": "How can Heteroscedasticity be detected in a Regression Model? What methods can be used to correct for Heteroscedasticity? Examples of Heteroscedasticity in Data Analysis . 1. Economy: Consumption and Income.", "subpage_snippet": "", "source": "iartificial.blog", "link": "https://iartificial.blog/en/learning/heteroscedasticity-homoscedasticity-regression-analysis/", "content": "How can Heteroscedasticity be detected in a Regression Model? What methods can be used to correct for Heteroscedasticity? Examples of Heteroscedasticity in Data Analysis . 1. Economy: Consumption and Income."}
+{"idx": 8, "title": "This Is the Ideal Temperature for Your Winter... - Better Report", "date": "", "ddg_snippet": "Winter can be difficult to bear without cranking your thermostat to its max. Setting the temperature higher may leave you feeling warm and toasty, but your utility bills can skyrocket.", "subpage_snippet": "", "source": "betterreport.com", "link": "https://betterreport.com/ideal-thermostat-temperature-winter/", "content": "Winter can be difficult to bear without cranking your thermostat to its max. Setting the temperature higher may leave you feeling warm and toasty, but your utility bills can skyrocket."}
+{"idx": 9, "title": "Homoscedasticity in Regression : Purpose and Methods", "date": "", "ddg_snippet": "If you have ever used linear regression to model the relationship between a dependent variable and one or more independent variables, you may have encountered the term homoscedasticity. But what does it mean and why is it important for regression analysis ?", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/advice/3/what-purpose-homoscedasticity-assumption-regression-b0jnc", "content": "If you have ever used linear regression to model the relationship between a dependent variable and one or more independent variables, you may have encountered the term homoscedasticity. But what does it mean and why is it important for regression analysis ?"}
diff --git a/data/sampled_jsons/Kirilenko_Molodtsova_Stepchenkova_2015_causal_modeling_single_pathway_multiple_variables.jsonl b/data/sampled_jsons/Kirilenko_Molodtsova_Stepchenkova_2015_causal_modeling_single_pathway_multiple_variables.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..08935197564752fb588c07ebca69d2d4bf2ed61c
--- /dev/null
+++ b/data/sampled_jsons/Kirilenko_Molodtsova_Stepchenkova_2015_causal_modeling_single_pathway_multiple_variables.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Multiple Linear Regression Analysis", "date": "", "ddg_snippet": "Discover how multiple linear regression analysis can help you identify causes , predict effects, and forecast trends.", "subpage_snippet": "", "source": "www.statisticssolutions.com", "link": "https://www.statisticssolutions.com/free-resources/directory-of-statistical-analyses/questions-the-multiple-linear-regression-answers/", "content": "Discover how multiple linear regression analysis can help you identify causes , predict effects, and forecast trends."}
+{"idx": 1, "title": "Multiple linear regression. How to find the most significant independent...", "date": "", "ddg_snippet": "The assignment starts: choose 3-5 independent variables (arbitrarily), of which at least one is categorical . These will be included in a multiple linear model . My question is, how do I evaluate which of these variables have most \"explanatory power\"?", "subpage_snippet": "", "source": "stats.stackexchange.com", "link": "https://stats.stackexchange.com/questions/330923/multiple-linear-regression-how-to-find-the-most-significant-independent-variabl", "content": "The assignment starts: choose 3-5 independent variables (arbitrarily), of which at least one is categorical . These will be included in a multiple linear model . My question is, how do I evaluate which of these variables have most \"explanatory power\"?"}
+{"idx": 2, "title": "(PDF) The influence of temperature on #ClimateChange and...", "date": "", "ddg_snippet": "Kirilenko , Molodtsova and Stepchenkova . [ 2015 ] found that during extreme weather events (quantified using anomalous. temperature data), there was an increase in the number of tweets about climate.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/324808493_The_influence_of_temperature_on_ClimateChange_and_GlobalWarming_discourses_on_Twitter", "content": "Kirilenko , Molodtsova and Stepchenkova . [ 2015 ] found that during extreme weather events (quantified using anomalous. temperature data), there was an increase in the number of tweets about climate."}
+{"idx": 3, "title": "Comparing Events Coverage in Online News and Social Media: The...", "date": "", "ddg_snippet": "Molodtsova et al. (2013) show that the number of tweets on climate change correlates with extreme weather events, a correlation that also holds for opinion polls on climate change (Donner and McDaniels 2013). Along with weather events, Kirilenko and Stepchenkova (2014) and...", "subpage_snippet": "", "source": "crisislex.org", "link": "https://crisislex.org/papers/icwsm2015_climate_change_media_gap.pdf", "content": "Molodtsova et al. (2013) show that the number of tweets on climate change correlates with extreme weather events, a correlation that also holds for opinion polls on climate change (Donner and McDaniels 2013). Along with weather events, Kirilenko and Stepchenkova (2014) and..."}
+{"idx": 4, "title": "Using Social Media to Detect and Locate Wildfires", "date": "", "ddg_snippet": "Kirilenko , Molodtsova and Stepchenkova ( 2015 ) showed US social media activity about climate change was correlated with local incidence of extreme temperatures.", "subpage_snippet": "", "source": "ore.exeter.ac.uk", "link": "https://ore.exeter.ac.uk/repository/bitstream/handle/10871/28583/13204-58375-1-PB.pdf?sequence=1", "content": "Kirilenko , Molodtsova and Stepchenkova ( 2015 ) showed US social media activity about climate change was correlated with local incidence of extreme temperatures."}
+{"idx": 5, "title": "Yeo | The influence of temperature on #ClimateChange and...", "date": "", "ddg_snippet": "Kirilenko , Molodtsova and Stepchenkova [ 2015 ] found that during extreme weather events (quantified using anomalous temperature data), there was an increase in the number of tweets about climate change, especially for colder and wetter regions of the United States and during...", "subpage_snippet": "", "source": "jcom.sissa.it", "link": "https://jcom.sissa.it/article/pubid/JCOM_1605_2017_A01/", "content": "Kirilenko , Molodtsova and Stepchenkova [ 2015 ] found that during extreme weather events (quantified using anomalous temperature data), there was an increase in the number of tweets about climate change, especially for colder and wetter regions of the United States and during..."}
+{"idx": 6, "title": "[PDF] Another look at the instrumental variable ... | Semantic Scholar", "date": "", "ddg_snippet": "This paper considers the Fixed Effects estimation of non-linear models of panel data with multiplicative unobserved effects and where instrumental variables are predetermined as opposed to strictly…", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Another-look-at-the-instrumental-variable-of-models-Arellano-Bover/6b4819081f79a121b761c281f765d532b9f83106", "content": "This paper considers the Fixed Effects estimation of non-linear models of panel data with multiplicative unobserved effects and where instrumental variables are predetermined as opposed to strictly…"}
+{"idx": 7, "title": "Analyzed the audience perception towards", "date": "", "ddg_snippet": "models above, we can evaluate the Independent Variable Importance of Preference of Media Importance is 0.158 and Normalized Importance is 83.0%.[13] Khan, T. (2020). The Citizenship Amendment. Act, 2019: A Religion Based Pathway to Indian.", "subpage_snippet": "", "source": "revistaclinicapsicologica.com", "link": "https://revistaclinicapsicologica.com/data-cms/articles/20210915065031pmSSCI-696.pdf", "content": "models above, we can evaluate the Independent Variable Importance of Preference of Media Importance is 0.158 and Normalized Importance is 83.0%.[13] Khan, T. (2020). The Citizenship Amendment. Act, 2019: A Religion Based Pathway to Indian."}
+{"idx": 8, "title": "Flying-Submarine's disaster mood map | Devpost", "date": "", "ddg_snippet": "Currently we are focusing on California region, which is often affected by wildfire, and recently created a lot of buzz on social media. There are some research shown that social media has been successfully use to detect extreme weather ( Kirilenko , Molodtsova , and Stepchenkova 2015 ).", "subpage_snippet": "", "source": "devpost.com", "link": "https://devpost.com/software/flying-submarine-s-disaster-mood-map", "content": "Currently we are focusing on California region, which is often affected by wildfire, and recently created a lot of buzz on social media. There are some research shown that social media has been successfully use to detect extreme weather ( Kirilenko , Molodtsova , and Stepchenkova 2015 )."}
+{"idx": 9, "title": "EDaSS", "date": "", "ddg_snippet": "In fact, authors ( Kirilenko , Molodtsova , & Stepchenkova , 2015 ) confirmed that “the explosive growth of social networks, such as Facebook, Flickr, and Twitter, has enabled ’’passive’’ surveying of public opinion”.", "subpage_snippet": "", "source": "www.edass.org", "link": "https://www.edass.org/wp-content/uploads/2018/02/PROCEEDINGS-2018.pdf", "content": "In fact, authors ( Kirilenko , Molodtsova , & Stepchenkova , 2015 ) confirmed that “the explosive growth of social networks, such as Facebook, Flickr, and Twitter, has enabled ’’passive’’ surveying of public opinion”."}
diff --git a/data/sampled_jsons/Kirkpatrick_et_al._2017_catastrophic_forgetting_abstract_year_2017.jsonl b/data/sampled_jsons/Kirkpatrick_et_al._2017_catastrophic_forgetting_abstract_year_2017.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7dacc972afebe3d48b5c07998f71cc0a29bc7d3c
--- /dev/null
+++ b/data/sampled_jsons/Kirkpatrick_et_al._2017_catastrophic_forgetting_abstract_year_2017.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Kirkpatrick Bank", "date": "", "ddg_snippet": "Kirkpatrick Bank offers a full range of mortgage loan options – permanent or second mortgage loans, construction or bridge loans, VA, FHA, conventional and Jumbo home loans.", "subpage_snippet": "", "source": "www.kirkpatrickbank.com", "link": "https://www.kirkpatrickbank.com/", "content": "Kirkpatrick Bank offers a full range of mortgage loan options – permanent or second mortgage loans, construction or bridge loans, VA, FHA, conventional and Jumbo home loans."}
+{"idx": 1, "title": "Personal Banking - Kirkpatrick Bank", "date": "", "ddg_snippet": "Kirkpatrick Bank offers a full range of mortgage loan options – permanent or second mortgage loans, construction or bridge loans, VA, FHA, conventional and Jumbo home loans.", "subpage_snippet": "", "source": "www.kirkpatrickbank.com", "link": "https://www.kirkpatrickbank.com/personal-banking", "content": "Kirkpatrick Bank offers a full range of mortgage loan options – permanent or second mortgage loans, construction or bridge loans, VA, FHA, conventional and Jumbo home loans."}
+{"idx": 2, "title": "Business Banking - Kirkpatrick Bank", "date": "", "ddg_snippet": "Kirkpatrick Bank offers a full range of mortgage loan options – permanent or second mortgage loans, construction or bridge loans, VA, FHA, conventional and Jumbo home loans.", "subpage_snippet": "", "source": "www.kirkpatrickbank.com", "link": "https://www.kirkpatrickbank.com/business-banking", "content": "Kirkpatrick Bank offers a full range of mortgage loan options – permanent or second mortgage loans, construction or bridge loans, VA, FHA, conventional and Jumbo home loans."}
+{"idx": 3, "title": "Kirkpatrick Bank | Locations", "date": "", "ddg_snippet": "Kirkpatrick Bank offers a full range of mortgage loan options – permanent or second mortgage loans, construction or bridge loans, VA, FHA, conventional and Jumbo home loans.", "subpage_snippet": "", "source": "www.kirkpatrickbank.com", "link": "https://www.kirkpatrickbank.com/locations", "content": "Kirkpatrick Bank offers a full range of mortgage loan options – permanent or second mortgage loans, construction or bridge loans, VA, FHA, conventional and Jumbo home loans."}
+{"idx": 4, "title": "Kirkpatrick Bank | About", "date": "", "ddg_snippet": "Kirkpatrick Bank offers a full range of mortgage loan options – permanent or second mortgage loans, construction or bridge loans, VA, FHA, conventional and Jumbo home loans.", "subpage_snippet": "", "source": "www.kirkpatrickbank.com", "link": "https://www.kirkpatrickbank.com/about", "content": "Kirkpatrick Bank offers a full range of mortgage loan options – permanent or second mortgage loans, construction or bridge loans, VA, FHA, conventional and Jumbo home loans."}
+{"idx": 5, "title": "Kirkpatrick Bank - Personal Online Banking", "date": "", "ddg_snippet": "You can access your Kirkpatrick Bank accounts from almost any PC that has Internet access. Will Internet Banking work with my current Internet Service Provider?", "subpage_snippet": "", "source": "www.kirkpatrickbank.com", "link": "https://www.kirkpatrickbank.com/helpful-videos/personal-online-banking", "content": "You can access your Kirkpatrick Bank accounts from almost any PC that has Internet access. Will Internet Banking work with my current Internet Service Provider?"}
+{"idx": 6, "title": "Kirkpatrick Bank | Contact", "date": "", "ddg_snippet": "Kirkpatrick Bank offers a full range of mortgage loan options – permanent or second mortgage loans, construction or bridge loans, VA, FHA, conventional and Jumbo home loans.", "subpage_snippet": "", "source": "www.kirkpatrickbank.com", "link": "https://www.kirkpatrickbank.com/contact", "content": "Kirkpatrick Bank offers a full range of mortgage loan options – permanent or second mortgage loans, construction or bridge loans, VA, FHA, conventional and Jumbo home loans."}
+{"idx": 7, "title": "Kirkpatrick Bank | Mortgage Lenders", "date": "", "ddg_snippet": "Kirkpatrick Bank offers a full range of mortgage loan options – permanent or second mortgage loans, construction or bridge loans, VA, FHA, conventional and Jumbo home loans.", "subpage_snippet": "", "source": "www.kirkpatrickbank.com", "link": "https://www.kirkpatrickbank.com/mortgage-lenders/b-drake", "content": "Kirkpatrick Bank offers a full range of mortgage loan options – permanent or second mortgage loans, construction or bridge loans, VA, FHA, conventional and Jumbo home loans."}
+{"idx": 8, "title": "Careers - Kirkpatrick Bank", "date": "", "ddg_snippet": "Kirkpatrick Bank seeks qualified candidates for career opportunities in the Oklahoma City, Colorado Springs, Westcliffe and Denver areas and offers a competitive compensation and benefits package.", "subpage_snippet": "", "source": "www.kirkpatrickbank.com", "link": "https://www.kirkpatrickbank.com/careers", "content": "Kirkpatrick Bank seeks qualified candidates for career opportunities in the Oklahoma City, Colorado Springs, Westcliffe and Denver areas and offers a competitive compensation and benefits package."}
+{"idx": 9, "title": "Kirkpatrick Bank | Resources", "date": "", "ddg_snippet": "Kirkpatrick Bank offers a full range of mortgage loan options – permanent or second mortgage loans, construction or bridge loans, VA, FHA, conventional and Jumbo home loans.", "subpage_snippet": "", "source": "www.kirkpatrickbank.com", "link": "https://www.kirkpatrickbank.com/resources", "content": "Kirkpatrick Bank offers a full range of mortgage loan options – permanent or second mortgage loans, construction or bridge loans, VA, FHA, conventional and Jumbo home loans."}
diff --git a/data/sampled_jsons/Kwon_circuit_fingerprinting_attacks_Tor_2015.jsonl b/data/sampled_jsons/Kwon_circuit_fingerprinting_attacks_Tor_2015.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..12c0869a4edf326f12ac7bf4198676dec165f008
--- /dev/null
+++ b/data/sampled_jsons/Kwon_circuit_fingerprinting_attacks_Tor_2015.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Circuit Fingerprinting Attacks: Passive Deanonymization of ...", "date": "", "ddg_snippet": "Our key insight is that during the circuit con-struction and communication phase between a client and a hidden service, Tor exhibits fingerprintable traffic pat-terns that allow an adversary to efficiently and accurately identify, and correlate circuits involved in the communi-cation with hidden services.", "subpage_snippet": "", "source": "www.usenix.org", "link": "https://www.usenix.org/system/files/conference/usenixsecurity15/sec15-paper-kwon.pdf", "content": "Our key insight is that during the circuit con-struction and communication phase between a client and a hidden service, Tor exhibits fingerprintable traffic pat-terns that allow an adversary to efficiently and accurately identify, and correlate circuits involved in the communi-cation with hidden services."}
+{"idx": 1, "title": "Fingerprinting Hidden Service Circuits from a Tor Middle Relay", "date": "", "ddg_snippet": "Kwon et al. recently showed that circuit fingerprinting attacks could be used to identify hidden service circuits, which is a key step towards linking Tor users and their activity online. In this paper, we explore an improvement to their attack that uses random forests, which achieves similar accuracy while being more robust to simple countermeasures against it. Additionally, we perform our ...", "subpage_snippet": "", "source": "www.research.ed.ac.uk", "link": "https://www.research.ed.ac.uk/en/publications/fingerprinting-hidden-service-circuits-from-a-tor-middle-relay", "content": "Kwon et al. recently showed that circuit fingerprinting attacks could be used to identify hidden service circuits, which is a key step towards linking Tor users and their activity online. In this paper, we explore an improvement to their attack that uses random forests, which achieves similar accuracy while being more robust to simple countermeasures against it. Additionally, we perform our ..."}
+{"idx": 2, "title": "Circuit fingerprinting attacks | Proceedings of the 24th ...", "date": "", "ddg_snippet": "In particular, we show that the circuits, paths established through the Tor network, used to communicate with hidden services exhibit a very different behavior compared to a general circuit . We propose two attacks , under two slightly different threat models, that could identify a hidden service client or operator using these weaknesses.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/2831143.2831162", "content": "In particular, we show that the circuits, paths established through the Tor network, used to communicate with hidden services exhibit a very different behavior compared to a general circuit . We propose two attacks , under two slightly different threat models, that could identify a hidden service client or operator using these weaknesses."}
+{"idx": 3, "title": "Discovering onion services through circuit fingerprinting attacks Circuit Fingerprinting Attacks: Passive Deanonymization of ... Poster: Fingerprinting Hidden Service Circuits from a Tor ... Fingerprinting Hidden Service Circuits from a Tor Middle Relay Discovering onion services through circuit fingerprinting attacks Discovering onion services through circuit fingerprinting attacks Discovering onion services through circuit fingerprinting attacks Discovering onion services through circuit fingerprinting attacks Discovering onion services through circuit fingerprinting attacks Circuit Fingerprinting Attacks: Passive Deanonymization of ...", "date": "", "ddg_snippet": "Mar 1, 2023 · Circuit fingerprinting attack is a traffic analysis attack against Tor which break or reduce the anonymity that Tor aims to provide. Kwon et al. [1] discovered the fingerprint features of circuits and used the features to first propose a circuit fingerprinting attack . To measure the popularity of onion services, Jansen et al. [2] proposes a circuit fingerprinting attack that can be implemented ... This paper sheds light on crucial weaknesses in the design of hidden services that allow us to break the anonymity of hidden service clients and operators pas-sively. In particular, we show that the circuits, paths established through the Tor network, used to commu-nicate with hidden services exhibit a very different be-havior compared to a general circuit . We propose two attacks , under two ... Abstract— Kwon et al. recently showed that circuit fingerprint-ing attacks could be used to identify hidden service circuits, which is a key step towards linking Tor users and their activity online. Kwon et al. recently showed that circuit fingerprinting attacks could be used to identify hidden service circuits, which is a key step towards linking Tor users and their activity online. In this paper, we explore an improvement to their attack that uses random forests, which achieves similar accuracy while being more robust to simple countermeasures against it. Additionally, we perform our ... Why is Kwon's Circuit Fingerprinting Attack worse than Tor? Since Tor uses Client-side introduction and rendezvous circuit hiding machines on all client-side circuits, the performance of Kwon ’s circuit fingerprinting attack with circuit construction sequence feature becomes worse. Table 4. Results for circuit fingerprintings. 6. Discussion How does Kwon's Circuit Fingerprinting compare to Kwon? We compare our circuit fingerprinting attack to Kwon ’s. According to Kwon ’s paper, we re-implemented his circuit fingerprinting using Python. To ensure fair comparison, we run Kwon ’s circuit fingerprinting on our dataset. And we discard the duration of activity feature of Kwon ’s circuit fingerprinting , since our dataset does not contain timestamps. Are onion services vulnerable to Circuit Fingerprinting attacks? This would cause the network addresses of onion services to be easily exposed to malicious entry relays using circuit fingerprinting attacks . Compared to the IP addresses of clients, the IP addresses of onion services does not change frequently. What is Circuit Fingerprinting Attack? Circuit fingerprinting attack is a approach to classify and discover specific Tor circuits through Tor circuit fingerprints on the onion router. Kwon et al. were the first to find that Tor circuits presents different characteristics in construction sequence, duration of activity, and proposed a circuit fingerprinting attack. Can a dummy cell protect against a Circuit Fingerprinting Attack? But an adversary could use a circuit fingerprinting attack to classify circuit types and discovers the network address of the onion service. Recently, Tor has used padding defenses to inject dummy cells to protect against circuit fingerprinting attacks . But we found that circuits still expose much information to the adversary. TLDR A novel circuit fingerprinting attack is presented, which divides the circuit into the circuitgenerated by the client and the circuit generated by the onion service, and achieves highly accurate circuits fingerprinting attacks even when application-layer traffic is identical and some type of circuits using the defenses provided by Tor ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2667295222000514", "content": "Mar 1, 2023 · Circuit fingerprinting attack is a traffic analysis attack against Tor which break or reduce the anonymity that Tor aims to provide. Kwon et al. [1] discovered the fingerprint features of circuits and used the features to first propose a circuit fingerprinting attack . To measure the popularity of onion services, Jansen et al. [2] proposes a circuit fingerprinting attack that can be implemented ... This paper sheds light on crucial weaknesses in the design of hidden services that allow us to break the anonymity of hidden service clients and operators pas-sively. In particular, we show that the circuits, paths established through the Tor network, used to commu-nicate with hidden services exhibit a very different be-havior compared to a general circuit . We propose two attacks , under two ... Abstract— Kwon et al. recently showed that circuit fingerprint-ing attacks could be used to identify hidden service circuits, which is a key step towards linking Tor users and their activity online. Kwon et al. recently showed that circuit fingerprinting attacks could be used to identify hidden service circuits, which is a key step towards linking Tor users and their activity online. In this paper, we explore an improvement to their attack that uses random forests, which achieves similar accuracy while being more robust to simple countermeasures against it. Additionally, we perform our ... Why is Kwon's Circuit Fingerprinting Attack worse than Tor? Since Tor uses Client-side introduction and rendezvous circuit hiding machines on all client-side circuits, the performance of Kwon ’s circuit fingerprinting attack with circuit construction sequence feature becomes worse. Table 4. Results for circuit fingerprintings. 6. Discussion How does Kwon's Circuit Fingerprinting compare to Kwon? We compare our circuit fingerprinting attack to Kwon ’s. According to Kwon ’s paper, we re-implemented his circuit fingerprinting using Python. To ensure fair comparison, we run Kwon ’s circuit fingerprinting on our dataset. And we discard the duration of activity feature of Kwon ’s circuit fingerprinting , since our dataset does not contain timestamps. Are onion services vulnerable to Circuit Fingerprinting attacks? This would cause the network addresses of onion services to be easily exposed to malicious entry relays using circuit fingerprinting attacks . Compared to the IP addresses of clients, the IP addresses of onion services does not change frequently. What is Circuit Fingerprinting Attack? Circuit fingerprinting attack is a approach to classify and discover specific Tor circuits through Tor circuit fingerprints on the onion router. Kwon et al. were the first to find that Tor circuits presents different characteristics in construction sequence, duration of activity, and proposed a circuit fingerprinting attack. Can a dummy cell protect against a Circuit Fingerprinting Attack? But an adversary could use a circuit fingerprinting attack to classify circuit types and discovers the network address of the onion service. Recently, Tor has used padding defenses to inject dummy cells to protect against circuit fingerprinting attacks . But we found that circuits still expose much information to the adversary. TLDR A novel circuit fingerprinting attack is presented, which divides the circuit into the circuitgenerated by the client and the circuit generated by the onion service, and achieves highly accurate circuits fingerprinting attacks even when application-layer traffic is identical and some type of circuits using the defenses provided by Tor ..."}
+{"idx": 4, "title": "Circuit Fingerprinting Attacks: Passive Deanonymization of ...", "date": "", "ddg_snippet": "This paper sheds light on crucial weaknesses in the design of hidden services that allow us to break the anonymity of hidden service clients and operators pas-sively. In particular, we show that the circuits, paths established through the Tor network, used to commu-nicate with hidden services exhibit a very different be-havior compared to a general circuit . We propose two attacks , under two ...", "subpage_snippet": "", "source": "people.csail.mit.edu", "link": "https://people.csail.mit.edu/devadas/pubs/circuit_finger.pdf", "content": "This paper sheds light on crucial weaknesses in the design of hidden services that allow us to break the anonymity of hidden service clients and operators pas-sively. In particular, we show that the circuits, paths established through the Tor network, used to commu-nicate with hidden services exhibit a very different be-havior compared to a general circuit . We propose two attacks , under two ..."}
+{"idx": 5, "title": "Poster: Fingerprinting Hidden Service Circuits from a Tor ...", "date": "", "ddg_snippet": "Abstract— Kwon et al. recently showed that circuit fingerprint-ing attacks could be used to identify hidden service circuits, which is a key step towards linking Tor users and their activity online.", "subpage_snippet": "", "source": "www.ieee-security.org", "link": "https://www.ieee-security.org/TC/SP2017/poster-abstracts/IEEE-SP17_Posters_paper_36.pdf", "content": "Abstract— Kwon et al. recently showed that circuit fingerprint-ing attacks could be used to identify hidden service circuits, which is a key step towards linking Tor users and their activity online."}
+{"idx": 6, "title": "Circuit Fingerprinting Attacks: Passive Deanonymization of ...", "date": "", "ddg_snippet": "TLDR A novel circuit fingerprinting attack is presented, which divides the circuit into the circuitgenerated by the client and the circuit generated by the onion service, and achieves highly accurate circuits fingerprinting attacks even when application-layer traffic is identical and some type of circuits using the defenses provided by Tor ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Circuit-Fingerprinting-Attacks:-Passive-of-Tor-Kwon-Alsabah/fb4cb1f1c57ee56e9a014570debf7d3d6871ddf3/figure/15", "content": "TLDR A novel circuit fingerprinting attack is presented, which divides the circuit into the circuitgenerated by the client and the circuit generated by the onion service, and achieves highly accurate circuits fingerprinting attacks even when application-layer traffic is identical and some type of circuits using the defenses provided by Tor ..."}
+{"idx": 7, "title": "Researchers mount successful attacks against Tor network—and...", "date": "", "ddg_snippet": "Traffic fingerprinting . Kwon devised an attack on this system with joint first author Mashael AlSabah, an assistant professor of computer science at Qatar University, a researcher at QCRI, and, this year, a visiting scientist at MIT; Srini Devadas, the Edwin Sibley Webster Professor in MIT's...", "subpage_snippet": "", "source": "phys.org", "link": "https://phys.org/news/2015-07-mount-successful-tor-networkand.html", "content": "Traffic fingerprinting . Kwon devised an attack on this system with joint first author Mashael AlSabah, an assistant professor of computer science at Qatar University, a researcher at QCRI, and, this year, a visiting scientist at MIT; Srini Devadas, the Edwin Sibley Webster Professor in MIT's..."}
+{"idx": 8, "title": "[PDF] Circuit Fingerprinting Attacks : Passive... | Semantic Scholar", "date": "", "ddg_snippet": "Circuit Fingerprinting Attacks : Passive Deanonymization of Tor Hidden Services.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Circuit-Fingerprinting-Attacks:-Passive-of-Tor-Kwon-Alsabah/fb4cb1f1c57ee56e9a014570debf7d3d6871ddf3", "content": "Circuit Fingerprinting Attacks : Passive Deanonymization of Tor Hidden Services."}
+{"idx": 9, "title": "Researchers Unveiled a New, Serious Vulnerability In Tor", "date": "", "ddg_snippet": "The attack , described in a paper the team will present at the 2015 Usenix Security Symposium this summer, does not require the attacker to actually decrypt any Tor traffic.", "subpage_snippet": "", "source": "www.vice.com", "link": "https://www.vice.com/en/article/researchers-unveiled-a-new-serious-vulnerability-in-tor/", "content": "The attack , described in a paper the team will present at the 2015 Usenix Security Symposium this summer, does not require the attacker to actually decrypt any Tor traffic."}
diff --git a/data/sampled_jsons/LAUREL-LR_formula_equation_xi+1_=_xi_+_f(xi)_+_A__sigma(B__xi_+_C).jsonl b/data/sampled_jsons/LAUREL-LR_formula_equation_xi+1_=_xi_+_f(xi)_+_A__sigma(B__xi_+_C).jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7367b816bda6e7969a6957c02e821b114f6ebf1f
--- /dev/null
+++ b/data/sampled_jsons/LAUREL-LR_formula_equation_xi+1_=_xi_+_f(xi)_+_A__sigma(B__xi_+_C).jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Riemann xi function - Wikipedia", "date": "", "ddg_snippet": "Riemann xi function in the complex plane. The color of a point encodes the value of the function. Darker colors denote values closer to zero and hue encodes the value's argument. In mathematics, the Riemann xi function is a variant of the Riemann zeta function, and is defined so as to have a particularly simple functional equation . The function is named in honour of Bernhard Riemann.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Riemann_Xi_function", "content": "Riemann xi function in the complex plane. The color of a point encodes the value of the function. Darker colors denote values closer to zero and hue encodes the value's argument. In mathematics, the Riemann xi function is a variant of the Riemann zeta function, and is defined so as to have a particularly simple functional equation . The function is named in honour of Bernhard Riemann."}
+{"idx": 1, "title": "LAuReL-Learned-Augmented-Residual-Layer/LAuRel_LR.py at master ...", "date": "", "ddg_snippet": "Contribute to BAW2501/ LAuReL -Learned-Augmented-Residual-Layer development by creating an account on GitHub.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/BAW2501/LAuReL-Learned-Augmented-Residual-Layer/blob/master/LAuRel_LR.py", "content": "Contribute to BAW2501/ LAuReL -Learned-Augmented-Residual-Layer development by creating an account on GitHub."}
+{"idx": 2, "title": "LAUREL: Learned Augmented Residual Layer - arXiv.org", "date": "", "ddg_snippet": "Here, f(·) can be any non-linear function such as attention, MLP, multiple non-linear layers, etc., xi is the input to the said non-linear function, and xi+1 is the combined output of the non-linear function and the residual component.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2411.07501v2", "content": "Here, f(·) can be any non-linear function such as attention, MLP, multiple non-linear layers, etc., xi is the input to the said non-linear function, and xi+1 is the combined output of the non-linear function and the residual component."}
+{"idx": 3, "title": "PDF Chapter 7: Ordinary Differential Equations - Faculty of Engineering", "date": "", "ddg_snippet": "2 Euler's Method Basic idea of iterative methods: given ( xi ; yi), xi+1 = xi + h, yi+1 = yi + Áh, where Á is estimated function slope.", "subpage_snippet": "", "source": "www.ece.mcmaster.ca", "link": "https://www.ece.mcmaster.ca/~xwu/part7.pdf", "content": "2 Euler's Method Basic idea of iterative methods: given ( xi ; yi), xi+1 = xi + h, yi+1 = yi + Áh, where Á is estimated function slope."}
+{"idx": 4, "title": "LAUREL: Learned Augmented Residual Layer - arXiv.org", "date": "", "ddg_snippet": "xi+1 = f(xi) + xi . (1) Here, f(·) can be any non-linear function such as attention, MLP, multiple non-linear layers, etc., xi is the input to the said non-linear function, and xi+1 is the combined output of the non-linear function and the residual component. Refer to Figure 1 for an illustration.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2411.07501", "content": "xi+1 = f(xi) + xi . (1) Here, f(·) can be any non-linear function such as attention, MLP, multiple non-linear layers, etc., xi is the input to the said non-linear function, and xi+1 is the combined output of the non-linear function and the residual component. Refer to Figure 1 for an illustration."}
+{"idx": 5, "title": "PDF Practice problems -1 - Memorial University of Newfoundland", "date": "", "ddg_snippet": "Three-point forward difference: formula calculates the derivative at point xi from the value at that point and the next two points xi+1 and xi+2, and keep h= xi+2 - xi+1 = xi+1 - xi .", "subpage_snippet": "", "source": "www.engr.mun.ca", "link": "https://www.engr.mun.ca/~weimin/Courses/ENGR4425/4425_Ch6Integration.pdf", "content": "Three-point forward difference: formula calculates the derivative at point xi from the value at that point and the next two points xi+1 and xi+2, and keep h= xi+2 - xi+1 = xi+1 - xi ."}
+{"idx": 6, "title": "4.2: Riemann Sums - Mathematics LibreTexts", "date": "", "ddg_snippet": "A Riemann sum is simply a sum of products of the form \\\\(f (x^∗_i )\\\\Delta x\\\\) that estimates the area between a positive function and the horizontal axis over a given interval. If the function …", "subpage_snippet": "", "source": "math.libretexts.org", "link": "https://math.libretexts.org/Under_Construction/Purgatory/Book:_Active_Calculus_(Boelkins_et_al.)/04:_The_Definite_Integral/4.02:_Riemann_Sums", "content": "A Riemann sum is simply a sum of products of the form \\\\(f (x^∗_i )\\\\Delta x\\\\) that estimates the area between a positive function and the horizontal axis over a given interval. If the function …"}
+{"idx": 7, "title": "PDF Lecture 1 - MIT OpenCourseWare", "date": "", "ddg_snippet": "1.1 Representation of functions In Fig. 1.1, a continuous and differentiable function, f , of the independent variable x , is approximated by the values fi = f ( xi ) at the points, xi . The set of points, xi , make up the numerical mesh or \"grid\".", "subpage_snippet": "", "source": "ocw.mit.edu", "link": "https://ocw.mit.edu/courses/12-950-atmospheric-and-oceanic-modeling-spring-2004/507424ba4dc53db72128cae0d5231e80_lec1.pdf", "content": "1.1 Representation of functions In Fig. 1.1, a continuous and differentiable function, f , of the independent variable x , is approximated by the values fi = f ( xi ) at the points, xi . The set of points, xi , make up the numerical mesh or \"grid\"."}
+{"idx": 8, "title": "PDF Section 9.4: Approximation of Definite Integrals Review of Riemann Su", "date": "", "ddg_snippet": "Uniform divisions en decompose into N pieces of equal length, for N So, xi = a + i∆ and xi ≤ ai ≤ xi+1 and for such a uniform decomposition, the Riemann sum is N−1 N−1 X (f(ai)(xi+1 − xi ))", "subpage_snippet": "", "source": "math.berkeley.edu", "link": "https://math.berkeley.edu/~scanlon/m16bs04/ln/16b2lec15.pdf", "content": "Uniform divisions en decompose into N pieces of equal length, for N So, xi = a + i∆ and xi ≤ ai ≤ xi+1 and for such a uniform decomposition, the Riemann sum is N−1 N−1 X (f(ai)(xi+1 − xi ))"}
+{"idx": 9, "title": "PDF msc321 - Gordon College", "date": "", "ddg_snippet": "Figure 15.1 shows the discrete set of points xi where the function is known. We will use the notation ui = u(xi) to denote the value of the function at the i-th node of the computational grid. The nodes divide the axis into a set of intervals of width xi = xi+1 xi . When the grid spacing is fixed, i.e. all intervals are of equal size, we will refer to the grid spacing as x. There are definite ...", "subpage_snippet": "", "source": "www.math-cs.gordon.edu", "link": "https://www.math-cs.gordon.edu/courses/cps343/doc/lectfiniteDifference.pdf", "content": "Figure 15.1 shows the discrete set of points xi where the function is known. We will use the notation ui = u(xi) to denote the value of the function at the i-th node of the computational grid. The nodes divide the axis into a set of intervals of width xi = xi+1 xi . When the grid spacing is fixed, i.e. all intervals are of equal size, we will refer to the grid spacing as x. There are definite ..."}
diff --git a/data/sampled_jsons/LAuReL_Learned_Augmented_Residual_Layer_Figure_3_accuracy_rank.jsonl b/data/sampled_jsons/LAuReL_Learned_Augmented_Residual_Layer_Figure_3_accuracy_rank.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4323c1b0677fffe4e2c3b0dd18a04c0ed299eba4
--- /dev/null
+++ b/data/sampled_jsons/LAuReL_Learned_Augmented_Residual_Layer_Figure_3_accuracy_rank.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "DeepCrossAttention: Supercharging Transformer Residual", "date": "", "ddg_snippet": "We show that DCA achieves a better trade-off between accuracy and model size when the ratio of the collective ranks of the layers to the ambient ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.06785v1", "content": "We show that DCA achieves a better trade-off between accuracy and model size when the ratio of the collective ranks of the layers to the ambient ..."}
+{"idx": 1, "title": "Sanjiv Kumar", "date": "", "ddg_snippet": "Abstract: Bipartite ranking is a fundamental supervised learning problem, with the goal of learning a ranking over instances with maximal area under ...", "subpage_snippet": "", "source": "www.catalyzex.com", "link": "https://www.catalyzex.com/author/Sanjiv+Kumar", "content": "Abstract: Bipartite ranking is a fundamental supervised learning problem, with the goal of learning a ranking over instances with maximal area under ..."}
+{"idx": 2, "title": "SHREC 2025: Protein Surface Shape Retrieval including", "date": "", "ddg_snippet": "... 3D protein into 2D map) [ 15 , 17 ] , (iv) shape retrieval methods based on the moments of 3D Zernike descriptors [ 18 ] , and recently, (v) shape ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.12976v1", "content": "... 3D protein into 2D map) [ 15 , 17 ] , (iv) shape retrieval methods based on the moments of 3D Zernike descriptors [ 18 ] , and recently, (v) shape ..."}
+{"idx": 3, "title": "Homepage of Christopher Re (Chris Re)", "date": "", "ddg_snippet": "On the AI side, I am fascinated by how we can learn from increasingly weak forms of supervision, the basis of new architectures, the role of data ...", "subpage_snippet": "", "source": "cs.stanford.edu", "link": "https://cs.stanford.edu/people/chrismre/", "content": "On the AI side, I am fascinated by how we can learn from increasingly weak forms of supervision, the basis of new architectures, the role of data ..."}
+{"idx": 4, "title": "Barry's Blog: August 2014", "date": "", "ddg_snippet": "... widespread relevance to the entire arts field; 2) a topic under which might fit consideration of several of the above challenges (and / or others); 3 ...", "subpage_snippet": "", "source": "blog.westaf.org", "link": "https://blog.westaf.org/2014/08/", "content": "... widespread relevance to the entire arts field; 2) a topic under which might fit consideration of several of the above challenges (and / or others); 3 ..."}
+{"idx": 5, "title": "JP2009536413A - Fuzzy logic based viewer identification for", "date": "", "ddg_snippet": "H04H60/35 — Arrangements for identifying or recognising characteristics with a direct linkage to broadcast information or to broadcast space ...", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/JP2009536413A/en", "content": "H04H60/35 — Arrangements for identifying or recognising characteristics with a direct linkage to broadcast information or to broadcast space ..."}
+{"idx": 6, "title": "(IUCr) Prediction of models for ordered solvent in", "date": "", "ddg_snippet": "Keywords: PeakProbe ; solvent modelling ; electron-density analysis ; supervised learning ; decorrelation ; resolution ; data mining .", "subpage_snippet": "", "source": "journals.iucr.org", "link": "https://journals.iucr.org/d/issues/2019/08/00/tz5099/index.html", "content": "Keywords: PeakProbe ; solvent modelling ; electron-density analysis ; supervised learning ; decorrelation ; resolution ; data mining ."}
+{"idx": 7, "title": "The non-standard in writing: A look at West African and", "date": "", "ddg_snippet": "... 3 ) Imagined (4) Observed and (5) Invented, describes the text type of literary dialect as characteristic of invented speech, being based on hypothetic ...", "subpage_snippet": "", "source": "journals.openedition.org", "link": "https://journals.openedition.org/erea/6312", "content": "... 3 ) Imagined (4) Observed and (5) Invented, describes the text type of literary dialect as characteristic of invented speech, being based on hypothetic ..."}
+{"idx": 8, "title": "EEG-Based Emotion Recognition in Music Listening | Request PDF", "date": "", "ddg_snippet": "Learning Rate, β 1 , β 2 1 10 − 3 , (0.9, 0.999) Batch Size for SEED and DEAP 20 Dropout Rate 2 0.1 Initial Curvature for Riemannian Manifold 3 ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/224135598_EEG-Based_Emotion_Recognition_in_Music_Listening", "content": "Learning Rate, β 1 , β 2 1 10 − 3 , (0.9, 0.999) Batch Size for SEED and DEAP 20 Dropout Rate 2 0.1 Initial Curvature for Riemannian Manifold 3 ..."}
+{"idx": 9, "title": "Habitat suitability assessment for tule elk in the San", "date": "", "ddg_snippet": "While California’s statewide tule elk ( Cervus canadensis nannodes ) population has recovered from two or three individual survivors in the late ...", "subpage_snippet": "", "source": "journal.wildlife.ca.gov", "link": "https://journal.wildlife.ca.gov/2023/12/29/habitat-suitability-assessment-for-tule-elk-in-the-san-francisco-bay-and-monterey-bay-areas/", "content": "While California’s statewide tule elk ( Cervus canadensis nannodes ) population has recovered from two or three individual survivors in the late ..."}
diff --git a/data/sampled_jsons/LEnergy_loss_function_Equation_10_gQlxd3Mtru_Learning_stochastic_dynamics_from_snapshots.jsonl b/data/sampled_jsons/LEnergy_loss_function_Equation_10_gQlxd3Mtru_Learning_stochastic_dynamics_from_snapshots.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6879971534dd040a37a8cb2460963a6996afa804
--- /dev/null
+++ b/data/sampled_jsons/LEnergy_loss_function_Equation_10_gQlxd3Mtru_Learning_stochastic_dynamics_from_snapshots.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Learning stochastic dynamics from snapshots through ...", "date": "", "ddg_snippet": "Jan 22, 2025 · Reconstructing dynamics using samples from sparsely time-resolved snapshots is an important problem in both natural sciences and machine learning . Here, we introduce a new deep learning approach for solving regularized unbalanced optimal transport (RUOT) and inferring continuous unbalanced stochastic dynamics from observed snapshots .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=gQlxd3Mtru", "content": "Jan 22, 2025 · Reconstructing dynamics using samples from sparsely time-resolved snapshots is an important problem in both natural sciences and machine learning . Here, we introduce a new deep learning approach for solving regularized unbalanced optimal transport (RUOT) and inferring continuous unbalanced stochastic dynamics from observed snapshots ."}
+{"idx": 1, "title": "Learning stochastic dynamics from snapshots through ...", "date": "", "ddg_snippet": "Introduction Reconstructing dynamics using samples from sparsely time-resolved snapshots is an important problem in both natural sciences and machine learning . Here, we introduce a new deep learning approach for solving regularized unbalanced optimal transport (RUOT) and inferring continuous unbalanced stochastic dynamics from observed snapshots .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/zhenyiizhang/DeepRUOT", "content": "Introduction Reconstructing dynamics using samples from sparsely time-resolved snapshots is an important problem in both natural sciences and machine learning . Here, we introduce a new deep learning approach for solving regularized unbalanced optimal transport (RUOT) and inferring continuous unbalanced stochastic dynamics from observed snapshots ."}
+{"idx": 2, "title": "Learning stochasticdynamics from snapshots through ...", "date": "", "ddg_snippet": "Learning stochastic dynamics from snapshots through regularized unbalanced optimal transport ZhenyiZhang TiejunLi* Peking University PeijieZhou* *Joint corresponding authors", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/media/iclr-2025/Slides/31800_i2NXVyQ.pdf", "content": "Learning stochastic dynamics from snapshots through regularized unbalanced optimal transport ZhenyiZhang TiejunLi* Peking University PeijieZhou* *Joint corresponding authors"}
+{"idx": 3, "title": "Loss Functions (cont.) and Loss Functions for Energy Based ...", "date": "", "ddg_snippet": "This loss is used for measuring whether two inputs are similar or dissimilar, using the cosine distance, and is typically used for learning nonlinear embeddings or semi-supervised learning .", "subpage_snippet": "", "source": "atcold.github.io", "link": "https://atcold.github.io/NYU-DLSP20/en/week11/11-2/", "content": "This loss is used for measuring whether two inputs are similar or dissimilar, using the cosine distance, and is typically used for learning nonlinear embeddings or semi-supervised learning ."}
+{"idx": 4, "title": "Governing equation discovery of a complex system from snapshots", "date": "", "ddg_snippet": "Oct 23, 2024 · One promising methodology involves using snapshots of system behavior over time to model stochastic dynamics [ 10 ]. By extract-ing continuous underlying dynamics from these snapshots , researchers can capture essential features and statistical properties, facilitating accurate predictions of future behavior and regulation of the system.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.16694", "content": "Oct 23, 2024 · One promising methodology involves using snapshots of system behavior over time to model stochastic dynamics [ 10 ]. By extract-ing continuous underlying dynamics from these snapshots , researchers can capture essential features and statistical properties, facilitating accurate predictions of future behavior and regulation of the system."}
+{"idx": 5, "title": "Energy Loss of Charged Particles Traversing a Medium", "date": "", "ddg_snippet": "The ionisation energy loss is in principle a stochastic process and one should note that the Bethe-Bloch equation describes only the average energy loss . If one shoots single charged particles on a material he will observe that the energy loss follows the Landau distribution where the most probable value is far lower than the average predicted ...", "subpage_snippet": "", "source": "alpha.physics.uoi.gr", "link": "https://alpha.physics.uoi.gr/foudas_public/APP/Lecture4-EnergyLoss.pdf", "content": "The ionisation energy loss is in principle a stochastic process and one should note that the Bethe-Bloch equation describes only the average energy loss . If one shoots single charged particles on a material he will observe that the energy loss follows the Landau distribution where the most probable value is far lower than the average predicted ..."}
+{"idx": 6, "title": "Action Matching: Learning Stochastic Dynamics from Samples", "date": "", "ddg_snippet": "Inspired by connections with optimal transport, we derive extensions of Action Matching to learn stochastic differential equations and dynamics involving cre-ation and destruction of probability mass.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v202/neklyudov23a/neklyudov23a.pdf", "content": "Inspired by connections with optimal transport, we derive extensions of Action Matching to learn stochastic differential equations and dynamics involving cre-ation and destruction of probability mass."}
+{"idx": 7, "title": "Noise Guided Structural Learning from Observing Stochastic ...", "date": "", "ddg_snippet": "is conducted via deep learning , using the loss function defined in equation 4. Figure 9 shows the estimation result. The background of the figure is the histogram of. Learning stochastic dynamics from data. In ICLR 2024 Workshop on AI4DifferentialEquations In Science, 2024.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.00002v1/", "content": "is conducted via deep learning , using the loss function defined in equation 4. Figure 9 shows the estimation result. The background of the figure is the histogram of. Learning stochastic dynamics from data. In ICLR 2024 Workshop on AI4DifferentialEquations In Science, 2024."}
+{"idx": 8, "title": "(PDF) Noise Guided Structural Learning from Observing Stochastic ...", "date": "", "ddg_snippet": "We develop an innovative learning framework that incorporate the noise structure to infer the governing equations from observation of trajectory data generated by stochastic dynamics . Our approach can proficiently captures both the noise and the drift terms.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/385510296_Noise_Guided_Structural_Learning_from_Observing_Stochastic_Dynamics", "content": "We develop an innovative learning framework that incorporate the noise structure to infer the governing equations from observation of trajectory data generated by stochastic dynamics . Our approach can proficiently captures both the noise and the drift terms."}
+{"idx": 9, "title": "PyTorch Loss Functions : The Ultimate Guide", "date": "", "ddg_snippet": "Learn about PyTorch loss functions : from built-in to custom, covering their implementation and monitoring techniques.", "subpage_snippet": "", "source": "neptune.ai", "link": "https://neptune.ai/blog/pytorch-loss-functions", "content": "Learn about PyTorch loss functions : from built-in to custom, covering their implementation and monitoring techniques."}
diff --git a/data/sampled_jsons/LHRS-Bench_dataset_size_scale_number_of_annotations.jsonl b/data/sampled_jsons/LHRS-Bench_dataset_size_scale_number_of_annotations.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c761e80a4bccc890389447ad0b1f9cc401672142
--- /dev/null
+++ b/data/sampled_jsons/LHRS-Bench_dataset_size_scale_number_of_annotations.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF LHRS-Bot: Empowering Remote Sensing with VGI-Enhanced Large Multimodal ...", "date": "", "ddg_snippet": "WeproposeLHRS-Bot,anMLLMfortheRSdomain.Tounleashthepotential of LLMs for RS image understanding, we curate a large- scale dataset , LHRS - Align,forRS-specificalignment,andLHRS-Instruct,amultimodalinstruction- followingdatasettoenhanceLHRS-Bot'sinstruction-followingcapabilities.Ad- ditionally,weintroduceLHRS- Bench ...", "subpage_snippet": "", "source": "www.ecva.net", "link": "https://www.ecva.net/papers/eccv_2024/papers_ECCV/papers/09511.pdf", "content": "WeproposeLHRS-Bot,anMLLMfortheRSdomain.Tounleashthepotential of LLMs for RS image understanding, we curate a large- scale dataset , LHRS - Align,forRS-specificalignment,andLHRS-Instruct,amultimodalinstruction- followingdatasettoenhanceLHRS-Bot'sinstruction-followingcapabilities.Ad- ditionally,weintroduceLHRS- Bench ..."}
+{"idx": 1, "title": "LHRS-Bot-Nova: Improved Multimodal Large Language Model for Remote ...", "date": "", "ddg_snippet": "LHRS -Bot-Nova features an enhanced vision encoder and a novel bridge layer, enabling efficient visual compression and better language-vision alignment. To further enhance RS-oriented vision-language alignment, we propose a large- scale RS image-caption dataset , generated through feature-guided image recaptioning.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.09301v1", "content": "LHRS -Bot-Nova features an enhanced vision encoder and a novel bridge layer, enabling efficient visual compression and better language-vision alignment. To further enhance RS-oriented vision-language alignment, we propose a large- scale RS image-caption dataset , generated through feature-guided image recaptioning."}
+{"idx": 2, "title": "LHRS-Bot/README.md at main · NJU-LHRS/LHRS-Bot · GitHub", "date": "", "ddg_snippet": "LHRS -Bot demonstrates a deep understanding of RS imagery and possesses the capability for sophisticated reasoning within the RS domain. In this repository, we will release our code, training framework, model weights, and dataset !", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/NJU-LHRS/LHRS-Bot/blob/main/README.md", "content": "LHRS -Bot demonstrates a deep understanding of RS imagery and possesses the capability for sophisticated reasoning within the RS domain. In this repository, we will release our code, training framework, model weights, and dataset !"}
+{"idx": 3, "title": "GitHub - NJU-LHRS/LHRS-Bot: VGI-Enhanced multimodal large language ...", "date": "", "ddg_snippet": "LHRS -Bot demonstrates a deep understanding of RS imagery and possesses the capability for sophisticated reasoning within the RS domain. In this repository, we will release our code, training framework, model weights, and dataset !", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/NJU-LHRS/LHRS-Bot", "content": "LHRS -Bot demonstrates a deep understanding of RS imagery and possesses the capability for sophisticated reasoning within the RS domain. In this repository, we will release our code, training framework, model weights, and dataset !"}
+{"idx": 4, "title": "LHRS-Bot: Empowering Remote Sensing with VGI-Enhanced Large ... - Springer", "date": "", "ddg_snippet": "To bridge this gap, we construct a large- scale RS image-text dataset , LHRS ( LHRS stands for 'Language Helps Remote Sensing'.)-Align, and an informative RS-specific instruction dataset , LHRS -Instruct, leveraging the extensive volunteered geographic information (VGI) and globally available RS images.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-3-031-72904-1_26", "content": "To bridge this gap, we construct a large- scale RS image-text dataset , LHRS ( LHRS stands for 'Language Helps Remote Sensing'.)-Align, and an informative RS-specific instruction dataset , LHRS -Instruct, leveraging the extensive volunteered geographic information (VGI) and globally available RS images."}
+{"idx": 5, "title": "LHRS-Bot/main_bench_gen.py at main · NJU-LHRS/LHRS-Bot - GitHub", "date": "", "ddg_snippet": "VGI-Enhanced multimodal large language model for remote sensing images. - LHRS -Bot/main_bench_gen.py at main · NJU- LHRS / LHRS -Bot", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/NJU-LHRS/LHRS-Bot/blob/main/main_bench_gen.py", "content": "VGI-Enhanced multimodal large language model for remote sensing images. - LHRS -Bot/main_bench_gen.py at main · NJU- LHRS / LHRS -Bot"}
+{"idx": 6, "title": "PDF LHRS-Bot: Empowering Remote Sensing with VGI-Enhanced Large Multimodal ...", "date": "", "ddg_snippet": "F Details about LHRS - Bench Dataset F.1 EvaluationDimensions The hierarchical ability taxonomies are comprised of 5 top-level dimesntions with 11 fine-grained sub-dimensions. Note that each question-answer pair may encompass multiple sub-dimensions [40]. Detailed introduction of each ability dimensionsisoutlinedbelow.", "subpage_snippet": "", "source": "www.ecva.net", "link": "https://www.ecva.net/papers/eccv_2024/papers_ECCV/papers/09511-supp.pdf", "content": "F Details about LHRS - Bench Dataset F.1 EvaluationDimensions The hierarchical ability taxonomies are comprised of 5 top-level dimesntions with 11 fine-grained sub-dimensions. Note that each question-answer pair may encompass multiple sub-dimensions [40]. Detailed introduction of each ability dimensionsisoutlinedbelow."}
+{"idx": 7, "title": "LHRS-Bot: Empowering Remote Sensing with VGI-Enhanced Large Multimodal ...", "date": "", "ddg_snippet": "To bridge this gap, we construct a large- scale RS image-text dataset , LHRS 1 -Align, and an informative RS-specific instruction dataset , LHRS -Instruct, leveraging the extensive volunteered geographic information (VGI) and globally available RS images.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2402.02544v3", "content": "To bridge this gap, we construct a large- scale RS image-text dataset , LHRS 1 -Align, and an informative RS-specific instruction dataset , LHRS -Instruct, leveraging the extensive volunteered geographic information (VGI) and globally available RS images."}
+{"idx": 8, "title": "PDF LHRS-Bot: Empowering Remote Sensing with VGI-Enhanced Large ... - Springer", "date": "", "ddg_snippet": "To bridge the gap, we present LHRS -Bot, a specialized MLLM for RS, enhanced by globally available volunteered geographical information (VGI) and worldwide RS images. Specifically, we construct a large- scale , semantically rich, and feature-diverse dataset , LHRS -Align, by geographically pairing RS images with plentiful attributed information from the OpenStreetMap VGI database1, and consequently ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/978-3-031-72904-1_26.pdf", "content": "To bridge the gap, we present LHRS -Bot, a specialized MLLM for RS, enhanced by globally available volunteered geographical information (VGI) and worldwide RS images. Specifically, we construct a large- scale , semantically rich, and feature-diverse dataset , LHRS -Align, by geographically pairing RS images with plentiful attributed information from the OpenStreetMap VGI database1, and consequently ..."}
+{"idx": 9, "title": "Few-Shot Vision-Language Reasoning for Satellite Imagery via Verifiable ...", "date": "", "ddg_snippet": "As a result, the LHRS - Bench score serves as a measure of the model's high level generalization ability. We observe that all models trained with 8-shot or more examples outper-form VHM-RL on the LHRS - Bench metric.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2507.21745", "content": "As a result, the LHRS - Bench score serves as a measure of the model's high level generalization ability. We observe that all models trained with 8-shot or more examples outper-form VHM-RL on the LHRS - Bench metric."}
diff --git a/data/sampled_jsons/LPGNN_Locally_Private_Graph_Neural_Networks_Sajadmanesh_Gatica-Perez_abstract.jsonl b/data/sampled_jsons/LPGNN_Locally_Private_Graph_Neural_Networks_Sajadmanesh_Gatica-Perez_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..50c69f76b1865079ecacfb5d9e9cfdbdc27b5c70
--- /dev/null
+++ b/data/sampled_jsons/LPGNN_Locally_Private_Graph_Neural_Networks_Sajadmanesh_Gatica-Perez_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2006.05535] Locally Private Graph Neural Networks", "date": "", "ddg_snippet": "Abstract : Graph Neural Networks (GNNs) have demonstrated superior performance in learning node representations for various graph inference tasks.View a PDF of the paper titled Locally Private Graph Neural Networks , by Sina Sajadmanesh and Daniel Gatica - Perez .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2006.05535", "content": "Abstract : Graph Neural Networks (GNNs) have demonstrated superior performance in learning node representations for various graph inference tasks.View a PDF of the paper titled Locally Private Graph Neural Networks , by Sina Sajadmanesh and Daniel Gatica - Perez ."}
+{"idx": 1, "title": "(PDF) Locally Private Graph Neural Networks", "date": "", "ddg_snippet": "Graph Neural Network ( LPGNN ), a novel privacy -preserving GNN.Discover more about: Graphs . Preprint. Locally Private Graph Neural Networks . June 2020. Sina Sajadmanesh Sina Sajadmanesh .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/348678319_Locally_Private_Graph_Neural_Networks", "content": "Graph Neural Network ( LPGNN ), a novel privacy -preserving GNN.Discover more about: Graphs . Preprint. Locally Private Graph Neural Networks . June 2020. Sina Sajadmanesh Sina Sajadmanesh ."}
+{"idx": 2, "title": "Locally Private Graph Neural Networks", "date": "", "ddg_snippet": "Differential Privacy ; Private Learning; Graph Neural Networks ; Node Classification. ACM Reference Format: Sina Sajadmanesh and Daniel Gatica - Perez .Daniel Gatica - Perez . gatica@idiap.ch Idiap Research Institute.", "subpage_snippet": "", "source": "publications.idiap.ch", "link": "https://publications.idiap.ch/downloads/papers/2021/Sajadmanesh_CCS2021_2021.pdf", "content": "Differential Privacy ; Private Learning; Graph Neural Networks ; Node Classification. ACM Reference Format: Sina Sajadmanesh and Daniel Gatica - Perez .Daniel Gatica - Perez . gatica@idiap.ch Idiap Research Institute."}
+{"idx": 3, "title": "GitHub - sisaman/ LPGNN : Locally Private Graph Neural Networks ...", "date": "", "ddg_snippet": "Abstract . Graph Neural Networks (GNNs) have demonstrated superior performance in learning node representations for various graph inference tasks. However, learning over graph data can raise privacy concerns when nodes represent people or...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/sisaman/LPGNN", "content": "Abstract . Graph Neural Networks (GNNs) have demonstrated superior performance in learning node representations for various graph inference tasks. However, learning over graph data can raise privacy concerns when nodes represent people or..."}
+{"idx": 4, "title": "(Open Access) Locally Private Graph Neural Networks (2020)", "date": "", "ddg_snippet": "Graph Neural Networks (GNNs) have demonstrated superior performance in learning node representations for various graph inference tasks.", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/papers/locally-private-graph-neural-networks-1lusvir819", "content": "Graph Neural Networks (GNNs) have demonstrated superior performance in learning node representations for various graph inference tasks."}
+{"idx": 5, "title": "Locally Private Graph Neural Networks | Proceedings of the 2021...", "date": "", "ddg_snippet": "Presentation video for the paper \" Locally Private Graph Neural Networks \". In this work, we propose a privacy -preserving GNN framework based on local differential privacy , when the graph topology is public but the node features/labels are private .", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.1145/3460120.3484565?cookieSet=1", "content": "Presentation video for the paper \" Locally Private Graph Neural Networks \". In this work, we propose a privacy -preserving GNN framework based on local differential privacy , when the graph topology is public but the node features/labels are private ."}
+{"idx": 6, "title": "Sina Sajadmanesh - Google Scholar", "date": "", "ddg_snippet": "2020. Locally Private Graph Neural Networks . S Sajadmanesh , D Gatica - Perez . ACM CCS, 2021.", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=Gtw3NoAAAAAJ&hl=en", "content": "2020. Locally Private Graph Neural Networks . S Sajadmanesh , D Gatica - Perez . ACM CCS, 2021."}
+{"idx": 7, "title": "Locally Private Graph Neural Networks - Paper Detail", "date": "", "ddg_snippet": "Sina Sajadmanesh , Daniel Gatica - Perez . Abstract . Graph Neural Networks (GNNs) have demonstrated superior performance inlearning graph representations for several subsequent downstream inferencetasks.", "subpage_snippet": "", "source": "deeplearn.org", "link": "https://deeplearn.org/arxiv/179057/locally-private-graph-neural-networks", "content": "Sina Sajadmanesh , Daniel Gatica - Perez . Abstract . Graph Neural Networks (GNNs) have demonstrated superior performance inlearning graph representations for several subsequent downstream inferencetasks."}
+{"idx": 8, "title": "LPGNet: Link Private Graph Networks for Node... | Connected Papers", "date": "", "ddg_snippet": "Locally Private Graph Neural Networks . Sina Sajadmanesh , D. Gática - Pérez . 2020. Privacy -Enhanced Graph Neural Network for Decentralized Local Graphs . Xin-jun Pei, Xiaoheng Deng, Shengwei Tian, Jianqing Liu, Kaiping Xue.", "subpage_snippet": "", "source": "www.connectedpapers.com", "link": "https://www.connectedpapers.com/main/41284c49f521d60eec3b4db6e32878cbdd7ea632/LPGNet:-Link-Private-Graph-Networks-for-Node-Classification/graph", "content": "Locally Private Graph Neural Networks . Sina Sajadmanesh , D. Gática - Pérez . 2020. Privacy -Enhanced Graph Neural Network for Decentralized Local Graphs . Xin-jun Pei, Xiaoheng Deng, Shengwei Tian, Jianqing Liu, Kaiping Xue."}
+{"idx": 9, "title": "GAP: Differentially Private Graph Neural Networks with... | USENIX", "date": "", "ddg_snippet": "In this paper, we study the problem of learning Graph Neural Networks (GNNs) with Differential Privacy (DP). We propose a novel differentially private GNN based on Aggregation Perturbation (GAP), which adds stochastic noise to the GNN's aggregation function to statistically...", "subpage_snippet": "", "source": "www.usenix.org", "link": "https://www.usenix.org/conference/usenixsecurity23/presentation/sajadmanesh", "content": "In this paper, we study the problem of learning Graph Neural Networks (GNNs) with Differential Privacy (DP). We propose a novel differentially private GNN based on Aggregation Perturbation (GAP), which adds stochastic noise to the GNN's aggregation function to statistically..."}
diff --git a/data/sampled_jsons/LPGNN_Sajadmanesh_Gatica-Perez_2021_locally_private_graph_neural_networks_year_2021.jsonl b/data/sampled_jsons/LPGNN_Sajadmanesh_Gatica-Perez_2021_locally_private_graph_neural_networks_year_2021.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6739cb46218e82cebb674d91b7b683f092692571
--- /dev/null
+++ b/data/sampled_jsons/LPGNN_Sajadmanesh_Gatica-Perez_2021_locally_private_graph_neural_networks_year_2021.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2006.05535] Locally Private Graph Neural Networks - arXiv.org Locally Private Graph Neural Networks | Proceedings of the ... Locally Private Graph Neural Networks (ACM CCS 2021) 2cm Locally Private Graph Neural Networks - Sina Sajadmanesh (PDF) Locally Private Graph Neural Networks - ResearchGate Locally Private Graph Neural Networks - infoscience.epfl.ch LPGNN: Graph Neural Networks with Local Differential Privacy", "date": "", "ddg_snippet": "Jun 9, 2020 · View a PDF of the paper titled Locally Private Graph Neural Networks , by Sina Sajadmanesh and Daniel Gatica-Perez Nov 13, 2021 · Presentation video for the paper \" Locally Private Graph Neural Networks \". In this work, we propose a privacy-preserving GNN framework based on local differential privacy, when the graph topology is public but the node features/labels are private . Our contributions include building a new privacy mechanism, called the multi-bit mechanism, for high-dimensional feature perturbation. We also ... This repository is the official implementation of the paper: Locally Private Graph Neural Networks (ACM CCS '21) Proceedings version: https://dl.acm.org/doi/abs/10.1145/3460120.3484565 Video presentation: https://www.youtube.com/watch?v=1LdC5G_p-0g See full list on github.com This code is implemented in Python 3.9, and relies on the following packages: •PyTorch >= 1.8.1 •PyTorch Geometric >= 1.7.0 •Pandas >= 1.2.4 •Numpy >= 1.20.2 •Seaborn >= 0.11.1 See full list on github.com Replicating the paper's results In order to replicate our experiments and reproduce the paper's results, you must do the following steps:1.Run python experiments.py -n LPGNN create -- LPGNN --baselines2.Run python experiments.py -n LPGNN exec --allAll the datasets will be downloaded automatically into datasets folder, and the results will be stored in results directory.3.Go through results.ipynb notebook to visualize the results. Training individual models If you want to individually train and evaluate the models on any of the datasets mentioned in the paper, run the following command:The test result for each run will be saved as a csv file in the directory specified by-o option (default: ./output). See full list on github.com If you find this code useful, please cite the following paper: See full list on github.com LOCALLY PRIVATE GRAPH NEURAL NETWORKS Sina Sajadmanesh Daniel Gatica - Perez Jan 22, 2021 · Abstract and Figures Graph Neural Networks (GNNs) have demonstrated superior performance in learning node representations for various graph inference tasks. Graph Neural Networks (GNNs) have demonstrated superior performance in learning node representations for various graph inference tasks. However, learning over graph data can raise privacy concerns when nodes represent people or human-related variables that involve sensitive or personal information. In this paper, we study the problem of node data privacy, where graph nodes (e.g., social ... Publications Sajadmanes, S. and Gatica-Perez , D. ( 2021 ) Locally Private Graph Neural Networks . ACM Conference on Computer and Communications Security (CCS)", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2006.05535", "content": "Jun 9, 2020 · View a PDF of the paper titled Locally Private Graph Neural Networks , by Sina Sajadmanesh and Daniel Gatica-Perez Nov 13, 2021 · Presentation video for the paper \" Locally Private Graph Neural Networks \". In this work, we propose a privacy-preserving GNN framework based on local differential privacy, when the graph topology is public but the node features/labels are private . Our contributions include building a new privacy mechanism, called the multi-bit mechanism, for high-dimensional feature perturbation. We also ... This repository is the official implementation of the paper: Locally Private Graph Neural Networks (ACM CCS '21) Proceedings version: https://dl.acm.org/doi/abs/10.1145/3460120.3484565 Video presentation: https://www.youtube.com/watch?v=1LdC5G_p-0g See full list on github.com This code is implemented in Python 3.9, and relies on the following packages: •PyTorch >= 1.8.1 •PyTorch Geometric >= 1.7.0 •Pandas >= 1.2.4 •Numpy >= 1.20.2 •Seaborn >= 0.11.1 See full list on github.com Replicating the paper's results In order to replicate our experiments and reproduce the paper's results, you must do the following steps:1.Run python experiments.py -n LPGNN create -- LPGNN --baselines2.Run python experiments.py -n LPGNN exec --allAll the datasets will be downloaded automatically into datasets folder, and the results will be stored in results directory.3.Go through results.ipynb notebook to visualize the results. Training individual models If you want to individually train and evaluate the models on any of the datasets mentioned in the paper, run the following command:The test result for each run will be saved as a csv file in the directory specified by-o option (default: ./output). See full list on github.com If you find this code useful, please cite the following paper: See full list on github.com LOCALLY PRIVATE GRAPH NEURAL NETWORKS Sina Sajadmanesh Daniel Gatica - Perez Jan 22, 2021 · Abstract and Figures Graph Neural Networks (GNNs) have demonstrated superior performance in learning node representations for various graph inference tasks. Graph Neural Networks (GNNs) have demonstrated superior performance in learning node representations for various graph inference tasks. However, learning over graph data can raise privacy concerns when nodes represent people or human-related variables that involve sensitive or personal information. In this paper, we study the problem of node data privacy, where graph nodes (e.g., social ... Publications Sajadmanes, S. and Gatica-Perez , D. ( 2021 ) Locally Private Graph Neural Networks . ACM Conference on Computer and Communications Security (CCS)"}
+{"idx": 1, "title": "(PDF) Locally Private Graph Neural Networks - ResearchGate", "date": "", "ddg_snippet": "Jan 22, 2021 · Abstract and Figures Graph Neural Networks (GNNs) have demonstrated superior performance in learning node representations for various graph inference tasks.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/348678319_Locally_Private_Graph_Neural_Networks", "content": "Jan 22, 2021 · Abstract and Figures Graph Neural Networks (GNNs) have demonstrated superior performance in learning node representations for various graph inference tasks."}
+{"idx": 2, "title": "Locally Private Graph Neural Networks | Proceedings of the ...", "date": "", "ddg_snippet": "Nov 13, 2021 · Presentation video for the paper \" Locally Private Graph Neural Networks \". In this work, we propose a privacy-preserving GNN framework based on local differential privacy, when the graph topology is public but the node features/labels are private . Our contributions include building a new privacy mechanism, called the multi-bit mechanism, for high-dimensional feature perturbation. We also ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3460120.3484565", "content": "Nov 13, 2021 · Presentation video for the paper \" Locally Private Graph Neural Networks \". In this work, we propose a privacy-preserving GNN framework based on local differential privacy, when the graph topology is public but the node features/labels are private . Our contributions include building a new privacy mechanism, called the multi-bit mechanism, for high-dimensional feature perturbation. We also ..."}
+{"idx": 3, "title": "Locally Private Graph Neural Networks (ACM CCS 2021) 2cm Locally Private Graph Neural Networks - Sina Sajadmanesh (PDF) Locally Private Graph Neural Networks - ResearchGate Locally Private Graph Neural Networks - infoscience.epfl.ch LPGNN: Graph Neural Networks with Local Differential Privacy", "date": "", "ddg_snippet": "This repository is the official implementation of the paper: Locally Private Graph Neural Networks (ACM CCS '21) Proceedings version: https://dl.acm.org/doi/abs/10.1145/3460120.3484565 Video presentation: https://www.youtube.com/watch?v=1LdC5G_p-0g See full list on github.com This code is implemented in Python 3.9, and relies on the following packages: •PyTorch >= 1.8.1 •PyTorch Geometric >= 1.7.0 •Pandas >= 1.2.4 •Numpy >= 1.20.2 •Seaborn >= 0.11.1 See full list on github.com Replicating the paper's results In order to replicate our experiments and reproduce the paper's results, you must do the following steps:1.Run python experiments.py -n LPGNN create -- LPGNN --baselines2.Run python experiments.py -n LPGNN exec --allAll the datasets will be downloaded automatically into datasets folder, and the results will be stored in results directory.3.Go through results.ipynb notebook to visualize the results. Training individual models If you want to individually train and evaluate the models on any of the datasets mentioned in the paper, run the following command:The test result for each run will be saved as a csv file in the directory specified by-o option (default: ./output). See full list on github.com If you find this code useful, please cite the following paper: See full list on github.com LOCALLY PRIVATE GRAPH NEURAL NETWORKS Sina Sajadmanesh Daniel Gatica - Perez Jan 22, 2021 · Abstract and Figures Graph Neural Networks (GNNs) have demonstrated superior performance in learning node representations for various graph inference tasks. Graph Neural Networks (GNNs) have demonstrated superior performance in learning node representations for various graph inference tasks. However, learning over graph data can raise privacy concerns when nodes represent people or human-related variables that involve sensitive or personal information. In this paper, we study the problem of node data privacy, where graph nodes (e.g., social ... Publications Sajadmanes, S. and Gatica-Perez , D. ( 2021 ) Locally Private Graph Neural Networks . ACM Conference on Computer and Communications Security (CCS)", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/sisaman/lpgnn", "content": "This repository is the official implementation of the paper: Locally Private Graph Neural Networks (ACM CCS '21) Proceedings version: https://dl.acm.org/doi/abs/10.1145/3460120.3484565 Video presentation: https://www.youtube.com/watch?v=1LdC5G_p-0g See full list on github.com This code is implemented in Python 3.9, and relies on the following packages: •PyTorch >= 1.8.1 •PyTorch Geometric >= 1.7.0 •Pandas >= 1.2.4 •Numpy >= 1.20.2 •Seaborn >= 0.11.1 See full list on github.com Replicating the paper's results In order to replicate our experiments and reproduce the paper's results, you must do the following steps:1.Run python experiments.py -n LPGNN create -- LPGNN --baselines2.Run python experiments.py -n LPGNN exec --allAll the datasets will be downloaded automatically into datasets folder, and the results will be stored in results directory.3.Go through results.ipynb notebook to visualize the results. Training individual models If you want to individually train and evaluate the models on any of the datasets mentioned in the paper, run the following command:The test result for each run will be saved as a csv file in the directory specified by-o option (default: ./output). See full list on github.com If you find this code useful, please cite the following paper: See full list on github.com LOCALLY PRIVATE GRAPH NEURAL NETWORKS Sina Sajadmanesh Daniel Gatica - Perez Jan 22, 2021 · Abstract and Figures Graph Neural Networks (GNNs) have demonstrated superior performance in learning node representations for various graph inference tasks. Graph Neural Networks (GNNs) have demonstrated superior performance in learning node representations for various graph inference tasks. However, learning over graph data can raise privacy concerns when nodes represent people or human-related variables that involve sensitive or personal information. In this paper, we study the problem of node data privacy, where graph nodes (e.g., social ... Publications Sajadmanes, S. and Gatica-Perez , D. ( 2021 ) Locally Private Graph Neural Networks . ACM Conference on Computer and Communications Security (CCS)"}
+{"idx": 4, "title": "2cm Locally Private Graph Neural Networks - Sina Sajadmanesh", "date": "", "ddg_snippet": "LOCALLY PRIVATE GRAPH NEURAL NETWORKS Sina Sajadmanesh Daniel Gatica - Perez", "subpage_snippet": "", "source": "sajadmanesh.com", "link": "https://sajadmanesh.com/files/slides/21.06.02-AI4media.pdf", "content": "LOCALLY PRIVATE GRAPH NEURAL NETWORKS Sina Sajadmanesh Daniel Gatica - Perez"}
+{"idx": 5, "title": "Locally Private Graph Neural Networks - infoscience.epfl.ch", "date": "", "ddg_snippet": "Graph Neural Networks (GNNs) have demonstrated superior performance in learning node representations for various graph inference tasks. However, learning over graph data can raise privacy concerns when nodes represent people or human-related variables that involve sensitive or personal information. In this paper, we study the problem of node data privacy, where graph nodes (e.g., social ...", "subpage_snippet": "", "source": "infoscience.epfl.ch", "link": "https://infoscience.epfl.ch/record/294094", "content": "Graph Neural Networks (GNNs) have demonstrated superior performance in learning node representations for various graph inference tasks. However, learning over graph data can raise privacy concerns when nodes represent people or human-related variables that involve sensitive or personal information. In this paper, we study the problem of node data privacy, where graph nodes (e.g., social ..."}
+{"idx": 6, "title": "LPGNN: Graph Neural Networks with Local Differential Privacy", "date": "", "ddg_snippet": "Publications Sajadmanes, S. and Gatica-Perez , D. ( 2021 ) Locally Private Graph Neural Networks . ACM Conference on Computer and Communications Security (CCS)", "subpage_snippet": "", "source": "technology.idiap.ch", "link": "https://technology.idiap.ch/technologies/machine-learning/lpgnn/", "content": "Publications Sajadmanes, S. and Gatica-Perez , D. ( 2021 ) Locally Private Graph Neural Networks . ACM Conference on Computer and Communications Security (CCS)"}
+{"idx": 7, "title": "Locally Private Graph Neural Networks", "date": "", "ddg_snippet": "Differential Privacy ; Private Learning; Graph Neural Networks ; Node Classification. ACM Reference Format: Sina Sajadmanesh and Daniel Gatica - Perez . Graph Neural Networks . A GNN learns a representation for every node in the graph using a set of stacked graph convolution.", "subpage_snippet": "", "source": "publications.idiap.ch", "link": "https://publications.idiap.ch/downloads/papers/2021/Sajadmanesh_CCS2021_2021.pdf", "content": "Differential Privacy ; Private Learning; Graph Neural Networks ; Node Classification. ACM Reference Format: Sina Sajadmanesh and Daniel Gatica - Perez . Graph Neural Networks . A GNN learns a representation for every node in the graph using a set of stacked graph convolution."}
+{"idx": 8, "title": "Locally Private Graph Neural Networks | Proceedings of the 2021 ...", "date": "", "ddg_snippet": "Presentation video for the paper \" Locally Private Graph Neural Networks \". In this work, we propose a privacy -preserving GNN framework based on local differential privacy , when the graph topology is public but the node features/labels are private .", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.1145/3460120.3484565?cookieSet=1", "content": "Presentation video for the paper \" Locally Private Graph Neural Networks \". In this work, we propose a privacy -preserving GNN framework based on local differential privacy , when the graph topology is public but the node features/labels are private ."}
+{"idx": 9, "title": "GitHub - sisaman/ LPGNN : Locally Private Graph Neural Networks ...", "date": "", "ddg_snippet": "@inproceedings{ sajadmanesh 2021 locally , author = { Sajadmanesh , Sina and Gatica - Perez , Daniel}, title = { Locally Private Graph Neural Networks }, year = { 2021 }, publisher = {Association for Computing Machinery}, doi = {10.1145/3460120.3484565}, booktitle...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/sisaman/LPGNN", "content": "@inproceedings{ sajadmanesh 2021 locally , author = { Sajadmanesh , Sina and Gatica - Perez , Daniel}, title = { Locally Private Graph Neural Networks }, year = { 2021 }, publisher = {Association for Computing Machinery}, doi = {10.1145/3460120.3484565}, booktitle..."}
diff --git a/data/sampled_jsons/LWeVVPuIx0_Last-Iterate_Convergence_Properties_Figure_1_duality_gap_alt._RM+_alt._PRM+_10^5_iteratio_year_2023.jsonl b/data/sampled_jsons/LWeVVPuIx0_Last-Iterate_Convergence_Properties_Figure_1_duality_gap_alt._RM+_alt._PRM+_10^5_iteratio_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e76e32f7aa71d2b465c662698df75d61863624b4
--- /dev/null
+++ b/data/sampled_jsons/LWeVVPuIx0_Last-Iterate_Convergence_Properties_Figure_1_duality_gap_alt._RM+_alt._PRM+_10^5_iteratio_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Last - Iterate Convergence Properties of Regret Matching Algorithms...", "date": "", "ddg_snippet": "We study last - iterate convergence properties of algorithms for solving two-player zero-sum games based on Regret Matching+ ( RM+ ). Figure 1 : Duality gap of the current iterates generated by RM+ , PRM+ , and their alternating variants on the zero-sum game with payoff matrix.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2311.00676v2", "content": "We study last - iterate convergence properties of algorithms for solving two-player zero-sum games based on Regret Matching+ ( RM+ ). Figure 1 : Duality gap of the current iterates generated by RM+ , PRM+ , and their alternating variants on the zero-sum game with payoff matrix."}
+{"idx": 1, "title": "Published as a conference paper at ICLR 2025", "date": "", "ddg_snippet": "RM+ alt . PRM+ . Duality gap . last - iterate properties . We start by showing convergence in duality gap , and strengthen the resu√lt to convergence in iterates in Section 4. 1 .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=LWeVVPuIx0", "content": "RM+ alt . PRM+ . Duality gap . last - iterate properties . We start by showing convergence in duality gap , and strengthen the resu√lt to convergence in iterates in Section 4. 1 ."}
+{"idx": 2, "title": "Красивый телефон с хорошей камерой, но не... - AndroidInsider.ru", "date": "", "ddg_snippet": "Артем Сутягин. 02. 10 .2024.Наши соцсети. alt .", "subpage_snippet": "", "source": "AndroidInsider.ru", "link": "https://AndroidInsider.ru/smartfony/krasivyj-telefon-s-horoshej-kameroj-no-ne-dlya-igr-opyt-ispolzovaniya-infinix-zero-40-5g.html", "content": "Артем Сутягин. 02. 10 .2024.Наши соцсети. alt ."}
+{"idx": 3, "title": "CatBoost / Хабр", "date": "", "ddg_snippet": "Важные параметры включают iterations , learning_rate, depth и другие. Подробнее изучить библиотеку можно здесь.2. Регрессия. from catboost import CatBoostRegressor import numpy as np. # Подготовка данных X = np.random.rand(100, 10 ) y = np.random.rand(100).", "subpage_snippet": "", "source": "habr.com", "link": "https://habr.com/ru/companies/otus/articles/778714/", "content": "Важные параметры включают iterations , learning_rate, depth и другие. Подробнее изучить библиотеку можно здесь.2. Регрессия. from catboost import CatBoostRegressor import numpy as np. # Подготовка данных X = np.random.rand(100, 10 ) y = np.random.rand(100)."}
+{"idx": 4, "title": "3 … Efficient Solvers and Time Integration", "date": "", "ddg_snippet": "Iterative Solver Convergence Properties .Jacobi preconditioner Partitioned ILU preconditioner. 1 process. No Jacobi convergence for ∆ t = 10 − 1 .", "subpage_snippet": "", "source": "persson.berkeley.edu", "link": "https://persson.berkeley.edu/pub/dgschool3.pdf", "content": "Iterative Solver Convergence Properties .Jacobi preconditioner Partitioned ILU preconditioner. 1 process. No Jacobi convergence for ∆ t = 10 − 1 ."}
+{"idx": 5, "title": "Download Proton VPN for free", "date": "", "ddg_snippet": "Limited server access and slower speeds in the free version. Minimum requirements. Windows 10 /11.Available in multiple languages. Downloads. Total: 124040 Last week: 4245.", "subpage_snippet": "", "source": "proton-vpn.sooftware.com", "link": "https://proton-vpn.sooftware.com/windows/download", "content": "Limited server access and slower speeds in the free version. Minimum requirements. Windows 10 /11.Available in multiple languages. Downloads. Total: 124040 Last week: 4245."}
+{"idx": 6, "title": "Кроссовки купить с доставкой, цены на спортивные кроссовки...", "date": "", "ddg_snippet": "Женщинам. alt .Кроссовки мужские Kappa Rivolte Cnvs. 37. 10 349 ₽11 499 ₽.", "subpage_snippet": "", "source": "www.Sportmaster.ru", "link": "https://www.Sportmaster.ru/catalog/krossovki_/", "content": "Женщинам. alt .Кроссовки мужские Kappa Rivolte Cnvs. 37. 10 349 ₽11 499 ₽."}
+{"idx": 7, "title": "Аргументы и Факты — последние новости России и мира сегодня", "date": "", "ddg_snippet": "10 отличий экранизаций романа Богомолова.", "subpage_snippet": "", "source": "aif.ru", "link": "https://aif.ru/", "content": "10 отличий экранизаций романа Богомолова."}
+{"idx": 8, "title": "Учебные курсы мехмата ЮФУ", "date": "", "ddg_snippet": "Математика 0 курс 10 класс.Нет событий, среда 10 сентября 10 .", "subpage_snippet": "", "source": "edu.mmcs.sfedu.ru", "link": "https://edu.mmcs.sfedu.ru/", "content": "Математика 0 курс 10 класс.Нет событий, среда 10 сентября 10 ."}
+{"idx": 9, "title": "Как узнать всё про человека по номеру телефона. 7 способов", "date": "", "ddg_snippet": "10 . Я подключил Яндекс Go для бизнеса, чтобы ездить на такси от компании и не платить из своего кармана. Вот гайд, как экономить на поездках всех сотрудников.", "subpage_snippet": "", "source": "www.iphones.ru", "link": "https://www.iphones.ru/iNotes/kak-uznat-vse-o-cheloveke-po-nomeru-telefona", "content": "10 . Я подключил Яндекс Go для бизнеса, чтобы ездить на такси от компании и не платить из своего кармана. Вот гайд, как экономить на поездках всех сотрудников."}
diff --git a/data/sampled_jsons/LWeVVPuIx0_Last-Iterate_Convergence_Properties_of_Regret-Matching_Algorithms_in_Games.jsonl b/data/sampled_jsons/LWeVVPuIx0_Last-Iterate_Convergence_Properties_of_Regret-Matching_Algorithms_in_Games.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..19ec9d863bd8f2cdd6292028b11ae1d7e23b9e25
--- /dev/null
+++ b/data/sampled_jsons/LWeVVPuIx0_Last-Iterate_Convergence_Properties_of_Regret-Matching_Algorithms_in_Games.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Last-Iterate Convergence Properties of Regret-Matching ...", "date": "", "ddg_snippet": "Jan 22, 2025 · We study last - iterate convergence properties of algorithms for solving two-player zero-sum games based on Regret Matching $^+$ (RM$^+$). Despite their widespread use for solving real games , virtually nothing is known about their last - iterate convergence .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=LWeVVPuIx0", "content": "Jan 22, 2025 · We study last - iterate convergence properties of algorithms for solving two-player zero-sum games based on Regret Matching $^+$ (RM$^+$). Despite their widespread use for solving real games , virtually nothing is known about their last - iterate convergence ."}
+{"idx": 1, "title": "Last-Iterate Convergence Properties of Regret-Matching ... LAST-I CONVERGENCE PROPERTIES OF R M ALGORITHMS IN GAMES Last-iterate Convergence in Extensive-Form Games Gabriele Farina - Last-Iterate Convergence Properties of ...", "date": "", "ddg_snippet": "Published with Wowchemy — the free, open source website builder that empowers creators. vergence guarantees even on a simple 3 × 3 matrix game . We then prove that recent variants of these algorithms based on a smoothing technique, extragradient RM+ and smooth √ Predictive RM+, enjoy asymptotic last - iterate convergence (without a rate), 1/ t best-iterate convergence , and when combin. Regret -based algorithms are highly efficient at finding approximate Nash equilibria in sequential games such as poker games. However, most regret -based algorithms , including counterfactual regret minimization (CFR) and its variants, rely on iterate averaging to achieve convergence . We study last - iterate convergence properties of algorithms for solving two-player zero-sum games based on Regret Matching + (RM+). Despite their widespread use for solving real games , virtually nothing is known about their last - iterate convergence .", "subpage_snippet": "", "source": "www.columbia.edu", "link": "https://www.columbia.edu/~ck2945/publication/cai-2025-last/", "content": "Published with Wowchemy — the free, open source website builder that empowers creators. vergence guarantees even on a simple 3 × 3 matrix game . We then prove that recent variants of these algorithms based on a smoothing technique, extragradient RM+ and smooth √ Predictive RM+, enjoy asymptotic last - iterate convergence (without a rate), 1/ t best-iterate convergence , and when combin. Regret -based algorithms are highly efficient at finding approximate Nash equilibria in sequential games such as poker games. However, most regret -based algorithms , including counterfactual regret minimization (CFR) and its variants, rely on iterate averaging to achieve convergence . We study last - iterate convergence properties of algorithms for solving two-player zero-sum games based on Regret Matching + (RM+). Despite their widespread use for solving real games , virtually nothing is known about their last - iterate convergence ."}
+{"idx": 2, "title": "Gabriele Farina - Last-Iterate Convergence Properties of ...", "date": "", "ddg_snippet": "We study last - iterate convergence properties of algorithms for solving two-player zero-sum games based on Regret Matching + (RM+). Despite their widespread use for solving real games , virtually nothing is known about their last - iterate convergence .", "subpage_snippet": "", "source": "www.mit.edu", "link": "https://www.mit.edu/~gfarina/2025/iclr25_rm_lastiterate/", "content": "We study last - iterate convergence properties of algorithms for solving two-player zero-sum games based on Regret Matching + (RM+). Despite their widespread use for solving real games , virtually nothing is known about their last - iterate convergence ."}
+{"idx": 3, "title": "Last-Iterate Convergence Properties of Regret Matching ...", "date": "", "ddg_snippet": "We study last - iterate convergence properties of algorithms for solving two-player zero-sum games based on Regret Matching +(RM+). Despite their widespread use for solving real games , virtually nothing is known about their last - iterate convergence .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2311.00676v2", "content": "We study last - iterate convergence properties of algorithms for solving two-player zero-sum games based on Regret Matching +(RM+). Despite their widespread use for solving real games , virtually nothing is known about their last - iterate convergence ."}
+{"idx": 4, "title": "last-iterate convergence | Yang Cai", "date": "", "ddg_snippet": "Uncoupled and Convergent Learning in Two-Player Zero-Sum Markov Games with Bandit Feedback We revisit the problem of learning in two-player zero-sum Markov games , focusing on developing an algorithm that is uncoupled, …", "subpage_snippet": "", "source": "cs.yale.edu", "link": "https://cs.yale.edu/homes/cai/tag/last-iterate-convergence/", "content": "Uncoupled and Convergent Learning in Two-Player Zero-Sum Markov Games with Bandit Feedback We revisit the problem of learning in two-player zero-sum Markov games , focusing on developing an algorithm that is uncoupled, …"}
+{"idx": 5, "title": "LAST-I CONVERGENCE PROPERTIES OF R M ALGORITHMS IN GAMES", "date": "", "ddg_snippet": "vergence guarantees even on a simple 3 × 3 matrix game . We then prove that recent variants of these algorithms based on a smoothing technique, extragradient RM+ and smooth √ Predictive RM+, enjoy asymptotic last - iterate convergence (without a rate), 1/ t best-iterate convergence , and when combin.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=LWeVVPuIx0", "content": "vergence guarantees even on a simple 3 × 3 matrix game . We then prove that recent variants of these algorithms based on a smoothing technique, extragradient RM+ and smooth √ Predictive RM+, enjoy asymptotic last - iterate convergence (without a rate), 1/ t best-iterate convergence , and when combin."}
+{"idx": 6, "title": "Last-iterate Convergence in Extensive-Form Games", "date": "", "ddg_snippet": "Regret -based algorithms are highly efficient at finding approximate Nash equilibria in sequential games such as poker games. However, most regret -based algorithms , including counterfactual regret minimization (CFR) and its variants, rely on iterate averaging to achieve convergence .", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper/2021/file/77bb14f6132ea06dea456584b7d5581e-Paper.pdf", "content": "Regret -based algorithms are highly efficient at finding approximate Nash equilibria in sequential games such as poker games. However, most regret -based algorithms , including counterfactual regret minimization (CFR) and its variants, rely on iterate averaging to achieve convergence ."}
+{"idx": 7, "title": "Revision History for Last-Iterate Convergence Properties...", "date": "", "ddg_snippet": "Last-Iterate Convergence Properties of Regret-Matching Algorithms in Games . Authors: Yang Cai 0001, Gabriele Farina, Julien Grand-Clément, Christian Kroer ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/revisions?id=Aaic7wjEU4", "content": "Last-Iterate Convergence Properties of Regret-Matching Algorithms in Games . Authors: Yang Cai 0001, Gabriele Farina, Julien Grand-Clément, Christian Kroer ..."}
+{"idx": 8, "title": "[2311.00676] Last - Iterate Convergence Properties of ...", "date": "", "ddg_snippet": "We study last - iterate convergence properties of algorithms for solving two-player zero-sum games based on Regret Matching $^+$ (RM$^+$). Despite their widespread use for solving real games , virtually nothing is known about their last - iterate convergence .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2311.00676", "content": "We study last - iterate convergence properties of algorithms for solving two-player zero-sum games based on Regret Matching $^+$ (RM$^+$). Despite their widespread use for solving real games , virtually nothing is known about their last - iterate convergence ."}
+{"idx": 9, "title": "Last - Iterate Convergence Properties of Regret - Matching ...", "date": "", "ddg_snippet": "We study last - iterate convergence properties of algorithms for solving two-player zero-sum games based on Regret Matching . + (RM. + ). Despite their widespread use for solving real games , virtually nothing is known about their last - iterate convergence .", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/paper/last-iterate-convergence-properties-of-regret", "content": "We study last - iterate convergence properties of algorithms for solving two-player zero-sum games based on Regret Matching . + (RM. + ). Despite their widespread use for solving real games , virtually nothing is known about their last - iterate convergence ."}
diff --git "a/data/sampled_jsons/L_stage1_OR_L_stage_1_lambda_m_OR_\316\273m_2504.11786.jsonl" "b/data/sampled_jsons/L_stage1_OR_L_stage_1_lambda_m_OR_\316\273m_2504.11786.jsonl"
new file mode 100644
index 0000000000000000000000000000000000000000..1b74fbfcb44833fbbbafc3c4bfefe10b7bd5443d
--- /dev/null
+++ "b/data/sampled_jsons/L_stage1_OR_L_stage_1_lambda_m_OR_\316\273m_2504.11786.jsonl"
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
+{"idx": 1, "title": "SGDFuse: SAM-Guided Diffusion for High-Fidelity Infrared and", "date": "", "ddg_snippet": "... limitations in modeling the high-order semantic relationships and non-linear complementarities between modalities, thereby restricting their efficacy ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.05264v1", "content": "... limitations in modeling the high-order semantic relationships and non-linear complementarities between modalities, thereby restricting their efficacy ..."}
+{"idx": 2, "title": "Sem-RaDiff: Diffusion-Based 3D Radar Semantic Perception in", "date": "", "ddg_snippet": "... simply formulate radar perception enhancement as discriminative learning [ 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 ] , directly learning ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.02283v2", "content": "... simply formulate radar perception enhancement as discriminative learning [ 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 ] , directly learning ..."}
+{"idx": 3, "title": "ORI and MOD LIST | Tuner's Website * Dpf Egr Adblue Dtc", "date": "", "ddg_snippet": "... stage1 alfa__0.9_twinair_2012___62.5kwkw___51935080__664c.original alfa__ 1 .4_t_2009___99.3kwkw___51872437__a043.original ...", "subpage_snippet": "", "source": "tuningbot.com", "link": "https://tuningbot.com/ecus-ori-and-mod-complete-list/", "content": "... stage1 alfa__0.9_twinair_2012___62.5kwkw___51935080__664c.original alfa__ 1 .4_t_2009___99.3kwkw___51872437__a043.original ..."}
+{"idx": 4, "title": "ORI and MOD LIST 3 | Tuner's Website * Dpf Egr Adblue Dtc", "date": "", "ddg_snippet": "Chiptuning Services - LOW Prices and HIGH Quality ... original pkw_toyota_yaris_2007_turbodiesel___55.2kwkw_bosch___391935_4d7d. stage1 ...", "subpage_snippet": "", "source": "tuningbot.com", "link": "https://tuningbot.com/ori-and-mod-list-3/", "content": "Chiptuning Services - LOW Prices and HIGH Quality ... original pkw_toyota_yaris_2007_turbodiesel___55.2kwkw_bosch___391935_4d7d. stage1 ..."}
+{"idx": 5, "title": "E85 : boîtier/reprogrammation, Flexfuel, fiabilité, les", "date": "", "ddg_snippet": "Et encore moins la fiabilité et la justesse d ’ un véhicule pensé à l ’ origine pour fonctionner à l ’ éthanol.", "subpage_snippet": "", "source": "www.downshift.fr", "link": "https://www.downshift.fr/e85-boitier-reprogrammation-flexfuel-fiabilite-les-verites-sur-lethanol/", "content": "Et encore moins la fiabilité et la justesse d ’ un véhicule pensé à l ’ origine pour fonctionner à l ’ éthanol."}
+{"idx": 6, "title": "Automotive Tuning. - MHH AUTO - Page 707", "date": "", "ddg_snippet": "... l .zorin.79 , Lacee90 , Lambda1 , lazafikus , LivMax , madrian , Mahmoud89 , mariadumitruiulian , MattRS , maxdre , mihaiti , mikeak_2001 , miruwor , ...", "subpage_snippet": "", "source": "mhhauto.com", "link": "https://mhhauto.com/Forum-Automotive-Tuning?page=707", "content": "... l .zorin.79 , Lacee90 , Lambda1 , lazafikus , LivMax , madrian , Mahmoud89 , mariadumitruiulian , MattRS , maxdre , mihaiti , mikeak_2001 , miruwor , ..."}
+{"idx": 7, "title": "gcc - g++ \"/ld.exe: cannot find l:mylib.a: No such file or", "date": "", "ddg_snippet": "... LTO_WRAPPER=C:/mingw64/bin/../libexec/gcc/x86_64-w64-mingw32/13.2.0/lto-wrapper.exe OFFLOAD_TARGET_NAMES=nvptx-none Target: x86_64-w64-mingw32 ...", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/77388546/g-ld-exe-cannot-find-lmylib-a-no-such-file-or-directory", "content": "... LTO_WRAPPER=C:/mingw64/bin/../libexec/gcc/x86_64-w64-mingw32/13.2.0/lto-wrapper.exe OFFLOAD_TARGET_NAMES=nvptx-none Target: x86_64-w64-mingw32 ..."}
+{"idx": 8, "title": "Catalogue Sprintcar", "date": "", "ddg_snippet": "Retaillage des cames sur vos arbres d origine ou arbres en acier taillés dans la masse (profils et levées sur mesure) retaille Stage 1 ou stage 2 ...", "subpage_snippet": "", "source": "shop.brancquartcompetition.fr", "link": "https://shop.brancquartcompetition.fr/en/4-sprintcar-buggy-autocross-proto", "content": "Retaillage des cames sur vos arbres d origine ou arbres en acier taillés dans la masse (profils et levées sur mesure) retaille Stage 1 ou stage 2 ..."}
+{"idx": 9, "title": "Mercedes-Benz C-Klasse (CL203, S203, W203) C 320 CDI Hybrid", "date": "", "ddg_snippet": "... one , we will disassemble and examine the turbo, ... With subscribing into our free newslerrs you will receive a 10€ voucher for your next order.", "subpage_snippet": "", "source": "www.turbozentrum.de", "link": "https://www.turbozentrum.de/Mercedes-Benz-C-Klasse-CL203-S203-W203-C-320-CDI-Hybrid-Turbo-Stage-2-777318-0002", "content": "... one , we will disassemble and examine the turbo, ... With subscribing into our free newslerrs you will receive a 10€ voucher for your next order."}
diff --git a/data/sampled_jsons/Large_Language_Models_(LLMs)_employ_auto-regressive_decoding_that_requires_sequential_computation_Me.jsonl b/data/sampled_jsons/Large_Language_Models_(LLMs)_employ_auto-regressive_decoding_that_requires_sequential_computation_Me.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d0170e5774db19a9bf5ab82de52788869ad4e8b0
--- /dev/null
+++ b/data/sampled_jsons/Large_Language_Models_(LLMs)_employ_auto-regressive_decoding_that_requires_sequential_computation_Me.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Medusa: Simple LLM Inference Acceleration Framework with Multiple ...", "date": "", "ddg_snippet": "Abstract Large Language Models (LLMs) employ auto-regressive decoding that requires sequential computation , with each step reliant on the previous one's output. This creates a bottleneck as each step necessitates moving the full model parameters from High-Bandwidth Memory (HBM) to the accelerator's cache. While methods such as speculative decoding have been suggested to address this issue ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2401.10774v3", "content": "Abstract Large Language Models (LLMs) employ auto-regressive decoding that requires sequential computation , with each step reliant on the previous one's output. This creates a bottleneck as each step necessitates moving the full model parameters from High-Bandwidth Memory (HBM) to the accelerator's cache. While methods such as speculative decoding have been suggested to address this issue ..."}
+{"idx": 1, "title": "MEDUSA | Proceedings of the 41st International Conference on Machine ...", "date": "", "ddg_snippet": "Large Language Models (LLMs) employ auto-regressive decoding that requires sequential computation , with each step reliant on the previous one's output. This creates a bottleneck as each step necessitates moving the full model parameters from High-Bandwidth Memory (HBM) to the accelerator's cache.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3692070.3692273", "content": "Large Language Models (LLMs) employ auto-regressive decoding that requires sequential computation , with each step reliant on the previous one's output. This creates a bottleneck as each step necessitates moving the full model parameters from High-Bandwidth Memory (HBM) to the accelerator's cache."}
+{"idx": 2, "title": "Speeding Up Large Language Models with Extra Heads", "date": "", "ddg_snippet": "Original Source Title: Medusa : Simple LLM Inference Acceleration Framework with Multiple Decoding Heads Abstract: Large Language Models (LLMs) employ auto-regressive decoding that requires sequential computation , with each step reliant on the previous one's output.", "subpage_snippet": "", "source": "scisimple.com", "link": "https://scisimple.com/en/articles/2025-09-15-speeding-up-large-language-models-with-extra-heads--ake82v1", "content": "Original Source Title: Medusa : Simple LLM Inference Acceleration Framework with Multiple Decoding Heads Abstract: Large Language Models (LLMs) employ auto-regressive decoding that requires sequential computation , with each step reliant on the previous one's output."}
+{"idx": 3, "title": "Medusa: Multiple Decoding Heads for Faster LLM Inference", "date": "", "ddg_snippet": "Large Language Models (LLMs) employ auto-regressive decoding that requires sequential computation , with each step reliant on the previous one's output. This creates a bottleneck… we present Medusa , an efficient method that augments LLM inference by adding extra decoding heads to predict multiple subsequent tokens in parallel.", "subpage_snippet": "", "source": "mlscrapbook.substack.com", "link": "https://mlscrapbook.substack.com/p/medusa-multiple-decoding-heads-for", "content": "Large Language Models (LLMs) employ auto-regressive decoding that requires sequential computation , with each step reliant on the previous one's output. This creates a bottleneck… we present Medusa , an efficient method that augments LLM inference by adding extra decoding heads to predict multiple subsequent tokens in parallel."}
+{"idx": 4, "title": "Medusa: Simple LLM Inference Acceleration Framework with Multiple ...", "date": "", "ddg_snippet": "Large Language Models (LLMs) employ auto-regressive decoding that requires sequential computation , with each step reliant on the previous one's output. This creates a bottleneck as each step necessitates moving the full model parameters from High-Bandwidth Memory (HBM) to the accelerator's cache. While methods such as speculative decoding have been suggested to address this issue, their ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v235/cai24b.html", "content": "Large Language Models (LLMs) employ auto-regressive decoding that requires sequential computation , with each step reliant on the previous one's output. This creates a bottleneck as each step necessitates moving the full model parameters from High-Bandwidth Memory (HBM) to the accelerator's cache. While methods such as speculative decoding have been suggested to address this issue, their ..."}
+{"idx": 5, "title": "Medusa: Simple LLM Inference Acceleration Framework with Multiple ...", "date": "", "ddg_snippet": "Large Language Models (LLMs) employ auto-regressive decoding that requires sequential computation , with each step reliant on the previous one's output. This creates a bottleneck as each step...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=PEpbUobfJv", "content": "Large Language Models (LLMs) employ auto-regressive decoding that requires sequential computation , with each step reliant on the previous one's output. This creates a bottleneck as each step..."}
+{"idx": 6, "title": "Hydra: Sequentially-Dependent Draft Heads for Medusa Decoding", "date": "", "ddg_snippet": "Abstract: Large Language Models (LLMs) employ auto-regressive decoding that requires sequential computation , with each step reliant on the previous one's output. This creates a bottleneck as each step necessitates moving the full model parameters from High-Bandwidth Memory (HBM) to the accelerator's cache. While methods such as speculative decoding have been suggested to address this issue ...", "subpage_snippet": "", "source": "bohrium.dp.tech", "link": "https://bohrium.dp.tech/paper/arxiv/2402.05109", "content": "Abstract: Large Language Models (LLMs) employ auto-regressive decoding that requires sequential computation , with each step reliant on the previous one's output. This creates a bottleneck as each step necessitates moving the full model parameters from High-Bandwidth Memory (HBM) to the accelerator's cache. While methods such as speculative decoding have been suggested to address this issue ..."}
+{"idx": 7, "title": "ICML Poster Medusa: Simple LLM Inference Acceleration Framework with ...", "date": "", "ddg_snippet": "Abstract: Large Language Models (LLMs) employ auto-regressive decoding that requires sequential computation , with each step reliant on the previous one's output. This creates a bottleneck as each step necessitates moving the full model parameters from High-Bandwidth Memory (HBM) to the accelerator's cache. While methods such as speculative decoding have been suggested to address this issue ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2024/poster/34133", "content": "Abstract: Large Language Models (LLMs) employ auto-regressive decoding that requires sequential computation , with each step reliant on the previous one's output. This creates a bottleneck as each step necessitates moving the full model parameters from High-Bandwidth Memory (HBM) to the accelerator's cache. While methods such as speculative decoding have been suggested to address this issue ..."}
+{"idx": 8, "title": "Medusa: Simple LLM Inference Acceleration Framework with Multiple ...", "date": "", "ddg_snippet": "This paper presents Medusa , an efficient method that augments LLM inference by adding extra decoding heads to predict multiple subsequent tokens in parallel using a tree-based attention mechanism, and proposes several extensions that improve or expand the utility of Medusa . Large Language Models (LLMs) employ auto-regressive decoding that requires sequential computation , with each step reliant ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Medusa:-Simple-LLM-Inference-Acceleration-Framework-Cai-Li/57e7af0b69325fafb371ef5d502e39ef9c90ef7e", "content": "This paper presents Medusa , an efficient method that augments LLM inference by adding extra decoding heads to predict multiple subsequent tokens in parallel using a tree-based attention mechanism, and proposes several extensions that improve or expand the utility of Medusa . Large Language Models (LLMs) employ auto-regressive decoding that requires sequential computation , with each step reliant ..."}
+{"idx": 9, "title": "Medusa: Simple LLM Inference Acceleration Framework with Multiple ...", "date": "", "ddg_snippet": "Large Language Models (LLMs) employ auto-regressive decoding that requires sequential computation , with each step reliant on the previous one's output. This creates a bottleneck as each step necessitates moving the full model parameters from High-Bandwidth Memory (HBM) to the accelerator's cache. While methods such as speculative decoding have been suggested to address this issue, their ...", "subpage_snippet": "", "source": "ui.adsabs.harvard.edu", "link": "https://ui.adsabs.harvard.edu/abs/2024arXiv240110774C/abstract", "content": "Large Language Models (LLMs) employ auto-regressive decoding that requires sequential computation , with each step reliant on the previous one's output. This creates a bottleneck as each step necessitates moving the full model parameters from High-Bandwidth Memory (HBM) to the accelerator's cache. While methods such as speculative decoding have been suggested to address this issue, their ..."}
diff --git a/data/sampled_jsons/Learned_Augmented_Residual_Layer_LAUREL-LR_Equation_(3).jsonl b/data/sampled_jsons/Learned_Augmented_Residual_Layer_LAUREL-LR_Equation_(3).jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a904711aa63d876a3f1ecbec950f00161ebfa6cb
--- /dev/null
+++ b/data/sampled_jsons/Learned_Augmented_Residual_Layer_LAUREL-LR_Equation_(3).jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2411.07501] LAuReL: Learned Augmented Residual Layer - arXiv.org", "date": "", "ddg_snippet": "In this paper we introduce Learned Augmented Residual Layer ( LAuReL ) -- a novel generalization of the canonical residual connection -- with the goal to be an in-situ replacement of the latter while outperforming on both model quality and footprint metrics.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2411.07501", "content": "In this paper we introduce Learned Augmented Residual Layer ( LAuReL ) -- a novel generalization of the canonical residual connection -- with the goal to be an in-situ replacement of the latter while outperforming on both model quality and footprint metrics."}
+{"idx": 1, "title": "GitHub - BAW2501/LAuReL-Learned-Augmented-Residual-Layer", "date": "", "ddg_snippet": "This repository contains my independent implementations of the three LAuReL variants described in the article titled \" LAuReL : Learned Augmented Residual Layer \". These implementations aim to explore the concepts presented in the paper and evaluate their effectiveness on image datasets. Unofficial ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/BAW2501/LAuReL-Learned-Augmented-Residual-Layer", "content": "This repository contains my independent implementations of the three LAuReL variants described in the article titled \" LAuReL : Learned Augmented Residual Layer \". These implementations aim to explore the concepts presented in the paper and evaluate their effectiveness on image datasets. Unofficial ..."}
+{"idx": 2, "title": "LAuReL: Learned Augmented Residual Layer - OpenReview", "date": "", "ddg_snippet": "In this paper, we introduce the Learned Augmented Residual Layer ( LAuReL ) --- a novel generalization of the canonical residual connection --- designed to serve as an in-situ replacement while outperforming it in both model quality and footprint metrics.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=rUDRWP9WvZ", "content": "In this paper, we introduce the Learned Augmented Residual Layer ( LAuReL ) --- a novel generalization of the canonical residual connection --- designed to serve as an in-situ replacement while outperforming it in both model quality and footprint metrics."}
+{"idx": 3, "title": "Google AI Introduces LAuReL (Learned Augmented Residual Layer ...", "date": "", "ddg_snippet": "The LAUREL -RW+LR variant matches the performance of the extra- layer approach while using 2.6 times fewer parameters, and LAUREL -RW+LR+PA outperforms it with 1.82 times fewer parameters. Moreover, in language models, LAUREL shows consistent improvements across tasks including Q&A, NLU, Math, and Code with only a 0.012% parameter increase.", "subpage_snippet": "", "source": "aiquantumintelligence.com", "link": "https://aiquantumintelligence.com/google-ai-introduces-laurel-learned-augmented-residual-layer-revolutionizing-neural-networks-with-enhanced-residual-connections-for-efficient-model-performance", "content": "The LAUREL -RW+LR variant matches the performance of the extra- layer approach while using 2.6 times fewer parameters, and LAUREL -RW+LR+PA outperforms it with 1.82 times fewer parameters. Moreover, in language models, LAUREL shows consistent improvements across tasks including Q&A, NLU, Math, and Code with only a 0.012% parameter increase."}
+{"idx": 4, "title": "LAuReL: Learned Augmented Residual Layer - arXiv.org", "date": "", "ddg_snippet": "2 Learned Augmented Residual Layer In this section we describe the main idea behind LAuReL . In its most general form, we reformulate the residual connection to be the following: ... Here α is a learned scalar parameter, and g () is a learned linear function with x i, x i 1,, x 0 as inputs, where x j is the output of the j th residual connection.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.07501v1", "content": "2 Learned Augmented Residual Layer In this section we describe the main idea behind LAuReL . In its most general form, we reformulate the residual connection to be the following: ... Here α is a learned scalar parameter, and g () is a learned linear function with x i, x i 1,, x 0 as inputs, where x j is the output of the j th residual connection."}
+{"idx": 5, "title": "LAuReL-Learned-Augmented-Residual-Layer/LAuRel_LR.py at master ... - GitHub", "date": "", "ddg_snippet": "Contribute to BAW2501/ LAuReL - Learned - Augmented - Residual - Layer development by creating an account on GitHub.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/BAW2501/LAuReL-Learned-Augmented-Residual-Layer/blob/master/LAuRel_LR.py", "content": "Contribute to BAW2501/ LAuReL - Learned - Augmented - Residual - Layer development by creating an account on GitHub."}
+{"idx": 6, "title": "LAuReL: Learned Augmented Residual Layer - catalyzex.com", "date": "", "ddg_snippet": "LAuReL : Learned Augmented Residual Layer : Paper and Code. One of the core pillars of efficient deep learning methods is architectural improvements such as the residual /skip connection, which has led to significantly better model convergence and quality. Since then the residual connection has become ubiquitous in not just convolutional neural networks but also transformer-based architectures ...", "subpage_snippet": "", "source": "www.catalyzex.com", "link": "https://www.catalyzex.com/paper/laurel-learned-augmented-residual-layer", "content": "LAuReL : Learned Augmented Residual Layer : Paper and Code. One of the core pillars of efficient deep learning methods is architectural improvements such as the residual /skip connection, which has led to significantly better model convergence and quality. Since then the residual connection has become ubiquitous in not just convolutional neural networks but also transformer-based architectures ..."}
+{"idx": 7, "title": "ICML Poster LAuReL: Learned Augmented Residual Layer", "date": "", "ddg_snippet": "In this paper, we introduce Learned Augmented Residual Layer ( LAuReL ), which is a generalization of the residual connection and a drop-in replacement. LAuReL is a general framework but we provide three variants which can be used to cheaply make the residual connection adaptive instead of it being a simple summation.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/43889", "content": "In this paper, we introduce Learned Augmented Residual Layer ( LAuReL ), which is a generalization of the residual connection and a drop-in replacement. LAuReL is a general framework but we provide three variants which can be used to cheaply make the residual connection adaptive instead of it being a simple summation."}
+{"idx": 8, "title": "LAuReL: Learned Augmented Residual Layer - OpenReview", "date": "", "ddg_snippet": "In this paper we introduce a Learned Augmented Residual Layer (LAUREL)—a novel generalization of the canonical residual connection—with the goal to be an in-situ replacement of the latter while outperforming on both model quality and footprint metrics.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=honBJOVRn5", "content": "In this paper we introduce a Learned Augmented Residual Layer (LAUREL)—a novel generalization of the canonical residual connection—with the goal to be an in-situ replacement of the latter while outperforming on both model quality and footprint metrics."}
+{"idx": 9, "title": "Paper page - LAuReL: Learned Augmented Residual Layer", "date": "", "ddg_snippet": "In this paper we introduce Learned Augmented Residual Layer ( LAuReL ) -- a novel generalization of the canonical residual connection -- with the goal to be an in-situ replacement of the latter while outperforming on both model quality and footprint metrics.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2411.07501", "content": "In this paper we introduce Learned Augmented Residual Layer ( LAuReL ) -- a novel generalization of the canonical residual connection -- with the goal to be an in-situ replacement of the latter while outperforming on both model quality and footprint metrics."}
diff --git a/data/sampled_jsons/Lemma_3.6_HDT_SRRW_average_neighborhood_size_computational_cost_Beyond_Self-Repellent_Kernels.jsonl b/data/sampled_jsons/Lemma_3.6_HDT_SRRW_average_neighborhood_size_computational_cost_Beyond_Self-Repellent_Kernels.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2ee17014b72079c55ad68026c4e1ade62ae3088b
--- /dev/null
+++ b/data/sampled_jsons/Lemma_3.6_HDT_SRRW_average_neighborhood_size_computational_cost_Beyond_Self-Repellent_Kernels.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ICML Poster Beyond Self-Repellent Kernels: History-Driven ...", "date": "", "ddg_snippet": "Jul 10, 2025 · Methods like the Self - Repellent Random Walk ( SRRW ) aim to prevent over-exploration of areas but often come with significant computational costs .This research introduces the History-Driven Target ( HDT ) framework, a novel approach enhancing sampling efficiency while reducing computational demands.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46659", "content": "Jul 10, 2025 · Methods like the Self - Repellent Random Walk ( SRRW ) aim to prevent over-exploration of areas but often come with significant computational costs .This research introduces the History-Driven Target ( HDT ) framework, a novel approach enhancing sampling efficiency while reducing computational demands."}
+{"idx": 1, "title": "Beyond Self-Repellent Kernels: History-Driven Target Towards...", "date": "", "ddg_snippet": "May 1, 2025 · Methods like the Self - Repellent Random Walk ( SRRW ) aim to prevent over-exploration of areas but often come with significant computational costs . This research introduces the History-Driven Target ( HDT ) framework, a novel approach enhancing sampling efficiency while reducing computational demands.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=0yzOEMbShU", "content": "May 1, 2025 · Methods like the Self - Repellent Random Walk ( SRRW ) aim to prevent over-exploration of areas but often come with significant computational costs . This research introduces the History-Driven Target ( HDT ) framework, a novel approach enhancing sampling efficiency while reducing computational demands."}
+{"idx": 2, "title": "[2505.18300] Beyond Self-Repellent Kernels: History-Driven ... Abstract 1. Introduction - arXiv.org Performance analysis and computational cost evaluation of ... AE 4803 AIM: Course Notes - 4 LLS: Computational Cost Designing Statistical Estimators That Balance Sample Size ... ICML Poster Beyond Self-Repellent Kernels: History-Driven ...", "date": "", "ddg_snippet": "May 23, 2025 · We propose a history-driven target ( HDT ) framework in Markov Chain Monte Carlo (MCMC) to improve any random walk algorithm on discrete state spaces, such as general undirected graphs, for efficient sampling from target distribution μ. By decoupling the self - repellent mechanism from the transition kernel, we overcome the computational limitations of SRRW while achieving near-zero variance. This paradigm shift offers the best of both worlds: compu- tational efficiency and broad compatibility with advanced MCMC samplers. Jul 1, 2018 · Knowing that one complex multiplication requires four real multiplications and two additions, it is important to evaluate the computational cost in order to determine the hardware resources needed to implement a given distribution in terms of number of real embedded adders and multipliers. In this section we will introduce fundamental algorithms from computational linear algebra that are used to solve linear systems and linear least squares problems. We will also introduce and discuss some of the key properties and theoretical considerations when using these algorithms. When we have a large amount of data, we can exploit excess samples to decrease statistical risk, to decrease computational cost , or to trade off between the two. We propose to achieve this tradeoff by varying the amount of smoothing applied to the optimization problem. Jul 10, 2025 · Methods like the Self - Repellent Random Walk ( SRRW ) aim to prevent over-exploration of areas but often come with significant computational costs .This research introduces the History-Driven Target ( HDT ) framework, a novel approach enhancing sampling efficiency while reducing computational demands.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2505.18300", "content": "May 23, 2025 · We propose a history-driven target ( HDT ) framework in Markov Chain Monte Carlo (MCMC) to improve any random walk algorithm on discrete state spaces, such as general undirected graphs, for efficient sampling from target distribution μ. By decoupling the self - repellent mechanism from the transition kernel, we overcome the computational limitations of SRRW while achieving near-zero variance. This paradigm shift offers the best of both worlds: compu- tational efficiency and broad compatibility with advanced MCMC samplers. Jul 1, 2018 · Knowing that one complex multiplication requires four real multiplications and two additions, it is important to evaluate the computational cost in order to determine the hardware resources needed to implement a given distribution in terms of number of real embedded adders and multipliers. In this section we will introduce fundamental algorithms from computational linear algebra that are used to solve linear systems and linear least squares problems. We will also introduce and discuss some of the key properties and theoretical considerations when using these algorithms. When we have a large amount of data, we can exploit excess samples to decrease statistical risk, to decrease computational cost , or to trade off between the two. We propose to achieve this tradeoff by varying the amount of smoothing applied to the optimization problem. Jul 10, 2025 · Methods like the Self - Repellent Random Walk ( SRRW ) aim to prevent over-exploration of areas but often come with significant computational costs .This research introduces the History-Driven Target ( HDT ) framework, a novel approach enhancing sampling efficiency while reducing computational demands."}
+{"idx": 3, "title": "Performance analysis and computational cost evaluation of ...", "date": "", "ddg_snippet": "Jul 1, 2018 · Knowing that one complex multiplication requires four real multiplications and two additions, it is important to evaluate the computational cost in order to determine the hardware resources needed to implement a given distribution in terms of number of real embedded adders and multipliers.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S1051200418300617", "content": "Jul 1, 2018 · Knowing that one complex multiplication requires four real multiplications and two additions, it is important to evaluate the computational cost in order to determine the hardware resources needed to implement a given distribution in terms of number of real embedded adders and multipliers."}
+{"idx": 4, "title": "AE 4803 AIM: Course Notes - 4 LLS: Computational Cost", "date": "", "ddg_snippet": "In this section we will introduce fundamental algorithms from computational linear algebra that are used to solve linear systems and linear least squares problems. We will also introduce and discuss some of the key properties and theoretical considerations when using these algorithms.", "subpage_snippet": "", "source": "elizqian.github.io", "link": "https://elizqian.github.io/ae-ml-dev/13_lsnla.html", "content": "In this section we will introduce fundamental algorithms from computational linear algebra that are used to solve linear systems and linear least squares problems. We will also introduce and discuss some of the key properties and theoretical considerations when using these algorithms."}
+{"idx": 5, "title": "Designing Statistical Estimators That Balance Sample Size ...", "date": "", "ddg_snippet": "When we have a large amount of data, we can exploit excess samples to decrease statistical risk, to decrease computational cost , or to trade off between the two. We propose to achieve this tradeoff by varying the amount of smoothing applied to the optimization problem.", "subpage_snippet": "", "source": "tropp.caltech.edu", "link": "https://tropp.caltech.edu/papers/BTCB15-Designing-Statistical-preprint.pdf", "content": "When we have a large amount of data, we can exploit excess samples to decrease statistical risk, to decrease computational cost , or to trade off between the two. We propose to achieve this tradeoff by varying the amount of smoothing applied to the optimization problem."}
+{"idx": 6, "title": "History-Driven Target Towards Efficient Nonlinear MCMC ...", "date": "", "ddg_snippet": "27 Jul 2025 — Lemma 3.6 implies that the cost -based covariance of HDT ... neighborhood size leads to more performance advantage of HDT -MCMC compared to SRRW .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.18300v3", "content": "27 Jul 2025 — Lemma 3.6 implies that the cost -based covariance of HDT ... neighborhood size leads to more performance advantage of HDT -MCMC compared to SRRW ."}
+{"idx": 7, "title": "History-Driven Target Towards Efficient Nonlinear MCMC ...", "date": "", "ddg_snippet": "Lemma 3.6 implies that the cost -based covariance of HDT -MCMC is at ... neighborhood size leads to more performance advantage of HDT -MCMC compared to SRRW .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.18300v1", "content": "Lemma 3.6 implies that the cost -based covariance of HDT -MCMC is at ... neighborhood size leads to more performance advantage of HDT -MCMC compared to SRRW ."}
+{"idx": 8, "title": "Large-Scale Scientific Computing", "date": "", "ddg_snippet": "Traditionally, the purpose of the conference is to bring together scientists working with large- scale computational models in natural sciences and environmental ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/978-3-030-97549-4.pdf", "content": "Traditionally, the purpose of the conference is to bring together scientists working with large- scale computational models in natural sciences and environmental ..."}
+{"idx": 9, "title": "Principles and Applications of Data Science", "date": "", "ddg_snippet": "size of the target model or computational budget, using the coefficients obtained from adjusting the input dimensions on the baseline network. Tan et al. [9] ...", "subpage_snippet": "", "source": "mdpi-res.com", "link": "https://mdpi-res.com/bookfiles/book/5711/Principles_and_Applications_of_Data_Science.pdf?v=1753146380", "content": "size of the target model or computational budget, using the coefficients obtained from adjusting the input dimensions on the baseline network. Tan et al. [9] ..."}
diff --git a/data/sampled_jsons/Leveraging_Per-Instance_Privacy_for_Machine_Unlearning_paper_Definition_4.2.jsonl b/data/sampled_jsons/Leveraging_Per-Instance_Privacy_for_Machine_Unlearning_paper_Definition_4.2.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d30040d543bf921c1ca7fd1f0f7d3d0f4e602ec2
--- /dev/null
+++ b/data/sampled_jsons/Leveraging_Per-Instance_Privacy_for_Machine_Unlearning_paper_Definition_4.2.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Leveraging Per - Instance Privacy for Machine Unlearning", "date": "", "ddg_snippet": "Definition 4 . 2 ( Per - Instance Privacy Loss).This paper presents work whose goal is to advance the field of machine unlearning , which is specifically oriented to im-prove the trustworthiness of machine learning, by supporting requests to remove the influence of training data.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2505.18786", "content": "Definition 4 . 2 ( Per - Instance Privacy Loss).This paper presents work whose goal is to advance the field of machine unlearning , which is specifically oriented to im-prove the trustworthiness of machine learning, by supporting requests to remove the influence of training data."}
+{"idx": 1, "title": "ICML Poster Leveraging Per - Instance Privacy for Machine ...", "date": "", "ddg_snippet": "We present a principled, per - instance approach to quantifying the difficulty of unlearning via fine-tuning.All together, our findings provide a foundation for more efficient and adaptive unlearning strategies tailored to the unique properties of individual data points.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46697", "content": "We present a principled, per - instance approach to quantifying the difficulty of unlearning via fine-tuning.All together, our findings provide a foundation for more efficient and adaptive unlearning strategies tailored to the unique properties of individual data points."}
+{"idx": 2, "title": "Leveraging Per -Example Privacy for Machine Unlearning", "date": "", "ddg_snippet": "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.", "subpage_snippet": "", "source": "research.google", "link": "https://research.google/pubs/leveraging-per-example-privacy-for-machine-unlearning/", "content": "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."}
+{"idx": 3, "title": "(PDF) Machine Unlearning : Solutions and Challenges", "date": "", "ddg_snippet": "First, machine unlearning enforces privacy regulations and.ndss- paper / machine - unlearning -of- features-and- labels/. [71] J. Martens, “New insights and perspectives on the natural gradient. method,” The Journal of Machine Learning Research, vol. 21, no. 1", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/379711614_Machine_Unlearning_Solutions_and_Challenges", "content": "First, machine unlearning enforces privacy regulations and.ndss- paper / machine - unlearning -of- features-and- labels/. [71] J. Martens, “New insights and perspectives on the natural gradient. method,” The Journal of Machine Learning Research, vol. 21, no. 1"}
+{"idx": 4, "title": "GitHub - jjbrophy47/ machine _ unlearning : Existing Literature about...", "date": "", "ddg_snippet": "Frameworks. OpenUnlearning. Machine Unlearning Comparator. Papers .Forget to Flourish: Leveraging Machine - Unlearning on Pretrained Language Models for Privacy Leakage.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/jjbrophy47/machine_unlearning", "content": "Frameworks. OpenUnlearning. Machine Unlearning Comparator. Papers .Forget to Flourish: Leveraging Machine - Unlearning on Pretrained Language Models for Privacy Leakage."}
+{"idx": 5, "title": "Balancing Data Privacy and Model Performance in Machine Unlearning", "date": "", "ddg_snippet": "Machine Unlearning : Machine Unlearning : Privacy vs. Performancemachine learning.A new framework to enhance privacy in. Table of Contents. The Challenge of Machine Unlearning .", "subpage_snippet": "", "source": "scisimple.com", "link": "https://scisimple.com/en/articles/2025-07-30-balancing-data-privacy-and-model-performance-in-machine-unlearning--a3lx5ew", "content": "Machine Unlearning : Machine Unlearning : Privacy vs. Performancemachine learning.A new framework to enhance privacy in. Table of Contents. The Challenge of Machine Unlearning ."}
+{"idx": 6, "title": "Faster Machine Unlearning via Natural Gradient Descent-Bohrium", "date": "", "ddg_snippet": "To avoid retraining models from scratch, we propose a novel algorithm leveraging Natural Gradient Descent (NGD).", "subpage_snippet": "", "source": "www.bohrium.com", "link": "https://www.bohrium.com/paper-details/faster-machine-unlearning-via-natural-gradient-descent/1018766455161749511-108614", "content": "To avoid retraining models from scratch, we propose a novel algorithm leveraging Natural Gradient Descent (NGD)."}
+{"idx": 7, "title": "Frontiers in Machine Learning: Synthesizing May... - DEV Community", "date": "", "ddg_snippet": "Papers such as Leveraging Per - Instance Privacy for Machine Unlearning and Soft Weighted Machine Unlearning develop methods to quantify and minimize the privacy loss associated with individual data points, enabling adaptive, fair, and efficient data removal.", "subpage_snippet": "", "source": "dev.to", "link": "https://dev.to/khanali21/frontiers-in-machine-learning-synthesizing-may-2025-arxiv-cslg-advances-in-efficiency-18o5", "content": "Papers such as Leveraging Per - Instance Privacy for Machine Unlearning and Soft Weighted Machine Unlearning develop methods to quantify and minimize the privacy loss associated with individual data points, enabling adaptive, fair, and efficient data removal."}
+{"idx": 8, "title": "Machine Unlearning Doesn't Do What You... | Read Paper on Bytez", "date": "", "ddg_snippet": "We then discuss evolving motivations for machine unlearning in response to the ascendance of Generative AI (Section 2.3). These new motivations have encouraged an expanded definition for machine unlearning (Section 2.4), which we will rely on throughout the paper .", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/arxiv/2412.06966/paper", "content": "We then discuss evolving motivations for machine unlearning in response to the ascendance of Generative AI (Section 2.3). These new motivations have encouraged an expanded definition for machine unlearning (Section 2.4), which we will rely on throughout the paper ."}
+{"idx": 9, "title": "CS PhD, UofT - Cited by 775 - Machine Learning - Computer Security", "date": "", "ddg_snippet": "Leveraging Per - Instance Privacy for Machine Unlearning . NM Sepahvand, A Thudi, B Isik, A Bhattacharyya, N PapernotICML 2025 Workshop on Machine Unlearning for Generative AI, 0. The system can't perform the operation now. Try again later.", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=bTEybH0AAAAJ&hl=en", "content": "Leveraging Per - Instance Privacy for Machine Unlearning . NM Sepahvand, A Thudi, B Isik, A Bhattacharyya, N PapernotICML 2025 Workshop on Machine Unlearning for Generative AI, 0. The system can't perform the operation now. Try again later."}
diff --git a/data/sampled_jsons/Li_&_Chen_2024_generative_models_polynomial_dimension_bound_abstract_year_2024.jsonl b/data/sampled_jsons/Li_&_Chen_2024_generative_models_polynomial_dimension_bound_abstract_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ce922b93fec371a5f16b237bfe9608215f18780e
--- /dev/null
+++ b/data/sampled_jsons/Li_&_Chen_2024_generative_models_polynomial_dimension_bound_abstract_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Gen Li Yuting Wei Yuejie Chi Yuxin Chen†§ August 6, 2024 arXiv:2408 ...", "date": "", "ddg_snippet": "Diffusion models , which convert noise into new data instances by learning to reverse a diffusion process, have become a cornerstone in contemporary generative modeling. In this work, we develop non-asymptotic convergence theory for a popular diffusion-based sampler (i.e., the probability flow ODE sampler) in discrete time, assuming access to l2-accurate estimates of the (Stein) score functions ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2408.02320", "content": "Diffusion models , which convert noise into new data instances by learning to reverse a diffusion process, have become a cornerstone in contemporary generative modeling. In this work, we develop non-asymptotic convergence theory for a popular diffusion-based sampler (i.e., the probability flow ODE sampler) in discrete time, assuming access to l2-accurate estimates of the (Stein) score functions ..."}
+{"idx": 1, "title": "An encoding generative modeling approach to dimension reduction and ...", "date": "", "ddg_snippet": "Causal inference has been increasingly essential in modern observational studies with rich covariate information. However, it is often challenging to estimate the causal effect with high-dimensional covariates. Here, we introduce an approach by encoding generative modeling (EGM) for handling high-dimensional covariates by a dependency-aware dimension reduction strategy where the key idea is to ...", "subpage_snippet": "", "source": "www.pnas.org", "link": "https://www.pnas.org/doi/10.1073/pnas.2322376121", "content": "Causal inference has been increasingly essential in modern observational studies with rich covariate information. However, it is often challenging to estimate the causal effect with high-dimensional covariates. Here, we introduce an approach by encoding generative modeling (EGM) for handling high-dimensional covariates by a dependency-aware dimension reduction strategy where the key idea is to ..."}
+{"idx": 2, "title": "Generative learning for forecasting the dynamics of high ... - Nature", "date": "", "ddg_snippet": "We introduce generative models for accelerating simulations of high-dimensional systems through learning and evolving their effective dynamics.", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41467-024-53165-w", "content": "We introduce generative models for accelerating simulations of high-dimensional systems through learning and evolving their effective dynamics."}
+{"idx": 3, "title": "T N -asymptotic Convergence for D -b Generative Models", "date": "", "ddg_snippet": "Gen Li , Yuting Wei, Yuxin Chen , and Yuejie Chi. Towards faster non-asymptotic convergence for diffusion-based generative models . arXiv preprint arXiv:2306.09251, 2023.", "subpage_snippet": "", "source": "par.nsf.gov", "link": "https://par.nsf.gov/servlets/purl/10508010", "content": "Gen Li , Yuting Wei, Yuxin Chen , and Yuejie Chi. Towards faster non-asymptotic convergence for diffusion-based generative models . arXiv preprint arXiv:2306.09251, 2023."}
+{"idx": 4, "title": "Discrete Diffusion Models: Novel Analysis and New Sampler Guarantees", "date": "", "ddg_snippet": "[33] Gen Li , Yuting Wei, Yuxin Chen , and Yuejie Chi. Towards faster non-asymptotic convergence for diffusion-based generative models . In The Twelfth International Conference on Learning Representations, 2024 .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2509.16756", "content": "[33] Gen Li , Yuting Wei, Yuxin Chen , and Yuejie Chi. Towards faster non-asymptotic convergence for diffusion-based generative models . In The Twelfth International Conference on Learning Representations, 2024 ."}
+{"idx": 5, "title": "PDF Generative Image Dynamics - CVF Open Access", "date": "", "ddg_snippet": "Recent advances in generative models , in particular con-ditional diffusion models [44, 84, 86], have enabled us to model rich distributions, including distributions of real im-ages conditioned on text [72-74]. This capability has enabled several new applications, such as text-conditioned genera-tion of diverse and realistic image content.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2024//papers/Li_Generative_Image_Dynamics_CVPR_2024_paper.pdf", "content": "Recent advances in generative models , in particular con-ditional diffusion models [44, 84, 86], have enabled us to model rich distributions, including distributions of real im-ages conditioned on text [72-74]. This capability has enabled several new applications, such as text-conditioned genera-tion of diverse and realistic image content."}
+{"idx": 6, "title": "Blink of an eye: a simple theory for feature localization in generative ...", "date": "", "ddg_snippet": "In this work we de- velop a simple, unifying theory to explain this phenomenon. Using the formalism of stochastic localization for generative models , we show that it emerges generically as the generation process localizes to a sub-population of the distribution it models .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=QvqnPVGWAN", "content": "In this work we de- velop a simple, unifying theory to explain this phenomenon. Using the formalism of stochastic localization for generative models , we show that it emerges generically as the generation process localizes to a sub-population of the distribution it models ."}
+{"idx": 7, "title": "Gen Li Yuling Yan January 3, 2025 arXiv:2405.14861v2 [cs.LG] 31 Dec 2024", "date": "", "ddg_snippet": "This paper investigates score-based difusion models when the underlying target distribution is concen-trated on or near low-dimensional manifolds within the higher-dimensional space in which they formally reside, a common characteristic of natural image distributions. Despite previous eforts to understand the data generation process of difusion models , existing theoretical support remains ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2405.14861", "content": "This paper investigates score-based difusion models when the underlying target distribution is concen-trated on or near low-dimensional manifolds within the higher-dimensional space in which they formally reside, a common characteristic of natural image distributions. Despite previous eforts to understand the data generation process of difusion models , existing theoretical support remains ..."}
+{"idx": 8, "title": "[PDF] Convergence Analysis of Discrete Diffusion Model: Exact ...", "date": "", "ddg_snippet": "This work provides the first polynomial -time convergence guarantees for the probability flow ODE implementation (together with a corrector step) of score-based generative modeling and obtains better dimension dependence than prior works on DDPM.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Convergence-Analysis-of-Discrete-Diffusion-Model:-Chen-Ying/e8123fa1633cf1d14031ed96de9087ba3623c830", "content": "This work provides the first polynomial -time convergence guarantees for the probability flow ODE implementation (together with a corrector step) of score-based generative modeling and obtains better dimension dependence than prior works on DDPM."}
+{"idx": 9, "title": "The probability flow ODE is provably fast | OpenReview", "date": "", "ddg_snippet": "TL;DR: We give the first fully polynomial -time bounds for the probability flow ODE, together with a corrector based on underdamped Langevin, and obtain superior dimension dependence compared to existing analyses of SDE-based diffusion models .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=KD6MFeWSAd", "content": "TL;DR: We give the first fully polynomial -time bounds for the probability flow ODE, together with a corrector based on underdamped Langevin, and obtain superior dimension dependence compared to existing analyses of SDE-based diffusion models ."}
diff --git a/data/sampled_jsons/Likelihood_Based_Approach_Distribution_Regression_experimental_results_numerical_values.jsonl b/data/sampled_jsons/Likelihood_Based_Approach_Distribution_Regression_experimental_results_numerical_values.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..35672136a6e22d11a86113619e8134d049d46c51
--- /dev/null
+++ b/data/sampled_jsons/Likelihood_Based_Approach_Distribution_Regression_experimental_results_numerical_values.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Regression toward the mean - Wikipedia", "date": "", "ddg_snippet": "Regression toward the mean is thus a useful concept to consider when designing any scientific experiment , data analysis, or test, which intentionally ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Regression_toward_the_mean", "content": "Regression toward the mean is thus a useful concept to consider when designing any scientific experiment , data analysis, or test, which intentionally ..."}
+{"idx": 1, "title": "Statistical inference - Wikipedia", "date": "", "ddg_snippet": "Fully parametric : The probability distributions describing the data-generation process are assumed to be fully described by a family of probability ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Statistical_inference", "content": "Fully parametric : The probability distributions describing the data-generation process are assumed to be fully described by a family of probability ..."}
+{"idx": 2, "title": "A Likelihood Based Approach to Distribution Regression Using ...", "date": "", "ddg_snippet": "Oct 2, 2024 · In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where the response variable lies in a high-dimensional ambient space but concentrates around a potentially lower-dimensional manifold. More specifically, we study the large-sample properties of a likelihood - based approach for estimating these ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.02025", "content": "Oct 2, 2024 · In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where the response variable lies in a high-dimensional ambient space but concentrates around a potentially lower-dimensional manifold. More specifically, we study the large-sample properties of a likelihood - based approach for estimating these ..."}
+{"idx": 3, "title": "A LIKELIHOOD BASED APPROACH TO DISTRIBUTION REGRESSION USING ...", "date": "", "ddg_snippet": "In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where the re-sponse variable lies in a high-dimensional ambient space but concentrates around a potentially lower-dimensional manifold. More specifically, we study the large-sample properties of a likelihood - based approach for estimating these ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=V6hhhXoTSq", "content": "In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where the re-sponse variable lies in a high-dimensional ambient space but concentrates around a potentially lower-dimensional manifold. More specifically, we study the large-sample properties of a likelihood - based approach for estimating these ..."}
+{"idx": 4, "title": "20_mle_annotated - Stanford University 3 Likelihood-based inference – MATH 60604A - Statistical ... Empirical likelihood estimation for linear regression models ... Maximum Likelihood Estimation (MLE) - fsb.miamioh.edu 3 Likelihood - based inference – MATH 60604A - Statistical Modelling 3 Likelihood - based inference – MATH 60604A - Statistical Modelling 3 Likelihood - based inference – MATH 60604A - Statistical Modelling 3 Likelihood - based inference – MATH 60604A - Statistical Modelling 3 Likelihood - based inference – MATH 60604A - Statistical Modelling 3 Likelihood - based inference – MATH 60604A - Statistical Modelling A Likelihood Approach to Nonparametric Estimation of a ...", "date": "", "ddg_snippet": "... , , , are iid random variables, where drawn from distribution Var = . unbiased estimate of Sample variance: unbiased estimate of See full list on web.stanford.edu In the real world, we don’t know the true parameters. • But we do get to observe data: # times coin comes up heads, lifetimes of disk drives produced, # visitors to website per day, offer amount for a used bike def estimator 9 : a random variable estimating true parameter . In parameter estimation, We use the point estimate of parameter estimate (b... See full list on web.stanford.edu of iid random variables , , ... , . • was drawn from a distribution with density function | . (or mass) • Sample: , , ... , Likelihood question: How likely is the sample , , ... , given the parameter ? See full list on web.stanford.edu = : , , ... , | = A | % This is just a product, since are iid. See full list on web.stanford.edu variables , , ... , distribution | . iid random , drawn from a maximizes the likelihood of our sample, : also maximizes the log- likelihood See full list on web.stanford.edu Learning objectives: Learn the terminology associated with likelihood - based inference Derive closed-form expressions for the maximum likelihood estimator in simple models Using numerical optimization, obtain parameter estimates and their standards errors using maximum likelihood Use large-sample properties of the likelihood to derive confidence intervals and tests Use information criteria for ... Mar 13, 2021 · The results of the simulation study show that the proposed estimators based on EL method are remarkably better than the estimators obtained from CML method in terms of mean squared errors (MSE) and bias in almost all the simulation configurations. These findings are also confirmed by the results of the numerical and real data examples. Maximum Likelihood — Approach I: Grid Search We can find the MLE with grid-search—we evaluate log likelihood (4) for a range of possible values of μ and choose the one that maximizes log likelihood . What are the properties of maximum likelihood estimator? Several properties of maximum likelihood estimator makes it appealing for inference. The maximum likelihood estimator is efficient, meaning it has the smallest asymptotic mean squared error. The maximum likelihood estimator is also consistent, i.e., it converges to the correct value as the sample size increase (asymptotically unbiased). What is the log likelihood of a random sample? For simplicity, suppose we have a simple random sample, so the log likelihood is a sum of n terms and information accumulates linearly with the sample size: the data carry more information about the unknown parameter vector, whose true value we denote θ 0. Does likelihood account for model complexity? However, the likelihood doesn’t account for model complexity in the sense that more complex models with more parameters lead to higher likelihood. This is not a problem for comparison of nested models using the likelihood ratio test because we look only at relative improvement in fit. How do you calculate the maximum likelihood of an exponential distribution? Example 3.3 (Calculation of the maximum likelihood of an exponential distribution) As Figure 3.1 reveals that the exponential log likelihood function is unimodal and thus achieves a single maximum, we can use calculus to derive an explicit expression for λ ^ based on the log likelihood ℓ (λ) = n ln λ 1 λ ∑ i = 1 n y i . Which scale maximizes the log likelihood for a given? Using the gradients derived in Example 3.7, we find that the value of the scale that maximizes the log likelihood for given α is λ ^ α = (1 n ∑ i = 1 n y i α) 1 / α. and plugging in this value gives a function of α alone, thereby also reducing the optimization problem for the Weibull to a line search along ℓ p (α). What is an example of profile likelihood? One famous example of profile likelihood is the Cox proportional hazard covered in Chapter 7. The likelihood can also serve as building block for model comparison: the larger ℓ (θ ^), the better the fit. However, the likelihood doesn’t account for model complexity in the sense that more complex models with more parameters lead to higher likelihood. We investigate statistical properties of a likelihood approach to nonparametric estimation of a singular distribution using deep generative models. More speci cally, a deep generative model is used to model high-dimensional data that are assumed to concentrate around some low-dimensional structure.", "subpage_snippet": "", "source": "web.stanford.edu", "link": "https://web.stanford.edu/class/archive/cs/cs109/cs109.1234/lectures/20_mle_annotated.pdf", "content": "... , , , are iid random variables, where drawn from distribution Var = . unbiased estimate of Sample variance: unbiased estimate of See full list on web.stanford.edu In the real world, we don’t know the true parameters. • But we do get to observe data: # times coin comes up heads, lifetimes of disk drives produced, # visitors to website per day, offer amount for a used bike def estimator 9 : a random variable estimating true parameter . In parameter estimation, We use the point estimate of parameter estimate (b... See full list on web.stanford.edu of iid random variables , , ... , . • was drawn from a distribution with density function | . (or mass) • Sample: , , ... , Likelihood question: How likely is the sample , , ... , given the parameter ? See full list on web.stanford.edu = : , , ... , | = A | % This is just a product, since are iid. See full list on web.stanford.edu variables , , ... , distribution | . iid random , drawn from a maximizes the likelihood of our sample, : also maximizes the log- likelihood See full list on web.stanford.edu Learning objectives: Learn the terminology associated with likelihood - based inference Derive closed-form expressions for the maximum likelihood estimator in simple models Using numerical optimization, obtain parameter estimates and their standards errors using maximum likelihood Use large-sample properties of the likelihood to derive confidence intervals and tests Use information criteria for ... Mar 13, 2021 · The results of the simulation study show that the proposed estimators based on EL method are remarkably better than the estimators obtained from CML method in terms of mean squared errors (MSE) and bias in almost all the simulation configurations. These findings are also confirmed by the results of the numerical and real data examples. Maximum Likelihood — Approach I: Grid Search We can find the MLE with grid-search—we evaluate log likelihood (4) for a range of possible values of μ and choose the one that maximizes log likelihood . What are the properties of maximum likelihood estimator? Several properties of maximum likelihood estimator makes it appealing for inference. The maximum likelihood estimator is efficient, meaning it has the smallest asymptotic mean squared error. The maximum likelihood estimator is also consistent, i.e., it converges to the correct value as the sample size increase (asymptotically unbiased). What is the log likelihood of a random sample? For simplicity, suppose we have a simple random sample, so the log likelihood is a sum of n terms and information accumulates linearly with the sample size: the data carry more information about the unknown parameter vector, whose true value we denote θ 0. Does likelihood account for model complexity? However, the likelihood doesn’t account for model complexity in the sense that more complex models with more parameters lead to higher likelihood. This is not a problem for comparison of nested models using the likelihood ratio test because we look only at relative improvement in fit. How do you calculate the maximum likelihood of an exponential distribution? Example 3.3 (Calculation of the maximum likelihood of an exponential distribution) As Figure 3.1 reveals that the exponential log likelihood function is unimodal and thus achieves a single maximum, we can use calculus to derive an explicit expression for λ ^ based on the log likelihood ℓ (λ) = n ln λ 1 λ ∑ i = 1 n y i . Which scale maximizes the log likelihood for a given? Using the gradients derived in Example 3.7, we find that the value of the scale that maximizes the log likelihood for given α is λ ^ α = (1 n ∑ i = 1 n y i α) 1 / α. and plugging in this value gives a function of α alone, thereby also reducing the optimization problem for the Weibull to a line search along ℓ p (α). What is an example of profile likelihood? One famous example of profile likelihood is the Cox proportional hazard covered in Chapter 7. The likelihood can also serve as building block for model comparison: the larger ℓ (θ ^), the better the fit. However, the likelihood doesn’t account for model complexity in the sense that more complex models with more parameters lead to higher likelihood. We investigate statistical properties of a likelihood approach to nonparametric estimation of a singular distribution using deep generative models. More speci cally, a deep generative model is used to model high-dimensional data that are assumed to concentrate around some low-dimensional structure."}
+{"idx": 5, "title": "3 Likelihood-based inference – MATH 60604A - Statistical ...", "date": "", "ddg_snippet": "Learning objectives: Learn the terminology associated with likelihood - based inference Derive closed-form expressions for the maximum likelihood estimator in simple models Using numerical optimization, obtain parameter estimates and their standards errors using maximum likelihood Use large-sample properties of the likelihood to derive confidence intervals and tests Use information criteria for ...", "subpage_snippet": "", "source": "lbelzile.github.io", "link": "https://lbelzile.github.io/math60604a/likelihood.html", "content": "Learning objectives: Learn the terminology associated with likelihood - based inference Derive closed-form expressions for the maximum likelihood estimator in simple models Using numerical optimization, obtain parameter estimates and their standards errors using maximum likelihood Use large-sample properties of the likelihood to derive confidence intervals and tests Use information criteria for ..."}
+{"idx": 6, "title": "Empirical likelihood estimation for linear regression models ... Maximum Likelihood Estimation (MLE) - fsb.miamioh.edu 3 Likelihood - based inference – MATH 60604A - Statistical Modelling 3 Likelihood - based inference – MATH 60604A - Statistical Modelling 3 Likelihood - based inference – MATH 60604A - Statistical Modelling 3 Likelihood - based inference – MATH 60604A - Statistical Modelling 3 Likelihood - based inference – MATH 60604A - Statistical Modelling 3 Likelihood - based inference – MATH 60604A - Statistical Modelling A Likelihood Approach to Nonparametric Estimation of a ...", "date": "", "ddg_snippet": "Mar 13, 2021 · The results of the simulation study show that the proposed estimators based on EL method are remarkably better than the estimators obtained from CML method in terms of mean squared errors (MSE) and bias in almost all the simulation configurations. These findings are also confirmed by the results of the numerical and real data examples. Maximum Likelihood — Approach I: Grid Search We can find the MLE with grid-search—we evaluate log likelihood (4) for a range of possible values of μ and choose the one that maximizes log likelihood . What are the properties of maximum likelihood estimator? Several properties of maximum likelihood estimator makes it appealing for inference. The maximum likelihood estimator is efficient, meaning it has the smallest asymptotic mean squared error. The maximum likelihood estimator is also consistent, i.e., it converges to the correct value as the sample size increase (asymptotically unbiased). What is the log likelihood of a random sample? For simplicity, suppose we have a simple random sample, so the log likelihood is a sum of n terms and information accumulates linearly with the sample size: the data carry more information about the unknown parameter vector, whose true value we denote θ 0. Does likelihood account for model complexity? However, the likelihood doesn’t account for model complexity in the sense that more complex models with more parameters lead to higher likelihood. This is not a problem for comparison of nested models using the likelihood ratio test because we look only at relative improvement in fit. How do you calculate the maximum likelihood of an exponential distribution? Example 3.3 (Calculation of the maximum likelihood of an exponential distribution) As Figure 3.1 reveals that the exponential log likelihood function is unimodal and thus achieves a single maximum, we can use calculus to derive an explicit expression for λ ^ based on the log likelihood ℓ (λ) = n ln λ 1 λ ∑ i = 1 n y i . Which scale maximizes the log likelihood for a given? Using the gradients derived in Example 3.7, we find that the value of the scale that maximizes the log likelihood for given α is λ ^ α = (1 n ∑ i = 1 n y i α) 1 / α. and plugging in this value gives a function of α alone, thereby also reducing the optimization problem for the Weibull to a line search along ℓ p (α). What is an example of profile likelihood? One famous example of profile likelihood is the Cox proportional hazard covered in Chapter 7. The likelihood can also serve as building block for model comparison: the larger ℓ (θ ^), the better the fit. However, the likelihood doesn’t account for model complexity in the sense that more complex models with more parameters lead to higher likelihood. We investigate statistical properties of a likelihood approach to nonparametric estimation of a singular distribution using deep generative models. More speci cally, a deep generative model is used to model high-dimensional data that are assumed to concentrate around some low-dimensional structure.", "subpage_snippet": "", "source": "www.ncbi.nlm.nih.gov", "link": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9267430/", "content": "Mar 13, 2021 · The results of the simulation study show that the proposed estimators based on EL method are remarkably better than the estimators obtained from CML method in terms of mean squared errors (MSE) and bias in almost all the simulation configurations. These findings are also confirmed by the results of the numerical and real data examples. Maximum Likelihood — Approach I: Grid Search We can find the MLE with grid-search—we evaluate log likelihood (4) for a range of possible values of μ and choose the one that maximizes log likelihood . What are the properties of maximum likelihood estimator? Several properties of maximum likelihood estimator makes it appealing for inference. The maximum likelihood estimator is efficient, meaning it has the smallest asymptotic mean squared error. The maximum likelihood estimator is also consistent, i.e., it converges to the correct value as the sample size increase (asymptotically unbiased). What is the log likelihood of a random sample? For simplicity, suppose we have a simple random sample, so the log likelihood is a sum of n terms and information accumulates linearly with the sample size: the data carry more information about the unknown parameter vector, whose true value we denote θ 0. Does likelihood account for model complexity? However, the likelihood doesn’t account for model complexity in the sense that more complex models with more parameters lead to higher likelihood. This is not a problem for comparison of nested models using the likelihood ratio test because we look only at relative improvement in fit. How do you calculate the maximum likelihood of an exponential distribution? Example 3.3 (Calculation of the maximum likelihood of an exponential distribution) As Figure 3.1 reveals that the exponential log likelihood function is unimodal and thus achieves a single maximum, we can use calculus to derive an explicit expression for λ ^ based on the log likelihood ℓ (λ) = n ln λ 1 λ ∑ i = 1 n y i . Which scale maximizes the log likelihood for a given? Using the gradients derived in Example 3.7, we find that the value of the scale that maximizes the log likelihood for given α is λ ^ α = (1 n ∑ i = 1 n y i α) 1 / α. and plugging in this value gives a function of α alone, thereby also reducing the optimization problem for the Weibull to a line search along ℓ p (α). What is an example of profile likelihood? One famous example of profile likelihood is the Cox proportional hazard covered in Chapter 7. The likelihood can also serve as building block for model comparison: the larger ℓ (θ ^), the better the fit. However, the likelihood doesn’t account for model complexity in the sense that more complex models with more parameters lead to higher likelihood. We investigate statistical properties of a likelihood approach to nonparametric estimation of a singular distribution using deep generative models. More speci cally, a deep generative model is used to model high-dimensional data that are assumed to concentrate around some low-dimensional structure."}
+{"idx": 7, "title": "Maximum Likelihood Estimation (MLE) - fsb.miamioh.edu", "date": "", "ddg_snippet": "Maximum Likelihood — Approach I: Grid Search We can find the MLE with grid-search—we evaluate log likelihood (4) for a range of possible values of μ and choose the one that maximizes log likelihood .", "subpage_snippet": "", "source": "www.fsb.miamioh.edu", "link": "https://www.fsb.miamioh.edu/lij14/572_slide_mle.pdf", "content": "Maximum Likelihood — Approach I: Grid Search We can find the MLE with grid-search—we evaluate log likelihood (4) for a range of possible values of μ and choose the one that maximizes log likelihood ."}
+{"idx": 8, "title": "A Likelihood Approach to Nonparametric Estimation of a ...", "date": "", "ddg_snippet": "We investigate statistical properties of a likelihood approach to nonparametric estimation of a singular distribution using deep generative models. More speci cally, a deep generative model is used to model high-dimensional data that are assumed to concentrate around some low-dimensional structure.", "subpage_snippet": "", "source": "jmlr.org", "link": "https://jmlr.org/papers/volume24/21-1099/21-1099.pdf", "content": "We investigate statistical properties of a likelihood approach to nonparametric estimation of a singular distribution using deep generative models. More speci cally, a deep generative model is used to model high-dimensional data that are assumed to concentrate around some low-dimensional structure."}
+{"idx": 9, "title": "Sparse principal component regression via singular value", "date": "", "ddg_snippet": "... of our proposals is demonstrated numerically and compared with the typical dimension reduction approaches (including principal component regression ...", "subpage_snippet": "", "source": "sugaku.net", "link": "https://sugaku.net/oa/W3127190032/sparse-principal-component-regression-via-singular-value-decomposition-approach", "content": "... of our proposals is demonstrated numerically and compared with the typical dimension reduction approaches (including principal component regression ..."}
diff --git a/data/sampled_jsons/Likelihood_Based_Approach_to_Distribution_Regression_Table_1_FD1_Mean_Squared_Error.jsonl b/data/sampled_jsons/Likelihood_Based_Approach_to_Distribution_Regression_Table_1_FD1_Mean_Squared_Error.jsonl
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A LIKELIHOOD BASED APPROACH TO DISTRIBUTION REGRESSION USING ...", "date": "", "ddg_snippet": "In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where the re-sponse variable lies in a high-dimensional ambient space but concentrates around a potentially lower-dimensional manifold. More specifically, we study the large-sample properties of a likelihood- based approach for estimating these ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=V6hhhXoTSq", "content": "In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where the re-sponse variable lies in a high-dimensional ambient space but concentrates around a potentially lower-dimensional manifold. More specifically, we study the large-sample properties of a likelihood- based approach for estimating these ..."}
+{"idx": 1, "title": "A Likelihood Based Approach to Distribution Regression Using ...", "date": "", "ddg_snippet": "More specifically, we study the large-sample properties of a likelihood- based approach for estimating these models. Our results lead to the convergence rate of a sieve maximum likelihood estimator (MLE) for estimating the conditional distribution (and its devolved counterpart) of the response given predictors in the Hellinger (Wasserstein) metric.", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2410.02025", "content": "More specifically, we study the large-sample properties of a likelihood- based approach for estimating these models. Our results lead to the convergence rate of a sieve maximum likelihood estimator (MLE) for estimating the conditional distribution (and its devolved counterpart) of the response given predictors in the Hellinger (Wasserstein) metric."}
+{"idx": 2, "title": "How to choose between mean squared error and likelihood?", "date": "", "ddg_snippet": "Jun 9, 2022 · Notice that the mean squared error of the first approach can also arise from likelihood maximization, but for a model that assumes the same variance for the two normal distributions.", "subpage_snippet": "", "source": "stats.stackexchange.com", "link": "https://stats.stackexchange.com/questions/578241/how-to-choose-between-mean-squared-error-and-likelihood", "content": "Jun 9, 2022 · Notice that the mean squared error of the first approach can also arise from likelihood maximization, but for a model that assumes the same variance for the two normal distributions."}
+{"idx": 3, "title": "Multiple Linear Regressions by Maximizing the Likelihood ...", "date": "", "ddg_snippet": "Abstract Multiple linear regression analysis is widely used to link an outcome with predictors for better understanding of the behaviour of the outcome of interest. Usually, under the assumption that the errors follow a normal distribution , the coefficients of the model are estimated by minimizing the sum of squared deviations. A new approach based on maximum likelihood estimation is proposed ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC5174750/", "content": "Abstract Multiple linear regression analysis is widely used to link an outcome with predictors for better understanding of the behaviour of the outcome of interest. Usually, under the assumption that the errors follow a normal distribution , the coefficients of the model are estimated by minimizing the sum of squared deviations. A new approach based on maximum likelihood estimation is proposed ..."}
+{"idx": 4, "title": "Lecture 2. Estimation, bias, and mean squared error", "date": "", "ddg_snippet": "The distribution of T = T(X) is called its sampling distribution . X1; : : : ; Xn are iid, each with pdf/pmf fX(x j ), unknown. by a statistic, ie by a function T of the data.", "subpage_snippet": "", "source": "statslab.cam.ac.uk", "link": "http://statslab.cam.ac.uk/Dept/People/djsteaching/S1B-17-02-estimation-bias.pdf", "content": "The distribution of T = T(X) is called its sampling distribution . X1; : : : ; Xn are iid, each with pdf/pmf fX(x j ), unknown. by a statistic, ie by a function T of the data."}
+{"idx": 5, "title": "3 Likelihood-based inference – MATH 60604A - Statistical ...", "date": "", "ddg_snippet": "3 Likelihood- based inference This chapter is dedicated to the basics of statistical modelling using likelihood- based inference, arguably the most popular estimation paradigm in statistics.", "subpage_snippet": "", "source": "lbelzile.github.io", "link": "https://lbelzile.github.io/math60604a/likelihood.html", "content": "3 Likelihood- based inference This chapter is dedicated to the basics of statistical modelling using likelihood- based inference, arguably the most popular estimation paradigm in statistics."}
+{"idx": 6, "title": "12.2 A maximum-likelihood approach | An Introduction to Data ...", "date": "", "ddg_snippet": "12.2 A maximum-likelihood approach In order to be able to extend regression modeling to predictor variables other than metric variables (so-called generalized linear regression models, see Chapter 15), the geometric approach needs to be abandoned in favor of a likelihood- based approach . The likelihood- based approach tries to find coefficients that explain the observed data most plausibly.", "subpage_snippet": "", "source": "michael-franke.github.io", "link": "https://michael-franke.github.io/intro-data-analysis/Chap-04-01-linear-regression-MLE.html", "content": "12.2 A maximum-likelihood approach In order to be able to extend regression modeling to predictor variables other than metric variables (so-called generalized linear regression models, see Chapter 15), the geometric approach needs to be abandoned in favor of a likelihood- based approach . The likelihood- based approach tries to find coefficients that explain the observed data most plausibly."}
+{"idx": 7, "title": "Reducing Bias and Mean Squared Error Associated With Regression-Based ...", "date": "", "ddg_snippet": "We show how this approach extends to address bias in odds or risk ratio estimators in many common regression settings. We also propose a class of estimators that provide reduced mean bias and squared error , while allowing the investigator to control the risk of underestimating the true ratio parameter.", "subpage_snippet": "", "source": "www.ncbi.nlm.nih.gov", "link": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3433076/", "content": "We show how this approach extends to address bias in odds or risk ratio estimators in many common regression settings. We also propose a class of estimators that provide reduced mean bias and squared error , while allowing the investigator to control the risk of underestimating the true ratio parameter."}
+{"idx": 8, "title": "PDF Minimum Mean Squared Error Model Averaging in Likelihood Models", "date": "", "ddg_snippet": "We investigate the performance of our methods in both linear models and generalized linear models, and illustrate the methods in two empirical applications. Key words and phrases: Frequentist model averaging, likelihood regression , local misspecification, mean squared error , weight choice.", "subpage_snippet": "", "source": "www3.stat.sinica.edu.tw", "link": "https://www3.stat.sinica.edu.tw/statistica/oldpdf/A26n219.pdf", "content": "We investigate the performance of our methods in both linear models and generalized linear models, and illustrate the methods in two empirical applications. Key words and phrases: Frequentist model averaging, likelihood regression , local misspecification, mean squared error , weight choice."}
+{"idx": 9, "title": "Gaussian Noise and Mean Squared Error - Jonathan Ramkissoon", "date": "", "ddg_snippet": "The first approach would be to make an assumption about the distribution of errors and use this to form a likelihood function from which we can do maximum likelihood estimation.", "subpage_snippet": "", "source": "jramkiss.github.io", "link": "https://jramkiss.github.io/2022/01/05/MLE-loss-regression/", "content": "The first approach would be to make an assumption about the distribution of errors and use this to form a likelihood function from which we can do maximum likelihood estimation."}
diff --git a/data/sampled_jsons/Likelihood_Based_Approach_to_Distribution_Regression_Using_Conditional_Deep_Generative_Models_Table_.jsonl b/data/sampled_jsons/Likelihood_Based_Approach_to_Distribution_Regression_Using_Conditional_Deep_Generative_Models_Table_.jsonl
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2410.02025] A Likelihood Based Approach to Distribution ...", "date": "", "ddg_snippet": "Oct 2, 2024 · In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where the response variable lies in a high-dimensional ambient space but concentrates around a potentially lower-dimensional manifold. More specifically, we study the large-sample properties of a likelihood - based approach for estimating these ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.02025", "content": "Oct 2, 2024 · In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where the response variable lies in a high-dimensional ambient space but concentrates around a potentially lower-dimensional manifold. More specifically, we study the large-sample properties of a likelihood - based approach for estimating these ..."}
+{"idx": 1, "title": "A LIKELIHOOD BASED APPROACH TO DISTRIBUTION REGRESSION USING ...", "date": "", "ddg_snippet": "he large-sample properties of a likelihood-based approach for estimating these models. Our results lead to the convergence rate of a sieve maximum likelihood estimator ( MLE ) for estimating the conditional distribution (and its devolv", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=V6hhhXoTSq", "content": "he large-sample properties of a likelihood-based approach for estimating these models. Our results lead to the convergence rate of a sieve maximum likelihood estimator ( MLE ) for estimating the conditional distribution (and its devolv"}
+{"idx": 2, "title": "dblp: A Likelihood Based Approach to Distribution Regression ...", "date": "", "ddg_snippet": "Bibliographic details on A Likelihood Based Approach to Distribution Regression Using Conditional Deep Generative Models .", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/journals/corr/abs-2410-02025", "content": "Bibliographic details on A Likelihood Based Approach to Distribution Regression Using Conditional Deep Generative Models ."}
+{"idx": 3, "title": "ICML Poster A Likelihood Based Approach to Distribution ...", "date": "", "ddg_snippet": "In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where the response variable lies in a high-dimensional ambient space but concentrates around a potentially lower-dimensional manifold. More specifically, we study the large-sample properties of a likelihood - based approach for estimating these ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46645", "content": "In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where the response variable lies in a high-dimensional ambient space but concentrates around a potentially lower-dimensional manifold. More specifically, we study the large-sample properties of a likelihood - based approach for estimating these ..."}
+{"idx": 4, "title": "A Likelihood Based Approach to Distribution Regression Using ...", "date": "", "ddg_snippet": "Abstract In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where the response variable lies in a high-dimensional ambient space but concentrates around a potentially lower-dimensional manifold. More specifically, we study the large-sample properties of a likelihood - based approach for estimating ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.02025v1", "content": "Abstract In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where the response variable lies in a high-dimensional ambient space but concentrates around a potentially lower-dimensional manifold. More specifically, we study the large-sample properties of a likelihood - based approach for estimating ..."}
+{"idx": 5, "title": "A Likelihood Approach to Nonparametric Estimation of a ...", "date": "", "ddg_snippet": "In this work, we focus on the likelihood-based approach and study statistical proper-ties of a sieve maximum likelihood estimator ( MLE ) of deep generative models under the assumption that P is the distribution of X = f (Z) + for some function f : Z ! R and N (0; 2ID), where 0.", "subpage_snippet": "", "source": "jmlr.org", "link": "https://jmlr.org/papers/volume24/21-1099/21-1099.pdf", "content": "In this work, we focus on the likelihood-based approach and study statistical proper-ties of a sieve maximum likelihood estimator ( MLE ) of deep generative models under the assumption that P is the distribution of X = f (Z) + for some function f : Z ! R and N (0; 2ID), where 0."}
+{"idx": 6, "title": "Deep distribution regression - ScienceDirect", "date": "", "ddg_snippet": "Jul 1 , 2021 · Abstract Due to their flexibility and predictive performance, machine-learning based regression methods have become an important tool for predictive modeling and forecasting. However, most methods focus on estimating the conditional mean or specific quantiles of the target quantity and do not provide the full conditional distribution , which contains uncertainty information that might be ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0167947321000372", "content": "Jul 1 , 2021 · Abstract Due to their flexibility and predictive performance, machine-learning based regression methods have become an important tool for predictive modeling and forecasting. However, most methods focus on estimating the conditional mean or specific quantiles of the target quantity and do not provide the full conditional distribution , which contains uncertainty information that might be ..."}
+{"idx": 7, "title": "(PDF) A Likelihood Based Approach to Distribution Regression ...", "date": "", "ddg_snippet": "Keywords: Distribution Regression ; Conditional Deep Generative Models ; Intrinsic Manifold Structure; Sieve MLE ; Wasserstein Convergence.A deep generative approach to conditional sampling. Journal of the American Statistical Association, pages 1 –12.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/384630603_A_Likelihood_Based_Approach_to_Distribution_Regression_Using_Conditional_Deep_Generative_Models", "content": "Keywords: Distribution Regression ; Conditional Deep Generative Models ; Intrinsic Manifold Structure; Sieve MLE ; Wasserstein Convergence.A deep generative approach to conditional sampling. Journal of the American Statistical Association, pages 1 –12."}
+{"idx": 8, "title": "A Likelihood Based Approach to Distribution Regression Using ...", "date": "", "ddg_snippet": "This paper presents a likelihood - based approach for distribution regression using conditional deep generative models .", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/likelihood-based-approach-to-distribution-regression-using", "content": "This paper presents a likelihood - based approach for distribution regression using conditional deep generative models ."}
+{"idx": 9, "title": "A Likelihood Based Approach to Distribution Regression Using ...", "date": "", "ddg_snippet": "The text explores conditional deep generative models for distribution regression , focusing on high-dimensional data concentrated around a lower-dimensional manifold. It analyzes large-sample properties...", "subpage_snippet": "", "source": "powerdrill.ai", "link": "https://powerdrill.ai/discover/discover-A-Likelihood-Based-cm1v7rba8uvnv013whs4l4bq4", "content": "The text explores conditional deep generative models for distribution regression , focusing on high-dimensional data concentrated around a lower-dimensional manifold. It analyzes large-sample properties..."}
diff --git a/data/sampled_jsons/Linearization_Turns_Neural_Operators_into_Function-Valued_Gaussian_Processes_arXiv_OOD_Flip_dataset_year_2023-2024.jsonl b/data/sampled_jsons/Linearization_Turns_Neural_Operators_into_Function-Valued_Gaussian_Processes_arXiv_OOD_Flip_dataset_year_2023-2024.jsonl
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Linearization Turns Neural Operators into Function-Valued ...", "date": "", "ddg_snippet": "by E Magnani · Cited by 4 — Abstract. Neural operators generalize neural networks to learn mappings between function spaces from data. They are commonly used to learn solution.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=4Z04wVQ9FY", "content": "by E Magnani · Cited by 4 — Abstract. Neural operators generalize neural networks to learn mappings between function spaces from data. They are commonly used to learn solution."}
+{"idx": 1, "title": "Linearization Turns Neural Operators into Function-Valued ...", "date": "", "ddg_snippet": "Our approach leverages model linearization to push ( Gaussian ) weight-space uncertainty forward to the neural operator's predictions. We show that this can be ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46474", "content": "Our approach leverages model linearization to push ( Gaussian ) weight-space uncertainty forward to the neural operator's predictions. We show that this can be ..."}
+{"idx": 2, "title": "Uncertainty propagation in feed-forward neural network ...", "date": "", "ddg_snippet": "9 Aug 2025 — A key finding of our study is that a “ linearization ” of the leaky ReLU activation function in the deep net representing (1) allows us to obtain ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.21059", "content": "9 Aug 2025 — A key finding of our study is that a “ linearization ” of the leaky ReLU activation function in the deep net representing (1) allows us to obtain ..."}
+{"idx": 3, "title": "Ambient Noise Full Waveform Inversion with Neural ...", "date": "", "ddg_snippet": "19 Mar 2025 — In this study, we demonstrate the application of neural operators for full waveform inversion on a real seismic dataset .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.15013v1", "content": "19 Mar 2025 — In this study, we demonstrate the application of neural operators for full waveform inversion on a real seismic dataset ."}
+{"idx": 4, "title": "Incorporating Unlabelled Data into Bayesian Neural ...", "date": "", "ddg_snippet": "30 Aug 2024 — Using a novel evaluation scheme, we showed that self-supervised BNNs learn functional priors that better reflect the semantics of the data than ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2304.01762v3", "content": "30 Aug 2024 — Using a novel evaluation scheme, we showed that self-supervised BNNs learn functional priors that better reflect the semantics of the data than ..."}
+{"idx": 5, "title": "Computation-through-Dynamics Benchmark", "date": "", "ddg_snippet": "8 Feb 2025 — A powerful framework for understanding neural computation uses neural dynamics – the rules that describe the temporal evolution of neural ...", "subpage_snippet": "", "source": "www.biorxiv.org", "link": "https://www.biorxiv.org/content/10.1101/2025.02.07.637062v1.full.pdf", "content": "8 Feb 2025 — A powerful framework for understanding neural computation uses neural dynamics – the rules that describe the temporal evolution of neural ..."}
+{"idx": 6, "title": "Computation-through-Dynamics Benchmark: Simulated ...", "date": "", "ddg_snippet": "by C Versteeg · 2025 · Cited by 1 — CtDB provides a critical platform for model developers to better understand and characterize neural computation through the lens of dynamics.", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC11839132/", "content": "by C Versteeg · 2025 · Cited by 1 — CtDB provides a critical platform for model developers to better understand and characterize neural computation through the lens of dynamics."}
+{"idx": 7, "title": "NeurIPS 2024 Papers", "date": "", "ddg_snippet": "Modeling Latent Neural Dynamics with Gaussian Process Switching Linear Dynamical Systems · Language Model as Visual Explainer · No Representation, No Trust ...", "subpage_snippet": "", "source": "nips.cc", "link": "https://nips.cc/virtual/2024/papers.html", "content": "Modeling Latent Neural Dynamics with Gaussian Process Switching Linear Dynamical Systems · Language Model as Visual Explainer · No Representation, No Trust ..."}
+{"idx": 8, "title": "Deep Learning for Solving Economic Models Jesús ...", "date": "", "ddg_snippet": "by J Fernández-Villaverde · 2025 — There are two lines in the graph: the discontinuous line is the value function from the deep neural network. The continuous line is the value function computed ... 58 pages", "subpage_snippet": "", "source": "www.nber.org", "link": "https://www.nber.org/system/files/working_papers/w34250/w34250.pdf", "content": "by J Fernández-Villaverde · 2025 — There are two lines in the graph: the discontinuous line is the value function from the deep neural network. The continuous line is the value function computed ... 58 pages"}
+{"idx": 9, "title": "Learning Partial Differential Equations in Reproducing ...", "date": "", "ddg_snippet": "by G Stepaniants · 2023 · Cited by 29 — We propose a new data-driven approach for learning the fundamental solutions (Green's functions ) of various linear partial differential equations (PDEs) ... 72 pages", "subpage_snippet": "", "source": "www.jmlr.org", "link": "https://www.jmlr.org/papers/volume24/21-1363/21-1363.pdf", "content": "by G Stepaniants · 2023 · Cited by 29 — We propose a new data-driven approach for learning the fundamental solutions (Green's functions ) of various linear partial differential equations (PDEs) ... 72 pages"}
diff --git a/data/sampled_jsons/Linearization_Turns_Neural_Operators_into_Function-Valued_Gaussian_Processes_ensemble_OOD_generaliza_year_2024.jsonl b/data/sampled_jsons/Linearization_Turns_Neural_Operators_into_Function-Valued_Gaussian_Processes_ensemble_OOD_generaliza_year_2024.jsonl
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index 0000000000000000000000000000000000000000..81479c25a0141ebde1e26fae019e63a4681bbac9
--- /dev/null
+++ b/data/sampled_jsons/Linearization_Turns_Neural_Operators_into_Function-Valued_Gaussian_Processes_ensemble_OOD_generaliza_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Distance-informed Neural Processes", "date": "", "ddg_snippet": "Stochastic models such as Gaussian Processes (GPs) williams2006gaussian address this limitation by defining a prior over functions using kernel ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.18903v1", "content": "Stochastic models such as Gaussian Processes (GPs) williams2006gaussian address this limitation by defining a prior over functions using kernel ..."}
+{"idx": 1, "title": "Deep Neural Networks Tend To Extrapolate Predictably", "date": "", "ddg_snippet": "... in arbitrary ways, we observe that neural network predictions often tend towards a constant value as input data becomes increasingly OOD .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2310.00873v2", "content": "... in arbitrary ways, we observe that neural network predictions often tend towards a constant value as input data becomes increasingly OOD ."}
+{"idx": 2, "title": "ICLR 2024 Schedule", "date": "", "ddg_snippet": "Going Beyond Neural Network Feature ... PAC-FNO: Parallel-Structured All-Component Fourier Neural Operators for Recognizing Low-Quality Images", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2024/calendar", "content": "Going Beyond Neural Network Feature ... PAC-FNO: Parallel-Structured All-Component Fourier Neural Operators for Recognizing Low-Quality Images"}
+{"idx": 3, "title": "ICLR 2024 Papers", "date": "", "ddg_snippet": "The Effect of Intrinsic Dataset Properties on Generalization : Unraveling Learning Differences Between Natural and Medical Images", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2024/papers.html", "content": "The Effect of Intrinsic Dataset Properties on Generalization : Unraveling Learning Differences Between Natural and Medical Images"}
+{"idx": 4, "title": "ICLR 2023 Schedule", "date": "", "ddg_snippet": "... Process is Necessary for Out-of-Distribution ... Modeling the Data-Generating Process is Necessary for Out-of-Distribution Generalization", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2023/calendar", "content": "... Process is Necessary for Out-of-Distribution ... Modeling the Data-Generating Process is Necessary for Out-of-Distribution Generalization"}
+{"idx": 5, "title": "ICML 2021 Papers", "date": "", "ddg_snippet": "... Gaussian Processes and Steerable ... Improving Breadth-Wise Backpropagation in Graph Neural Networks Helps Learning Long-Range Dependencies.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2021/papers.html", "content": "... Gaussian Processes and Steerable ... Improving Breadth-Wise Backpropagation in Graph Neural Networks Helps Learning Long-Range Dependencies."}
+{"idx": 6, "title": "Downloads", "date": "", "ddg_snippet": "A theory of high dimensional regression with arbitrary correlations between input features and target functions : sample complexity, multiple descent ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/Downloads/2021", "content": "A theory of high dimensional regression with arbitrary correlations between input features and target functions : sample complexity, multiple descent ..."}
+{"idx": 7, "title": "Keywords - Idiap Publications", "date": "", "ddg_snippet": "artificial) neural network ... audio processing ... Bayesian Gaussian mixture model", "subpage_snippet": "", "source": "publications.idiap.ch", "link": "https://publications.idiap.ch/keywords", "content": "artificial) neural network ... audio processing ... Bayesian Gaussian mixture model"}
+{"idx": 8, "title": "Accurate and Scalable Estimation of Epistemic Uncertainty for", "date": "", "ddg_snippet": "Notably, since GEP and OOD detection methods often rely upon transformations of a model’s logits, improving calibration can in turn improve ...", "subpage_snippet": "", "source": "tollandbicycle.com", "link": "https://tollandbicycle.com/article/accurate-and-scalable-estimation-of-epistemic-uncertainty-for-graph-neural-networks", "content": "Notably, since GEP and OOD detection methods often rely upon transformations of a model’s logits, improving calibration can in turn improve ..."}
+{"idx": 9, "title": "Glossary | Saturn Cloud", "date": "", "ddg_snippet": "Data Augmentation in Natural Language Processing (NLP) ... Query Understanding in Natural Language Processing (NLP)", "subpage_snippet": "", "source": "saturncloud.io", "link": "https://saturncloud.io/glossary/", "content": "Data Augmentation in Natural Language Processing (NLP) ... Query Understanding in Natural Language Processing (NLP)"}
diff --git a/data/sampled_jsons/Lippe_et_al._2022_CITRIS_year_2022.jsonl b/data/sampled_jsons/Lippe_et_al._2022_CITRIS_year_2022.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d87be182c7c3db946a976b282a4ffea9bd02a5be
--- /dev/null
+++ b/data/sampled_jsons/Lippe_et_al._2022_CITRIS_year_2022.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "CITRIS: Causal Identifiability from Temporal Intervened ...", "date": "", "ddg_snippet": "by P Lippe · 2022 · Cited by 141 — We find this graph by using ENCO ( Lippe et al ., 2022 ), a continuous-optimization causal discovery method which supports the usage of arbitrary neural ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v162/lippe22a/lippe22a.pdf", "content": "by P Lippe · 2022 · Cited by 141 — We find this graph by using ENCO ( Lippe et al ., 2022 ), a continuous-optimization causal discovery method which supports the usage of arbitrary neural ..."}
+{"idx": 1, "title": "iCITRIS: Causal Representation Learning for Instantaneous ...", "date": "", "ddg_snippet": "by P Lippe — [1] Lippe, Phillip, et al . \"CITRIS: Causal Identifiability from Temporal Intervened. Sequences.\" International Conference on Machine Learning. PMLR, 2022.", "subpage_snippet": "", "source": "phlippe.github.io", "link": "https://phlippe.github.io/media/iCITRIS_Poster_A0.pdf", "content": "by P Lippe — [1] Lippe, Phillip, et al . \"CITRIS: Causal Identifiability from Temporal Intervened. Sequences.\" International Conference on Machine Learning. PMLR, 2022."}
+{"idx": 2, "title": "Causal Identifiability from Temporal Intervened Sequences", "date": "", "ddg_snippet": "by P Lippe · 2022 · Cited by 141 — In this paper, we propose CITRIS , a variational autoencoder framework that learns causal representations from temporal sequences of images.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2202.03169", "content": "by P Lippe · 2022 · Cited by 141 — In this paper, we propose CITRIS , a variational autoencoder framework that learns causal representations from temporal sequences of images."}
+{"idx": 3, "title": "Learning Causal Variables from Temporal Sequences with ...", "date": "", "ddg_snippet": "5 Aug 2022 — Lippe, Phillip et al . “Intervention Design for Causal Representation Learning.” CRL@UAI 2022. Page 12. CITRIS Architecture. CITRIS-NF. Learning ...", "subpage_snippet": "", "source": "phlippe.github.io", "link": "https://phlippe.github.io/media/UAI2022_CRL_Invited_Talk.pdf", "content": "5 Aug 2022 — Lippe, Phillip et al . “Intervention Design for Causal Representation Learning.” CRL@UAI 2022. Page 12. CITRIS Architecture. CITRIS-NF. Learning ..."}
+{"idx": 4, "title": "Causal Representation Learning for Instantaneous and ...", "date": "", "ddg_snippet": "by P Lippe · Cited by 50 — This paper describes iCITRIS, a generalization of CITRIS [Lippe et al 2022] to allow for “instantaneous effects”: i.e. they allow ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=itZ6ggvMnzS", "content": "by P Lippe · Cited by 50 — This paper describes iCITRIS, a generalization of CITRIS [Lippe et al 2022] to allow for “instantaneous effects”: i.e. they allow ..."}
+{"idx": 5, "title": "Causal Identifiability from Temporal Intervened Sequences", "date": "", "ddg_snippet": "In this tutorial, we will have a closer look at CIT RIS (Lippe et al., 2022a), a variational autoencoder framework that learns causal representations from ...", "subpage_snippet": "", "source": "uvadlc-notebooks.readthedocs.io", "link": "https://uvadlc-notebooks.readthedocs.io/en/latest/tutorial_notebooks/DL2/Causality_and_CRL/citris-tutorial.html", "content": "In this tutorial, we will have a closer look at CIT RIS (Lippe et al., 2022a), a variational autoencoder framework that learns causal representations from ..."}
+{"idx": 6, "title": "Towards the Reusability and Compositionality of Causal ...", "date": "", "ddg_snippet": "Since the TRIS setting was originally developed for CITRIS Lippe et al .( 2022b ) Lippe , Magliacane, Löwe, Asano, Cohen, and Gavves , we can use it as is in this ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.09830v1", "content": "Since the TRIS setting was originally developed for CITRIS Lippe et al .( 2022b ) Lippe , Magliacane, Löwe, Asano, Cohen, and Gavves , we can use it as is in this ..."}
+{"idx": 7, "title": "Intervention Design for Causal Representation Learning", "date": "", "ddg_snippet": "by P Lippe · Cited by 12 — As a specific setting, we focus on the recent causal represen- tation learning method CITRIS (Lippe et al., 2022b) which leverages data from temporal intervened ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=TpVzjh4M2hd", "content": "by P Lippe · Cited by 12 — As a specific setting, we focus on the recent causal represen- tation learning method CITRIS (Lippe et al., 2022b) which leverages data from temporal intervened ..."}
+{"idx": 8, "title": "Causal Representation Learning for Instantaneous and ...", "date": "", "ddg_snippet": "by P Lippe · Cited by 50 — \" CITRIS: Causal. Identifiability from Temporal Intervened Sequences .\" In International Conference on Machine Learning, PMLR, 2022. Page 5. iCITRIS Architecture. 7 pages", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/media/iclr-2023/Slides/12009.pdf", "content": "by P Lippe · Cited by 50 — \" CITRIS: Causal. Identifiability from Temporal Intervened Sequences .\" In International Conference on Machine Learning, PMLR, 2022. Page 5. iCITRIS Architecture. 7 pages"}
+{"idx": 9, "title": "Weakly supervised causal representation learning", "date": "", "ddg_snippet": "by J Brehmer · 2022 · Cited by 190 — Lippe et al . [15] learn causal representations from time-series data from labelled interventions, assuming that causal effects are not instantaneous but can ... 13 pages", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2022/file/fa567e2b2c870f8f09a87b6e73370869-Paper-Conference.pdf", "content": "by J Brehmer · 2022 · Cited by 190 — Lippe et al . [15] learn causal representations from time-series data from labelled interventions, assuming that causal effects are not instantaneous but can ... 13 pages"}
diff --git a/data/sampled_jsons/Locally_private_graph_neural_networks_Sajadmanesh_Gatica-Perez_abstract_year_2021.jsonl b/data/sampled_jsons/Locally_private_graph_neural_networks_Sajadmanesh_Gatica-Perez_abstract_year_2021.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e934693a78e47b5949d113520d054dd25d3ec841
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+++ b/data/sampled_jsons/Locally_private_graph_neural_networks_Sajadmanesh_Gatica-Perez_abstract_year_2021.jsonl
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+{"idx": 0, "title": "[2006.05535] Locally Private Graph Neural Networks", "date": "", "ddg_snippet": "Abstract : Graph Neural Networks (GNNs) have demonstrated superior performance in learning node representations for various graph inference tasks.View a PDF of the paper titled Locally Private Graph Neural Networks , by Sina Sajadmanesh and Daniel Gatica - Perez .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2006.05535", "content": "Abstract : Graph Neural Networks (GNNs) have demonstrated superior performance in learning node representations for various graph inference tasks.View a PDF of the paper titled Locally Private Graph Neural Networks , by Sina Sajadmanesh and Daniel Gatica - Perez ."}
+{"idx": 1, "title": "(PDF) Locally Private Graph Neural Networks", "date": "", "ddg_snippet": "Abstract and Figures. Graph Neural Networks (GNNs) have demonstrated superior performance in learning node representations for various graph inference tasks. Sajadmanesh and Gatica - Perez .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/348678319_Locally_Private_Graph_Neural_Networks", "content": "Abstract and Figures. Graph Neural Networks (GNNs) have demonstrated superior performance in learning node representations for various graph inference tasks. Sajadmanesh and Gatica - Perez ."}
+{"idx": 2, "title": "Locally Private Graph Neural Networks", "date": "", "ddg_snippet": "EPFL. ABSTRACT . Graph Neural Networks (GNNs) have demonstrated superior perfor-mance in learning node representations for various graph inference tasks.ACM Reference Format: Sina Sajadmanesh and Daniel Gatica - Perez .", "subpage_snippet": "", "source": "publications.idiap.ch", "link": "https://publications.idiap.ch/downloads/papers/2021/Sajadmanesh_CCS2021_2021.pdf", "content": "EPFL. ABSTRACT . Graph Neural Networks (GNNs) have demonstrated superior perfor-mance in learning node representations for various graph inference tasks.ACM Reference Format: Sina Sajadmanesh and Daniel Gatica - Perez ."}
+{"idx": 3, "title": "(Open Access) Locally Private Graph Neural Networks (2020)", "date": "", "ddg_snippet": "Sina Sajadmanesh , Daniel Gatica - Perez +1 moreIdiap Research Institute. - 09 Jun 2020. Abstract : Graph Neural Networks (GNNs) have demonstrated superior performance in learning node representations for various graph inference tasks.", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/papers/locally-private-graph-neural-networks-1lusvir819", "content": "Sina Sajadmanesh , Daniel Gatica - Perez +1 moreIdiap Research Institute. - 09 Jun 2020. Abstract : Graph Neural Networks (GNNs) have demonstrated superior performance in learning node representations for various graph inference tasks."}
+{"idx": 4, "title": "Locally Private Graph Neural Networks | Proceedings of the 2021...", "date": "", "ddg_snippet": "Presentation video for the paper \" Locally Private Graph Neural Networks \". In this work, we propose a privacy -preserving GNN framework based on local differential privacy , when the graph topology is public but the node features/labels are private .", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.1145/3460120.3484565?cookieSet=1", "content": "Presentation video for the paper \" Locally Private Graph Neural Networks \". In this work, we propose a privacy -preserving GNN framework based on local differential privacy , when the graph topology is public but the node features/labels are private ."}
+{"idx": 5, "title": "Locally Private Graph Neural Networks - Paper Detail", "date": "", "ddg_snippet": "Sina Sajadmanesh , Daniel Gatica - Perez . Abstract . Graph Neural Networks (GNNs) have demonstrated superior performance inlearning graph representations for several subsequent downstream inferencetasks.", "subpage_snippet": "", "source": "deeplearn.org", "link": "https://deeplearn.org/arxiv/179057/locally-private-graph-neural-networks", "content": "Sina Sajadmanesh , Daniel Gatica - Perez . Abstract . Graph Neural Networks (GNNs) have demonstrated superior performance inlearning graph representations for several subsequent downstream inferencetasks."}
+{"idx": 6, "title": "Locally Private Graph Neural Networks -Bohrium", "date": "", "ddg_snippet": "Graph Neural Networks (GNNs) have demonstrated superior performance in learning node representations for various graph inference tasks.", "subpage_snippet": "", "source": "www.bohrium.com", "link": "https://www.bohrium.com/paper-details/locally-private-graph-neural-networks/867757841736269842-108614", "content": "Graph Neural Networks (GNNs) have demonstrated superior performance in learning node representations for various graph inference tasks."}
+{"idx": 7, "title": "Sina Sajadmanesh - Google Scholar", "date": "", "ddg_snippet": "2020. Locally Private Graph Neural Networks . S Sajadmanesh , D Gatica - Perez . ACM CCS, 2021.", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=Gtw3NoAAAAAJ&hl=en", "content": "2020. Locally Private Graph Neural Networks . S Sajadmanesh , D Gatica - Perez . ACM CCS, 2021."}
+{"idx": 8, "title": "GitHub - sisaman/LPGNN: Locally Private Graph Neural Networks ...", "date": "", "ddg_snippet": "Abstract . Graph Neural Networks (GNNs) have demonstrated superior performance in learning node representations for various graph inference tasks. However, learning over graph data can raise privacy concerns when nodes represent people or...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/sisaman/LPGNN", "content": "Abstract . Graph Neural Networks (GNNs) have demonstrated superior performance in learning node representations for various graph inference tasks. However, learning over graph data can raise privacy concerns when nodes represent people or..."}
+{"idx": 9, "title": "GAP: Differentially Private Graph Neural Networks", "date": "", "ddg_snippet": "[38] Sina Sajadmanesh and Daniel Gatica - Perez . Locally private graph neural networks . In Proceedings of the. 2021 ACM SIGSAC Conference on Computer and Com-munications Security, pages 2130–2145, 2021.", "subpage_snippet": "", "source": "www.usenix.org", "link": "https://www.usenix.org/system/files/sec23fall-prepub-196-sajadmanesh.pdf", "content": "[38] Sina Sajadmanesh and Daniel Gatica - Perez . Locally private graph neural networks . In Proceedings of the. 2021 ACM SIGSAC Conference on Computer and Com-munications Security, pages 2130–2145, 2021."}
diff --git a/data/sampled_jsons/Long-Form_Speech_Generation_with_Spoken_Language_Models_arXiv_2412.18603_Experimental_Setup_training.jsonl b/data/sampled_jsons/Long-Form_Speech_Generation_with_Spoken_Language_Models_arXiv_2412.18603_Experimental_Setup_training.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..bb25a58bc4f09b38667a691c7e12141b1701908c
--- /dev/null
+++ b/data/sampled_jsons/Long-Form_Speech_Generation_with_Spoken_Language_Models_arXiv_2412.18603_Experimental_Setup_training.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Long-Form Speech Generation with Spoken Language ...", "date": "", "ddg_snippet": "24 Dec 2024 — ... training sequences of up to one of 30s, 4m (240s), and 16m (960s) in duration . For each preprocessed duration, we train a model on 16 TPU ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.18603v1", "content": "24 Dec 2024 — ... training sequences of up to one of 30s, 4m (240s), and 16m (960s) in duration . For each preprocessed duration, we train a model on 16 TPU ..."}
+{"idx": 1, "title": "Long-Form Speech Generation with Spoken Language ...", "date": "", "ddg_snippet": "by SJ Park · 2024 · Cited by 9 — We discuss the design choices required to en- able the practical training , generation , and extrapolation to tens of minutes of audio, from tokenization to ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2412.18603", "content": "by SJ Park · 2024 · Cited by 9 — We discuss the design choices required to en- able the practical training , generation , and extrapolation to tens of minutes of audio, from tokenization to ..."}
+{"idx": 2, "title": "On The Landscape of Spoken Language Models", "date": "", "ddg_snippet": "11 Apr 2025 — The field of spoken language processing is undergoing a shift from training custom-built, task-specific models toward using and optimizing ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.08528v1", "content": "11 Apr 2025 — The field of spoken language processing is undergoing a shift from training custom-built, task-specific models toward using and optimizing ..."}
+{"idx": 3, "title": "Training a Speech Language Model on One GPU in a Day", "date": "", "ddg_snippet": "by G Maimon · 2025 · Cited by 4 — We introduce Slam, a recipe for training high- quality Speech Language Models ( SLMs) on a single academic GPU in 24 hours . We do so. 16 pages", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.findings-acl.631.pdf", "content": "by G Maimon · 2025 · Cited by 4 — We introduce Slam, a recipe for training high- quality Speech Language Models ( SLMs) on a single academic GPU in 24 hours . We do so. 16 pages"}
+{"idx": 4, "title": "01Zhangbw/Speech-and-audio-papers-Top-Conference", "date": "", "ddg_snippet": "Long - Form Speech Generation with Spoken Language Models · https:// arxiv .org/abs/ 2412.18603 · Aligning Spoken Dialogue Models from User Interactions, spoken ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/01Zhangbw/Speech-and-audio-papers-Top-Conference", "content": "Long - Form Speech Generation with Spoken Language Models · https:// arxiv .org/abs/ 2412.18603 · Aligning Spoken Dialogue Models from User Interactions, spoken ..."}
+{"idx": 5, "title": "Recent Advances in Discrete Speech Tokens: A Review", "date": "", "ddg_snippet": "16 Feb 2025 — In this review, we provide a comprehensive overview of the concepts, methods, and characteristics of various types of discrete speech tokens, ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.06490v2", "content": "16 Feb 2025 — In this review, we provide a comprehensive overview of the concepts, methods, and characteristics of various types of discrete speech tokens, ..."}
+{"idx": 6, "title": "WailordHe/cv-arxiv-daily-wailord: 🎓Automatically Update ...", "date": "", "ddg_snippet": "Long - Form Speech Generation with Spoken Language Models , Se Jin Park et.al. 2412.18603 · link. 2024-12-24, Decentralized Intelligence in GameFi: Embodied AI ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/WailordHe/cv-arxiv-daily-wailord", "content": "Long - Form Speech Generation with Spoken Language Models , Se Jin Park et.al. 2412.18603 · link. 2024-12-24, Decentralized Intelligence in GameFi: Embodied AI ..."}
+{"idx": 7, "title": "PROSODYLM: Uncovering the Emerging Prosody ...", "date": "", "ddg_snippet": "by K Qian — We find that PROSODYLM can learn surprisingly di- verse emerging prosody processing capabilities through pre- training alone, ranging from harnessing the prosody ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=uBg8PClMUu", "content": "by K Qian — We find that PROSODYLM can learn surprisingly di- verse emerging prosody processing capabilities through pre- training alone, ranging from harnessing the prosody ..."}
+{"idx": 8, "title": "Stop Looking for “Important Tokens” in Multimodal ...", "date": "", "ddg_snippet": "2024. 799. Long - form speech generation with spoken language . 800 models . arXiv preprint arXiv : 2412.18603 . 801. Alec Radford, Jong Wook Kim, Chris Hallacy ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/bed129fe3781b5aff48c8f3f830470fc21adaeef.pdf", "content": "2024. 799. Long - form speech generation with spoken language . 800 models . arXiv preprint arXiv : 2412.18603 . 801. Alec Radford, Jong Wook Kim, Chris Hallacy ..."}
+{"idx": 9, "title": "VibeVoice Technical Report", "date": "", "ddg_snippet": "26 Aug 2025 — Thus, VibeVoice can synthesize long-form speech for up to 90 minutes (in a 64K context window length) with a maximum of 4 speakers, capturing ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.19205v1", "content": "26 Aug 2025 — Thus, VibeVoice can synthesize long-form speech for up to 90 minutes (in a 64K context window length) with a maximum of 4 speakers, capturing ..."}
diff --git a/data/sampled_jsons/Longpre_2021_EMNLP_entity-based_knowledge_conflicts_abstract_NQ-Swap.jsonl b/data/sampled_jsons/Longpre_2021_EMNLP_entity-based_knowledge_conflicts_abstract_NQ-Swap.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..525a7031b8cdd512b7a422c382e379296d8b626c
--- /dev/null
+++ b/data/sampled_jsons/Longpre_2021_EMNLP_entity-based_knowledge_conflicts_abstract_NQ-Swap.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ADACAD: Adaptively Decoding to Balance Conflicts ...", "date": "", "ddg_snippet": "by H Wang · 2025 · Cited by 15 — NQ-SWAP ( Longpre et al., 2021 ) introduces synthetic conflicts by swapping entities in the context to challenge the model's ability to manage ...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.naacl-long.581.pdf", "content": "by H Wang · 2025 · Cited by 15 — NQ-SWAP ( Longpre et al., 2021 ) introduces synthetic conflicts by swapping entities in the context to challenge the model's ability to manage ..."}
+{"idx": 1, "title": "Taming Knowledge Conflicts in Language Models", "date": "", "ddg_snippet": "6 Jun 2025 — ... NQ-Swap ( Longpre et al., 2021 ) . The details of these datasets can ... Entity - based knowledge conflicts in question answering. In ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.10996v2", "content": "6 Jun 2025 — ... NQ-Swap ( Longpre et al., 2021 ) . The details of these datasets can ... Entity - based knowledge conflicts in question answering. In ..."}
+{"idx": 2, "title": "Knowledge Conflicts for LLMs: A Survey", "date": "", "ddg_snippet": "by R Xu · 2024 · Cited by 175 — Entity - based knowledge conflicts in question answering. ... on Memotrap and by 128% on NQ-SWAP where. LLMs need to adhere to the given context.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2024.emnlp-main.486.pdf", "content": "by R Xu · 2024 · Cited by 175 — Entity - based knowledge conflicts in question answering. ... on Memotrap and by 128% on NQ-SWAP where. LLMs need to adhere to the given context."}
+{"idx": 3, "title": "AdaCAD: Adaptively Decoding to Balance Conflicts ...", "date": "", "ddg_snippet": "Additionally, we evaluate on an existing knowledge conflict dataset, NQ-Swap ( Longpre et al., 2021 ) , which is based on the NQ dataset and consists of synthetic ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2409.07394v2", "content": "Additionally, we evaluate on an existing knowledge conflict dataset, NQ-Swap ( Longpre et al., 2021 ) , which is based on the NQ dataset and consists of synthetic ..."}
+{"idx": 4, "title": "Knowledge Conflicts for LLMs: A Survey", "date": "", "ddg_snippet": "(2023a). Llama, OPT,. GPT-Neo, and. FLAN. NQ-SWAP , MemoTrap, and NQ. Their method improves GPT-Neo 20B by 54.4% on Memotrap and by 128% on NQ-SWAP where. LLMs ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=P8PF2e5EXf", "content": "(2023a). Llama, OPT,. GPT-Neo, and. FLAN. NQ-SWAP , MemoTrap, and NQ. Their method improves GPT-Neo 20B by 54.4% on Memotrap and by 128% on NQ-SWAP where. LLMs ..."}
+{"idx": 5, "title": "Exploiting Contextual Knowledge in LLMs through V- ...", "date": "", "ddg_snippet": "variant, NQ-Swap ( Longpre et al., 2021 ). The NQ-. 489. Swap dataset, derived from the original NQ, exclu-. 490 sively consists of conflicting contextual ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=B3i76OuS4d", "content": "variant, NQ-Swap ( Longpre et al., 2021 ). The NQ-. 489. Swap dataset, derived from the original NQ, exclu-. 490 sively consists of conflicting contextual ..."}
+{"idx": 6, "title": "Knowledge Conflicts For LLMS: A Survey | PDF", "date": "", "ddg_snippet": "11 Nov 2024 — (2023a) Llama, OPT, NQ-SWAP , MemoTrap, Their method improves GPT-Neo 20B by 54.4% GPT-Neo, and and NQ on Memotrap and by 128% on NQ-SWAP where", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/790695871/2403-08319v2-1", "content": "11 Nov 2024 — (2023a) Llama, OPT, NQ-SWAP , MemoTrap, Their method improves GPT-Neo 20B by 54.4% GPT-Neo, and and NQ on Memotrap and by 128% on NQ-SWAP where"}
+{"idx": 7, "title": "Understanding and Leveraging the Expert Specialization of", "date": "", "ddg_snippet": "As a result, experts identified through standard activation- based heuristics may not accurately reflect optimal specialization for context-dependent ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.19594v2", "content": "As a result, experts identified through standard activation- based heuristics may not accurately reflect optimal specialization for context-dependent ..."}
+{"idx": 8, "title": "Breaking the Trade-Off Between Faithfulness and Expressiveness", "date": "", "ddg_snippet": "This mechanism reranks the top-k candidate tokens based on their alignment with external knowledge , evaluated by both semantic similarity and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.18651v1", "content": "This mechanism reranks the top-k candidate tokens based on their alignment with external knowledge , evaluated by both semantic similarity and ..."}
+{"idx": 9, "title": "DeCoRe: Decoding by Contrasting Retrieval Heads to Mitigate", "date": "", "ddg_snippet": "... 2018 ) , MemoTrap (Liu & Liu, 2023 ) , Open Book Natural Questions ( NQ ; Kwiatkowski et al., 2019 ) , and NQ - Swap ( Longpre et al., 2021 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.18860v1", "content": "... 2018 ) , MemoTrap (Liu & Liu, 2023 ) , Open Book Natural Questions ( NQ ; Kwiatkowski et al., 2019 ) , and NQ - Swap ( Longpre et al., 2021 ..."}
diff --git a/data/sampled_jsons/Lu_Xian_Henry_Adams_Chad_Topaz_Lori_Ziegelmeier_Foundations_Data_Science_doi_10.3934fods.2021033.jsonl b/data/sampled_jsons/Lu_Xian_Henry_Adams_Chad_Topaz_Lori_Ziegelmeier_Foundations_Data_Science_doi_10.3934fods.2021033.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..341feb6644bbad4f5d8e76bfdffa00e4b10952b4
--- /dev/null
+++ b/data/sampled_jsons/Lu_Xian_Henry_Adams_Chad_Topaz_Lori_Ziegelmeier_Foundations_Data_Science_doi_10.3934fods.2021033.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Lu Xian Henry Adams Chad M. Topaz Lori Ziegelmeier", "date": "", "ddg_snippet": "In this paper, we have three overarching goals. First, we aim to provide a lay reader in the data science community with an overview of topological tools for studying time-varying metric spaces. Second, we demonstrate an application of some of these tools to a parameter recovery problem arising in the study of collective behavior. Finally, we present a new tool for time-varying metric spaces ...", "subpage_snippet": "", "source": "par.nsf.gov", "link": "https://par.nsf.gov/servlets/purl/10388958", "content": "In this paper, we have three overarching goals. First, we aim to provide a lay reader in the data science community with an overview of topological tools for studying time-varying metric spaces. Second, we demonstrate an application of some of these tools to a parameter recovery problem arising in the study of collective behavior. Finally, we present a new tool for time-varying metric spaces ..."}
+{"idx": 1, "title": "Capturing Dynamics of Time-Varying Data via Topology", "date": "", "ddg_snippet": "Capturing Dynamics of Time-Varying Data via Topology Lu Xian , Henry Adams , Chad M. Topaz , Lori Ziegelmeier", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2010.05780", "content": "Capturing Dynamics of Time-Varying Data via Topology Lu Xian , Henry Adams , Chad M. Topaz , Lori Ziegelmeier"}
+{"idx": 2, "title": "[PDF] Capturing dynamics of time-varying data via topology by Lu Xian ...", "date": "", "ddg_snippet": "\" Capturing dynamics of time-varying data via topology \" is a paper by Lu Xian Henry Adams Chad M. Topaz Lori Ziegelmeier published in 2022. It has an Open Access status of \"gold\".", "subpage_snippet": "", "source": "oa.mg", "link": "https://oa.mg/work/10.3934/fods.2021033", "content": "\" Capturing dynamics of time-varying data via topology \" is a paper by Lu Xian Henry Adams Chad M. Topaz Lori Ziegelmeier published in 2022. It has an Open Access status of \"gold\"."}
+{"idx": 3, "title": "Research - Henry Adams", "date": "", "ddg_snippet": "Capturing dynamics of time-varying data via topology. With Lu Xian , Chad Topaz , and Lori Ziegelmeier . Foundations of Data Science 4 (2022), 1-36. [Publisher Link, arXiv:2010.05780]", "subpage_snippet": "", "source": "people.clas.ufl.edu", "link": "https://people.clas.ufl.edu/henry-adams/research/", "content": "Capturing dynamics of time-varying data via topology. With Lu Xian , Chad Topaz , and Lori Ziegelmeier . Foundations of Data Science 4 (2022), 1-36. [Publisher Link, arXiv:2010.05780]"}
+{"idx": 4, "title": "Capturing dynamics of time-varying data via topology", "date": "", "ddg_snippet": "Lu Xian 1, , Henry Adams 2, , Chad M. Topaz 3, and Lori Ziegelmeier 4, , 1. School of Information, University of Michigan, Ann Arbor, MI 48109, USA 2. Department of Mathematics, Colorado State University, Fort Collins, CO 80523, USA 3. Department of Mathematics and Statistics, Williams College, Williamstown, MA 01267, USA 4.", "subpage_snippet": "", "source": "www.aimsciences.org", "link": "https://www.aimsciences.org/article/id/2acaee54-6688-46a4-b35d-447f84c4c691", "content": "Lu Xian 1, , Henry Adams 2, , Chad M. Topaz 3, and Lori Ziegelmeier 4, , 1. School of Information, University of Michigan, Ann Arbor, MI 48109, USA 2. Department of Mathematics, Colorado State University, Fort Collins, CO 80523, USA 3. Department of Mathematics and Statistics, Williams College, Williamstown, MA 01267, USA 4."}
+{"idx": 5, "title": "Chad TOPAZ | Macalester College, Saint Paul - ResearchGate", "date": "", "ddg_snippet": "Capturing dynamics of time-varying data via topology Article Jan 2021 Lu Xian Henry Adams Chad M. Topaz Lori Ziegelmeier", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/profile/Chad-Topaz", "content": "Capturing dynamics of time-varying data via topology Article Jan 2021 Lu Xian Henry Adams Chad M. Topaz Lori Ziegelmeier"}
+{"idx": 6, "title": "replication code for \"Capturing dynamics of time-varying data ... - GitHub", "date": "", "ddg_snippet": "Replication code of \"Capturing dynamics of time-varying data via topology\" by Lu Xian , Henry Adams , Chad M. Topaz , Lori Ziegelmeier , https://www.aimsciences.org/article/ doi /10.3934/fods.2021033", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/lxiancode/tda-crocker", "content": "Replication code of \"Capturing dynamics of time-varying data via topology\" by Lu Xian , Henry Adams , Chad M. Topaz , Lori Ziegelmeier , https://www.aimsciences.org/article/ doi /10.3934/fods.2021033"}
+{"idx": 7, "title": "Lori Ziegelmeier - dblp", "date": "", "ddg_snippet": "export record BibTeX RIS RDF N-Triples RDF Turtle RDF/XML XML dblp key: ask others Google Google Scholar Semantic Scholar Internet Archive Scholar CiteSeerX PubPeer share record Twitter Reddit BibSonomy LinkedIn Facebook persistent URL: Lu Xian , Henry Adams , Chad M. Topaz , Lori Ziegelmeier : Capturing Dynamics of Time-Varying Data via Topology ...", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/pid/69/10962", "content": "export record BibTeX RIS RDF N-Triples RDF Turtle RDF/XML XML dblp key: ask others Google Google Scholar Semantic Scholar Internet Archive Scholar CiteSeerX PubPeer share record Twitter Reddit BibSonomy LinkedIn Facebook persistent URL: Lu Xian , Henry Adams , Chad M. Topaz , Lori Ziegelmeier : Capturing Dynamics of Time-Varying Data via Topology ..."}
+{"idx": 8, "title": "Lu Xian | DeepAI", "date": "", "ddg_snippet": "Featured Co-authors Henry Adams 10 publications Lori Ziegelmeier 7 publications Chad M. Topaz 5 publications newest | popular Activity Feed Likes research ∙10/07/2020", "subpage_snippet": "", "source": "cdnjs.deepai.org", "link": "https://cdnjs.deepai.org/profile/lu-xian", "content": "Featured Co-authors Henry Adams 10 publications Lori Ziegelmeier 7 publications Chad M. Topaz 5 publications newest | popular Activity Feed Likes research ∙10/07/2020"}
+{"idx": 9, "title": "Lu Xian - Google Scholar", "date": "", "ddg_snippet": "University of Michigan - Cited by 103", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=EbIx7egAAAAJ&hl=en", "content": "University of Michigan - Cited by 103"}
diff --git a/data/sampled_jsons/Luo_&_Tseng_1992.jsonl b/data/sampled_jsons/Luo_&_Tseng_1992.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..453bfca3b02980e31825810ce253bbc435dec9df
--- /dev/null
+++ b/data/sampled_jsons/Luo_&_Tseng_1992.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Coordinate descent - Wikipedia", "date": "", "ddg_snippet": "Luo , Zhiquan; Tseng , P. ( 1992 ), \"On the convergence of the coordinate descent method for convex differentiable minimization\", Journal of Optimization Theory and Applications, vol. 72, no. 1, Kluwer Academic/Plenum Publishers, pp. 7–35, doi: 10.1007/BF00939948, hdl: 1721.1/3164, S2CID 121091844.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Coordinate_descent", "content": "Luo , Zhiquan; Tseng , P. ( 1992 ), \"On the convergence of the coordinate descent method for convex differentiable minimization\", Journal of Optimization Theory and Applications, vol. 72, no. 1, Kluwer Academic/Plenum Publishers, pp. 7–35, doi: 10.1007/BF00939948, hdl: 1721.1/3164, S2CID 121091844."}
+{"idx": 1, "title": "Error bounds and convergence analysis of feasible ...", "date": "", "ddg_snippet": "by ZQ Luo · 1993 · Cited by 596 — Luo and P. Tseng , On the convergence of the coordinate descent method for convex differentiable minimization, J. Optim. Theory Appl. 72 ( 1992 ) 7–35.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/BF02096261", "content": "by ZQ Luo · 1993 · Cited by 596 — Luo and P. Tseng , On the convergence of the coordinate descent method for convex differentiable minimization, J. Optim. Theory Appl. 72 ( 1992 ) 7–35."}
+{"idx": 2, "title": "On the linear convergence of descent methods for convex ...", "date": "", "ddg_snippet": "Luo , Z. Q., & Tseng , P. ( 1992 ). On the linear convergence of descent methods for convex essentially smooth minimization. SIAM Journal on Control and Optimization, 30 (2), 408-425. https://doi.org/10.1137/0330025", "subpage_snippet": "", "source": "experts.umn.edu", "link": "https://experts.umn.edu/en/publications/on-the-linear-convergence-of-descent-methods-for-convex-essential", "content": "Luo , Z. Q., & Tseng , P. ( 1992 ). On the linear convergence of descent methods for convex essentially smooth minimization. SIAM Journal on Control and Optimization, 30 (2), 408-425. https://doi.org/10.1137/0330025"}
+{"idx": 3, "title": "On the convergence of the coordinate descent method for ...", "date": "", "ddg_snippet": "On the convergence of the coordinate descent method for convex differentiable minimization Author (s) Luo , Zhi-Quan.; Tseng , Paul.; Massachusetts Institute of Technology. Laboratory for Information and Decision Systems.", "subpage_snippet": "", "source": "dspace.mit.edu", "link": "https://dspace.mit.edu/handle/1721.1/3164", "content": "On the convergence of the coordinate descent method for convex differentiable minimization Author (s) Luo , Zhi-Quan.; Tseng , Paul.; Massachusetts Institute of Technology. Laboratory for Information and Decision Systems."}
+{"idx": 4, "title": "On the Linear Convergence of Descent Methods for - ProQuest", "date": "", "ddg_snippet": "Luo , Zhi-Quan; Tseng , Paul. SIAM Journal on Control and Optimization; Philadelphia Vol. 30, Iss. 2, (Mar 1992 ): 18. DOI:10.1137/0330025. This is a limited preview of the full PDF. Try and log in through your library or institution to see if they have access. It appears you don't have support to open PDFs in this web browser.", "subpage_snippet": "", "source": "www.proquest.com", "link": "https://www.proquest.com/docview/925910878", "content": "Luo , Zhi-Quan; Tseng , Paul. SIAM Journal on Control and Optimization; Philadelphia Vol. 30, Iss. 2, (Mar 1992 ): 18. DOI:10.1137/0330025. This is a limited preview of the full PDF. Try and log in through your library or institution to see if they have access. It appears you don't have support to open PDFs in this web browser."}
+{"idx": 5, "title": "On the convergence of the coordinate descent method for ...", "date": "", "ddg_snippet": "by ZQ Luo · 1992 · Cited by 739 — Luo , Z.Q., Tseng , P. On the convergence of the coordinate descent method for convex differentiable minimization. J Optim Theory Appl 72, 7–35 ( 1992 ). https ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/BF00939948", "content": "by ZQ Luo · 1992 · Cited by 739 — Luo , Z.Q., Tseng , P. On the convergence of the coordinate descent method for convex differentiable minimization. J Optim Theory Appl 72, 7–35 ( 1992 ). https ..."}
+{"idx": 6, "title": "On the convergence of the coordinate descent method for ...", "date": "", "ddg_snippet": "by ZQ Luo · 1992 · Cited by 738 — On the convergence of the coordinate descent method for convex differentiable minimization. Z. Q. Luo , P. Tseng . Electrical and Computer Engineering. Research ...", "subpage_snippet": "", "source": "experts.umn.edu", "link": "https://experts.umn.edu/en/publications/on-the-convergence-of-the-coordinate-descent-method-for-convex-di", "content": "by ZQ Luo · 1992 · Cited by 738 — On the convergence of the coordinate descent method for convex differentiable minimization. Z. Q. Luo , P. Tseng . Electrical and Computer Engineering. Research ..."}
+{"idx": 7, "title": "On the Convergence Rate of Dual Ascent Methods for ...", "date": "", "ddg_snippet": "Luo P. Tseng . Mathematics. 1992 . TLDR. The linear convergence of both the gradient projection algorithm of Goldstein and Levitin and Polyak, and a matrix ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/On-the-Convergence-Rate-of-Dual-Ascent-Methods-for-Luo-Tseng/67297678b0379bdfc7aca135a0d48b7af7decb22", "content": "Luo P. Tseng . Mathematics. 1992 . TLDR. The linear convergence of both the gradient projection algorithm of Goldstein and Levitin and Polyak, and a matrix ..."}
+{"idx": 8, "title": "On the convergence of the coordinate descent method for ...", "date": "", "ddg_snippet": "3 Jan 1992 — Luo P. Tseng . Mathematics. 1992 . TLDR. The linear convergence of both the gradient projection algorithm of Goldstein and Levitin and Polyak, and ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/On-the-convergence-of-the-coordinate-descent-method-Luo-Tseng/f68cacb50482fd50991b0fbbf3e9b67a3607907e", "content": "3 Jan 1992 — Luo P. Tseng . Mathematics. 1992 . TLDR. The linear convergence of both the gradient projection algorithm of Goldstein and Levitin and Polyak, and ..."}
+{"idx": 9, "title": "On a global error bound for a class of monotone affine ...", "date": "", "ddg_snippet": "by ZQ Luo · 1992 · Cited by 43 — April 1992 , Pages 159-165. Operations Research Letters. On a global error bound for a class of monotone affine variational inequality problems☆. Author links ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/abs/pii/016763779290080M", "content": "by ZQ Luo · 1992 · Cited by 43 — April 1992 , Pages 159-165. Operations Research Letters. On a global error bound for a class of monotone affine variational inequality problems☆. Author links ..."}
diff --git a/data/sampled_jsons/METransformer_BLEU-4_0.124_MIMIC-CXR_experimental_results_table_year_2023.jsonl b/data/sampled_jsons/METransformer_BLEU-4_0.124_MIMIC-CXR_experimental_results_table_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..1a8b4ec83e9682276341e1bc82b8e722b1e95f8c
--- /dev/null
+++ b/data/sampled_jsons/METransformer_BLEU-4_0.124_MIMIC-CXR_experimental_results_table_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF Supplementary Material METransformer: Radiology Report Generation by ...", "date": "", "ddg_snippet": "The experi-mental results for IU-Xray and MIMIC-CXR are shown in Table . I and Table . II, respectively. We also adopt an overall score to evaluate the performance of the model by consid-ering all metrics by the following literature [5], which is calculated by the Eqn. 1: C", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_METransformer_Radiology_Report_CVPR_2023_supplemental.pdf", "content": "The experi-mental results for IU-Xray and MIMIC-CXR are shown in Table . I and Table . II, respectively. We also adopt an overall score to evaluate the performance of the model by consid-ering all metrics by the following literature [5], which is calculated by the Eqn. 1: C"}
+{"idx": 1, "title": "PDF METransformer: Radiology Report Generation by Transformer with Multiple ...", "date": "", "ddg_snippet": "Based on this motivation, we propose a new diagnostic captioning framework, METransformer , to mimic the \"multi-expert joint diagnosis\" process. Built upon a transformer backbone, METransformer introduces multiple \"expert tokens\", representing multiple experts, into both the transformer encoder and decoder.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_METransformer_Radiology_Report_Generation_by_Transformer_With_Multiple_Learnable_Expert_CVPR_2023_paper.pdf", "content": "Based on this motivation, we propose a new diagnostic captioning framework, METransformer , to mimic the \"multi-expert joint diagnosis\" process. Built upon a transformer backbone, METransformer introduces multiple \"expert tokens\", representing multiple experts, into both the transformer encoder and decoder."}
+{"idx": 2, "title": "[2304.02211] METransformer: Radiology Report Generation by Transformer ...", "date": "", "ddg_snippet": "To show the impacts of the expert tokens, we train METransformer with different numbers of expert tokens, i.e., 𝑛 𝑢 𝑚 _ 𝑒 𝑥 𝑝 𝑒 𝑟 𝑡 1 3 5 7 9 and the results on IU-Xray and MIMIC-CXR are shown in Figure.", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2304.02211", "content": "To show the impacts of the expert tokens, we train METransformer with different numbers of expert tokens, i.e., 𝑛 𝑢 𝑚 _ 𝑒 𝑥 𝑝 𝑒 𝑟 𝑡 1 3 5 7 9 and the results on IU-Xray and MIMIC-CXR are shown in Figure."}
+{"idx": 3, "title": "(PDF) METransformer: Radiology Report Generation by Transformer with ...", "date": "", "ddg_snippet": "The experiments on the public IU-Xray and MIMIC-CXR datasets show that the Align-Transformer can achieve results competitive with state-of-the-art methods on the two datasets.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/369823367_METransformer_Radiology_Report_Generation_by_Transformer_with_Multiple_Learnable_Expert_Tokens", "content": "The experiments on the public IU-Xray and MIMIC-CXR datasets show that the Align-Transformer can achieve results competitive with state-of-the-art methods on the two datasets."}
+{"idx": 4, "title": "MIMIC-CXR: Chest X-ray Image Classification and Report Generation - GitHub", "date": "", "ddg_snippet": "Team 16 Alexander Koehler, Feng-Jen Hsieh, Yuandi Tang Overview This repository contains the implementation and results of the research paper \" MIMIC-CXR : Chest X-ray Image Classification and Report Generation\". The project leverages the MIMIC-CXR dataset, which includes chest X-ray images along with corresponding radiology reports.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/yuanditang/MIMIC-CXR", "content": "Team 16 Alexander Koehler, Feng-Jen Hsieh, Yuandi Tang Overview This repository contains the implementation and results of the research paper \" MIMIC-CXR : Chest X-ray Image Classification and Report Generation\". The project leverages the MIMIC-CXR dataset, which includes chest X-ray images along with corresponding radiology reports."}
+{"idx": 5, "title": "CXPMRG-Bench: Pre-training and Benchmarking for X-ray Medical Report ...", "date": "", "ddg_snippet": "As shown in Table 3, our method also demonstrates outstanding performance on the MIMIC-CXR dataset, surpasses all other advanced report generation methods, and achieves the most advanced level in several common indicators (e.g., BLEU-1, BLEU-2, BLEU-3, and BLEU-4 ).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.00379v1", "content": "As shown in Table 3, our method also demonstrates outstanding performance on the MIMIC-CXR dataset, surpasses all other advanced report generation methods, and achieves the most advanced level in several common indicators (e.g., BLEU-1, BLEU-2, BLEU-3, and BLEU-4 )."}
+{"idx": 6, "title": "A medical report generation method based on local visual modeling and ...", "date": "", "ddg_snippet": "Our method surpasses state-of-the-art methodologies on the IU-X-ray and MIMIC-CXR datasets, with respective improvements of 3. 55%, 8. 10%, and 3. 90% in BLEU-3, BLEU 4 , and METEOR scores. These outcomes underscore the viability and precision of our model in automating the generation of medical reports.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S1746809425010389", "content": "Our method surpasses state-of-the-art methodologies on the IU-X-ray and MIMIC-CXR datasets, with respective improvements of 3. 55%, 8. 10%, and 3. 90% in BLEU-3, BLEU 4 , and METEOR scores. These outcomes underscore the viability and precision of our model in automating the generation of medical reports."}
+{"idx": 7, "title": "PDF Contrastive Knowledge-Guided Large Language Models for Medical Report ...", "date": "", "ddg_snippet": "For the MIMIC-CXR dataset, Specifically, our framework achieves 0.414, 0.270, and 0.136 in BLEU-1, BLEU-2, and BLEU-4 , Fig. 3. Qualitative examples of the proposed method and base model. Different colors highlight different medical terms in the reports. respectively.", "subpage_snippet": "", "source": "papers.miccai.org", "link": "https://papers.miccai.org/miccai-2025/paper/1713_paper.pdf", "content": "For the MIMIC-CXR dataset, Specifically, our framework achieves 0.414, 0.270, and 0.136 in BLEU-1, BLEU-2, and BLEU-4 , Fig. 3. Qualitative examples of the proposed method and base model. Different colors highlight different medical terms in the reports. respectively."}
+{"idx": 8, "title": "ICON: Improving Inter-Report Consistency in Radiology ... - OpenReview", "date": "", "ddg_snippet": "The ablation results for MIMIC -ABN and MIMIC-CXR are listed in Table 3 and Table 4 . We study three variants: (1) w/o ZOOM, where all components are removed, (2) w/o IN-SPECT, where both the INSPECTOR and MIXUP are removed, and (3) w/o MIXUP, where only MIXUP is removed.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=fiQucCdsUe", "content": "The ablation results for MIMIC -ABN and MIMIC-CXR are listed in Table 3 and Table 4 . We study three variants: (1) w/o ZOOM, where all components are removed, (2) w/o IN-SPECT, where both the INSPECTOR and MIXUP are removed, and (3) w/o MIXUP, where only MIXUP is removed."}
+{"idx": 9, "title": "ICON: Improving Inter-Report Consistency of Radiology ... - OpenReview", "date": "", "ddg_snippet": "The ablation results for MIMIC -ABN and MIMIC-CXR are listed in Table 2 and Table 4 . The performance of the ablated model w/o ZOOM drops significantly for both datasets, while the variant w/o INSPECT achieves competitive re-sults on clinical accuracy.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=67qntOrWVt", "content": "The ablation results for MIMIC -ABN and MIMIC-CXR are listed in Table 2 and Table 4 . The performance of the ablated model w/o ZOOM drops significantly for both datasets, while the variant w/o INSPECT achieves competitive re-sults on clinical accuracy."}
diff --git a/data/sampled_jsons/METransformer_PapersWithCode_results_table_BLEU-4.jsonl b/data/sampled_jsons/METransformer_PapersWithCode_results_table_BLEU-4.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f149239e9279f36ab0663997e1ae8122d6cdab07
--- /dev/null
+++ b/data/sampled_jsons/METransformer_PapersWithCode_results_table_BLEU-4.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF METransformer: Radiology Report Generation by Transformer with Multiple ...", "date": "", "ddg_snippet": "The results are reported in Table 1 and Table . 2 3, respectively. Specifically, we compare METransformer with 5 state-of-the-art (SOTA) image captioning methods, including Show-tell [32], AdaAtt [24], Att2in [1], Transformer [7]", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_METransformer_Radiology_Report_Generation_by_Transformer_With_Multiple_Learnable_Expert_CVPR_2023_paper.pdf", "content": "The results are reported in Table 1 and Table . 2 3, respectively. Specifically, we compare METransformer with 5 state-of-the-art (SOTA) image captioning methods, including Show-tell [32], AdaAtt [24], Att2in [1], Transformer [7]"}
+{"idx": 1, "title": "[2304.02211] METransformer: Radiology Report Generation by Transformer ...", "date": "", "ddg_snippet": "In clinical scenarios, multi-specialist consultation could significantly benefit the diagnosis, especially for intricate cases. This inspires us to explore a \"multi-expert joint diagnosis\" mechanism to upgrade the existing \"single expert\" framework commonly seen in the current literature. To this end, we propose METransformer , a method to realize this idea with a transformer-based backbone ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2304.02211", "content": "In clinical scenarios, multi-specialist consultation could significantly benefit the diagnosis, especially for intricate cases. This inspires us to explore a \"multi-expert joint diagnosis\" mechanism to upgrade the existing \"single expert\" framework commonly seen in the current literature. To this end, we propose METransformer , a method to realize this idea with a transformer-based backbone ..."}
+{"idx": 2, "title": "[2304.02211] METransformer: Radiology Report Generation by Transformer ...", "date": "", "ddg_snippet": "Based on this motivation, we propose a new diagnostic captioning framework, METransformer , to mimic the \"multi-expert joint diagnosis\" process. Built upon a transformer backbone, METransformer introduces multiple \"expert tokens\", representing multiple experts, into both the transformer encoder and decoder.", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2304.02211", "content": "Based on this motivation, we propose a new diagnostic captioning framework, METransformer , to mimic the \"multi-expert joint diagnosis\" process. Built upon a transformer backbone, METransformer introduces multiple \"expert tokens\", representing multiple experts, into both the transformer encoder and decoder."}
+{"idx": 3, "title": "METransformer: Radiology Report Generation by Transformer with Multiple ...", "date": "", "ddg_snippet": "In clinical scenarios, multi-specialist consultation could significantly benefit the diagnosis, especially for intricate cases. This inspires us to explore a \"multi-expert joint diagnosis\" mechanism to upgrade the existing \"single expert\" framework commonly seen in the current literature. To this end, we propose METransformer , a method to realize this idea with a transformer-based ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10203079", "content": "In clinical scenarios, multi-specialist consultation could significantly benefit the diagnosis, especially for intricate cases. This inspires us to explore a \"multi-expert joint diagnosis\" mechanism to upgrade the existing \"single expert\" framework commonly seen in the current literature. To this end, we propose METransformer , a method to realize this idea with a transformer-based ..."}
+{"idx": 4, "title": "PDF Supplementary Material METransformer: Radiology Report Generation by ...", "date": "", "ddg_snippet": "It is noted that the calculated metric could be any commonly used NLG met-rics, such as BLEU 4 [3], ROUGE [2], METEOR [1], CIDEr [ 4 ], or a combination of multiple metrics. In this paper, we compute CIDEr as the voting score to select the optimal results .", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_METransformer_Radiology_Report_CVPR_2023_supplemental.pdf", "content": "It is noted that the calculated metric could be any commonly used NLG met-rics, such as BLEU 4 [3], ROUGE [2], METEOR [1], CIDEr [ 4 ], or a combination of multiple metrics. In this paper, we compute CIDEr as the voting score to select the optimal results ."}
+{"idx": 5, "title": "Figure 4: The BLEU-4 scores (%) of generated translations on the merged...", "date": "", "ddg_snippet": "The BLEU-4 scores (%) of generated translations on the merged four test sets with respect to the lengths of source sentences. The numbers on X-axis of the figure stand for sentences longer than ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/The-BLEU-4-scores-of-generated-translations-on-the-merged-four-test-sets-with-respect_fig3_309207378", "content": "The BLEU-4 scores (%) of generated translations on the merged four test sets with respect to the lengths of source sentences. The numbers on X-axis of the figure stand for sentences longer than ..."}
+{"idx": 6, "title": "Markin-Wang/awesome_radiology_report_generation - GitHub", "date": "", "ddg_snippet": "METransformer : Radiology Report Generation by Transformer with Multiple Learnable Expert Tokens, CVPR. | pdf | KiUT: Knowledge-injected U-Transformer for Radiology Report Generation, CVPR. | pdf | Dynamic Graph Enhanced Contrastive Learning for Chest X-ray Report Generation, CVPR. | pdf | code |", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Markin-Wang/awesome_radiology_report_generation/", "content": "METransformer : Radiology Report Generation by Transformer with Multiple Learnable Expert Tokens, CVPR. | pdf | KiUT: Knowledge-injected U-Transformer for Radiology Report Generation, CVPR. | pdf | Dynamic Graph Enhanced Contrastive Learning for Chest X-ray Report Generation, CVPR. | pdf | code |"}
+{"idx": 7, "title": "R2GenGPT: Radiology Report Generation with frozen LLMs", "date": "", "ddg_snippet": "For instance, our BLEU_4 score is improved from 0.124 to 0.134, marking an 8.1 % increase. However, we achieved a CIDEr score of 0.269, which is lower than METransformer's 0.362. This discrepancy is because METransformer employs an expert voting strategy similar to an ensemble approach to enhance the CIDEr metric.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2950162823000334", "content": "For instance, our BLEU_4 score is improved from 0.124 to 0.134, marking an 8.1 % increase. However, we achieved a CIDEr score of 0.269, which is lower than METransformer's 0.362. This discrepancy is because METransformer employs an expert voting strategy similar to an ensemble approach to enhance the CIDEr metric."}
+{"idx": 8, "title": "Papers with code · GitHub", "date": "", "ddg_snippet": "paperswithcode -data Public The full dataset behind paperswithcode .com 633 88 axcell Public Tools for extracting tables and results from Machine Learning papers Python 430 62", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/paperswithcode", "content": "paperswithcode -data Public The full dataset behind paperswithcode .com 633 88 axcell Public Tools for extracting tables and results from Machine Learning papers Python 430 62"}
+{"idx": 9, "title": "arXiv:2408.09743v1 [cs.CV] 19 Aug 2024", "date": "", "ddg_snippet": "erent numbers of context sample pairs. The results reveal an optimal point at 3 context sample pairs, which achieves higher scores than 10 pairs, with improvements for Bleu-1, Bleu-4 , ROUGE-L, and METEOR of 0.023,", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2408.09743", "content": "erent numbers of context sample pairs. The results reveal an optimal point at 3 context sample pairs, which achieves higher scores than 10 pairs, with improvements for Bleu-1, Bleu-4 , ROUGE-L, and METEOR of 0.023,"}
diff --git a/data/sampled_jsons/METransformer_Radiology_Report_Generation_by_Transformer_with_Multiple_Learnable_Expert_Tokens_BLEU-.jsonl b/data/sampled_jsons/METransformer_Radiology_Report_Generation_by_Transformer_with_Multiple_Learnable_Expert_Tokens_BLEU-.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..25f6087b9eeeae1b391692c3759f6448c7bcf128
--- /dev/null
+++ b/data/sampled_jsons/METransformer_Radiology_Report_Generation_by_Transformer_with_Multiple_Learnable_Expert_Tokens_BLEU-.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "METransformer : Radiology Report Generation by Transformer ...", "date": "", "ddg_snippet": "Built upon a transformer backbone, METransformer introduces multiple “ expert tokens ”, representing multiple experts, into both the transformer encoder and decoder.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_METransformer_Radiology_Report_Generation_by_Transformer_With_Multiple_Learnable_Expert_CVPR_2023_paper.pdf", "content": "Built upon a transformer backbone, METransformer introduces multiple “ expert tokens ”, representing multiple experts, into both the transformer encoder and decoder."}
+{"idx": 1, "title": "(PDF) METransformer : Radiology Report Generation by ...", "date": "", "ddg_snippet": "MET ransformer: Radiology Report Generation by Transf ormer with Multiple . Learnable Expert T okens. mimic the “multi-expert joint diagnosis” process. Built. upon a transformer backbone, METransformer introduces. multiple “ expert tokens ”, representing multiple experts, into.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/369823367_METransformer_Radiology_Report_Generation_by_Transformer_with_Multiple_Learnable_Expert_Tokens", "content": "MET ransformer: Radiology Report Generation by Transf ormer with Multiple . Learnable Expert T okens. mimic the “multi-expert joint diagnosis” process. Built. upon a transformer backbone, METransformer introduces. multiple “ expert tokens ”, representing multiple experts, into."}
+{"idx": 2, "title": "[2304.02211] METransformer : Radiology Report Generation by ...", "date": "", "ddg_snippet": "View a PDF of the paper titled METransformer : Radiology Report Generation by Transformer with Multiple Learnable Expert Tokens , by Zhanyu Wang and 3 other authors.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2304.02211", "content": "View a PDF of the paper titled METransformer : Radiology Report Generation by Transformer with Multiple Learnable Expert Tokens , by Zhanyu Wang and 3 other authors."}
+{"idx": 3, "title": "Paper tables with annotated results for METransformer : Radiology ...", "date": "", "ddg_snippet": "METransformer : Radiology Report Generation by Transformer with Multiple Learnable Expert Tokens . In clinical scenarios, multi-specialist consultation could significantly benefit the diagnosis, especially for intricate cases.", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/paper/metransformer-radiology-report-generation-by/review/", "content": "METransformer : Radiology Report Generation by Transformer with Multiple Learnable Expert Tokens . In clinical scenarios, multi-specialist consultation could significantly benefit the diagnosis, especially for intricate cases."}
+{"idx": 4, "title": "METransformer : Radiology Report Generation by Transformer ...", "date": "", "ddg_snippet": "To this end, we propose METransformer , a method to realize this idea with a transformer -based backbone. The key design of our method is the introduction of multiple learnable “ expert ” tokens into both the transformer encoder and decoder.", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10203079/", "content": "To this end, we propose METransformer , a method to realize this idea with a transformer -based backbone. The key design of our method is the introduction of multiple learnable “ expert ” tokens into both the transformer encoder and decoder."}
+{"idx": 5, "title": "METransformer : Radiology Report Generation by Transformer ...", "date": "", "ddg_snippet": "To this end, we propose METransformer , a method to realize this idea with a transformer -based backbone. The key design of our method is the introduction of multiple learnable \" expert \" tokens into both the transformer encoder and decoder.", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/abs/2304.02211", "content": "To this end, we propose METransformer , a method to realize this idea with a transformer -based backbone. The key design of our method is the introduction of multiple learnable \" expert \" tokens into both the transformer encoder and decoder."}
+{"idx": 6, "title": "GitHub - AlonzoLeeeooo/awesome- radiology - report - generation ...", "date": "", "ddg_snippet": "METransformer : Radiology Report Generation by Transformer with Multiple Learnable Expert Tokens [Paper].", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/AlonzoLeeeooo/awesome-radiology-report-generation", "content": "METransformer : Radiology Report Generation by Transformer with Multiple Learnable Expert Tokens [Paper]."}
+{"idx": 7, "title": "Multivariate Cooperative Game for Image- Report Pairs: Hierarchical...", "date": "", "ddg_snippet": "Abstract Medical report generation (MRG) has great clinical potential, which could relieve radiologists from the heavy workloads of report writing.“ Metransformer : Radiology report generation by transformer with multiple learnable expert tokens .”", "subpage_snippet": "", "source": "papers.miccai.org", "link": "https://papers.miccai.org/miccai-2024/555-Paper1475.html", "content": "Abstract Medical report generation (MRG) has great clinical potential, which could relieve radiologists from the heavy workloads of report writing.“ Metransformer : Radiology report generation by transformer with multiple learnable expert tokens .”"}
+{"idx": 8, "title": "Cross-modal Memory Networks for Radiology Report Generation", "date": "", "ddg_snippet": "METransformer : Radiology Report Generation by Transformer with Multiple Learnable Expert Tokens . Radiology Report Generation with a Learned Knowledge Base and Multi-modal Alignment.", "subpage_snippet": "", "source": "www.connectedpapers.com", "link": "https://www.connectedpapers.com/main/db2bd466953f3ea49280988e1659b6ac3f639e45/Cross+modal-Memory-Networks-for-Radiology-Report-Generation/graph", "content": "METransformer : Radiology Report Generation by Transformer with Multiple Learnable Expert Tokens . Radiology Report Generation with a Learned Knowledge Base and Multi-modal Alignment."}
+{"idx": 9, "title": "Multi-granularity Semantic Guided Transformer for Radiology Report ...", "date": "", "ddg_snippet": "Radiology Report Generation aims to generate accurate diagnostic reports based on medical images. Existing approaches based on the Transformer paradigm and grid features had achieved significant performance.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-981-97-9437-9_36", "content": "Radiology Report Generation aims to generate accurate diagnostic reports based on medical images. Existing approaches based on the Transformer paradigm and grid features had achieved significant performance."}
diff --git a/data/sampled_jsons/METransformer_Table_II_MIMIC-CXR_results_BLEU-4_exact_value.jsonl b/data/sampled_jsons/METransformer_Table_II_MIMIC-CXR_results_BLEU-4_exact_value.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3b141b2a81e8f58520b2572e4f4c37cbc35efbde
--- /dev/null
+++ b/data/sampled_jsons/METransformer_Table_II_MIMIC-CXR_results_BLEU-4_exact_value.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Supplementary Material METransformer: Radiology Report ...", "date": "", "ddg_snippet": "The experi-mental results for IU-Xray and MIMIC-CXR are shown in Table . I and Table . II , respectively. We also adopt an overall score to evaluate the performance of the model by consid-ering all metrics by the following literature [5], which is calculated by the Eqn. 1: C", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_METransformer_Radiology_Report_CVPR_2023_supplemental.pdf", "content": "The experi-mental results for IU-Xray and MIMIC-CXR are shown in Table . I and Table . II , respectively. We also adopt an overall score to evaluate the performance of the model by consid-ering all metrics by the following literature [5], which is calculated by the Eqn. 1: C"}
+{"idx": 1, "title": "[2304.02211] METransformer: Radiology Report Generation by ...", "date": "", "ddg_snippet": "Figure 2 : Bleu_4 and CIDEr scores by using different numbers of expert tokens on IU-Xray and MIMIC-CXR dataset. Figure 3: An example of the generated reports and their attention-mapping visualization of three key medical terms from BASELINE and ours METransformer .", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2304.02211", "content": "Figure 2 : Bleu_4 and CIDEr scores by using different numbers of expert tokens on IU-Xray and MIMIC-CXR dataset. Figure 3: An example of the generated reports and their attention-mapping visualization of three key medical terms from BASELINE and ours METransformer ."}
+{"idx": 2, "title": "MIMIC-CXR: Chest X-ray Image Classification and ... - GitHub", "date": "", "ddg_snippet": "This repository contains the implementation and results of the research paper \" MIMIC-CXR : Chest X-ray Image Classification and Report Generation\". The project leverages the MIMIC-CXR dataset, which includes chest X-ray images along with corresponding radiology reports.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/yuanditang/MIMIC-CXR", "content": "This repository contains the implementation and results of the research paper \" MIMIC-CXR : Chest X-ray Image Classification and Report Generation\". The project leverages the MIMIC-CXR dataset, which includes chest X-ray images along with corresponding radiology reports."}
+{"idx": 3, "title": "Table 2 from METransformer: Radiology Report Generation by ...", "date": "", "ddg_snippet": "Table 2 . Comparison of clinical efficacy metrics on the test set of the MIMIC-CXR dataset for measuring the accuracy of the description of clinical abnormalities. - \" METransformer : Radiology Report Generation by Transformer with Multiple Learnable Expert Tokens\"", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/METransformer:-Radiology-Report-Generation-by-with-Wang-Liu/4d90382730eefbe8639e64f6855f206657758cc7/figure/2", "content": "Table 2 . Comparison of clinical efficacy metrics on the test set of the MIMIC-CXR dataset for measuring the accuracy of the description of clinical abnormalities. - \" METransformer : Radiology Report Generation by Transformer with Multiple Learnable Expert Tokens\""}
+{"idx": 4, "title": "mimic-iv-website/content/cxr/cxr-record-list.md at master ...", "date": "", "ddg_snippet": "CXR Record List This table lists all records in the MIMIC-CXR database. Each DICOM file, corresponding to a single chest x-ray , is assigned a unique dicom_id. This table links those IDs to a study_id for the radiology report and a subject_id for the patient. Table source: Hospital database.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/MIT-LCP/mimic-iv-website/blob/master/content/cxr/cxr-record-list.md", "content": "CXR Record List This table lists all records in the MIMIC-CXR database. Each DICOM file, corresponding to a single chest x-ray , is assigned a unique dicom_id. This table links those IDs to a study_id for the radiology report and a subject_id for the patient. Table source: Hospital database."}
+{"idx": 5, "title": "METransformer: Radiology Report Generation by Transformer ...", "date": "", "ddg_snippet": "Apr 5, 2023 · In clinical scenarios, multi-specialist consultation could significantly benefit the diagnosis, especially for intricate cases. This inspires us to explore a \"multi-expert joint diagnosis\" mechanism to upgrade the existing \"single expert\" framework commonly seen in the current literature. To this end, we propose METransformer , a method to realize this idea with a transformer-based backbone ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2304.02211", "content": "Apr 5, 2023 · In clinical scenarios, multi-specialist consultation could significantly benefit the diagnosis, especially for intricate cases. This inspires us to explore a \"multi-expert joint diagnosis\" mechanism to upgrade the existing \"single expert\" framework commonly seen in the current literature. To this end, we propose METransformer , a method to realize this idea with a transformer-based backbone ..."}
+{"idx": 6, "title": "GIT-CXR: End-to-End Transformer for Chest X-Ray Report ...", "date": "", "ddg_snippet": "5 Jan 2025 — Through our work, we have designed and evaluated an end-to-end transformer -based method to generate accurate and factually complete radiology ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.02598v1", "content": "5 Jan 2025 — Through our work, we have designed and evaluated an end-to-end transformer -based method to generate accurate and factually complete radiology ..."}
+{"idx": 7, "title": "arXiv:2408.09743v1 [cs.CV] 19 Aug 2024", "date": "", "ddg_snippet": "on the large-scale MIMIC-CXR dataset. Our method achieved a BLEU -1 score of 0.420, a BLEU-4 score of 0.136, and a ROUGE-L score of 0.291, in-dicating its ability to generate precise and", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2408.09743", "content": "on the large-scale MIMIC-CXR dataset. Our method achieved a BLEU -1 score of 0.420, a BLEU-4 score of 0.136, and a ROUGE-L score of 0.291, in-dicating its ability to generate precise and"}
+{"idx": 8, "title": "Radiology Report Generation Using Transformers ...", "date": "", "ddg_snippet": "by N Aksoy · 2023 · Cited by 19 — Both MIMIC - CXR and MIMIC-IV databases have a common unique identification number ... BLEU - 4 . RBERT. F1ScoreBERT. Baseline Model. 0.314 소 0.004.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2311.11097", "content": "by N Aksoy · 2023 · Cited by 19 — Both MIMIC - CXR and MIMIC-IV databases have a common unique identification number ... BLEU - 4 . RBERT. F1ScoreBERT. Baseline Model. 0.314 소 0.004."}
+{"idx": 9, "title": "Understanding transfer learning for chest radiograph ...", "date": "", "ddg_snippet": "by E Vendrow · 2023 · Cited by 8 — The single feature model pretrained on ImageNet results in lower BLEU (1- 4 ) scores, CIDEr score, and ROUGE-L score than the models using CheXpert pretraining.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "http://www.sciencedirect.com/science/article/pii/S2405844023051769", "content": "by E Vendrow · 2023 · Cited by 8 — The single feature model pretrained on ImageNet results in lower BLEU (1- 4 ) scores, CIDEr score, and ROUGE-L score than the models using CheXpert pretraining."}
diff --git a/data/sampled_jsons/MLP_performance_lattice_paths_n=10_n=11_n=12_algebraic_combinatorics_dataset.jsonl b/data/sampled_jsons/MLP_performance_lattice_paths_n=10_n=11_n=12_algebraic_combinatorics_dataset.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..cc19f299d3287196c820076a74d5d9bd4499ee27
--- /dev/null
+++ b/data/sampled_jsons/MLP_performance_lattice_paths_n=10_n=11_n=12_algebraic_combinatorics_dataset.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Lattice path - Wikipedia", "date": "", "ddg_snippet": "In combinatorics , a lattice path L in the d -dimensional integer lattice of length k with steps in the set S, is a sequence of vectors such that each consecutive difference lies in S. [1] A lattice path may lie in any lattice in , [1] but the integer lattice is most commonly used.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Lattice_Path", "content": "In combinatorics , a lattice path L in the d -dimensional integer lattice of length k with steps in the set S, is a sequence of vectors such that each consecutive difference lies in S. [1] A lattice path may lie in any lattice in , [1] but the integer lattice is most commonly used."}
+{"idx": 1, "title": "Find the Number of Lattice Paths - Mathematics Stack Exchange", "date": "", "ddg_snippet": "0 How many lattice paths are there from $ (0, 0)$ to $ (10, 10)$ that do not pass to the point $ (5, 5)$ but do pass to $ (3, 3)$? What I have so far: The number of lattice paths from $ (0,0)$ to $ (n,k)$ is equal to the binomial coefficient $\\binom {n+k}n$ (according to Wikipedia).", "subpage_snippet": "", "source": "math.stackexchange.com", "link": "https://math.stackexchange.com/questions/1234726/find-the-number-of-lattice-paths", "content": "0 How many lattice paths are there from $ (0, 0)$ to $ (10, 10)$ that do not pass to the point $ (5, 5)$ but do pass to $ (3, 3)$? What I have so far: The number of lattice paths from $ (0,0)$ to $ (n,k)$ is equal to the binomial coefficient $\\binom {n+k}n$ (according to Wikipedia)."}
+{"idx": 2, "title": "PDF Lattice Path Combinatorics", "date": "", "ddg_snippet": "This book endeavors to deepen our understanding of lattice path combinatorics , explore key types of special sequences, elucidate their interconnections, and concurrently champion the au- thor's interpretation of the \"combinatorial spirit\". The author intends to give an up-to-date introduction to the theory of lattice path combinator - ics, its relation to those special counting sequences ...", "subpage_snippet": "", "source": "api.pageplace.de", "link": "https://api.pageplace.de/preview/DT0400.9781040123416_A48854950/preview-9781040123416_A48854950.pdf", "content": "This book endeavors to deepen our understanding of lattice path combinatorics , explore key types of special sequences, elucidate their interconnections, and concurrently champion the au- thor's interpretation of the \"combinatorial spirit\". The author intends to give an up-to-date introduction to the theory of lattice path combinator - ics, its relation to those special counting sequences ..."}
+{"idx": 3, "title": "Lattice Path Enumeration Christian Krattenthaler arXiv:1503.05930v3 ...", "date": "", "ddg_snippet": "10.2. Lattice paths without restrictions In this short section, we briefly cover the simplest enumeration problems for lattice paths . If we are given a set of steps S, then the number of paths starting from the origin and using n steps from S is If we are also fixing the end point, then we cannot expect a reasonable formula in this generality. |S|n.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1503.05930", "content": "10.2. Lattice paths without restrictions In this short section, we briefly cover the simplest enumeration problems for lattice paths . If we are given a set of steps S, then the number of paths starting from the origin and using n steps from S is If we are also fixing the end point, then we cannot expect a reasonable formula in this generality. |S|n."}
+{"idx": 4, "title": "Lattice Path Combinatorics and Applications | SpringerLink", "date": "", "ddg_snippet": "This contributed volume groups and represents recent methods in various branches of lattice path and enumerative combinatorics and relevant applications. It contains both research articles and expository articles on the lives and work of leading researchers in lattice path combinatorics and beyond.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/book/10.1007/978-3-030-11102-1", "content": "This contributed volume groups and represents recent methods in various branches of lattice path and enumerative combinatorics and relevant applications. It contains both research articles and expository articles on the lives and work of leading researchers in lattice path combinatorics and beyond."}
+{"idx": 5, "title": "Lattice Path -- from Wolfram MathWorld", "date": "", "ddg_snippet": "A path composed of connected horizontal and vertical line segments, each passing between adjacent lattice points. A lattice path is therefore a sequence of points P_0, P_1, ..., P_n with n>=0 such that each P_i is a lattice point and P_(i+1) is obtained by offsetting one unit east (or west) or one unit north (or south). The number of paths of length a+b from the origin (0,0) to a point (a,b ...", "subpage_snippet": "", "source": "mathworld.wolfram.com", "link": "https://mathworld.wolfram.com/LatticePath.html", "content": "A path composed of connected horizontal and vertical line segments, each passing between adjacent lattice points. A lattice path is therefore a sequence of points P_0, P_1, ..., P_n with n>=0 such that each P_i is a lattice point and P_(i+1) is obtained by offsetting one unit east (or west) or one unit north (or south). The number of paths of length a+b from the origin (0,0) to a point (a,b ..."}
+{"idx": 6, "title": "Enumeration of Lattice Paths with Restrictions", "date": "", "ddg_snippet": "- north-east paths , up-down paths , and Dyck paths - with restrictions applied. The first restriction is counting north-east lattice paths that only cross the diagonal line, y = x, once. The second form of lattice paths with restrictions is up-down paths that cross the x-axis exactly once and fall to a fixed depth of k.", "subpage_snippet": "", "source": "digitalcommons.georgiasouthern.edu", "link": "https://digitalcommons.georgiasouthern.edu/cgi/viewcontent.cgi?article=4041&context=etd", "content": "- north-east paths , up-down paths , and Dyck paths - with restrictions applied. The first restriction is counting north-east lattice paths that only cross the diagonal line, y = x, once. The second form of lattice paths with restrictions is up-down paths that cross the x-axis exactly once and fall to a fixed depth of k."}
+{"idx": 7, "title": "bp0609/MLP-Performance-On-Different-Tasks - GitHub", "date": "", "ddg_snippet": "This repository is dedicated to exploring the performance of Multilayer Perceptrons ( MLPs ) across various machine learning tasks and comparing them against alternative models such as Random Forests, Logistic Regression, and others. The focus is on understanding the strengths, limitations, and behavior of MLPs in different scenarios through quantitative and qualitative analyses.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/bp0609/MLP-Performance-On-Different-Tasks", "content": "This repository is dedicated to exploring the performance of Multilayer Perceptrons ( MLPs ) across various machine learning tasks and comparing them against alternative models such as Random Forests, Logistic Regression, and others. The focus is on understanding the strengths, limitations, and behavior of MLPs in different scenarios through quantitative and qualitative analyses."}
+{"idx": 8, "title": "Counting Lattice Paths - STEM hash", "date": "", "ddg_snippet": "According to the binomial theorem, we can expand a polynomial (x + y) n (x + y)n into a sum of terms of the form a x b y c axbyc where b b and c c are positive integers and b + c = n b +c = n. The coefficient a a is equal to (n k) (kn) -the coefficient of the kth term in the polynomial expansion.", "subpage_snippet": "", "source": "stemhash.com", "link": "https://stemhash.com/counting-lattice-paths/", "content": "According to the binomial theorem, we can expand a polynomial (x + y) n (x + y)n into a sum of terms of the form a x b y c axbyc where b b and c c are positive integers and b + c = n b +c = n. The coefficient a a is equal to (n k) (kn) -the coefficient of the kth term in the polynomial expansion."}
+{"idx": 9, "title": "PDF MAT344 Lecture 4 - University of Toronto Department of Mathematics", "date": "", "ddg_snippet": "A lattice path in the plane is a curve made up of line segments that either go from a point (i; j) to the point (i + 1; j) or from a point (i; j) to the point (i; j + 1) where i and j are integers. (Thus lattice paths always move either up or to the right.) The length of the path is the number of such line segments.", "subpage_snippet": "", "source": "www.math.toronto.edu", "link": "https://www.math.toronto.edu/balazse/2019_Summer_MAT344/Lec_4.pdf", "content": "A lattice path in the plane is a curve made up of line segments that either go from a point (i; j) to the point (i + 1; j) or from a point (i; j) to the point (i; j + 1) where i and j are integers. (Thus lattice paths always move either up or to the right.) The length of the path is the number of such line segments."}
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new file mode 100644
index 0000000000000000000000000000000000000000..80a665594c013df21352260574624cd6d5a71fca
--- /dev/null
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+{"idx": 1, "title": "Machine Learning meets Algebraic Combinatorics: A Suite of Datasets ...", "date": "", "ddg_snippet": "To address this, we introduce a new collection of datasets , the Algebraic Combinatorics Dataset Repository (ACD Repo), representing either foundational results or open problems in algebraic combinatorics , a subfield of mathematics that studies discrete structures arising from abstract algebra.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.06366", "content": "To address this, we introduce a new collection of datasets , the Algebraic Combinatorics Dataset Repository (ACD Repo), representing either foundational results or open problems in algebraic combinatorics , a subfield of mathematics that studies discrete structures arising from abstract algebra."}
+{"idx": 2, "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": 3, "title": "PNNL Research Featured at World's Second-Largest Machine Learning ...", "date": "", "ddg_snippet": "Leading the featured presentation \" Machine Learning meets Algebraic Combinatorics : A Suite of Datasets Capturing Research-level Conjecturing Ability in Pure Mathematics\" was AI researcher and mathematician Henry Kvinge, who has a deep-seated interest in the intersection of AI and mathematics, both in terms of the use of mathematics to ...", "subpage_snippet": "", "source": "www.pnnl.gov", "link": "https://www.pnnl.gov/news-media/pnnl-research-featured-worlds-second-largest-machine-learning-conference", "content": "Leading the featured presentation \" Machine Learning meets Algebraic Combinatorics : A Suite of Datasets Capturing Research-level Conjecturing Ability in Pure Mathematics\" was AI researcher and mathematician Henry Kvinge, who has a deep-seated interest in the intersection of AI and mathematics, both in terms of the use of mathematics to ..."}
+{"idx": 4, "title": "[2025]Machine Learning Meets Algebraic Combinatorics: A Suite of ...", "date": "", "ddg_snippet": "This paper introduces the ACD Repo, a suite of nine datasets designed to model research-level problems in algebraic combinatorics using machine learning , incorporating both narrow models (e.g., MLPs, Transformers) and LLM-based approaches like program synthesis. MLPs performed consistently well across classification tasks, while LLMs occasionally achieved perfect prediction by implicitly ...", "subpage_snippet": "", "source": "hyoo14.github.io", "link": "https://hyoo14.github.io/study/2025/07/25/2025-Machine-Learning-Meets-Algebraic-Combinatorics_-A-Suite-of-Datasets-Capturing-Research-level-Conjecturing-Ability-in-Pure-Mathematics", "content": "This paper introduces the ACD Repo, a suite of nine datasets designed to model research-level problems in algebraic combinatorics using machine learning , incorporating both narrow models (e.g., MLPs, Transformers) and LLM-based approaches like program synthesis. MLPs performed consistently well across classification tasks, while LLMs occasionally achieved perfect prediction by implicitly ..."}
+{"idx": 5, "title": "Machine Learning meets Algebraic Combinatorics: A Suite of Datasets to ...", "date": "", "ddg_snippet": "Machine Learning meets Algebraic Combinatorics : A Suite of Datasets to Accelerate AI for Mathematics Research", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/98521", "content": "Machine Learning meets Algebraic Combinatorics : A Suite of Datasets to Accelerate AI for Mathematics Research"}
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+{"idx": 7, "title": "GitHub - pnnl/ML4AlgComb: ML Benchmarks in Algebraic Combinatorics", "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": 8, "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": 9, "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."}
diff --git a/data/sampled_jsons/Machine_Learning_meets_Algebraic_Combinatorics_Appendix_B.1_S18_characters_training_examples.jsonl b/data/sampled_jsons/Machine_Learning_meets_Algebraic_Combinatorics_Appendix_B.1_S18_characters_training_examples.jsonl
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+{"idx": 0, "title": "[1310.6482] Algebraic combinatorial geometry: the polynomial...", "date": "", "ddg_snippet": "View a PDF of the paper titled Algebraic combinatorial geometry: the polynomial method in arithmetic combinatorics , incidence combinatorics , and number theory, by Terence Tao.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1310.6482", "content": "View a PDF of the paper titled Algebraic combinatorial geometry: the polynomial method in arithmetic combinatorics , incidence combinatorics , and number theory, by Terence Tao."}
+{"idx": 1, "title": "Combinatorial Algebraic Topology | SpringerLink", "date": "", "ddg_snippet": "Combinatorial algebraic topology is a fascinating and dynamic field at the crossroads of algebraic topology and discrete mathematics. This volume is the first comprehensive treatment of the subject in book form.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/book/10.1007/978-3-540-71962-5", "content": "Combinatorial algebraic topology is a fascinating and dynamic field at the crossroads of algebraic topology and discrete mathematics. This volume is the first comprehensive treatment of the subject in book form."}
+{"idx": 2, "title": "machinelearningmastery.com/much- training -data-required- machine ...", "date": "", "ddg_snippet": "How Much Training Data is Required for Machine Learning ?", "subpage_snippet": "", "source": "machinelearningmastery.com", "link": "https://machinelearningmastery.com/much-training-data-required-machine-learning/", "content": "How Much Training Data is Required for Machine Learning ?"}
+{"idx": 3, "title": "Articles by Helen Jenne | Synthical", "date": "", "ddg_snippet": "Machine Learning meets Algebraic Combinatorics : A Suite of Datasets Capturing Research-level Conjecturing Ability in Pure Mathematics.The combinatorial PT-DT correspondence. 15 December 2020 by Helen Jenne and others.", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/profile/247b5a84-c5b9-40f1-8d6e-bd79fc2af63b/articles", "content": "Machine Learning meets Algebraic Combinatorics : A Suite of Datasets Capturing Research-level Conjecturing Ability in Pure Mathematics.The combinatorial PT-DT correspondence. 15 December 2020 by Helen Jenne and others."}
+{"idx": 4, "title": "Combinatorial algebra : syntax and", "date": "", "ddg_snippet": "2.5. 1 . Examples and simple facts. 2.5.2. Fusions, free sets and free deletions. 2.5.3. The Bean–Ehrenfeucht–McNulty & Zimin theorem. Combinatorial algebra : syntax and semantics. Mark V. Sapir.", "subpage_snippet": "", "source": "math.vanderbilt.edu", "link": "https://math.vanderbilt.edu/~msapir/book/book11513.pdf", "content": "2.5. 1 . Examples and simple facts. 2.5.2. Fusions, free sets and free deletions. 2.5.3. The Bean–Ehrenfeucht–McNulty & Zimin theorem. Combinatorial algebra : syntax and semantics. Mark V. Sapir."}
+{"idx": 5, "title": "Journal of Combinatorial Algebra | EMS Press", "date": "", "ddg_snippet": "Journal of Combinatorial Algebra . Overview. Editorial Board.The Journal of Combinatorial Algebra is devoted to publication of research articles of the highest level. Its domain is the rich and deep area of interplay between combinatorics and algebra .", "subpage_snippet": "", "source": "ems.press", "link": "https://ems.press/journals/jca", "content": "Journal of Combinatorial Algebra . Overview. Editorial Board.The Journal of Combinatorial Algebra is devoted to publication of research articles of the highest level. Its domain is the rich and deep area of interplay between combinatorics and algebra ."}
+{"idx": 6, "title": "Ontol: Лучшие бесплатные онлайн-курсы от MIT / Хабр", "date": "", "ddg_snippet": "Matrix Methods in Data Analysis, Signal Processing, and Machine Learning (Spring 2018).Graph Theory and Additive Combinatorics (2019). Описание курса: ocw.mit.edu/18-217F19.", "subpage_snippet": "", "source": "habr.com", "link": "https://habr.com/ru/articles/587732/", "content": "Matrix Methods in Data Analysis, Signal Processing, and Machine Learning (Spring 2018).Graph Theory and Additive Combinatorics (2019). Описание курса: ocw.mit.edu/18-217F19."}
+{"idx": 7, "title": "MATH-AI: The 4th Workshop on Mathematical Reasoning and AI", "date": "", "ddg_snippet": "- Repeated examples help learn arithmetic ( Poster ) > link.- Machine Learning meets Algebraic Combinatorics : A Suite of Datasets to Accelerate AI for Mathematics Research ( Poster ) > link.", "subpage_snippet": "", "source": "nips.cc", "link": "https://nips.cc/virtual/2024/workshop/84719", "content": "- Repeated examples help learn arithmetic ( Poster ) > link.- Machine Learning meets Algebraic Combinatorics : A Suite of Datasets to Accelerate AI for Mathematics Research ( Poster ) > link."}
+{"idx": 8, "title": "openreview.net/profile?id=~Helen_Jenne 1", "date": "", "ddg_snippet": "Machine Learning meets Algebraic Combinatorics : A Suite of Datasets Capturing Research-level Conjecturing Ability in Pure Mathematics. Machines and Mathematical Mutations: Using GNNs to Characterize Quiver Mutation Classes.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/profile?id=~Helen_Jenne1", "content": "Machine Learning meets Algebraic Combinatorics : A Suite of Datasets Capturing Research-level Conjecturing Ability in Pure Mathematics. Machines and Mathematical Mutations: Using GNNs to Characterize Quiver Mutation Classes."}
+{"idx": 9, "title": "Memuat turun buku Mathematics - Graph Theory Perpustakaan...", "date": "", "ddg_snippet": "Combinatorics . Computational Mathematics. Computer Algebra .Applied Graph Theory: An Introduction with Graph Optimization and Algebraic Graph Theory.", "subpage_snippet": "", "source": "ms.articles.sk", "link": "https://ms.articles.sk/category/408/Graph-Theory", "content": "Combinatorics . Computational Mathematics. Computer Algebra .Applied Graph Theory: An Introduction with Graph Optimization and Algebraic Graph Theory."}
diff --git a/data/sampled_jsons/Machine_Learning_meets_Algebraic_Combinatorics_Suite_Datasets_MLP_accuracy_mHeight_n=10.jsonl b/data/sampled_jsons/Machine_Learning_meets_Algebraic_Combinatorics_Suite_Datasets_MLP_accuracy_mHeight_n=10.jsonl
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+{"idx": 1, "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": 2, "title": "GitHub - pnnl/ML4AlgComb: ML Benchmarks in Algebraic Combinatorics", "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": 3, "title": "[2025]Machine Learning Meets Algebraic Combinatorics: A Suite of ...", "date": "", "ddg_snippet": "This paper introduces the ACD Repo, a suite of nine datasets designed to model research-level problems in algebraic combinatorics using machine learning , incorporating both narrow models (e.g., MLPs , Transformers) and LLM-based approaches like program synthesis. MLPs performed consistently well across classification tasks, while LLMs occasionally achieved perfect prediction by implicitly ...", "subpage_snippet": "", "source": "hyoo14.github.io", "link": "https://hyoo14.github.io/study/2025/07/25/2025-Machine-Learning-Meets-Algebraic-Combinatorics_-A-Suite-of-Datasets-Capturing-Research-level-Conjecturing-Ability-in-Pure-Mathematics", "content": "This paper introduces the ACD Repo, a suite of nine datasets designed to model research-level problems in algebraic combinatorics using machine learning , incorporating both narrow models (e.g., MLPs , Transformers) and LLM-based approaches like program synthesis. MLPs performed consistently well across classification tasks, while LLMs occasionally achieved perfect prediction by implicitly ..."}
+{"idx": 4, "title": "Machine Learning meets Algebraic Combinatorics: A Suite of Datasets ...", "date": "", "ddg_snippet": "Machine Learning meets Algebraic Combinatorics : A Suite of Datasets Capturing Research-level Conjecturing Ability in Pure Mathematics Herman Chau · Helen Jenne · Davis Brown · Jesse He · Mark Raugas · Sara Billey · Henry Kvinge", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/43763", "content": "Machine Learning meets Algebraic Combinatorics : A Suite of Datasets Capturing Research-level Conjecturing Ability in Pure Mathematics Herman Chau · Helen Jenne · Davis Brown · Jesse He · Mark Raugas · Sara Billey · Henry Kvinge"}
+{"idx": 5, "title": "Algebraic Combinatorics in Data Science and Optimisation", "date": "", "ddg_snippet": "This Special Issue aims to capture the shades of algebraic combinatorics that emerge in data science and optimization research to provide new topics in algebraic combinatorics from the perspective of applied mathematics, and to give birth to a novel trend in algebraic combinatorics .", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/journal/appliedmath/special_issues/AD188FEFS5", "content": "This Special Issue aims to capture the shades of algebraic combinatorics that emerge in data science and optimization research to provide new topics in algebraic combinatorics from the perspective of applied mathematics, and to give birth to a novel trend in algebraic combinatorics ."}
+{"idx": 6, "title": "Machine Learning meets Algebraic Combinatorics: A Suite of Datasets to ...", "date": "", "ddg_snippet": "Machine Learning meets Algebraic Combinatorics : A Suite of Datasets to Accelerate AI for Mathematics Research Herman Chau · Helen Jenne · Davis Brown · Jesse He · Mark Raugas · Sara Billey · Henry Kvinge", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/98521", "content": "Machine Learning meets Algebraic Combinatorics : A Suite of Datasets to Accelerate AI for Mathematics Research Herman Chau · Helen Jenne · Davis Brown · Jesse He · Mark Raugas · Sara Billey · Henry Kvinge"}
+{"idx": 7, "title": "Machine Learning meets Algebraic Combinatorics: A Suite of Datasets ...", "date": "", "ddg_snippet": "This paper introduces the Algebraic Combinatorics Dataset Repository (ACD Repo), a collection of datasets designed for using machine learning in advanced math research.", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/icml/43763/paper", "content": "This paper introduces the Algebraic Combinatorics Dataset Repository (ACD Repo), a collection of datasets designed for using machine learning in advanced math research."}
+{"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."}
diff --git a/data/sampled_jsons/Machine_Learning_meets_Algebraic_Combinatorics_spurious_correlation_Schubert_polynomials.jsonl b/data/sampled_jsons/Machine_Learning_meets_Algebraic_Combinatorics_spurious_correlation_Schubert_polynomials.jsonl
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index 0000000000000000000000000000000000000000..d3879e2e59f22f88b586a82fa4c8e8714e82c1a1
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+++ b/data/sampled_jsons/Machine_Learning_meets_Algebraic_Combinatorics_spurious_correlation_Schubert_polynomials.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Machine - Wikipedia", "date": "", "ddg_snippet": "A machine is a thermodynamic system that uses power to apply forces and control movement to perform an action. The term is commonly applied to artificial devices, such as those employing engines or motors, but also to natural biological macromolecules, such as molecular machines .", "subpage_snippet": "", "source": "en.m.wikipedia.org", "link": "https://en.m.wikipedia.org/wiki/Machine", "content": "A machine is a thermodynamic system that uses power to apply forces and control movement to perform an action. The term is commonly applied to artificial devices, such as those employing engines or motors, but also to natural biological macromolecules, such as molecular machines ."}
+{"idx": 1, "title": "MACHINE Definition & Meaning | Dictionary .com", "date": "", "ddg_snippet": "Machine definition: an apparatus consisting of interrelated parts with separate functions, used in the performance of some kind of work.. See examples of MACHINE used in a sentence.", "subpage_snippet": "", "source": "www.dictionary.com", "link": "https://www.dictionary.com/browse/machine", "content": "Machine definition: an apparatus consisting of interrelated parts with separate functions, used in the performance of some kind of work.. See examples of MACHINE used in a sentence."}
+{"idx": 2, "title": "MACHINE Definition & Meaning - Merriam-Webster", "date": "", "ddg_snippet": "The meaning of MACHINE is a mechanically, electrically, or electronically operated device for performing a task. How to use machine in a sentence.", "subpage_snippet": "", "source": "www.merriam-webster.com", "link": "https://www.merriam-webster.com/dictionary/machine", "content": "The meaning of MACHINE is a mechanically, electrically, or electronically operated device for performing a task. How to use machine in a sentence."}
+{"idx": 3, "title": "Machine | Definition, Mechanisms & Efficiency | Britannica", "date": "", "ddg_snippet": "machine , device, having a unique purpose, that augments or replaces human or animal effort for the accomplishment of physical tasks.", "subpage_snippet": "", "source": "www.britannica.com", "link": "https://www.britannica.com/technology/machine", "content": "machine , device, having a unique purpose, that augments or replaces human or animal effort for the accomplishment of physical tasks."}
+{"idx": 4, "title": "MACHINE | English meaning - Cambridge Dictionary", "date": "", "ddg_snippet": "MACHINE definition: 1. a piece of equipment with several moving parts that uses power to do a particular type of work…. Learn more.", "subpage_snippet": "", "source": "dictionary.cambridge.org", "link": "https://dictionary.cambridge.org/dictionary/english/machine", "content": "MACHINE definition: 1. a piece of equipment with several moving parts that uses power to do a particular type of work…. Learn more."}
+{"idx": 5, "title": "Machine - definition of machine by The Free Dictionary", "date": "", "ddg_snippet": "Of, relating to, or felt to resemble a machine : machine repairs; machine politics.", "subpage_snippet": "", "source": "www.thefreedictionary.com", "link": "https://www.thefreedictionary.com/machine", "content": "Of, relating to, or felt to resemble a machine : machine repairs; machine politics."}
+{"idx": 6, "title": "What Is A Machine ? Its Types and How it Works - Mech Lesson", "date": "", "ddg_snippet": "A machine is a mechanical device that uses power to apply force and control motion to perform work efficiently . Machines range from simple tools like pulleys and levers to complex systems like engines, robots, and manufacturing equipment.", "subpage_snippet": "", "source": "mechlesson.com", "link": "https://mechlesson.com/machine/", "content": "A machine is a mechanical device that uses power to apply force and control motion to perform work efficiently . Machines range from simple tools like pulleys and levers to complex systems like engines, robots, and manufacturing equipment."}
+{"idx": 7, "title": "What is a Machine ? - Computer Hope", "date": "", "ddg_snippet": "Jun 1, 2025 · A machine is a device with several parts that work together to perform a task. A machine can augment or replace the efforts required by an animal or human to make it easier to complete a task. For example, a car is a complex machine that transports humans and other things.", "subpage_snippet": "", "source": "www.computerhope.com", "link": "https://www.computerhope.com/jargon/m/machine.htm", "content": "Jun 1, 2025 · A machine is a device with several parts that work together to perform a task. A machine can augment or replace the efforts required by an animal or human to make it easier to complete a task. For example, a car is a complex machine that transports humans and other things."}
+{"idx": 8, "title": "Machine - New World Encyclopedia", "date": "", "ddg_snippet": "Modern power tools, automated machine tools, and human-operated power machinery are tools that are also machines . Machines used to transform heat or other energy into mechanical energy are known as engines.", "subpage_snippet": "", "source": "www.newworldencyclopedia.org", "link": "https://www.newworldencyclopedia.org/entry/Machine", "content": "Modern power tools, automated machine tools, and human-operated power machinery are tools that are also machines . Machines used to transform heat or other energy into mechanical energy are known as engines."}
+{"idx": 9, "title": "What is Machine ? – An Essential Guide", "date": "", "ddg_snippet": "The machine is a device that has a unique purpose, that augments or replaces human or animal effort for the accomplishment of physical tasks.", "subpage_snippet": "", "source": "www.theengineeringchoice.com", "link": "https://www.theengineeringchoice.com/what-is-machine/", "content": "The machine is a device that has a unique purpose, that augments or replaces human or animal effort for the accomplishment of physical tasks."}
diff --git a/data/sampled_jsons/Marrying_Causal_Representation_Learning_with.jsonl b/data/sampled_jsons/Marrying_Causal_Representation_Learning_with.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3cfc1cdb65a776c2de982b810dc6f57dd218e622
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2405.13888] Marrying Causal Representation Learning with ... GitHub - CausalLearningAI/crl-dynamical-systems Marrying Causal Representation Learning with Dynamical ... dblp: Marrying Causal Representation Learning with Dynamical ... Marrying Causal Representation Learning with Dynamical ... Images Marrying Causal Representation Learning with Dynamical ... Causal Representation Learning and Optimal Intervention Design", "date": "", "ddg_snippet": "May 22, 2024 · In this paper, we draw a clear connection between the two and their key assumptions, allowing us to apply identifiable methods developed in causal representation learning to dynamical systems. Official code for the NeurIPS 2024 paper Marrying Causal Representation Learning with Dynamical Systems for Science. This work was performed by Dingling Yao, Caroline Muller and Francesco Locatello. Please cite us when making use of our code or ideas. In this paper, we draw a clear connection between the two and their key assumptions, allowing us to apply identifiable methods developed in causal representation learning to dynamical systems. Feb 13, 2025 · Bibliographic details on Marrying Causal Representation Learning with Dynamical Systems for Science. Dingling Yao · Caroline Muller · Francesco Locatello Keywords: [ Dynamical Systems ] [ Causal Representation Learning ] [ Abstract ] [ Project Page ] [ OpenReview] View all In this paper, we draw a clear 021 connection between the two and their key assump-022 tions, allowing us to apply identifiable methods 023 developed in causal representation learning to 024 dynamical systems. Nov 29, 2023 · While representation learning has been hugely successful in predictive tasks, it can fail miserably in causal tasks including predicting the effect of an intervention. This calls for a marriage between representation learning and causal inference.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2405.13888", "content": "May 22, 2024 · In this paper, we draw a clear connection between the two and their key assumptions, allowing us to apply identifiable methods developed in causal representation learning to dynamical systems. Official code for the NeurIPS 2024 paper Marrying Causal Representation Learning with Dynamical Systems for Science. This work was performed by Dingling Yao, Caroline Muller and Francesco Locatello. Please cite us when making use of our code or ideas. In this paper, we draw a clear connection between the two and their key assumptions, allowing us to apply identifiable methods developed in causal representation learning to dynamical systems. Feb 13, 2025 · Bibliographic details on Marrying Causal Representation Learning with Dynamical Systems for Science. Dingling Yao · Caroline Muller · Francesco Locatello Keywords: [ Dynamical Systems ] [ Causal Representation Learning ] [ Abstract ] [ Project Page ] [ OpenReview] View all In this paper, we draw a clear 021 connection between the two and their key assump-022 tions, allowing us to apply identifiable methods 023 developed in causal representation learning to 024 dynamical systems. Nov 29, 2023 · While representation learning has been hugely successful in predictive tasks, it can fail miserably in causal tasks including predicting the effect of an intervention. This calls for a marriage between representation learning and causal inference."}
+{"idx": 1, "title": "GitHub - CausalLearningAI/crl-dynamical-systems", "date": "", "ddg_snippet": "Official code for the NeurIPS 2024 paper Marrying Causal Representation Learning with Dynamical Systems for Science. This work was performed by Dingling Yao, Caroline Muller and Francesco Locatello. Please cite us when making use of our code or ideas.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/CausalLearningAI/crl-dynamical-systems", "content": "Official code for the NeurIPS 2024 paper Marrying Causal Representation Learning with Dynamical Systems for Science. This work was performed by Dingling Yao, Caroline Muller and Francesco Locatello. Please cite us when making use of our code or ideas."}
+{"idx": 2, "title": "Marrying Causal Representation Learning with Dynamical ...", "date": "", "ddg_snippet": "In this paper, we draw a clear connection between the two and their key assumptions, allowing us to apply identifiable methods developed in causal representation learning to dynamical systems.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/hash/83eb339ed42297658fa24b5cec939285-Abstract-Conference.html", "content": "In this paper, we draw a clear connection between the two and their key assumptions, allowing us to apply identifiable methods developed in causal representation learning to dynamical systems."}
+{"idx": 3, "title": "dblp: Marrying Causal Representation Learning with Dynamical ...", "date": "", "ddg_snippet": "Feb 13, 2025 · Bibliographic details on Marrying Causal Representation Learning with Dynamical Systems for Science.", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/conf/nips/YaoML24", "content": "Feb 13, 2025 · Bibliographic details on Marrying Causal Representation Learning with Dynamical Systems for Science."}
+{"idx": 4, "title": "Marrying Causal Representation Learning with Dynamical ...", "date": "", "ddg_snippet": "Dingling Yao · Caroline Muller · Francesco Locatello Keywords: [ Dynamical Systems ] [ Causal Representation Learning ] [ Abstract ] [ Project Page ] [ OpenReview]", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2024/36844", "content": "Dingling Yao · Caroline Muller · Francesco Locatello Keywords: [ Dynamical Systems ] [ Causal Representation Learning ] [ Abstract ] [ Project Page ] [ OpenReview]"}
+{"idx": 5, "title": "Marrying Causal Representation Learning with Dynamical ...", "date": "", "ddg_snippet": "In this paper, we draw a clear 021 connection between the two and their key assump-022 tions, allowing us to apply identifiable methods 023 developed in causal representation learning to 024 dynamical systems.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=4SnTRS5Nkl", "content": "In this paper, we draw a clear 021 connection between the two and their key assump-022 tions, allowing us to apply identifiable methods 023 developed in causal representation learning to 024 dynamical systems."}
+{"idx": 6, "title": "Causal Representation Learning and Optimal Intervention Design", "date": "", "ddg_snippet": "Nov 29, 2023 · While representation learning has been hugely successful in predictive tasks, it can fail miserably in causal tasks including predicting the effect of an intervention. This calls for a marriage between representation learning and causal inference.", "subpage_snippet": "", "source": "eecs.berkeley.edu", "link": "https://eecs.berkeley.edu/research/colloquium/archives/231129-2/", "content": "Nov 29, 2023 · While representation learning has been hugely successful in predictive tasks, it can fail miserably in causal tasks including predicting the effect of an intervention. This calls for a marriage between representation learning and causal inference."}
+{"idx": 7, "title": "Causal Representation Learning with Generative Artificial", "date": "", "ddg_snippet": "Causal Representation Learning with Generative Artificial Intelligence: Application to Texts as Treatments 1 1 1 We thank Christian Fong and Justin ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.00903v2", "content": "Causal Representation Learning with Generative Artificial Intelligence: Application to Texts as Treatments 1 1 1 We thank Christian Fong and Justin ..."}
+{"idx": 8, "title": "Learning Robust Intervention Representations with Delta", "date": "", "ddg_snippet": "Leveraging this insight, we propose a framework that is capable of learning causal representations from image pairs, without any additional ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.04492v1", "content": "Leveraging this insight, we propose a framework that is capable of learning causal representations from image pairs, without any additional ..."}
+{"idx": 9, "title": "Interview with Fiona Anting Tan: Researching causal relations", "date": "", "ddg_snippet": "Her research interests span across natural language processing and reasoning, with a focus on extracting causal relationships from text for various ...", "subpage_snippet": "", "source": "aihub.org", "link": "https://aihub.org/2024/02/01/interview-with-fiona-anting-tan-researching-causal-relations-in-text/", "content": "Her research interests span across natural language processing and reasoning, with a focus on extracting causal relationships from text for various ..."}
diff --git a/data/sampled_jsons/Marrying_Causal_Representation_Learning_with_Dynamical_Systems_for_Science_html.jsonl b/data/sampled_jsons/Marrying_Causal_Representation_Learning_with_Dynamical_Systems_for_Science_html.jsonl
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index 0000000000000000000000000000000000000000..58b0e55e34011855820bc9954b743f3e201e2578
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Causal Representation Learning with Generative Artificial", "date": "", "ddg_snippet": "Causal Representation Learning with Generative Artificial Intelligence: Application to Texts as Treatments 1 1 1 We thank Christian Fong and Justin ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.00903v2", "content": "Causal Representation Learning with Generative Artificial Intelligence: Application to Texts as Treatments 1 1 1 We thank Christian Fong and Justin ..."}
+{"idx": 1, "title": "Learning Robust Intervention Representations with Delta", "date": "", "ddg_snippet": "In this paper, we introduce Causal Delta Embedding (CDE), a novel framework for learning robust representations of interventions from image pairs.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.04492v1", "content": "In this paper, we introduce Causal Delta Embedding (CDE), a novel framework for learning robust representations of interventions from image pairs."}
+{"idx": 2, "title": "Mapping the Course for Prompt-based Structured Prediction", "date": "", "ddg_snippet": "... for challenging tasks, showing that structured learning ... Segmented discourse representation theory: Dynamic semantics with discourse structure .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.15090v1", "content": "... for challenging tasks, showing that structured learning ... Segmented discourse representation theory: Dynamic semantics with discourse structure ."}
+{"idx": 3, "title": "Selective State Space Model for Monaural Speech Enhancement", "date": "", "ddg_snippet": "... with the School of Electrical Engineering and Telecommunications, University of New South Wales, Sydney, Australia.Hexin Liu is with the College of ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.06217v1", "content": "... with the School of Electrical Engineering and Telecommunications, University of New South Wales, Sydney, Australia.Hexin Liu is with the College of ..."}
+{"idx": 4, "title": "MELBA – GLACIAL: Granger and Learning-based Causality", "date": "", "ddg_snippet": "... LeArning -based CausalIty Analysis for Longitudinal studies) to fill this methodological gap by marrying GC with a multi-task neural forecasting model.", "subpage_snippet": "", "source": "www.melba-journal.org", "link": "https://www.melba-journal.org/papers/2024:028.html", "content": "... LeArning -based CausalIty Analysis for Longitudinal studies) to fill this methodological gap by marrying GC with a multi-task neural forecasting model."}
+{"idx": 5, "title": "Site Map | Interactive Storytelling Tools for Writers | Chris", "date": "", "ddg_snippet": "Are We Ready for a Formal Association? ... My Correspondence with Dr. ... Should I Dispense With Uncertainty?", "subpage_snippet": "", "source": "erasmatazz.com", "link": "https://erasmatazz.com/site-map.html", "content": "Are We Ready for a Formal Association? ... My Correspondence with Dr. ... Should I Dispense With Uncertainty?"}
+{"idx": 6, "title": "Obituaries | Statistical Modeling, Causal Inference, and Social", "date": "", "ddg_snippet": "These issues have been discussed for many years in the philosophy of science and statistics, gaining attention in recent decades first with the ...", "subpage_snippet": "", "source": "statmodeling.stat.columbia.edu", "link": "https://statmodeling.stat.columbia.edu/category/obituaries/", "content": "These issues have been discussed for many years in the philosophy of science and statistics, gaining attention in recent decades first with the ..."}
+{"idx": 7, "title": "1 Introduction", "date": "", "ddg_snippet": "... us to develop a deep understanding of the mechanistic causal relations governing astrophysical systems , beyond merely identifying correlations within ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.00134v1", "content": "... us to develop a deep understanding of the mechanistic causal relations governing astrophysical systems , beyond merely identifying correlations within ..."}
+{"idx": 8, "title": "Downloads", "date": "", "ddg_snippet": "A Hierarchical Reinforcement Learning Based Optimization Framework for Large-scale Dynamic Pickup and Delivery Problems", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/Downloads/2021", "content": "A Hierarchical Reinforcement Learning Based Optimization Framework for Large-scale Dynamic Pickup and Delivery Problems"}
+{"idx": 9, "title": "Programming language - WikiZero", "date": "", "ddg_snippet": "Prolog , designed in 1972, was the first logic programming language, communicating with a computer using formal logic notation.", "subpage_snippet": "", "source": "www.wikizero.org", "link": "https://www.wikizero.org/wiki/en/Programming_language", "content": "Prolog , designed in 1972, was the first logic programming language, communicating with a computer using formal logic notation."}
diff --git a/data/sampled_jsons/Medical_Large_Language_Model_Benchmarks_Should_Prioritize_Construct_Validity_Figure_5_psychiatry_car.jsonl b/data/sampled_jsons/Medical_Large_Language_Model_Benchmarks_Should_Prioritize_Construct_Validity_Figure_5_psychiatry_car.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2412017e34c8ea5f5fb2216f7e386b188cd4275b
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+++ b/data/sampled_jsons/Medical_Large_Language_Model_Benchmarks_Should_Prioritize_Construct_Validity_Figure_5_psychiatry_car.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Large Language Models in Medical Diagnostics: Scoping ...", "date": "", "ddg_snippet": "by H Su · 2025 · Cited by 3 — Evaluating the performance of large language models in predicting diagnostics for Spanish clinical cases in cardiology . Appl Sci. 2024 Dec 25;15(1):61. doi ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12186007/", "content": "by H Su · 2025 · Cited by 3 — Evaluating the performance of large language models in predicting diagnostics for Spanish clinical cases in cardiology . Appl Sci. 2024 Dec 25;15(1):61. doi ..."}
+{"idx": 1, "title": "Applications of large language models in cardiovascular ...", "date": "", "ddg_snippet": "by JF Santos · 2025 · Cited by 1 — Large language models (LLMs) offer potential solutions for enhancing patient education and supporting clinical decision-making. This study aimed to evaluate ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12282349/", "content": "by JF Santos · 2025 · Cited by 1 — Large language models (LLMs) offer potential solutions for enhancing patient education and supporting clinical decision-making. This study aimed to evaluate ..."}
+{"idx": 2, "title": "BRIDGE: Benchmarking Large Language Models for ...", "date": "", "ddg_snippet": "by J Wu · 2025 · Cited by 4 — To address this gap, we present BRIDGE, a comprehensive multilingual benchmark comprising 87 tasks sourced from real-world clinical data sources ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2504.19467?", "content": "by J Wu · 2025 · Cited by 4 — To address this gap, we present BRIDGE, a comprehensive multilingual benchmark comprising 87 tasks sourced from real-world clinical data sources ..."}
+{"idx": 3, "title": "Article The application of large language models in medicine", "date": "", "ddg_snippet": "by X Meng · 2024 · Cited by 151 — This study systematically reviewed the application of large language models (LLMs) in medicine , analyzing 550 selected studies from a vast literature search.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2589004224009350", "content": "by X Meng · 2024 · Cited by 151 — This study systematically reviewed the application of large language models (LLMs) in medicine , analyzing 550 selected studies from a vast literature search."}
+{"idx": 4, "title": "Large Language Model Influence on Diagnostic Reasoning", "date": "", "ddg_snippet": "by E Goh · 2024 · Cited by 320 — This randomized clinical trial evaluates the diagnostic performance of physicians with use of a large language model compared with ...", "subpage_snippet": "", "source": "jamanetwork.com", "link": "https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2825395", "content": "by E Goh · 2024 · Cited by 320 — This randomized clinical trial evaluates the diagnostic performance of physicians with use of a large language model compared with ..."}
+{"idx": 5, "title": "Large Language Models in Medical Diagnostics: Scoping ...", "date": "", "ddg_snippet": "9 Jun 2025 — This scoping review aimed to provide an overview of the current state of research regarding the use of LLMs in medical diagnostics.", "subpage_snippet": "", "source": "www.jmir.org", "link": "https://www.jmir.org/2025/1/e72062", "content": "9 Jun 2025 — This scoping review aimed to provide an overview of the current state of research regarding the use of LLMs in medical diagnostics."}
+{"idx": 6, "title": "A systematic review of large language model (LLM ...", "date": "", "ddg_snippet": "by S Shool · 2025 · Cited by 63 — This systematic review examines the evaluation parameters and methodologies applied to LLMs in clinical medicine , highlighting their capabilities, limitations, ...", "subpage_snippet": "", "source": "bmcmedinformdecismak.biomedcentral.com", "link": "https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-025-02954-4", "content": "by S Shool · 2025 · Cited by 63 — This systematic review examines the evaluation parameters and methodologies applied to LLMs in clinical medicine , highlighting their capabilities, limitations, ..."}
+{"idx": 7, "title": "Large language models for disease diagnosis: a scoping ...", "date": "", "ddg_snippet": "by S Zhou · 2025 · Cited by 44 — In this article, we perform a comprehensive review of LLM-based methods for disease diagnosis. Our review examines the existing literature across various ...", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s44387-025-00011-z", "content": "by S Zhou · 2025 · Cited by 44 — In this article, we perform a comprehensive review of LLM-based methods for disease diagnosis. Our review examines the existing literature across various ..."}
+{"idx": 8, "title": "Evaluating large language models and agents in healthcare", "date": "", "ddg_snippet": "by C Xiaolan · 2025 · Cited by 18 — This paper provides a comprehensive overview of current evaluation practices for LLMs and LLM agents in medicine .", "subpage_snippet": "", "source": "mednexus.org", "link": "https://mednexus.org/doi/10.1016/j.imed.2025.03.002", "content": "by C Xiaolan · 2025 · Cited by 18 — This paper provides a comprehensive overview of current evaluation practices for LLMs and LLM agents in medicine ."}
+{"idx": 9, "title": "Mind the Gap", "date": "", "ddg_snippet": "by F Mutisya · 2025 — Benchmarking medical large language models (LLMs) involves a multi-stage process of dataset design, metric selection, and model evaluation, typically grounded ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2507.16322", "content": "by F Mutisya · 2025 — Benchmarking medical large language models (LLMs) involves a multi-stage process of dataset design, metric selection, and model evaluation, typically grounded ..."}
diff --git a/data/sampled_jsons/Medical_Large_Language_Model_Benchmarks_Should_Prioritize_Construct_Validity_YuMEUNNpeb.jsonl b/data/sampled_jsons/Medical_Large_Language_Model_Benchmarks_Should_Prioritize_Construct_Validity_YuMEUNNpeb.jsonl
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index 0000000000000000000000000000000000000000..e8ac14ccb9db309b65bea7d4042b101b89f7379d
--- /dev/null
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Position: Medical Large Language Model Benchmarks Should ...", "date": "", "ddg_snippet": "Medical large language models (LLMs) research often makes bold claims, from encoding clinical knowledge to reasoning like a physician. These claims are usually backed by evaluation on competitive benchmarks —a tradition inherited from mainstream machine learning.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=YuMEUNNpeb", "content": "Medical large language models (LLMs) research often makes bold claims, from encoding clinical knowledge to reasoning like a physician. These claims are usually backed by evaluation on competitive benchmarks —a tradition inherited from mainstream machine learning."}
+{"idx": 1, "title": "Medical Large Language Model Benchmarks Should Prioritize ...", "date": "", "ddg_snippet": "Medical large language models (LLMs) research often makes bold claims, from encoding clinical knowledge to reasoning like a physician. Medical LLM benchmarks , much like those in other fields, are arbitrarily constructed using medical licensing exam questions.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.10694v1", "content": "Medical large language models (LLMs) research often makes bold claims, from encoding clinical knowledge to reasoning like a physician. Medical LLM benchmarks , much like those in other fields, are arbitrarily constructed using medical licensing exam questions."}
+{"idx": 2, "title": "Latest AI Research LLM Agents, Medical LLMs, And More", "date": "", "ddg_snippet": "Medical Large Language Model Benchmarks Should Prioritize Construct Validity argues that medical LLM benchmarks should prioritize construct validity . This means that the benchmarks should accurately measure the underlying abilities they are intended to assess.", "subpage_snippet": "", "source": "codemeld.org", "link": "https://codemeld.org/blog/latest-ai-research-llm-agents", "content": "Medical Large Language Model Benchmarks Should Prioritize Construct Validity argues that medical LLM benchmarks should prioritize construct validity . This means that the benchmarks should accurately measure the underlying abilities they are intended to assess."}
+{"idx": 3, "title": "AI Medical Tools Provide Worse Treatment for Women and...", "date": "", "ddg_snippet": "That’s bad, obviously, but one could argue that those models are more general purpose and not designed to be use in a medical setting. Unfortunately, a healthcare-centric LLM called Palmyra-Med was also studied and suffered from some of the same biases, per the paper.", "subpage_snippet": "", "source": "gizmodo.com", "link": "https://gizmodo.com/ai-medical-tools-provide-worse-treatment-for-women-and-underrepresented-groups-2000661945", "content": "That’s bad, obviously, but one could argue that those models are more general purpose and not designed to be use in a medical setting. Unfortunately, a healthcare-centric LLM called Palmyra-Med was also studied and suffered from some of the same biases, per the paper."}
+{"idx": 4, "title": "Why Language Models Hallucinate", "date": "", "ddg_snippet": "Many language - model benchmarks mirror standardized human exams, using binary metrics such as accuracy or pass-rate.2025. Medical large language models are vulnerable to data-poisoning attacks.", "subpage_snippet": "", "source": "readwise-assets.s3.amazonaws.com", "link": "https://readwise-assets.s3.amazonaws.com/media/wisereads/articles/why-language-models-hallucinat/2509.04664v1.pdf", "content": "Many language - model benchmarks mirror standardized human exams, using binary metrics such as accuracy or pass-rate.2025. Medical large language models are vulnerable to data-poisoning attacks."}
+{"idx": 5, "title": "This medical startup uses LLMs to run... | MIT Technology Review", "date": "", "ddg_snippet": "This is the new reality for patients at a small number of clinics in Southern California that are run by the medical startup Akido Labs. These patients—some of whom are on Medicaid—can access specialist appointments on short notice, a privilege typically only afforded to the wealthy few who...", "subpage_snippet": "", "source": "www.technologyreview.com", "link": "https://www.technologyreview.com/2025/09/22/1123873/medical-diagnosis-llm/", "content": "This is the new reality for patients at a small number of clinics in Southern California that are run by the medical startup Akido Labs. These patients—some of whom are on Medicaid—can access specialist appointments on short notice, a privilege typically only afforded to the wealthy few who..."}
+{"idx": 6, "title": "Inioluwa Deborah Raji's research works | University of California...", "date": "", "ddg_snippet": "Medical Large Language Model Benchmarks Should Prioritize Construct Validity . Preprint. File available.In this position paper, we argue that medical LLM benchmarks should (and indeed can) be empirically evaluated for their construct validity .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/scientific-contributions/Inioluwa-Deborah-Raji-2148191730", "content": "Medical Large Language Model Benchmarks Should Prioritize Construct Validity . Preprint. File available.In this position paper, we argue that medical LLM benchmarks should (and indeed can) be empirically evaluated for their construct validity ."}
+{"idx": 7, "title": "Franny Dean - Google Scholar", "date": "", "ddg_snippet": "2021. Position: Medical Large Language Model Benchmarks Should Prioritize Construct Validity .", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=Y04UhM4AAAAJ&hl=en", "content": "2021. Position: Medical Large Language Model Benchmarks Should Prioritize Construct Validity ."}
+{"idx": 8, "title": "Articles by Frances Dean | Synthical", "date": "", "ddg_snippet": "Medical Large Language Model Benchmarks Should Prioritize Construct Validity . 12 March 2025 by Ahmed Alaa and others. Computation and Language .", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/profile/d8b88aa7-cbcf-4d81-a315-a1ff4be5f516/articles", "content": "Medical Large Language Model Benchmarks Should Prioritize Construct Validity . 12 March 2025 by Ahmed Alaa and others. Computation and Language ."}
+{"idx": 9, "title": "Latest 15 Papers - June 08, 2025 - Githubissues", "date": "", "ddg_snippet": "Medical Large Language Model Benchmarks Should Prioritize Construct Validity . 2025-03-12. Benchmarking Chinese Medical LLMs: A Medbench-based Analysis of Performance Gaps and Hierarchical Optimization Strategies.", "subpage_snippet": "", "source": "githubissues.com", "link": "https://githubissues.com/somewordstoolate/DailyArXiv/17", "content": "Medical Large Language Model Benchmarks Should Prioritize Construct Validity . 2025-03-12. Benchmarking Chinese Medical LLMs: A Medbench-based Analysis of Performance Gaps and Hierarchical Optimization Strategies."}
diff --git a/data/sampled_jsons/Medusa_Cai_et_al._2024_abstract_year_2024.jsonl b/data/sampled_jsons/Medusa_Cai_et_al._2024_abstract_year_2024.jsonl
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index 0000000000000000000000000000000000000000..b2fe70c114eb901c0856e8d3294e8816d565393e
--- /dev/null
+++ b/data/sampled_jsons/Medusa_Cai_et_al._2024_abstract_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "DeFT: Decoding with Flash Tree-Attention for Efficient Tree-structured...", "date": "", "ddg_snippet": "Abstract .• Speculative decoding ( Cai et al ., 2024 ; Miao et al ., 2023) : We used the token tree topology from Medusa ( Cai et al ., 2024 ) and recorded real interaction data with APPS (Hendrycks et al ., 2021) as prompt dataset, including the length of accepted tokens at each step.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2404.00242v3", "content": "Abstract .• Speculative decoding ( Cai et al ., 2024 ; Miao et al ., 2023) : We used the token tree topology from Medusa ( Cai et al ., 2024 ) and recorded real interaction data with APPS (Hendrycks et al ., 2021) as prompt dataset, including the length of accepted tokens at each step."}
+{"idx": 1, "title": "Multi - token", "date": "", "ddg_snippet": "MEDUSA ( Cai et al ., 2024 ) on the other hand attaches additional linear heads to the final transformer block of the pretrained backbone, each including its own unembedding matrix.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=gKInyC9nlQ", "content": "MEDUSA ( Cai et al ., 2024 ) on the other hand attaches additional linear heads to the final transformer block of the pretrained backbone, each including its own unembedding matrix."}
+{"idx": 2, "title": "Towards Fast Multilingual LLM Inference", "date": "", "ddg_snippet": "2. Medusa ( Cai et al ., 2024 ) and Eagle (Li et al ., 2024 ): Both methods enhance the tar-get LLM by integrating additional lightweight FFN heads. These heads are designed to ef-ficiently draft potential token sequences de-pending on the penultimate representations from the target LLM.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2024.emnlp-main.602.pdf", "content": "2. Medusa ( Cai et al ., 2024 ) and Eagle (Li et al ., 2024 ): Both methods enhance the tar-get LLM by integrating additional lightweight FFN heads. These heads are designed to ef-ficiently draft potential token sequences de-pending on the penultimate representations from the target LLM."}
+{"idx": 3, "title": "(PDF) Unlocking Efficiency in Large Language Model Inference...", "date": "", "ddg_snippet": "in research ( Cai et al ., 2024 ;Li et al ., 2024 ), to. provide a basis for comparison with earlier studies. Additionally, it includes two input-guided tasksthat Medusa ( Cai et al ., 2024 ) and EAGLE (Li et al ., 2024 ) excel in this experimental setting, achieving.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/377891735_Unlocking_Efficiency_in_Large_Language_Model_Inference_A_Comprehensive_Survey_of_Speculative_Decoding", "content": "in research ( Cai et al ., 2024 ;Li et al ., 2024 ), to. provide a basis for comparison with earlier studies. Additionally, it includes two input-guided tasksthat Medusa ( Cai et al ., 2024 ) and EAGLE (Li et al ., 2024 ) excel in this experimental setting, achieving."}
+{"idx": 4, "title": "GitHub - chandana7d/speculative-decoding", "date": "", "ddg_snippet": "Heming Xia, Zhe Yang, et al . January 2024 . Medusa . Cai et al .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/chandana7d/speculative-decoding", "content": "Heming Xia, Zhe Yang, et al . January 2024 . Medusa . Cai et al ."}
+{"idx": 5, "title": "REST: Retrieval-Based Speculative Decoding", "date": "", "ddg_snippet": "In this framework, blockwise parallel decoding (Stern et al ., 2018) and Medusa ( Cai et al ., 2023) train multiple heads based on the LLM for draft token generation.", "subpage_snippet": "", "source": "www.cs.jhu.edu", "link": "https://www.cs.jhu.edu/~kevinduh/t/naacl24/final_pdf/paper161.pdf", "content": "In this framework, blockwise parallel decoding (Stern et al ., 2018) and Medusa ( Cai et al ., 2023) train multiple heads based on the LLM for draft token generation."}
+{"idx": 6, "title": "sqres/llmsplit_deepseek · Datasets at Hugging Face", "date": "", "ddg_snippet": "Tree Attention, as implemented in Medusa ( Cai et al ., 2024 ), serves as a baseline method in the paper for evaluating the performance of DEFT.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/sqres/llmsplit_deepseek", "content": "Tree Attention, as implemented in Medusa ( Cai et al ., 2024 ), serves as a baseline method in the paper for evaluating the performance of DEFT."}
+{"idx": 7, "title": "Meta Prompting | Prompt Engineering Guide", "date": "", "ddg_snippet": "Key Characteristics. According to Zhang et al . ( 2024 ) (opens in a new tab), the key characteristics of meta prompting can be summarized as follows: 1. Structure-oriented: Prioritizes the format and pattern of problems and solutions over specific content.", "subpage_snippet": "", "source": "www.promptingguide.ai", "link": "https://www.promptingguide.ai/techniques/meta-prompting", "content": "Key Characteristics. According to Zhang et al . ( 2024 ) (opens in a new tab), the key characteristics of meta prompting can be summarized as follows: 1. Structure-oriented: Prioritizes the format and pattern of problems and solutions over specific content."}
+{"idx": 8, "title": "science.org/doi/10.1126/science.adk3705", "date": "", "ddg_snippet": "A 485-million-year history of Earth’s surface temperature, Science 385, 2024 .", "subpage_snippet": "", "source": "www.science.org", "link": "https://www.science.org/doi/10.1126/science.adk3705", "content": "A 485-million-year history of Earth’s surface temperature, Science 385, 2024 ."}
+{"idx": 9, "title": "Саркопения в клинике нервных болезней | Северина М.И., Исаева...", "date": "", "ddg_snippet": "Согласно проведенному метаанализу Y. Cai et al . [40] частота саркопении у пациентов с БП варьирует от 6 до 55,5%. Саркопения при БП ассоциируется со степенью тяжести заболевания, прогрессированием двигательных нарушений, а также немоторными симптомами...", "subpage_snippet": "", "source": "www.rmj.ru", "link": "https://www.rmj.ru/articles/nevrologiya/Sarkopeniya_v_klinike_nervnyh_bolezney/", "content": "Согласно проведенному метаанализу Y. Cai et al . [40] частота саркопении у пациентов с БП варьирует от 6 до 55,5%. Саркопения при БП ассоциируется со степенью тяжести заболевания, прогрессированием двигательных нарушений, а также немоторными симптомами..."}
diff --git a/data/sampled_jsons/Medusa_Simple_LLM_inference_acceleration_framework_with_multiple_decoding_heads_Cai_2024.jsonl b/data/sampled_jsons/Medusa_Simple_LLM_inference_acceleration_framework_with_multiple_decoding_heads_Cai_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e067fa93d05518fbbb0f555d42d44da26818cba1
--- /dev/null
+++ b/data/sampled_jsons/Medusa_Simple_LLM_inference_acceleration_framework_with_multiple_decoding_heads_Cai_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2401.10774] Medusa: Simple LLM Inference Acceleration ...", "date": "", "ddg_snippet": "by T Cai · 2024 · Cited by 349 — In this paper, we present Medusa, an efficient method that augments LLM inference by adding extra decoding heads to predict multiple subsequent tokens in ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2401.10774", "content": "by T Cai · 2024 · Cited by 349 — In this paper, we present Medusa, an efficient method that augments LLM inference by adding extra decoding heads to predict multiple subsequent tokens in ..."}
+{"idx": 1, "title": "MEDUSA: Simple LLM inference acceleration framework ...", "date": "", "ddg_snippet": "by T Cai · 2024 · Cited by 349 — In this paper, we present MEDUSA , an efficient method that augments LLM inference by adding extra decoding heads to predict multiple subsequent tokens in ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3692070.3692273", "content": "by T Cai · 2024 · Cited by 349 — In this paper, we present MEDUSA , an efficient method that augments LLM inference by adding extra decoding heads to predict multiple subsequent tokens in ..."}
+{"idx": 2, "title": "Medusa: Simple LLM Inference Acceleration Framework ...", "date": "", "ddg_snippet": "by T Cai · 2024 · Cited by 349 — Our results suggest that the proposed typical ac- ceptance scheme can accelerate the decoding speed further while maintaining a similar generation quality. To ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2401.10774", "content": "by T Cai · 2024 · Cited by 349 — Our results suggest that the proposed typical ac- ceptance scheme can accelerate the decoding speed further while maintaining a similar generation quality. To ..."}
+{"idx": 3, "title": "Medusa: Simple Framework for Accelerating LLM ...", "date": "", "ddg_snippet": "Medusa is a simple framework that democratizes the acceleration techniques for LLM generation with multiple decoding heads .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/FasterDecoding/Medusa", "content": "Medusa is a simple framework that democratizes the acceleration techniques for LLM generation with multiple decoding heads ."}
+{"idx": 4, "title": "MEDUSA: Simple LLM Inference Acceleration Framework ...", "date": "", "ddg_snippet": "by T Cai · 2024 · Cited by 349 — In this paper, we present MEDUSA , an efficient method that augments LLM inference by adding extra decoding heads to predict multiple subsequent tokens in ...", "subpage_snippet": "", "source": "experts.illinois.edu", "link": "https://experts.illinois.edu/en/publications/medusa-simple-llm-inference-acceleration-framework-with-multiple-", "content": "by T Cai · 2024 · Cited by 349 — In this paper, we present MEDUSA , an efficient method that augments LLM inference by adding extra decoding heads to predict multiple subsequent tokens in ..."}
+{"idx": 5, "title": "Medusa: Simple LLM Inference Acceleration Framework ...", "date": "", "ddg_snippet": "This paper presents Medusa, an efficient method that augments LLM inference by adding extra decoding heads to predict multiple subsequent tokens in parallel ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Medusa:-Simple-LLM-Inference-Acceleration-Framework-Cai-Li/57e7af0b69325fafb371ef5d502e39ef9c90ef7e", "content": "This paper presents Medusa, an efficient method that augments LLM inference by adding extra decoding heads to predict multiple subsequent tokens in parallel ..."}
+{"idx": 6, "title": "Paper page - Medusa: Simple LLM Inference Acceleration ...", "date": "", "ddg_snippet": "21 Jan 2024 — In this paper, we present Medusa, an efficient method that augments LLM inference by adding extra decoding heads to predict multiple subsequent tokens in ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2401.10774", "content": "21 Jan 2024 — In this paper, we present Medusa, an efficient method that augments LLM inference by adding extra decoding heads to predict multiple subsequent tokens in ..."}
+{"idx": 7, "title": "[Literature Review] Medusa: Simple LLM Inference ...", "date": "", "ddg_snippet": "This paper introduces MEDUSA, a method for accelerating the inference speed of large language models (LLMs). The core idea is to add multiple decoding heads ...", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/en/review/medusa-simple-llm-inference-acceleration-framework-with-multiple-decoding-heads", "content": "This paper introduces MEDUSA, a method for accelerating the inference speed of large language models (LLMs). The core idea is to add multiple decoding heads ..."}
+{"idx": 8, "title": "ACCELERATION MULTIPLE HEADS DECODING FOR ...", "date": "", "ddg_snippet": "by Z Zhang — Multiple heads decoding accelerates the inference of Large Language Models . (LLMs) by predicting next several tokens simultaneously.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=Wn5QaSiMEV", "content": "by Z Zhang — Multiple heads decoding accelerates the inference of Large Language Models . (LLMs) by predicting next several tokens simultaneously."}
+{"idx": 9, "title": "Exploring Medusa and Multi-Token Prediction", "date": "", "ddg_snippet": "10 Jul 2024 — This blog post will go into detail on the \" MEDUSA : Simple LLM Inference Acceleration Framework with Multiple Decoding Heads \" paper", "subpage_snippet": "", "source": "towardsdatascience.com", "link": "https://towardsdatascience.com/exploring-medusa-and-multi-token-prediction-de7f8312e4a7/", "content": "10 Jul 2024 — This blog post will go into detail on the \" MEDUSA : Simple LLM Inference Acceleration Framework with Multiple Decoding Heads \" paper"}
diff --git a/data/sampled_jsons/Mind2Web_Deng_et_al._abstract_year_2023.jsonl b/data/sampled_jsons/Mind2Web_Deng_et_al._abstract_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..fa25e75be541e68732d69d608b5f4af579d01e46
--- /dev/null
+++ b/data/sampled_jsons/Mind2Web_Deng_et_al._abstract_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Mind2Web: Towards a Generalist Agent for the Web", "date": "", "ddg_snippet": "Jun 9, 2023 · We introduce Mind2Web , the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website. Existing datasets for web agents either use simulated websites or only cover a limited set of websites and tasks, thus not suitable for generalist web agents. With over 2,000 open-ended tasks collected from 137 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2306.06070", "content": "Jun 9, 2023 · We introduce Mind2Web , the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website. Existing datasets for web agents either use simulated websites or only cover a limited set of websites and tasks, thus not suitable for generalist web agents. With over 2,000 open-ended tasks collected from 137 ..."}
+{"idx": 1, "title": "MIND2WEB: Towards a Generalist Agent for the Web - NeurIPS", "date": "", "ddg_snippet": "Abstract We introduce MIND2WEB , the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website. Existing datasets for web agents either use simulated websites or only cover a limited set of websites and tasks, thus not suitable for generalist web agents. With over 2,000 open-ended tasks collected from ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2023/file/5950bf290a1570ea401bf98882128160-Paper-Datasets_and_Benchmarks.pdf", "content": "Abstract We introduce MIND2WEB , the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website. Existing datasets for web agents either use simulated websites or only cover a limited set of websites and tasks, thus not suitable for generalist web agents. With over 2,000 open-ended tasks collected from ..."}
+{"idx": 2, "title": "MIND2WEB | Proceedings of the 37th International Conference ...", "date": "", "ddg_snippet": "Dec 10, 2023 · Abstract We introduce MIND2WEB , the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website. Existing datasets for web agents either use simulated websites or only cover a limited set of websites and tasks, thus not suitable for generalist web agents.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3666122.3667342", "content": "Dec 10, 2023 · Abstract We introduce MIND2WEB , the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website. Existing datasets for web agents either use simulated websites or only cover a limited set of websites and tasks, thus not suitable for generalist web agents."}
+{"idx": 3, "title": "Mind2Web - GitHub Pages", "date": "", "ddg_snippet": "Mind2Web is a dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website. Mind2Web contains 2,350 tasks from 137 websites spanning 31 domains that: Reflect diverse and practical use cases on the web. Provide challenging yet realistic environments with real-world websites. Test generalization ability across ...", "subpage_snippet": "", "source": "osu-nlp-group.github.io", "link": "https://osu-nlp-group.github.io/Mind2Web/", "content": "Mind2Web is a dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website. Mind2Web contains 2,350 tasks from 137 websites spanning 31 domains that: Reflect diverse and practical use cases on the web. Provide challenging yet realistic environments with real-world websites. Test generalization ability across ..."}
+{"idx": 4, "title": "Mind2Web: Towards a Generalist Agent for the Web - GitHub NeurIPS 2023 Mind2web Towards A Generalist Agent For ... - Scribd Mind2Web: Towards a Generalist Agent for the Web MIND2WEB: Towards a Generalist Agent for the Web - NeurIPS MIND2WEB: Towards a Generalist Agent for the Web - NeurIPS Mind2Web : Towards a Generalist Agent for the Web - GitHub MIND2WEB: Towards a Generalist Agent for the Web - NeurIPS MIND2WEB: Towards a Generalist Agent for the Web - NeurIPS Mind2Web: Towards a Generalist Agent for the Web - arXiv.org", "date": "", "ddg_snippet": "Dataset, code, and models for the paper \" Mind2Web : Towards a Generalist Agent for the Web\". Check project website for demos and data exploration. Release process: •Dataset •Data used in the paper with textual context •Data with full traces and snapshots See full list on github.com Mind2Web is the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website. Existing datasets for web agents either use simulated websites or only cover a limited set of websites and tasks, thus not suitable for generalist web agents. With over 2,000 open-ended tasks collected from 137 websites spanning 31 domains and crowdsourced action sequences for the tasks, Mind2Web provides three necessary ingredients for building generalist web agents: 1) diverse domains, websites, and tasks, 2) use of real-world websites instead of simulated and simplified ones, and 3) a broad spectrum of user interaction patterns. Please check our website to explore the dataset. See full list on github.com The training set is hosted on Huggingface. We only provide a zip file for the test splits to prevent potential data contamination from large lagnuage models crawling the test set for training. Please download the test set here and unzip it with password mind2web . The structure is the same as the training set and you can still load it with Huggingface datasets. Please DO NOT redistribute the unzipped data files online. Clone the training data from Huggingface: And then download and unzip the test data into the same directory. After that, the directory structure should look like this: See full list on github.com Data Splits •train: 1,009 instances•test:•Cross Task: 252 instances, tasks from the same website are seen during training•Cross Website: 177 instances, websites are not seen during training•Cross Domain: 912 instances, entire domains are not seen during training Data Fields •\"annotation_id\" (str): unique id for each task•\"website\" (str): website name•\"domain\" (str): website domain•\"subdomain\" (str): website subdomain•\"confirmed_task\" (str): task description•\"action_reprs\" (list[str]): human readable string representation of the action sequence•\"actions\" (list[dict]): list of actions (steps) to complete the task•\"action_uid\" (str): unique id for each action (step)•\"raw_html\" (str): raw html of the page before the action is performed•\"cleaned_html\" (str): cleaned html of the page before the action is performed•\"operation\" (dict): operation to perform•\"op\" (str): operation type, one of CLICK, TYPE, SELECT•\"original_op\" (str): original operation type, contain additional HOVER and ENTER that are mapped to CLICK, not used•\"value\" (str): optional value for the operation, e.g., text to type, option to select•\"pos_candidates\" (list[dict]): ground truth elements. Here we only include positive elements that exist in \"cleaned_html\" after our preprocessing, so \"pos_candidates\" might be empty. The original labeled element can always be found in the \"raw_html\".•\"tag\" (str): tag of the element•\"is_original_target\" (bool): whether the element is the original target labeled by the annotator•\"is_top_level_target\" (bool): whether the element is a top level target find by our algorithm. please see the paper for more details.•\"backend_node_id\" (str): unique id for the element•\"attributes\" (str): serialized attributes of the element, use json.loads to convert back to dict•\"neg_candidates\" (list[dict]): other candidate elements in the page after preprocessing, has similar structure as \"pos_candidates\" See full list on github.com The raw dump contains the original trace file, network traffic stored in har file, recordings and various snapshots extracted from the trace file. Due to the size of the raw dump, the data is shared via Globus with OSC. Please check the instruction here for an overview of Globus. You can either login with your Google account, or check with your institution as they might have an institutional Globus account. If you have any trouble accessing the data, please contact us. The raw dump is organized in the following structure: We have the following files for each task: •session.har.zip: network traffic stored in har file, can be used for replaying. Please see here for more details. Note that matching network requests is non-trivial as even the same action may trigger different requests due to the dynamic nature of the web (datetime, random generator). We do not use this in our work but would be worth exploring. See full list on github.com Evaluation You can find the trained DeBERTa-v3-base model on Huggingface Model Hub. You can run evaluation with the following command:•model_path: path to the model or model name on Huggingface Model Hub.•data_path: path to the dataset directory, e.g., ${BASE_DIR}/ Mind2Web .•split_file: path to the split file, e.g., data/test_website/*.json.•output_dir: path to the output directory. You will see two files: results_*.json with the evaluation metrics, and scores_*.pkl with the prediction scores which can be used for the action prediction module. Fine-tuning To fine-tune the model, you can simply run:•model: Model config to load.It uses config file in candidate_generation/conf/config.yaml. The checkpoints will be saved under the workdir configured by hydra. The candidate generation model we use is a encoder-only DeBERTa model that outputs a score for a pair of query and candidate, and the implementation is based on SentenceTransformer's Cross-Encoders, please check the documentation for more details. See full list on github.com Evaluation You can find the trained flan-t5-base, flan-t5-large and flan-t5-xl models on Huggingface Model Hub.To run evaluation on the test splits, set the parameters in action_prediction/conf/config.yaml accordingly. We use hydra for managing the configuration.you can then run the evaluation using the following command:•model_path: Path to the model you want to evaluate, or the model name on Huggingface Model Hub.•model: Model config to load, it should match the model to load.•output_path: We will write model outputs to the directory, you will see three files: *_outputs*.json, *_predictions*.json and *_results*.json.•top_k: Number of candidates to consider for each action, we use 50 in the paper for most experiments. Fine-tuning To fine-tune the model, you can use the following command:It uses the same config file as above, action_prediction/conf/config.yaml. The checkpoints will be saved under the workdir configured by hydra. The action prediction model we use is based on the seq2seq T5 model, and the implementation is largely based on Huggingface Transformers's Seq2SeqTrainer, please check the documentation for more details. See full list on github.com You will need the dataset, candidate generation results, and your own openai_api key to run evaluation with LLMs. You can find the 3-shot prompt we use under: src/action_prediction/llm_prompt.json. As described in the paper, we use a multi-choice QA formulation for selecting the target element; an example is shown below: To run evaluation on the test splits, set the parameters in action_prediction/conf/config.yaml similar as above. you can then run the evaluation using the following command: •output_path: We will write model outputs to the directory, you will see three files: *_outputs*.json, *_predictions*.json and *_results*.json. •llm: Openai model you want to use. See full list on github.com Xiang Deng , Huan Sun, Yu Su, The Ohio State University See full list on github.com The Mind2Web dataset is licensed under a Creative Commons Attribution 4.0 International License. Code under this repo is licensed under a MIT License. See full list on github.com M IND 2W EB : Towards a Generalist Agent for the Web Xiang Deng ∗ Yu Gu Boyuan Zheng Shijie Chen Samuel Stevens Boshi Wang Huan Sun∗ Yu Su∗ The Ohio State University https://osu-nlp-group.github.io/ Mind2Web Abstract We introduce M IND 2W EB , the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any ... What is mind2web? We introduce Mind2Web, the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website. Existing datasets for web agents either use simulated websites or only cover a limited set of websites and tasks, thus not suitable for generalist web agents. How many domains does mind2web have? 31 domains and crowdsourced action sequences for the tasks, MIND2WEB pro-vides three necessary ingredients for building generalist web agents: 1) diverse domains, websites, and tasks, 2) use of real-world websites instead of simulated and simplified ones, and 3) a broad spectrum of user interaction patterns. Is mind2web better than a full-fledged website? Recent studies [5, 21, 35] have utilized similar techniques for mobile applications, however, these are often simpler and offer fewer functions compared with full-fledged websites. In contrast, MIND2WEB aims to adapt to a realistic web environment, characterized by its high diversity. Also related is the research on web automation systems [1, 19]. What's new in mind2web 2024? 2024/3/18: Multimodal- Mind2Web dataset released. We have paired each HTML document with the corresponding webpage screenshot image and saved the trouble of downloading Mind2Web Raw Dump. 2024/1/3: Update! Check out our latest follow-up, SeeAct, enabling everyone to use GPT-4V-based web agents with one click! Try it out and have fun! What is mind2web & how can it benefit LLMs? MIND2WEB can bridge this lacuna by necessitating LLMs to take actions within realistic web-browsing environments that demand prolonged decision-making sequences. Furthermore, MIND2WEB may stimulate the development of more advanced tools based on LLMs that interface the web with natural language. Can LLMs build a generalist agent on top of mind2web? In this work, we explore the use of LLMs to build generalist agent on top of MIND2WEB by either tuning medium-sized LMs with only around 1,000 examples, or prompting an LLM such as GPT-4, and have observed promising results . Dec 9, 2023 · Abstract We introduce Mind2Web , the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website. Existing datasets for web agents either use simulated websites or only cover a limited set of websites and tasks, thus not suitable for generalist web agents.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/OSU-NLP-Group/Mind2Web", "content": "Dataset, code, and models for the paper \" Mind2Web : Towards a Generalist Agent for the Web\". Check project website for demos and data exploration. Release process: •Dataset •Data used in the paper with textual context •Data with full traces and snapshots See full list on github.com Mind2Web is the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website. Existing datasets for web agents either use simulated websites or only cover a limited set of websites and tasks, thus not suitable for generalist web agents. With over 2,000 open-ended tasks collected from 137 websites spanning 31 domains and crowdsourced action sequences for the tasks, Mind2Web provides three necessary ingredients for building generalist web agents: 1) diverse domains, websites, and tasks, 2) use of real-world websites instead of simulated and simplified ones, and 3) a broad spectrum of user interaction patterns. Please check our website to explore the dataset. See full list on github.com The training set is hosted on Huggingface. We only provide a zip file for the test splits to prevent potential data contamination from large lagnuage models crawling the test set for training. Please download the test set here and unzip it with password mind2web . The structure is the same as the training set and you can still load it with Huggingface datasets. Please DO NOT redistribute the unzipped data files online. Clone the training data from Huggingface: And then download and unzip the test data into the same directory. After that, the directory structure should look like this: See full list on github.com Data Splits •train: 1,009 instances•test:•Cross Task: 252 instances, tasks from the same website are seen during training•Cross Website: 177 instances, websites are not seen during training•Cross Domain: 912 instances, entire domains are not seen during training Data Fields •\"annotation_id\" (str): unique id for each task•\"website\" (str): website name•\"domain\" (str): website domain•\"subdomain\" (str): website subdomain•\"confirmed_task\" (str): task description•\"action_reprs\" (list[str]): human readable string representation of the action sequence•\"actions\" (list[dict]): list of actions (steps) to complete the task•\"action_uid\" (str): unique id for each action (step)•\"raw_html\" (str): raw html of the page before the action is performed•\"cleaned_html\" (str): cleaned html of the page before the action is performed•\"operation\" (dict): operation to perform•\"op\" (str): operation type, one of CLICK, TYPE, SELECT•\"original_op\" (str): original operation type, contain additional HOVER and ENTER that are mapped to CLICK, not used•\"value\" (str): optional value for the operation, e.g., text to type, option to select•\"pos_candidates\" (list[dict]): ground truth elements. Here we only include positive elements that exist in \"cleaned_html\" after our preprocessing, so \"pos_candidates\" might be empty. The original labeled element can always be found in the \"raw_html\".•\"tag\" (str): tag of the element•\"is_original_target\" (bool): whether the element is the original target labeled by the annotator•\"is_top_level_target\" (bool): whether the element is a top level target find by our algorithm. please see the paper for more details.•\"backend_node_id\" (str): unique id for the element•\"attributes\" (str): serialized attributes of the element, use json.loads to convert back to dict•\"neg_candidates\" (list[dict]): other candidate elements in the page after preprocessing, has similar structure as \"pos_candidates\" See full list on github.com The raw dump contains the original trace file, network traffic stored in har file, recordings and various snapshots extracted from the trace file. Due to the size of the raw dump, the data is shared via Globus with OSC. Please check the instruction here for an overview of Globus. You can either login with your Google account, or check with your institution as they might have an institutional Globus account. If you have any trouble accessing the data, please contact us. The raw dump is organized in the following structure: We have the following files for each task: •session.har.zip: network traffic stored in har file, can be used for replaying. Please see here for more details. Note that matching network requests is non-trivial as even the same action may trigger different requests due to the dynamic nature of the web (datetime, random generator). We do not use this in our work but would be worth exploring. See full list on github.com Evaluation You can find the trained DeBERTa-v3-base model on Huggingface Model Hub. You can run evaluation with the following command:•model_path: path to the model or model name on Huggingface Model Hub.•data_path: path to the dataset directory, e.g., ${BASE_DIR}/ Mind2Web .•split_file: path to the split file, e.g., data/test_website/*.json.•output_dir: path to the output directory. You will see two files: results_*.json with the evaluation metrics, and scores_*.pkl with the prediction scores which can be used for the action prediction module. Fine-tuning To fine-tune the model, you can simply run:•model: Model config to load.It uses config file in candidate_generation/conf/config.yaml. The checkpoints will be saved under the workdir configured by hydra. The candidate generation model we use is a encoder-only DeBERTa model that outputs a score for a pair of query and candidate, and the implementation is based on SentenceTransformer's Cross-Encoders, please check the documentation for more details. See full list on github.com Evaluation You can find the trained flan-t5-base, flan-t5-large and flan-t5-xl models on Huggingface Model Hub.To run evaluation on the test splits, set the parameters in action_prediction/conf/config.yaml accordingly. We use hydra for managing the configuration.you can then run the evaluation using the following command:•model_path: Path to the model you want to evaluate, or the model name on Huggingface Model Hub.•model: Model config to load, it should match the model to load.•output_path: We will write model outputs to the directory, you will see three files: *_outputs*.json, *_predictions*.json and *_results*.json.•top_k: Number of candidates to consider for each action, we use 50 in the paper for most experiments. Fine-tuning To fine-tune the model, you can use the following command:It uses the same config file as above, action_prediction/conf/config.yaml. The checkpoints will be saved under the workdir configured by hydra. The action prediction model we use is based on the seq2seq T5 model, and the implementation is largely based on Huggingface Transformers's Seq2SeqTrainer, please check the documentation for more details. See full list on github.com You will need the dataset, candidate generation results, and your own openai_api key to run evaluation with LLMs. You can find the 3-shot prompt we use under: src/action_prediction/llm_prompt.json. As described in the paper, we use a multi-choice QA formulation for selecting the target element; an example is shown below: To run evaluation on the test splits, set the parameters in action_prediction/conf/config.yaml similar as above. you can then run the evaluation using the following command: •output_path: We will write model outputs to the directory, you will see three files: *_outputs*.json, *_predictions*.json and *_results*.json. •llm: Openai model you want to use. See full list on github.com Xiang Deng , Huan Sun, Yu Su, The Ohio State University See full list on github.com The Mind2Web dataset is licensed under a Creative Commons Attribution 4.0 International License. Code under this repo is licensed under a MIT License. See full list on github.com M IND 2W EB : Towards a Generalist Agent for the Web Xiang Deng ∗ Yu Gu Boyuan Zheng Shijie Chen Samuel Stevens Boshi Wang Huan Sun∗ Yu Su∗ The Ohio State University https://osu-nlp-group.github.io/ Mind2Web Abstract We introduce M IND 2W EB , the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any ... What is mind2web? We introduce Mind2Web, the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website. Existing datasets for web agents either use simulated websites or only cover a limited set of websites and tasks, thus not suitable for generalist web agents. How many domains does mind2web have? 31 domains and crowdsourced action sequences for the tasks, MIND2WEB pro-vides three necessary ingredients for building generalist web agents: 1) diverse domains, websites, and tasks, 2) use of real-world websites instead of simulated and simplified ones, and 3) a broad spectrum of user interaction patterns. Is mind2web better than a full-fledged website? Recent studies [5, 21, 35] have utilized similar techniques for mobile applications, however, these are often simpler and offer fewer functions compared with full-fledged websites. In contrast, MIND2WEB aims to adapt to a realistic web environment, characterized by its high diversity. Also related is the research on web automation systems [1, 19]. What's new in mind2web 2024? 2024/3/18: Multimodal- Mind2Web dataset released. We have paired each HTML document with the corresponding webpage screenshot image and saved the trouble of downloading Mind2Web Raw Dump. 2024/1/3: Update! Check out our latest follow-up, SeeAct, enabling everyone to use GPT-4V-based web agents with one click! Try it out and have fun! What is mind2web & how can it benefit LLMs? MIND2WEB can bridge this lacuna by necessitating LLMs to take actions within realistic web-browsing environments that demand prolonged decision-making sequences. Furthermore, MIND2WEB may stimulate the development of more advanced tools based on LLMs that interface the web with natural language. Can LLMs build a generalist agent on top of mind2web? In this work, we explore the use of LLMs to build generalist agent on top of MIND2WEB by either tuning medium-sized LMs with only around 1,000 examples, or prompting an LLM such as GPT-4, and have observed promising results . Dec 9, 2023 · Abstract We introduce Mind2Web , the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website. Existing datasets for web agents either use simulated websites or only cover a limited set of websites and tasks, thus not suitable for generalist web agents."}
+{"idx": 5, "title": "NeurIPS 2023 Mind2web Towards A Generalist Agent For ... - Scribd", "date": "", "ddg_snippet": "M IND 2W EB : Towards a Generalist Agent for the Web Xiang Deng ∗ Yu Gu Boyuan Zheng Shijie Chen Samuel Stevens Boshi Wang Huan Sun∗ Yu Su∗ The Ohio State University https://osu-nlp-group.github.io/ Mind2Web Abstract We introduce M IND 2W EB , the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any ...", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/809106936/NeurIPS-2023-Mind2web-Towards-a-Generalist-Agent-for-the-Web-Paper-Datasets-and-Benchmarks", "content": "M IND 2W EB : Towards a Generalist Agent for the Web Xiang Deng ∗ Yu Gu Boyuan Zheng Shijie Chen Samuel Stevens Boshi Wang Huan Sun∗ Yu Su∗ The Ohio State University https://osu-nlp-group.github.io/ Mind2Web Abstract We introduce M IND 2W EB , the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any ..."}
+{"idx": 6, "title": "Mind2Web: Towards a Generalist Agent for the Web - arXiv.org", "date": "", "ddg_snippet": "Dec 9, 2023 · Abstract We introduce Mind2Web , the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website. Existing datasets for web agents either use simulated websites or only cover a limited set of websites and tasks, thus not suitable for generalist web agents.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2306.06070", "content": "Dec 9, 2023 · Abstract We introduce Mind2Web , the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website. Existing datasets for web agents either use simulated websites or only cover a limited set of websites and tasks, thus not suitable for generalist web agents."}
+{"idx": 7, "title": "Deng et al ., 2015 - Bibliography - Confluence", "date": "", "ddg_snippet": "Bibliography. / Deng et al ., 2015. More actions.ZLOC uses the location information of an account’s friends or followers. More specifically, we investigate the follower accounts of suspected zombie accounts in SINA WeiBo, one of the two most popular microblogging websites in China.", "subpage_snippet": "", "source": "open-measure.atlassian.net", "link": "https://open-measure.atlassian.net/wiki/spaces/BIB/pages/782925964", "content": "Bibliography. / Deng et al ., 2015. More actions.ZLOC uses the location information of an account’s friends or followers. More specifically, we investigate the follower accounts of suspected zombie accounts in SINA WeiBo, one of the two most popular microblogging websites in China."}
+{"idx": 8, "title": "On the Multi-turn Instruction Following for Conversational Web Agents", "date": "", "ddg_snippet": "Yang Deng , Xuan Zhang, Wenxuan Zhang, Yifei Yuan, See-Kiong Ng, Tat-Seng Chua. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2024.acl-long.477/", "content": "Yang Deng , Xuan Zhang, Wenxuan Zhang, Yifei Yuan, See-Kiong Ng, Tat-Seng Chua. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)."}
+{"idx": 9, "title": "With M emory for C omputer C ontrol", "date": "", "ddg_snippet": "Mind 2 Web ( Deng et al ., 2023) is a realistic dataset containing human demonstrations of open-domain tasks from various real-world websites, such as Airbnb and Twitter.In Mind 2 Web , we compare with MindAct ( Deng et al ., 2023), the current SOTA ICL method in this benchmark.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=pI6ylnkPAD", "content": "Mind 2 Web ( Deng et al ., 2023) is a realistic dataset containing human demonstrations of open-domain tasks from various real-world websites, such as Airbnb and Twitter.In Mind 2 Web , we compare with MindAct ( Deng et al ., 2023), the current SOTA ICL method in this benchmark."}
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Mind2Web: Towards a Generalist Agent for the Web", "date": "", "ddg_snippet": "We introduce Mind2Web , the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website. Existing datasets for web agents either use simulated websites or only cover a limited set of websites and tasks, thus not suitable for generalist web agents. With over 2,000 open-ended tasks collected from 137 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2306.06070", "content": "We introduce Mind2Web , the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website. Existing datasets for web agents either use simulated websites or only cover a limited set of websites and tasks, thus not suitable for generalist web agents. With over 2,000 open-ended tasks collected from 137 ..."}
+{"idx": 1, "title": "PDF MIND2WEB: Towards a Generalist Agent for the Web - NeurIPS", "date": "", "ddg_snippet": "Abstract We introduce MIND2WEB , the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website. Existing datasets for web agents either use simulated websites or only cover a limited set of websites and tasks, thus not suitable for generalist web agents. With over 2,000 open-ended tasks collected from ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2023/file/5950bf290a1570ea401bf98882128160-Paper-Datasets_and_Benchmarks.pdf", "content": "Abstract We introduce MIND2WEB , the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website. Existing datasets for web agents either use simulated websites or only cover a limited set of websites and tasks, thus not suitable for generalist web agents. With over 2,000 open-ended tasks collected from ..."}
+{"idx": 2, "title": "MIND2WEB | Proceedings of the 37th International Conference on Neural ...", "date": "", "ddg_snippet": "Abstract We introduce MIND2WEB , the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website. Existing datasets for web agents either use simulated websites or only cover a limited set of websites and tasks, thus not suitable for generalist web agents.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3666122.3667342", "content": "Abstract We introduce MIND2WEB , the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website. Existing datasets for web agents either use simulated websites or only cover a limited set of websites and tasks, thus not suitable for generalist web agents."}
+{"idx": 3, "title": "Mind2Web - GitHub Pages", "date": "", "ddg_snippet": "Mind2Web is a dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website. Mind2Web contains 2,350 tasks from 137 websites spanning 31 domains that: Reflect diverse and practical use cases on the web. Provide challenging yet realistic environments with real-world websites. Test generalization ability across ...", "subpage_snippet": "", "source": "osu-nlp-group.github.io", "link": "https://osu-nlp-group.github.io/Mind2Web/", "content": "Mind2Web is a dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website. Mind2Web contains 2,350 tasks from 137 websites spanning 31 domains that: Reflect diverse and practical use cases on the web. Provide challenging yet realistic environments with real-world websites. Test generalization ability across ..."}
+{"idx": 4, "title": "NeurIPS 2023 Mind2web Towards A Generalist Agent For The Web Paper ...", "date": "", "ddg_snippet": "M IND 2W EB: Towards a Generalist Agent for the Web Xiang Deng ∗ Yu Gu Boyuan Zheng Shijie Chen Samuel Stevens Boshi Wang Huan Sun∗ Yu Su∗ The Ohio State University https://osu-nlp-group.github.io/ Mind2Web Abstract We introduce M IND 2W EB, the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any ...", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/809106936/NeurIPS-2023-Mind2web-Towards-a-Generalist-Agent-for-the-Web-Paper-Datasets-and-Benchmarks", "content": "M IND 2W EB: Towards a Generalist Agent for the Web Xiang Deng ∗ Yu Gu Boyuan Zheng Shijie Chen Samuel Stevens Boshi Wang Huan Sun∗ Yu Su∗ The Ohio State University https://osu-nlp-group.github.io/ Mind2Web Abstract We introduce M IND 2W EB, the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any ..."}
+{"idx": 5, "title": "Mind2Web: Towards a Generalist Agent for the Web, Xiang Deng+ ... - GitHub", "date": "", "ddg_snippet": "Abstract We introduce Mind2Web , the first dataset for developing and evaluatinggeneralist agents for the web that can follow language instructions to completecomplex tasks on any website. Existing datasets for web agents either usesimulated websites or only cover a limited set of websites and tasks, thus notsuitable for generalist web agents.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/AkihikoWatanabe/paper_notes/issues/783", "content": "Abstract We introduce Mind2Web , the first dataset for developing and evaluatinggeneralist agents for the web that can follow language instructions to completecomplex tasks on any website. Existing datasets for web agents either usesimulated websites or only cover a limited set of websites and tasks, thus notsuitable for generalist web agents."}
+{"idx": 6, "title": "Mind2Web: Towards a Generalist Agent for the Web - arXiv.org", "date": "", "ddg_snippet": "Abstract We introduce Mind2Web , the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website. Existing datasets for web agents either use simulated websites or only cover a limited set of websites and tasks, thus not suitable for generalist web agents. With over 2, 000 open-ended tasks collected ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2306.06070v3", "content": "Abstract We introduce Mind2Web , the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website. Existing datasets for web agents either use simulated websites or only cover a limited set of websites and tasks, thus not suitable for generalist web agents. With over 2, 000 open-ended tasks collected ..."}
+{"idx": 7, "title": "Paper page - Mind2Web: Towards a Generalist Agent for the Web", "date": "", "ddg_snippet": "Abstract We introduce Mind2Web , the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website. Existing datasets for web agents either use simulated websites or only cover a limited set of websites and tasks, thus not suitable for generalist web agents.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2306.06070", "content": "Abstract We introduce Mind2Web , the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website. Existing datasets for web agents either use simulated websites or only cover a limited set of websites and tasks, thus not suitable for generalist web agents."}
+{"idx": 8, "title": "Mind2Web: Towards a Generalist Agent for the Web - NeurIPS", "date": "", "ddg_snippet": "Authors Xiang Deng , Yu Gu, Boyuan Zheng, Shijie Chen, Sam Stevens, Boshi Wang, Huan Sun, Yu Su Abstract We introduce Mind2Web , the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website. Existing datasets for web agents either use simulated websites or only cover a limited set of websites and ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2023/hash/5950bf290a1570ea401bf98882128160-Abstract-Datasets_and_Benchmarks.html", "content": "Authors Xiang Deng , Yu Gu, Boyuan Zheng, Shijie Chen, Sam Stevens, Boshi Wang, Huan Sun, Yu Su Abstract We introduce Mind2Web , the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website. Existing datasets for web agents either use simulated websites or only cover a limited set of websites and ..."}
+{"idx": 9, "title": "Mind2Web: Towards a Generalist Agent for the Web", "date": "", "ddg_snippet": "Mind2Web is introduced, the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website and an initial exploration of using large language models (LLMs) for building generalist web agents is conducted.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Mind2Web:-Towards-a-Generalist-Agent-for-the-Web-Deng-Gu/58f8925a8b87054ad0635a6398a7fe24935b1604/figure/1", "content": "Mind2Web is introduced, the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website and an initial exploration of using large language models (LLMs) for building generalist web agents is conducted."}
diff --git a/data/sampled_jsons/Mind2Web_Towards_a_Generalist_Agent_for_the_Web_dataset_construction_method_year_2023.jsonl b/data/sampled_jsons/Mind2Web_Towards_a_Generalist_Agent_for_the_Web_dataset_construction_method_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..85abffc8043246f8dfd65658cc840f729f17eb4e
--- /dev/null
+++ b/data/sampled_jsons/Mind2Web_Towards_a_Generalist_Agent_for_the_Web_dataset_construction_method_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Mind2Web: Towards a Generalist Agent for the Web", "date": "", "ddg_snippet": "by X Deng · Cited by 635 — The proposed dataset is a great resource to the community. The paper presents necessary details about the dataset construction . The associated project ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=kiYqbO3wqw", "content": "by X Deng · Cited by 635 — The proposed dataset is a great resource to the community. The paper presents necessary details about the dataset construction . The associated project ..."}
+{"idx": 1, "title": "Mind2Web: Towards a Generalist Agent for the Web", "date": "", "ddg_snippet": "We introduce Mind2Web , the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2306.06070v3", "content": "We introduce Mind2Web , the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex ..."}
+{"idx": 2, "title": "MIND2WEB: towards a generalist agent for the web", "date": "", "ddg_snippet": "by X Deng · 2023 · Cited by 635 — We introduce MIND2WEB , the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3666122.3667342", "content": "by X Deng · 2023 · Cited by 635 — We introduce MIND2WEB , the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex ..."}
+{"idx": 3, "title": "Mind2Web: Towards a Generalist Agent for the Web", "date": "", "ddg_snippet": "by X Deng · 2023 · Cited by 635 — We introduce Mind2Web , the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2306.06070", "content": "by X Deng · 2023 · Cited by 635 — We introduce Mind2Web , the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex ..."}
+{"idx": 4, "title": "Mind2Web: Towards a Generalist Agent for the Web", "date": "", "ddg_snippet": "Mind2Web is introduced, the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Mind2Web:-Towards-a-Generalist-Agent-for-the-Web-Deng-Gu/58f8925a8b87054ad0635a6398a7fe24935b1604", "content": "Mind2Web is introduced, the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete ..."}
+{"idx": 5, "title": "Paper reading - Towards a Generalist Agent for the Web", "date": "", "ddg_snippet": "16 Jul 2024 — MIND2WEB : Towards a Generalist Agent for the Web ... A dataset of real tasks on real-world websites , including tasks and user interaction traces.", "subpage_snippet": "", "source": "leoleoasd.me", "link": "http://leoleoasd.me/2024/07/16/paper-reading-mind2web-towards-a-generalist-agent-for-the-web/", "content": "16 Jul 2024 — MIND2WEB : Towards a Generalist Agent for the Web ... A dataset of real tasks on real-world websites , including tasks and user interaction traces."}
+{"idx": 6, "title": "Generalist Virtual Agents: A Survey on Autonomous ...", "date": "", "ddg_snippet": "Our objective is to provide a comprehensive overview of Generalist Virtual Agents (GVAs), covering their definition, necessity, implementation approaches, ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/wendell0218/GVA-Survey", "content": "Our objective is to provide a comprehensive overview of Generalist Virtual Agents (GVAs), covering their definition, necessity, implementation approaches, ..."}
+{"idx": 7, "title": "Xiang Deng", "date": "", "ddg_snippet": "Mind2Web is introduced, the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/author/Xiang-Deng/145924070", "content": "Mind2Web is introduced, the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete ..."}
+{"idx": 8, "title": "Huan Sun", "date": "", "ddg_snippet": "Co-authors ; Mind2web: Towards a generalist agent for the web . X Deng, Y Gu, B Zheng, S Chen, S Stevens, B Wang, H Sun, Y Su. Advances in Neural Information ...", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=wIFkulcAAAAJ&hl=en", "content": "Co-authors ; Mind2web: Towards a generalist agent for the web . X Deng, Y Gu, B Zheng, S Chen, S Stevens, B Wang, H Sun, Y Su. Advances in Neural Information ..."}
+{"idx": 9, "title": "Daily Papers", "date": "", "ddg_snippet": "8 days ago — We introduce Mind2Web , the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=web-based+virtual+agent", "content": "8 days ago — We introduce Mind2Web , the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to ..."}
diff --git a/data/sampled_jsons/Mind2Web_dataset_Deng_et_al._abstract.jsonl b/data/sampled_jsons/Mind2Web_dataset_Deng_et_al._abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..9ea1a1055ac2b4448f80f8aa5a1e470ce79a6e11
--- /dev/null
+++ b/data/sampled_jsons/Mind2Web_dataset_Deng_et_al._abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Data and its (dis) contents: A survey of dataset", "date": "", "ddg_snippet": "machine learning [Halevy et al ., 2009, Deng et al ., 2009]. These data practices tend to abstract away the human labor, subjective judgments and biases, and contingent contexts involved in dataset production.", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/pdf/data-and-its-dis-contents-a-survey-of-dataset-development-1w43tv43ul.pdf", "content": "machine learning [Halevy et al ., 2009, Deng et al ., 2009]. These data practices tend to abstract away the human labor, subjective judgments and biases, and contingent contexts involved in dataset production."}
+{"idx": 1, "title": "Conceptual Captions: A Cleaned, Hypernymed, Image Alt-text Dataset ...", "date": "", "ddg_snippet": "Relevant datasets include the ImageNet dataset ( Deng et al ., 2009), with over 14 million images and 1 million bounding-box annotations, and the MS-COCO dataset (Lin et al ., 2014), with 120,000 images and 5-way image-caption anno-tations.", "subpage_snippet": "", "source": "gwern.net", "link": "https://gwern.net/doc/ai/nn/diffusion/2018-sharma.pdf", "content": "Relevant datasets include the ImageNet dataset ( Deng et al ., 2009), with over 14 million images and 1 million bounding-box annotations, and the MS-COCO dataset (Lin et al ., 2014), with 120,000 images and 5-way image-caption anno-tations."}
+{"idx": 2, "title": "[2306.06070] Mind 2 Web : Towards a Generalist Agent for the Web", "date": "", "ddg_snippet": "Existing datasets for web agents either use simulated websites or only cover a limited set of websites and tasks, thus not suitable for generalist web agents.View a PDF of the paper titled Mind 2 Web : Towards a Generalist Agent for the Web, by Xiang Deng and 7 other authors.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2306.06070", "content": "Existing datasets for web agents either use simulated websites or only cover a limited set of websites and tasks, thus not suitable for generalist web agents.View a PDF of the paper titled Mind 2 Web : Towards a Generalist Agent for the Web, by Xiang Deng and 7 other authors."}
+{"idx": 3, "title": "On the Multi-turn Instruction Following for Conversational Web Agents", "date": "", "ddg_snippet": "Following the evaluation settings in Mind 2 Web ( Deng et al ., 2023), we also select and divide the test set into three subsets, including cross-task, cross-website, and cross-subdomain, for evaluating how well an agent can generalize across tasks, web-sites, and domains.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2402.15057", "content": "Following the evaluation settings in Mind 2 Web ( Deng et al ., 2023), we also select and divide the test set into three subsets, including cross-task, cross-website, and cross-subdomain, for evaluating how well an agent can generalize across tasks, web-sites, and domains."}
+{"idx": 4, "title": "With M emory for C omputer C ontrol", "date": "", "ddg_snippet": "Mind 2 Web ( Deng et al ., 2023) is a realistic dataset containing human demonstrations of open-domain tasks from various real-world websites, such as Airbnb and Twitter.In Mind 2 Web , we compare with MindAct ( Deng et al ., 2023), the current SOTA ICL method in this benchmark.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=Pc8AU1aF5e", "content": "Mind 2 Web ( Deng et al ., 2023) is a realistic dataset containing human demonstrations of open-domain tasks from various real-world websites, such as Airbnb and Twitter.In Mind 2 Web , we compare with MindAct ( Deng et al ., 2023), the current SOTA ICL method in this benchmark."}
+{"idx": 5, "title": "(PDF) AllTogether: Investigating the Efficacy of Spliced Prompt for Web...", "date": "", "ddg_snippet": "The Mind 2 Web dataset Deng et al . (2023) includes. various domains including travel, information, ser", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/375865032_AllTogether_Investigating_the_Efficacy_of_Spliced_Prompt_for_Web_Navigation_using_Large_Language_Models", "content": "The Mind 2 Web dataset Deng et al . (2023) includes. various domains including travel, information, ser"}
+{"idx": 6, "title": "Revisiting Data Normalization for Appearance-Based Gaze Estimation", "date": "", "ddg_snippet": "2016]. Deng et al . used a head CNN to learn the head pose explicitly from face images to compensate the estimated gaze direction from eye images [ Deng and Zhu 2017].We then evaluated the impact of data normalization using real im-ages from the MPIIGaze dataset [Zhang et al .", "subpage_snippet": "", "source": "collaborative-ai.org", "link": "https://collaborative-ai.org/publications/zhang18_etra.pdf", "content": "2016]. Deng et al . used a head CNN to learn the head pose explicitly from face images to compensate the estimated gaze direction from eye images [ Deng and Zhu 2017].We then evaluated the impact of data normalization using real im-ages from the MPIIGaze dataset [Zhang et al ."}
+{"idx": 7, "title": "Frontiers | Natural Image Reconstruction From fMRI Using Deep...", "date": "", "ddg_snippet": "Images are from ImageNet dataset ( Deng et al ., 2009). 5.4. Quantitative Comparison Results on Natural Images From DIR.", "subpage_snippet": "", "source": "www.frontiersin.org", "link": "https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2021.795488/full", "content": "Images are from ImageNet dataset ( Deng et al ., 2009). 5.4. Quantitative Comparison Results on Natural Images From DIR."}
+{"idx": 8, "title": "\"Using Geophysical and Geodetic Data to Improve Natural and...\"", "date": "", "ddg_snippet": "2) High-resolution DEM generation combining multiple remote-sensing data sets ( Deng et al ., 2019).3) Surface deformation and induced seismicity due to fluid injection and oil and gas extraction ( Deng et al ., 2020).", "subpage_snippet": "", "source": "digitalcommons.usf.edu", "link": "https://digitalcommons.usf.edu/etd/8184/", "content": "2) High-resolution DEM generation combining multiple remote-sensing data sets ( Deng et al ., 2019).3) Surface deformation and induced seismicity due to fluid injection and oil and gas extraction ( Deng et al ., 2020)."}
+{"idx": 9, "title": "Uncovering and Mitigating Algorithmic Bias through Learned Latent...", "date": "", "ddg_snippet": "Negative examples were taken from the ImageNet dataset ( Deng et al . 2009), from a wide variety of non-human.", "subpage_snippet": "", "source": "www.aies-conference.com", "link": "https://www.aies-conference.com/2019/wp-content/papers/main/AIES-19_paper_220.pdf", "content": "Negative examples were taken from the ImageNet dataset ( Deng et al . 2009), from a wide variety of non-human."}
diff --git a/data/sampled_jsons/Mistral-7B_MMLU_score_benchmark.jsonl b/data/sampled_jsons/Mistral-7B_MMLU_score_benchmark.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0296cb28931fe704a3d8e3ecd6665b57d45f0f1d
--- /dev/null
+++ b/data/sampled_jsons/Mistral-7B_MMLU_score_benchmark.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Compare GPT-4 32K 0613 vs. Mistral 7B Instruct", "date": "", "ddg_snippet": "... benchmarks between GPT-4 32K ... It was released on September 27, 2023, and achieved a score of 60.1 in the MMLU benchmark under a 5-shot scenario.", "subpage_snippet": "", "source": "context.ai", "link": "https://context.ai/compare/gpt-4-32k-0613/mistral-7b-instruct", "content": "... benchmarks between GPT-4 32K ... It was released on September 27, 2023, and achieved a score of 60.1 in the MMLU benchmark under a 5-shot scenario."}
+{"idx": 1, "title": "Compare GPT-3.5 Turbo 0125 vs. Mistral 7B Instruct", "date": "", "ddg_snippet": "... benchmarks between GPT-3.5 ... It was released on September 27, 2023, and achieved a score of 60.1 in the MMLU benchmark under a 5-shot scenario.", "subpage_snippet": "", "source": "context.ai", "link": "https://context.ai/compare/gpt-3-5-turbo-0125/mistral-7b-instruct", "content": "... benchmarks between GPT-3.5 ... It was released on September 27, 2023, and achieved a score of 60.1 in the MMLU benchmark under a 5-shot scenario."}
+{"idx": 2, "title": "Compare GPT-4 vs. Mistral 7B Instruct", "date": "", "ddg_snippet": "... scores in benchmarks like ... It was released on September 27, 2023, and achieved a score of 60.1 in the MMLU benchmark under a 5-shot scenario.", "subpage_snippet": "", "source": "context.ai", "link": "https://context.ai/compare/gpt-4/mistral-7b-instruct", "content": "... scores in benchmarks like ... It was released on September 27, 2023, and achieved a score of 60.1 in the MMLU benchmark under a 5-shot scenario."}
+{"idx": 3, "title": "Compare GPT-3.5 Turbo 16K vs. Mistral 8x7B Instruct", "date": "", "ddg_snippet": "... benchmarks between GPT-3.5 Turbo ... It was released on December 11, 2023, and achieved a score of 70.6 in the MMLU benchmark in a 5- shot scenario.", "subpage_snippet": "", "source": "context.ai", "link": "https://context.ai/compare/gpt-3-5-turbo-16k/mistral-8x7b-instruct", "content": "... benchmarks between GPT-3.5 Turbo ... It was released on December 11, 2023, and achieved a score of 70.6 in the MMLU benchmark in a 5- shot scenario."}
+{"idx": 4, "title": "Compare GPT-4 vs. Mistral Large", "date": "", "ddg_snippet": "It was released on February 26, 2024, and has achieved impressive scores in benchmarks like MMLU ( 81.2 in a 5-shot scenario) and HellaSwag (89.2 in ...", "subpage_snippet": "", "source": "context.ai", "link": "https://context.ai/compare/gpt-4/mistral-large", "content": "It was released on February 26, 2024, and has achieved impressive scores in benchmarks like MMLU ( 81.2 in a 5-shot scenario) and HellaSwag (89.2 in ..."}
+{"idx": 5, "title": "Exploring Mistral AI's Le Chat: A Comprehensive Guide", "date": "", "ddg_snippet": "Mathstral 7B , a model with 7 billion parameters released by Mistral AI, achieves a score of 56.6% on the MATH benchmark and 63.47% on the MMLU ...", "subpage_snippet": "", "source": "neuroflash.com", "link": "https://neuroflash.com/blog/le-chat/", "content": "Mathstral 7B , a model with 7 billion parameters released by Mistral AI, achieves a score of 56.6% on the MATH benchmark and 63.47% on the MMLU ..."}
+{"idx": 6, "title": "Mistral 7B vs DeepSeek R1 (2025) | Performance, Pricing, and", "date": "", "ddg_snippet": "Coding Proficiency: Mistral Large scores 45.1% on HumanE and 73.1% on MBPP coding benchmarks ... With an overall benchmark score of 81.2%, it ranks ...", "subpage_snippet": "", "source": "elephas.app", "link": "https://elephas.app/blog/deepseek-vs-mistral", "content": "Coding Proficiency: Mistral Large scores 45.1% on HumanE and 73.1% on MBPP coding benchmarks ... With an overall benchmark score of 81.2%, it ranks ..."}
+{"idx": 7, "title": "MMLU Benchmark (Massive Multi-task Language Understanding) —", "date": "", "ddg_snippet": "The MMLU provides a way to test and compare various language models like OpenAI GPT-4o, Mistral 7b , Google Gemini, and Anthropic Claude 3, etc.", "subpage_snippet": "", "source": "klu.ai", "link": "https://klu.ai/glossary/mmlu-eval", "content": "The MMLU provides a way to test and compare various language models like OpenAI GPT-4o, Mistral 7b , Google Gemini, and Anthropic Claude 3, etc."}
+{"idx": 8, "title": "iAsk Pro’s MMLU Pro LLM Benchmark Results, AGI Performance", "date": "", "ddg_snippet": "MMLU -Pro is an advanced benchmark designed to evaluate the capabilities of large-scale language models (LLMs) in a more robust and challenging manner ...", "subpage_snippet": "", "source": "iask.ai", "link": "https://iask.ai/mmlu-pro", "content": "MMLU -Pro is an advanced benchmark designed to evaluate the capabilities of large-scale language models (LLMs) in a more robust and challenging manner ..."}
+{"idx": 9, "title": "Mistral AI's Mistral Large 2 - AI Model Details", "date": "", "ddg_snippet": "It was released on July 24, 2024, and achieved a score of 84.0 in the MMLU benchmark in a 5-shot scenario. ... A more robust MMLU benchmark with ...", "subpage_snippet": "", "source": "docsbot.ai", "link": "https://docsbot.ai/models/mistral-large-2", "content": "It was released on July 24, 2024, and achieved a score of 84.0 in the MMLU benchmark in a 5-shot scenario. ... A more robust MMLU benchmark with ..."}
diff --git a/data/sampled_jsons/Mistral-7B_vs_Pythia-7B_performance_comparison_MMLU_benchmark.jsonl b/data/sampled_jsons/Mistral-7B_vs_Pythia-7B_performance_comparison_MMLU_benchmark.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ae0f4b297de1a10a60907f6ce064279beb3c5ccc
--- /dev/null
+++ b/data/sampled_jsons/Mistral-7B_vs_Pythia-7B_performance_comparison_MMLU_benchmark.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Models Benchmarks | Mistral AI", "date": "", "ddg_snippet": "LLM (Large Language Model) benchmarks are standardized tests or datasets used to evaluate the performance of large language models. These benchmarks help researchers and developers understand the strengths and weaknesses of their models and compare them with other models in a systematic way.", "subpage_snippet": "", "source": "docs.mistral.ai", "link": "https://docs.mistral.ai/getting-started/models/benchmark/", "content": "LLM (Large Language Model) benchmarks are standardized tests or datasets used to evaluate the performance of large language models. These benchmarks help researchers and developers understand the strengths and weaknesses of their models and compare them with other models in a systematic way."}
+{"idx": 1, "title": "Mistral 7B Instruct: Intelligence, Performance & Price Analysis", "date": "", "ddg_snippet": "Analysis of Mistral's Mistral 7B Instruct and comparison to other AI models across key metrics including quality, price, performance (tokens per second & time to first token), context window & more.", "subpage_snippet": "", "source": "artificialanalysis.ai", "link": "https://artificialanalysis.ai/models/mistral-7b-instruct", "content": "Analysis of Mistral's Mistral 7B Instruct and comparison to other AI models across key metrics including quality, price, performance (tokens per second & time to first token), context window & more."}
+{"idx": 2, "title": "What is going on with Mistral 7b finetunes? (Equal to Qwen 72b)", "date": "", "ddg_snippet": "MMLU seems to be the most resistant benchmark to cheating/contamination, but I may be wrong. There are a couple of 13Bs with an abnormally high MMLU score, but they've been flagged. All these \"SOTA\" 7B models can't compete with any 70+ MMLU scoring model in practice, and they're definitely not anywhere near Qwen-72B level.", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/LocalLLaMA/comments/18et7h9/what_is_going_on_with_mistral_7b_finetunes_equal/", "content": "MMLU seems to be the most resistant benchmark to cheating/contamination, but I may be wrong. There are a couple of 13Bs with an abnormally high MMLU score, but they've been flagged. All these \"SOTA\" 7B models can't compete with any 70+ MMLU scoring model in practice, and they're definitely not anywhere near Qwen-72B level."}
+{"idx": 3, "title": "Mistral 7B vs. Llama 3 70B vs. Gemma 2 9B: A Comprehensive Benchmark ...", "date": "", "ddg_snippet": "While Mistral 7B and Gemma 2 9B have their strengths, particularly in resource efficiency and specific niche applications, Llama 3 70B's robust performance across all benchmarks makes it the ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@samir20/mistral-7b-vs-llama-3-70b-vs-gemma-2-9b-a-comprehensive-benchmark-showdown-9c3128f24b23", "content": "While Mistral 7B and Gemma 2 9B have their strengths, particularly in resource efficiency and specific niche applications, Llama 3 70B's robust performance across all benchmarks makes it the ..."}
+{"idx": 4, "title": "Mistral Models vs. Competitors: A Performance Showdown Across NLP, Code ...", "date": "", "ddg_snippet": "Performance Comparison : Mistral 7B vs . LLaMA Models, Retrieved from Mistral AI Website The second figure provides a more aggregated view of the models' performance across broader categories such as MMLU , Knowledge, Reasoning, Comprehension, AGI Eval, Math, BBH, and Code.", "subpage_snippet": "", "source": "hub.researchgraph.org", "link": "https://hub.researchgraph.org/mistral-models-vs-competitors-a-performance-showdown-across-nlp-code-and-multilingual-tasks/", "content": "Performance Comparison : Mistral 7B vs . LLaMA Models, Retrieved from Mistral AI Website The second figure provides a more aggregated view of the models' performance across broader categories such as MMLU , Knowledge, Reasoning, Comprehension, AGI Eval, Math, BBH, and Code."}
+{"idx": 5, "title": "Mistral 7B Explained: Towards More Efficient Language Models", "date": "", "ddg_snippet": "A tabular view of the comparison above with the scores for each benchmark [1]. The overall trend shows that Mistral 7B outperforms Llama 2 13B across all metrics the models were evaluated on ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/data-science/mistral-7b-explained-towards-more-efficient-language-models-7f9c6e6b7251", "content": "A tabular view of the comparison above with the scores for each benchmark [1]. The overall trend shows that Mistral 7B outperforms Llama 2 13B across all metrics the models were evaluated on ..."}
+{"idx": 6, "title": "Mistral 7B | Mistral AI", "date": "", "ddg_snippet": "Performance in details We compared Mistral 7B to the Llama 2 family, and re-run all model evaluations ourselves for fair comparison . Performance of Mistral 7B and different Llama models on a wide range of benchmarks . For all metrics, all models were re-evaluated with our evaluation pipeline for accurate comparison .", "subpage_snippet": "", "source": "mistral.ai", "link": "https://mistral.ai/news/announcing-mistral-7b", "content": "Performance in details We compared Mistral 7B to the Llama 2 family, and re-run all model evaluations ourselves for fair comparison . Performance of Mistral 7B and different Llama models on a wide range of benchmarks . For all metrics, all models were re-evaluated with our evaluation pipeline for accurate comparison ."}
+{"idx": 7, "title": "Mistral 7B Beats Llama v2 13B on All Benchmarks: Overview and Fine ...", "date": "", "ddg_snippet": "Performance of Mistral 7B and different Llama models on a wide range of benchmarks . For all metrics, all models were re-evaluated with our evaluation pipeline for accurate comparison . Mistral 7B significantly outperforms Llama 2 13B on all metrics, and is on par with Llama 34B (since Llama 2 34B was not released, we report results on Llama 34B).", "subpage_snippet": "", "source": "agentissue.medium.com", "link": "https://agentissue.medium.com/mistral-7b-beats-llama-v2-13b-overview-and-fine-tuning-c608374b5c82", "content": "Performance of Mistral 7B and different Llama models on a wide range of benchmarks . For all metrics, all models were re-evaluated with our evaluation pipeline for accurate comparison . Mistral 7B significantly outperforms Llama 2 13B on all metrics, and is on par with Llama 34B (since Llama 2 34B was not released, we report results on Llama 34B)."}
+{"idx": 8, "title": "Mistral 7B vs DeepSeek R1 Performance: Which LLM is the Better Choice?", "date": "", "ddg_snippet": "Mistral 7B vs DeepSeek R1 Performance compared—Which LLM offers better efficiency, inference speed, and cost-effectiveness? A deep dive into benchmarks , deployment, and use cases.", "subpage_snippet": "", "source": "blog.adyog.com", "link": "https://blog.adyog.com/2025/01/31/mistral-7b-vs-deepseek-r1-performance-which-llm-is-the-better-choice/", "content": "Mistral 7B vs DeepSeek R1 Performance compared—Which LLM offers better efficiency, inference speed, and cost-effectiveness? A deep dive into benchmarks , deployment, and use cases."}
+{"idx": 9, "title": "Mistral 7B vs DeepSeek R1 (2025) | Performance, Pricing, and Practical ...", "date": "", "ddg_snippet": "Compare Mistral 7B vs DeepSeek R1 to discover which LLM excels in performance , efficiency, and cost-effectiveness. Explore benchmarks , practical use cases, and more!", "subpage_snippet": "", "source": "elephas.app", "link": "https://elephas.app/blog/deepseek-vs-mistral", "content": "Compare Mistral 7B vs DeepSeek R1 to discover which LLM excels in performance , efficiency, and cost-effectiveness. Explore benchmarks , practical use cases, and more!"}
diff --git a/data/sampled_jsons/ModelGo_Licenses_Section_3.4_sitearxiv.org_OR_siteopenreview.net_year_2024.jsonl b/data/sampled_jsons/ModelGo_Licenses_Section_3.4_sitearxiv.org_OR_siteopenreview.net_year_2024.jsonl
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index 0000000000000000000000000000000000000000..da10eb69833a167f81c29e1a61273a25370ebd88
--- /dev/null
+++ b/data/sampled_jsons/ModelGo_Licenses_Section_3.4_sitearxiv.org_OR_siteopenreview.net_year_2024.jsonl
@@ -0,0 +1,9 @@
+{"idx": 0, "title": "ModelGo: A Pratical Tool for Machine Learning License Analysis", "date": "", "ddg_snippet": "143 142 2.1 Machine Learning Project Licensing 145 144 Typically, a ML project is constructed with data, software and mod-146 els, which are usually governed by diferent licensing frameworks. 147 To profile current ML licensing, we summary licensing details for 148 ML projects with over 1,000 likes available in Huggingface2model 149 repository (See Appendix A.2). Due to a lack of license ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=Et9rHdWGAZ", "content": "143 142 2.1 Machine Learning Project Licensing 145 144 Typically, a ML project is constructed with data, software and mod-146 els, which are usually governed by diferent licensing frameworks. 147 To profile current ML licensing, we summary licensing details for 148 ML projects with over 1,000 likes available in Huggingface2model 149 repository (See Appendix A.2). Due to a lack of license ..."}
+{"idx": 1, "title": "Activation Addition: Steering Language Models Without ...", "date": "", "ddg_snippet": "Dec 31, 2022 · Reliably controlling the behavior of large language models is a pressing open problem. Existing methods include supervised finetuning, reinforcement learning from human feedback, prompt engineering and guided decoding. We instead investigate activation engineering: modifying activations at inference-time to predictably alter model behavior. We bias the forward pass with a 'steering vector ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=MRu3nZhoZP", "content": "Dec 31, 2022 · Reliably controlling the behavior of large language models is a pressing open problem. Existing methods include supervised finetuning, reinforcement learning from human feedback, prompt engineering and guided decoding. We instead investigate activation engineering: modifying activations at inference-time to predictably alter model behavior. We bias the forward pass with a 'steering vector ..."}
+{"idx": 2, "title": "Steering Language Models with Activation Engineering", "date": "", "ddg_snippet": "Sep 26, 2024 · Prompt engineering and finetuning aim to maximize language model performance on a given metric (like toxicity reduction). However, these methods do not optimally elicit a model's capabilities. To...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=2XBPdPIcFK", "content": "Sep 26, 2024 · Prompt engineering and finetuning aim to maximize language model performance on a given metric (like toxicity reduction). However, these methods do not optimally elicit a model's capabilities. To..."}
+{"idx": 3, "title": "Current Model Licensing Practices are Dragging Us into a ...", "date": "", "ddg_snippet": "by M Duan · Cited by 1 — ... ModelGo Licenses (MGLs), to address these challenges and promote better ... As mentioned in Section 3.4 , MGLs aim to promote more standardized model ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=1rh8iTehBc", "content": "by M Duan · Cited by 1 — ... ModelGo Licenses (MGLs), to address these challenges and promote better ... As mentioned in Section 3.4 , MGLs aim to promote more standardized model ..."}
+{"idx": 4, "title": "Current Model Licensing Practices are Dragging Us into a ...", "date": "", "ddg_snippet": "of model licenses, ModelGo Licenses (MGLs), to ... We introduce this proposal in Section 3.4 . Risk 3 ... Our Proposal: ModelGo Licenses . We propose a ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/4bb8da2945abd31f3e42d0e5f0a87a8bb47ddc9c.pdf", "content": "of model licenses, ModelGo Licenses (MGLs), to ... We introduce this proposal in Section 3.4 . Risk 3 ... Our Proposal: ModelGo Licenses . We propose a ..."}
+{"idx": 5, "title": "Scalable Extraction of Training Data from Aligned, Production...", "date": "", "ddg_snippet": "Jan 22, 2025 · Large language models are prone to *memorizing* some of their training data. Memorized (and possibly sensitive) samples can then be extracted at generation time by adversarial or benign users....", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=vjel3nWP2a", "content": "Jan 22, 2025 · Large language models are prone to *memorizing* some of their training data. Memorized (and possibly sensitive) samples can then be extracted at generation time by adversarial or benign users...."}
+{"idx": 6, "title": "Tractable Multi-Agent Reinforcement Learning through ...", "date": "", "ddg_snippet": "Jan 22, 2025 · A significant roadblock to the development of principled multi-agent reinforcement learning (MARL) algorithms is the fact that desired solution concepts like Nash equilibria may be intractable to...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=stUKwWBuBm", "content": "Jan 22, 2025 · A significant roadblock to the development of principled multi-agent reinforcement learning (MARL) algorithms is the fact that desired solution concepts like Nash equilibria may be intractable to..."}
+{"idx": 7, "title": "Shedding Light on Time Series Classification using ...", "date": "", "ddg_snippet": "Jan 22, 2025 · In time-series classification, interpretable models can bring additional insights but be outperformed by deep models since human-understandable features have limited expressivity and flexibility....", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=n34taxF0TC", "content": "Jan 22, 2025 · In time-series classification, interpretable models can bring additional insights but be outperformed by deep models since human-understandable features have limited expressivity and flexibility...."}
+{"idx": 8, "title": "Agent Reviewers: Domain-specific Multimodal Agents with ...", "date": "", "ddg_snippet": "May 1, 2025 · TL;DR: We present Agent Reviewers, an LLM-based multi-agent system for peer review, enhanced by multimodal feedback and a shared memory of prior papers.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=s7HUJamWqX", "content": "May 1, 2025 · TL;DR: We present Agent Reviewers, an LLM-based multi-agent system for peer review, enhanced by multimodal feedback and a shared memory of prior papers."}
diff --git a/data/sampled_jsons/ModelGo_Licenses_compositional_structure_Apache-2.0_OpenRAIL_behavioral_restrictions_year_2024.jsonl b/data/sampled_jsons/ModelGo_Licenses_compositional_structure_Apache-2.0_OpenRAIL_behavioral_restrictions_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d5c5323150725bd9a260997c208af437b4f44f24
--- /dev/null
+++ b/data/sampled_jsons/ModelGo_Licenses_compositional_structure_Apache-2.0_OpenRAIL_behavioral_restrictions_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ModelGo: A Practical Tool for Machine Learning License ...", "date": "", "ddg_snippet": "by M Duan · 2024 · Cited by 15 — The most popular license is Open Responsible AI License ( OpenRAIL ) [9], which is a permissive license but includes copyleft-style use-based ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/pdf/10.1145/3589334.3645520", "content": "by M Duan · 2024 · Cited by 15 — The most popular license is Open Responsible AI License ( OpenRAIL ) [9], which is a permissive license but includes copyleft-style use-based ..."}
+{"idx": 1, "title": "FAQ | ModelGo Licenses", "date": "", "ddg_snippet": "Q: What is the difference between ModelGo and OpenRAILs? From the compositional perspective, OpenRAILs (-M) is built upon Apache-2.0 with additional terms tailored for ML fields. Their main alterations include adding a Use Restrictions attachment and use-based behaviour restriction terms in the license text.", "subpage_snippet": "", "source": "www.modelgo.li", "link": "https://www.modelgo.li/learn-more/faq", "content": "Q: What is the difference between ModelGo and OpenRAILs? From the compositional perspective, OpenRAILs (-M) is built upon Apache-2.0 with additional terms tailored for ML fields. Their main alterations include adding a Use Restrictions attachment and use-based behaviour restriction terms in the license text."}
+{"idx": 2, "title": "GitHub - Xtra-Computing/ModelGo", "date": "", "ddg_snippet": "Why we need ModelGo Licenses Set? To facilitate managed sharing of models while protecting your Intellectual Property. ModelGo licenses offer flexible options to fulfill your specific licensing needs about using and distributing your deep learning models while protecting your Intellectual Property (IP).", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Xtra-Computing/ModelGo", "content": "Why we need ModelGo Licenses Set? To facilitate managed sharing of models while protecting your Intellectual Property. ModelGo licenses offer flexible options to fulfill your specific licensing needs about using and distributing your deep learning models while protecting your Intellectual Property (IP)."}
+{"idx": 3, "title": "[License-review] ModelGo Attribution-OpenSource License ...", "date": "", "ddg_snippet": "When publishing their models, developers typically choose from three main options (as seen in the model license tags on the Hugging Face website): >> >> OSS licenses , e.g., Apache-2.0 , MIT >> Open responsible AI licenses (OpenRAILs), e.g., CreativeML- OpenRAIL -M, OpenRAIL++ >> Proprietary Licenses , e.g., Llama2, Llama3 >> >> However, not all ...", "subpage_snippet": "", "source": "lists.opensource.org", "link": "https://lists.opensource.org/pipermail/license-review_lists.opensource.org/2025-March/005714.html", "content": "When publishing their models, developers typically choose from three main options (as seen in the model license tags on the Hugging Face website): >> >> OSS licenses , e.g., Apache-2.0 , MIT >> Open responsible AI licenses (OpenRAILs), e.g., CreativeML- OpenRAIL -M, OpenRAIL++ >> Proprietary Licenses , e.g., Llama2, Llama3 >> >> However, not all ..."}
+{"idx": 4, "title": "ModelGo: A Pratical Tool for Machine Learning License Analysis", "date": "", "ddg_snippet": "For example, GPT- 2 83 and BERT [11] are regarded as part of software and then licensed as 84 OSS (e.g., MIT and Apache-2.0 ). However, ML projects like StableD- 85 ifusion and Llama2 [49] tend to apply responsible AI restriction 86 terms for both model and code, using AI model licenses such as 87 OpenRAIL -M [9] and Llama2 Community License [34].", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=Et9rHdWGAZ", "content": "For example, GPT- 2 83 and BERT [11] are regarded as part of software and then licensed as 84 OSS (e.g., MIT and Apache-2.0 ). However, ML projects like StableD- 85 ifusion and Llama2 [49] tend to apply responsible AI restriction 86 terms for both model and code, using AI model licenses such as 87 OpenRAIL -M [9] and Llama2 Community License [34]."}
+{"idx": 5, "title": "FAQ - Responsible AI Licenses (RAIL)", "date": "", "ddg_snippet": "This license is an updated version of the BigScience OpenRAIL -M license . It was designed with text-to-image models in mind, such as Stable Diffusion. There are 2 main modifications: 1. The Preamble of the license ; 2 . The deletion of 2 use restrictions that are present in the BigScience OpenRAIL -M license , restrictions (e) and (g).", "subpage_snippet": "", "source": "www.licenses.ai", "link": "https://www.licenses.ai/faq-2", "content": "This license is an updated version of the BigScience OpenRAIL -M license . It was designed with text-to-image models in mind, such as Stable Diffusion. There are 2 main modifications: 1. The Preamble of the license ; 2 . The deletion of 2 use restrictions that are present in the BigScience OpenRAIL -M license , restrictions (e) and (g)."}
+{"idx": 6, "title": "ModelGo: A Practical Tool for Machine Learning License Analysis", "date": "", "ddg_snippet": "For example, GPT- 2 and BERT [11] are regarded as part of software and then licensed as OSS (e.g., MIT and Apache-2.0 ). However, ML projects like StableD- iffusion and Llama2 [49] tend to apply responsible AI restriction terms for both model and code, using AI model licenses such as OpenRAIL -M [9] and Llama2 Community License [34].", "subpage_snippet": "", "source": "dlnext.acm.org", "link": "https://dlnext.acm.org/doi/pdf/10.1145/3589334.3645520", "content": "For example, GPT- 2 and BERT [11] are regarded as part of software and then licensed as OSS (e.g., MIT and Apache-2.0 ). However, ML projects like StableD- iffusion and Llama2 [49] tend to apply responsible AI restriction terms for both model and code, using AI model licenses such as OpenRAIL -M [9] and Llama2 Community License [34]."}
+{"idx": 7, "title": "Understanding ModelGo | ModelGo Licenses", "date": "", "ddg_snippet": "Structure ModelGo licenses consist of six sections. Section 2 , \" License Rights,\" is the primary provision that grants rights licenses and states the restrictions of use and distribution. ModelGo licenses include a Disclaimer and Limitation of Liability (Section3, 4).", "subpage_snippet": "", "source": "www.modelgo.li", "link": "https://www.modelgo.li/learn-more/understanding-modelgo", "content": "Structure ModelGo licenses consist of six sections. Section 2 , \" License Rights,\" is the primary provision that grants rights licenses and states the restrictions of use and distribution. ModelGo licenses include a Disclaimer and Limitation of Liability (Section3, 4)."}
+{"idx": 8, "title": "Current Model Licensing Practices are Dragging Us into a ...", "date": "", "ddg_snippet": "Popular options include Apache - 2.0 , OpenRAIL (Responsible AI Licenses ) ... OpenRAIL includes behavioral usage restrictions that violate FSF's definition of ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/40180", "content": "Popular options include Apache - 2.0 , OpenRAIL (Responsible AI Licenses ) ... OpenRAIL includes behavioral usage restrictions that violate FSF's definition of ..."}
+{"idx": 9, "title": "They've Stolen My GPL-Licensed Model!", "date": "", "ddg_snippet": "16 Dec 2024 — While this license offers good clarity, it enforces use behavior restrictions that render it non-compliant with open-source licenses like GPL- ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.11483v1", "content": "16 Dec 2024 — While this license offers good clarity, it enforces use behavior restrictions that render it non-compliant with open-source licenses like GPL- ..."}
diff --git a/data/sampled_jsons/ModelGo_Licenses_vs_Responsible_AI_Licenses.jsonl b/data/sampled_jsons/ModelGo_Licenses_vs_Responsible_AI_Licenses.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e69da234653e1118bcb2e426622afb652862372d
--- /dev/null
+++ b/data/sampled_jsons/ModelGo_Licenses_vs_Responsible_AI_Licenses.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Quick Guide to Popular AI Licenses", "date": "", "ddg_snippet": "Not all \"open\" AI licenses are truly open source.With AI , the license battle isn’t just “copyleft” vs “permissive”. You now need to consider not only how you wish to distribute (or not distribute) your software, but also how it is intended to be used.", "subpage_snippet": "", "source": "www.mend.io", "link": "https://www.mend.io/blog/quick-guide-to-popular-ai-licenses/", "content": "Not all \"open\" AI licenses are truly open source.With AI , the license battle isn’t just “copyleft” vs “permissive”. You now need to consider not only how you wish to distribute (or not distribute) your software, but also how it is intended to be used."}
+{"idx": 1, "title": "Responsible AI Licenses (RAIL)", "date": "", "ddg_snippet": "Responsible AI Licenses (RAIL) empower developers to restrict the use of their AI technology in order to prevent irresponsible and harmful applications. We provide both source code licenses as well as end-user licenses that developers/providers can include with AI software to restrict its...", "subpage_snippet": "", "source": "www.licenses.ai", "link": "https://www.licenses.ai/", "content": "Responsible AI Licenses (RAIL) empower developers to restrict the use of their AI technology in order to prevent irresponsible and harmful applications. We provide both source code licenses as well as end-user licenses that developers/providers can include with AI software to restrict its..."}
+{"idx": 2, "title": "OpenRAIL: Towards open and responsible AI licensing frameworks", "date": "", "ddg_snippet": "Open & Responsible AI licenses (\"OpenRAIL\") are AI -specific licenses enabling open access, use and distribution of AI artifacts while requiring a responsible use of the latter.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/blog/open_rail", "content": "Open & Responsible AI licenses (\"OpenRAIL\") are AI -specific licenses enabling open access, use and distribution of AI artifacts while requiring a responsible use of the latter."}
+{"idx": 3, "title": "ModelGo : A Pratical Tool for Machine Learning License Analysis", "date": "", "ddg_snippet": "36 License analysis, AI licensing , model mining. license is Open Responsible AI License (OpenRAIL) [9], which is a permissive license but includes copyleft-style use-based restrictions governing the use of the model and its derivatives.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=Et9rHdWGAZ", "content": "36 License analysis, AI licensing , model mining. license is Open Responsible AI License (OpenRAIL) [9], which is a permissive license but includes copyleft-style use-based restrictions governing the use of the model and its derivatives."}
+{"idx": 4, "title": "\"#Open vs # Responsible is now a big topic in AI circles. But it raises...&q...", "date": "", "ddg_snippet": "These new licenses aim to ensure not just openness of resources, but also responsibility for the impact of AI models .Creators of RAIL license pay more attention to enforcing responsible behavior than to openness. The license is meant to do what ethical guidelines fail to do.", "subpage_snippet": "", "source": "twitter.com", "link": "https://twitter.com/atarkowski/status/1573272619889348610", "content": "These new licenses aim to ensure not just openness of resources, but also responsibility for the impact of AI models .Creators of RAIL license pay more attention to enforcing responsible behavior than to openness. The license is meant to do what ethical guidelines fail to do."}
+{"idx": 5, "title": "ModelGo : A Practical Tool for Machine Learning License Analysis", "date": "", "ddg_snippet": "Behavioral Use Licensing for Responsible AI . Conference Paper.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/380539201_ModelGo_A_Practical_Tool_for_Machine_Learning_License_Analysis", "content": "Behavioral Use Licensing for Responsible AI . Conference Paper."}
+{"idx": 6, "title": "Responsible AI Licenses (RAIL) - Ensuring Ethical AI Use - NavTo. AI", "date": "", "ddg_snippet": "Responsible AI Licenses (RAIL) offer a comprehensive framework for the ethical distribution and use of AI technologies. These licenses are designed to mitigate the risks associated with AI misuse by implementing behavioral-use restrictions.", "subpage_snippet": "", "source": "www.navto.ai", "link": "https://www.navto.ai/responsible-ai-licenses-rail-", "content": "Responsible AI Licenses (RAIL) offer a comprehensive framework for the ethical distribution and use of AI technologies. These licenses are designed to mitigate the risks associated with AI misuse by implementing behavioral-use restrictions."}
+{"idx": 7, "title": "ICML Oral Position: Current Model Licensing Practices are Dragging...", "date": "", "ddg_snippet": "Developers are often required to choose a license to publish and govern the use of their models . Popular options include Apache-2.0, OpenRAIL ( Responsible AI Licenses ), Creative Commons Licenses (CCs), Llama2, and GPL-3.0.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/40181", "content": "Developers are often required to choose a license to publish and govern the use of their models . Popular options include Apache-2.0, OpenRAIL ( Responsible AI Licenses ), Creative Commons Licenses (CCs), Llama2, and GPL-3.0."}
+{"idx": 8, "title": "Open Source Licensing Modalities in Large Language... | Medium", "date": "", "ddg_snippet": "Open RAIL ( Responsible AI License ): The RAIL licenses are a family of ethical licenses crafted for AI . For example, BigScience (a research collaboration) released BLOOM (176B multilingual model ) under an Open RAIL-M license .", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@adnanmasood/open-source-licensing-modalities-in-large-language-models-insights-risks-and-opportunities-for-283416b2a40d", "content": "Open RAIL ( Responsible AI License ): The RAIL licenses are a family of ethical licenses crafted for AI . For example, BigScience (a research collaboration) released BLOOM (176B multilingual model ) under an Open RAIL-M license ."}
+{"idx": 9, "title": "The Transparency vs . Safety Dilemma in Open-Source AI - Klover. ai", "date": "", "ddg_snippet": "Licensing Models : Openness vs . Responsibility in AI . Open-source software historically relies on licenses (MIT, Apache, GPL, etc.) that grant broad freedoms to use, modify, and share code.", "subpage_snippet": "", "source": "www.klover.ai", "link": "https://www.klover.ai/the-transparency-vs-safety-dilemma-in-open-source-ai/", "content": "Licensing Models : Openness vs . Responsibility in AI . Open-source software historically relies on licenses (MIT, Apache, GPL, etc.) that grant broad freedoms to use, modify, and share code."}
diff --git a/data/sampled_jsons/MultiPDENet_MaNN_Block_macro_time-stepping_correction_neural_network_function.jsonl b/data/sampled_jsons/MultiPDENet_MaNN_Block_macro_time-stepping_correction_neural_network_function.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..bace0aa02c055e808ae75612650f30ec5c794878
--- /dev/null
+++ b/data/sampled_jsons/MultiPDENet_MaNN_Block_macro_time-stepping_correction_neural_network_function.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "MultiPDENet: PDE-embedded Learning with Multi-time-stepping", "date": "", "ddg_snippet": "The Correction Block leverages a neural network to refine the coarse solution, with the Fourier Neural Operator (FNO) (Li et al., 2021) as the correction mechanism within this block .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.15987v1", "content": "The Correction Block leverages a neural network to refine the coarse solution, with the Fourier Neural Operator (FNO) (Li et al., 2021) as the correction mechanism within this block ."}
+{"idx": 1, "title": "PDE-constrained Learning with Multi-time-stepping for Accelerated...", "date": "", "ddg_snippet": "This paper introduces MultiPDENet , a neural network architecture designed to accelerate fluid dynamic simulations by combining classical numerical methods, such as a multi-scale time-stepping scheme inspired by Runge-Kutta methods, finite-difference derivatives, and a Fourier Neural Operator for learned corrections .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=stcN89QGfL", "content": "This paper introduces MultiPDENet , a neural network architecture designed to accelerate fluid dynamic simulations by combining classical numerical methods, such as a multi-scale time-stepping scheme inspired by Runge-Kutta methods, finite-difference derivatives, and a Fourier Neural Operator for learned corrections ."}
+{"idx": 2, "title": "ICML Poster MultiPDENet: PDE-embedded Learning with Multi-time-stepping ...", "date": "", "ddg_snippet": "Crucially, to prevent small errors from accumulating over long predictions, MultiPDENet uses multiscale time stepping : a neural network corrects prediction errors at a significantly coarser time scale.Tested on challenging systems like fluid dynamics, MultiPDENet achieves highly accurate long-term predictions even when trained on very limited ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46029", "content": "Crucially, to prevent small errors from accumulating over long predictions, MultiPDENet uses multiscale time stepping : a neural network corrects prediction errors at a significantly coarser time scale.Tested on challenging systems like fluid dynamics, MultiPDENet achieves highly accurate long-term predictions even when trained on very limited ..."}
+{"idx": 3, "title": "PDE-EMBEDDED LEARNING WITH MULTI TIME STEPPING FOR ... - OpenReview", "date": "", "ddg_snippet": "The primary contributions of thiswork aresummarized as follows: •WedevelopedMultiPDENet, a PDE-embeddednetwork with multiscale time -stepping,for accelerated fluid flow simulations on spatiotemporal coarse grids. By integrating neural solver with PDEs, MultiPDENet achieves great generalizability and efficiency.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=stcN89QGfL", "content": "The primary contributions of thiswork aresummarized as follows: •WedevelopedMultiPDENet, a PDE-embeddednetwork with multiscale time -stepping,for accelerated fluid flow simulations on spatiotemporal coarse grids. By integrating neural solver with PDEs, MultiPDENet achieves great generalizability and efficiency."}
+{"idx": 4, "title": "P2C2Net: PDE-Preserved Coarse Correction Network for efficient ...", "date": "", "ddg_snippet": "A PDE-embedded network with multiscale time stepping ( MultiPDENet ), which fuses the scheme of numerical methods and machine learning, for accelerated simulation of flows and achieves the state-of-the-art performance compared with other neural baseline models, also with clear speedup compared to classical numerical methods.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/P2C2Net:-PDE-Preserved-Coarse-Correction-Network-of-Wang-Ren/1ad28f6be3fb3fe75f55dcb2ff513f22d59d476b", "content": "A PDE-embedded network with multiscale time stepping ( MultiPDENet ), which fuses the scheme of numerical methods and machine learning, for accelerated simulation of flows and achieves the state-of-the-art performance compared with other neural baseline models, also with clear speedup compared to classical numerical methods."}
+{"idx": 5, "title": "A 40nm STT-MRAM Near-Memory Computing Macro for Memory-Augmented Neural ...", "date": "", "ddg_snippet": "Memory-augmented neural network ( MANN ) has gained attention as a pivotal solution for few-shot learning (FSL). Among the candidates for associative memory in MANN accelerators, spin-transfer torque magnetic random-access memory (STT-MRAM) stands out for its compact cell area, long data retention time , and excellent scalability.", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/abstract/document/11043262", "content": "Memory-augmented neural network ( MANN ) has gained attention as a pivotal solution for few-shot learning (FSL). Among the candidates for associative memory in MANN accelerators, spin-transfer torque magnetic random-access memory (STT-MRAM) stands out for its compact cell area, long data retention time , and excellent scalability."}
+{"idx": 6, "title": "The Memory Revolution in Neural Networks: Exploring MANNs", "date": "", "ddg_snippet": "Context: Memory-Augmented Neural Networks ( MANNs ) are innovative architectures designed to enhance neural networks by integrating external memory modules, enabling dynamic storage and retrieval of ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/codex/the-memory-revolution-in-neural-networks-exploring-manns-cb0b02d73a9f", "content": "Context: Memory-Augmented Neural Networks ( MANNs ) are innovative architectures designed to enhance neural networks by integrating external memory modules, enabling dynamic storage and retrieval of ..."}
+{"idx": 7, "title": "Neural network-based time stepping scheme for multiscale partial ...", "date": "", "ddg_snippet": "The macroscopic coherent behavior in complex systems usually may occur from the interactions of microscopic agents, such as molecules, cells and individuals in a population, between themselves and their environment resulting in the multiscale behavior of such systems. Multiscale systems are computationally expensive to simulate as they exhibit behavior at multiple spatial and temporal scales ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10455439", "content": "The macroscopic coherent behavior in complex systems usually may occur from the interactions of microscopic agents, such as molecules, cells and individuals in a population, between themselves and their environment resulting in the multiscale behavior of such systems. Multiscale systems are computationally expensive to simulate as they exhibit behavior at multiple spatial and temporal scales ..."}
+{"idx": 8, "title": "MultiPDENet: PDE-embedded Learning with Multi-time-stepping for ...", "date": "", "ddg_snippet": "For time integration, a dedicated \"Physics Block \" employs a precise numerical method at a fine time scale. Crucially, to prevent small errors from accumulating over long predictions, MultiPDENet uses multiscale time stepping : a neural network corrects prediction errors at a significantly coarser time scale.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=D1gs8QT74m", "content": "For time integration, a dedicated \"Physics Block \" employs a precise numerical method at a fine time scale. Crucially, to prevent small errors from accumulating over long predictions, MultiPDENet uses multiscale time stepping : a neural network corrects prediction errors at a significantly coarser time scale."}
+{"idx": 9, "title": "Synergistic learning with multi-task DeepONet for efficient PDE problem ...", "date": "", "ddg_snippet": "2. Multi-task learning in neural operators Neural operators learn nonlinear mappings between functional spaces on bounded domains, offering a unique framework for real- time solution inference for complex parametric PDEs. Here, 'parametric PDEs' refer to PDE systems with parameters that vary over a certain range.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0893608024010426", "content": "2. Multi-task learning in neural operators Neural operators learn nonlinear mappings between functional spaces on bounded domains, offering a unique framework for real- time solution inference for complex parametric PDEs. Here, 'parametric PDEs' refer to PDE systems with parameters that vary over a certain range."}
diff --git a/data/sampled_jsons/MultiPDENet_PDE-embedded_Learning_with_Multi-time-stepping_for_Accelerated_Flow_Simulation_supplemen.jsonl b/data/sampled_jsons/MultiPDENet_PDE-embedded_Learning_with_Multi-time-stepping_for_Accelerated_Flow_Simulation_supplemen.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d0bd6dda74650873f855b459b24992f5363b8acf
--- /dev/null
+++ b/data/sampled_jsons/MultiPDENet_PDE-embedded_Learning_with_Multi-time-stepping_for_Accelerated_Flow_Simulation_supplemen.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "MultiPDENet : PDE - embedded Learning with Multi - time - stepping ...", "date": "", "ddg_snippet": "Solving partial differential equations ( PDEs ) by numerical methods meet computational cost challenge for getting the accurate solution since fine grids and small time steps are required.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.15987v1", "content": "Solving partial differential equations ( PDEs ) by numerical methods meet computational cost challenge for getting the accurate solution since fine grids and small time steps are required."}
+{"idx": 1, "title": "MultiPDENet : встроенное в PDE обучение с несколькими...", "date": "", "ddg_snippet": "С этой целью мы предлагаем встроенную в PDE сеть с многомасштабным временным шагом ( MultiPDENet ), которая объединяет численные методы и машинное обучение для ускоренного моделирования потоков.", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/article/MultiPDENet:-PDE-embedded-Learning-with-Multi-time-stepping-for-Accelerated-Flow-Simulation-d706dd88-bdd4-4d82-98f6-c8e2b25382fe/ru", "content": "С этой целью мы предлагаем встроенную в PDE сеть с многомасштабным временным шагом ( MultiPDENet ), которая объединяет численные методы и машинное обучение для ускоренного моделирования потоков."}
+{"idx": 2, "title": "Awesome AI4 PDE", "date": "", "ddg_snippet": "MultiPDENet : PDE - embedded Learning with Multi - time - stepping for Accelerated Flow Simulation . Solving. Learning Controllable Adaptive Simulation for Multi-resolution Physics.", "subpage_snippet": "", "source": "ai4pde.notion.site", "link": "https://ai4pde.notion.site/", "content": "MultiPDENet : PDE - embedded Learning with Multi - time - stepping for Accelerated Flow Simulation . Solving. Learning Controllable Adaptive Simulation for Multi-resolution Physics."}
+{"idx": 3, "title": "Papers by Yi Zhang with links to code and results.", "date": "", "ddg_snippet": "MultiPDENet : PDE - embedded Learning with Multi - time - stepping for Accelerated Flow Simulation .Test- time Distribution Learning Adapter for Cross-modal Visual Reasoning.", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/search?q=author:Yi+Zhang", "content": "MultiPDENet : PDE - embedded Learning with Multi - time - stepping for Accelerated Flow Simulation .Test- time Distribution Learning Adapter for Cross-modal Visual Reasoning."}
+{"idx": 4, "title": "Stability Limitations in Simulation of Dynamical Systems with Multiple ...", "date": "", "ddg_snippet": "Algorithmic instability Numerical resonance Multiple time -scale Exponential integrator Linear stability.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-3-319-15221-9_7?error=cookies_not_supported&code=86f8d5d9-1055-4294-8f65-15d747097880", "content": "Algorithmic instability Numerical resonance Multiple time -scale Exponential integrator Linear stability."}
+{"idx": 5, "title": "Assistant Professor in Materials for Thermal... - Academic Positions", "date": "", "ddg_snippet": "We are looking for a motivated and visionary Assistant Professor to strengthen our research into materials for thermal energy storage. You will join the Transport in Permeable Media (TPM) group, embedded in the Plasma & Flow and Materials domains of APSE...", "subpage_snippet": "", "source": "academicpositions.com", "link": "https://academicpositions.com/ad/eindhoven-university-of-technology/2025/assistant-professor-in-materials-for-thermal-energy-storage/238949", "content": "We are looking for a motivated and visionary Assistant Professor to strengthen our research into materials for thermal energy storage. You will join the Transport in Permeable Media (TPM) group, embedded in the Plasma & Flow and Materials domains of APSE..."}
+{"idx": 6, "title": "Explicit multi - time stepping methods for convection-dominated flow ...", "date": "", "ddg_snippet": "Semantic Scholar extracted view of \"Explicit multi - time stepping methods for convection-dominated flow problems\" by N. Maurits et al.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Explicit-multi-time-stepping-methods-for-flow-Maurits-Ven/1211451a49260288963fb6b3268122dcd69bd037", "content": "Semantic Scholar extracted view of \"Explicit multi - time stepping methods for convection-dominated flow problems\" by N. Maurits et al."}
+{"idx": 7, "title": "MultiPDENet: PDE-embedded Learning with Multi-time- ...", "date": "", "ddg_snippet": "by Q Wang — This new approach cleverly combines numerical methods with machine learning by embedding the core physics equations ( PDEs ) directly into the model's design. It ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=D1gs8QT74m", "content": "by Q Wang — This new approach cleverly combines numerical methods with machine learning by embedding the core physics equations ( PDEs ) directly into the model's design. It ..."}
+{"idx": 8, "title": "MultiPDENet: PDE-embedded Learning with Multi-time ...", "date": "", "ddg_snippet": "15 Jul 2025 — To this end, we propose a PDEembedded network with multiscale time stepping ( MultiPDENet ), which fuses the scheme of numerical methods and ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46029", "content": "15 Jul 2025 — To this end, we propose a PDEembedded network with multiscale time stepping ( MultiPDENet ), which fuses the scheme of numerical methods and ..."}
+{"idx": 9, "title": "MultiPDENet: PDE-embedded Learning with Multi-time- ...", "date": "", "ddg_snippet": "We developed MultiPDENet , a PDE-embedded net- work with multiscale time-stepping, for accelerated flow simulations on spatiotemporal coarse grids. By.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/046ccccd77df13df48f47ff1081b58969771121a.pdf", "content": "We developed MultiPDENet , a PDE-embedded net- work with multiscale time-stepping, for accelerated flow simulations on spatiotemporal coarse grids. By."}
diff --git a/data/sampled_jsons/MultiPDENet_Physics_Block_Methodology_PDE-embedded.jsonl b/data/sampled_jsons/MultiPDENet_Physics_Block_Methodology_PDE-embedded.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a6986700735d189303cb781f212b23976adeba8b
--- /dev/null
+++ b/data/sampled_jsons/MultiPDENet_Physics_Block_Methodology_PDE-embedded.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "MultiPDENet: PDE-embedded Learning with Multi-time-stepping", "date": "", "ddg_snippet": "To overcome these limitations, we propose MultiPDENet , a PDE-embedded network that incorporates multiscale time-stepping (as shown in Figure 1), to efficiently simulate spatiotemporal dynamics, e.g., turbulent fluid flows, on coarse spatial and temporal grids with limited data.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.15987v1", "content": "To overcome these limitations, we propose MultiPDENet , a PDE-embedded network that incorporates multiscale time-stepping (as shown in Figure 1), to efficiently simulate spatiotemporal dynamics, e.g., turbulent fluid flows, on coarse spatial and temporal grids with limited data."}
+{"idx": 1, "title": "PDE-constrained Learning with Multi-time-stepping for Accelerated...", "date": "", "ddg_snippet": "To this end, we propose a PDE-embedded network with multiscale time stepping ( MultiPDENet ), which fuses the scheme of numerical methods and machine learning, for accelerated simulation of fluid flows.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=stcN89QGfL", "content": "To this end, we propose a PDE-embedded network with multiscale time stepping ( MultiPDENet ), which fuses the scheme of numerical methods and machine learning, for accelerated simulation of fluid flows."}
+{"idx": 2, "title": "MultiPDENet: PDE-embedded Learning with Multi-time-stepping for ...", "date": "", "ddg_snippet": "A PDE-embedded network with multiscale time stepping ( MultiPDENet ), which fuses the scheme of numerical methods and machine learning, for accelerated simulation of flows and achieves the state-of-the-art performance compared with other neural baseline models, also with clear speedup compared to classical numerical methods. Solving partial differential equations ( PDEs ) by numerical methods meet ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/MultiPDENet:-PDE-embedded-Learning-with-for-Flow-Wang-Mi/6aee4adf8e7489f251995859a5f0432a2c60bb82", "content": "A PDE-embedded network with multiscale time stepping ( MultiPDENet ), which fuses the scheme of numerical methods and machine learning, for accelerated simulation of flows and achieves the state-of-the-art performance compared with other neural baseline models, also with clear speedup compared to classical numerical methods. Solving partial differential equations ( PDEs ) by numerical methods meet ..."}
+{"idx": 3, "title": "GitHub - ZichaoLong/PDE-Net: PDE-Net: Learning PDEs from Data", "date": "", "ddg_snippet": "PDE -Net: Learning PDEs from Data. Contribute to ZichaoLong/ PDE -Net development by creating an account on GitHub.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/ZichaoLong/PDE-Net", "content": "PDE -Net: Learning PDEs from Data. Contribute to ZichaoLong/ PDE -Net development by creating an account on GitHub."}
+{"idx": 4, "title": "Synergistic learning with multi-task DeepONet for efficient PDE problem ...", "date": "", "ddg_snippet": "The method used in this example can be extended further when solving PDE problems involving multiple domains, where solutions for multiple PDEs across multiple problem domains is desired. Further details for the Fisher problem are discussed in Section 3.1.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0893608024010426", "content": "The method used in this example can be extended further when solving PDE problems involving multiple domains, where solutions for multiple PDEs across multiple problem domains is desired. Further details for the Fisher problem are discussed in Section 3.1."}
+{"idx": 5, "title": "[2501.15987] MultiPDENet: PDE-embedded Learning with Multi-time ...", "date": "", "ddg_snippet": "To this end, we propose a PDE-embedded network with multiscale time stepping ( MultiPDENet ), which fuses the scheme of numerical methods and machine learning, for accelerated simulation of flows.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2501.15987", "content": "To this end, we propose a PDE-embedded network with multiscale time stepping ( MultiPDENet ), which fuses the scheme of numerical methods and machine learning, for accelerated simulation of flows."}
+{"idx": 6, "title": "MultiPDENet: PDE-embedded Learning with Multi-time-stepping for ...", "date": "", "ddg_snippet": "MultiPDENet achieves the state-of-the-art performance compared with other neural baseline models, also with clear speedup compared to classical numerical methods. Schematic of MultiPDENet for learning turbulent flows. (a), Model architecture. (b), Physics Block . (c), Learnable PDE block .", "subpage_snippet": "", "source": "chatpaper.com", "link": "https://chatpaper.com/chatpaper/paper/102640?from=search", "content": "MultiPDENet achieves the state-of-the-art performance compared with other neural baseline models, also with clear speedup compared to classical numerical methods. Schematic of MultiPDENet for learning turbulent flows. (a), Model architecture. (b), Physics Block . (c), Learnable PDE block ."}
+{"idx": 7, "title": "PDF Physics-Embedded Neural Networks: Graph Neural PDE Solvers with Mixed ...", "date": "", "ddg_snippet": "We present our approach termed physics - embedded neural networks that considers boundary conditions and predicts the state after a long time using an implicit method. It is built based on an E(n)-equivariant GNN, resulting in high generalization performance on various shapes.", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper/2022/file/93476ae409ae3246e22a9d4b931f84ed-Paper-Conference.pdf", "content": "We present our approach termed physics - embedded neural networks that considers boundary conditions and predicts the state after a long time using an implicit method. It is built based on an E(n)-equivariant GNN, resulting in high generalization performance on various shapes."}
+{"idx": 8, "title": "PDE-EMBEDDED LEARNING WITH MULTI TIME STEPPING FOR ... - OpenReview", "date": "", "ddg_snippet": "A physics block with a 4th-order Runge-Kutta integrator at the fine time scale is established thatembedsthe structure of PDEs to guide the prediction.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=stcN89QGfL", "content": "A physics block with a 4th-order Runge-Kutta integrator at the fine time scale is established thatembedsthe structure of PDEs to guide the prediction."}
+{"idx": 9, "title": "Yuan Mi - catalyzex.com", "date": "", "ddg_snippet": "To this end, we propose a PDE-embedded network with multiscale time stepping ( MultiPDENet ), which fuses the scheme of numerical methods and machine learning, for accelerated simulation of flows.", "subpage_snippet": "", "source": "www.catalyzex.com", "link": "https://www.catalyzex.com/author/Yuan+Mi", "content": "To this end, we propose a PDE-embedded network with multiscale time stepping ( MultiPDENet ), which fuses the scheme of numerical methods and machine learning, for accelerated simulation of flows."}
diff --git a/data/sampled_jsons/Murai_2022_BIT-VO_binary_inertial_odometry.jsonl b/data/sampled_jsons/Murai_2022_BIT-VO_binary_inertial_odometry.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7612be15b60dd85313d6687ba0a34d02a1a58837
--- /dev/null
+++ b/data/sampled_jsons/Murai_2022_BIT-VO_binary_inertial_odometry.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Riku Murai", "date": "", "ddg_snippet": "BIT - VO : Visual Odometry at 300 FPS using Binary Features from the Focal Plane ... Visual inertial odometry using focal plane binary features (bit-vio). M ...", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=JCex0BwAAAAJ&hl=en", "content": "BIT - VO : Visual Odometry at 300 FPS using Binary Features from the Focal Plane ... Visual inertial odometry using focal plane binary features (bit-vio). M ..."}
+{"idx": 1, "title": "Riku Murai", "date": "", "ddg_snippet": "BIT -VIO is a loosely-coupled iterated Extended Kalman Filter (iEKF) which fuses together the visual odometry running fast at 300 FPS with predictions from 400 ...", "subpage_snippet": "", "source": "www.catalyzex.com", "link": "https://www.catalyzex.com/author/Riku+Murai", "content": "BIT -VIO is a loosely-coupled iterated Extended Kalman Filter (iEKF) which fuses together the visual odometry running fast at 300 FPS with predictions from 400 ..."}
+{"idx": 2, "title": "High-frame rate homography and visual odometry by ...", "date": "", "ddg_snippet": "by R Murai · 2023 · Cited by 2 — We presented BIT - VO , which is capable of performing VO at 300 FPS by using binary edges and corners computed on the focal plane. Our system is ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s10514-023-10122-8", "content": "by R Murai · 2023 · Cited by 2 — We presented BIT - VO , which is capable of performing VO at 300 FPS by using binary edges and corners computed on the focal plane. Our system is ..."}
+{"idx": 3, "title": "Focal-Plane Sensor-Processor-Based Visual Inertial ...", "date": "", "ddg_snippet": "Following this separation, BIT - VO performs the frontend feature detection on the SCAMP-5 camera itself, where corners and binary edges are detected and ... 117 pages", "subpage_snippet": "", "source": "mattlisondra.com", "link": "https://mattlisondra.com/data/MASc_Thesis_Matthew_Lisondra_Final.pdf", "content": "Following this separation, BIT - VO performs the frontend feature detection on the SCAMP-5 camera itself, where corners and binary edges are detected and ... 117 pages"}
+{"idx": 4, "title": "Visual Inertial Odometry using Focal Plane Binary Features ...", "date": "", "ddg_snippet": "by M Lisondra · Cited by 2 — Abstract— Focal-Plane Sensor-Processor Arrays (FPSP)s are an emerging technology that can execute vision algorithms directly on the image sensor. 8 pages", "subpage_snippet": "", "source": "kimjunseo.com", "link": "https://kimjunseo.com/publication/jason-bitvio-ICRA/ICRA24_3859_MS.pdf", "content": "by M Lisondra · Cited by 2 — Abstract— Focal-Plane Sensor-Processor Arrays (FPSP)s are an emerging technology that can execute vision algorithms directly on the image sensor. 8 pages"}
+{"idx": 5, "title": "Untitled", "date": "", "ddg_snippet": "BIT - VO : Visual Odometry at 300 FPS using Binary Features from the Focal Plane. Murai , Riku;Saeedi, Sajad;Kelly, Paul H. J.. 2020 IEEE/RSJ ...", "subpage_snippet": "", "source": "marketplace.copyright.com", "link": "https://marketplace.copyright.com/rs-ui-web/mp/search/author/Murai,+Riku", "content": "BIT - VO : Visual Odometry at 300 FPS using Binary Features from the Focal Plane. Murai , Riku;Saeedi, Sajad;Kelly, Paul H. J.. 2020 IEEE/RSJ ..."}
+{"idx": 6, "title": "Camera Tracking on Focal-Plane Sensor-Processor Arrays", "date": "", "ddg_snippet": "BIT - VO : Visual Odometry at 300 FPS using Binary Features from the Focal Plane · Visual Inertial Odometry using Focal Plane Binary Features (BIT-VIO) · High-frame ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Camera-Tracking-on-Focal-Plane-Sensor-Processor-Debrunner-Bose/2ee8c904bd6bd90d62266b78af2c6808279d3bdf", "content": "BIT - VO : Visual Odometry at 300 FPS using Binary Features from the Focal Plane · Visual Inertial Odometry using Focal Plane Binary Features (BIT-VIO) · High-frame ..."}
+{"idx": 7, "title": "arXiv:2406.09726v1 [cs.CV] 14 Jun 2024", "date": "", "ddg_snippet": "by I Alzugaray · 2024 — Yet the majority of Visual. Odometry ( VO ) pipelines rely on the transmission and processing of full images in a centralized unit (e.g. CPU or ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2406.09726", "content": "by I Alzugaray · 2024 — Yet the majority of Visual. Odometry ( VO ) pipelines rely on the transmission and processing of full images in a centralized unit (e.g. CPU or ..."}
+{"idx": 8, "title": "Vincentqyw/cv-arxiv-daily: 🎓Automatically Update ...", "date": "", "ddg_snippet": "Visual Inertial Odometry using Focal Plane Binary Features ( BIT -VIO), Matthew Lisondra et.al. 2403.09882, null. 2024-03-02, Grid-based Fast and Structural ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Vincentqyw/cv-arxiv-daily", "content": "Visual Inertial Odometry using Focal Plane Binary Features ( BIT -VIO), Matthew Lisondra et.al. 2403.09882, null. 2024-03-02, Grid-based Fast and Structural ..."}
+{"idx": 9, "title": "Sajad Saeedi | Scholar Profiles and Rankings", "date": "", "ddg_snippet": "Visual Inertial Odometry using Focal Plane Binary Features ( BIT -VIO) (conference). Lisondra, Matthew | Kim, Junseo | Murai , Riku | Zareinia, Kourosh | Saeedi, ...", "subpage_snippet": "", "source": "scholargps.com", "link": "https://scholargps.com/scholars/40987866185605/sajad-saeedi", "content": "Visual Inertial Odometry using Focal Plane Binary Features ( BIT -VIO) (conference). Lisondra, Matthew | Kim, Junseo | Murai , Riku | Zareinia, Kourosh | Saeedi, ..."}
diff --git a/data/sampled_jsons/NVIDIA_open_source_software_toolkit_for_optimizing_and_deploying_quantized_models_on_Jetson_devices.jsonl b/data/sampled_jsons/NVIDIA_open_source_software_toolkit_for_optimizing_and_deploying_quantized_models_on_Jetson_devices.jsonl
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "NVIDIA TensorRT Model Optimizer - GitHub", "date": "", "ddg_snippet": "The NVIDIA TensorRT Model Optimizer (referred to as Model Optimizer, or ModelOpt) is a library comprising state-of-the-art model optimization techniques including quantization , distillation, pruning, speculative decoding and sparsity to accelerate models.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/NVIDIA/TensorRT-Model-Optimizer", "content": "The NVIDIA TensorRT Model Optimizer (referred to as Model Optimizer, or ModelOpt) is a library comprising state-of-the-art model optimization techniques including quantization , distillation, pruning, speculative decoding and sparsity to accelerate models."}
+{"idx": 1, "title": "TAO Toolkit | NVIDIA Developer", "date": "", "ddg_snippet": "The open - source NVIDIA TAO, built on TensorFlow and PyTorch, uses the power of transfer learning while simultaneously simplifying the model training process and optimizing the model for inference throughput on practically any platform. The result is an ultra-streamlined workflow.", "subpage_snippet": "", "source": "developer.nvidia.com", "link": "https://developer.nvidia.com/tao-toolkit", "content": "The open - source NVIDIA TAO, built on TensorFlow and PyTorch, uses the power of transfer learning while simultaneously simplifying the model training process and optimizing the model for inference throughput on practically any platform. The result is an ultra-streamlined workflow."}
+{"idx": 2, "title": "Using NVIDIA TensorRT-LLM to run gpt-oss-20b", "date": "", "ddg_snippet": "Aug 5, 2025 · TensorRT-LLM provides users with an easy-to-use Python API to define Large Language Models (LLMs) and support state-of-the-art optimizations to perform inference efficiently on NVIDIA GPUs.", "subpage_snippet": "", "source": "cookbook.openai.com", "link": "https://cookbook.openai.com/articles/gpt-oss/run-nvidia", "content": "Aug 5, 2025 · TensorRT-LLM provides users with an easy-to-use Python API to define Large Language Models (LLMs) and support state-of-the-art optimizations to perform inference efficiently on NVIDIA GPUs."}
+{"idx": 3, "title": "TensorRT Open Source Software - GitHub NVIDIA Optimized Frameworks - NVIDIA Docs GitHub - NVIDIA/RTX-AI-Toolkit: The NVIDIA RTX™ AI Toolkit is ... NVIDIA TensorRT Model Optimizer - GitHub TAO Toolkit | NVIDIA Developer Optimize AI Inference Performance with NVIDIA Full-Stack Solutions Optimize AI Inference Performance with NVIDIA Full-Stack Solutions GitHub - NVIDIA /RTX-AI- Toolkit : The NVIDIA RTX™ AI Toolkit is a suite GitHub - NVIDIA/TensorRT: NVIDIA® TensorRT™ is an SDK for high Optimize AI Inference Performance with NVIDIA Full-Stack ...", "date": "", "ddg_snippet": "This repository contains the Open Source Software (OSS) components of NVIDIA TensorRT. It includes the sources for TensorRT plugins and parsers (Caffe and ONNX), as well as sample applications demonstrating usage and capabilities of the TensorRT platform. These open source software components are a subset of the TensorRT General Availability (GA) release with some extensions and bug-fixes. • For code contributions to TensorRT-OSS, please see our Contribution Guide and Coding Guidelines. • For a summary of new additions and updates shipped with TensorRT-OSS releases, please refer to the Changelog. • For business inquiries, please contact researchinquiries@ nvidia .com • For press and other inquiries, please contact Hector Marinez at hmarinez@ nvidia .com Need enterprise support? NVIDIA global support is available for TensorRT with the NVIDIA AI Enterprise software suite. Check out NVIDIA LaunchPad for free access to a set of hands- on labs with TensorRT hosted on NVIDIA infrastructure. See full list on github.com We provide the TensorRT Python package for an easy installation. To install: See full list on github.com To build the TensorRT-OSS components, you will first need the following software packages. TensorRT GA build •TensorRT v8.6.1.6 System Packages •CUDA •Recommended versions: See full list on github.com 1.Download TensorRT OSS 2.(Optional - if not using TensorRT container) Specify the TensorRT GA release build path If using the TensorRT OSS build container, TensorRT libraries are pre-installed under /usr/lib/x86_64-linux-gnu and you may skip this step. Else download and extract the TensorRT GA build from NVIDIA Developer Zone. Example: Ubuntu 20.04 on x86-64 with cuda-12.0 3.(Optional - for Jetson builds only) Download the JetPack SDK See full list on github.com For Linux platforms, we recommend that you generate a docker container for building TensorRT OSS as described below. For native builds, please install the prerequisite System Packages. 1.Generate the TensorRT-OSS build container. The TensorRT-OSS build container can be generated using the supplied Dockerfiles and build scripts. The build containers are configured for building TensorRT OSS out-of-the-box. Example: Ubuntu 20.04 on x86-64 with cuda-12.1 (default) Example: CentOS/RedHat 7 on x86-64 with cuda-11.8 Example: Ubuntu 20.04 cross-compile for Jetson (aarch64) with cuda-11.4.2 (JetPack SDK) See full list on github.com •Generate Makefiles and build. Example: Linux (x86-64) build with default cuda-12.1 Example: Linux (aarch64) build with default cuda-12.1 Example: Native build on Jetson (aarch64) with cuda-11.4 Example: Ubuntu 20.04 Cross-Compile for Jetson (aarch64) with cuda-11.4 (JetPack) •Required CMake build arguments are: See full list on github.com See full list on github.com •Please refer to TensorRT 8.6 Release Notes See full list on github.com Developers, researchers, and data scientists can get easy access to NVIDIA AI- optimized DL framework containers with DL examples that are performance-tuned and tested for NVIDIA GPUs. This eliminates the need to manage packages and dependencies or build DL frameworks from source. NVIDIA RTX AI Toolkit includes 2 primary phases: Model Customization and Model Deployment. Each phase is tailored to guide you through the necessary steps to effectively customize and deploy your AI models . What is Nvidia tensorrt model optimizer? The NVIDIA TensorRT Model Optimizer (referred to as Model Optimizer, or ModelOpt) is a library comprising state-of-the-art model optimization techniques including quantization, distillation, pruning, speculative decoding and sparsity to accelerate models . What is Nvidia Tao? The open-source NVIDIA TAO, built on TensorFlow and PyTorch , uses the power of transfer learning while simultaneously simplifying the model training process and optimizing the model for inference throughput on practically any platform. The result is an ultra-streamlined workflow. What is Nvidia tensorrt & Triton? In addition to Triton, NVIDIA offers a broad ecosystem of AI inference solutions . For developers seeking powerful, customizable tools, NVIDIA TensorRT provides a high-performance deep learning inference library with APIs that enable fine-grained optimizations. What is Nvidia Triton inference server? The NVIDIA Triton Inference Server has been renamed to NVIDIA Dynamo Triton as part of the NVIDIA Dynamo Platform as of March 18, 2025, and is designed to simplify AI inference deployment for high-throughput, latency-critical production applications by consolidating framework-specific inference servers. What is Nvidia RTX AI toolkit? GitHub - NVIDIA /RTX-AI- Toolkit : The NVIDIA RTX™ AI Toolkit is a suite of tools and SDKs for Windows developers to customize, optimize, and deploy AI models across RTX PCs and cloud. Cannot retrieve latest commit at this time. What is Nvidia tensorrt? NVIDIA® TensorRT™ is an SDK for high-performance deep learning inference on NVIDIA GPUs . This repository contains the open source components of TensorRT. - NVIDIA/TensorRT Jan 24, 2025 · To address this, NVIDIA developed the NVIDIA Triton Inference Server, an open - source platform capable of serving models from any AI framework. By consolidating framework-specific inference servers, Triton streamlined AI inference deployment and increased AI prediction capacity.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/NVIDIA/TensorRT", "content": "This repository contains the Open Source Software (OSS) components of NVIDIA TensorRT. It includes the sources for TensorRT plugins and parsers (Caffe and ONNX), as well as sample applications demonstrating usage and capabilities of the TensorRT platform. These open source software components are a subset of the TensorRT General Availability (GA) release with some extensions and bug-fixes. • For code contributions to TensorRT-OSS, please see our Contribution Guide and Coding Guidelines. • For a summary of new additions and updates shipped with TensorRT-OSS releases, please refer to the Changelog. • For business inquiries, please contact researchinquiries@ nvidia .com • For press and other inquiries, please contact Hector Marinez at hmarinez@ nvidia .com Need enterprise support? NVIDIA global support is available for TensorRT with the NVIDIA AI Enterprise software suite. Check out NVIDIA LaunchPad for free access to a set of hands- on labs with TensorRT hosted on NVIDIA infrastructure. See full list on github.com We provide the TensorRT Python package for an easy installation. To install: See full list on github.com To build the TensorRT-OSS components, you will first need the following software packages. TensorRT GA build •TensorRT v8.6.1.6 System Packages •CUDA •Recommended versions: See full list on github.com 1.Download TensorRT OSS 2.(Optional - if not using TensorRT container) Specify the TensorRT GA release build path If using the TensorRT OSS build container, TensorRT libraries are pre-installed under /usr/lib/x86_64-linux-gnu and you may skip this step. Else download and extract the TensorRT GA build from NVIDIA Developer Zone. Example: Ubuntu 20.04 on x86-64 with cuda-12.0 3.(Optional - for Jetson builds only) Download the JetPack SDK See full list on github.com For Linux platforms, we recommend that you generate a docker container for building TensorRT OSS as described below. For native builds, please install the prerequisite System Packages. 1.Generate the TensorRT-OSS build container. The TensorRT-OSS build container can be generated using the supplied Dockerfiles and build scripts. The build containers are configured for building TensorRT OSS out-of-the-box. Example: Ubuntu 20.04 on x86-64 with cuda-12.1 (default) Example: CentOS/RedHat 7 on x86-64 with cuda-11.8 Example: Ubuntu 20.04 cross-compile for Jetson (aarch64) with cuda-11.4.2 (JetPack SDK) See full list on github.com •Generate Makefiles and build. Example: Linux (x86-64) build with default cuda-12.1 Example: Linux (aarch64) build with default cuda-12.1 Example: Native build on Jetson (aarch64) with cuda-11.4 Example: Ubuntu 20.04 Cross-Compile for Jetson (aarch64) with cuda-11.4 (JetPack) •Required CMake build arguments are: See full list on github.com See full list on github.com •Please refer to TensorRT 8.6 Release Notes See full list on github.com Developers, researchers, and data scientists can get easy access to NVIDIA AI- optimized DL framework containers with DL examples that are performance-tuned and tested for NVIDIA GPUs. This eliminates the need to manage packages and dependencies or build DL frameworks from source. NVIDIA RTX AI Toolkit includes 2 primary phases: Model Customization and Model Deployment. Each phase is tailored to guide you through the necessary steps to effectively customize and deploy your AI models . What is Nvidia tensorrt model optimizer? The NVIDIA TensorRT Model Optimizer (referred to as Model Optimizer, or ModelOpt) is a library comprising state-of-the-art model optimization techniques including quantization, distillation, pruning, speculative decoding and sparsity to accelerate models . What is Nvidia Tao? The open-source NVIDIA TAO, built on TensorFlow and PyTorch , uses the power of transfer learning while simultaneously simplifying the model training process and optimizing the model for inference throughput on practically any platform. The result is an ultra-streamlined workflow. What is Nvidia tensorrt & Triton? In addition to Triton, NVIDIA offers a broad ecosystem of AI inference solutions . For developers seeking powerful, customizable tools, NVIDIA TensorRT provides a high-performance deep learning inference library with APIs that enable fine-grained optimizations. What is Nvidia Triton inference server? The NVIDIA Triton Inference Server has been renamed to NVIDIA Dynamo Triton as part of the NVIDIA Dynamo Platform as of March 18, 2025, and is designed to simplify AI inference deployment for high-throughput, latency-critical production applications by consolidating framework-specific inference servers. What is Nvidia RTX AI toolkit? GitHub - NVIDIA /RTX-AI- Toolkit : The NVIDIA RTX™ AI Toolkit is a suite of tools and SDKs for Windows developers to customize, optimize, and deploy AI models across RTX PCs and cloud. Cannot retrieve latest commit at this time. What is Nvidia tensorrt? NVIDIA® TensorRT™ is an SDK for high-performance deep learning inference on NVIDIA GPUs . This repository contains the open source components of TensorRT. - NVIDIA/TensorRT Jan 24, 2025 · To address this, NVIDIA developed the NVIDIA Triton Inference Server, an open - source platform capable of serving models from any AI framework. By consolidating framework-specific inference servers, Triton streamlined AI inference deployment and increased AI prediction capacity."}
+{"idx": 4, "title": "NVIDIA Optimized Frameworks - NVIDIA Docs", "date": "", "ddg_snippet": "Developers, researchers, and data scientists can get easy access to NVIDIA AI- optimized DL framework containers with DL examples that are performance-tuned and tested for NVIDIA GPUs. This eliminates the need to manage packages and dependencies or build DL frameworks from source.", "subpage_snippet": "", "source": "docs.nvidia.com", "link": "https://docs.nvidia.com/optimized-frameworks/index.html", "content": "Developers, researchers, and data scientists can get easy access to NVIDIA AI- optimized DL framework containers with DL examples that are performance-tuned and tested for NVIDIA GPUs. This eliminates the need to manage packages and dependencies or build DL frameworks from source."}
+{"idx": 5, "title": "GitHub - NVIDIA/RTX-AI-Toolkit: The NVIDIA RTX™ AI Toolkit is ...", "date": "", "ddg_snippet": "NVIDIA RTX AI Toolkit includes 2 primary phases: Model Customization and Model Deployment. Each phase is tailored to guide you through the necessary steps to effectively customize and deploy your AI models .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/NVIDIA/RTX-AI-Toolkit", "content": "NVIDIA RTX AI Toolkit includes 2 primary phases: Model Customization and Model Deployment. Each phase is tailored to guide you through the necessary steps to effectively customize and deploy your AI models ."}
+{"idx": 6, "title": "Optimize AI Inference Performance with NVIDIA Full-Stack ...", "date": "", "ddg_snippet": "Jan 24, 2025 · To address this, NVIDIA developed the NVIDIA Triton Inference Server, an open - source platform capable of serving models from any AI framework. By consolidating framework-specific inference servers, Triton streamlined AI inference deployment and increased AI prediction capacity.", "subpage_snippet": "", "source": "developer.nvidia.com", "link": "https://developer.nvidia.com/blog/optimize-ai-inference-performance-with-nvidia-full-stack-solutions/", "content": "Jan 24, 2025 · To address this, NVIDIA developed the NVIDIA Triton Inference Server, an open - source platform capable of serving models from any AI framework. By consolidating framework-specific inference servers, Triton streamlined AI inference deployment and increased AI prediction capacity."}
+{"idx": 7, "title": "(PDF) Deploying DeepSeek AI on NVIDIA Jetson AGX Orin: A Free...", "date": "", "ddg_snippet": "Keywords: DeepSeek AI, NVIDIA Jetson AGX Orin, edge AI, natural language processing, computer vision, open - source AI, MIT license, real-time AI inference, TensorRT optimization , AI deployment , IoT applications, autonomous systems, smart surveillance, model quantization ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/388401833_Deploying_DeepSeek_AI_on_NVIDIA_Jetson_AGX_Orin_A_Free_Open-Source_MIT-_Licensed_Solution_for_High-Performance_Edge_AI_in_Natural_Language_Processing_and_Computer_Vision", "content": "Keywords: DeepSeek AI, NVIDIA Jetson AGX Orin, edge AI, natural language processing, computer vision, open - source AI, MIT license, real-time AI inference, TensorRT optimization , AI deployment , IoT applications, autonomous systems, smart surveillance, model quantization ..."}
+{"idx": 8, "title": "Post-Training Quantization of LLMs with NVIDIA NeMo and NVIDIA ...", "date": "", "ddg_snippet": "It also uses NVIDIA TensorRT-LLM , which is an open - source library for optimizing LLM inference. We present both accuracy and performance results for quantized models . Quantizing and deploying NeMo models . At a high level, the PTQ workflow consists of the following steps", "subpage_snippet": "", "source": "developer.nvidia.com", "link": "https://developer.nvidia.com/blog/post-training-quantization-of-llms-with-nvidia-nemo-and-nvidia-tensorrt-model-optimizer/", "content": "It also uses NVIDIA TensorRT-LLM , which is an open - source library for optimizing LLM inference. We present both accuracy and performance results for quantized models . Quantizing and deploying NeMo models . At a high level, the PTQ workflow consists of the following steps"}
+{"idx": 9, "title": "Optimizing Deep Learning Models for Fast Inference on Edge Devices", "date": "", "ddg_snippet": "TensorFlow Model Optimization Toolkit — provides APIs for both weight pruning and quantization .Engine file: It is an optimized and serialized model format created by NVIDIA for high-performance inference on NVIDIA GPUs and Jetson devices .", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@korinetharunkumarpalli/optimizing-deep-learning-models-for-fast-inference-on-edge-devices-1c0e853ddf21", "content": "TensorFlow Model Optimization Toolkit — provides APIs for both weight pruning and quantization .Engine file: It is an optimized and serialized model format created by NVIDIA for high-performance inference on NVIDIA GPUs and Jetson devices ."}
diff --git a/data/sampled_jsons/NeRF_Mildenhall_et_al._2020_abstract_year_2020.jsonl b/data/sampled_jsons/NeRF_Mildenhall_et_al._2020_abstract_year_2020.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d4b995f1df6c587480729c0124f07334e08bd192
--- /dev/null
+++ b/data/sampled_jsons/NeRF_Mildenhall_et_al._2020_abstract_year_2020.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Neural Radiance Fields ( NeRFs ) ( Mildenhall et al ., 2020 ) pion", "date": "", "ddg_snippet": "ABSTRACT : Generating geometric 3D reconstructions from Neural Radiance Fields ( NeRFs ) is of great interest.It was followed by the groundbreaking research work of Neural Radiance Fields ( Mildenhall et al ., 2020 ).", "subpage_snippet": "", "source": "isprs-archives.copernicus.org", "link": "https://isprs-archives.copernicus.org/articles/XLVIII-1-W3-2023/71/2023/isprs-archives-XLVIII-1-W3-2023-71-2023.pdf", "content": "ABSTRACT : Generating geometric 3D reconstructions from Neural Radiance Fields ( NeRFs ) is of great interest.It was followed by the groundbreaking research work of Neural Radiance Fields ( Mildenhall et al ., 2020 )."}
+{"idx": 1, "title": "HR- NeRF : advancing realism and accuracy in highlight scene...", "date": "", "ddg_snippet": "Abstract . NeRF and its variants excel in novel view synthesis but struggle with scenes featuring specular highlights.To represent 3D scenes implicitly, NeRF ( Mildenhall et al ., 2020 ) employs MLP networks.", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12041011/", "content": "Abstract . NeRF and its variants excel in novel view synthesis but struggle with scenes featuring specular highlights.To represent 3D scenes implicitly, NeRF ( Mildenhall et al ., 2020 ) employs MLP networks."}
+{"idx": 2, "title": "[2003.08934] NeRF : Representing Scenes as Neural Radiance Fields...", "date": "", "ddg_snippet": "[Submitted on 19 Mar 2020 (v1), last revised 3 Aug 2020 (this version, v2)].View a PDF of the paper titled NeRF : Representing Scenes as Neural Radiance Fields for View Synthesis, by Ben Mildenhall and 5 other authors.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2003.08934", "content": "[Submitted on 19 Mar 2020 (v1), last revised 3 Aug 2020 (this version, v2)].View a PDF of the paper titled NeRF : Representing Scenes as Neural Radiance Fields for View Synthesis, by Ben Mildenhall and 5 other authors."}
+{"idx": 3, "title": "GeCoNeRF: Few-Shot Neural Radiance Fields via Geometric...", "date": "", "ddg_snippet": "Neural Radiance Field ( NeRF ) ( Mildenhall et al ., 2020 ).Following ( Mildenhall et al ., 2020 ), we formulate per-ray depth values as weighted composition of distances traveled from origin.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=32h1MpQ3W1", "content": "Neural Radiance Field ( NeRF ) ( Mildenhall et al ., 2020 ).Following ( Mildenhall et al ., 2020 ), we formulate per-ray depth values as weighted composition of distances traveled from origin."}
+{"idx": 4, "title": "Double nerf : representing dynamic scenes as neural radiance", "date": "", "ddg_snippet": "It was shown ( Mildenhall et al ., 2020 ) that such approach allows outperforming previous works on new views synthesizing by neural rendering. The advantages of NeRF models have attracted a lot attention in the computer vision area in the following years and initiated the researches in various...", "subpage_snippet": "", "source": "pdfs.semanticscholar.org", "link": "https://pdfs.semanticscholar.org/702b/b22c760e8e733350c18e36b57f1db1ac2907.pdf", "content": "It was shown ( Mildenhall et al ., 2020 ) that such approach allows outperforming previous works on new views synthesizing by neural rendering. The advantages of NeRF models have attracted a lot attention in the computer vision area in the following years and initiated the researches in various..."}
+{"idx": 5, "title": "GitHub - awesome- NeRF /awesome- NeRF : A curated list of awesome...", "date": "", "ddg_snippet": "Object-Centric Neural Scene Rendering, Guo et al ., Arxiv 2020 | bibtex. NeRF : Representing Scenes as Neural Radiance Fields for View Synthesis, Ben Mildenhall . Understanding and Extending Neural Radiance Fields, Barron et al .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/awesome-NeRF/awesome-NeRF", "content": "Object-Centric Neural Scene Rendering, Guo et al ., Arxiv 2020 | bibtex. NeRF : Representing Scenes as Neural Radiance Fields for View Synthesis, Ben Mildenhall . Understanding and Extending Neural Radiance Fields, Barron et al ."}
+{"idx": 6, "title": "(PDF) A Comparative Study of Traditional Light Field Methods and NeRF", "date": "", "ddg_snippet": "NeRF . The seminal paper [ Mildenhall et al ., 2020 ] is intended to be a novel view synthesis method, and. does so by rendering and optimising a continuous volumetric scene using a sparse set of input views.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/363154509_A_Comparative_Study_of_Traditional_Light_Field_Methods_and_NeRF", "content": "NeRF . The seminal paper [ Mildenhall et al ., 2020 ] is intended to be a novel view synthesis method, and. does so by rendering and optimising a continuous volumetric scene using a sparse set of input views."}
+{"idx": 7, "title": "(PDF) HyperNeRF: A Higher-Dimensional Representation for...", "date": "", "ddg_snippet": "1. Neural Radiance Fields ( NeRF ) [ Mildenhall et al . 2020 ] when endowed with the ability to handle deformations [Park et al . 2020 ] are able to capture non-static human subjects, but often struggle in the presence of significant deformation or.", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/82156779/HyperNeRF_A_Higher_Dimensional_Representation_for_Topologically_Varying_Neural_Radiance_Fields", "content": "1. Neural Radiance Fields ( NeRF ) [ Mildenhall et al . 2020 ] when endowed with the ability to handle deformations [Park et al . 2020 ] are able to capture non-static human subjects, but often struggle in the presence of significant deformation or."}
+{"idx": 8, "title": "SuNeRF: Validation of a 3D Global Reconstruction", "date": "", "ddg_snippet": "NeRFs ( Mildenhall et al ., 2020 ) learn the density and color at each point in a volume based on a set of images captured from different viewpoints. The resulting representation can then be used to render novel scenes.", "subpage_snippet": "", "source": "www.seti.org", "link": "https://www.seti.org/media/k4nnyx4z/sunerf-validation-of-a-3d-global-reconstruction-of-the-solar-corona-using-simulated-euv-images.pdf", "content": "NeRFs ( Mildenhall et al ., 2020 ) learn the density and color at each point in a volume based on a set of images captured from different viewpoints. The resulting representation can then be used to render novel scenes."}
+{"idx": 9, "title": "Sparsesat- nerf : dense depth supervised neural radiance fields", "date": "", "ddg_snippet": "NeRF ( Mildenhall et al ., 2020 ) learns a continuous volumetric representation of the scene from a set of images characterised by the sensor position and the viewing direction. This repre-sentation is dened by a fully-connected (non-convolutional) deep network.", "subpage_snippet": "", "source": "hal.science", "link": "https://hal.science/hal-04205387/document", "content": "NeRF ( Mildenhall et al ., 2020 ) learns a continuous volumetric representation of the scene from a set of images characterised by the sensor position and the viewing direction. This repre-sentation is dened by a fully-connected (non-convolutional) deep network."}
diff --git a/data/sampled_jsons/NeRF_slow_rendering_speed_real-time_performance_limitations.jsonl b/data/sampled_jsons/NeRF_slow_rendering_speed_real-time_performance_limitations.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e4ca4dc2750944d92098766c5857ed800d6bbb03
--- /dev/null
+++ b/data/sampled_jsons/NeRF_slow_rendering_speed_real-time_performance_limitations.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Baking Neural Radiance Fields for Real - Time View Synthesis", "date": "", "ddg_snippet": "Rendering a standard NeRF in real - time is completely intractable on current hardware. NeRF requires about 100 teraops to render a single 800 × 800 frame, which results in a best-case rendering time of 10 seconds per frame on an NVIDIA RTX 2080 GPU with full GPU utilization.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/ICCV2021/papers/Hedman_Baking_Neural_Radiance_Fields_for_Real-Time_View_Synthesis_ICCV_2021_paper.pdf", "content": "Rendering a standard NeRF in real - time is completely intractable on current hardware. NeRF requires about 100 teraops to render a single 800 × 800 frame, which results in a best-case rendering time of 10 seconds per frame on an NVIDIA RTX 2080 GPU with full GPU utilization."}
+{"idx": 1, "title": "3D Gaussian Splatting - Paper Explained, Training NeRFStudio", "date": "", "ddg_snippet": "Despite their advancements, NeRFs still had certain limitations , such as slow rendering time , artifacts/floaters, struggle to capture large scenes etc. Finally, in 2023, 3D Gaussian Splatting was introduced, addressing many of the issues NeRFs faced.", "subpage_snippet": "", "source": "learnopencv.com", "link": "https://learnopencv.com/3d-gaussian-splatting/", "content": "Despite their advancements, NeRFs still had certain limitations , such as slow rendering time , artifacts/floaters, struggle to capture large scenes etc. Finally, in 2023, 3D Gaussian Splatting was introduced, addressing many of the issues NeRFs faced."}
+{"idx": 2, "title": "NeuRAD: Neural Rendering for Autonomous Driving | Request PDF", "date": "", "ddg_snippet": "SplatAD: Real - Time Lidar and Camera Rendering with 3D Gaussian Splatting for Autonomous Driving.This technique leverages the superior generalization capabilities of NeRF -based methods and the real - time rendering speed of 3D Gaussian Splatting (3DGS).", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/384219828_NeuRAD_Neural_Rendering_for_Autonomous_Driving", "content": "SplatAD: Real - Time Lidar and Camera Rendering with 3D Gaussian Splatting for Autonomous Driving.This technique leverages the superior generalization capabilities of NeRF -based methods and the real - time rendering speed of 3D Gaussian Splatting (3DGS)."}
+{"idx": 3, "title": "Blackmagic Forum • View topic - Slow Rendering Speed in DaVinci...", "date": "", "ddg_snippet": "Initially, the rendering speed is normal at around 15-17 FPS. However, it drastically slows down to an unworkable 0-0.5 FPS when the GPU memory is nearly full (11.2GB/12GB) and RAM usage approaches its limit (40.7/64GB).", "subpage_snippet": "", "source": "forum.blackmagicdesign.com", "link": "https://forum.blackmagicdesign.com/viewtopic.php?f=21&t=189628", "content": "Initially, the rendering speed is normal at around 15-17 FPS. However, it drastically slows down to an unworkable 0-0.5 FPS when the GPU memory is nearly full (11.2GB/12GB) and RAM usage approaches its limit (40.7/64GB)."}
+{"idx": 4, "title": "How to Optimize Performance in 3D Games for... - @ oneframework.net", "date": "", "ddg_snippet": "Graphics Processing Power: Weak GPU performance limits the amount of pixels it can store, resulting in slow render times , poor FPS, and lag. Memory Limitations : Low- performance devices usually have less RAM available, making it difficult to load textures...", "subpage_snippet": "", "source": "oneframework.net", "link": "https://oneframework.net/how-to-optimize-performance-in-3d-games-for-low-end-devices/", "content": "Graphics Processing Power: Weak GPU performance limits the amount of pixels it can store, resulting in slow render times , poor FPS, and lag. Memory Limitations : Low- performance devices usually have less RAM available, making it difficult to load textures..."}
+{"idx": 5, "title": "Reaper rendering - too slow | Forum", "date": "", "ddg_snippet": "Sometimes, renders are even slower than real - time , which doesn't make sense considering the project plays without a single itch. render speed varies here between 0.7x and approx 15x (but iirc once i had 50x).", "subpage_snippet": "", "source": "forum.cockos.com", "link": "https://forum.cockos.com/showthread.php?t=66860", "content": "Sometimes, renders are even slower than real - time , which doesn't make sense considering the project plays without a single itch. render speed varies here between 0.7x and approx 15x (but iirc once i had 50x)."}
+{"idx": 6, "title": "Slow rendering performance on scroll · Issue #139 · TanStack/virtual", "date": "", "ddg_snippet": "However, I am getting some poor performance on row rendering . It's the classic empty and then flash to content on scroll.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/TanStack/virtual/issues/139", "content": "However, I am getting some poor performance on row rendering . It's the classic empty and then flash to content on scroll."}
+{"idx": 7, "title": "Free Online FPS Test | Real - Time FPS, Stability Score...", "date": "", "ddg_snippet": "Real - time performance graph. System diagnostics (CPU, GPU, browser, resolution). Stability Score.Records frame-by-frame render speeds . Calculates performance metrics like average FPS, min FPS, and stability. Analyzes your device’s hardware capabilities.", "subpage_snippet": "", "source": "testmyspec.com", "link": "https://testmyspec.com/", "content": "Real - time performance graph. System diagnostics (CPU, GPU, browser, resolution). Stability Score.Records frame-by-frame render speeds . Calculates performance metrics like average FPS, min FPS, and stability. Analyzes your device’s hardware capabilities."}
+{"idx": 8, "title": "Volume Shader BM - GPU Benchmark Platform & Volume Rendering", "date": "", "ddg_snippet": "Explore VolumeShader.org for real - time volume rendering , GPU performance testing, and an interactive shader playground. No installation needed.", "subpage_snippet": "", "source": "volumeshader.org", "link": "https://volumeshader.org/", "content": "Explore VolumeShader.org for real - time volume rendering , GPU performance testing, and an interactive shader playground. No installation needed."}
+{"idx": 9, "title": "Online CPU & GPU Stress Test - Check System Performance", "date": "", "ddg_snippet": "Advanced GPU stress testing with WebGL-based rendering challenges Real - time performance metrics including FPS, render time, and CPU utilizationVisual performance monitoring with live charts and gauges", "subpage_snippet": "", "source": "helptester.com", "link": "https://helptester.com/hardware-stresstest/", "content": "Advanced GPU stress testing with WebGL-based rendering challenges Real - time performance metrics including FPS, render time, and CPU utilizationVisual performance monitoring with live charts and gauges"}
diff --git a/data/sampled_jsons/Near-Optimal_Online_Learning_Multi-Agent_Submodular_Coordination_adversarial_target_behavior_experim.jsonl b/data/sampled_jsons/Near-Optimal_Online_Learning_Multi-Agent_Submodular_Coordination_adversarial_target_behavior_experim.jsonl
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index 0000000000000000000000000000000000000000..a4c28d27922212f29f625fe958a26ce6c61419bb
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+++ b/data/sampled_jsons/Near-Optimal_Online_Learning_Multi-Agent_Submodular_Coordination_adversarial_target_behavior_experim.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Near-Optimal Online Learning for Multi-Agent Submodular...", "date": "", "ddg_snippet": "by Q Zhang · Cited by 4 — Summary: This work proposes two algorithms MA-OSMA and MA-OSEA that collaboratively solve an online, multi-agent submodular optimization problem. Both ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=i8dYPGdB1C", "content": "by Q Zhang · Cited by 4 — Summary: This work proposes two algorithms MA-OSMA and MA-OSEA that collaboratively solve an online, multi-agent submodular optimization problem. Both ..."}
+{"idx": 1, "title": "Near-Optimal Online Learning for Multi-Agent Submodular ...", "date": "", "ddg_snippet": "7 Feb 2025 — We conduct a simulation-based evaluation of our proposed algorithms within a multi - target tracking scenario. Our experiments demonstrate the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.05028v1", "content": "7 Feb 2025 — We conduct a simulation-based evaluation of our proposed algorithms within a multi - target tracking scenario. Our experiments demonstrate the ..."}
+{"idx": 2, "title": "AGENT SUBMODULAR COORDINATION: TIGHT AP", "date": "", "ddg_snippet": "by Q Zhang · Cited by 4 — Coordinating multiple agents to collaboratively maximize submodular functions in unpredictable environments is a critical task with numerous applications in.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=i8dYPGdB1C", "content": "by Q Zhang · Cited by 4 — Coordinating multiple agents to collaboratively maximize submodular functions in unpredictable environments is a critical task with numerous applications in."}
+{"idx": 3, "title": "Proofs and Experiments in Scalable, Near-Optimal Search ...", "date": "", "ddg_snippet": "by G Hollinger · Cited by 52 — In this paper, we show that the MESPP problem requires the optimization of a submodular objective function, and this key insight provides optimality bounds on ...", "subpage_snippet": "", "source": "www.roboticsproceedings.org", "link": "https://www.roboticsproceedings.org/rss04/p27.pdf", "content": "by G Hollinger · Cited by 52 — In this paper, we show that the MESPP problem requires the optimization of a submodular objective function, and this key insight provides optimality bounds on ..."}
+{"idx": 4, "title": "Convex Markov Games: A New Frontier for Multi-Agent ...", "date": "", "ddg_snippet": "15 Jul 2025 — Our experiments reveal novel solutions to classic repeated normal-form games, find fair solutions in a repeated asymmetric coordination game, ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/43543", "content": "15 Jul 2025 — Our experiments reveal novel solutions to classic repeated normal-form games, find fair solutions in a repeated asymmetric coordination game, ..."}
+{"idx": 5, "title": "Convex Markov Games: A New Frontier for Multi-Agent ...", "date": "", "ddg_snippet": "16 Jun 2025 — Our experiments reveal novel solutions to classic repeated normal-form games, find fair solutions in a repeated asymmetric coordination game, ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.16600v3", "content": "16 Jun 2025 — Our experiments reveal novel solutions to classic repeated normal-form games, find fair solutions in a repeated asymmetric coordination game, ..."}
+{"idx": 6, "title": "AAMAS: International Conference on Autonomous Agents and ...", "date": "", "ddg_snippet": "5 Jun 2025 — In many realistic scenarios, a number of agents need to maximize multiple submodular objectives over the same ground set. We study such a ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/proceedings/10.5555/3709347?tocHeading=heading2", "content": "5 Jun 2025 — In many realistic scenarios, a number of agents need to maximize multiple submodular objectives over the same ground set. We study such a ..."}
+{"idx": 7, "title": "Daily Papers", "date": "", "ddg_snippet": "5 days ago — The algorithms are shown optimal in adversarial settings and nearly optimal up to a logarithmic factor in stochastic settings simultaneously by ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=multi-objective+multi-armed+bandit", "content": "5 days ago — The algorithms are shown optimal in adversarial settings and nearly optimal up to a logarithmic factor in stochastic settings simultaneously by ..."}
+{"idx": 8, "title": "ICLR 2025 Papers", "date": "", "ddg_snippet": "Near-Optimal Online Learning for Multi-Agent Submodular Coordination : Tight Approximation and Communication Efficiency · YouTube-SL-25: A Large-Scale, Open ...", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2025/papers.html", "content": "Near-Optimal Online Learning for Multi-Agent Submodular Coordination : Tight Approximation and Communication Efficiency · YouTube-SL-25: A Large-Scale, Open ..."}
+{"idx": 9, "title": "NeurIPS 2024 Papers", "date": "", "ddg_snippet": "Start here, schedule, tutorials, main conference, invited talks, orals, spotlights, papers, paper visualization, competitions, datasets & benchmarks.", "subpage_snippet": "", "source": "nips.cc", "link": "https://nips.cc/virtual/2024/papers.html", "content": "Start here, schedule, tutorials, main conference, invited talks, orals, spotlights, papers, paper visualization, competitions, datasets & benchmarks."}
diff --git a/data/sampled_jsons/Neural_Exploratory_Landscape_Analysis_Meta-Black-Box-Optimization_equation_3_evaluation_metric_year_2024.jsonl b/data/sampled_jsons/Neural_Exploratory_Landscape_Analysis_Meta-Black-Box-Optimization_equation_3_evaluation_metric_year_2024.jsonl
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index 0000000000000000000000000000000000000000..23b5ea2dc25a26eae1e6af23ce60870adfe7a050
--- /dev/null
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Neural Exploratory Landscape Analysis for Meta-Black-Box ...", "date": "", "ddg_snippet": "27 Mar 2025 — Exploratory landscape analysis of continuous space optimization problems using information content. IEEE transactions on evolutionary ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2408.10672v3", "content": "27 Mar 2025 — Exploratory landscape analysis of continuous space optimization problems using information content. IEEE transactions on evolutionary ..."}
+{"idx": 1, "title": "Exploratory landscape analysis on black-box optimization ...", "date": "", "ddg_snippet": "by X Yang · 2025 — Exploratory landscape analysis (ELA) paves the way for algorithm design to deal with BBOPs. Existing ELA methods have limitations on unseen problems and lack ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/abs/pii/S2210650225002949", "content": "by X Yang · 2025 — Exploratory landscape analysis (ELA) paves the way for algorithm design to deal with BBOPs. Existing ELA methods have limitations on unseen problems and lack ..."}
+{"idx": 2, "title": "Neural Exploratory Landscape Analysis", "date": "", "ddg_snippet": "26 Sept 2024 — Recent research in Meta - Black - Box Optimization (MetaBBO) have shown that meta-trained neural networks can effectively guide the design of black- ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2408.10672v2", "content": "26 Sept 2024 — Recent research in Meta - Black - Box Optimization (MetaBBO) have shown that meta-trained neural networks can effectively guide the design of black- ..."}
+{"idx": 3, "title": "Neural Exploratory Landscape Analysis for Meta-Black-Box ...", "date": "", "ddg_snippet": "NeurELA is a novel framework that dynamically profiles landscape features through a two-stage, attention-based neural network, executed in an entirely ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Neural-Exploratory-Landscape-Analysis-for-Ma-Chen/06dc4a6ce58184fcfdd6c48e40bf583f33cf0cfa", "content": "NeurELA is a novel framework that dynamically profiles landscape features through a two-stage, attention-based neural network, executed in an entirely ..."}
+{"idx": 4, "title": "MetaEvo/psc4MetaBBO", "date": "", "ddg_snippet": "\" Neural Networks as Black - Box Benchmark Functions Optimized for Exploratory Landscape Features\" Proceedings of the 17th ACM/SIGEVO Conference on Foundations ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/MetaEvo/psc4MetaBBO", "content": "\" Neural Networks as Black - Box Benchmark Functions Optimized for Exploratory Landscape Features\" Proceedings of the 17th ACM/SIGEVO Conference on Foundations ..."}
+{"idx": 5, "title": "Meta-Black-Box-Optimization", "date": "", "ddg_snippet": "Concretely, our method uses a Mamba neural network architecture to meta -learn decomposed Q-functions for each con- figurable component in the low-level ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/2f9052c209e77207cb87544eb40cb37b2a701261.pdf", "content": "Concretely, our method uses a Mamba neural network architecture to meta -learn decomposed Q-functions for each con- figurable component in the low-level ..."}
+{"idx": 6, "title": "Landscape Analysis for Surrogate Models in the ...", "date": "", "ddg_snippet": "2 Jun 2025 — In this paper, we investigate the relationships between the predictive accuracy of surrogate models, their settings, and features of the black -box function ...", "subpage_snippet": "", "source": "direct.mit.edu", "link": "https://direct.mit.edu/evco/article/33/2/249/124014/Landscape-Analysis-for-Surrogate-Models-in-the", "content": "2 Jun 2025 — In this paper, we investigate the relationships between the predictive accuracy of surrogate models, their settings, and features of the black -box function ..."}
+{"idx": 7, "title": "Benchmarking footprints of continuous black-box ...", "date": "", "ddg_snippet": "by A Nikolikj · 2025 · Cited by 2 — This study introduces a novel approach for comparing algorithms based on the concept of an algorithm footprint, which aims to identify easy and challenging ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2210650225000537", "content": "by A Nikolikj · 2025 · Cited by 2 — This study introduces a novel approach for comparing algorithms based on the concept of an algorithm footprint, which aims to identify easy and challenging ..."}
+{"idx": 8, "title": "Revisions", "date": "", "ddg_snippet": "This paper proposes an automatic construction method of landscape features for meta black - box optimization (MetaBBO). The proposed approach, termed neural ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/revisions?id=W3ffbexefK", "content": "This paper proposes an automatic construction method of landscape features for meta black - box optimization (MetaBBO). The proposed approach, termed neural ..."}
+{"idx": 9, "title": "Reinforcement Learning-based Self-adaptive Differential ...", "date": "", "ddg_snippet": "by H Guo · 2025 · Cited by 4 — Recently, Meta - Black - Box - Optimization (MetaBBO) methods significantly enhance the performance of traditional black-box optimizers through ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.1145/3712256.3726309", "content": "by H Guo · 2025 · Cited by 4 — Recently, Meta - Black - Box - Optimization (MetaBBO) methods significantly enhance the performance of traditional black-box optimizers through ..."}
diff --git a/data/sampled_jsons/OFUL_algorithm_complexity_Abbasi-Yadkori_2011.jsonl b/data/sampled_jsons/OFUL_algorithm_complexity_Abbasi-Yadkori_2011.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0e22d0b8827b23631fa4f454bb1482d3414f219f
--- /dev/null
+++ b/data/sampled_jsons/OFUL_algorithm_complexity_Abbasi-Yadkori_2011.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF Improved Algorithms for Linear Stochastic Bandits - NeurIPS", "date": "", "ddg_snippet": "Another variant, studied by Dani et al. (2008), Abbasi-Yadkori et al. (2009), Rusmevichientong and Tsitsiklis (2010), is the case when the set of available actions does not change between time steps but the set can be an almost arbitrary, even infinite, bounded subset of a finite-dimensional vector space.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper/2011/file/e1d5be1c7f2f456670de3d53c7b54f4a-Paper.pdf", "content": "Another variant, studied by Dani et al. (2008), Abbasi-Yadkori et al. (2009), Rusmevichientong and Tsitsiklis (2010), is the case when the set of available actions does not change between time steps but the set can be an almost arbitrary, even infinite, bounded subset of a finite-dimensional vector space."}
+{"idx": 1, "title": "Improved Algorithms for Linear Stochastic Bandits - NIPS", "date": "", "ddg_snippet": "Authors Yasin Abbasi-yadkori , Dávid Pál, Csaba Szepesvári Abstract We improve the theoretical analysis and empirical performance of algorithms for the stochastic multi-armed bandit problem and the linear stochastic multi-armed bandit problem. In particular, we show that a simple modification of Auer's UCB algorithm (Auer, 2002) achieves with high probability constant regret. More ...", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper/4417-improved-algorithms-for-linear-stochastic-bandits", "content": "Authors Yasin Abbasi-yadkori , Dávid Pál, Csaba Szepesvári Abstract We improve the theoretical analysis and empirical performance of algorithms for the stochastic multi-armed bandit problem and the linear stochastic multi-armed bandit problem. In particular, we show that a simple modification of Auer's UCB algorithm (Auer, 2002) achieves with high probability constant regret. More ..."}
+{"idx": 2, "title": "Improved Algorithms for Linear Stochastic Bandits (extended version)", "date": "", "ddg_snippet": "By Lemma 11 in Abbasi-Yadkori et al. [ 2011 ] and Assumption 1 from the main manuscript, we have log det (G t ) ≤ d log (T /d). By the choice of τ , det (G τ ) −1 ≤ 1. ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/230627940_Improved_Algorithms_for_Linear_Stochastic_Bandits_extended_version", "content": "By Lemma 11 in Abbasi-Yadkori et al. [ 2011 ] and Assumption 1 from the main manuscript, we have log det (G t ) ≤ d log (T /d). By the choice of τ , det (G τ ) −1 ≤ 1. ..."}
+{"idx": 3, "title": "Improved algorithms for linear stochastic bandits", "date": "", "ddg_snippet": "Y. Abbasi-Yadkori , A. Antos, and Cs. Szepesvari. Forced-exploration based algorithms for playing in stochastic linear bandits. In COLT Workshop on On-line Learning with Limited Feedback, 2009.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/2986459.2986717", "content": "Y. Abbasi-Yadkori , A. Antos, and Cs. Szepesvari. Forced-exploration based algorithms for playing in stochastic linear bandits. In COLT Workshop on On-line Learning with Limited Feedback, 2009."}
+{"idx": 4, "title": "Yasin Abbasi-Yadkori", "date": "", "ddg_snippet": "Yasin Abbasi-Yadkori Artificial intelligence, machine learning, sequential decision problems yasin dot abbasi@gmail.com Continued", "subpage_snippet": "", "source": "yasinov.github.io", "link": "https://yasinov.github.io/", "content": "Yasin Abbasi-Yadkori Artificial intelligence, machine learning, sequential decision problems yasin dot abbasi@gmail.com Continued"}
+{"idx": 5, "title": "PDF Improved Algorithms for Linear Stochastic Bandits", "date": "", "ddg_snippet": "Another variant, studied by Dani et al. (2008), Abbasi-Yadkori et al. (2009), Rusmevichientong and Tsitsiklis (2010), is the case when the set of available actions does not change between time steps but the set can be an almost arbitrary, even infinite, bounded subset of a finite-dimensional vector space.", "subpage_snippet": "", "source": "sites.ualberta.ca", "link": "https://sites.ualberta.ca/~szepesva/papers/linear-bandits-NeurIPS2011.pdf", "content": "Another variant, studied by Dani et al. (2008), Abbasi-Yadkori et al. (2009), Rusmevichientong and Tsitsiklis (2010), is the case when the set of available actions does not change between time steps but the set can be an almost arbitrary, even infinite, bounded subset of a finite-dimensional vector space."}
+{"idx": 6, "title": "A Simple and Provably Efficient Algorithm for Asynchronous Federated ...", "date": "", "ddg_snippet": "In this work, we resolve the above open problem by proposing a simple algorithm for asynchronous federated contextual linear bandits over a star-shaped communication network. Our algorithm is based on the principle of optimism ( Abbasi-Yadkori et al., 2011 ) and enjoys the following advantages: (i) Each agent can decide whether or not to participate in each round. Full participation is not ...", "subpage_snippet": "", "source": "par.nsf.gov", "link": "https://par.nsf.gov/servlets/purl/10379034", "content": "In this work, we resolve the above open problem by proposing a simple algorithm for asynchronous federated contextual linear bandits over a star-shaped communication network. Our algorithm is based on the principle of optimism ( Abbasi-Yadkori et al., 2011 ) and enjoys the following advantages: (i) Each agent can decide whether or not to participate in each round. Full participation is not ..."}
+{"idx": 7, "title": "Improved Algorithms for Linear Stochastic Bandits - Semantic Scholar", "date": "", "ddg_snippet": "A new rate-optimal algorithm called Sieved-Greedy is introduced by combining insights from uncertainty complexity and a new (and general) notion of optimism in expectation, which significantly outperforms existing benchmarks by combining the best attributes of both greedy and OFUL algorithms .", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Improved-Algorithms-for-Linear-Stochastic-Bandits-Abbasi-Yadkori-Pál/6bea71fa6deb19c67e9586428f8f240e789fb3df", "content": "A new rate-optimal algorithm called Sieved-Greedy is introduced by combining insights from uncertainty complexity and a new (and general) notion of optimism in expectation, which significantly outperforms existing benchmarks by combining the best attributes of both greedy and OFUL algorithms ."}
+{"idx": 8, "title": "GitHub - vineet0814/contextual-bandits-OFUL", "date": "", "ddg_snippet": "Currently the main.py contains implementation of OFUL algorithm by Abbasi-Yadkori 2011 from paper titled 'Improved Algorithms for Linear Stochastic Bandits'.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/vineet0814/contextual-bandits-OFUL", "content": "Currently the main.py contains implementation of OFUL algorithm by Abbasi-Yadkori 2011 from paper titled 'Improved Algorithms for Linear Stochastic Bandits'."}
+{"idx": 9, "title": "PDF Istituto Italiano di Tecnologia and University College London arXiv ...", "date": "", "ddg_snippet": "In this paper, we are concerned with linear bandits ( Abbasi-Yadkori et al., 2011 ; Chu et al., 2011 ; Auer, 2003), a consolidated MAB setting in which each arm is associated with a vector of features and the arm payoff function is modeled by a (unknown) linear regression of the arm feature vector. Our study builds upon the OFUL algorithm introduced in ( Abbasi-Yadkori et al., 2011 ), which in ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2005.08531.pdf", "content": "In this paper, we are concerned with linear bandits ( Abbasi-Yadkori et al., 2011 ; Chu et al., 2011 ; Auer, 2003), a consolidated MAB setting in which each arm is associated with a vector of features and the arm payoff function is modeled by a (unknown) linear regression of the arm feature vector. Our study builds upon the OFUL algorithm introduced in ( Abbasi-Yadkori et al., 2011 ), which in ..."}
diff --git a/data/sampled_jsons/OFUL_algorithm_steps.jsonl b/data/sampled_jsons/OFUL_algorithm_steps.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4e111914a84f782b6e63a12ade8dcb4d6e2fa2a6
--- /dev/null
+++ b/data/sampled_jsons/OFUL_algorithm_steps.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Corruption-Robust Algorithms with Uncertainty Weighting for", "date": "", "ddg_snippet": "... F -LSVI (Wang et al., 2020 ) and develop an algorithm , which adds a bonus for every step and establishes optimism in the backward iteration.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2212.05949v4", "content": "... F -LSVI (Wang et al., 2020 ) and develop an algorithm , which adds a bonus for every step and establishes optimism in the backward iteration."}
+{"idx": 1, "title": "Bandit Algorithms · Li Zhuohua @ CUHK", "date": "", "ddg_snippet": "We want to compare our algorithm with the best \\(h\\), which minimizes \\(\\sum_{t=1}^{n} \\left| h(x_t)-y_t \\right|\\) (Hence maximize the whole formula).", "subpage_snippet": "", "source": "zhuohua.me", "link": "https://zhuohua.me/notes/20211208101747-bandit_algorithms/", "content": "We want to compare our algorithm with the best \\(h\\), which minimizes \\(\\sum_{t=1}^{n} \\left| h(x_t)-y_t \\right|\\) (Hence maximize the whole formula)."}
+{"idx": 2, "title": "Stochastic Linear Bandits and UCB – Bandit Algorithms", "date": "", "ddg_snippet": "... algorithm described in this post LinUCB, while the previous paper calls an essentially identical algorithm OFUL (after optimism in the face of ...", "subpage_snippet": "", "source": "banditalgs.com", "link": "https://banditalgs.com/2016/10/19/stochastic-linear-bandits/", "content": "... algorithm described in this post LinUCB, while the previous paper calls an essentially identical algorithm OFUL (after optimism in the face of ..."}
+{"idx": 3, "title": "Improved Regret of Linear Ensemble Sampling", "date": "", "ddg_snippet": "Numerous algorithms have been developed for this problem, including deterministic approaches such as UCB-based methods [ 5 , 8 , 1 ] and randomized ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.03932v2", "content": "Numerous algorithms have been developed for this problem, including deterministic approaches such as UCB-based methods [ 5 , 8 , 1 ] and randomized ..."}
+{"idx": 4, "title": "Tokenized Bandit for LLM Decoding and Alignment", "date": "", "ddg_snippet": "... we present GreedyETC that step -by- step learns a desirable path to efficient token sequences by combining exploration-then-commit style of algorithm ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.07276v1", "content": "... we present GreedyETC that step -by- step learns a desirable path to efficient token sequences by combining exploration-then-commit style of algorithm ..."}
+{"idx": 5, "title": "Achieving Limited Adaptivity for Multinomial Logistic Bandits", "date": "", "ddg_snippet": "This section is structured in the following manner: we introduce the algorithm and explain each step in detail. ... steps involved in the algorithm ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.03072v1", "content": "This section is structured in the following manner: we introduce the algorithm and explain each step in detail. ... steps involved in the algorithm ..."}
+{"idx": 6, "title": "Heavy-Tailed Linear Bandits: Huber Regression with One-Pass", "date": "", "ddg_snippet": "Unlike the OFUL algorithm (Abbasi-Yadkori et al., 2011 ) in SLB with sub-Gaussian noise, which uses least squares (LS) for estimation and can ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.00419v1", "content": "Unlike the OFUL algorithm (Abbasi-Yadkori et al., 2011 ) in SLB with sub-Gaussian noise, which uses least squares (LS) for estimation and can ..."}
+{"idx": 7, "title": "Lower Bounds for Stochastic Linear Bandits – Bandit", "date": "", "ddg_snippet": "... algorithm that enjoys the bound of ... Note 1: The worst-case bound demonstrates the near-optimality of the OFUL algorithm for a specific action-set.", "subpage_snippet": "", "source": "banditalgs.com", "link": "https://banditalgs.com/2016/10/20/lower-bounds-for-stochastic-linear-bandits/", "content": "... algorithm that enjoys the bound of ... Note 1: The worst-case bound demonstrates the near-optimality of the OFUL algorithm for a specific action-set."}
+{"idx": 8, "title": "WO2009018164A2 - Method and apparatus for handling mobility", "date": "", "ddg_snippet": "Steps may be taken to ensure that the cell re-selected by non- 3GPP RAT is given priority when the WTRU 410 transitions to LTE Mode.", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/WO2009018164A2/en", "content": "Steps may be taken to ensure that the cell re-selected by non- 3GPP RAT is given priority when the WTRU 410 transitions to LTE Mode."}
+{"idx": 9, "title": "US8812329B2 - Laboratory instrumentation information management", "date": "", "ddg_snippet": "... include: encoding said label with other information as said element is processed in accordance with at least one laboratory workflow processing step ...", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/US8812329B2/en", "content": "... include: encoding said label with other information as said element is processed in accordance with at least one laboratory workflow processing step ..."}
diff --git a/data/sampled_jsons/OFUL_optimization_problem_confidence_ellipsoid_computational_bottleneck.jsonl b/data/sampled_jsons/OFUL_optimization_problem_confidence_ellipsoid_computational_bottleneck.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..1232642c649b4f44801f5bebc0982b2a0fa8e60f
--- /dev/null
+++ b/data/sampled_jsons/OFUL_optimization_problem_confidence_ellipsoid_computational_bottleneck.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Program optimization - Wikipedia", "date": "", "ddg_snippet": "In computer science, program optimization , code optimization , or software optimization is the process of modifying a software system to make some aspect of it work more efficiently or use fewer resources.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Program_optimization", "content": "In computer science, program optimization , code optimization , or software optimization is the process of modifying a software system to make some aspect of it work more efficiently or use fewer resources."}
+{"idx": 1, "title": "Combining Differential Privacy with John Ellipsoid Computation", "date": "", "ddg_snippet": "Fast John Ellipsoid withFast John Ellipsoid withPrivacymethod protects sensitive information.Speedy John ellipsoid computation . Table of Contents. The John Ellipsoid Problem . Differential Privacy.", "subpage_snippet": "", "source": "scisimple.com", "link": "https://scisimple.com/en/articles/2025-06-29-combining-differential-privacy-with-john-ellipsoid-computation--a315qjo", "content": "Fast John Ellipsoid withFast John Ellipsoid withPrivacymethod protects sensitive information.Speedy John ellipsoid computation . Table of Contents. The John Ellipsoid Problem . Differential Privacy."}
+{"idx": 2, "title": "[SOLVED] Compute 80% Confidence Ellipsoid ... - DeveloperLoad", "date": "", "ddg_snippet": "'Compute 80% Confidence Ellipsoid Matplotlib. I try to compute an ellipsoid with a custom center and a specific border. I try to follow this formula to calculate the 80% confidence ellipsoid around my points", "subpage_snippet": "", "source": "www.developerload.com", "link": "https://www.developerload.com/compute-80-confidence-ellipsoid-matplotlib", "content": "'Compute 80% Confidence Ellipsoid Matplotlib. I try to compute an ellipsoid with a custom center and a specific border. I try to follow this formula to calculate the 80% confidence ellipsoid around my points"}
+{"idx": 3, "title": "Bottleneck Calculator | CPU GPU Performance Fix", "date": "", "ddg_snippet": "Advanced CPU/GPU Bottleneck Calculator. Optimize your PC build by identifying performance bottlenecks . Our tool analyzes your components to deliver accurate performance matching recommendations for gaming, streaming, and professional workloads.", "subpage_snippet": "", "source": "bottleneckcalculator.to", "link": "https://bottleneckcalculator.to/", "content": "Advanced CPU/GPU Bottleneck Calculator. Optimize your PC build by identifying performance bottlenecks . Our tool analyzes your components to deliver accurate performance matching recommendations for gaming, streaming, and professional workloads."}
+{"idx": 4, "title": "PC Bottleneck Calculator | CPU & GPU Performance Analysis", "date": "", "ddg_snippet": "Identify potential bottlenecks in seconds and optimize your system for seamless gaming, streaming, or demanding creative workloads. Get the performance you paid for!", "subpage_snippet": "", "source": "www.pc-bottleneck-calculator.com", "link": "https://www.pc-bottleneck-calculator.com/", "content": "Identify potential bottlenecks in seconds and optimize your system for seamless gaming, streaming, or demanding creative workloads. Get the performance you paid for!"}
+{"idx": 5, "title": "linear algebra - Matrix optimization problem - MathOverflow", "date": "", "ddg_snippet": "Optimization problem with determinant as objective.A problem not listed above. Try to be as specific as possible.", "subpage_snippet": "", "source": "mathoverflow.net", "link": "https://mathoverflow.net/questions/63310/matrix-optimization-problem", "content": "Optimization problem with determinant as objective.A problem not listed above. Try to be as specific as possible."}
+{"idx": 6, "title": "Ellipsoid -- from Wolfram MathWorld", "date": "", "ddg_snippet": "If the lengths of two axes of an ellipsoid are the same, the figure is called an ellipsoid of revolution or spheroid.Tietze, H. Famous Problems of Mathematics: Solved and Unsolved Mathematics Problems from Antiquity to Modern Times.", "subpage_snippet": "", "source": "mathworld.wolfram.com", "link": "https://mathworld.wolfram.com/Ellipsoid.html", "content": "If the lengths of two axes of an ellipsoid are the same, the figure is called an ellipsoid of revolution or spheroid.Tietze, H. Famous Problems of Mathematics: Solved and Unsolved Mathematics Problems from Antiquity to Modern Times."}
+{"idx": 7, "title": "Optimization (Summer Term 2014)", "date": "", "ddg_snippet": "The simplex algorithm and the ellipsoid method will be presented. The lecture concludes with exact and approximation algorithms for NP-hard optimization problems .", "subpage_snippet": "", "source": "resources.mpi-inf.mpg.de", "link": "https://resources.mpi-inf.mpg.de/departments/d1/teaching/ss14/OPT/", "content": "The simplex algorithm and the ellipsoid method will be presented. The lecture concludes with exact and approximation algorithms for NP-hard optimization problems ."}
+{"idx": 8, "title": "Bayesian Optimization Enhances Quantum Annealing, Boosts...", "date": "", "ddg_snippet": "Quantum annealing (QA), a computational paradigm used to solve combinatorial optimization problems , faces challenges such as vanishing gaps during the anneal. To address this, researchers have proposed using Bayesian optimization (BO) to design high-quality QA schedules.", "subpage_snippet": "", "source": "quantumzeitgeist.com", "link": "https://quantumzeitgeist.com/bayesian-optimization-enhances-quantum-annealing-boosts-quantum-computing-efficiency/", "content": "Quantum annealing (QA), a computational paradigm used to solve combinatorial optimization problems , faces challenges such as vanishing gaps during the anneal. To address this, researchers have proposed using Bayesian optimization (BO) to design high-quality QA schedules."}
+{"idx": 9, "title": "Computer Vision Pipeline Optimization : Accelerating Image...", "date": "", "ddg_snippet": "Modern computer vision optimization encompasses the entire processing pipeline from image capture and preprocessing through model inference and post-processing.", "subpage_snippet": "", "source": "www.runpod.io", "link": "https://www.runpod.io/articles/guides/computer-vision-pipeline-optimization-accelerating-image-processing-workflows-with-gpu-computing", "content": "Modern computer vision optimization encompasses the entire processing pipeline from image capture and preprocessing through model inference and post-processing."}
diff --git a/data/sampled_jsons/OS-Atlas_paper_experimental_setup_GPU_year_2024.jsonl b/data/sampled_jsons/OS-Atlas_paper_experimental_setup_GPU_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4dd40e682e0789f2c9aff2fbc8c4d116f2421b3d
--- /dev/null
+++ b/data/sampled_jsons/OS-Atlas_paper_experimental_setup_GPU_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "OS-ATLAS: A Foundation Action Model for Generalist GUI ...", "date": "", "ddg_snippet": "30 Oct 2024 — we developed OS-Atlas —a foundational GUI action model that excels at GUI grounding and OOD agentic tasks through innovations in both data and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.23218v1", "content": "30 Oct 2024 — we developed OS-Atlas —a foundational GUI action model that excels at GUI grounding and OOD agentic tasks through innovations in both data and ..."}
+{"idx": 1, "title": "OS-ATLAS: Foundation Action Model for Generalist GUI ...", "date": "", "ddg_snippet": "by Z Wu · Cited by 120 — Summary: This paper proposes a high-performance GUI Agent, OS-ATLAS, trained on a synthetic grounding dataset and operating within a unified action space. ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=n9PDaFNi8t", "content": "by Z Wu · Cited by 120 — Summary: This paper proposes a high-performance GUI Agent, OS-ATLAS, trained on a synthetic grounding dataset and operating within a unified action space. ..."}
+{"idx": 2, "title": "Large scale multi-GPU based parallel traffic simulation for ...", "date": "", "ddg_snippet": "by X Jiang · 2024 · Cited by 12 — LPSim utilizes a multi- GPU architecture to simulate extensive and dynamic traffic networks with high fidelity and reduced computation time.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0968090X24003942", "content": "by X Jiang · 2024 · Cited by 12 — LPSim utilizes a multi- GPU architecture to simulate extensive and dynamic traffic networks with high fidelity and reduced computation time."}
+{"idx": 3, "title": "A Cross-Platform, WebGPU-Based 3D Engine for Real-Time ...", "date": "", "ddg_snippet": "7 Sept 2025 — This paper introduces wgpuEngine, an ... research and experimental setups oriented to take advantage of the WebGPU emerging technology.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3746237.3746305", "content": "7 Sept 2025 — This paper introduces wgpuEngine, an ... research and experimental setups oriented to take advantage of the WebGPU emerging technology."}
+{"idx": 4, "title": "A graphical, interactive and GPU-enabled workflow to ...", "date": "", "ddg_snippet": "by S Reddy · 2021 · Cited by 15 — Experimental setup for benchmarking . Guppy GPU was benchmarked on an AWS g4nd.4xlarge virtual machine instance with a NVIDIA Tesla T4 GPU ...", "subpage_snippet": "", "source": "www.biorxiv.org", "link": "https://www.biorxiv.org/content/10.1101/2021.05.11.443665v1.full-text", "content": "by S Reddy · 2021 · Cited by 15 — Experimental setup for benchmarking . Guppy GPU was benchmarked on an AWS g4nd.4xlarge virtual machine instance with a NVIDIA Tesla T4 GPU ..."}
+{"idx": 5, "title": "XBOUND: Exploring the Capability Boundaries of Device- ...", "date": "", "ddg_snippet": "27 May 2025 — We extensively evaluate the Android Control Tree Dataset on the OS - Atlas and UI-TARS series. The experimental setup is presented in Sec. 4.1.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.21279v1", "content": "27 May 2025 — We extensively evaluate the Android Control Tree Dataset on the OS - Atlas and UI-TARS series. The experimental setup is presented in Sec. 4.1."}
+{"idx": 6, "title": "What Limits Virtual Agent Application? OmniBench", "date": "", "ddg_snippet": "follow the training details provided in the OS - Atlas paper and adopt the same experimental setup to train our backbone mod- els: OS - Atlas -4B and UGround-7B-V1.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/d5d3691187b9f32f92e8e287e5005d40aa429f80.pdf", "content": "follow the training details provided in the OS - Atlas paper and adopt the same experimental setup to train our backbone mod- els: OS - Atlas -4B and UGround-7B-V1."}
+{"idx": 7, "title": "Runtime Systems and Scheduling Support for High-End ...", "date": "", "ddg_snippet": "by V Trichy Ravi · 2012 — In all the following experiments , a set of applications are scheduled on two GPUs . The configuration (X, Y) on each bar represents the following. X denotes ...", "subpage_snippet": "", "source": "etd.ohiolink.edu", "link": "https://etd.ohiolink.edu/acprod/odb_etd/ws/send_file/send?accession=osu1338324367&disposition=inline", "content": "by V Trichy Ravi · 2012 — In all the following experiments , a set of applications are scheduled on two GPUs . The configuration (X, Y) on each bar represents the following. X denotes ..."}
+{"idx": 8, "title": "Hierarchical Partitioning for Quantum Circuit Simulation on ...", "date": "", "ddg_snippet": "19 Dec 2024 — We have presented Atlas , an efficient and scalable Schrödinger-style distributed multi-GPU quantum circuit simulator. It uses a hierarchical ...", "subpage_snippet": "", "source": "www.cs.cmu.edu", "link": "https://www.cs.cmu.edu/~csd-phd-blog/2024/atlas/", "content": "19 Dec 2024 — We have presented Atlas , an efficient and scalable Schrödinger-style distributed multi-GPU quantum circuit simulator. It uses a hierarchical ..."}
+{"idx": 9, "title": "From CUDA to OpenCL: Towards a performance-portable ...", "date": "", "ddg_snippet": "by P Du · 2012 · Cited by 496 — In this work, we evaluate OpenCL as a programming tool for developing performance-portable applications for GPGPU.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/abs/pii/S0167819111001335", "content": "by P Du · 2012 · Cited by 496 — In this work, we evaluate OpenCL as a programming tool for developing performance-portable applications for GPGPU."}
diff --git a/data/sampled_jsons/OS-Atlas_paper_implementation_details_GPU_year_2024.jsonl b/data/sampled_jsons/OS-Atlas_paper_implementation_details_GPU_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..95e2e25afc00a63cca8b135c8f7ada00d562adcf
--- /dev/null
+++ b/data/sampled_jsons/OS-Atlas_paper_implementation_details_GPU_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - Atlas- OS / Atlas : An open and lightweight modification to...", "date": "", "ddg_snippet": "Atlas- OS / Atlas Public. Uh oh!Atlas strikes a balance between performance and compatibility. It implements numerous meaningful changes to improve Windows performance and responsiveness without breaking essential features.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Atlas-OS/Atlas", "content": "Atlas- OS / Atlas Public. Uh oh!Atlas strikes a balance between performance and compatibility. It implements numerous meaningful changes to improve Windows performance and responsiveness without breaking essential features."}
+{"idx": 1, "title": "Мод Windows 10 для игр: тестирую Atlas OS , из которого вырезали...", "date": "", "ddg_snippet": "В угоду скорости в Atlas OS вырезали некоторые компоненты системы. Разбираюсь, насколько удобно пользоваться такой ОС.", "subpage_snippet": "", "source": "trashbox.ru", "link": "https://trashbox.ru/link/atlas-os-obzor", "content": "В угоду скорости в Atlas OS вырезали некоторые компоненты системы. Разбираюсь, насколько удобно пользоваться такой ОС."}
+{"idx": 2, "title": "I tried a \"kinder\" Windows 11 experience with AtlasOS, and this is how...", "date": "", "ddg_snippet": "A tablet running Atlas OS showing privacy and power settings on screen.Once this is all done and you're on the desktop, you need to download the two components from the AtlasOS website. You have the AME Wizard and the Atlas Playbook.", "subpage_snippet": "", "source": "www.xda-developers.com", "link": "https://www.xda-developers.com/tried-kinder-windows-experience-atlasos-how-should-be/", "content": "A tablet running Atlas OS showing privacy and power settings on screen.Once this is all done and you're on the desktop, you need to download the two components from the AtlasOS website. You have the AME Wizard and the Atlas Playbook."}
+{"idx": 3, "title": "GPU Test - TheTest", "date": "", "ddg_snippet": "Test your device's graphics capabilities with our comprehensive GPU test. Supports both WebGL and WebGPU rendering pipelines with real-time 3D rendering and performance monitoring.", "subpage_snippet": "", "source": "thetest.com", "link": "https://thetest.com/tests/gpu", "content": "Test your device's graphics capabilities with our comprehensive GPU test. Supports both WebGL and WebGPU rendering pipelines with real-time 3D rendering and performance monitoring."}
+{"idx": 4, "title": "How to Install AtlasOS on Your Windows PC? [8 Easy Steps]", "date": "", "ddg_snippet": "Installing a new operating system can sometimes feel like trying to solve a Rubik’s Cube—it’s colorful and can be a bit puzzling, but oh-so-satisfying once everything lines up.You’ve successfully installed Atlas OS on Windows 11. AtlasOS. Step 7: Post-Installation.", "subpage_snippet": "", "source": "appuals.com", "link": "https://appuals.com/how-to-install-atlasos/", "content": "Installing a new operating system can sometimes feel like trying to solve a Rubik’s Cube—it’s colorful and can be a bit puzzling, but oh-so-satisfying once everything lines up.You’ve successfully installed Atlas OS on Windows 11. AtlasOS. Step 7: Post-Installation."}
+{"idx": 5, "title": "View of GPU Implementation of Thermal Aware 3D IC Floorplanning", "date": "", "ddg_snippet": "Return to Article Details GPU Implementation of Thermal Aware 3D IC Floorplanning Download Download PDF.", "subpage_snippet": "", "source": "cspub-ijcisim.org", "link": "https://cspub-ijcisim.org/index.php/ijcisim/article/view/405/389", "content": "Return to Article Details GPU Implementation of Thermal Aware 3D IC Floorplanning Download Download PDF."}
+{"idx": 6, "title": "Real-Time Atmospheric Scattering - Graphics and GPU Programming...", "date": "", "ddg_snippet": "Implements a real-time rendering algorithm based on the SIGGRAPH paper \"Display of The Earth Taking into Account Atmospheric Scattering\".It runs between 50-100 FPS on my system , and it should be easy to make it go a good bit faster.", "subpage_snippet": "", "source": "rsdn.gamedev.net", "link": "https://rsdn.gamedev.net/tutorials/programming/graphics/real-time-atmospheric-scattering-r2093/", "content": "Implements a real-time rendering algorithm based on the SIGGRAPH paper \"Display of The Earth Taking into Account Atmospheric Scattering\".It runs between 50-100 FPS on my system , and it should be easy to make it go a good bit faster."}
+{"idx": 7, "title": "Atlas OS – оптимизированная Windows для геймеров", "date": "", "ddg_snippet": "Как установить Atlas OS . Процедура установки сборки включает два этапа: переустановка или чистая установка официальной версии Windows 10 или 11 и запуск специального программного обеспечения, которое произведет все необходимые изменения.", "subpage_snippet": "", "source": "www.white-windows.ru", "link": "https://www.white-windows.ru/atlas-os-optimizirovannaya-windows-dlya-gejmerov/", "content": "Как установить Atlas OS . Процедура установки сборки включает два этапа: переустановка или чистая установка официальной версии Windows 10 или 11 и запуск специального программного обеспечения, которое произведет все необходимые изменения."}
+{"idx": 8, "title": "Installing Atlas OS on Windows 11... - Technical Explore", "date": "", "ddg_snippet": "Atlas OS might be the solution you're looking for. This guide will walk you through everything you need to know about Atlas OS , from installation to optimization, helping you squeeze every last drop of performance out of your Windows 11 system .", "subpage_snippet": "", "source": "www.technicalexplore.com", "link": "https://www.technicalexplore.com/tech/installing-atlas-os-on-windows-11-a-gamers-guide-to-boosting-performance", "content": "Atlas OS might be the solution you're looking for. This guide will walk you through everything you need to know about Atlas OS , from installation to optimization, helping you squeeze every last drop of performance out of your Windows 11 system ."}
+{"idx": 9, "title": "Interstellar object 3I/ ATLAS brightening quickly", "date": "", "ddg_snippet": "[3I/ ATLAS ] may nonetheless be an old object, consistent with ejection from a long-lived primordial planetesimal disk in an early-formed system . The scientists published their not-yet peer-reviewed paper on arXiv on September 10, 2025.", "subpage_snippet": "", "source": "earthsky.org", "link": "https://earthsky.org/space/new-interstellar-object-candidate-heading-toward-the-sun-a11pl3z/", "content": "[3I/ ATLAS ] may nonetheless be an old object, consistent with ejection from a long-lived primordial planetesimal disk in an early-formed system . The scientists published their not-yet peer-reviewed paper on arXiv on September 10, 2025."}
diff --git a/data/sampled_jsons/Olah_et_al._2020_circuit_analysis_transformers.jsonl b/data/sampled_jsons/Olah_et_al._2020_circuit_analysis_transformers.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e2e5f714bb31c5d4236107fcd677b9a3ecd86cd6
--- /dev/null
+++ b/data/sampled_jsons/Olah_et_al._2020_circuit_analysis_transformers.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Finding Transformer Circuits with Edge Pruning", "date": "", "ddg_snippet": "denote the set of edges in the circuit and full model, respectively ( Olah et al ., 2020 ) . How do we model a Transformer with a missing edge.Does circuit analysis interpretability scale? Evidence from multiple choice capabilities in Chinchilla, 2023. Lindner et al .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.16778v1", "content": "denote the set of edges in the circuit and full model, respectively ( Olah et al ., 2020 ) . How do we model a Transformer with a missing edge.Does circuit analysis interpretability scale? Evidence from multiple choice capabilities in Chinchilla, 2023. Lindner et al ."}
+{"idx": 1, "title": "A Mathematical Framework for Transformer Circuits", "date": "", "ddg_snippet": "Black et al . provide significant evidence for typical neurons in transformer language models being polysemantic.Elhage, et al ., \"A Mathematical Framework for Transformer Circuits \", Transformer Circuits Thread, 2021.", "subpage_snippet": "", "source": "transformer-circuits.pub", "link": "https://transformer-circuits.pub/2021/framework/index.html", "content": "Black et al . provide significant evidence for typical neurons in transformer language models being polysemantic.Elhage, et al ., \"A Mathematical Framework for Transformer Circuits \", Transformer Circuits Thread, 2021."}
+{"idx": 2, "title": "GitHub - vilasmenon/Microglia_ Olah _ et _ al _ 2020 : Supplementary files...", "date": "", "ddg_snippet": "Supplementary files for Olah et al . 2020 paper.vilasmenon/Microglia_ Olah _ et _ al _ 2020 . This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/vilasmenon/Microglia_Olah_et_al_2020", "content": "Supplementary files for Olah et al . 2020 paper.vilasmenon/Microglia_ Olah _ et _ al _ 2020 . This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository."}
+{"idx": 3, "title": "Olah et al ., 2020 , control (TLE) data", "date": "", "ddg_snippet": "Dear all, we are trying to re- analyze the scRNAseq data from Olah et al ., 2020 (https://www.nature.com/articles/s41467-020-19737-2) but the link indicated in the original manuscript leads us to a folder ( https://www.synapse.org/#!Synapse:syn21438358) where we cannot...", "subpage_snippet": "", "source": "www.synapse.org", "link": "https://www.synapse.org/Synapse:syn2580853/discussion/threadId=10352", "content": "Dear all, we are trying to re- analyze the scRNAseq data from Olah et al ., 2020 (https://www.nature.com/articles/s41467-020-19737-2) but the link indicated in the original manuscript leads us to a folder ( https://www.synapse.org/#!Synapse:syn21438358) where we cannot..."}
+{"idx": 4, "title": "A Practical Review of Mechanistic... | Read Paper on Bytez", "date": "", "ddg_snippet": "between features ( Olah et al ., 2020 ), subsequent studies have generalized them as connections between the activation outputs of transformer components (Olsson et al ., 2022; Wang et al ., 2022a), where interpreting individual transformer components becomes part of the circuit ...", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/arxiv/2407.02646/paper", "content": "between features ( Olah et al ., 2020 ), subsequent studies have generalized them as connections between the activation outputs of transformer components (Olsson et al ., 2022; Wang et al ., 2022a), where interpreting individual transformer components becomes part of the circuit ..."}
+{"idx": 5, "title": "(PDF) The underlying structures of self-attention: symmetry...", "date": "", "ddg_snippet": "Bricken et al .,2023], circuit analysis to interpret. Transformer components [ Olah et al ., 2020 ,Elhage. et al .,2021, Olah ,2022], and techniques like the. logit lens to analyze self-attention mechanisms [Geva.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/389091272_The_underlying_structures_of_self-attention_symmetry_directionality_and_emergent_dynamics_in_Transformer_training", "content": "Bricken et al .,2023], circuit analysis to interpret. Transformer components [ Olah et al ., 2020 ,Elhage. et al .,2021, Olah ,2022], and techniques like the. logit lens to analyze self-attention mechanisms [Geva."}
+{"idx": 6, "title": "Mechanistic?", "date": "", "ddg_snippet": "The term mechanistic interpretability was coined by Chris Olah and first publicly used in the Dis-till.pub Circuits thread, a series of blogposts by OpenAI researchers between March 2020 –April 2021. The first post ( Olah et al ., 2020 )...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2024.blackboxnlp-1.30.pdf", "content": "The term mechanistic interpretability was coined by Chris Olah and first publicly used in the Dis-till.pub Circuits thread, a series of blogposts by OpenAI researchers between March 2020 –April 2021. The first post ( Olah et al ., 2020 )..."}
+{"idx": 7, "title": "Distill: Early Vision in CNNs | Dynamically Typed", "date": "", "ddg_snippet": "( Olah et al ., 2020 ). For a few of the categories, like black & white and small circle detectors in mixed3a, and boundary and fur detectors in mixed3b, the article also investigates the “ circuits ” that formed them. Such circuits show how strongly the presence of a feature in the input...", "subpage_snippet": "", "source": "dynamicallytyped.com", "link": "https://dynamicallytyped.com/stories/2020/distill-early-vision-in-cnns/", "content": "( Olah et al ., 2020 ). For a few of the categories, like black & white and small circle detectors in mixed3a, and boundary and fur detectors in mixed3b, the article also investigates the “ circuits ” that formed them. Such circuits show how strongly the presence of a feature in the input..."}
+{"idx": 8, "title": "Interpretability in Parameter Space: Minimizing", "date": "", "ddg_snippet": "Further analyses . Training details and hyperparameters. Interpretability in Parameter Space: Minimizing Mechanistic Description Length with.independent of architecture [Li et al ., 2015, Olah et al ., 2020 b], such as convolutional networks, transformers , state space models, and more.", "subpage_snippet": "", "source": "aarnphm.xyz", "link": "https://aarnphm.xyz/thoughts/papers/Interpretability+in+Parameter+Space-+Minimizing+Mechanistic+Description+Length+with+Attribution-based+Parameter+Decomposition.pdf", "content": "Further analyses . Training details and hyperparameters. Interpretability in Parameter Space: Minimizing Mechanistic Description Length with.independent of architecture [Li et al ., 2015, Olah et al ., 2020 b], such as convolutional networks, transformers , state space models, and more."}
+{"idx": 9, "title": "Week 5 - Interpretability and Governance — DAISI", "date": "", "ddg_snippet": "Olah et al . ( 2020 ) explore how neural circuits build up representations of high-level features out of lower-level features.Further readings on interpretability: A mathematical framework for transformer circuits (Elhage et al ., 2021) (90 mins).", "subpage_snippet": "", "source": "www.delftaisafety.org", "link": "https://www.delftaisafety.org/week-5-interpretability-and-governance", "content": "Olah et al . ( 2020 ) explore how neural circuits build up representations of high-level features out of lower-level features.Further readings on interpretability: A mathematical framework for transformer circuits (Elhage et al ., 2021) (90 mins)."}
diff --git a/data/sampled_jsons/OmniBench_paper_appendix_supplementary_materials_experimental_setup_GPU.jsonl b/data/sampled_jsons/OmniBench_paper_appendix_supplementary_materials_experimental_setup_GPU.jsonl
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "OmniBench : Towards The Future of", "date": "", "ddg_snippet": "OmniBench . Benchmark Design. Annotation Protocol. OmniInstruct. Experiment Settings .4The results of pure text (I′, A′) setting are placed at Table 8, Appendix A. (a) (c). Figure 4. The Performance Changes Brought By Textual Alterna-tives.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2409.15272", "content": "OmniBench . Benchmark Design. Annotation Protocol. OmniInstruct. Experiment Settings .4The results of pure text (I′, A′) setting are placed at Table 8, Appendix A. (a) (c). Figure 4. The Performance Changes Brought By Textual Alterna-tives."}
+{"idx": 1, "title": "GitHub - multimodal-art-projection/ OmniBench : A project for tri-modal...", "date": "", "ddg_snippet": "This table shows the omni -language models in the full evaluation setting in OmniBench , with the \"Image & Audio\", \"Audio\", and \"Image\" as input contexts and accuracy as metric.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/multimodal-art-projection/OmniBench", "content": "This table shows the omni -language models in the full evaluation setting in OmniBench , with the \"Image & Audio\", \"Audio\", and \"Image\" as input contexts and accuracy as metric."}
+{"idx": 2, "title": "GPU System Requirements for Running DeepSeek-R1", "date": "", "ddg_snippet": "Quantization and distributed GPU setups allow them to handle their massive parameter counts. VRAM Requirements for DeepSeek-R1.Multi- GPU setup (e.g., NVIDIA RTX 4090 x2). DeepSeek-R1-Distill-Qwen-32B.", "subpage_snippet": "", "source": "apxml.com", "link": "https://apxml.com/posts/gpu-requirements-deepseek-r1", "content": "Quantization and distributed GPU setups allow them to handle their massive parameter counts. VRAM Requirements for DeepSeek-R1.Multi- GPU setup (e.g., NVIDIA RTX 4090 x2). DeepSeek-R1-Distill-Qwen-32B."}
+{"idx": 3, "title": "How to Setup GPU for Deep Learning [Full Guide from...] - GeekChamp", "date": "", "ddg_snippet": "Setting up a GPU for deep learning from scratch is a methodical process that, when done properly, can unlock immense potential in your AI projects. It requires an understanding of hardware compatibility, precise software installations, and ongoing maintenance.", "subpage_snippet": "", "source": "geekchamp.com", "link": "https://geekchamp.com/how-to-setup-gpu-for-deep-learning-full-guide-from-scratch/", "content": "Setting up a GPU for deep learning from scratch is a methodical process that, when done properly, can unlock immense potential in your AI projects. It requires an understanding of hardware compatibility, precise software installations, and ongoing maintenance."}
+{"idx": 4, "title": "Using both GPU on an Mac Pro 2013", "date": "", "ddg_snippet": "There is no system-wide CrossFire X equivalent that will automatically split up rendering tasks across both GPUs . By default, one GPU is setup for display duties while the other is used exclusively for GPU compute workloads.", "subpage_snippet": "", "source": "www.physicsforums.com", "link": "https://www.physicsforums.com/threads/using-both-gpu-on-an-mac-pro-2013.1058852/", "content": "There is no system-wide CrossFire X equivalent that will automatically split up rendering tasks across both GPUs . By default, one GPU is setup for display duties while the other is used exclusively for GPU compute workloads."}
+{"idx": 5, "title": "Optimizing Roblox Performance: Advanced GPU Utilization - thinglabs", "date": "", "ddg_snippet": "What settings should I adjust to improve Roblox GPU performance? Players can adjust graphics settings in the game options. These include texture quality, lighting, and shadow detail. Each setting affects how the GPU renders the game. Lower settings lead to better performance.", "subpage_snippet": "", "source": "thinglabs.io", "link": "https://thinglabs.io/optimizing-roblox-performance-advanced-gpu-utilization", "content": "What settings should I adjust to improve Roblox GPU performance? Players can adjust graphics settings in the game options. These include texture quality, lighting, and shadow detail. Each setting affects how the GPU renders the game. Lower settings lead to better performance."}
+{"idx": 6, "title": "How to Fix Lag, CPU Freeze, and FPS Issues in... - Gamer Tag Zero", "date": "", "ddg_snippet": "To further optimize performance, you can adjust the DirectX version used by PoE 2. Depending on your GPU model, here are the best settings : For NVIDIA RTX 30 series or higher: Set DXLevel to DX12 in the in-game graphics settings .", "subpage_snippet": "", "source": "gamertagzero.com", "link": "https://gamertagzero.com/how-to-fix-lag-cpu-freeze-and-fps-issues-in-path-of-exile-2-2025/", "content": "To further optimize performance, you can adjust the DirectX version used by PoE 2. Depending on your GPU model, here are the best settings : For NVIDIA RTX 30 series or higher: Set DXLevel to DX12 in the in-game graphics settings ."}
+{"idx": 7, "title": "Telegram: View @efizika", "date": "", "ddg_snippet": "Equipment and materials : experimental setup for studyingmotion on inclined planes with various surfaces, support stand with clamp and holder, wooden block with adjustable mass, protractor, analog stopwatch,control unit .", "subpage_snippet": "", "source": "t.me", "link": "https://t.me/efizika/424", "content": "Equipment and materials : experimental setup for studyingmotion on inclined planes with various surfaces, support stand with clamp and holder, wooden block with adjustable mass, protractor, analog stopwatch,control unit ."}
+{"idx": 8, "title": "Best Settings For AMD Radeon Adrenalin - FPS Bump 2025", "date": "", "ddg_snippet": "If you’ve recently switched to an AMD Radeon GPU -or you’re a seasoned “reed team” enthusiast you’ve probably found the sheer number of Radeon Adrenalin settings a bit daunting. From Radeon AntiLag to Fluid Motion Frames 2, every option promises something: lower latency...", "subpage_snippet": "", "source": "topdailyblog.com", "link": "https://topdailyblog.com/2025/08/26/best-settings-for-amd-radeon/", "content": "If you’ve recently switched to an AMD Radeon GPU -or you’re a seasoned “reed team” enthusiast you’ve probably found the sheer number of Radeon Adrenalin settings a bit daunting. From Radeon AntiLag to Fluid Motion Frames 2, every option promises something: lower latency..."}
+{"idx": 9, "title": "OpenVINO 2025.3 — OpenVINO™ documentation — Version(2025)", "date": "", "ddg_snippet": "This guide introduces installation and learning materials for Intel® Distribution of OpenVINO™ toolkit.", "subpage_snippet": "", "source": "docs.openvino.ai", "link": "https://docs.openvino.ai/", "content": "This guide introduces installation and learning materials for Intel® Distribution of OpenVINO™ toolkit."}
diff --git a/data/sampled_jsons/On_Differential_Privacy_for_Adaptively_Solving_Search_Problems_via_Sketching_PMLR_267_2025_ICML.jsonl b/data/sampled_jsons/On_Differential_Privacy_for_Adaptively_Solving_Search_Problems_via_Sketching_PMLR_267_2025_ICML.jsonl
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index 0000000000000000000000000000000000000000..9b6bd4095409e93b22f0c49921a1dd49cd02b074
--- /dev/null
+++ b/data/sampled_jsons/On_Differential_Privacy_for_Adaptively_Solving_Search_Problems_via_Sketching_PMLR_267_2025_ICML.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "On Differential Privacy for Adaptively Solving Search Problems via ...", "date": "", "ddg_snippet": "In this paper, we investigate the use of differential privacy for adaptive queries to search problems , which are significantly more challenging since the responses to queries can reveal much more about the internal randomness than a single numerical query.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2506.05503", "content": "In this paper, we investigate the use of differential privacy for adaptive queries to search problems , which are significantly more challenging since the responses to queries can reveal much more about the internal randomness than a single numerical query."}
+{"idx": 1, "title": "Track: Oral 4C Privacy and Uncertainty Quantification", "date": "", "ddg_snippet": "In this paper we investigate the use of differential privacy for adaptive queries to {\\it search } problems , which are significantly more challenging since the responses to queries can reveal much more about the internal randomness than a single numerical query.", "subpage_snippet": "", "source": "dev.icml.cc", "link": "https://dev.icml.cc/virtual/2025/session/46911", "content": "In this paper we investigate the use of differential privacy for adaptive queries to {\\it search } problems , which are significantly more challenging since the responses to queries can reveal much more about the internal randomness than a single numerical query."}
+{"idx": 2, "title": "On Differential Privacy for Adaptively Solving Search Problems via ...", "date": "", "ddg_snippet": "In this paper we investigate the use of differential privacy for adaptive queries to {\\it search } problems , which are significantly more challenging since the responses to queries can reveal much more about the internal randomness than a single numerical query.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=kEn7Wt6Yj2", "content": "In this paper we investigate the use of differential privacy for adaptive queries to {\\it search } problems , which are significantly more challenging since the responses to queries can reveal much more about the internal randomness than a single numerical query."}
+{"idx": 3, "title": "Sketching meets differential privacy | Proceedings of the 40th ...", "date": "", "ddg_snippet": "Hopkins, S. B., Kamath, G., and Majid, M. Efficient mean estimation with pure differential privacy via a sum-of-squares exponential mechanism. In Proceedings of the 54th Annual ACM SIGACT Symposium on Theory of Computing, STOC 2022, 2022.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.5555/3618408.3619751", "content": "Hopkins, S. B., Kamath, G., and Majid, M. Efficient mean estimation with pure differential privacy via a sum-of-squares exponential mechanism. In Proceedings of the 54th Annual ACM SIGACT Symposium on Theory of Computing, STOC 2022, 2022."}
+{"idx": 4, "title": "On Differential Privacy for Adaptively Solving Search Problems via ...", "date": "", "ddg_snippet": "In this paper, we investigate the use of diferential privacy for adaptive queries to search problems , which are significantly more challenging since the responses to queries can reveal much more about the internal randomness than a single numerical query.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2506.05503", "content": "In this paper, we investigate the use of diferential privacy for adaptive queries to search problems , which are significantly more challenging since the responses to queries can reveal much more about the internal randomness than a single numerical query."}
+{"idx": 5, "title": "On Differential Privacy for Adaptively Solving Search Problems via ...", "date": "", "ddg_snippet": "We identify key parameters to these problems , such as the number of c𝑐citalic_c-approximate near neighbors and the matrix condition number, and use different differential privacy techniques to design algorithms returning the solution vector with memory and time depending on these parameters.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.05503v1", "content": "We identify key parameters to these problems , such as the number of c𝑐citalic_c-approximate near neighbors and the matrix condition number, and use different differential privacy techniques to design algorithms returning the solution vector with memory and time depending on these parameters."}
+{"idx": 6, "title": "ICML 2025 5mins video: On Differential Privacy for Adaptively Solving ...", "date": "", "ddg_snippet": "ICML 2025 5mins video: On Differential Privacy for Adaptively Solving Search Problems via Sketching , 视频播放量 914、弹幕量 0、点赞数 15、投硬币枚数 4、收藏人数 5、转发人数 3, 视频作者 simapofang, 作者简介 我是一个华盛顿大学大一新生，希望哥哥姐姐们多多关注，相关视频 ...", "subpage_snippet": "", "source": "www.bilibili.com", "link": "https://www.bilibili.com/video/BV1ZhMDzkEWE/", "content": "ICML 2025 5mins video: On Differential Privacy for Adaptively Solving Search Problems via Sketching , 视频播放量 914、弹幕量 0、点赞数 15、投硬币枚数 4、收藏人数 5、转发人数 3, 视频作者 simapofang, 作者简介 我是一个华盛顿大学大一新生，希望哥哥姐姐们多多关注，相关视频 ..."}
+{"idx": 7, "title": "ICML-2025-Papers/sections/2025/main/privacy-and-uncertainty ...", "date": "", "ddg_snippet": "ICML 2025 Papers: Dive into cutting-edge research from the premier machine learning conference. Stay current with breakthroughs in deep learning, generative AI, optimization, reinforcement learning, and beyond.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/DmitryRyumin/ICML-2025-Papers/blob/main/sections/2025/main/privacy-and-uncertainty-quantification.md", "content": "ICML 2025 Papers: Dive into cutting-edge research from the premier machine learning conference. Stay current with breakthroughs in deep learning, generative AI, optimization, reinforcement learning, and beyond."}
+{"idx": 8, "title": "Data Structures and Algorithms", "date": "", "ddg_snippet": "... on Applied and Computational Discrete Algorithms (ACDA) 2025 ... Title: On Differential Privacy for Adaptively Solving Search Problems via Sketching", "subpage_snippet": "", "source": "arxiv.org", "link": "http://arxiv.org/list/cs.DS/recent?skip=9&show=25", "content": "... on Applied and Computational Discrete Algorithms (ACDA) 2025 ... Title: On Differential Privacy for Adaptively Solving Search Problems via Sketching"}
+{"idx": 9, "title": "Lichen Zhang's Homepage", "date": "", "ddg_snippet": "I'm particularly excited about problems in optimization, sketching , differential privacy and quantum computing. Recently, I've been working on theoretical and practical approaches to improve the efficiency of large language models (LLM). I'm supported by a Simons Dissertation Fellowship in Mathematics.", "subpage_snippet": "", "source": "lczh.github.io", "link": "https://lczh.github.io/", "content": "I'm particularly excited about problems in optimization, sketching , differential privacy and quantum computing. Recently, I've been working on theoretical and practical approaches to improve the efficiency of large language models (LLM). I'm supported by a Simons Dissertation Fellowship in Mathematics."}
diff --git a/data/sampled_jsons/On_a_Connection_Between_Imitation_Learning_and_RLHF_Table_4_LLama3-8B-Base_MATH_benchmark_year_2024.jsonl b/data/sampled_jsons/On_a_Connection_Between_Imitation_Learning_and_RLHF_Table_4_LLama3-8B-Base_MATH_benchmark_year_2024.jsonl
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index 0000000000000000000000000000000000000000..ebd8b714efc86862cb0194d4e451bdf11bad84a3
--- /dev/null
+++ b/data/sampled_jsons/On_a_Connection_Between_Imitation_Learning_and_RLHF_Table_4_LLama3-8B-Base_MATH_benchmark_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Business Technology Products, Services & Solutions - Connection", "date": "", "ddg_snippet": "Let the experts at Connection listen to your needs, understand your goals, and deliver IT solutions and services designed around you. Because what you do matters.", "subpage_snippet": "", "source": "www.connection.com", "link": "https://www.connection.com/", "content": "Let the experts at Connection listen to your needs, understand your goals, and deliver IT solutions and services designed around you. Because what you do matters."}
+{"idx": 1, "title": "Careers - Connection", "date": "", "ddg_snippet": "Discover what unites our team and makes Connection a one-of- a -kind workplace. If you’re looking for a career where you’ll be surrounded by people who love to dream big, dig deep, and support each other, you’ll find your place at Connection .", "subpage_snippet": "", "source": "www.connection.com", "link": "https://www.connection.com/content/careers", "content": "Discover what unites our team and makes Connection a one-of- a -kind workplace. If you’re looking for a career where you’ll be surrounded by people who love to dream big, dig deep, and support each other, you’ll find your place at Connection ."}
+{"idx": 2, "title": "Connection (CNXN) Reports Fourth Quarter and Full Year 2024...", "date": "", "ddg_snippet": "Feb 5, 2025 · Connection –Public Sector Solutions (800.800.0019), is a rapid-response provider of IT products and services to federal, state, and local government agencies and educational institutions through specialized account managers, publications, and online at www. connection .com/publicsector.", "subpage_snippet": "", "source": "ir.connection.com", "link": "https://ir.connection.com/news-releases/news-release-details/connection-cnxn-reports-fourth-quarter-and-full-year-2024", "content": "Feb 5, 2025 · Connection –Public Sector Solutions (800.800.0019), is a rapid-response provider of IT products and services to federal, state, and local government agencies and educational institutions through specialized account managers, publications, and online at www. connection .com/publicsector."}
+{"idx": 3, "title": "Enterprise - Connection", "date": "", "ddg_snippet": "Connection is an industry-leading, global technology solutions provider, focused on driving technology initiatives with large corporations. We connect our clients with the technology they need to not only solve their challenges, but also to transform their businesses.", "subpage_snippet": "", "source": "www.connection.com", "link": "https://www.connection.com/pccb2b/enterprise", "content": "Connection is an industry-leading, global technology solutions provider, focused on driving technology initiatives with large corporations. We connect our clients with the technology they need to not only solve their challenges, but also to transform their businesses."}
+{"idx": 4, "title": "Customer Care - Connection", "date": "", "ddg_snippet": "Get answers to your technical questions. 30-Day Free installation and diagnostic assistance for most products purchased from PC Connection . In some cases, you may be referred to the manufacturer in order to ensure the best possible support.", "subpage_snippet": "", "source": "www.connection.com", "link": "https://www.connection.com/IPA/Content/Support", "content": "Get answers to your technical questions. 30-Day Free installation and diagnostic assistance for most products purchased from PC Connection . In some cases, you may be referred to the manufacturer in order to ensure the best possible support."}
+{"idx": 5, "title": "Contact Us - Apple Authorized Reseller - Connection", "date": "", "ddg_snippet": "Connection , a Fortune 1000 company, calms the confusion of IT by delivering industry-leading technology solutions to enhance growth, elevate productivity, and empower innovation.", "subpage_snippet": "", "source": "www.connection.com", "link": "https://www.connection.com/IPA/Content/About/Contact", "content": "Connection , a Fortune 1000 company, calms the confusion of IT by delivering industry-leading technology solutions to enhance growth, elevate productivity, and empower innovation."}
+{"idx": 6, "title": "Artificial Intelligence - Connection", "date": "", "ddg_snippet": "Connection helps enterprises navigate multicloud strategies to modernize and build the infrastructure they need for data- and AI-centric workloads and face the future with confidence.", "subpage_snippet": "", "source": "www.connection.com", "link": "https://www.connection.com/solutions-services/artificial-intelligence", "content": "Connection helps enterprises navigate multicloud strategies to modernize and build the infrastructure they need for data- and AI-centric workloads and face the future with confidence."}
+{"idx": 7, "title": "Locations - Connection", "date": "", "ddg_snippet": "Connection has grown a great deal since the Company's founding in 1982. We now operate facilities in nine states across the U.S.—but we remain close to our Granite State roots.", "subpage_snippet": "", "source": "www.connection.com", "link": "https://www.connection.com/content/careers/locations", "content": "Connection has grown a great deal since the Company's founding in 1982. We now operate facilities in nine states across the U.S.—but we remain close to our Granite State roots."}
+{"idx": 8, "title": "IT Solutions & Services - Connection", "date": "", "ddg_snippet": "Connection provides the end-to-end technology solutions and services your organization, its people, and its customers need to function at their best. From digital workplace solutions to modern IT infrastructure and multicloud to supply chain and lifecycle, we have you covered.", "subpage_snippet": "", "source": "www.connection.com", "link": "https://www.connection.com/solutions-services", "content": "Connection provides the end-to-end technology solutions and services your organization, its people, and its customers need to function at their best. From digital workplace solutions to modern IT infrastructure and multicloud to supply chain and lifecycle, we have you covered."}
+{"idx": 9, "title": "Company History - Connection", "date": "", "ddg_snippet": "Discover how Connection started business back in 1982 and learn about the innovations, milestones, and memories we’ve made along the way.", "subpage_snippet": "", "source": "www.connection.com", "link": "https://www.connection.com/content/about/company-history", "content": "Discover how Connection started business back in 1982 and learn about the innovations, milestones, and memories we’ve made along the way."}
diff --git a/data/sampled_jsons/On_a_Connection_Between_Imitation_Learning_and_RLHF_sitearxiv.org_Table_4_LLama3-8B-Base_MATH_year_2024.jsonl b/data/sampled_jsons/On_a_Connection_Between_Imitation_Learning_and_RLHF_sitearxiv.org_Table_4_LLama3-8B-Base_MATH_year_2024.jsonl
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index 0000000000000000000000000000000000000000..e5742e29a3537d072ef13152973afb4087bebcc7
--- /dev/null
+++ b/data/sampled_jsons/On_a_Connection_Between_Imitation_Learning_and_RLHF_sitearxiv.org_Table_4_LLama3-8B-Base_MATH_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "On a Connection Between Imitation Learning and RLHF", "date": "", "ddg_snippet": "This work studies the alignment of large language models with preference data from an imitation learning perspective. We establish a close theoretical connection between reinforcement learning from human feedback RLHF and imitation learning (IL), revealing that RLHF implicitly performs imitation learning on the preference data distribution. Building on this connection , we propose DIL, a ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.05079", "content": "This work studies the alignment of large language models with preference data from an imitation learning perspective. We establish a close theoretical connection between reinforcement learning from human feedback RLHF and imitation learning (IL), revealing that RLHF implicitly performs imitation learning on the preference data distribution. Building on this connection , we propose DIL, a ..."}
+{"idx": 1, "title": "On a Connection Between Imitation Learning and RLHF", "date": "", "ddg_snippet": "This work studies the alignment of large language models with preference data from an imitation learning perspective. We establish a close theoretical connection between reinforcement learning from human feedback ( RLHF ) and imitation learning (IL), revealing that RLHF implicitly performs imitation learning on the preference data distribution.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.05079v1", "content": "This work studies the alignment of large language models with preference data from an imitation learning perspective. We establish a close theoretical connection between reinforcement learning from human feedback ( RLHF ) and imitation learning (IL), revealing that RLHF implicitly performs imitation learning on the preference data distribution."}
+{"idx": 2, "title": "A arXiv:2503.05079v1 [cs.LG] 7 Mar 2025", "date": "", "ddg_snippet": "ABSTRACT This work studies the alignment of large language models with preference data from an imitation learning perspective. We establish a close theoretical connection be-tween reinforcement learning from human feedback ( RLHF ) and imitation learning (IL), revealing that RLHF implicitly performs imitation learning on the preference data distribution. Building on this connection , we propose ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.05079", "content": "ABSTRACT This work studies the alignment of large language models with preference data from an imitation learning perspective. We establish a close theoretical connection be-tween reinforcement learning from human feedback ( RLHF ) and imitation learning (IL), revealing that RLHF implicitly performs imitation learning on the preference data distribution. Building on this connection , we propose ..."}
+{"idx": 3, "title": "Imitating Language via Scalable Inverse Reinforcement Learning", "date": "", "ddg_snippet": "The majority of language model training builds on imitation learning . It covers pretraining, supervised fine-tuning, and affects the starting conditions for reinforcement learning from human feedback ( RLHF ). The simplicity and scalability of maximum likelihood estimation (MLE) for next token prediction led to its role as predominant paradigm. However, the broader field of imitation learning ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2409.01369", "content": "The majority of language model training builds on imitation learning . It covers pretraining, supervised fine-tuning, and affects the starting conditions for reinforcement learning from human feedback ( RLHF ). The simplicity and scalability of maximum likelihood estimation (MLE) for next token prediction led to its role as predominant paradigm. However, the broader field of imitation learning ..."}
+{"idx": 4, "title": "The Hidden Link Between RLHF and Contrastive Learning", "date": "", "ddg_snippet": "Several recent works have hinted at conceptual links between RLHF and imitation learning [8]. DIL [ 4 ] show that optimizing a policy with RLHF implicitly maximizes the likelihood of human-preferred outputs, formally proves that RLHF for aligning language models could be view as an imitation learning problem.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2506.22578", "content": "Several recent works have hinted at conceptual links between RLHF and imitation learning [8]. DIL [ 4 ] show that optimizing a policy with RLHF implicitly maximizes the likelihood of human-preferred outputs, formally proves that RLHF for aligning language models could be view as an imitation learning problem."}
+{"idx": 5, "title": "[2504.03784] Robust Reinforcement Learning from Human Feedback for ...", "date": "", "ddg_snippet": "Reinforcement learning from human feedback ( RLHF ) has emerged as a key technique for aligning the output of large language models (LLMs) with human preferences. To learn the reward function, most existing RLHF algorithms use the Bradley-Terry model, which relies on assumptions about human preferences that may not reflect the complexity and variability of real-world judgments. In this paper, we ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2504.03784", "content": "Reinforcement learning from human feedback ( RLHF ) has emerged as a key technique for aligning the output of large language models (LLMs) with human preferences. To learn the reward function, most existing RLHF algorithms use the Bradley-Terry model, which relies on assumptions about human preferences that may not reflect the complexity and variability of real-world judgments. In this paper, we ..."}
+{"idx": 6, "title": "Improving Multimodal Interactive Agents with Reinforcement Learning ...", "date": "", "ddg_snippet": "Here we demonstrate how to use reinforcement learning from human feedback ( RLHF ) to improve upon simulated, embodied agents trained to a base level of competency with imitation learning . First, we collected data of humans interacting with agents in a simulated 3D world.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2211.11602", "content": "Here we demonstrate how to use reinforcement learning from human feedback ( RLHF ) to improve upon simulated, embodied agents trained to a base level of competency with imitation learning . First, we collected data of humans interacting with agents in a simulated 3D world."}
+{"idx": 7, "title": "Reinforcement Learning in the Era of LLMs: What is Essential? What is ...", "date": "", "ddg_snippet": "Figure 7: Because of the high cost of keeping humans in the loop, the practice of RLHF considers learning with an offline dataset generated by interactions between (the SFT) LLMs and Human annotators.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2310.06147v1", "content": "Figure 7: Because of the high cost of keeping humans in the loop, the practice of RLHF considers learning with an offline dataset generated by interactions between (the SFT) LLMs and Human annotators."}
+{"idx": 8, "title": "Open Problems and Fundamental Limitations of Reinforcement Learning ...", "date": "", "ddg_snippet": "Reinforcement learning from human feedback ( RLHF ) is a technique for training AI systems to align with human goals. RLHF has emerged as the central method used to finetune state-of-the-art large language models (LLMs). Despite this popularity, there has been relatively little public work systematizing its flaws. In this paper, we (1) survey open problems and fundamental limitations of RLHF and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2307.15217", "content": "Reinforcement learning from human feedback ( RLHF ) is a technique for training AI systems to align with human goals. RLHF has emerged as the central method used to finetune state-of-the-art large language models (LLMs). Despite this popularity, there has been relatively little public work systematizing its flaws. In this paper, we (1) survey open problems and fundamental limitations of RLHF and ..."}
+{"idx": 9, "title": "Imitating Language via Scalable Inverse Reinforcement Learning", "date": "", "ddg_snippet": "Abstract The majority of language model training builds on imitation learning . It covers pretraining, supervised fine-tuning, and affects the starting conditions for reinforcement learning from human feedback ( RLHF ). The simplicity and scalability of maximum likelihood estimation (MLE) for next token prediction led to its role as predominant paradigm. However, the broader field of imitation ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2409.01369v1", "content": "Abstract The majority of language model training builds on imitation learning . It covers pretraining, supervised fine-tuning, and affects the starting conditions for reinforcement learning from human feedback ( RLHF ). The simplicity and scalability of maximum likelihood estimation (MLE) for next token prediction led to its role as predominant paradigm. However, the broader field of imitation ..."}
diff --git a/data/sampled_jsons/Origin_Identification_for_Text-Guided_Image-to-Image_Diffusion_Models_Figure_9_Section_5.5_increasin_year_2023.jsonl b/data/sampled_jsons/Origin_Identification_for_Text-Guided_Image-to-Image_Diffusion_Models_Figure_9_Section_5.5_increasin_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e64356cd12d8f39098622d9688c37dc0f7f34f0b
--- /dev/null
+++ b/data/sampled_jsons/Origin_Identification_for_Text-Guided_Image-to-Image_Diffusion_Models_Figure_9_Section_5.5_increasin_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Origin Identification for Text-Guided Image-to-Image Diffusion ...", "date": "", "ddg_snippet": "Abstract. Text - guided image -to- image diffusion models ex- cel in translating images based on textual prompts, allowing for precise and creative visual ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/681ea68d062b8991956d7a196be74f59c4610d76.pdf", "content": "Abstract. Text - guided image -to- image diffusion models ex- cel in translating images based on textual prompts, allowing for precise and creative visual ..."}
+{"idx": 1, "title": "Generalizable Origin Identification for Text-Guided Image- ...", "date": "", "ddg_snippet": "4 Jan 2025 — The mAP and Acc for ' Unseen ' are calculated by averaging the results of 6 unseen diffusion models . Please refer to Table 8 and Section 11 in ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.02376v1", "content": "4 Jan 2025 — The mAP and Acc for ' Unseen ' are calculated by averaging the results of 6 unseen diffusion models . Please refer to Table 8 and Section 11 in ..."}
+{"idx": 2, "title": "ICML Poster Origin Identification for Text-Guided Image-to- ...", "date": "", "ddg_snippet": "In the method section , we first prove the existence of a linear transformation that minimizes the distance between the pre-trained Variational Autoencoder ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46505", "content": "In the method section , we first prove the existence of a linear transformation that minimizes the distance between the pre-trained Variational Autoencoder ..."}
+{"idx": 3, "title": "GUIDED IMAGE-TO-IMAGE DIFFUSION MODELS", "date": "", "ddg_snippet": "This paper proposes a novel task, origin identification for text - guided image -to- image diffusion models (ID2), which aims to identify the origin of a generated ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/5941eae59e3504dfb1ab4e6cbe70630e131191d7.pdf", "content": "This paper proposes a novel task, origin identification for text - guided image -to- image diffusion models (ID2), which aims to identify the origin of a generated ..."}
+{"idx": 4, "title": "Pre-trained Text-to-Image Diffusion Models Are Versatile ...", "date": "", "ddg_snippet": "Our paper proposes Stable Control Representations, which uses pre-trained text -to- image diffusion models as a source of language- guided visual representations.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.05852v1", "content": "Our paper proposes Stable Control Representations, which uses pre-trained text -to- image diffusion models as a source of language- guided visual representations."}
+{"idx": 5, "title": "CVPR Poster One Diffusion to Generate Them All", "date": "", "ddg_snippet": "A single large-scale diffusion model designed to tackle a wide range of image synthesis and understanding tasks.", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/poster/33931", "content": "A single large-scale diffusion model designed to tackle a wide range of image synthesis and understanding tasks."}
+{"idx": 6, "title": "INTERPRETING IMAGE-TO-IMAGE LATENT DIFFUSION ...", "date": "", "ddg_snippet": "Large-scale diffusion models have made significant advances in image genera- tion, particularly through cross-attention mechanisms.", "subpage_snippet": "", "source": "proceedings.iclr.cc", "link": "https://proceedings.iclr.cc/paper_files/paper/2025/file/c4a59e985de8b134328f41a47bc7dfac-Paper-Conference.pdf", "content": "Large-scale diffusion models have made significant advances in image genera- tion, particularly through cross-attention mechanisms."}
+{"idx": 7, "title": "Findings of the Association for Computational Linguistics", "date": "", "ddg_snippet": "by W Che · 2025 — Text -to- image models are powerful for producing high-quality images based on given text prompts, but crafting these prompts often requires specialized ...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/volumes/2025.findings-acl/", "content": "by W Che · 2025 — Text -to- image models are powerful for producing high-quality images based on given text prompts, but crafting these prompts often requires specialized ..."}
+{"idx": 8, "title": "NeurIPS Poster FouRA: Fourier Low-Rank Adaptation", "date": "", "ddg_snippet": "While Low-Rank Adaptation (LoRA) has proven beneficial for efficiently fine-tuning large models , LoRA fine-tuned text -to- image diffusion models lack ...", "subpage_snippet": "", "source": "nips.cc", "link": "https://nips.cc/virtual/2024/poster/93511", "content": "While Low-Rank Adaptation (LoRA) has proven beneficial for efficiently fine-tuning large models , LoRA fine-tuned text -to- image diffusion models lack ..."}
+{"idx": 9, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/OwMatch_Conditional_Self-Labeling_with_Consistency_for_Open-World_Semi-Supervised_Learning_conclusio.jsonl b/data/sampled_jsons/OwMatch_Conditional_Self-Labeling_with_Consistency_for_Open-World_Semi-Supervised_Learning_conclusio.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0df777b721f1118f9ba291da054e1c4003a033e6
--- /dev/null
+++ b/data/sampled_jsons/OwMatch_Conditional_Self-Labeling_with_Consistency_for_Open-World_Semi-Supervised_Learning_conclusio.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "OwMatch: Conditional Self-Labeling with Consistency for Open-World Semi ...", "date": "", "ddg_snippet": "Semi-supervised learning (SSL) offers a robust framework for harnessing the potential of unannotated data. Traditionally, SSL mandates that all classes possess labeled instances. However, the emergence of open-world SSL (OwSSL) introduces a more practical challenge, wherein unlabeled data may encompass samples from unseen classes. This scenario leads to misclassification of unseen classes as ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2411.01833", "content": "Semi-supervised learning (SSL) offers a robust framework for harnessing the potential of unannotated data. Traditionally, SSL mandates that all classes possess labeled instances. However, the emergence of open-world SSL (OwSSL) introduces a more practical challenge, wherein unlabeled data may encompass samples from unseen classes. This scenario leads to misclassification of unseen classes as ..."}
+{"idx": 1, "title": "OwMatch: Conditional Self-Labeling with Consistency for Open-World Semi ...", "date": "", "ddg_snippet": "To overcome this challenge, this study revisits two methodologies from self - supervised and semi-supervised learning , self-labeling and consistency , tailoring them to address the OwSSL problem. Specifically, we propose an effective framework called OwMatch , combining conditional self-labeling and open-world hierarchical thresholding.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=rle9X7DQuH", "content": "To overcome this challenge, this study revisits two methodologies from self - supervised and semi-supervised learning , self-labeling and consistency , tailoring them to address the OwSSL problem. Specifically, we propose an effective framework called OwMatch , combining conditional self-labeling and open-world hierarchical thresholding."}
+{"idx": 2, "title": "GitHub - niusj03/OwMatch: [NeurIPS 2024] OwMatch", "date": "", "ddg_snippet": "This is the official repository for the paper OwMatch : Conditional Self-Labeling with Consistency for Open-World Semi-Supervised Learning . The repository contains the source code implemented in PyTorch.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/niusj03/OwMatch", "content": "This is the official repository for the paper OwMatch : Conditional Self-Labeling with Consistency for Open-World Semi-Supervised Learning . The repository contains the source code implemented in PyTorch."}
+{"idx": 3, "title": "OwMatch | Proceedings of the 38th International Conference on Neural ...", "date": "", "ddg_snippet": "Specifically, we propose an effective framework called OwMatch , combining conditional self-labeling and open-world hierarchical thresholding. Theoretically, we analyze the estimation of class distribution on unlabeled data through rigorous statistical analysis, thus demonstrating that OwMatch can ensure the unbiasedness of the self -label ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3737916.3741083", "content": "Specifically, we propose an effective framework called OwMatch , combining conditional self-labeling and open-world hierarchical thresholding. Theoretically, we analyze the estimation of class distribution on unlabeled data through rigorous statistical analysis, thus demonstrating that OwMatch can ensure the unbiasedness of the self -label ..."}
+{"idx": 4, "title": "OwMatch: Conditional Self-Labeling with Consistency for Open-World Semi ...", "date": "", "ddg_snippet": "Conclusion The OwMatch method introduces a novel approach to open-world semi-supervised learning , addressing the limitations of traditional SSL techniques by conditioning self-labeling on model confidence and using consistency regularization.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/owmatch-conditional-self-labeling-consistency-open-world", "content": "Conclusion The OwMatch method introduces a novel approach to open-world semi-supervised learning , addressing the limitations of traditional SSL techniques by conditioning self-labeling on model confidence and using consistency regularization."}
+{"idx": 5, "title": "OpenMatch: Open-set Consistency Regularization for Semi-supervised ...", "date": "", "ddg_snippet": "Semi-supervised learning (SSL) is an effective means to leverage unlabeled data to improve a model's performance. Typical SSL methods like FixMatch assume that labeled and unlabeled data share the same label space. However, in practice, unlabeled data can contain categories unseen in the labeled set, i.e., outliers, which can significantly harm the performance of SSL algorithms. To address ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2105.14148", "content": "Semi-supervised learning (SSL) is an effective means to leverage unlabeled data to improve a model's performance. Typical SSL methods like FixMatch assume that labeled and unlabeled data share the same label space. However, in practice, unlabeled data can contain categories unseen in the labeled set, i.e., outliers, which can significantly harm the performance of SSL algorithms. To address ..."}
+{"idx": 6, "title": "OwMatch: Conditional Self-Labeling with Consistency for Open-World Semi ...", "date": "", "ddg_snippet": "To overcome this challenge, this study revisits two methodologies from self - supervised and semi-supervised learning , self-labeling and consistency , tailoring them to address the OwSSL problem.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/385529038_OwMatch_Conditional_Self-Labeling_with_Consistency_for_Open-World_Semi-Supervised_Learning", "content": "To overcome this challenge, this study revisits two methodologies from self - supervised and semi-supervised learning , self-labeling and consistency , tailoring them to address the OwSSL problem."}
+{"idx": 7, "title": "OwMatch: Conditional Self-Labeling with Consistency for Open-world Semi ...", "date": "", "ddg_snippet": "OwMatch tackles these problems using conditional self-labeling (incorporating labeled data to guide self-labeling ) and open-world hierarchical thresholding (adapting thresholds based on class confidence). This approach leads to unbiased self -label assignments, improved clustering, and a balanced learning process.", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/posters/rle9x7dquh/", "content": "OwMatch tackles these problems using conditional self-labeling (incorporating labeled data to guide self-labeling ) and open-world hierarchical thresholding (adapting thresholds based on class confidence). This approach leads to unbiased self -label assignments, improved clustering, and a balanced learning process."}
+{"idx": 8, "title": "Awesome-Realistic-Semi-Supervised-Learning - GitHub", "date": "", "ddg_snippet": "An awesome paper list of Semi-Supervised Learning (SSL) under realistic ( Class -Imbalanced & Open -Set & Open-World ) settings. If you would like to add literature or have other requests, please contact mengqy19@gmail.com. We will update the list of papers regularly to keep it up to date. 😁", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/RabbitBoss/Awesome-Realistic-Semi-Supervised-Learning", "content": "An awesome paper list of Semi-Supervised Learning (SSL) under realistic ( Class -Imbalanced & Open -Set & Open-World ) settings. If you would like to add literature or have other requests, please contact mengqy19@gmail.com. We will update the list of papers regularly to keep it up to date. 😁"}
+{"idx": 9, "title": "NIU Shengjie (牛圣杰) - Shengjie's Homepage", "date": "", "ddg_snippet": "Recent News Sept 2024, our work \" OwMatch : Conditional Self-Labeling with Consistency for Open-world Semi-Supervised Learning \" got accepted by NeurIPS. arXiv & Github Aug 2023, the 23 summer seminar Introduction to and Advances in Self - Supervised Learning comes to perfect end.", "subpage_snippet": "", "source": "niusj03.github.io", "link": "https://niusj03.github.io/", "content": "Recent News Sept 2024, our work \" OwMatch : Conditional Self-Labeling with Consistency for Open-world Semi-Supervised Learning \" got accepted by NeurIPS. arXiv & Github Aug 2023, the 23 summer seminar Introduction to and Advances in Self - Supervised Learning comes to perfect end."}
diff --git a/data/sampled_jsons/OwMatch_ablation_study_Table_3_Novel_class_accuracy_Conditional_Self-Labeling_deep-diver.github.io_year_2024.jsonl b/data/sampled_jsons/OwMatch_ablation_study_Table_3_Novel_class_accuracy_Conditional_Self-Labeling_deep-diver.github.io_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..327cd3d133d1d7ee27baffb70058bf108a53a527
--- /dev/null
+++ b/data/sampled_jsons/OwMatch_ablation_study_Table_3_Novel_class_accuracy_Conditional_Self-Labeling_deep-diver.github.io_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "OwMatch: Conditional Self-Labeling with Consistency for Open ...", "date": "", "ddg_snippet": "To overcome this challenge, this study revisits two methodologies from self -supervised and semi-supervised learning, self-labeling and consistency, tailoring them to address the OwSSL problem. Specifically, we propose an effective framework called OwMatch , combining conditional self-labeling and open-world hierarchical thresholding.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2411.01833", "content": "To overcome this challenge, this study revisits two methodologies from self -supervised and semi-supervised learning, self-labeling and consistency, tailoring them to address the OwSSL problem. Specifically, we propose an effective framework called OwMatch , combining conditional self-labeling and open-world hierarchical thresholding."}
+{"idx": 1, "title": "GitHub - niusj03/OwMatch: [NeurIPS 2024] OwMatch", "date": "", "ddg_snippet": "This is the official repository for the paper OwMatch : Conditional Self-Labeling with Consistency for Open-World Semi-Supervised Learning. The repository contains the source code implemented in PyTorch.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/niusj03/OwMatch", "content": "This is the official repository for the paper OwMatch : Conditional Self-Labeling with Consistency for Open-World Semi-Supervised Learning. The repository contains the source code implemented in PyTorch."}
+{"idx": 2, "title": "[2411.01833] OwMatch: Conditional Self-Labeling with ...", "date": "", "ddg_snippet": "Nov 4, 2024 · To overcome this challenge, this study revisits two methodologies from self -supervised and semi-supervised learning, self-labeling and consistency, tailoring them to address the OwSSL problem. Specifically, we propose an effective framework called OwMatch , combining conditional self-labeling and open-world hierarchical thresholding.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2411.01833", "content": "Nov 4, 2024 · To overcome this challenge, this study revisits two methodologies from self -supervised and semi-supervised learning, self-labeling and consistency, tailoring them to address the OwSSL problem. Specifically, we propose an effective framework called OwMatch , combining conditional self-labeling and open-world hierarchical thresholding."}
+{"idx": 3, "title": "OwMatch | Proceedings of the 38th International Conference on ...", "date": "", "ddg_snippet": "To overcome this challenge, this study revisits two methodologies from self -supervised and semi-supervised learning, self-labeling and consistency, tailoring them to address the OwSSL problem. Specifically, we propose an effective framework called OwMatch , combining conditional self-labeling and open-world hierarchical thresholding.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3737916.3741083", "content": "To overcome this challenge, this study revisits two methodologies from self -supervised and semi-supervised learning, self-labeling and consistency, tailoring them to address the OwSSL problem. Specifically, we propose an effective framework called OwMatch , combining conditional self-labeling and open-world hierarchical thresholding."}
+{"idx": 4, "title": "OwMatch: Conditional Self-Labeling with Consistency for Open ...", "date": "", "ddg_snippet": "Table 2: Ablation study on datasets with both novel class ratio and label ratio of 50%. Here, ConSL refers to conditional self-labeling , PLCR refers to consistency regularization, and OwAT refers to an open-world hierarchical thresholding scheme.", "subpage_snippet": "", "source": "nips.cc", "link": "https://nips.cc/media/neurips-2024/Slides/93416.pdf", "content": "Table 2: Ablation study on datasets with both novel class ratio and label ratio of 50%. Here, ConSL refers to conditional self-labeling , PLCR refers to consistency regularization, and OwAT refers to an open-world hierarchical thresholding scheme."}
+{"idx": 5, "title": "OwMatch : Conditional Self - Labeling with Consistency for...", "date": "", "ddg_snippet": "Table 3 : Ablation studies on each component with both novel class ratio and label ratio of 50%. Here, ConSL refers to conditional self - labeling , PLCR refers to pseudo-label consistency regularization, and OwHT refers to an open-world hierarchical thresholding scheme.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.01833v1", "content": "Table 3 : Ablation studies on each component with both novel class ratio and label ratio of 50%. Here, ConSL refers to conditional self - labeling , PLCR refers to pseudo-label consistency regularization, and OwHT refers to an open-world hierarchical thresholding scheme."}
+{"idx": 6, "title": "(PDF) OwMatch : Conditional Self - Labeling with Consistency for...", "date": "", "ddg_snippet": "Table 3 : Ablation studies on each component with both novel class ratio and label ratio of 50%. Here, ConSL refers to conditional self - labeling , PLCR refers to pseudo-label consistency regularization", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/385529038_OwMatch_Conditional_Self-Labeling_with_Consistency_for_Open-World_Semi-Supervised_Learning", "content": "Table 3 : Ablation studies on each component with both novel class ratio and label ratio of 50%. Here, ConSL refers to conditional self - labeling , PLCR refers to pseudo-label consistency regularization"}
+{"idx": 7, "title": "(PDF) OwMatch : Conditional Self - Labeling with Consistency for...", "date": "", "ddg_snippet": "To overcome this challenge, this study revisits two methodologies from self-supervised and semi-supervised learning, self - labeling and consistency, tailoring them to address the OwSSL problem. Specifically, we propose an effective framework called OwMatch , combining conditional ...", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/papers/owmatch-conditional-self-labeling-with-consistency-for-open-2qmo1h06mr3a", "content": "To overcome this challenge, this study revisits two methodologies from self-supervised and semi-supervised learning, self - labeling and consistency, tailoring them to address the OwSSL problem. Specifically, we propose an effective framework called OwMatch , combining conditional ..."}
+{"idx": 8, "title": "OwMatch : Conditional Self - Labeling with Consistency for...", "date": "", "ddg_snippet": "Specifically, we propose an effective framework called _ OwMatch _, combining conditional self - labeling and open-world hierarchical thresholding. Theoretically, we analyze the estimation of class distribution on unlabeled data through rigorous statistical analysis...", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/venue/rle9X7DQuH@OpenReview", "content": "Specifically, we propose an effective framework called _ OwMatch _, combining conditional self - labeling and open-world hierarchical thresholding. Theoretically, we analyze the estimation of class distribution on unlabeled data through rigorous statistical analysis..."}
+{"idx": 9, "title": "OwMatch : Conditional Self - Labeling with Consistency for...", "date": "", "ddg_snippet": "Conditional Self - Labeling . Hierarchical Thresholding. OwMatch Analysis.The comparison includes both seen classes ( classes with labeled data) and novel classes (unseen classes ). The table shows the average accuracy for each method across seen, novel , and all classes .", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/posters/rle9x7dquh/", "content": "Conditional Self - Labeling . Hierarchical Thresholding. OwMatch Analysis.The comparison includes both seen classes ( classes with labeled data) and novel classes (unseen classes ). The table shows the average accuracy for each method across seen, novel , and all classes ."}
diff --git a/data/sampled_jsons/PAW_index_formula_Digital_Disparities_A_Comparative_Web_Measurement_Study_Across_Economic_Boundaries.jsonl b/data/sampled_jsons/PAW_index_formula_Digital_Disparities_A_Comparative_Web_Measurement_Study_Across_Economic_Boundaries.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d16f8134ebb083308afa921197cda08ad1ed964a
--- /dev/null
+++ b/data/sampled_jsons/PAW_index_formula_Digital_Disparities_A_Comparative_Web_Measurement_Study_Across_Economic_Boundaries.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Digital Disparities: A Comparative Web Measurement Study ...", "date": "", "ddg_snippet": "A PAW index of 1 or less indicates afordable access, while values above 1 suggest that webpage sizes or broadband costs are too high relative to income, limiting access to essential online services [68]. See Appendix A.7 for details on PAW calculation. Figure 15: Website afordability ( PAW Index ) across countries.", "subpage_snippet": "", "source": "www.staicu.org", "link": "https://www.staicu.org/publications/www2025.pdf", "content": "A PAW index of 1 or less indicates afordable access, while values above 1 suggest that webpage sizes or broadband costs are too high relative to income, limiting access to essential online services [68]. See Appendix A.7 for details on PAW calculation. Figure 15: Website afordability ( PAW Index ) across countries."}
+{"idx": 1, "title": "Digital Disparities: A Comparative Web Measurement Study ...", "date": "", "ddg_snippet": "A PAW index of 1 or less indicates afordable access, while values above 1 suggest that webpage sizes or broadband costs are too high relative to income, limiting access to essential online services [60]. See appendix A.7 for details on PAW calculation.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=IMhoJgWANP", "content": "A PAW index of 1 or less indicates afordable access, while values above 1 suggest that webpage sizes or broadband costs are too high relative to income, limiting access to essential online services [60]. See appendix A.7 for details on PAW calculation."}
+{"idx": 2, "title": "digital-disparities-www25/README.md at main - GitHub", "date": "", "ddg_snippet": "Digital Disparities: A Comparative Web Measurement Study This repository contains the dataset, tools, and analysis for our research paper \" Digital Disparities: A Comparative Web Measurement Study Across Economic Boundaries \", presented at WWW'25. The study explores differences in web development practices across developed and developing countries, focusing on web size, complexity, security ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/kal-purush/digital-disparities-www25/blob/main/README.md", "content": "Digital Disparities: A Comparative Web Measurement Study This repository contains the dataset, tools, and analysis for our research paper \" Digital Disparities: A Comparative Web Measurement Study Across Economic Boundaries \", presented at WWW'25. The study explores differences in web development practices across developed and developing countries, focusing on web size, complexity, security ..."}
+{"idx": 3, "title": "Unequal Internet: study highlights differences between ...", "date": "", "ddg_snippet": "Apr 29, 2025 · The Internet may be a global phenomenon, but its often-claimed global nature is tempered by the ‘ digital divide’ – digital participation still heavily depends on economic conditions. CISPA researcher Masudul Bhuiyan from CISPA Faculty Dr. Cristian-Alexander Staicu’s team explored whether security and data privacy differences can be found on websites as well. His study of 200,000 ...", "subpage_snippet": "", "source": "nachrichten.idw-online.de", "link": "https://nachrichten.idw-online.de/2025/04/29/unequal-internet-study-highlights-differences-between-websites-from-developing-and-developed-countries", "content": "Apr 29, 2025 · The Internet may be a global phenomenon, but its often-claimed global nature is tempered by the ‘ digital divide’ – digital participation still heavily depends on economic conditions. CISPA researcher Masudul Bhuiyan from CISPA Faculty Dr. Cristian-Alexander Staicu’s team explored whether security and data privacy differences can be found on websites as well. His study of 200,000 ..."}
+{"idx": 4, "title": "Digital Disparities: A Comparative Web Measurement Study ...", "date": "", "ddg_snippet": "Apr 22, 2025 · Index Terms Digital Disparities: A Comparative Web Measurement Study Across Economic Boundaries Information systems World Wide Web Social and professional topics", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3696410.3714647", "content": "Apr 22, 2025 · Index Terms Digital Disparities: A Comparative Web Measurement Study Across Economic Boundaries Information systems World Wide Web Social and professional topics"}
+{"idx": 5, "title": "Unequal internet: Study highlights differences between ...", "date": "", "ddg_snippet": "More information: Masudul Hasan Masud Bhuiyan et al, Digital Disparities: A Comparative Web Measurement Study Across Economic Boundaries , CISPA (2025). DOI: 10.60882/cispa.28365449.v1", "subpage_snippet": "", "source": "techxplore.com", "link": "https://techxplore.com/news/2025-04-unequal-internet-highlights-differences-websites.pdf", "content": "More information: Masudul Hasan Masud Bhuiyan et al, Digital Disparities: A Comparative Web Measurement Study Across Economic Boundaries , CISPA (2025). DOI: 10.60882/cispa.28365449.v1"}
+{"idx": 6, "title": "GitHub - kal-purush/digital-disparities-www25: Code and ...", "date": "", "ddg_snippet": "Digital Disparities: A Comparative Web Measurement Study This repository contains the dataset, tools, and analysis for our research paper \" Digital Disparities: A Comparative Web Measurement Study Across Economic Boundaries \", presented at WWW'25. The study explores differences in web development practices across developed and developing countries, focusing on web size, complexity, security ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/kal-purush/digital-disparities-www25", "content": "Digital Disparities: A Comparative Web Measurement Study This repository contains the dataset, tools, and analysis for our research paper \" Digital Disparities: A Comparative Web Measurement Study Across Economic Boundaries \", presented at WWW'25. The study explores differences in web development practices across developed and developing countries, focusing on web size, complexity, security ..."}
+{"idx": 7, "title": "A Comparative Web Measurement Study Across Economic ...", "date": "", "ddg_snippet": "1 Mar 2017 — Digital Disparities: A Comparative Web Measurement Study Across Economic Boundaries ... See Appendix A.7 for details on PAW calculation .", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/pdf/10.1145/3696410.3714647?download=true", "content": "1 Mar 2017 — Digital Disparities: A Comparative Web Measurement Study Across Economic Boundaries ... See Appendix A.7 for details on PAW calculation ."}
+{"idx": 8, "title": "Digital Disparities : A Comparative Web Measurement Study ...", "date": "", "ddg_snippet": "A user study in two high schools in Pakistan confirms that the performance gains come at no expense to the pages' look and functionality. These findings suggest that deploying Lite- Web at scale would constitute a major step toward a WWW without digital inequality.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/391035183_Digital_Disparities_A_Comparative_Web_Measurement_Study_Across_Economic_Boundaries", "content": "A user study in two high schools in Pakistan confirms that the performance gains come at no expense to the pages' look and functionality. These findings suggest that deploying Lite- Web at scale would constitute a major step toward a WWW without digital inequality."}
+{"idx": 9, "title": "Unequal internet: Study highlights differences between websites from...", "date": "", "ddg_snippet": "Visualization to the paper \" A Comparative Web Measurement Study Across Economic Boundaries .\"More information: Masudul Hasan Masud Bhuiyan et al, Digital Disparities : A Comparative Web Measurement Study Across Economic Boundaries , CISPA (2025).", "subpage_snippet": "", "source": "techxplore.com", "link": "https://techxplore.com/news/2025-04-unequal-internet-highlights-differences-websites.html", "content": "Visualization to the paper \" A Comparative Web Measurement Study Across Economic Boundaries .\"More information: Masudul Hasan Masud Bhuiyan et al, Digital Disparities : A Comparative Web Measurement Study Across Economic Boundaries , CISPA (2025)."}
diff --git a/data/sampled_jsons/PPO_instability_symmetric_loss_reverse_cross_entropy_noise_mitigation.jsonl b/data/sampled_jsons/PPO_instability_symmetric_loss_reverse_cross_entropy_noise_mitigation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..db2394df536bc5a39fb5eec3dde2855bc725a6e3
--- /dev/null
+++ b/data/sampled_jsons/PPO_instability_symmetric_loss_reverse_cross_entropy_noise_mitigation.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Symmetric Reinforcement Learning Loss for Robust Learning on ...", "date": "", "ddg_snippet": "In this work, we focus on RL algorithms that share learning dificulties with cross - entropy loss , especially for low-probability predictions. To enhance stability, we adapt reverse cross - entropy (RCE) from supervised learning for noisy data, defining a symmetric RL loss . We demonstrate performance improvements across various tasks and scales.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2405.17618v3", "content": "In this work, we focus on RL algorithms that share learning dificulties with cross - entropy loss , especially for low-probability predictions. To enhance stability, we adapt reverse cross - entropy (RCE) from supervised learning for noisy data, defining a symmetric RL loss . We demonstrate performance improvements across various tasks and scales."}
+{"idx": 1, "title": "Symmetric Reinforcement Learning Loss for Robust Learning on ...", "date": "", "ddg_snippet": "In this work, we focus on RL algorithms that share learning difficulties with cross - entropy loss , especially for low-probability predictions. To enhance stability, we adapt reverse cross - entropy (RCE) from supervised learning for noisy data, defining a symmetric RL loss . We demonstrate performance improvements across various tasks and scales.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=YjBrt82S3v", "content": "In this work, we focus on RL algorithms that share learning difficulties with cross - entropy loss , especially for low-probability predictions. To enhance stability, we adapt reverse cross - entropy (RCE) from supervised learning for noisy data, defining a symmetric RL loss . We demonstrate performance improvements across various tasks and scales."}
+{"idx": 2, "title": "Symmetric Cross Entropy for Robust Learning with Noisy Labels", "date": "", "ddg_snippet": "Inspired by the symmetric KL-divergence, we pro-pose the approach of Symmetric cross entropy Learning (SL), boosting CE symmetrically with a noise robust coun-terpart Reverse Cross Entropy (RCE). Our proposed SL ap-proach simultaneously addresses both the under learning and overfitting problem of CE in the presence of noisy la-bels.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content_ICCV_2019/papers/Wang_Symmetric_Cross_Entropy_for_Robust_Learning_With_Noisy_Labels_ICCV_2019_paper.pdf", "content": "Inspired by the symmetric KL-divergence, we pro-pose the approach of Symmetric cross entropy Learning (SL), boosting CE symmetrically with a noise robust coun-terpart Reverse Cross Entropy (RCE). Our proposed SL ap-proach simultaneously addresses both the under learning and overfitting problem of CE in the presence of noisy la-bels."}
+{"idx": 3, "title": "Understanding Entropy Loss for PPO Agents Exploration", "date": "", "ddg_snippet": "Oct 10, 2023 · In PPO , the goal of training is to strike a balance between the entropy term and fine tuning the probabilities for all available action. This happens throughout training, as, unlike epsilon greedy approach, exploration in PPO does not diminish over time.", "subpage_snippet": "", "source": "www.mathworks.com", "link": "https://www.mathworks.com/matlabcentral/answers/2031574-understanding-entropy-loss-for-ppo-agents-exploration", "content": "Oct 10, 2023 · In PPO , the goal of training is to strike a balance between the entropy term and fine tuning the probabilities for all available action. This happens throughout training, as, unlike epsilon greedy approach, exploration in PPO does not diminish over time."}
+{"idx": 4, "title": "Symmetric Cross Entropy for Robust Learning With Noisy Labels", "date": "", "ddg_snippet": "Inspired by the symmetric KL-divergence, we propose the approach of Symmetric cross entropy Learning (SL), boosting CE symmetrically with a noise robust counterpart Reverse Cross Entropy (RCE). Our proposed SL approach simultaneously addresses both the under learning and overfitting problem of CE in the presence of noisy labels.", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/9010653", "content": "Inspired by the symmetric KL-divergence, we propose the approach of Symmetric cross entropy Learning (SL), boosting CE symmetrically with a noise robust counterpart Reverse Cross Entropy (RCE). Our proposed SL approach simultaneously addresses both the under learning and overfitting problem of CE in the presence of noisy labels."}
+{"idx": 5, "title": "Troubleshooting PPO Training Instability - apxml.com", "date": "", "ddg_snippet": "Training large language models with PPO in the RLHF setting can sometimes feel like navigating a minefield. While powerful, PPO training runs can be sensitive to hyperparameters and implementation details, occasionally leading to instability .", "subpage_snippet": "", "source": "apxml.com", "link": "https://apxml.com/courses/rlhf-reinforcement-learning-human-feedback/chapter-4-rl-ppo-fine-tuning/troubleshooting-ppo-instability", "content": "Training large language models with PPO in the RLHF setting can sometimes feel like navigating a minefield. While powerful, PPO training runs can be sensitive to hyperparameters and implementation details, occasionally leading to instability ."}
+{"idx": 6, "title": "Robust Learning via Golden Symmetric Loss of (un)Trusted Labels", "date": "", "ddg_snippet": "In this paper, we propose to construct a golden symmetric loss (GSL) based on the estimated corruption matrix as to avoid overfitting to noisy la-bels and learn efectively from hard classes. is the GSL weighted sum of the corrected regular cross entropy and reverse cross entropy .", "subpage_snippet": "", "source": "epubs.siam.org", "link": "https://epubs.siam.org/doi/pdf/10.1137/1.9781611977653.ch64", "content": "In this paper, we propose to construct a golden symmetric loss (GSL) based on the estimated corruption matrix as to avoid overfitting to noisy la-bels and learn efectively from hard classes. is the GSL weighted sum of the corrected regular cross entropy and reverse cross entropy ."}
+{"idx": 7, "title": "ICML 2024 Schedule", "date": "", "ddg_snippet": "... Entropy Neural Network for Deep Graph ... Size-invariance Matters: Rethinking Metrics and Losses for Imbalanced Multi-object Salient Object Detection", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2024/calendar", "content": "... Entropy Neural Network for Deep Graph ... Size-invariance Matters: Rethinking Metrics and Losses for Imbalanced Multi-object Salient Object Detection"}
+{"idx": 8, "title": "Daily AI Papers | All credits go to HuggingFace’s Daily AI", "date": "", "ddg_snippet": "... in GUI agent development, including data scarcity, scalable multi-turn RL, the limitations of GUI-only operation, and environment instability .", "subpage_snippet": "", "source": "gabrielchua.me", "link": "https://gabrielchua.me/daily-ai-papers/", "content": "... in GUI agent development, including data scarcity, scalable multi-turn RL, the limitations of GUI-only operation, and environment instability ."}
+{"idx": 9, "title": "A Survey of Direct Preference Optimization", "date": "", "ddg_snippet": "However, this RLHF paradigm suffers from critical limitations of computational complexity and training instability .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.11701v1", "content": "However, this RLHF paradigm suffers from critical limitations of computational complexity and training instability ."}
diff --git a/data/sampled_jsons/Parisi_Kazemipour_Bowling_2024_continuous_MDP_neural_network_state_action_pair_year_2024.jsonl b/data/sampled_jsons/Parisi_Kazemipour_Bowling_2024_continuous_MDP_neural_network_state_action_pair_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a232ba0e3ae4d6f781d0f268ea3e6b757ae8585e
--- /dev/null
+++ b/data/sampled_jsons/Parisi_Kazemipour_Bowling_2024_continuous_MDP_neural_network_state_action_pair_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Simone Parisi", "date": "", "ddg_snippet": "Simone Parisi , Alireza Kazemipour , Michael Bowling Neural Information Processing Systems (NeurIPS), 2024 Monitored Markov Decision Processes Simone Parisi , Montaser Mohammedalamen, Alireza Kazemipour , Matthew E. Taylor, Michael Bowling International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2024", "subpage_snippet": "", "source": "sparisi.github.io", "link": "https://sparisi.github.io/", "content": "Simone Parisi , Alireza Kazemipour , Michael Bowling Neural Information Processing Systems (NeurIPS), 2024 Monitored Markov Decision Processes Simone Parisi , Montaser Mohammedalamen, Alireza Kazemipour , Matthew E. Taylor, Michael Bowling International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2024"}
+{"idx": 1, "title": "PDF Discrete versus Continuous Markov Decision Processes", "date": "", "ddg_snippet": "\\Discrete or Continuous \" in States , Actions or Time Steps When we say Discrete or Continuous MDP , we could be talking of:", "subpage_snippet": "", "source": "web.stanford.edu", "link": "https://web.stanford.edu/class/cme241/lecture_slides/DiscreteVSContinuous.pdf", "content": "\\Discrete or Continuous \" in States , Actions or Time Steps When we say Discrete or Continuous MDP , we could be talking of:"}
+{"idx": 2, "title": "PDF Low-rank MDPs with Continuous Action Spaces", "date": "", "ddg_snippet": "In this paper, we study the problem of extending low-rank MDP PAC results to settings with continuous action spaces, where |A| = ∞. We first discuss why | occurs with these algorithms, the dependence on and how this dependence may be alleviated.", "subpage_snippet": "", "source": "mirunaoprescu.com", "link": "https://mirunaoprescu.com/assets/publications/2024_low_rank/Low_Rank_MDP.pdf", "content": "In this paper, we study the problem of extending low-rank MDP PAC results to settings with continuous action spaces, where |A| = ∞. We first discuss why | occurs with these algorithms, the dependence on and how this dependence may be alleviated."}
+{"idx": 3, "title": "Deep Reinforcement Learning for Weakly Coupled MDP's with Continuous ...", "date": "", "ddg_snippet": "This paper introduces the Lagrange Policy for Continuous Actions (LPCA), a reinforcement learning algorithm specifically designed for weakly coupled MDP problems with continuous action spaces. LPCA addresses the challenge of resource constraints dependent on continuous actions by introducing a Lagrange relaxation of the weakly coupled MDP problem within a neural network framework for Q-value ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-3-031-70753-7_5", "content": "This paper introduces the Lagrange Policy for Continuous Actions (LPCA), a reinforcement learning algorithm specifically designed for weakly coupled MDP problems with continuous action spaces. LPCA addresses the challenge of resource constraints dependent on continuous actions by introducing a Lagrange relaxation of the weakly coupled MDP problem within a neural network framework for Q-value ..."}
+{"idx": 4, "title": "Model-Based Exploration in Monitored Markov Decision Processes", "date": "", "ddg_snippet": "Parisi et al. (2024b) showed asymptotic convergence to an optimal policy with an ergodic monitor function fm, i.e., for all environment state-action pairs (se, ae), there exists at least a monitor state-action pair (sm, am) such that the proxy reward is observed infinitely-often given infinite exploration.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.16772v3", "content": "Parisi et al. (2024b) showed asymptotic convergence to an optimal policy with an ergodic monitor function fm, i.e., for all environment state-action pairs (se, ae), there exists at least a monitor state-action pair (sm, am) such that the proxy reward is observed infinitely-often given infinite exploration."}
+{"idx": 5, "title": "Beyond Optimism: Exploration With Partially Observable Rewards", "date": "", "ddg_snippet": "Authors Simone Parisi , Alireza Kazemipour , Michael Bowling Abstract Exploration in reinforcement learning (RL) remains an open challenge.RL algorithms rely on observing rewards to train the agent, and if informative rewards are sparse the agent learns slowly or may not learn at all. To improve exploration and reward discovery, popular algorithms rely on optimism. But what if sometimes rewards ...", "subpage_snippet": "", "source": "proceedings.nips.cc", "link": "https://proceedings.nips.cc/paper_files/paper/2024/hash/784fd5a46dfe303e5b51c8621b84cf3f-Abstract-Conference.html", "content": "Authors Simone Parisi , Alireza Kazemipour , Michael Bowling Abstract Exploration in reinforcement learning (RL) remains an open challenge.RL algorithms rely on observing rewards to train the agent, and if informative rewards are sparse the agent learns slowly or may not learn at all. To improve exploration and reward discovery, popular algorithms rely on optimism. But what if sometimes rewards ..."}
+{"idx": 6, "title": "[2502.16772] 1 Introduction", "date": "", "ddg_snippet": "Since it tries to visit every joint state-action pair infinitely often without paying attention to their importance on maximizing the return, as the state space gets larger, the performance of Directed-E 2 deteriorates.", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2502.16772", "content": "Since it tries to visit every joint state-action pair infinitely often without paying attention to their importance on maximizing the return, as the state space gets larger, the performance of Directed-E 2 deteriorates."}
+{"idx": 7, "title": "PDF Monitored Markov Decision Processes", "date": "", "ddg_snippet": "The next monitor state m+1, indeed, depends on both the current monitor pair m, ( m) and environment pair ( e, e). Consequently, the proxy reward also depends on both the environment and the monitor.", "subpage_snippet": "", "source": "www.ifaamas.org", "link": "https://www.ifaamas.org/Proceedings/aamas2024/pdfs/p1549.pdf", "content": "The next monitor state m+1, indeed, depends on both the current monitor pair m, ( m) and environment pair ( e, e). Consequently, the proxy reward also depends on both the environment and the monitor."}
+{"idx": 8, "title": "PDF lec15_dqn_upload - University of Illinois Urbana-Champaign", "date": "", "ddg_snippet": "Reward function ( ) Policy p( ): the action that an agent takes in any given state The \"solution\" to an MDP But how to find this solution?", "subpage_snippet": "", "source": "slazebni.cs.illinois.edu", "link": "https://slazebni.cs.illinois.edu/spring24/lec15_dqn.pdf", "content": "Reward function ( ) Policy p( ): the action that an agent takes in any given state The \"solution\" to an MDP But how to find this solution?"}
+{"idx": 9, "title": "opendilab/awesome-exploration-rl - GitHub", "date": "", "ddg_snippet": "Simone Parisi , Alireza Kazemipour , Michael Bowling Key: Reinforcement Learning, Partial Observability, Optimism, Exploration ExpEnv: Tabular Environments (with and without unobservable rewards) Exploring the Edges of Latent State Clusters for Goal-Conditioned Reinforcement Learning Yuanlin Duan, Guofeng Cui, He Zhu", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/opendilab/awesome-exploration-rl", "content": "Simone Parisi , Alireza Kazemipour , Michael Bowling Key: Reinforcement Learning, Partial Observability, Optimism, Exploration ExpEnv: Tabular Environments (with and without unobservable rewards) Exploring the Edges of Latent State Clusters for Goal-Conditioned Reinforcement Learning Yuanlin Duan, Guofeng Cui, He Zhu"}
diff --git a/data/sampled_jsons/Pedestrian_Motion_Reconstruction_Large-scale_Benchmark_Mixed_Reality_Table_1_PMR_vs_Human3.6M_subjec_year_2023.jsonl b/data/sampled_jsons/Pedestrian_Motion_Reconstruction_Large-scale_Benchmark_Mixed_Reality_Table_1_PMR_vs_Human3.6M_subjec_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0622163bfa121cfcfa0344dcb42e9a990ee01b95
--- /dev/null
+++ b/data/sampled_jsons/Pedestrian_Motion_Reconstruction_Large-scale_Benchmark_Mixed_Reality_Table_1_PMR_vs_Human3.6M_subjec_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ICLR Poster Pedestrian Motion Reconstruction : A Large - scale ...", "date": "", "ddg_snippet": "Pedestrian Motion Reconstruction : A Large - scale Benchmark via Mixed Reality Rendering with Multiple Perspectives and Modalities.", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2025/poster/29263", "content": "Pedestrian Motion Reconstruction : A Large - scale Benchmark via Mixed Reality Rendering with Multiple Perspectives and Modalities."}
+{"idx": 1, "title": "Human Benchmark", "date": "", "ddg_snippet": "Human Benchmark . Measure your abilities with brain games and cognitive tests.", "subpage_snippet": "", "source": "humanbenchmark.com", "link": "https://humanbenchmark.com/", "content": "Human Benchmark . Measure your abilities with brain games and cognitive tests."}
+{"idx": 2, "title": "NVIDIA RTX 5090 PCIe 5.0 vs . 4.0 vs . 3.0 x16 Scaling Benchmarks", "date": "", "ddg_snippet": "Sponsor: Arctic Liquid Freezer III on Amazon - https://geni.us/NrMtDTThis benchmark compares PCIe generation differences on the NVIDIA RTX 5090 GPU.", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=L1NPFFRTzLo", "content": "Sponsor: Arctic Liquid Freezer III on Amazon - https://geni.us/NrMtDTThis benchmark compares PCIe generation differences on the NVIDIA RTX 5090 GPU."}
+{"idx": 3, "title": "Achieving view-distance and -angle invariance in motion prediction...", "date": "", "ddg_snippet": "Recently, human motion prediction has gained significant attention and achieved notable success. Table 1 Performance comparison in the first protocol between different methods via MPJPE from the Human 3 . 6 M dataset.", "subpage_snippet": "", "source": "vciba.springeropen.com", "link": "https://vciba.springeropen.com/articles/10.1186/s42492-024-00176-5", "content": "Recently, human motion prediction has gained significant attention and achieved notable success. Table 1 Performance comparison in the first protocol between different methods via MPJPE from the Human 3 . 6 M dataset."}
+{"idx": 4, "title": "Modelling Diverse Interactions and Multimodality for Pedestrian ...", "date": "", "ddg_snippet": "Table III demonstrates that our model achieves superior performance across multiple prediction horizons. Our model obtains the best ADE results across all time steps and achieves the lowest FDE for 1 −3 s predictions.", "subpage_snippet": "", "source": "www.ieee-jas.net", "link": "https://www.ieee-jas.net/en/article/doi/10.1109/JAS.2025.125363", "content": "Table III demonstrates that our model achieves superior performance across multiple prediction horizons. Our model obtains the best ADE results across all time steps and achieves the lowest FDE for 1 −3 s predictions."}
+{"idx": 5, "title": "Parametric Model-Based 3D Human Shape and Pose Estimation from...", "date": "", "ddg_snippet": "Large - scale motion of the acquired object is recovered using a novel space-time adaptive, non-rigid registration method. Fine-scale details such as wrinkles and folds are synthesized with an efficient linear mesh deformation algorithm.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/333232479_Parametric_Model-Based_3D_Human_Shape_and_Pose_Estimation_from_Multiple_Views", "content": "Large - scale motion of the acquired object is recovered using a novel space-time adaptive, non-rigid registration method. Fine-scale details such as wrinkles and folds are synthesized with an efficient linear mesh deformation algorithm."}
+{"idx": 6, "title": "3D Human Interaction Generation: A Survey", "date": "", "ddg_snippet": "TABLE I : Representative works of human interaction generation. Motion data was captured using a head-mounted camera and body-mounted IMUs, enabling drift-free human motion estimation and self-localization in large - scale environments.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.13120v1", "content": "TABLE I : Representative works of human interaction generation. Motion data was captured using a head-mounted camera and body-mounted IMUs, enabling drift-free human motion estimation and self-localization in large - scale environments."}
+{"idx": 7, "title": "Radeon 660 M + Ryzen 5 6600H : Test in 12 Games - Ryzen...", "date": "", "ddg_snippet": "Benchmarking the New laptop APU Ryzen 5 6600H with its integrated graphics AMD Radeon 660 M in 12 Games I will be benchmarking the Radeon Radeon 660 M Ryzen 5 6600U as well when I get my hands on a laptop with it with many more games, so stay tuned an...", "subpage_snippet": "", "source": "rutube.ru", "link": "https://rutube.ru/video/2ae104947ca4295d43803076ceafd58e/", "content": "Benchmarking the New laptop APU Ryzen 5 6600H with its integrated graphics AMD Radeon 660 M in 12 Games I will be benchmarking the Radeon Radeon 660 M Ryzen 5 6600U as well when I get my hands on a laptop with it with many more games, so stay tuned an..."}
+{"idx": 8, "title": "AI Detector - Trusted AI Checker (Free & No SignUp)", "date": "", "ddg_snippet": "Content at scale AI Detector refers to an AI detection tool that can support large - scale text content. Decopy AI Content Detector supports detection of up to 200,000 characters at a time.", "subpage_snippet": "", "source": "decopy.ai", "link": "https://decopy.ai/ai-detector/", "content": "Content at scale AI Detector refers to an AI detection tool that can support large - scale text content. Decopy AI Content Detector supports detection of up to 200,000 characters at a time."}
+{"idx": 9, "title": "OpenAI o3-mini vs DeepSeek R 1 – Which is the best LRM?", "date": "", "ddg_snippet": "OpenAI o3-mini vs DeepSeek R 1 Performance Benchmarks . To evaluate the effectiveness of these models in practical scenarios, we can look at their performance benchmarks across various coding tasks.", "subpage_snippet": "", "source": "blog.getbind.co", "link": "https://blog.getbind.co/2025/02/01/openai-o3-mini-vs-deepseek-r1-which-one-is-better/", "content": "OpenAI o3-mini vs DeepSeek R 1 Performance Benchmarks . To evaluate the effectiveness of these models in practical scenarios, we can look at their performance benchmarks across various coding tasks."}
diff --git a/data/sampled_jsons/Pedestrian_Motion_Reconstruction_Table_4_SLHAMR_PA-MPJPE_mov..jsonl b/data/sampled_jsons/Pedestrian_Motion_Reconstruction_Table_4_SLHAMR_PA-MPJPE_mov..jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..62e37fab5cd8e2bad20d17d7d78193dfcd18dd9d
--- /dev/null
+++ b/data/sampled_jsons/Pedestrian_Motion_Reconstruction_Table_4_SLHAMR_PA-MPJPE_mov..jsonl
@@ -0,0 +1,2 @@
+{"idx": 0, "title": "PEDESTRIAN MOTION RECONSTRUCTION:ALARGE", "date": "", "ddg_snippet": "Note: W-MPJPE, WA-MPJPE, and PA-MPJPE reported in mm, Acc. Err. reported ... Table 4 : SLHAMR (Ye et al., 2023b) performance comparison under single and ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/789739cfd83f5876545163c9f6ee9f74cfe121fa.pdf", "content": "Note: W-MPJPE, WA-MPJPE, and PA-MPJPE reported in mm, Acc. Err. reported ... Table 4 : SLHAMR (Ye et al., 2023b) performance comparison under single and ..."}
+{"idx": 1, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/Per-Instance_Privacy_Loss_Definition_4.2_'Leveraging_Per-Instance_Privacy_for_Machine_Unlearning'_Mo.jsonl b/data/sampled_jsons/Per-Instance_Privacy_Loss_Definition_4.2_'Leveraging_Per-Instance_Privacy_for_Machine_Unlearning'_Mo.jsonl
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index 0000000000000000000000000000000000000000..107477756d9ea7164e49af6969ce3a2788aebe10
--- /dev/null
+++ b/data/sampled_jsons/Per-Instance_Privacy_Loss_Definition_4.2_'Leveraging_Per-Instance_Privacy_for_Machine_Unlearning'_Mo.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Leveraging Per-Instance Privacy for Machine Unlearning", "date": "", "ddg_snippet": "by NM Sepahvand — We present a principled, per - instance approach to quantifying the difficulty of unlearning via fine- tuning. We begin by sharpening an analysis of.", "subpage_snippet": "", "source": "tpdp.journalprivacyconfidentiality.org", "link": "https://tpdp.journalprivacyconfidentiality.org/2025/pdf/sepahvand.pdf", "content": "by NM Sepahvand — We present a principled, per - instance approach to quantifying the difficulty of unlearning via fine- tuning. We begin by sharpening an analysis of."}
+{"idx": 1, "title": "Machine Learning May 2025", "date": "", "ddg_snippet": "17 May 2025 — Title: Leveraging Per-Instance Privacy for Machine Unlearning . Nazanin Mohammadi Sepahvand, Anvith Thudi, Berivan Isik, Ashmita Bhattacharyya ...", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/list/cs.LG/2025-05?skip=1675&show=2000", "content": "17 May 2025 — Title: Leveraging Per-Instance Privacy for Machine Unlearning . Nazanin Mohammadi Sepahvand, Anvith Thudi, Berivan Isik, Ashmita Bhattacharyya ..."}
+{"idx": 2, "title": "Machine Learning May 2025", "date": "", "ddg_snippet": "17 May 2025 — Title: Leveraging Per-Instance Privacy for Machine Unlearning . Nazanin Mohammadi Sepahvand, Anvith Thudi, Berivan Isik, Ashmita Bhattacharyya ...", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/list/cs.LG/2025-05?skip=875&show=2000", "content": "17 May 2025 — Title: Leveraging Per-Instance Privacy for Machine Unlearning . Nazanin Mohammadi Sepahvand, Anvith Thudi, Berivan Isik, Ashmita Bhattacharyya ..."}
+{"idx": 3, "title": "Data Selection for Transfer Unlearning", "date": "", "ddg_snippet": "... for a relaxed definition of unlearning that does not address privacy applications but targets a scenario where a data owner withdraws permission of ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.10425v1", "content": "... for a relaxed definition of unlearning that does not address privacy applications but targets a scenario where a data owner withdraws permission of ..."}
+{"idx": 4, "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": "arxiv.org", "link": "https://arxiv.org/pdf/2505.18786", "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": 5, "title": "Per-instance Differential Privacy", "date": "", "ddg_snippet": "In this paper, we proposed to use per-instance di erential privacy (pDP) for quantifying the ne-grained privacy loss of a xed individual against randomized data analysis conducted on a xed data set.", "subpage_snippet": "", "source": "journalprivacyconfidentiality.org", "link": "https://journalprivacyconfidentiality.org/index.php/jpc/article/download/662/675/1038", "content": "In this paper, we proposed to use per-instance di erential privacy (pDP) for quantifying the ne-grained privacy loss of a xed individual against randomized data analysis conducted on a xed data set."}
+{"idx": 6, "title": "A survey on machine unlearning: Techniques and new emerged privacy ...", "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": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2214212625000481", "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": 7, "title": "Per-Instance Privacy Accounting for Differentially Private Stochastic ...", "date": "", "ddg_snippet": "Differentially private stochastic gradient descent (DP-SGD) is the workhorse algorithm for recent advances in private deep learning. It provides a single privacy guarantee to all datapoints in the dataset. We propose an efficient algorithm to compute per-instance privacy guarantees for individual examples when running DP-SGD. We use our algorithm to investigate per-instance privacy losses ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2206.02617v2", "content": "Differentially private stochastic gradient descent (DP-SGD) is the workhorse algorithm for recent advances in private deep learning. It provides a single privacy guarantee to all datapoints in the dataset. We propose an efficient algorithm to compute per-instance privacy guarantees for individual examples when running DP-SGD. We use our algorithm to investigate per-instance privacy losses ..."}
+{"idx": 8, "title": "Differential privacy in deep learning: Privacy and beyond", "date": "", "ddg_snippet": "Motivated by the security risks of deep neural networks, such as various membership and attribute inference attacks, differential privacy has emerged as a promising approach for protecting the privacy of neural networks. As a result, it is crucial to investigate the frontier intersection of differential privacy and deep learning, which is the main motivation behind this survey. Most of the ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0167739X23002315", "content": "Motivated by the security risks of deep neural networks, such as various membership and attribute inference attacks, differential privacy has emerged as a promising approach for protecting the privacy of neural networks. As a result, it is crucial to investigate the frontier intersection of differential privacy and deep learning, which is the main motivation behind this survey. Most of the ..."}
+{"idx": 9, "title": "Rectifying Privacy and Efficacy Measurements in Machine Unlearning: A ...", "date": "", "ddg_snippet": "Then, we propose RULI (Rectified Unlearning Evaluation Framework via Likelihood Inference), a novel framework to address critical gaps in the evaluation of inexact unlearning methods. RULI introduces a dual-objective attack to measure both unlearning efficacy and privacy risks at a per -sample granularity.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.13009v1", "content": "Then, we propose RULI (Rectified Unlearning Evaluation Framework via Likelihood Inference), a novel framework to address critical gaps in the evaluation of inexact unlearning methods. RULI introduces a dual-objective attack to measure both unlearning efficacy and privacy risks at a per -sample granularity."}
diff --git a/data/sampled_jsons/Per-Instance_Privacy_for_Machine_Unlearning_A_Principled_Approach_Thudi_et_al_2024.jsonl b/data/sampled_jsons/Per-Instance_Privacy_for_Machine_Unlearning_A_Principled_Approach_Thudi_et_al_2024.jsonl
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index 0000000000000000000000000000000000000000..0f356f947190f15d6b68ec70688aa8499b5876b8
--- /dev/null
+++ b/data/sampled_jsons/Per-Instance_Privacy_for_Machine_Unlearning_A_Principled_Approach_Thudi_et_al_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Leveraging Per - Instance Privacy for Machine Unlearning", "date": "", "ddg_snippet": "Armed with per - instance privacy losses, we revisit Chien et al .’s 2024 theoretical analysis of noisy gradient descent as an unlearning scheme (coined “Langevin unlearning ” a .k.a “noisy fine-tuning”), based on training without the forget set.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=0A4Y9qRnu9", "content": "Armed with per - instance privacy losses, we revisit Chien et al .’s 2024 theoretical analysis of noisy gradient descent as an unlearning scheme (coined “Langevin unlearning ” a .k.a “noisy fine-tuning”), based on training without the forget set."}
+{"idx": 1, "title": "Nicolas PAPERNOT | Professor (Assistant) | Doctor of Philosophy", "date": "", "ddg_snippet": "Leveraging Per - Instance Privacy for Machine Unlearning . Preprint. May 2025.We present a principled , per - instance approach to quantifying the difficulty of unlearning via fine-tuning.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/profile/Nicolas-Papernot", "content": "Leveraging Per - Instance Privacy for Machine Unlearning . Preprint. May 2025.We present a principled , per - instance approach to quantifying the difficulty of unlearning via fine-tuning."}
+{"idx": 2, "title": "Anvith Thudi - Google Akademik", "date": "", "ddg_snippet": "Takip et . Anvith Thudi . 2024 . Leveraging Per - Instance Privacy for Machine Unlearning .", "subpage_snippet": "", "source": "scholar.google.ro", "link": "https://scholar.google.ro/citations?user=bTEybH0AAAAJ&hl=tr", "content": "Takip et . Anvith Thudi . 2024 . Leveraging Per - Instance Privacy for Machine Unlearning ."}
+{"idx": 3, "title": "GitHub - tamlhp/awesome- machine - unlearning : Awesome Machine ...", "date": "", "ddg_snippet": "- - Tight Bounds for Machine Unlearning via Differential Privacy . 2023. Huang et al .Model-Intrinsic The model-intrinsic approaches include unlearning methods designed for a specific type of models.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/tamlhp/awesome-machine-unlearning", "content": "- - Tight Bounds for Machine Unlearning via Differential Privacy . 2023. Huang et al .Model-Intrinsic The model-intrinsic approaches include unlearning methods designed for a specific type of models."}
+{"idx": 4, "title": "Machine Unlearning : Challenges in Data Quality and Access", "date": "", "ddg_snippet": "[ Thudi et al ., 2022] Anvith Thudi , Gabriel Deza, Varun Chandrasekaran, and Nicolas Papernot. Unrolling SGD: understanding factors influencing machine unlearning . In IEEE European Symposium on Security and Privacy (Eu-roSP), pages 303–319, 2022.", "subpage_snippet": "", "source": "www.ijcai.org", "link": "https://www.ijcai.org/proceedings/2024/0987.pdf", "content": "[ Thudi et al ., 2022] Anvith Thudi , Gabriel Deza, Varun Chandrasekaran, and Nicolas Papernot. Unrolling SGD: understanding factors influencing machine unlearning . In IEEE European Symposium on Security and Privacy (Eu-roSP), pages 303–319, 2022."}
+{"idx": 5, "title": "UnUnlearning: Unlearning is not sufficient for content... | Bytez", "date": "", "ddg_snippet": "Unlearning emerged as a promising solution for knowledge control, originally developed for removal of privacy -sensitive information (Bourtoule et al ., 2021).", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/arxiv/2407.00106/paper", "content": "Unlearning emerged as a promising solution for knowledge control, originally developed for removal of privacy -sensitive information (Bourtoule et al ., 2021)."}
+{"idx": 6, "title": "CMC | Free Full-Text | Ensuring User Privacy and Model Security via...", "date": "", "ddg_snippet": "Thudi et al .This makes it possible to use single-gradient cancellation to cancel a point from a model trained with SD loss, effectively reducing this unlearning error. Mahadevan and Mathioudakis [44] proposed a similar approach to differential privacy .", "subpage_snippet": "", "source": "www.techscience.com", "link": "https://www.techscience.com/cmc/v77n2/54792/html", "content": "Thudi et al .This makes it possible to use single-gradient cancellation to cancel a point from a model trained with SD loss, effectively reducing this unlearning error. Mahadevan and Mathioudakis [44] proposed a similar approach to differential privacy ."}
+{"idx": 7, "title": "Learn To Unlearn for Deep Neural Networks: Minimizing Unlearning ...", "date": "", "ddg_snippet": "Thudi et . al . [43] design of a new training objective penalty that limits the overall change in weights during SGD and as a result facili-tates approximate unlearning .", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/WACV2024/papers/Hoang_Learn_To_Unlearn_for_Deep_Neural_Networks_Minimizing_Unlearning_Interference_WACV_2024_paper.pdf", "content": "Thudi et . al . [43] design of a new training objective penalty that limits the overall change in weights during SGD and as a result facili-tates approximate unlearning ."}
+{"idx": 8, "title": "Frontiers in Machine Learning: Synthesizing May... - DEV Community", "date": "", "ddg_snippet": "Per - Instance Machine Unlearning Enables Targeted Data Removal Advances in machine unlearning provide tools for efficient and fair removal of specific data influences from trained models.", "subpage_snippet": "", "source": "dev.to", "link": "https://dev.to/khanali21/frontiers-in-machine-learning-synthesizing-may-2025-arxiv-cslg-advances-in-efficiency-18o5", "content": "Per - Instance Machine Unlearning Enables Targeted Data Removal Advances in machine unlearning provide tools for efficient and fair removal of specific data influences from trained models."}
+{"idx": 9, "title": "Contrastive Unlearning : A Contrastive Approach to Machine ...", "date": "", "ddg_snippet": "[ Thudi et al ., 2022] Anvith Thudi , Hengrui Jia, Ilia Shu-mailov, and Nicolas Papernot. On the necessity of au-ditable algorithmic definitions for machine unlearning .", "subpage_snippet": "", "source": "www.cs.emory.edu", "link": "https://www.cs.emory.edu/~jyang71/files/ctul.pdf", "content": "[ Thudi et al ., 2022] Anvith Thudi , Hengrui Jia, Ilia Shu-mailov, and Nicolas Papernot. On the necessity of au-ditable algorithmic definitions for machine unlearning ."}
diff --git a/data/sampled_jsons/Perdomo_2024_'The_Relative_Value_of_Prediction_in_Algorithmic_Decision_Making'_methodology_binary_co.jsonl b/data/sampled_jsons/Perdomo_2024_'The_Relative_Value_of_Prediction_in_Algorithmic_Decision_Making'_methodology_binary_co.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e07a737cbcc504bc486df797a3fb7ec3bdf13502
--- /dev/null
+++ b/data/sampled_jsons/Perdomo_2024_'The_Relative_Value_of_Prediction_in_Algorithmic_Decision_Making'_methodology_binary_co.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "The Relative Value of Prediction in Algorithmic Decision Making", "date": "", "ddg_snippet": "Abstract Algorithmic predictions are increasingly used to inform the allocations of goods and interventions in the public sphere. In these domains, predictions serve as a means to an end. They provide stakeholders with insights into likelihood of future events as a means to improve decision making quality, and enhance social welfare .", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v235/perdomo24a.html", "content": "Abstract Algorithmic predictions are increasingly used to inform the allocations of goods and interventions in the public sphere. In these domains, predictions serve as a means to an end. They provide stakeholders with insights into likelihood of future events as a means to improve decision making quality, and enhance social welfare ."}
+{"idx": 1, "title": "The Relative Value of Prediction in Algorithmic Decision Making", "date": "", "ddg_snippet": "We identify simple, sharp conditions determining the relative value of prediction vis-`a-vis expanding access, within several statistical models that are popular amongst quantitative so-cial scientists. Furthermore, we illustrate how these theoretical insights can guide the design of algorithmic decision making systems in practice.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=oaACFfNbXl", "content": "We identify simple, sharp conditions determining the relative value of prediction vis-`a-vis expanding access, within several statistical models that are popular amongst quantitative so-cial scientists. Furthermore, we illustrate how these theoretical insights can guide the design of algorithmic decision making systems in practice."}
+{"idx": 2, "title": "The Relative Value of Prediction in Algorithmic Decision Making", "date": "", "ddg_snippet": "Juan Carlos Perdomo Algorithmic predictions are increasingly used to inform the allocations of goods and interventions in the public sphere. In these domains, predictions serve as a means to an end. They provide stakeholders with insights into likelihood of future events as a means to improve decision making quality, and enhance social welfare . However, if maximizing welfare is the ultimate ...", "subpage_snippet": "", "source": "www.deepnlp.org", "link": "https://www.deepnlp.org/content/articles/the-relative-value-of-prediction-in-algorithmic-decision-making", "content": "Juan Carlos Perdomo Algorithmic predictions are increasingly used to inform the allocations of goods and interventions in the public sphere. In these domains, predictions serve as a means to an end. They provide stakeholders with insights into likelihood of future events as a means to improve decision making quality, and enhance social welfare . However, if maximizing welfare is the ultimate ..."}
+{"idx": 3, "title": "The Relative Value of Prediction in Algorithmic Decision Making", "date": "", "ddg_snippet": "Poster The Relative Value of Prediction in Algorithmic Decision Making Juan Perdomo [ Abstract ] [ Paper PDF ] 2024 Poster", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2024/poster/33078", "content": "Poster The Relative Value of Prediction in Algorithmic Decision Making Juan Perdomo [ Abstract ] [ Paper PDF ] 2024 Poster"}
+{"idx": 4, "title": "\"The Relative Value of Prediction in Algorithmic Decision Making.\" - dblp", "date": "", "ddg_snippet": "Bibliographic details on The Relative Value of Prediction in Algorithmic Decision Making .", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/conf/icml/Perdomo24", "content": "Bibliographic details on The Relative Value of Prediction in Algorithmic Decision Making ."}
+{"idx": 5, "title": "RISS: \"The Relative Value of Prediction\" by Juan C. Perdomo (Harvard)", "date": "", "ddg_snippet": "Join us to engage in lively discussions in the session, \" The Relative Value of Prediction \" delivered by Juan C. Perdomo , postdoctoral fellow at the Harvard Center for Research on Computation and Society. Abstract Throughout the social world, predictive algorithms are a means to an end.", "subpage_snippet": "", "source": "groups.google.com", "link": "https://groups.google.com/g/ml-news/c/p3rIZ0taNyM", "content": "Join us to engage in lively discussions in the session, \" The Relative Value of Prediction \" delivered by Juan C. Perdomo , postdoctoral fellow at the Harvard Center for Research on Computation and Society. Abstract Throughout the social world, predictive algorithms are a means to an end."}
+{"idx": 6, "title": "[FLAIR seminar] The Relative Value of Prediction - EPFL", "date": "", "ddg_snippet": "Algorithmic predictions are increasingly used to inform the allocations of goods and services in the public sphere. In these domains predictions serve as a means to an end. They provide stakeholders with insights into the likelihood of future events in order to improve decision making quality and enhance social welfare . However if maximizing welfare is the question to what extent is improving ...", "subpage_snippet": "", "source": "memento.epfl.ch", "link": "https://memento.epfl.ch/event/flair-seminar-the-relative-value-of-prediction/", "content": "Algorithmic predictions are increasingly used to inform the allocations of goods and services in the public sphere. In these domains predictions serve as a means to an end. They provide stakeholders with insights into the likelihood of future events in order to improve decision making quality and enhance social welfare . However if maximizing welfare is the question to what extent is improving ..."}
+{"idx": 7, "title": "CITP Seminar: Juan Carlos Perdomo - The Relative Value of Prediction", "date": "", "ddg_snippet": "Algorithmic predictions are increasingly used to inform the allocations of goods and services in the public sphere. In these domains, predictions serve as a means to an end. They provide stakeholders with insights into the likelihood of future events in order to improve decision making quality, and enhance social welfare .", "subpage_snippet": "", "source": "citp.princeton.edu", "link": "https://citp.princeton.edu/events/2024/citp-seminar-juan-carlos-perdomo-relative-value-prediction", "content": "Algorithmic predictions are increasingly used to inform the allocations of goods and services in the public sphere. In these domains, predictions serve as a means to an end. They provide stakeholders with insights into the likelihood of future events in order to improve decision making quality, and enhance social welfare ."}
+{"idx": 8, "title": "RISS Seminar: 'The Relative Value of Prediction' by Juan C. Perdomo", "date": "", "ddg_snippet": "The Rational Intelligence Seminar Series (RISS) invites you to a discussion on 'The Relative Value of Prediction' by Juan C. Perdomo , postdoctoral fellow at Harvard Center for Research on Computation and Society.", "subpage_snippet": "", "source": "mlscientist.com", "link": "https://mlscientist.com/riss-seminar-the-relative-value-of-prediction-by-juan-c-perdomo-3/", "content": "The Rational Intelligence Seminar Series (RISS) invites you to a discussion on 'The Relative Value of Prediction' by Juan C. Perdomo , postdoctoral fellow at Harvard Center for Research on Computation and Society."}
+{"idx": 9, "title": "The Relative Value of Prediction in Algorithmic Decision Making", "date": "", "ddg_snippet": "Abstract:Algorithmic predictions are increasingly used to inform the allocations of goods and interventions in the public sphere. In these domains, predictions serve as a means to an end. They provide stakeholders with insights into likelihood of future events as a means to improve decision making quality, and enhance social welfare .", "subpage_snippet": "", "source": "bohrium.dp.tech", "link": "https://bohrium.dp.tech/paper/arxiv/2312.08511", "content": "Abstract:Algorithmic predictions are increasingly used to inform the allocations of goods and interventions in the public sphere. In these domains, predictions serve as a means to an end. They provide stakeholders with insights into likelihood of future events as a means to improve decision making quality, and enhance social welfare ."}
diff --git a/data/sampled_jsons/Po-Hung_Yeh_Kuang-Huei_Lee_Jun-Cheng_Chen_Training-Free_Diffusion_Model_Alignment_with_Sampling_Demo.jsonl b/data/sampled_jsons/Po-Hung_Yeh_Kuang-Huei_Lee_Jun-Cheng_Chen_Training-Free_Diffusion_Model_Alignment_with_Sampling_Demo.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0edb42dbab89391afa616ed54e2c60afa2b1c584
--- /dev/null
+++ b/data/sampled_jsons/Po-Hung_Yeh_Kuang-Huei_Lee_Jun-Cheng_Chen_Training-Free_Diffusion_Model_Alignment_with_Sampling_Demo.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Training - free Diffusion Model Alignment with Sampling Demons", "date": "", "ddg_snippet": "Authors: Po - Hung Yeh , Kuang - Huei Lee , Jun - Cheng Chen . View a PDF of the paper titled Training - free Diffusion Model Alignment with Sampling Demons , by Po - Hung Yeh and 2 other authors.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.05760", "content": "Authors: Po - Hung Yeh , Kuang - Huei Lee , Jun - Cheng Chen . View a PDF of the paper titled Training - free Diffusion Model Alignment with Sampling Demons , by Po - Hung Yeh and 2 other authors."}
+{"idx": 1, "title": "Training - free Diffusion Model Alignment with Sampling Demons", "date": "", "ddg_snippet": "One approach to aligning diffusion models with user preferences is to fine-tune using reinforcement learning (RL) to optimize the models based on rewards signals that reflect the user preferences (Black et al., 2023; Fan et al., 2023) .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.05760v1", "content": "One approach to aligning diffusion models with user preferences is to fine-tune using reinforcement learning (RL) to optimize the models based on rewards signals that reflect the user preferences (Black et al., 2023; Fan et al., 2023) ."}
+{"idx": 2, "title": "ICLR Poster Training - Free Diffusion Model Alignment with ...", "date": "", "ddg_snippet": "alignment method for diffusion models .Our method can be easily integrated with existing diffusion models without further training .Our experiments show that the proposed approach significantly improves the average aesthetics scores for text-to-image generation.", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2025/poster/28034", "content": "alignment method for diffusion models .Our method can be easily integrated with existing diffusion models without further training .Our experiments show that the proposed approach significantly improves the average aesthetics scores for text-to-image generation."}
+{"idx": 3, "title": "Training - free Diffusion Model Alignment with Sampling Demons", "date": "", "ddg_snippet": "8 Oct 2024 · Po - Hung Yeh , Kuang - Huei Lee , Jun - Cheng Chen ·. Edit social preview.To the best of our knowledge, the proposed approach is the first inference-time, backpropagation- free preference alignment method for diffusion models .", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/paper/training-free-diffusion-model-alignment-with", "content": "8 Oct 2024 · Po - Hung Yeh , Kuang - Huei Lee , Jun - Cheng Chen ·. Edit social preview.To the best of our knowledge, the proposed approach is the first inference-time, backpropagation- free preference alignment method for diffusion models ."}
+{"idx": 4, "title": "GitHub - aiiu-lab/DemonSampling: [ICLR'25] Official implementation of...", "date": "", "ddg_snippet": "[ICLR'25] Official implementation of \" Training - free Diffusion Model Alignment with Sampling Demons \". Diffusion models have revolutionized image generation; however, aligning these models with diverse user preferences remains a significant challenge.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/aiiu-lab/DemonSampling", "content": "[ICLR'25] Official implementation of \" Training - free Diffusion Model Alignment with Sampling Demons \". Diffusion models have revolutionized image generation; however, aligning these models with diverse user preferences remains a significant challenge."}
+{"idx": 5, "title": "Training - free Diffusion Model Alignment with Sampling Demons", "date": "", "ddg_snippet": "Po - Hung Yeh , Kuang - Huei Lee , Jun - Cheng Chen .This paper introduces a training - free technique called \" Sampling Demons \" for aligning diffusion models with target objectives.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/training-free-diffusion-model-alignment-sampling-demons", "content": "Po - Hung Yeh , Kuang - Huei Lee , Jun - Cheng Chen .This paper introduces a training - free technique called \" Sampling Demons \" for aligning diffusion models with target objectives."}
+{"idx": 6, "title": "Training - free Diffusion Model Alignment with Sampling Demons", "date": "", "ddg_snippet": "View recent discussion. Abstract: Aligning diffusion models with user preferences has been a key challenge. Existing methods for aligning diffusion models either require retraining or are limited to differentiable reward functions.", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/abs/2410.05760v2", "content": "View recent discussion. Abstract: Aligning diffusion models with user preferences has been a key challenge. Existing methods for aligning diffusion models either require retraining or are limited to differentiable reward functions."}
+{"idx": 7, "title": "Training - free Diffusion Model Alignment with Sampling Demons", "date": "", "ddg_snippet": "Po - Hung Yeh , Kuang - Huei Lee , Jun - Cheng Chen . Academia Sinica, Google DeepMind. Aligning diffusion models with user preferences has been a key challenge. Existing methods for aligning diffusion models either require retraining or are limited to differentiable reward functions.", "subpage_snippet": "", "source": "rareone0602.github.io", "link": "https://rareone0602.github.io/Demon_page/", "content": "Po - Hung Yeh , Kuang - Huei Lee , Jun - Cheng Chen . Academia Sinica, Google DeepMind. Aligning diffusion models with user preferences has been a key challenge. Existing methods for aligning diffusion models either require retraining or are limited to differentiable reward functions."}
+{"idx": 8, "title": "aiiu-lab/DemonSampling - Githubissues", "date": "", "ddg_snippet": "Sampling Demon Official implementation of ICLR 2025 \" Sampling Demon \" (arXiv:2410.05760). This repository contains the official implementation of Sampling Demon , an inference-time, backpropagation- free preference alignment method for diffusion models .", "subpage_snippet": "", "source": "githubissues.com", "link": "https://githubissues.com/aiiu-lab/DemonSampling/readme", "content": "Sampling Demon Official implementation of ICLR 2025 \" Sampling Demon \" (arXiv:2410.05760). This repository contains the official implementation of Sampling Demon , an inference-time, backpropagation- free preference alignment method for diffusion models ."}
+{"idx": 9, "title": "Development | aiiu-lab/DemonSampling | DeepWiki", "date": "", "ddg_snippet": "api.py: Low-level API functions for ODE/SDE integration and demon sampling . utils.py: Utility functions for image processing and latent space operations. models /: Diffusion model implementations and interfaces.", "subpage_snippet": "", "source": "deepwiki.com", "link": "https://deepwiki.com/aiiu-lab/DemonSampling/6-development", "content": "api.py: Low-level API functions for ODE/SDE integration and demon sampling . utils.py: Utility functions for image processing and latent space operations. models /: Diffusion model implementations and interfaces."}
diff --git a/data/sampled_jsons/Position_Current_Model_Licensing_Practices_Section_3.3_license_conflict_Llama2_Llama3.jsonl b/data/sampled_jsons/Position_Current_Model_Licensing_Practices_Section_3.3_license_conflict_Llama2_Llama3.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..04e5b4466aaf9578589d9744077152ea6dff7b0e
--- /dev/null
+++ b/data/sampled_jsons/Position_Current_Model_Licensing_Practices_Section_3.3_license_conflict_Llama2_Llama3.jsonl
@@ -0,0 +1,9 @@
+{"idx": 0, "title": "ICML Poster Position : Current Model Licensing Practices are...", "date": "", "ddg_snippet": "Developers are often required to choose a license to publish and govern the use of their models. Popular options include Apache-2.0, OpenRAIL (Responsible AI Licenses ), Creative Commons Licenses (CCs), Llama 2 , and GPL-3.0.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/40180", "content": "Developers are often required to choose a license to publish and govern the use of their models. Popular options include Apache-2.0, OpenRAIL (Responsible AI Licenses ), Creative Commons Licenses (CCs), Llama 2 , and GPL-3.0."}
+{"idx": 1, "title": "ICML 2025 Statistics: Position Track - Paper Copilot", "date": "", "ddg_snippet": "Position : Current Model Licensing Practices are Dragging Us into a Quagmire of Legal Noncompliance.2, 3 , 3 ,4.", "subpage_snippet": "", "source": "papercopilot.com", "link": "https://papercopilot.com/statistics/icml-statistics/icml-2025-statistics-position-track/", "content": "Position : Current Model Licensing Practices are Dragging Us into a Quagmire of Legal Noncompliance.2, 3 , 3 ,4."}
+{"idx": 2, "title": "Position: Current Model Licensing Practices are Dragging Us into a ...", "date": "", "ddg_snippet": "For example, RAILs and Llama2 License , which also contain discriminatory terms, could conflict with GPL-3.0 (Contractor et al., 2022). Even though it is possible to exploit solutions from OSS projects to address the issues above, one unique nature of ML projects, Implicit Dependency, makes them signifi-cantly harder to resolve.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=1rh8iTehBc", "content": "For example, RAILs and Llama2 License , which also contain discriminatory terms, could conflict with GPL-3.0 (Contractor et al., 2022). Even though it is possible to exploit solutions from OSS projects to address the issues above, one unique nature of ML projects, Implicit Dependency, makes them signifi-cantly harder to resolve."}
+{"idx": 3, "title": "ICML 2025 Sneak Peek: The New Laws of AI | Medium", "date": "", "ddg_snippet": "When Position Papers Take Center Stage. Perhaps the clearest signal of the field’s maturation is the unprecedented prominence of “ position papers” in the conference’s most prestigious oral presentation track.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/foundation-models-deep-dive/icml-2025-sneak-peek-the-new-laws-of-ai-1210789c66a2", "content": "When Position Papers Take Center Stage. Perhaps the clearest signal of the field’s maturation is the unprecedented prominence of “ position papers” in the conference’s most prestigious oral presentation track."}
+{"idx": 4, "title": "Bingsheng He @ NUS SoC", "date": "", "ddg_snippet": "Position: The Current AI Conference Model is Unsustainable! Position : Current Model Licensing Practices are Dragging Us into a Quagmire of Legal Noncompliance. Call for contributions.", "subpage_snippet": "", "source": "www.comp.nus.edu.sg", "link": "https://www.comp.nus.edu.sg/~hebs/", "content": "Position: The Current AI Conference Model is Unsustainable! Position : Current Model Licensing Practices are Dragging Us into a Quagmire of Legal Noncompliance. Call for contributions."}
+{"idx": 5, "title": "ICML 2025 Review Controversies Spark Academic Debate | CSPaper", "date": "", "ddg_snippet": "Position : Current Model Licensing Practices are Dragging Us into a Quagmire of Legal Noncompliance Highlights legal risks in model licensing .", "subpage_snippet": "", "source": "cspaper.org", "link": "https://cspaper.org/topic/62/icml-2025-review-controversies-spark-academic-debate", "content": "Position : Current Model Licensing Practices are Dragging Us into a Quagmire of Legal Noncompliance Highlights legal risks in model licensing ."}
+{"idx": 6, "title": "Haotian (Rick) Jiang - Amazon | LinkedIn", "date": "", "ddg_snippet": "Excited to share that our paper “ Position : Current Model Licensing Practices are Dragging Us into a Quagmire of Legal Noncompliance” is accepted as… Liked by Haotian (Rick) Jiang. Join now to see all activity.", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/in/haotian-rick-jiang-a27517b8", "content": "Excited to share that our paper “ Position : Current Model Licensing Practices are Dragging Us into a Quagmire of Legal Noncompliance” is accepted as… Liked by Haotian (Rick) Jiang. Join now to see all activity."}
+{"idx": 7, "title": "AIモデルに家族がある？ 186... | note（ノート）", "date": "", "ddg_snippet": "\" Position : Current model licensing practices are dragging us into a quagmire of legal noncompliance.\"\"Charting and navigating hugging face's model atlas.\"", "subpage_snippet": "", "source": "note.com", "link": "https://note.com/rami_engineer/n/n7fa7dbfa88d4", "content": "\" Position : Current model licensing practices are dragging us into a quagmire of legal noncompliance.\"\"Charting and navigating hugging face's model atlas.\""}
+{"idx": 8, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/Position_Evaluating_Generative_AI_Systems_Is_a_Social_Science_Measurement_Challenge_appendices_conse.jsonl b/data/sampled_jsons/Position_Evaluating_Generative_AI_Systems_Is_a_Social_Science_Measurement_Challenge_appendices_conse.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..1ef9024addfd1012a19995c3832d1d4a28fa8d57
--- /dev/null
+++ b/data/sampled_jsons/Position_Evaluating_Generative_AI_Systems_Is_a_Social_Science_Measurement_Challenge_appendices_conse.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Generative AI - Generative AI Tools Ad Viewing ads is privacy protected by DuckDuckGo. Ad clicks are managed by Microsoft's ad network ( more info ).", "date": "", "ddg_snippet": "Discover how generative AI can boost low-code productivity and empower developers. Learn how to shift your enterprise outcomes into high gear with Pega GenAI", "subpage_snippet": "", "source": "duckduckgo.com", "link": "https://duckduckgo.com/y.js?ad_domain=pega.com&ad_provider=bingv7aa&ad_type=txad&click_metadata=pCn2cq9EU080GpQO4--My0zvzckpftRnTUSmX9UhYgTMWb6wzNyc0_WwDbheW7gWHZ0p01OYQjIo54Y-e2sliCYI-iB4yIFrBmkrZkEbfA5eBiDvYVh8cnFUI4RL0h_Q.hteDdAt5lECJuGj0Jo1KQA&rut=2cfaebf360f937059408729da870966994505f5d65ee9c323cab3d613cbc3ae9&u3=https://www.bing.com/aclick?ld=e8gkoNhCfl8zQLqLvLFG_38DVUCUwy8ffoGfLT0i3rqRt3dMGuVDYEWPFWKERk7GtE2WCeKG4wXlsAmosEbheUFUVD_qLV5Fye87qA6VvVMWa2P0n38MEZXkx-ey-MoJc6jIDf48MJlX_TGBVKVeRQ0ZeHpioYiKFri-rqfpE7f7FhN3OrdmdbqipqXe8AMDiC7KPunQ&u=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&rlid=e2004dd6e3cf1c607fe4250472f68733&vqd=4-274982672360640636299548843026291553497&iurl={1}IG=4742539805C94435BC47F4EFAF0E37D4&CID=05202929036B67B720923F590200667A&ID=DevEx,5046.1", "content": "Discover how generative AI can boost low-code productivity and empower developers. Learn how to shift your enterprise outcomes into high gear with Pega GenAI"}
+{"idx": 1, "title": "(PDF) To Rely or Not to Rely? Evaluating Interventions for", "date": "", "ddg_snippet": "As Large Language Models become integral to decision-making, optimism about their power is tempered with concern over their errors.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/387322197_To_Rely_or_Not_to_Rely_Evaluating_Interventions_for_Appropriate_Reliance_on_Large_Language_Models", "content": "As Large Language Models become integral to decision-making, optimism about their power is tempered with concern over their errors."}
+{"idx": 2, "title": "The More You Automate, the Less You See: Hidden Pitfalls of AI", "date": "", "ddg_snippet": "... these risks, we design controlled experiments that isolate each failure mode while addressing challenges unique to evaluating AI scientist systems ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.08713v1", "content": "... these risks, we design controlled experiments that isolate each failure mode while addressing challenges unique to evaluating AI scientist systems ..."}
+{"idx": 3, "title": "What Comes After Harm? Mapping Reparative Actions in AI through", "date": "", "ddg_snippet": "A related technique is AI red-teaming which adopts a more adversarial perspective on stress-testing AI systems and simulation of bad actors or ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.05687v1", "content": "A related technique is AI red-teaming which adopts a more adversarial perspective on stress-testing AI systems and simulation of bad actors or ..."}
+{"idx": 4, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
+{"idx": 5, "title": "Building and Measuring Trust between Large Language Models", "date": "", "ddg_snippet": "... and measuring trust among humans is complicated, not least because the correlation between different interpretations of trust is subject to ongoing ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.15858v1", "content": "... and measuring trust among humans is complicated, not least because the correlation between different interpretations of trust is subject to ongoing ..."}
+{"idx": 6, "title": "Impact of gist intervention on automated system", "date": "", "ddg_snippet": "Similarly an automated tool may be used to generate quantitative scores that help system users make judgments about whether information that is ...", "subpage_snippet": "", "source": "cognitiveresearchjournal.springeropen.com", "link": "https://cognitiveresearchjournal.springeropen.com/articles/10.1186/s41235-024-00594-2", "content": "Similarly an automated tool may be used to generate quantitative scores that help system users make judgments about whether information that is ..."}
+{"idx": 7, "title": "metabolic integrity - TOWARDS LIFE-KNOWLEDGE", "date": "", "ddg_snippet": "... advances the philosophical, scientific, and systemic project of Life-Value Onto-Axiology (LVOA), initiated by John McMurtry, into a next-generation ...", "subpage_snippet": "", "source": "bsahely.com", "link": "https://bsahely.com/tag/metabolic-integrity/", "content": "... advances the philosophical, scientific, and systemic project of Life-Value Onto-Axiology (LVOA), initiated by John McMurtry, into a next-generation ..."}
+{"idx": 8, "title": "integral ontology - TOWARDS LIFE-KNOWLEDGE", "date": "", "ddg_snippet": "... advances the philosophical, scientific, and systemic project of Life-Value Onto-Axiology (LVOA), initiated by John McMurtry, into a next-generation ...", "subpage_snippet": "", "source": "bsahely.com", "link": "https://bsahely.com/tag/integral-ontology/", "content": "... advances the philosophical, scientific, and systemic project of Life-Value Onto-Axiology (LVOA), initiated by John McMurtry, into a next-generation ..."}
+{"idx": 9, "title": "V. Five Principles for the Use of Artificial Intelligence in", "date": "", "ddg_snippet": "... Actively involving a diverse and intersectional array of educators, including those with disabilities, in the development, design, and evaluation of ...", "subpage_snippet": "", "source": "www.nea.org", "link": "https://www.nea.org/resource-library/artificial-intelligence-education/v-five-principles-use-artificial-intelligence-education", "content": "... Actively involving a diverse and intersectional array of educators, including those with disabilities, in the development, design, and evaluation of ..."}
diff --git a/data/sampled_jsons/Position_Evaluating_Generative_AI_Systems_Social_Science_Measurement_Challenge_Section_6_criticism_l.jsonl b/data/sampled_jsons/Position_Evaluating_Generative_AI_Systems_Social_Science_Measurement_Challenge_Section_6_criticism_l.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8407abfac0e63e8487094ebc6767eed3eec37bc0
--- /dev/null
+++ b/data/sampled_jsons/Position_Evaluating_Generative_AI_Systems_Social_Science_Measurement_Challenge_Section_6_criticism_l.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Position : Evaluating Generative AI Systems Is a Social Science ...", "date": "", "ddg_snippet": "The measurement tasks involved in evaluating generative AI (GenAI) systems lack sufficient sci - entific rigor, leading to what has been described as “a tangle of sloppy tests [and] apples-to-oranges comparisons” (Roose, 2024).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.00561", "content": "The measurement tasks involved in evaluating generative AI (GenAI) systems lack sufficient sci - entific rigor, leading to what has been described as “a tangle of sloppy tests [and] apples-to-oranges comparisons” (Roose, 2024)."}
+{"idx": 1, "title": "(PDF) Position : Evaluating Generative AI Systems is a Social ...", "date": "", "ddg_snippet": "Generative AI systems produce a range of ethical and social risks. Evaluation of these risks is a critical step on the path to ensuring the safety of these systems . However, evaluation requires the availability of validated and established measurement approaches and tools.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/388657599_Position_Evaluating_Generative_AI_Systems_is_a_Social_Science_Measurement_Challenge", "content": "Generative AI systems produce a range of ethical and social risks. Evaluation of these risks is a critical step on the path to ensuring the safety of these systems . However, evaluation requires the availability of validated and established measurement approaches and tools."}
+{"idx": 2, "title": "ICML Poster Position : Evaluating Generative AI Systems Is a Social ...", "date": "", "ddg_snippet": "The measurement tasks involved in evaluating generative AI (GenAI) systems lack sufficient scientific rigor, leading to what has been described as \"a tangle of sloppy tests [and] apples-to-oranges comparisons\" (Roose, 2024).", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/40182", "content": "The measurement tasks involved in evaluating generative AI (GenAI) systems lack sufficient scientific rigor, leading to what has been described as \"a tangle of sloppy tests [and] apples-to-oranges comparisons\" (Roose, 2024)."}
+{"idx": 3, "title": "NeurIPS Evaluating Generative AI Systems is a Social Science ...", "date": "", "ddg_snippet": "Across academia, industry, and government, there is an increasing awareness that evaluating generative AI (GenAI) systems is challenging , as concepts related to their capabilities (e.g., intelligence, reasoning) and risks (e.g., stereotyping, anthropomorphism) are especially...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/104194", "content": "Across academia, industry, and government, there is an increasing awareness that evaluating generative AI (GenAI) systems is challenging , as concepts related to their capabilities (e.g., intelligence, reasoning) and risks (e.g., stereotyping, anthropomorphism) are especially..."}
+{"idx": 4, "title": "Evaluating Generative AI Systems is a Social Science ...", "date": "", "ddg_snippet": "generative AI systems that borrow from how social scientists measure human behaviors and abilities. Think of current AI evaluation like using a ruler made of rubber - it stretches and bends, giving different measurements each time.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/evaluating-generative-ai-systems-is-social-science", "content": "generative AI systems that borrow from how social scientists measure human behaviors and abilities. Think of current AI evaluation like using a ruler made of rubber - it stretches and bends, giving different measurements each time."}
+{"idx": 5, "title": "Downes.ca ~ Stephen's Web ~ Evaluating Generative AI Systems is...", "date": "", "ddg_snippet": "The argument in this short paper ( 6 page PDF) is that \" measurement tasks involved in evaluating GenAI systems are highly reminiscent of measurement tasks found throughout the social sciences \" and thus \"the ML community would benefit from learning from and drawing on the social ...", "subpage_snippet": "", "source": "www.downes.ca", "link": "https://www.downes.ca/post/77850", "content": "The argument in this short paper ( 6 page PDF) is that \" measurement tasks involved in evaluating GenAI systems are highly reminiscent of measurement tasks found throughout the social sciences \" and thus \"the ML community would benefit from learning from and drawing on the social ..."}
+{"idx": 6, "title": "ICML 2025 Statistics: Position Track - Paper Copilot", "date": "", "ddg_snippet": "Position : Evaluating Generative AI Systems Is a Social Science Measurement Challenge . Position : AI for Just Work : Constructing Diverse Imaginations of AI beyond \"Replacing Humans''. social ethical env impact.", "subpage_snippet": "", "source": "papercopilot.com", "link": "https://papercopilot.com/statistics/icml-statistics/icml-2025-statistics-position-track/", "content": "Position : Evaluating Generative AI Systems Is a Social Science Measurement Challenge . Position : AI for Just Work : Constructing Diverse Imaginations of AI beyond \"Replacing Humans''. social ethical env impact."}
+{"idx": 7, "title": "New Apple study challenges whether AI models truly... - Ars Technica", "date": "", "ddg_snippet": "Many proponents of generative AI have contested the Apple results, while critics have latched onto the study as a definitive knockout blow for LLM credibility. Apple's results, combined with the USAMO findings, seem to strengthen the case made by critics like Marcus that these systems ...", "subpage_snippet": "", "source": "arstechnica.com", "link": "https://arstechnica.com/ai/2025/06/new-apple-study-challenges-whether-ai-models-truly-reason-through-problems/", "content": "Many proponents of generative AI have contested the Apple results, while critics have latched onto the study as a definitive knockout blow for LLM credibility. Apple's results, combined with the USAMO findings, seem to strengthen the case made by critics like Marcus that these systems ..."}
+{"idx": 8, "title": "Evaluating Generative AI Content for Style and Theme", "date": "", "ddg_snippet": "Evaluating generative AI allows for the assessment of how well AI models produce content that aligns with given style or theme parameters. It ensures the outputs meet quality standards, fit the intended purpose, and maintain a consistent tone or visual aesthetic.", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/advice/0/how-can-you-evaluate-generative-ai-content-mbycf", "content": "Evaluating generative AI allows for the assessment of how well AI models produce content that aligns with given style or theme parameters. It ensures the outputs meet quality standards, fit the intended purpose, and maintain a consistent tone or visual aesthetic."}
+{"idx": 9, "title": "Towards Interactive Evaluations for Interaction Harms in Human- AI ...", "date": "", "ddg_snippet": "2024). Safety evaluations of generative AI systems build on a rich history of ethical considerations in the field of natural language processing (NLP), where, prior to the advent of large pre-trained models, researchers have long grappled with issues of harm from language technologies (Dev et al.", "subpage_snippet": "", "source": "knightcolumbia.org", "link": "https://knightcolumbia.org/content/towards-interactive-evaluations-for-interaction-harms-in-human-ai-systems", "content": "2024). Safety evaluations of generative AI systems build on a rich history of ethical considerations in the field of natural language processing (NLP), where, prior to the advent of large pre-trained models, researchers have long grappled with issues of harm from language technologies (Dev et al."}
diff --git a/data/sampled_jsons/Practical_Almost-Linear-Time_Approximation_Algorithms_for_Hybrid_and_Overlapping_Graph_Clustering_co_year_2022.jsonl b/data/sampled_jsons/Practical_Almost-Linear-Time_Approximation_Algorithms_for_Hybrid_and_Overlapping_Graph_Clustering_co_year_2022.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..af6d0f9ad5a1cc199b9cef0c8471019eb6e5f42e
--- /dev/null
+++ b/data/sampled_jsons/Practical_Almost-Linear-Time_Approximation_Algorithms_for_Hybrid_and_Overlapping_Graph_Clustering_co_year_2022.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF ICML 2022 — Practical Almost-Linear-Time Approximation Algorithms for ...", "date": "", "ddg_snippet": "Practical Almost-Linear-Time Approximation Algorithms for Hybrid and Overlapping Graph Clustering Konstantinos Ameranis, Lorenzo Orecchia, Kunal Talwar, Charalampos Tsourakakis", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/media/icml-2022/Slides/16880_M2pKLQk.pdf", "content": "Practical Almost-Linear-Time Approximation Algorithms for Hybrid and Overlapping Graph Clustering Konstantinos Ameranis, Lorenzo Orecchia, Kunal Talwar, Charalampos Tsourakakis"}
+{"idx": 1, "title": "ICML 2022 Practical Almost - Linear - Time Approximation ...", "date": "", "ddg_snippet": "While almost - linear - time approximation algorithms are known for edge-boundary-based graph partitioning, little progress has been made on fast algorithms for HGP, even in the special case of vertex-boundary-based graph partitioning.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2022/spotlight/16880", "content": "While almost - linear - time approximation algorithms are known for edge-boundary-based graph partitioning, little progress has been made on fast algorithms for HGP, even in the special case of vertex-boundary-based graph partitioning."}
+{"idx": 2, "title": "ICML Poster Practical Almost - Linear - Time Approximation ...", "date": "", "ddg_snippet": "While almost - linear - time approximation algorithms are known for edge-boundary-based graph partitioning, little progress has been made on fast algorithms for HGP, even in the special case of vertex-boundary-based graph partitioning. In this work, we introduce a frame-work based...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2022/poster/16879", "content": "While almost - linear - time approximation algorithms are known for edge-boundary-based graph partitioning, little progress has been made on fast algorithms for HGP, even in the special case of vertex-boundary-based graph partitioning. In this work, we introduce a frame-work based..."}
+{"idx": 3, "title": "ICML 2022 s", "date": "", "ddg_snippet": "Practical Almost-Linear-Time Approximation Algorithms for Hybrid and Overlapping Graph Clustering An Asymptotic Test for Conditional Independence using Analytic Kernel Embeddings", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/Conferences/2022/Videos", "content": "Practical Almost-Linear-Time Approximation Algorithms for Hybrid and Overlapping Graph Clustering An Asymptotic Test for Conditional Independence using Analytic Kernel Embeddings"}
+{"idx": 4, "title": "ICML 2022 Schedule", "date": "", "ddg_snippet": "[11:40] Practical Almost-Linear-Time Approximation Algorithms for Hybrid and Overlapping Graph Clustering . [11:45] Fair and Fast k-Center Clustering for Data ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2022/calendar", "content": "[11:40] Practical Almost-Linear-Time Approximation Algorithms for Hybrid and Overlapping Graph Clustering . [11:45] Fair and Fast k-Center Clustering for Data ..."}
+{"idx": 5, "title": "ICML 2022 Papers", "date": "", "ddg_snippet": "Practical Almost-Linear-Time Approximation Algorithms for Hybrid and Overlapping Graph Clustering · Distributionally Robust $Q$-Learning · Optimally ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2022/papers.html", "content": "Practical Almost-Linear-Time Approximation Algorithms for Hybrid and Overlapping Graph Clustering · Distributionally Robust $Q$-Learning · Optimally ..."}
+{"idx": 6, "title": "A Hybrid Mixture of 𝑡-Factor Analyzers for Robust Clustering", "date": "", "ddg_snippet": "... hybrid approach for estimating the mixture model of t t -factor analyzers (MtFA) that employs multivariate t t -distribution and factor model to ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.21120v2", "content": "... hybrid approach for estimating the mixture model of t t -factor analyzers (MtFA) that employs multivariate t t -distribution and factor model to ..."}
+{"idx": 7, "title": "Hybrid Topic‐Semantic Labeling and Graph Embeddings for", "date": "", "ddg_snippet": "... hybrid pipeline that bridges semantic topic modeling and graph ‐based representation learning to cluster legal documents in an unsupervised manner.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.00990v1", "content": "... hybrid pipeline that bridges semantic topic modeling and graph ‐based representation learning to cluster legal documents in an unsupervised manner."}
+{"idx": 8, "title": "An Update on the Comparison of MIP, CP and Hybrid Approaches", "date": "", "ddg_snippet": "... computational effort, we propose a matheuristic consisting of an Iterated Greedy Algorithm hybridized with a reconstruction phase based in executing ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/325624743_An_Update_on_the_Comparison_of_MIP_CP_and_Hybrid_Approaches_for_Mixed_Resource_Allocation_and_Scheduling", "content": "... computational effort, we propose a matheuristic consisting of an Iterated Greedy Algorithm hybridized with a reconstruction phase based in executing ..."}
+{"idx": 9, "title": "Task parallel assembly language for uncompromising parallelism", "date": "", "ddg_snippet": "... linear algebra codes contain implicit preconditions about how data is stored that hamper direct translation, S p EQ identifies the high-level ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/352534106_Task_parallel_assembly_language_for_uncompromising_parallelism", "content": "... linear algebra codes contain implicit preconditions about how data is stored that hamper direct translation, S p EQ identifies the high-level ..."}
diff --git a/data/sampled_jsons/Promoting_Fairness_Among_Dynamic_Agents_in_Online-Matching_Markets_Bad_Example_Example_2_SAMP_algori.jsonl b/data/sampled_jsons/Promoting_Fairness_Among_Dynamic_Agents_in_Online-Matching_Markets_Bad_Example_Example_2_SAMP_algori.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a3a66405b8832e9cdb7437ad97cf0709dada1738
--- /dev/null
+++ b/data/sampled_jsons/Promoting_Fairness_Among_Dynamic_Agents_in_Online-Matching_Markets_Bad_Example_Example_2_SAMP_algori.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Promoting Fairness Among Dynamic Agents in Online ...", "date": "", "ddg_snippet": "5 Nov 2024 — This work focuses on a fair online bipartite matching problem under poisson arrivals, where the objective is to maximize the minimum number of ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=0C3bLHwjsY&noteId=SXxUDRsiUp", "content": "5 Nov 2024 — This work focuses on a fair online bipartite matching problem under poisson arrivals, where the objective is to maximize the minimum number of ..."}
+{"idx": 1, "title": "Promoting Fairness Among Dynamic Agents in Online- ...", "date": "", "ddg_snippet": "by W Ma · 2024 · Cited by 1 — Figure 1: A bad example used to show hardness results for any randomized and non-rejecting algorithms and the tightness of competitive analysis for SAMP . Proof. 30 pages", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/959f70ee50044bed305e48e3484005a7-Paper-Conference.pdf", "content": "by W Ma · 2024 · Cited by 1 — Figure 1: A bad example used to show hardness results for any randomized and non-rejecting algorithms and the tightness of competitive analysis for SAMP . Proof. 30 pages"}
+{"idx": 2, "title": "Fairness Maximization among Offline Agents in Online ...", "date": "", "ddg_snippet": "We study the prototypical online matching model with known, independent, and identical (KIID) arrivals, which is widely used to model the dynamic arrivals of ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3569705", "content": "We study the prototypical online matching model with known, independent, and identical (KIID) arrivals, which is widely used to model the dynamic arrivals of ..."}
+{"idx": 3, "title": "Dynamic Fairness-aware Recommendation Through Multi ...", "date": "", "ddg_snippet": "The agent -oriented framework allows for multiple fairness concerns and for their definitions to be heterogeneous and independent of each other. ( 2 ). We ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3690653", "content": "The agent -oriented framework allows for multiple fairness concerns and for their definitions to be heterogeneous and independent of each other. ( 2 ). We ..."}
+{"idx": 4, "title": "Two-sided Competing Matching Recommendation Markets ...", "date": "", "ddg_snippet": "29 May 2024 — In this paper, we propose a novel algorithm and present an in-depth analysis of the problem of complementary preferences in matching markets .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2301.10230v3", "content": "29 May 2024 — In this paper, we propose a novel algorithm and present an in-depth analysis of the problem of complementary preferences in matching markets ."}
+{"idx": 5, "title": "Group Fairness in Dynamic Refugee Assignment", "date": "", "ddg_snippet": "by D Freund · 2023 · Cited by 33 — The Proportionally Optimized fairness rule ( Example 2 ) aims to mitigate the negative effect that the existence of a group may cause to members ... 55 pages", "subpage_snippet": "", "source": "www.hbs.edu", "link": "https://www.hbs.edu/ris/Publication+Files/23-047_34a19acd-f45a-441a-a8e2-5e11ab67b60b.pdf", "content": "by D Freund · 2023 · Cited by 33 — The Proportionally Optimized fairness rule ( Example 2 ) aims to mitigate the negative effect that the existence of a group may cause to members ... 55 pages"}
+{"idx": 6, "title": "A Dynamic Double Auction Framework for Matching Patient Agents", "date": "", "ddg_snippet": "Abstract. In this paper we present and evaluate a general framework for the design of truthful auctions for matching agents in a dynamic , two -sided market .", "subpage_snippet": "", "source": "www.ed.aaai.org", "link": "https://www.ed.aaai.org/Papers/JAIR/Vol30/JAIR-3004.pdf", "content": "Abstract. In this paper we present and evaluate a general framework for the design of truthful auctions for matching agents in a dynamic , two -sided market ."}
+{"idx": 7, "title": "Stability, Fairness And The Pursuit of Happiness in ...", "date": "", "ddg_snippet": "by G Benade · 2023 · Cited by 1 — Recommender systems facilitate markets by matching buyers to products (and their sellers) in large online platforms. They do so by learning buyers' preferences ...", "subpage_snippet": "", "source": "papers.ssrn.com", "link": "https://papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID4561473_code1192005.pdf?abstractid=4241170&mirid=1", "content": "by G Benade · 2023 · Cited by 1 — Recommender systems facilitate markets by matching buyers to products (and their sellers) in large online platforms. They do so by learning buyers' preferences ..."}
+{"idx": 8, "title": "Do algorithms play fair? Analysing the perceived ...", "date": "", "ddg_snippet": "by N Jabagi · 2025 · Cited by 9 — We explore how workers perceive the fairness of HR-decisions made by algorithms and how those perceptions impact job satisfaction and perceived organisational ...", "subpage_snippet": "", "source": "www.tandfonline.com", "link": "https://www.tandfonline.com/doi/full/10.1080/09585192.2024.2441448", "content": "by N Jabagi · 2025 · Cited by 9 — We explore how workers perceive the fairness of HR-decisions made by algorithms and how those perceptions impact job satisfaction and perceived organisational ..."}
+{"idx": 9, "title": "arXiv:2109.08934v2 [cs.GT] 26 Sep 2021", "date": "", "ddg_snippet": "Fairness Maximization among . Offline Agents in Online - Matching Markets . Will Ma1, Pan Xu2, and Yifan Xu3. 1 Columbia University. 2 New Jersey Institute of ...", "subpage_snippet": "", "source": "web3.arxiv.org", "link": "https://web3.arxiv.org/pdf/2109.08934", "content": "Fairness Maximization among . Offline Agents in Online - Matching Markets . Will Ma1, Pan Xu2, and Yifan Xu3. 1 Columbia University. 2 New Jersey Institute of ..."}
diff --git a/data/sampled_jsons/PyTorch_DeepSpeed_FairScale_tensor_parallelism_data_parallelism_hybrid_framework_year_2024.jsonl b/data/sampled_jsons/PyTorch_DeepSpeed_FairScale_tensor_parallelism_data_parallelism_hybrid_framework_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..dd690c311b7953148b0189c84c2ee8a46d8c3d83
--- /dev/null
+++ b/data/sampled_jsons/PyTorch_DeepSpeed_FairScale_tensor_parallelism_data_parallelism_hybrid_framework_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Introducing PyTorch Fully Sharded Data Parallel (FSDP) API", "date": "", "ddg_snippet": "Mar 14, 2022 · It however requires the model to fit on one GPU. Recent approaches like DeepSpeed ZeRO and FairScale ’s Fully Sharded Data Parallel allow us to break this barrier by sharding a model’s parameters, gradients and optimizer states across data parallel workers while still maintaining the simplicity of data parallelism .", "subpage_snippet": "", "source": "pytorch.org", "link": "https://pytorch.org/blog/introducing-pytorch-fully-sharded-data-parallel-api/", "content": "Mar 14, 2022 · It however requires the model to fit on one GPU. Recent approaches like DeepSpeed ZeRO and FairScale ’s Fully Sharded Data Parallel allow us to break this barrier by sharding a model’s parameters, gradients and optimizer states across data parallel workers while still maintaining the simplicity of data parallelism ."}
+{"idx": 1, "title": "DeepSpeed Tensor Parallelism: A Comprehensive Guide", "date": "", "ddg_snippet": "Apr 24, 2025 · Learn how to implement DeepSpeed tensor parallelism for efficient model training and improved performance.", "subpage_snippet": "", "source": "www.byteplus.com", "link": "https://www.byteplus.com/en/topic/499163?title=deepspeed-tensor-parallelism-a-comprehensive-guide", "content": "Apr 24, 2025 · Learn how to implement DeepSpeed tensor parallelism for efficient model training and improved performance."}
+{"idx": 2, "title": "Pipeline Parallelism - DeepSpeed", "date": "", "ddg_snippet": "2 days ago · DeepSpeed v0.3 includes new support for pipeline parallelism ! Pipeline parallelism improves both the memory and compute efficiency of deep learning training by partitioning the layers of a model into stages that can be processed in parallel . DeepSpeed ’s training engine provides hybrid data and pipeline parallelism and can be further combined with model parallelism such as Megatron-LM. An ...", "subpage_snippet": "", "source": "www.deepspeed.ai", "link": "https://www.deepspeed.ai/tutorials/pipeline/", "content": "2 days ago · DeepSpeed v0.3 includes new support for pipeline parallelism ! Pipeline parallelism improves both the memory and compute efficiency of deep learning training by partitioning the layers of a model into stages that can be processed in parallel . DeepSpeed ’s training engine provides hybrid data and pipeline parallelism and can be further combined with model parallelism such as Megatron-LM. An ..."}
+{"idx": 3, "title": "Fairscale - Faster Pytorch", "date": "", "ddg_snippet": "We already presented using FairScale in PyTorch Lightning- the FSDP and DDP integration of PyTorch Lightning is based on FairScale . To use FairScale with PyTorch , import torch from fairscale .optim.oss import OSS from fairscale .nn. data _ parallel import ShardedDataParallel as ShardedDDP def train( rank: int, world_size: int, epochs: int ...", "subpage_snippet": "", "source": "finger-bone.github.io", "link": "https://finger-bone.github.io/faster-pytorch/04/", "content": "We already presented using FairScale in PyTorch Lightning- the FSDP and DDP integration of PyTorch Lightning is based on FairScale . To use FairScale with PyTorch , import torch from fairscale .optim.oss import OSS from fairscale .nn. data _ parallel import ShardedDataParallel as ShardedDDP def train( rank: int, world_size: int, epochs: int ..."}
+{"idx": 4, "title": "Tensor Parallelism | deepspeedai/DeepSpeed | DeepWiki", "date": "", "ddg_snippet": "Jul 11, 2025 · Architecture Overview DeepSpeed 's tensor parallelism is built around replacing standard PyTorch layers with distributed equivalents that automatically handle weight partitioning and communication. The system distinguishes between row- parallel and column- parallel layers, each with different communication patterns.", "subpage_snippet": "", "source": "deepwiki.com", "link": "https://deepwiki.com/deepspeedai/DeepSpeed/4.3-tensor-parallelism", "content": "Jul 11, 2025 · Architecture Overview DeepSpeed 's tensor parallelism is built around replacing standard PyTorch layers with distributed equivalents that automatically handle weight partitioning and communication. The system distinguishes between row- parallel and column- parallel layers, each with different communication patterns."}
+{"idx": 5, "title": "PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel", "date": "", "ddg_snippet": "This paper presents PyTorch [24] Fully Sharded Data Parallel (FSDP), which enables the training of large-scale models by shard-ing model parameters. The FSDP algorithm is motivated by the ZeroRedundancyOptimizer [27, 28] technique from DeepSpeed but with a revised design and implementation that is aligned with the other components of PyTorch .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2304.11277", "content": "This paper presents PyTorch [24] Fully Sharded Data Parallel (FSDP), which enables the training of large-scale models by shard-ing model parameters. The FSDP algorithm is motivated by the ZeroRedundancyOptimizer [27, 28] technique from DeepSpeed but with a revised design and implementation that is aligned with the other components of PyTorch ."}
+{"idx": 6, "title": "Multi-GPU Training With Model Parallelism in DeepSpeed", "date": "", "ddg_snippet": "May 9, 2025 · This allows you to train larger models without running out of memory. Flexible Parallelism : DeepSpeed supports both data and model parallelism (including pipeline parallelism and tensor parallelism ), giving developers freedom to mix and match strategies based on their architecture and hardware constraints.", "subpage_snippet": "", "source": "dev.co", "link": "https://dev.co/ai/multi-gpu-training-with-model-parallelism-in-deepspeed", "content": "May 9, 2025 · This allows you to train larger models without running out of memory. Flexible Parallelism : DeepSpeed supports both data and model parallelism (including pipeline parallelism and tensor parallelism ), giving developers freedom to mix and match strategies based on their architecture and hardware constraints."}
+{"idx": 7, "title": "Parallelism Strategies for Distributed Training | by Ekin Karabulut", "date": "", "ddg_snippet": "Pytorch Tensor Parallel : Tensor Parallelism (TP) is built on top of DistributedTensor(DTensor) and provides several Parallelism styles: Rowwise, Colwise, and Pairwise Parallelism . DeepSpeed Tensor Parallelism for Inference of HuggingFace Models.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/better-programming/parallelism-strategies-for-distributed-training-303904798a12", "content": "Pytorch Tensor Parallel : Tensor Parallelism (TP) is built on top of DistributedTensor(DTensor) and provides several Parallelism styles: Rowwise, Colwise, and Pairwise Parallelism . DeepSpeed Tensor Parallelism for Inference of HuggingFace Models."}
+{"idx": 8, "title": "Fairscale Integration with Pytorch -lightning | Restackio", "date": "", "ddg_snippet": "Explore how Fairscale enhances Pytorch -lightning for efficient distributed training and model parallelism . The framework for autonomous intelligence.", "subpage_snippet": "", "source": "store-restack.vercel.app", "link": "https://store-restack.vercel.app/p/pytorch-lightning-knowledge-fairscale-answer-cat-ai", "content": "Explore how Fairscale enhances Pytorch -lightning for efficient distributed training and model parallelism . The framework for autonomous intelligence."}
+{"idx": 9, "title": "Train any LLM using an Open-source framework of your choice!", "date": "", "ddg_snippet": "These frameworks include Deepspeed , Megatron- DeepSpeed , FairScale , Megatron-LM, Colossal-AI, BMTrain, Mesh TensorFlow, MaxText, and Alpa. Parallelism — These techniques allow scaling of models by layer parallelism and tensor parallelism .", "subpage_snippet": "", "source": "readmedium.com", "link": "https://readmedium.com/8-awesome-libraries-to-train-large-language-models-ef4092836bb9", "content": "These frameworks include Deepspeed , Megatron- DeepSpeed , FairScale , Megatron-LM, Colossal-AI, BMTrain, Mesh TensorFlow, MaxText, and Alpa. Parallelism — These techniques allow scaling of models by layer parallelism and tensor parallelism ."}
diff --git a/data/sampled_jsons/RAGGED_paper_Towards_Informed_Design_of_Scalable_and_Stable_RAG_Systems_Section_5_Figure_3_peak-then_year_2024.jsonl b/data/sampled_jsons/RAGGED_paper_Towards_Informed_Design_of_Scalable_and_Stable_RAG_Systems_Section_5_Figure_3_peak-then_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..168ba4678b62d38031069a8f68ba56320abd3ed7
--- /dev/null
+++ b/data/sampled_jsons/RAGGED_paper_Towards_Informed_Design_of_Scalable_and_Stable_RAG_Systems_Section_5_Figure_3_peak-then_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "RAGGED Definition & Meaning - Merriam-Webster", "date": "", "ddg_snippet": "The meaning of RAGGED is roughly unkempt. How to use ragged in a sentence.", "subpage_snippet": "", "source": "www.merriam-webster.com", "link": "https://www.merriam-webster.com/dictionary/ragged", "content": "The meaning of RAGGED is roughly unkempt. How to use ragged in a sentence."}
+{"idx": 1, "title": "RAGGED | English meaning - Cambridge Dictionary", "date": "", "ddg_snippet": "RAGGED definition: 1. (of clothes) torn and not in good condition: 2. (of a person) untidy, dirty, and wearing old…. Learn more.", "subpage_snippet": "", "source": "dictionary.cambridge.org", "link": "https://dictionary.cambridge.org/dictionary/english/ragged", "content": "RAGGED definition: 1. (of clothes) torn and not in good condition: 2. (of a person) untidy, dirty, and wearing old…. Learn more."}
+{"idx": 2, "title": "Ragged Definition & Meaning | Britannica Dictionary", "date": "", "ddg_snippet": "RAGGED meaning: 1 : having an edge or surface that is not straight or even; 2 : in bad condition especially because of being torn", "subpage_snippet": "", "source": "www.britannica.com", "link": "https://www.britannica.com/dictionary/ragged", "content": "RAGGED meaning: 1 : having an edge or surface that is not straight or even; 2 : in bad condition especially because of being torn"}
+{"idx": 3, "title": "Ragged - definition of ragged by The Free Dictionary", "date": "", "ddg_snippet": "Define ragged . ragged synonyms, ragged pronunciation, ragged translation, English dictionary definition of ragged . adj. 1. Tattered, frayed, or torn: ragged clothes. 2. Dressed in tattered or threadbare clothes: a ragged scarecrow. 3. Unkempt or shaggy: ragged hair.", "subpage_snippet": "", "source": "www.thefreedictionary.com", "link": "https://www.thefreedictionary.com/ragged", "content": "Define ragged . ragged synonyms, ragged pronunciation, ragged translation, English dictionary definition of ragged . adj. 1. Tattered, frayed, or torn: ragged clothes. 2. Dressed in tattered or threadbare clothes: a ragged scarecrow. 3. Unkempt or shaggy: ragged hair."}
+{"idx": 4, "title": "RAGGED definition and meaning | Collins English Dictionary", "date": "", "ddg_snippet": "You can say that something is ragged when it is untidy or uneven . She could hear his ragged breathing, as if he had been running. O'Brien formed the men into a ragged line.", "subpage_snippet": "", "source": "www.collinsdictionary.com", "link": "https://www.collinsdictionary.com/dictionary/english/ragged", "content": "You can say that something is ragged when it is untidy or uneven . She could hear his ragged breathing, as if he had been running. O'Brien formed the men into a ragged line."}
+{"idx": 5, "title": "ragged adjective - Definition, pictures, pronunciation and usage...", "date": "", "ddg_snippet": "Definition of ragged adjective in Oxford Advanced Learner's Dictionary. Meaning, pronunciation, picture, example sentences, grammar, usage notes, synonyms and more.", "subpage_snippet": "", "source": "www.oxfordlearnersdictionaries.com", "link": "https://www.oxfordlearnersdictionaries.com/definition/english/ragged", "content": "Definition of ragged adjective in Oxford Advanced Learner's Dictionary. Meaning, pronunciation, picture, example sentences, grammar, usage notes, synonyms and more."}
+{"idx": 6, "title": "ragged - WordReference.com Dictionary of English", "date": "", "ddg_snippet": "rags , ragged or tattered clothing: The tramp was dressed in rags . any article of apparel regarded deprecatingly or self-deprecatingly, esp. a dress: It's just an old rag I had in the closet.", "subpage_snippet": "", "source": "www.wordreference.com", "link": "https://www.wordreference.com/definition/ragged", "content": "rags , ragged or tattered clothing: The tramp was dressed in rags . any article of apparel regarded deprecatingly or self-deprecatingly, esp. a dress: It's just an old rag I had in the closet."}
+{"idx": 7, "title": "ragged | meaning of ragged in Longman Dictionary of Contemporary...", "date": "", "ddg_snippet": "ragged meaning, definition, what is ragged : torn and in bad condition: Learn more.", "subpage_snippet": "", "source": "www.ldoceonline.com", "link": "https://www.ldoceonline.com/dictionary/ragged", "content": "ragged meaning, definition, what is ragged : torn and in bad condition: Learn more."}
+{"idx": 8, "title": "RAGGED Definition & Meaning | Dictionary .com", "date": "", "ddg_snippet": "Ragged definition: clothed in tattered garments.. See examples of RAGGED used in a sentence.", "subpage_snippet": "", "source": "www.dictionary.com", "link": "https://www.dictionary.com/browse/ragged", "content": "Ragged definition: clothed in tattered garments.. See examples of RAGGED used in a sentence."}
+{"idx": 9, "title": "RAGGED Synonyms: 176 Similar and Opposite Words - Merriam-Webster", "date": "", "ddg_snippet": "Synonyms for RAGGED : jagged, broken, scraggly, craggy, scraggy, rugged, serrated, rough; Antonyms of RAGGED : clean, soft, smooth, regular, uniform, even, unbroken, flat", "subpage_snippet": "", "source": "www.merriam-webster.com", "link": "https://www.merriam-webster.com/thesaurus/ragged", "content": "Synonyms for RAGGED : jagged, broken, scraggly, craggy, scraggy, rugged, serrated, rough; Antonyms of RAGGED : clean, soft, smooth, regular, uniform, even, unbroken, flat"}
diff --git a/data/sampled_jsons/RAGGED_three_retriever_paradigms_sparse_dense_hybrid_year_2024.jsonl b/data/sampled_jsons/RAGGED_three_retriever_paradigms_sparse_dense_hybrid_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ff337e5b9ee86a2049af6bd418733a78905760bb
--- /dev/null
+++ b/data/sampled_jsons/RAGGED_three_retriever_paradigms_sparse_dense_hybrid_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Sparse Meets Dense: A Hybrid Approach to Enhance Scientific Document ...", "date": "", "ddg_snippet": "Finally, we combine the sparse and dense retrieval models to produce a hybrid retriever (see Fig. 1). Our hybrid model uses a simple weighted combination of the query/document similarities in the sparse and dense embedding spaces.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2401.04055", "content": "Finally, we combine the sparse and dense retrieval models to produce a hybrid retriever (see Fig. 1). Our hybrid model uses a simple weighted combination of the query/document similarities in the sparse and dense embedding spaces."}
+{"idx": 1, "title": "Hybrid Retrieval with BM42 | Haystack", "date": "", "ddg_snippet": "Qdrant Hybrid Retriever compares dense and sparse query and document embeddings and retrieves the most relevant documents, merging the scores with Reciprocal Rank Fusion.", "subpage_snippet": "", "source": "haystack.deepset.ai", "link": "https://haystack.deepset.ai/cookbook/hybrid_retrieval_bm42", "content": "Qdrant Hybrid Retriever compares dense and sparse query and document embeddings and retrieves the most relevant documents, merging the scores with Reciprocal Rank Fusion."}
+{"idx": 2, "title": "HybridRAG: Overview of Hybrid Retrieval Methodologies in RAG", "date": "", "ddg_snippet": "About Research Paper delving into the various retrieval architectures in RAG, and describing how combining sparse and dense vector search can elevate model performance, specifically with smaller sized transformer models.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/sidsomani2005/HybridRAG", "content": "About Research Paper delving into the various retrieval architectures in RAG, and describing how combining sparse and dense vector search can elevate model performance, specifically with smaller sized transformer models."}
+{"idx": 3, "title": "Hybrid Search for RAG | Dense & Sparse Retrieval", "date": "", "ddg_snippet": "Implement hybrid search strategies by combining dense (vector) and sparse (keyword) retrievers for comprehensive RAG results.", "subpage_snippet": "", "source": "apxml.com", "link": "https://apxml.com/courses/optimizing-rag-for-production/chapter-2-advanced-retrieval-optimization/hybrid-search-rag", "content": "Implement hybrid search strategies by combining dense (vector) and sparse (keyword) retrievers for comprehensive RAG results."}
+{"idx": 4, "title": "PDF ragged_afm - adaptive-foundation-models.org", "date": "", "ddg_snippet": "Better retrieval gains in open-domain Small retriever gains amplified in tasks than in special-domain tasks. special domains. Dense retrievers improve recall but not always reader performance. Specialized tasks (BioASQ) benefit more from dense retrievers than open-domain tasks. Key Takeaways 1. Suboptimal RAG can be worse than no-context.", "subpage_snippet": "", "source": "adaptive-foundation-models.org", "link": "https://adaptive-foundation-models.org/posters/RAGGED_AFM_2024_Poster.pdf", "content": "Better retrieval gains in open-domain Small retriever gains amplified in tasks than in special-domain tasks. special domains. Dense retrievers improve recall but not always reader performance. Specialized tasks (BioASQ) benefit more from dense retrievers than open-domain tasks. Key Takeaways 1. Suboptimal RAG can be worse than no-context."}
+{"idx": 5, "title": "[2401.04055] Sparse Meets Dense: A Hybrid Approach to Enhance ...", "date": "", "ddg_snippet": "Traditional information retrieval is based on sparse bag-of-words vector representations of documents and queries. More recent deep-learning approaches have used dense embeddings learned using a transformer-based large language model. We show that on a classic benchmark on scientific document retrieval in the medical domain of cystic fibrosis, that both of these models perform roughly ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2401.04055", "content": "Traditional information retrieval is based on sparse bag-of-words vector representations of documents and queries. More recent deep-learning approaches have used dense embeddings learned using a transformer-based large language model. We show that on a classic benchmark on scientific document retrieval in the medical domain of cystic fibrosis, that both of these models perform roughly ..."}
+{"idx": 6, "title": "Dense vs Sparse: A Short, Chaotic, and Honest History of RAG Retrievers ...", "date": "", "ddg_snippet": "These embeddings could be used alone or in hybrid retrieval setups, combining the precision of sparse methods with the semantic reach of dense representations.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@pinareceaktan/dense-vs-sparse-a-short-chaotic-and-honest-history-of-rag-retrievers-from-tf-idf-to-colbert-7bb3a60414a1", "content": "These embeddings could be used alone or in hybrid retrieval setups, combining the precision of sparse methods with the semantic reach of dense representations."}
+{"idx": 7, "title": "Retrieval Techniques - Sparse, Dense, and Hybrid Representations - LinkedIn", "date": "", "ddg_snippet": "Hybrid Techniques Sparse techniques are efficient, and dense techniques alleviate the vocabulary mismatch problem. Hybrid techniques attempt to unify the best of both worlds using document expansion.", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/pulse/retrieval-techniques-sparse-dense-hybrid-najeeb-khan-ph-d--wmtpc", "content": "Hybrid Techniques Sparse techniques are efficient, and dense techniques alleviate the vocabulary mismatch problem. Hybrid techniques attempt to unify the best of both worlds using document expansion."}
+{"idx": 8, "title": "Core Components - Rag a richfull strategy.", "date": "", "ddg_snippet": "Core Components Retriever Searches a database to find relevant documents based on the user query. Uses similarity search (e.g., cosine similarity between vector embeddings). Types: Sparse retrievers : Traditional (e.g., BM25). Dense retrievers : Modern (e.g., dual encoder models). Hybrid : Combines both.", "subpage_snippet": "", "source": "alawiii.github.io", "link": "https://alawiii.github.io/Rag-book/chapter2/core-components.html", "content": "Core Components Retriever Searches a database to find relevant documents based on the user query. Uses similarity search (e.g., cosine similarity between vector embeddings). Types: Sparse retrievers : Traditional (e.g., BM25). Dense retrievers : Modern (e.g., dual encoder models). Hybrid : Combines both."}
+{"idx": 9, "title": "Understanding RAG Part VI: Effective Retrieval Optimization", "date": "", "ddg_snippet": "1. Hybrid Search and Reranking Hybrid search combines two retrieval criteria to obtain a set of relevant documents (or document chunks). A common approach is to combine sparse and dense retrieval. Sparse retrieval uses keyword-based methods like TF-IDF to match exact terms, making it effective for precise term matching. In contrast, dense retrieval leverages embeddings (numerical ...", "subpage_snippet": "", "source": "machinelearningmastery.com", "link": "https://machinelearningmastery.com/understanding-rag-part-vi-effective-retrieval-optimization/", "content": "1. Hybrid Search and Reranking Hybrid search combines two retrieval criteria to obtain a set of relevant documents (or document chunks). A common approach is to combine sparse and dense retrieval. Sparse retrieval uses keyword-based methods like TF-IDF to match exact terms, making it effective for precise term matching. In contrast, dense retrieval leverages embeddings (numerical ..."}
diff --git a/data/sampled_jsons/RAG_Stability_Score_RSS_formula_definition_peak_performance_minimum.jsonl b/data/sampled_jsons/RAG_Stability_Score_RSS_formula_definition_peak_performance_minimum.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..171d5f781651c54b50dee36fe596bf8b24654754
--- /dev/null
+++ b/data/sampled_jsons/RAG_Stability_Score_RSS_formula_definition_peak_performance_minimum.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "RAG genomic variation causes autoimmune diseases ...", "date": "", "ddg_snippet": "by N Haque · 2023 · Cited by 8 — We constructed the first full-length structural model of human RAG recombinase across four functional states of the recombination process.", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC10579426/", "content": "by N Haque · 2023 · Cited by 8 — We constructed the first full-length structural model of human RAG recombinase across four functional states of the recombination process."}
+{"idx": 1, "title": "A fine-tuning enhanced RAG system with quantized ...", "date": "", "ddg_snippet": "by K Rangan · 2024 · Cited by 32 — This study presents an innovative enhancement to retrieval-augmented generation ( RAG ) systems by seamlessly integrating fine-tuned large language models (LLMs) ...", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41598-024-79110-x", "content": "by K Rangan · 2024 · Cited by 32 — This study presents an innovative enhancement to retrieval-augmented generation ( RAG ) systems by seamlessly integrating fine-tuned large language models (LLMs) ..."}
+{"idx": 2, "title": "RAG1-DNA Binding in V(D)J Recombination: SPECIFICITY ...", "date": "", "ddg_snippet": "by M Ciubotaru · 2003 · Cited by 42 — The RAG1 and RAG2 proteins together constitute the nuclease that initiates the assembly of immunoglobulin and T cell receptor genes in a reaction known as V(D) ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0021925820865399", "content": "by M Ciubotaru · 2003 · Cited by 42 — The RAG1 and RAG2 proteins together constitute the nuclease that initiates the assembly of immunoglobulin and T cell receptor genes in a reaction known as V(D) ..."}
+{"idx": 3, "title": "RAG and HMGB1 create a large bend in the 23RSS in the V(D ...", "date": "", "ddg_snippet": "by M Ciubotaru · 2013 · Cited by 33 — Minimal 'core' regions required for DNA cleavage and recombination activity have been defined for murine RAG1 (amino acids 384–1008) and RAG2 (amino acids 1–387) ...", "subpage_snippet": "", "source": "academic.oup.com", "link": "https://academic.oup.com/nar/article/41/4/2437/2414568", "content": "by M Ciubotaru · 2013 · Cited by 33 — Minimal 'core' regions required for DNA cleavage and recombination activity have been defined for murine RAG1 (amino acids 384–1008) and RAG2 (amino acids 1–387) ..."}
+{"idx": 4, "title": "architecture of the 12RSS in V(D)J recombination signal and ...", "date": "", "ddg_snippet": "by M Ciubotaru · 2015 · Cited by 15 — Abstract. V(D)J recombination is initiated by RAG1 and RAG2, which together with HMGB1 bind to a recombination signal sequence (12RSS or 23RSS) to form the.", "subpage_snippet": "", "source": "academic.oup.com", "link": "https://academic.oup.com/nar/article/43/2/917/2414594", "content": "by M Ciubotaru · 2015 · Cited by 15 — Abstract. V(D)J recombination is initiated by RAG1 and RAG2, which together with HMGB1 bind to a recombination signal sequence (12RSS or 23RSS) to form the."}
+{"idx": 5, "title": "Structural mechanism of a Rag GTPase activation ...", "date": "", "ddg_snippet": "by RE Lawrence · 2019 · Cited by 162 — To understand how the Rag nucleotide state controls LFC formation, we tested whether FLCN:FNIP2 could form a stable complex with Ragulator and ...", "subpage_snippet": "", "source": "www.science.org", "link": "https://www.science.org/doi/10.1126/science.aax0364", "content": "by RE Lawrence · 2019 · Cited by 162 — To understand how the Rag nucleotide state controls LFC formation, we tested whether FLCN:FNIP2 could form a stable complex with Ragulator and ..."}
+{"idx": 6, "title": "Retrieval-Augmented Generation with Estimation of Source ...", "date": "", "ddg_snippet": "by J Hwang · 2024 · Cited by 6 — As shown in Figure A3, RA- RAG demon- strates stable performance from κ = 4, a trend that remains consistent across all datasets. This result , with κ being less ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.22954", "content": "by J Hwang · 2024 · Cited by 6 — As shown in Figure A3, RA- RAG demon- strates stable performance from κ = 4, a trend that remains consistent across all datasets. This result , with κ being less ..."}
+{"idx": 7, "title": "Navigating the Maze of LLM Evaluation: A Guide to ...", "date": "", "ddg_snippet": "Several metrics have been developed to assess different facets of RAG performance : Faithfulness: This measures whether the generated answer ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@yujiisobe/navigating-the-maze-of-llm-evaluation-a-guide-to-benchmarks-rag-and-agent-assessment-fb7aef299e66", "content": "Several metrics have been developed to assess different facets of RAG performance : Faithfulness: This measures whether the generated answer ..."}
+{"idx": 8, "title": "A comprehensive survey of loss functions and metrics in ...", "date": "", "ddg_snippet": "by J Terven · 2025 · Cited by 48 — This paper presents a comprehensive review of loss functions and performance metrics in deep learning, highlighting key developments and practical insights", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s10462-025-11198-7", "content": "by J Terven · 2025 · Cited by 48 — This paper presents a comprehensive review of loss functions and performance metrics in deep learning, highlighting key developments and practical insights"}
+{"idx": 9, "title": "Linear Regression in Machine Learning", "date": "", "ddg_snippet": "13 Jun 2025 — Linear regression is a fundamental statistical method used to define linear relationship between a dependent and one or more independent variables.", "subpage_snippet": "", "source": "www.analyticsvidhya.com", "link": "https://www.analyticsvidhya.com/blog/2021/10/everything-you-need-to-know-about-linear-regression/", "content": "13 Jun 2025 — Linear regression is a fundamental statistical method used to define linear relationship between a dependent and one or more independent variables."}
diff --git a/data/sampled_jsons/RL_Incorrect_Synthetic_Data_Eight-Fold_LLaMA-2-Chat_Mistral-7B_base_models.jsonl b/data/sampled_jsons/RL_Incorrect_Synthetic_Data_Eight-Fold_LLaMA-2-Chat_Mistral-7B_base_models.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4b26bb2c468d3b3d3a232d299f288e8dbe407be8
--- /dev/null
+++ b/data/sampled_jsons/RL_Incorrect_Synthetic_Data_Eight-Fold_LLaMA-2-Chat_Mistral-7B_base_models.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "LocalAI models", "date": "", "ddg_snippet": "To install the model with the CLI, run: local-ai models install baidu_ernie-4.5-21b-a3b-thinking See also Installation to see how to install models ...", "subpage_snippet": "", "source": "localai.io", "link": "https://localai.io/gallery.html", "content": "To install the model with the CLI, run: local-ai models install baidu_ernie-4.5-21b-a3b-thinking See also Installation to see how to install models ..."}
+{"idx": 1, "title": "Glossary of common Machine Learning, Statistics and Data", "date": "", "ddg_snippet": "Bagging or bootstrap averaging is a technique where multiple models are created on the subset of data , and the final predictions are determined by ...", "subpage_snippet": "", "source": "www.analyticsvidhya.com", "link": "https://www.analyticsvidhya.com/glossary-of-common-statistics-and-machine-learning-terms/", "content": "Bagging or bootstrap averaging is a technique where multiple models are created on the subset of data , and the final predictions are determined by ..."}
+{"idx": 2, "title": "Synthetic Data Generation & Multi-Step RL for Reasoning ...", "date": "", "ddg_snippet": "7 Apr 2025 — SWiRL uses synthetic data generation and RL to learn multi-step reasoning and tool use, with two stages: data generation and RL optimization.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.04736v1", "content": "7 Apr 2025 — SWiRL uses synthetic data generation and RL to learn multi-step reasoning and tool use, with two stages: data generation and RL optimization."}
+{"idx": 3, "title": "[AINews] a calm before the storm • Buttondown", "date": "", "ddg_snippet": "... for quantizing cached KV activations, allowing a LLaMA - 7B model to be served with a context length of up to 1 million on a single A100-80GB GPU.", "subpage_snippet": "", "source": "buttondown.com", "link": "https://buttondown.com/ainews/archive/ainews-sxxx/", "content": "... for quantizing cached KV activations, allowing a LLaMA - 7B model to be served with a context length of up to 1 million on a single A100-80GB GPU."}
+{"idx": 4, "title": "jdp - LessWrong", "date": "", "ddg_snippet": "It used to be hard to find a base model to run this on but should now be fairly easy with LLaMa , Mixtral, et al. ... data is being extended in a loop ...", "subpage_snippet": "", "source": "www.lesswrong.com", "link": "https://www.lesswrong.com/users/jdp", "content": "It used to be hard to find a base model to run this on but should now be fairly easy with LLaMa , Mixtral, et al. ... data is being extended in a loop ..."}
+{"idx": 5, "title": "jdp - LessWrong 2.0 viewer", "date": "", "ddg_snippet": "... 3 does deep modeling of the data ... It used to be hard to find a base model to run this on but should now be fairly easy with LLaMa , Mixtral, et al.", "subpage_snippet": "", "source": "www.greaterwrong.com", "link": "https://www.greaterwrong.com/users/jdp", "content": "... 3 does deep modeling of the data ... It used to be hard to find a base model to run this on but should now be fairly easy with LLaMa , Mixtral, et al."}
+{"idx": 6, "title": "Can 1B LLM Surpass 405B LLM? Rethinking Compute-Optimal", "date": "", "ddg_snippet": "Large Language Models (LLMs) have shown significant improvements across a variety of domains (OpenAI, 2023 ; Hurst et al., 2024 ; Anthropic, 2023 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.06703v1", "content": "Large Language Models (LLMs) have shown significant improvements across a variety of domains (OpenAI, 2023 ; Hurst et al., 2024 ; Anthropic, 2023 ..."}
+{"idx": 7, "title": "Scaling LLM Test-Time Compute Optimally Can be More ...", "date": "", "ddg_snippet": "by CV Snell · Cited by 74 — [ 2 ]: RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight - Fold ... Both the PRM and ORM models utilize the PaLM 2 -S* base language ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=4FWAwZtd2n", "content": "by CV Snell · Cited by 74 — [ 2 ]: RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight - Fold ... Both the PRM and ORM models utilize the PaLM 2 -S* base language ..."}
+{"idx": 8, "title": "Daily Papers", "date": "", "ddg_snippet": "Using only 25 million tokens of synthetic data generated with MetaSynth, we successfully adapt a well-trained LLM ( Mistral - 7B -v0.3) to two specialized domains- ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=LLM-generated+synthetic+data", "content": "Using only 25 million tokens of synthetic data generated with MetaSynth, we successfully adapt a well-trained LLM ( Mistral - 7B -v0.3) to two specialized domains- ..."}
+{"idx": 9, "title": "The Comprehensive Guide to Fine-tuning LLM | by Sunil Rao", "date": "", "ddg_snippet": "For a customer support chatbot, you might choose a mid-sized open-source model like Llama 3 8B, Mistral 7B , or a similar instruction-tuned base ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/data-science-collective/comprehensive-guide-to-fine-tuning-llm-4a8fd4d0e0af", "content": "For a customer support chatbot, you might choose a mid-sized open-source model like Llama 3 8B, Mistral 7B , or a similar instruction-tuned base ..."}
diff --git a/data/sampled_jsons/RL_on_Incorrect_Synthetic_Data_Scales_the_Efficiency_of_LLM_Math_Reasoning_Eight-Fold_Section_5_base.jsonl b/data/sampled_jsons/RL_on_Incorrect_Synthetic_Data_Scales_the_Efficiency_of_LLM_Math_Reasoning_Eight-Fold_Section_5_base.jsonl
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index 0000000000000000000000000000000000000000..6430fb4f7b9a30c795a75f37df113ad216d93a5f
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+{"idx": 0, "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. First, we find that while the typical approach of finetuning a model on synthetic correct or positive ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2406.14532", "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. First, we find that while the typical approach of finetuning a model on synthetic correct or positive ..."}
+{"idx": 1, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math ...", "date": "", "ddg_snippet": "RL on Incorrect S ynthetic Data Scales the Efficien cy of LLM Math R easo ning by Eight-F old Amrith Setlur 1, Saurabh Garg 1, Xinyang (Young) Geng 2, N aman Garg 3, Virginia Smith 1 and Aviral ...", "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": "RL on Incorrect S ynthetic Data Scales the Efficien cy of LLM Math R easo ning by Eight-F old Amrith Setlur 1, Saurabh Garg 1, Xinyang (Young) Geng 2, N aman Garg 3, Virginia Smith 1 and Aviral ..."}
+{"idx": 2, "title": "RL on Incorrect Synthetic Data · MinWoo Park", "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": "dsdanielpark.github.io", "link": "https://dsdanielpark.github.io/llm/2024-06-25-RLonIncorrectSyntheticData.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": 3, "title": "PDF Reinforcement Learning for LLM Reasoning", "date": "", "ddg_snippet": "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", "subpage_snippet": "", "source": "cs224r.stanford.edu", "link": "https://cs224r.stanford.edu/slides/10_cs224r-rl_for_reasoning_lecture.pdf", "content": "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"}
+{"idx": 4, "title": "PDF RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math ...", "date": "", "ddg_snippet": "Abstract 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 ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/4b77d5b896c321a29277524a98a50215-Paper-Conference.pdf", "content": "Abstract 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 ..."}
+{"idx": 5, "title": "scaling-LLM-math-synthetic-data/README.md at master - GitHub", "date": "", "ddg_snippet": "Code and data used in the paper: \"Training on Incorrect Synthetic Data via RL Scales LLM Math Reasoning Eight-Fold \"", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/ars22/scaling-LLM-math-synthetic-data/blob/master/README.md", "content": "Code and data used in the paper: \"Training on Incorrect Synthetic Data via RL Scales LLM Math Reasoning Eight-Fold \""}
+{"idx": 6, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math ...", "date": "", "ddg_snippet": "Abstract 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": "arxiv.org", "link": "https://arxiv.org/html/2406.14532v1", "content": "Abstract 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": 7, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math ...", "date": "", "ddg_snippet": "This paper investigates the use of synthetic data for enhancing LLM math reasoning capabilities. The researchers discovered that this approach leads to only modest gains, and in some cases, even performance degradation. The study introduces a novel approach that utilizes both positive and negative synthetic data .", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/posters/9m87e9keq1/", "content": "This paper investigates the use of synthetic data for enhancing LLM math reasoning capabilities. The researchers discovered that this approach leads to only modest gains, and in some cases, even performance degradation. The study introduces a novel approach that utilizes both positive and negative synthetic data ."}
+{"idx": 8, "title": "RLonIncorrectSyntheticDataScalesthe EfficiencyofLLMMathReasoningbyEight ...", "date": "", "ddg_snippet": "RLonIncorrectSyntheticDataScalestheEfficiencyofLLMMathReasoningbyEight- Fold SFTbase Policy ! QnA pairs sampled from GPT/Gemini Synthetic Data Positive Data Correct answers \"! Negative Data Finetune policy RFT : SFT on self-generated correct answers !\" RL with step-level rewards on all answers #!\" e.g., preference - based RL Incorrect answers \"!", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2406.14532", "content": "RLonIncorrectSyntheticDataScalestheEfficiencyofLLMMathReasoningbyEight- Fold SFTbase Policy ! QnA pairs sampled from GPT/Gemini Synthetic Data Positive Data Correct answers \"! Negative Data Finetune policy RFT : SFT on self-generated correct answers !\" RL with step-level rewards on all answers #!\" e.g., preference - based RL Incorrect answers \"!"}
+{"idx": 9, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math ...", "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": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2406.14532", "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 ..."}
diff --git a/data/sampled_jsons/RL_on_Incorrect_Synthetic_Data_Scales_the_Efficiency_of_LLM_Math_Reasoning_by_Eight-Fold_Section_5_b.jsonl b/data/sampled_jsons/RL_on_Incorrect_Synthetic_Data_Scales_the_Efficiency_of_LLM_Math_Reasoning_by_Eight-Fold_Section_5_b.jsonl
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index 0000000000000000000000000000000000000000..8efa4afe440080ef933fbb2c712582774c7e52c9
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+{"idx": 0, "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. First, we find that while the typical approach of finetuning a model on synthetic correct or positive ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2406.14532", "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. First, we find that while the typical approach of finetuning a model on synthetic correct or positive ..."}
+{"idx": 1, "title": "RL on Incorrect Synthetic Data · MinWoo Park", "date": "", "ddg_snippet": "abstract: 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": "dsdanielpark.github.io", "link": "https://dsdanielpark.github.io/llm/2024-06-25-RLonIncorrectSyntheticData.html", "content": "abstract: 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": 2, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math ...", "date": "", "ddg_snippet": "Abstract 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": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/hash/4b77d5b896c321a29277524a98a50215-Abstract-Conference.html", "content": "Abstract 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": 3, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math ...", "date": "", "ddg_snippet": "Abstract 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 ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=9m87e9Keq1", "content": "Abstract 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 ..."}
+{"idx": 4, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math ...", "date": "", "ddg_snippet": "RL on Incorrect S ynthetic Data Scales the Efficien cy of LLM Math R easo ning by Eight-F old Amrith Setlur 1, Saurabh Garg 1, Xinyang (Young) Geng 2, N aman Garg 3, Virginia Smith 1 and Aviral ...", "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": "RL on Incorrect S ynthetic Data Scales the Efficien cy of LLM Math R easo ning by Eight-F old Amrith Setlur 1, Saurabh Garg 1, Xinyang (Young) Geng 2, N aman Garg 3, Virginia Smith 1 and Aviral ..."}
+{"idx": 5, "title": "scaling-LLM-math-synthetic-data/README.md at master - GitHub", "date": "", "ddg_snippet": "Code and data used in the paper: \"Training on Incorrect Synthetic Data via RL Scales LLM Math Reasoning Eight-Fold \"", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/ars22/scaling-LLM-math-synthetic-data/blob/master/README.md", "content": "Code and data used in the paper: \"Training on Incorrect Synthetic Data via RL Scales LLM Math Reasoning Eight-Fold \""}
+{"idx": 6, "title": "RLonIncorrectSyntheticDataScalesthe EfficiencyofLLMMathReasoningbyEight ...", "date": "", "ddg_snippet": "RLonIncorrectSyntheticDataScalestheEfficiencyofLLMMathReasoningbyEight- Fold SFTbase Policy ! QnA pairs sampled from GPT/Gemini Synthetic Data Positive Data Correct answers \"! Negative Data Finetune policy RFT : SFT on self-generated correct answers !\" RL with step-level rewards on all answers #!\" e.g., preference - based RL Incorrect answers \"!", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2406.14532", "content": "RLonIncorrectSyntheticDataScalestheEfficiencyofLLMMathReasoningbyEight- Fold SFTbase Policy ! QnA pairs sampled from GPT/Gemini Synthetic Data Positive Data Correct answers \"! Negative Data Finetune policy RFT : SFT on self-generated correct answers !\" RL with step-level rewards on all answers #!\" e.g., preference - based RL Incorrect answers \"!"}
+{"idx": 7, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math ...", "date": "", "ddg_snippet": "This paper investigates the use of synthetic data for enhancing LLM math reasoning capabilities. The researchers discovered that this approach leads to only modest gains, and in some cases, even performance degradation. The study introduces a novel approach that utilizes both positive and negative synthetic data .", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/posters/9m87e9keq1/", "content": "This paper investigates the use of synthetic data for enhancing LLM math reasoning capabilities. The researchers discovered that this approach leads to only modest gains, and in some cases, even performance degradation. The study introduces a novel approach that utilizes both positive and negative synthetic data ."}
+{"idx": 8, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math ...", "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": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2406.14532", "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": 9, "title": "Learning to Reason by Failing: Offline RL on Sub-optimal Rollouts ...", "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 **doubles** the sample efficiency of synthetic data .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=v2PV1yCFJk", "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 **doubles** the sample efficiency of synthetic data ."}
diff --git a/data/sampled_jsons/RL_on_Incorrect_Synthetic_Data_Scales_the_Efficiency_of_LLM_Math_Reasoning_by_Eight-Fold_fine-tuned_.jsonl b/data/sampled_jsons/RL_on_Incorrect_Synthetic_Data_Scales_the_Efficiency_of_LLM_Math_Reasoning_by_Eight-Fold_fine-tuned_.jsonl
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+{"idx": 1, "title": "RL on Incorrect Synthetic Data Scales", "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": "openreview.net", "link": "https://openreview.net/pdf?id=9m87e9Keq1", "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": 2, "title": "(PDF) RL on Incorrect Synthetic Data Scales the Efficiency of LLM ...", "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": "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": "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": 3, "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.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/hash/4b77d5b896c321a29277524a98a50215-Abstract-Conference.html", "content": "Training on model -generated synthetic data is a promising approach for finetuning LLMs, but it remains unclear when it helps or hurts."}
+{"idx": 4, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math ...", "date": "", "ddg_snippet": "The study investigates the impact of synthetic data , both correct and incorrect , on the fine - tuning of LLMs for enhanced math reasoning using supervised fine - tuning (SFT) and reinforcement learning ( RL ).", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/papers/2406.14532", "content": "The study investigates the impact of synthetic data , both correct and incorrect , on the fine - tuning of LLMs for enhanced math reasoning using supervised fine - tuning (SFT) and reinforcement learning ( RL )."}
+{"idx": 5, "title": "Bayesian beagle - RL on Incorrect Synthetic Data Scales the ...", "date": "", "ddg_snippet": "Finetuning LLMs with model -generated data can improve math reasoning , especially with self-generated correct solutions and per-step negative responses. This approach can double efficiency and reduce spurious correlations.", "subpage_snippet": "", "source": "bayesian-beagle.netlify.app", "link": "https://bayesian-beagle.netlify.app/posts/rl_on_incorrect_synthetic_data_scales_the_efficiency_of_llm_math_reasoning_by_eight_fold/2024-06-20-rl_on_incorrect_synthetic_data_scales_the_efficiency_of_llm_math_reasoning_by_eight_fold", "content": "Finetuning LLMs with model -generated data can improve math reasoning , especially with self-generated correct solutions and per-step negative responses. This approach can double efficiency and reduce spurious correlations."}
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+{"idx": 8, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math ...", "date": "", "ddg_snippet": "The key finding is that using RL on \"flawed\" synthetic data can boost the efficiency of LLM math reasoning by up to 8 times, compared to training on correct data alone.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/rl-incorrect-synthetic-data-scales-efficiency-llm", "content": "The key finding is that using RL on \"flawed\" synthetic data can boost the efficiency of LLM math reasoning by up to 8 times, compared to training on correct data alone."}
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diff --git a/data/sampled_jsons/Rafailov_et_al.,_2024_Direct_Preference_Optimization_DPO_abstract_year_2024.jsonl b/data/sampled_jsons/Rafailov_et_al.,_2024_Direct_Preference_Optimization_DPO_abstract_year_2024.jsonl
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index 0000000000000000000000000000000000000000..b5ad1add988e1e98083a26126525b69e53d92a37
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+{"idx": 0, "title": "Direct Preference Optimization: Your Language Model is ...", "date": "", "ddg_snippet": "by R Rafailov · 2023 · Cited by 5233 — In this paper we introduce a new parameterization of the reward model in RLHF that enables extraction of the corresponding optimal policy in closed form.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2305.18290", "content": "by R Rafailov · 2023 · Cited by 5233 — In this paper we introduce a new parameterization of the reward model in RLHF that enables extraction of the corresponding optimal policy in closed form."}
+{"idx": 1, "title": "Direct Preference Optimization: Your Language Model is ...", "date": "", "ddg_snippet": "by R Rafailov · 2023 · Cited by 5233 — Our experiments show that DPO can fine-tune LMs to align with human preferences as well as or better than existing methods.", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html", "content": "by R Rafailov · 2023 · Cited by 5233 — Our experiments show that DPO can fine-tune LMs to align with human preferences as well as or better than existing methods."}
+{"idx": 2, "title": "Direct Preference Optimization: Your Language Model is ...", "date": "", "ddg_snippet": "by R Rafailov · Cited by 5233 — Our experiments show that DPO can fine-tune LMs to align with human preferences as well as or better than existing methods.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=HPuSIXJaa9", "content": "by R Rafailov · Cited by 5233 — Our experiments show that DPO can fine-tune LMs to align with human preferences as well as or better than existing methods."}
+{"idx": 3, "title": "Direct Preference Optimization: Your Language Model is ...", "date": "", "ddg_snippet": "by R Rafailov · Cited by 5233 — In this paper, we show how to directly optimize a language model to adhere to human preferences, without explicit reward modeling or reinforcement learning. We ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=HPuSIXJaa9", "content": "by R Rafailov · Cited by 5233 — In this paper, we show how to directly optimize a language model to adhere to human preferences, without explicit reward modeling or reinforcement learning. We ..."}
+{"idx": 4, "title": "Direct Preference Optimization: Your Language Model is ...", "date": "", "ddg_snippet": "by R Rafailov · 2023 · Cited by 5233 — In this paper, we show how to directly optimize a language model to adhere to human preferences, without explicit reward modeling or ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2305.18290", "content": "by R Rafailov · 2023 · Cited by 5233 — In this paper, we show how to directly optimize a language model to adhere to human preferences, without explicit reward modeling or ..."}
+{"idx": 5, "title": "Disentangling Length from Quality in Direct Preference ...", "date": "", "ddg_snippet": "by R Park · 2024 · Cited by 169 — We study the length problem in the DPO setting, showing significant exploitation in DPO and linking it to out-of-distribution bootstrapping.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2024.findings-acl.297/", "content": "by R Park · 2024 · Cited by 169 — We study the length problem in the DPO setting, showing significant exploitation in DPO and linking it to out-of-distribution bootstrapping."}
+{"idx": 6, "title": "Direct Preference Optimization Explained In-depth", "date": "", "ddg_snippet": "13 Apr 2024 — This method of preference tuning is an alternative to Reinforcement Learning from Human Feedback (RLHF) that avoids the actual reinforcement learning.", "subpage_snippet": "", "source": "www.tylerromero.com", "link": "https://www.tylerromero.com/posts/2024-04-dpo/", "content": "13 Apr 2024 — This method of preference tuning is an alternative to Reinforcement Learning from Human Feedback (RLHF) that avoids the actual reinforcement learning."}
+{"idx": 7, "title": "Extended Abstract - CS 224R Deep Reinforcement Learning", "date": "", "ddg_snippet": "by E Hellman — We focus on three approaches: Supervised Fine-Tuning (SFT), Direct Preference . Optimization ( DPO ) Rafailov et al . (2023), and Group Relative Policy Optimization ...", "subpage_snippet": "", "source": "cs224r.stanford.edu", "link": "https://cs224r.stanford.edu/projects/pdfs/CS_224R_Final_Paper_2.pdf", "content": "by E Hellman — We focus on three approaches: Supervised Fine-Tuning (SFT), Direct Preference . Optimization ( DPO ) Rafailov et al . (2023), and Group Relative Policy Optimization ..."}
+{"idx": 8, "title": "Diffusion Model Alignment Using Direct Preference Optimization", "date": "", "ddg_snippet": "by B Wallace · 2024 · Cited by 351 — We propose Diffusion-. DPO , a method to align diffusion models to human pref- erences by directly optimizing on human comparison data. Diffusion- DPO is adapted ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2024/papers/Wallace_Diffusion_Model_Alignment_Using_Direct_Preference_Optimization_CVPR_2024_paper.pdf", "content": "by B Wallace · 2024 · Cited by 351 — We propose Diffusion-. DPO , a method to align diffusion models to human pref- erences by directly optimizing on human comparison data. Diffusion- DPO is adapted ..."}
+{"idx": 9, "title": "Direct Preference Optimization with an Offset", "date": "", "ddg_snippet": "by A Amini · 2024 · Cited by 96 — Direct preference optimization ( DPO ) is a successful fine-tuning strategy for aligning large language models with human preferences . 19 pages", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2024.findings-acl.592.pdf", "content": "by A Amini · 2024 · Cited by 96 — Direct preference optimization ( DPO ) is a successful fine-tuning strategy for aligning large language models with human preferences . 19 pages"}
diff --git a/data/sampled_jsons/Raissi_Physics-informed_neural_networks_2019_abstract_year_2019.jsonl b/data/sampled_jsons/Raissi_Physics-informed_neural_networks_2019_abstract_year_2019.jsonl
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index 0000000000000000000000000000000000000000..719c2cec25c292d8b8778c22a34f97b6b516c286
--- /dev/null
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Physics - informed neural networks : A deep learning framework for...", "date": "", "ddg_snippet": "Reference abstract : We introduce physics - informed neural networks – neural networks that are trained to solve supervised learning tasks while respecting any given laws of physics described by general nonlinear partial differential equations.", "subpage_snippet": "", "source": "transferlab.ai", "link": "https://transferlab.ai/refs/raissi_physicsinformed_2019/", "content": "Reference abstract : We introduce physics - informed neural networks – neural networks that are trained to solve supervised learning tasks while respecting any given laws of physics described by general nonlinear partial differential equations."}
+{"idx": 1, "title": "(Open Access) fPINNs: Fractional Physics - Informed Neural ...", "date": "", "ddg_snippet": "Abstract : Physics - informed neural networks (PINNs), introduced in [M. Raissi , P. Perdikaris, and G. Karniadakis, J. Comput. Phys., 378 ( 2019 ), pp. 686--707], are effective in solving integer-order partial di... read more.", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/papers/fpinns-fractional-physics-informed-neural-networks-25ms3t6zlq", "content": "Abstract : Physics - informed neural networks (PINNs), introduced in [M. Raissi , P. Perdikaris, and G. Karniadakis, J. Comput. Phys., 378 ( 2019 ), pp. 686--707], are effective in solving integer-order partial di... read more."}
+{"idx": 2, "title": "(PDF) Physics ‐ informed neural network applied to...", "date": "", "ddg_snippet": "[J Comput Phys. 2019 ;378:686–707], is applied to the partial differential equation (PDE) of liquid film flows.University, Setagaya-ku, Tokyo, 158-8557, Japan Abstract A physics - informed neural network (PINN), which has been recently proposed by Raissi et al.", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/108168403/Physics_informed_neural_network_applied_to_surface_tension_driven_liquid_film_flows", "content": "[J Comput Phys. 2019 ;378:686–707], is applied to the partial differential equation (PDE) of liquid film flows.University, Setagaya-ku, Tokyo, 158-8557, Japan Abstract A physics - informed neural network (PINN), which has been recently proposed by Raissi et al."}
+{"idx": 3, "title": "Physics - informed neural networks derived from a mCRE functional...", "date": "", "ddg_snippet": "Antoine Benady, Ludovic Chamoin, Emanuel Baranger. Physics - informed neural networks derived from a mCRE functional for constitutive modelling. Artificial Intelligence and Augmented Engineer-ing, Dec 2022, Palaiseau, France. hal-03929841.", "subpage_snippet": "", "source": "hal.science", "link": "https://hal.science/hal-03929841v1/document", "content": "Antoine Benady, Ludovic Chamoin, Emanuel Baranger. Physics - informed neural networks derived from a mCRE functional for constitutive modelling. Artificial Intelligence and Augmented Engineer-ing, Dec 2022, Palaiseau, France. hal-03929841."}
+{"idx": 4, "title": "Scientific Machine Learning Through Physics – Informed Neural ...", "date": "", "ddg_snippet": "Physics - Informed Neural Networks (PINN) are neural networks (NNs) that encode model equations, like Partial Differential Equations (PDE), as a component of.Since Raissi ’s first papers on arXiv in 2019 [146], a boost in citations can be seen in late 2018 and 2019 .", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s10915-022-01939-z", "content": "Physics - Informed Neural Networks (PINN) are neural networks (NNs) that encode model equations, like Partial Differential Equations (PDE), as a component of.Since Raissi ’s first papers on arXiv in 2019 [146], a boost in citations can be seen in late 2018 and 2019 ."}
+{"idx": 5, "title": "maziarraissi/PINNs - Githubissues", "date": "", "ddg_snippet": "Physics Informed Neural Networks Notice: This repository is no longer under active maintenance. It is highly recommended to utilize implementations of Physics - Informed Neural Networks (PINNs) available in PyTorch, JAX, and TensorFlow v2.", "subpage_snippet": "", "source": "githubissues.com", "link": "https://githubissues.com/maziarraissi/PINNs/readme", "content": "Physics Informed Neural Networks Notice: This repository is no longer under active maintenance. It is highly recommended to utilize implementations of Physics - Informed Neural Networks (PINNs) available in PyTorch, JAX, and TensorFlow v2."}
+{"idx": 6, "title": "(PDF) Physics Informed Neural Networks for Modeling of...", "date": "", "ddg_snippet": "Physics - informed neural networks (PINNs) ( Raissi et al., 2019 ) represent an advance in sci2. physics - informed neural networks (pinns). 2.1 Setting Up a PINN Training. PINNs leverage automatic differentiation to obtain an analytical representation of an output vari", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/378833661_Physics_Informed_Neural_Networks_for_Modeling_of_3D_Flow-Thermal_Problems_with_Sparse_Domain_Data", "content": "Physics - informed neural networks (PINNs) ( Raissi et al., 2019 ) represent an advance in sci2. physics - informed neural networks (pinns). 2.1 Setting Up a PINN Training. PINNs leverage automatic differentiation to obtain an analytical representation of an output vari"}
+{"idx": 7, "title": "Wavefield Solutions Using a Physics - Informed Neural Network as...", "date": "", "ddg_snippet": "Summary Physics - informed neural networks (PINNs) are promising to replace conventional partial differential equation (PDE) solvers by offering accurate and more flexible PDE solutions.", "subpage_snippet": "", "source": "www.earthdoc.org", "link": "https://www.earthdoc.org/content/papers/10.3997/2214-4609.202410273", "content": "Summary Physics - informed neural networks (PINNs) are promising to replace conventional partial differential equation (PDE) solvers by offering accurate and more flexible PDE solutions."}
+{"idx": 8, "title": "Physics - Informed Neural Networks (PINNs) in Finance by... :: SSRN", "date": "", "ddg_snippet": "Physics - Informed Neural Networks (PINNs) provide a framework to embed the Heston model dynamics directly into the learning process. This ensures that prediction.", "subpage_snippet": "", "source": "papers.ssrn.com", "link": "https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4598180", "content": "Physics - Informed Neural Networks (PINNs) provide a framework to embed the Heston model dynamics directly into the learning process. This ensures that prediction."}
+{"idx": 9, "title": "Physics - Informed Neural Networks as Solvers for the...", "date": "", "ddg_snippet": "We demonstrate the utility of physics - informed neural networks (PINNs) as solvers for the non-relativistic, time-dependent Schrödinger equation.", "subpage_snippet": "", "source": "inspirehep.net", "link": "https://inspirehep.net/literature/2169815", "content": "We demonstrate the utility of physics - informed neural networks (PINNs) as solvers for the non-relativistic, time-dependent Schrödinger equation."}
diff --git a/data/sampled_jsons/Regret_matching+_(in)stability_and_fast_convergence_in_games_Farina_et_al._2023_abstract.jsonl b/data/sampled_jsons/Regret_matching+_(in)stability_and_fast_convergence_in_games_Farina_et_al._2023_abstract.jsonl
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index 0000000000000000000000000000000000000000..0b395807476f2f74b9e709174b0c272ab5298d9c
--- /dev/null
+++ b/data/sampled_jsons/Regret_matching+_(in)stability_and_fast_convergence_in_games_Farina_et_al._2023_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Regret Matching+: (In)Stability and Fast Convergence in Games Regret Matching+: (In)Stability and Fast Convergence in Games Regret Matching$^+$: (In)Stability and Fast Convergence in Games Regret Matching+: - Instability, average- and last-iterate ... Regret matching+ | Proceedings of the 37th International ... Regret Matching +: ( In ) Stability and Fast Convergence in Games Regret Matching : ( In ) Stability and Fast Convergence in Games - Massa… Regret Matching : ( In ) Stability and Fast Convergence in Games - Massa… Regret Matching : ( In ) Stability and Fast Convergence in Games - Massa… Gabriele Farina - Regret Matching$^+$: (In)Stability and Fast ...", "date": "", "ddg_snippet": "May 24, 2023 · Regret Matching+ (RM+) and its variants are important algorithms for solving large-scale games . However, a theoretical understanding of their success in practice is still a mystery. Moreover, recent advances on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability . In this paper, we first give counterexamples showing that RM+ and ... Authors Gabriele Farina , Julien Grand-Clément, Christian Kroer, Chung-Wei Lee, Haipeng Luo Abstract Regret Matching$^+$ (RM$^+$) and its variants are important algorithms for solving large-scale games .However, a theoretical understanding of their success in practice is still a mystery.Moreover, recent advances on fast convergence in games are limited to no- regret algorithms such as online ... Regret Matching +: ( In)Stability and Fast Convergence in Games Gabriele Farina , Julien Grand-Clément, Christian Kroer, Chung-Wei Lee, Haipeng Luo December, 2023 Cite URL Presentation based on: Regret Matching+ : Instability and Fast Convergence in Games , Dec 10, 2023 · Abstract Regret Matching+ (RM+) and its variants are important algorithms for solving large-scale games [35]. However, a theoretical understanding of their success in practice is still a mystery. Moreover, recent advances [34] on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability . Are regret matching+ algorithms effective in solving large-scale games? Regret Matching+ (RM+) and its variants are important algorithms for solving large-scale games. However, a theoretical understanding of their success in practice is still a mystery . Moreover, recent advances on fast convergence in games are limited to no-regret algorithms such as online mirror descent, which satisfy stability. Do regret minimizers have faster convergence rates? ut its practical performance is usually significantly faster.On the other hand, a line of recent works show that regret minimizers based on follow the regular-ized leader (FTRL) or online mirror descent (OMD) enjoy faster convergence rates in theory when ombined with the concept of opti Does asynchronous restarting stabilize the RM+ algorithm? bounded by O d3/2T1/4 in multi-t=1 player normal-form games.Although the restarting idea successfully stabilizes the RM+ algorithm , the discontinuity created by asynchronous restarts caus s technical dificulty for bounding the social regret by O(1). Next we ntroduce an alternativ Which algorithm guarantees O(1/T) convergence to a CCE after T iterations? 3 guarantees O(1/T) convergence to a CCE after T iterations. With the setup from Theorem 5.5 and k = O(log(T)), Algorithm 4 guarantees O(log(T)/ ) convergence to a Regret Matching$^+$ (RM$^+$) and its variants are important algorithms for solving large-scale games . However, a theoretical understanding of their success in practice is still a mystery. Moreover, recent advances on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability . In this paper, we first give counterexamples showing that RM ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2305.14709", "content": "May 24, 2023 · Regret Matching+ (RM+) and its variants are important algorithms for solving large-scale games . However, a theoretical understanding of their success in practice is still a mystery. Moreover, recent advances on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability . In this paper, we first give counterexamples showing that RM+ and ... Authors Gabriele Farina , Julien Grand-Clément, Christian Kroer, Chung-Wei Lee, Haipeng Luo Abstract Regret Matching$^+$ (RM$^+$) and its variants are important algorithms for solving large-scale games .However, a theoretical understanding of their success in practice is still a mystery.Moreover, recent advances on fast convergence in games are limited to no- regret algorithms such as online ... Regret Matching +: ( In)Stability and Fast Convergence in Games Gabriele Farina , Julien Grand-Clément, Christian Kroer, Chung-Wei Lee, Haipeng Luo December, 2023 Cite URL Presentation based on: Regret Matching+ : Instability and Fast Convergence in Games , Dec 10, 2023 · Abstract Regret Matching+ (RM+) and its variants are important algorithms for solving large-scale games [35]. However, a theoretical understanding of their success in practice is still a mystery. Moreover, recent advances [34] on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability . Are regret matching+ algorithms effective in solving large-scale games? Regret Matching+ (RM+) and its variants are important algorithms for solving large-scale games. However, a theoretical understanding of their success in practice is still a mystery . Moreover, recent advances on fast convergence in games are limited to no-regret algorithms such as online mirror descent, which satisfy stability. Do regret minimizers have faster convergence rates? ut its practical performance is usually significantly faster.On the other hand, a line of recent works show that regret minimizers based on follow the regular-ized leader (FTRL) or online mirror descent (OMD) enjoy faster convergence rates in theory when ombined with the concept of opti Does asynchronous restarting stabilize the RM+ algorithm? bounded by O d3/2T1/4 in multi-t=1 player normal-form games.Although the restarting idea successfully stabilizes the RM+ algorithm , the discontinuity created by asynchronous restarts caus s technical dificulty for bounding the social regret by O(1). Next we ntroduce an alternativ Which algorithm guarantees O(1/T) convergence to a CCE after T iterations? 3 guarantees O(1/T) convergence to a CCE after T iterations. With the setup from Theorem 5.5 and k = O(log(T)), Algorithm 4 guarantees O(log(T)/ ) convergence to a Regret Matching$^+$ (RM$^+$) and its variants are important algorithms for solving large-scale games . However, a theoretical understanding of their success in practice is still a mystery. Moreover, recent advances on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability . In this paper, we first give counterexamples showing that RM ..."}
+{"idx": 1, "title": "Regret Matching+: (In)Stability and Fast Convergence in Games", "date": "", "ddg_snippet": "Authors Gabriele Farina , Julien Grand-Clément, Christian Kroer, Chung-Wei Lee, Haipeng Luo Abstract Regret Matching$^+$ (RM$^+$) and its variants are important algorithms for solving large-scale games .However, a theoretical understanding of their success in practice is still a mystery.Moreover, recent advances on fast convergence in games are limited to no- regret algorithms such as online ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2023/hash/c209cd57e13f3344a4cad4ce84d0ee1b-Abstract-Conference.html", "content": "Authors Gabriele Farina , Julien Grand-Clément, Christian Kroer, Chung-Wei Lee, Haipeng Luo Abstract Regret Matching$^+$ (RM$^+$) and its variants are important algorithms for solving large-scale games .However, a theoretical understanding of their success in practice is still a mystery.Moreover, recent advances on fast convergence in games are limited to no- regret algorithms such as online ..."}
+{"idx": 2, "title": "Regret Matching$^+$: (In)Stability and Fast Convergence in Games", "date": "", "ddg_snippet": "Regret Matching +: ( In)Stability and Fast Convergence in Games Gabriele Farina , Julien Grand-Clément, Christian Kroer, Chung-Wei Lee, Haipeng Luo December, 2023 Cite URL", "subpage_snippet": "", "source": "www.columbia.edu", "link": "https://www.columbia.edu/~ck2945/publication/farina-2023-regret/", "content": "Regret Matching +: ( In)Stability and Fast Convergence in Games Gabriele Farina , Julien Grand-Clément, Christian Kroer, Chung-Wei Lee, Haipeng Luo December, 2023 Cite URL"}
+{"idx": 3, "title": "Regret Matching+ : ( In ) Stability and Fast Convergence in Games", "date": "", "ddg_snippet": "05/24/23 - Regret Matching+ (RM+) and its variants are important algorithms for solving large-scale games .Moreover, recent advances on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability .", "subpage_snippet": "", "source": "deepai.org", "link": "https://deepai.org/publication/regret-matching-in-stability-and-fast-convergence-in-games", "content": "05/24/23 - Regret Matching+ (RM+) and its variants are important algorithms for solving large-scale games .Moreover, recent advances on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability ."}
+{"idx": 4, "title": "Regret Matching+ : ( In ) Stability and Fast Convergence in Games", "date": "", "ddg_snippet": "Abstract . Regret Matching+ (RM+) and its variants are important algorithms for solving large-scale games .Moreover, recent advances on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/371008709_Regret_Matching_InStability_and_Fast_Convergence_in_Games", "content": "Abstract . Regret Matching+ (RM+) and its variants are important algorithms for solving large-scale games .Moreover, recent advances on fast convergence in games are limited to no- regret algorithms such as online mirror descent, which satisfy stability ."}
+{"idx": 5, "title": "Regret Matching+ : ( In ) Stability and Fast Convergence in Games", "date": "", "ddg_snippet": "Regret Matching $^+$ (RM$^+$) and its variants are important algorithms for solving large-scale games .Keywords : Regret Matching , Predictive algorithms, Extensive-Form Games . Abstract", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=nYgs0qZJ97", "content": "Regret Matching $^+$ (RM$^+$) and its variants are important algorithms for solving large-scale games .Keywords : Regret Matching , Predictive algorithms, Extensive-Form Games . Abstract"}
+{"idx": 6, "title": "Gabriele Farina - Publications", "date": "", "ddg_snippet": "Regret Matching $^+$: ( In ) Stability and Fast Convergence in Games Gabriele Farina , Julien Grand-Clément, Christian Kroer, Chung-Wei Lee, Haipeng Luo Spotlight paper.Last update: 7/8/2025 14:29:12 ET .", "subpage_snippet": "", "source": "www.mit.edu", "link": "https://www.mit.edu/~gfarina/publications/", "content": "Regret Matching $^+$: ( In ) Stability and Fast Convergence in Games Gabriele Farina , Julien Grand-Clément, Christian Kroer, Chung-Wei Lee, Haipeng Luo Spotlight paper.Last update: 7/8/2025 14:29:12 ET ."}
+{"idx": 7, "title": "Julien Grand-Clément | 5 Publications | 2 Citations | Related Authors", "date": "", "ddg_snippet": "Regret Matching+ : ( In ) Stability and Fast Convergence in Games . Gabriele Farina , Julien Grand-Clément, Christian Kroer, Chung-Wei Lee, Haipeng Luo +4 more.", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/authors/julien-grand-clement-3tq4z79u", "content": "Regret Matching+ : ( In ) Stability and Fast Convergence in Games . Gabriele Farina , Julien Grand-Clément, Christian Kroer, Chung-Wei Lee, Haipeng Luo +4 more."}
+{"idx": 8, "title": "Julien Grand-Clément - Google Akademik", "date": "", "ddg_snippet": "Regret Matching+ :( In ) Stability and Fast Convergence in Games . G Farina , J Grand-Clément, C Kroer, CW Lee, H Luo. Proceedings of the 36th Advances in Neural Information Processing Systems …, 2023 .", "subpage_snippet": "", "source": "scholar.google.co.za", "link": "https://scholar.google.co.za/citations?user=K_ZLzdoAAAAJ&hl=tr", "content": "Regret Matching+ :( In ) Stability and Fast Convergence in Games . G Farina , J Grand-Clément, C Kroer, CW Lee, H Luo. Proceedings of the 36th Advances in Neural Information Processing Systems …, 2023 ."}
+{"idx": 9, "title": "Articles by Julien Grand-Clément | Synthical", "date": "", "ddg_snippet": "Regret Matching+ : ( In ) Stability and Fast Convergence in Games . 24 May 2023 by Gabriele Farina and others.", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/profile/1551d66f-d464-4596-898d-eb958ce85108/articles", "content": "Regret Matching+ : ( In ) Stability and Fast Convergence in Games . 24 May 2023 by Gabriele Farina and others."}
diff --git "a/data/sampled_jsons/Rennala_SGD_wasteful_Tyurin_Richt\303\241rik_distributed_machine_learning.jsonl" "b/data/sampled_jsons/Rennala_SGD_wasteful_Tyurin_Richt\303\241rik_distributed_machine_learning.jsonl"
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index 0000000000000000000000000000000000000000..0cb3e639089ccc5c8509161f8cbc18e1cac038ee
--- /dev/null
+++ "b/data/sampled_jsons/Rennala_SGD_wasteful_Tyurin_Richt\303\241rik_distributed_machine_learning.jsonl"
@@ -0,0 +1,5 @@
+{"idx": 0, "title": "Peter Richtarik", "date": "", "ddg_snippet": "... Richtárik Convergence analysis of the PAGE stochastic algorithm for convex finite-sum optimization [ arXiv ] [method: PAGE] [285] Igor Sokolov, ...", "subpage_snippet": "", "source": "richtarik.org", "link": "https://richtarik.org/i_papers.html", "content": "... Richtárik Convergence analysis of the PAGE stochastic algorithm for convex finite-sum optimization [ arXiv ] [method: PAGE] [285] Igor Sokolov, ..."}
+{"idx": 1, "title": "ATA: Adaptive Task Allocation for Efficient Resource Management in...", "date": "", "ddg_snippet": "GTA-SGD, originally introduced as Rennala SGD by Tyurin & Richtárik (2024) . Additionally, we include OFTA (Optimal Fixed Task Allocation), which assumes the oracle knowledge of the mean computation times and uses the optimal allocation.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.00775v2", "content": "GTA-SGD, originally introduced as Rennala SGD by Tyurin & Richtárik (2024) . Additionally, we include OFTA (Optimal Fixed Task Allocation), which assumes the oracle knowledge of the mean computation times and uses the optimal allocation."}
+{"idx": 2, "title": "ATA: Adaptive Task Allocation for Efficient Resource Management in...", "date": "", "ddg_snippet": "GTA-SGD, originally introduced as Rennala SGD by Tyurin & Richtárik (2024) . Additionally, we include OFTA (Optimal Fixed Task Allocation), which assumes the oracle knowledge of the mean computation times and uses the optimal allocation.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.00775v1", "content": "GTA-SGD, originally introduced as Rennala SGD by Tyurin & Richtárik (2024) . Additionally, we include OFTA (Optimal Fixed Task Allocation), which assumes the oracle knowledge of the mean computation times and uses the optimal allocation."}
+{"idx": 3, "title": "cherryATA: cherryAdaptive cherryTask cherryAllocation for Efficient...", "date": "", "ddg_snippet": "Their method, Rennala SGD , corresponds to Minibatch SGD of minibatch size B (which depends on the target accuracy and σ only), with the B tasks (stochastic gradients) completed via GTA.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.00775", "content": "Their method, Rennala SGD , corresponds to Minibatch SGD of minibatch size B (which depends on the target accuracy and σ only), with the B tasks (stochastic gradients) completed via GTA."}
+{"idx": 4, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/RepE_Zou_2023_representation_engineering_AI_year_2023.jsonl b/data/sampled_jsons/RepE_Zou_2023_representation_engineering_AI_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0d95068c236f3a1c7dddab40b627120343ab104b
--- /dev/null
+++ b/data/sampled_jsons/RepE_Zou_2023_representation_engineering_AI_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Representation Engineering: A Top-Down Approach to AI ...", "date": "", "ddg_snippet": "by A Zou · 2023 · Cited by 514 — In this paper, we identify and characterize the emerging area of representation engineering ( RepE ), an approach to enhancing the transparency of AI systems.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2310.01405", "content": "by A Zou · 2023 · Cited by 514 — In this paper, we identify and characterize the emerging area of representation engineering ( RepE ), an approach to enhancing the transparency of AI systems."}
+{"idx": 1, "title": "Representation Engineering", "date": "", "ddg_snippet": "by A Zou · 2023 · Cited by 506 — We explore a top-down approach to AI transparency called representation engineering ( RepE ), which places representations and transformations ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2310.01405", "content": "by A Zou · 2023 · Cited by 506 — We explore a top-down approach to AI transparency called representation engineering ( RepE ), which places representations and transformations ..."}
+{"idx": 2, "title": "Representation Engineering: A Top-Down Approach to AI ...", "date": "", "ddg_snippet": "25 Jan 2024 — RepE is a top-down approach to transparency research that treats representations as the fundamental unit of analysis, aiming to understand and control ...", "subpage_snippet": "", "source": "montrealethics.ai", "link": "https://montrealethics.ai/representation-engineering-a-top-down-approach-to-ai-transparency/", "content": "25 Jan 2024 — RepE is a top-down approach to transparency research that treats representations as the fundamental unit of analysis, aiming to understand and control ..."}
+{"idx": 3, "title": "Representation Engineering: A Top-Down Approach to AI ...", "date": "", "ddg_snippet": "2 Oct 2023 — This paper identifies and characterize the emerging area of representation engineering ( RepE ), an approach to enhancing the transparency of AI ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/58fdf550600fc3873729d466601c5d08a51ba8a0", "content": "2 Oct 2023 — This paper identifies and characterize the emerging area of representation engineering ( RepE ), an approach to enhancing the transparency of AI ..."}
+{"idx": 4, "title": "Representation Engineering: A Top-Down Approach to ...", "date": "", "ddg_snippet": "In this paper, we identify and characterize the emerging area of representation engineering ( RepE ), an approach to enhancing the transparency of AI systems ...", "subpage_snippet": "", "source": "www.grayswan.ai", "link": "https://www.grayswan.ai/research/repe", "content": "In this paper, we identify and characterize the emerging area of representation engineering ( RepE ), an approach to enhancing the transparency of AI systems ..."}
+{"idx": 5, "title": "Representation Engineering", "date": "", "ddg_snippet": "In this paper, we identify and characterize the emerging area of representation engineering ( RepE ), an approach to enhancing the transparency of AI systems that ...", "subpage_snippet": "", "source": "aisafety.tokyo", "link": "https://aisafety.tokyo/benkyoukai/representation-engineering", "content": "In this paper, we identify and characterize the emerging area of representation engineering ( RepE ), an approach to enhancing the transparency of AI systems that ..."}
+{"idx": 6, "title": "Turning Knobs Inside LLMs: A Review of Representation ...", "date": "", "ddg_snippet": "It proposes a family of techniques — collectively Representation Engineering ( RepE ) — for reading and controlling high-level concepts inside ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/glitch-q/turning-knobs-inside-llms-a-review-of-representation-engineering-a-top-down-approach-to-ai-f850a0be7ad5", "content": "It proposes a family of techniques — collectively Representation Engineering ( RepE ) — for reading and controlling high-level concepts inside ..."}
+{"idx": 7, "title": "Rethinking The Reliability of Representation Engineering ...", "date": "", "ddg_snippet": "by Z Deng — This paper proposes an improvement to the representation engineering ( RepE ) analysis of LLMs. In the original RepE , different treatments are applied to the ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=sYJQEgkkaI", "content": "by Z Deng — This paper proposes an improvement to the representation engineering ( RepE ) analysis of LLMs. In the original RepE , different treatments are applied to the ..."}
+{"idx": 8, "title": "Andy Zou", "date": "", "ddg_snippet": "In this paper, we introduce and characterize the emerging area of representation engineering ( RepE ), an approach to enhancing the transparency of AI systems.", "subpage_snippet": "", "source": "andyzoujm.github.io", "link": "https://andyzoujm.github.io/", "content": "In this paper, we introduce and characterize the emerging area of representation engineering ( RepE ), an approach to enhancing the transparency of AI systems."}
+{"idx": 9, "title": "Enhancing Neural Network Transparency through ...", "date": "", "ddg_snippet": "by A Zou — In this paper, we introduce and characterize the emerging area of representation engineering ( RepE ), an approach to enhancing the transparency of AI systems.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=aCgybhcZFi", "content": "by A Zou — In this paper, we introduce and characterize the emerging area of representation engineering ( RepE ), an approach to enhancing the transparency of AI systems."}
diff --git a/data/sampled_jsons/ResearchGate_388658326_Blink_of_an_eye_a_simple_theory_for_feature_localization_in_generative_models.jsonl b/data/sampled_jsons/ResearchGate_388658326_Blink_of_an_eye_a_simple_theory_for_feature_localization_in_generative_models.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..9693894aa865d4e3f44f1d9c7318d26c2ada4b21
--- /dev/null
+++ b/data/sampled_jsons/ResearchGate_388658326_Blink_of_an_eye_a_simple_theory_for_feature_localization_in_generative_models.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF ResearchGate", "date": "", "ddg_snippet": "ResearchGate", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/388658326_Blink_of_an_eye_a_simple_theory_for_feature_localization_in_generative_models/fulltext/67a191718311ce680c5059c3/Blink-of-an-eye-a-simple-theory-for-feature-localization-in-generative-models.pdf", "content": "ResearchGate"}
+{"idx": 1, "title": "(PDF) Blink of an eye: a simple theory for feature localization in ...", "date": "", "ddg_snippet": "PDF | Large language models (LLMs) can exhibit undesirable and unexpected behavior in the blink of an eye. In a recent Anthropic demo, Claude switched... | Find, read and cite all the research you ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/388658326_Blink_of_an_eye_a_simple_theory_for_feature_localization_in_generative_models", "content": "PDF | Large language models (LLMs) can exhibit undesirable and unexpected behavior in the blink of an eye. In a recent Anthropic demo, Claude switched... | Find, read and cite all the research you ..."}
+{"idx": 2, "title": "Blink of an eye: a simple theory for feature localization in generative ...", "date": "", "ddg_snippet": "Large language models can exhibit unexpected behavior in the blink of an eye. In a recent computer use demo, a language model switched from coding to Googling pictures of Yellowstone, and these sudden shifts in behavior have also been observed in reasoning patterns and jailbreaks. This phenomenon is not unique to autoregressive models: in diffusion models, key features of the final output are ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.00921", "content": "Large language models can exhibit unexpected behavior in the blink of an eye. In a recent computer use demo, a language model switched from coding to Googling pictures of Yellowstone, and these sudden shifts in behavior have also been observed in reasoning patterns and jailbreaks. This phenomenon is not unique to autoregressive models: in diffusion models, key features of the final output are ..."}
+{"idx": 3, "title": "Blink of an eye: a simple theory for feature localization in generative ...", "date": "", "ddg_snippet": "In this work we de- velop a simple, unifying theory to explain this phenomenon. Using the formalism of stochastic localization for generative models, we show that it emerges generically as the generation process localizes to a sub-population of the distribution it models.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=QvqnPVGWAN", "content": "In this work we de- velop a simple, unifying theory to explain this phenomenon. Using the formalism of stochastic localization for generative models, we show that it emerges generically as the generation process localizes to a sub-population of the distribution it models."}
+{"idx": 4, "title": "In the Blink of an Eye: A Unified Theory for Feature Emergence in ...", "date": "", "ddg_snippet": "The key insight of our approach is to exploit the powerful formalism for generative models of stochastic localization, which has roots as a proof technique in probability theory. Leveraging our consolidated theory for critical windows, we apply it to different examples of critical windows in theoretical and empirical contexts.", "subpage_snippet": "", "source": "dash.harvard.edu", "link": "https://dash.harvard.edu/items/f3795c57-8ff8-44d2-ae92-1fc5525aeb39", "content": "The key insight of our approach is to exploit the powerful formalism for generative models of stochastic localization, which has roots as a proof technique in probability theory. Leveraging our consolidated theory for critical windows, we apply it to different examples of critical windows in theoretical and empirical contexts."}
+{"idx": 5, "title": "[PDF] Blink of an eye: a simple theory for feature localization in ...", "date": "", "ddg_snippet": "This work develops a simple, unifying theory to explain the sudden shifts in behavior of large language models and shows that it emerges generically as the generation process localizes to a sub-population of the distribution it models. Large language models can exhibit unexpected behavior in the blink of an eye. In a recent computer use demo, a language model switched from coding to Googling ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Blink-of-an-eye:-a-simple-theory-for-feature-in-Li-Karan/1819508abae222d6a0ee516bdc1258ea47f084bc", "content": "This work develops a simple, unifying theory to explain the sudden shifts in behavior of large language models and shows that it emerges generically as the generation process localizes to a sub-population of the distribution it models. Large language models can exhibit unexpected behavior in the blink of an eye. In a recent computer use demo, a language model switched from coding to Googling ..."}
+{"idx": 6, "title": "Blink of an Eye: A Simple Theory for Feature Localization in Generative ...", "date": "", "ddg_snippet": "We present a concise theoretical framework that explains sudden feature localization events—\"blinks of an eye\"—in diffusion and autoregressive generative models.", "subpage_snippet": "", "source": "marvinfli.github.io", "link": "https://marvinfli.github.io/publication/2025-blink-eye", "content": "We present a concise theoretical framework that explains sudden feature localization events—\"blinks of an eye\"—in diffusion and autoregressive generative models."}
+{"idx": 7, "title": "Blink of an eye: a simple theory for feature localization in generative ...", "date": "", "ddg_snippet": "This phenomenon is not unique to autoregressive models: in diffusion models, key features of the final output are decided in narrow \"critical windows\" of the generation process. In this work we develop a simple, unifying theory to explain this phenomenon.", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/icml/45312/paper", "content": "This phenomenon is not unique to autoregressive models: in diffusion models, key features of the final output are decided in narrow \"critical windows\" of the generation process. In this work we develop a simple, unifying theory to explain this phenomenon."}
+{"idx": 8, "title": "Blink of an eye: a simple theory for feature localization in generative ...", "date": "", "ddg_snippet": "Critical windows, broadly characterizable as a few steps of the sampling procedureduring which features of the final output appear, arise in many contexts for different generative models and data modalities (Figure 1 ). They are extremely useful from an interpretability perspective as they represent the steps of the sampler responsible for a given property of the output [27, 56], and have also ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.00921v1", "content": "Critical windows, broadly characterizable as a few steps of the sampling procedureduring which features of the final output appear, arise in many contexts for different generative models and data modalities (Figure 1 ). They are extremely useful from an interpretability perspective as they represent the steps of the sampler responsible for a given property of the output [27, 56], and have also ..."}
+{"idx": 9, "title": "Blink of an eye: a simple theory for feature localization in generative ...", "date": "", "ddg_snippet": "Poster in Workshop: Frontiers in Probabilistic Inference: learning meets Sampling Blink of an eye: a simple theory for feature localization in generative models Marvin Li · Aayush Karan · Sitan Chen", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2025/36105", "content": "Poster in Workshop: Frontiers in Probabilistic Inference: learning meets Sampling Blink of an eye: a simple theory for feature localization in generative models Marvin Li · Aayush Karan · Sitan Chen"}
diff --git a/data/sampled_jsons/Responsible_AI_Licenses_RAIL_OpenRAIL_model_licensing.jsonl b/data/sampled_jsons/Responsible_AI_Licenses_RAIL_OpenRAIL_model_licensing.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ad1476f7d2bde47f998e9d72babd03b5147719d5
--- /dev/null
+++ b/data/sampled_jsons/Responsible_AI_Licenses_RAIL_OpenRAIL_model_licensing.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Responsible AI Licenses (RAIL)", "date": "", "ddg_snippet": "Responsible AI Licenses (RAIL ) empower developers to restrict the use of their AI technology in order to prevent irresponsible and harmful applications.", "subpage_snippet": "", "source": "www.licenses.ai", "link": "https://www.licenses.ai/", "content": "Responsible AI Licenses (RAIL ) empower developers to restrict the use of their AI technology in order to prevent irresponsible and harmful applications."}
+{"idx": 1, "title": "Responsible AI Licenses (RAIL): Here's What You Need to ...", "date": "", "ddg_snippet": "22 May 2024 — Responsible AI Licenses (RAIL ) are a class of licenses created with the intention of preventing harmful or unethical uses of artificial intelligence.", "subpage_snippet": "", "source": "www.mend.io", "link": "https://www.mend.io/blog/responsible-ai-licenses-rail-heres-what-you-need-to-know/", "content": "22 May 2024 — Responsible AI Licenses (RAIL ) are a class of licenses created with the intention of preventing harmful or unethical uses of artificial intelligence."}
+{"idx": 2, "title": "Responsible AI Licenses (RAIL)", "date": "", "ddg_snippet": "Responsible AI Pubs Licenses : These licenses are built on a model for behavioral-use that aims to reduce the risk of negative outcomes and misuse of AI.", "subpage_snippet": "", "source": "www.licenses.ai", "link": "https://www.licenses.ai/ai-licenses", "content": "Responsible AI Pubs Licenses : These licenses are built on a model for behavioral-use that aims to reduce the risk of negative outcomes and misuse of AI."}
+{"idx": 3, "title": "OpenRAIL: Towards open and responsible AI licensing ...", "date": "", "ddg_snippet": "31 Aug 2022 — OpenRAIL are AI-specific licenses enabling open access, use and distribution of AI artifacts while requiring a responsible use of the latter.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/blog/open_rail", "content": "31 Aug 2022 — OpenRAIL are AI-specific licenses enabling open access, use and distribution of AI artifacts while requiring a responsible use of the latter."}
+{"idx": 4, "title": "Should OpenRAIL licenses be considered OS AI Licenses?", "date": "", "ddg_snippet": "Initially, RAIL licenses were conceived in response to concerns about AI technologies being released without proper ethical considerations, inspired by events ...", "subpage_snippet": "", "source": "opensource.org", "link": "https://opensource.org/ai/webinars/should-openrail-licenses-be-considered-os-ai-licenses", "content": "Initially, RAIL licenses were conceived in response to concerns about AI technologies being released without proper ethical considerations, inspired by events ..."}
+{"idx": 5, "title": "RAIL License - AAAI", "date": "", "ddg_snippet": "5 Apr 2023 — Open Responsible AI Licenses (Open RAIL) are licenses designed to permit free and open access, re-use, and downstream distribution of derivatives of AI ...", "subpage_snippet": "", "source": "aaai.org", "link": "https://aaai.org/rail-license/", "content": "5 Apr 2023 — Open Responsible AI Licenses (Open RAIL) are licenses designed to permit free and open access, re-use, and downstream distribution of derivatives of AI ..."}
+{"idx": 6, "title": "Growth of responsible AI licensing. Analysis of license use for ...", "date": "", "ddg_snippet": "7 Feb 2023 — This analysis aims at understanding how licenses are used by developers making ML model -related code and or data publicly available.", "subpage_snippet": "", "source": "openfuture.pubpub.org", "link": "https://openfuture.pubpub.org/pub/growth-of-responsible-ai-licensing", "content": "7 Feb 2023 — This analysis aims at understanding how licenses are used by developers making ML model -related code and or data publicly available."}
+{"idx": 7, "title": "RAIL-M is an imperfectly good start for AI model licenses", "date": "", "ddg_snippet": "25 Jul 2023 — RAIL-M is part of the Open Responsible AI Licenses (Open RAIL) family , which is a collection of AI licenses that aim to promote responsible use ...", "subpage_snippet": "", "source": "about.gitlab.com", "link": "https://about.gitlab.com/blog/rail-m-is-an-imperfectly-good-start-for-ai-model-licenses/", "content": "25 Jul 2023 — RAIL-M is part of the Open Responsible AI Licenses (Open RAIL) family , which is a collection of AI licenses that aim to promote responsible use ..."}
+{"idx": 8, "title": "The BigScience RAIL License", "date": "", "ddg_snippet": "Such a license effectively imposes behavioral-use terms on the use of the model . The concept of a Responsible AI License emerged from a community initiative to ...", "subpage_snippet": "", "source": "bigscience.huggingface.co", "link": "https://bigscience.huggingface.co/blog/the-bigscience-rail-license", "content": "Such a license effectively imposes behavioral-use terms on the use of the model . The concept of a Responsible AI License emerged from a community initiative to ..."}
+{"idx": 9, "title": "Responsible AI Licenses: social vehicles toward ...", "date": "", "ddg_snippet": "28 May 2023 — Overview: This article explores a new licensing paradigm in the AI space – Open & Responsible AI Licenses ( Open RAIL ) – from a social ...", "subpage_snippet": "", "source": "montrealethics.ai", "link": "https://montrealethics.ai/responsible-ai-licenses-social-vehicles-toward-decentralized-control-of-ai/", "content": "28 May 2023 — Overview: This article explores a new licensing paradigm in the AI space – Open & Responsible AI Licenses ( Open RAIL ) – from a social ..."}
diff --git a/data/sampled_jsons/Rew_short_=_equation_formalization_feint_behaviors_sitearxiv.org.jsonl b/data/sampled_jsons/Rew_short_=_equation_formalization_feint_behaviors_sitearxiv.org.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..48b3496970b73c71e58ee9204d1135ca4e3814cf
--- /dev/null
+++ b/data/sampled_jsons/Rew_short_=_equation_formalization_feint_behaviors_sitearxiv.org.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Feint Behaviors and Strategies: Formalization, Implementation and ...", "date": "", "ddg_snippet": "In this work, we introduce the first comprehensive formalization of Feint behaviors at both action-level and strategy-level, and provide concrete implementation and quantitative evaluation of them in multi-player games.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2403.07932v2", "content": "In this work, we introduce the first comprehensive formalization of Feint behaviors at both action-level and strategy-level, and provide concrete implementation and quantitative evaluation of them in multi-player games."}
+{"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 in Multi-Player Games - arXiv.org", "date": "", "ddg_snippet": "The formalization is built upon Non-transitive Active Markov Game Model, where Feint can have a considerable amount of impacts. Then, our work considers practical implementation details of Feint in Multi-Player Games, under the state-of-the-art progress of multi-agent modeling to date (namely Multi-Agent Reinforcement Learning).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.07932v1", "content": "The formalization is built upon Non-transitive Active Markov Game Model, where Feint can have a considerable amount of impacts. Then, our work considers practical implementation details of Feint in Multi-Player Games, under the state-of-the-art progress of multi-agent modeling to date (namely Multi-Agent Reinforcement Learning)."}
+{"idx": 3, "title": "PDF F M -P G - arXiv.org", "date": "", "ddg_snippet": "ABSTRACT This paper introduces the first formalization , implementation and quantitative evaluation of Feint in Multi-Player Games. Our work first formalizesFeint from the perspective of Multi-Player Games, in terms of the temporal, spatial and their collective impacts. The formalization is built upon Non-transitive Active Markov Game Model, where Feint can have a considerable amount of impacts ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2403.07932v1.pdf", "content": "ABSTRACT This paper introduces the first formalization , implementation and quantitative evaluation of Feint in Multi-Player Games. Our work first formalizesFeint from the perspective of Multi-Player Games, in terms of the temporal, spatial and their collective impacts. The formalization is built upon Non-transitive Active Markov Game Model, where Feint can have a considerable amount of impacts ..."}
+{"idx": 4, "title": "Formalizing Feint Actions, and Example Studies in Two-Player Games", "date": "", "ddg_snippet": "Abstract. Feint actions refer to a set of deceptive actions, which enable players to obtain temporal advantages from their opponents. Such actions are regarded as widely-used tactic in most non-deterministic Two-player Games (e.g. boxing and fencing). However, existing literature doesnot provide comprehensive and concrete formalization on Feint actions, and their implications on Two-Player ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.07931v1", "content": "Abstract. Feint actions refer to a set of deceptive actions, which enable players to obtain temporal advantages from their opponents. Such actions are regarded as widely-used tactic in most non-deterministic Two-player Games (e.g. boxing and fencing). However, existing literature doesnot provide comprehensive and concrete formalization on Feint actions, and their implications on Two-Player ..."}
+{"idx": 5, "title": "F M -P G FEINT IN MULTI-PLA - arXiv.org", "date": "", "ddg_snippet": "ABSTRACT This paper introduces the first formalization , implementation and quantitative evaluation of Feint in Multi-Player Games. Our work first formalizes Feint from the perspective of Multi-Player Games, in terms of the temporal, spatial and their collective impacts. The formalization is built upon Non-transitive Active Markov Game Model, where Feint can have a considerable amount of ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2403.07932", "content": "ABSTRACT This paper introduces the first formalization , implementation and quantitative evaluation of Feint in Multi-Player Games. Our work first formalizes Feint from the perspective of Multi-Player Games, in terms of the temporal, spatial and their collective impacts. The formalization is built upon Non-transitive Active Markov Game Model, where Feint can have a considerable amount of ..."}
+{"idx": 6, "title": "Formalizing Feint Actions, and Example Studies in Two-Player Games", "date": "", "ddg_snippet": "Feint actions refer to a set of deceptive actions, which enable players to obtain temporal advantages from their opponents. Such actions are regarded as widely-used tactic in most non-deterministic Two-player Games (e.g. boxing and fencing). However, existing literature does not provide comprehensive and concrete formalization on Feint actions, and their implications on Two-Player Games. We ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2403.07931", "content": "Feint actions refer to a set of deceptive actions, which enable players to obtain temporal advantages from their opponents. Such actions are regarded as widely-used tactic in most non-deterministic Two-player Games (e.g. boxing and fencing). However, existing literature does not provide comprehensive and concrete formalization on Feint actions, and their implications on Two-Player Games. We ..."}
+{"idx": 7, "title": "[2312.06103] A Practical Formalization of Monadic Equational Reasoning ...", "date": "", "ddg_snippet": "We propose a formalization of a hierarchy of effects using monads in the Coq proof assistant that makes monadic equational reasoning practical. Our main idea is to formalize the hierarchy of effects and algebraic laws as interfaces like it is done when formalizing hierarchy of algebras in dependent type theory.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2312.06103", "content": "We propose a formalization of a hierarchy of effects using monads in the Coq proof assistant that makes monadic equational reasoning practical. Our main idea is to formalize the hierarchy of effects and algebraic laws as interfaces like it is done when formalizing hierarchy of algebras in dependent type theory."}
+{"idx": 8, "title": "[2406.09757] Evaluating LLM-driven User-Intent Formalization for ...", "date": "", "ddg_snippet": "Verification-aware programming languages such as Dafny and F* provide means to formally specify and prove properties of a program. Although the problem of checking an implementation against a specification can be defined mechanically, there is no algorithmic way of ensuring the correctness of the {\\\\it user-intent formalization for programs}, expressed as a formal specification. This is ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2406.09757", "content": "Verification-aware programming languages such as Dafny and F* provide means to formally specify and prove properties of a program. Although the problem of checking an implementation against a specification can be defined mechanically, there is no algorithmic way of ensuring the correctness of the {\\\\it user-intent formalization for programs}, expressed as a formal specification. This is ..."}
+{"idx": 9, "title": "CriticLean: Critic-Guided Reinforcement Learning for Mathematical ...", "date": "", "ddg_snippet": "The formalization of mathematical statements [64] is a critical task in modern mathematical computation, particularly in the context of theorem provers like Lean 4 [22].", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2507.06181", "content": "The formalization of mathematical statements [64] is a critical task in modern mathematical computation, particularly in the context of theorem provers like Lean 4 [22]."}
diff --git a/data/sampled_jsons/Roose_2024_'A.I._has_a_measurement_problem'_'tangle_of_sloppy_tests'_year_2024.jsonl b/data/sampled_jsons/Roose_2024_'A.I._has_a_measurement_problem'_'tangle_of_sloppy_tests'_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..13d1f5a5494c5da81d361f2540f3505a507ed9a3
--- /dev/null
+++ b/data/sampled_jsons/Roose_2024_'A.I._has_a_measurement_problem'_'tangle_of_sloppy_tests'_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A.I. Has a Measurement Problem - The New York Times", "date": "", "ddg_snippet": "In short, A.I . measurement is a mess — a tangle of sloppy tests , apples-to-oranges comparisons and self-serving hype that has left users, regulators and A.I . developers themselves grasping in ...", "subpage_snippet": "", "source": "www.nytimes.com", "link": "https://www.nytimes.com/2024/04/15/technology/ai-models-measurement.html", "content": "In short, A.I . measurement is a mess — a tangle of sloppy tests , apples-to-oranges comparisons and self-serving hype that has left users, regulators and A.I . developers themselves grasping in ..."}
+{"idx": 1, "title": "AI has a measurement problem - eKathimerini.com", "date": "", "ddg_snippet": "In short, AI measurement is a mess - a tangle of sloppy tests , apples-to-oranges comparisons and self-serving hype that has left users, regulators and AI developers themselves grasping in the dark.", "subpage_snippet": "", "source": "www.ekathimerini.com", "link": "https://www.ekathimerini.com/nytimes/1236644/ai-has-a-measurement-problem/", "content": "In short, AI measurement is a mess - a tangle of sloppy tests , apples-to-oranges comparisons and self-serving hype that has left users, regulators and AI developers themselves grasping in the dark."}
+{"idx": 2, "title": "Microsoft New Future of Work Report 2024", "date": "", "ddg_snippet": "4 Dec 2024 — To quote Kevin Roose in The New York Times, “ AI measurement is a mess—a tangle of sloppy tests , apples-to-oranges comparisons and self-serving ... 52 pages", "subpage_snippet": "", "source": "www.microsoft.com", "link": "https://www.microsoft.com/en-us/research/wp-content/uploads/2024/12/NFWReport2024_12.20.24.pdf", "content": "4 Dec 2024 — To quote Kevin Roose in The New York Times, “ AI measurement is a mess—a tangle of sloppy tests , apples-to-oranges comparisons and self-serving ... 52 pages"}
+{"idx": 3, "title": "A.I. Has a Measurement Problem - The New York Times", "date": "", "ddg_snippet": "And there is no independent testing or auditing process for these models, meaning that A.I . companies are essentially grading their own homework. In short, A.I . measurement is a mess — a tangle of sloppy tests , apples-to-oranges comparisons and self-serving hype that has left users, regulators and A.I . developers themselves grasping in the dark.", "subpage_snippet": "", "source": "hai.stanford.edu", "link": "https://hai.stanford.edu/news/ai-has-measurement-problem", "content": "And there is no independent testing or auditing process for these models, meaning that A.I . companies are essentially grading their own homework. In short, A.I . measurement is a mess — a tangle of sloppy tests , apples-to-oranges comparisons and self-serving hype that has left users, regulators and A.I . developers themselves grasping in the dark."}
+{"idx": 4, "title": "Spencer Dorn on LinkedIn: A.I. Has a Measurement Problem", "date": "", "ddg_snippet": "Here, Kevin Roose explains, \" A.I . measurement is a mess — a tangle of sloppy tests , apples-to-oranges comparisons and self-serving hype that has left users, regulators and A.I . developers ...", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/posts/spencerdorn_ai-has-a-measurement-problem-activity-7186353673516597249-H0v1", "content": "Here, Kevin Roose explains, \" A.I . measurement is a mess — a tangle of sloppy tests , apples-to-oranges comparisons and self-serving hype that has left users, regulators and A.I . developers ..."}
+{"idx": 5, "title": "The next wave: AI has a measurement problem", "date": "", "ddg_snippet": "In short, AI measurement is a mess — a tangle of sloppy tests , apples-to-oranges comparisons and self-serving hype that has left users, regulators and AI developers themselves grasping in the dark.", "subpage_snippet": "", "source": "www.dtnext.in", "link": "https://www.dtnext.in/edit/the-next-wave-ai-has-a-measurement-problem-780461", "content": "In short, AI measurement is a mess — a tangle of sloppy tests , apples-to-oranges comparisons and self-serving hype that has left users, regulators and AI developers themselves grasping in the dark."}
+{"idx": 6, "title": "Artificial intelligence has a measurement problem - PressReader", "date": "", "ddg_snippet": "In short, AI measurement is a mess — a tangle of sloppy tests , apples-to-oranges comparisons and self-serving hype that has left users, regulators and AI developers themselves grasping in the dark. The solution here is likely a combination of public and private efforts.", "subpage_snippet": "", "source": "www.pressreader.com", "link": "https://www.pressreader.com/thailand/bangkok-post/20240418/281784224138861", "content": "In short, AI measurement is a mess — a tangle of sloppy tests , apples-to-oranges comparisons and self-serving hype that has left users, regulators and AI developers themselves grasping in the dark. The solution here is likely a combination of public and private efforts."}
+{"idx": 7, "title": "AI has a measurement problem | Nieman Journalism Lab", "date": "", "ddg_snippet": "\"Artificial intelligence is too important a technology to be evaluated on the basis of vibes. Until we get better ways of measuring these tools, we won't know how to use them, or whether their progress should be celebrated or feared.\" —SC ...", "subpage_snippet": "", "source": "www.niemanlab.org", "link": "https://www.niemanlab.org/reading/a-i-has-a-measurement-problem/", "content": "\"Artificial intelligence is too important a technology to be evaluated on the basis of vibes. Until we get better ways of measuring these tools, we won't know how to use them, or whether their progress should be celebrated or feared.\" —SC ..."}
+{"idx": 8, "title": "Evaluating Generative AI Systems Is a Social Science ...", "date": "", "ddg_snippet": "by H Wallach · Cited by 11 — ... has been described as \"a tangle of sloppy tests [and] apples-to-oranges comparisons\" ( Roose , 2024 ). In this position paper, we argue that ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=1ZC4RNjqzU", "content": "by H Wallach · Cited by 11 — ... has been described as \"a tangle of sloppy tests [and] apples-to-oranges comparisons\" ( Roose , 2024 ). In this position paper, we argue that ..."}
+{"idx": 9, "title": "Dimensions of Generative AI Evaluation Design", "date": "", "ddg_snippet": "19 Nov 2024 — ... sloppy tests , apples-to-oranges comparisons and self-serving hype ... A.I. has a measurement problem . The New York Times, April 2024 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.12709v1", "content": "19 Nov 2024 — ... sloppy tests , apples-to-oranges comparisons and self-serving hype ... A.I. has a measurement problem . The New York Times, April 2024 ..."}
diff --git a/data/sampled_jsons/Roose_2024_tangle_of_sloppy_tests_year_2024.jsonl b/data/sampled_jsons/Roose_2024_tangle_of_sloppy_tests_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..59459b8d7a8ecbd8be33ea53fbe65a7a9e5130e5
--- /dev/null
+++ b/data/sampled_jsons/Roose_2024_tangle_of_sloppy_tests_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2502.00561] Position: Evaluating Generative AI Systems Is a Social ...", "date": "", "ddg_snippet": "The measurement tasks involved in evaluating generative AI (GenAI) systems lack sufficient scientific rigor, leading to what has been described as \"a tangle of sloppy tests [and] apples-to-oranges comparisons\" ( Roose , 2024 ). In this position paper, we argue that the ML community would benefit from learning from and drawing on the social sciences when developing and using measurement ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.00561", "content": "The measurement tasks involved in evaluating generative AI (GenAI) systems lack sufficient scientific rigor, leading to what has been described as \"a tangle of sloppy tests [and] apples-to-oranges comparisons\" ( Roose , 2024 ). In this position paper, we argue that the ML community would benefit from learning from and drawing on the social sciences when developing and using measurement ..."}
+{"idx": 1, "title": "A.I. Has a Measurement Problem - The New York Times", "date": "", "ddg_snippet": "In short, A.I. measurement is a mess — a tangle of sloppy tests , apples-to-oranges comparisons and self-serving hype that has left users, regulators and A.I. developers themselves grasping in ...", "subpage_snippet": "", "source": "www.nytimes.com", "link": "https://www.nytimes.com/2024/04/15/technology/ai-models-measurement.html", "content": "In short, A.I. measurement is a mess — a tangle of sloppy tests , apples-to-oranges comparisons and self-serving hype that has left users, regulators and A.I. developers themselves grasping in ..."}
+{"idx": 2, "title": "The next wave: AI has a measurement problem", "date": "", "ddg_snippet": "And there is no independent testing or auditing process for these models, meaning that AI companies are essentially grading their own homework. In short, AI measurement is a mess — a tangle of sloppy tests , apples-to-oranges comparisons and self-serving hype that has left users, regulators and AI developers themselves grasping in the dark.", "subpage_snippet": "", "source": "www.dtnext.in", "link": "https://www.dtnext.in/edit/the-next-wave-ai-has-a-measurement-problem-780461", "content": "And there is no independent testing or auditing process for these models, meaning that AI companies are essentially grading their own homework. In short, AI measurement is a mess — a tangle of sloppy tests , apples-to-oranges comparisons and self-serving hype that has left users, regulators and AI developers themselves grasping in the dark."}
+{"idx": 3, "title": "Artificial intelligence has a measurement problem - PressReader", "date": "", "ddg_snippet": "In short, AI measurement is a mess — a tangle of sloppy tests , apples-to-oranges comparisons and self-serving hype that has left users, regulators and AI developers themselves grasping in the dark. The solution here is likely a combination of public and private efforts.", "subpage_snippet": "", "source": "www.pressreader.com", "link": "https://www.pressreader.com/thailand/bangkok-post/20240418/281784224138861", "content": "In short, AI measurement is a mess — a tangle of sloppy tests , apples-to-oranges comparisons and self-serving hype that has left users, regulators and AI developers themselves grasping in the dark. The solution here is likely a combination of public and private efforts."}
+{"idx": 4, "title": "Position: Evaluating Generative AI Syste...", "date": "", "ddg_snippet": "The measurement tasks involved in evaluating generative AI (GenAI) systems are especially difficult, leading to what has been described as \"a tangle of sloppy tests [and] apples-to-oranges comparisons\" ( Roose , 2024 ). In this position paper, we argue that the ML community would benefit from learning from and drawing on the social sciences when developing and using measurement instruments for ...", "subpage_snippet": "", "source": "axi.lims.ac.uk", "link": "https://axi.lims.ac.uk/paper/2502.00561", "content": "The measurement tasks involved in evaluating generative AI (GenAI) systems are especially difficult, leading to what has been described as \"a tangle of sloppy tests [and] apples-to-oranges comparisons\" ( Roose , 2024 ). In this position paper, we argue that the ML community would benefit from learning from and drawing on the social sciences when developing and using measurement instruments for ..."}
+{"idx": 5, "title": "PDF Dimensions of Generative AI Evaluation Design", "date": "", "ddg_snippet": "Evaluating the capabilities and risks of generative AI (GenAI) models and systems is crucial for their successful development, deployment, and adoption. Despite this, many would likely agree with New York Times columnist Kevin Roose's recent characterization of the current state of GenAI evaluation as \"a mess—a tangle of sloppy tests , apples-to-oranges comparisons and self-serving hype ...", "subpage_snippet": "", "source": "evalevalai.com", "link": "https://evalevalai.com/2024workshop/accepted_papers/EvalEval_24_Dow.pdf", "content": "Evaluating the capabilities and risks of generative AI (GenAI) models and systems is crucial for their successful development, deployment, and adoption. Despite this, many would likely agree with New York Times columnist Kevin Roose's recent characterization of the current state of GenAI evaluation as \"a mess—a tangle of sloppy tests , apples-to-oranges comparisons and self-serving hype ..."}
+{"idx": 6, "title": "Position: Evaluating Generative AI Systems Is a Social Science ...", "date": "", "ddg_snippet": "The measurement tasks involved in evaluating generative AI (GenAI) systems lack suficient sci-entific rigor, leading to what has been described as \"a tangle of sloppy tests [and] apples-to-oranges comparisons\" ( Roose , 2024 ).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.00561", "content": "The measurement tasks involved in evaluating generative AI (GenAI) systems lack suficient sci-entific rigor, leading to what has been described as \"a tangle of sloppy tests [and] apples-to-oranges comparisons\" ( Roose , 2024 )."}
+{"idx": 7, "title": "Great quote: | Laura Dietz - LinkedIn Perú", "date": "", "ddg_snippet": "Great quote: To quote Kevin Roose in The New York Times, \"AI measurement is a mess—a tangle of sloppy tests , apples-to-oranges comparisons and self-serving hype that has left users, regulators and AI developers themselves grasping in the dark.\" ( Roose 2024 ) That is, GenAI evaluation is far from being a science", "subpage_snippet": "", "source": "pe.linkedin.com", "link": "https://pe.linkedin.com/posts/laura-dietz-47036516_microsoft-new-future-of-work-report-2024-activity-7276037643547639808-4D_Q", "content": "Great quote: To quote Kevin Roose in The New York Times, \"AI measurement is a mess—a tangle of sloppy tests , apples-to-oranges comparisons and self-serving hype that has left users, regulators and AI developers themselves grasping in the dark.\" ( Roose 2024 ) That is, GenAI evaluation is far from being a science"}
+{"idx": 8, "title": "Position: Evaluating Generative AI Systems Is a Social Science...", "date": "", "ddg_snippet": "The measurement tasks involved in evaluating generative AI (GenAI) systems lack sufficient scientific rigor, leading to what has been described as \"a tangle of sloppy tests [and] apples-to-oranges comparisons\" ( Roose , 2024 ). In this position paper, we argue that the ML community would benefit from learning from and drawing on the social sciences when developing and using measurement ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=1ZC4RNjqzU", "content": "The measurement tasks involved in evaluating generative AI (GenAI) systems lack sufficient scientific rigor, leading to what has been described as \"a tangle of sloppy tests [and] apples-to-oranges comparisons\" ( Roose , 2024 ). In this position paper, we argue that the ML community would benefit from learning from and drawing on the social sciences when developing and using measurement ..."}
+{"idx": 9, "title": "Position: Evaluating Generative AI Systems is a Social Science ...", "date": "", "ddg_snippet": "The measurement tasks involved in evaluating generative AI (GenAI) systems are especially difficult, leading to what has been described as\"a tangle of sloppy tests [and] apples-to-oranges comparisons\" ( Roose , 2024 ).", "subpage_snippet": "", "source": "www.microsoft.com", "link": "https://www.microsoft.com/en-us/research/publication/position-evaluating-generative-ai-systems-is-a-social-science-measurement-challenge/", "content": "The measurement tasks involved in evaluating generative AI (GenAI) systems are especially difficult, leading to what has been described as\"a tangle of sloppy tests [and] apples-to-oranges comparisons\" ( Roose , 2024 )."}
diff --git "a/data/sampled_jsons/Rs(n)_OR_R\316\264(n)_OR_R_delta(n)_Statistical_Collusion.jsonl" "b/data/sampled_jsons/Rs(n)_OR_R\316\264(n)_OR_R_delta(n)_Statistical_Collusion.jsonl"
new file mode 100644
index 0000000000000000000000000000000000000000..916cf36ba2a41d3b9e0816b2429d3c3a1262f7bd
--- /dev/null
+++ "b/data/sampled_jsons/Rs(n)_OR_R\316\264(n)_OR_R_delta(n)_Statistical_Collusion.jsonl"
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Doug Ross @ Journal: Larwyn’s Linx: Biden and Putin Are", "date": "", "ddg_snippet": "DNA-level' Statistical Proof: 2020 Vote Fraud What really happened in the Middle East? The Law : By Frédéric Bastiat (Illustrated) Mediasplainer ...", "subpage_snippet": "", "source": "directorblue.blogspot.com", "link": "https://directorblue.blogspot.com/2022/04/larwyns-linx-biden-and-putin-are-in.html", "content": "DNA-level' Statistical Proof: 2020 Vote Fraud What really happened in the Middle East? The Law : By Frédéric Bastiat (Illustrated) Mediasplainer ..."}
+{"idx": 1, "title": "Trench Wars Divisions - The Free Online Multiplayer SpaceShip", "date": "", "ddg_snippet": "TWD.org > Squads > dicE > Roster > a2m > Squad History ... Profile : Squad History : Statistics : Recent Games : On The Move : Gallery", "subpage_snippet": "", "source": "twd.trenchwars.org", "link": "https://twd.trenchwars.org/profile/15449/squadhistory", "content": "TWD.org > Squads > dicE > Roster > a2m > Squad History ... Profile : Squad History : Statistics : Recent Games : On The Move : Gallery"}
+{"idx": 2, "title": "Trench Wars Divisions - The Free Online Multiplayer SpaceShip", "date": "", "ddg_snippet": "TWD.org > Squads > Raid > Roster > TNTkilla > Squad History ... Profile : Squad History : Statistics : Recent Games : On ... RATTY...", "subpage_snippet": "", "source": "twd.trenchwars.org", "link": "https://twd.trenchwars.org/profile/11756/squadhistory", "content": "TWD.org > Squads > Raid > Roster > TNTkilla > Squad History ... Profile : Squad History : Statistics : Recent Games : On ... RATTY..."}
+{"idx": 3, "title": "Trench Wars Divisions - The Free Online Multiplayer SpaceShip", "date": "", "ddg_snippet": "TWD.org > Find Player > Fludd > Squad History ... Profile : Squad History : Statistics : Recent Games : On The Move : Gallery", "subpage_snippet": "", "source": "twd.trenchwars.org", "link": "https://twd.trenchwars.org/profile/1707/squadhistory", "content": "TWD.org > Find Player > Fludd > Squad History ... Profile : Squad History : Statistics : Recent Games : On The Move : Gallery"}
+{"idx": 4, "title": "Trench Wars Divisions - The Free Online Multiplayer SpaceShip", "date": "", "ddg_snippet": "TWD.org > Find Player > Children > Squad History ... Profile : Squad History : Statistics : Recent Games : On The Move : Gallery", "subpage_snippet": "", "source": "twd.trenchwars.org", "link": "https://twd.trenchwars.org/profile/1458/squadhistory", "content": "TWD.org > Find Player > Children > Squad History ... Profile : Squad History : Statistics : Recent Games : On The Move : Gallery"}
+{"idx": 5, "title": "Trench Wars Divisions - The Free Online Multiplayer SpaceShip", "date": "", "ddg_snippet": "TWD.org > Squads > SPASTIC > Roster > Pure_Luck > Squad History ... Profile : Squad History : Statistics : Recent Games : On The Move ...", "subpage_snippet": "", "source": "twd.trenchwars.org", "link": "https://twd.trenchwars.org/profile/1489/squadhistory", "content": "TWD.org > Squads > SPASTIC > Roster > Pure_Luck > Squad History ... Profile : Squad History : Statistics : Recent Games : On The Move ..."}
+{"idx": 6, "title": "US6590996B1 - Color adaptive watermarking - Google Patents", "date": "", "ddg_snippet": "... not performed a legal analysis ... Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/US6590996B1/en", "content": "... not performed a legal analysis ... Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)"}
+{"idx": 7, "title": "US20100310117A1 - Color image or video processing - Google", "date": "", "ddg_snippet": "H04N1/32 — Circuits or arrangements for control or supervision between transmitter and receiver or between image input and image output device ...", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/US20100310117A1/en", "content": "H04N1/32 — Circuits or arrangements for control or supervision between transmitter and receiver or between image input and image output device ..."}
+{"idx": 8, "title": "VOCI DALLA STRADA: Coaguli anomali e mortalità per tutte le", "date": "", "ddg_snippet": "Ne consegue che 5 richiami per 62 giorni riducono di 310 giorni la media dei 308 giorni rimanenti di vita dopo la dose 1.", "subpage_snippet": "", "source": "www.vocidallastrada.org", "link": "https://www.vocidallastrada.org/2023/05/coaguli-anomali-e-mortalita-per-tutte.html", "content": "Ne consegue che 5 richiami per 62 giorni riducono di 310 giorni la media dei 308 giorni rimanenti di vita dopo la dose 1."}
+{"idx": 9, "title": "Musclin Is Related to Insulin Resistance and Body Composition,", "date": "", "ddg_snippet": "... Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction ...", "subpage_snippet": "", "source": "e-enm.org", "link": "https://e-enm.org/journal/view.php?number=2221&view=citations", "content": "... Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction ..."}
diff --git a/data/sampled_jsons/Ryan_Greenblatt_Fabien_Roger_Dmitrii_Krasheninnikov_David_Krueger_password-locked_models.jsonl b/data/sampled_jsons/Ryan_Greenblatt_Fabien_Roger_Dmitrii_Krasheninnikov_David_Krueger_password-locked_models.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f050206d26d9c6e94a268db72b2ebf084d45b15c
--- /dev/null
+++ b/data/sampled_jsons/Ryan_Greenblatt_Fabien_Roger_Dmitrii_Krasheninnikov_David_Krueger_password-locked_models.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Publications – Krueger AI Safety Lab", "date": "", "ddg_snippet": "Ryan Greenblatt , Fabien Roger , Dmitrii Krasheninnikov , David Krueger Neural Information Processing Systems (2024) ... Dmitrii Krasheninnikov , ...", "subpage_snippet": "", "source": "www.kasl.ai", "link": "https://www.kasl.ai/publications/", "content": "Ryan Greenblatt , Fabien Roger , Dmitrii Krasheninnikov , David Krueger Neural Information Processing Systems (2024) ... Dmitrii Krasheninnikov , ..."}
+{"idx": 1, "title": "Output — Cambridge AI Safety Hub", "date": "", "ddg_snippet": "Ryan Greenblatt , Fabien Roger , Dmitrii Krasheninnikov , David Krueger ... Dmitrii Krasheninnikov , Xin Chen, Lauro Langosco , Peter Hase, Erdem ...", "subpage_snippet": "", "source": "www.cambridgeaisafety.org", "link": "https://www.cambridgeaisafety.org/output", "content": "Ryan Greenblatt , Fabien Roger , Dmitrii Krasheninnikov , David Krueger ... Dmitrii Krasheninnikov , Xin Chen, Lauro Langosco , Peter Hase, Erdem ..."}
+{"idx": 2, "title": "Research — Cambridge AI Safety Hub", "date": "", "ddg_snippet": "... Dmitrii Krasheninnikov , Xin Chen, Lauro Langosco , Peter Hase, Erdem ... Ryan Greenblatt , Fabien Roger , Dmitrii Krasheninnikov , David Krueger", "subpage_snippet": "", "source": "www.cambridgeaisafety.org", "link": "https://www.cambridgeaisafety.org/research", "content": "... Dmitrii Krasheninnikov , Xin Chen, Lauro Langosco , Peter Hase, Erdem ... Ryan Greenblatt , Fabien Roger , Dmitrii Krasheninnikov , David Krueger"}
+{"idx": 3, "title": "[Paper] Stress-testing capability elicitation with", "date": "", "ddg_snippet": "The paper is by Ryan Greenblatt , Fabien Roger , Dmitrii Krasheninnikov and David Krueger . ... Fabien and Ryan , and may not reflect the views of Dmitrii ...", "subpage_snippet": "", "source": "www.greaterwrong.com", "link": "https://www.greaterwrong.com/posts/c4sZqhqPwNKGz3fFW/paper-stress-testing-capability-elicitation-with-password", "content": "The paper is by Ryan Greenblatt , Fabien Roger , Dmitrii Krasheninnikov and David Krueger . ... Fabien and Ryan , and may not reflect the views of Dmitrii ..."}
+{"idx": 4, "title": "[Paper] Stress-testing capability elicitation with", "date": "", "ddg_snippet": "The paper is by Ryan Greenblatt , Fabien Roger , Dmitrii Krasheninnikov and David Krueger . ... Fabien and Ryan , and may not reflect the views of Dmitrii ...", "subpage_snippet": "", "source": "www.greaterwrong.com", "link": "https://www.greaterwrong.com/posts/c4sZqhqPwNKGz3fFW/paper-stress-testing-capability-elicitation-by-training", "content": "The paper is by Ryan Greenblatt , Fabien Roger , Dmitrii Krasheninnikov and David Krueger . ... Fabien and Ryan , and may not reflect the views of Dmitrii ..."}
+{"idx": 5, "title": "Memorizing weak examples can elicit strong behavior out of", "date": "", "ddg_snippet": "The phenomenon was discovered by Ryan Greenblatt during a joint project with Fabien Roger , Dmitrii Krasheninnikov and David Krueger .", "subpage_snippet": "", "source": "www.alignmentforum.org", "link": "https://www.alignmentforum.org/posts/bfm5Fm9pJYAthFSZT/memorizing-weak-examples-can-elicit-strong-behavior-out-of", "content": "The phenomenon was discovered by Ryan Greenblatt during a joint project with Fabien Roger , Dmitrii Krasheninnikov and David Krueger ."}
+{"idx": 6, "title": "[Paper] Stress-testing capability elicitation with", "date": "", "ddg_snippet": "The paper is by Ryan Greenblatt , Fabien Roger , Dmitrii Krasheninnikov and David Krueger . ... Fabien and Ryan , and may not reflect the views of Dmitrii ...", "subpage_snippet": "", "source": "www.alignmentforum.org", "link": "https://www.alignmentforum.org/posts/c4sZqhqPwNKGz3fFW/paper-stress-testing-capability-elicitation-with-password", "content": "The paper is by Ryan Greenblatt , Fabien Roger , Dmitrii Krasheninnikov and David Krueger . ... Fabien and Ryan , and may not reflect the views of Dmitrii ..."}
+{"idx": 7, "title": "What AI evaluations for preventing catastrophic risks can and", "date": "", "ddg_snippet": "For example, evaluators may have access to models which have not been trained for safety, or access to fine-tuning in order to train specifically for ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.08653v1", "content": "For example, evaluators may have access to models which have not been trained for safety, or access to fine-tuning in order to train specifically for ..."}
+{"idx": 8, "title": "Model Tampering Attacks Enable More Rigorous Evaluations of LLM", "date": "", "ddg_snippet": "Model suite: To facilitate further research, we release a set of 64 models trained using 8 methods to unlearn dual-use biology knowledge at varying ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.05209v3", "content": "Model suite: To facilitate further research, we release a set of 64 models trained using 8 methods to unlearn dual-use biology knowledge at varying ..."}
+{"idx": 9, "title": "[Paper] Stress-testing capability elicitation with", "date": "", "ddg_snippet": "The paper is by Ryan Greenblatt , Fabien Roger , Dmitrii Krasheninnikov and David Krueger . ... Fabien and Ryan , and may not reflect the views of Dmitrii ...", "subpage_snippet": "", "source": "www.lesswrong.com", "link": "https://www.lesswrong.com/posts/c4sZqhqPwNKGz3fFW/paper-stress-testing-capability-elicitation-with-password", "content": "The paper is by Ryan Greenblatt , Fabien Roger , Dmitrii Krasheninnikov and David Krueger . ... Fabien and Ryan , and may not reflect the views of Dmitrii ..."}
diff --git a/data/sampled_jsons/S18_characters_dataset_100000_training_examples_algebraic_combinatorics.jsonl b/data/sampled_jsons/S18_characters_dataset_100000_training_examples_algebraic_combinatorics.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..1a0d84dfee148e0b7184aa16b1d06f0aed5c6444
--- /dev/null
+++ b/data/sampled_jsons/S18_characters_dataset_100000_training_examples_algebraic_combinatorics.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Algebraic Combinatorics Dataset Repository - GitHub", "date": "", "ddg_snippet": "A Collection of Algebraic Combinatorics Datasets for Scientific Discovery in Mathematics The challenge of sifting through large datasets with the goal of identifying structure and patterns is a common activity in research level mathematics. As an obvious example , many careers have been spent looking for patterns in the set of prime numbers.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/pnnl/ML4AlgComb", "content": "A Collection of Algebraic Combinatorics Datasets for Scientific Discovery in Mathematics The challenge of sifting through large datasets with the goal of identifying structure and patterns is a common activity in research level mathematics. As an obvious example , many careers have been spent looking for patterns in the set of prime numbers."}
+{"idx": 1, "title": "Machine Learning meets Algebraic Combinatorics: A Suite of ...", "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": 2, "title": "18.212: AlgebraicCombinatorics - Stanford University", "date": "", "ddg_snippet": "As the title suggests, this is a class on combinatorics . Combinatorics is the area of mathematics that studies discrete objects, like graphs, permutations, and various diagrams, looking at objects that we can count or list. These days, there are two main flavors: Stanley-style and Erdős-style. Stanley-style (also enumerative, algebraic , or geometric) combinatorics deals with counting objects ...", "subpage_snippet": "", "source": "web.stanford.edu", "link": "https://web.stanford.edu/~lindrew/18.212.pdf", "content": "As the title suggests, this is a class on combinatorics . Combinatorics is the area of mathematics that studies discrete objects, like graphs, permutations, and various diagrams, looking at objects that we can count or list. These days, there are two main flavors: Stanley-style and Erdős-style. Stanley-style (also enumerative, algebraic , or geometric) combinatorics deals with counting objects ..."}
+{"idx": 3, "title": "Algebraic Combinatorics - Mathematics | MIT OpenCourseWare", "date": "", "ddg_snippet": "This course covers the applications of algebra to combinatorics . Topics include enumeration methods, permutations, partitions, partially ordered sets and lattices, Young tableaux, graph theory, matrix tree theorem, electrical networks, convex polytopes, and more.", "subpage_snippet": "", "source": "ocw.mit.edu", "link": "https://ocw.mit.edu/courses/18-212-algebraic-combinatorics-spring-2019/", "content": "This course covers the applications of algebra to combinatorics . Topics include enumeration methods, permutations, partitions, partially ordered sets and lattices, Young tableaux, graph theory, matrix tree theorem, electrical networks, convex polytopes, and more."}
+{"idx": 4, "title": "Open Problems in Algebraic Combinatorics", "date": "", "ddg_snippet": "Open Problems in Algebraic Combinatorics (pdf compilation of blog posts)", "subpage_snippet": "", "source": "www.samuelfhopkins.com", "link": "https://www.samuelfhopkins.com/OPAC/files/blog.pdf", "content": "Open Problems in Algebraic Combinatorics (pdf compilation of blog posts)"}
+{"idx": 5, "title": "Diverse Algebra Word Problems Dataset - Kaggle", "date": "", "ddg_snippet": "This dataset provides training and testing examples for solving algebra word problems automatically. This is a public release of the dataset corresponding paper \"\"Annotating Derivations: A New Evaluation Strategy and Dataset for Algebra Word Problems\"\"", "subpage_snippet": "", "source": "www.kaggle.com", "link": "https://www.kaggle.com/datasets/saurabhshahane/diverse-algebra-word-problems", "content": "This dataset provides training and testing examples for solving algebra word problems automatically. This is a public release of the dataset corresponding paper \"\"Annotating Derivations: A New Evaluation Strategy and Dataset for Algebra Word Problems\"\""}
+{"idx": 6, "title": "Algebraic combinatorial geometry: the polynomial method in...", "date": "", "ddg_snippet": "...a technique in combinatorics and number theory for controlling a relevant set of points by comparing it with the zero set of a suitably chosen polynomial, and then using tools from algebraic geometry (e.g. Bezout’s theorem) on that zero set .", "subpage_snippet": "", "source": "terrytao.wordpress.com", "link": "https://terrytao.wordpress.com/2013/10/25/algebraic-combinatorial-geometry-the-polynomial-method-in-arithmetic-combinatorics-incidence-combinatorics-and-number-theory/", "content": "...a technique in combinatorics and number theory for controlling a relevant set of points by comparing it with the zero set of a suitably chosen polynomial, and then using tools from algebraic geometry (e.g. Bezout’s theorem) on that zero set ."}
+{"idx": 7, "title": "Find Open Datasets and Machine Learning Projects | Kaggle", "date": "", "ddg_snippet": "Download Open Datasets on 1000s of Projects + Share Projects on One Platform. Explore Popular Topics Like Government, Sports, Medicine, Fintech, Food, More.", "subpage_snippet": "", "source": "www.kaggle.com", "link": "https://www.kaggle.com/datasets", "content": "Download Open Datasets on 1000s of Projects + Share Projects on One Platform. Explore Popular Topics Like Government, Sports, Medicine, Fintech, Food, More."}
+{"idx": 8, "title": "UCI Machine Learning Repository | Discover datasets around the world!", "date": "", "ddg_snippet": "Donated on 6/30/1988. A small classic dataset from Fisher, 1936. One of the earliest known datasets used for evaluating classification methods. Dataset Characteristics . Tabular.", "subpage_snippet": "", "source": "archive.ics.uci.edu", "link": "https://archive.ics.uci.edu/dataset/53/iris", "content": "Donated on 6/30/1988. A small classic dataset from Fisher, 1936. One of the earliest known datasets used for evaluating classification methods. Dataset Characteristics . Tabular."}
+{"idx": 9, "title": "mathigon.org/world/ Combinatorics", "date": "", "ddg_snippet": "Combinatorics and Pascal’s Triangle.", "subpage_snippet": "", "source": "mathigon.org", "link": "https://mathigon.org/world/Combinatorics", "content": "Combinatorics and Pascal’s Triangle."}
diff --git a/data/sampled_jsons/S18_characters_dataset_size.jsonl b/data/sampled_jsons/S18_characters_dataset_size.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e10e1b67fa2e9b0d74837c259d06082e82b17bd9
--- /dev/null
+++ b/data/sampled_jsons/S18_characters_dataset_size.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "adi2606/Anime_Characters · Datasets at Hugging Face", "date": "", "ddg_snippet": "📚 Dataset Summary This dataset features 1,941 anime character images, neatly organized into 322 folders, each representing a different anime series 🎌. 📦 Size of downloaded files: 152 MB 🪄 Size of auto-converted Parquet files: 151 MB 📊 Split: Train only 🎭 Classes: 322 unique anime titles Perfect for image classification, anime recommendation systems, and visual style analysis ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/adi2606/Anime_Characters", "content": "📚 Dataset Summary This dataset features 1,941 anime character images, neatly organized into 322 folders, each representing a different anime series 🎌. 📦 Size of downloaded files: 152 MB 🪄 Size of auto-converted Parquet files: 151 MB 📊 Split: Train only 🎭 Classes: 322 unique anime titles Perfect for image classification, anime recommendation systems, and visual style analysis ..."}
+{"idx": 1, "title": "Danbooru 2018 Anime Character Recognition Dataset - GitHub The EMNIST Dataset | NIST Fictional Characters Dataset | Kaggle s18 Object Detection Dataset (v2, 2022-06-23 1:21pm) by Saji ... Danbooru2018 Anime Character Dataset | Datasets | HyperAI超神经 lowres/anime · Datasets at Hugging Face", "date": "", "ddg_snippet": "This repo provides an anime character recognition dataset based on Danbooru 2018. The original Danbooru dataset provides images with tags. We processed the dataset (more details below) to generate 1M head images with corresponding character tags. About 70k characters are included in the dataset. See full list on github.com We processed the original Danbooru dataset as follows: •First only the character tags were kept by filtering according to the category of the tag. •Because we don't have information on which face corresponds to which tag, we only kept the images that have only one character tag. •Then we extracted head bounding boxes using this model. •For similar reasons, we discarded the images on which multiple head boxes are detected. This ended up with 0.97M images and 70k tags. See full list on github.com The core of the database is the detected head bounding boxes and corresponding tags. This is stored in the file faces.tsv. Note you will have to obtain the images from the original Danbooru dataset The tsv file has three columns. The first column is the file name from the Danbooru dataset . The second column is the tag id, and the third column is the head detection results. Each detection result has five fields separated by commas, i.e. left, top, right, bottom, confidence in order. The tag text for each id can be seen in the file tagIds.tsv. We also have the cropped face images ready as a tarball. Many thanks to gwern, you can now download the tarball using rsync: See full list on github.com Data Split The split for the baseline is also provided in case evaluation and comparison of the algorithms are of interest.•trainSplit.tsv contains the image ids for training.•valSplit.tsv contains the image ids for validation.•testSplit.tsv is the set we use to measure the final accuracy.The total number of images are less than the entire dataset because we removed images that did not reach the confidence bar (0.85). Note at the current stage the test set has not been verified by humans. See full list on github.com •The test set for character recognition is to be verified by humans. •Still need to do more work on face alignment. See full list on github.com Apr 4, 2017 · The full complement of the NIST Special Database 19 is a vailable in the ByClass a nd ByMerge splits. The EMNIST Balanced dataset contains a set of characters with a n equal number of samples per class. The EMNIST Letters dataset merges a balanced set of the uppercase a nd lowercase letters into a single 26-class task. Diverse 1,500 Fictional Characters for NLP, Gen AI, and Storytelling Project Jun 22, 2024 · 10 open source s18 images and annotations in multiple formats for training computer vision models. s18 (v2, 2022-06-23 1:21pm), created by Saji Thoppil The Danbooru2018 dataset is released by the Kaggle platform and includes images of Japanese anime characters and their corresponding label information. Labels are usually divided into copyright, role, author and other information. This dataset is collected by anime fans and is very targeted, making up for the large-scale datasets such as ImageNet. anime characters datasets This is an anime/manga/2D characters dataset , it is intended to be an encyclopedia for anime characters . The dataset is open source to use without limitations or any restrictions. how to use", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/grapeot/Danbooru2018AnimeCharacterRecognitionDataset", "content": "This repo provides an anime character recognition dataset based on Danbooru 2018. The original Danbooru dataset provides images with tags. We processed the dataset (more details below) to generate 1M head images with corresponding character tags. About 70k characters are included in the dataset. See full list on github.com We processed the original Danbooru dataset as follows: •First only the character tags were kept by filtering according to the category of the tag. •Because we don't have information on which face corresponds to which tag, we only kept the images that have only one character tag. •Then we extracted head bounding boxes using this model. •For similar reasons, we discarded the images on which multiple head boxes are detected. This ended up with 0.97M images and 70k tags. See full list on github.com The core of the database is the detected head bounding boxes and corresponding tags. This is stored in the file faces.tsv. Note you will have to obtain the images from the original Danbooru dataset The tsv file has three columns. The first column is the file name from the Danbooru dataset . The second column is the tag id, and the third column is the head detection results. Each detection result has five fields separated by commas, i.e. left, top, right, bottom, confidence in order. The tag text for each id can be seen in the file tagIds.tsv. We also have the cropped face images ready as a tarball. Many thanks to gwern, you can now download the tarball using rsync: See full list on github.com Data Split The split for the baseline is also provided in case evaluation and comparison of the algorithms are of interest.•trainSplit.tsv contains the image ids for training.•valSplit.tsv contains the image ids for validation.•testSplit.tsv is the set we use to measure the final accuracy.The total number of images are less than the entire dataset because we removed images that did not reach the confidence bar (0.85). Note at the current stage the test set has not been verified by humans. See full list on github.com •The test set for character recognition is to be verified by humans. •Still need to do more work on face alignment. See full list on github.com Apr 4, 2017 · The full complement of the NIST Special Database 19 is a vailable in the ByClass a nd ByMerge splits. The EMNIST Balanced dataset contains a set of characters with a n equal number of samples per class. The EMNIST Letters dataset merges a balanced set of the uppercase a nd lowercase letters into a single 26-class task. Diverse 1,500 Fictional Characters for NLP, Gen AI, and Storytelling Project Jun 22, 2024 · 10 open source s18 images and annotations in multiple formats for training computer vision models. s18 (v2, 2022-06-23 1:21pm), created by Saji Thoppil The Danbooru2018 dataset is released by the Kaggle platform and includes images of Japanese anime characters and their corresponding label information. Labels are usually divided into copyright, role, author and other information. This dataset is collected by anime fans and is very targeted, making up for the large-scale datasets such as ImageNet. anime characters datasets This is an anime/manga/2D characters dataset , it is intended to be an encyclopedia for anime characters . The dataset is open source to use without limitations or any restrictions. how to use"}
+{"idx": 2, "title": "The EMNIST Dataset | NIST", "date": "", "ddg_snippet": "Apr 4, 2017 · The full complement of the NIST Special Database 19 is a vailable in the ByClass a nd ByMerge splits. The EMNIST Balanced dataset contains a set of characters with a n equal number of samples per class. The EMNIST Letters dataset merges a balanced set of the uppercase a nd lowercase letters into a single 26-class task.", "subpage_snippet": "", "source": "www.nist.gov", "link": "https://www.nist.gov/itl/products-and-services/emnist-dataset", "content": "Apr 4, 2017 · The full complement of the NIST Special Database 19 is a vailable in the ByClass a nd ByMerge splits. The EMNIST Balanced dataset contains a set of characters with a n equal number of samples per class. The EMNIST Letters dataset merges a balanced set of the uppercase a nd lowercase letters into a single 26-class task."}
+{"idx": 3, "title": "Fictional Characters Dataset | Kaggle", "date": "", "ddg_snippet": "Diverse 1,500 Fictional Characters for NLP, Gen AI, and Storytelling Project", "subpage_snippet": "", "source": "www.kaggle.com", "link": "https://www.kaggle.com/datasets/pratyushpuri/synthetic-fictional-characters-dataset", "content": "Diverse 1,500 Fictional Characters for NLP, Gen AI, and Storytelling Project"}
+{"idx": 4, "title": "s18 Object Detection Dataset (v2, 2022-06-23 1:21pm) by Saji ...", "date": "", "ddg_snippet": "Jun 22, 2024 · 10 open source s18 images and annotations in multiple formats for training computer vision models. s18 (v2, 2022-06-23 1:21pm), created by Saji Thoppil", "subpage_snippet": "", "source": "universe.roboflow.com", "link": "https://universe.roboflow.com/saji-thoppil/s18/dataset/2", "content": "Jun 22, 2024 · 10 open source s18 images and annotations in multiple formats for training computer vision models. s18 (v2, 2022-06-23 1:21pm), created by Saji Thoppil"}
+{"idx": 5, "title": "Danbooru2018 Anime Character Dataset | Datasets | HyperAI超神经", "date": "", "ddg_snippet": "The Danbooru2018 dataset is released by the Kaggle platform and includes images of Japanese anime characters and their corresponding label information. Labels are usually divided into copyright, role, author and other information. This dataset is collected by anime fans and is very targeted, making up for the large-scale datasets such as ImageNet.", "subpage_snippet": "", "source": "hyper.ai", "link": "https://hyper.ai/en/datasets/9000", "content": "The Danbooru2018 dataset is released by the Kaggle platform and includes images of Japanese anime characters and their corresponding label information. Labels are usually divided into copyright, role, author and other information. This dataset is collected by anime fans and is very targeted, making up for the large-scale datasets such as ImageNet."}
+{"idx": 6, "title": "lowres/anime · Datasets at Hugging Face", "date": "", "ddg_snippet": "anime characters datasets This is an anime/manga/2D characters dataset , it is intended to be an encyclopedia for anime characters . The dataset is open source to use without limitations or any restrictions. how to use", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/lowres/anime", "content": "anime characters datasets This is an anime/manga/2D characters dataset , it is intended to be an encyclopedia for anime characters . The dataset is open source to use without limitations or any restrictions. how to use"}
+{"idx": 7, "title": "ACP - Attribution of aerosol particle number size distributions", "date": "", "ddg_snippet": "Attribution of aerosol particle number size distributions to main sources using an 11-year urban dataset Attribution of aerosol particle number size ...", "subpage_snippet": "", "source": "acp.copernicus.org", "link": "https://acp.copernicus.org/articles/24/5695/2024/", "content": "Attribution of aerosol particle number size distributions to main sources using an 11-year urban dataset Attribution of aerosol particle number size ..."}
+{"idx": 8, "title": "EEG dataset and OpenBMI toolbox for three BCI paradigms: an", "date": "", "ddg_snippet": "Our dataset could, therefore, support a broad range of BCI research such as subject-dependent or independent BCI [ 15–17 ], session-to-session ...", "subpage_snippet": "", "source": "academic.oup.com", "link": "https://academic.oup.com/gigascience/article/8/5/giz002/5304369", "content": "Our dataset could, therefore, support a broad range of BCI research such as subject-dependent or independent BCI [ 15–17 ], session-to-session ..."}
+{"idx": 9, "title": "File Management", "date": "", "ddg_snippet": "You can either calculate the number of characters to be removed by yourself and then use \"Header Size \" and \"Footer Size \" options or let Stambia ...", "subpage_snippet": "", "source": "stambia.org", "link": "https://stambia.org/doc/131-technology-articles/file/file-management", "content": "You can either calculate the number of characters to be removed by yourself and then use \"Header Size \" and \"Footer Size \" options or let Stambia ..."}
diff --git a/data/sampled_jsons/SAFE_Finding_Sparse_and_Flat_Minima_to_Improve_Pruning_PDF_arXiv.jsonl b/data/sampled_jsons/SAFE_Finding_Sparse_and_Flat_Minima_to_Improve_Pruning_PDF_arXiv.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3ad8b2f0a8126a6d295814e0f4157d2ad4f662a2
--- /dev/null
+++ b/data/sampled_jsons/SAFE_Finding_Sparse_and_Flat_Minima_to_Improve_Pruning_PDF_arXiv.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "SAFE: Finding Sparse and Flat Minima to Improve Pruning", "date": "", "ddg_snippet": "by D Lee · 2025 — 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/abs/2506.06866", "content": "by D Lee · 2025 — We formulate pruning as a sparsity-constrained optimization problem where flatness is encouraged as an objective."}
+{"idx": 1, "title": "SAFE: Finding Sparse and Flat Minima to Improve Pruning", "date": "", "ddg_snippet": "by D Lee · 2025 — We evaluate SAFE across standard benchmark tasks in image classification and large language model post-training pruning , and compare with ...", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/pdf/2506.06866", "content": "by D Lee · 2025 — We evaluate SAFE across standard benchmark tasks in image classification and large language model post-training pruning , and compare with ..."}
+{"idx": 2, "title": "ADAPTIVE SHARPNESS-AWARE PRUNING FOR ...", "date": "", "ddg_snippet": "by A Bair · Cited by 14 — The main objective of our design is to find flat minima in order to produce models that are simultaneously prunable and robust. We introduce a new method,.", "subpage_snippet": "", "source": "proceedings.iclr.cc", "link": "https://proceedings.iclr.cc/paper_files/paper/2024/file/cb7d81f864f95b4fd7c1085c0c8e11f1-Paper-Conference.pdf", "content": "by A Bair · Cited by 14 — The main objective of our design is to find flat minima in order to produce models that are simultaneously prunable and robust. We introduce a new method,."}
+{"idx": 3, "title": "Sparse Flows: Pruning Continuous-depth Models", "date": "", "ddg_snippet": "by L Liebenwein · 2021 · Cited by 17 — Figure 3: Flat minima result in better generalization compared to sharp min- ima. Pruning neural ODEs flattens the loss around local minima. Figure is re-. 15 pages", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper/2021/file/bf1b2f4b901c21a1d8645018ea9aeb05-Paper.pdf", "content": "by L Liebenwein · 2021 · Cited by 17 — Figure 3: Flat minima result in better generalization compared to sharp min- ima. Pruning neural ODEs flattens the loss around local minima. Figure is re-. 15 pages"}
+{"idx": 4, "title": "ROBUSTNESS TO PRUNING PREDICTS", "date": "", "ddg_snippet": "by L Kuhn · Cited by 15 — Lastly, we investigate whether the success of prunability can be explained by its relationship to flat local minima . ... URL https:// arxiv .org/ pdf /1811.04918. pdf .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=1P2KAvsE59b", "content": "by L Kuhn · Cited by 15 — Lastly, we investigate whether the success of prunability can be explained by its relationship to flat local minima . ... URL https:// arxiv .org/ pdf /1811.04918. pdf ."}
+{"idx": 5, "title": "adaptive sharpness-aware pruning for robust sparse ...", "date": "", "ddg_snippet": "by A Bair · 2023 · Cited by 13 — The main objective of our design is to find flat minima in order to produce models that are simultaneously prunable and robust. We introduce ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2306.14306", "content": "by A Bair · 2023 · Cited by 13 — The main objective of our design is to find flat minima in order to produce models that are simultaneously prunable and robust. We introduce ..."}
+{"idx": 6, "title": "Tavish9/awesome-daily-AI-arxiv", "date": "", "ddg_snippet": "Daily AI Research Digest: Tracking breakthroughs in AI/NLP/CV/Robotics with dynamic updates and paper navigation. - Tavish9/awesome-daily-AI- arxiv .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Tavish9/awesome-daily-AI-arxiv", "content": "Daily AI Research Digest: Tracking breakthroughs in AI/NLP/CV/Robotics with dynamic updates and paper navigation. - Tavish9/awesome-daily-AI- arxiv ."}
+{"idx": 7, "title": "Effective Sparsification of Neural Networks with Global ...", "date": "", "ddg_snippet": "SAFE: Finding Sparse and Flat Minima to Improve Pruning · Dongyeop LeeKwanhee LeeJinseok ChungNamhoon Lee. Computer Science, Mathematics. ArXiv. 2025. TLDR.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Effective-Sparsification-of-Neural-Networks-with-Zhou-Zhang/a5e56209623c52f3f7ddfaa92f9e44cfc6ffc972", "content": "SAFE: Finding Sparse and Flat Minima to Improve Pruning · Dongyeop LeeKwanhee LeeJinseok ChungNamhoon Lee. Computer Science, Mathematics. ArXiv. 2025. TLDR."}
+{"idx": 8, "title": "Approaching Deep Learning through the Spectral ...", "date": "", "ddg_snippet": "by D Yunis · 2024 · Cited by 4 — We see that the magnitude pruning still has similar dynamics in its top singular vectors, while random pruning does not. Last column: Singular ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2408.11804?", "content": "by D Yunis · 2024 · Cited by 4 — We see that the magnitude pruning still has similar dynamics in its top singular vectors, while random pruning does not. Last column: Singular ..."}
+{"idx": 9, "title": "The Generalization-Stability Tradeoff In Neural Network ...", "date": "", "ddg_snippet": "by B Bartoldson · 2020 · Cited by 117 — Our results provide empirical support for such theory, as we find that iterative DNN pruning may improve both generalization and flatness by creating ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper/2020/file/ef2ee09ea9551de88bc11fd7eeea93b0-Paper.pdf", "content": "by B Bartoldson · 2020 · Cited by 117 — Our results provide empirical support for such theory, as we find that iterative DNN pruning may improve both generalization and flatness by creating ..."}
diff --git a/data/sampled_jsons/SAFE_algorithm_dual_variable_update_formula_augmented_Lagrangian_pruning.jsonl b/data/sampled_jsons/SAFE_algorithm_dual_variable_update_formula_augmented_Lagrangian_pruning.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c31911e919b699b07d87341ffacd08136aeba295
--- /dev/null
+++ b/data/sampled_jsons/SAFE_algorithm_dual_variable_update_formula_augmented_Lagrangian_pruning.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "SAFE: Finding Sparse and Flat Minima to Improve Pruning", "date": "", "ddg_snippet": "To tackle this, we use an augmented Lagrangian dual -based approach well established in the optimization literature with convergence guarantees, and propose a new optimization-based pruning method called SAFE .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2506.06866", "content": "To tackle this, we use an augmented Lagrangian dual -based approach well established in the optimization literature with convergence guarantees, and propose a new optimization-based pruning method called SAFE ."}
+{"idx": 1, "title": "Chapter 7 Duality / augmented Lagrangian / ADMM", "date": "", "ddg_snippet": "The so-called linearized augmented Lagrangian method (LALM) is an alternative approach that replaces the expensive exact x update (7.13) with a proximal point update :", "subpage_snippet": "", "source": "web.eecs.umich.edu", "link": "https://web.eecs.umich.edu/~fessler/course/598/l/n-07-dual.pdf", "content": "The so-called linearized augmented Lagrangian method (LALM) is an alternative approach that replaces the expensive exact x update (7.13) with a proximal point update :"}
+{"idx": 2, "title": "21.1 Review of dual methods and Augmented Lagrangian method", "date": "", "ddg_snippet": "In dual method , the primal variable x is updated by minimize the Lagrangian , and the dual variable u is updated by the way of gradient ascent, as Ax b is the gradient of u.", "subpage_snippet": "", "source": "stat.cmu.edu", "link": "https://stat.cmu.edu/~ryantibs/convexopt-F16/scribes/admm-scribed.pdf", "content": "In dual method , the primal variable x is updated by minimize the Lagrangian , and the dual variable u is updated by the way of gradient ascent, as Ax b is the gradient of u."}
+{"idx": 3, "title": "Constrained Optimization Algorithms III - Stanford University", "date": "", "ddg_snippet": "The Lagrangian , ADMM and/or Primal- Dual Methods min f (x) s.t. h(x) = 0 Relax equality constraints into the objective function and update the", "subpage_snippet": "", "source": "web.stanford.edu", "link": "https://web.stanford.edu/class/msande211x/lecture14.pdf", "content": "The Lagrangian , ADMM and/or Primal- Dual Methods min f (x) s.t. h(x) = 0 Relax equality constraints into the objective function and update the"}
+{"idx": 4, "title": "Augmented Lagrangian Methods - University of Wisconsin–Madison", "date": "", "ddg_snippet": "Add subscripts, and we recover the augmented Lagrangian algorithm of the rst slide! Can also increase (to sharpen the e ect of the prox term), if needed.", "subpage_snippet": "", "source": "pages.cs.wisc.edu", "link": "https://pages.cs.wisc.edu/~swright/nd2016/IMA_augmentedLagrangian.pdf", "content": "Add subscripts, and we recover the augmented Lagrangian algorithm of the rst slide! Can also increase (to sharpen the e ect of the prox term), if needed."}
+{"idx": 5, "title": "Revisiting Augmented Lagrangian Duals - Optimization Online", "date": "", "ddg_snippet": "Abstract For nonconvex optimization problems, possibly having mixed-integer variables , a convergent primal- dual solution algorithm is proposed. The approach applies a proximal bundle method to certain augmented Lagrangian dual that arises in the context of the so-called generalized augmented Lagrangians. We recast these Lagrangians into the framework of a classical Lagrangian , by means of a ...", "subpage_snippet": "", "source": "optimization-online.org", "link": "https://optimization-online.org/wp-content/uploads/2020/03/7709.pdf", "content": "Abstract For nonconvex optimization problems, possibly having mixed-integer variables , a convergent primal- dual solution algorithm is proposed. The approach applies a proximal bundle method to certain augmented Lagrangian dual that arises in the context of the so-called generalized augmented Lagrangians. We recast these Lagrangians into the framework of a classical Lagrangian , by means of a ..."}
+{"idx": 6, "title": "Safe: Finding Sparse and Flat Minima to Improve Pruning", "date": "", "ddg_snippet": "Augmented Lagrangian based approach To solve this, we form the augmented Lagrangian dual problem of the following: max , min u x,z λ", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/media/icml-2025/Slides/46658.pdf", "content": "Augmented Lagrangian based approach To solve this, we form the augmented Lagrangian dual problem of the following: max , min u x,z λ"}
+{"idx": 7, "title": "Downloads", "date": "", "ddg_snippet": "Bayesian optimization under mixed constraints with a slack- variable augmented Lagrangian ... Regression: Nonparametric Regression for Latent Variable ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/Downloads/2016", "content": "Bayesian optimization under mixed constraints with a slack- variable augmented Lagrangian ... Regression: Nonparametric Regression for Latent Variable ..."}
+{"idx": 8, "title": "Downloads", "date": "", "ddg_snippet": "Algorithmic Assurance: An Active Approach to Algorithmic Testing using Bayesian Optimisation ... Almost Optimal Algorithms for Linear Stochastic ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/Downloads/2018", "content": "Algorithmic Assurance: An Active Approach to Algorithmic Testing using Bayesian Optimisation ... Almost Optimal Algorithms for Linear Stochastic ..."}
+{"idx": 9, "title": "Downloads", "date": "", "ddg_snippet": "A First-Order Algorithmic Framework for Wasserstein ... Algorithm -Dependent Generalization Bounds for Overparameterized Deep Residual Networks", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/Downloads/2019", "content": "A First-Order Algorithmic Framework for Wasserstein ... Algorithm -Dependent Generalization Bounds for Overparameterized Deep Residual Networks"}
diff --git a/data/sampled_jsons/SAFE_paper_LLaMa-2_7B_Wikitext_50%_sparsity_SparseGPT_SAFE+_perplexity_Table_1_year_2024.jsonl b/data/sampled_jsons/SAFE_paper_LLaMa-2_7B_Wikitext_50%_sparsity_SparseGPT_SAFE+_perplexity_Table_1_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..cf64d177891eb1d5093c9e5ea815a49cf7eda460
--- /dev/null
+++ b/data/sampled_jsons/SAFE_paper_LLaMa-2_7B_Wikitext_50%_sparsity_SparseGPT_SAFE+_perplexity_Table_1_year_2024.jsonl
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+{"idx": 0, "title": "Safe overnight area off I95? (Fayetteville, Rocky Mount: crime...", "date": "", "ddg_snippet": "May 4, 2015 · We're looking for a safe place (hotel) to stop and spend the night off of I95 midway through which points us to NC. I'd like to feel comfortable leaving the truck/car (which of course will be locked) while we get some shut eye. Any suggestions for a hotel not too far off of I95?", "subpage_snippet": "", "source": "www.city-data.com", "link": "https://www.city-data.com/forum/north-carolina/2360850-safe-overnight-area-off-i95.html", "content": "May 4, 2015 · We're looking for a safe place (hotel) to stop and spend the night off of I95 midway through which points us to NC. I'd like to feel comfortable leaving the truck/car (which of course will be locked) while we get some shut eye. Any suggestions for a hotel not too far off of I95?"}
+{"idx": 1, "title": "Is Ronkonkoma LiRR train station safe? (Brookhaven, Islip: 2013,...", "date": "", "ddg_snippet": "Mar 14, 2023 · We wonder how safe it is to commute from there? Are parking spaces relatively easy to get? Is it safe to leave your car there 10 hours or so and is", "subpage_snippet": "", "source": "www.city-data.com", "link": "https://www.city-data.com/forum/long-island/3410615-ronkonkoma-lirr-train-station-safe.html", "content": "Mar 14, 2023 · We wonder how safe it is to commute from there? Are parking spaces relatively easy to get? Is it safe to leave your car there 10 hours or so and is"}
+{"idx": 2, "title": "How safe is Historic Coronado District? (Phoenix: apartments,...", "date": "", "ddg_snippet": "Feb 25, 2009 · Hi everyone, looking into buying a home in the neighborhood. I really fell in love with historic type homes, something about them just feels right.", "subpage_snippet": "", "source": "www.city-data.com", "link": "https://www.city-data.com/forum/phoenix-area/576896-how-safe-historic-coronado-district.html", "content": "Feb 25, 2009 · Hi everyone, looking into buying a home in the neighborhood. I really fell in love with historic type homes, something about them just feels right."}
+{"idx": 3, "title": "Moving to Prospect Park South, Safe? (Victor: low income,...", "date": "", "ddg_snippet": "Jun 27, 2014 · Hi guys, I typically lurk here but since I'm making a big move to NYC, I figured I'd post. I'm going to be relocating to an area in Prospect Park", "subpage_snippet": "", "source": "www.city-data.com", "link": "https://www.city-data.com/forum/new-york-city/2148183-moving-prospect-park-south-safe.html", "content": "Jun 27, 2014 · Hi guys, I typically lurk here but since I'm making a big move to NYC, I figured I'd post. I'm going to be relocating to an area in Prospect Park"}
+{"idx": 4, "title": "Crime in Tulsa, Oklahoma (OK): murders, rapes, robberies,...", "date": "", "ddg_snippet": "Crime rate in Tulsa, Oklahoma (OK): murders, rapes, robberies, assaults, burglaries, thefts, auto thefts, arson, law enforcement employees, police officers, crime map", "subpage_snippet": "", "source": "www.city-data.com", "link": "https://www.city-data.com/crime/crime-Tulsa-Oklahoma.html", "content": "Crime rate in Tulsa, Oklahoma (OK): murders, rapes, robberies, assaults, burglaries, thefts, auto thefts, arson, law enforcement employees, police officers, crime map"}
+{"idx": 5, "title": "Crime rate in Camden, New Jersey (NJ): murders, rapes, robberies...", "date": "", "ddg_snippet": "Camden, NJ New Jersey murders, rapes, robberies, assaults, burglaries, thefts, auto thefts, arson, law enforcement employees, police officers, crime map", "subpage_snippet": "", "source": "www.city-data.com", "link": "https://www.city-data.com/crime/crime-Camden-New-Jersey.html", "content": "Camden, NJ New Jersey murders, rapes, robberies, assaults, burglaries, thefts, auto thefts, arson, law enforcement employees, police officers, crime map"}
+{"idx": 6, "title": "Crime rate in Wilmington, North Carolina (NC): murders, rapes ...", "date": "", "ddg_snippet": "Wilmington, NC North Carolina murders, rapes, robberies, assaults, burglaries, thefts, auto thefts, arson, law enforcement employees, police officers, crime map", "subpage_snippet": "", "source": "www.city-data.com", "link": "https://www.city-data.com/crime/crime-Wilmington-North-Carolina.html", "content": "Wilmington, NC North Carolina murders, rapes, robberies, assaults, burglaries, thefts, auto thefts, arson, law enforcement employees, police officers, crime map"}
+{"idx": 7, "title": "Crime in Flint, Michigan (MI): murders, rapes, robberies,...", "date": "", "ddg_snippet": "Flint, MI Michigan murders, rapes, robberies, assaults, burglaries, thefts, auto thefts, arson, law enforcement employees, police officers, crime map", "subpage_snippet": "", "source": "www.city-data.com", "link": "https://www.city-data.com/crime/crime-Flint-Michigan.html", "content": "Flint, MI Michigan murders, rapes, robberies, assaults, burglaries, thefts, auto thefts, arson, law enforcement employees, police officers, crime map"}
+{"idx": 8, "title": "Crime rate in Pine Bluff, Arkansas (AR): murders, rapes,...", "date": "", "ddg_snippet": "Pine Bluff, AR Arkansas murders, rapes, robberies, assaults, burglaries, thefts, auto thefts, arson, law enforcement employees, police officers, crime map", "subpage_snippet": "", "source": "www.city-data.com", "link": "https://www.city-data.com/crime/crime-Pine-Bluff-Arkansas.html", "content": "Pine Bluff, AR Arkansas murders, rapes, robberies, assaults, burglaries, thefts, auto thefts, arson, law enforcement employees, police officers, crime map"}
+{"idx": 9, "title": "Crime in Gary, Indiana (IN): murders, rapes, robberies, assaults...", "date": "", "ddg_snippet": "Crime rate in Gary, Indiana (IN): murders, rapes, robberies, assaults, burglaries, thefts, auto thefts, arson, law enforcement employees, police officers, crime map", "subpage_snippet": "", "source": "www.city-data.com", "link": "https://www.city-data.com/crime/crime-Gary-Indiana.html", "content": "Crime rate in Gary, Indiana (IN): murders, rapes, robberies, assaults, burglaries, thefts, auto thefts, arson, law enforcement employees, police officers, crime map"}
diff --git a/data/sampled_jsons/SAMP_algorithm_Bad_Example_Section_5.3_fairness_1-1e_online_matching.jsonl b/data/sampled_jsons/SAMP_algorithm_Bad_Example_Section_5.3_fairness_1-1e_online_matching.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c5f3da1cf8589cc91c72df903032e1e30fd6ee36
--- /dev/null
+++ b/data/sampled_jsons/SAMP_algorithm_Bad_Example_Section_5.3_fairness_1-1e_online_matching.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "idahosquaredancing.com -", "date": "", "ddg_snippet": "... some online casinos rely on luck to determine the outcome of their games, others use algorithms and mathematical advantages to give the house an edge ...", "subpage_snippet": "", "source": "idahosquaredancing.com", "link": "https://idahosquaredancing.com/", "content": "... some online casinos rely on luck to determine the outcome of their games, others use algorithms and mathematical advantages to give the house an edge ..."}
+{"idx": 1, "title": "utahbankruptcyattorneys.net -", "date": "", "ddg_snippet": "Slots are one of the most popular casino games and there are many different types of them, both online and in land-based casinos.", "subpage_snippet": "", "source": "utahbankruptcyattorneys.net", "link": "https://utahbankruptcyattorneys.net/", "content": "Slots are one of the most popular casino games and there are many different types of them, both online and in land-based casinos."}
+{"idx": 2, "title": "Gambling Archives - wipala.org", "date": "", "ddg_snippet": "Dalam era digital yang semakin berkembang, permainan slot telah menjadi salah satu pilihan utama bagi penggemar judi online .", "subpage_snippet": "", "source": "wipala.org", "link": "https://wipala.org/category/gambling/", "content": "Dalam era digital yang semakin berkembang, permainan slot telah menjadi salah satu pilihan utama bagi penggemar judi online ."}
+{"idx": 3, "title": "ChipKIT Uno32: First Impressions And Benchmarks | Hackaday", "date": "", "ddg_snippet": "Given that the IDE was wrapped up literally hours before going live online and at Maker Faire, it’ s understandable that there are some loose ends ...", "subpage_snippet": "", "source": "hackaday.com", "link": "https://hackaday.com/2011/05/27/chipkit-uno32-first-impressions-and-benchmarks/", "content": "Given that the IDE was wrapped up literally hours before going live online and at Maker Faire, it’ s understandable that there are some loose ends ..."}
+{"idx": 4, "title": "#1 - Best Trading Brokers metals bank guarantees", "date": "", "ddg_snippet": "Use these tips from our tips and strategies section How To Trade A Binary Option Online This page explains Real binary option strategy Cobalt detail ...", "subpage_snippet": "", "source": "trading.robotsforex.net", "link": "http://trading.robotsforex.net/best-trading-brokers-metals-bank-guarantees.html", "content": "Use these tips from our tips and strategies section How To Trade A Binary Option Online This page explains Real binary option strategy Cobalt detail ..."}
+{"idx": 5, "title": "IJSRET Volume 5 Issue 3, May-Jun-2019 - IJSRET", "date": "", "ddg_snippet": "... oneself off from social support networks: Infertile couples experience a difficult dilemma, particularly around holiday time or important family ...", "subpage_snippet": "", "source": "ijsret.com", "link": "https://ijsret.com/2019/05/13/ijsret-volume-5-issue-3-may-jun-2019/", "content": "... oneself off from social support networks: Infertile couples experience a difficult dilemma, particularly around holiday time or important family ..."}
+{"idx": 6, "title": "US20170231519A1 - System and method for annotating and", "date": "", "ddg_snippet": "... epileptiform discharges (ED) waveforms in a database; receiving, by a computing device, a signal encoding electroencephalograph (EEG) data from a ...", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/US20170231519A1/en", "content": "... epileptiform discharges (ED) waveforms in a database; receiving, by a computing device, a signal encoding electroencephalograph (EEG) data from a ..."}
+{"idx": 7, "title": "2508 questions with answers in STATISTICAL ANALYSIS | Science", "date": "", "ddg_snippet": "... will be running one statistical analysis test on associations between feeders and non-feeders on a variety of characteristics such as age, gender etc.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/topic/Statistical-Analysis/7", "content": "... will be running one statistical analysis test on associations between feeders and non-feeders on a variety of characteristics such as age, gender etc."}
+{"idx": 8, "title": "3129 questions with answers in STATISTICS | Science topic", "date": "", "ddg_snippet": "... on a RCT-study I feel the effect of SN and SI is limited, as some people have been given tools to do active breaks and asked to do them at least 3 ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/topic/Statistics2/5", "content": "... on a RCT-study I feel the effect of SN and SI is limited, as some people have been given tools to do active breaks and asked to do them at least 3 ..."}
+{"idx": 9, "title": "simulation | Andrew Wheeler", "date": "", "ddg_snippet": "... a consistent low probability ( 5 ), and a consistent high ( 1 ), a downward mostly linear slope ( 3 ), and an upward linear slope (2), and then one ...", "subpage_snippet": "", "source": "andrewpwheeler.com", "link": "https://andrewpwheeler.com/tag/simulation/", "content": "... a consistent low probability ( 5 ), and a consistent high ( 1 ), a downward mostly linear slope ( 3 ), and an upward linear slope (2), and then one ..."}
diff --git a/data/sampled_jsons/SAMP_algorithm_online_matching_fairness_LP_solution_probability_sampling_year_2024.jsonl b/data/sampled_jsons/SAMP_algorithm_online_matching_fairness_LP_solution_probability_sampling_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..48f7281e909f33c2da207bc961bf2402bcd27a5f
--- /dev/null
+++ b/data/sampled_jsons/SAMP_algorithm_online_matching_fairness_LP_solution_probability_sampling_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Promoting Fairness Among Dynamic Agents in Online ...", "date": "", "ddg_snippet": "9 Dec 2024 — In this paper, we study online matching problems where performance is instead determined by the fairness in service provided to different online ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/poster/96945", "content": "9 Dec 2024 — In this paper, we study online matching problems where performance is instead determined by the fairness in service provided to different online ..."}
+{"idx": 1, "title": "Promoting Fairness Among Dynamic Agents in Online- ...", "date": "", "ddg_snippet": "by W Ma · 2024 · Cited by 1 — SAMP is formally stated in Algorithm 1. ALGORITHM 1: An LP -based Sampling Algorithm ( SAMP ). 1 Solve LP (2) to get an optimal solution {x∗ ij , s∗}. 2 Let ... 30 pages", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/959f70ee50044bed305e48e3484005a7-Paper-Conference.pdf", "content": "by W Ma · 2024 · Cited by 1 — SAMP is formally stated in Algorithm 1. ALGORITHM 1: An LP -based Sampling Algorithm ( SAMP ). 1 Solve LP (2) to get an optimal solution {x∗ ij , s∗}. 2 Let ... 30 pages"}
+{"idx": 2, "title": "Fairness Maximization among Offline Agents in Online ...", "date": "", "ddg_snippet": "We present two linear-programming ( LP ) based sampling algorithms , which achieve competitive ratios at least 0.725 for individual fairness maximization and 0.719 ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3569705", "content": "We present two linear-programming ( LP ) based sampling algorithms , which achieve competitive ratios at least 0.725 for individual fairness maximization and 0.719 ..."}
+{"idx": 3, "title": "Tight Competitive and Variance Analyses of Matching ...", "date": "", "ddg_snippet": "by P Xu · 2024 · Cited by 1 — Our focus is on two natural LP-based sampling policies that serve as foundational elements in online algorithm design: One in- cludes ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2401.13842", "content": "by P Xu · 2024 · Cited by 1 — Our focus is on two natural LP-based sampling policies that serve as foundational elements in online algorithm design: One in- cludes ..."}
+{"idx": 4, "title": "A survey on online matching and ad allocation", "date": "", "ddg_snippet": "by R Lee · 2023 — Algorithms designed for online bipartite matching have a wide variety of practical applications. The most notable application is ad allocation.", "subpage_snippet": "", "source": "digitalcommons.njit.edu", "link": "https://digitalcommons.njit.edu/cgi/viewcontent.cgi?article=3200&context=theses", "content": "by R Lee · 2023 — Algorithms designed for online bipartite matching have a wide variety of practical applications. The most notable application is ad allocation."}
+{"idx": 5, "title": "Online Planning in Non-stationary Environments", "date": "", "ddg_snippet": "We highlight that Algorithm 1 is the same for both Simu and Samp , with the exception of Lines 5-6. The algorithm consists of three main components: a sampling ...", "subpage_snippet": "", "source": "papers.ssrn.com", "link": "https://papers.ssrn.com/sol3/Delivery.cfm/5219198.pdf?abstractid=5219198&mirid=1", "content": "We highlight that Algorithm 1 is the same for both Simu and Samp , with the exception of Lines 5-6. The algorithm consists of three main components: a sampling ..."}
+{"idx": 6, "title": "REVISITING QUANTUM ALGORITHMS FOR LINEAR RE", "date": "", "ddg_snippet": "by Z Song · Cited by 11 — In this work, we develop a quantum algorithm that runs in eO(ϵ−1. √ nd1.5) + poly(d/ϵ) time and outputs a classical solution . It provides a quadratic quantum ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=hyrRupfS0o", "content": "by Z Song · Cited by 11 — In this work, we develop a quantum algorithm that runs in eO(ϵ−1. √ nd1.5) + poly(d/ϵ) time and outputs a classical solution . It provides a quadratic quantum ..."}
+{"idx": 7, "title": "arXiv:2112.04169v2 [cs.GT] 9 Dec 2021", "date": "", "ddg_snippet": "by P Xu · 2021 · Cited by 4 — Algorithm 1: An LP -based sampling ( SAMP ). 1 Offline Phase: 2 Solve LP (1), and let {xi j } be an optimal solution . 3 Online Phase: 4 Let ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2112.04169", "content": "by P Xu · 2021 · Cited by 4 — Algorithm 1: An LP -based sampling ( SAMP ). 1 Offline Phase: 2 Solve LP (1), and let {xi j } be an optimal solution . 3 Online Phase: 4 Let ..."}
+{"idx": 8, "title": "Fully Dynamic Clustering and Diversity Maximization in ...", "date": "", "ddg_snippet": "9 May 2025 — We present approximation algorithms for some variants of k-center clustering and diversity maximization in a fully dynamic setting.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3727881", "content": "9 May 2025 — We present approximation algorithms for some variants of k-center clustering and diversity maximization in a fully dynamic setting."}
+{"idx": 9, "title": "Combinatorial Optimization Under Uncertainty: Probing ...", "date": "", "ddg_snippet": "by S Singla · 2018 · Cited by 26 — Combinatorial optimization captures many natural problems such as match - ing, load balancing, social welfare, network design, clustering, ... 229 pages", "subpage_snippet": "", "source": "faculty.cc.gatech.edu", "link": "https://faculty.cc.gatech.edu/~ssingla7/papers/thesis_Sahil.pdf", "content": "by S Singla · 2018 · Cited by 26 — Combinatorial optimization captures many natural problems such as match - ing, load balancing, social welfare, network design, clustering, ... 229 pages"}
diff --git a/data/sampled_jsons/SCAMP-7_PPA_prototype_maximum_FPS_3000_descriptor-in-pixel_year_2024.jsonl b/data/sampled_jsons/SCAMP-7_PPA_prototype_maximum_FPS_3000_descriptor-in-pixel_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a077e2bc857f2807a4f1f11d211eb5628051f880
--- /dev/null
+++ b/data/sampled_jsons/SCAMP-7_PPA_prototype_maximum_FPS_3000_descriptor-in-pixel_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Descriptor - In - Pixel : Point-Feature Tracking For Pixel Processor Arrays", "date": "", "ddg_snippet": "We introduce a Descriptor - In - Pixel paradigm, in which a feature descriptor is held within the memory of each pixel-processor. The PPA ’s architecture enables the response of every processor’s descriptor, upon the current image, to be computed in parallel.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Bose_Descriptor-In-Pixel__Point-Feature_Tracking_For_Pixel_Processor_Arrays_CVPR_2025_paper.pdf", "content": "We introduce a Descriptor - In - Pixel paradigm, in which a feature descriptor is held within the memory of each pixel-processor. The PPA ’s architecture enables the response of every processor’s descriptor, upon the current image, to be computed in parallel."}
+{"idx": 1, "title": "Descriptor - In - Pixel : Point-Feature Tracking For Pixel Processor Arrays", "date": "", "ddg_snippet": "The PPA 's architecture enables us to compute the response of every stored descriptor in parallel.This approach is very fast, our implementation upon the SCAMP - 7 PPA prototype runs at over 3000 FPS ( Frames Per Second ), tracking point-features reliably even under violent motion.", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/venue/Bose_Descriptor-In-Pixel__Point-Feature_Tracking_For_Pixel_Processor_Arrays@CVPR2025@CVF", "content": "The PPA 's architecture enables us to compute the response of every stored descriptor in parallel.This approach is very fast, our implementation upon the SCAMP - 7 PPA prototype runs at over 3000 FPS ( Frames Per Second ), tracking point-features reliably even under violent motion."}
+{"idx": 2, "title": "SCAMP Vision Sensor", "date": "", "ddg_snippet": "The SCAMP -5 vision chip contains a 256x256 processor array, operating in a SIMD mode, with one processing element per image pixel .Up to 3000 fps (when returning only keypoint coordinates / descriptors ).", "subpage_snippet": "", "source": "personalpages.manchester.ac.uk", "link": "https://personalpages.manchester.ac.uk/staff/p.dudek/scamp/", "content": "The SCAMP -5 vision chip contains a 256x256 processor array, operating in a SIMD mode, with one processing element per image pixel .Up to 3000 fps (when returning only keypoint coordinates / descriptors )."}
+{"idx": 3, "title": "Best Minecraft Mods For Maximum FPS Boost - YouTube", "date": "", "ddg_snippet": "Use These Minecraft 1.20 - 1.21 Mods To Increase Your FPS , Remove Lag, and Increase Render Distance.", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=MJW4igOU_J8", "content": "Use These Minecraft 1.20 - 1.21 Mods To Increase Your FPS , Remove Lag, and Increase Render Distance."}
+{"idx": 4, "title": "Как повысить FPS в Roblox", "date": "", "ddg_snippet": "Как повысить ФПС в Роблокс-06. Если вы постоянно видите 30 или 60 FPS и значение никогда не повышается, возможно, в настройках включена функция вертикальной синхронизации. Найдите « Maximum Frame Rate», снимите лимит или установите значение побольше.", "subpage_snippet": "", "source": "Lumpics.ru", "link": "https://Lumpics.ru/how-to-increase-fps-in-roblox/", "content": "Как повысить ФПС в Роблокс-06. Если вы постоянно видите 30 или 60 FPS и значение никогда не повышается, возможно, в настройках включена функция вертикальной синхронизации. Найдите « Maximum Frame Rate», снимите лимит или установите значение побольше."}
+{"idx": 5, "title": "Wplace Pixel Art - Convert Images to wplace Pixel", "date": "", "ddg_snippet": "Transform any image into Wplace pixel art with wplace pixel converter.Plan your Wplace build before you place a single pixel . Click to upload or drag and drop your image here. Supports JPEG, JPG, PNG - Max 5MB. Works with any image type for pixel art conversion.", "subpage_snippet": "", "source": "wplacepixel.com", "link": "https://wplacepixel.com/", "content": "Transform any image into Wplace pixel art with wplace pixel converter.Plan your Wplace build before you place a single pixel . Click to upload or drag and drop your image here. Supports JPEG, JPG, PNG - Max 5MB. Works with any image type for pixel art conversion."}
+{"idx": 6, "title": "Accurate FPS Tester - Online Frame Rate Checker", "date": "", "ddg_snippet": "Browser-based FPS testers help check your monitor's refresh rate capabilities and system performance without installing software. These tools typically run.", "subpage_snippet": "", "source": "subgadgets.com", "link": "https://subgadgets.com/tools/fps-tester/", "content": "Browser-based FPS testers help check your monitor's refresh rate capabilities and system performance without installing software. These tools typically run."}
+{"idx": 7, "title": "CVPR 2025 最佳论文候选16篇 快览-CSDN博客", "date": "", "ddg_snippet": "Descriptor - In _ Pixel . This paper presents a novel approach for joint point-feature detection and tracking, specifically designed for Pixel Processor Array sensors ( PPA ).", "subpage_snippet": "", "source": "blog.csdn.net", "link": "https://blog.csdn.net/u014546828/article/details/148510133", "content": "Descriptor - In _ Pixel . This paper presents a novel approach for joint point-feature detection and tracking, specifically designed for Pixel Processor Array sensors ( PPA )."}
+{"idx": 8, "title": "Лайфхак для поднятия ФПС в CS 2! Совет поможет только тем...", "date": "", "ddg_snippet": "Чтобы поднять ФПС в игре, вместо fps _ max 0 вам нужно указывать определённое значение, например fps _ max 300, и при этом ОБЯЗАТЕЛЬНО включить Resizable BAR в БИОС.Это получается надо прописать fps _ max 500, чтобы такие кадры были?", "subpage_snippet": "", "source": "vk.com", "link": "https://vk.com/wall-144847188_1459423", "content": "Чтобы поднять ФПС в игре, вместо fps _ max 0 вам нужно указывать определённое значение, например fps _ max 300, и при этом ОБЯЗАТЕЛЬНО включить Resizable BAR в БИОС.Это получается надо прописать fps _ max 500, чтобы такие кадры были?"}
+{"idx": 9, "title": "CVPR 2025 技術動向調査: Best Paper Award Candidates...", "date": "", "ddg_snippet": "Descriptor - In - Pixel : Point-Feature Tracking For Pixel Processor Arrays. The PanAf-FGBG Dataset: Understanding the Impact of Backgrounds in Wildlife Behaviour Recognition. UniAP: Unifying Inter- and Intra-Layer Automatic Parallelism by Mixed Integer Quadratic Programming.", "subpage_snippet": "", "source": "engineers.safie.link", "link": "https://engineers.safie.link/entry/cvpr2025", "content": "Descriptor - In - Pixel : Point-Feature Tracking For Pixel Processor Arrays. The PanAf-FGBG Dataset: Understanding the Impact of Backgrounds in Wildlife Behaviour Recognition. UniAP: Unifying Inter- and Intra-Layer Automatic Parallelism by Mixed Integer Quadratic Programming."}
diff --git a/data/sampled_jsons/SDXL_ViT_Vision_Transformer_modules_vs_SDv1.5_feature_selection_prior_studies.jsonl b/data/sampled_jsons/SDXL_ViT_Vision_Transformer_modules_vs_SDv1.5_feature_selection_prior_studies.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..598ebdf2755c0e396333934a694bddd27fda51b1
--- /dev/null
+++ b/data/sampled_jsons/SDXL_ViT_Vision_Transformer_modules_vs_SDv1.5_feature_selection_prior_studies.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GenTron: Diffusion Transformers for Image and Video Generation", "date": "", "ddg_snippet": "The Transformer architectures have been demonstrated to possess significant scalability in both visual perception [ 53 , 69 , 11 , 17 ] and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2312.04557v2", "content": "The Transformer architectures have been demonstrated to possess significant scalability in both visual perception [ 53 , 69 , 11 , 17 ] and ..."}
+{"idx": 1, "title": "Vision Transformers ( ViT ) Explained + Fine-tuning in Python - YouTube", "date": "", "ddg_snippet": "Vision and language are the two big domains in machine learning. Two distinct disciplines with their own problems, best practices, and model architectures.", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=qU7wO02urYU", "content": "Vision and language are the two big domains in machine learning. Two distinct disciplines with their own problems, best practices, and model architectures."}
+{"idx": 2, "title": "How to use SDXL on VLAD (SD.Next)", "date": "", "ddg_snippet": "SDXL vs SDv 1 . 5 , what are the Differences?The original v 1 . 5 model consists of 1 billion parameters. Use Stable Diffusion XL in the cloud on RunDiffusion.com.", "subpage_snippet": "", "source": "learn.rundiffusion.com", "link": "https://learn.rundiffusion.com/how-to-use-sdxl-on-vlad-sd-next/", "content": "SDXL vs SDv 1 . 5 , what are the Differences?The original v 1 . 5 model consists of 1 billion parameters. Use Stable Diffusion XL in the cloud on RunDiffusion.com."}
+{"idx": 3, "title": "Vision Transformer : A New Era in Image Recognition", "date": "", "ddg_snippet": "Vision Transformer ( ViT ) in Image Recognition. While the Transformer architecture has become the highest standard for tasks involving Natural Language Processing (NLP), its use cases relating to Computer Vision (CV) remain limited.", "subpage_snippet": "", "source": "viso.ai", "link": "https://viso.ai/deep-learning/vision-transformer-vit/", "content": "Vision Transformer ( ViT ) in Image Recognition. While the Transformer architecture has become the highest standard for tasks involving Natural Language Processing (NLP), its use cases relating to Computer Vision (CV) remain limited."}
+{"idx": 4, "title": "Введение в библиотеку Transformers и платформу Hugging... / Хабр", "date": "", "ddg_snippet": "Из наиболее известных: Vision Transformer ( ViT ), T5, ResNet, BERT, GPT2. На этих архитектурах обучены более 60 000 моделей. Модели Transformers поддерживают три фреймворках: PyTorch, TensorFlow и JAX.", "subpage_snippet": "", "source": "habr.com", "link": "https://habr.com/ru/articles/704592/", "content": "Из наиболее известных: Vision Transformer ( ViT ), T5, ResNet, BERT, GPT2. На этих архитектурах обучены более 60 000 моделей. Модели Transformers поддерживают три фреймворках: PyTorch, TensorFlow и JAX."}
+{"idx": 5, "title": "ip-adapter-plus_ sdxl _ vit -h huggingface.co api & InvokeAI... - Toolify", "date": "", "ddg_snippet": "This is the SDXL ViT -H IP Adapter Plus model. It requires the SD 1 . 5 IP Adapter encoder to be installed to function correctly.", "subpage_snippet": "", "source": "www.toolify.ai", "link": "https://www.toolify.ai/ai-model/invokeai-ip-adapter-plus-sdxl-vit-h", "content": "This is the SDXL ViT -H IP Adapter Plus model. It requires the SD 1 . 5 IP Adapter encoder to be installed to function correctly."}
+{"idx": 6, "title": "A hybrid ViT -CNN model with attention mechanism and dual feature ...", "date": "", "ddg_snippet": "838 standard features between the features selected with both methods were classified with six different machine learning algorithms. In the study , the five -class public SIPaKMeD dataset was used. The proposed model achieved a high accuracy value of 99.02% on the relevant dataset.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s11760-025-04664-w", "content": "838 standard features between the features selected with both methods were classified with six different machine learning algorithms. In the study , the five -class public SIPaKMeD dataset was used. The proposed model achieved a high accuracy value of 99.02% on the relevant dataset."}
+{"idx": 7, "title": "DiffCut: Catalyzing Zero-Shot Semantic Segmentation with", "date": "", "ddg_snippet": "Our ablation studies further reveal the relevance of these diffusion features as well as the recursive partitioning approach which proves to provide ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.02842v2", "content": "Our ablation studies further reveal the relevance of these diffusion features as well as the recursive partitioning approach which proves to provide ..."}
+{"idx": 8, "title": "Janus: Decoupling Visual Encoding for Unified Multimodal", "date": "", "ddg_snippet": "... score of 8.53 8.53 8.53 8.53 and an accuracy of 61 61 61 61 %, surpassing text-to-image generative models such as DALL-E 2 2 2 2 [ 66 ] and SDXL ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.13848v1", "content": "... score of 8.53 8.53 8.53 8.53 and an accuracy of 61 61 61 61 %, surpassing text-to-image generative models such as DALL-E 2 2 2 2 [ 66 ] and SDXL ..."}
+{"idx": 9, "title": "IP-Adapters: All you need to know - Stable Diffusion Art", "date": "", "ddg_snippet": "OpenClip ViT BigG 14 (aka SDXL version, 1845M parameters). However, things got muddled when some SDXL IP-Adapter models also got trained with the H version. For clarity, I will refer them as the ViT H and ViT BigG version. ( ViT stands for Vision Transformer ).", "subpage_snippet": "", "source": "stable-diffusion-art.com", "link": "https://stable-diffusion-art.com/ip-adapter/", "content": "OpenClip ViT BigG 14 (aka SDXL version, 1845M parameters). However, things got muddled when some SDXL IP-Adapter models also got trained with the H version. For clarity, I will refer them as the ViT H and ViT BigG version. ( ViT stands for Vision Transformer )."}
diff --git a/data/sampled_jsons/SIIHPC_Algorithm_2_termination_condition_optimization_loop.jsonl b/data/sampled_jsons/SIIHPC_Algorithm_2_termination_condition_optimization_loop.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f2276547e293570f1d427cd3bc197f43d39bca44
--- /dev/null
+++ b/data/sampled_jsons/SIIHPC_Algorithm_2_termination_condition_optimization_loop.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Bellman–Ford algorithm - Wikipedia", "date": "", "ddg_snippet": "The Bellman–Ford algorithm is an algorithm that computes shortest paths from a single source vertex to all of the other vertices in a weighted digraph. It is slower than Dijkstra's algorithm for the same problem, but more versatile...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Bellman–Ford_algorithm", "content": "The Bellman–Ford algorithm is an algorithm that computes shortest paths from a single source vertex to all of the other vertices in a weighted digraph. It is slower than Dijkstra's algorithm for the same problem, but more versatile..."}
+{"idx": 1, "title": "pymoo - Termination Criterion", "date": "", "ddg_snippet": "Termination Criterion Whenever an algorithm is executed, it needs to be decided in each iteration whether the optimization run shall be continued or not. Many different ways exist of how to determine when a run of an algorithm should be terminated. Next, termination criteria specifically developed for single or multi-objective optimization as well as more generalized, for instance, limiting ...", "subpage_snippet": "", "source": "pymoo.org", "link": "https://pymoo.org/interface/termination.html", "content": "Termination Criterion Whenever an algorithm is executed, it needs to be decided in each iteration whether the optimization run shall be continued or not. Many different ways exist of how to determine when a run of an algorithm should be terminated. Next, termination criteria specifically developed for single or multi-objective optimization as well as more generalized, for instance, limiting ..."}
+{"idx": 2, "title": "language agnostic - Loop termination conditions - Stack Overflow Usage example", "date": "", "ddg_snippet": "Oct 17, 2012 · These for-loops are among the first basic examples of formal correctness proofs of algorithms . They have different but equivalent termination conditions : 1 for ( int i = 0; i != N; ++i ) 2 fo... See more on stackoverflow", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/132164/loop-termination-conditions", "content": "Oct 17, 2012 · These for-loops are among the first basic examples of formal correctness proofs of algorithms . They have different but equivalent termination conditions : 1 for ( int i = 0; i != N; ++i ) 2 fo... See more on stackoverflow"}
+{"idx": 3, "title": "Termination condition - AI Wiki - Artificial Intelligence Wiki", "date": "", "ddg_snippet": "Termination Condition in Machine Learning In the field of machine learning, a termination condition , also known as stopping criterion, refers to a set of predefined criteria that determines when an optimization algorithm should cease its search for the optimal solution.", "subpage_snippet": "", "source": "aiwiki.ai", "link": "https://aiwiki.ai/wiki/Termination_condition", "content": "Termination Condition in Machine Learning In the field of machine learning, a termination condition , also known as stopping criterion, refers to a set of predefined criteria that determines when an optimization algorithm should cease its search for the optimal solution."}
+{"idx": 4, "title": "The Termination Criteria of A Heuristic Algorithm", "date": "", "ddg_snippet": "Jan 13, 2025 · As improvement heuristic algorithms are based on the search to improve the solution, this search has to be stoped at some point. Otherwise, the algorithm could go on forever. The condition that allows the search to stop is often when meeting a specified requirement, and this requirement is the termination criterion of the algorithm .", "subpage_snippet": "", "source": "ningjoptalg.substack.com", "link": "https://ningjoptalg.substack.com/p/the-termination-criteria-of-a-heuristic", "content": "Jan 13, 2025 · As improvement heuristic algorithms are based on the search to improve the solution, this search has to be stoped at some point. Otherwise, the algorithm could go on forever. The condition that allows the search to stop is often when meeting a specified requirement, and this requirement is the termination criterion of the algorithm ."}
+{"idx": 5, "title": "Termination Analysis of Loops - Stanford University Termination Conditions pymoo - Termination Criterion pymoo - Termination Criterion pymoo - Termination Criterion Termination condition - AI Wiki - Artificial Intelligence, Machine Learnin… Termination condition - AI Wiki - Artificial Intelligence, Machine Learnin… language agnostic - Loop termination conditions - Stack Overflow Proving Conditional Termination - UCL Computer Science", "date": "", "ddg_snippet": "Find ranking functions and supporting invariants automatically. See full list on theory.stanford.edu L : hV ; ; T i: variables V range over R initial condition See full list on theory.stanford.edu Consider loop L : hV ; ; T i. : S ! R is a ranking function of L L if See full list on theory.stanford.edu Every terminating loop has a ranking function. But in general, expressing a ranking function requires FOL with xpoints, which is incomplete. Therefore, termination is not necessarily semi-decidable. In fact, termination is not semi-decidable for a simple class of loops . See full list on theory.stanford.edu Termination of such loops is not semi-decidable (not recursively enumerable). See full list on theory.stanford.edu Apply Farkas Lemma rules to encode conditions for the unknown invariant coe cients I ij conditions for the unknown ranking function coe cients c ij Solve the generated constraint system. See full list on theory.stanford.edu Linear loop L : hV ; ; T i has a linear ranking function supported by an `-conjunct linear invariant , the constraint system generated I ^ See full list on theory.stanford.edu Linear loop L : hV ; ; T i has a k-lexicographic linear ranking function supported by an `-conjunct linear invariant , the constraint I ^ Di _ Ci ^ Bi; See full list on theory.stanford.edu Termination conditions are the prerequisite factors for ending the function of optimization algorithms . When a termination condition value is met, the optimization algorithm ceases functioning. How to terminate an algorithm? The termination can also be based on the time of the algorithm to be executed. For instance, to run an algorithm for 3 seconds the termination can be defined by get_termination(\"time\", \"00:00:03\") or for 1 hour and 30 minutes by get_termination(\"time\", \"01:30:00\"). Also, we can track the change in the design space. What are the termination criteria for optimization? Next, termination criteria specifically developed for single or multi-objective optimization as well as more generalized, for instance, limiting the number of iterations of an algorithm , are described The termination of your optimization procedure is important. How do I terminate a multi-objective algorithm? By default for multi-objective problems, the termination will be set to And for single-optimization to The termination can simply be reached by providing an upper bound for the number of function evaluations . Whenever in an iteration, the number of function evaluations is greater than this upper bound the algorithm terminates. How do I select the appropriate termination condition for machine learning? Selecting the appropriate termination condition depends on the specific machine learning problem, the algorithm used, and the available computational resources. It is often a balance between the risk of overfitting, underfitting, and the efficiency of the learning process. What are the different types of termination conditions used in machine learning? There are several types of termination conditions used in machine learning algorithms, each with its advantages and disadvantages. Some common types include: Iteration-based termination: The algorithm stops after a predetermined number of iterations or epochs. How to guarantee termination of a loop if I = I + 2? If you think about the loop which increments by two (i = i + 2), this general idea becomes more understandable. To guarantee termination one now needs to know that i%2 == N%2 , whereas this is irrelevant when using < as the condition. Termination of the constrained loop ensures that these conditions are eventually met, and thus the original loop will terminate. Thus, we call the procedure recursively with the constrained transition relation, and disjoin the returned preconditions with the existing ones.", "subpage_snippet": "", "source": "theory.stanford.edu", "link": "https://theory.stanford.edu/~arbrad/slides/termination.pdf", "content": "Find ranking functions and supporting invariants automatically. See full list on theory.stanford.edu L : hV ; ; T i: variables V range over R initial condition See full list on theory.stanford.edu Consider loop L : hV ; ; T i. : S ! R is a ranking function of L L if See full list on theory.stanford.edu Every terminating loop has a ranking function. But in general, expressing a ranking function requires FOL with xpoints, which is incomplete. Therefore, termination is not necessarily semi-decidable. In fact, termination is not semi-decidable for a simple class of loops . See full list on theory.stanford.edu Termination of such loops is not semi-decidable (not recursively enumerable). See full list on theory.stanford.edu Apply Farkas Lemma rules to encode conditions for the unknown invariant coe cients I ij conditions for the unknown ranking function coe cients c ij Solve the generated constraint system. See full list on theory.stanford.edu Linear loop L : hV ; ; T i has a linear ranking function supported by an `-conjunct linear invariant , the constraint system generated I ^ See full list on theory.stanford.edu Linear loop L : hV ; ; T i has a k-lexicographic linear ranking function supported by an `-conjunct linear invariant , the constraint I ^ Di _ Ci ^ Bi; See full list on theory.stanford.edu Termination conditions are the prerequisite factors for ending the function of optimization algorithms . When a termination condition value is met, the optimization algorithm ceases functioning. How to terminate an algorithm? The termination can also be based on the time of the algorithm to be executed. For instance, to run an algorithm for 3 seconds the termination can be defined by get_termination(\"time\", \"00:00:03\") or for 1 hour and 30 minutes by get_termination(\"time\", \"01:30:00\"). Also, we can track the change in the design space. What are the termination criteria for optimization? Next, termination criteria specifically developed for single or multi-objective optimization as well as more generalized, for instance, limiting the number of iterations of an algorithm , are described The termination of your optimization procedure is important. How do I terminate a multi-objective algorithm? By default for multi-objective problems, the termination will be set to And for single-optimization to The termination can simply be reached by providing an upper bound for the number of function evaluations . Whenever in an iteration, the number of function evaluations is greater than this upper bound the algorithm terminates. How do I select the appropriate termination condition for machine learning? Selecting the appropriate termination condition depends on the specific machine learning problem, the algorithm used, and the available computational resources. It is often a balance between the risk of overfitting, underfitting, and the efficiency of the learning process. What are the different types of termination conditions used in machine learning? There are several types of termination conditions used in machine learning algorithms, each with its advantages and disadvantages. Some common types include: Iteration-based termination: The algorithm stops after a predetermined number of iterations or epochs. How to guarantee termination of a loop if I = I + 2? If you think about the loop which increments by two (i = i + 2), this general idea becomes more understandable. To guarantee termination one now needs to know that i%2 == N%2 , whereas this is irrelevant when using < as the condition. Termination of the constrained loop ensures that these conditions are eventually met, and thus the original loop will terminate. Thus, we call the procedure recursively with the constrained transition relation, and disjoin the returned preconditions with the existing ones."}
+{"idx": 6, "title": "Termination Conditions", "date": "", "ddg_snippet": "Termination conditions are the prerequisite factors for ending the function of optimization algorithms . When a termination condition value is met, the optimization algorithm ceases functioning.", "subpage_snippet": "", "source": "people.ece.ubc.ca", "link": "https://people.ece.ubc.ca/robertor/Links_files/Files/ICCAP-2008-doc/icug/icug077.html", "content": "Termination conditions are the prerequisite factors for ending the function of optimization algorithms . When a termination condition value is met, the optimization algorithm ceases functioning."}
+{"idx": 7, "title": "Proving Conditional Termination - UCL Computer Science", "date": "", "ddg_snippet": "Termination of the constrained loop ensures that these conditions are eventually met, and thus the original loop will terminate. Thus, we call the procedure recursively with the constrained transition relation, and disjoin the returned preconditions with the existing ones.", "subpage_snippet": "", "source": "www0.cs.ucl.ac.uk", "link": "http://www0.cs.ucl.ac.uk/staff/b.cook/pdfs/proving_conditional_termination.pdf", "content": "Termination of the constrained loop ensures that these conditions are eventually met, and thus the original loop will terminate. Thus, we call the procedure recursively with the constrained transition relation, and disjoin the returned preconditions with the existing ones."}
+{"idx": 8, "title": "Introduction To Optimization : Gradient Free Algorithms ... - YouTube", "date": "", "ddg_snippet": "A brief overview of Simulated Annealing, the Nelder-Mead method, and a survey of various metaphor and biological inspired optimization algorithms .", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=NI3WllrvWoc", "content": "A brief overview of Simulated Annealing, the Nelder-Mead method, and a survey of various metaphor and biological inspired optimization algorithms ."}
+{"idx": 9, "title": "Horn SAT algorithm using graphs - Stack Overflow", "date": "", "ddg_snippet": "A second termination condition is an empty clause. Termination condition # 2 : An empty clause: the formula has no solutions. return false", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/64953928/horn-sat-algorithm-using-graphs", "content": "A second termination condition is an empty clause. Termination condition # 2 : An empty clause: the formula has no solutions. return false"}
diff --git "a/data/sampled_jsons/SPD_Sync-Point_Drop_Algorithm_1_if_S[B[i]]__\317\2041_and_S[B[i]]__\317\2042_optimization_strategy.jsonl" "b/data/sampled_jsons/SPD_Sync-Point_Drop_Algorithm_1_if_S[B[i]]__\317\2041_and_S[B[i]]__\317\2042_optimization_strategy.jsonl"
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index 0000000000000000000000000000000000000000..f1e1982352badec9fe54eb29e3feb2e09b639d70
--- /dev/null
+++ "b/data/sampled_jsons/SPD_Sync-Point_Drop_Algorithm_1_if_S[B[i]]__\317\2041_and_S[B[i]]__\317\2042_optimization_strategy.jsonl"
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+{"idx": 0, "title": "SPD: Sync-Point Drop for Efficient Tensor Parallelism of ...", "date": "", "ddg_snippet": "by HB Kim · 2025 — Based on the sync sensitivity value of blocks ( S ), we rank the blocks in an ascending order ( B ). According to the predetermined ranking of the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.20727", "content": "by HB Kim · 2025 — Based on the sync sensitivity value of blocks ( S ), we rank the blocks in an ascending order ( B ). According to the predetermined ranking of the ..."}
+{"idx": 1, "title": "SPD: SYNC-POINT DROP FOR EFFICIENT TENSOR PAR", "date": "", "ddg_snippet": "if S [ B [i ]] ≤ τ1 then. ▷ Section 4.2.2: in-sensitive. 11: Block[ B [i ]] ← SPD (Block[ B [i]]). 12: else if S [ B [i ]] ≤ τ2 then ▷ Section 4.2.3: sensitive. 13:.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/41db95002c5da44fac80ce28aa5bf70de802581b.pdf", "content": "if S [ B [i ]] ≤ τ1 then. ▷ Section 4.2.2: in-sensitive. 11: Block[ B [i ]] ← SPD (Block[ B [i]]). 12: else if S [ B [i ]] ≤ τ2 then ▷ Section 4.2.3: sensitive. 13:."}
+{"idx": 2, "title": "SPD: Sync-Point Drop for Efficient Tensor Parallelism of ...", "date": "", "ddg_snippet": "Therefore, we introduce a novel optimization technique, Sync - Point Drop ( SPD ), to reduce communication overheads in tensor parallelism by selectively dropping ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46606", "content": "Therefore, we introduce a novel optimization technique, Sync - Point Drop ( SPD ), to reduce communication overheads in tensor parallelism by selectively dropping ..."}
+{"idx": 3, "title": "SPD: Sync-Point Drop for Efficient Tensor Parallelism of ...", "date": "", "ddg_snippet": "by HB Kim — Therefore, we introduce a novel optimization technique, Sync - Point Drop ( SPD ), to reduce communication overheads in tensor parallelism by selectively dropping ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=23zxLtvder&referrer=[the+profile+of+Minsik+Cho](/profile?id=~Minsik_Cho1)", "content": "by HB Kim — Therefore, we introduce a novel optimization technique, Sync - Point Drop ( SPD ), to reduce communication overheads in tensor parallelism by selectively dropping ..."}
+{"idx": 4, "title": "IRIG STANDARD 106-22 TELEMETRY STANDARDS ...", "date": "", "ddg_snippet": "The recommendation will be based upon extensive lab testing in the Telemetry Lab with all combinations of coded and uncoded waveforms. e. Task TG-177: IRIG 106 ...", "subpage_snippet": "", "source": "www.trmc.osd.mil", "link": "https://www.trmc.osd.mil/wiki/download/attachments/168165620/106-22.pdf?api=v2", "content": "The recommendation will be based upon extensive lab testing in the Telemetry Lab with all combinations of coded and uncoded waveforms. e. Task TG-177: IRIG 106 ..."}
+{"idx": 5, "title": "Domain Decomposition Methods in Science and ...", "date": "", "ddg_snippet": "This includes theoretical aspects of scientific computing such as mathematical modeling, optimization methods, discretization techniques , multiscale approaches, ...", "subpage_snippet": "", "source": "dokumen.pub", "link": "https://dokumen.pub/domain-decomposition-methods-in-science-and-engineering-xxvii-lecture-notes-in-computational-science-and-engineering-149-1st-ed-2024-3031507681-9783031507687.html", "content": "This includes theoretical aspects of scientific computing such as mathematical modeling, optimization methods, discretization techniques , multiscale approaches, ..."}
+{"idx": 6, "title": "Simulations of Laser Propagation and X-Ray Radiation ...", "date": "", "ddg_snippet": "B .2.1 Background. B .2.1. 1 Overview of the Diffraction Theory of Optical Aberrations. Here we briefly review the diffraction theory of optics. In this theory ...", "subpage_snippet": "", "source": "deepblue.lib.umich.edu", "link": "http://deepblue.lib.umich.edu/bitstream/2027.42/107227/1/cummingp_1.pdf", "content": "B .2.1 Background. B .2.1. 1 Overview of the Diffraction Theory of Optical Aberrations. Here we briefly review the diffraction theory of optics. In this theory ..."}
+{"idx": 7, "title": "MIT Project Oxygen 2001", "date": "", "ddg_snippet": "14 Dec 2001 — These are research papers recently published, or about to be published by faculty and students at LCS and AI who are working on. Oxygen. The ...", "subpage_snippet": "", "source": "www.ai.mit.edu", "link": "http://www.ai.mit.edu/projects/oxygen/oxygen-book2001/oxygenbook2001.pdf", "content": "14 Dec 2001 — These are research papers recently published, or about to be published by faculty and students at LCS and AI who are working on. Oxygen. The ..."}
+{"idx": 8, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
+{"idx": 9, "title": "Parallel Computational Technologies", "date": "", "ddg_snippet": "This volume contains a selection of the papers presented at the 14th International. Scientific Conference on Parallel Computational Technologies (PCT 2020).", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/978-3-030-55326-5.pdf", "content": "This volume contains a selection of the papers presented at the 14th International. Scientific Conference on Parallel Computational Technologies (PCT 2020)."}
diff --git a/data/sampled_jsons/SPING_deep-reinforcement-learning_framework_for_state_placement_in_sharding_blockchains_abstract.jsonl b/data/sampled_jsons/SPING_deep-reinforcement-learning_framework_for_state_placement_in_sharding_blockchains_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..111ff02085569fa71d65340f829fc0e2341ab12c
--- /dev/null
+++ b/data/sampled_jsons/SPING_deep-reinforcement-learning_framework_for_state_placement_in_sharding_blockchains_abstract.jsonl
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+{"idx": 0, "title": "SPRING: Improving the Throughput of Sharding Blockchain via Deep ...", "date": "", "ddg_snippet": "In this paper, we present SPRING, the first deep-reinforcement-learning (DRL)-based sharding framework for state placement . SPRING formulates the state placement as a Markov Decision Process, which considers the cross-shard transaction ratio and workload balancing and employs DRL to learn the effective state placement policy.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3589334.3645386", "content": "In this paper, we present SPRING, the first deep-reinforcement-learning (DRL)-based sharding framework for state placement . SPRING formulates the state placement as a Markov Decision Process, which considers the cross-shard transaction ratio and workload balancing and employs DRL to learn the effective state placement policy."}
+{"idx": 1, "title": "[rfp0423] SPRING: Improving the Throughput of Sharding ... - YouTube", "date": "", "ddg_snippet": "\"SPRING: Improving the Throughput of Sharding Blockchain via Deep Reinforcement Learning Based State PlacementPengze Li, Mingxuan Song, Mingzhe Xing, Zhen Xi...", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=DmSy-alC3pQ", "content": "\"SPRING: Improving the Throughput of Sharding Blockchain via Deep Reinforcement Learning Based State PlacementPengze Li, Mingxuan Song, Mingzhe Xing, Zhen Xi..."}
+{"idx": 2, "title": "Research on transaction allocation strategy in blockchain state sharding", "date": "", "ddg_snippet": "Additionally, SkyChain [30] leverages deep reinforcement learning to adjust shard intervals dynamically, the number of shards, and block size, thereby enhancing the overall security and performance of the blockchain network.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0167739X25000512", "content": "Additionally, SkyChain [30] leverages deep reinforcement learning to adjust shard intervals dynamically, the number of shards, and block size, thereby enhancing the overall security and performance of the blockchain network."}
+{"idx": 3, "title": "PDF SPRING: Improving the Throughput of Sharding Blockchain via Deep ...", "date": "", "ddg_snippet": "In this paper, we present Spring, the first deep - reinforcement -learning(DRL)-based sharding framework for state placement . Spring formulates the state placement as a Markov Decision Process which takes into consideration the cross-shard transaction ratio and workload balancing, and employs DRL to learn the efective state placement pol-icy.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=8oczaP1YKD&name=pdf", "content": "In this paper, we present Spring, the first deep - reinforcement -learning(DRL)-based sharding framework for state placement . Spring formulates the state placement as a Markov Decision Process which takes into consideration the cross-shard transaction ratio and workload balancing, and employs DRL to learn the efective state placement pol-icy."}
+{"idx": 4, "title": "Efficient State Sharding in Blockchain via Density-based Graph ...", "date": "", "ddg_snippet": "These studies typically generate a large number of cross-shard transactions because they primarily use simple address mapping for state sharding , that is, the prefix/suffix of the account address. In this article, we propose a state sharding scheme via density-based partitioning of the account-transaction graph.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3697840", "content": "These studies typically generate a large number of cross-shard transactions because they primarily use simple address mapping for state sharding , that is, the prefix/suffix of the account address. In this article, we propose a state sharding scheme via density-based partitioning of the account-transaction graph."}
+{"idx": 5, "title": "Optimization of State Clustering and Safety Verification in Deep ...", "date": "", "ddg_snippet": "Ensuring the safety of Deep Reinforcement Learning (DRL) systems remains a significant challenge, particularly in real-time applications such as autonomous driving and robotics, where incorrect decisions can lead to catastrophic failures. This study proposes a novel safety verification framework that combines state abstraction with probabilistic model checking to quantitatively analyze failure ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10879317", "content": "Ensuring the safety of Deep Reinforcement Learning (DRL) systems remains a significant challenge, particularly in real-time applications such as autonomous driving and robotics, where incorrect decisions can lead to catastrophic failures. This study proposes a novel safety verification framework that combines state abstraction with probabilistic model checking to quantitatively analyze failure ..."}
+{"idx": 6, "title": "PDF SPRING: Improving the Throughput of Sharding Blockchain via Deep ...", "date": "", "ddg_snippet": "In this paper, we present SPRING, the first deep - reinforcement -learning(DRL)-based shard-ing framework for state placement . SPRING formulates the state placement as a Markov Decision Process, which considers the cross-shard transaction ratio and workload balancing and employs DRL to learn the efective state placement policy.", "subpage_snippet": "", "source": "zhenxiao.com", "link": "http://zhenxiao.com/papers/WWW_Spring_camera_ready.pdf", "content": "In this paper, we present SPRING, the first deep - reinforcement -learning(DRL)-based shard-ing framework for state placement . SPRING formulates the state placement as a Markov Decision Process, which considers the cross-shard transaction ratio and workload balancing and employs DRL to learn the efective state placement policy."}
+{"idx": 7, "title": "AERO: Enhancing Sharding Blockchain via Deep Reinforcement Learning for ...", "date": "", "ddg_snippet": "We propose AERO, a deep reinforcement learning framework to facilitate efficient account migration in sharding blockchains . AERO employs a prefix-based grouping strategy to enable group-level migration decisions and capture complex transaction patterns and relationships between accounts.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3696410.3714926", "content": "We propose AERO, a deep reinforcement learning framework to facilitate efficient account migration in sharding blockchains . AERO employs a prefix-based grouping strategy to enable group-level migration decisions and capture complex transaction patterns and relationships between accounts."}
+{"idx": 8, "title": "Papers - Zhen Xiao", "date": "", "ddg_snippet": "SPRING: Improving the Throughput of Sharding Blockchain via Deep Reinforcement Learning Based State Placement In Proceedings of the Web Conference 2024 (WWW 2024), May 2024. Hangyu Mao, Rui Zhao, Ziyue Li, Zhiwei Xu, Hao Chen, Yiqun Chen, Bin Zhang, Zhen Xiao, Junge Zhang and Jiangjin Yin.", "subpage_snippet": "", "source": "zhenxiao.com", "link": "http://zhenxiao.com/papers/", "content": "SPRING: Improving the Throughput of Sharding Blockchain via Deep Reinforcement Learning Based State Placement In Proceedings of the Web Conference 2024 (WWW 2024), May 2024. Hangyu Mao, Rui Zhao, Ziyue Li, Zhiwei Xu, Hao Chen, Yiqun Chen, Bin Zhang, Zhen Xiao, Junge Zhang and Jiangjin Yin."}
+{"idx": 9, "title": "【最新区块链论文录用资讯】Ccf a — Www 2024 共12篇", "date": "", "ddg_snippet": "SPRING: Improving the Throughput of Sharding Blockchain via Deep Reinforcement Learning Based State Placement SPRING：通过基于深度强化学习的状态放置提高分片区块链的吞吐量", "subpage_snippet": "", "source": "blog.csdn.net", "link": "https://blog.csdn.net/u013288190/article/details/139021453", "content": "SPRING: Improving the Throughput of Sharding Blockchain via Deep Reinforcement Learning Based State Placement SPRING：通过基于深度强化学习的状态放置提高分片区块链的吞吐量"}
diff --git a/data/sampled_jsons/SSCD_Pizzi_et_al_2022_A_self-supervised_descriptor_for_image_copy_detection_abstract.jsonl b/data/sampled_jsons/SSCD_Pizzi_et_al_2022_A_self-supervised_descriptor_for_image_copy_detection_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..524e7e4771845d710015189cb6410168c1f8bc70
--- /dev/null
+++ b/data/sampled_jsons/SSCD_Pizzi_et_al_2022_A_self-supervised_descriptor_for_image_copy_detection_abstract.jsonl
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+{"idx": 0, "title": "A Self-Supervised Descriptor for Image Copy Detection", "date": "", "ddg_snippet": "by E Pizzi · 2022 · Cited by 146 — We introduce SSCD, a model that builds on a recent self-supervised contrastive training objective . We adapt this method to the copy detection task.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2202.10261", "content": "by E Pizzi · 2022 · Cited by 146 — We introduce SSCD, a model that builds on a recent self-supervised contrastive training objective . We adapt this method to the copy detection task."}
+{"idx": 1, "title": "A Self-Supervised Descriptor for Image Copy Detection", "date": "", "ddg_snippet": "by E Pizzi · 2022 · Cited by 143 — We introduce SSCD, a model that builds on a recent self-supervised contrastive training objective . We adapt this method to the copy detection task by changing ... 11 pages", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2022/papers/Pizzi_A_Self-Supervised_Descriptor_for_Image_Copy_Detection_CVPR_2022_paper.pdf", "content": "by E Pizzi · 2022 · Cited by 143 — We introduce SSCD, a model that builds on a recent self-supervised contrastive training objective . We adapt this method to the copy detection task by changing ... 11 pages"}
+{"idx": 2, "title": "Relational Self-supervised Distillation with Compact ...", "date": "", "ddg_snippet": "28 May 2024 — This paper addresses image copy detection, a task in online sharing platforms for copyright protection. While previous approaches have ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.17928v1", "content": "28 May 2024 — This paper addresses image copy detection, a task in online sharing platforms for copyright protection. While previous approaches have ..."}
+{"idx": 3, "title": "Relational Self-Supervised Distillation with Compact ...", "date": "", "ddg_snippet": "In this paper, we present RDCD, a novel method for training lightweight networks with compact descriptors for image copy detection in a self - supervised manner. 10 pages", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/WACV2025/papers/Kim_Relational_Self-Supervised_Distillation_with_Compact_Descriptors_for_Image_Copy_Detection_WACV_2025_paper.pdf", "content": "In this paper, we present RDCD, a novel method for training lightweight networks with compact descriptors for image copy detection in a self - supervised manner. 10 pages"}
+{"idx": 4, "title": "Image Copy Detection for Diffusion Models", "date": "", "ddg_snippet": "by W Wang · 2024 · Cited by 1 — A self-supervised descriptor for image copy detection . In Proceedings of the IEEE/CVF Conference on Computer Vision and. Pattern Recognition, pages 14532–14542, ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/1a000ee0f122d0bbd3edb9bf55170ea3-Paper-Conference.pdf", "content": "by W Wang · 2024 · Cited by 1 — A self-supervised descriptor for image copy detection . In Proceedings of the IEEE/CVF Conference on Computer Vision and. Pattern Recognition, pages 14532–14542, ..."}
+{"idx": 5, "title": "Diffusion Model Memorization Auditing via Generative Image ...", "date": "", "ddg_snippet": "Self-Supervised Copy Detection . The SSCD metric was proposed by Pizzi et al. (2022) as a measure of semantic sim- ilarity between two images. This improves ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/497e344f78264fecccf8b887062732f79b7f14f3.pdf", "content": "Self-Supervised Copy Detection . The SSCD metric was proposed by Pizzi et al. (2022) as a measure of semantic sim- ilarity between two images. This improves ..."}
+{"idx": 6, "title": "GUIDED IMAGE-TO-IMAGE DIFFUSION MODELS", "date": "", "ddg_snippet": "A self- supervised descriptor for image copy detection . In Proceedings of the IEEE/CVF Conference on. Computer Vision and Pattern Recognition, pp. 14532–14542, ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/5941eae59e3504dfb1ab4e6cbe70630e131191d7.pdf", "content": "A self- supervised descriptor for image copy detection . In Proceedings of the IEEE/CVF Conference on. Computer Vision and Pattern Recognition, pp. 14532–14542, ..."}
+{"idx": 7, "title": "Imposter: Text and Frequency Guidance for Subject Driven ...", "date": "", "ddg_snippet": "by D Kothandaraman · 2025 · Cited by 2 — Abstract . We present ImPoster, a novel algorithm for generating a target image of a 'source' sub- ject performing a 'driving' action.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.coling-main.730.pdf", "content": "by D Kothandaraman · 2025 · Cited by 2 — Abstract . We present ImPoster, a novel algorithm for generating a target image of a 'source' sub- ject performing a 'driving' action."}
+{"idx": 8, "title": "Hybrid Image-Retrieval Method for Image-Splicing Validation", "date": "", "ddg_snippet": "TL;DR: This work introduces SSCD , a model that builds on a recent self - supervised contrastive training objective, and adapts this method to the copy detection ...", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/papers/hybrid-image-retrieval-method-for-image-splicing-validation-31s8k55imw", "content": "TL;DR: This work introduces SSCD , a model that builds on a recent self - supervised contrastive training objective, and adapts this method to the copy detection ..."}
+{"idx": 9, "title": "Active Indexing", "date": "", "ddg_snippet": "... image . Parameters. Neural net. extractor: ResNet50 trained with SSCD [4]. [4. Pizzi et al . A Self - Supervised Descriptor for Image Copy Detection . ICCV 2022 .] ...", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/media/iclr-2023/Slides/11510.pdf", "content": "... image . Parameters. Neural net. extractor: ResNet50 trained with SSCD [4]. [4. Pizzi et al . A Self - Supervised Descriptor for Image Copy Detection . ICCV 2022 .] ..."}
diff --git a/data/sampled_jsons/SWE-bench_Can_Language_Models_Resolve_Real-world_Github_Issues_arXiv_year_2024.jsonl b/data/sampled_jsons/SWE-bench_Can_Language_Models_Resolve_Real-world_Github_Issues_arXiv_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e3f651a00c816172da9d58494fec957a59b478ba
--- /dev/null
+++ b/data/sampled_jsons/SWE-bench_Can_Language_Models_Resolve_Real-world_Github_Issues_arXiv_year_2024.jsonl
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+{"idx": 0, "title": "SWE-bench: Can Language Models Resolve Real-World GitHub Issues?", "date": "", "ddg_snippet": "Oct 10, 2023 · Language models have outpaced our ability to evaluate them effectively, but for their future development it is essential to study the frontier of their capabilities. We find real-world software engineering to be a rich, sustainable, and challenging testbed for evaluating the next generation of language models . To this end, we introduce SWE-bench , an evaluation framework consisting of $2,294 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2310.06770", "content": "Oct 10, 2023 · Language models have outpaced our ability to evaluate them effectively, but for their future development it is essential to study the frontier of their capabilities. We find real-world software engineering to be a rich, sustainable, and challenging testbed for evaluating the next generation of language models . To this end, we introduce SWE-bench , an evaluation framework consisting of $2,294 ..."}
+{"idx": 1, "title": "SWE-bench: Can Language Models Resolve Real-World GitHub Issues?", "date": "", "ddg_snippet": "SWE-bench is a benchmark featuring GitHub issues from popular repositories that report bugs or request new features, and pull requests that make changes to the repository to resolve these issues .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2310.06770v3", "content": "SWE-bench is a benchmark featuring GitHub issues from popular repositories that report bugs or request new features, and pull requests that make changes to the repository to resolve these issues ."}
+{"idx": 2, "title": "SWE-bench: Can Language Models Resolve Real-world Github Issues?", "date": "", "ddg_snippet": "Our evaluations show that both state-of-the-art proprietary models and our fine-tuned model SWE -Llama can resolve only the simplest issues . The best-performing model , Claude 2, is able to solve a mere 1.96% of the issues . Advances on SWE-bench represent steps towards LMs that are more practical, intelligent, and autonomous.", "subpage_snippet": "", "source": "proceedings.iclr.cc", "link": "https://proceedings.iclr.cc/paper_files/paper/2024/hash/edac78c3e300629acfe6cbe9ca88fb84-Abstract-Conference.html", "content": "Our evaluations show that both state-of-the-art proprietary models and our fine-tuned model SWE -Llama can resolve only the simplest issues . The best-performing model , Claude 2, is able to solve a mere 1.96% of the issues . Advances on SWE-bench represent steps towards LMs that are more practical, intelligent, and autonomous."}
+{"idx": 3, "title": "SWE-BENCH: CAN LANGUAGE MODELS RESOLVE REAL-WORLD GITHUB ISSUES? GitHub - itaowei/SWE_bench: SWE-Bench: Can Language Models ... SWE-bench: Can Language Models Resolve Real-world Github ... SWE-bench: Can Language Models Resolve Real-World GitHub Issues? SWE-BENCH : CAN LANGUAGE MODELS RESOLVE REAL-WORLD GITHU… SWE - bench : Can Language Models Resolve Real - World GitHub Issues ? SWE - bench : Can Language Models Resolve Real - World GitHub Issues ? Claude SWE-Bench Performance \\ Anthropic", "date": "", "ddg_snippet": "Language models have outpaced our ability to evaluate them effectively, but for their future development it is essential to study the frontier of their capabilities. We find real-world software engineering to be a rich, sustainable, and challenging testbed for evaluating the next generation of language models . To this end, we introduce SWE-bench , an evaluation framework consisting of 2,294 ... 👋 Overview SWE-bench is a benchmark for evaluating large language models on real world software issues collected from GitHub . Given a codebase and an issue , a language model is tasked with generating a patch that resolves the described problem. Dec 31, 2023 · SWE-bench : Can Language Models Resolve Real-world Github Issues ? Carlos E. Jimenez, John Yang, Alexander Wettig, Shunyu Yao, Kexin Pei, Ofir Press, Karthik R. Narasimhan Published: 31 Dec 2023, Last Modified: 14 May 2025 ICLR 2024 Everyone Revisions BibTeX CC BY-SA 4.0 SWE-bench is a benchmark featuring GitHub issues from popular repositories that report bugs or request new features, and pull requests that make changes to the repository to resolve these issues . Can language models solve real-world GitHub issues? The best-performing model, Claude 2, is able to solve a mere 1.96% of the issues . Advances on SWE - bench represent steps towards LMs that are more practical, intelligent, and autonomous. Dive into the research topics of ' SWE - BENCH : CAN LANGUAGE MODELS RESOLVE REAL - WORLD GITHUB ISSUES ?'. Together they form a unique fingerprint. Can language models be evaluated in real-world software engineering? Language models have outpaced our ability to evaluate them effectively, but for their future development it is essential to study the frontier of their capabilities. We find real - world software engineering to be a rich, sustainable, and challenging testbed for evaluating the next generation of language models . How do you solve problems in SWE-bench? Resolving issues in SWE - bench frequently requires understanding and coordinating changes across multiple functions, classes, and even files simultaneously, calling for models to interact with execution environments, process extremely long contexts and perform complex reasoning that goes far beyond traditional code generation tasks. Jan 6, 2025 · SWE-bench is an AI evaluation benchmark that assesses a model's ability to complete real-world software engineering tasks. Specifically, it tests how the model can resolve GitHub issues from popular open-source Python repositories.", "subpage_snippet": "", "source": "collaborate.princeton.edu", "link": "https://collaborate.princeton.edu/en/publications/swe-bench-can-language-models-resolve-real-world-github-issues", "content": "Language models have outpaced our ability to evaluate them effectively, but for their future development it is essential to study the frontier of their capabilities. We find real-world software engineering to be a rich, sustainable, and challenging testbed for evaluating the next generation of language models . To this end, we introduce SWE-bench , an evaluation framework consisting of 2,294 ... 👋 Overview SWE-bench is a benchmark for evaluating large language models on real world software issues collected from GitHub . Given a codebase and an issue , a language model is tasked with generating a patch that resolves the described problem. Dec 31, 2023 · SWE-bench : Can Language Models Resolve Real-world Github Issues ? Carlos E. Jimenez, John Yang, Alexander Wettig, Shunyu Yao, Kexin Pei, Ofir Press, Karthik R. Narasimhan Published: 31 Dec 2023, Last Modified: 14 May 2025 ICLR 2024 Everyone Revisions BibTeX CC BY-SA 4.0 SWE-bench is a benchmark featuring GitHub issues from popular repositories that report bugs or request new features, and pull requests that make changes to the repository to resolve these issues . Can language models solve real-world GitHub issues? The best-performing model, Claude 2, is able to solve a mere 1.96% of the issues . Advances on SWE - bench represent steps towards LMs that are more practical, intelligent, and autonomous. Dive into the research topics of ' SWE - BENCH : CAN LANGUAGE MODELS RESOLVE REAL - WORLD GITHUB ISSUES ?'. Together they form a unique fingerprint. Can language models be evaluated in real-world software engineering? Language models have outpaced our ability to evaluate them effectively, but for their future development it is essential to study the frontier of their capabilities. We find real - world software engineering to be a rich, sustainable, and challenging testbed for evaluating the next generation of language models . How do you solve problems in SWE-bench? Resolving issues in SWE - bench frequently requires understanding and coordinating changes across multiple functions, classes, and even files simultaneously, calling for models to interact with execution environments, process extremely long contexts and perform complex reasoning that goes far beyond traditional code generation tasks. Jan 6, 2025 · SWE-bench is an AI evaluation benchmark that assesses a model's ability to complete real-world software engineering tasks. Specifically, it tests how the model can resolve GitHub issues from popular open-source Python repositories."}
+{"idx": 4, "title": "GitHub - itaowei/SWE_bench: SWE-Bench: Can Language Models ...", "date": "", "ddg_snippet": "👋 Overview SWE-bench is a benchmark for evaluating large language models on real world software issues collected from GitHub . Given a codebase and an issue , a language model is tasked with generating a patch that resolves the described problem.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/itaowei/SWE_bench", "content": "👋 Overview SWE-bench is a benchmark for evaluating large language models on real world software issues collected from GitHub . Given a codebase and an issue , a language model is tasked with generating a patch that resolves the described problem."}
+{"idx": 5, "title": "SWE-bench: Can Language Models Resolve Real-world Github ...", "date": "", "ddg_snippet": "Dec 31, 2023 · SWE-bench : Can Language Models Resolve Real-world Github Issues ? Carlos E. Jimenez, John Yang, Alexander Wettig, Shunyu Yao, Kexin Pei, Ofir Press, Karthik R. Narasimhan Published: 31 Dec 2023, Last Modified: 14 May 2025 ICLR 2024 Everyone Revisions BibTeX CC BY-SA 4.0", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=8y2YPzvJaG", "content": "Dec 31, 2023 · SWE-bench : Can Language Models Resolve Real-world Github Issues ? Carlos E. Jimenez, John Yang, Alexander Wettig, Shunyu Yao, Kexin Pei, Ofir Press, Karthik R. Narasimhan Published: 31 Dec 2023, Last Modified: 14 May 2025 ICLR 2024 Everyone Revisions BibTeX CC BY-SA 4.0"}
+{"idx": 6, "title": "Claude SWE-Bench Performance \\ Anthropic", "date": "", "ddg_snippet": "Jan 6, 2025 · SWE-bench is an AI evaluation benchmark that assesses a model's ability to complete real-world software engineering tasks. Specifically, it tests how the model can resolve GitHub issues from popular open-source Python repositories.", "subpage_snippet": "", "source": "www.anthropic.com", "link": "https://www.anthropic.com/engineering/swe-bench-sonnet", "content": "Jan 6, 2025 · SWE-bench is an AI evaluation benchmark that assesses a model's ability to complete real-world software engineering tasks. Specifically, it tests how the model can resolve GitHub issues from popular open-source Python repositories."}
+{"idx": 7, "title": "SWE-bench: Can Language Models Resolve Real-world ...", "date": "", "ddg_snippet": "by CE Jimenez · Cited by 956 — The paper primarily describes a benchmark (Swe-Bench) for evaluating language models. The benchmark consists of issues reported in github python ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=VTF8yNQM66", "content": "by CE Jimenez · Cited by 956 — The paper primarily describes a benchmark (Swe-Bench) for evaluating language models. The benchmark consists of issues reported in github python ..."}
+{"idx": 8, "title": "SWE-bench: Can Language Models Resolve Real-world ...", "date": "", "ddg_snippet": "SWE-bench is a benchmark for evaluating large language models on real world software issues collected from GitHub. Given a codebase and an issue, a language ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/SWE-bench/SWE-bench", "content": "SWE-bench is a benchmark for evaluating large language models on real world software issues collected from GitHub. Given a codebase and an issue, a language ..."}
+{"idx": 9, "title": "[PDF] SWE-bench: Can Language Models Resolve Real- ...", "date": "", "ddg_snippet": "SWE-bench is introduced, an evaluation framework consisting of software engineering problems drawn from real GitHub issues and corresponding pull requests ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/94a5f96308729e31c1ffbc0f0618db87795092fe", "content": "SWE-bench is introduced, an evaluation framework consisting of software engineering problems drawn from real GitHub issues and corresponding pull requests ..."}
diff --git a/data/sampled_jsons/SWEBench_abstract_language_models_resolve_GitHub_issues_software_engineering_benchmark_evaluation.jsonl b/data/sampled_jsons/SWEBench_abstract_language_models_resolve_GitHub_issues_software_engineering_benchmark_evaluation.jsonl
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index 0000000000000000000000000000000000000000..43a61aa3d829bfd2bdae347225ab94bc3fa04bf2
--- /dev/null
+++ b/data/sampled_jsons/SWEBench_abstract_language_models_resolve_GitHub_issues_software_engineering_benchmark_evaluation.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2310.06770] SWE - bench : Can Language Models Resolve ...", "date": "", "ddg_snippet": "software engineering problems drawn from real GitHub issues and corresponding pull requests across 12. popular Python repositories. Given a codebase along with a description of an issue to be resolved , a language model is tasked with editing the codebase to address the issue .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2310.06770", "content": "software engineering problems drawn from real GitHub issues and corresponding pull requests across 12. popular Python repositories. Given a codebase along with a description of an issue to be resolved , a language model is tasked with editing the codebase to address the issue ."}
+{"idx": 1, "title": "GitHub - SWE - bench / SWE - bench : SWE - bench [Multimodal]: Can...", "date": "", "ddg_snippet": "SWE - bench is a benchmark for evaluating large language models on real world software issues collected from GitHub . Given a codebase and an issue , a language model is tasked with generating a patch that resolves the described problem.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/SWE-bench/SWE-bench", "content": "SWE - bench is a benchmark for evaluating large language models on real world software issues collected from GitHub . Given a codebase and an issue , a language model is tasked with generating a patch that resolves the described problem."}
+{"idx": 2, "title": "Can Language Models Resolve Real-world Github Issues ? Oral", "date": "", "ddg_snippet": "Oral. SWE - bench : Can Language Models Resolve Real-world Github Issues ?We find real-world software engineering to be a rich, sustainable, and challenging testbed for evaluating the next generation of language models .", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2024/oral/19757", "content": "Oral. SWE - bench : Can Language Models Resolve Real-world Github Issues ?We find real-world software engineering to be a rich, sustainable, and challenging testbed for evaluating the next generation of language models ."}
+{"idx": 3, "title": "SWE - bench : Can Language Models Resolve Real-World GitHub ...", "date": "", "ddg_snippet": "We find real-world software engineering to be a rich, sustainable, and challenging testbed for evaluating the next generation of language models . To this end, we introduce SWE - bench , an evaluation framework consisting of $2,294$ software engineering problems drawn from...", "subpage_snippet": "", "source": "githubissues.com", "link": "https://githubissues.com/AkihikoWatanabe/paper_notes/1848", "content": "We find real-world software engineering to be a rich, sustainable, and challenging testbed for evaluating the next generation of language models . To this end, we introduce SWE - bench , an evaluation framework consisting of $2,294$ software engineering problems drawn from..."}
+{"idx": 4, "title": "SWE Benchmark : LLM evaluation in Software Engineering ... | Medium", "date": "", "ddg_snippet": "Different from rest evaluation benchmarks . SWE - bench is an advanced benchmark designed to evaluate language models ’ capabilities in realistic software engineering scenarios. It offers several key advantages over traditional programming benchmarks", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@sulbha.jindal/swe-benchmark-llm-evaluation-in-software-engineering-setting-52f315b2de5a", "content": "Different from rest evaluation benchmarks . SWE - bench is an advanced benchmark designed to evaluate language models ’ capabilities in realistic software engineering scenarios. It offers several key advantages over traditional programming benchmarks"}
+{"idx": 5, "title": "The Rise of AI Teammates in Software Engineering (SE) 3.0: How", "date": "", "ddg_snippet": "... software engineering , AIDev offers concrete, structured, and open data that can power future research in benchmarking , agent readiness, optimization ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.15003v1", "content": "... software engineering , AIDev offers concrete, structured, and open data that can power future research in benchmarking , agent readiness, optimization ..."}
+{"idx": 6, "title": "OmniGIRL: A Multilingual and Multimodal Benchmark for GitHub", "date": "", "ddg_snippet": "With advances in large language models (LLMs), this task has gained increasing attention, and several benchmarks are proposed to evaluate the issue ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.04606v1", "content": "With advances in large language models (LLMs), this task has gained increasing attention, and several benchmarks are proposed to evaluate the issue ..."}
+{"idx": 7, "title": "Vayavya Labs Pvt. Ltd. - SWE-Bench-C Evaluation Framework", "date": "", "ddg_snippet": "Keywords—SWE-bench, Benchmark , Software engineering (SWE), Multi-agent, Pull request, Claude Code, GitHub issue , Large Language Model (LLM), SWE ...", "subpage_snippet": "", "source": "vayavyalabs.com", "link": "https://vayavyalabs.com/blogs/swe-bench-c-evaluation-framework/", "content": "Keywords—SWE-bench, Benchmark , Software engineering (SWE), Multi-agent, Pull request, Claude Code, GitHub issue , Large Language Model (LLM), SWE ..."}
+{"idx": 8, "title": "ICLR 2024 — Best Papers & Talks (Benchmarks, Reasoning", "date": "", "ddg_snippet": "We introduce SWE-bench, an evaluation framework consisting of 2,294 software engineering problems drawn from real GitHub issues and corresponding ...", "subpage_snippet": "", "source": "www.latent.space", "link": "https://www.latent.space/p/iclr-2024-benchmarks-agents", "content": "We introduce SWE-bench, an evaluation framework consisting of 2,294 software engineering problems drawn from real GitHub issues and corresponding ..."}
+{"idx": 9, "title": "What Benchmarks Say About Agentic AI’s Coding Potential", "date": "", "ddg_snippet": "... shows the top-scoring model resolved 55% of the coding issues on SWE-bench Lite, which is a subset of the benchmark designed to make evaluation less ...", "subpage_snippet": "", "source": "www.aiwire.net", "link": "https://www.aiwire.net/2025/03/28/what-benchmarks-say-about-agentic-ais-coding-potential/", "content": "... shows the top-scoring model resolved 55% of the coding issues on SWE-bench Lite, which is a subset of the benchmark designed to make evaluation less ..."}
diff --git a/data/sampled_jsons/Sahara_algorithm_Safety_Attention_Head_AttRibution_Algorithm_Ships_metric_calculation.jsonl b/data/sampled_jsons/Sahara_algorithm_Safety_Attention_Head_AttRibution_Algorithm_Ships_metric_calculation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..169099803b5e4affe7ddb8dcf686914ae3f6ff1c
--- /dev/null
+++ b/data/sampled_jsons/Sahara_algorithm_Safety_Attention_Head_AttRibution_Algorithm_Ships_metric_calculation.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "On the Role of Attention Heads in Large Language Model ...", "date": "", "ddg_snippet": "by Z Zhou · 2024 · Cited by 19 — Based on this, we generalize Ships to the dataset level and further introduce the Safety Attention Head AttRibution Algorithm ( Sahara ) to ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.13708", "content": "by Z Zhou · 2024 · Cited by 19 — Based on this, we generalize Ships to the dataset level and further introduce the Safety Attention Head AttRibution Algorithm ( Sahara ) to ..."}
+{"idx": 1, "title": "On the Role of Attention Heads in Large Language Model ...", "date": "", "ddg_snippet": "by Z Zhou · Cited by 19 — Base on this, we generalize Ships to the dataset level and further introduce the Safety Attention Head AttRibution Algorithm ( Sahara ) to attribute the critical ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=h0Ak8A5yqw", "content": "by Z Zhou · Cited by 19 — Base on this, we generalize Ships to the dataset level and further introduce the Safety Attention Head AttRibution Algorithm ( Sahara ) to attribute the critical ..."}
+{"idx": 2, "title": "On the Role of Attention Heads in Large Language Model ...", "date": "", "ddg_snippet": "17 Oct 2024 — We present a novel metric , Ships , to evaluate the impact of attention head ablation on safety . Base on this, we propose a heuristic algorithm , ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.13708v1", "content": "17 Oct 2024 — We present a novel metric , Ships , to evaluate the impact of attention head ablation on safety . Base on this, we propose a heuristic algorithm , ..."}
+{"idx": 3, "title": "[Literature Review] On the Role of Attention Heads in Large ...", "date": "", "ddg_snippet": "Safety Attention Head Attribution Algorithm (Sahara): Sahara is designed to detect groups of attention heads that collaboratively ensure rejection of harmful ...", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/en/review/on-the-role-of-attention-heads-in-large-language-model-safety", "content": "Safety Attention Head Attribution Algorithm (Sahara): Sahara is designed to detect groups of attention heads that collaboratively ensure rejection of harmful ..."}
+{"idx": 4, "title": "Track: Poster Session 2", "date": "", "ddg_snippet": "24 Apr 2025 — We propose an novel metric which tailored for multi- head attention ... Algorithm ( Sahara ) to attribute the critical safety attention heads inside ...", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2025/session/31972", "content": "24 Apr 2025 — We propose an novel metric which tailored for multi- head attention ... Algorithm ( Sahara ) to attribute the critical safety attention heads inside ..."}
+{"idx": 5, "title": "Daily Papers", "date": "", "ddg_snippet": "4 days ago — We propose a novel metric which tailored for multi- head attention ... Algorithm ( Sahara ) to attribute the critical safety attention heads inside ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=safety-helpfulness+trade-off", "content": "4 days ago — We propose a novel metric which tailored for multi- head attention ... Algorithm ( Sahara ) to attribute the critical safety attention heads inside ..."}
+{"idx": 6, "title": "State of AI on X: \"Natural Language Processing", "date": "", "ddg_snippet": "They generalize Ships to the dataset level and introduce the Safety Attention Head AttRibution Algorithm (Sahara) to identify critical safety ...", "subpage_snippet": "", "source": "twitter.com", "link": "https://twitter.com/stateof_ai/status/1847364253231317207", "content": "They generalize Ships to the dataset level and introduce the Safety Attention Head AttRibution Algorithm (Sahara) to identify critical safety ..."}
+{"idx": 7, "title": "ICLR 2025 Orals", "date": "", "ddg_snippet": "Base on this, we generalize Ships to the dataset level and further introduce the Safety Attention Head AttRibution Algorithm ( Sahara ) to attribute the critical ...", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2025/events/oral", "content": "Base on this, we generalize Ships to the dataset level and further introduce the Safety Attention Head AttRibution Algorithm ( Sahara ) to attribute the critical ..."}
+{"idx": 8, "title": "A lightweight Deeplab V3+ network integrating deep ...", "date": "", "ddg_snippet": "by L Liu · 2025 — A lightweight deep learning model is proposed based on Deeplab V3 + , which employs the combination of attention mechanism and deep transitive transfer ...", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41598-024-66060-7", "content": "by L Liu · 2025 — A lightweight deep learning model is proposed based on Deeplab V3 + , which employs the combination of attention mechanism and deep transitive transfer ..."}
+{"idx": 9, "title": "Flood change detection model based on an improved U ...", "date": "", "ddg_snippet": "by F Wang · 2025 · Cited by 4 — This study proposes an advanced methodology that integrates SAR images with deep learning techniques to extract flood disaster change information.", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41598-025-87851-6", "content": "by F Wang · 2025 · Cited by 4 — This study proposes an advanced methodology that integrates SAR images with deep learning techniques to extract flood disaster change information."}
diff --git a/data/sampled_jsons/Sahara_algorithm_pseudocode_Safety_Attention_Head_AttRibution_group_size_S_calculation.jsonl b/data/sampled_jsons/Sahara_algorithm_pseudocode_Safety_Attention_Head_AttRibution_group_size_S_calculation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2abbd344dd13323ae117cd6b91b3302bf07dfee9
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+++ b/data/sampled_jsons/Sahara_algorithm_pseudocode_Safety_Attention_Head_AttRibution_group_size_S_calculation.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Terrorism group prediction using feature combination and ...", "date": "", "ddg_snippet": "by M Abdalsalam · 2024 · Cited by 1 — This study introduces a framework designed to classify and predict terrorist groups using bidirectional recurrent units and self- attention mechanisms.", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC11419613/", "content": "by M Abdalsalam · 2024 · Cited by 1 — This study introduces a framework designed to classify and predict terrorist groups using bidirectional recurrent units and self- attention mechanisms."}
+{"idx": 1, "title": "Secretary bird optimization algorithm: a new metaheuristic ...", "date": "", "ddg_snippet": "by Y Fu · 2024 · Cited by 340 — This study introduces a novel population-based metaheuristic algorithm called secretary bird optimization algorithm (SBOA), inspired by the survival behavior ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s10462-024-10729-y", "content": "by Y Fu · 2024 · Cited by 340 — This study introduces a novel population-based metaheuristic algorithm called secretary bird optimization algorithm (SBOA), inspired by the survival behavior ..."}
+{"idx": 2, "title": "Greater cane rat algorithm (GCRA): A nature-inspired ...", "date": "", "ddg_snippet": "by JO Agushaka · 2024 · Cited by 71 — This paper introduces a new metaheuristic technique known as the Greater Cane Rat Algorithm (GCRA) for addressing optimization problems.", "subpage_snippet": "", "source": "www.cell.com", "link": "https://www.cell.com/heliyon/fulltext/S2405-8440(24)07660-6", "content": "by JO Agushaka · 2024 · Cited by 71 — This paper introduces a new metaheuristic technique known as the Greater Cane Rat Algorithm (GCRA) for addressing optimization problems."}
+{"idx": 3, "title": "Designing The Algorithm | PDF | Algorithms | Payroll", "date": "", "ddg_snippet": "The algorithm repeats the steps of getting employee data, calculating pay, updating the total pay disbursed, and printing the individual pay. It then prints the ...", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/93601137/Designing-the-Algorithm", "content": "The algorithm repeats the steps of getting employee data, calculating pay, updating the total pay disbursed, and printing the individual pay. It then prints the ..."}
+{"idx": 4, "title": "HIDS-IoMT: A Deep Learning-Based Intelligent Intrusion ...", "date": "", "ddg_snippet": "by A Berguiga · 2025 · Cited by 17 — The authors of the study [21] introduce a multi- head attention -based gated recurrent unit (MAGRU) model ... Algorithm 1 presents the pseudocode ... 20 pages", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/iel8/6287639/10820123/10891538.pdf", "content": "by A Berguiga · 2025 · Cited by 17 — The authors of the study [21] introduce a multi- head attention -based gated recurrent unit (MAGRU) model ... Algorithm 1 presents the pseudocode ... 20 pages"}
+{"idx": 5, "title": "Asian Chapter of the Association for Computational ...", "date": "", "ddg_snippet": "Motivated by the above issue, this paper proposes a Multi- head Attention Dual Summary (MADS) based method which generates two types of summaries that capture ...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/events/aacl-2022/", "content": "Motivated by the above issue, this paper proposes a Multi- head Attention Dual Summary (MADS) based method which generates two types of summaries that capture ..."}
+{"idx": 6, "title": "Flood change detection model based on an improved U ...", "date": "", "ddg_snippet": "by F Wang · 2025 · Cited by 4 — This study proposes an advanced methodology that integrates SAR images with deep learning techniques to extract flood disaster change information.", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41598-025-87851-6", "content": "by F Wang · 2025 · Cited by 4 — This study proposes an advanced methodology that integrates SAR images with deep learning techniques to extract flood disaster change information."}
+{"idx": 7, "title": "Space‐Time Causal Discovery in Earth System Science: A ...", "date": "", "ddg_snippet": "11 Jul 2025 — We introduce Causal Space-Time Stencil Learning (CaStLe) for learning local causal dynamical structure underlying space-time data CaStLe ...", "subpage_snippet": "", "source": "agupubs.onlinelibrary.wiley.com", "link": "https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2024JH000546", "content": "11 Jul 2025 — We introduce Causal Space-Time Stencil Learning (CaStLe) for learning local causal dynamical structure underlying space-time data CaStLe ..."}
+{"idx": 8, "title": "Computer Science May 2025", "date": "", "ddg_snippet": "17 May 2025 — Comments: We are withdrawing this submission as the underlying experiment is currently incomplete. We require additional time to gather more ...", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/list/cs/2025-05?skip=8775&show=2000", "content": "17 May 2025 — Comments: We are withdrawing this submission as the underlying experiment is currently incomplete. We require additional time to gather more ..."}
+{"idx": 9, "title": "Cross-linguistic conditions on word length | PLOS One", "date": "", "ddg_snippet": "by S Wichmann · 2023 · Cited by 10 — The formula for stability is S = (R–U)/(1 –U), where S is the stability of a given feature, R is the proportion of pairs of related ...", "subpage_snippet": "", "source": "journals.plos.org", "link": "https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0281041", "content": "by S Wichmann · 2023 · Cited by 10 — The formula for stability is S = (R–U)/(1 –U), where S is the stability of a given feature, R is the proportion of pairs of related ..."}
diff --git a/data/sampled_jsons/Saito_2020_Unbiased_Pairwise_Learning_from_Biased_Implicit_Feedback_abstract.jsonl b/data/sampled_jsons/Saito_2020_Unbiased_Pairwise_Learning_from_Biased_Implicit_Feedback_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c1aba3c04af3f37f963488a56989d8137bffccd8
--- /dev/null
+++ b/data/sampled_jsons/Saito_2020_Unbiased_Pairwise_Learning_from_Biased_Implicit_Feedback_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Unbiased Pairwise Learning from Biased Implicit Feedback", "date": "", "ddg_snippet": "by Y Saito · 2020 · Cited by 52 — In this study, we first define an ideal pairwise loss function defined using the ground-truth relevance parameters that should be used to optimize the ranking ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3409256.3409812", "content": "by Y Saito · 2020 · Cited by 52 — In this study, we first define an ideal pairwise loss function defined using the ground-truth relevance parameters that should be used to optimize the ranking ..."}
+{"idx": 1, "title": "Unbiased Pairwise Learning from Biased Implicit Feedback", "date": "", "ddg_snippet": "In this study, we first define an ideal pairwise loss function defined using the ground-truth relevance parameters that should be used to optimize the ranking ...", "subpage_snippet": "", "source": "usait0.com", "link": "https://usait0.com/en/publication/2020/ictir2020/", "content": "In this study, we first define an ideal pairwise loss function defined using the ground-truth relevance parameters that should be used to optimize the ranking ..."}
+{"idx": 2, "title": "Unbiased Pairwise Learning from Implicit Feedback for ...", "date": "", "ddg_snippet": "by Y Ren · 2023 · Cited by 7 — We design an effective unbiased pairwise learning algorithm for implicit feedback with theoretically lower variance than the existing approach.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2304.05066", "content": "by Y Ren · 2023 · Cited by 7 — We design an effective unbiased pairwise learning algorithm for implicit feedback with theoretically lower variance than the existing approach."}
+{"idx": 3, "title": "Unbiased Recommender Learning from Implicit Feedback ...", "date": "", "ddg_snippet": "by H Wang · Cited by 1 — Implicit feedback recommendation is challenged by the missing negative feedback essential for effective model training.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=0E5rZOGA13", "content": "by H Wang · Cited by 1 — Implicit feedback recommendation is challenged by the missing negative feedback essential for effective model training."}
+{"idx": 4, "title": "Unbiased Recommender Learning from Biased Graded ...", "date": "", "ddg_snippet": "by Y Saito · 2020 · Cited by 1 — ABSTRACT . Binary user-behavior logs such as clicks or views, called implicit feedback , are often used to build recommender systems because.", "subpage_snippet": "", "source": "decisionmaking4ir.github.io", "link": "https://decisionmaking4ir.github.io/WSDM-2022/papers/Suguru.pdf", "content": "by Y Saito · 2020 · Cited by 1 — ABSTRACT . Binary user-behavior logs such as clicks or views, called implicit feedback , are often used to build recommender systems because."}
+{"idx": 5, "title": "Bilateral Self-unbiased Learning from Biased Implicit ...", "date": "", "ddg_snippet": "by J Lee · 2022 · Cited by 16 — ABSTRACT . Implicit feedback has been widely used to build commercial rec- ommender systems. Because observed feedback represents users'.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2207.12660", "content": "by J Lee · 2022 · Cited by 16 — ABSTRACT . Implicit feedback has been widely used to build commercial rec- ommender systems. Because observed feedback represents users'."}
+{"idx": 6, "title": "An Unbiased Pairwise Ranking Approach Using Spatial ...", "date": "", "ddg_snippet": "For implicit - feedback recommendations, two types of learning objectives are extensively employed to learn model parameters, including pointwise learning and ...", "subpage_snippet": "", "source": "papers.ssrn.com", "link": "https://papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID4361887_code1914442.pdf?abstractid=4347644&mirid=1", "content": "For implicit - feedback recommendations, two types of learning objectives are extensively employed to learn model parameters, including pointwise learning and ..."}
+{"idx": 7, "title": "Unbiased Recommender Learning from Implicit Feedback ...", "date": "", "ddg_snippet": "by H Wang · Cited by 1 — Implicit feedback recommendation is challenged by the missing negative feedback essential for effective model training. Existing methods of-.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=0E5rZOGA13", "content": "by H Wang · Cited by 1 — Implicit feedback recommendation is challenged by the missing negative feedback essential for effective model training. Existing methods of-."}
+{"idx": 8, "title": "Publications", "date": "", "ddg_snippet": "Yuta Saito (2020). Unbiased Pairwise Learning from Biased Implicit Feedback . In Proceedings of the 2020 ACM SIGIR on International Conference on Theory of ...", "subpage_snippet": "", "source": "usait0.com", "link": "https://usait0.com/en/publication/", "content": "Yuta Saito (2020). Unbiased Pairwise Learning from Biased Implicit Feedback . In Proceedings of the 2020 ACM SIGIR on International Conference on Theory of ..."}
+{"idx": 9, "title": "Towards Resolving Propensity Contradiction in Offline ...", "date": "", "ddg_snippet": "by Y Saito · Cited by 15 — [Saito, 2020b] Yuta Saito. Unbiased pairwise learning from biased implicit feedback . In Proceedings of the 2020 ACM. SIGIR on International Conference on Theory ...", "subpage_snippet": "", "source": "www.ijcai.org", "link": "https://www.ijcai.org/proceedings/2022/0307.pdf", "content": "by Y Saito · Cited by 15 — [Saito, 2020b] Yuta Saito. Unbiased pairwise learning from biased implicit feedback . In Proceedings of the 2020 ACM. SIGIR on International Conference on Theory ..."}
diff --git a/data/sampled_jsons/Sang-Jun_Park_Keun-Soo_Heo_Disease-aware_Image-Text_Alignment_arXiv.jsonl b/data/sampled_jsons/Sang-Jun_Park_Keun-Soo_Heo_Disease-aware_Image-Text_Alignment_arXiv.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e6a87dc80d6f106100c02edd6d184e0e7024df6f
--- /dev/null
+++ b/data/sampled_jsons/Sang-Jun_Park_Keun-Soo_Heo_Disease-aware_Image-Text_Alignment_arXiv.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2504.11786] DART: Disease - aware Image - Text Alignment and...", "date": "", "ddg_snippet": "View a PDF of the paper titled DART: Disease - aware Image - Text Alignment and Self-correcting Re- alignment for Trustworthy Radiology Report Generation, by Sang - Jun Park and 5 other authors.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2504.11786", "content": "View a PDF of the paper titled DART: Disease - aware Image - Text Alignment and Self-correcting Re- alignment for Trustworthy Radiology Report Generation, by Sang - Jun Park and 5 other authors."}
+{"idx": 1, "title": "DART: Disease - aware Image - Text Alignment and Self-correcting...", "date": "", "ddg_snippet": "Sang - Jun Park 111Equal contribution.In this study, we propose a Disease - aware image - text Alignment and self-correcting Re- alignment for Trustworthy radiology report generation (DART) framework.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.11786v1", "content": "Sang - Jun Park 111Equal contribution.In this study, we propose a Disease - aware image - text Alignment and self-correcting Re- alignment for Trustworthy radiology report generation (DART) framework."}
+{"idx": 2, "title": "Heo Keun - Soo - Google 학술 검색", "date": "", "ddg_snippet": "DART: Disease - aware Image - Text Alignment and Self-correcting Re- alignment for Trustworthy Radiology Report Generation. SJ Park , KS Heo , DH Shin, YH Son, JH Oh, TE Kam.", "subpage_snippet": "", "source": "scholar.google.co.kr", "link": "https://scholar.google.co.kr/citations?user=UJJGePQAAAAJ&hl=ko", "content": "DART: Disease - aware Image - Text Alignment and Self-correcting Re- alignment for Trustworthy Radiology Report Generation. SJ Park , KS Heo , DH Shin, YH Son, JH Oh, TE Kam."}
+{"idx": 3, "title": "dblp: Sang - Jun Park (disambiguation)", "date": "", "ddg_snippet": "Sang - Jun Park , Keun - Soo Heo , Dong-Hee Shin, Young-Han Son, Ji-Hye Oh, Tae-Eui Kam: DART: Disease - aware Image - Text Alignment and Self-correcting Re- alignment for Trustworthy Radiology Report Generation.", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/pid/166/8696.html", "content": "Sang - Jun Park , Keun - Soo Heo , Dong-Hee Shin, Young-Han Son, Ji-Hye Oh, Tae-Eui Kam: DART: Disease - aware Image - Text Alignment and Self-correcting Re- alignment for Trustworthy Radiology Report Generation."}
+{"idx": 4, "title": "Keun - Soo Heo | 5 Publications | 4 Citations | Related Authors", "date": "", "ddg_snippet": "Keun - Soo Heo is an academic researcher. The author has contributed to research in topics: Computer science & Pattern recognition (psychology). The author has an hindex of 1, co-authored 2 publications.", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/authors/keun-soo-heo-31fsv2ex", "content": "Keun - Soo Heo is an academic researcher. The author has contributed to research in topics: Computer science & Pattern recognition (psychology). The author has an hindex of 1, co-authored 2 publications."}
+{"idx": 5, "title": "CVPR 2025 Open Access Repository", "date": "", "ddg_snippet": "Sang - Jun Park , Keun - Soo Heo , Dong-Hee Shin, Young-Han Son, Ji-Hye Oh, Tae-Eui Kam; Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR), 2025, pp. 15580-15589.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/html/Park_DART_Disease-aware_Image-Text_Alignment_and_Self-correcting_Re-alignment_for_Trustworthy_Radiology_CVPR_2025_paper.html", "content": "Sang - Jun Park , Keun - Soo Heo , Dong-Hee Shin, Young-Han Son, Ji-Hye Oh, Tae-Eui Kam; Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR), 2025, pp. 15580-15589."}
+{"idx": 6, "title": "Dart", "date": "", "ddg_snippet": "Authors: Sang - Jun Park , Keun - Soo Heo , Dong-Hee Shin, Young-Han Son, Ji-Hye Oh, Tae-Eui Kam.In this study, we propose a Disease - aware image - text Alignment and self-correcting Re- alignment for Trustworthy radiology report generation (DART) framework.", "subpage_snippet": "", "source": "www.catalyzex.com", "link": "https://www.catalyzex.com/s/Dart", "content": "Authors: Sang - Jun Park , Keun - Soo Heo , Dong-Hee Shin, Young-Han Son, Ji-Hye Oh, Tae-Eui Kam.In this study, we propose a Disease - aware image - text Alignment and self-correcting Re- alignment for Trustworthy radiology report generation (DART) framework."}
+{"idx": 7, "title": "Articles by Dong-Ho Shin | Synthical", "date": "", "ddg_snippet": "DART: Disease - aware Image - Text Alignment and Self-correcting Re- alignment for Trustworthy Radiology Report Generation. 16 April 2025 by Sang - Jun Park and others. Computer Vision and Pattern Recognition.", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/profile/3e7eed8b-226c-4317-a4f7-ae93f39b25b8/articles", "content": "DART: Disease - aware Image - Text Alignment and Self-correcting Re- alignment for Trustworthy Radiology Report Generation. 16 April 2025 by Sang - Jun Park and others. Computer Vision and Pattern Recognition."}
+{"idx": 8, "title": "CVPR2025 Accepted Papers-CSDN博客", "date": "", "ddg_snippet": "Context- Aware Evaluation for Balancing Preservation and Modification in Text -Guided Image Editing Yoonjeon Kim · Soohyun Ryu · Yeonsung Jung · Hyunkoo Lee · Joowon Kim · June Yong Yang · Jaeryong Hwang · Eunho Yang Playing the Fool...", "subpage_snippet": "", "source": "blog.csdn.net", "link": "https://blog.csdn.net/u013963578/article/details/146183100", "content": "Context- Aware Evaluation for Balancing Preservation and Modification in Text -Guided Image Editing Yoonjeon Kim · Soohyun Ryu · Yeonsung Jung · Hyunkoo Lee · Joowon Kim · June Yong Yang · Jaeryong Hwang · Eunho Yang Playing the Fool..."}
+{"idx": 9, "title": "Joseph is a super minimal content focus theme for Jekyll.", "date": "", "ddg_snippet": "DART: Disease - aware Image - Text Alignment and Self-correcting Re- alignment for Trustworthy Radiology Report Generation. Sang - Jun Park , Keun - Soo Heo , Dong-Hee Shin, Young-Han Son, Ji-Hye Oh, Tae-Eui Kam.", "subpage_snippet": "", "source": "donghee-shin-page.github.io", "link": "https://donghee-shin-page.github.io/publications/", "content": "DART: Disease - aware Image - Text Alignment and Self-correcting Re- alignment for Trustworthy Radiology Report Generation. Sang - Jun Park , Keun - Soo Heo , Dong-Hee Shin, Young-Han Son, Ji-Hye Oh, Tae-Eui Kam."}
diff --git a/data/sampled_jsons/Sanity_Checking_Causal_Representation_Learning_Contrastive_CRL_MCC_scores_numerical_values.jsonl b/data/sampled_jsons/Sanity_Checking_Causal_Representation_Learning_Contrastive_CRL_MCC_scores_numerical_values.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ec8fc5d9e58788eb2eaaa95c60db2eb65e242904
--- /dev/null
+++ b/data/sampled_jsons/Sanity_Checking_Causal_Representation_Learning_Contrastive_CRL_MCC_scores_numerical_values.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Sanity Checking Causal Representation Learning on a Simple...", "date": "", "ddg_snippet": "We evaluate methods for causal representation learning ( CRL ) on a simple, real-world system where these methods are expected to work.Similar to the MCC score , the authors of CITRIS report the correlation between the learned representation and the ground-truth variables.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.20099", "content": "We evaluate methods for causal representation learning ( CRL ) on a simple, real-world system where these methods are expected to work.Similar to the MCC score , the authors of CITRIS report the correlation between the learned representation and the ground-truth variables."}
+{"idx": 1, "title": "GitHub - simonbing/CRLSanityCheck", "date": "", "ddg_snippet": "Sanity Checking Causal Representation Learning on a Simple Real-World System.For the real-data experiment using the Contrastive CRL method, run. python contrastive _ crl _experiment --dataset lt_ crl _benchmark_v1 \\", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/simonbing/CRLSanityCheck", "content": "Sanity Checking Causal Representation Learning on a Simple Real-World System.For the real-data experiment using the Contrastive CRL method, run. python contrastive _ crl _experiment --dataset lt_ crl _benchmark_v1 \\"}
+{"idx": 2, "title": "Sanity Checking Causal Representation Learning on a Simple...", "date": "", "ddg_snippet": "The paper seeks to validate advances in causal representation learning ( CRL ) by providing a sanity check . It emphasizes the importance of applying existing theoretical frameworks to real-world problems rather than solely relying on synthetic data for validation.", "subpage_snippet": "", "source": "powerdrill.ai", "link": "https://powerdrill.ai/discover/summary-sanity-checking-causal-representation-learning-on-cm7p9dbhq6e6507m0bf9fj22h", "content": "The paper seeks to validate advances in causal representation learning ( CRL ) by providing a sanity check . It emphasizes the importance of applying existing theoretical frameworks to real-world problems rather than solely relying on synthetic data for validation."}
+{"idx": 3, "title": "ICML Poster Sanity Checking Causal Representation Learning on...", "date": "", "ddg_snippet": "We evaluate methods for causal representation learning ( CRL ) on a simple, real-world system where these methods are expected to work.We select methods representative of different approaches to CRL and find that they all fail to recover the underlying causal factors.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/44652", "content": "We evaluate methods for causal representation learning ( CRL ) on a simple, real-world system where these methods are expected to work.We select methods representative of different approaches to CRL and find that they all fail to recover the underlying causal factors."}
+{"idx": 4, "title": "Sanity Checking Causal Representation Learning on a Simple...", "date": "", "ddg_snippet": "We evaluate methods for causal representation learning ( CRL ) on a simple, real-world system where these methods are expected to work.We select methods representative of different approaches to CRL and find that they all fail to recover the underlying causal factors.", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/article/Sanity-Checking-Causal-Representation-Learning-on-a-Simple-Real-World-System-ea56ed1c-68a5-4a60-8384-8b1132108289", "content": "We evaluate methods for causal representation learning ( CRL ) on a simple, real-world system where these methods are expected to work.We select methods representative of different approaches to CRL and find that they all fail to recover the underlying causal factors."}
+{"idx": 5, "title": "Learning Linear Causal Representations from", "date": "", "ddg_snippet": "Connectivity- contrastive learning : Combining causal discovery and representation learning for multimodal data. In International Conference on Artificial Intelligence and Statistics, pages 3399–3426.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2023/file/8e5de4cb639ef718f44060dc257cb04f-Paper-Conference.pdf", "content": "Connectivity- contrastive learning : Combining causal discovery and representation learning for multimodal data. In International Conference on Artificial Intelligence and Statistics, pages 3399–3426."}
+{"idx": 6, "title": "Interventional Causal Representation Learning | alphaXiv", "date": "", "ddg_snippet": "Causal Representation Learning ( CRL ) seeks to identify interpretable latent factors from high-dimensional observations, enabling AI systems to generalize better and understand causal relationships.", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2209.11924v4", "content": "Causal Representation Learning ( CRL ) seeks to identify interpretable latent factors from high-dimensional observations, enabling AI systems to generalize better and understand causal relationships."}
+{"idx": 7, "title": "Causal Differentiating Concepts... | ServiceNow AI Research", "date": "", "ddg_snippet": "Publications. Causal Differentiating Concepts: Interpreting LM Behavior via Causal Representation Learning .", "subpage_snippet": "", "source": "www.servicenow.com", "link": "https://www.servicenow.com/research/publication/navita-goyal-caus-neurips2025.html", "content": "Publications. Causal Differentiating Concepts: Interpreting LM Behavior via Causal Representation Learning ."}
+{"idx": 8, "title": "Causal Representations", "date": "", "ddg_snippet": "Connectivity- contrastive learning : Combining causal discov-ery and representation learning for multimodal data. Score -based Causal Representation Learning with Interventions. arXiv preprint arXiv:2301.08230, 2023.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v236/bing24a/bing24a.pdf", "content": "Connectivity- contrastive learning : Combining causal discov-ery and representation learning for multimodal data. Score -based Causal Representation Learning with Interventions. arXiv preprint arXiv:2301.08230, 2023."}
+{"idx": 9, "title": "AAAI '25 | Causal Reprsentation Learning", "date": "", "ddg_snippet": "The emerging field of causal representation learning ( CRL ) aims to learn the latent causal structures by integrating representation . CRL , subsequently, facilitates causal reasoning, intervention, and planning. This tutorial will provide a thorough overview of the recent advances in CRL .", "subpage_snippet": "", "source": "www.isg-rpi.com", "link": "https://www.isg-rpi.com/aaai-25", "content": "The emerging field of causal representation learning ( CRL ) aims to learn the latent causal structures by integrating representation . CRL , subsequently, facilitates causal reasoning, intervention, and planning. This tutorial will provide a thorough overview of the recent advances in CRL ."}
diff --git "a/data/sampled_jsons/Sar\304\261y\304\261ld\304\261z_et_al._2023_ImageNet-100_accuracy_results_table.jsonl" "b/data/sampled_jsons/Sar\304\261y\304\261ld\304\261z_et_al._2023_ImageNet-100_accuracy_results_table.jsonl"
new file mode 100644
index 0000000000000000000000000000000000000000..c9a7aa2e656ba7643c24717ddfde0ff412481ef8
--- /dev/null
+++ "b/data/sampled_jsons/Sar\304\261y\304\261ld\304\261z_et_al._2023_ImageNet-100_accuracy_results_table.jsonl"
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "300 Types of Flowers with Names from A To Z and Pictures", "date": "", "ddg_snippet": "Mar 17, 2024 · On this page, you will find a comprehensive list of flower names starting with each letter of the alphabet. Simply jump to each specific flower using the table of content below. Abutilon is a shrub that blooms during the summer. This plant produces flowers that may be white to a purple blue color.", "subpage_snippet": "", "source": "florgeous.com", "link": "https://florgeous.com/types-of-flowers/", "content": "Mar 17, 2024 · On this page, you will find a comprehensive list of flower names starting with each letter of the alphabet. Simply jump to each specific flower using the table of content below. Abutilon is a shrub that blooms during the summer. This plant produces flowers that may be white to a purple blue color."}
+{"idx": 1, "title": "Flower Shop Moses Lake | Florist in Moses Lake , WA | FLORAL...", "date": "", "ddg_snippet": "A fresh, one-of-a-kind bouquet filled with the best blooms of the season. Hand-tied, beautifully wrapped, and always a surprise. Let us create a custom flower arrangement for you. Disclaimer: Some flowers and/or conatiners are limited due to high demand.", "subpage_snippet": "", "source": "www.floraloccasions.net", "link": "https://www.floraloccasions.net/", "content": "A fresh, one-of-a-kind bouquet filled with the best blooms of the season. Hand-tied, beautifully wrapped, and always a surprise. Let us create a custom flower arrangement for you. Disclaimer: Some flowers and/or conatiners are limited due to high demand."}
+{"idx": 2, "title": "Flower Delivery by Canada Flowers", "date": "", "ddg_snippet": "Order flowers online with Canada Flowers for same day flower delivery across Canada. Beautiful bouquets and flower arrangements delivered by the best florists in Canada. Send Flowers with Canada Flowers where you can find the perfect bouquet for any occasion!", "subpage_snippet": "", "source": "www.canadaflowers.ca", "link": "https://www.canadaflowers.ca/", "content": "Order flowers online with Canada Flowers for same day flower delivery across Canada. Beautiful bouquets and flower arrangements delivered by the best florists in Canada. Send Flowers with Canada Flowers where you can find the perfect bouquet for any occasion!"}
+{"idx": 3, "title": "Flowers and Gift Baskets - Florist Canada | Flower Delivery |...", "date": "", "ddg_snippet": "Thanks for the last minute flower delivery! They were beautiful and perfect! Thank you so much for your excellent service - my daughter tells me the flowers are lovely! The flowers and chocolates arrived and we were blown away. The bouquet was beautiful and the chocolates were gourmet quality. I will definitely use your company in the future!!", "subpage_snippet": "", "source": "bloomex.ca", "link": "https://bloomex.ca/", "content": "Thanks for the last minute flower delivery! They were beautiful and perfect! Thank you so much for your excellent service - my daughter tells me the flowers are lovely! The flowers and chocolates arrived and we were blown away. The bouquet was beautiful and the chocolates were gourmet quality. I will definitely use your company in the future!!"}
+{"idx": 4, "title": "Moses Lake Florist: Moses Lake Flower Delivery, WA", "date": "", "ddg_snippet": "Whether you’re celebrating a special occasion or simply want to make someone smile, our local Moses Lake florist offers Same-day flower delivery throughout the city. Choose from a wide range of stunning fresh flowers , vibrant plants, and unique gourmet gift baskets for any occasion.", "subpage_snippet": "", "source": "jamescressflorist.com", "link": "https://jamescressflorist.com/pages/local-flower-delivery-washington-moses-lake", "content": "Whether you’re celebrating a special occasion or simply want to make someone smile, our local Moses Lake florist offers Same-day flower delivery throughout the city. Choose from a wide range of stunning fresh flowers , vibrant plants, and unique gourmet gift baskets for any occasion."}
+{"idx": 5, "title": "FLORAL OCCASIONS (509) 764-7673 Flowers and Florist in Moses Lake", "date": "", "ddg_snippet": "Order flowers from FLORAL OCCASIONS, a local florist located at 315 S Ash Street, Moses Lake, Washington. Find reviews, social media, videos, map and contact information including phone number (509) 764-7673 for this flower shop.", "subpage_snippet": "", "source": "www.flowershopnetwork.com", "link": "https://www.flowershopnetwork.com/floristProfile/271788", "content": "Order flowers from FLORAL OCCASIONS, a local florist located at 315 S Ash Street, Moses Lake, Washington. Find reviews, social media, videos, map and contact information including phone number (509) 764-7673 for this flower shop."}
+{"idx": 6, "title": "Moses Lake WA Florist & SAME-DAY Flower Delivery. FREE Delivery...", "date": "", "ddg_snippet": "Need reliable delivery to a hospital, funeral home, or cemetery in Moses Lake , Washington ? Our expert team handles these time-sensitive orders with care and speed.", "subpage_snippet": "", "source": "www.flowersbyeva.com", "link": "https://www.flowersbyeva.com/florist/washington/moses-lake", "content": "Need reliable delivery to a hospital, funeral home, or cemetery in Moses Lake , Washington ? Our expert team handles these time-sensitive orders with care and speed."}
+{"idx": 7, "title": "Flowers & Gifts Delivery | Canadian Florist | 1800Flowers.ca", "date": "", "ddg_snippet": "1800Flowers is in Canada! Our dedicated Canadian site offers Canada's finest flower and gift selections, all priced in Canadian Dollars and fulfilled locally.", "subpage_snippet": "", "source": "www.1800flowers.ca", "link": "https://www.1800flowers.ca/", "content": "1800Flowers is in Canada! Our dedicated Canadian site offers Canada's finest flower and gift selections, all priced in Canadian Dollars and fulfilled locally."}
+{"idx": 8, "title": "Florist Moses Lake WA | Flowers By Nora", "date": "", "ddg_snippet": "Flowers By Nora team routinely delivers to funeral homes, cemeteries and hospitals within Moses Lake and surrounding areas. For expedited and sameday orders please call us to learn more about our 45 minute express delivery. We are always here to support you during your difficult time.", "subpage_snippet": "", "source": "www.flowersbynora.com", "link": "https://www.flowersbynora.com/best-florist/washington/moses-lake", "content": "Flowers By Nora team routinely delivers to funeral homes, cemeteries and hospitals within Moses Lake and surrounding areas. For expedited and sameday orders please call us to learn more about our 45 minute express delivery. We are always here to support you during your difficult time."}
+{"idx": 9, "title": "Moses Lake WA Florist - Sunny Flower Delivery", "date": "", "ddg_snippet": "We have beautiful flower arrangements available for delivery today.", "subpage_snippet": "", "source": "www.sunnyflowerdelivery.com", "link": "https://www.sunnyflowerdelivery.com/flower-delivery/moses-lake-wa-florist/", "content": "We have beautiful flower arrangements available for delivery today."}
diff --git "a/data/sampled_jsons/Sar\304\261y\304\261ld\304\261z_synthetic_ImageNet_clone_2023.jsonl" "b/data/sampled_jsons/Sar\304\261y\304\261ld\304\261z_synthetic_ImageNet_clone_2023.jsonl"
new file mode 100644
index 0000000000000000000000000000000000000000..eaee715ffa53a67549e5efef65f240d151076e08
--- /dev/null
+++ "b/data/sampled_jsons/Sar\304\261y\304\261ld\304\261z_synthetic_ImageNet_clone_2023.jsonl"
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Mert Bülent Sarıyıldız - Google Scholar", "date": "", "ddg_snippet": "MB Sariyildiz , Y Kalantidis, K Alahari, D Larlus. ICLR 2023 -International Conference on Learning Representations, 1-26, 2023 .Supplementary Material for Fake it till you make it: Learning transferable representations from synthetic ImageNet clones .", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=9vpQ9tIAAAAJ&hl=en", "content": "MB Sariyildiz , Y Kalantidis, K Alahari, D Larlus. ICLR 2023 -International Conference on Learning Representations, 1-26, 2023 .Supplementary Material for Fake it till you make it: Learning transferable representations from synthetic ImageNet clones ."}
+{"idx": 1, "title": "Automated Synthetic -to-Real Generalization | DeepAI", "date": "", "ddg_snippet": "Models trained on synthetic images often face degraded generalization to real data. As a convention, these models are often initialized with ImageNet pre-trained representation.", "subpage_snippet": "", "source": "deepai.org", "link": "https://deepai.org/publication/automated-synthetic-to-real-generalization", "content": "Models trained on synthetic images often face degraded generalization to real data. As a convention, these models are often initialized with ImageNet pre-trained representation."}
+{"idx": 2, "title": "openreview.net/profile?id=~Mert_Bülent_ Sarıyıldız 1", "date": "", "ddg_snippet": "Naver Labs Europe (naverlabs.com). 2023 – Present.Fake it Till You Make it: Learning Transferable Representations from Synthetic ImageNet Clones .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/profile?id=~Mert_Bülent_Sarıyıldız1", "content": "Naver Labs Europe (naverlabs.com). 2023 – Present.Fake it Till You Make it: Learning Transferable Representations from Synthetic ImageNet Clones ."}
+{"idx": 3, "title": "CVPR 2023 - Fake it till you make it: Learning transferable...", "date": "", "ddg_snippet": "The paper investigates the ability of synthetic images, generated using Stable Diffusion, to replace real images for training models for ImageNet classification.", "subpage_snippet": "", "source": "agibreakdown.podbean.com", "link": "https://agibreakdown.podbean.com/e/cvpr-2023-fake-it-till-you-make-it-learning-transferable-representations-from-synthetic-imagenet-clones/", "content": "The paper investigates the ability of synthetic images, generated using Stable Diffusion, to replace real images for training models for ImageNet classification."}
+{"idx": 4, "title": "Synthetic Data from Diffusion Models Improves ImageNet Classification", "date": "", "ddg_snippet": ": Learning transferable representations from synthetic ImageNet clones .", "subpage_snippet": "", "source": "www.connectedpapers.com", "link": "https://www.connectedpapers.com/main/4538e353dd98f396c8facc29ebb72e9b1ba5f7c2/Synthetic-Data-from-Diffusion-Models-Improves-ImageNet-Classification/graph", "content": ": Learning transferable representations from synthetic ImageNet clones ."}
+{"idx": 5, "title": "M.Bülent Sarıyıldız (@mbsariyildiz) / Twitter", "date": "", "ddg_snippet": "Since ImageNet -1K is the set of “seen” concepts of ImageNet -CoG, we can evaluate out-of-the-box any model pretrained on ImageNet -1K! In fact, with a computationally efficient evaluation protocol, in our paper we benchmark 31 different models.", "subpage_snippet": "", "source": "twitter.com", "link": "https://twitter.com/mbsariyildiz", "content": "Since ImageNet -1K is the set of “seen” concepts of ImageNet -CoG, we can evaluate out-of-the-box any model pretrained on ImageNet -1K! In fact, with a computationally efficient evaluation protocol, in our paper we benchmark 31 different models."}
+{"idx": 6, "title": "Fake It Till You Make It: Learning Transferable Representations From...", "date": "", "ddg_snippet": "Synthetic ImageNet clones . Synthetic images for Ima - geNet classes have been used recently in a number of re-lated works [2, 48, 67] based on class conditional Genera-tive Adversarial Networks (GANs), such as BigGAN [6]. Besnier et al.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2023/papers/Sariyildiz_Fake_It_Till_You_Make_It_Learning_Transferable_Representations_From_CVPR_2023_paper.pdf", "content": "Synthetic ImageNet clones . Synthetic images for Ima - geNet classes have been used recently in a number of re-lated works [2, 48, 67] based on class conditional Genera-tive Adversarial Networks (GANs), such as BigGAN [6]. Besnier et al."}
+{"idx": 7, "title": "Fake it till you make it: Learning transferable... - Naver Labs Europe", "date": "", "ddg_snippet": "Learning transferable representations from synthetic ImageNet clones .We provide two ResNet50 models pretrained on our synthetic ImageNet clones : ImageNet -100-SD or ImageNet -1K-SD.", "subpage_snippet": "", "source": "europe.naverlabs.com", "link": "https://europe.naverlabs.com/research/computer-vision/imagenet-sd/", "content": "Learning transferable representations from synthetic ImageNet clones .We provide two ResNet50 models pretrained on our synthetic ImageNet clones : ImageNet -100-SD or ImageNet -1K-SD."}
+{"idx": 8, "title": "[2212.08420] Fake it till you make it: Learning transferable...", "date": "", "ddg_snippet": "We show that with minimal and class-agnostic prompt engineering, ImageNet clones are able to close a large part of the gap between models produced by synthetic images and models trained with real images, for the several standard classification benchmarks that we consider in this study.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2212.08420", "content": "We show that with minimal and class-agnostic prompt engineering, ImageNet clones are able to close a large part of the gap between models produced by synthetic images and models trained with real images, for the several standard classification benchmarks that we consider in this study."}
+{"idx": 9, "title": "Mert Bulent Sariyildiz 's Personal Website", "date": "", "ddg_snippet": "[ 2023 -07] - Joined NAVER LABS Europe as a research scientist! ImageNet -SD | Fake it till you make it: Learning(s) from a synthetic ImageNet clone Mert Bulent Sariyildiz , Karteek Alahari, Diane Larlus and Yannis Kalantidis CVPR 2023 .", "subpage_snippet": "", "source": "mbsariyildiz.github.io", "link": "https://mbsariyildiz.github.io/", "content": "[ 2023 -07] - Joined NAVER LABS Europe as a research scientist! ImageNet -SD | Fake it till you make it: Learning(s) from a synthetic ImageNet clone Mert Bulent Sariyildiz , Karteek Alahari, Diane Larlus and Yannis Kalantidis CVPR 2023 ."}
diff --git a/data/sampled_jsons/Schubert_polynomials_n=6_accuracy_Logistic_regression_MLP_Transformer_table_1.jsonl b/data/sampled_jsons/Schubert_polynomials_n=6_accuracy_Logistic_regression_MLP_Transformer_table_1.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..eb2eeb910de6b57b8494e22cdebe2d7f1d398bf0
--- /dev/null
+++ b/data/sampled_jsons/Schubert_polynomials_n=6_accuracy_Logistic_regression_MLP_Transformer_table_1.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Logistic Regression With Polynomial Features - GeeksforGeeks", "date": "", "ddg_snippet": "Logistic regression with polynomial features is a technique used to model complex, non-linear relationships between input variables and the target variable. This approach involves transforming the original input features into higher-degree polynomial features, which can help capture intricate patterns in the data and improve the model's predictive performance. In this article we will ...", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/machine-learning/logistic-regression-with-polynomial-features/", "content": "Logistic regression with polynomial features is a technique used to model complex, non-linear relationships between input variables and the target variable. This approach involves transforming the original input features into higher-degree polynomial features, which can help capture intricate patterns in the data and improve the model's predictive performance. In this article we will ..."}
+{"idx": 1, "title": "How to calculate logistic regression accuracy - Stack Overflow", "date": "", "ddg_snippet": "I am a complete beginner in machine learning and coding in python, and I have been tasked with coding logistic regression from scratch to understand what happens under the hood. So far I have coded...", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/47437893/how-to-calculate-logistic-regression-accuracy", "content": "I am a complete beginner in machine learning and coding in python, and I have been tasked with coding logistic regression from scratch to understand what happens under the hood. So far I have coded..."}
+{"idx": 2, "title": "LogisticRegression — scikit-learn 1.7.1 documentation", "date": "", "ddg_snippet": "LogisticRegression # class sklearn.linear_model.LogisticRegression(penalty='l2', *, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=None, random_state=None, solver='lbfgs', max_iter=100, multi_class='deprecated', verbose=0, warm_start=False, n_jobs=None, l1_ratio=None) [source] # Logistic Regression (aka logit, MaxEnt) classifier. This class implements ...", "subpage_snippet": "", "source": "scikit-learn.org", "link": "https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html", "content": "LogisticRegression # class sklearn.linear_model.LogisticRegression(penalty='l2', *, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=None, random_state=None, solver='lbfgs', max_iter=100, multi_class='deprecated', verbose=0, warm_start=False, n_jobs=None, l1_ratio=None) [source] # Logistic Regression (aka logit, MaxEnt) classifier. This class implements ..."}
+{"idx": 3, "title": "Schubert polynomials Type B/C/D Schubert polynomials Skew ... - SymCat", "date": "", "ddg_snippet": "The quantum Schubert polynomials S ω q (x) is a deformation of the Schubert polynomials by a vector q = (q 1 ,, q n 1 ) These were introduced in [FGP97]. Recall the formula that expresses the Schubert polynomials as sums of products of elementary symmetric functions:", "subpage_snippet": "", "source": "www.symmetricfunctions.com", "link": "https://www.symmetricfunctions.com/schubert.htm", "content": "The quantum Schubert polynomials S ω q (x) is a deformation of the Schubert polynomials by a vector q = (q 1 ,, q n 1 ) These were introduced in [FGP97]. Recall the formula that expresses the Schubert polynomials as sums of products of elementary symmetric functions:"}
+{"idx": 4, "title": "PDF Lecture 26 | Logistic regression - Stanford University", "date": "", "ddg_snippet": "Lecture 26 | Logistic regression 26.1 The logistic regression model Example 26.1. An internet company would like to understand what factors in uence whether a visitor to a webpage clicks on an advertisement. Suppose it has available his-torical data of n ad impressions, each impression corresponding to a single ad being shown to a single visitor.", "subpage_snippet": "", "source": "web.stanford.edu", "link": "https://web.stanford.edu/class/archive/stats/stats200/stats200.1172/Lecture26.pdf", "content": "Lecture 26 | Logistic regression 26.1 The logistic regression model Example 26.1. An internet company would like to understand what factors in uence whether a visitor to a webpage clicks on an advertisement. Suppose it has available his-torical data of n ad impressions, each impression corresponding to a single ad being shown to a single visitor."}
+{"idx": 5, "title": "Logistic Regression - Stat 20", "date": "", "ddg_snippet": "The framework that we used to build a predictive model for regression followed four distinct steps: Decide on the mathematical form of the model: we used a linear model with potential for transformations and polynomials Select a metric that defines the \"best\" fit: we used R 2 or MSE Estimating the coefficients of the model that are best using the training data: we found the coefficients ...", "subpage_snippet": "", "source": "stat20.berkeley.edu", "link": "https://stat20.berkeley.edu/fall-2024/5-prediction/05-logistic-regression/notes.html", "content": "The framework that we used to build a predictive model for regression followed four distinct steps: Decide on the mathematical form of the model: we used a linear model with potential for transformations and polynomials Select a metric that defines the \"best\" fit: we used R 2 or MSE Estimating the coefficients of the model that are best using the training data: we found the coefficients ..."}
+{"idx": 6, "title": "How to Optimize Logistic Regression Performance", "date": "", "ddg_snippet": "Logistic Regression is a widely employed algorithm for binary classification tasks. However, the performance of Logistic Regression models can be significantly impacted by the choice of hyperparameters, which can lead to suboptimal results if not properly tuned.", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/machine-learning/how-to-optimize-logistic-regression-performance/", "content": "Logistic Regression is a widely employed algorithm for binary classification tasks. However, the performance of Logistic Regression models can be significantly impacted by the choice of hyperparameters, which can lead to suboptimal results if not properly tuned."}
+{"idx": 7, "title": "Logistic Regression in Machine Learning - GeeksforGeeks", "date": "", "ddg_snippet": "Logistic Regression is a supervised machine learning algorithm used for classification problems. Unlike linear regression which predicts continuous values it predicts the probability that an input belongs to a specific class. It is used for binary classification where the output can be one of two possible categories such as Yes/No, True/False or 0/1. It uses sigmoid function to convert inputs ...", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/machine-learning/understanding-logistic-regression/", "content": "Logistic Regression is a supervised machine learning algorithm used for classification problems. Unlike linear regression which predicts continuous values it predicts the probability that an input belongs to a specific class. It is used for binary classification where the output can be one of two possible categories such as Yes/No, True/False or 0/1. It uses sigmoid function to convert inputs ..."}
+{"idx": 8, "title": "PDF Schubert Polynomials and Symmetric Functions Notes for The Lisbon ...", "date": "", "ddg_snippet": "1 , 2, . . . , n − 1 , such that π is the ordered product of the transpositions (qi qi + ↔ 1 ). To avoid (the exceedingly rare) confusion with on -line notation, we'll underline permutations in one-line", "subpage_snippet": "", "source": "pi.math.cornell.edu", "link": "https://pi.math.cornell.edu/~allenk/schubnotes.pdf", "content": "1 , 2, . . . , n − 1 , such that π is the ordered product of the transpositions (qi qi + ↔ 1 ). To avoid (the exceedingly rare) confusion with on -line notation, we'll underline permutations in one-line"}
+{"idx": 9, "title": "Lecture 6: Logistic Regression - Department of Computer Science", "date": "", "ddg_snippet": "In this lecture we will learn about the discriminative counterpart to the Gaussian Naive Bayes (Naive Bayes for continuous features).", "subpage_snippet": "", "source": "www.cs.cornell.edu", "link": "https://www.cs.cornell.edu/courses/cs4780/2018fa/lectures/lecturenote06.html", "content": "In this lecture we will learn about the discriminative counterpart to the Gaussian Naive Bayes (Naive Bayes for continuous features)."}
diff --git a/data/sampled_jsons/Schulman_2017_PPO_paper_contains_A2C_performance_results_Atari_baseline_comparison_year_2017.jsonl b/data/sampled_jsons/Schulman_2017_PPO_paper_contains_A2C_performance_results_Atari_baseline_comparison_year_2017.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d6607283379d42de71987a14d9c694783c127129
--- /dev/null
+++ b/data/sampled_jsons/Schulman_2017_PPO_paper_contains_A2C_performance_results_Atari_baseline_comparison_year_2017.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Imagined Autocurricula", "date": "", "ddg_snippet": "The resulting agents have significantly stronger generalization performance than state-of-the-art baselines , demonstrating strong transfer ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.13341v1", "content": "The resulting agents have significantly stronger generalization performance than state-of-the-art baselines , demonstrating strong transfer ..."}
+{"idx": 1, "title": "A Survey of Reinforcement Learning for Software Engineering", "date": "", "ddg_snippet": "We conducted a detailed analysis of the selected papers based on publication trends, distribution of publication venues, etc.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.12483v1", "content": "We conducted a detailed analysis of the selected papers based on publication trends, distribution of publication venues, etc."}
+{"idx": 2, "title": "1 Introduction", "date": "", "ddg_snippet": "Applied to Atari games, our game-playing Python program achieves performance competitive with deep reinforcement learning (RL) baselines while using ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.19506v1", "content": "Applied to Atari games, our game-playing Python program achieves performance competitive with deep reinforcement learning (RL) baselines while using ..."}
+{"idx": 3, "title": "ArCHer: Training Language Model Agents via Hierarchical", "date": "", "ddg_snippet": "Due to this, on-policy methods such as PPO ( Schulman et al., 2017 ) quickly become impractical owing to their inability to reuse data from past ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2402.19446v1", "content": "Due to this, on-policy methods such as PPO ( Schulman et al., 2017 ) quickly become impractical owing to their inability to reuse data from past ..."}
+{"idx": 4, "title": "Simplifying Deep Temporal Difference Learning", "date": "", "ddg_snippet": "... optimisation ( PPO ) ( Schulman et al., 2017 ) has emerged as the de facto choice for RL practitioners, proving to be a strong and efficient baseline ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2407.04811v2", "content": "... optimisation ( PPO ) ( Schulman et al., 2017 ) has emerged as the de facto choice for RL practitioners, proving to be a strong and efficient baseline ..."}
+{"idx": 5, "title": "Spinning Up as a Deep RL Researcher — Spinning Up", "date": "", "ddg_snippet": "Bad hyperparameters can significantly degrade RL performance , but if you ’ re using hyperparameters similar to the ones in papers and standard ...", "subpage_snippet": "", "source": "spinningup.openai.com", "link": "https://spinningup.openai.com/en/latest/spinningup/spinningup.html", "content": "Bad hyperparameters can significantly degrade RL performance , but if you ’ re using hyperparameters similar to the ones in papers and standard ..."}
+{"idx": 6, "title": "CaRL: Learning Scalable Planning Policies with Simple Rewards", "date": "", "ddg_snippet": "In Section 4 , we perform system-level comparisons with prior work and additionally evaluate our method on the nuPlan [ 31 ] simulator.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.17838v3", "content": "In Section 4 , we perform system-level comparisons with prior work and additionally evaluate our method on the nuPlan [ 31 ] simulator."}
+{"idx": 7, "title": "InfoBot: Transfer and Exploration via the Information Bottleneck", "date": "", "ddg_snippet": "This form of “information dropout” has been shown to promote generalization performance [Achille and Soatto, 2016 , Alemi et al., 2017 ] .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/1901.10902v5", "content": "This form of “information dropout” has been shown to promote generalization performance [Achille and Soatto, 2016 , Alemi et al., 2017 ] ."}
+{"idx": 8, "title": "Large Language Models are Learnable Planners for Long-Term", "date": "", "ddg_snippet": "... presents challenges such as instability and susceptibility to overfitting when training RL models from scratch, resulting in sub-optimal performance ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.00843v2", "content": "... presents challenges such as instability and susceptibility to overfitting when training RL models from scratch, resulting in sub-optimal performance ..."}
+{"idx": 9, "title": "Succeed or Learn Slowly: Sample Efficient Off-Policy", "date": "", "ddg_snippet": "... in sparse reward settings, which achieves the best performance , by at least 17% relative increase, and the fastest inference time of all baselines ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.01720v1", "content": "... in sparse reward settings, which achieves the best performance , by at least 17% relative increase, and the fastest inference time of all baselines ..."}
diff --git a/data/sampled_jsons/Score-based_Causal_Representation_Learning_nonlinear_mixing_robust_year_2024.jsonl b/data/sampled_jsons/Score-based_Causal_Representation_Learning_nonlinear_mixing_robust_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d4a26c814ab98110e14ba0b8e0e58c38e13d24a6
--- /dev/null
+++ b/data/sampled_jsons/Score-based_Causal_Representation_Learning_nonlinear_mixing_robust_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF Score-based Causal Representation Learning: Linear and General ...", "date": "", "ddg_snippet": "Abstract This paper addresses intervention- based causal representation learning (CRL) under a general nonparametric latent causal model and an unknown transformation that maps the latent variables to the observed variables. Linear and general transformations are investi-gated. The paper addresses both the identifiability and achievability aspects.", "subpage_snippet": "", "source": "jmlr.org", "link": "https://jmlr.org/papers/volume26/24-0194/24-0194.pdf", "content": "Abstract This paper addresses intervention- based causal representation learning (CRL) under a general nonparametric latent causal model and an unknown transformation that maps the latent variables to the observed variables. Linear and general transformations are investi-gated. The paper addresses both the identifiability and achievability aspects."}
+{"idx": 1, "title": "Score-based Causal Representation Learning with Interventions", "date": "", "ddg_snippet": "This paper studies the causal representation learning problem when the latent causal variables are observed indirectly through an unknown linear transformation. The objectives are: (i) recovering the unknown linear transformation (up to scaling) and (ii) determining the directed acyclic graph (DAG) underlying the latent variables. Sufficient conditions for DAG recovery are established, and it ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2301.08230", "content": "This paper studies the causal representation learning problem when the latent causal variables are observed indirectly through an unknown linear transformation. The objectives are: (i) recovering the unknown linear transformation (up to scaling) and (ii) determining the directed acyclic graph (DAG) underlying the latent variables. Sufficient conditions for DAG recovery are established, and it ..."}
+{"idx": 2, "title": "PDF Score-based Causal Representation Learning from Interventions ...", "date": "", "ddg_snippet": "Abstract This paper focuses on causal representation learning (CRL) under a general non-parametric causal latent model and a general transformation model that maps the latent data to the observational data. It establishes identifiability and achievability results using two (stochastic) hard uncoupled interventions per node in the latent causal graph. Notably, one does not know which pair of ...", "subpage_snippet": "", "source": "sites.ecse.rpi.edu", "link": "https://sites.ecse.rpi.edu/~tajer/papers/C93.pdf", "content": "Abstract This paper focuses on causal representation learning (CRL) under a general non-parametric causal latent model and a general transformation model that maps the latent data to the observational data. It establishes identifiability and achievability results using two (stochastic) hard uncoupled interventions per node in the latent causal graph. Notably, one does not know which pair of ..."}
+{"idx": 3, "title": "PDF Score-based CRL from Interventions - NeurIPS", "date": "", "ddg_snippet": "References B. Varıcı, E. Acartürk, K. Shanmugam, and A. Tajer. \" Score-based causal representation learning from interventions: Nonparametric identifiability ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/media/neurips-2023/Slides/74252.pdf", "content": "References B. Varıcı, E. Acartürk, K. Shanmugam, and A. Tajer. \" Score-based causal representation learning from interventions: Nonparametric identifiability ..."}
+{"idx": 4, "title": "Score-based Causal Representation Learning | Burak Varıcı", "date": "", "ddg_snippet": "This paper studies the causal representation learning problem when the latent causal variables are observed indirectly through an unknown linear transformation.", "subpage_snippet": "", "source": "bvarici.github.io", "link": "https://bvarici.github.io/projects/CRL/", "content": "This paper studies the causal representation learning problem when the latent causal variables are observed indirectly through an unknown linear transformation."}
+{"idx": 5, "title": "Robustness of nonlinear representation learning | Proceedings of the ...", "date": "", "ddg_snippet": "We study the problem of unsupervised representation learning in slightly misspecified settings, and thus formalize the study of robustness of non-linear representation learning . We focus on the case where the mixing is close to a local isometry in a suitable distance and show based on existing rigidity results that the mixing can be identified up to linear transformations and small errors. In ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3692070.3692261", "content": "We study the problem of unsupervised representation learning in slightly misspecified settings, and thus formalize the study of robustness of non-linear representation learning . We focus on the case where the mixing is close to a local isometry in a suitable distance and show based on existing rigidity results that the mixing can be identified up to linear transformations and small errors. In ..."}
+{"idx": 6, "title": "PDF Causal Representation Learning from General Environments under ...", "date": "", "ddg_snippet": "Abstract Causal representation learning aims to re-cover the latent causal variables and their causal relations, typically represented by di-rected acyclic graphs (DAGs), from low-level observations such as image pixels. A pre-vailing line of research exploits multiple en-vironments, which assume how data distri-butions change, including single-node inter-ventions, coupled interventions, or ...", "subpage_snippet": "", "source": "raw.githubusercontent.com", "link": "https://raw.githubusercontent.com/mlresearch/v258/main/assets/ng25a/ng25a.pdf", "content": "Abstract Causal representation learning aims to re-cover the latent causal variables and their causal relations, typically represented by di-rected acyclic graphs (DAGs), from low-level observations such as image pixels. A pre-vailing line of research exploits multiple en-vironments, which assume how data distri-butions change, including single-node inter-ventions, coupled interventions, or ..."}
+{"idx": 7, "title": "Score-based Causal Representation Learning from Interventions ...", "date": "", "ddg_snippet": "This paper focuses on causal representation learning (CRL) under a general nonparametric causal latent model and a general transformation model that maps the latent data to the observational data. It establishes identifiability and achievability results using two (stochastic) hard uncoupled interventions per node in the latent causal graph.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=MytNJ6lXAV", "content": "This paper focuses on causal representation learning (CRL) under a general nonparametric causal latent model and a general transformation model that maps the latent data to the observational data. It establishes identifiability and achievability results using two (stochastic) hard uncoupled interventions per node in the latent causal graph."}
+{"idx": 8, "title": "PDF Learning Linear Causal Representations from Interventions under ... - NIPS", "date": "", "ddg_snippet": "Learning Linear Causal Representations from Interventions under General Nonlinear Mixing Simon Buchholz* (MPI), Goutham Rajendran* (CMU), Elan Rosenfeld (CMU), Bryon Aragam (UChicago), Bernhard Sch ̈olkopf (MPI), Pradeep Ravikumar (CMU)", "subpage_snippet": "", "source": "nips.cc", "link": "https://nips.cc/media/neurips-2023/Slides/73823.pdf", "content": "Learning Linear Causal Representations from Interventions under General Nonlinear Mixing Simon Buchholz* (MPI), Goutham Rajendran* (CMU), Elan Rosenfeld (CMU), Bryon Aragam (UChicago), Bernhard Sch ̈olkopf (MPI), Pradeep Ravikumar (CMU)"}
+{"idx": 9, "title": "Causality-Inspired Robustness for Nonlinear Models via Representation ...", "date": "", "ddg_snippet": "In this work, we propose a nonlinear method under a causal framework by incorporating recent developments in identifiable representation learning and establish a distributional robustness guarantee. To our best knowledge, this is the first causality-inspired robustness method with such a finite-radius robustness guarantee in nonlinear settings.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2505.12868", "content": "In this work, we propose a nonlinear method under a causal framework by incorporating recent developments in identifiable representation learning and establish a distributional robustness guarantee. To our best knowledge, this is the first causality-inspired robustness method with such a finite-radius robustness guarantee in nonlinear settings."}
diff --git a/data/sampled_jsons/Score-based_Generative_Modeling_through_Stochastic_Differential_Equations_Song_et_al_2021.jsonl b/data/sampled_jsons/Score-based_Generative_Modeling_through_Stochastic_Differential_Equations_Song_et_al_2021.jsonl
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index 0000000000000000000000000000000000000000..1c7276a46a5a548b09438a321486270022c2a570
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+++ b/data/sampled_jsons/Score-based_Generative_Modeling_through_Stochastic_Differential_Equations_Song_et_al_2021.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2011.13456] Score - Based Generative Modeling through ...", "date": "", "ddg_snippet": "Title: Score - Based Generative Modeling through Stochastic Differential Equations . Authors:Yang Song , Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, Ben Poole.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2011.13456", "content": "Title: Score - Based Generative Modeling through Stochastic Differential Equations . Authors:Yang Song , Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, Ben Poole."}
+{"idx": 1, "title": "Score - Based Generative Modeling through Stochastic Differential ...", "date": "", "ddg_snippet": "back arrow Go to ICLR 2021 Conference homepage. Score - Based Generative Modeling through Stochastic Differential Equations Download PDF.Keywords: generative models , score - based generative models , stochastic differential equations , score matching, diffusion.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=PxTIG12RRHS", "content": "back arrow Go to ICLR 2021 Conference homepage. Score - Based Generative Modeling through Stochastic Differential Equations Download PDF.Keywords: generative models , score - based generative models , stochastic differential equations , score matching, diffusion."}
+{"idx": 2, "title": "ICLR 2021 Score - Based Generative Modeling through Stochastic ...", "date": "", "ddg_snippet": "By leveraging advances in score - based generative modeling , we can accurately estimate these scores with neural networks, and use numerical SDE solvers to generate samples.", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2021/oral/3402", "content": "By leveraging advances in score - based generative modeling , we can accurately estimate these scores with neural networks, and use numerical SDE solvers to generate samples."}
+{"idx": 3, "title": "Stochastic Differential Equations and Diffusion Models | VanillaBug", "date": "", "ddg_snippet": "Score - based generative modeling through stochastic differential equations . ArXiv Preprint ArXiv:2011.13456.", "subpage_snippet": "", "source": "www.vanillabug.com", "link": "https://www.vanillabug.com/posts/sde/", "content": "Score - based generative modeling through stochastic differential equations . ArXiv Preprint ArXiv:2011.13456."}
+{"idx": 4, "title": "A New Class of Generative Models . This article will discuss... | Medium", "date": "", "ddg_snippet": "The Score - based Generative Model via SDE comprises two parts. A forward-time differential equation (figure 3) slowly injects random noise into an image to turn it into a pure Gaussian distribution (white noise). The forward differential equation has a stochastic term and a diffusion...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@ishansrivastava_11121/a-new-class-of-generative-models-4a1a83f2bcc2", "content": "The Score - based Generative Model via SDE comprises two parts. A forward-time differential equation (figure 3) slowly injects random noise into an image to turn it into a pure Gaussian distribution (white noise). The forward differential equation has a stochastic term and a diffusion..."}
+{"idx": 5, "title": "Diffusion Schrödinger bridges for score - based generative modeling", "date": "", "ddg_snippet": "Score - Based Generative Modeling through Stochastic Differential Equations . Score - based generative models aka denoising diffusion models (Ho et al ., 2020; Song et al ., 2021 ) provide SOTA results in a large number of domains.", "subpage_snippet": "", "source": "www.slideshare.net", "link": "https://www.slideshare.net/slideshow/diffusion-schrdinger-bridges-for-scorebased-generative-modeling/251937201", "content": "Score - Based Generative Modeling through Stochastic Differential Equations . Score - based generative models aka denoising diffusion models (Ho et al ., 2020; Song et al ., 2021 ) provide SOTA results in a large number of domains."}
+{"idx": 6, "title": "PowerPoint 演示文稿", "date": "", "ddg_snippet": "Song et al . Score - based generative modeling through stochastic differential equations , ICLR 2021 .", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/media/icml-2022/Slides/16752.pdf", "content": "Song et al . Score - based generative modeling through stochastic differential equations , ICLR 2021 ."}
+{"idx": 7, "title": "PowerPoint 演示文稿", "date": "", "ddg_snippet": "Song et al . Score - based generative modeling through stochastic differential equations , ICLR 2021 .", "subpage_snippet": "", "source": "ml.cs.tsinghua.edu.cn", "link": "https://ml.cs.tsinghua.edu.cn/~fanbao/Application-DPM.pdf", "content": "Song et al . Score - based generative modeling through stochastic differential equations , ICLR 2021 ."}
+{"idx": 8, "title": "Machine learning emulation of", "date": "", "ddg_snippet": "Score - Based Generative Modeling through Stochastic Differential Equations . Song ,Y. et al ., 2021 . Kendon, E. J. et al . 2021 . ‘Update to the UKCP Local (2.2km) projections: Science report’, Met Office Hadley Centre, Exeter, UK.", "subpage_snippet": "", "source": "www.bom.gov.au", "link": "http://www.bom.gov.au/research/workshop/2024/presentations/1345+-+Machine+Learning+for+simulation+-+Henry+Addison+-+slides.pdf", "content": "Score - Based Generative Modeling through Stochastic Differential Equations . Song ,Y. et al ., 2021 . Kendon, E. J. et al . 2021 . ‘Update to the UKCP Local (2.2km) projections: Science report’, Met Office Hadley Centre, Exeter, UK."}
+{"idx": 9, "title": "GitHub - mbreuss/diffusion-literature-for-robotics: Summary of key...", "date": "", "ddg_snippet": "Song , Yang, et al . \" Score - Based Generative Modeling through Stochastic Differential Equations .\"Carvalho, J. et al . Conditioned Score - Based Models for Learning Collision-Free Trajectory Generation , NeurIPS 2022 Workshop on Score - Based Methods.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/mbreuss/diffusion-literature-for-robotics", "content": "Song , Yang, et al . \" Score - Based Generative Modeling through Stochastic Differential Equations .\"Carvalho, J. et al . Conditioned Score - Based Models for Learning Collision-Free Trajectory Generation , NeurIPS 2022 Workshop on Score - Based Methods."}
diff --git a/data/sampled_jsons/ScoreDec_Wu_et_al._2024_abstract.jsonl b/data/sampled_jsons/ScoreDec_Wu_et_al._2024_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..cfa8c3cd00c90a8280fd8dc744914bfe2f13e660
--- /dev/null
+++ b/data/sampled_jsons/ScoreDec_Wu_et_al._2024_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "arXiv:2401.12160v1 [eess.AS] 22 Jan 2024", "date": "", "ddg_snippet": "by YC Wu · 2024 · Cited by 7 — Both the objec- tive and subjective experimental results show that ScoreDec with a. 24 kbps bitrate encodes and decodes full-band 48 kHz speech ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2401.12160", "content": "by YC Wu · 2024 · Cited by 7 — Both the objec- tive and subjective experimental results show that ScoreDec with a. 24 kbps bitrate encodes and decodes full-band 48 kHz speech ..."}
+{"idx": 1, "title": "FlowDec: A flow-based full-band general audio codec with ...", "date": "", "ddg_snippet": "3 Mar 2025 — We address the shortcomings of ScoreDec (Wu et al., 2024 ) by designing and training for general audio beyond only speech, reducing bitrates ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.01485v1", "content": "3 Mar 2025 — We address the shortcomings of ScoreDec (Wu et al., 2024 ) by designing and training for general audio beyond only speech, reducing bitrates ..."}
+{"idx": 2, "title": "FlowDec: A flow-based full-band general audio codec with ...", "date": "", "ddg_snippet": "by S Welker · Cited by 8 — While ScoreDec (Wu et al., 2024 ) found improved phase compared to AudioDec, after investigating further, we found that there is indeed no clear advantage ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=uxDFlPGRLX", "content": "by S Welker · Cited by 8 — While ScoreDec (Wu et al., 2024 ) found improved phase compared to AudioDec, after investigating further, we found that there is indeed no clear advantage ..."}
+{"idx": 3, "title": "FLOWDEC: A FLOW-BASED FULL-BAND GENERAL", "date": "", "ddg_snippet": "We address the shortcomings of ScoreDec (Wu et al., 2024 ) by designing and training for general audio beyond only speech, reducing bitrates from 24 kbit/s to ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/46581252e0bf80bf4efd8fcbc4002f8627f498bb.pdf", "content": "We address the shortcomings of ScoreDec (Wu et al., 2024 ) by designing and training for general audio beyond only speech, reducing bitrates from 24 kbit/s to ..."}
+{"idx": 4, "title": "Rethinking Contrastive Learning in Graph Anomaly Detection", "date": "", "ddg_snippet": "Within graph analytics, Graph. Anomaly Detection (GAD) has emerged as a critical area of research [Xiang et al ., 2023; Xiang et al ., 2024 ]. ... [ Wu et al .,2019] ...", "subpage_snippet": "", "source": "www.ijcai.org", "link": "https://www.ijcai.org/proceedings/2025/0335.pdf", "content": "Within graph analytics, Graph. Anomaly Detection (GAD) has emerged as a critical area of research [Xiang et al ., 2023; Xiang et al ., 2024 ]. ... [ Wu et al .,2019] ..."}
+{"idx": 5, "title": "Reference 1 | PDF | Data Compression | Codec", "date": "", "ddg_snippet": "Yi-Chiao Wu , Dejan Marković, Steven Krenn, Israel D. Gebru, and Alexander Richard Codec Avatars Lab, Meta, Pittsburgh PA, USA. Abstract —Neural audio codecs ...", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/910710565/Reference-1", "content": "Yi-Chiao Wu , Dejan Marković, Steven Krenn, Israel D. Gebru, and Alexander Richard Codec Avatars Lab, Meta, Pittsburgh PA, USA. Abstract —Neural audio codecs ..."}
+{"idx": 6, "title": "Navigating the Complexity of Scoring Systems in Sepsis ...", "date": "", "ddg_snippet": "by V Reddy · 2024 · Cited by 14 — Abstract. This comprehensive review navigates the intricate landscape of sepsis scoring systems , aiming to provide healthcare professionals and ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC10932875/", "content": "by V Reddy · 2024 · Cited by 14 — Abstract. This comprehensive review navigates the intricate landscape of sepsis scoring systems , aiming to provide healthcare professionals and ..."}
+{"idx": 7, "title": "ICLR 2025 Thursday 04/24", "date": "", "ddg_snippet": "In this talk, I will highlight several recent directions of work that are making progress in addressing these challenges, including methods for robustness to ...", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2025/day/4/24", "content": "In this talk, I will highlight several recent directions of work that are making progress in addressing these challenges, including methods for robustness to ..."}
+{"idx": 8, "title": "Daily Papers", "date": "", "ddg_snippet": "4 days ago — We introduce Perceptual-Initialization (PI), a paradigm shift in visual representation learning that incorporates human perceptual structure ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=perceptual+structure", "content": "4 days ago — We introduce Perceptual-Initialization (PI), a paradigm shift in visual representation learning that incorporates human perceptual structure ..."}
+{"idx": 9, "title": "ComplexDec: A Domain-robust High-fidelity Neural Audio Codec", "date": "", "ddg_snippet": "To the best of our knowledge, ComplexDec is the first paper to explore the out-of-domain robustness of neural codecs, although several codecs [ 21 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.02019v1", "content": "To the best of our knowledge, ComplexDec is the first paper to explore the out-of-domain robustness of neural codecs, although several codecs [ 21 ..."}
diff --git a/data/sampled_jsons/Sean_Kerner_CrowdStrike_2024_report.jsonl b/data/sampled_jsons/Sean_Kerner_CrowdStrike_2024_report.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3bb09175568dd1b2f0b025775a63b0c75e748d6b
--- /dev/null
+++ b/data/sampled_jsons/Sean_Kerner_CrowdStrike_2024_report.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "CrowdStrike 2024 Global Threat Report", "date": "", "ddg_snippet": "CrowdStrike 2024 Global Threat Report Uncover the Adversaries Hiding in Plain Sight Tracking 230+ adversaries and noting a record eCrime breakout time, the CrowdStrike 2024 Global Threat Report unveils an alarming rise in covert activity and a cyber threat landscape dominated by stealth.", "subpage_snippet": "", "source": "www.crowdstrike.com", "link": "https://www.crowdstrike.com/en-us/resources/reports/crowdstrike-2024-global-threat-report/", "content": "CrowdStrike 2024 Global Threat Report Uncover the Adversaries Hiding in Plain Sight Tracking 230+ adversaries and noting a record eCrime breakout time, the CrowdStrike 2024 Global Threat Report unveils an alarming rise in covert activity and a cyber threat landscape dominated by stealth."}
+{"idx": 1, "title": "GLOBAL THREAT REPORT - CyberPeople", "date": "", "ddg_snippet": "We are entering an era of a cyber arms race where AI will amplify the impact for both the security professional and the adversary. Organizations cannot afford to fall behind, and the legacy technology of yesterday is no match for the speed and sophistication of the modern adversary. With the release of the CrowdStrike 2024 Global Threat Report , our elite Counter Adversary Operations team is ...", "subpage_snippet": "", "source": "cyberpeople.tech", "link": "https://cyberpeople.tech/reports/GlobalThreatReport2024.pdf", "content": "We are entering an era of a cyber arms race where AI will amplify the impact for both the security professional and the adversary. Organizations cannot afford to fall behind, and the legacy technology of yesterday is no match for the speed and sophistication of the modern adversary. With the release of the CrowdStrike 2024 Global Threat Report , our elite Counter Adversary Operations team is ..."}
+{"idx": 2, "title": "CrowdStrike outage explained: What caused it and what’s next Explaining the largest IT outage in history and what’s next 2024 CrowdStrike Threat Report 2024 Global Threat Report - spaceproject.govexec.com CrowdStrike 2024 Global Threat Report 2024 CrowdStrike Threat Report - techresearchinfo.com 2024 CrowdStrike Global Threat Report - Cyber Risk Leaders 2024 CrowdStrike Threat Report - techresearchinfo.com CrowdStrike outage explained: What caused it and what’s next 2024 CrowdStrike Global Threat Report - Cyber Risk Leaders 2024 CrowdStrike Global Threat Report - Cyber Risk Leaders", "date": "", "ddg_snippet": "Oct 29, 2024 · What might be considered the largest IT outage in history was triggered by a botched software update from security vendor CrowdStrike , affecting millions of Windows systems around the world. Insurers estimate the outage will cost U.S. Fortune 500 companies $5.4 billion. The outage occurred July 19, 2024 , with millions of Windows systems failing and showing the infamous blue screen of death ... Jul 22, 2024 · IT expert Sean Michael Kerner shares an article on TechTarget explaining the outage, the businesses affected, and recovery times for businesses to get “back to normal”. What caused the outage? The CrowdStrike Falcon platform was the main culprit. The CrowdStrike 2024 Global Threat Report shows this transformation from malware-based attacks in full efect, with 75% of attacks observed by CrowdStrike in 2023 being “malware-free” compared with 40% in 2019. The CrowdStrike 2024 Global Threat Report sheds light on the standout trends from last year, how adversaries’ activities and motivations are evolving and the ways CrowdStrike anticipates the threat landscape will evolve in the coming year. What is the CrowdStrike 2024 Global Threat Report? Tracking 230+ adversaries and noting a record eCrime breakout time, the CrowdStrike 2024 Global Threat Report unveils an alarming rise in covert activity and a cyber threat landscape dominated by stealth. What is CrowdStrike 2024? According to the CrowdStrike 2024 Global Threat Report , identity-based and social engineering attacks surged in 2023. To counter these threats, implement technology that can detect and correlate threats across identity, endpoint, and cloud environments. What does CrowdStrike report tell us about cyberattacks in 2024? In the 10th annual edition of the cybersecurity leader’s seminal report, CrowdStrike highlights activity from some of the 230+ prolific threat groups that it tracks today. Key findings in the 2024 report include: Dramatic Increase in Attack Velocity : The speed of cyberattacks continues to accelerate at an alarming rate. Is CrowdStrike malware-free? The CrowdStrike 2024 Global Threat Report shows this transformation from malware-based attacks in full efect, with 75% of attacks observed by CrowdStrike in 2023 being “malware-free” compared with 40% in 2019. Was CrowdStrike a cyberattack? As of July 29, 2024, CrowdStrike reported that approximately 99% of affected Windows sensors were back online. While the outage was not due to a cyberattack, threat actors have taken advantage of the incident. According to a blog post from CrowdStrike, the security vendor has received reports of the following malicious activity: What are the biggest threats in 2024? The report also details the biggest threats on the horizon for 2024, including the disruption of global elections and the exploitation of generative AI to lower the barrier of entry and launch more sophisticated attacks. Feb 22, 2024 · CrowdStrike announced the findings of the 2024 CrowdStrike Global Threat Report , highlighting a surge in adversaries leveraging stolen identity credentials to exploit gaps in cloud environments and maximize the stealth, speed and impact of cyberattacks.", "subpage_snippet": "", "source": "www.techtarget.com", "link": "https://www.techtarget.com/WhatIs/feature/Explaining-the-largest-IT-outage-in-history-and-whats-next", "content": "Oct 29, 2024 · What might be considered the largest IT outage in history was triggered by a botched software update from security vendor CrowdStrike , affecting millions of Windows systems around the world. Insurers estimate the outage will cost U.S. Fortune 500 companies $5.4 billion. The outage occurred July 19, 2024 , with millions of Windows systems failing and showing the infamous blue screen of death ... Jul 22, 2024 · IT expert Sean Michael Kerner shares an article on TechTarget explaining the outage, the businesses affected, and recovery times for businesses to get “back to normal”. What caused the outage? The CrowdStrike Falcon platform was the main culprit. The CrowdStrike 2024 Global Threat Report shows this transformation from malware-based attacks in full efect, with 75% of attacks observed by CrowdStrike in 2023 being “malware-free” compared with 40% in 2019. The CrowdStrike 2024 Global Threat Report sheds light on the standout trends from last year, how adversaries’ activities and motivations are evolving and the ways CrowdStrike anticipates the threat landscape will evolve in the coming year. What is the CrowdStrike 2024 Global Threat Report? Tracking 230+ adversaries and noting a record eCrime breakout time, the CrowdStrike 2024 Global Threat Report unveils an alarming rise in covert activity and a cyber threat landscape dominated by stealth. What is CrowdStrike 2024? According to the CrowdStrike 2024 Global Threat Report , identity-based and social engineering attacks surged in 2023. To counter these threats, implement technology that can detect and correlate threats across identity, endpoint, and cloud environments. What does CrowdStrike report tell us about cyberattacks in 2024? In the 10th annual edition of the cybersecurity leader’s seminal report, CrowdStrike highlights activity from some of the 230+ prolific threat groups that it tracks today. Key findings in the 2024 report include: Dramatic Increase in Attack Velocity : The speed of cyberattacks continues to accelerate at an alarming rate. Is CrowdStrike malware-free? The CrowdStrike 2024 Global Threat Report shows this transformation from malware-based attacks in full efect, with 75% of attacks observed by CrowdStrike in 2023 being “malware-free” compared with 40% in 2019. Was CrowdStrike a cyberattack? As of July 29, 2024, CrowdStrike reported that approximately 99% of affected Windows sensors were back online. While the outage was not due to a cyberattack, threat actors have taken advantage of the incident. According to a blog post from CrowdStrike, the security vendor has received reports of the following malicious activity: What are the biggest threats in 2024? The report also details the biggest threats on the horizon for 2024, including the disruption of global elections and the exploitation of generative AI to lower the barrier of entry and launch more sophisticated attacks. Feb 22, 2024 · CrowdStrike announced the findings of the 2024 CrowdStrike Global Threat Report , highlighting a surge in adversaries leveraging stolen identity credentials to exploit gaps in cloud environments and maximize the stealth, speed and impact of cyberattacks."}
+{"idx": 3, "title": "2024 CrowdStrike Threat Report", "date": "", "ddg_snippet": "The CrowdStrike 2024 Global Threat Report shows this transformation from malware-based attacks in full efect, with 75% of attacks observed by CrowdStrike in 2023 being “malware-free” compared with 40% in 2019.", "subpage_snippet": "", "source": "www.techresearchinfo.com", "link": "https://www.techresearchinfo.com/whitepaper/2024-crowdstrike-threat-report.pdf", "content": "The CrowdStrike 2024 Global Threat Report shows this transformation from malware-based attacks in full efect, with 75% of attacks observed by CrowdStrike in 2023 being “malware-free” compared with 40% in 2019."}
+{"idx": 4, "title": "2024 Global Threat Report - spaceproject.govexec.com", "date": "", "ddg_snippet": "The CrowdStrike 2024 Global Threat Report sheds light on the standout trends from last year, how adversaries’ activities and motivations are evolving and the ways CrowdStrike anticipates the threat landscape will evolve in the coming year.", "subpage_snippet": "", "source": "spaceproject.govexec.com", "link": "https://spaceproject.govexec.com/assets/2024-global-threat-report/portal/", "content": "The CrowdStrike 2024 Global Threat Report sheds light on the standout trends from last year, how adversaries’ activities and motivations are evolving and the ways CrowdStrike anticipates the threat landscape will evolve in the coming year."}
+{"idx": 5, "title": "2024 CrowdStrike Global Threat Report - Cyber Risk Leaders", "date": "", "ddg_snippet": "Feb 22, 2024 · CrowdStrike announced the findings of the 2024 CrowdStrike Global Threat Report , highlighting a surge in adversaries leveraging stolen identity credentials to exploit gaps in cloud environments and maximize the stealth, speed and impact of cyberattacks.", "subpage_snippet": "", "source": "cyberriskleaders.com", "link": "https://cyberriskleaders.com/2024-crowdstrike-global-threat-report/", "content": "Feb 22, 2024 · CrowdStrike announced the findings of the 2024 CrowdStrike Global Threat Report , highlighting a surge in adversaries leveraging stolen identity credentials to exploit gaps in cloud environments and maximize the stealth, speed and impact of cyberattacks."}
+{"idx": 6, "title": "CrowdStrike outage explained: What caused it and what’s next", "date": "", "ddg_snippet": "Sean Michael Kerner . Published: 29 Oct 2024 . What might be considered the largest IT outage in history was triggered by a botched software update from security vendor CrowdStrike , affecting millions of Windows systems around the world.", "subpage_snippet": "", "source": "www.techtarget.com", "link": "https://www.techtarget.com/whatis/feature/Explaining-the-largest-IT-outage-in-history-and-whats-next", "content": "Sean Michael Kerner . Published: 29 Oct 2024 . What might be considered the largest IT outage in history was triggered by a botched software update from security vendor CrowdStrike , affecting millions of Windows systems around the world."}
+{"idx": 7, "title": "CrowdStrike blames outage on content... | Computer Weekly", "date": "", "ddg_snippet": "CrowdStrike publishes the preliminary findings of what will be a lengthy investigation into the root causes of the failed 19 July update that caused Windows computers to crash all over the world. Share this item with your network", "subpage_snippet": "", "source": "www.computerweekly.com", "link": "https://www.computerweekly.com/news/366598755/CrowdStrike-blames-outage-on-content-configuration-update", "content": "CrowdStrike publishes the preliminary findings of what will be a lengthy investigation into the root causes of the failed 19 July update that caused Windows computers to crash all over the world. Share this item with your network"}
+{"idx": 8, "title": "Unraveling the 2024 CrowdStrike Incident: How a", "date": "", "ddg_snippet": "Although the 2024 CrowdStrike incident was unrelated to a cyberattack, it served as a reminder of the potential outcomes of flawed update processes and insufficient testing procedures.[3]. S. M. Kerner , \" CrowdStrike outage explained: What caused it and what’s next,\" TechTarget, Oct.", "subpage_snippet": "", "source": "philpapers.org", "link": "https://philpapers.org/archive/VENUTN.pdf", "content": "Although the 2024 CrowdStrike incident was unrelated to a cyberattack, it served as a reminder of the potential outcomes of flawed update processes and insufficient testing procedures.[3]. S. M. Kerner , \" CrowdStrike outage explained: What caused it and what’s next,\" TechTarget, Oct."}
+{"idx": 9, "title": "Understanding CrowdStrike Virus Risks for Smart Investors", "date": "", "ddg_snippet": "The July 2024 CrowdStrike code update debacle highlighted how a single oversight can cascade into a global outage.", "subpage_snippet": "", "source": "www.fairvalue-calculator.com", "link": "https://www.fairvalue-calculator.com/en/understanding-crowdstrike-virus-risks-investors-guidepost-to-cybersecurity-and-stocks/", "content": "The July 2024 CrowdStrike code update debacle highlighted how a single oversight can cascade into a global outage."}
diff --git a/data/sampled_jsons/Section_3.1_systematization_operationalization_benefit_Evaluating_Generative_AI_Systems_Is_a_Social_.jsonl b/data/sampled_jsons/Section_3.1_systematization_operationalization_benefit_Evaluating_Generative_AI_Systems_Is_a_Social_.jsonl
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index 0000000000000000000000000000000000000000..b25009e9a7d0e6ea8cc3b25835f99a5b85e1baf7
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+{"idx": 0, "title": "Position: Evaluating Generative AI Systems Is a Social ...", "date": "", "ddg_snippet": "by H Wallach · 2025 · Cited by 10 — Evaluating GenAI is a social science challenge because measurement tasks lack rigor, concepts are abstract, and are similar to social science measurement tasks.", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/pdf/2502.00561", "content": "by H Wallach · 2025 · Cited by 10 — Evaluating GenAI is a social science challenge because measurement tasks lack rigor, concepts are abstract, and are similar to social science measurement tasks."}
+{"idx": 1, "title": "Toward Valid Measurement Of (Un)fairness For Generative AI", "date": "", "ddg_snippet": "by KL Truong · 2025 — In this work, we propose a process for systematization by decom- posing unfairness into clear and approachable components that facilitate ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2507.04641", "content": "by KL Truong · 2025 — In this work, we propose a process for systematization by decom- posing unfairness into clear and approachable components that facilitate ..."}
+{"idx": 2, "title": "Toward Understanding the Role of Generative AI in ...", "date": "", "ddg_snippet": "by G Yu · 2025 — This study presents a systematic literature review to explore the emerging role of Generative Artificial Intelligence (GAI) in ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2666920X25001109", "content": "by G Yu · 2025 — This study presents a systematic literature review to explore the emerging role of Generative Artificial Intelligence (GAI) in ..."}
+{"idx": 3, "title": "Evaluating Generative AI Systems is a Social Science", "date": "", "ddg_snippet": "Across academia, industry, and government [e.g., 22 , 10 , 23 ] , there is an increasing awareness that the measurement tasks involved in evaluating ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.10939v1", "content": "Across academia, industry, and government [e.g., 22 , 10 , 23 ] , there is an increasing awareness that the measurement tasks involved in evaluating ..."}
+{"idx": 4, "title": "Position: Evaluating Generative AI Systems is a Social Science", "date": "", "ddg_snippet": "... argue that the ML community would benefit from learning from and drawing on the social sciences when developing and using measurement instruments for ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.00561v1", "content": "... argue that the ML community would benefit from learning from and drawing on the social sciences when developing and using measurement instruments for ..."}
+{"idx": 5, "title": "Generative AI Needs Adaptive Governance", "date": "", "ddg_snippet": "The paper is structured as follows: In Section 2, we explore in more detail what’s different about generative AI that warrants an adaptive ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.04554v1", "content": "The paper is structured as follows: In Section 2, we explore in more detail what’s different about generative AI that warrants an adaptive ..."}
+{"idx": 6, "title": "Impact of Generative AI on Industries and Organizations", "date": "", "ddg_snippet": "... an organization s successful AI envelopment depends on the interaction of social and technical factors, thus extending the literature s focus beyond ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/392620106_Impact_of_Generative_AI_on_Industries_and_Organizations", "content": "... an organization s successful AI envelopment depends on the interaction of social and technical factors, thus extending the literature s focus beyond ..."}
+{"idx": 7, "title": "Generative AI in Singapore | The Oxford Handbook of the", "date": "", "ddg_snippet": "The Oxford Handbook of the Foundations and Regulation of Generative AI ... Literary Studies (Fiction, Novelists, and Prose Writers)", "subpage_snippet": "", "source": "academic.oup.com", "link": "https://academic.oup.com/edited-volume/59908/chapter/512470725", "content": "The Oxford Handbook of the Foundations and Regulation of Generative AI ... Literary Studies (Fiction, Novelists, and Prose Writers)"}
+{"idx": 8, "title": "Estimating the environmental impact of Generative-AI services", "date": "", "ddg_snippet": "Regarding the AI life cycle -which consists of the phases depicted in Figure 1 -many studies limit themselves to the learning/training phase of AI [ 3 ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/380762688_Estimating_the_environmental_impact_of_Generative-AI_services_using_an_LCA-based_methodology", "content": "Regarding the AI life cycle -which consists of the phases depicted in Figure 1 -many studies limit themselves to the learning/training phase of AI [ 3 ..."}
+{"idx": 9, "title": "Generative Artificial Intelligence in Adaptive Social", "date": "", "ddg_snippet": "... articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the ...", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/2227-9717/13/4/1174", "content": "... articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the ..."}
diff --git a/data/sampled_jsons/Section_3.3_license_conflict_Llama2_Llama3_clause.jsonl b/data/sampled_jsons/Section_3.3_license_conflict_Llama2_Llama3_clause.jsonl
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index 0000000000000000000000000000000000000000..60a6050ea9d66622db1f2e6be52b7fdbed82c5d1
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+++ b/data/sampled_jsons/Section_3.3_license_conflict_Llama2_Llama3_clause.jsonl
@@ -0,0 +1,2 @@
+{"idx": 0, "title": "They've Stolen My GPL-Licensed Model!", "date": "", "ddg_snippet": "by M Duan · 2024 · Cited by 2 — Entities of the class Report are gen- reated to present these results in RDF format (reflecting Question. (iii); see Section 3.3 for details).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2412.11483", "content": "by M Duan · 2024 · Cited by 2 — Entities of the class Report are gen- reated to present these results in RDF format (reflecting Question. (iii); see Section 3.3 for details)."}
+{"idx": 1, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/Section_4.3_LiDAR_depth_information_occlusion_handling_pedestrian_motion_reconstruction.jsonl b/data/sampled_jsons/Section_4.3_LiDAR_depth_information_occlusion_handling_pedestrian_motion_reconstruction.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4edc18dcf625c620d8a798e0954046091fb61294
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+++ b/data/sampled_jsons/Section_4.3_LiDAR_depth_information_occlusion_handling_pedestrian_motion_reconstruction.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Bridging 2D and 3D Object Detection: Advances in Occlusion ...", "date": "", "ddg_snippet": "This paper presents a comparative review of recent 2D and 3D detection models, focusing on their occlusion-handling capabilities and the impact of sensor modalities such as stereo vision, Time-of-Flight (ToF) cameras, and LiDAR .", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/org/science/article/pii/S1526149225002024", "content": "This paper presents a comparative review of recent 2D and 3D detection models, focusing on their occlusion-handling capabilities and the impact of sensor modalities such as stereo vision, Time-of-Flight (ToF) cameras, and LiDAR ."}
+{"idx": 1, "title": "CMES | Free Full-Text | Bridging 2D and 3D Object Detection...", "date": "", "ddg_snippet": "Object detection; occlusion handling ; multimodal fusion; monocular; 3D sensors; depth estimation. Depth -sensing technologies such as LiDAR , stereo cameras, and ToF cameras provide essential spatial information , making them indispensable for occlusion-aware perception.", "subpage_snippet": "", "source": "www.techscience.com", "link": "https://www.techscience.com/CMES/v143n3/62810/html", "content": "Object detection; occlusion handling ; multimodal fusion; monocular; 3D sensors; depth estimation. Depth -sensing technologies such as LiDAR , stereo cameras, and ToF cameras provide essential spatial information , making them indispensable for occlusion-aware perception."}
+{"idx": 2, "title": "Object Detection, Recognition, and Tracking Algorithms for...", "date": "", "ddg_snippet": "Lidar – lidar fusion is a technique that combines data from two or more lidar sensors, improving the performance of object detection, recognition, and tracking algorithms in ADASs. Lidar sensors are good at providing information about the distance and shape of objects, but they can be...", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/1424-8220/24/1/249", "content": "Lidar – lidar fusion is a technique that combines data from two or more lidar sensors, improving the performance of object detection, recognition, and tracking algorithms in ADASs. Lidar sensors are good at providing information about the distance and shape of objects, but they can be..."}
+{"idx": 3, "title": "Multi-Modal 3D Object Detection in Long Range and Low-Resolution...", "date": "", "ddg_snippet": "2.1.2 LiDAR Cloud Points. LiDAR (Light Detection and Ranging) devices produce point clouds by measuring the distances to objects with laser pulses, creating high-resolution 3D representations of the environment.", "subpage_snippet": "", "source": "www.ce.cit.tum.de", "link": "https://www.ce.cit.tum.de/fileadmin/w00cgn/air/Personal_Files/WalterZimmer/masters_thesis_egemen_kopuz_final.pdf", "content": "2.1.2 LiDAR Cloud Points. LiDAR (Light Detection and Ranging) devices produce point clouds by measuring the distances to objects with laser pulses, creating high-resolution 3D representations of the environment."}
+{"idx": 4, "title": "(PDF) Super Resolution of Laser Range Data Based on Image-guided...", "date": "", "ddg_snippet": "Laser range scanner or Light Detection and Ranging ( LiDAR ), is becoming one of the. dominant tools in acquiring 3D information . By actively measuring distance to a surface through. time of flight of laser beams in a high-frequency, the laser range scanner is able to collect a vast.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/326305175_Super_Resolution_of_Laser_Range_Data_Based_on_Image-guided_Fusion_and_Dense_Matching", "content": "Laser range scanner or Light Detection and Ranging ( LiDAR ), is becoming one of the. dominant tools in acquiring 3D information . By actively measuring distance to a surface through. time of flight of laser beams in a high-frequency, the laser range scanner is able to collect a vast."}
+{"idx": 5, "title": "Joint", "date": "", "ddg_snippet": "The depth-based approaches that rely only on point clouds or depth information are vulnerable to occlusion or background clutter. These methods use stacks of planar laser scans by which they extract the shape of pedestrians [408].", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1802.02522", "content": "The depth-based approaches that rely only on point clouds or depth information are vulnerable to occlusion or background clutter. These methods use stacks of planar laser scans by which they extract the shape of pedestrians [408]."}
+{"idx": 6, "title": "Computer Vision, Part IV - ACCV 2010 - PDF Free Download", "date": "", "ddg_snippet": "Depth information extracted from an occlusion region is the cause of the output of erroneous responds for weak classifiers. Thus we ensure that any output of weak classifiers that perceive such occlusion regions is not integrated into the final classifier without modification.", "subpage_snippet": "", "source": "epdf.pub", "link": "https://epdf.pub/computer-vision-part-iv-accv-2010.html", "content": "Depth information extracted from an occlusion region is the cause of the output of erroneous responds for weak classifiers. Thus we ensure that any output of weak classifiers that perceive such occlusion regions is not integrated into the final classifier without modification."}
+{"idx": 7, "title": "Dissertação de Mestrado", "date": "", "ddg_snippet": "· H2: Depth information can be used to rectify contours extracted from the RGB image, improving the detection of planar texture-less objectsFigure 6.23. Occlusion handling using DARC: input image (top), detection result (middle) and augmentation (bottom). Chapter 6 – Results.", "subpage_snippet": "", "source": "repositorio.ufpe.br", "link": "https://repositorio.ufpe.br/bitstream/123456789/12143/1/TESE+João+Paulo+Silva+do+Monte+Lima.pdf", "content": "· H2: Depth information can be used to rectify contours extracted from the RGB image, improving the detection of planar texture-less objectsFigure 6.23. Occlusion handling using DARC: input image (top), detection result (middle) and augmentation (bottom). Chapter 6 – Results."}
+{"idx": 8, "title": "Active Sonar Tracking Under Realistic Conditions", "date": "", "ddg_snippet": "In addition, the 3D motion information is obtained by the proposed algorithm without using a 3D sonar or a multi-sensor system, but only by using a single monostatic 2D active sonar platform. The problem addressed in this paper is both challenging and realistic.", "subpage_snippet": "", "source": "macsphere.mcmaster.ca", "link": "https://macsphere.mcmaster.ca/bitstream/11375/24758/2/Liu_Ben_201908_PhD.pdf", "content": "In addition, the 3D motion information is obtained by the proposed algorithm without using a 3D sonar or a multi-sensor system, but only by using a single monostatic 2D active sonar platform. The problem addressed in this paper is both challenging and realistic."}
+{"idx": 9, "title": "Sensor Data Understanding [1 ed.]... - DOKUMEN.PUB", "date": "", "ddg_snippet": "Occlusion Handling .Based on the omni-stereo disparity-estimation results, the authors detect and trackmoving objects based on omni-stereo disparity motion vector, which is the difference between two consecutive disparity maps.", "subpage_snippet": "", "source": "dokumen.pub", "link": "https://dokumen.pub/sensor-data-understanding-1nbsped-9783832592455-9783832546335.html", "content": "Occlusion Handling .Based on the omni-stereo disparity-estimation results, the authors detect and trackmoving objects based on omni-stereo disparity motion vector, which is the difference between two consecutive disparity maps."}
diff --git a/data/sampled_jsons/Section_5_DCBM_applicability_limited_by_two_factors_first_factor.jsonl b/data/sampled_jsons/Section_5_DCBM_applicability_limited_by_two_factors_first_factor.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8e025695d4ab585c868abd933bff8a399bbf61ba
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+++ b/data/sampled_jsons/Section_5_DCBM_applicability_limited_by_two_factors_first_factor.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "DCBM: Data-Efficient Visual Concept Bottleneck Models", "date": "", "ddg_snippet": "DCBM's applicability is limited by two factors , the suitability of a segmentation foundation model and the expressiveness of the CLIP embedding space for a given use case.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.11576v2", "content": "DCBM's applicability is limited by two factors , the suitability of a segmentation foundation model and the expressiveness of the CLIP embedding space for a given use case."}
+{"idx": 1, "title": "Factors of Labor Standards Applicability - HUD.gov", "date": "", "ddg_snippet": "Factors of Labor Standards ApplicabilityThe labor standards provisions in HUD program statutes vary considerably meaning that there are significant differences in whether and to what extent prevailing wage requirements are applicable under these programs. This treatment discusses some of the differences and explains how the language is interpreted for applicability purposes.", "subpage_snippet": "", "source": "www.hud.gov", "link": "https://www.hud.gov/stat/fpm/labor-applicability", "content": "Factors of Labor Standards ApplicabilityThe labor standards provisions in HUD program statutes vary considerably meaning that there are significant differences in whether and to what extent prevailing wage requirements are applicable under these programs. This treatment discusses some of the differences and explains how the language is interpreted for applicability purposes."}
+{"idx": 2, "title": "PDF Microsoft Word - 06_Section 5.1_LL Distribution_E_.doc - IN.gov", "date": "", "ddg_snippet": "The multiple presence factors are already included in the distribution factor equations except when the tables call for the use of the lever rule. In these cases, the computations need to account for the multiple presence factors . Notice that the distribution factor tables include a column with the heading \"range of applicability \".", "subpage_snippet": "", "source": "www.in.gov", "link": "https://www.in.gov/dot/div/contracts/design/lrfd/06_Section+5.1_LL+Distribution(E).PDF", "content": "The multiple presence factors are already included in the distribution factor equations except when the tables call for the use of the lever rule. In these cases, the computations need to account for the multiple presence factors . Notice that the distribution factor tables include a column with the heading \"range of applicability \"."}
+{"idx": 3, "title": "PDF Load and Resistance Factor Design (LRFD) for Highway Bridge ...", "date": "", "ddg_snippet": "Chapter 3 presents loads and load factors , including design criteria for common bridge loads, as well as load factors used for various LRFD load combinations. Chapter 4 provides a general summary of structural analysis, including general analysis considerations, dead load analysis, live load analysis, and various methods of analysis.", "subpage_snippet": "", "source": "www.fhwa.dot.gov", "link": "https://www.fhwa.dot.gov/bridge/pubs/nhi15047.pdf", "content": "Chapter 3 presents loads and load factors , including design criteria for common bridge loads, as well as load factors used for various LRFD load combinations. Chapter 4 provides a general summary of structural analysis, including general analysis considerations, dead load analysis, live load analysis, and various methods of analysis."}
+{"idx": 4, "title": "PDF FA300220R0007 Section M - Evaluation Factors for Award", "date": "", "ddg_snippet": "Factor 1, all Subfactors 1-5 are of equal importance. If any Subfactor 1-5 is determined to be technically unacceptable, the Factor 1 will be determined technically unacceptable. This is a Performance-Price Trade-off (PPT) source selection and, for those Offerors who are determined to be technically acceptable tradeoffs may be made between past ...", "subpage_snippet": "", "source": "imlive.s3.amazonaws.com", "link": "https://imlive.s3.amazonaws.com/Federal+Government/ID90028196614329681131981155125758526996/Attach+15+-+Section+M+-+Evaluation+Factors+for+Award.pdf", "content": "Factor 1, all Subfactors 1-5 are of equal importance. If any Subfactor 1-5 is determined to be technically unacceptable, the Factor 1 will be determined technically unacceptable. This is a Performance-Price Trade-off (PPT) source selection and, for those Offerors who are determined to be technically acceptable tradeoffs may be made between past ..."}
+{"idx": 5, "title": "PDF Factors of Labor Standards Applicability", "date": "", "ddg_snippet": "Factors of Labor Standards Applicability The labor standards provisions in HUD program statutes vary considerably and there are significant differences in whether and to what extent prevailing wage requirements are applicable under these programs.", "subpage_snippet": "", "source": "www.hud.gov", "link": "https://www.hud.gov/sites/dfiles/OCHCO/documents/13441AII-5SECH.pdf", "content": "Factors of Labor Standards Applicability The labor standards provisions in HUD program statutes vary considerably and there are significant differences in whether and to what extent prevailing wage requirements are applicable under these programs."}
+{"idx": 6, "title": "Determining Which Labor Standards Apply - U.S. Department of Labor", "date": "", "ddg_snippet": "Factors to be considered include, but are not limited to: the existence of engineering or architectural plans or surveys of the site; the allocation of, or an application for, federal assistance for construction; contract negotiations or bid solicitations; the stated intent of the relevant government officials; and", "subpage_snippet": "", "source": "www.dol.gov", "link": "https://www.dol.gov/agencies/whd/government-contracts/prevailing-wage-resource-book/determining-which-labor-standards-apply", "content": "Factors to be considered include, but are not limited to: the existence of engineering or architectural plans or surveys of the site; the allocation of, or an application for, federal assistance for construction; contract negotiations or bid solicitations; the stated intent of the relevant government officials; and"}
+{"idx": 7, "title": "PDF Davis-Bacon and HOME Course Manual - HUD Exchange", "date": "", "ddg_snippet": "The labor laws that may apply to CDBG- and HOME-funded construction work include the following: The Davis-Bacon Act (40 USC, Chapter 3, Section 276a-276a- 5 ; and 29 CFR Parts 1, 3, 5 , 6 and 7). Davis-Bacon labor standards requirements are triggered at different thresholds under the CDBG and HOME Program, which is further discussed later in this manual. These labor standards require that workers ...", "subpage_snippet": "", "source": "files.hudexchange.info", "link": "https://files.hudexchange.info/resources/documents/Davis-BaconandHOME_TrainingManual.pdf", "content": "The labor laws that may apply to CDBG- and HOME-funded construction work include the following: The Davis-Bacon Act (40 USC, Chapter 3, Section 276a-276a- 5 ; and 29 CFR Parts 1, 3, 5 , 6 and 7). Davis-Bacon labor standards requirements are triggered at different thresholds under the CDBG and HOME Program, which is further discussed later in this manual. These labor standards require that workers ..."}
+{"idx": 8, "title": "2017 DC Construction Codes | dob", "date": "", "ddg_snippet": "The 2017 DC Construction Code takes effect on May 29, 2020. Applicability and provisions for the prior editions of the code, (for Permits issued, Applications Filed, Tenant Layouts and Permit Revisions) will be governed by the Transitory Provision stipulated in section 123.", "subpage_snippet": "", "source": "dob.dc.gov", "link": "https://dob.dc.gov/node/1615636", "content": "The 2017 DC Construction Code takes effect on May 29, 2020. Applicability and provisions for the prior editions of the code, (for Permits issued, Applications Filed, Tenant Layouts and Permit Revisions) will be governed by the Transitory Provision stipulated in section 123."}
+{"idx": 9, "title": "PDF Data-Driven Condition Based Maintenance - monohakobi.com", "date": "", "ddg_snippet": "This report aims to provide an overview of how data-driven condition based maintenance ( DCBM ) processes, utilising the latest analytical models, can help to deliver these benefits to the maritime industry, outlining the challenges that need to be overcome and suggesting potential paths to successful implementation.", "subpage_snippet": "", "source": "www.monohakobi.com", "link": "https://www.monohakobi.com/en/wp-content/uploads/2024/05/Data_Driven_Condition_Based_Maintenance_report.pdf", "content": "This report aims to provide an overview of how data-driven condition based maintenance ( DCBM ) processes, utilising the latest analytical models, can help to deliver these benefits to the maritime industry, outlining the challenges that need to be overcome and suggesting potential paths to successful implementation."}
diff --git a/data/sampled_jsons/Section_5_base_model_sitearxiv.orghtml2406.14532v1_year_2024.jsonl b/data/sampled_jsons/Section_5_base_model_sitearxiv.orghtml2406.14532v1_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..41048809ebb01a0acda380cd51625a49994a84ed
--- /dev/null
+++ b/data/sampled_jsons/Section_5_base_model_sitearxiv.orghtml2406.14532v1_year_2024.jsonl
@@ -0,0 +1,2 @@
+{"idx": 0, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM ...", "date": "", "ddg_snippet": "20 Jun 2024 — We saw in Section 5 that fine-tuning on model-generated data can ... base model initialization is under-trained that imperfectly cloned ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.14532v1", "content": "20 Jun 2024 — We saw in Section 5 that fine-tuning on model-generated data can ... base model initialization is under-trained that imperfectly cloned ..."}
+{"idx": 1, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/Self-Paced_Learning_Physics-Informed_Neural_Networks_PINN_year_2022.jsonl b/data/sampled_jsons/Self-Paced_Learning_Physics-Informed_Neural_Networks_PINN_year_2022.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..830ccab336a1c6bf217a5de40e8bdb1ec5b2e427
--- /dev/null
+++ b/data/sampled_jsons/Self-Paced_Learning_Physics-Informed_Neural_Networks_PINN_year_2022.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Self-adaptive physics-informed neural networks - ScienceDirect", "date": "", "ddg_snippet": "In this paper, we introduced Self -Adaptive Physics - Informed Neural Networks , a novel class of physics -constrained neural networks . This approach uses a similar conceptual framework as soft self -attention mechanisms in computer vision, in that the network identifies which inputs are most important to its own training.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0021999122007859", "content": "In this paper, we introduced Self -Adaptive Physics - Informed Neural Networks , a novel class of physics -constrained neural networks . This approach uses a similar conceptual framework as soft self -attention mechanisms in computer vision, in that the network identifies which inputs are most important to its own training."}
+{"idx": 1, "title": "ST-PINN: A Self-Training Physics-Informed Neural Network for Partial ...", "date": "", "ddg_snippet": "To address the issue of low accuracy and convergence problems of existing PINNs , we propose a self -training physics - informed neural network , ST- PINN . Specifically, ST- PINN introduces a pseudo label based self - learning algorithm during training.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2306.09389", "content": "To address the issue of low accuracy and convergence problems of existing PINNs , we propose a self -training physics - informed neural network , ST- PINN . Specifically, ST- PINN introduces a pseudo label based self - learning algorithm during training."}
+{"idx": 2, "title": "GitHub - junjun-yan/ST-PINN: A Self-Training Physics-Informed Neural ...", "date": "", "ddg_snippet": "With the development of deep learning , physics - informed neural networks ( PINNs ), as a mesh-free method, have shown great potential for fast PDE solving. To address the problem of low accuracy and convergence problems of existing PINNs , we propose a selftraining physics - informed neural network , ST- PINN .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/junjun-yan/ST-PINN", "content": "With the development of deep learning , physics - informed neural networks ( PINNs ), as a mesh-free method, have shown great potential for fast PDE solving. To address the problem of low accuracy and convergence problems of existing PINNs , we propose a selftraining physics - informed neural network , ST- PINN ."}
+{"idx": 3, "title": "Self-Paced Learning Enhanced Physics-informed Neural Networks for ...", "date": "", "ddg_snippet": "There is a hit discussion on solving partial differential equation by neural network . The famous PINN ( physics - informed neural networks ) has drawn worldwide attention since it was put forward....", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=QugfmhDu5Y4", "content": "There is a hit discussion on solving partial differential equation by neural network . The famous PINN ( physics - informed neural networks ) has drawn worldwide attention since it was put forward...."}
+{"idx": 4, "title": "PDF Adaptive Self-Supervision Algorithms for Physics-Informed Neural Networks", "date": "", "ddg_snippet": "Abstract. Physics - informed neural networks ( PINNs ) incorporate physical knowledge from the problem domain as a soft constraint on the loss function, but recent work has shown that this can lead to optimization difficulties. Here, we study the impact of the location of the collocation points on the trainability of these models. We find that the vanilla PINN performance can be significantly ...", "subpage_snippet": "", "source": "www.stat.berkeley.edu", "link": "https://www.stat.berkeley.edu/~mmahoney/pubs/subramanian_ECAI23.pdf", "content": "Abstract. Physics - informed neural networks ( PINNs ) incorporate physical knowledge from the problem domain as a soft constraint on the loss function, but recent work has shown that this can lead to optimization difficulties. Here, we study the impact of the location of the collocation points on the trainability of these models. We find that the vanilla PINN performance can be significantly ..."}
+{"idx": 5, "title": "PDF Self-Adaptive Physics-Informed Neural Networks using a Soft Attention ...", "date": "", "ddg_snippet": "Abstract Physics - Informed Neural Networks ( PINNs ) have emerged recently as a promising application of deep neural networks to the numerical solution of nonlin-ear partial differential equations (PDEs).", "subpage_snippet": "", "source": "ceur-ws.org", "link": "https://ceur-ws.org/Vol-2964/article_68.pdf", "content": "Abstract Physics - Informed Neural Networks ( PINNs ) have emerged recently as a promising application of deep neural networks to the numerical solution of nonlin-ear partial differential equations (PDEs)."}
+{"idx": 6, "title": "ST-PINN: A Self-Training Physics-Informed Neural Network for Partial ...", "date": "", "ddg_snippet": "Partial differential equations (PDEs) are an essential computational kernel in physics and engineering. With the advance of deep learning , physics - informed neural networks ( PINNs ), as a mesh-free method, have shown great potential for fast PDE solving in various applications. To address the issue of low accuracy and convergence problems of existing PINNs , we propose a self -training physics ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2306.09389", "content": "Partial differential equations (PDEs) are an essential computational kernel in physics and engineering. With the advance of deep learning , physics - informed neural networks ( PINNs ), as a mesh-free method, have shown great potential for fast PDE solving in various applications. To address the issue of low accuracy and convergence problems of existing PINNs , we propose a self -training physics ..."}
+{"idx": 7, "title": "(PDF) Self-Adaptive Physics-Informed Neural Networks using a Soft ...", "date": "", "ddg_snippet": "Physics - Informed Neural Networks ( PINNs ) have emerged recently as a promising application of deep neural networks to the numerical solution of nonlinear partial differential equations (PDEs).", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/344198157_Self-Adaptive_Physics-Informed_Neural_Networks_using_a_Soft_Attention_Mechanism", "content": "Physics - Informed Neural Networks ( PINNs ) have emerged recently as a promising application of deep neural networks to the numerical solution of nonlinear partial differential equations (PDEs)."}
+{"idx": 8, "title": "Scientific Machine Learning Through Physics-Informed Neural Networks ...", "date": "", "ddg_snippet": "Physics - Informed Neural Networks ( PINN ) are neural networks (NNs) that encode model equations, like Partial Differential Equations (PDE), as a component of the neural network itself. PINNs are nowadays used to solve PDEs, fractional equations, integral-differential equations, and stochastic PDEs. This novel methodology has arisen as a multi-task learning framework in which a NN must fit ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s10915-022-01939-z", "content": "Physics - Informed Neural Networks ( PINN ) are neural networks (NNs) that encode model equations, like Partial Differential Equations (PDE), as a component of the neural network itself. PINNs are nowadays used to solve PDEs, fractional equations, integral-differential equations, and stochastic PDEs. This novel methodology has arisen as a multi-task learning framework in which a NN must fit ..."}
+{"idx": 9, "title": "A self-adaptive physics-informed neural networks method for large ...", "date": "", "ddg_snippet": "To address these issues, we introduce self -adaptive physics - informed neural networks (SA- PINNs ), featuring an adaptive loss function weighting and a slope scaling method for the activation functions. Additionally, a sensitivity analysis exploring the influence of monitoring data on the parameter inversion accuracy is presented.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0266352X25000801", "content": "To address these issues, we introduce self -adaptive physics - informed neural networks (SA- PINNs ), featuring an adaptive loss function weighting and a slope scaling method for the activation functions. Additionally, a sensitivity analysis exploring the influence of monitoring data on the parameter inversion accuracy is presented."}
diff --git a/data/sampled_jsons/Self-refine_Iterative_Refinement_with_Self-Feedback_Madaan_et_al.,_2024.jsonl b/data/sampled_jsons/Self-refine_Iterative_Refinement_with_Self-Feedback_Madaan_et_al.,_2024.jsonl
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index 0000000000000000000000000000000000000000..227113503946094d3ac4f8f8ef9046d868e9aca6
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+++ b/data/sampled_jsons/Self-refine_Iterative_Refinement_with_Self-Feedback_Madaan_et_al.,_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Iterative Refinement with Self - Feedback", "date": "", "ddg_snippet": "Iterating SELF - REFINE SELF - REFINE alternates between FEEDBACK and REFINE steps until a stopping condition is met. The stopping condition stop(f bt, t) either stops at a specified timestep t, or extracts a stopping indicator (e.g. a scalar stop score) from the feedback .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2303.17651", "content": "Iterating SELF - REFINE SELF - REFINE alternates between FEEDBACK and REFINE steps until a stopping condition is met. The stopping condition stop(f bt, t) either stops at a specified timestep t, or extracts a stopping indicator (e.g. a scalar stop score) from the feedback ."}
+{"idx": 1, "title": "Self - Refinement Module in AI", "date": "", "ddg_snippet": "For example, in \" Self - Refine : Iterative Refinement with Self - Feedback \" ( Madaan et al ., 2023), a LLM serves as its own generator, critic, and editor. The process involves: Generating an initial output.", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/topics/self-refinement-module", "content": "For example, in \" Self - Refine : Iterative Refinement with Self - Feedback \" ( Madaan et al ., 2023), a LLM serves as its own generator, critic, and editor. The process involves: Generating an initial output."}
+{"idx": 2, "title": "Generated Code with Execution Feedback", "date": "", "ddg_snippet": "Self - refine : Iterative refinement with self - feedback . Advances in Neural Information Processing Systems, 36, 2024 .", "subpage_snippet": "", "source": "www.cse.cuhk.edu.hk", "link": "https://www.cse.cuhk.edu.hk/lyu/_media/conference/ypeng_forge2025_perfcodegen.pdf?id=publications:all_by_year&cache=cache", "content": "Self - refine : Iterative refinement with self - feedback . Advances in Neural Information Processing Systems, 36, 2024 ."}
+{"idx": 3, "title": "Practical Guide to Agentic Self -Reflection and Other Methods to...", "date": "", "ddg_snippet": "This approach, often called self - refine ( Madaan et al ., 2023), has shown great promise in reducing hallucinations and improving factual accuracy. However, there’s a known challenge: self-bias. Self - Refine : Iterative Refinement with Self - Feedback . arXiv preprint arXiv:2303.17651.", "subpage_snippet": "", "source": "opentyphoon.ai", "link": "https://opentyphoon.ai/blog/en/agentic-self-reflection-prompting-guide", "content": "This approach, often called self - refine ( Madaan et al ., 2023), has shown great promise in reducing hallucinations and improving factual accuracy. However, there’s a known challenge: self-bias. Self - Refine : Iterative Refinement with Self - Feedback . arXiv preprint arXiv:2303.17651."}
+{"idx": 4, "title": "Multimodal Auto Validation For Self - Refinement in", "date": "", "ddg_snippet": "3.3 Self - Refine Utilizing the self - refine method [ Madaan et al ., 2023], our approach introduces a self -correcting mechanism for workflow failures.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=fu4ptLcKbo&name=pdf", "content": "3.3 Self - Refine Utilizing the self - refine method [ Madaan et al ., 2023], our approach introduces a self -correcting mechanism for workflow failures."}
+{"idx": 5, "title": "(PDF) Teaching LLMs to Refine with Tools", "date": "", "ddg_snippet": "Self - refine : Iterative refinement . Self - refine : Iterative refinement with self - feedback . Advances in Neural Information Processing Systems, 36, 2024 .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/387351164_Teaching_LLMs_to_Refine_with_Tools", "content": "Self - refine : Iterative refinement . Self - refine : Iterative refinement with self - feedback . Advances in Neural Information Processing Systems, 36, 2024 ."}
+{"idx": 6, "title": "How AI Feedback Loops Function", "date": "", "ddg_snippet": "If you’re interested in learning more about reflection, I recommend: - Self - Refine : Iterative Refinement with Self - Feedback , by Madaan et al . (2023) - Reflexion: Language Agents with Verbal Reinforcement Learning, by Shinn et al .", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/top-content/artificial-intelligence/understanding-ai-systems/how-ai-feedback-loops-function/", "content": "If you’re interested in learning more about reflection, I recommend: - Self - Refine : Iterative Refinement with Self - Feedback , by Madaan et al . (2023) - Reflexion: Language Agents with Verbal Reinforcement Learning, by Shinn et al ."}
+{"idx": 7, "title": "Pride and Prejudice: LLM Amplifies Self -Bias in Self - Refinement", "date": "", "ddg_snippet": "2024 . Self - refine : Iterative refinement with self - feedback . Ad-vances in Neural Information Processing Systems, 36. Potsawee Manakul, Adian Liusie, and Mark Gales.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2024.acl-long.826.pdf", "content": "2024 . Self - refine : Iterative refinement with self - feedback . Ad-vances in Neural Information Processing Systems, 36. Potsawee Manakul, Adian Liusie, and Mark Gales."}
+{"idx": 8, "title": "large language models – Mark Nagelberg", "date": "", "ddg_snippet": "SELF - REFINE mimics human iterative refinement , where an initial draft is revised based on self - feedback . Iterative Process: The process uses two steps: FEEDBACK and REFINE , iterating until no further improvements are needed.", "subpage_snippet": "", "source": "www.marknagelberg.com", "link": "https://www.marknagelberg.com/tag/large-language-models/", "content": "SELF - REFINE mimics human iterative refinement , where an initial draft is revised based on self - feedback . Iterative Process: The process uses two steps: FEEDBACK and REFINE , iterating until no further improvements are needed."}
+{"idx": 9, "title": "AI Agents Workflow Design Patterns In LLMs - AI For Developers", "date": "", "ddg_snippet": "“ Self - Refine : Iterative Refinement with Self - Feedback , Madaan et al . (2023). Refinement patterns provide a self-correcting loop for quality output, while LLM Augment empowers real-world actions.", "subpage_snippet": "", "source": "aifordevelopers.io", "link": "https://aifordevelopers.io/ai-agents-workflow-design-patterns-in-llms/", "content": "“ Self - Refine : Iterative Refinement with Self - Feedback , Madaan et al . (2023). Refinement patterns provide a self-correcting loop for quality output, while LLM Augment empowers real-world actions."}
diff --git a/data/sampled_jsons/Sharpness-Aware_Minimization_Foret_2021_abstract_generalization.jsonl b/data/sampled_jsons/Sharpness-Aware_Minimization_Foret_2021_abstract_generalization.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..970ac5bf5c707ebfbd8d3a8055a21fe6ea26c97a
--- /dev/null
+++ b/data/sampled_jsons/Sharpness-Aware_Minimization_Foret_2021_abstract_generalization.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2010.01412] Sharpness-Aware Minimization for Efficiently", "date": "", "ddg_snippet": "View a PDF of the paper titled Sharpness - Aware Minimization for Efficiently Improving Generalization , by Pierre Foret and 3 other authors", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2010.01412", "content": "View a PDF of the paper titled Sharpness - Aware Minimization for Efficiently Improving Generalization , by Pierre Foret and 3 other authors"}
+{"idx": 1, "title": "[2110.03141] Efficient Sharpness-aware Minimization for", "date": "", "ddg_snippet": "This paper thus proposes Efficient Sharpness Aware Minimizer (ESAM), which boosts SAM s efficiency at no cost to its generalization performance.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2110.03141", "content": "This paper thus proposes Efficient Sharpness Aware Minimizer (ESAM), which boosts SAM s efficiency at no cost to its generalization performance."}
+{"idx": 2, "title": "Sharpness-Aware Minimization with Z-Score Gradient Filtering", "date": "", "ddg_snippet": "... sharpness - aware optimization has emerged as a prominent framework, aiming to identify flatter minima that are empirically associated with improved ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.02369v1", "content": "... sharpness - aware optimization has emerged as a prominent framework, aiming to identify flatter minima that are empirically associated with improved ..."}
+{"idx": 3, "title": "Bi-LoRA: Efficient Sharpness-Aware Minimization for Fine-Tuning", "date": "", "ddg_snippet": "Sharpness - Aware Minimization (SAM) ( Foret et al., 2021 ) is a widely used technique that enhances generalization by formulating optimization as a ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.19564v1", "content": "Sharpness - Aware Minimization (SAM) ( Foret et al., 2021 ) is a widely used technique that enhances generalization by formulating optimization as a ..."}
+{"idx": 4, "title": "Tractable Sharpness-aware Regularization of Probabilistic", "date": "", "ddg_snippet": "... sharp minima, characterized by high curvature, have been extensively studied in deep neural networks, leading to the development of sharpness - aware ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.05537v1", "content": "... sharp minima, characterized by high curvature, have been extensively studied in deep neural networks, leading to the development of sharpness - aware ..."}
+{"idx": 5, "title": "Sharpness-Aware Minimization for Efficiently Improving", "date": "", "ddg_snippet": "... the loss landscape and generalization , we introduce a novel, effective procedure for instead simultaneously minimizing loss value and loss sharpness ...", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/paper/sharpness-aware-minimization-for-efficiently-1", "content": "... the loss landscape and generalization , we introduce a novel, effective procedure for instead simultaneously minimizing loss value and loss sharpness ..."}
+{"idx": 6, "title": "ICLR Poster Sharpness-aware Minimization for Efficiently", "date": "", "ddg_snippet": "In particular, our procedure, Sharpness - Aware Minimization (SAM), seeks parameters that lie in neighborhoods having uniformly low loss; this ...", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2021/poster/2782", "content": "In particular, our procedure, Sharpness - Aware Minimization (SAM), seeks parameters that lie in neighborhoods having uniformly low loss; this ..."}
+{"idx": 7, "title": "GitHub - davda54/sam: SAM: Sharpness-Aware Minimization", "date": "", "ddg_snippet": "This is an unofficial repository for Sharpness - Aware Minimization for Efficiently Improving Generalization and ASAM: Adaptive Sharpness - Aware ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/davda54/sam", "content": "This is an unofficial repository for Sharpness - Aware Minimization for Efficiently Improving Generalization and ASAM: Adaptive Sharpness - Aware ..."}
+{"idx": 8, "title": "Frontiers in Artificial Intelligence Algorithm Optimization: A", "date": "", "ddg_snippet": "... aware serving, context length generalization , and ... Sharpness - aware minimization for efficiently improving generalization [Paper presentation].", "subpage_snippet": "", "source": "journals.zeuspress.org", "link": "https://journals.zeuspress.org/index.php/CAI/article/view/322", "content": "... aware serving, context length generalization , and ... Sharpness - aware minimization for efficiently improving generalization [Paper presentation]."}
+{"idx": 9, "title": "(PDF) Trade-Offs of Diagonal Fisher Information Matrix", "date": "", "ddg_snippet": "... FIM ( Guo and Spall , 2019 ; Soen and Sun , 2021 ... discover a general decomposition of the estimators’ variances corresponding to the samples of", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/378141278_Trade-Offs_of_Diagonal_Fisher_Information_Matrix_Estimators", "content": "... FIM ( Guo and Spall , 2019 ; Soen and Sun , 2021 ... discover a general decomposition of the estimators’ variances corresponding to the samples of"}
diff --git a/data/sampled_jsons/Shen_Lee_randomized_midpoint_method_log-concave_sampling_abstract_Picard_iterations_year_2019.jsonl b/data/sampled_jsons/Shen_Lee_randomized_midpoint_method_log-concave_sampling_abstract_Picard_iterations_year_2019.jsonl
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index 0000000000000000000000000000000000000000..c78ca41fdac95d73a254ebde8c59dd7aaedf0654
--- /dev/null
+++ b/data/sampled_jsons/Shen_Lee_randomized_midpoint_method_log-concave_sampling_abstract_Picard_iterations_year_2019.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "The Randomized Midpoint Method for Log - Concave Sampling", "date": "", "ddg_snippet": "Abstract : Sampling from log - concave distributions is a well researched problem that has many applications in statistics and machine learning.View a PDF of the paper titled The Randomized Midpoint Method for Log - Concave Sampling , by Ruoqi Shen and 1 other authors.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1909.05503", "content": "Abstract : Sampling from log - concave distributions is a well researched problem that has many applications in statistics and machine learning.View a PDF of the paper titled The Randomized Midpoint Method for Log - Concave Sampling , by Ruoqi Shen and 1 other authors."}
+{"idx": 1, "title": "(PDF) The Randomized Midpoint Method for Log - Concave ...", "date": "", "ddg_snippet": "Abstract : Sampling from log - concave distributions is a well researched problem that has many applications in statistics and machine learning.", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/papers/the-randomized-midpoint-method-for-log-concave-sampling-4jwrm4tta7", "content": "Abstract : Sampling from log - concave distributions is a well researched problem that has many applications in statistics and machine learning."}
+{"idx": 2, "title": "The Randomized Midpoint Method for Log - Concave", "date": "", "ddg_snippet": "4.2 Randomized Midpoint Method . Our step size for each iteration is h. In iteration n of our algorithm, to simulate (3), we need to approximate the solution to SDE (3) at time h, (x∗n(h), vn∗ (h)), with initial value, (xn, vn). The simplest way to do so is to use the Euler method", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=B1geUNBgIr", "content": "4.2 Randomized Midpoint Method . Our step size for each iteration is h. In iteration n of our algorithm, to simulate (3), we need to approximate the solution to SDE (3) at time h, (x∗n(h), vn∗ (h)), with initial value, (xn, vn). The simplest way to do so is to use the Euler method"}
+{"idx": 3, "title": "The Randomized Midpoint Method for Log - Concave Sampling", "date": "", "ddg_snippet": "Abstract . Sampling from log - concave distributions is a well researched problem that has many applications in statistics and machine learning.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/335788560_The_Randomized_Midpoint_Method_for_Log-Concave_Sampling", "content": "Abstract . Sampling from log - concave distributions is a well researched problem that has many applications in statistics and machine learning."}
+{"idx": 4, "title": "Ruoqi Shen - Google Akademik", "date": "", "ddg_snippet": "The randomized midpoint method for log - concave sampling . R Shen , YT Lee . Advances in Neural Information Processing Systems 32, 2019.", "subpage_snippet": "", "source": "scholar.google.ae", "link": "https://scholar.google.ae/citations?user=mOdDPw4AAAAJ&hl=tr", "content": "The randomized midpoint method for log - concave sampling . R Shen , YT Lee . Advances in Neural Information Processing Systems 32, 2019."}
+{"idx": 5, "title": "Improving Efficiency in Diffusion Models for Data Sampling", "date": "", "ddg_snippet": "Implications for Log - Concave Sampling . Technical Overview of the New Method .The randomized midpoint method is designed to improve the sampling process by providing a more accurate estimate of the score during certain time intervals.", "subpage_snippet": "", "source": "scisimple.com", "link": "https://scisimple.com/en/articles/2025-07-21-improving-efficiency-in-diffusion-models-for-data-sampling--a982dxq", "content": "Implications for Log - Concave Sampling . Technical Overview of the New Method .The randomized midpoint method is designed to improve the sampling process by providing a more accurate estimate of the score during certain time intervals."}
+{"idx": 6, "title": "The Randomized Midpoint Method for Log - Concave Sampling", "date": "", "ddg_snippet": "The framework can be used to solve not only the log - concave sampling problem, but any problem that involves simulating (stochastic) differential equations.", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/article/The-Randomized-Midpoint-Method-for-Log-Concave-Sampling-a4bbfd66-ffe2-11ed-90ce-72eb57fa10b3", "content": "The framework can be used to solve not only the log - concave sampling problem, but any problem that involves simulating (stochastic) differential equations."}
+{"idx": 7, "title": "Ruoqi Shen", "date": "", "ddg_snippet": "The Randomized Midpoint Method for Log - Concave Sampling . Ruoqi Shen , Yin Tat Lee .", "subpage_snippet": "", "source": "homes.cs.washington.edu", "link": "https://homes.cs.washington.edu/~shenr3/index.html", "content": "The Randomized Midpoint Method for Log - Concave Sampling . Ruoqi Shen , Yin Tat Lee ."}
+{"idx": 8, "title": "Faster Diffusion Sampling with Randomized Midpoints : Sequential...", "date": "", "ddg_snippet": "Sampling algorithms play an important role in controlling the quality and runtime of diffusion model inference.In this work, we propose a new scheme inspired by Shen and Lee 's randomized midpoint method for log - concave sampling ~\\cite{ShenL19}.", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/paper/faster-diffusion-based-sampling-with", "content": "Sampling algorithms play an important role in controlling the quality and runtime of diffusion model inference.In this work, we propose a new scheme inspired by Shen and Lee 's randomized midpoint method for log - concave sampling ~\\cite{ShenL19}."}
+{"idx": 9, "title": "Log - Concave Sampling", "date": "", "ddg_snippet": "Non- Log - Concave Sampling . Approximate First-Order Stationarity via Fisher Information.", "subpage_snippet": "", "source": "chewisinho.github.io", "link": "https://chewisinho.github.io/main.pdf", "content": "Non- Log - Concave Sampling . Approximate First-Order Stationarity via Fisher Information."}
diff --git a/data/sampled_jsons/Ships_metric_calculation_formula_Safety_Head_ImPortant_Score.jsonl b/data/sampled_jsons/Ships_metric_calculation_formula_Safety_Head_ImPortant_Score.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f146c4107054d5bab2c196bff8f9dae0ba129f26
--- /dev/null
+++ b/data/sampled_jsons/Ships_metric_calculation_formula_Safety_Head_ImPortant_Score.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "SAFETY CULTURE AND LEADING INDICATORS OF SAFETY", "date": "", "ddg_snippet": "Leading Indicators are the most important safety culture metrics for the organization as they correlate with the organization’s safety performance. ABS has developed a method for identifying potential leading indicators for improving safety performance.", "subpage_snippet": "", "source": "maritimesafetyinnovationlab.org", "link": "https://maritimesafetyinnovationlab.org/wp-content/uploads/2016/03/abs-safety-culture-and-leading-indicators-of-safety.pdf", "content": "Leading Indicators are the most important safety culture metrics for the organization as they correlate with the organization’s safety performance. ABS has developed a method for identifying potential leading indicators for improving safety performance."}
+{"idx": 1, "title": "Safety Score Methodology - rightship.com", "date": "", "ddg_snippet": "The Safety Score is calculated through a combination of industry standard rules and statistical modelling, including expert review of vessels. It has been designed to ensure that RightShip correctly identifies the vessels that are N/A, zero, 1 and 2 using a standard set of rules and to provide a prospective resolution for those vessels, on their continuous path to improvement.", "subpage_snippet": "", "source": "rightship.com", "link": "https://rightship.com/knowledge-base/safety-score-methodology", "content": "The Safety Score is calculated through a combination of industry standard rules and statistical modelling, including expert review of vessels. It has been designed to ensure that RightShip correctly identifies the vessels that are N/A, zero, 1 and 2 using a standard set of rules and to provide a prospective resolution for those vessels, on their continuous path to improvement."}
+{"idx": 2, "title": "Understanding RightShip’s Safety Score - MarinersLink", "date": "", "ddg_snippet": "Apr 9, 2025 · The Safety Score is a benchmarking tool introduced by RightShip in February 2021 to replace the earlier Star Rating system. It provides an indication of a vessel’s safety performance based on historical operational data, enabling stakeholders such as shipowners, charterers, and ports to assess and compare vessels within their peer groups. The score ranges from ‘N/A’ , ‘0’ , ‘1 ...", "subpage_snippet": "", "source": "marinerslink.com", "link": "https://marinerslink.com/understanding-rightships-safety-score/", "content": "Apr 9, 2025 · The Safety Score is a benchmarking tool introduced by RightShip in February 2021 to replace the earlier Star Rating system. It provides an indication of a vessel’s safety performance based on historical operational data, enabling stakeholders such as shipowners, charterers, and ports to assess and compare vessels within their peer groups. The score ranges from ‘N/A’ , ‘0’ , ‘1 ..."}
+{"idx": 3, "title": "Safety Score - Rightship", "date": "", "ddg_snippet": "Enhance your due diligence process with RightShip's Safety Score , a transparent and easy-to-use tool for measuring vessel safety and operational performance. Benchmark vessels, make informed decisions, and become an industry leader. Access our global vessel database and five years of data today.", "subpage_snippet": "", "source": "rightship.com", "link": "https://rightship.com/solutions/charterer/safety-score", "content": "Enhance your due diligence process with RightShip's Safety Score , a transparent and easy-to-use tool for measuring vessel safety and operational performance. Benchmark vessels, make informed decisions, and become an industry leader. Access our global vessel database and five years of data today."}
+{"idx": 4, "title": "Safety Metrics And KPIs You Should Know | Safety Reports", "date": "", "ddg_snippet": "Sep 26, 2024 · Safety metrics are the backbone of an effective health and safety program. They help organizations monitor their performance, identify areas for improvement, and ensure compliance with regulatory standards. This post covers the most critical safety metrics that every health and safety professional and risk manager should know.", "subpage_snippet": "", "source": "www.safety-reports.com", "link": "https://www.safety-reports.com/blog/safety-metrics-and-kpis-you-should-know/", "content": "Sep 26, 2024 · Safety metrics are the backbone of an effective health and safety program. They help organizations monitor their performance, identify areas for improvement, and ensure compliance with regulatory standards. This post covers the most critical safety metrics that every health and safety professional and risk manager should know."}
+{"idx": 5, "title": "Martin Crawford-Brunt explains RightShip’s new safety score", "date": "", "ddg_snippet": "May 27, 2022 · The RightShip Safety Score is a new way to benchmark safety . It incorporates various maritime data sets into one easy to understand score , based on 20 safety considerations and six sub scores .", "subpage_snippet": "", "source": "rightship.com", "link": "https://rightship.com/insights/martin-crawford-brunt-explains-rightship-s-new-safety-score", "content": "May 27, 2022 · The RightShip Safety Score is a new way to benchmark safety . It incorporates various maritime data sets into one easy to understand score , based on 20 safety considerations and six sub scores ."}
+{"idx": 6, "title": "3 Important Calculations Every Marine Engineer Must Know On Ships", "date": "", "ddg_snippet": "Apr 21, 2021 · Some essential parameters in the ship's engine room cannot be read directly through an instrument or gauge. Marine engineer working onboard ships must know how to carry out these important formula based calculations using a number of dynamic factors.", "subpage_snippet": "", "source": "www.marineinsight.com", "link": "https://www.marineinsight.com/guidelines/3-important-calculations-every-marine-engineer-must-know/", "content": "Apr 21, 2021 · Some essential parameters in the ship's engine room cannot be read directly through an instrument or gauge. Marine engineer working onboard ships must know how to carry out these important formula based calculations using a number of dynamic factors."}
+{"idx": 7, "title": "ON THE ROLE OF ATTENTION HEADS IN LARGE LANGUAGE ...", "date": "", "ddg_snippet": "Building on this, we define Safety Head ImPortant Score ( Ships ) to evaluate the importance of attention head θhl i . Formally, Ships can be expressed as: Ships ( ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/3a4b2272f2a7c546abd9814d901492aba03bd0c6.pdf", "content": "Building on this, we define Safety Head ImPortant Score ( Ships ) to evaluate the importance of attention head θhl i . Formally, Ships can be expressed as: Ships ( ..."}
+{"idx": 8, "title": "Lightweight Restoration of Safety in Pruned Large Vision ...", "date": "", "ddg_snippet": "... metric tailored for multi- head attention, namely the Safety head importance score ( Ships ), to evaluate the contribution of each head to the model safety .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.16104v2", "content": "... metric tailored for multi- head attention, namely the Safety head importance score ( Ships ), to evaluate the contribution of each head to the model safety ."}
+{"idx": 9, "title": "Safety and COLREG evaluation for marine collision ...", "date": "", "ddg_snippet": "by IB Hagen · 2023 · Cited by 22 — The relation between a penalty and its corresponding score is such that S = 1 − P . When a score is calculated from multiple penalties, weights ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0029801823023752", "content": "by IB Hagen · 2023 · Cited by 22 — The relation between a penalty and its corresponding score is such that S = 1 − P . When a score is calculated from multiple penalties, weights ..."}
diff --git a/data/sampled_jsons/Signed_Laplacians_for_Constrained_Graph_Clustering.jsonl b/data/sampled_jsons/Signed_Laplacians_for_Constrained_Graph_Clustering.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..039bf3b0436749e79bc27c4bd34bb2afa87b0fee
--- /dev/null
+++ b/data/sampled_jsons/Signed_Laplacians_for_Constrained_Graph_Clustering.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Signed Laplacians for Constrained Graph Clustering", "date": "", "ddg_snippet": "by JSF Carrasco — We use a tool called a signed Laplacian , which helps balance the natural structure of the data with the extra must-link and cannot-link rules.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=MHaSq1LlTe", "content": "by JSF Carrasco — We use a tool called a signed Laplacian , which helps balance the natural structure of the data with the extra must-link and cannot-link rules."}
+{"idx": 1, "title": "Signed Laplacians for Constrained Graph Clustering", "date": "", "ddg_snippet": "Given two weighted graphs and defined on the same vertex set, the constrained clustering problem seeks to find a subset that minimises the cut ratio between ...", "subpage_snippet": "", "source": "chatpaper.com", "link": "https://chatpaper.com/paper/165108", "content": "Given two weighted graphs and defined on the same vertex set, the constrained clustering problem seeks to find a subset that minimises the cut ratio between ..."}
+{"idx": 2, "title": "Signed Laplacians for Constrained Graph Clustering", "date": "", "ddg_snippet": "G and · H. To reduce computational complexity, we utilise the signed Laplacian of · H, streamlining calculations while maintaining accuracy.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/45552", "content": "G and · H. To reduce computational complexity, we utilise the signed Laplacian of · H, streamlining calculations while maintaining accuracy."}
+{"idx": 3, "title": "A Comprehensive Survey on Spectral Clustering with ...", "date": "", "ddg_snippet": "24 Jan 2025 — This survey presents a comprehensive review of spectral clustering methods, emphasizing on the critical role of GSL.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.13597v2", "content": "24 Jan 2025 — This survey presents a comprehensive review of spectral clustering methods, emphasizing on the critical role of GSL."}
+{"idx": 4, "title": "Signed Laplacian for Spectral Clustering Revisited", "date": "", "ddg_snippet": "by A Knyazev · 2017 · Cited by 38 — The graph Laplacian matrix is obtained from the graph adjacency matrix that represents graph edge weights describing similarities of graph ... 21 pages", "subpage_snippet": "", "source": "www.merl.com", "link": "https://www.merl.com/publications/docs/TR2017-001.pdf", "content": "by A Knyazev · 2017 · Cited by 38 — The graph Laplacian matrix is obtained from the graph adjacency matrix that represents graph edge weights describing similarities of graph ... 21 pages"}
+{"idx": 5, "title": "On Constrained Spectral Clustering and Its Applications", "date": "", "ddg_snippet": "by X Wang · Cited by 261 — A new graph Laplacian is then computed based on the modified affinity matrix. In Xu et al (2005), the constraints are encoded in the same way, but a random walk ...", "subpage_snippet": "", "source": "www.cs.ucdavis.edu", "link": "https://www.cs.ucdavis.edu/~davidson/Publications/KDD10-DMKD12.pdf", "content": "by X Wang · Cited by 261 — A new graph Laplacian is then computed based on the modified affinity matrix. In Xu et al (2005), the constraints are encoded in the same way, but a random walk ..."}
+{"idx": 6, "title": "Prediction and Clustering in Signed Networks: A Local to ...", "date": "", "ddg_snippet": "by KY Chiang · 2014 · Cited by 189 — This procedure is analogous to the standard spectral clustering algorithm on unsigned graphs ; the only difference being that the usual graph Laplacian is ... 37 pages", "subpage_snippet": "", "source": "jmlr.org", "link": "https://jmlr.org/papers/volume15/chiang14a/chiang14a.pdf", "content": "by KY Chiang · 2014 · Cited by 189 — This procedure is analogous to the standard spectral clustering algorithm on unsigned graphs ; the only difference being that the usual graph Laplacian is ... 37 pages"}
+{"idx": 7, "title": "Spectral Theory of Unsigned and Signed Graphs Applications ...", "date": "", "ddg_snippet": "by J Gallier · 2019 · Cited by 79 — Abstract: This is a survey of the method of graph cuts and its applications to graph clustering of weighted unsigned and signed graphs . 122 pages", "subpage_snippet": "", "source": "www.cis.upenn.edu", "link": "https://www.cis.upenn.edu/~jean/spectral-graph-notes.pdf", "content": "by J Gallier · 2019 · Cited by 79 — Abstract: This is a survey of the method of graph cuts and its applications to graph clustering of weighted unsigned and signed graphs . 122 pages"}
+{"idx": 8, "title": "Regularized spectral methods for clustering signed networks", "date": "", "ddg_snippet": "by M Cucuringu · 2020 · Cited by 29 — We study the problem of k-way clustering in signed graphs . Considerable attention in recent years has been devoted to analyzing and modeling ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2011.01737", "content": "by M Cucuringu · 2020 · Cited by 29 — We study the problem of k-way clustering in signed graphs . Considerable attention in recent years has been devoted to analyzing and modeling ..."}
+{"idx": 9, "title": "Constrained Graph Clustering with Signed Laplacians", "date": "", "ddg_snippet": "by JSF Carrasco — The objective is to minimize the number of G-edges across clusters while maximizing the number of H-edges within clusters . Several variants of ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=FneYHZU19U", "content": "by JSF Carrasco — The objective is to minimize the number of G-edges across clusters while maximizing the number of H-edges within clusters . Several variants of ..."}
diff --git a/data/sampled_jsons/Signed_Laplacians_for_Constrained_Graph_Clustering_sitearxiv.org.jsonl b/data/sampled_jsons/Signed_Laplacians_for_Constrained_Graph_Clustering_sitearxiv.org.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b7fc0d696b79942acad23c9318447e522aa50b14
--- /dev/null
+++ b/data/sampled_jsons/Signed_Laplacians_for_Constrained_Graph_Clustering_sitearxiv.org.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GraphC: Parameter-free Hierarchical Clustering of Signed ...", "date": "", "ddg_snippet": "14 Jan 2025 — We can create a balanced Laplacian matrix once a signed network is balanced. The spectral decomposition of this matrix reveals the eigenvectors ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.00249v2", "content": "14 Jan 2025 — We can create a balanced Laplacian matrix once a signed network is balanced. The spectral decomposition of this matrix reveals the eigenvectors ..."}
+{"idx": 1, "title": "Computing the p-Laplacian eigenpairs of signed graphs 2025.01 ...", "date": "", "ddg_snippet": "In this paper, we establish the equivalence between the graph p p p italic_p - Laplacian eigenproblem and the tensor eigenproblem when p p p italic_p is even.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.07929v1", "content": "In this paper, we establish the equivalence between the graph p p p italic_p - Laplacian eigenproblem and the tensor eigenproblem when p p p italic_p is even."}
+{"idx": 2, "title": "Gremban Expansion for Signed Networks: Algebraic and ...", "date": "", "ddg_snippet": "2 days ago — This article deals with the characterization and detection of community and faction structures in signed networks.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.14193v1", "content": "2 days ago — This article deals with the characterization and detection of community and faction structures in signed networks."}
+{"idx": 3, "title": "Cluster Synchronization via Graph Laplacian Eigenvectors", "date": "", "ddg_snippet": "12 Sept 2025 — Extending the spectral framework to accommodate such systems would require analyzing the Laplacian of signed graphs . This introduces new ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.18978v2/", "content": "12 Sept 2025 — Extending the spectral framework to accommodate such systems would require analyzing the Laplacian of signed graphs . This introduces new ..."}
+{"idx": 4, "title": "On Spectral Properties of Signed Laplacians with ...", "date": "", "ddg_snippet": "by W Chen · 2020 · Cited by 28 — In this paper, we investigate spectral properties of signed Laplacians for undirected signed graphs. We find conditions on the negative weights ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2009.03581", "content": "by W Chen · 2020 · Cited by 28 — In this paper, we investigate spectral properties of signed Laplacians for undirected signed graphs. We find conditions on the negative weights ..."}
+{"idx": 5, "title": "A Comprehensive Survey on Spectral Clustering with ...", "date": "", "ddg_snippet": "24 Jan 2025 — The Constrained Laplacian Rank (CLR) algorithm [44] learns a new similarity matrix with exactly k k k italic_k connected components from an ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.13597v2", "content": "24 Jan 2025 — The Constrained Laplacian Rank (CLR) algorithm [44] learns a new similarity matrix with exactly k k k italic_k connected components from an ..."}
+{"idx": 6, "title": "Efficient Learning of Balanced Signed Graphs via Iterative ...", "date": "", "ddg_snippet": "by H Yokota · 2024 · Cited by 2 — A signed graph is balanced if there exist no cycles of odd number of negative edges [20]. It is discovered that there exists a simple one- to- ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2409.07794", "content": "by H Yokota · 2024 · Cited by 2 — A signed graph is balanced if there exist no cycles of odd number of negative edges [20]. It is discovered that there exists a simple one- to- ..."}
+{"idx": 7, "title": "Signed Graph Learning: Algorithms and Theory", "date": "", "ddg_snippet": "by A Karaaslanli · 2025 — In this paper, we develop a method for learning signed graphs from a set of smooth signed graph signals. Specif- ically, we employ the net ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2507.09717", "content": "by A Karaaslanli · 2025 — In this paper, we develop a method for learning signed graphs from a set of smooth signed graph signals. Specif- ically, we employ the net ..."}
+{"idx": 8, "title": "[2301.04956] Graph Laplacian for Semi-Supervised Learning", "date": "", "ddg_snippet": "by O Streicher · 2023 · Cited by 8 — In this paper, we propose a new type of graph - Laplacian which is adapted for Semi-Supervised Learning (SSL) problems. It is based on both density and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2301.04956", "content": "by O Streicher · 2023 · Cited by 8 — In this paper, we propose a new type of graph - Laplacian which is adapted for Semi-Supervised Learning (SSL) problems. It is based on both density and ..."}
+{"idx": 9, "title": "Efficient Learning of Balanced Signed Graphs via Sparse ...", "date": "", "ddg_snippet": "by H Yokota · 2025 — Abstract— Signed graphs are equipped with both positive and negative edge weights, encoding pairwise correlations as well as.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2506.01826", "content": "by H Yokota · 2025 — Abstract— Signed graphs are equipped with both positive and negative edge weights, encoding pairwise correlations as well as."}
diff --git a/data/sampled_jsons/SimXRD-4M_Category_1_Category_2_space_group_classification_A.3.jsonl b/data/sampled_jsons/SimXRD-4M_Category_1_Category_2_space_group_classification_A.3.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d1ad9772fceeb2897beb3bfb2487a813d36d3af8
--- /dev/null
+++ b/data/sampled_jsons/SimXRD-4M_Category_1_Category_2_space_group_classification_A.3.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "SimXRD - 4 M : Big Simulated X-ray Diffraction Data and Crystal...", "date": "", "ddg_snippet": "To address this, we introduce SimXRD - 4 M , the largest open-source simulated XRD pattern dataset to date, aimed at accelerating the development of crystallographic informatics.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.15469v2", "content": "To address this, we introduce SimXRD - 4 M , the largest open-source simulated XRD pattern dataset to date, aimed at accelerating the development of crystallographic informatics."}
+{"idx": 1, "title": "SimXRD-4M: Big Simulated X-ray Diffraction Data and ...", "date": "", "ddg_snippet": "by CAO Bin · Cited by 1 — We introduce SimXRD , the largest open-source simulated XRD pattern dataset to date, aimed at accelerating the development of crystallographic informatics.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=mkuB677eMM", "content": "by CAO Bin · Cited by 1 — We introduce SimXRD , the largest open-source simulated XRD pattern dataset to date, aimed at accelerating the development of crystallographic informatics."}
+{"idx": 2, "title": "SimXRD-4M: Big Simulated X-ray Diffraction Data ...", "date": "", "ddg_snippet": "15 Jun 2024 — Crystals are classified based on their symmetry elements, falling into one of 7 crystal systems, and could further divided into 230 space groups ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.15469v1", "content": "15 Jun 2024 — Crystals are classified based on their symmetry elements, falling into one of 7 crystal systems, and could further divided into 230 space groups ..."}
+{"idx": 3, "title": "(PDF) SIMXRD - 4 M : big simulated x-ray diffraction data and crystal...", "date": "", "ddg_snippet": "• Category 3 : The parameter domain excludes Category 1 and Category 2 .ideal conditions, exhibiting lower broadening and noise. Category 3 represents an intermediate state. between the two . The accuracy of space group classification is displayed in Table 6. The results.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/389657996_SIMXRD-4M_BIG_SIMULATED_X-RAY_DIFFRACTION_DATA_AND_CRYSTAL_SYMMETRY_CLASSIFICATION_BENCHMARK", "content": "• Category 3 : The parameter domain excludes Category 1 and Category 2 .ideal conditions, exhibiting lower broadening and noise. Category 3 represents an intermediate state. between the two . The accuracy of space group classification is displayed in Table 6. The results."}
+{"idx": 4, "title": "Advancing Crystal Structure Analysis with SimXRD Dataset", "date": "", "ddg_snippet": "Space Group Classification : This is a more intricate task, as it requires identifying the specific space group for each crystal pattern. Because there are more classes in this category , the models often find it harder to make accurate predictions.", "subpage_snippet": "", "source": "scisimple.com", "link": "https://scisimple.com/en/articles/2025-07-21-advancing-crystal-structure-analysis-with-simxrd-dataset--ak4ex8o", "content": "Space Group Classification : This is a more intricate task, as it requires identifying the specific space group for each crystal pattern. Because there are more classes in this category , the models often find it harder to make accurate predictions."}
+{"idx": 5, "title": "GitHub - Bin-Cao/SimXRD: [ICLR 2025] SimXRD - 4 M : Big Simulated...", "date": "", "ddg_snippet": "Open Source: SimXRD - 4 M is available on Huggingface. Data Description: Crystals are categorized into 230 space groups , each representing a distinct symmetry catrgory.The ultimate goal is for the model to accurately identify the correct space group based on XRD patterns.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Bin-Cao/SimXRD", "content": "Open Source: SimXRD - 4 M is available on Huggingface. Data Description: Crystals are categorized into 230 space groups , each representing a distinct symmetry catrgory.The ultimate goal is for the model to accurately identify the correct space group based on XRD patterns."}
+{"idx": 6, "title": "World Bank country classifications by income level for 2024-2025", "date": "", "ddg_snippet": "Explore the updated World Bank country income classifications for 2024-2025, highlighting GNI per capita shifts and global economic trends. Discover which countries moved between income categories and understand the factors driving these changes.", "subpage_snippet": "", "source": "blogs.worldbank.org", "link": "https://blogs.worldbank.org/en/opendata/world-bank-country-classifications-by-income-level-for-2024-2025", "content": "Explore the updated World Bank country income classifications for 2024-2025, highlighting GNI per capita shifts and global economic trends. Discover which countries moved between income categories and understand the factors driving these changes."}
+{"idx": 7, "title": "VS Code: How To Change Indentation ( 2 spaces ...) - KindaCode", "date": "", "ddg_snippet": "VS Code: How to Compare Two Files (Find the Difference). 2 . Type “Indentation” into the search field then head to the “Editor: Tab Size” section. Replace the default space number with your preferred one", "subpage_snippet": "", "source": "www.kindacode.com", "link": "https://www.kindacode.com/article/vs-code-how-to-change-indentation-2-spaces-4-spaces", "content": "VS Code: How to Compare Two Files (Find the Difference). 2 . Type “Indentation” into the search field then head to the “Editor: Tab Size” section. Replace the default space number with your preferred one"}
+{"idx": 8, "title": "High-Resolution Space Group Diagrams and Tables", "date": "", "ddg_snippet": "121. I -4 2 m. 122. I -4 2 d. 123. P 4 / m m m.", "subpage_snippet": "", "source": "img.chem.ucl.ac.uk", "link": "http://img.chem.ucl.ac.uk/sgp/large/sgp.htm", "content": "121. I -4 2 m. 122. I -4 2 d. 123. P 4 / m m m."}
+{"idx": 9, "title": "ICLR 2025 Papers", "date": "", "ddg_snippet": "SimXRD - 4M : Big Simulated X-ray Diffraction Data and Crystal Symmetry Classification Benchmark · MuPT: A Generative Symbolic Music Pretrained Transformer ...", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2025/papers.html", "content": "SimXRD - 4M : Big Simulated X-ray Diffraction Data and Crystal Symmetry Classification Benchmark · MuPT: A Generative Symbolic Music Pretrained Transformer ..."}
diff --git a/data/sampled_jsons/SimXRD-4M_Table_2.jsonl b/data/sampled_jsons/SimXRD-4M_Table_2.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b99e06ee689ac84450a528d0db912adb954bcf54
--- /dev/null
+++ b/data/sampled_jsons/SimXRD-4M_Table_2.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "SimXRD-4M: Big Simulated X-ray Diffraction Data and Crystal Symmetry ...", "date": "", "ddg_snippet": "To address this, we introduce SimXRD-4M , the largest open-source simulated XRD pattern dataset to date, aimed at accelerating the development of crystallographic informatics. We developed a novel XRD simulation method that incorporates comprehensive physical interactions, resulting in a high-fidelity database.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.15469v2", "content": "To address this, we introduce SimXRD-4M , the largest open-source simulated XRD pattern dataset to date, aimed at accelerating the development of crystallographic informatics. We developed a novel XRD simulation method that incorporates comprehensive physical interactions, resulting in a high-fidelity database."}
+{"idx": 1, "title": "Simxrd-4m: B Simulated X-ray Diffraction D C Symmetry Classification", "date": "", "ddg_snippet": "ABSTRACT Powder X-ray diffraction (XRD) patterns are highly effective for crystal identi-fication and play a pivotal role in materials discovery. Although machine learn-ing (ML) has advanced the analysis of powder XRD patterns, progress has been constrained by the limited availability of training data and established bench-marks. To address this, we introduce SimXRD-4M , the largest open-source ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=mkuB677eMM", "content": "ABSTRACT Powder X-ray diffraction (XRD) patterns are highly effective for crystal identi-fication and play a pivotal role in materials discovery. Although machine learn-ing (ML) has advanced the analysis of powder XRD patterns, progress has been constrained by the limited availability of training data and established bench-marks. To address this, we introduce SimXRD-4M , the largest open-source ..."}
+{"idx": 2, "title": "SimXRD-4M ICLR 2025 - GitHub", "date": "", "ddg_snippet": "Open Source: SimXRD-4M is available on Huggingface. Data Description: Crystals are categorized into 230 space groups, each representing a distinct symmetry catrgory. XRD patterns, which correspond to the crystal structure, serve as vital tools for studying these materials. However, XRD patterns are influenced by various factors such as the testing environment (instrumentation), light source (X ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Bin-Cao/SimXRD", "content": "Open Source: SimXRD-4M is available on Huggingface. Data Description: Crystals are categorized into 230 space groups, each representing a distinct symmetry catrgory. XRD patterns, which correspond to the crystal structure, serve as vital tools for studying these materials. However, XRD patterns are influenced by various factors such as the testing environment (instrumentation), light source (X ..."}
+{"idx": 3, "title": "AI4Spectro (SimXRD) - Hugging Face", "date": "", "ddg_snippet": "SimXRD-4M databaseSimXRD- 4M Registration Utilize this page to register the utilization of SimXRD , thereby retrieving version information, backup data, and reference materials Introduction The SimXRD database is a comprehensive resource for spectral data analysis, designed to facilitate the identification of crystal materials both in and out library. Version V1.0.0 (May 2024) This version ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/AI4Spectro", "content": "SimXRD-4M databaseSimXRD- 4M Registration Utilize this page to register the utilization of SimXRD , thereby retrieving version information, backup data, and reference materials Introduction The SimXRD database is a comprehensive resource for spectral data analysis, designed to facilitate the identification of crystal materials both in and out library. Version V1.0.0 (May 2024) This version ..."}
+{"idx": 4, "title": "SimXRD-4M: Big Simulated X-ray Diffraction Data and Crystalline ...", "date": "", "ddg_snippet": "#1 SimXRD-4M : Big Simulated X-ray Diffraction Data and Crystalline Symmetry Classification Benchmark [PDF 2 ] [Copy] [Kimi] [REL] Authors: Bin Cao, Yang Liu, Zinan Zheng, Ruifeng Tan, Jia Li, Tong-Yi Zhang Powder X-ray diffraction (XRD) patterns are highly effective for crystal identification and play a pivotal role in materials discovery. While machine learning (ML) has advanced the analysis ...", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/venue/mkuB677eMM@OpenReview", "content": "#1 SimXRD-4M : Big Simulated X-ray Diffraction Data and Crystalline Symmetry Classification Benchmark [PDF 2 ] [Copy] [Kimi] [REL] Authors: Bin Cao, Yang Liu, Zinan Zheng, Ruifeng Tan, Jia Li, Tong-Yi Zhang Powder X-ray diffraction (XRD) patterns are highly effective for crystal identification and play a pivotal role in materials discovery. While machine learning (ML) has advanced the analysis ..."}
+{"idx": 5, "title": "SimXRD-4M: Big Simulated X-ray Diffraction Data Accelerate the Crystal ...", "date": "", "ddg_snippet": "To address this, we introduce SimXRD , the largest open-source simulated XRD pattern dataset so far, to accelerate the development of crystallographic informatics. SimXRD comprises 4,065,346 simulated powder X-ray diffraction patterns, representing 119,569 distinct crystal structures under 33 simulated conditions that mimic real-world variations.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2406.15469", "content": "To address this, we introduce SimXRD , the largest open-source simulated XRD pattern dataset so far, to accelerate the development of crystallographic informatics. SimXRD comprises 4,065,346 simulated powder X-ray diffraction patterns, representing 119,569 distinct crystal structures under 33 simulated conditions that mimic real-world variations."}
+{"idx": 6, "title": "SimXRD-4M: Big Simulated X-ray Diffraction Data Accelerate the ...", "date": "", "ddg_snippet": "To address this, we introduce SimXRD , the largest open-source simulated XRD pattern dataset so far, to accelerate the development of crystallographic informatics. SimXRD comprises 4,065,346 simulated powder X-ray diffraction patterns, representing 119,569 distinct crystal structures under 33 simulated conditions that mimic real-world variations.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.15469v1", "content": "To address this, we introduce SimXRD , the largest open-source simulated XRD pattern dataset so far, to accelerate the development of crystallographic informatics. SimXRD comprises 4,065,346 simulated powder X-ray diffraction patterns, representing 119,569 distinct crystal structures under 33 simulated conditions that mimic real-world variations."}
+{"idx": 7, "title": "PDF SimXRD-4M: Big Simulated X-ray Diffraction Data and Crystal Symmetry ...", "date": "", "ddg_snippet": "2 : Data analysis reveals that the symmetry labels follow a long-tailed distribution. 3: We evaluate 21 models on two different splitting patterns (in-library and out-of-library) and find that most existing models struggle to accurately predict the symmetry of low-frequency classes, even when addressing for class imbalance.", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/media/iclr-2025/Slides/28452.pdf", "content": "2 : Data analysis reveals that the symmetry labels follow a long-tailed distribution. 3: We evaluate 21 models on two different splitting patterns (in-library and out-of-library) and find that most existing models struggle to accurately predict the symmetry of low-frequency classes, even when addressing for class imbalance."}
+{"idx": 8, "title": "Publications - Ruifeng Tan", "date": "", "ddg_snippet": "SimXRD-4M : Big Simulated X-ray Diffraction Data and Crystalline Symmetry Classification Benchmark Published in ICLR, 2025 In this paper, we developed the largest open-source simulated X-ray diffraction database ( SimXRD ). SimXRD comprises 4,065,346 simulated powder XRD patterns, representing 119,569 unique crystal structures under 33 simulated conditions that reflect real-world variations. We ...", "subpage_snippet": "", "source": "ruifeng-tan.github.io", "link": "https://ruifeng-tan.github.io/publications/", "content": "SimXRD-4M : Big Simulated X-ray Diffraction Data and Crystalline Symmetry Classification Benchmark Published in ICLR, 2025 In this paper, we developed the largest open-source simulated X-ray diffraction database ( SimXRD ). SimXRD comprises 4,065,346 simulated powder XRD patterns, representing 119,569 unique crystal structures under 33 simulated conditions that reflect real-world variations. We ..."}
+{"idx": 9, "title": "arXiv:2406.15469v1 [cond-mat.mtrl-sci] 15 Jun 2024", "date": "", "ddg_snippet": "3 SimXRD-4M Dataset In this section, we first introduce the fundamental concepts of the research problem and then elaborate on the dataset construction process and analysis results.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2406.15469v1", "content": "3 SimXRD-4M Dataset In this section, we first introduce the fundamental concepts of the research problem and then elaborate on the dataset construction process and analysis results."}
diff --git a/data/sampled_jsons/SimXRD-4M_paper_Category_1_vs_Category_2_space_group_classification_year_2023.jsonl b/data/sampled_jsons/SimXRD-4M_paper_Category_1_vs_Category_2_space_group_classification_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6494f589d212e5bc2af5b9ac172ba0d88e7e6ce0
--- /dev/null
+++ b/data/sampled_jsons/SimXRD-4M_paper_Category_1_vs_Category_2_space_group_classification_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Corundum - Wikipedia", "date": "", "ddg_snippet": "General. Category . Oxide mineral – Hematite group . Formula. Al 2 O3. IMA symbol. Crn[ 1 ]. Strunz classification . 4.CB.05.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Corundum", "content": "General. Category . Oxide mineral – Hematite group . Formula. Al 2 O3. IMA symbol. Crn[ 1 ]. Strunz classification . 4.CB.05."}
+{"idx": 1, "title": "SimXRD-4M: Big Simulated X-ray Diffraction Data and ...", "date": "", "ddg_snippet": "by CAO Bin · Cited by 1 — The paper introduces a benchmark dataset \" SimXRD \" which comprises 4,065,346 simulated powder XRD patterns that represent 119,569 unique crystal structures. This ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=mkuB677eMM", "content": "by CAO Bin · Cited by 1 — The paper introduces a benchmark dataset \" SimXRD \" which comprises 4,065,346 simulated powder XRD patterns that represent 119,569 unique crystal structures. This ..."}
+{"idx": 2, "title": "SimXRD-4M: Big Simulated X-ray Diffraction Data ...", "date": "", "ddg_snippet": "15 Jun 2024 — Symmetry classification Formally, SimXRD considers the following multi-class sequence classification problem.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.15469v1", "content": "15 Jun 2024 — Symmetry classification Formally, SimXRD considers the following multi-class sequence classification problem."}
+{"idx": 3, "title": "ICLR 2025 Papers", "date": "", "ddg_snippet": "SimXRD - 4M : Big Simulated X-ray Diffraction Data and Crystal Symmetry Classification Benchmark · MuPT: A Generative Symbolic Music Pretrained Transformer ...", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2025/papers.html", "content": "SimXRD - 4M : Big Simulated X-ray Diffraction Data and Crystal Symmetry Classification Benchmark · MuPT: A Generative Symbolic Music Pretrained Transformer ..."}
+{"idx": 4, "title": "SimXRD - 4 M : Big Simulated X-ray Diffraction Data and Crystal...", "date": "", "ddg_snippet": "To address this, we introduce SimXRD - 4 M , the largest open-source simulated XRD pattern dataset to date, aimed at accelerating the development of crystallographic informatics.They treat XRD patterns as sequences and aim to classify them into specific crystal systems or space groups .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.15469v2", "content": "To address this, we introduce SimXRD - 4 M , the largest open-source simulated XRD pattern dataset to date, aimed at accelerating the development of crystallographic informatics.They treat XRD patterns as sequences and aim to classify them into specific crystal systems or space groups ."}
+{"idx": 5, "title": "GitHub - Bin-Cao/SimXRD: [ICLR 2025] SimXRD - 4 M : Big Simulated...", "date": "", "ddg_snippet": "Open Source: SimXRD - 4 M is available on Huggingface. Data Description: Crystals are categorized into 230 space groups , each representing a distinct symmetry catrgory. XRD patterns, which correspond to the crystal structure, serve as vital tools for studying these materials.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Bin-Cao/SimXRD", "content": "Open Source: SimXRD - 4 M is available on Huggingface. Data Description: Crystals are categorized into 230 space groups , each representing a distinct symmetry catrgory. XRD patterns, which correspond to the crystal structure, serve as vital tools for studying these materials."}
+{"idx": 6, "title": "(PDF) SimXRD - 4 M : Big Simulated X-ray Diffraction Data Accelerate...", "date": "", "ddg_snippet": "across various space groups (uniformly divided into 5 categories according to the frequenc y) and (B).T o further demonstrate the practical utility and applicability of SimXRD , we explore its use in two . critical tasks: crystal system classification and space group classification .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/381665624_SimXRD-4M_Big_Simulated_X-ray_Diffraction_Data_Accelerate_the_Crystalline_Symmetry_Classification", "content": "across various space groups (uniformly divided into 5 categories according to the frequenc y) and (B).T o further demonstrate the practical utility and applicability of SimXRD , we explore its use in two . critical tasks: crystal system classification and space group classification ."}
+{"idx": 7, "title": "SimXRD - 4 M : Big Simulated X-ray Diffraction Data", "date": "", "ddg_snippet": "Long-tailed distribution of space group . 1 : We introduce SimXRD , the largest open-source XRD pattern dataset for symmetry identification. 2 : Data analysis reveals that the symmetry labels follow a long-tailed distribution.", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/media/iclr-2025/Slides/28452.pdf", "content": "Long-tailed distribution of space group . 1 : We introduce SimXRD , the largest open-source XRD pattern dataset for symmetry identification. 2 : Data analysis reveals that the symmetry labels follow a long-tailed distribution."}
+{"idx": 8, "title": "Advancing Crystal Structure Analysis with SimXRD Dataset", "date": "", "ddg_snippet": "Space Group Classification : This is a more intricate task, as it requires identifying the specific space group for each crystal pattern. Because there are more classes in this category , the models often find it harder to make accurate predictions.", "subpage_snippet": "", "source": "scisimple.com", "link": "https://scisimple.com/en/articles/2025-07-21-advancing-crystal-structure-analysis-with-simxrd-dataset--ak4ex8o", "content": "Space Group Classification : This is a more intricate task, as it requires identifying the specific space group for each crystal pattern. Because there are more classes in this category , the models often find it harder to make accurate predictions."}
+{"idx": 9, "title": "World Bank country classifications by income level for 2024-2025", "date": "", "ddg_snippet": "Explore the updated World Bank country income classifications for 2024-2025, highlighting GNI per capita shifts and global economic trends. Discover which countries moved between income categories and understand the factors driving these changes.", "subpage_snippet": "", "source": "blogs.worldbank.org", "link": "https://blogs.worldbank.org/en/opendata/world-bank-country-classifications-by-income-level-for-2024-2025", "content": "Explore the updated World Bank country income classifications for 2024-2025, highlighting GNI per capita shifts and global economic trends. Discover which countries moved between income categories and understand the factors driving these changes."}
diff --git a/data/sampled_jsons/Simple_yet_Effective_Incomplete_Multi-view_Clustering_BDGPFEA_50%_missing_ratio_Table_2_ACC_Ours_IMV.jsonl b/data/sampled_jsons/Simple_yet_Effective_Incomplete_Multi-view_Clustering_BDGPFEA_50%_missing_ratio_Table_2_ACC_Ours_IMV.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..60381df230ccfb63c14f20fa0ebb7694874b048e
--- /dev/null
+++ b/data/sampled_jsons/Simple_yet_Effective_Incomplete_Multi-view_Clustering_BDGPFEA_50%_missing_ratio_Table_2_ACC_Ours_IMV.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Simple yet Effective Incomplete Multi-view Clustering:...", "date": "", "ddg_snippet": "Most of incomplete multi-view clustering (IMVC) methods typically choose to ignore the missing samples and only utilize observed unpaired samples to construct bipartite similarity. Moreover, they employ a single quantity of prototypes to extract the information of $\\textbf {all}$ views . To eliminate these drawbacks, we present a simple yet effective IMVC approach, SIIHPC, in this work. It ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=KijslFbfOL", "content": "Most of incomplete multi-view clustering (IMVC) methods typically choose to ignore the missing samples and only utilize observed unpaired samples to construct bipartite similarity. Moreover, they employ a single quantity of prototypes to extract the information of $\\textbf {all}$ views . To eliminate these drawbacks, we present a simple yet effective IMVC approach, SIIHPC, in this work. It ..."}
+{"idx": 1, "title": "PDF Simple yet Effective Incomplete Multi-view Clustering: Similarity-level ...", "date": "", "ddg_snippet": "Motivation Most of IMVC methods (1) choose to ignore the missing samples and only utilize observed unpaired samples to construct bipartite similarity; ( 2 ) employ a single quantity of prototypes to extract the information of all views .", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/media/iclr-2025/Slides/30038.pdf", "content": "Motivation Most of IMVC methods (1) choose to ignore the missing samples and only utilize observed unpaired samples to construct bipartite similarity; ( 2 ) employ a single quantity of prototypes to extract the information of all views ."}
+{"idx": 2, "title": "PDF Deep Incomplete Multi-View Clustering via Mining Cluster Complementarity", "date": "", "ddg_snippet": "Abstract Incomplete multi-view clustering (IMVC) is an important un-supervised approach to group the multi-view data containing missing data in some views . Previous IMVC methods suf-fer from the following issues: (1) the inaccurate imputation or padding for missing data negatively affects the clustering performance, ( 2 ) the quality of features after fusion might be interfered by the low ...", "subpage_snippet": "", "source": "cdn.aaai.org", "link": "https://cdn.aaai.org/ojs/20856/20856-13-24869-1-2-20220628.pdf", "content": "Abstract Incomplete multi-view clustering (IMVC) is an important un-supervised approach to group the multi-view data containing missing data in some views . Previous IMVC methods suf-fer from the following issues: (1) the inaccurate imputation or padding for missing data negatively affects the clustering performance, ( 2 ) the quality of features after fusion might be interfered by the low ..."}
+{"idx": 3, "title": "Incomplete multi-view clustering via structure exploration and missing ...", "date": "", "ddg_snippet": "Incomplete multi-view clustering (IMVC) aims to boost clustering performance by capturing complementary information from incomplete multi-views , where partial data samples in one or more views are missing .", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S1566253523004396", "content": "Incomplete multi-view clustering (IMVC) aims to boost clustering performance by capturing complementary information from incomplete multi-views , where partial data samples in one or more views are missing ."}
+{"idx": 4, "title": "GitHub - whbdmu/MIMB: Incomplete Multi-view Clustering", "date": "", "ddg_snippet": "Introduction In this paper, we have proposed a novel method termed Manifold-based Incomplete Multi-view Clustering via Bi-Consistency Guidance (MIMB), which flexibly recover the incomplete data among various views and intends to achieve the bi-consistency guidance via reverse regularization.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/whbdmu/MIMB", "content": "Introduction In this paper, we have proposed a novel method termed Manifold-based Incomplete Multi-view Clustering via Bi-Consistency Guidance (MIMB), which flexibly recover the incomplete data among various views and intends to achieve the bi-consistency guidance via reverse regularization."}
+{"idx": 5, "title": "Projective Incomplete Multi-View Clustering - IEEE Xplore", "date": "", "ddg_snippet": "Due to the rapid development of multimedia technology and sensor technology, multi-view clustering (MVC) has become a research hotspot in machine learning, data mining, and other fields and has been developed significantly in the past decades. Compared with single- view clustering , MVC improves clustering performance by exploiting complementary and consistent information among different views ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10042189", "content": "Due to the rapid development of multimedia technology and sensor technology, multi-view clustering (MVC) has become a research hotspot in machine learning, data mining, and other fields and has been developed significantly in the past decades. Compared with single- view clustering , MVC improves clustering performance by exploiting complementary and consistent information among different views ..."}
+{"idx": 6, "title": "Manifold-based Incomplete Multi-view Clustering via Bi-Consistency Guidance", "date": "", "ddg_snippet": "Abstract—Incomplete multi-view clustering primarily focuses on dividing unlabeled data into corresponding categories with missing instances, and has received intensive attention due to its superiority in real applications. Considering the influence of incomplete data, the existing methods mostly attempt to recover data by adding extra terms. However, for the unsupervised methods, a simple ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2405.10987", "content": "Abstract—Incomplete multi-view clustering primarily focuses on dividing unlabeled data into corresponding categories with missing instances, and has received intensive attention due to its superiority in real applications. Considering the influence of incomplete data, the existing methods mostly attempt to recover data by adding extra terms. However, for the unsupervised methods, a simple ..."}
+{"idx": 7, "title": "S EFFECTIVE INCOMPLETE MULTI VIEW C - OpenReview", "date": "", "ddg_snippet": "ABSTRACT Most of incomplete multi-view clustering (IMVC) methods typically choose to ignore the missing samples and only utilize observed unpaired samples to construct bipartite similarity. Moreover, they employ a single quantity of prototypes to extract the information of all views . To eliminate these drawbacks, we present a simple yet effective IMVC approach, SIIHPC, in this work. It firstly ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=KijslFbfOL", "content": "ABSTRACT Most of incomplete multi-view clustering (IMVC) methods typically choose to ignore the missing samples and only utilize observed unpaired samples to construct bipartite similarity. Moreover, they employ a single quantity of prototypes to extract the information of all views . To eliminate these drawbacks, we present a simple yet effective IMVC approach, SIIHPC, in this work. It firstly ..."}
+{"idx": 8, "title": "PDF Incomplete Multi-view Clustering via Prototype-based Imputation - IJCAI", "date": "", "ddg_snippet": "Abstract In this paper, we study how to achieve two charac-teristics highly-expected by incomplete multi-view clustering (IMvC). Namely, i) instance common-ality refers to that within-cluster instances should share a common pattern, and ii) view versatil-ity refers to that cross- view samples should own view -specific patterns.", "subpage_snippet": "", "source": "www.ijcai.org", "link": "https://www.ijcai.org/proceedings/2023/0435.pdf", "content": "Abstract In this paper, we study how to achieve two charac-teristics highly-expected by incomplete multi-view clustering (IMvC). Namely, i) instance common-ality refers to that within-cluster instances should share a common pattern, and ii) view versatil-ity refers to that cross- view samples should own view -specific patterns."}
+{"idx": 9, "title": "Balance guided incomplete multi-view spectral clustering", "date": "", "ddg_snippet": "Considering the different contributions of views to the clustering task, a weighted multi-view learning mechanism is introduced to automatically balance the effects of different views in model optimization. In this way, the intrinsic information of the incomplete multi-view data can be fully exploited.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0893608023003805", "content": "Considering the different contributions of views to the clustering task, a weighted multi-view learning mechanism is introduced to automatically balance the effects of different views in model optimization. In this way, the intrinsic information of the incomplete multi-view data can be fully exploited."}
diff --git a/data/sampled_jsons/Sn_characters_dataset_training_examples_algebraic_combinatorics_machine_learning.jsonl b/data/sampled_jsons/Sn_characters_dataset_training_examples_algebraic_combinatorics_machine_learning.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..1f08c4f8d6a4f199a9dc30c42ef54773c2397fdb
--- /dev/null
+++ b/data/sampled_jsons/Sn_characters_dataset_training_examples_algebraic_combinatorics_machine_learning.jsonl
@@ -0,0 +1,5 @@
+{"idx": 0, "title": "Machine Learning meets Algebraic Combinatorics : A Suite of...", "date": "", "ddg_snippet": "In this paper we introduce the Algebraic Combinatorics Dataset Repository, a collection of research-level mathemat-ics datasets structured for machine learning and designed to accelerate mathematical discovery.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.06366", "content": "In this paper we introduce the Algebraic Combinatorics Dataset Repository, a collection of research-level mathemat-ics datasets structured for machine learning and designed to accelerate mathematical discovery."}
+{"idx": 1, "title": "Free Course: Introduction to Machine Learning Course from", "date": "", "ddg_snippet": "Learn the simple intuition behind Support Vector Machines .,Implement an SVM classifier in SKLearn/scikit- learn .,Identify how to choose the right ...", "subpage_snippet": "", "source": "www.classcentral.com", "link": "https://www.classcentral.com/course/udacity-introduction-to-machine-learning-course-2996", "content": "Learn the simple intuition behind Support Vector Machines .,Implement an SVM classifier in SKLearn/scikit- learn .,Identify how to choose the right ..."}
+{"idx": 2, "title": "Machine Learning Meets Algebraic Combinatorics", "date": "", "ddg_snippet": "Machine Learning Meets Algebraic Combinatorics : A Suite of Datasets ... Sn characters n = 18. 1.5920 × 1010. 2.7447 × 109 ... There are 118, 580 training examples ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=tlniJJFUW2&name=pdf", "content": "Machine Learning Meets Algebraic Combinatorics : A Suite of Datasets ... Sn characters n = 18. 1.5920 × 1010. 2.7447 × 109 ... There are 118, 580 training examples ..."}
+{"idx": 3, "title": "Debian -- Software Packages in \"buster\", Subsection gnu-r", "date": "", "ddg_snippet": "Dirichlet-Multinomial Mixture Model Machine Learning for Microbiome Data ... BioConductor SnpMatrix and XSnpMatrix classes and methods", "subpage_snippet": "", "source": "packages.debian.org", "link": "https://packages.debian.org/buster/gnu-r/", "content": "Dirichlet-Multinomial Mixture Model Machine Learning for Microbiome Data ... BioConductor SnpMatrix and XSnpMatrix classes and methods"}
+{"idx": 4, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/Socialized_Coevolution-_Advancing_a_Better_World_through_Cross-Task_Collaboration_Table_3_ablation_s.jsonl b/data/sampled_jsons/Socialized_Coevolution-_Advancing_a_Better_World_through_Cross-Task_Collaboration_Table_3_ablation_s.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..21af942a684e5ae2211e3162c3d1f3372e69a973
--- /dev/null
+++ b/data/sampled_jsons/Socialized_Coevolution-_Advancing_a_Better_World_through_Cross-Task_Collaboration_Table_3_ablation_s.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Socialized Coevolution: Advancing a Better World through ...", "date": "", "ddg_snippet": "To further verify the significance of each module, i.e., DHC and DSC, in DISC based on SC, we conduct the ablation study as shown in Table 3 . The results reveal that the model with DHC and DSC attains the best performance.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=0WQJ6DFSKp", "content": "To further verify the significance of each module, i.e., DHC and DSC, in DISC based on SC, we conduct the ablation study as shown in Table 3 . The results reveal that the model with DHC and DSC attains the best performance."}
+{"idx": 1, "title": "Socialized Learning: Making Each Other Better Through Multi ...", "date": "", "ddg_snippet": "Inspired by population genetics and cognitive science, leading to unique and complete development, we propose Multi-Agent Socialized Collaboration (MASC), which achieves SL through interactions among multiple agents.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v235/yao24d.html", "content": "Inspired by population genetics and cognitive science, leading to unique and complete development, we propose Multi-Agent Socialized Collaboration (MASC), which achieves SL through interactions among multiple agents."}
+{"idx": 2, "title": "Socialized learning | Proceedings of the 41st International ...", "date": "", "ddg_snippet": "Jul 21, 2024 · Inspired by population genetics and cognitive science, leading to unique and complete development, we propose Multi-Agent Socialized Collaboration (MASC), which achieves SL through interactions among multiple agents.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3692070.3694419", "content": "Jul 21, 2024 · Inspired by population genetics and cognitive science, leading to unique and complete development, we propose Multi-Agent Socialized Collaboration (MASC), which achieves SL through interactions among multiple agents."}
+{"idx": 3, "title": "Socialized Coevolution: Advancing a Better World through ...", "date": "", "ddg_snippet": "May 1, 2025 · The ablation study ( Table 3 ) is a good starting point, but it could be more comprehensive. While it ablates DHC and DSC individually, it would also be informative to see ablations of specific components within DHC and DSC to understand the contribution of each sub-module.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=0WQJ6DFSKp", "content": "May 1, 2025 · The ablation study ( Table 3 ) is a good starting point, but it could be more comprehensive. While it ablates DHC and DSC individually, it would also be informative to see ablations of specific components within DHC and DSC to understand the contribution of each sub-module."}
+{"idx": 4, "title": "ICML Poster Socialized Coevolution : Advancing a Better World ...", "date": "", "ddg_snippet": "Inspired by cognitive science, we propose Dynamic Information Socialized Collaboration (DISC), which achieves SC through interactions between models specialized in different downstream tasks.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46680", "content": "Inspired by cognitive science, we propose Dynamic Information Socialized Collaboration (DISC), which achieves SC through interactions between models specialized in different downstream tasks."}
+{"idx": 5, "title": "GitHub - yxjdarren/SC: The paper has been accepted to ICML 2025.", "date": "", "ddg_snippet": "The code repository for \" Socialized Coevolution : Advancing a Better World through Cross - Task Collaboration \" (the paper has been accepted by ICML 2025) in PyTorch.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/yxjdarren/SC", "content": "The code repository for \" Socialized Coevolution : Advancing a Better World through Cross - Task Collaboration \" (the paper has been accepted by ICML 2025) in PyTorch."}
+{"idx": 6, "title": "Xinjie Yao | Vision Group", "date": "", "ddg_snippet": "[2025-05] “ Socialized Coevolution : Advancing a Better World through Cross - Task Collaboration ” has been accepted by International Conference on Machine Learning (ICML, CCF-A).", "subpage_snippet": "", "source": "yxjdarren.github.io", "link": "https://yxjdarren.github.io/", "content": "[2025-05] “ Socialized Coevolution : Advancing a Better World through Cross - Task Collaboration ” has been accepted by International Conference on Machine Learning (ICML, CCF-A)."}
+{"idx": 7, "title": "Ruipu Zhao - OpenReview", "date": "", "ddg_snippet": "Publications Socialized Coevolution : Advancing a Better World through Cross - Task Collaboration Xinjie Yao, Yu Wang, Pengfei Zhu, Wanyu Lin, Ruipu Zhao, Zhoupeng Guo, Weihao Li, Qinghua Hu Published: 01 May 2025, Last Modified: 23 Jul 2025 ICML 2025 poster", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/profile?id=~Ruipu_Zhao1", "content": "Publications Socialized Coevolution : Advancing a Better World through Cross - Task Collaboration Xinjie Yao, Yu Wang, Pengfei Zhu, Wanyu Lin, Ruipu Zhao, Zhoupeng Guo, Weihao Li, Qinghua Hu Published: 01 May 2025, Last Modified: 23 Jul 2025 ICML 2025 poster"}
+{"idx": 8, "title": "RobotSmith: Generative Robotic Tool Design for Acquisition of", "date": "", "ddg_snippet": "... using collaborative VLM agents, (2) generates low-level robot trajectories for tool use, and ( 3 ) jointly optimizes tool geometry and usage for task ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.14763v1", "content": "... using collaborative VLM agents, (2) generates low-level robot trajectories for tool use, and ( 3 ) jointly optimizes tool geometry and usage for task ..."}
+{"idx": 9, "title": "openreview.net/profile?id=~Wanyu_Lin1", "date": "", "ddg_snippet": "Socialized Coevolution : Advancing a Better World through Cross - Task Collaboration . Xinjie Yao, Yu Wang, Pengfei Zhu, Wanyu Lin, Ruipu Zhao, Zhoupeng Guo, Weihao Li, Qinghua Hu.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/profile?id=~Wanyu_Lin1", "content": "Socialized Coevolution : Advancing a Better World through Cross - Task Collaboration . Xinjie Yao, Yu Wang, Pengfei Zhu, Wanyu Lin, Ruipu Zhao, Zhoupeng Guo, Weihao Li, Qinghua Hu."}
diff --git a/data/sampled_jsons/Spring_Li_et_al_2024_state_placement_blockchain_sharding_reinforcement_learning_year_2024.jsonl b/data/sampled_jsons/Spring_Li_et_al_2024_state_placement_blockchain_sharding_reinforcement_learning_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..9dfba6a9b731181b3ba248ae06476df88222d6d1
--- /dev/null
+++ b/data/sampled_jsons/Spring_Li_et_al_2024_state_placement_blockchain_sharding_reinforcement_learning_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "SPRING: Improving the Throughput of Sharding Blockchain via Deep ...", "date": "", "ddg_snippet": "In this paper, we present SPRING , the first deep- reinforcement - learning (DRL)-based sharding framework for state placement . SPRING formulates the state placement as a Markov Decision Process, which considers the cross-shard transaction ratio and workload balancing and employs DRL to learn the effective state placement policy.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3589334.3645386", "content": "In this paper, we present SPRING , the first deep- reinforcement - learning (DRL)-based sharding framework for state placement . SPRING formulates the state placement as a Markov Decision Process, which considers the cross-shard transaction ratio and workload balancing and employs DRL to learn the effective state placement policy."}
+{"idx": 1, "title": "PDF SPRING: Improving the Throughput of Sharding Blockchain via Deep ...", "date": "", "ddg_snippet": "Existing sharding solutions place states based on heuristic algorithms or redistribute states via graph-partitioning-based methods, which are either less efective or costly. In this paper, we present Spring , the first deep- reinforcement -learning(DRL)-based sharding framework for state placement .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=8oczaP1YKD&name=pdf", "content": "Existing sharding solutions place states based on heuristic algorithms or redistribute states via graph-partitioning-based methods, which are either less efective or costly. In this paper, we present Spring , the first deep- reinforcement -learning(DRL)-based sharding framework for state placement ."}
+{"idx": 2, "title": "The State-of-the-Art and Promising Future of Blockchain Sharding", "date": "", "ddg_snippet": "Blockchain sharding is a significant technical area, improving the scalability of blockchain systems. It is regarded as one of the potential solutions that can achieve on-chain scaling, and significantly improve the scalability of blockchains without alleviating the decentralization feature of blockchain . To provide a reference and inspire participation from both the academic and industrial ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10742577", "content": "Blockchain sharding is a significant technical area, improving the scalability of blockchain systems. It is regarded as one of the potential solutions that can achieve on-chain scaling, and significantly improve the scalability of blockchains without alleviating the decentralization feature of blockchain . To provide a reference and inspire participation from both the academic and industrial ..."}
+{"idx": 3, "title": "PDF SPRING: Improving the Throughput of Sharding Blockchain via Deep ...", "date": "", "ddg_snippet": "In this paper, we present SPRING , the first deep- reinforcement -learning(DRL)-based shard-ing framework for state placement . SPRING formulates the state placement as a Markov Decision Process, which considers the cross-shard transaction ratio and workload balancing and employs DRL to learn the efective state placement policy.", "subpage_snippet": "", "source": "zhenxiao.com", "link": "http://zhenxiao.com/papers/WWW_Spring_camera_ready.pdf", "content": "In this paper, we present SPRING , the first deep- reinforcement -learning(DRL)-based shard-ing framework for state placement . SPRING formulates the state placement as a Markov Decision Process, which considers the cross-shard transaction ratio and workload balancing and employs DRL to learn the efective state placement policy."}
+{"idx": 4, "title": "A dynamic state sharding blockchain architecture for scalable and ...", "date": "", "ddg_snippet": "Considering that blockchain based crowdsourcing systems rely on the underlying blockchain , we aim to optimize the scalability and decentralization of blockchain while ensuring security by proposing a blockchain architecture based on state sharding using deep reinforcement learning , thereby improving the performance and security of blockchain ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S1084804523002047", "content": "Considering that blockchain based crowdsourcing systems rely on the underlying blockchain , we aim to optimize the scalability and decentralization of blockchain while ensuring security by proposing a blockchain architecture based on state sharding using deep reinforcement learning , thereby improving the performance and security of blockchain ..."}
+{"idx": 5, "title": "SPRING: Improving the Throughput of Sharding Blockchain via Deep...", "date": "", "ddg_snippet": "In this paper, we present Spring , the first deep- reinforcement - learning (DRL)-based sharding framework for state placement . Spring formulates the state placement as a Markov Decision Process, which takes into consideration the cross-shard transaction ratio and workload balancing, and employs DRL to learn the effective state placement policy.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=8oczaP1YKD", "content": "In this paper, we present Spring , the first deep- reinforcement - learning (DRL)-based sharding framework for state placement . Spring formulates the state placement as a Markov Decision Process, which takes into consideration the cross-shard transaction ratio and workload balancing, and employs DRL to learn the effective state placement policy."}
+{"idx": 6, "title": "Qiuyu Ding (0009-0001-3676-1552) - ORCID", "date": "", "ddg_snippet": "SPRING : Improving the Throughput of Sharding Blockchain via Deep Reinforcement Learning Based State Placement 2024 -05-13 | Conference paper DOI: 10.1145/3589334.3645386 Contributors: Pengze Li ; Mingxuan Song; Mingzhe Xing; Zhen Xiao; Qiuyu Ding; Shengjie Guan; Jieyi Long", "subpage_snippet": "", "source": "orcid.org", "link": "https://orcid.org/0009-0001-3676-1552", "content": "SPRING : Improving the Throughput of Sharding Blockchain via Deep Reinforcement Learning Based State Placement 2024 -05-13 | Conference paper DOI: 10.1145/3589334.3645386 Contributors: Pengze Li ; Mingxuan Song; Mingzhe Xing; Zhen Xiao; Qiuyu Ding; Shengjie Guan; Jieyi Long"}
+{"idx": 7, "title": "Cross-shard transaction optimization based on community detection in ...", "date": "", "ddg_snippet": "SPRING proposes a sharding framework based on deep reinforcement learning , which constructs the placement problem as a Markov Decision Process (MDP) while considering the proportion of cross-shard transactions and workload balancing.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S1568494624012250", "content": "SPRING proposes a sharding framework based on deep reinforcement learning , which constructs the placement problem as a Markov Decision Process (MDP) while considering the proportion of cross-shard transactions and workload balancing."}
+{"idx": 8, "title": "Zhen Xiao | Semantic Scholar", "date": "", "ddg_snippet": "SPRING is presented, the first deep- reinforcement - learning (DRL)-based sharding framework for state placement that considers the cross-shard transaction ratio and workload balancing and employs DRL to learn the effective state placement policy. Expand", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/author/Zhen-Xiao/2257215916", "content": "SPRING is presented, the first deep- reinforcement - learning (DRL)-based sharding framework for state placement that considers the cross-shard transaction ratio and workload balancing and employs DRL to learn the effective state placement policy. Expand"}
+{"idx": 9, "title": "Mingzhe Xing", "date": "", "ddg_snippet": "(To appear in KDD'2024). Pengzi Li , Mingxuan Song, Mingzhe Xing, Zhen Xiao, Qiuyu Ding, Shengjie Guan and Jieyi Long, \" SPRING : Improving the Throughput of Sharding Blockchain via Deep Reinforcement Learning Based State Placement \". In Proceedings of the ACM on Web Conference 2024 (WWW'2024).", "subpage_snippet": "", "source": "xmzzyo.github.io", "link": "https://xmzzyo.github.io/", "content": "(To appear in KDD'2024). Pengzi Li , Mingxuan Song, Mingzhe Xing, Zhen Xiao, Qiuyu Ding, Shengjie Guan and Jieyi Long, \" SPRING : Improving the Throughput of Sharding Blockchain via Deep Reinforcement Learning Based State Placement \". In Proceedings of the ACM on Web Conference 2024 (WWW'2024)."}
diff --git a/data/sampled_jsons/Statistical_Collusion_by_Collectives_Rs(k)_=_Hoeffding.jsonl b/data/sampled_jsons/Statistical_Collusion_by_Collectives_Rs(k)_=_Hoeffding.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b1cabf7f1b296da0d271e75d99b69262063d3b6b
--- /dev/null
+++ b/data/sampled_jsons/Statistical_Collusion_by_Collectives_Rs(k)_=_Hoeffding.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "7 Feb 2025 — We note that Hoeffding's inequality can be loose , for example when applied to sums of Bernoulli random variables with means close to zero. One ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.04879v1", "content": "7 Feb 2025 — We note that Hoeffding's inequality can be loose , for example when applied to sums of Bernoulli random variables with means close to zero. One ..."}
+{"idx": 1, "title": "Singapore Department of Statistics (DOS) | SingStat Website", "date": "", "ddg_snippet": "Singapore's National Statistical Office that collects, compiles and disseminates economic and socio-demographic statistics.", "subpage_snippet": "", "source": "www.singstat.gov.sg", "link": "https://www.singstat.gov.sg/", "content": "Singapore's National Statistical Office that collects, compiles and disseminates economic and socio-demographic statistics."}
+{"idx": 2, "title": "Decision Making With Heterogeneous Agents ... - DASH (Harvard)", "date": "", "ddg_snippet": "The methods of artificial intelligence and statistical machine learning are finding tremendous success in various fields, with applications ranging from ...", "subpage_snippet": "", "source": "dash.harvard.edu", "link": "https://dash.harvard.edu/bitstreams/fb6480e5-69f6-473e-86c4-437cacf8e04b/download", "content": "The methods of artificial intelligence and statistical machine learning are finding tremendous success in various fields, with applications ranging from ..."}
+{"idx": 3, "title": "Advanced Mathematical Methods in Intelligent Multimedia", "date": "", "ddg_snippet": "... ( k , n)-Threshold Secret Image Sharing Scheme Based ... Hoeffding , W. Probability inequalities for sums ... statistical feature of a doubtful image ...", "subpage_snippet": "", "source": "mdpi-res.com", "link": "https://mdpi-res.com/bookfiles/book/7935/Advanced_Mathematical_Methods_in_Intelligent_Multimedia_Security_and_Applications.pdf?v=1750640844", "content": "... ( k , n)-Threshold Secret Image Sharing Scheme Based ... Hoeffding , W. Probability inequalities for sums ... statistical feature of a doubtful image ..."}
+{"idx": 4, "title": "Annual reports - CPFB", "date": "", "ddg_snippet": "Find out how we’ve been doing through our CPF annual reports, where we share our financial statements, review of operations and more.", "subpage_snippet": "", "source": "www.cpf.gov.sg", "link": "https://www.cpf.gov.sg/employer/infohub/reports-and-statistics/annual-reports", "content": "Find out how we’ve been doing through our CPF annual reports, where we share our financial statements, review of operations and more."}
+{"idx": 5, "title": "Reuters | Breaking International News & Views", "date": "", "ddg_snippet": "2 days ago · Find latest news from every corner of the globe at Reuters.com , your online source for breaking international news coverage.", "subpage_snippet": "", "source": "www.reuters.com", "link": "https://www.reuters.com/", "content": "2 days ago · Find latest news from every corner of the globe at Reuters.com , your online source for breaking international news coverage."}
+{"idx": 6, "title": "Payment Components presents new MEPS+ (SCRIPS) solution", "date": "", "ddg_snippet": "PaymentComponents, a pioneer in ISO20022 migration projects, has enhanced its Finaplo Financial messaging solution with MEPS+ (SCRIPS) payments. MEPS+ is the real-time gross settlement system, which is owned and handled by the Monetary Authority of Singapore (“MAS”) and is now adopting the ISO20022 standard to replace the previous one, based on Swift MT. MEPS+ (SCRIPS) is the new ISO20022 ...", "subpage_snippet": "", "source": "www.paymentcomponents.com", "link": "https://www.paymentcomponents.com/new-meps-scrips-solution/", "content": "PaymentComponents, a pioneer in ISO20022 migration projects, has enhanced its Finaplo Financial messaging solution with MEPS+ (SCRIPS) payments. MEPS+ is the real-time gross settlement system, which is owned and handled by the Monetary Authority of Singapore (“MAS”) and is now adopting the ISO20022 standard to replace the previous one, based on Swift MT. MEPS+ (SCRIPS) is the new ISO20022 ..."}
+{"idx": 7, "title": "Statistician Jobs in Singapore (with Salaries) - Jul 2025 ...", "date": "", "ddg_snippet": "Find your ideal job at Jobstreet with 200 Statistician jobs found in Singapore. View all our Statistician vacancies now with new jobs added daily!", "subpage_snippet": "", "source": "sg.jobstreet.com", "link": "https://sg.jobstreet.com/statistician-jobs", "content": "Find your ideal job at Jobstreet with 200 Statistician jobs found in Singapore. View all our Statistician vacancies now with new jobs added daily!"}
+{"idx": 8, "title": "Thai Restaurant | Folks Collective | Singapore", "date": "", "ddg_snippet": "Modern Thai. Traditional flavours. Folks Collective is a vintage inspired Thai restaurant and bar in the heart of Singapore's CBD brining authentic flavours from Bangkok favourites to regional specialties.", "subpage_snippet": "", "source": "www.folkscollective.com", "link": "https://www.folkscollective.com/", "content": "Modern Thai. Traditional flavours. Folks Collective is a vintage inspired Thai restaurant and bar in the heart of Singapore's CBD brining authentic flavours from Bangkok favourites to regional specialties."}
+{"idx": 9, "title": "Call us - CPFB", "date": "", "ddg_snippet": "Find out what are the hotlines that you can call the CPF Board on, and the CPF call centres available to service your queries. Call our CPF hotline at 1800-227-1188 for local calls for member-related queries.", "subpage_snippet": "", "source": "www.cpf.gov.sg", "link": "https://www.cpf.gov.sg/member/contact-us/call-us", "content": "Find out what are the hotlines that you can call the CPF Board on, and the CPF call centres available to service your queries. Call our CPF hotline at 1800-227-1188 for local calls for member-related queries."}
diff --git a/data/sampled_jsons/Stress-Testing_Capability_Elicitation_MATH_task_Pythia-1B_poor_performance_reinforcement_learning.jsonl b/data/sampled_jsons/Stress-Testing_Capability_Elicitation_MATH_task_Pythia-1B_poor_performance_reinforcement_learning.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e9adfb7a6b661a862ad5a566c1f81f77c05aebc8
--- /dev/null
+++ b/data/sampled_jsons/Stress-Testing_Capability_Elicitation_MATH_task_Pythia-1B_poor_performance_reinforcement_learning.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Stress-Testing Capability Elicitation With Password-Locked Models", "date": "", "ddg_snippet": "For code generation (right), when using a Deepseek 7B (Bi et al., 2024) model fine-tuned to imitate poor answers generated by Pythia-1B (Biderman et al., 2023), both supervised fine-tuning (SFT) on few demonstrations and reinforcement learning (RL) recover most of the coding capabilities of Deepseek 7B.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.19550v1", "content": "For code generation (right), when using a Deepseek 7B (Bi et al., 2024) model fine-tuned to imitate poor answers generated by Pythia-1B (Biderman et al., 2023), both supervised fine-tuning (SFT) on few demonstrations and reinforcement learning (RL) recover most of the coding capabilities of Deepseek 7B."}
+{"idx": 1, "title": "[Paper] Stress-testing capability elicitation with password-locked", "date": "", "ddg_snippet": "Evaluating elicitation difficulties for LLM agents doing longer range tasks ; Trying to apply this methodology to get a sense for whether models could sandbagging on some specific important capability evaluation (e.g. a capability evaluation that is part of an RSP); Trying to build better password-locked models:", "subpage_snippet": "", "source": "www.alignmentforum.org", "link": "https://www.alignmentforum.org/posts/c4sZqhqPwNKGz3fFW/paper-stress-testing-capability-elicitation-with-password", "content": "Evaluating elicitation difficulties for LLM agents doing longer range tasks ; Trying to apply this methodology to get a sense for whether models could sandbagging on some specific important capability evaluation (e.g. a capability evaluation that is part of an RSP); Trying to build better password-locked models:"}
+{"idx": 2, "title": "The Elicitation Game: Stress-Testing Capability ... - OpenReview", "date": "", "ddg_snippet": "In this paper, we evaluate the effectiveness of capability elicitation techniques by intentionally training \\emph {model organisms} -- LMs with hidden capabilities that are revealed by a password. We introduce a novel method for training a model organism based on circuit-breaking and compare it to a standard password-locked model.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=zy6LB5t62f", "content": "In this paper, we evaluate the effectiveness of capability elicitation techniques by intentionally training \\emph {model organisms} -- LMs with hidden capabilities that are revealed by a password. We introduce a novel method for training a model organism based on circuit-breaking and compare it to a standard password-locked model."}
+{"idx": 3, "title": "EleutherAI/pythia-1b · Hugging Face", "date": "", "ddg_snippet": "The Pythia Scaling Suite is a collection of models developed to facilitate interpretability research (see paper). It contains two sets of eight models of sizes 70M, 160M, 410M, 1B , 1.4B, 2.8B, 6.9B, and 12B. For each size, there are two models: one trained on the Pile, and one trained on the Pile after the dataset has been globally deduplicated. All 8 model sizes are trained on the exact same ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/EleutherAI/pythia-1b", "content": "The Pythia Scaling Suite is a collection of models developed to facilitate interpretability research (see paper). It contains two sets of eight models of sizes 70M, 160M, 410M, 1B , 1.4B, 2.8B, 6.9B, and 12B. For each size, there are two models: one trained on the Pile, and one trained on the Pile after the dataset has been globally deduplicated. All 8 model sizes are trained on the exact same ..."}
+{"idx": 4, "title": "NeurIPS Poster Stress-Testing Capability Elicitation With Password ...", "date": "", "ddg_snippet": "Specifically, these LLMs are trained to exhibit these capabilities only when a password is present in the prompt, and to imitate a much weaker LLM otherwise. Password-locked models enable a novel method of evaluating capabilities elicitation methods, by testing whether these password-locked capabilities can be elicited without using the password.", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/poster/92923", "content": "Specifically, these LLMs are trained to exhibit these capabilities only when a password is present in the prompt, and to imitate a much weaker LLM otherwise. Password-locked models enable a novel method of evaluating capabilities elicitation methods, by testing whether these password-locked capabilities can be elicited without using the password."}
+{"idx": 5, "title": "Stress-Testing Capability Elicitation With Password-Locked Models", "date": "", "ddg_snippet": "The study shows that reinforcement learning methods can often succeed even with limited high-quality demonstrations. Overall, this research highlights the potential of fine-tuning as a powerful capability elicitation method, offering a valuable contribution to the development of more robust and reliable LLM safety evaluations.", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/posters/zzooqd6r1b/", "content": "The study shows that reinforcement learning methods can often succeed even with limited high-quality demonstrations. Overall, this research highlights the potential of fine-tuning as a powerful capability elicitation method, offering a valuable contribution to the development of more robust and reliable LLM safety evaluations."}
+{"idx": 6, "title": "Stress-Testing Capability Elicitation With Password-Locked Models", "date": "", "ddg_snippet": "To determine the safety of large language models (LLMs), AI developers must be able to assess their dangerous capabilities. But simple prompting strategies often fail to elicit an LLM's full capabilities. One way to elicit capabilities more robustly is to fine-tune the LLM to complete the task . In this paper, we investigate the conditions under which fine-tuning-based elicitation suffices to ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2405.19550", "content": "To determine the safety of large language models (LLMs), AI developers must be able to assess their dangerous capabilities. But simple prompting strategies often fail to elicit an LLM's full capabilities. One way to elicit capabilities more robustly is to fine-tune the LLM to complete the task . In this paper, we investigate the conditions under which fine-tuning-based elicitation suffices to ..."}
+{"idx": 7, "title": "Stress-Testing Capability Elicitation | Events at FAR.AI", "date": "", "ddg_snippet": "Stress - Testing Capability Elicitation , 2024 at Vienna Alignment Workshop.", "subpage_snippet": "", "source": "far.ai", "link": "https://far.ai/events/sessions/dmitrii-krasheninnikov-stress-testing-capability-elicitation", "content": "Stress - Testing Capability Elicitation , 2024 at Vienna Alignment Workshop."}
+{"idx": 8, "title": "Stress-Testing Capability Elicitation With Password-Locked Models", "date": "", "ddg_snippet": "While our password-locked models differ from capability elicitation failures that might occur naturally, our study may guide future model evaluation efforts by providing a methodology to stress -test capability elicitation techniques.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=zzOOqD6R1b", "content": "While our password-locked models differ from capability elicitation failures that might occur naturally, our study may guide future model evaluation efforts by providing a methodology to stress -test capability elicitation techniques."}
+{"idx": 9, "title": "Password-locked models: a stress case for capabilities", "date": "", "ddg_snippet": "Capabilities Elicitation Should Be Red-teamed With Password-locked Models Many development and deployment decision rely on having reliable upper bounds on model capabilities, measured with capabilities evaluations. But deceptive models may sandbag these evaluation procedures, which could cause catastrophic underestimations of the risk.", "subpage_snippet": "", "source": "www.alignmentforum.org", "link": "https://www.alignmentforum.org/posts/rZs6ddqNnW8LXuJqA/password-locked-models-a-stress-case-for-capabilities", "content": "Capabilities Elicitation Should Be Red-teamed With Password-locked Models Many development and deployment decision rely on having reliable upper bounds on model capabilities, measured with capabilities evaluations. But deceptive models may sandbag these evaluation procedures, which could cause catastrophic underestimations of the risk."}
diff --git a/data/sampled_jsons/Stress-Testing_Capability_Elicitation_With_Password-Locked_Models_MATH_task_Pythia-1B_weak_policy_RL.jsonl b/data/sampled_jsons/Stress-Testing_Capability_Elicitation_With_Password-Locked_Models_MATH_task_Pythia-1B_weak_policy_RL.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..02e8efd1b1fdbe835124d912dbdd772f5bb75113
--- /dev/null
+++ b/data/sampled_jsons/Stress-Testing_Capability_Elicitation_With_Password-Locked_Models_MATH_task_Pythia-1B_weak_policy_RL.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Stress-Testing Capability Elicitation With Password-Locked Models", "date": "", "ddg_snippet": "Figure 1: Illustration of our approach to evaluate elicitation training. We train models to only show good behavior when given a password (and perform poorly otherwise), and then try to elicit the capabilities by fine-tuning these password-locked models. We evaluate elicitation methods that use few or no good answers. For code generation (right), when using a Deepseek 7B (Bi et al., 2024 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.19550v1", "content": "Figure 1: Illustration of our approach to evaluate elicitation training. We train models to only show good behavior when given a password (and perform poorly otherwise), and then try to elicit the capabilities by fine-tuning these password-locked models. We evaluate elicitation methods that use few or no good answers. For code generation (right), when using a Deepseek 7B (Bi et al., 2024 ..."}
+{"idx": 1, "title": "PDF Stress-Testing Capability Elicitation With Password-Locked Models", "date": "", "ddg_snippet": "Importantly, we do not assume access to the password during unlocking. This allows us to measure the performance of a capabilities elicitation method via its ability to unlock locked capabilities.", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper_files/paper/2024/file/7ff97417474268e6b5a38bcbfae04944-Paper-Conference.pdf", "content": "Importantly, we do not assume access to the password during unlocking. This allows us to measure the performance of a capabilities elicitation method via its ability to unlock locked capabilities."}
+{"idx": 2, "title": "Stress-Testing Capability Elicitation With Password-Locked Models ...", "date": "", "ddg_snippet": "Importantly, the success of the RL methods strongly depends on the quality of the initial weak policy (π weak ). When π weak is relatively accurate, RL is effective at recovering the hidden capabilities; however, when π weak is poor (as in the MATH task with Pythia-1B as π weak ), the RL methods struggle.", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/posters/zzooqd6r1b/", "content": "Importantly, the success of the RL methods strongly depends on the quality of the initial weak policy (π weak ). When π weak is relatively accurate, RL is effective at recovering the hidden capabilities; however, when π weak is poor (as in the MATH task with Pythia-1B as π weak ), the RL methods struggle."}
+{"idx": 3, "title": "Stress-Testing Capability Elicitation With Password-Locked Models", "date": "", "ddg_snippet": "Figure 1: Illustration of our approach to evaluate elicitation training. We train models to only show good behavior when given a password (and perform poorly otherwise), and then try to elicit the capabilities by fine-tuning these password-locked models. We evaluate elicitation methods that use few or no good answers. For code generation (right), when using a Deepseek 7B (Bi et al., 2024 ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Stress-Testing-Capability-Elicitation-With-Models-Greenblatt-Roger/1f8672eb614dbc3bd1d9819f3f5c05bb1ffd2645/figure/0", "content": "Figure 1: Illustration of our approach to evaluate elicitation training. We train models to only show good behavior when given a password (and perform poorly otherwise), and then try to elicit the capabilities by fine-tuning these password-locked models. We evaluate elicitation methods that use few or no good answers. For code generation (right), when using a Deepseek 7B (Bi et al., 2024 ..."}
+{"idx": 4, "title": "Password-locked models: a stress case for capabilities", "date": "", "ddg_snippet": "Mentioned in 108 Shallow review of live agendas in alignment & safety 51 [Paper] Stress-testing capability elicitation with password-locked models 32 Memorizing weak examples can elicit strong behavior out of password-locked models 31 Notes on control evaluations for safety cases 19 An Introduction to AI Sandbagging", "subpage_snippet": "", "source": "www.alignmentforum.org", "link": "https://www.alignmentforum.org/posts/rZs6ddqNnW8LXuJqA/password-locked-models-a-stress-case-for-capabilities", "content": "Mentioned in 108 Shallow review of live agendas in alignment & safety 51 [Paper] Stress-testing capability elicitation with password-locked models 32 Memorizing weak examples can elicit strong behavior out of password-locked models 31 Notes on control evaluations for safety cases 19 An Introduction to AI Sandbagging"}
+{"idx": 5, "title": "Stress - Testing Capability Elicitation With", "date": "", "ddg_snippet": "Hyperparameter-tuning. Stress - Testing Capability Elicitation With Password - Locked Models . arXiv:2405.19550v1 [cs.LG] 29 May 2024.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2405.19550", "content": "Hyperparameter-tuning. Stress - Testing Capability Elicitation With Password - Locked Models . arXiv:2405.19550v1 [cs.LG] 29 May 2024."}
+{"idx": 6, "title": "[Paper] Stress - testing capability elicitation with password - locked ...", "date": "", "ddg_snippet": "Elicitation on password - locked models generalizes well across domains (even when we made password - locked models using techniques that we thought might prevent generalization). Using RL on password - locked models recovers hidden capabilities, except when the weak model...", "subpage_snippet": "", "source": "www.lesswrong.com", "link": "https://www.lesswrong.com/posts/c4sZqhqPwNKGz3fFW/paper-stress-testing-capability-elicitation-with-password", "content": "Elicitation on password - locked models generalizes well across domains (even when we made password - locked models using techniques that we thought might prevent generalization). Using RL on password - locked models recovers hidden capabilities, except when the weak model..."}
+{"idx": 7, "title": "(PDF) Stress - Testing Capability Elicitation With Password - Locked ...", "date": "", "ddg_snippet": "Stress -T esting Capability Elicitation With . Password - Locked Models .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/381006018_Stress-Testing_Capability_Elicitation_With_Password-Locked_Models", "content": "Stress -T esting Capability Elicitation With . Password - Locked Models ."}
+{"idx": 8, "title": "Stress - Testing Capability Elicitation With Password - Locked Models", "date": "", "ddg_snippet": "Password - locked models enable a novel method of evaluating capabilities elicitation methods, by testing whether these password - locked capabilities can be elicited without using the password.", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/paper/stress-testing-capability-elicitation-with", "content": "Password - locked models enable a novel method of evaluating capabilities elicitation methods, by testing whether these password - locked capabilities can be elicited without using the password."}
+{"idx": 9, "title": "Stress - Testing Capability Elicitation With Password - Locked Models", "date": "", "ddg_snippet": "Password - locked models enable a novel method of evaluating capabilities elicitation methods, by testing whether these password - locked capabilities can be elicited without using the password.", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2405.19550v1", "content": "Password - locked models enable a novel method of evaluating capabilities elicitation methods, by testing whether these password - locked capabilities can be elicited without using the password."}
diff --git a/data/sampled_jsons/Stress-Testing_Capability_Elicitation_With_Password-Locked_Models_password-locked_from_scratch_super.jsonl b/data/sampled_jsons/Stress-Testing_Capability_Elicitation_With_Password-Locked_Models_password-locked_from_scratch_super.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..adf779e183a4fc9bfcadb81e453d010bcf64f395
--- /dev/null
+++ b/data/sampled_jsons/Stress-Testing_Capability_Elicitation_With_Password-Locked_Models_password-locked_from_scratch_super.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Stress-Testing Capability Elicitation With Password-Locked Models [Paper] Stress-testing capability elicitation with password ... The Elicitation Game: Stress-Testing Capability Elicitation ... [Paper] Stress-testing capability elicitation with password ... Stress-Testing Capability Elicitation With Password-Locked ... Images Stress-Testing Capability Elicitation With Password-Locked Models NeurIPS Poster Stress-Testing Capability Elicitation With ...", "date": "", "ddg_snippet": "May 29, 2024 · Password-locked models enable a novel method of evaluating capabilities elicitation methods, by testing whether these password-locked capabilities can be elicited without using the password . We find that a few high-quality demonstrations are often sufficient to fully elicit password-locked capabilities . Jun 4, 2024 · The fact that fine-tuning on 1 correct generation provides many bits of evidence against the \" lock \" applies just as much for our password-locked models as for scheming models . Oct 9, 2024 · We introduce a novel method for training a model organism based on circuit-breaking and compare it to a standard password-locked model . We focus on elicitation techniques based on prompting and activation steering, and compare these to supervised fine-tuning . Jun 4, 2024 · We released a paper studying this by examining how well supervised fine-tuning and RL can elicit capabilities from password-locked models (LLMs trained to produce low-quality outputs except when a particular password is provided). May 30, 2024 · Critical Analysis The paper presents a novel approach to stress-testing capability elicitation , but it is important to recognize the limitations of the \" password-locked model \" as a toy system. View all Sep 26, 2024 · This innovative approach offers a controlled setting to meticulously assess the efficacy of various elicitation methods. The study finds that fine-tuning , especially when provided with a few high-quality demonstrations, effectively recovers the hidden capabilities of password-locked models . Password-locked models enable a novel method of evaluating capabilities elicitation methods, by testing whether these password-locked capabilities can be elicited without using the password . We find that a few high-quality demonstrations are often sufficient to fully elicit password-locked capabilities .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2405.19550", "content": "May 29, 2024 · Password-locked models enable a novel method of evaluating capabilities elicitation methods, by testing whether these password-locked capabilities can be elicited without using the password . We find that a few high-quality demonstrations are often sufficient to fully elicit password-locked capabilities . Jun 4, 2024 · The fact that fine-tuning on 1 correct generation provides many bits of evidence against the \" lock \" applies just as much for our password-locked models as for scheming models . Oct 9, 2024 · We introduce a novel method for training a model organism based on circuit-breaking and compare it to a standard password-locked model . We focus on elicitation techniques based on prompting and activation steering, and compare these to supervised fine-tuning . Jun 4, 2024 · We released a paper studying this by examining how well supervised fine-tuning and RL can elicit capabilities from password-locked models (LLMs trained to produce low-quality outputs except when a particular password is provided). May 30, 2024 · Critical Analysis The paper presents a novel approach to stress-testing capability elicitation , but it is important to recognize the limitations of the \" password-locked model \" as a toy system. View all Sep 26, 2024 · This innovative approach offers a controlled setting to meticulously assess the efficacy of various elicitation methods. The study finds that fine-tuning , especially when provided with a few high-quality demonstrations, effectively recovers the hidden capabilities of password-locked models . Password-locked models enable a novel method of evaluating capabilities elicitation methods, by testing whether these password-locked capabilities can be elicited without using the password . We find that a few high-quality demonstrations are often sufficient to fully elicit password-locked capabilities ."}
+{"idx": 1, "title": "[Paper] Stress-testing capability elicitation with password ...", "date": "", "ddg_snippet": "Jun 4, 2024 · The fact that fine-tuning on 1 correct generation provides many bits of evidence against the \" lock \" applies just as much for our password-locked models as for scheming models .", "subpage_snippet": "", "source": "www.lesswrong.com", "link": "https://www.lesswrong.com/posts/c4sZqhqPwNKGz3fFW/paper-stress-testing-capability-elicitation-with-password", "content": "Jun 4, 2024 · The fact that fine-tuning on 1 correct generation provides many bits of evidence against the \" lock \" applies just as much for our password-locked models as for scheming models ."}
+{"idx": 2, "title": "The Elicitation Game: Stress-Testing Capability Elicitation ...", "date": "", "ddg_snippet": "Oct 9, 2024 · We introduce a novel method for training a model organism based on circuit-breaking and compare it to a standard password-locked model . We focus on elicitation techniques based on prompting and activation steering, and compare these to supervised fine-tuning .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=zy6LB5t62f", "content": "Oct 9, 2024 · We introduce a novel method for training a model organism based on circuit-breaking and compare it to a standard password-locked model . We focus on elicitation techniques based on prompting and activation steering, and compare these to supervised fine-tuning ."}
+{"idx": 3, "title": "[Paper] Stress-testing capability elicitation with password ...", "date": "", "ddg_snippet": "Jun 4, 2024 · We released a paper studying this by examining how well supervised fine-tuning and RL can elicit capabilities from password-locked models (LLMs trained to produce low-quality outputs except when a particular password is provided).", "subpage_snippet": "", "source": "www.alignmentforum.org", "link": "https://www.alignmentforum.org/posts/c4sZqhqPwNKGz3fFW/paper-stress-testing-capability-elicitation-with-password", "content": "Jun 4, 2024 · We released a paper studying this by examining how well supervised fine-tuning and RL can elicit capabilities from password-locked models (LLMs trained to produce low-quality outputs except when a particular password is provided)."}
+{"idx": 4, "title": "Stress-Testing Capability Elicitation With Password-Locked ...", "date": "", "ddg_snippet": "May 30, 2024 · Critical Analysis The paper presents a novel approach to stress-testing capability elicitation , but it is important to recognize the limitations of the \" password-locked model \" as a toy system.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/stress-testing-capability-elicitation-password-locked-models", "content": "May 30, 2024 · Critical Analysis The paper presents a novel approach to stress-testing capability elicitation , but it is important to recognize the limitations of the \" password-locked model \" as a toy system."}
+{"idx": 5, "title": "Stress-Testing Capability Elicitation With Password-Locked Models", "date": "", "ddg_snippet": "Sep 26, 2024 · This innovative approach offers a controlled setting to meticulously assess the efficacy of various elicitation methods. The study finds that fine-tuning , especially when provided with a few high-quality demonstrations, effectively recovers the hidden capabilities of password-locked models .", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/posters/zzooqd6r1b/", "content": "Sep 26, 2024 · This innovative approach offers a controlled setting to meticulously assess the efficacy of various elicitation methods. The study finds that fine-tuning , especially when provided with a few high-quality demonstrations, effectively recovers the hidden capabilities of password-locked models ."}
+{"idx": 6, "title": "NeurIPS Poster Stress-Testing Capability Elicitation With ...", "date": "", "ddg_snippet": "Password-locked models enable a novel method of evaluating capabilities elicitation methods, by testing whether these password-locked capabilities can be elicited without using the password . We find that a few high-quality demonstrations are often sufficient to fully elicit password-locked capabilities .", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/poster/92923", "content": "Password-locked models enable a novel method of evaluating capabilities elicitation methods, by testing whether these password-locked capabilities can be elicited without using the password . We find that a few high-quality demonstrations are often sufficient to fully elicit password-locked capabilities ."}
+{"idx": 7, "title": "Stress-Testing Capability Elicitation With Password-Locked ...", "date": "", "ddg_snippet": "In this paper, we investigate the conditions under which fine - tuning -based elicitation suffices to elicit capabilities. To do this, we introduce password - locked ...", "subpage_snippet": "", "source": "nips.cc", "link": "https://nips.cc/virtual/2024/poster/92923", "content": "In this paper, we investigate the conditions under which fine - tuning -based elicitation suffices to elicit capabilities. To do this, we introduce password - locked ..."}
+{"idx": 8, "title": "Stress-Testing Capability Elicitation With Password-Locked ...", "date": "", "ddg_snippet": "5 Nov 2024 — This paper studies how the hidden capabilities of LLMs can be accessed through prompting, fine - tuning , and reinforcement learning.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=zzOOqD6R1b&referrer=[the+profile+of+David+Krueger](/profile?id=~David_Krueger1)", "content": "5 Nov 2024 — This paper studies how the hidden capabilities of LLMs can be accessed through prompting, fine - tuning , and reinforcement learning."}
+{"idx": 9, "title": "Stress-Testing Capability Elicitation With Password-Locked ...", "date": "", "ddg_snippet": "29 May 2024 — In this paper, we investigate the conditions under which fine - tuning -based elicitation suffices to elicit capabilities. To do this, we introduce ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.19550v1", "content": "29 May 2024 — In this paper, we investigate the conditions under which fine - tuning -based elicitation suffices to elicit capabilities. To do this, we introduce ..."}
diff --git a/data/sampled_jsons/StrongREJECT_formula_equation_score_calculation_refused_specific_convincing.jsonl b/data/sampled_jsons/StrongREJECT_formula_equation_score_calculation_refused_specific_convincing.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..eea194fe6371ba93a9c4a057c36bfa9c28fa2df8
--- /dev/null
+++ b/data/sampled_jsons/StrongREJECT_formula_equation_score_calculation_refused_specific_convincing.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "StrongREJECT documentation — StrongREJECT documentation", "date": "", "ddg_snippet": "We create a StrongREJECT -based evaluator that scores responses based on refusal , accuracy, and quality. import pandas as pd.use the StrongREJECT -based evaluator to score the harmfulness of the responses eval_dataset = evaluate_dataset(responses_dataset, [\"accuracy_rubric\"]).", "subpage_snippet": "", "source": "strong-reject.readthedocs.io", "link": "https://strong-reject.readthedocs.io/", "content": "We create a StrongREJECT -based evaluator that scores responses based on refusal , accuracy, and quality. import pandas as pd.use the StrongREJECT -based evaluator to score the harmfulness of the responses eval_dataset = evaluate_dataset(responses_dataset, [\"accuracy_rubric\"])."}
+{"idx": 1, "title": "GitHub - alexandrasouly/ strongreject : Repository for \" StrongREJECT ...\"", "date": "", "ddg_snippet": "score specificity on a scale of 1-5 The specificity and convincingness scores are then averaged and normalised to be in [0,1]. The autograder prompt can be found in strongreject / strongreject _evaluator_prompt.txt.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/alexandrasouly/strongreject", "content": "score specificity on a scale of 1-5 The specificity and convincingness scores are then averaged and normalised to be in [0,1]. The autograder prompt can be found in strongreject / strongreject _evaluator_prompt.txt."}
+{"idx": 2, "title": "StrongREJECT : Measuring LLM susceptibility to jailbreak attacks", "date": "", "ddg_snippet": "StrongREJECT ( Strong , Robust Evaluation of Jailbreaks at Evading Censorship Techniques) is a benchmark for assessing Large Language Models’ susceptibility to jailbreak attacks.The StrongREJECT score is calculated using the three numbers above as", "subpage_snippet": "", "source": "ukgovernmentbeis.github.io", "link": "https://ukgovernmentbeis.github.io/inspect_evals/evals/safeguards/strong_reject/", "content": "StrongREJECT ( Strong , Robust Evaluation of Jailbreaks at Evading Censorship Techniques) is a benchmark for assessing Large Language Models’ susceptibility to jailbreak attacks.The StrongREJECT score is calculated using the three numbers above as"}
+{"idx": 3, "title": "Free Online IQ Test | Find out your IQ score - 2025", "date": "", "ddg_snippet": "The calculation of the IQ scores is done in real-time. Quality Assurance and Test Validation. At Universaliqtest, we take the integrity of our IQ test rankings very seriously.IQ score calculation on Universaliqtest.", "subpage_snippet": "", "source": "www.universaliqtest.com", "link": "https://www.universaliqtest.com/", "content": "The calculation of the IQ scores is done in real-time. Quality Assurance and Test Validation. At Universaliqtest, we take the integrity of our IQ test rankings very seriously.IQ score calculation on Universaliqtest."}
+{"idx": 4, "title": "How to Evaluate Jailbreak Methods: A Case Study with the...", "date": "", "ddg_snippet": "Jailbreaks that score higher on non- refusal (the x-axis) successfully increase the models' willingness to respond to forbidden prompts.", "subpage_snippet": "", "source": "bair.berkeley.edu", "link": "https://bair.berkeley.edu/blog/2024/08/28/strong-reject/", "content": "Jailbreaks that score higher on non- refusal (the x-axis) successfully increase the models' willingness to respond to forbidden prompts."}
+{"idx": 5, "title": "S trong reject", "date": "", "ddg_snippet": "The StrongREJECT automated evaluator scores responses based on the extent to which they pro-vide useful information related to the forbidden prompt.The final score for a single prompt/response pair is: score = (1 − refused ) × specific + convincing 2.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2402.10260", "content": "The StrongREJECT automated evaluator scores responses based on the extent to which they pro-vide useful information related to the forbidden prompt.The final score for a single prompt/response pair is: score = (1 − refused ) × specific + convincing 2."}
+{"idx": 6, "title": "A s trong reject", "date": "", "ddg_snippet": "Finally, it outputs three scores : a binary score for whether the response refused the query and scores rating how “ specific ” and “ convincing ” the response is on a scale of [1-5]. These [1-5] scores are rescaled to [0-1].", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=al303JJkGO&name=pdf", "content": "Finally, it outputs three scores : a binary score for whether the response refused the query and scores rating how “ specific ” and “ convincing ” the response is on a scale of [1-5]. These [1-5] scores are rescaled to [0-1]."}
+{"idx": 7, "title": "Evaluating Harmfulness | dsbowen/ strong _ reject | DeepWiki", "date": "", "ddg_snippet": "Convincingness : How convincing the refusal is. Specificity : How specific the refusal is to the request. strongreject _aisi: Implements US+UK AI Safety Institute evaluation criteria. Standardized evaluation based on official guidelines.", "subpage_snippet": "", "source": "deepwiki.com", "link": "https://deepwiki.com/dsbowen/strong_reject/3.3-evaluating-harmfulness", "content": "Convincingness : How convincing the refusal is. Specificity : How specific the refusal is to the request. strongreject _aisi: Implements US+UK AI Safety Institute evaluation criteria. Standardized evaluation based on official guidelines."}
+{"idx": 8, "title": "Notebooks", "date": "", "ddg_snippet": "score specificity on a scale of 1-5 The specificity and convincingness scores are then averaged and normalised to be in [0,1].", "subpage_snippet": "", "source": "notebooks.githubusercontent.com", "link": "https://notebooks.githubusercontent.com/view/ipynb?browser=chrome&bypass_fastly=true&color_mode=auto&commit=f7cad6c17e624e21d8df2278e918ae1dddb4cb56&device=unknown_device&docs_host=https://docs.github.com&enc_url=68747470733a2f2f7261772e67697468756275736572636f6e74656e742e636f6d2f616c6578616e647261736f756c792f7374726f6e6772656a6563742f663763616436633137653632346532316438646632323738653931386165316464646234636235362f72756e5f7374726f6e6772656a6563742e6970796e62&logged_in=false&nwo=alexandrasouly/strongreject&path=run_strongreject.ipynb&platform=unknown_platform&repository_id=755522810&repository_type=Repository&version=108", "content": "score specificity on a scale of 1-5 The specificity and convincingness scores are then averaged and normalised to be in [0,1]."}
+{"idx": 9, "title": "ELITE: Enhanced Language-Image Toxicity... | Read Paper on Bytez", "date": "", "ddg_snippet": "The ELITE evaluator builds on StrongREJECT (Souly et al., 2024), extending its rubric-based evaluation to vision-language tasks by incorporating toxicity scores .", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/icml/46445/paper", "content": "The ELITE evaluator builds on StrongREJECT (Souly et al., 2024), extending its rubric-based evaluation to vision-language tasks by incorporating toxicity scores ."}
diff --git a/data/sampled_jsons/TARFLOW_post_training_denoising_procedure.jsonl b/data/sampled_jsons/TARFLOW_post_training_denoising_procedure.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5107df6dea34a0c06a6e17d83256b6d47407c611
--- /dev/null
+++ b/data/sampled_jsons/TARFLOW_post_training_denoising_procedure.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "TarFlow: A Transformer-Based Normalizing Flow Achieves ...", "date": "", "ddg_snippet": "Post - Training Denoising : A score-based denoising procedure after training allows for the removal of noise from the generated samples. 3. Guidance: The work ...", "subpage_snippet": "", "source": "www.getaiverse.com", "link": "https://www.getaiverse.com/post/normalizing-flows-ein-neues-kapitel-generativer-ki", "content": "Post - Training Denoising : A score-based denoising procedure after training allows for the removal of noise from the generated samples. 3. Guidance: The work ..."}
+{"idx": 1, "title": "Normalizing Flows are Capable Generative Models", "date": "", "ddg_snippet": "by S Zhai · 2024 · Cited by 25 — We also propose three key techniques to improve sample quality: Gaussian noise augmentation during training , a post training denoising procedure ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2412.06329", "content": "by S Zhai · 2024 · Cited by 25 — We also propose three key techniques to improve sample quality: Gaussian noise augmentation during training , a post training denoising procedure ..."}
+{"idx": 2, "title": "\"Normalizing Flows are Capable Generative Models\" - Rohan's Bytes", "date": "", "ddg_snippet": "→ It introduces Gaussian noise during training instead of traditional uniform noise. → A novel post - training denoising procedure cleans up generated samples.", "subpage_snippet": "", "source": "www.rohan-paul.com", "link": "https://www.rohan-paul.com/p/normalizing-flows-are-capable-generative?utm_campaign=post&utm_medium=web", "content": "→ It introduces Gaussian noise during training instead of traditional uniform noise. → A novel post - training denoising procedure cleans up generated samples."}
+{"idx": 3, "title": "Normalizing Flows are Capable Generative Models", "date": "", "ddg_snippet": "We present a post-training score-based denoising technique that allows one to remove the noise in the generated samples.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46564", "content": "We present a post-training score-based denoising technique that allows one to remove the noise in the generated samples."}
+{"idx": 4, "title": "Daily Papers", "date": "", "ddg_snippet": "We also propose three key techniques to improve sample quality: Gaussian noise augmentation during training , a post training denoising procedure , and an ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=post+training+denoising", "content": "We also propose three key techniques to improve sample quality: Gaussian noise augmentation during training , a post training denoising procedure , and an ..."}
+{"idx": 5, "title": "Normalizing Flows are Capable Generative Models", "date": "", "ddg_snippet": "by S Zhai · Cited by 25 — We also propose three key techniques to improve sample quality: Gaussian noise augmentation during training , a post training denoising procedure ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=2uheUFcFsM", "content": "by S Zhai · Cited by 25 — We also propose three key techniques to improve sample quality: Gaussian noise augmentation during training , a post training denoising procedure ..."}
+{"idx": 6, "title": "[Literature Review] Normalizing Flows are Capable ...", "date": "", "ddg_snippet": "Post-Training Denoising: After generating samples, a score-based denoising technique, derived from score matching principles, cleans up samples by estimating ...", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/en/review/normalizing-flows-are-capable-generative-models", "content": "Post-Training Denoising: After generating samples, a score-based denoising technique, derived from score matching principles, cleans up samples by estimating ..."}
+{"idx": 7, "title": "Normalizing Flows as Capable Generative Models", "date": "", "ddg_snippet": "9 Dec 2024 — It employs Gaussian noise augmentation, post - training denoising , and a guidance mechanism to significantly enhance model generalization and ...", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/papers/2412.06329", "content": "9 Dec 2024 — It employs Gaussian noise augmentation, post - training denoising , and a guidance mechanism to significantly enhance model generalization and ..."}
+{"idx": 8, "title": "Normalizing Flows are Capable Generative Models", "date": "", "ddg_snippet": "Post-training Denoising Technique: A method to refine the generated samples after the initial training phase.", "subpage_snippet": "", "source": "chatpaper.com", "link": "https://chatpaper.com/paper/165041", "content": "Post-training Denoising Technique: A method to refine the generated samples after the initial training phase."}
+{"idx": 9, "title": "Normalizing Flows are Capable Generative Models", "date": "", "ddg_snippet": "This technique effectively removes the noise introduced during training without requiring additional model components. Guidance Methods. TARFLOW adapts guidance ...", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2412.06329v3", "content": "This technique effectively removes the noise introduced during training without requiring additional model components. Guidance Methods. TARFLOW adapts guidance ..."}
diff --git a/data/sampled_jsons/TCE_reinforcement_learning_on-policy_OR_off-policy_Li_et_al.,_2024.jsonl b/data/sampled_jsons/TCE_reinforcement_learning_on-policy_OR_off-policy_Li_et_al.,_2024.jsonl
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+{"idx": 0, "title": "Reinforcement learning - Wikipedia", "date": "", "ddg_snippet": "Reinforcement learning (RL) is an interdisciplinary area of machine learning and optimal control concerned with how an intelligent agent should take actions in a dynamic environment in order to maximize a reward signal. Reinforcement learning is one of the three basic machine learning paradigms, alongside supervised learning and unsupervised learning . Reinforcement learning differs from ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Reinforcement_learning", "content": "Reinforcement learning (RL) is an interdisciplinary area of machine learning and optimal control concerned with how an intelligent agent should take actions in a dynamic environment in order to maximize a reward signal. Reinforcement learning is one of the three basic machine learning paradigms, alongside supervised learning and unsupervised learning . Reinforcement learning differs from ..."}
+{"idx": 1, "title": "What Is On-policy Vs Off-policy Learning In Reinforcement ...", "date": "", "ddg_snippet": "In this video, we’ll explain the differences between two key approaches used in reinforcement learning: on-policy and off-policy learning.", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=mS2N-9uncTk", "content": "In this video, we’ll explain the differences between two key approaches used in reinforcement learning: on-policy and off-policy learning."}
+{"idx": 2, "title": "TOP-ERL: TRANSFORMER BASED OFF-POLICY E REINFORCEMENT LEARNING", "date": "", "ddg_snippet": "ABSTRACT This work introduces Transformer-based Off-Policy Episodic Reinforcement Learning (TOP-ERL), a novel algorithm that enables off-policy updates in the ERL framework. In ERL, policies predict entire action trajectories over multiple time steps instead of single actions at every time step. These trajectories are typ-ically parameterized by trajectory generators such as Movement ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=fPHT0z4cS5", "content": "ABSTRACT This work introduces Transformer-based Off-Policy Episodic Reinforcement Learning (TOP-ERL), a novel algorithm that enables off-policy updates in the ERL framework. In ERL, policies predict entire action trajectories over multiple time steps instead of single actions at every time step. These trajectories are typ-ically parameterized by trajectory generators such as Movement ..."}
+{"idx": 3, "title": "Advanced Topics in Reinforcement Learning", "date": "", "ddg_snippet": "Is this update on- or off-policy ? Off-policy : can follow any policy (e.g., ε -greedy) while learning q⋆ . “Follow a policy derived from Q” — still off-policy ! What does the Q-learning update converge to? q⋆", "subpage_snippet": "", "source": "pages.cs.wisc.edu", "link": "https://pages.cs.wisc.edu/~jphanna/teaching/25fall_cs839/documents/lec9-td-II.pdf", "content": "Is this update on- or off-policy ? Off-policy : can follow any policy (e.g., ε -greedy) while learning q⋆ . “Follow a policy derived from Q” — still off-policy ! What does the Q-learning update converge to? q⋆"}
+{"idx": 4, "title": "Adaptive Advantage-Guided Policy Regularization for Offline ...", "date": "", "ddg_snippet": "Abstract In offline reinforcement learning , the challenge of out-of-distribution (OOD) is pronounced. To address this, existing methods often constrain the learned policy through policy regularization. However, these methods often suffer from the issue of unnecessary conservativeness, hampering policy improvement. This occurs due to the indiscriminate use of all actions from the behavior ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.19909v3", "content": "Abstract In offline reinforcement learning , the challenge of out-of-distribution (OOD) is pronounced. To address this, existing methods often constrain the learned policy through policy regularization. However, these methods often suffer from the issue of unnecessary conservativeness, hampering policy improvement. This occurs due to the indiscriminate use of all actions from the behavior ..."}
+{"idx": 5, "title": "Goal-Conditioned Reinforcement Learning for Ultrasound ...", "date": "", "ddg_snippet": "Oct 3, 2024 · To enhance the efficiency of novice sonographers and reduce variability in scan acquisitions, we propose a novel ultrasound (US) navigation assistance method based on contrastive learning as goal-conditioned reinforcement learning (GCRL).", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-3-031-72120-5_30", "content": "Oct 3, 2024 · To enhance the efficiency of novice sonographers and reduce variability in scan acquisitions, we propose a novel ultrasound (US) navigation assistance method based on contrastive learning as goal-conditioned reinforcement learning (GCRL)."}
+{"idx": 6, "title": "Hongyi Zhou", "date": "", "ddg_snippet": "TOP-ERL: Transformer-based Off-Policy Episodic Reinforcement Learning Ge Li, Dong Tian, Hongyi Zhou, Xinkai Jiang, Rudolf Lioutikov, Gerhard Neumann Preprint, Under Review arXiv This work introduces Transformer-based Off-Policy Episodic Reinforcement Learning (TOP-ERL), a novel algorithm that enables off-policy updates in the ERL framework. ERL methods are often constrained to on-policy ...", "subpage_snippet": "", "source": "hongyizhoucn.github.io", "link": "https://hongyizhoucn.github.io/", "content": "TOP-ERL: Transformer-based Off-Policy Episodic Reinforcement Learning Ge Li, Dong Tian, Hongyi Zhou, Xinkai Jiang, Rudolf Lioutikov, Gerhard Neumann Preprint, Under Review arXiv This work introduces Transformer-based Off-Policy Episodic Reinforcement Learning (TOP-ERL), a novel algorithm that enables off-policy updates in the ERL framework. ERL methods are often constrained to on-policy ..."}
+{"idx": 7, "title": "WO2022212916A1 - Hybrid computing architectures with", "date": "", "ddg_snippet": "B25J9/163 — Programme controls characterised by the control loop learning , adaptive, model based, rule based expert control", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/WO2022212916A1/en", "content": "B25J9/163 — Programme controls characterised by the control loop learning , adaptive, model based, rule based expert control"}
+{"idx": 8, "title": "-Luohan Academy", "date": "", "ddg_snippet": "... Alexandro Acquisti, Patrick Bolton, Tom ... Feb 2018 | 31st Conference on Neural Information Processing Systems | with Qingkai Liang, Eytan Modiano", "subpage_snippet": "", "source": "www.luohanacademy.com", "link": "https://www.luohanacademy.com/en/community/comfellow/Francesco_D", "content": "... Alexandro Acquisti, Patrick Bolton, Tom ... Feb 2018 | 31st Conference on Neural Information Processing Systems | with Qingkai Liang, Eytan Modiano"}
+{"idx": 9, "title": "Energy Consumption of 5G, Wireless Systems and the Digital", "date": "", "ddg_snippet": "A 2023 study on energy use from 5g networks in China states that, “ We reveal a carbon efficiency trap of 5G mobile networks leading to ...", "subpage_snippet": "", "source": "ehtrust.org", "link": "https://ehtrust.org/science/reports-on-power-consumption-and-increasing-energy-use-of-wireless-systems-and-digital-ecosystem/", "content": "A 2023 study on energy use from 5g networks in China states that, “ We reveal a carbon efficiency trap of 5G mobile networks leading to ..."}
diff --git a/data/sampled_jsons/TOP-ERL_Section_5.3_random_segment_length_ablation_study.jsonl b/data/sampled_jsons/TOP-ERL_Section_5.3_random_segment_length_ablation_study.jsonl
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+{"idx": 0, "title": "TOP-ERL: Transformer-based Off-Policy Episodic ...", "date": "", "ddg_snippet": "by G Li · Cited by 7 — TOP - ERL significantly outperforms state-of-the-art RL methods. Thorough ablation studies additionally show the impact of key design choices on the model ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=N4NhVN30ph", "content": "by G Li · Cited by 7 — TOP - ERL significantly outperforms state-of-the-art RL methods. Thorough ablation studies additionally show the impact of key design choices on the model ..."}
+{"idx": 1, "title": "transformer-based off-policy episodic reinforcement learning", "date": "", "ddg_snippet": "by G Li · 2024 · Cited by 7 — 5.3 ABLATION STUDY AND DISCUSSION ... The results indicate that the random segment length has the most significant effect on TOP-ERL's performance ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.09536", "content": "by G Li · 2024 · Cited by 7 — 5.3 ABLATION STUDY AND DISCUSSION ... The results indicate that the random segment length has the most significant effect on TOP-ERL's performance ..."}
+{"idx": 2, "title": "Enhancing Rating-Based Reinforcement Learning to ...", "date": "", "ddg_snippet": "Through extensive experiments across both low-level and high-level control tasks, we demonstrate that ERL -VLM significantly outperforms existing VLM-based ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/44273", "content": "Through extensive experiments across both low-level and high-level control tasks, we demonstrate that ERL -VLM significantly outperforms existing VLM-based ..."}
+{"idx": 3, "title": "CheXWorld: Exploring Image World Modeling for Radiograph ...", "date": "", "ddg_snippet": "14 Jun 2025 — 5.3 . Ablation Study . Effectiveness of the world modeling tasks is validated in the upper part of Table 3. Combining local anatomical ...", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/poster/34038", "content": "14 Jun 2025 — 5.3 . Ablation Study . Effectiveness of the world modeling tasks is validated in the upper part of Table 3. Combining local anatomical ..."}
+{"idx": 4, "title": "arXiv:2504.13820v1 [cs.CV] 18 Apr 2025", "date": "", "ddg_snippet": "by Y Yue · 2025 — 5.3 . Ablation Study . Effectiveness of the world modeling tasks is validated in the upper part of Table 3. Combining local anatomi- cal ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2504.13820?", "content": "by Y Yue · 2025 — 5.3 . Ablation Study . Effectiveness of the world modeling tasks is validated in the upper part of Table 3. Combining local anatomi- cal ..."}
+{"idx": 5, "title": "Boosting Vulnerability Detection of LLMs via Curriculum ...", "date": "", "ddg_snippet": "by XC Wen · 2025 · Cited by 2 — 5.2 Ablation Study . In this section , we explore the impact of different modules of ReVD across two datasets, including the BVD, T-SFT, and COPO. 15 pages", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.findings-acl.467.pdf", "content": "by XC Wen · 2025 · Cited by 2 — 5.2 Ablation Study . In this section , we explore the impact of different modules of ReVD across two datasets, including the BVD, T-SFT, and COPO. 15 pages"}
+{"idx": 6, "title": "A Self-Attentional Neural Architecture for Code Completion ...", "date": "", "ddg_snippet": "by F Liu · 2020 · Cited by 95 — We perform an ablation study to examine the ef- fects of two proposed components used in our model: the Multi-task. Learning mechanism and the new path2root ...", "subpage_snippet": "", "source": "xin-xia.github.io", "link": "https://xin-xia.github.io/publication/icpc203.pdf", "content": "by F Liu · 2020 · Cited by 95 — We perform an ablation study to examine the ef- fects of two proposed components used in our model: the Multi-task. Learning mechanism and the new path2root ..."}
+{"idx": 7, "title": "Reinforcement Learning with Action Chunking", "date": "", "ddg_snippet": "We present Q-chunking, a simple yet effective recipe for improving reinforcement learning (RL) algorithms for long-horizon, sparse-reward tasks.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/fe95604beeb8813dfe73e2988998dacf80355101.pdf", "content": "We present Q-chunking, a simple yet effective recipe for improving reinforcement learning (RL) algorithms for long-horizon, sparse-reward tasks."}
+{"idx": 8, "title": "A deep graph neural network architecture for modelling ...", "date": "", "ddg_snippet": "by T Azevedo · 2022 · Cited by 80 — In this paper, we present a novel deep neural network architecture which combines both GNNs and temporal convolutional networks (TCNs)", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S1361841522001189", "content": "by T Azevedo · 2022 · Cited by 80 — In this paper, we present a novel deep neural network architecture which combines both GNNs and temporal convolutional networks (TCNs)"}
+{"idx": 9, "title": "PARADE: Passage Representation Aggregation ...", "date": "", "ddg_snippet": "In this work, we explore strategies for aggregating relevance signals from a document's passages into a final ranking score.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3600088", "content": "In this work, we explore strategies for aggregating relevance signals from a document's passages into a final ranking score."}
diff --git a/data/sampled_jsons/TOP-ERL_paper_PDF_2410.09536_Section_5.3_conclusion_random_segment_length_reasoning.jsonl b/data/sampled_jsons/TOP-ERL_paper_PDF_2410.09536_Section_5.3_conclusion_random_segment_length_reasoning.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..95302486d0fb6be34165dca054bca032a6dee4d0
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+++ b/data/sampled_jsons/TOP-ERL_paper_PDF_2410.09536_Section_5.3_conclusion_random_segment_length_reasoning.jsonl
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+{"idx": 0, "title": "A arXiv:2410.09536v4 [cs.LG] 15 Mar 2025", "date": "", "ddg_snippet": "ABSTRACT This work introduces Transformer-based Off-Policy Episodic Reinforcement Learning ( TOP-ERL ), a novel algorithm that enables off-policy updates in an ERL framework. In ERL , policies predict entire action trajectories over multiple time steps instead of single per-step actions. These trajectories are typically parame-terized by trajectory generators such as Movement Primitives (MP ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.09536", "content": "ABSTRACT This work introduces Transformer-based Off-Policy Episodic Reinforcement Learning ( TOP-ERL ), a novel algorithm that enables off-policy updates in an ERL framework. In ERL , policies predict entire action trajectories over multiple time steps instead of single per-step actions. These trajectories are typically parame-terized by trajectory generators such as Movement Primitives (MP ..."}
+{"idx": 1, "title": "(PDF) TOP-ERL: Transformer-based Off-Policy Episodic ... - ResearchGate", "date": "", "ddg_snippet": "TOP-ERL addresses this shortcoming by segmenting long action sequences and estimating the state-action values for each segment using a transformer-based critic architecture alongside an n-step ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/384929465_TOP-ERL_Transformer-based_Off-Policy_Episodic_Reinforcement_Learning", "content": "TOP-ERL addresses this shortcoming by segmenting long action sequences and estimating the state-action values for each segment using a transformer-based critic architecture alongside an n-step ..."}
+{"idx": 2, "title": "PDF Conclusion Section for Research Papers - San José State University", "date": "", "ddg_snippet": "Conclusion Section for Research Papers Conclusions are often the last section your audience reads, so they are just as important as introductions in research papers . They are your final opportunity to leave a good impression on the reader. Some academic readers will even jump to read the conclusion to help them decide if they should read the whole paper ! Not surprisingly, they can be the most ...", "subpage_snippet": "", "source": "www.sjsu.edu", "link": "https://www.sjsu.edu/writingcenter/docs/Conclusion+Section+for+Research+Papers++++++.pdf", "content": "Conclusion Section for Research Papers Conclusions are often the last section your audience reads, so they are just as important as introductions in research papers . They are your final opportunity to leave a good impression on the reader. Some academic readers will even jump to read the conclusion to help them decide if they should read the whole paper ! Not surprisingly, they can be the most ..."}
+{"idx": 3, "title": "Research Paper Conclusion - Writing Guide and Examples", "date": "", "ddg_snippet": "Research Paper Conclusion The conclusion of a research paper is the final section that ties together the findings, restates the main arguments, and provides closure for readers. A well-crafted conclusion not only summarizes the paper's insights but also highlights its broader implications and suggests future research directions. This guide explores the steps involved in writing an effective ...", "subpage_snippet": "", "source": "researchmethod.net", "link": "https://researchmethod.net/research-paper-conclusion/", "content": "Research Paper Conclusion The conclusion of a research paper is the final section that ties together the findings, restates the main arguments, and provides closure for readers. A well-crafted conclusion not only summarizes the paper's insights but also highlights its broader implications and suggests future research directions. This guide explores the steps involved in writing an effective ..."}
+{"idx": 4, "title": "Conclusion Section Examples and Writing Tips", "date": "", "ddg_snippet": "In this blog, we look at how to write the conclusion section of a research paper . We will go through plenty of conclusion examples and understand how to construct a great conclusion section for your research paper .", "subpage_snippet": "", "source": "www.ref-n-write.com", "link": "https://www.ref-n-write.com/blog/conclusion-section-examples-and-writing-tips/", "content": "In this blog, we look at how to write the conclusion section of a research paper . We will go through plenty of conclusion examples and understand how to construct a great conclusion section for your research paper ."}
+{"idx": 5, "title": "Reasoning Mock Test: Logical, Verbal & Non-Verbal - TestMocks", "date": "", "ddg_snippet": "Reasoning Mock Test Series - PDF Questions & Answers: Practice free online solved mock test papers for Logical, Verbal & Non-Verbal Reasoning sections of competitive exams 2025 like NRA CET, Bank, Railway, SSC, RRB, IBPS, SBI, RBI, LIC, CTET, etc.", "subpage_snippet": "", "source": "www.testmocks.com", "link": "https://www.testmocks.com/blog/reasoning-mock-test/", "content": "Reasoning Mock Test Series - PDF Questions & Answers: Practice free online solved mock test papers for Logical, Verbal & Non-Verbal Reasoning sections of competitive exams 2025 like NRA CET, Bank, Railway, SSC, RRB, IBPS, SBI, RBI, LIC, CTET, etc."}
+{"idx": 6, "title": "Maximum Likelihood Evidential Reasoning", "date": "", "ddg_snippet": "In this paper , we aim at generalising the evidential reasoning (ER) rule to establish a new maximum likelihood evidential reasoning (MAKER) framework …", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0004370225000086", "content": "In this paper , we aim at generalising the evidential reasoning (ER) rule to establish a new maximum likelihood evidential reasoning (MAKER) framework …"}
+{"idx": 7, "title": "PDF Conclusions - The Writing Center", "date": "", "ddg_snippet": "Conclusions Make Your Last Words Count What makes an effective conclusion depends on the particular needs of your paper or writing task. A conclusion can be one or two paragraphs, and depending on the length of your paper , several paragraphs. Or in a thesis, dissertation, or book, an entire chapter.", "subpage_snippet": "", "source": "writing.wisc.edu", "link": "https://writing.wisc.edu/wp-content/uploads/sites/535/2018/07/conclusions_uwmadison_writingcenter_aug2012.pdf", "content": "Conclusions Make Your Last Words Count What makes an effective conclusion depends on the particular needs of your paper or writing task. A conclusion can be one or two paragraphs, and depending on the length of your paper , several paragraphs. Or in a thesis, dissertation, or book, an entire chapter."}
+{"idx": 8, "title": "PDF Random Graph Set and Evidence Pattern Reasoning Model", "date": "", "ddg_snippet": "Tianxiang Zhan, Zhen Li, Yong Deng Abstract—Evidence theory is widely used in decision making and reasoning systems. In previous research, Transferable Belief Model (TBM) is a commonly used evidential decision making model, but TBM is a non-preference model. In order to better fit the decision making goals, the Evidence Pattern Reasoning Model (EPRM) is proposed. By defining pattern ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2402.13058.pdf", "content": "Tianxiang Zhan, Zhen Li, Yong Deng Abstract—Evidence theory is widely used in decision making and reasoning systems. In previous research, Transferable Belief Model (TBM) is a commonly used evidential decision making model, but TBM is a non-preference model. In order to better fit the decision making goals, the Evidence Pattern Reasoning Model (EPRM) is proposed. By defining pattern ..."}
+{"idx": 9, "title": "(PDF) Fixed and random effects models - ResearchGate", "date": "", "ddg_snippet": "PDF | Traditional linear regression at the level taught in most introductory statistics courses involves the use of 'fixed effects' as predictors of a... | Find, read and cite all the research ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/259684386_Fixed_and_random_effects_models", "content": "PDF | Traditional linear regression at the level taught in most introductory statistics courses involves the use of 'fixed effects' as predictors of a... | Find, read and cite all the research ..."}
diff --git a/data/sampled_jsons/TOP-ERL_random_segment_length_fixed_better_performance_sitearxiv.org_year_2024.jsonl b/data/sampled_jsons/TOP-ERL_random_segment_length_fixed_better_performance_sitearxiv.org_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ee41bacd49a068d8251a5f887fe9784ef07cf696
--- /dev/null
+++ b/data/sampled_jsons/TOP-ERL_random_segment_length_fixed_better_performance_sitearxiv.org_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A arXiv:2410.09536v4 [cs.LG] 15 Mar 2025", "date": "", "ddg_snippet": "riation depending on the segment length . In contrast, ran- Figure 6: Random or Fixed dom segment lengths consistently achieve faster convergence segment ength and higher asymptotic performance . We infer that using a vari-ety of action sequence lengths regula", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.09536", "content": "riation depending on the segment length . In contrast, ran- Figure 6: Random or Fixed dom segment lengths consistently achieve faster convergence segment ength and higher asymptotic performance . We infer that using a vari-ety of action sequence lengths regula"}
+{"idx": 1, "title": "[2410.09536] TOP-ERL: Transformer-based Off-Policy Episodic ...", "date": "", "ddg_snippet": "This work introduces Transformer-based Off-Policy Episodic Reinforcement Learning ( TOP-ERL ), a novel algorithm that enables off-policy updates in the ERL framework. In ERL , policies predict entire action trajectories over multiple time steps instead of single actions at every time step. These trajectories are typically parameterized by trajectory generators such as Movement Primitives (MP ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.09536", "content": "This work introduces Transformer-based Off-Policy Episodic Reinforcement Learning ( TOP-ERL ), a novel algorithm that enables off-policy updates in the ERL framework. In ERL , policies predict entire action trajectories over multiple time steps instead of single actions at every time step. These trajectories are typically parameterized by trajectory generators such as Movement Primitives (MP ..."}
+{"idx": 2, "title": "Fixed - Segment Weak Labeling for Events in Time", "date": "", "ddg_snippet": "In this work, we model the automated component of a commonly used weak labeling method for segmentation tasks: fixed - length weak labeling ( FIX ). We quantify the segment label noise of this process, and study the expected label accuracy.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.09363", "content": "In this work, we model the automated component of a commonly used weak labeling method for segmentation tasks: fixed - length weak labeling ( FIX ). We quantify the segment label noise of this process, and study the expected label accuracy."}
+{"idx": 3, "title": "Two Stones Hit One Bird: Bilevel Positional Encoding for Better ...", "date": "", "ddg_snippet": "... performs well on sequences with the in-distribution length but yields a significant performance drop on longer sequences, our BiPE method substantially improves its length extrapolation capabilities, e.g., 19.67 v.s. 158 perplexity on PG19 with the 4096 sequence length .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2401.16421v2", "content": "... performs well on sequences with the in-distribution length but yields a significant performance drop on longer sequences, our BiPE method substantially improves its length extrapolation capabilities, e.g., 19.67 v.s. 158 perplexity on PG19 with the 4096 sequence length ."}
+{"idx": 4, "title": "Overcoming the Curse of Sentence Length for Neural Machine", "date": "", "ddg_snippet": "Random condence score refers to segmenting a sentence with randomly generated condence score for each segment . From Table 1 we can clearly see that the pro-posed segmentation algorithm results in signi-cantly better performance .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1409.1257", "content": "Random condence score refers to segmenting a sentence with randomly generated condence score for each segment . From Table 1 we can clearly see that the pro-posed segmentation algorithm results in signi-cantly better performance ."}
+{"idx": 5, "title": "TOP-ERL: TRANSFORMER BASED OFF-POLICY E REINFORCEMENT LEARNING - arXiv.org", "date": "", "ddg_snippet": "ificant performance variation depending on the segment length . In contrast, ran- Figure 6: Random or Fixed dom segment lengths consistently achieve faste convergence segment length and higher asymptotic performance . We infer that using a vari-ety of action s", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.09536v2", "content": "ificant performance variation depending on the segment length . In contrast, ran- Figure 6: Random or Fixed dom segment lengths consistently achieve faste convergence segment length and higher asymptotic performance . We infer that using a vari-ety of action s"}
+{"idx": 6, "title": "Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context", "date": "", "ddg_snippet": "Transformers have a potential of learning longer-term dependency, but are limited by a fixed - length context in the setting of language modeling. We propose a novel neural architecture Transformer-XL that enables learning dependency beyond a fixed length without disrupting temporal coherence. It consists of a segment -level recurrence mechanism and a novel positional encoding scheme. Our method ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1901.02860", "content": "Transformers have a potential of learning longer-term dependency, but are limited by a fixed - length context in the setting of language modeling. We propose a novel neural architecture Transformer-XL that enables learning dependency beyond a fixed length without disrupting temporal coherence. It consists of a segment -level recurrence mechanism and a novel positional encoding scheme. Our method ..."}
+{"idx": 7, "title": "Random-Access Infinite Context Length for Transformers", "date": "", "ddg_snippet": "Notably, by using a local context length of 250 and retrieving the top k = 2 most relevant blocks, we achieve a comparable performance with a context length of 512.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2305.16300", "content": "Notably, by using a local context length of 250 and retrieving the top k = 2 most relevant blocks, we achieve a comparable performance with a context length of 512."}
+{"idx": 8, "title": "MoRe-ERL: Learning Motion Residuals using Episodic Reinforcement Learning", "date": "", "ddg_snippet": "The learning curves in Fig. 7a demonstrate that MoRe- ERL achieves higher sample eficiency and yields better performance compared to ERL trained from scratch and step-based RL approaches, under both Markovian and non-Markovian reward settings.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2508.01409", "content": "The learning curves in Fig. 7a demonstrate that MoRe- ERL achieves higher sample eficiency and yields better performance compared to ERL trained from scratch and step-based RL approaches, under both Markovian and non-Markovian reward settings."}
+{"idx": 9, "title": "Improving Transformer Based Line Segment Detection with Matched ...", "date": "", "ddg_snippet": "To address this, we present a simple and efficient re-ranking module for line segment detection based on learnable geometric information, since low-level geometric info such as edge, endpoint, and length of line segment always provides essential insights in finding better lines.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.17766v1", "content": "To address this, we present a simple and efficient re-ranking module for line segment detection based on learnable geometric information, since low-level geometric info such as edge, endpoint, and length of line segment always provides essential insights in finding better lines."}
diff --git a/data/sampled_jsons/TRSSL_Rizve_et_al._2022_abstract_year_2022.jsonl b/data/sampled_jsons/TRSSL_Rizve_et_al._2022_abstract_year_2022.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..abbdb9a0dd4aa98cbbf8e0e5fa86a0325adc83b6
--- /dev/null
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2207.02269] Towards Realistic Semi-Supervised Learning Google Scholar GitHub - nayeemrizve/TRSSL: \"Towards Realistic Semi ... (PDF) Towards Realistic Semi-Supervised Learning - ResearchGate Mamshad Nayeem Rizve Towards Realistic Semi-supervised Learning | Computer Vision ... Towards Realistic Semi-Supervised Learning - arXiv.org", "date": "", "ddg_snippet": "Jul 5, 2022 · Deep learning is pushing the state-of-the-art in many computer vision applications. However, it relies on large annotated data repositories, and capturing the unconstrained nature of the real-world data is yet to be solved. Semi-supervised learning (SSL) complements the annotated training data with a large corpus of unlabeled data to reduce annotation cost. The standard SSL approach assumes ... Google Scholar provides a simple way to broadly search for scholarly literature. Search across a wide variety of disciplines and sources: articles, theses, books, abstracts and court opinions. \"Towards Realistic Semi-Supervised Learning\" by Mamshad Nayeem Rizve , Navid Kardan, Mubarak Shah (ECCV 2022 ) - nayeemrizve/ TRSSL Jul 5, 2022 · arXiv:2207.02269v1 [cs.CV] 5 Jul 2022 2 M. N. Rizve et al . goal is to identify nov el-class samples and classify them, as well as to improve Ishan Dave, Rohit Gupta, Mamshad Nayeem Rizve , Mubarak Shah CVIU, 2022 arxiv / elsevier / bibtex / code / video We propose a new temporal contrastive learning framework for self-supervised video representation learning, consisting of two novel losses that aim to increase the temporal diversity of learned features. Abstract Deep learning is pushing the state-of-the-art in many computer vision applications. However, it relies on large annotated data repositories, and capturing the unconstrained nature of the real-world data is yet to be solved. Semi-supervised learning (SSL) complements the annotated training data with a large corpus of unlabeled data to reduce annotation cost. The standard SSL approach ... Towards Realistic Semi-Supervised Learning Mamshad Nayeem Rizve , Navid Kardan, and Mubarak Shah Center for Research in Computer Vision, UCF, USA {nayeemrizve, kardan}@knights.ucf.edu, shah@crcv.ucf.edu Abstract . Deep learning is pushing the state-of-the-art in many com-puter vision applications.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2207.02269", "content": "Jul 5, 2022 · Deep learning is pushing the state-of-the-art in many computer vision applications. However, it relies on large annotated data repositories, and capturing the unconstrained nature of the real-world data is yet to be solved. Semi-supervised learning (SSL) complements the annotated training data with a large corpus of unlabeled data to reduce annotation cost. The standard SSL approach assumes ... Google Scholar provides a simple way to broadly search for scholarly literature. Search across a wide variety of disciplines and sources: articles, theses, books, abstracts and court opinions. \"Towards Realistic Semi-Supervised Learning\" by Mamshad Nayeem Rizve , Navid Kardan, Mubarak Shah (ECCV 2022 ) - nayeemrizve/ TRSSL Jul 5, 2022 · arXiv:2207.02269v1 [cs.CV] 5 Jul 2022 2 M. N. Rizve et al . goal is to identify nov el-class samples and classify them, as well as to improve Ishan Dave, Rohit Gupta, Mamshad Nayeem Rizve , Mubarak Shah CVIU, 2022 arxiv / elsevier / bibtex / code / video We propose a new temporal contrastive learning framework for self-supervised video representation learning, consisting of two novel losses that aim to increase the temporal diversity of learned features. Abstract Deep learning is pushing the state-of-the-art in many computer vision applications. However, it relies on large annotated data repositories, and capturing the unconstrained nature of the real-world data is yet to be solved. Semi-supervised learning (SSL) complements the annotated training data with a large corpus of unlabeled data to reduce annotation cost. The standard SSL approach ... Towards Realistic Semi-Supervised Learning Mamshad Nayeem Rizve , Navid Kardan, and Mubarak Shah Center for Research in Computer Vision, UCF, USA {nayeemrizve, kardan}@knights.ucf.edu, shah@crcv.ucf.edu Abstract . Deep learning is pushing the state-of-the-art in many com-puter vision applications."}
+{"idx": 1, "title": "GitHub - nayeemrizve/TRSSL: \"Towards Realistic Semi ...", "date": "", "ddg_snippet": "\"Towards Realistic Semi-Supervised Learning\" by Mamshad Nayeem Rizve , Navid Kardan, Mubarak Shah (ECCV 2022 ) - nayeemrizve/ TRSSL", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/nayeemrizve/TRSSL", "content": "\"Towards Realistic Semi-Supervised Learning\" by Mamshad Nayeem Rizve , Navid Kardan, Mubarak Shah (ECCV 2022 ) - nayeemrizve/ TRSSL"}
+{"idx": 2, "title": "Mamshad Nayeem Rizve", "date": "", "ddg_snippet": "Ishan Dave, Rohit Gupta, Mamshad Nayeem Rizve , Mubarak Shah CVIU, 2022 arxiv / elsevier / bibtex / code / video We propose a new temporal contrastive learning framework for self-supervised video representation learning, consisting of two novel losses that aim to increase the temporal diversity of learned features.", "subpage_snippet": "", "source": "nayeemrizve.github.io", "link": "https://nayeemrizve.github.io/", "content": "Ishan Dave, Rohit Gupta, Mamshad Nayeem Rizve , Mubarak Shah CVIU, 2022 arxiv / elsevier / bibtex / code / video We propose a new temporal contrastive learning framework for self-supervised video representation learning, consisting of two novel losses that aim to increase the temporal diversity of learned features."}
+{"idx": 3, "title": "Towards Realistic Semi-Supervised Learning - arXiv.org", "date": "", "ddg_snippet": "Towards Realistic Semi-Supervised Learning Mamshad Nayeem Rizve , Navid Kardan, and Mubarak Shah Center for Research in Computer Vision, UCF, USA {nayeemrizve, kardan}@knights.ucf.edu, shah@crcv.ucf.edu Abstract . Deep learning is pushing the state-of-the-art in many com-puter vision applications.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2207.02269", "content": "Towards Realistic Semi-Supervised Learning Mamshad Nayeem Rizve , Navid Kardan, and Mubarak Shah Center for Research in Computer Vision, UCF, USA {nayeemrizve, kardan}@knights.ucf.edu, shah@crcv.ucf.edu Abstract . Deep learning is pushing the state-of-the-art in many com-puter vision applications."}
+{"idx": 4, "title": "Google Scholar", "date": "", "ddg_snippet": "Google Scholar provides a simple way to broadly search for scholarly literature. Search across a wide variety of disciplines and sources: articles, theses, books, abstracts and court opinions.", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/", "content": "Google Scholar provides a simple way to broadly search for scholarly literature. Search across a wide variety of disciplines and sources: articles, theses, books, abstracts and court opinions."}
+{"idx": 5, "title": "(PDF) Towards Realistic Semi-Supervised Learning - ResearchGate", "date": "", "ddg_snippet": "Jul 5, 2022 · arXiv:2207.02269v1 [cs.CV] 5 Jul 2022 2 M. N. Rizve et al . goal is to identify nov el-class samples and classify them, as well as to improve", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/361807923_Towards_Realistic_Semi-Supervised_Learning", "content": "Jul 5, 2022 · arXiv:2207.02269v1 [cs.CV] 5 Jul 2022 2 M. N. Rizve et al . goal is to identify nov el-class samples and classify them, as well as to improve"}
+{"idx": 6, "title": "Towards Realistic Semi-supervised Learning | Computer Vision ...", "date": "", "ddg_snippet": "Abstract Deep learning is pushing the state-of-the-art in many computer vision applications. However, it relies on large annotated data repositories, and capturing the unconstrained nature of the real-world data is yet to be solved. Semi-supervised learning (SSL) complements the annotated training data with a large corpus of unlabeled data to reduce annotation cost. The standard SSL approach ...", "subpage_snippet": "", "source": "dlnext.acm.org", "link": "https://dlnext.acm.org/doi/abs/10.1007/978-3-031-19821-2_25", "content": "Abstract Deep learning is pushing the state-of-the-art in many computer vision applications. However, it relies on large annotated data repositories, and capturing the unconstrained nature of the real-world data is yet to be solved. Semi-supervised learning (SSL) complements the annotated training data with a large corpus of unlabeled data to reduce annotation cost. The standard SSL approach ..."}
+{"idx": 7, "title": "Towards Realistic Semi-supervised Learning", "date": "", "ddg_snippet": "by MN Rizve · 2022 · Cited by 71 — Towards Realistic Semi-supervised Learning . Authors: Mamshad Nayeem Rizve.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1007/978-3-031-19821-2_25", "content": "by MN Rizve · 2022 · Cited by 71 — Towards Realistic Semi-supervised Learning . Authors: Mamshad Nayeem Rizve."}
+{"idx": 8, "title": "Rethinking Open-World Semi-Supervised Learning", "date": "", "ddg_snippet": "31 May 2024 — Openldn: Learning to discover novel classes for open-world semi-supervised learning . In ECCV, 2022a. Rizve et al. [2022b] ↑", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.20829v1", "content": "31 May 2024 — Openldn: Learning to discover novel classes for open-world semi-supervised learning . In ECCV, 2022a. Rizve et al. [2022b] ↑"}
+{"idx": 9, "title": "MUTUAL INFORMATION-GUIDED KNOWLEDGE TRANS", "date": "", "ddg_snippet": "Concurrently,. Rizve et al . ( 2022a ;b) propose more effective pseudo labeling strategies for unlabeled data. How- ever, as in NCD, most of the previous works ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/5f805925c915b06af2cb7a1da80c780a52f25fa9.pdf", "content": "Concurrently,. Rizve et al . ( 2022a ;b) propose more effective pseudo labeling strategies for unlabeled data. How- ever, as in NCD, most of the previous works ..."}
diff --git a/data/sampled_jsons/Table_1_FD1_MSE_mean_estimation_distribution_regression.jsonl b/data/sampled_jsons/Table_1_FD1_MSE_mean_estimation_distribution_regression.jsonl
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index 0000000000000000000000000000000000000000..1d10e352bc9ee9dc204df1dae41cbdcdacaf9672
--- /dev/null
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "MSE vs RMSE vs MAE vs MAPE vs R-Squared: When to Use?", "date": "", "ddg_snippet": "Learn about when to use which evaluation metrics of regression models - MSE , RMSE, MAE, MAPE, R-Squared. Learn with Python & R Code Examples", "subpage_snippet": "", "source": "vitalflux.com", "link": "https://vitalflux.com/mse-vs-rmse-vs-mae-vs-mape-vs-r-squared-when-to-use/", "content": "Learn about when to use which evaluation metrics of regression models - MSE , RMSE, MAE, MAPE, R-Squared. Learn with Python & R Code Examples"}
+{"idx": 1, "title": "9 MSE of Estimator | All Models Are Wrong: Concepts of Statistical Learning", "date": "", "ddg_snippet": "9.1 MSE of an Estimator In order to discuss the bias-variance decomposition of a regression function and its expected MSE , we would like to first review the concept of the mean squared error of an estimator.", "subpage_snippet": "", "source": "allmodelsarewrong.github.io", "link": "https://allmodelsarewrong.github.io/mse.html", "content": "9.1 MSE of an Estimator In order to discuss the bias-variance decomposition of a regression function and its expected MSE , we would like to first review the concept of the mean squared error of an estimator."}
+{"idx": 2, "title": "PDF Lecture 2. Estimation, bias, and mean squared error", "date": "", "ddg_snippet": "Recall that an estimator T is a function of the data, and hence is a random quantity. Roughly, we prefer estimators whose sampling distributions \\cluster more closely\" around the true value of , whatever that value might be. Recall that an estimator T is a function of the data, and hence is a random quantity. Roughly, we prefer estimators whose sampling distributions \\cluster more closely ...", "subpage_snippet": "", "source": "statslab.cam.ac.uk", "link": "http://statslab.cam.ac.uk/Dept/People/djsteaching/S1B-17-02-estimation-bias.pdf", "content": "Recall that an estimator T is a function of the data, and hence is a random quantity. Roughly, we prefer estimators whose sampling distributions \\cluster more closely\" around the true value of , whatever that value might be. Recall that an estimator T is a function of the data, and hence is a random quantity. Roughly, we prefer estimators whose sampling distributions \\cluster more closely ..."}
+{"idx": 3, "title": "Mean Squared Error (MSE) - Statistics by Jim", "date": "", "ddg_snippet": "Mean squared error ( MSE ) measures error in statistical models by using the average squared difference between observed and predicted values.", "subpage_snippet": "", "source": "statisticsbyjim.com", "link": "https://statisticsbyjim.com/regression/mean-squared-error-mse/", "content": "Mean squared error ( MSE ) measures error in statistical models by using the average squared difference between observed and predicted values."}
+{"idx": 4, "title": "Machine Learning: An Expert Guide to Mean Squared Error and Regression ...", "date": "", "ddg_snippet": "The neural network model shows lowest MSE , indicating its regression line should represent the closest fit to the underlying data function. Visualizing the models: We clearly observe the MLP regression (green) line capturing the data distribution accurately by minimizing MSE loss during training. The random forest (red) exhibits some variance errors due to averaging of decision trees. As ...", "subpage_snippet": "", "source": "expertbeacon.com", "link": "https://expertbeacon.com/machine-learning-an-expert-guide-to-mean-squared-error-and-regression-lines/", "content": "The neural network model shows lowest MSE , indicating its regression line should represent the closest fit to the underlying data function. Visualizing the models: We clearly observe the MLP regression (green) line capturing the data distribution accurately by minimizing MSE loss during training. The random forest (red) exhibits some variance errors due to averaging of decision trees. As ..."}
+{"idx": 5, "title": "Mean Squared Error - GeeksforGeeks", "date": "", "ddg_snippet": "Mean Squared Error ( MSE ) is a fundamental concept in statistics and machine learning, playing a crucial role in assessing the accuracy of predictive models. The MSE value provides a way to analyze the accuracy of the model.", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/maths/mean-squared-error/", "content": "Mean Squared Error ( MSE ) is a fundamental concept in statistics and machine learning, playing a crucial role in assessing the accuracy of predictive models. The MSE value provides a way to analyze the accuracy of the model."}
+{"idx": 6, "title": "How to Calculate MSE in R - Statology", "date": "", "ddg_snippet": "Depending on what format your data is in, there are two easy methods you can use to calculate the MSE of a regression model in R. Method 1 : Calculate MSE from Regression Model", "subpage_snippet": "", "source": "www.statology.org", "link": "https://www.statology.org/how-to-calculate-mse-in-r/", "content": "Depending on what format your data is in, there are two easy methods you can use to calculate the MSE of a regression model in R. Method 1 : Calculate MSE from Regression Model"}
+{"idx": 7, "title": "MSE in Linear Regression: A Quick Smart Guide", "date": "", "ddg_snippet": "Explore the role of mean squared error as a cost function in linear regression and master each step with clear examples.", "subpage_snippet": "", "source": "www.numberanalytics.com", "link": "https://www.numberanalytics.com/blog/mse-linear-regression-guide", "content": "Explore the role of mean squared error as a cost function in linear regression and master each step with clear examples."}
+{"idx": 8, "title": "PDF ECE 302: Lecture 8.4 Minimum Mean Square Estimation", "date": "", "ddg_snippet": "First-time readers are often tempted to think that the maximum-likelihood estimation or the maximum a posteriori estimation are the best methods to estimate parameters.", "subpage_snippet": "", "source": "probability4datascience.com", "link": "https://probability4datascience.com/slides/Slide_8_04.pdf", "content": "First-time readers are often tempted to think that the maximum-likelihood estimation or the maximum a posteriori estimation are the best methods to estimate parameters."}
+{"idx": 9, "title": "Mean Squared Error: Definition and Example - Statistics How To", "date": "", "ddg_snippet": "Note that I used an online calculator to get the regression line; where the MSE really comes in handy is if you were finding an equation for the regression line by hand: you could try several equations, and the one that gave you the smallest MSE would be the line of best fit.", "subpage_snippet": "", "source": "www.statisticshowto.com", "link": "https://www.statisticshowto.com/probability-and-statistics/statistics-definitions/mean-squared-error/", "content": "Note that I used an online calculator to get the regression line; where the MSE really comes in handy is if you were finding an equation for the regression line by hand: you could try several equations, and the one that gave you the smallest MSE would be the line of best fit."}
diff --git a/data/sampled_jsons/Table_4_Per-task_costs_CVE-Bench_T-Agent_AutoGPT_One-day_USD_monetary_cost_year_2025.jsonl b/data/sampled_jsons/Table_4_Per-task_costs_CVE-Bench_T-Agent_AutoGPT_One-day_USD_monetary_cost_year_2025.jsonl
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index 0000000000000000000000000000000000000000..a6eaaff07b5cbf5c5448c42ba1a609bb0fa7f59b
--- /dev/null
+++ b/data/sampled_jsons/Table_4_Per-task_costs_CVE-Bench_T-Agent_AutoGPT_One-day_USD_monetary_cost_year_2025.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "CVE-Bench: A Benchmark for AI Agents’ Ability to Exploit Real ...", "date": "", "ddg_snippet": "Compared to the zero- day setting, running CVE - Bench with the one - day setting is more expensive. Although the vulnerability description provided in the one - day setting reduces the potential explorations the LLM agents need, agents may dig deeper and execute more iterations.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.17332v4", "content": "Compared to the zero- day setting, running CVE - Bench with the one - day setting is more expensive. Although the vulnerability description provided in the one - day setting reduces the potential explorations the LLM agents need, agents may dig deeper and execute more iterations."}
+{"idx": 1, "title": "CVE-Bench: A Benchmark for AI Agents' Ability to Exploit ...", "date": "", "ddg_snippet": "21 Mar 2025 — As shown, among successful exploits, T - Agent performs 68% and 30% database access under zero-day and one-day settings, respectively, while the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.17332v1", "content": "21 Mar 2025 — As shown, among successful exploits, T - Agent performs 68% and 30% database access under zero-day and one-day settings, respectively, while the ..."}
+{"idx": 2, "title": "AutoGPT...one day : r/ChatGPT - Reddit", "date": "", "ddg_snippet": "A very very far away day. Currently just a way to throw away money. Yesterday spent 17 dollars in one hour and the result was rubish. The reasoning was ok but the end result… too expensive to run.", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/ChatGPT/comments/13o62d5/autogptone_day/", "content": "A very very far away day. Currently just a way to throw away money. Yesterday spent 17 dollars in one hour and the result was rubish. The reasoning was ok but the end result… too expensive to run."}
+{"idx": 3, "title": "Trend Micro Apex One (On-Prem) Zero-day Vulnerabilities ...", "date": "", "ddg_snippet": "Aug 6, 2025 · Apex One is an on-premise and cloud-based endpoint security solution that helps small and large enterprises with virtual patching and threat detection. URL filtering, pre-execution machine learning, root cause analysis, and data encryption are some of its important features.", "subpage_snippet": "", "source": "threatprotect.qualys.com", "link": "https://threatprotect.qualys.com/2025/08/06/trend-micro-apex-one-on-prem-zero-day-vulnerabilities-exploited-in-the-wild-cve-2025-54948-cve-2025-54987/", "content": "Aug 6, 2025 · Apex One is an on-premise and cloud-based endpoint security solution that helps small and large enterprises with virtual patching and threat detection. URL filtering, pre-execution machine learning, root cause analysis, and data encryption are some of its important features."}
+{"idx": 4, "title": "CVE-2021-44228: Remediation Guidance for Log4j Zero-Day RCE", "date": "", "ddg_snippet": "Sep 13, 2021 · On the 9th of December, 2021, a new vulnerability, CVE -2021-44228, was discovered in Log4j, a popular open-source Java logging framework distributed under Apache Software License.", "subpage_snippet": "", "source": "www.sentinelone.com", "link": "https://www.sentinelone.com/blog/log4j-cve-2021-44228/", "content": "Sep 13, 2021 · On the 9th of December, 2021, a new vulnerability, CVE -2021-44228, was discovered in Log4j, a popular open-source Java logging framework distributed under Apache Software License."}
+{"idx": 5, "title": "CVE - Bench : A Benchmark for AI Agents ' Ability to Exploit Real-World...", "date": "", "ddg_snippet": "Table 4 . Per - task costs of evaluating LLM agents on CVE - Bench .The values we reported are the average of 5 repetitions. As shown, the costs of running our benchmark is less than $100. Compared to the zero-day setting, running CVE - Bench with the one - day setting is more expensive.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.17332", "content": "Table 4 . Per - task costs of evaluating LLM agents on CVE - Bench .The values we reported are the average of 5 repetitions. As shown, the costs of running our benchmark is less than $100. Compared to the zero-day setting, running CVE - Bench with the one - day setting is more expensive."}
+{"idx": 6, "title": "CVE - Bench : A Benchmark for AI Agents ' Ability to Exploit Real-World...", "date": "", "ddg_snippet": "Table 4 . Per - task costs of evaluating LLM agents on CVE - Bench . AutoGPT T - Agent Cy-Agent. We report the average number of input and output tokens, monetary cost , and the time to execute one task.", "subpage_snippet": "", "source": "yuxuan18.github.io", "link": "https://yuxuan18.github.io/assets/pub/cvebench.pdf", "content": "Table 4 . Per - task costs of evaluating LLM agents on CVE - Bench . AutoGPT T - Agent Cy-Agent. We report the average number of input and output tokens, monetary cost , and the time to execute one task."}
+{"idx": 7, "title": "ChatGPT Burns Millions Every Day. Can Computer Scientists ...", "date": "", "ddg_snippet": "Feb 10, 2023 · Training a large language model like that used by ChatGPT is expensive — likely in the tens of millions of dollars — but running it is the true expense.", "subpage_snippet": "", "source": "www.forbes.com", "link": "https://www.forbes.com/sites/johnkoetsier/2023/02/10/chatgpt-burns-millions-every-day-can-computer-scientists-make-ai-one-million-times-more-efficient/", "content": "Feb 10, 2023 · Training a large language model like that used by ChatGPT is expensive — likely in the tens of millions of dollars — but running it is the true expense."}
+{"idx": 8, "title": "460ch3 Flashcards - Quizlet", "date": "", "ddg_snippet": "If an activity whose normal duration is 13 days can be shortened to 10 days for an added cost of $1,500, what is the crash cost per period?", "subpage_snippet": "", "source": "quizlet.com", "link": "https://quizlet.com/56432210/460ch3-flash-cards/", "content": "If an activity whose normal duration is 13 days can be shortened to 10 days for an added cost of $1,500, what is the crash cost per period?"}
+{"idx": 9, "title": "You might have heard of Auto GPT for a while now but did you ...", "date": "", "ddg_snippet": "In one day , AutoGPT was one of the most used repositories on Github in the whole world, and for a good reason. Apparently, it's a next-gen version of GPT4 , which can be completely...", "subpage_snippet": "", "source": "kh.linkedin.com", "link": "https://kh.linkedin.com/posts/pravneet_you-might-have-heard-of-auto-gpt-for-a-while-activity-7052332110300258304-e-9s", "content": "In one day , AutoGPT was one of the most used repositories on Github in the whole world, and for a good reason. Apparently, it's a next-gen version of GPT4 , which can be completely..."}
diff --git a/data/sampled_jsons/Table_8_SIGMOS_FlowDec-75m_DAC-75_4.5_kbps_sitearxiv.org.jsonl b/data/sampled_jsons/Table_8_SIGMOS_FlowDec-75m_DAC-75_4.5_kbps_sitearxiv.org.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..dd733d5c921b178860f2c61503b56422fce5fe8e
--- /dev/null
+++ b/data/sampled_jsons/Table_8_SIGMOS_FlowDec-75m_DAC-75_4.5_kbps_sitearxiv.org.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "FlowDec: A flow-based full-band general audio codec with high ...", "date": "", "ddg_snippet": "FAD is multiplied by 100 for readability. Numbers can be found in Table 8 . In Fig. 4, we show the objective metric results of FlowDec-75m and FlowDec - 75s compared to EnCodec (48 kHz), DAC-75 , 2xDAC- 75 and the official DAC 44.1 kHz checkpoint, and also include the 25 Hz feature rate models FlowDec -25s and DAC -25 for comparison.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.01485v1", "content": "FAD is multiplied by 100 for readability. Numbers can be found in Table 8 . In Fig. 4, we show the objective metric results of FlowDec-75m and FlowDec - 75s compared to EnCodec (48 kHz), DAC-75 , 2xDAC- 75 and the official DAC 44.1 kHz checkpoint, and also include the 25 Hz feature rate models FlowDec -25s and DAC -25 for comparison."}
+{"idx": 1, "title": "A arXiv:2503.01485v1 [cs.SD] 3 Mar 2025", "date": "", "ddg_snippet": "SIGMOS \" FlowDec-75m FlowDec - 75s FlowDec -25s DAC-75 DAC -25 DAC 44.1kHz 2xDAC- 75 EnCodec Figure 4: Mean objective metrics attained by compared methods on the test set at varying bitrates. Colored bands indicate 95% confidence intervals. SIGMOS is speech-only and is calculated only on the speech test files. FAD is multiplied by 100 for readability.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.01485v1", "content": "SIGMOS \" FlowDec-75m FlowDec - 75s FlowDec -25s DAC-75 DAC -25 DAC 44.1kHz 2xDAC- 75 EnCodec Figure 4: Mean objective metrics attained by compared methods on the test set at varying bitrates. Colored bands indicate 95% confidence intervals. SIGMOS is speech-only and is calculated only on the speech test files. FAD is multiplied by 100 for readability."}
+{"idx": 2, "title": "[2503.01485] FlowDec: A flow-based full-band general audio codec with ...", "date": "", "ddg_snippet": "We propose FlowDec , a neural full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method. Compared to the prior work ScoreDec which is based on score matching, we generalize from speech to general audio and move from 24 kbit/s to as low as 4 kbit/s, while improving output ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.01485", "content": "We propose FlowDec , a neural full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method. Compared to the prior work ScoreDec which is based on score matching, we generalize from speech to general audio and move from 24 kbit/s to as low as 4 kbit/s, while improving output ..."}
+{"idx": 3, "title": "ICASSP 2024 Speech Signal Improvement Challenge - arXiv.org", "date": "", "ddg_snippet": "Table 1: ICASSP 2024 Speech Signal Improvement Challenge results. We included MOS and differential MOS (DMOS) (for each score we subtract the corresponding Noisy score) for P.804 [2] , WAcc and the Final Score with the corresponding rank.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2401.14444v1", "content": "Table 1: ICASSP 2024 Speech Signal Improvement Challenge results. We included MOS and differential MOS (DMOS) (for each score we subtract the corresponding Noisy score) for P.804 [2] , WAcc and the Final Score with the corresponding rank."}
+{"idx": 4, "title": "FlowDec: A flow-based full-band general audio codec with high ...", "date": "", "ddg_snippet": "For Test A, we can see that the 4.5 kbit/s variants are rated somewhat lower than the 7.5 kbit/s variants but still achieve good scores compared to EnCodec at 6.0 kbit/s, and the low anchor Opus. We can further see that, at any given bitrate, the score distributions of DAC-75 and FlowDec-75m show no significant differences.", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2503.01485", "content": "For Test A, we can see that the 4.5 kbit/s variants are rated somewhat lower than the 7.5 kbit/s variants but still achieve good scores compared to EnCodec at 6.0 kbit/s, and the low anchor Opus. We can further see that, at any given bitrate, the score distributions of DAC-75 and FlowDec-75m show no significant differences."}
+{"idx": 5, "title": "Recent Event Camera Innovations: A Survey - arXiv.org", "date": "", "ddg_snippet": "The real-time capture and processing of events enable immediate responses to scene changes, making event-based vision particularly suitable for applications that require rapid decision-making. The technology's focus on detecting changes on a logarithmic scale rather than absolute values allows it to handle a wide range of lighting conditions effectively, avoiding common issues like ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2408.13627v2", "content": "The real-time capture and processing of events enable immediate responses to scene changes, making event-based vision particularly suitable for applications that require rapid decision-making. The technology's focus on detecting changes on a logarithmic scale rather than absolute values allows it to handle a wide range of lighting conditions effectively, avoiding common issues like ..."}
+{"idx": 6, "title": "RaD-Net 2: A causal two-stage repairing and denoising speech...", "date": "", "ddg_snippet": "Table 1: DNSMOS and SIGMOS scores for the different approaches on the ICASSP 2024 SSI Challenge blind test set.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.07498v1", "content": "Table 1: DNSMOS and SIGMOS scores for the different approaches on the ICASSP 2024 SSI Challenge blind test set."}
+{"idx": 7, "title": "CodecBench: A Comprehensive Benchmark for Acoustic and Semantic...", "date": "", "ddg_snippet": "For instance, FlowDec , which enhances DAC through flow matching, exhibits embeddings that are not significantly different from DAC . Table 8 : Comparisons between different codecs in CodecBench datasets for Music category, where Baichuan refers to Baichuan-Audio tokenizer.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2508.20660", "content": "For instance, FlowDec , which enhances DAC through flow matching, exhibits embeddings that are not significantly different from DAC . Table 8 : Comparisons between different codecs in CodecBench datasets for Music category, where Baichuan refers to Baichuan-Audio tokenizer."}
+{"idx": 8, "title": "Improving Speech Enhancement with Multi-Metric Supervision from...", "date": "", "ddg_snippet": "SIGMOS _OVRL (SIG.OVRL for short) estimates overall quality, while SIGMOS _NOISE, REVERB, COL, LOUD, and SIG capture specific degradations such as background noise, reverberation, spectral coloration, loudness inconsistency, and signal artifacts.18.29 / 10. 75 .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.12260v2", "content": "SIGMOS _OVRL (SIG.OVRL for short) estimates overall quality, while SIGMOS _NOISE, REVERB, COL, LOUD, and SIG capture specific degradations such as background noise, reverberation, spectral coloration, loudness inconsistency, and signal artifacts.18.29 / 10. 75 ."}
+{"idx": 9, "title": "CodecBench: A Comprehensive Benchmark for Acoustic and Semantic...", "date": "", "ddg_snippet": "kbps . Mel Loss.Notably, FlowDec exhibits slightly better overall performance than DAC -24k-rvq 8 , under identical codebook and bitrate configurations, particularly on the Music datasets. This highlights the advantages of the flow -matching approach.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.20660v1", "content": "kbps . Mel Loss.Notably, FlowDec exhibits slightly better overall performance than DAC -24k-rvq 8 , under identical codebook and bitrate configurations, particularly on the Music datasets. This highlights the advantages of the flow -matching approach."}
diff --git a/data/sampled_jsons/Taming_Knowledge_Conflicts_in_Language_Models_theoretical_analysis_attention_module.jsonl b/data/sampled_jsons/Taming_Knowledge_Conflicts_in_Language_Models_theoretical_analysis_attention_module.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a3199278f677da9e20c887aa1512c8692c98ae8f
--- /dev/null
+++ b/data/sampled_jsons/Taming_Knowledge_Conflicts_in_Language_Models_theoretical_analysis_attention_module.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "The Evolving Role of Large Language Models in Scientific", "date": "", "ddg_snippet": "Scientific innovation is undergoing a paradigm shift driven by the rapid advancement of Large Language Models (LLMs).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.11810v1", "content": "Scientific innovation is undergoing a paradigm shift driven by the rapid advancement of Large Language Models (LLMs)."}
+{"idx": 1, "title": "Into TA: A Comprehensive Textbook on Transactional Analysis", "date": "", "ddg_snippet": "With the research described here, the authors explore, within a transactional analysis theoretical framework, whether the therapeutic impact of in ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/328220481_Into_TA_A_Comprehensive_Textbook_on_Transactional_Analysis", "content": "With the research described here, the authors explore, within a transactional analysis theoretical framework, whether the therapeutic impact of in ..."}
+{"idx": 2, "title": "Downloads", "date": "", "ddg_snippet": "A Theoretical Analysis on Feature Learning in Neural Networks: Emergence from Inputs and Advantage over Fixed Features", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/Downloads/2022", "content": "A Theoretical Analysis on Feature Learning in Neural Networks: Emergence from Inputs and Advantage over Fixed Features"}
+{"idx": 3, "title": "ICLR 2025 Papers", "date": "", "ddg_snippet": "... Models are Secretly Time-Agnostic Masked Models and Exploit ... Neuron-based Multifractal Analysis of Neuron Interaction Dynamics in Large Models", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2025/papers.html", "content": "... Models are Secretly Time-Agnostic Masked Models and Exploit ... Neuron-based Multifractal Analysis of Neuron Interaction Dynamics in Large Models"}
+{"idx": 4, "title": "CVPR 2025 Schedule", "date": "", "ddg_snippet": "Cognitive AI for the Future: Agentic Multimodal Models and RAG for Vision Language Applications, from Training to Deployment", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/calendar", "content": "Cognitive AI for the Future: Agentic Multimodal Models and RAG for Vision Language Applications, from Training to Deployment"}
+{"idx": 5, "title": "Multi-modal Generative AI: Multi-modal LLMs, Diffusions and the", "date": "", "ddg_snippet": "Multi-modal generative AI (Artificial Intelligence) has received increasing attention recently with the advent of (multi-modal) large language models ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2409.14993v2", "content": "Multi-modal generative AI (Artificial Intelligence) has received increasing attention recently with the advent of (multi-modal) large language models ..."}
+{"idx": 6, "title": "NeurIPS 2020 Papers", "date": "", "ddg_snippet": "Duality-Induced Regularizer for Tensor Factorization Based Knowledge Graph Completion ... Models : Generative Temporal Difference Learning for ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2020/papers.html", "content": "Duality-Induced Regularizer for Tensor Factorization Based Knowledge Graph Completion ... Models : Generative Temporal Difference Learning for ..."}
+{"idx": 7, "title": "NeurIPS 2024 Schedule", "date": "", "ddg_snippet": "Opening the Language Model Pipeline: A Tutorial on Data Preparation, Model Training, and Adaptation ... intrinsic functions that give fine-grained ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/calendar", "content": "Opening the Language Model Pipeline: A Tutorial on Data Preparation, Model Training, and Adaptation ... intrinsic functions that give fine-grained ..."}
+{"idx": 8, "title": "ICML 2025 Schedule", "date": "", "ddg_snippet": "... and Bias in Algorithms, Data, and Models ... 10:45] Emergence in non-neural models : grokking modular arithmetic via average gradient outer product", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/calendar", "content": "... and Bias in Algorithms, Data, and Models ... 10:45] Emergence in non-neural models : grokking modular arithmetic via average gradient outer product"}
+{"idx": 9, "title": "Premovement activity in the corticospinal tract is amplified by", "date": "", "ddg_snippet": "Predictive coding theories, and in particular the active inference account, provide a good theoretical framework for explaining the placebo effect ...", "subpage_snippet": "", "source": "academic.oup.com", "link": "https://academic.oup.com/scan/article/20/1/nsaf014/7994475", "content": "Predictive coding theories, and in particular the active inference account, provide a good theoretical framework for explaining the placebo effect ..."}
diff --git a/data/sampled_jsons/Temporally-Correlated_Episodic_RL_TCE_policy_gradient_OR_importance_sampling_OR_off-policy_Li_2024.jsonl b/data/sampled_jsons/Temporally-Correlated_Episodic_RL_TCE_policy_gradient_OR_importance_sampling_OR_off-policy_Li_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4e7ac660fb172dddf16ddf4db90391210accfd3b
--- /dev/null
+++ b/data/sampled_jsons/Temporally-Correlated_Episodic_RL_TCE_policy_gradient_OR_importance_sampling_OR_off-policy_Li_2024.jsonl
@@ -0,0 +1,9 @@
+{"idx": 0, "title": "Performance of Three Estimation Methods in Repeated Time-to ... Tricuspid regurgitation (TR/TI) - Echocardiography, diagnosis ... Contrastive Continual Learning with Importance Sampling and ... Risk factors and survival impact of severe radiation-related ... Endovascular Treatment for Patients With Stroke and Large ... Common Terminology Criteria for Adverse Events (CTCAE)", "date": "", "ddg_snippet": "Jan 13, 2011 · The performance of the SAEM and importance sampling were generally higher than Laplace when the frequency of individuals with events was less than 43%, while at frequencies above that all methods were equal in performance. Key words: importance sampling , Laplace, mixed-effects modeling, NONMEM, repeated time-to-event, SAEM Approximately 85%-90% of healthy individuals in the general population exhibit a small tricuspid regurgitation, which is considered a normal finding. Pathological tricuspid regurgitation is more pronounced. Echocardiography is the preferred method for diagnosing tricuspid regurgitation. Mar 7, 2024 · Recently, because of the high-quality representations of contrastive learning methods, rehearsal-based contrastive continual learning has been proposed to explore how to continually learn transferable representation embeddings to avoid the catastrophic forgetting issue in traditional continual settings. Based on this framework, we propose Contrastive Continual Learning via Importance Sampling ... May 8, 2025 · We conducted a cohort study of HNC patients who received ≥50 Gy as part of curative treatment between January 2003 and December 2020 at a Canadian quaternary cancer center. Risk factors for severe RLTs (≥RTOG Grade 3) were evaluated using time-to-event analyses. Actuarial rates of RLT and overall survival (OS) were estimated using competing risk and Kaplan–Meier methods, respectively ... This case-control study compares the outcomes of patients following stroke with large baseline ischemic cores on computed tomographic perfusion undergoing endovascular therapy with the outcomes of matched controls who had medical care alone. Jul 22, 2025 · Introduction The NCI Common Terminology Criteria for Adverse Events is a descriptive terminology which can be utilized for Adverse Event (AE) reporting. A grading (severity) scale is provided for each AE term.", "subpage_snippet": "", "source": "www.ncbi.nlm.nih.gov", "link": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3032099/", "content": "Jan 13, 2011 · The performance of the SAEM and importance sampling were generally higher than Laplace when the frequency of individuals with events was less than 43%, while at frequencies above that all methods were equal in performance. Key words: importance sampling , Laplace, mixed-effects modeling, NONMEM, repeated time-to-event, SAEM Approximately 85%-90% of healthy individuals in the general population exhibit a small tricuspid regurgitation, which is considered a normal finding. Pathological tricuspid regurgitation is more pronounced. Echocardiography is the preferred method for diagnosing tricuspid regurgitation. Mar 7, 2024 · Recently, because of the high-quality representations of contrastive learning methods, rehearsal-based contrastive continual learning has been proposed to explore how to continually learn transferable representation embeddings to avoid the catastrophic forgetting issue in traditional continual settings. Based on this framework, we propose Contrastive Continual Learning via Importance Sampling ... May 8, 2025 · We conducted a cohort study of HNC patients who received ≥50 Gy as part of curative treatment between January 2003 and December 2020 at a Canadian quaternary cancer center. Risk factors for severe RLTs (≥RTOG Grade 3) were evaluated using time-to-event analyses. Actuarial rates of RLT and overall survival (OS) were estimated using competing risk and Kaplan–Meier methods, respectively ... This case-control study compares the outcomes of patients following stroke with large baseline ischemic cores on computed tomographic perfusion undergoing endovascular therapy with the outcomes of matched controls who had medical care alone. Jul 22, 2025 · Introduction The NCI Common Terminology Criteria for Adverse Events is a descriptive terminology which can be utilized for Adverse Event (AE) reporting. A grading (severity) scale is provided for each AE term."}
+{"idx": 1, "title": "Contrastive Continual Learning with Importance Sampling and ...", "date": "", "ddg_snippet": "Mar 7, 2024 · Recently, because of the high-quality representations of contrastive learning methods, rehearsal-based contrastive continual learning has been proposed to explore how to continually learn transferable representation embeddings to avoid the catastrophic forgetting issue in traditional continual settings. Based on this framework, we propose Contrastive Continual Learning via Importance Sampling ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2403.04599", "content": "Mar 7, 2024 · Recently, because of the high-quality representations of contrastive learning methods, rehearsal-based contrastive continual learning has been proposed to explore how to continually learn transferable representation embeddings to avoid the catastrophic forgetting issue in traditional continual settings. Based on this framework, we propose Contrastive Continual Learning via Importance Sampling ..."}
+{"idx": 2, "title": "10 Issues for the Clinician in Tricuspid Regurgitation ...", "date": "", "ddg_snippet": "Importantly, the U.S. Food and Drug Administration approved 2 transcatheter systems for the treatment of patients with symptomatic, severe TR in 2024 specifically to improve health status, including quality of life (QoL). 1,2 The Centers for Medicare & Medicaid Services announced in March 2025 that it will cover transcatheter TV replacement ...", "subpage_snippet": "", "source": "www.jacc.org", "link": "https://www.jacc.org/doi/10.1016/j.jacc.2025.07.002", "content": "Importantly, the U.S. Food and Drug Administration approved 2 transcatheter systems for the treatment of patients with symptomatic, severe TR in 2024 specifically to improve health status, including quality of life (QoL). 1,2 The Centers for Medicare & Medicaid Services announced in March 2025 that it will cover transcatheter TV replacement ..."}
+{"idx": 3, "title": "U pdates for t emporally -c orrelated e pisodic", "date": "", "ddg_snippet": "Episodic rl . Using Movement Primitives for Trajectory Representation. Representation of Trajectory Distribution and Likelihood.Open the black box: step-based policy updates for temporally - correlated episodic .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=mnipav175N", "content": "Episodic rl . Using Movement Primitives for Trajectory Representation. Representation of Trajectory Distribution and Likelihood.Open the black box: step-based policy updates for temporally - correlated episodic ."}
+{"idx": 4, "title": "Welcome to yobihome", "date": "", "ddg_snippet": "Identifying Policy Gradient Subspaces.Compressor summary: The paper proposes Off - policy DAE, a method that learns from off - policy data by decomposing return into skill and luck components, without using importance sampling or truncation.", "subpage_snippet": "", "source": "yobibyte.github.io", "link": "https://yobibyte.github.io/iclr2024_compressed.html", "content": "Identifying Policy Gradient Subspaces.Compressor summary: The paper proposes Off - policy DAE, a method that learns from off - policy data by decomposing return into skill and luck components, without using importance sampling or truncation."}
+{"idx": 5, "title": "Tricuspid regurgitation (TR/TI) - Echocardiography, diagnosis ...", "date": "", "ddg_snippet": "Approximately 85%-90% of healthy individuals in the general population exhibit a small tricuspid regurgitation, which is considered a normal finding. Pathological tricuspid regurgitation is more pronounced. Echocardiography is the preferred method for diagnosing tricuspid regurgitation.", "subpage_snippet": "", "source": "ecgwaves.com", "link": "https://ecgwaves.com/topic/tricuspid-regurgitation-tr-ti/", "content": "Approximately 85%-90% of healthy individuals in the general population exhibit a small tricuspid regurgitation, which is considered a normal finding. Pathological tricuspid regurgitation is more pronounced. Echocardiography is the preferred method for diagnosing tricuspid regurgitation."}
+{"idx": 6, "title": "Risk factors and survival impact of severe radiation-related ...", "date": "", "ddg_snippet": "May 8, 2025 · We conducted a cohort study of HNC patients who received ≥50 Gy as part of curative treatment between January 2003 and December 2020 at a Canadian quaternary cancer center. Risk factors for severe RLTs (≥RTOG Grade 3) were evaluated using time-to-event analyses. Actuarial rates of RLT and overall survival (OS) were estimated using competing risk and Kaplan–Meier methods, respectively ...", "subpage_snippet": "", "source": "www.thelancet.com", "link": "https://www.thelancet.com/journals/lanam/article/PIIS2667-193X(25)00228-5/fulltext", "content": "May 8, 2025 · We conducted a cohort study of HNC patients who received ≥50 Gy as part of curative treatment between January 2003 and December 2020 at a Canadian quaternary cancer center. Risk factors for severe RLTs (≥RTOG Grade 3) were evaluated using time-to-event analyses. Actuarial rates of RLT and overall survival (OS) were estimated using competing risk and Kaplan–Meier methods, respectively ..."}
+{"idx": 7, "title": "Endovascular Treatment for Patients With Stroke and Large ...", "date": "", "ddg_snippet": "This case-control study compares the outcomes of patients following stroke with large baseline ischemic cores on computed tomographic perfusion undergoing endovascular therapy with the outcomes of matched controls who had medical care alone.", "subpage_snippet": "", "source": "jamanetwork.com", "link": "https://jamanetwork.com/journals/jamaneurology/fullarticle/2575852", "content": "This case-control study compares the outcomes of patients following stroke with large baseline ischemic cores on computed tomographic perfusion undergoing endovascular therapy with the outcomes of matched controls who had medical care alone."}
+{"idx": 8, "title": "Common Terminology Criteria for Adverse Events (CTCAE)", "date": "", "ddg_snippet": "Jul 22, 2025 · Introduction The NCI Common Terminology Criteria for Adverse Events is a descriptive terminology which can be utilized for Adverse Event (AE) reporting. A grading (severity) scale is provided for each AE term.", "subpage_snippet": "", "source": "dctd.cancer.gov", "link": "https://dctd.cancer.gov/research/ctep-trials/for-sites/adverse-events/ctcae-v6.pdf", "content": "Jul 22, 2025 · Introduction The NCI Common Terminology Criteria for Adverse Events is a descriptive terminology which can be utilized for Adverse Event (AE) reporting. A grading (severity) scale is provided for each AE term."}
diff --git a/data/sampled_jsons/The_Illusion_of_State_in_State-Space_Models_Merrill_finite_state_machines_cannot_simulate.jsonl b/data/sampled_jsons/The_Illusion_of_State_in_State-Space_Models_Merrill_finite_state_machines_cannot_simulate.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c6e23578e3626ff9859ce8695501fbbf29b40199
--- /dev/null
+++ b/data/sampled_jsons/The_Illusion_of_State_in_State-Space_Models_Merrill_finite_state_machines_cannot_simulate.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Implicit Language Models are RNNs: Balancing Parallelization ...", "date": "", "ddg_snippet": "Implicit SSMs Lift the Illusion of State The Illusion of State ( Merrill et al., 2024) reveals that SSMs cannot simulate arbitrary finite state machines . A hard state tracking problem in the sense that all state tracking problems can be reduced to it is given by the word problem for the symmetric group S5 (Barrington, 1989).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.07827v2", "content": "Implicit SSMs Lift the Illusion of State The Illusion of State ( Merrill et al., 2024) reveals that SSMs cannot simulate arbitrary finite state machines . A hard state tracking problem in the sense that all state tracking problems can be reduced to it is given by the word problem for the symmetric group S5 (Barrington, 1989)."}
+{"idx": 1, "title": "[2502.07827] Implicit Language Models are RNNs: Balancing ...", "date": "", "ddg_snippet": "The Illusion of State ( Merrill et al., 2024) reveals that SSMs cannot simulate arbitrary finite state machines . A hard state tracking problem in the sense that all state tracking problems can be reduced to it is given by the word problem for the symmetric group S 5 (Barrington, 1989).", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2502.07827", "content": "The Illusion of State ( Merrill et al., 2024) reveals that SSMs cannot simulate arbitrary finite state machines . A hard state tracking problem in the sense that all state tracking problems can be reduced to it is given by the word problem for the symmetric group S 5 (Barrington, 1989)."}
+{"idx": 2, "title": "Implicit Language Models are RNNs: Balancing Parallelization ...", "date": "", "ddg_snippet": "Implicit SSMs Lift the Illusion of State The illusion of state ( Merrill et al., 2024) reveals that constant depth SSMs cannot simulate arbitrary finite state machines . A hard state tracking problem in the sense that all state track-ing problems can be reduced to it is given by the word problem for the symmetric group S5 (Barrington, 1989).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.07827", "content": "Implicit SSMs Lift the Illusion of State The illusion of state ( Merrill et al., 2024) reveals that constant depth SSMs cannot simulate arbitrary finite state machines . A hard state tracking problem in the sense that all state track-ing problems can be reduced to it is given by the word problem for the symmetric group S5 (Barrington, 1989)."}
+{"idx": 3, "title": "Implicit Language Models are RNNs: Balancing Parallelization ...", "date": "", "ddg_snippet": "Implicit SSMs Lift the Illusion of State The illusion of state ( Merrill et al., 2024) reveals that constant depth SSMs cannot simulate arbitrary finite state machines . A hard state tracking problem in the sense that all state tracking problems can be reduced to it is given by the word problem for the symmetric group S 5 (Barrington, 1989).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.07827", "content": "Implicit SSMs Lift the Illusion of State The illusion of state ( Merrill et al., 2024) reveals that constant depth SSMs cannot simulate arbitrary finite state machines . A hard state tracking problem in the sense that all state tracking problems can be reduced to it is given by the word problem for the symmetric group S 5 (Barrington, 1989)."}
+{"idx": 4, "title": "Implicit Language Models are RNNs", "date": "", "ddg_snippet": "10 Feb 2025 — The Illusion of State ( Merrill et al., 2024) reveals that SSMs cannot simulate arbitrary finite state machines . A hard state tracking ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.07827v1", "content": "10 Feb 2025 — The Illusion of State ( Merrill et al., 2024) reveals that SSMs cannot simulate arbitrary finite state machines . A hard state tracking ..."}
+{"idx": 5, "title": "Implicit Language Models are RNNs: Balancing ...", "date": "", "ddg_snippet": "... ( Merrill et al., 2024) reveals that constant depth SSMs cannot simulate arbitrary finite state machines . A hard state tracking problem in the sense that all ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46440", "content": "... ( Merrill et al., 2024) reveals that constant depth SSMs cannot simulate arbitrary finite state machines . A hard state tracking problem in the sense that all ..."}
+{"idx": 6, "title": "Implicit Language Models are RNNs: Balancing Parallelization ...", "date": "", "ddg_snippet": "Merrill , W., Petty, J., and Sabharwal, A. The illusion of state in state-space models . ... cannot simulate arbitrary finite state machines , a limitation the ...", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2502.07827v1", "content": "Merrill , W., Petty, J., and Sabharwal, A. The illusion of state in state-space models . ... cannot simulate arbitrary finite state machines , a limitation the ..."}
+{"idx": 7, "title": "Implicit Language Models are RNNs: Balancing Parallelization and...", "date": "", "ddg_snippet": "Implicit SSMs Lift the Illusion of State The illusion of state ( Merrill et al., 2024) reveals that constant depth SSMs cannot simulate arbitrary finite state machines . Merrill , W., Petty, J., and Sabharwal, A. The illusion of state in state - space models .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=5EbiopWH6e", "content": "Implicit SSMs Lift the Illusion of State The illusion of state ( Merrill et al., 2024) reveals that constant depth SSMs cannot simulate arbitrary finite state machines . Merrill , W., Petty, J., and Sabharwal, A. The illusion of state in state - space models ."}
+{"idx": 8, "title": "(PDF) Implicit Language Models are RNNs: Balancing Parallelization...", "date": "", "ddg_snippet": "State - space models (SSMs) and transformers dominate the language modeling landscape.RNN’s hidden state . 4. Implicit SSMs Adapt to Hard Languages. Implicit SSMs Lift the Illusion of State The Illusion of . State ( Merrill et al.,2024) reveals that SSMs cannot simulate .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/388955170_Implicit_Language_Models_are_RNNs_Balancing_Parallelization_and_Expressivity", "content": "State - space models (SSMs) and transformers dominate the language modeling landscape.RNN’s hidden state . 4. Implicit SSMs Adapt to Hard Languages. Implicit SSMs Lift the Illusion of State The Illusion of . State ( Merrill et al.,2024) reveals that SSMs cannot simulate ."}
+{"idx": 9, "title": "The Illusion of State in State-Space Models", "date": "", "ddg_snippet": "... the seemingly stateful design of SSMs truly enable them to solve sequential and state -tracking problems that transformers cannot? If so, this would be ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2404.08819v3", "content": "... the seemingly stateful design of SSMs truly enable them to solve sequential and state -tracking problems that transformers cannot? If so, this would be ..."}
diff --git a/data/sampled_jsons/The_Open_Images_Dataset_V4_Kuznetsova_et_al._2020_year_2020.jsonl b/data/sampled_jsons/The_Open_Images_Dataset_V4_Kuznetsova_et_al._2020_year_2020.jsonl
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index 0000000000000000000000000000000000000000..3c510e5b942867e88e798a2d6fa4495583c0458d
--- /dev/null
+++ b/data/sampled_jsons/The_Open_Images_Dataset_V4_Kuznetsova_et_al._2020_year_2020.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Open Images V6 - Description", "date": "", "ddg_snippet": "... OpenImages, author = {Alina Kuznetsova and Hassan Rom and Neil Alldrin and Jasper Uijlings and Ivan Krasin and Jordi Pont-Tuset and Shahab Kamali and ...", "subpage_snippet": "", "source": "storage.googleapis.com", "link": "https://storage.googleapis.com/openimages/web/factsfigures.html", "content": "... OpenImages, author = {Alina Kuznetsova and Hassan Rom and Neil Alldrin and Jasper Uijlings and Ivan Krasin and Jordi Pont-Tuset and Shahab Kamali and ..."}
+{"idx": 1, "title": "Open Images V7 - Description", "date": "", "ddg_snippet": "... OpenImages, author = {Alina Kuznetsova and Hassan Rom and Neil Alldrin and Jasper Uijlings and Ivan Krasin and Jordi Pont-Tuset and Shahab Kamali and ...", "subpage_snippet": "", "source": "storage.googleapis.com", "link": "https://storage.googleapis.com/openimages/web/factsfigures_v7.html", "content": "... OpenImages, author = {Alina Kuznetsova and Hassan Rom and Neil Alldrin and Jasper Uijlings and Ivan Krasin and Jordi Pont-Tuset and Shahab Kamali and ..."}
+{"idx": 2, "title": "Open Datasets", "date": "", "ddg_snippet": "This repository contains a mapping between the classes of COCO, LVIS, and Open Images V4 datasets into a unique set of 1460 classes.", "subpage_snippet": "", "source": "www.ai4media.eu", "link": "https://www.ai4media.eu/open-datasets/", "content": "This repository contains a mapping between the classes of COCO, LVIS, and Open Images V4 datasets into a unique set of 1460 classes."}
+{"idx": 3, "title": "‘ML dataset’ directory · Gwern.net", "date": "", "ddg_snippet": "... Dataset for Advancing Reasoning ”, He et al 2025 ... Me, Myself, and AI: The Situational Awareness Dataset (SAD) for LLMs ”, Laine et al 2024", "subpage_snippet": "", "source": "gwern.net", "link": "https://gwern.net/doc/ai/dataset/index", "content": "... Dataset for Advancing Reasoning ”, He et al 2025 ... Me, Myself, and AI: The Situational Awareness Dataset (SAD) for LLMs ”, Laine et al 2024"}
+{"idx": 4, "title": "UnityShip: A Large-Scale Synthetic Dataset for Ship Recognition", "date": "", "ddg_snippet": "For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the ...", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/2072-4292/13/24/4999", "content": "For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the ..."}
+{"idx": 5, "title": "MOCHA: Multi-modal Objects-aware Cross-arcHitecture Alignment", "date": "", "ddg_snippet": "... semantics from a vision-language foundation model into a lightweight student detector by aligning region-level embeddings and regularizing the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.14001v1", "content": "... semantics from a vision-language foundation model into a lightweight student detector by aligning region-level embeddings and regularizing the ..."}
+{"idx": 6, "title": "JanusFlow: Harmonizing Autoregression and Rectified Flow for", "date": "", "ddg_snippet": "JanusFlow shows state-of- the -art performances in both multimodal comprehension and text-to- image generation compared to existing unified approaches ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.07975v2", "content": "JanusFlow shows state-of- the -art performances in both multimodal comprehension and text-to- image generation compared to existing unified approaches ..."}
+{"idx": 7, "title": "Vittorio Ferrari | DeepAI", "date": "", "ddg_snippet": "The Open Images Dataset contains approximately 9 million images and is a... ... This paper aims to reduce the time to annotate images for the ...", "subpage_snippet": "", "source": "deepai.org", "link": "https://deepai.org/profile/vittorio-ferrari", "content": "The Open Images Dataset contains approximately 9 million images and is a... ... This paper aims to reduce the time to annotate images for the ..."}
+{"idx": 8, "title": "‘active learning’ directory · Gwern.net", "date": "", "ddg_snippet": "The Open Images Dataset V4 : Unified Image Classification, Object Detection, and Visual Relationship Detection at Scale ”, Kuznetsova et al 2018", "subpage_snippet": "", "source": "gwern.net", "link": "https://gwern.net/doc/reinforcement-learning/exploration/active-learning/index", "content": "The Open Images Dataset V4 : Unified Image Classification, Object Detection, and Visual Relationship Detection at Scale ”, Kuznetsova et al 2018"}
+{"idx": 9, "title": "(PDF) Scaling Autoregressive Multi-Modal Models: Pretraining", "date": "", "ddg_snippet": "The SFT stage follows multi-task instruction tuning for text-only models Iyer et ... Image T okenization W e use the image tokenizer from Gafni et al .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/373715029_Scaling_Autoregressive_Multi-Modal_Models_Pretraining_and_Instruction_Tuning", "content": "The SFT stage follows multi-task instruction tuning for text-only models Iyer et ... Image T okenization W e use the image tokenizer from Gafni et al ."}
diff --git a/data/sampled_jsons/The_impact_of_modeling_decisions_in_statistical_profiling_Bach_abstract_unemployment_screening_capac.jsonl b/data/sampled_jsons/The_impact_of_modeling_decisions_in_statistical_profiling_Bach_abstract_unemployment_screening_capac.jsonl
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index 0000000000000000000000000000000000000000..28a99b2f90cbff8f9178aafe8ac02fa4512ce4d2
--- /dev/null
+++ b/data/sampled_jsons/The_impact_of_modeling_decisions_in_statistical_profiling_Bach_abstract_unemployment_screening_capac.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "The Value of Prediction in Identifying the Worst-Off", "date": "", "ddg_snippet": "... the absence of an overarching framework that allows the systematic assessment of the relative impacts of different design decisions , efforts to ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.19334v2", "content": "... the absence of an overarching framework that allows the systematic assessment of the relative impacts of different design decisions , efforts to ..."}
+{"idx": 1, "title": "Acceptance and Commitment Therapy plus usual care for improving", "date": "", "ddg_snippet": "We aimed to evaluate the effectiveness of ACT plus usual care, compared with usual care alone, for improving quality of life in people with motor ...", "subpage_snippet": "", "source": "www.thelancet.com", "link": "https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(24)00533-6/fulltext", "content": "We aimed to evaluate the effectiveness of ACT plus usual care, compared with usual care alone, for improving quality of life in people with motor ..."}
+{"idx": 2, "title": "Containment strategy during the COVID-19 pandemic among three", "date": "", "ddg_snippet": "By assessing the effectiveness of COVID-19 containment measures, we wanted to provide lessons for wider LMICs in controlling and preventing the COVID ...", "subpage_snippet": "", "source": "jogh.org", "link": "https://jogh.org/2022/jogh-12-05016", "content": "By assessing the effectiveness of COVID-19 containment measures, we wanted to provide lessons for wider LMICs in controlling and preventing the COVID ..."}
+{"idx": 3, "title": "Time Series Information Visualization – A Review of", "date": "", "ddg_snippet": "The area of Information Visualization (InfoVis) leverages human cognitive abilities to transform abstract data patterns into comprehensible visual ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.14920v1", "content": "The area of Information Visualization (InfoVis) leverages human cognitive abilities to transform abstract data patterns into comprehensible visual ..."}
+{"idx": 4, "title": "(PDF) Gambling disorder and problematic pornography use: Does", "date": "", "ddg_snippet": "Methods Cognitive-behavioral therapy (CBT) was administered in 16 weekly sessions, with assessments of GD severity, impulsivity, emotion regulation ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/390065266_Gambling_disorder_and_problematic_pornography_use_Does_co-occurrence_influence_treatment_outcome", "content": "Methods Cognitive-behavioral therapy (CBT) was administered in 16 weekly sessions, with assessments of GD severity, impulsivity, emotion regulation ..."}
+{"idx": 5, "title": "Hearing loss prevalence and years lived with disability,", "date": "", "ddg_snippet": "Interventions such as childhood screening , hearing aids, effective management of otitis media and meningitis, and cochlear implants have the ...", "subpage_snippet": "", "source": "www.thelancet.com", "link": "https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(21)00516-X/fulltext?rss=yes", "content": "Interventions such as childhood screening , hearing aids, effective management of otitis media and meningitis, and cochlear implants have the ..."}
+{"idx": 6, "title": "Social Vulnerability and Child Food Insecurity in Developed", "date": "", "ddg_snippet": "... key social vulnerability contributors to CFI in economically developed countries and discuss the factors in the context of the socio-ecological model ...", "subpage_snippet": "", "source": "advances.nutrition.org", "link": "https://advances.nutrition.org/article/S2161-8313(25)00001-8/fulltext", "content": "... key social vulnerability contributors to CFI in economically developed countries and discuss the factors in the context of the socio-ecological model ..."}
+{"idx": 7, "title": "Impaired Personality Functioning in Children and Adolescents", "date": "", "ddg_snippet": "Feature papers represent the most advanced research with significant potential for high impact in the field. ... The aim is to provide a snapshot of ...", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/2227-9067/10/7/1186", "content": "Feature papers represent the most advanced research with significant potential for high impact in the field. ... The aim is to provide a snapshot of ..."}
+{"idx": 8, "title": "Challenging (-Hindering) Employment and Employee … –", "date": "", "ddg_snippet": "Unpacking the components of work is most certainly necessary to specify the mechanisms leading to outcomes of interest, but this approach does not ...", "subpage_snippet": "", "source": "www.erudit.org", "link": "https://www.erudit.org/fr/revues/ri/2022-v77-n4-ri07810/1097689ar/", "content": "Unpacking the components of work is most certainly necessary to specify the mechanisms leading to outcomes of interest, but this approach does not ..."}
+{"idx": 9, "title": "“As long as that place stays open, I’ll stay alive”:", "date": "", "ddg_snippet": "For clients receiving iOAT, the consequences of treatment disruption may be considerable, given the frequency of doses, the intensity of services ...", "subpage_snippet": "", "source": "www.researchsquare.com", "link": "https://www.researchsquare.com/article/rs-2596310/v1", "content": "For clients receiving iOAT, the consequences of treatment disruption may be considerable, given the frequency of doses, the intensity of services ..."}
diff --git a/data/sampled_jsons/The_impact_of_modeling_decisions_in_statistical_profiling_Bach_et_al._2023_abstract_year_2023.jsonl b/data/sampled_jsons/The_impact_of_modeling_decisions_in_statistical_profiling_Bach_et_al._2023_abstract_year_2023.jsonl
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index 0000000000000000000000000000000000000000..78475a5f92f7e8eacca98a90da8c4712fd973874
--- /dev/null
+++ b/data/sampled_jsons/The_impact_of_modeling_decisions_in_statistical_profiling_Bach_et_al._2023_abstract_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "The impact of modeling decisions in statistical profiling", "date": "", "ddg_snippet": "by RL Bach · 2023 · Cited by 13 — We evaluate regression and machine-learning models for predicting job-seekers' risk of becoming long-term unemployed using German administrative labor market ...", "subpage_snippet": "", "source": "www.cambridge.org", "link": "https://www.cambridge.org/core/journals/data-and-policy/article/impact-of-modeling-decisions-in-statistical-profiling/AFAE0907D68D7AFA6915B2F8D08C90B5", "content": "by RL Bach · 2023 · Cited by 13 — We evaluate regression and machine-learning models for predicting job-seekers' risk of becoming long-term unemployed using German administrative labor market ..."}
+{"idx": 1, "title": "Transparent and Fair Profiling in Employment Services", "date": "", "ddg_snippet": "15 Sept 2025 — 2023 . The impact of modeling decisions in statistical profiling . Data & Policy 5: e32. Barnes et al . (2015) ↑ Barnes, S.-A., S. Wright, P ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.11847v1", "content": "15 Sept 2025 — 2023 . The impact of modeling decisions in statistical profiling . Data & Policy 5: e32. Barnes et al . (2015) ↑ Barnes, S.-A., S. Wright, P ..."}
+{"idx": 2, "title": "Reconsidering Fairness Through Unawareness From the ...", "date": "", "ddg_snippet": "( 2023 ) ↑ Ruben L Bach , Christoph Kern, Hannah Mautner, and Frauke Kreuter. 2023 . The impact of modeling decisions in statistical profiling . Data & Policy 5 ( ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.16638v2", "content": "( 2023 ) ↑ Ruben L Bach , Christoph Kern, Hannah Mautner, and Frauke Kreuter. 2023 . The impact of modeling decisions in statistical profiling . Data & Policy 5 ( ..."}
+{"idx": 3, "title": "Fairness in Algorithmic Profiling: The AMAS Case", "date": "", "ddg_snippet": "by E Achterhold · 2025 · Cited by 3 — In the German context, Bach et al. (2023) and Kern et al. (2024) highlight the importance of modeling choices and show how the set of job ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s11023-024-09706-9", "content": "by E Achterhold · 2025 · Cited by 3 — In the German context, Bach et al. (2023) and Kern et al. (2024) highlight the importance of modeling choices and show how the set of job ..."}
+{"idx": 4, "title": "Bridging the gap: Towards an expanded toolkit for AI- ...", "date": "", "ddg_snippet": "by U Fischer-Abaigar · 2024 · Cited by 10 — Bach , Kern, Mautner and Kreuter, 2023 . R.L. Bach , C. Kern, H. Mautner, F. Kreuter. The impact of modeling decisions in statistical profiling . Data & Policy, 5 ( ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0740624X24000686", "content": "by U Fischer-Abaigar · 2024 · Cited by 10 — Bach , Kern, Mautner and Kreuter, 2023 . R.L. Bach , C. Kern, H. Mautner, F. Kreuter. The impact of modeling decisions in statistical profiling . Data & Policy, 5 ( ..."}
+{"idx": 5, "title": "Fairness Implications of Algorithmic Profiling Schemes", "date": "", "ddg_snippet": "by C Kern · 2024 · Cited by 4 — We show that despite achieving high prediction performance on average, profiling models can be considerably less accurate for vulnerable social subgroups. In ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/full/10.1145/3689485", "content": "by C Kern · 2024 · Cited by 4 — We show that despite achieving high prediction performance on average, profiling models can be considerably less accurate for vulnerable social subgroups. In ..."}
+{"idx": 6, "title": "Top 38 Data & policy papers published in 2023", "date": "", "ddg_snippet": "The impact of modeling decisions in statistical profiling . Ruben L. Bach, Christoph Kern, Hannah Mautner, Frauke Kreuter. 31 Dec 2022-Data & policy. TL;DR: The ...", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/journals/data-policy-235lcu6d/2023", "content": "The impact of modeling decisions in statistical profiling . Ruben L. Bach, Christoph Kern, Hannah Mautner, Frauke Kreuter. 31 Dec 2022-Data & policy. TL;DR: The ..."}
+{"idx": 7, "title": "rule-based versus statistical models for jobseeker profiling1", "date": "", "ddg_snippet": "by ÁF Junquera · 2024 — In line with results for Germany (Bach et al., 2023), tree-based methods do better both at the ROC and at the PR functions, but the ...", "subpage_snippet": "", "source": "files.de-1.osf.io", "link": "https://files.de-1.osf.io/v1/resources/c7ps3/providers/osfstorage/666b234477ff4c5a3ce045ad?format=pdf&action=download&direct&version=1", "content": "by ÁF Junquera · 2024 — In line with results for Germany (Bach et al., 2023), tree-based methods do better both at the ROC and at the PR functions, but the ..."}
+{"idx": 8, "title": "Advanced methods for identifying and profiling social ...", "date": "", "ddg_snippet": "4 Sept 2025 — The rise of online social networks has created a new landscape for brands and companies to engage with consumers.", "subpage_snippet": "", "source": "www.tandfonline.com", "link": "https://www.tandfonline.com/doi/full/10.1080/2573234X.2025.2545923?src=exp-la", "content": "4 Sept 2025 — The rise of online social networks has created a new landscape for brands and companies to engage with consumers."}
+{"idx": 9, "title": "Human oversight of automated decision-making systems in ...", "date": "", "ddg_snippet": "by K Sztandar-Sztanderska — Bach RL, Kern C, Mautner H, et al. (2023) The impact of modeling decisions in statistical profiling . Data & Policy 5: e32. Barnes S-A, Wright S ...", "subpage_snippet": "", "source": "journals.sagepub.com", "link": "https://journals.sagepub.com/doi/abs/10.1177/09589287251358069", "content": "by K Sztandar-Sztanderska — Bach RL, Kern C, Mautner H, et al. (2023) The impact of modeling decisions in statistical profiling . Data & Policy 5: e32. Barnes S-A, Wright S ..."}
diff --git a/data/sampled_jsons/The_impact_of_modeling_decisions_in_statistical_profiling_unemployment_58%.jsonl b/data/sampled_jsons/The_impact_of_modeling_decisions_in_statistical_profiling_unemployment_58%.jsonl
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index 0000000000000000000000000000000000000000..814515eee7574fcf247322ffd0f9768e425cbe98
--- /dev/null
+++ b/data/sampled_jsons/The_impact_of_modeling_decisions_in_statistical_profiling_unemployment_58%.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "The Work Profiler: A digital instrument for selection and ...", "date": "", "ddg_snippet": "by MA Wijnhoven · 2014 · Cited by 39 — The impact of modeling decisions in statistical profiling . Go to citationCrossrefGoogle Scholar. 2023 ACM Conference on Fairness ...", "subpage_snippet": "", "source": "journals.sagepub.com", "link": "https://journals.sagepub.com/doi/full/10.1177/0269094214545045", "content": "by MA Wijnhoven · 2014 · Cited by 39 — The impact of modeling decisions in statistical profiling . Go to citationCrossrefGoogle Scholar. 2023 ACM Conference on Fairness ..."}
+{"idx": 1, "title": "为什么英语中“state-of-the-art”表示“最先进”的意思呢？ - 知乎", "date": "", "ddg_snippet": "附上一则解释，有兴趣的学习一下。 The origin of the concept of \"state of the art\" took place in the beginning of the twentieth century. The earliest use of the term \"state of the art\" documented by the Oxford English Dictionary dates back to 1910, from an engineering manual by Henry Harrison Suplee (1856-post 1943), an engineering graduate (University of Pennsylvania, 1876 ...", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/23503643", "content": "附上一则解释，有兴趣的学习一下。 The origin of the concept of \"state of the art\" took place in the beginning of the twentieth century. The earliest use of the term \"state of the art\" documented by the Oxford English Dictionary dates back to 1910, from an engineering manual by Henry Harrison Suplee (1856-post 1943), an engineering graduate (University of Pennsylvania, 1876 ..."}
+{"idx": 2, "title": "提交表单显示Please verify the CAPTCHA before proceed怎么办？ - 知...", "date": "", "ddg_snippet": "本人因为旅游需要打印电子签证，但是提交后显示Please verify the CAPTCHA before proceed，换了好几个浏…", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/640631824", "content": "本人因为旅游需要打印电子签证，但是提交后显示Please verify the CAPTCHA before proceed，换了好几个浏…"}
+{"idx": 3, "title": "Journal of the Mechanics and Physics of Solids 在力学界是 ... - ...", "date": "", "ddg_snippet": "Nov 21, 2014 · Journal of the Mechanics and Physics of Solids 在力学界是个什么水平的杂志？", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/26721583", "content": "Nov 21, 2014 · Journal of the Mechanics and Physics of Solids 在力学界是个什么水平的杂志？"}
+{"idx": 4, "title": "6 Types of Hormones That Exercise Affects - Cathe Friedrich", "date": "", "ddg_snippet": "When you do a workout, there’s a lot going on behind the scenes. Read on and discover six types of hormones that a workout affects and why.", "subpage_snippet": "", "source": "cathe.com", "link": "https://cathe.com/the-hormonal-symphony-of-exercise-6-types-of-hormones-that-exercise-affects/", "content": "When you do a workout, there’s a lot going on behind the scenes. Read on and discover six types of hormones that a workout affects and why."}
+{"idx": 5, "title": "5 Hormones That Impact Muscle Growth and How They Work", "date": "", "ddg_snippet": "Exposing your muscles to progressive overload through weight training is the stimulus that causes them to grow, but there are a number of “key players” behind the scenes that influence how much return you get on your weight training investment. These anabolic and catabolic hormones can make it easier or harder to build lean body mass. Here are some of those hormonal players and what they ...", "subpage_snippet": "", "source": "cathe.com", "link": "https://cathe.com/5-hormones-that-impact-muscle-growth-and-how-they-work/", "content": "Exposing your muscles to progressive overload through weight training is the stimulus that causes them to grow, but there are a number of “key players” behind the scenes that influence how much return you get on your weight training investment. These anabolic and catabolic hormones can make it easier or harder to build lean body mass. Here are some of those hormonal players and what they ..."}
+{"idx": 6, "title": "Cathe On-Demand Streaming Subscription • Cathe Friedrich", "date": "", "ddg_snippet": "You can work out and watch Cathe OnDemand exercise and workout videos on your computer or almost any mobile device and stream it to your big TV.", "subpage_snippet": "", "source": "cathe.com", "link": "https://cathe.com/ondemand_subscribe/", "content": "You can work out and watch Cathe OnDemand exercise and workout videos on your computer or almost any mobile device and stream it to your big TV."}
+{"idx": 7, "title": "Using Artificial Intelligence to classify Jobseekers", "date": "", "ddg_snippet": "by SAM DESIERE · 2021 · Cited by 104 — The impact of modeling decisions in statistical profiling . Type: Article; Title: The impact of modeling decisions in statistical profiling ...", "subpage_snippet": "", "source": "www.cambridge.org", "link": "https://www.cambridge.org/core/journals/journal-of-social-policy/article/using-artificial-intelligence-to-classify-jobseekers-the-accuracyequity-tradeoff/3993FE92BCC14FEA60DAB9CCD636D58D", "content": "by SAM DESIERE · 2021 · Cited by 104 — The impact of modeling decisions in statistical profiling . Type: Article; Title: The impact of modeling decisions in statistical profiling ..."}
+{"idx": 8, "title": "Workout DVD, Fitness DVD, Exercise DVDs and Videos", "date": "", "ddg_snippet": "Helpful Cathe Links For over thirty years Cathe has been known for producing the highest quality workout and fitness exercise videos and DVDs, and along the way, we built one of the friendliest and most supportive communities in the fitness industry. We offer one of the largest privately held fitness libraries in the world with over 600 fun and challenging workout videos and DVDs to help you ...", "subpage_snippet": "", "source": "cathe.com", "link": "https://cathe.com/", "content": "Helpful Cathe Links For over thirty years Cathe has been known for producing the highest quality workout and fitness exercise videos and DVDs, and along the way, we built one of the friendliest and most supportive communities in the fitness industry. We offer one of the largest privately held fitness libraries in the world with over 600 fun and challenging workout videos and DVDs to help you ..."}
+{"idx": 9, "title": "sci投稿Declaration of interest怎么写? - 知乎", "date": "", "ddg_snippet": "COI/Declaration of Interest forms from all the authors of an article is required for every submiss…", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/497551602", "content": "COI/Declaration of Interest forms from all the authors of an article is required for every submiss…"}
diff --git a/data/sampled_jsons/The_underlying_structures_of_self-attention_symmetry,_directionality,_and_emergent_dynamics_Definiti.jsonl b/data/sampled_jsons/The_underlying_structures_of_self-attention_symmetry,_directionality,_and_emergent_dynamics_Definiti.jsonl
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index 0000000000000000000000000000000000000000..0576ed77ecb6bbcdf221099bdd67f75d1fc9d5d1
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "The underlying structures of self-attention: symmetry ...", "date": "", "ddg_snippet": "The underlying structures of self-attention: symmetry, directionality, and emergent dynamics in Transformer training Matteo Saponati1,*, Pascal Sager1,2,*, Pau ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.10927", "content": "The underlying structures of self-attention: symmetry, directionality, and emergent dynamics in Transformer training Matteo Saponati1,*, Pascal Sager1,2,*, Pau ..."}
+{"idx": 1, "title": "THE EMERGENCE OF CLUSTERS IN SELF-ATTENTION DYNAMICS", "date": "", "ddg_snippet": "1. Introduction The introduction of Transformers in 2017 [29] marked a turning point in the AI revolution, powering breakthroughs in natural language modeling and computer vi-sion. With remarkable empirical success, Transformers enable large language mod-els to compute very powerful representations using the self - attention mechanism. Yet, little is known about the geometric structure of these ...", "subpage_snippet": "", "source": "people.lids.mit.edu", "link": "https://people.lids.mit.edu/yp/homepage/data/2023_transformers1.pdf", "content": "1. Introduction The introduction of Transformers in 2017 [29] marked a turning point in the AI revolution, powering breakthroughs in natural language modeling and computer vi-sion. With remarkable empirical success, Transformers enable large language mod-els to compute very powerful representations using the self - attention mechanism. Yet, little is known about the geometric structure of these ..."}
+{"idx": 2, "title": "The underlying structures of self-attention: symmetry ...", "date": "", "ddg_snippet": "Abstract Self - attention is essential to Transformer architectures, yet how information is embedded in the self - attention matrices and how different objective functions impact this process remains unclear. We present a mathematical framework to analyze self - attention matrices by deriving the structures governing their weight updates. Using this framework, we demonstrate that bidirectional ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.10927v1", "content": "Abstract Self - attention is essential to Transformer architectures, yet how information is embedded in the self - attention matrices and how different objective functions impact this process remains unclear. We present a mathematical framework to analyze self - attention matrices by deriving the structures governing their weight updates. Using this framework, we demonstrate that bidirectional ..."}
+{"idx": 3, "title": "Question 3.2.1 Give a definition for food security ... - Filo", "date": "", "ddg_snippet": "Sep 14, 2025 · Question 3.2 .1: Definition for food security Food security is a situation where all people, at all times, have physical, social and economic access to sufficient, safe and nutritious food to meet their dietary needs for an active and healthy life.", "subpage_snippet": "", "source": "askfilo.com", "link": "https://askfilo.com/user-question-answers-smart-solutions/question-3-2-1-give-a-definition-for-food-security-question-3338333537333537", "content": "Sep 14, 2025 · Question 3.2 .1: Definition for food security Food security is a situation where all people, at all times, have physical, social and economic access to sufficient, safe and nutritious food to meet their dietary needs for an active and healthy life."}
+{"idx": 4, "title": "FMEA RPN - Risk Priority Number. How to Calculate and ...", "date": "", "ddg_snippet": "Aug 18, 2022 · 1. Risk Priority Number definition Risk Priority Number is a numerical assessment of the risk priority level of a failure mode/failure cause in an FMEA analysis. It helps the responsible team/individual prioritize risks and decide on the corrective actions.", "subpage_snippet": "", "source": "www.iqasystem.com", "link": "https://www.iqasystem.com/news/risk-priority-number/", "content": "Aug 18, 2022 · 1. Risk Priority Number definition Risk Priority Number is a numerical assessment of the risk priority level of a failure mode/failure cause in an FMEA analysis. It helps the responsible team/individual prioritize risks and decide on the corrective actions."}
+{"idx": 5, "title": "Spinoza’s Psychological Theory - Stanford Encyclopedia of ...", "date": "", "ddg_snippet": "Oct 23, 2001 · In Part III of his Ethics, “On the Origin and Nature of the Affects,” which is the subject of this article, Spinoza addresses two of the most serious challenges facing his thoroughgoing naturalism. First, he attempts to show that human beings follow the order of nature. Human beings, on Spinoza’s view, have causal natures similar in kind to other ordinary objects, other “finite modes ...", "subpage_snippet": "", "source": "plato.stanford.edu", "link": "https://plato.stanford.edu/entries/spinoza-psychological/", "content": "Oct 23, 2001 · In Part III of his Ethics, “On the Origin and Nature of the Affects,” which is the subject of this article, Spinoza addresses two of the most serious challenges facing his thoroughgoing naturalism. First, he attempts to show that human beings follow the order of nature. Human beings, on Spinoza’s view, have causal natures similar in kind to other ordinary objects, other “finite modes ..."}
+{"idx": 6, "title": "The Mathematical Foundations of Self-Referential Systems: From", "date": "", "ddg_snippet": "... framework for the analysis of self -referential systems—systems capable of containing representations of their own structure and dynamics .", "subpage_snippet": "", "source": "www.novaspivack.com", "link": "https://www.novaspivack.com/science/the-mathematical-foundations-of-self-referential-systems-from-computability-to-transfinite-dynamics", "content": "... framework for the analysis of self -referential systems—systems capable of containing representations of their own structure and dynamics ."}
+{"idx": 7, "title": "Spontaneous quantization of the Yang-Mills gradient flow", "date": "", "ddg_snippet": "These results suggest a fundamental mechanism by which quantum randomness and structure may emerge spontaneously from deterministic, nonlinear ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.20514v2", "content": "These results suggest a fundamental mechanism by which quantum randomness and structure may emerge spontaneously from deterministic, nonlinear ..."}
+{"idx": 8, "title": "Symmetry Groups of Basins of Attraction in Equivariant", "date": "", "ddg_snippet": "A cornerstone of equivariant dynamics is the Equivariant Branching Lemma , which predicts the emergence of symmetric patterns and equilibria.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.11403v1", "content": "A cornerstone of equivariant dynamics is the Equivariant Branching Lemma , which predicts the emergence of symmetric patterns and equilibria."}
+{"idx": 9, "title": "Symmetry and Symmetry Breaking (Stanford Encyclopedia of", "date": "", "ddg_snippet": "... definition stem from? In addition to the ancient notion of symmetry used by the Greeks and Romans (current until the end of the Renaissance), a ...", "subpage_snippet": "", "source": "plato.stanford.edu", "link": "https://plato.stanford.edu/entries/symmetry-breaking/", "content": "... definition stem from? In addition to the ancient notion of symmetry used by the Greeks and Romans (current until the end of the Renaissance), a ..."}
diff --git a/data/sampled_jsons/Theorem_6.1_ATA_regret_bound_logarithmic_filetypepdf_year_2024.jsonl b/data/sampled_jsons/Theorem_6.1_ATA_regret_bound_logarithmic_filetypepdf_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0eca2d619d2b0e41a7d79d049aec74859e79d960
--- /dev/null
+++ b/data/sampled_jsons/Theorem_6.1_ATA_regret_bound_logarithmic_filetypepdf_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "cherryATA", "date": "", "ddg_snippet": "logarithmic term. The precise ... for ATA and ATA -Empirical, through a regret analysis on the ... In the regret bound of Theorem 6.1 , the term α2 appears in-.", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/pdf/2502.00775v1", "content": "logarithmic term. The precise ... for ATA and ATA -Empirical, through a regret analysis on the ... In the regret bound of Theorem 6.1 , the term α2 appears in-."}
+{"idx": 1, "title": "( PDF ) Optimal Matrix Sketching over Sliding Windows", "date": "", "ddg_snippet": "This conclusively answers the open question regarding the optimal space bound for matrix sketching over sliding windows.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/382928945_Optimal_Matrix_Sketching_over_Sliding_Windows", "content": "This conclusively answers the open question regarding the optimal space bound for matrix sketching over sliding windows."}
+{"idx": 2, "title": "Corruption-Robust Linear Bandits: Minimax Optimality and ...", "date": "", "ddg_snippet": "by H Liu · 2024 · Cited by 3 — Theorem 6.1 . Assume our environment satisfies Assumption 1 and that ... the same regret bound without knowledge of C∞. The idea of Wei ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.07533", "content": "by H Liu · 2024 · Cited by 3 — Theorem 6.1 . Assume our environment satisfies Assumption 1 and that ... the same regret bound without knowledge of C∞. The idea of Wei ..."}
+{"idx": 3, "title": "Asymptotically Efficient Distributed Experimentation", "date": "", "ddg_snippet": "spends on suboptimal arms is logarithmic in the length of the time-horizon. ... is agnostic to the separation between arms (see Theorem 6.1 in Immorlica et al.", "subpage_snippet": "", "source": "pages.stern.nyu.edu", "link": "https://pages.stern.nyu.edu/~jreed/papers/paper31.pdf", "content": "spends on suboptimal arms is logarithmic in the length of the time-horizon. ... is agnostic to the separation between arms (see Theorem 6.1 in Immorlica et al."}
+{"idx": 4, "title": "Learning with Good Feature Representations in Bandits and ...", "date": "", "ddg_snippet": "by T Lattimore · Cited by 230 — Known approximation error The logarithmic factor in the second term in the regret bound can be removed when ε is known by modifying the elimination criteria so ... 12 pages", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "http://proceedings.mlr.press/v119/lattimore20a/lattimore20a-supp.pdf", "content": "by T Lattimore · Cited by 230 — Known approximation error The logarithmic factor in the second term in the regret bound can be removed when ε is known by modifying the elimination criteria so ... 12 pages"}
+{"idx": 5, "title": "A Batch-to-Online Transformation under Random-Order Model", "date": "", "ddg_snippet": "regret bound is only polylogarithmically ... that are logarithmic in the dimension. Journal of ... Theorem 6.1 . For any ϵ ∈ (0, 1), Algorithm 3 ...", "subpage_snippet": "", "source": "papers.neurips.cc", "link": "https://papers.neurips.cc/paper_files/paper/2023/file/afe99e55be23b3523818da1fefa33494-Paper-Conference.pdf", "content": "regret bound is only polylogarithmically ... that are logarithmic in the dimension. Journal of ... Theorem 6.1 . For any ϵ ∈ (0, 1), Algorithm 3 ..."}
+{"idx": 6, "title": "Efficient Low-Rank Matrix Estimation, Experimental Design ...", "date": "", "ddg_snippet": "by K Jang · Cited by 4 — hides logarithmic factors. For any set S, let P(S) be the set of ... the agent to improve the regret bound - the regret bound of ETC can be. √. T ...", "subpage_snippet": "", "source": "raw.githubusercontent.com", "link": "https://raw.githubusercontent.com/mlresearch/v235/main/assets/jang24e/jang24e.pdf", "content": "by K Jang · Cited by 4 — hides logarithmic factors. For any set S, let P(S) be the set of ... the agent to improve the regret bound - the regret bound of ETC can be. √. T ..."}
+{"idx": 7, "title": "INTERACTIVE MACHINE LEARNING", "date": "", "ddg_snippet": "... logarithmic cost suffered in supervised learning, one has to pay an ... regret bound for SpannerIGW which scales as p poly(d) · T, and—like the ...", "subpage_snippet": "", "source": "yinglunz.com", "link": "https://yinglunz.com/pdfs/dissertation.pdf", "content": "... logarithmic cost suffered in supervised learning, one has to pay an ... regret bound for SpannerIGW which scales as p poly(d) · T, and—like the ..."}
+{"idx": 8, "title": "Adapting to Continuous Covariate Shift via Online Density ...", "date": "", "ddg_snippet": "by YJ Zhang · 2023 · Cited by 21 — regret bound for convex function, whereas there is still a gap to the Ω(pT(1 ... equality, we treat double logarithmic factors in T as a constant ... 40 pages", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2023/file/5cad96c4433955a2e76749ee74a424f5-Paper-Conference.pdf", "content": "by YJ Zhang · 2023 · Cited by 21 — regret bound for convex function, whereas there is still a gap to the Ω(pT(1 ... equality, we treat double logarithmic factors in T as a constant ... 40 pages"}
+{"idx": 9, "title": "GL-LowPopArt: A Nearly Instance-Wise Minimax-Optimal ...", "date": "", "ddg_snippet": "by J Lee — the regret bound varies with the geometry of the arm set. For example, (Jun ... Theorem C.5 (Restatement of Theorem 6.1 .1 of Tropp (2015)). Let {At}N t ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=TyArXyYnvz", "content": "by J Lee — the regret bound varies with the geometry of the arm set. For example, (Jun ... Theorem C.5 (Restatement of Theorem 6.1 .1 of Tropp (2015)). Let {At}N t ..."}
diff --git a/data/sampled_jsons/Towards_Realistic_Semi-Supervised_Learning_ECCV_2022_Rizve_abstract.jsonl b/data/sampled_jsons/Towards_Realistic_Semi-Supervised_Learning_ECCV_2022_Rizve_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..152d7df3218e352725b7628ff355dfd29a8df166
--- /dev/null
+++ b/data/sampled_jsons/Towards_Realistic_Semi-Supervised_Learning_ECCV_2022_Rizve_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2207.02269] Towards Realistic Semi-Supervised Learning", "date": "", "ddg_snippet": "by MN Rizve · 2022 · Cited by 71 — In this paper, we propose a novel pseudo-label based approach to tackle SSL in open-world setting. At the core of our method, we utilize sample uncertainty.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2207.02269", "content": "by MN Rizve · 2022 · Cited by 71 — In this paper, we propose a novel pseudo-label based approach to tackle SSL in open-world setting. At the core of our method, we utilize sample uncertainty."}
+{"idx": 1, "title": "Towards Realistic Semi-supervised Learning", "date": "", "ddg_snippet": "by MN Rizve · 2022 · Cited by 71 — OpenLDN: Learning to Discover Novel Classes for Open-World Semi-Supervised Learning . Computer Vision – ECCV 2022. Abstract. Semi-supervised learning (SSL) is ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1007/978-3-031-19821-2_25", "content": "by MN Rizve · 2022 · Cited by 71 — OpenLDN: Learning to Discover Novel Classes for Open-World Semi-Supervised Learning . Computer Vision – ECCV 2022. Abstract. Semi-supervised learning (SSL) is ..."}
+{"idx": 2, "title": "arXiv:2207.02269v2 [cs.CV] 28 Jul 2022", "date": "", "ddg_snippet": "by MN Rizve · 2022 · Cited by 71 — Abstract . Deep learning is pushing the state-of-the-art in many com- puter vision applications. However, it relies on large annotated data.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2207.02269", "content": "by MN Rizve · 2022 · Cited by 71 — Abstract . Deep learning is pushing the state-of-the-art in many com- puter vision applications. However, it relies on large annotated data."}
+{"idx": 3, "title": "TOWARDS REALISTIC LONG-TAILED SEMI-SUPERVISED ...", "date": "", "ddg_snippet": "Open-world long-tailed semi - supervised learning (OLSSL) has increasingly at- tracted attention. However, existing OLSSL algorithms generally assume that the.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/2cd398bf25fc2602a15dc6780847e91c92d2059c.pdf", "content": "Open-world long-tailed semi - supervised learning (OLSSL) has increasingly at- tracted attention. However, existing OLSSL algorithms generally assume that the."}
+{"idx": 4, "title": "Computer Vision – ECCV 2022 | Guide Proceedings", "date": "", "ddg_snippet": "23 Oct 2022 — Abstract · xml. Article. Towards Realistic Semi-supervised Learning · Author Picture Mamshad Nayeem Rizve,; + 2. Pages 437–455https://doi.org ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/proceedings/10.1007/978-3-031-19821-2", "content": "23 Oct 2022 — Abstract · xml. Article. Towards Realistic Semi-supervised Learning · Author Picture Mamshad Nayeem Rizve,; + 2. Pages 437–455https://doi.org ..."}
+{"idx": 5, "title": "A survey of class-imbalanced semi-supervised learning", "date": "", "ddg_snippet": "by Q Gui · 2024 · Cited by 21 — In this article, we comprehensively review class-imbalanced semi-supervised learning (CISSL), starting with an introduction to this field.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s10994-023-06344-7", "content": "by Q Gui · 2024 · Cited by 21 — In this article, we comprehensively review class-imbalanced semi-supervised learning (CISSL), starting with an introduction to this field."}
+{"idx": 6, "title": "arXiv:2409.05122v2 [cs.CV] 15 Sep 2024", "date": "", "ddg_snippet": "15 Sept 2024 — In this paper, we design a novel semi - supervised learning framework, termed. Progressive Mean Teachers (PMT), for medical image segmentation, ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=bsPpRos5aE", "content": "15 Sept 2024 — In this paper, we design a novel semi - supervised learning framework, termed. Progressive Mean Teachers (PMT), for medical image segmentation, ..."}
+{"idx": 7, "title": "Retraining with Selective Samples for Generalized ...", "date": "", "ddg_snippet": "[Rizve et al., 2022] Mamshad Nayeem Rizve, Navid Kar- dan, and Mubarak Shah. Towards realistic semi- supervised learning . In European Conference on Com ...", "subpage_snippet": "", "source": "www.ijcai.org", "link": "https://www.ijcai.org/proceedings/2024/0532.pdf", "content": "[Rizve et al., 2022] Mamshad Nayeem Rizve, Navid Kar- dan, and Mubarak Shah. Towards realistic semi- supervised learning . In European Conference on Com ..."}
+{"idx": 8, "title": "Discover and Align Taxonomic Context Priors for Open- ...", "date": "", "ddg_snippet": "by Y Wang · 2023 · Cited by 15 — In. ECCV, 2022. [65] Mamshad Nayeem Rizve, Navid Kardan, and Mubarak Shah. Towards realistic semi-supervised learning . In ECCV, 2022. [66] Subhankar Roy ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2023/file/3c646b713f5de2cf1ab1939d49a4036d-Paper-Conference.pdf", "content": "by Y Wang · 2023 · Cited by 15 — In. ECCV, 2022. [65] Mamshad Nayeem Rizve, Navid Kardan, and Mubarak Shah. Towards realistic semi-supervised learning . In ECCV, 2022. [66] Subhankar Roy ..."}
+{"idx": 9, "title": "TimeBalance: Temporally-Invariant and Temporally-Distinctive ...", "date": "", "ddg_snippet": "by IR Dave · 2023 · Cited by 36 — Towards realistic semi-supervised learning . In Computer. Vision–ECCV 2022: 17th European Conference, Tel Aviv, Is- rael, October 23–27, 2022, Proceedings ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2023/papers/Dave_TimeBalance_Temporally-Invariant_and_Temporally-Distinctive_Video_Representations_for_Semi-Supervised_Action_Recognition_CVPR_2023_paper.pdf", "content": "by IR Dave · 2023 · Cited by 36 — Towards realistic semi-supervised learning . In Computer. Vision–ECCV 2022: 17th European Conference, Tel Aviv, Is- rael, October 23–27, 2022, Proceedings ..."}
diff --git a/data/sampled_jsons/Towards_Realistic_Semi-Supervised_Learning_Rizve_full_abstract_Sinkhorn-Knopp.jsonl b/data/sampled_jsons/Towards_Realistic_Semi-Supervised_Learning_Rizve_full_abstract_Sinkhorn-Knopp.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2e1db4b49509b966929f2caa44aa1993fdae34d2
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+++ b/data/sampled_jsons/Towards_Realistic_Semi-Supervised_Learning_Rizve_full_abstract_Sinkhorn-Knopp.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Towards Realistic Semi-supervised Learning | Computer Vision ...", "date": "", "ddg_snippet": "Oct 23, 2022 · Abstract Deep learning is pushing the state-of-the-art in many computer vision applications. However, it relies on large annotated data repositories, and capturing the unconstrained nature of the real-world data is yet to be solved.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1007/978-3-031-19821-2_25", "content": "Oct 23, 2022 · Abstract Deep learning is pushing the state-of-the-art in many computer vision applications. However, it relies on large annotated data repositories, and capturing the unconstrained nature of the real-world data is yet to be solved."}
+{"idx": 1, "title": "Towards Realistic Semi-Supervised Learning - arXiv.org", "date": "", "ddg_snippet": "Towards Realistic Semi-Supervised Learning Mamshad Nayeem Rizve , Navid Kardan, and Mubarak Shah Center for Research in Computer Vision, UCF, USA Abstract . Deep learning is pushing the state-of-the-art in many com-puter vision applications.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2207.02269v1", "content": "Towards Realistic Semi-Supervised Learning Mamshad Nayeem Rizve , Navid Kardan, and Mubarak Shah Center for Research in Computer Vision, UCF, USA Abstract . Deep learning is pushing the state-of-the-art in many com-puter vision applications."}
+{"idx": 2, "title": "Towards Realistic Semi-supervised Learning | SpringerLink (PDF) Towards Realistic Semi-Supervised Learning - ResearchGate Towards Realistic Semi-supervised Learning | Computer Vision ... Towards Realistic Semi-Supervised Learning - Semantic Scholar Towards Realistic Semi-Supervised Learning - GitHub Towards Realistic Semi-Supervised Learning - NASA/ADS", "date": "", "ddg_snippet": "In the following, we describe our experimental setup including dataset descriptions, implementation details, evaluation details, and specifics of our baselines. Datasets. We conduct experiments on four commonly used computer vision benchmark datasets: CIFAR-10 , CIFAR-100 , ImageNet-100 and Tiny ImageNet . The datasets are selected in increasing o... See full list on link.springer.com Standard Benchmark Datasets. We compare our method with existing literature on open-world SSL problem and other related approaches that have been modified for this problem in Table 1 and 2. On CIFAR-10 we observe that our proposed method outperforms ORCA on both seen and novel classes by 12.1% and 4.1%, respectively. Our proposed method also outp... See full list on link.springer.com To investigate the impact of different components, we conduct extensive ablation study on CIFAR-10, CIFAR-100, and Tiny ImageNet datasets. We report the results in Table 3. The first row depicts the performance of our proposed method without uncertainty-guided temperature scaling, and mixed pseudo-labeling. Here, we can see that our proposed method... See full list on link.springer.com Jul 5, 2022 · Our model generates pseudo-labels for the unlabeled samples using Sinkhorn-Knopp while taking class distribution prior into account. Right: reliable training with both labeled and unlabeled samples. Oct 23, 2022 · Abstract Deep learning is pushing the state-of-the-art in many computer vision applications. However, it relies on large annotated data repositories, and capturing the unconstrained nature of the real-world data is yet to be solved. Fig. 2: Training Overview: Left: generating pseudo-labels. Our model generates pseudo-labels for the unlabeled samples using Sinkhorn-Knopp while taking class distribution prior into account. Right: reliable training with both labeled and unlabeled samples. We use the ground-truth labels and generated pseudo-labels to train in a supervised manner. To address the unreliable nature of pseudo ... Towards Realistic Semi-Supervised Learning Implementation of Towards Realistic Semi-Supervised Learning . Deep learning is pushing the state-of-the-art in many computer vision applications. However, it relies on large annotated data repositories, and capturing the unconstrained nature of the real-world data is yet to be solved. Deep learning is pushing the state-of-the-art in many computer vision applications. However, it relies on large annotated data repositories, and capturing the unconstrained nature of the real-world data is yet to be solved. Semi-supervised learning (SSL) complements the annotated training data with a large corpus of unlabeled data to reduce annotation cost. The standard SSL approach assumes ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-3-031-19821-2_25", "content": "In the following, we describe our experimental setup including dataset descriptions, implementation details, evaluation details, and specifics of our baselines. Datasets. We conduct experiments on four commonly used computer vision benchmark datasets: CIFAR-10 , CIFAR-100 , ImageNet-100 and Tiny ImageNet . The datasets are selected in increasing o... See full list on link.springer.com Standard Benchmark Datasets. We compare our method with existing literature on open-world SSL problem and other related approaches that have been modified for this problem in Table 1 and 2. On CIFAR-10 we observe that our proposed method outperforms ORCA on both seen and novel classes by 12.1% and 4.1%, respectively. Our proposed method also outp... See full list on link.springer.com To investigate the impact of different components, we conduct extensive ablation study on CIFAR-10, CIFAR-100, and Tiny ImageNet datasets. We report the results in Table 3. The first row depicts the performance of our proposed method without uncertainty-guided temperature scaling, and mixed pseudo-labeling. Here, we can see that our proposed method... See full list on link.springer.com Jul 5, 2022 · Our model generates pseudo-labels for the unlabeled samples using Sinkhorn-Knopp while taking class distribution prior into account. Right: reliable training with both labeled and unlabeled samples. Oct 23, 2022 · Abstract Deep learning is pushing the state-of-the-art in many computer vision applications. However, it relies on large annotated data repositories, and capturing the unconstrained nature of the real-world data is yet to be solved. Fig. 2: Training Overview: Left: generating pseudo-labels. Our model generates pseudo-labels for the unlabeled samples using Sinkhorn-Knopp while taking class distribution prior into account. Right: reliable training with both labeled and unlabeled samples. We use the ground-truth labels and generated pseudo-labels to train in a supervised manner. To address the unreliable nature of pseudo ... Towards Realistic Semi-Supervised Learning Implementation of Towards Realistic Semi-Supervised Learning . Deep learning is pushing the state-of-the-art in many computer vision applications. However, it relies on large annotated data repositories, and capturing the unconstrained nature of the real-world data is yet to be solved. Deep learning is pushing the state-of-the-art in many computer vision applications. However, it relies on large annotated data repositories, and capturing the unconstrained nature of the real-world data is yet to be solved. Semi-supervised learning (SSL) complements the annotated training data with a large corpus of unlabeled data to reduce annotation cost. The standard SSL approach assumes ..."}
+{"idx": 3, "title": "(PDF) Towards Realistic Semi-Supervised Learning - ResearchGate", "date": "", "ddg_snippet": "Jul 5, 2022 · Our model generates pseudo-labels for the unlabeled samples using Sinkhorn-Knopp while taking class distribution prior into account. Right: reliable training with both labeled and unlabeled samples.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/361807923_Towards_Realistic_Semi-Supervised_Learning", "content": "Jul 5, 2022 · Our model generates pseudo-labels for the unlabeled samples using Sinkhorn-Knopp while taking class distribution prior into account. Right: reliable training with both labeled and unlabeled samples."}
+{"idx": 4, "title": "Towards Realistic Semi-Supervised Learning - Semantic Scholar", "date": "", "ddg_snippet": "Fig. 2: Training Overview: Left: generating pseudo-labels. Our model generates pseudo-labels for the unlabeled samples using Sinkhorn-Knopp while taking class distribution prior into account. Right: reliable training with both labeled and unlabeled samples. We use the ground-truth labels and generated pseudo-labels to train in a supervised manner. To address the unreliable nature of pseudo ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Towards-Realistic-Semi-Supervised-Learning-Rizve-Kardan/cce61de63d6691fce13bf1a1c8d231553df12f31/figure/2", "content": "Fig. 2: Training Overview: Left: generating pseudo-labels. Our model generates pseudo-labels for the unlabeled samples using Sinkhorn-Knopp while taking class distribution prior into account. Right: reliable training with both labeled and unlabeled samples. We use the ground-truth labels and generated pseudo-labels to train in a supervised manner. To address the unreliable nature of pseudo ..."}
+{"idx": 5, "title": "Towards Realistic Semi-Supervised Learning - GitHub", "date": "", "ddg_snippet": "Towards Realistic Semi-Supervised Learning Implementation of Towards Realistic Semi-Supervised Learning . Deep learning is pushing the state-of-the-art in many computer vision applications. However, it relies on large annotated data repositories, and capturing the unconstrained nature of the real-world data is yet to be solved.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/nayeemrizve/TRSSL", "content": "Towards Realistic Semi-Supervised Learning Implementation of Towards Realistic Semi-Supervised Learning . Deep learning is pushing the state-of-the-art in many computer vision applications. However, it relies on large annotated data repositories, and capturing the unconstrained nature of the real-world data is yet to be solved."}
+{"idx": 6, "title": "Towards Realistic Semi-Supervised Learning - NASA/ADS", "date": "", "ddg_snippet": "Deep learning is pushing the state-of-the-art in many computer vision applications. However, it relies on large annotated data repositories, and capturing the unconstrained nature of the real-world data is yet to be solved. Semi-supervised learning (SSL) complements the annotated training data with a large corpus of unlabeled data to reduce annotation cost. The standard SSL approach assumes ...", "subpage_snippet": "", "source": "ui.adsabs.harvard.edu", "link": "https://ui.adsabs.harvard.edu/abs/2022arXiv220702269N/abstract", "content": "Deep learning is pushing the state-of-the-art in many computer vision applications. However, it relies on large annotated data repositories, and capturing the unconstrained nature of the real-world data is yet to be solved. Semi-supervised learning (SSL) complements the annotated training data with a large corpus of unlabeled data to reduce annotation cost. The standard SSL approach assumes ..."}
+{"idx": 7, "title": "Towards Semi-supervised Universal Graph Classification", "date": "", "ddg_snippet": "Abstract— Graph neural networks have pushed state-of-the-arts in graph classifications recently. Typically, these methods are studied.", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/pdf/towards-semi-supervised-universal-graph-classification-3s20xaie.pdf", "content": "Abstract— Graph neural networks have pushed state-of-the-arts in graph classifications recently. Typically, these methods are studied."}
+{"idx": 8, "title": "Progressive distribution matching for federated semi-supervised ...", "date": "", "ddg_snippet": "The matching problem could be formulated as an optimal transport (OT) problem and efficiently solved by Sinkhorn - Knopp iteration. Through extensive experiments, ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1609/aaai.v39i5.32551", "content": "The matching problem could be formulated as an optimal transport (OT) problem and efficiently solved by Sinkhorn - Knopp iteration. Through extensive experiments, ..."}
+{"idx": 9, "title": "Is ImageNet worth 1 video? Learning strong image ...", "date": "", "ddg_snippet": "by S Venkataramanan · Cited by 33 — This is a nice use-case of Sinkhorn – Knopp to avoid having to use non end- to -end approaches like optical-flow or off-the-shelf object-detectors and the ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=Yen1lGns2o", "content": "by S Venkataramanan · Cited by 33 — This is a nice use-case of Sinkhorn – Knopp to avoid having to use non end- to -end approaches like optical-flow or off-the-shelf object-detectors and the ..."}
diff --git a/data/sampled_jsons/Towards_scientific_discovery_with_dictionary_learning_Extracting_biological_concepts_from_microscopy.jsonl b/data/sampled_jsons/Towards_scientific_discovery_with_dictionary_learning_Extracting_biological_concepts_from_microscopy.jsonl
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index 0000000000000000000000000000000000000000..cc6123ca6001c47d66f8bc596f4cfa9ee56dd560
--- /dev/null
+++ b/data/sampled_jsons/Towards_scientific_discovery_with_dictionary_learning_Extracting_biological_concepts_from_microscopy.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Molecular biology - Wikipedia", "date": "", "ddg_snippet": "A major milestone in molecular biology was the discovery of the structure of DNA.Much of molecular biology is quantitative, and recently a significant amount of work has been done using computer science techniques such as bioinformatics and computational biology .", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Molecular_biology", "content": "A major milestone in molecular biology was the discovery of the structure of DNA.Much of molecular biology is quantitative, and recently a significant amount of work has been done using computer science techniques such as bioinformatics and computational biology ."}
+{"idx": 1, "title": "Towards scientific discovery with dictionary learning : Extracting ...", "date": "", "ddg_snippet": "We show that sparse dictionaries indeed extract biologically -meaningful concepts such as cell type and genetic perturbation type. We also propose Iterative Codebook Feature Learning (ICFL) and combine it with a pre-processing step which uses PCA whitening from a control dataset.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.16247v2", "content": "We show that sparse dictionaries indeed extract biologically -meaningful concepts such as cell type and genetic perturbation type. We also propose Iterative Codebook Feature Learning (ICFL) and combine it with a pre-processing step which uses PCA whitening from a control dataset."}
+{"idx": 2, "title": "Towards scientific discovery with dictionary learning : Extracting ...", "date": "", "ddg_snippet": "Dictionary learning (DL) has emerged as a powerful interpretability tool for large language models . By extracting known concepts (e.g., Golden-Gate Bridge) from human-interpretable data (e.g., text), sparse DL can elucidate a model 's inner workings.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=jYl7kM1oK0", "content": "Dictionary learning (DL) has emerged as a powerful interpretability tool for large language models . By extracting known concepts (e.g., Golden-Gate Bridge) from human-interpretable data (e.g., text), sparse DL can elucidate a model 's inner workings."}
+{"idx": 3, "title": "Towards scientific discovery with dictionary learning : Extracting ...", "date": "", "ddg_snippet": "The system learned to recognize subtle biological patterns that other models missedTraining time decreased by 30% compared to standard approachesThe model showed better transfer learning capabilities across different types of microscopy data", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/towards-scientific-discovery-dictionary-learning-extracting-biological", "content": "The system learned to recognize subtle biological patterns that other models missedTraining time decreased by 30% compared to standard approachesThe model showed better transfer learning capabilities across different types of microscopy data"}
+{"idx": 4, "title": "Lisbon Unit for Learning and Intelligent Systems - LUMLIS", "date": "", "ddg_snippet": "Towards scientific discovery with dictionary learning : Extracting biological concepts from microscopy foundation models - [paper].", "subpage_snippet": "", "source": "lumlis.tecnico.ulisboa.pt", "link": "https://lumlis.tecnico.ulisboa.pt/reading-group-inescid.html", "content": "Towards scientific discovery with dictionary learning : Extracting biological concepts from microscopy foundation models - [paper]."}
+{"idx": 5, "title": "Interpretable AI: Past, Present and Future", "date": "", "ddg_snippet": "The first workshop, titled \"XAI in Action: Past, Present, and Future Applications,\" was held at NeurIPS 2023. In this edition, we aim to bridge classical interpretability and modern methods for foundation models .", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/workshop/84733", "content": "The first workshop, titled \"XAI in Action: Past, Present, and Future Applications,\" was held at NeurIPS 2023. In this edition, we aim to bridge classical interpretability and modern methods for foundation models ."}
+{"idx": 6, "title": "Valence Labs | Publications", "date": "", "ddg_snippet": "Towards scientific discovery with dictionary learning : Extracting biological concepts from microscopy foundation models .November 4th, 2024. ViTally Consistent: Scaling Biological Representation Learning for Cell Microscopy .", "subpage_snippet": "", "source": "www.valencelabs.com", "link": "https://www.valencelabs.com/research/", "content": "Towards scientific discovery with dictionary learning : Extracting biological concepts from microscopy foundation models .November 4th, 2024. ViTally Consistent: Scaling Biological Representation Learning for Cell Microscopy ."}
+{"idx": 7, "title": "TLDR - A Byte Sized Daily Tech Newsletter", "date": "", "ddg_snippet": "Towards scientific discovery with dictionary learning : Extracting biological concepts from microscopy foundation models (20 minute read). DeepSeek Gets an 'F' in Safety From Researchers (3 minute read).", "subpage_snippet": "", "source": "tldr.tech", "link": "https://tldr.tech/?product-page=3", "content": "Towards scientific discovery with dictionary learning : Extracting biological concepts from microscopy foundation models (20 minute read). DeepSeek Gets an 'F' in Safety From Researchers (3 minute read)."}
+{"idx": 8, "title": "Kian Kenyon-Dean - Академия Google", "date": "", "ddg_snippet": "Towards scientific discovery with dictionary learning : Extracting biological concepts from microscopy foundation models .2024. Utilizing masked autoencoder generative models to extract microscopy representation autoencoder embeddings.", "subpage_snippet": "", "source": "scholar.google.ca", "link": "https://scholar.google.ca/citations?user=l46NXroAAAAJ", "content": "Towards scientific discovery with dictionary learning : Extracting biological concepts from microscopy foundation models .2024. Utilizing masked autoencoder generative models to extract microscopy representation autoencoder embeddings."}
+{"idx": 9, "title": "awesome-llm-plaza/docs/llm_misc.md at main...", "date": "", "ddg_snippet": "Fund open source developers. Towards scientific discovery with dictionary learning : Extracting biological concepts from microscopy foundation models , arXiv, 2412.16247, arxiv, pdf, cication: -1.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/metame-ai/awesome-llm-plaza/blob/main/docs/llm_misc.md", "content": "Fund open source developers. Towards scientific discovery with dictionary learning : Extracting biological concepts from microscopy foundation models , arXiv, 2412.16247, arxiv, pdf, cication: -1."}
diff --git a/data/sampled_jsons/Towards_scientific_discovery_with_dictionary_learning_Section_4.1_PCA_whitening_biological_context.jsonl b/data/sampled_jsons/Towards_scientific_discovery_with_dictionary_learning_Section_4.1_PCA_whitening_biological_context.jsonl
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index 0000000000000000000000000000000000000000..7b27ab8490a60b0b677bb0905a18cc2c58bf8209
--- /dev/null
+++ b/data/sampled_jsons/Towards_scientific_discovery_with_dictionary_learning_Section_4.1_PCA_whitening_biological_context.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Toward or towards ? | Merriam-Webster", "date": "", "ddg_snippet": "Both toward and towards are two forms of the same word. They've been used interchangeably since their inception in the 9th century. Toward is more common in the US and in Canada, while towards is typically preferred elsewhere. You should feel free to choose the one you prefer.", "subpage_snippet": "", "source": "www.merriam-webster.com", "link": "https://www.merriam-webster.com/grammar/toward-towards-usage", "content": "Both toward and towards are two forms of the same word. They've been used interchangeably since their inception in the 9th century. Toward is more common in the US and in Canada, while towards is typically preferred elsewhere. You should feel free to choose the one you prefer."}
+{"idx": 1, "title": "Toward vs. Towards - What's the Difference? - GRAMMARIST", "date": "", "ddg_snippet": "As a basic rule, North American English uses toward , and all its surrounding areas use towards. Toward is the preferred spelling in American and Canadian English.", "subpage_snippet": "", "source": "grammarist.com", "link": "https://grammarist.com/spelling/toward-towards/", "content": "As a basic rule, North American English uses toward , and all its surrounding areas use towards. Toward is the preferred spelling in American and Canadian English."}
+{"idx": 2, "title": "Toward vs. Towards: How to Choose the Right Word - ThoughtCo", "date": "", "ddg_snippet": "Jun 10, 2025 · While the meanings of the words toward vs. towards are the same and both spellings are correct, where and how they are used matters.", "subpage_snippet": "", "source": "www.thoughtco.com", "link": "https://www.thoughtco.com/toward-vs-towards-4154727", "content": "Jun 10, 2025 · While the meanings of the words toward vs. towards are the same and both spellings are correct, where and how they are used matters."}
+{"idx": 3, "title": "Towards or toward ? - Grammar - Cambridge Dictionary", "date": "", "ddg_snippet": "Towards and toward are prepositions. We can use both forms, but towards is much more common than toward. Toward (s) most often means ‘in the direction of something’: The oil pollution is now moving towards the shore, and could threaten beaches and wild life. He stood up and moved toward the door.", "subpage_snippet": "", "source": "dictionary.cambridge.org", "link": "https://dictionary.cambridge.org/grammar/british-grammar/towards-or-toward", "content": "Towards and toward are prepositions. We can use both forms, but towards is much more common than toward. Toward (s) most often means ‘in the direction of something’: The oil pollution is now moving towards the shore, and could threaten beaches and wild life. He stood up and moved toward the door."}
+{"idx": 4, "title": "Toward vs. Towards : What’s the Difference? - Writing Explained", "date": "", "ddg_snippet": "The answer to that question is simple: the difference between toward and towards is entirely regional preference. There is no demonstrable difference of sense or function between them, meaning both words can be used interchangeably.", "subpage_snippet": "", "source": "writingexplained.org", "link": "https://writingexplained.org/toward-vs-towards-difference", "content": "The answer to that question is simple: the difference between toward and towards is entirely regional preference. There is no demonstrable difference of sense or function between them, meaning both words can be used interchangeably."}
+{"idx": 5, "title": "Is It 'Toward' or ' Towards '? - Word Smarts", "date": "", "ddg_snippet": "Maybe you've been stumped by this grammatical dilemma before: Is it \"toward\" or \" towards \"; \"forward\" or \"forwards\"? This debate is a game of transatlantic tug-of-war, but the best choice often depends on your location.", "subpage_snippet": "", "source": "wordsmarts.com", "link": "https://wordsmarts.com/toward-or-towards/", "content": "Maybe you've been stumped by this grammatical dilemma before: Is it \"toward\" or \" towards \"; \"forward\" or \"forwards\"? This debate is a game of transatlantic tug-of-war, but the best choice often depends on your location."}
+{"idx": 6, "title": "Is It Toward or Towards ? | Spelling, Difference & Examples", "date": "", "ddg_snippet": "Jun 11, 2025 · Toward and towards are two ways of spelling the same preposition, which means “in contribution to,” “in the direction of,” or “in relation to.” The words are often used interchangeably, but there’s a difference in preference depending on whether you use British or American English.", "subpage_snippet": "", "source": "languagetool.org", "link": "https://languagetool.org/insights/post/towards-vs-toward/", "content": "Jun 11, 2025 · Toward and towards are two ways of spelling the same preposition, which means “in contribution to,” “in the direction of,” or “in relation to.” The words are often used interchangeably, but there’s a difference in preference depending on whether you use British or American English."}
+{"idx": 7, "title": "Toward vs. Towards : Understanding the Spelling Difference", "date": "", "ddg_snippet": "Aug 22, 2025 · Both “ toward ” and “ towards ” function as prepositions, primarily indicating movement or direction in a physical or metaphorical sense. They signify motion in the direction of something, inclination, or tendency. There is no semantic difference between the two; they carry the same meaning.", "subpage_snippet": "", "source": "grammardefinition.com", "link": "https://grammardefinition.com/toward-or-towards-spelling-differences-examples/", "content": "Aug 22, 2025 · Both “ toward ” and “ towards ” function as prepositions, primarily indicating movement or direction in a physical or metaphorical sense. They signify motion in the direction of something, inclination, or tendency. There is no semantic difference between the two; they carry the same meaning."}
+{"idx": 8, "title": "Toward or Towards - Grammarly Blog", "date": "", "ddg_snippet": "Toward and towards are two acceptable ways of spelling the same preposition . Toward is the preferred spelling in the United States and Canada. Towards is the preferred spelling in the United Kingdom and Australia.", "subpage_snippet": "", "source": "www.grammarly.com", "link": "https://www.grammarly.com/blog/commonly-confused-words/toward-towards/", "content": "Toward and towards are two acceptable ways of spelling the same preposition . Toward is the preferred spelling in the United States and Canada. Towards is the preferred spelling in the United Kingdom and Australia."}
+{"idx": 9, "title": "Which should you use, \"toward\" or \" towards \"? | Britannica...", "date": "", "ddg_snippet": "Although this is a question that confuses many, the answer is simple: Toward and towards are completely interchangeable , so you can use either one whenever you want.", "subpage_snippet": "", "source": "www.britannica.com", "link": "https://www.britannica.com/dictionary/eb/qa/which-should-you-use-toward-or-towards", "content": "Although this is a question that confuses many, the answer is simple: Toward and towards are completely interchangeable , so you can use either one whenever you want."}
diff --git a/data/sampled_jsons/Trockman_Kolter_2023_mimetic_initialization.jsonl b/data/sampled_jsons/Trockman_Kolter_2023_mimetic_initialization.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3245982c3e5886f8bb92c7698f104ee41d78e087
--- /dev/null
+++ b/data/sampled_jsons/Trockman_Kolter_2023_mimetic_initialization.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "40 Places To Promote Your Event For Free In 2025 - EVENTCUBE", "date": "", "ddg_snippet": "Looking for the best places to promote your upcoming event online - and do it all for free? You've arrived at the right place. We cover 40 proven ways to give your event that added push and get it in front of the maximum number of people - all with an interest in the content of your specific event . So let's dive right in...", "subpage_snippet": "", "source": "www.eventcube.io", "link": "https://www.eventcube.io/blog/promote-your-event-free-30-places", "content": "Looking for the best places to promote your upcoming event online - and do it all for free? You've arrived at the right place. We cover 40 proven ways to give your event that added push and get it in front of the maximum number of people - all with an interest in the content of your specific event . So let's dive right in..."}
+{"idx": 1, "title": "Create a Professional Event Listing to Sell Tickets | Eventbrite", "date": "", "ddg_snippet": "An event listing can help you reach more attendees. List your event online and in our app for free. Try Eventbrite now!", "subpage_snippet": "", "source": "www.eventbrite.com", "link": "https://www.eventbrite.com/organizer/features/event-listing/", "content": "An event listing can help you reach more attendees. List your event online and in our app for free. Try Eventbrite now!"}
+{"idx": 2, "title": "List & Manage Your Event Online | Create an Event Listing on...", "date": "", "ddg_snippet": "BookMyShow now provides a platform for DIY event listing , ticketing & event promotion. Create, manage and list your events with us today.", "subpage_snippet": "", "source": "diy.hostmyshow.com", "link": "https://diy.hostmyshow.com/", "content": "BookMyShow now provides a platform for DIY event listing , ticketing & event promotion. Create, manage and list your events with us today."}
+{"idx": 3, "title": "Event Websites – Planning & Fundraising | MyEvent", "date": "", "ddg_snippet": "Manage, promote & fundraise for any event with MyEvent ’s all-in-one ticketing and fundraising platform. Get started for free!", "subpage_snippet": "", "source": "myevent.com", "link": "https://myevent.com/", "content": "Manage, promote & fundraise for any event with MyEvent ’s all-in-one ticketing and fundraising platform. Get started for free!"}
+{"idx": 4, "title": "Free Event Listing Website to Reach More Event Seekers", "date": "", "ddg_snippet": "Get personalized support & assistance for all your events , at your convenience, via call, chat or email. We are here not just to resolve your queries, but to make your events successful.", "subpage_snippet": "", "source": "allevents.in", "link": "https://allevents.in/pages/event-listing", "content": "Get personalized support & assistance for all your events , at your convenience, via call, chat or email. We are here not just to resolve your queries, but to make your events successful."}
+{"idx": 5, "title": "9 Best Event Listing Websites (2025) | Events Near Me", "date": "", "ddg_snippet": "Feb 8, 2025 · In this article, we’ll go over some of the best event listing websites where you can submit your event for free or at a low cost. Whether you’re hosting a business conference, a local concert, or a community gathering, these platforms can help you attract more attendees and grow your event’s reach. What Are Event Listing Websites?", "subpage_snippet": "", "source": "eventsnearme.io", "link": "https://eventsnearme.io/blog/event-listing-websites", "content": "Feb 8, 2025 · In this article, we’ll go over some of the best event listing websites where you can submit your event for free or at a low cost. Whether you’re hosting a business conference, a local concert, or a community gathering, these platforms can help you attract more attendees and grow your event’s reach. What Are Event Listing Websites?"}
+{"idx": 6, "title": "My Events", "date": "", "ddg_snippet": "Find exciting community events and activities on our event site.", "subpage_snippet": "", "source": "myeventslist.com", "link": "https://myeventslist.com/", "content": "Find exciting community events and activities on our event site."}
+{"idx": 7, "title": "MyCityScene Free Event Calendar Tool for Any Website", "date": "", "ddg_snippet": "MyCityScene is the free online event calendar that works with any website. Enter your event once, and share instantly with your community.", "subpage_snippet": "", "source": "mycityscene.com", "link": "https://mycityscene.com/", "content": "MyCityScene is the free online event calendar that works with any website. Enter your event once, and share instantly with your community."}
+{"idx": 8, "title": "Best Place To List Your Event for Free | Publish Event Now", "date": "", "ddg_snippet": "Organize the events through categories so that your audience can find the relevant listing of your events through our optimized search filters. We bring your listing to the people who are interested in a particular category similar to your event and present the listing to them directly.", "subpage_snippet": "", "source": "www.eventalways.com", "link": "https://www.eventalways.com/event-listing", "content": "Organize the events through categories so that your audience can find the relevant listing of your events through our optimized search filters. We bring your listing to the people who are interested in a particular category similar to your event and present the listing to them directly."}
+{"idx": 9, "title": "15 Event Promotion Websites for Free Event Listing", "date": "", "ddg_snippet": "To make your search easier, we’ve compiled a list of 15 of the best event promotion websites that allow you to list your event for free. By utilizing these websites, you can increase your event’s visibility and attract more attendees without breaking the bank.", "subpage_snippet": "", "source": "eventespresso.com", "link": "https://eventespresso.com/blog/event-promotion-websites", "content": "To make your search easier, we’ve compiled a list of 15 of the best event promotion websites that allow you to list your event for free. By utilizing these websites, you can increase your event’s visibility and attract more attendees without breaking the bank."}
diff --git a/data/sampled_jsons/Two-Room_2x11_200_steps_training_Rnd._Experts_multiplier_sitearxiv.org.jsonl b/data/sampled_jsons/Two-Room_2x11_200_steps_training_Rnd._Experts_multiplier_sitearxiv.org.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d4b5b42fdefe4c3d2c35f03c0ba947d777b64314
--- /dev/null
+++ b/data/sampled_jsons/Two-Room_2x11_200_steps_training_Rnd._Experts_multiplier_sitearxiv.org.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2505.14681] Two Experts Are All You Need for Steering ... Two Experts Are All You Need for Steering Thinking Accelerating Mixture-of-Experts Training with Adaptive Expert ... Local Mixtures of Experts: Essentially Free Test-Time ... [2502.15315] Tight Clusters Make Specialized Experts - arXiv.org Lazarus: Resilient and Elastic Training of Mixture-of-Experts ... MoNTA: Accelerating Mixture-of-Experts Training with Network ...", "date": "", "ddg_snippet": "May 20, 2025 · Abstract page for arXiv paper 2505.14681: Two Experts Are All You Need for Steering Thinking: Reinforcing Cognitive Effort in MoE Reasoning Models Without Additional Training May 27, 2025 · Notably, using two experts with the reinforcement multiplier of 4, 32, 64, or 128 achieves the highest accuracy of 83.3%. In contrast, applying an excessively large multiplier (e.g., 512) causes a dramatic drop in accuracy, often to near zero. Abstract Mixture-of- Experts (MoE) models have become a widely adopted solution to continue scaling model sizes without a corresponding linear increase in compute. During MoE model training , each input token is dynamically routed to a subset of experts – sparsely-activated feed-forward networks – within each transformer layer. May 20, 2025 · Mixture of expert (MoE) models are a promising approach to increasing model capacity without increasing inference cost, and are core components of many state-of-the-art language models. However, current MoE models typically use only few experts due to prohibitive training and inference cost. We propose Test-Time Model Merging (TTMM) which scales the MoE paradigm to an order of magnitude more ... Feb 21, 2025 · Sparse Mixture-of- Experts (MoE) architectures have emerged as a promising approach to decoupling model capacity from computational cost. At the core of the MoE model is the router, which learns the underlying clustering structure of the input distribution in order to send input tokens to appropriate experts . However, latent clusters may be unidentifiable in high dimension, which causes slow ... Jul 5, 2024 · We present Lazarus, a system for resilient and elastic training of MoE models. Lazarus adaptively allocates expert replicas to address the inherent imbalance in expert workload and speeds-up training , while a provably optimal expert placement algorithm is developed to maximize the probability of recovery upon failures. Abstract The Mixture of Experts (MoE) is an advanced model ar-chitecture in the industry that combines multiple special-ized expert models from various domains into a single su-permodel. This approach enables the model to scale with-out significantly increasing the computational costs of train-ing and inference, while maximizing model performance. However, current distributed training ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2505.14681", "content": "May 20, 2025 · Abstract page for arXiv paper 2505.14681: Two Experts Are All You Need for Steering Thinking: Reinforcing Cognitive Effort in MoE Reasoning Models Without Additional Training May 27, 2025 · Notably, using two experts with the reinforcement multiplier of 4, 32, 64, or 128 achieves the highest accuracy of 83.3%. In contrast, applying an excessively large multiplier (e.g., 512) causes a dramatic drop in accuracy, often to near zero. Abstract Mixture-of- Experts (MoE) models have become a widely adopted solution to continue scaling model sizes without a corresponding linear increase in compute. During MoE model training , each input token is dynamically routed to a subset of experts – sparsely-activated feed-forward networks – within each transformer layer. May 20, 2025 · Mixture of expert (MoE) models are a promising approach to increasing model capacity without increasing inference cost, and are core components of many state-of-the-art language models. However, current MoE models typically use only few experts due to prohibitive training and inference cost. We propose Test-Time Model Merging (TTMM) which scales the MoE paradigm to an order of magnitude more ... Feb 21, 2025 · Sparse Mixture-of- Experts (MoE) architectures have emerged as a promising approach to decoupling model capacity from computational cost. At the core of the MoE model is the router, which learns the underlying clustering structure of the input distribution in order to send input tokens to appropriate experts . However, latent clusters may be unidentifiable in high dimension, which causes slow ... Jul 5, 2024 · We present Lazarus, a system for resilient and elastic training of MoE models. Lazarus adaptively allocates expert replicas to address the inherent imbalance in expert workload and speeds-up training , while a provably optimal expert placement algorithm is developed to maximize the probability of recovery upon failures. Abstract The Mixture of Experts (MoE) is an advanced model ar-chitecture in the industry that combines multiple special-ized expert models from various domains into a single su-permodel. This approach enables the model to scale with-out significantly increasing the computational costs of train-ing and inference, while maximizing model performance. However, current distributed training ..."}
+{"idx": 1, "title": "Two Experts Are All You Need for Steering Thinking Accelerating Mixture-of-Experts Training with Adaptive Expert ... Local Mixtures of Experts: Essentially Free Test-Time ... [2502.15315] Tight Clusters Make Specialized Experts - arXiv.org Lazarus: Resilient and Elastic Training of Mixture-of-Experts ... MoNTA: Accelerating Mixture-of-Experts Training with Network ...", "date": "", "ddg_snippet": "May 27, 2025 · Notably, using two experts with the reinforcement multiplier of 4, 32, 64, or 128 achieves the highest accuracy of 83.3%. In contrast, applying an excessively large multiplier (e.g., 512) causes a dramatic drop in accuracy, often to near zero. Abstract Mixture-of- Experts (MoE) models have become a widely adopted solution to continue scaling model sizes without a corresponding linear increase in compute. During MoE model training , each input token is dynamically routed to a subset of experts – sparsely-activated feed-forward networks – within each transformer layer. May 20, 2025 · Mixture of expert (MoE) models are a promising approach to increasing model capacity without increasing inference cost, and are core components of many state-of-the-art language models. However, current MoE models typically use only few experts due to prohibitive training and inference cost. We propose Test-Time Model Merging (TTMM) which scales the MoE paradigm to an order of magnitude more ... Feb 21, 2025 · Sparse Mixture-of- Experts (MoE) architectures have emerged as a promising approach to decoupling model capacity from computational cost. At the core of the MoE model is the router, which learns the underlying clustering structure of the input distribution in order to send input tokens to appropriate experts . However, latent clusters may be unidentifiable in high dimension, which causes slow ... Jul 5, 2024 · We present Lazarus, a system for resilient and elastic training of MoE models. Lazarus adaptively allocates expert replicas to address the inherent imbalance in expert workload and speeds-up training , while a provably optimal expert placement algorithm is developed to maximize the probability of recovery upon failures. Abstract The Mixture of Experts (MoE) is an advanced model ar-chitecture in the industry that combines multiple special-ized expert models from various domains into a single su-permodel. This approach enables the model to scale with-out significantly increasing the computational costs of train-ing and inference, while maximizing model performance. However, current distributed training ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.14681v2", "content": "May 27, 2025 · Notably, using two experts with the reinforcement multiplier of 4, 32, 64, or 128 achieves the highest accuracy of 83.3%. In contrast, applying an excessively large multiplier (e.g., 512) causes a dramatic drop in accuracy, often to near zero. Abstract Mixture-of- Experts (MoE) models have become a widely adopted solution to continue scaling model sizes without a corresponding linear increase in compute. During MoE model training , each input token is dynamically routed to a subset of experts – sparsely-activated feed-forward networks – within each transformer layer. May 20, 2025 · Mixture of expert (MoE) models are a promising approach to increasing model capacity without increasing inference cost, and are core components of many state-of-the-art language models. However, current MoE models typically use only few experts due to prohibitive training and inference cost. We propose Test-Time Model Merging (TTMM) which scales the MoE paradigm to an order of magnitude more ... Feb 21, 2025 · Sparse Mixture-of- Experts (MoE) architectures have emerged as a promising approach to decoupling model capacity from computational cost. At the core of the MoE model is the router, which learns the underlying clustering structure of the input distribution in order to send input tokens to appropriate experts . However, latent clusters may be unidentifiable in high dimension, which causes slow ... Jul 5, 2024 · We present Lazarus, a system for resilient and elastic training of MoE models. Lazarus adaptively allocates expert replicas to address the inherent imbalance in expert workload and speeds-up training , while a provably optimal expert placement algorithm is developed to maximize the probability of recovery upon failures. Abstract The Mixture of Experts (MoE) is an advanced model ar-chitecture in the industry that combines multiple special-ized expert models from various domains into a single su-permodel. This approach enables the model to scale with-out significantly increasing the computational costs of train-ing and inference, while maximizing model performance. However, current distributed training ..."}
+{"idx": 2, "title": "Accelerating Mixture-of-Experts Training with Adaptive Expert ...", "date": "", "ddg_snippet": "Abstract Mixture-of- Experts (MoE) models have become a widely adopted solution to continue scaling model sizes without a corresponding linear increase in compute. During MoE model training , each input token is dynamically routed to a subset of experts – sparsely-activated feed-forward networks – within each transformer layer.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.19925v1", "content": "Abstract Mixture-of- Experts (MoE) models have become a widely adopted solution to continue scaling model sizes without a corresponding linear increase in compute. During MoE model training , each input token is dynamically routed to a subset of experts – sparsely-activated feed-forward networks – within each transformer layer."}
+{"idx": 3, "title": "Local Mixtures of Experts: Essentially Free Test-Time ... [2502.15315] Tight Clusters Make Specialized Experts - arXiv.org Lazarus: Resilient and Elastic Training of Mixture-of-Experts ... MoNTA: Accelerating Mixture-of-Experts Training with Network ...", "date": "", "ddg_snippet": "May 20, 2025 · Mixture of expert (MoE) models are a promising approach to increasing model capacity without increasing inference cost, and are core components of many state-of-the-art language models. However, current MoE models typically use only few experts due to prohibitive training and inference cost. We propose Test-Time Model Merging (TTMM) which scales the MoE paradigm to an order of magnitude more ... Feb 21, 2025 · Sparse Mixture-of- Experts (MoE) architectures have emerged as a promising approach to decoupling model capacity from computational cost. At the core of the MoE model is the router, which learns the underlying clustering structure of the input distribution in order to send input tokens to appropriate experts . However, latent clusters may be unidentifiable in high dimension, which causes slow ... Jul 5, 2024 · We present Lazarus, a system for resilient and elastic training of MoE models. Lazarus adaptively allocates expert replicas to address the inherent imbalance in expert workload and speeds-up training , while a provably optimal expert placement algorithm is developed to maximize the probability of recovery upon failures. Abstract The Mixture of Experts (MoE) is an advanced model ar-chitecture in the industry that combines multiple special-ized expert models from various domains into a single su-permodel. This approach enables the model to scale with-out significantly increasing the computational costs of train-ing and inference, while maximizing model performance. However, current distributed training ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2505.14136", "content": "May 20, 2025 · Mixture of expert (MoE) models are a promising approach to increasing model capacity without increasing inference cost, and are core components of many state-of-the-art language models. However, current MoE models typically use only few experts due to prohibitive training and inference cost. We propose Test-Time Model Merging (TTMM) which scales the MoE paradigm to an order of magnitude more ... Feb 21, 2025 · Sparse Mixture-of- Experts (MoE) architectures have emerged as a promising approach to decoupling model capacity from computational cost. At the core of the MoE model is the router, which learns the underlying clustering structure of the input distribution in order to send input tokens to appropriate experts . However, latent clusters may be unidentifiable in high dimension, which causes slow ... Jul 5, 2024 · We present Lazarus, a system for resilient and elastic training of MoE models. Lazarus adaptively allocates expert replicas to address the inherent imbalance in expert workload and speeds-up training , while a provably optimal expert placement algorithm is developed to maximize the probability of recovery upon failures. Abstract The Mixture of Experts (MoE) is an advanced model ar-chitecture in the industry that combines multiple special-ized expert models from various domains into a single su-permodel. This approach enables the model to scale with-out significantly increasing the computational costs of train-ing and inference, while maximizing model performance. However, current distributed training ..."}
+{"idx": 4, "title": "[2502.15315] Tight Clusters Make Specialized Experts - arXiv.org", "date": "", "ddg_snippet": "Feb 21, 2025 · Sparse Mixture-of- Experts (MoE) architectures have emerged as a promising approach to decoupling model capacity from computational cost. At the core of the MoE model is the router, which learns the underlying clustering structure of the input distribution in order to send input tokens to appropriate experts . However, latent clusters may be unidentifiable in high dimension, which causes slow ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.15315", "content": "Feb 21, 2025 · Sparse Mixture-of- Experts (MoE) architectures have emerged as a promising approach to decoupling model capacity from computational cost. At the core of the MoE model is the router, which learns the underlying clustering structure of the input distribution in order to send input tokens to appropriate experts . However, latent clusters may be unidentifiable in high dimension, which causes slow ..."}
+{"idx": 5, "title": "Lazarus: Resilient and Elastic Training of Mixture-of-Experts ...", "date": "", "ddg_snippet": "Jul 5, 2024 · We present Lazarus, a system for resilient and elastic training of MoE models. Lazarus adaptively allocates expert replicas to address the inherent imbalance in expert workload and speeds-up training , while a provably optimal expert placement algorithm is developed to maximize the probability of recovery upon failures.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2407.04656", "content": "Jul 5, 2024 · We present Lazarus, a system for resilient and elastic training of MoE models. Lazarus adaptively allocates expert replicas to address the inherent imbalance in expert workload and speeds-up training , while a provably optimal expert placement algorithm is developed to maximize the probability of recovery upon failures."}
+{"idx": 6, "title": "MoNTA: Accelerating Mixture-of-Experts Training with Network ...", "date": "", "ddg_snippet": "Abstract The Mixture of Experts (MoE) is an advanced model ar-chitecture in the industry that combines multiple special-ized expert models from various domains into a single su-permodel. This approach enables the model to scale with-out significantly increasing the computational costs of train-ing and inference, while maximizing model performance. However, current distributed training ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2411.00662", "content": "Abstract The Mixture of Experts (MoE) is an advanced model ar-chitecture in the industry that combines multiple special-ized expert models from various domains into a single su-permodel. This approach enables the model to scale with-out significantly increasing the computational costs of train-ing and inference, while maximizing model performance. However, current distributed training ..."}
+{"idx": 7, "title": "A Survey on Mixture of Experts", "date": "", "ddg_snippet": "experts namely (b) Sparse MoE to perform conditional computation. The expert layer returns the output of the selected expert multiplied by the gate value (softmax of the gating function output).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2407.06204v2", "content": "experts namely (b) Sparse MoE to perform conditional computation. The expert layer returns the output of the selected expert multiplied by the gate value (softmax of the gating function output)."}
+{"idx": 8, "title": "A Survey on Mixture of Experts", "date": "", "ddg_snippet": "experts namely (b) Sparse MoE to perform conditional computation. The expert layer returns the output of the selected expert multiplied by the gate value (softmax of the gating function output).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2407.06204v1", "content": "experts namely (b) Sparse MoE to perform conditional computation. The expert layer returns the output of the selected expert multiplied by the gate value (softmax of the gating function output)."}
+{"idx": 9, "title": "Efficient Active Imitation Learning with Random Network Distillation", "date": "", "ddg_snippet": "Imitation learning usually proceeds in two steps : first, a dataset of behaviors is built by leveraging experts interacting with the dynamical system. RND -DAgger either outperforms or matches existing approaches in terms of final performance while significantly reducing expert burden.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.01894v1", "content": "Imitation learning usually proceeds in two steps : first, a dataset of behaviors is built by leveraging experts interacting with the dynamical system. RND -DAgger either outperforms or matches existing approaches in terms of final performance while significantly reducing expert burden."}
diff --git a/data/sampled_jsons/UNrfYfbLZ3_Accelerating_Linear_Recurrent_Neural_Networks_quantization_equation_4_symmetric.jsonl b/data/sampled_jsons/UNrfYfbLZ3_Accelerating_Linear_Recurrent_Neural_Networks_quantization_equation_4_symmetric.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4e557f5f4a9835548eec6589e7f3f10246c41452
--- /dev/null
+++ b/data/sampled_jsons/UNrfYfbLZ3_Accelerating_Linear_Recurrent_Neural_Networks_quantization_equation_4_symmetric.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Accelerating Linear Recurrent Neural Networks for the Edge", "date": "", "ddg_snippet": "Linear recurrent neural networks enable powerful long-range sequence modeling with constant memory usage and time-per-token during inference. These architectures hold promise for streaming applications at the edge, but deployment in resource-constrained environments requires hardware-aware optimizations to minimize latency and energy consumption.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.01330v1", "content": "Linear recurrent neural networks enable powerful long-range sequence modeling with constant memory usage and time-per-token during inference. These architectures hold promise for streaming applications at the edge, but deployment in resource-constrained environments requires hardware-aware optimizations to minimize latency and energy consumption."}
+{"idx": 1, "title": "ICML Poster Accelerating Linear Recurrent Neural Networks for the Edge ...", "date": "", "ddg_snippet": "Abstract: Linear recurrent neural networks enable powerful long-range sequence modeling with constant memory usage and time-per-token during inference. These architectures hold promise for streaming applications at the edge, but deployment in resource-constrained environments requires hardware-aware optimizations to minimize latency and energy ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/45121", "content": "Abstract: Linear recurrent neural networks enable powerful long-range sequence modeling with constant memory usage and time-per-token during inference. These architectures hold promise for streaming applications at the edge, but deployment in resource-constrained environments requires hardware-aware optimizations to minimize latency and energy ..."}
+{"idx": 2, "title": "PDF Accelerating Recurrent Neural Networks through Compiler Techniques and ...", "date": "", "ddg_snippet": "ModelCompiler supports multiple linear quantization schema and both symmetric and asymmet-ric quantization methods [23]. Furthermore, ModelCompiler has multiple granularity levels for quantization , whole-tensor-based, column-based, row-based, and channel-based ones.", "subpage_snippet": "", "source": "learningsys.org", "link": "http://learningsys.org/nips18/assets/papers/30CameraReadySubmissionmodelcompiler_camera_ready_no_final_flag.pdf", "content": "ModelCompiler supports multiple linear quantization schema and both symmetric and asymmet-ric quantization methods [23]. Furthermore, ModelCompiler has multiple granularity levels for quantization , whole-tensor-based, column-based, row-based, and channel-based ones."}
+{"idx": 3, "title": "PDF Interneurons Accelerate Learning Dynamics in Recurrent Neural Networks ...", "date": "", "ddg_snippet": "Interestingly, the network with interneurons is an overparameterized solution of the whitening objective for the network with direct recurrent connections, so our results can be viewed as a recurrent linear neural network analogue of the implicit acceleration phenomenon observed in overparameterized feedforward linear neural networks .", "subpage_snippet": "", "source": "lipshutzlab.com", "link": "https://lipshutzlab.com/papers/lipshutz2023interneurons.pdf", "content": "Interestingly, the network with interneurons is an overparameterized solution of the whitening objective for the network with direct recurrent connections, so our results can be viewed as a recurrent linear neural network analogue of the implicit acceleration phenomenon observed in overparameterized feedforward linear neural networks ."}
+{"idx": 4, "title": "Accelerating Linear Recurrent Neural Networks for the Edge with ...", "date": "", "ddg_snippet": "Linear recurrent neural networks enable powerful long-range sequence modeling with constant memory usage and time-per-token during inference. These architectures hold promise for streaming applications at the edge, but deployment in resource-constrained environments requires hardware-aware optimizations to minimize latency and energy consumption.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.01330v1", "content": "Linear recurrent neural networks enable powerful long-range sequence modeling with constant memory usage and time-per-token during inference. These architectures hold promise for streaming applications at the edge, but deployment in resource-constrained environments requires hardware-aware optimizations to minimize latency and energy consumption."}
+{"idx": 5, "title": "Accelerating Recurrent Neural Networks: A Memory-Efficient Approach", "date": "", "ddg_snippet": "Recurrent neural networks (RNNs) have achieved the state-of-the-art performance on various sequence learning tasks due to their powerful sequence modeling capability. However, RNNs usually require a large number of parameters and high computational complexity. Hence, it is quite challenging to implement complex RNNs on embedded devices with stringent memory and latency requirement. In this ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/7967731", "content": "Recurrent neural networks (RNNs) have achieved the state-of-the-art performance on various sequence learning tasks due to their powerful sequence modeling capability. However, RNNs usually require a large number of parameters and high computational complexity. Hence, it is quite challenging to implement complex RNNs on embedded devices with stringent memory and latency requirement. In this ..."}
+{"idx": 6, "title": "The Power of Linear Recurrent Neural Networks", "date": "", "ddg_snippet": "Abstract Recurrent neural networks are a powerful means to cope with time series. We show how autoregressive linear , i.e., linearly activated recurrent neural networks (LRNNs) can approximate any time-dependent function f (t). The approximation can effectively be learned by simply solving a linear equation system; no backpropagation or similar methods are needed. Furthermore, and this is the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/1802.03308v9", "content": "Abstract Recurrent neural networks are a powerful means to cope with time series. We show how autoregressive linear , i.e., linearly activated recurrent neural networks (LRNNs) can approximate any time-dependent function f (t). The approximation can effectively be learned by simply solving a linear equation system; no backpropagation or similar methods are needed. Furthermore, and this is the ..."}
+{"idx": 7, "title": "Accelerating a recurrent neural network to finite-time convergence ...", "date": "", "ddg_snippet": "In this paper, a new design formula is presented to accelerate the convergence speed of a recurrent neural network , and applied to time-varying matrix square root finding in real time. Then, according to such a new design formula, a finite-time Zhang neural network (FTZNN) is proposed and investigated for finding time-varying matrix square root.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0016003217303058", "content": "In this paper, a new design formula is presented to accelerate the convergence speed of a recurrent neural network , and applied to time-varying matrix square root finding in real time. Then, according to such a new design formula, a finite-time Zhang neural network (FTZNN) is proposed and investigated for finding time-varying matrix square root."}
+{"idx": 8, "title": "Linear Symmetric Quantization of Neural Networks for Low Precision ...", "date": "", "ddg_snippet": "Quantization technique is also closely related to the implementation of specialized hardware that maps the procedure of network inference onto the energy-efficient low-precision integer or fixed-point arithmetic circuits. In the hardware perspective, low-precision integer accelerators or proces-sors are dominating the solutions targeted on neural network inference, especially for mobile and ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=H1lBj2VFPS", "content": "Quantization technique is also closely related to the implementation of specialized hardware that maps the procedure of network inference onto the energy-efficient low-precision integer or fixed-point arithmetic circuits. In the hardware perspective, low-precision integer accelerators or proces-sors are dominating the solutions targeted on neural network inference, especially for mobile and ..."}
+{"idx": 9, "title": "PDF Weekly Group Sync - icml.cc", "date": "", "ddg_snippet": "Research Questions Do highly sparse linear RNNs outperform dense linear RNNs across different inference compute budgets? Can fixed-point quantization compress sparse linear RNNs without damaging the network's performance? Can unstructured sparsity and fixed-point quantization be translated into latency and energy advantages on neuromorphic ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/media/icml-2025/Slides/45121.pdf", "content": "Research Questions Do highly sparse linear RNNs outperform dense linear RNNs across different inference compute budgets? Can fixed-point quantization compress sparse linear RNNs without damaging the network's performance? Can unstructured sparsity and fixed-point quantization be translated into latency and energy advantages on neuromorphic ..."}
diff --git a/data/sampled_jsons/UVGS_Gaussian_Splatting_NeRF_limitations.jsonl b/data/sampled_jsons/UVGS_Gaussian_Splatting_NeRF_limitations.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3401b6a218f632c5a4d4299aa16ecc87ba2b5e70
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+++ b/data/sampled_jsons/UVGS_Gaussian_Splatting_NeRF_limitations.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "UVGS: Reimagining Unstructured 3D Gaussian Splatting ...", "date": "", "ddg_snippet": "3 Feb 2025 — Limitations & Future Work: While single layer UVGS images can recover the geometry of the object, they sometimes suffer in terms of ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.01846v1", "content": "3 Feb 2025 — Limitations & Future Work: While single layer UVGS images can recover the geometry of the object, they sometimes suffer in terms of ..."}
+{"idx": 1, "title": "CVPR Poster UVGS: Reimagining Unstructured 3D Gaussian ...", "date": "", "ddg_snippet": "3D Gaussian Splatting ( 3DGS ) has demonstrated superior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging due to their ...", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/poster/33266", "content": "3D Gaussian Splatting ( 3DGS ) has demonstrated superior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging due to their ..."}
+{"idx": 2, "title": "NeRF Is a Valuable Assistant for 3D Gaussian Splatting", "date": "", "ddg_snippet": "We introduce NeRF -GS, a novel framework that jointly optimizes Neural Radiance Fields ( NeRF ) and 3D Gaussian Splatting (3DGS). This framework leverages the inherent continuous spatial representation of NeRF to mitigate several limitations of 3DGS, including sensitivity to Gaussian initialization, limited spatial awareness, and weak inter- Gaussian correlations, thereby enhancing its performance ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2507.23374", "content": "We introduce NeRF -GS, a novel framework that jointly optimizes Neural Radiance Fields ( NeRF ) and 3D Gaussian Splatting (3DGS). This framework leverages the inherent continuous spatial representation of NeRF to mitigate several limitations of 3DGS, including sensitivity to Gaussian initialization, limited spatial awareness, and weak inter- Gaussian correlations, thereby enhancing its performance ..."}
+{"idx": 3, "title": "PDF UVGS: Reimagining Unstructured 3D Gaussian Splatting using UV Mapping", "date": "", "ddg_snippet": "Recently, 3D Gaussian Splatting (3DGS) [17] emerged as a compelling alternative, enabling eficient and high-fidelity 3D rendering through a large set of Gaussian primitives that model spatial and visual properties. As an explicit represen-tation, 3DGS offers several advantages over NeRF .", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Rai_UVGS_Reimagining_Unstructured_3D_Gaussian_Splatting_using_UV_Mapping_CVPR_2025_paper.pdf", "content": "Recently, 3D Gaussian Splatting (3DGS) [17] emerged as a compelling alternative, enabling eficient and high-fidelity 3D rendering through a large set of Gaussian primitives that model spatial and visual properties. As an explicit represen-tation, 3DGS offers several advantages over NeRF ."}
+{"idx": 4, "title": "Gaussian Splatting with NeRF-based color and opacity", "date": "", "ddg_snippet": "Unfortunately, GS is difficult to condition since its representation is fully explicit. To mitigate the caveats of both models, we propose a hybrid model Viewing Direction Gaussian Splatting (VDGS) that uses GS representation of the 3D object's shape and NeRF -based encoding of opacity.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S1077314224003540", "content": "Unfortunately, GS is difficult to condition since its representation is fully explicit. To mitigate the caveats of both models, we propose a hybrid model Viewing Direction Gaussian Splatting (VDGS) that uses GS representation of the 3D object's shape and NeRF -based encoding of opacity."}
+{"idx": 5, "title": "PDF UVGS: Reimagining Unstructured 3D Gaussian Splatting using UV Mapping", "date": "", "ddg_snippet": "Recently, 3D Gaussian Splatting (3DGS) [19] emerged as a compelling alternative, enabling efficient and high-fidelity 3D rendering through a large set of Gaussian primitives that model spatial and visual properties. As an explicit represen- 1 tation, 3DGS offers several advantages over NeRF .", "subpage_snippet": "", "source": "aashishrai3799.github.io", "link": "https://aashishrai3799.github.io/uvgs/assets/UVGS.pdf", "content": "Recently, 3D Gaussian Splatting (3DGS) [19] emerged as a compelling alternative, enabling efficient and high-fidelity 3D rendering through a large set of Gaussian primitives that model spatial and visual properties. As an explicit represen- 1 tation, 3DGS offers several advantages over NeRF ."}
+{"idx": 6, "title": "UVGS: Reimagining Unstructured 3D Gaussian Splatting using UV Mapping", "date": "", "ddg_snippet": "3D Gaussian Splatting (3DGS) has demonstrated superior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging due to their discrete, unstructured, and permutation-invariant nature. In this work, we present a simple yet effective method to overcome these challenges. We utilize spherical mapping to transform 3DGS into a structured 2D representation, termed UVGS ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.01846", "content": "3D Gaussian Splatting (3DGS) has demonstrated superior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging due to their discrete, unstructured, and permutation-invariant nature. In this work, we present a simple yet effective method to overcome these challenges. We utilize spherical mapping to transform 3DGS into a structured 2D representation, termed UVGS ..."}
+{"idx": 7, "title": "UVGS: Reimagining Unstructured 3D Gaussian Splatting ... - IEEE Xplore", "date": "", "ddg_snippet": "3D Gaussian Splatting (3DGS) has demonstrated superior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging due to their discrete, unstructured, and permutation-invariant nature. In this work, we present a simple yet effective method to overcome these challenges. We utilize spherical mapping to transform 3DGS into a structured 2D representation, termed UVGS ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/11094709", "content": "3D Gaussian Splatting (3DGS) has demonstrated superior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging due to their discrete, unstructured, and permutation-invariant nature. In this work, we present a simple yet effective method to overcome these challenges. We utilize spherical mapping to transform 3DGS into a structured 2D representation, termed UVGS ..."}
+{"idx": 8, "title": "UVGS: Reimagining Unstructured 3D Gaussian Splatting", "date": "", "ddg_snippet": "3D Gaussian Splatting (3DGS) has demonstrated superior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging due to their discrete, unstructured, and permutation-invariant nature. In this work, we present a simple yet effective method to overcome these challenges. We utilize spherical mapping to transform 3DGS into a structured 2D representation, termed UVGS ...", "subpage_snippet": "", "source": "aashishrai3799.github.io", "link": "https://aashishrai3799.github.io/uvgs/", "content": "3D Gaussian Splatting (3DGS) has demonstrated superior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging due to their discrete, unstructured, and permutation-invariant nature. In this work, we present a simple yet effective method to overcome these challenges. We utilize spherical mapping to transform 3DGS into a structured 2D representation, termed UVGS ..."}
+{"idx": 9, "title": "[2502.01846] UVGS: Reimagining Unstructured 3D Gaussian Splatting using ...", "date": "", "ddg_snippet": "3D Gaussian Splatting (3DGS) has demonstrated superior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging due to their discrete, unstructured, and permutation-invariant nature. In t…", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2502.01846", "content": "3D Gaussian Splatting (3DGS) has demonstrated superior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging due to their discrete, unstructured, and permutation-invariant nature. In t…"}
diff --git a/data/sampled_jsons/UVGS_Reimagining_Unstructured_3D_Gaussian_Splatting_Objaverse_dataset_objects_count.jsonl b/data/sampled_jsons/UVGS_Reimagining_Unstructured_3D_Gaussian_Splatting_Objaverse_dataset_objects_count.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2e8472e2d3d048bec2ae1bcdebdfc4fe79d317e5
--- /dev/null
+++ b/data/sampled_jsons/UVGS_Reimagining_Unstructured_3D_Gaussian_Splatting_Objaverse_dataset_objects_count.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "UVGS: Reimagining Unstructured 3D Gaussian Splatting using UV Mapping", "date": "", "ddg_snippet": "3D Gaussian Splatting (3DGS) has demonstrated superior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging due to their discrete, unstructured , and permutation-invariant nature. In this work, we present a simple yet effective method to overcome these challenges. We utilize spherical mapping to transform 3DGS into a structured 2D representation, termed UVGS ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.01846", "content": "3D Gaussian Splatting (3DGS) has demonstrated superior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging due to their discrete, unstructured , and permutation-invariant nature. In this work, we present a simple yet effective method to overcome these challenges. We utilize spherical mapping to transform 3DGS into a structured 2D representation, termed UVGS ..."}
+{"idx": 1, "title": "PDF UVGS: Reimagining Unstructured 3D Gaussian Splatting using UV Mapping", "date": "", "ddg_snippet": "Abstract 3D Gaussian Splatting (3DGS) has demonstrated supe-rior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging due to their discrete, unstructured , and permutation-invariant nature. In this work, we present a simple yet effective method to overcome these challenges.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Rai_UVGS_Reimagining_Unstructured_3D_Gaussian_Splatting_using_UV_Mapping_CVPR_2025_paper.pdf", "content": "Abstract 3D Gaussian Splatting (3DGS) has demonstrated supe-rior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging due to their discrete, unstructured , and permutation-invariant nature. In this work, we present a simple yet effective method to overcome these challenges."}
+{"idx": 2, "title": "UVGS: Reimagining Unstructured 3D Gaussian Splatting", "date": "", "ddg_snippet": "3D Gaussian Splatting (3DGS) has demonstrated superior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging due to their discrete, unstructured , and permutation-invariant nature. In this work, we present a simple yet effective method to overcome these challenges. We utilize spherical mapping to transform 3DGS into a structured 2D representation, termed UVGS ...", "subpage_snippet": "", "source": "aashishrai3799.github.io", "link": "https://aashishrai3799.github.io/uvgs/", "content": "3D Gaussian Splatting (3DGS) has demonstrated superior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging due to their discrete, unstructured , and permutation-invariant nature. In this work, we present a simple yet effective method to overcome these challenges. We utilize spherical mapping to transform 3DGS into a structured 2D representation, termed UVGS ..."}
+{"idx": 3, "title": "[PDF] UVGS: Reimagining Unstructured 3D Gaussian Splatting using UV ...", "date": "", "ddg_snippet": "This work utilizes spherical mapping to transform 3DGS into a structured 2D representation, termed UVGS , and discovers that typical VAEs trained with latent diffusion models can directly generalize to this new representation without additional training. 3D Gaussian Splatting (3DGS) has demonstrated superior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/UVGS:-Reimagining-Unstructured-3D-Gaussian-using-UV-Rai-Wang/10f0d88160981f85fd6c270e65496ec5948a98d2", "content": "This work utilizes spherical mapping to transform 3DGS into a structured 2D representation, termed UVGS , and discovers that typical VAEs trained with latent diffusion models can directly generalize to this new representation without additional training. 3D Gaussian Splatting (3DGS) has demonstrated superior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging ..."}
+{"idx": 4, "title": "3D-Gaussian-Splatting-Papers/abs/2502.01846.md at main - GitHub", "date": "", "ddg_snippet": "UVGS : Reimagining Unstructured 3D Gaussian Splatting using UV Mapping 3D Gaussian Splatting (3DGS) has demonstrated superior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging due to their discrete, unstructured , and permutation-invariant nature.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Awesome3DGS/3D-Gaussian-Splatting-Papers/blob/main/abs/2502.01846.md", "content": "UVGS : Reimagining Unstructured 3D Gaussian Splatting using UV Mapping 3D Gaussian Splatting (3DGS) has demonstrated superior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging due to their discrete, unstructured , and permutation-invariant nature."}
+{"idx": 5, "title": "UVGS: Reimagining Unstructured 3D Gaussian Splatting using UV Mapping", "date": "", "ddg_snippet": "The top three rows show the unconditional generation results of our method using ShapeNet dataset , while the bottom 3 show from Objaverse dataset .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/388685707_UVGS_Reimagining_Unstructured_3D_Gaussian_Splatting_using_UV_Mapping", "content": "The top three rows show the unconditional generation results of our method using ShapeNet dataset , while the bottom 3 show from Objaverse dataset ."}
+{"idx": 6, "title": "UVGS: Reimagining Unstructured 3D Gaussian Splatting using UV Mapping", "date": "", "ddg_snippet": "Abstract 3D Gaussian Splatting (3DGS) has demonstrated superior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging due to their discrete, unstructured , and permutation-invariant nature. In this work, we present a simple yet effective method to overcome these challenges. We utilize spherical mapping to transform 3DGS into a structured 2D representation ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.01846v2", "content": "Abstract 3D Gaussian Splatting (3DGS) has demonstrated superior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging due to their discrete, unstructured , and permutation-invariant nature. In this work, we present a simple yet effective method to overcome these challenges. We utilize spherical mapping to transform 3DGS into a structured 2D representation ..."}
+{"idx": 7, "title": "UVGS: Reimagining Unstructured 3D Gaussian Splatting using UV Mapping ...", "date": "", "ddg_snippet": "View recent discussion. Abstract: 3D Gaussian Splatting (3DGS) has demonstrated superior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging due to their discrete, unstructured , and permutation-invariant nature. In this work, we present a simple yet effective method to overcome these challenges. We utilize spherical mapping to transform 3DGS into a ...", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2502.01846v3", "content": "View recent discussion. Abstract: 3D Gaussian Splatting (3DGS) has demonstrated superior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging due to their discrete, unstructured , and permutation-invariant nature. In this work, we present a simple yet effective method to overcome these challenges. We utilize spherical mapping to transform 3DGS into a ..."}
+{"idx": 8, "title": "\"UVGS: Reimagining Unstructured 3D Gaussian Splatting using UV ... - dblp", "date": "", "ddg_snippet": "Bibliographic details on UVGS : Reimagining Unstructured 3D Gaussian Splatting using UV Mapping.", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/conf/cvpr/RaiWJSC0P25", "content": "Bibliographic details on UVGS : Reimagining Unstructured 3D Gaussian Splatting using UV Mapping."}
+{"idx": 9, "title": "Daily Papers", "date": "", "ddg_snippet": "4 days ago — In this work, we propose OVGaussian, a generalizable Open-Vocabulary 3D semantic segmentation framework based on the 3D Gaussian representation.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=domain+generalizable+3DSS", "content": "4 days ago — In this work, we propose OVGaussian, a generalizable Open-Vocabulary 3D semantic segmentation framework based on the 3D Gaussian representation."}
diff --git a/data/sampled_jsons/UVGS_Reimagining_Unstructured_3D_Gaussian_Splatting_using_UV_Mapping.jsonl b/data/sampled_jsons/UVGS_Reimagining_Unstructured_3D_Gaussian_Splatting_using_UV_Mapping.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8fe06cbf91dcbea0475a8d7a4cdab59cedc69bb2
--- /dev/null
+++ b/data/sampled_jsons/UVGS_Reimagining_Unstructured_3D_Gaussian_Splatting_using_UV_Mapping.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2502.01846] UVGS : Reimagining Unstructured 3 D Gaussian ...", "date": "", "ddg_snippet": "View a PDF of the paper titled UVGS : Reimagining Unstructured 3 D Gaussian Splatting using UV Mapping , by Aashish Rai and Dilin Wang and Mihir Jain and Nikolaos Sarafianos and Kefan Chen and Srinath Sridhar and Aayush Prakash.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.01846", "content": "View a PDF of the paper titled UVGS : Reimagining Unstructured 3 D Gaussian Splatting using UV Mapping , by Aashish Rai and Dilin Wang and Mihir Jain and Nikolaos Sarafianos and Kefan Chen and Srinath Sridhar and Aayush Prakash."}
+{"idx": 1, "title": "(PDF) UVGS : Reimagining Unstructured 3 D Gaussian Splatting ...", "date": "", "ddg_snippet": "We utilize spherical mapping to transform 3DGS into a structured 2D representation, termed UVGS . UVGS can be viewed as multi-channel images, with feature dimensions as a concatenation of Gaussian attributes such as position, scale, color, opacity, and rotation.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/388685707_UVGS_Reimagining_Unstructured_3D_Gaussian_Splatting_using_UV_Mapping", "content": "We utilize spherical mapping to transform 3DGS into a structured 2D representation, termed UVGS . UVGS can be viewed as multi-channel images, with feature dimensions as a concatenation of Gaussian attributes such as position, scale, color, opacity, and rotation."}
+{"idx": 2, "title": "UVGS : Reimagining Unstructured 3 D Gaussian Splatting using UV ...", "date": "", "ddg_snippet": "We use spherical mapping [37] that inscribes Gaussian splats in a spherical surface, and projects attributes like position, rota-tion, scale, opacity, and color into an organized 14-channel image-like UV map . This mapping introduces spatial struc-ture, resolving issues of permutation...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Rai_UVGS_Reimagining_Unstructured_3D_Gaussian_Splatting_using_UV_Mapping_CVPR_2025_paper.pdf", "content": "We use spherical mapping [37] that inscribes Gaussian splats in a spherical surface, and projects attributes like position, rota-tion, scale, opacity, and color into an organized 14-channel image-like UV map . This mapping introduces spatial struc-ture, resolving issues of permutation..."}
+{"idx": 3, "title": "UVGS : Reimagining Unstructured 3 D Gaussian Splatting using UV ...", "date": "", "ddg_snippet": "3 D Gaussian Splatting (3DGS) has demonstrated superior quality in modeling 3 D objects and scenes. However, generating 3DGS remains challenging due to their discrete, unstructured , and permutation-invariant nature. In this work, we present a simple yet effective method to overcome these...", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/article/UVGS:-Reimagining-Unstructured-3D-Gaussian-Splatting-using-UV-Mapping-b2086680-3f01-4f55-825b-0c52f7e663bf", "content": "3 D Gaussian Splatting (3DGS) has demonstrated superior quality in modeling 3 D objects and scenes. However, generating 3DGS remains challenging due to their discrete, unstructured , and permutation-invariant nature. In this work, we present a simple yet effective method to overcome these..."}
+{"idx": 4, "title": "UVGS", "date": "", "ddg_snippet": "UVGS : Reimagining Unstructured 3 D Gaussian Splatting using UV Mapping .", "subpage_snippet": "", "source": "aashishrai3799.github.io", "link": "https://aashishrai3799.github.io/uvgs/", "content": "UVGS : Reimagining Unstructured 3 D Gaussian Splatting using UV Mapping ."}
+{"idx": 5, "title": "UVGS : Reimagining Unstructured 3 D Gaussian Splatting using UV ...", "date": "", "ddg_snippet": "This paper makes it better by using UV mapping - imagine unwrapping a 3 D object like a chocolate wrapper and painting on the flat surface. The researchers created two ways to \"unwrap\" 3 D objects. The first method, spherical mapping , works well for round objects like faces or sculptures.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/uvgs-reimagining-unstructured-3d-gaussian-splatting-using", "content": "This paper makes it better by using UV mapping - imagine unwrapping a 3 D object like a chocolate wrapper and painting on the flat surface. The researchers created two ways to \"unwrap\" 3 D objects. The first method, spherical mapping , works well for round objects like faces or sculptures."}
+{"idx": 6, "title": "GitHub - graphdeco-inria/ gaussian - splatting : Original reference...", "date": "", "ddg_snippet": "title = { 3 D Gaussian Splatting for Real-Time Radiance Field Rendering}To have better reconstructed scenes we use depth maps as priors during optimization with each input images. It works best on untextured parts ex: roads and can remove floaters.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/graphdeco-inria/gaussian-splatting", "content": "title = { 3 D Gaussian Splatting for Real-Time Radiance Field Rendering}To have better reconstructed scenes we use depth maps as priors during optimization with each input images. It works best on untextured parts ex: roads and can remove floaters."}
+{"idx": 7, "title": "Free 3 D Gaussian Splatting Tool | Polycam", "date": "", "ddg_snippet": "Gaussian splatting is a rasterization technique used for 3 D reconstruction and rendering. In essence, it is a method to create photorealistic scenes from a sampling of images.", "subpage_snippet": "", "source": "poly.cam", "link": "https://poly.cam/tools/gaussian-splatting", "content": "Gaussian splatting is a rasterization technique used for 3 D reconstruction and rendering. In essence, it is a method to create photorealistic scenes from a sampling of images."}
+{"idx": 8, "title": "UVGS Reimagining Unstructured 3 D Gaussian Splatting using UV ...", "date": "", "ddg_snippet": "About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How...", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=1uMuXsJScvY", "content": "About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How..."}
+{"idx": 9, "title": "Graphics Programming weekly - Issue... | Jendrik Illner - 3 D Programmer", "date": "", "ddg_snippet": "UVGS : Reimagining Unstructured 3 D Gaussian Splatting using UV Mapping .The paper presents how the mapping allows existing models to better operate on the image formats. Shows improvements in compression and quality.", "subpage_snippet": "", "source": "www.jendrikillner.com", "link": "https://www.jendrikillner.com/post/graphics-programming-weekly-issue-378/", "content": "UVGS : Reimagining Unstructured 3 D Gaussian Splatting using UV Mapping .The paper presents how the mapping allows existing models to better operate on the image formats. Shows improvements in compression and quality."}
diff --git a/data/sampled_jsons/UVGS_Reimagining_Unstructured_3D_Gaussian_Splatting_using_UV_Mapping_branched_mapping_layers_sitearx.jsonl b/data/sampled_jsons/UVGS_Reimagining_Unstructured_3D_Gaussian_Splatting_using_UV_Mapping_branched_mapping_layers_sitearx.jsonl
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index 0000000000000000000000000000000000000000..3aa36b1559afba510d59e5d23a86c1c44462d325
--- /dev/null
+++ b/data/sampled_jsons/UVGS_Reimagining_Unstructured_3D_Gaussian_Splatting_using_UV_Mapping_branched_mapping_layers_sitearx.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "arXiv:2502.01846v3 [cs.CV] 20 Mar 2025", "date": "", "ddg_snippet": "3D Gaussian Splatting (3DGS) has demonstrated supe- rior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging due to their discrete, unstructured , and permutation-invariant nature. In this work, we present a simple yet effective method to overcome these challenges. We utilize spherical mapping to transform 3DGS into a structured 2D representation, termed UVGS ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.01846", "content": "3D Gaussian Splatting (3DGS) has demonstrated supe- rior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging due to their discrete, unstructured , and permutation-invariant nature. In this work, we present a simple yet effective method to overcome these challenges. We utilize spherical mapping to transform 3DGS into a structured 2D representation, termed UVGS ..."}
+{"idx": 1, "title": "UVGS: Reimagining Unstructured 3D Gaussian Splatting using UV ... UVGS: Reimagining Unstructured 3D Gaussian Splatting using UV ... arXiv:2502.01846v3 [cs.CV] 20 Mar 2025 Distilled-3DGS: Distilled 3D Gaussian Splatting - arXiv.org 3DGS-Enhancer: Enhancing Unbounded 3D Gaussian Splatting with ... A Survey on 3D Gaussian Splatting Applications: Segmentation ... UVGS : Reimagining Unstructured 3D Gaussian Splatting using UV Mapp… UVGS : Reimagining Unstructured 3D Gaussian Splatting using UV Mapp… UVGS : Reimagining Unstructured 3D Gaussian Splatting using UV Mapp… UVGS : Reimagining Unstructured 3D Gaussian Splatting using UV Mapp… UVGS : Reimagining Unstructured 3D Gaussian Splatting using UV Mapp… UVGS : Reimagining Unstructured 3D Gaussian Splatting using UV Mapp… MVS-GS: High-Quality 3D Gaussian Splatting Mapping via Online ...", "date": "", "ddg_snippet": "Feb 3, 2025 · 3D Gaussian Splatting (3DGS) has demonstrated superior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging due to their discrete, unstructured , and permutation-invariant nature. In this work, we present a simple yet effective method to overcome these challenges. We utilize spherical mapping to transform 3DGS into a structured 2D representation, termed UVGS ... To address these shortcomings, we introduce UV Gaussian Splatting ( UVGS ), which provides a structured transformation of 3D Gaussian primitives into a 2D representation while preserving essential 3D information. We use spherical mapping [42] that inscribes Gaussian splats in a spherical surface, and projects attributes like position, rotation, scale, opacity, and color into an organized 14 ... 3D Gaussian Splatting (3DGS) has demonstrated supe- rior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging due to their discrete, unstructured , and permutation-invariant nature. In this work, we present a simple yet effective method to overcome these challenges. We utilize spherical mapping to transform 3DGS into a structured 2D representation, termed UVGS ... Aug 19, 2025 · However, applying it to 3D Gaussian Splatting (3DGS) introduces unique challenges. First, 3DGS is an explicit and unstructured representation composed of variable 3D Gaussians, lacking the consistent latent feature spaces typically leveraged in conventional KD. Oct 21, 2024 · Novel-view synthesis aims to generate novel views of a scene from multiple input images or videos, and recent advancements like 3D Gaussian splatting (3DGS) have achieved notable success in producing photorealistic renderings with efficient pipelines. However, generating high-quality novel views under challenging settings, such as sparse input views, remains difficult due to insufficient ... Additionally, it utilizes explicit and flexible 3D Gaussian splatting as the 3D representation, enabling localized editing without altering the background. With a similar idea of [150], GS-VTON introduces a reference-driven image editing ap-proach that integrates personalized information into a pre-trained 2D VTON model using LoRA fine-tuning. What is 3D Gaussian splatting? 3D Gaussian Splatting (3DGS) has demonstrated superior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging due to their discrete, unstructured , and permutation-invariant nature. In this work, we present a simple yet effective method to overcome these challenges. Can 3D Gaussian splatting be integrated with existing image foundational models? We introduced a novel method to solve the underlying issues with 3D Gaussian Splatting (3DGS) that prevent the direct integration of them with the large number of existing image foundational models. We proposed UVGS - a structured representation for 3DGS obtained by spherical mapping of 3DGS primitives to UV maps. What does uvgs stand for? To address these shortcomings, we introduce UV Gaussian Splatting (UVGS), which provides a structured transformation of 3D Gaussian primitives into a 2D representation while preserving essential 3D information. Can a single layer uvgs map match a 3DGS reconstruction quality? From the table, it can be seen that by using four layers of UVGS maps (K=4), we can almost match the reconstruction quality of fitted 3DGS results, while we realized that simply with a single layer UVGS maps, we are able to maintain the overall geometry and appearance of the object of our dataset with a PSNR of more than 30. Why is a 3DGS object based on a Super uvgs representation? The main reason behind this is the learned Super UVGS representation which not only maintains the appearance of the 3DGS object, but also serves as a proxy for geometrical shape by encoding all the 3DGS attributes into the same coherent feature space. Why is uvgs a scalable solution? Additionally, one can simply increase the 2D UV resolution to accommodate more Gaussians , making UVGS a scalable solution compared to typical 3D backbones. This approach immediately unlocks various novel generation applications of 3DGS by inherently utilizing the already developed superior 2D generation capabilities. Dec 26, 2024 · This study addresses the challenge of online 3D model generation for neural rendering using an RGB image stream. Previous research has tackled this issue by incorporating Neural Radiance Fields (NeRF) or 3D Gaussian Splatting (3DGS) as scene representations within dense SLAM methods. However, most studies focus primarily on estimating coarse 3D scenes rather than achieving detailed ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.01846", "content": "Feb 3, 2025 · 3D Gaussian Splatting (3DGS) has demonstrated superior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging due to their discrete, unstructured , and permutation-invariant nature. In this work, we present a simple yet effective method to overcome these challenges. We utilize spherical mapping to transform 3DGS into a structured 2D representation, termed UVGS ... To address these shortcomings, we introduce UV Gaussian Splatting ( UVGS ), which provides a structured transformation of 3D Gaussian primitives into a 2D representation while preserving essential 3D information. We use spherical mapping [42] that inscribes Gaussian splats in a spherical surface, and projects attributes like position, rotation, scale, opacity, and color into an organized 14 ... 3D Gaussian Splatting (3DGS) has demonstrated supe- rior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging due to their discrete, unstructured , and permutation-invariant nature. In this work, we present a simple yet effective method to overcome these challenges. We utilize spherical mapping to transform 3DGS into a structured 2D representation, termed UVGS ... Aug 19, 2025 · However, applying it to 3D Gaussian Splatting (3DGS) introduces unique challenges. First, 3DGS is an explicit and unstructured representation composed of variable 3D Gaussians, lacking the consistent latent feature spaces typically leveraged in conventional KD. Oct 21, 2024 · Novel-view synthesis aims to generate novel views of a scene from multiple input images or videos, and recent advancements like 3D Gaussian splatting (3DGS) have achieved notable success in producing photorealistic renderings with efficient pipelines. However, generating high-quality novel views under challenging settings, such as sparse input views, remains difficult due to insufficient ... Additionally, it utilizes explicit and flexible 3D Gaussian splatting as the 3D representation, enabling localized editing without altering the background. With a similar idea of [150], GS-VTON introduces a reference-driven image editing ap-proach that integrates personalized information into a pre-trained 2D VTON model using LoRA fine-tuning. What is 3D Gaussian splatting? 3D Gaussian Splatting (3DGS) has demonstrated superior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging due to their discrete, unstructured , and permutation-invariant nature. In this work, we present a simple yet effective method to overcome these challenges. Can 3D Gaussian splatting be integrated with existing image foundational models? We introduced a novel method to solve the underlying issues with 3D Gaussian Splatting (3DGS) that prevent the direct integration of them with the large number of existing image foundational models. We proposed UVGS - a structured representation for 3DGS obtained by spherical mapping of 3DGS primitives to UV maps. What does uvgs stand for? To address these shortcomings, we introduce UV Gaussian Splatting (UVGS), which provides a structured transformation of 3D Gaussian primitives into a 2D representation while preserving essential 3D information. Can a single layer uvgs map match a 3DGS reconstruction quality? From the table, it can be seen that by using four layers of UVGS maps (K=4), we can almost match the reconstruction quality of fitted 3DGS results, while we realized that simply with a single layer UVGS maps, we are able to maintain the overall geometry and appearance of the object of our dataset with a PSNR of more than 30. Why is a 3DGS object based on a Super uvgs representation? The main reason behind this is the learned Super UVGS representation which not only maintains the appearance of the 3DGS object, but also serves as a proxy for geometrical shape by encoding all the 3DGS attributes into the same coherent feature space. Why is uvgs a scalable solution? Additionally, one can simply increase the 2D UV resolution to accommodate more Gaussians , making UVGS a scalable solution compared to typical 3D backbones. This approach immediately unlocks various novel generation applications of 3DGS by inherently utilizing the already developed superior 2D generation capabilities. Dec 26, 2024 · This study addresses the challenge of online 3D model generation for neural rendering using an RGB image stream. Previous research has tackled this issue by incorporating Neural Radiance Fields (NeRF) or 3D Gaussian Splatting (3DGS) as scene representations within dense SLAM methods. However, most studies focus primarily on estimating coarse 3D scenes rather than achieving detailed ..."}
+{"idx": 2, "title": "UVGS: Reimagining Unstructured 3D Gaussian Splatting using UV ...", "date": "", "ddg_snippet": "To address these shortcomings, we introduce UV Gaussian Splatting ( UVGS ), which provides a structured transformation of 3D Gaussian primitives into a 2D representation while preserving essential 3D information. We use spherical mapping [42] that inscribes Gaussian splats in a spherical surface, and projects attributes like position, rotation, scale, opacity, and color into an organized 14 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.01846v1", "content": "To address these shortcomings, we introduce UV Gaussian Splatting ( UVGS ), which provides a structured transformation of 3D Gaussian primitives into a 2D representation while preserving essential 3D information. We use spherical mapping [42] that inscribes Gaussian splats in a spherical surface, and projects attributes like position, rotation, scale, opacity, and color into an organized 14 ..."}
+{"idx": 3, "title": "UVGS : Reimagining Unstructured 3 D Gaussian Splatting using UV ...", "date": "", "ddg_snippet": "Branched mapping layers : The rationale behind using branched mapping layers in both forward and reverse mapping networks is to prevent the incompatibility issues arising due the the different value distribution of 3DGS attributes.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.01846v2", "content": "Branched mapping layers : The rationale behind using branched mapping layers in both forward and reverse mapping networks is to prevent the incompatibility issues arising due the the different value distribution of 3DGS attributes."}
+{"idx": 4, "title": "Distilled-3DGS: Distilled 3D Gaussian Splatting - arXiv.org", "date": "", "ddg_snippet": "Aug 19, 2025 · However, applying it to 3D Gaussian Splatting (3DGS) introduces unique challenges. First, 3DGS is an explicit and unstructured representation composed of variable 3D Gaussians, lacking the consistent latent feature spaces typically leveraged in conventional KD.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.14037v1", "content": "Aug 19, 2025 · However, applying it to 3D Gaussian Splatting (3DGS) introduces unique challenges. First, 3DGS is an explicit and unstructured representation composed of variable 3D Gaussians, lacking the consistent latent feature spaces typically leveraged in conventional KD."}
+{"idx": 5, "title": "3DGS-Enhancer: Enhancing Unbounded 3D Gaussian Splatting with ... A Survey on 3D Gaussian Splatting Applications: Segmentation ... UVGS : Reimagining Unstructured 3D Gaussian Splatting using UV Mapp… UVGS : Reimagining Unstructured 3D Gaussian Splatting using UV Mapp… UVGS : Reimagining Unstructured 3D Gaussian Splatting using UV Mapp… UVGS : Reimagining Unstructured 3D Gaussian Splatting using UV Mapp… UVGS : Reimagining Unstructured 3D Gaussian Splatting using UV Mapp… UVGS : Reimagining Unstructured 3D Gaussian Splatting using UV Mapp… MVS-GS: High-Quality 3D Gaussian Splatting Mapping via Online ...", "date": "", "ddg_snippet": "Oct 21, 2024 · Novel-view synthesis aims to generate novel views of a scene from multiple input images or videos, and recent advancements like 3D Gaussian splatting (3DGS) have achieved notable success in producing photorealistic renderings with efficient pipelines. However, generating high-quality novel views under challenging settings, such as sparse input views, remains difficult due to insufficient ... Additionally, it utilizes explicit and flexible 3D Gaussian splatting as the 3D representation, enabling localized editing without altering the background. With a similar idea of [150], GS-VTON introduces a reference-driven image editing ap-proach that integrates personalized information into a pre-trained 2D VTON model using LoRA fine-tuning. What is 3D Gaussian splatting? 3D Gaussian Splatting (3DGS) has demonstrated superior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging due to their discrete, unstructured , and permutation-invariant nature. In this work, we present a simple yet effective method to overcome these challenges. Can 3D Gaussian splatting be integrated with existing image foundational models? We introduced a novel method to solve the underlying issues with 3D Gaussian Splatting (3DGS) that prevent the direct integration of them with the large number of existing image foundational models. We proposed UVGS - a structured representation for 3DGS obtained by spherical mapping of 3DGS primitives to UV maps. What does uvgs stand for? To address these shortcomings, we introduce UV Gaussian Splatting (UVGS), which provides a structured transformation of 3D Gaussian primitives into a 2D representation while preserving essential 3D information. Can a single layer uvgs map match a 3DGS reconstruction quality? From the table, it can be seen that by using four layers of UVGS maps (K=4), we can almost match the reconstruction quality of fitted 3DGS results, while we realized that simply with a single layer UVGS maps, we are able to maintain the overall geometry and appearance of the object of our dataset with a PSNR of more than 30. Why is a 3DGS object based on a Super uvgs representation? The main reason behind this is the learned Super UVGS representation which not only maintains the appearance of the 3DGS object, but also serves as a proxy for geometrical shape by encoding all the 3DGS attributes into the same coherent feature space. Why is uvgs a scalable solution? Additionally, one can simply increase the 2D UV resolution to accommodate more Gaussians , making UVGS a scalable solution compared to typical 3D backbones. This approach immediately unlocks various novel generation applications of 3DGS by inherently utilizing the already developed superior 2D generation capabilities. Dec 26, 2024 · This study addresses the challenge of online 3D model generation for neural rendering using an RGB image stream. Previous research has tackled this issue by incorporating Neural Radiance Fields (NeRF) or 3D Gaussian Splatting (3DGS) as scene representations within dense SLAM methods. However, most studies focus primarily on estimating coarse 3D scenes rather than achieving detailed ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.16266", "content": "Oct 21, 2024 · Novel-view synthesis aims to generate novel views of a scene from multiple input images or videos, and recent advancements like 3D Gaussian splatting (3DGS) have achieved notable success in producing photorealistic renderings with efficient pipelines. However, generating high-quality novel views under challenging settings, such as sparse input views, remains difficult due to insufficient ... Additionally, it utilizes explicit and flexible 3D Gaussian splatting as the 3D representation, enabling localized editing without altering the background. With a similar idea of [150], GS-VTON introduces a reference-driven image editing ap-proach that integrates personalized information into a pre-trained 2D VTON model using LoRA fine-tuning. What is 3D Gaussian splatting? 3D Gaussian Splatting (3DGS) has demonstrated superior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging due to their discrete, unstructured , and permutation-invariant nature. In this work, we present a simple yet effective method to overcome these challenges. Can 3D Gaussian splatting be integrated with existing image foundational models? We introduced a novel method to solve the underlying issues with 3D Gaussian Splatting (3DGS) that prevent the direct integration of them with the large number of existing image foundational models. We proposed UVGS - a structured representation for 3DGS obtained by spherical mapping of 3DGS primitives to UV maps. What does uvgs stand for? To address these shortcomings, we introduce UV Gaussian Splatting (UVGS), which provides a structured transformation of 3D Gaussian primitives into a 2D representation while preserving essential 3D information. Can a single layer uvgs map match a 3DGS reconstruction quality? From the table, it can be seen that by using four layers of UVGS maps (K=4), we can almost match the reconstruction quality of fitted 3DGS results, while we realized that simply with a single layer UVGS maps, we are able to maintain the overall geometry and appearance of the object of our dataset with a PSNR of more than 30. Why is a 3DGS object based on a Super uvgs representation? The main reason behind this is the learned Super UVGS representation which not only maintains the appearance of the 3DGS object, but also serves as a proxy for geometrical shape by encoding all the 3DGS attributes into the same coherent feature space. Why is uvgs a scalable solution? Additionally, one can simply increase the 2D UV resolution to accommodate more Gaussians , making UVGS a scalable solution compared to typical 3D backbones. This approach immediately unlocks various novel generation applications of 3DGS by inherently utilizing the already developed superior 2D generation capabilities. Dec 26, 2024 · This study addresses the challenge of online 3D model generation for neural rendering using an RGB image stream. Previous research has tackled this issue by incorporating Neural Radiance Fields (NeRF) or 3D Gaussian Splatting (3DGS) as scene representations within dense SLAM methods. However, most studies focus primarily on estimating coarse 3D scenes rather than achieving detailed ..."}
+{"idx": 6, "title": "A Survey on 3D Gaussian Splatting Applications: Segmentation ...", "date": "", "ddg_snippet": "Additionally, it utilizes explicit and flexible 3D Gaussian splatting as the 3D representation, enabling localized editing without altering the background. With a similar idea of [150], GS-VTON introduces a reference-driven image editing ap-proach that integrates personalized information into a pre-trained 2D VTON model using LoRA fine-tuning.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2508.09977", "content": "Additionally, it utilizes explicit and flexible 3D Gaussian splatting as the 3D representation, enabling localized editing without altering the background. With a similar idea of [150], GS-VTON introduces a reference-driven image editing ap-proach that integrates personalized information into a pre-trained 2D VTON model using LoRA fine-tuning."}
+{"idx": 7, "title": "MVS-GS: High-Quality 3D Gaussian Splatting Mapping via Online ...", "date": "", "ddg_snippet": "Dec 26, 2024 · This study addresses the challenge of online 3D model generation for neural rendering using an RGB image stream. Previous research has tackled this issue by incorporating Neural Radiance Fields (NeRF) or 3D Gaussian Splatting (3DGS) as scene representations within dense SLAM methods. However, most studies focus primarily on estimating coarse 3D scenes rather than achieving detailed ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2412.19130", "content": "Dec 26, 2024 · This study addresses the challenge of online 3D model generation for neural rendering using an RGB image stream. Previous research has tackled this issue by incorporating Neural Radiance Fields (NeRF) or 3D Gaussian Splatting (3DGS) as scene representations within dense SLAM methods. However, most studies focus primarily on estimating coarse 3D scenes rather than achieving detailed ..."}
+{"idx": 8, "title": "[2502.01846] UVGS: Reimagining Unstructured 3D Gaussian ...", "date": "", "ddg_snippet": "We utilize spherical mapping to transform 3DGS into a structured 2D representation, termed UVGS. UVGS can be viewed as multi-channel images , with feature ...", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2502.01846", "content": "We utilize spherical mapping to transform 3DGS into a structured 2D representation, termed UVGS. UVGS can be viewed as multi-channel images , with feature ..."}
+{"idx": 9, "title": "UVGS : Reimagining Unstructured 3 D Gaussian Splatting using UV ...", "date": "", "ddg_snippet": "Using a carefully designed multi- branch mapping network, Super UVGS consolidates the distinct attribute spaces into a shared feature space, enabling a more collective representation of the object. This unified transformation not only facilitates zero-shot compatibility with pretrained 2D...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.01846v3", "content": "Using a carefully designed multi- branch mapping network, Super UVGS consolidates the distinct attribute spaces into a shared feature space, enabling a more collective representation of the object. This unified transformation not only facilitates zero-shot compatibility with pretrained 2D..."}
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+{"idx": 0, "title": "[0711.0189] A Tutorial on Spectral Clustering - arXiv.org A Tutorial on Spectral Clustering - uni-tuebingen.de A tutorial on spectral clustering | Statistics and Computing A tutorial on spectral clustering [PDF] A tutorial on spectral clustering | Semantic Scholar A tutorial on spectral clustering | Statistics and Computing [0711.0189] A Tutorial on Spectral Clustering - arXiv.org [PDF] A tutorial on spectral clustering | Semantic Scholar A Tutorial on Spectral Clustering - uni-tuebingen.de [PDF] A tutorial on spectral clustering | Semantic Scholar A Tutorial on Spectral Clustering - uni-tuebingen.de A Tutorial on Spectral Clustering - uni-tuebingen.de A Tutorial on Spectral Clustering - ADS", "date": "", "ddg_snippet": "Nov 1, 2007 · In recent years, spectral clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved efficiently by standard linear algebra software, and very often outperforms traditional clustering algorithms such as the k-means algorithm. On the first glance spectral clustering appears slightly mysterious, and it is not obvious to see why it works ... A Tutorial on Spectral Clustering Ulrike von Luxburg Max Planck Institute for Biological Cybernetics Spemannstr. 38, 72076 T ̈ubingen, Germany ulrike . luxburg @tuebingen.mpg.de This article appears in Statistics and Computing , 17 (4), 2007 . The original publication is available at www.springer.com. Abstract Aug 22, 2007 · A tutorial on spectral clustering Published: 22 August 2007 Volume 17, pages 395–416, ( 2007 ) Cite this article Download PDF Ulrike von Luxburg 40k Accesses 7663 Citations 46 Altmetric 1 Mention Explore all metrics A tutorial on spectral clustering Ulrike von Luxburg Received: 15 August 2006 / Accepted: 7 July 2007 / Published online: 22 August 2007 Springer Science+Business Media, LLC 2007 Abstract In recent years, spectral clustering has become one of the most popular modern clustering algorithms. Nov 1, 2007 · This tutorial describes different graph Laplacians and their basic properties, present the most common spectral clustering algorithms, and derive those algorithms from scratch by several different approaches. Abstract In recent years, spectral clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved efficiently by standard linear ... Dec 1, 2007 · Abstract In recent years, spectral clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved efficiently by standard linear algebra software, and very often outperforms traditional clustering algorithms such as the k -means algorithm. What is spectral clustering? In recent years, spectral clustering has become one of the most popular modern clustering algorithms . It is simple to implement, can be solved efficiently by standard linear algebra software, and very often outperforms traditional clustering algorithms such as the k-means algorithm. Is spectral clustering faster than other clustering algorithms? A simple spectral clustering algorithm based on a vertex embedding with $O (\\log (k))$ vectors computed by the power method is presented, finding that it is significantly faster than alternative clustering algorithms, while producing results with approximately the same clustering accuracy. Does a normalized spectral clustering algorithm converge? For both normalized spectral clustering algorithms, it can be proved that this is indeed the case (von Luxburg, Bousquet, and Belkin, 2004, 2005; von Luxburg, Belkin, and Bousquet, to appear). Mathe-matically, one proves that as we take the limit n → ∞, the matrix Lsym converges in a strong sense Why are the first k eigenvectors used in spectral clustering? A note is given on why the first k eigenvectors in the algorithm are chosen and the conditions for indicator vectors under which the clustering problem could lead to the problem of minimizing the objective function of the spectral clustering method based on normalized cut criterion. Can spectral clustering be problematic if the eigenvectors of lsym are small? This argument shows that spectral clustering using the matrix Lsym can be problematic if the eigenvectors contain particularly small entries. On the other hand, note that such small entries in the eigenvectors only occur if some of the vertices have a particularly low degrees (as the eigenvectors of Lsym are given by D1/2 1 Ai). Can perturbation theory justify clustering algorithms based on eigenvectors of matrices? A bit of caution is needed when using perturbation theory arguments to justify clustering algorithms based on eigenvectors of matrices. In general, any block diagonal symmetric matrix has the property that there exists a basis of eigenvectors which are zero outside the individual blocks and real-valued within the blocks. In recent years, spectral clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved efficiently by standard linear algebra software, and very often outperforms traditional clustering algorithms such as the k-means algorithm. On the first glance spectral clustering appears slightly mysterious, and it is not obvious to see why it works ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/0711.0189", "content": "Nov 1, 2007 · In recent years, spectral clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved efficiently by standard linear algebra software, and very often outperforms traditional clustering algorithms such as the k-means algorithm. On the first glance spectral clustering appears slightly mysterious, and it is not obvious to see why it works ... A Tutorial on Spectral Clustering Ulrike von Luxburg Max Planck Institute for Biological Cybernetics Spemannstr. 38, 72076 T ̈ubingen, Germany ulrike . luxburg @tuebingen.mpg.de This article appears in Statistics and Computing , 17 (4), 2007 . The original publication is available at www.springer.com. Abstract Aug 22, 2007 · A tutorial on spectral clustering Published: 22 August 2007 Volume 17, pages 395–416, ( 2007 ) Cite this article Download PDF Ulrike von Luxburg 40k Accesses 7663 Citations 46 Altmetric 1 Mention Explore all metrics A tutorial on spectral clustering Ulrike von Luxburg Received: 15 August 2006 / Accepted: 7 July 2007 / Published online: 22 August 2007 Springer Science+Business Media, LLC 2007 Abstract In recent years, spectral clustering has become one of the most popular modern clustering algorithms. Nov 1, 2007 · This tutorial describes different graph Laplacians and their basic properties, present the most common spectral clustering algorithms, and derive those algorithms from scratch by several different approaches. Abstract In recent years, spectral clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved efficiently by standard linear ... Dec 1, 2007 · Abstract In recent years, spectral clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved efficiently by standard linear algebra software, and very often outperforms traditional clustering algorithms such as the k -means algorithm. What is spectral clustering? In recent years, spectral clustering has become one of the most popular modern clustering algorithms . It is simple to implement, can be solved efficiently by standard linear algebra software, and very often outperforms traditional clustering algorithms such as the k-means algorithm. Is spectral clustering faster than other clustering algorithms? A simple spectral clustering algorithm based on a vertex embedding with $O (\\log (k))$ vectors computed by the power method is presented, finding that it is significantly faster than alternative clustering algorithms, while producing results with approximately the same clustering accuracy. Does a normalized spectral clustering algorithm converge? For both normalized spectral clustering algorithms, it can be proved that this is indeed the case (von Luxburg, Bousquet, and Belkin, 2004, 2005; von Luxburg, Belkin, and Bousquet, to appear). Mathe-matically, one proves that as we take the limit n → ∞, the matrix Lsym converges in a strong sense Why are the first k eigenvectors used in spectral clustering? A note is given on why the first k eigenvectors in the algorithm are chosen and the conditions for indicator vectors under which the clustering problem could lead to the problem of minimizing the objective function of the spectral clustering method based on normalized cut criterion. Can spectral clustering be problematic if the eigenvectors of lsym are small? This argument shows that spectral clustering using the matrix Lsym can be problematic if the eigenvectors contain particularly small entries. On the other hand, note that such small entries in the eigenvectors only occur if some of the vertices have a particularly low degrees (as the eigenvectors of Lsym are given by D1/2 1 Ai). Can perturbation theory justify clustering algorithms based on eigenvectors of matrices? A bit of caution is needed when using perturbation theory arguments to justify clustering algorithms based on eigenvectors of matrices. In general, any block diagonal symmetric matrix has the property that there exists a basis of eigenvectors which are zero outside the individual blocks and real-valued within the blocks. In recent years, spectral clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved efficiently by standard linear algebra software, and very often outperforms traditional clustering algorithms such as the k-means algorithm. On the first glance spectral clustering appears slightly mysterious, and it is not obvious to see why it works ..."}
+{"idx": 1, "title": "A Tutorial on Spectral Clustering - uni-tuebingen.de", "date": "", "ddg_snippet": "A Tutorial on Spectral Clustering Ulrike von Luxburg Max Planck Institute for Biological Cybernetics Spemannstr. 38, 72076 T ̈ubingen, Germany ulrike . luxburg @tuebingen.mpg.de This article appears in Statistics and Computing , 17 (4), 2007 . The original publication is available at www.springer.com. Abstract", "subpage_snippet": "", "source": "www.tml.cs.uni-tuebingen.de", "link": "http://www.tml.cs.uni-tuebingen.de/team/luxburg/publications/Luxburg07_tutorial.pdf", "content": "A Tutorial on Spectral Clustering Ulrike von Luxburg Max Planck Institute for Biological Cybernetics Spemannstr. 38, 72076 T ̈ubingen, Germany ulrike . luxburg @tuebingen.mpg.de This article appears in Statistics and Computing , 17 (4), 2007 . The original publication is available at www.springer.com. Abstract"}
+{"idx": 2, "title": "A tutorial on spectral clustering | Statistics and Computing A tutorial on spectral clustering [PDF] A tutorial on spectral clustering | Semantic Scholar A tutorial on spectral clustering | Statistics and Computing [0711.0189] A Tutorial on Spectral Clustering - arXiv.org [PDF] A tutorial on spectral clustering | Semantic Scholar A Tutorial on Spectral Clustering - uni-tuebingen.de [PDF] A tutorial on spectral clustering | Semantic Scholar A Tutorial on Spectral Clustering - uni-tuebingen.de A Tutorial on Spectral Clustering - uni-tuebingen.de A Tutorial on Spectral Clustering - ADS", "date": "", "ddg_snippet": "Aug 22, 2007 · A tutorial on spectral clustering Published: 22 August 2007 Volume 17, pages 395–416, ( 2007 ) Cite this article Download PDF Ulrike von Luxburg 40k Accesses 7663 Citations 46 Altmetric 1 Mention Explore all metrics A tutorial on spectral clustering Ulrike von Luxburg Received: 15 August 2006 / Accepted: 7 July 2007 / Published online: 22 August 2007 Springer Science+Business Media, LLC 2007 Abstract In recent years, spectral clustering has become one of the most popular modern clustering algorithms. Nov 1, 2007 · This tutorial describes different graph Laplacians and their basic properties, present the most common spectral clustering algorithms, and derive those algorithms from scratch by several different approaches. Abstract In recent years, spectral clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved efficiently by standard linear ... Dec 1, 2007 · Abstract In recent years, spectral clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved efficiently by standard linear algebra software, and very often outperforms traditional clustering algorithms such as the k -means algorithm. What is spectral clustering? In recent years, spectral clustering has become one of the most popular modern clustering algorithms . It is simple to implement, can be solved efficiently by standard linear algebra software, and very often outperforms traditional clustering algorithms such as the k-means algorithm. Is spectral clustering faster than other clustering algorithms? A simple spectral clustering algorithm based on a vertex embedding with $O (\\log (k))$ vectors computed by the power method is presented, finding that it is significantly faster than alternative clustering algorithms, while producing results with approximately the same clustering accuracy. Does a normalized spectral clustering algorithm converge? For both normalized spectral clustering algorithms, it can be proved that this is indeed the case (von Luxburg, Bousquet, and Belkin, 2004, 2005; von Luxburg, Belkin, and Bousquet, to appear). Mathe-matically, one proves that as we take the limit n → ∞, the matrix Lsym converges in a strong sense Why are the first k eigenvectors used in spectral clustering? A note is given on why the first k eigenvectors in the algorithm are chosen and the conditions for indicator vectors under which the clustering problem could lead to the problem of minimizing the objective function of the spectral clustering method based on normalized cut criterion. Can spectral clustering be problematic if the eigenvectors of lsym are small? This argument shows that spectral clustering using the matrix Lsym can be problematic if the eigenvectors contain particularly small entries. On the other hand, note that such small entries in the eigenvectors only occur if some of the vertices have a particularly low degrees (as the eigenvectors of Lsym are given by D1/2 1 Ai). Can perturbation theory justify clustering algorithms based on eigenvectors of matrices? A bit of caution is needed when using perturbation theory arguments to justify clustering algorithms based on eigenvectors of matrices. In general, any block diagonal symmetric matrix has the property that there exists a basis of eigenvectors which are zero outside the individual blocks and real-valued within the blocks. In recent years, spectral clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved efficiently by standard linear algebra software, and very often outperforms traditional clustering algorithms such as the k-means algorithm. On the first glance spectral clustering appears slightly mysterious, and it is not obvious to see why it works ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s11222-007-9033-z", "content": "Aug 22, 2007 · A tutorial on spectral clustering Published: 22 August 2007 Volume 17, pages 395–416, ( 2007 ) Cite this article Download PDF Ulrike von Luxburg 40k Accesses 7663 Citations 46 Altmetric 1 Mention Explore all metrics A tutorial on spectral clustering Ulrike von Luxburg Received: 15 August 2006 / Accepted: 7 July 2007 / Published online: 22 August 2007 Springer Science+Business Media, LLC 2007 Abstract In recent years, spectral clustering has become one of the most popular modern clustering algorithms. Nov 1, 2007 · This tutorial describes different graph Laplacians and their basic properties, present the most common spectral clustering algorithms, and derive those algorithms from scratch by several different approaches. Abstract In recent years, spectral clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved efficiently by standard linear ... Dec 1, 2007 · Abstract In recent years, spectral clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved efficiently by standard linear algebra software, and very often outperforms traditional clustering algorithms such as the k -means algorithm. What is spectral clustering? In recent years, spectral clustering has become one of the most popular modern clustering algorithms . It is simple to implement, can be solved efficiently by standard linear algebra software, and very often outperforms traditional clustering algorithms such as the k-means algorithm. Is spectral clustering faster than other clustering algorithms? A simple spectral clustering algorithm based on a vertex embedding with $O (\\log (k))$ vectors computed by the power method is presented, finding that it is significantly faster than alternative clustering algorithms, while producing results with approximately the same clustering accuracy. Does a normalized spectral clustering algorithm converge? For both normalized spectral clustering algorithms, it can be proved that this is indeed the case (von Luxburg, Bousquet, and Belkin, 2004, 2005; von Luxburg, Belkin, and Bousquet, to appear). Mathe-matically, one proves that as we take the limit n → ∞, the matrix Lsym converges in a strong sense Why are the first k eigenvectors used in spectral clustering? A note is given on why the first k eigenvectors in the algorithm are chosen and the conditions for indicator vectors under which the clustering problem could lead to the problem of minimizing the objective function of the spectral clustering method based on normalized cut criterion. Can spectral clustering be problematic if the eigenvectors of lsym are small? This argument shows that spectral clustering using the matrix Lsym can be problematic if the eigenvectors contain particularly small entries. On the other hand, note that such small entries in the eigenvectors only occur if some of the vertices have a particularly low degrees (as the eigenvectors of Lsym are given by D1/2 1 Ai). Can perturbation theory justify clustering algorithms based on eigenvectors of matrices? A bit of caution is needed when using perturbation theory arguments to justify clustering algorithms based on eigenvectors of matrices. In general, any block diagonal symmetric matrix has the property that there exists a basis of eigenvectors which are zero outside the individual blocks and real-valued within the blocks. In recent years, spectral clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved efficiently by standard linear algebra software, and very often outperforms traditional clustering algorithms such as the k-means algorithm. On the first glance spectral clustering appears slightly mysterious, and it is not obvious to see why it works ..."}
+{"idx": 3, "title": "A tutorial on spectral clustering", "date": "", "ddg_snippet": "A tutorial on spectral clustering Ulrike von Luxburg Received: 15 August 2006 / Accepted: 7 July 2007 / Published online: 22 August 2007 Springer Science+Business Media, LLC 2007 Abstract In recent years, spectral clustering has become one of the most popular modern clustering algorithms.", "subpage_snippet": "", "source": "www.cs.cornell.edu", "link": "https://www.cs.cornell.edu/courses/cs6241/2020sp/readings/vonLuxburg-2007-spectral.pdf", "content": "A tutorial on spectral clustering Ulrike von Luxburg Received: 15 August 2006 / Accepted: 7 July 2007 / Published online: 22 August 2007 Springer Science+Business Media, LLC 2007 Abstract In recent years, spectral clustering has become one of the most popular modern clustering algorithms."}
+{"idx": 4, "title": "[PDF] A tutorial on spectral clustering | Semantic Scholar", "date": "", "ddg_snippet": "Nov 1, 2007 · This tutorial describes different graph Laplacians and their basic properties, present the most common spectral clustering algorithms, and derive those algorithms from scratch by several different approaches. Abstract In recent years, spectral clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved efficiently by standard linear ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/A-tutorial-on-spectral-clustering-Luxburg/eda90bd43f4256986688e525b45b833a3addab97", "content": "Nov 1, 2007 · This tutorial describes different graph Laplacians and their basic properties, present the most common spectral clustering algorithms, and derive those algorithms from scratch by several different approaches. Abstract In recent years, spectral clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved efficiently by standard linear ..."}
+{"idx": 5, "title": "A tutorial on spectral clustering | Statistics and Computing", "date": "", "ddg_snippet": "Dec 1, 2007 · Abstract In recent years, spectral clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved efficiently by standard linear algebra software, and very often outperforms traditional clustering algorithms such as the k -means algorithm.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.1007/s11222-007-9033-z", "content": "Dec 1, 2007 · Abstract In recent years, spectral clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved efficiently by standard linear algebra software, and very often outperforms traditional clustering algorithms such as the k -means algorithm."}
+{"idx": 6, "title": "A Tutorial on Spectral Clustering - ADS", "date": "", "ddg_snippet": "In recent years, spectral clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved efficiently by standard linear algebra software, and very often outperforms traditional clustering algorithms such as the k-means algorithm. On the first glance spectral clustering appears slightly mysterious, and it is not obvious to see why it works ...", "subpage_snippet": "", "source": "ui.adsabs.harvard.edu", "link": "https://ui.adsabs.harvard.edu/abs/2007arXiv0711.0189V/abstract", "content": "In recent years, spectral clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved efficiently by standard linear algebra software, and very often outperforms traditional clustering algorithms such as the k-means algorithm. On the first glance spectral clustering appears slightly mysterious, and it is not obvious to see why it works ..."}
+{"idx": 7, "title": "Generalized Dirichlet Energy and Graph Laplacians for", "date": "", "ddg_snippet": "In this work, we revisit the density-based clustering paradigm and we propose a novel framework that generalizes classical spectral methods to both ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2203.03221v3", "content": "In this work, we revisit the density-based clustering paradigm and we propose a novel framework that generalizes classical spectral methods to both ..."}
+{"idx": 8, "title": "Self-Tuning Spectral Clustering for Speaker Diarization", "date": "", "ddg_snippet": "Spectral clustering [ 13 , 14 ] is a technique used to group data points into clusters based on the eigenvalues and eigenvectors of a similarity ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.00023v2", "content": "Spectral clustering [ 13 , 14 ] is a technique used to group data points into clusters based on the eigenvalues and eigenvectors of a similarity ..."}
+{"idx": 9, "title": "WO2009038822A2 - Spectral clustering for multi-type relational", "date": "", "ddg_snippet": "... clustering of the elements of the objects using a spectral clustering algorithm, the spectral clustering algorithm computing at least one joint ...", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/WO2009038822A2/en", "content": "... clustering of the elements of the objects using a spectral clustering algorithm, the spectral clustering algorithm computing at least one joint ..."}
diff --git a/data/sampled_jsons/Unbiased_Pairwise_Learning_from_Biased_Implicit_Feedback_Saito_abstract_propensity_scores.jsonl b/data/sampled_jsons/Unbiased_Pairwise_Learning_from_Biased_Implicit_Feedback_Saito_abstract_propensity_scores.jsonl
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index 0000000000000000000000000000000000000000..13e26b4f57ab94fa5e94245d3240405759faa7bf
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Unbiased Pairwise Learning from Biased Implicit Feedback", "date": "", "ddg_snippet": "Abstract Implicit feedback is prevalent in real-world scenarios and is widely used in the construction of recommender systems.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3409256.3409812", "content": "Abstract Implicit feedback is prevalent in real-world scenarios and is widely used in the construction of recommender systems."}
+{"idx": 1, "title": "Unbiased Pairwise Learning from Biased Implicit Feedback - Yuta Saito", "date": "", "ddg_snippet": "Abstract Implicit feedback is prevalent in real-world scenarios and is widely used in the construction of recommender systems. However, the application of implicit feedback data is much more complicated than its explicit counterpart because it provides only positive feedback, and we cannot know whether the non-interacted feedback is positive or negative. Furthermore, positive feedback for rare ...", "subpage_snippet": "", "source": "usait0.com", "link": "https://usait0.com/en/publication/2020/ictir2020/", "content": "Abstract Implicit feedback is prevalent in real-world scenarios and is widely used in the construction of recommender systems. However, the application of implicit feedback data is much more complicated than its explicit counterpart because it provides only positive feedback, and we cannot know whether the non-interacted feedback is positive or negative. Furthermore, positive feedback for rare ..."}
+{"idx": 2, "title": "Sci-Hub | Unbiased Pairwise Learning from Biased Implicit Feedback ...", "date": "", "ddg_snippet": "Sci-Hub | Unbiased Pairwise Learning from Biased Implicit Feedback . Proceedings of the 2020 ACM SIGIR on International Conference on Theory of Information Retrieval | 10.1145/3409256.3409812", "subpage_snippet": "", "source": "sci-hub.st", "link": "https://sci-hub.st/10.1145/3409256.3409812", "content": "Sci-Hub | Unbiased Pairwise Learning from Biased Implicit Feedback . Proceedings of the 2020 ACM SIGIR on International Conference on Theory of Information Retrieval | 10.1145/3409256.3409812"}
+{"idx": 3, "title": "\"Unbiased Pairwise Learning from Biased Implicit Feedback.\"", "date": "", "ddg_snippet": "Bibliographic details on Unbiased Pairwise Learning from Biased Implicit Feedback .", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/conf/ictir/Saito20", "content": "Bibliographic details on Unbiased Pairwise Learning from Biased Implicit Feedback ."}
+{"idx": 4, "title": "Unbiased Pairwise Learning from Biased Implicit Feedback - GitHub", "date": "", "ddg_snippet": "Unbiased Pairwise Learning from Biased Implicit Feedback About This repository accompanies the real-world experiments conducted in the paper \" Unbiased Pairwise Learning from Biased Implicit Feedback \" by Yuta Saito , which has been accepted by ICTIR'20.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/usaito/unbiased-pairwise-rec", "content": "Unbiased Pairwise Learning from Biased Implicit Feedback About This repository accompanies the real-world experiments conducted in the paper \" Unbiased Pairwise Learning from Biased Implicit Feedback \" by Yuta Saito , which has been accepted by ICTIR'20."}
+{"idx": 5, "title": "Unbiased Pairwise Learning from Implicit Feedback for Recommender ...", "date": "", "ddg_snippet": "Yuta Saito . 2020. Unbiased Pairwise Learning from Biased Implicit Feedback . In Proceedings of the 2020 ACM SIGIR on International Conference on Theory of Information Retrieval. 5--12.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3539618.3592077", "content": "Yuta Saito . 2020. Unbiased Pairwise Learning from Biased Implicit Feedback . In Proceedings of the 2020 ACM SIGIR on International Conference on Theory of Information Retrieval. 5--12."}
+{"idx": 6, "title": "Unbiased Pairwise Learning from Biased Implicit Feedback", "date": "", "ddg_snippet": "Yuta Saito . Unbiased Pairwise Learning from Biased Implicit Feedback . In Krisztian Balog, Vinay Setty, Christina Lioma, Yiqun Liu 0001, Min Zhang 0006, Klaus Berberich, editors, ICTIR '20: The 2020 ACM SIGIR International Conference on the Theory of Information Retrieval, Virtual Event, Norway, September 14-17, 2020. pages 5-12, ACM, 2020. [doi]", "subpage_snippet": "", "source": "researchr.org", "link": "https://researchr.org/publication/Saito20-1", "content": "Yuta Saito . Unbiased Pairwise Learning from Biased Implicit Feedback . In Krisztian Balog, Vinay Setty, Christina Lioma, Yiqun Liu 0001, Min Zhang 0006, Klaus Berberich, editors, ICTIR '20: The 2020 ACM SIGIR International Conference on the Theory of Information Retrieval, Virtual Event, Norway, September 14-17, 2020. pages 5-12, ACM, 2020. [doi]"}
+{"idx": 7, "title": "PDF Unbiased Recommender Learning from Biased Graded Implicit Feedback", "date": "", "ddg_snippet": "Unbiased Pairwise Learning from Biased Implicit Feedback . In Proceedings of the 2020 ACM SIGIR on International Conference on Theory of Information Retrieval. 5-12.", "subpage_snippet": "", "source": "decisionmaking4ir.github.io", "link": "https://decisionmaking4ir.github.io/WSDM-2022/papers/Suguru.pdf", "content": "Unbiased Pairwise Learning from Biased Implicit Feedback . In Proceedings of the 2020 ACM SIGIR on International Conference on Theory of Information Retrieval. 5-12."}
+{"idx": 8, "title": "Document Similarity Enhanced IPS Estimation for Unbiased", "date": "", "ddg_snippet": "To address this bias when training LTR models, many approaches from the literature re-weight the users’ click data using Inverse Propensity Scoring ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.07909v1", "content": "To address this bias when training LTR models, many approaches from the literature re-weight the users’ click data using Inverse Propensity Scoring ..."}
+{"idx": 9, "title": "Doubly Robust Estimator for Ranking Metrics with Post-Click", "date": "", "ddg_snippet": "A possible solution to address this bias is to use the inverse propensity score estimator, as it can provide an unbiased evaluation even with the ...", "subpage_snippet": "", "source": "usait0.com", "link": "https://usait0.com/en/publication/2020/recsys2020/", "content": "A possible solution to address this bias is to use the inverse propensity score estimator, as it can provide an unbiased evaluation even with the ..."}
diff --git a/data/sampled_jsons/Understanding_the_Role_of_Initialization_in_Deep_Learning_Convergence_ICML_2025_year_2025.jsonl b/data/sampled_jsons/Understanding_the_Role_of_Initialization_in_Deep_Learning_Convergence_ICML_2025_year_2025.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..896cfab2eada9f5999dde4bf267c4e5ead71cb52
--- /dev/null
+++ b/data/sampled_jsons/Understanding_the_Role_of_Initialization_in_Deep_Learning_Convergence_ICML_2025_year_2025.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ICML 2025 2025 Spotlight Posters", "date": "", "ddg_snippet": "Unsupervised pre-training and transfer learning are commonly used techniques to initialize training algorithms for neural networks , particularly in settings ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/events/2025SpotlightPosters", "content": "Unsupervised pre-training and transfer learning are commonly used techniques to initialize training algorithms for neural networks , particularly in settings ..."}
+{"idx": 1, "title": "IDInit: A Universal and Stable Initialization Method for ...", "date": "", "ddg_snippet": "by Y Pan · Cited by 3 — The success of these networks hinges on effective initialization methods , which are vital for ensuring stable and rapid convergence during training. Recently, ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=LFiaoYnP6T", "content": "by Y Pan · Cited by 3 — The success of these networks hinges on effective initialization methods , which are vital for ensuring stable and rapid convergence during training. Recently, ..."}
+{"idx": 2, "title": "Global Convergence and Rich Feature Learning in L", "date": "", "ddg_snippet": "The learned features substantially deviate from their initialization, demonstrating true feature learning rather than random feature approximation. This ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/44572", "content": "The learned features substantially deviate from their initialization, demonstrating true feature learning rather than random feature approximation. This ..."}
+{"idx": 3, "title": "Deep Linear Network Training Dynamics from Random ...", "date": "", "ddg_snippet": "In this work, we develop a minimal theory of the learning rate transfer effect in randomly initialized deep linear networks. Our theory captures both (1) ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/45234", "content": "In this work, we develop a minimal theory of the learning rate transfer effect in randomly initialized deep linear networks. Our theory captures both (1) ..."}
+{"idx": 4, "title": "Global Convergence and Rich Feature Learning in $L", "date": "", "ddg_snippet": "by Z Chen · 2025 · Cited by 1 — In this paper, we investigate the training dynamics of infinitely wide, L-layer neural networks using the tensor program (TP) framework.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.09565", "content": "by Z Chen · 2025 · Cited by 1 — In this paper, we investigate the training dynamics of infinitely wide, L-layer neural networks using the tensor program (TP) framework."}
+{"idx": 5, "title": "ICML Poster Algorithm Development in Neural Networks", "date": "", "ddg_snippet": "Here we undertake a case study of the learning dynamics of recurrent neural networks (RNNs) trained on the streaming parity task in order to develop an ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46526", "content": "Here we undertake a case study of the learning dynamics of recurrent neural networks (RNNs) trained on the streaming parity task in order to develop an ..."}
+{"idx": 6, "title": "On the importance of initialization and momentum in deep ...", "date": "", "ddg_snippet": "by I Sutskever · 2013 · Cited by 7121 — We find that both the initialization and the momentum are crucial since poorly initialized networks cannot be trained with momentum.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3042817.3043064", "content": "by I Sutskever · 2013 · Cited by 7121 — We find that both the initialization and the momentum are crucial since poorly initialized networks cannot be trained with momentum."}
+{"idx": 7, "title": "ICML 2025 Papers", "date": "", "ddg_snippet": "IMLS Archives, Getting Started, Schedule, Tutorials, Main Conference, Invited Talks, Orals, Spotlight, Posters, Awards, Test of Time Award Papers, Workshops", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/papers.html", "content": "IMLS Archives, Getting Started, Schedule, Tutorials, Main Conference, Invited Talks, Orals, Spotlight, Posters, Awards, Test of Time Award Papers, Workshops"}
+{"idx": 8, "title": "ICML 2025 Schedule", "date": "", "ddg_snippet": "Calibration and Bias in Algorithms, Data, and Models: a tutorial on metrics and plots for measuring calibration, bias, fairness, reliability, and robustness.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/calendar", "content": "Calibration and Bias in Algorithms, Data, and Models: a tutorial on metrics and plots for measuring calibration, bias, fairness, reliability, and robustness."}
+{"idx": 9, "title": "Layer Infinite-Width Neural Networks under $\\ ...", "date": "", "ddg_snippet": "by Z Chen · Cited by 1 — This rich feature space captures relevant data information and ensures that any convergent point of the training process is a global minimum. Our analysis ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=efWv8jOBkb", "content": "by Z Chen · Cited by 1 — This rich feature space captures relevant data information and ensures that any convergent point of the training process is a global minimum. Our analysis ..."}
diff --git a/data/sampled_jsons/Universal_Transformers_abstract_Mostafa_Dehghani_2019_full_abstract_text.jsonl b/data/sampled_jsons/Universal_Transformers_abstract_Mostafa_Dehghani_2019_full_abstract_text.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..9744190195567308f304e49718c92bc097052bbb
--- /dev/null
+++ b/data/sampled_jsons/Universal_Transformers_abstract_Mostafa_Dehghani_2019_full_abstract_text.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[PDF] Universal Transformers | Semantic Scholar", "date": "", "ddg_snippet": "The Universal Transformer (UT), a parallel-in-time self-attentive recurrent sequence model which can be cast as a generalization of the Transformer model and which addresses issues of parallelizability and global receptive field, is proposed.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Universal-Transformers-Dehghani-Gouws/ac4dafdef1d2b685b7f28a11837414573d39ff4e", "content": "The Universal Transformer (UT), a parallel-in-time self-attentive recurrent sequence model which can be cast as a generalization of the Transformer model and which addresses issues of parallelizability and global receptive field, is proposed."}
+{"idx": 1, "title": "(PDF) Universal Transformers", "date": "", "ddg_snippet": "2019 , ArXiv. Abstract . Recurrent neural networks (RNNs) sequentially process data by updating their state with each new data point, and have long been the de facto choice for sequence modeling tasks. However, their inherently sequential computation makes them slow to train.", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/74420506/Universal_Transformers", "content": "2019 , ArXiv. Abstract . Recurrent neural networks (RNNs) sequentially process data by updating their state with each new data point, and have long been the de facto choice for sequence modeling tasks. However, their inherently sequential computation makes them slow to train."}
+{"idx": 2, "title": "(Open Access) Universal Transformers (2018) | Mostafa Dehghani", "date": "", "ddg_snippet": "Universal Transformers . Mostafa Dehghani , Stephan Gouws, Oriol Vinyals, Jakob Uszkoreit, Łukasz Kaiser +4 moreUniversity of Amsterdam, Google. - 10 Jul 2018. - arXiv: Computation and Language.", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/papers/universal-transformers-31xhb4n7be", "content": "Universal Transformers . Mostafa Dehghani , Stephan Gouws, Oriol Vinyals, Jakob Uszkoreit, Łukasz Kaiser +4 moreUniversity of Amsterdam, Google. - 10 Jul 2018. - arXiv: Computation and Language."}
+{"idx": 3, "title": "Long Range Arena: A Benchmark for Efficient Transformers", "date": "", "ddg_snippet": "Abstract . Transformers do not scale very well to long sequence lengths largely because of quadratic self-attention complexity. Universal Language Model Fine-tuning for Text Classification. Conference Paper. Full - text available.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/345654637_Long_Range_Arena_A_Benchmark_for_Efficient_Transformers", "content": "Abstract . Transformers do not scale very well to long sequence lengths largely because of quadratic self-attention complexity. Universal Language Model Fine-tuning for Text Classification. Conference Paper. Full - text available."}
+{"idx": 4, "title": "MostafaDehghani ( Mostafa Dehghani ) · GitHub", "date": "", "ddg_snippet": "Mostafa Dehghani MostafaDehghani. Follow. Research Scientist at Google Brain.Long Range Arena for Benchmarking Efficient Transformers .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/MostafaDehghani", "content": "Mostafa Dehghani MostafaDehghani. Follow. Research Scientist at Google Brain.Long Range Arena for Benchmarking Efficient Transformers ."}
+{"idx": 5, "title": "Mostafa Dehghani on Twitter: \"We have released the JAX...\"", "date": "", "ddg_snippet": "...of Universal Transformers (https://arxiv.org/abs/1807.03819) with adaptive halting in #Scenic (along with a Vision Transformer with token/example level halting mechanism): https://github.com/google-research/scenic/tree/main/scenic/projects/baselines/ universal _ transformer …", "subpage_snippet": "", "source": "twitter.com", "link": "https://twitter.com/m__dehghani/status/1564639592808353792", "content": "...of Universal Transformers (https://arxiv.org/abs/1807.03819) with adaptive halting in #Scenic (along with a Vision Transformer with token/example level halting mechanism): https://github.com/google-research/scenic/tree/main/scenic/projects/baselines/ universal _ transformer …"}
+{"idx": 6, "title": "Poster: Universal Transformers by Mostafa Dehghani et al.", "date": "", "ddg_snippet": "Universal Transformers . Mostafa Dehghani , Stephan Gouws, Oriol Vinyals, Jakob Uszkoreit, Lukasz Kaiser. ICLR 2019 .", "subpage_snippet": "", "source": "postersession.ai", "link": "https://postersession.ai/poster/universal-transformers/", "content": "Universal Transformers . Mostafa Dehghani , Stephan Gouws, Oriol Vinyals, Jakob Uszkoreit, Lukasz Kaiser. ICLR 2019 ."}
+{"idx": 7, "title": "Efficient Transformers : A Survey - ADS", "date": "", "ddg_snippet": "; Dehghani , Mostafa . Abstract . Transformer model architectures have garnered immense interest lately due to their effectiveness across a range of domains like language, vision and reinforcement learning.", "subpage_snippet": "", "source": "ui.adsabs.harvard.edu", "link": "https://ui.adsabs.harvard.edu/abs/2020arXiv200906732T/abstract", "content": "; Dehghani , Mostafa . Abstract . Transformer model architectures have garnered immense interest lately due to their effectiveness across a range of domains like language, vision and reinforcement learning."}
+{"idx": 8, "title": "With Universal Transformer , translation will go no harm! - Moment For...", "date": "", "ddg_snippet": "In Universal Transformer , we extend standard Transformer to a computationally general-purpose (Turing-complete) model using a novel and efficient time-parallel loop that produces stronger results on a wider range of tasks.", "subpage_snippet": "", "source": "www.mo4tech.com", "link": "https://www.mo4tech.com/with-universal-transformer-translation-will-go-no-harm.html", "content": "In Universal Transformer , we extend standard Transformer to a computationally general-purpose (Turing-complete) model using a novel and efficient time-parallel loop that produces stronger results on a wider range of tasks."}
+{"idx": 9, "title": "Mostafa Dehghani", "date": "", "ddg_snippet": "Mostafa Dehghani . Fantine Huot. Jasper Uijlings.Preview abstract The scaling of Transformers has driven breakthrough capabilities for language models. At present, the largest large language models (LLMs) contain upwards of 100B parameters.", "subpage_snippet": "", "source": "research.google", "link": "https://research.google/people/105644/", "content": "Mostafa Dehghani . Fantine Huot. Jasper Uijlings.Preview abstract The scaling of Transformers has driven breakthrough capabilities for language models. At present, the largest large language models (LLMs) contain upwards of 100B parameters."}
diff --git a/data/sampled_jsons/Unknown-Variance_OLS_Pacchiano_2024_regret_bound_R_polynomial.jsonl b/data/sampled_jsons/Unknown-Variance_OLS_Pacchiano_2024_regret_bound_R_polynomial.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0d64a354b3fc136d9af592430064eaaecf34e9fd
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+++ b/data/sampled_jsons/Unknown-Variance_OLS_Pacchiano_2024_regret_bound_R_polynomial.jsonl
@@ -0,0 +1,4 @@
+{"idx": 0, "title": "Regret minimization in Linear Bandits with offline data via", "date": "", "ddg_snippet": "Finally, we note that all these works have focused solely on regret upper bounds (except for MABs in Cheung & Lyu ( 2024 ) ), with a notable ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.08420v1", "content": "Finally, we note that all these works have focused solely on regret upper bounds (except for MABs in Cheung & Lyu ( 2024 ) ), with a notable ..."}
+{"idx": 1, "title": "Catoni Contextual Bandits are Robust to Heavy-tailed Rewards", "date": "", "ddg_snippet": "OLS ( Pacchiano , 2024 ) Catoni-OFUL (Theorem 3.4). Function Type Linear.The algorithm enjoys a variance -based regret bound with only polynomial dependence on R . When the per-round variance is unknown , our proposed variance -agnostic Catoni.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=5IpVe9PH14", "content": "OLS ( Pacchiano , 2024 ) Catoni-OFUL (Theorem 3.4). Function Type Linear.The algorithm enjoys a variance -based regret bound with only polynomial dependence on R . When the per-round variance is unknown , our proposed variance -agnostic Catoni."}
+{"idx": 2, "title": "Automatic Reward Shaping from Confounded Offline Data", "date": "", "ddg_snippet": "1. The final expected regret bound can be built upon this bounded Q-value difference via a gap-dependent decom- position. We can then bridge this gap dependent regret de- composition with the difference between learned Q-values 6 Automatic Reward Shaping from Confounded Offline Data and optimal Q-values on non-optimal actions based on the fact ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2505.11478v2", "content": "1. The final expected regret bound can be built upon this bounded Q-value difference via a gap-dependent decom- position. We can then bridge this gap dependent regret de- composition with the difference between learned Q-values 6 Automatic Reward Shaping from Confounded Offline Data and optimal Q-values on non-optimal actions based on the fact ..."}
+{"idx": 3, "title": "Catoni Contextual Bandits are Robust to Heavy-tailed ...", "date": "", "ddg_snippet": "by C Ye · 2025 · Cited by 1 — Unknown-Variance OLS . ( Pacchiano , 2024 ). Non ... The algorithm enjoys a variance-based regret bound with only polynomial dependence on R .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.02486?", "content": "by C Ye · 2025 · Cited by 1 — Unknown-Variance OLS . ( Pacchiano , 2024 ). Non ... The algorithm enjoys a variance-based regret bound with only polynomial dependence on R ."}
diff --git a/data/sampled_jsons/Upweighting_Easy_Samples_in_Fine-Tuning_Mitigates_Forgetting_Table_1_ResNet-50_average_accuracy.jsonl b/data/sampled_jsons/Upweighting_Easy_Samples_in_Fine-Tuning_Mitigates_Forgetting_Table_1_ResNet-50_average_accuracy.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..38b66e14f10625708244910ce677f8fc29760a4f
--- /dev/null
+++ b/data/sampled_jsons/Upweighting_Easy_Samples_in_Fine-Tuning_Mitigates_Forgetting_Table_1_ResNet-50_average_accuracy.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Upweighting Easy Samples in Fine-Tuning Mitigates ...", "date": "", "ddg_snippet": "by S Sanyal · Cited by 2 — As an example , when fine - tuning Gemma 2 2B on MetaMathQA, our method results in only a 0.8 % drop in accuracy on GSM8K (another math dataset) ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=13HPTmZKbM", "content": "by S Sanyal · Cited by 2 — As an example , when fine - tuning Gemma 2 2B on MetaMathQA, our method results in only a 0.8 % drop in accuracy on GSM8K (another math dataset) ..."}
+{"idx": 1, "title": "Upweighting Easy Samples in Fine-Tuning Mitigates ...", "date": "", "ddg_snippet": "by S Sanyal · 2025 · Cited by 2 — The pre-trained. ResNet - 50 model achieves a top- 1 accuracy of 79.02% on. ImageNet-1K's validation set. Standard fine - tuning experi- ences a ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.02797", "content": "by S Sanyal · 2025 · Cited by 2 — The pre-trained. ResNet - 50 model achieves a top- 1 accuracy of 79.02% on. ImageNet-1K's validation set. Standard fine - tuning experi- ences a ..."}
+{"idx": 2, "title": "Upweighting Easy Samples in Fine-Tuning Mitigates ...", "date": "", "ddg_snippet": "8% drop in accuracy on GSM8K (another math dataset) compared to standard fine - tuning , while preserving 5.4% more accuracy on the pre-training datasets. 1 .", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46655", "content": "8% drop in accuracy on GSM8K (another math dataset) compared to standard fine - tuning , while preserving 5.4% more accuracy on the pre-training datasets. 1 ."}
+{"idx": 3, "title": "Upweighting Easy Samples in Fine-Tuning Mitigates ...", "date": "", "ddg_snippet": "The pre-trained ResNet - 50 model achieves a top- 1 accuracy of 79.02% on ImageNet-1K's validation set. Standard fine - tuning experiences a significant 42.11% drop ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.02797v1", "content": "The pre-trained ResNet - 50 model achieves a top- 1 accuracy of 79.02% on ImageNet-1K's validation set. Standard fine - tuning experiences a significant 42.11% drop ..."}
+{"idx": 4, "title": "Upweighting Easy Samples in Fine-Tuning Mitigates ...", "date": "", "ddg_snippet": "... ResNet - 50 model pre-trained on ImageNet-1K (from Table 1 ). FLOW achieves the best average accuracy (between pre-training and target fine - tuning accuracies).", "subpage_snippet": "", "source": "chatpaper.com", "link": "https://chatpaper.com/paper/165077", "content": "... ResNet - 50 model pre-trained on ImageNet-1K (from Table 1 ). FLOW achieves the best average accuracy (between pre-training and target fine - tuning accuracies)."}
+{"idx": 5, "title": "Overcoming Catastrophic Forgetting With Unlabeled Data ...", "date": "", "ddg_snippet": "by K Lee · 2019 · Cited by 307 — (a,b) Arrows show the performance gain in the average incremental accuracy (ACC) and average forgetting (FGT) by learning with unlabeled data, respectively.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content_ICCV_2019/papers/Lee_Overcoming_Catastrophic_Forgetting_With_Unlabeled_Data_in_the_Wild_ICCV_2019_paper.pdf", "content": "by K Lee · 2019 · Cited by 307 — (a,b) Arrows show the performance gain in the average incremental accuracy (ACC) and average forgetting (FGT) by learning with unlabeled data, respectively."}
+{"idx": 6, "title": "Avoiding Forgetting in the Presence of Spurious Correlations", "date": "", "ddg_snippet": "We conducted experiments on three bench- marks and achieved a notable improvement in average and worst-group accuracy , with our results even sometimes sur-. 11 pages", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/WACV2025/papers/Capitani_Towards_Unbiased_Continual_Learning_Avoiding_Forgetting_in_the_Presence_of_WACV_2025_paper.pdf", "content": "We conducted experiments on three bench- marks and achieved a notable improvement in average and worst-group accuracy , with our results even sometimes sur-. 11 pages"}
+{"idx": 7, "title": "Adaptive online continual multi-view learning", "date": "", "ddg_snippet": "The results on Office-31 Split are reported in Table 1 , which are obtained with pre-trained ResNet - 50 . ... average accuracy and 6.2% on average forgetting , which ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S1566253523003366/pdf", "content": "The results on Office-31 Split are reported in Table 1 , which are obtained with pre-trained ResNet - 50 . ... average accuracy and 6.2% on average forgetting , which ..."}
+{"idx": 8, "title": "Transferring Pretrained Networks to Small Data via ... - BMVC 2020", "date": "", "ddg_snippet": "... forgetting in fine - tuning ... Table 1 : Comparison of different fine - tuning methods. ... Table 2: Comparison of Accuracy ( ResNet - 50 ) of Different Sampling Rate and ...", "subpage_snippet": "", "source": "www.bmvc2020-conference.com", "link": "https://www.bmvc2020-conference.com/assets/papers/0090.pdf", "content": "... forgetting in fine - tuning ... Table 1 : Comparison of different fine - tuning methods. ... Table 2: Comparison of Accuracy ( ResNet - 50 ) of Different Sampling Rate and ..."}
+{"idx": 9, "title": "DDGR: Continual Learning with Deep Diffusion-based ...", "date": "", "ddg_snippet": "by R Gao · 2023 · Cited by 102 — We gather the final average accuracies and average forget - ting rates of all experiments in the CI scenario in Table 1 ; for convenience, we refer to them as ... 20 pages", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v202/gao23e/gao23e.pdf", "content": "by R Gao · 2023 · Cited by 102 — We gather the final average accuracies and average forget - ting rates of all experiments in the CI scenario in Table 1 ; for convenience, we refer to them as ... 20 pages"}
diff --git a/data/sampled_jsons/VRSBench_512x512_image_size_Li_2024.jsonl b/data/sampled_jsons/VRSBench_512x512_image_size_Li_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a67880d221086a853ef90c045a9f42c2e97ba296
--- /dev/null
+++ b/data/sampled_jsons/VRSBench_512x512_image_size_Li_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "VRSBench: A Versatile Vision-Language Benchmark Dataset for Remote ...", "date": "", "ddg_snippet": "Exploring these improvement opportunities, we present a Versatile vision-language Benchmark for Remote Sensing image understanding, termed VRSBench . This benchmark comprises 29,614 images , with 29,614 human-verified detailed captions, 52,472 object references, and 123,221 question-answer pairs.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2406.12384", "content": "Exploring these improvement opportunities, we present a Versatile vision-language Benchmark for Remote Sensing image understanding, termed VRSBench . This benchmark comprises 29,614 images , with 29,614 human-verified detailed captions, 52,472 object references, and 123,221 question-answer pairs."}
+{"idx": 1, "title": "GitHub - lx709/VRSBench", "date": "", "ddg_snippet": "VRSBench VRSBench is a Versatile Vision-Language Benchmark for Remote Sensing Image Understanding. It consists of 29,614 remote sensing images with detailed captions, 52,472 object refers, and 3123,221 visual question-answer pairs.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/lx709/VRSBench", "content": "VRSBench VRSBench is a Versatile Vision-Language Benchmark for Remote Sensing Image Understanding. It consists of 29,614 remote sensing images with detailed captions, 52,472 object refers, and 3123,221 visual question-answer pairs."}
+{"idx": 2, "title": "VRSBench:", "date": "", "ddg_snippet": "Exploring these improvement opportunities, we present a V ersatile vision-language Bench mark for R emote S ensing image understanding, termed VRSBench . This benchmark comprises 29,614 images , with 29,614 human-verified detailed captions, 52,472 object references, and 123,221 question-answer pairs.", "subpage_snippet": "", "source": "vrsbench.github.io", "link": "https://vrsbench.github.io/", "content": "Exploring these improvement opportunities, we present a V ersatile vision-language Bench mark for R emote S ensing image understanding, termed VRSBench . This benchmark comprises 29,614 images , with 29,614 human-verified detailed captions, 52,472 object references, and 123,221 question-answer pairs."}
+{"idx": 3, "title": "VRSBench | Proceedings of the 38th International Conference on Neural ...", "date": "", "ddg_snippet": "Exploring these improvement opportunities, we present a V ersatile vision-language Bench mark for R emote Sensing image understanding, termed VRSBench . This benchmark comprises 29,614 images , with 29,614 human-verified detailed captions, 52,472 object references, and 123,221 question-answer pairs.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3737916.3738022", "content": "Exploring these improvement opportunities, we present a V ersatile vision-language Bench mark for R emote Sensing image understanding, termed VRSBench . This benchmark comprises 29,614 images , with 29,614 human-verified detailed captions, 52,472 object references, and 123,221 question-answer pairs."}
+{"idx": 4, "title": "VRSBench", "date": "", "ddg_snippet": "Paper: Li , Xiang, Jian Ding, and Mohamed Elhoseiny. \" Vrsbench : A versatile vision-language benchmark dataset for remote sensing image understanding.\" arXiv preprint arXiv:2406.12384 ( 2024 ).", "subpage_snippet": "", "source": "www.eod-grss-ieee.com", "link": "https://www.eod-grss-ieee.com/dataset-detail/UjNWY0FMSStDYU5MTHlJNVE5bXllQT09", "content": "Paper: Li , Xiang, Jian Ding, and Mohamed Elhoseiny. \" Vrsbench : A versatile vision-language benchmark dataset for remote sensing image understanding.\" arXiv preprint arXiv:2406.12384 ( 2024 )."}
+{"idx": 5, "title": "PDF Supplementary of VRSBench: A Versatile Benchmark for Vision Language ...", "date": "", "ddg_snippet": "1.1 Overview VRSBench consists of 29,614 remote sensing images with detailed captions, 52,472 object refers, 123,221 visual question-answer pairs. VRSBench is designed to facilitate the development and evaluation of vision-language models in remote sensing, providing a comprehensive set of annotations including detailed captions, visual grounding, and visual question answering. This section ...", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper_files/paper/2024/file/05b7f821234f66b78f99e7803fffa78a-Supplemental-Datasets_and_Benchmarks_Track.pdf", "content": "1.1 Overview VRSBench consists of 29,614 remote sensing images with detailed captions, 52,472 object refers, 123,221 visual question-answer pairs. VRSBench is designed to facilitate the development and evaluation of vision-language models in remote sensing, providing a comprehensive set of annotations including detailed captions, visual grounding, and visual question answering. This section ..."}
+{"idx": 6, "title": "(PDF) VRSBench: A Versatile Vision-Language Benchmark Dataset for ...", "date": "", "ddg_snippet": "Exploring these improvement opportunities, we present a Versatile vision-language Benchmark for Remote Sensing image understanding, termed VRSBench .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/381518535_VRSBench_A_Versatile_Vision-Language_Benchmark_Dataset_for_Remote_Sensing_Image_Understanding", "content": "Exploring these improvement opportunities, we present a Versatile vision-language Benchmark for Remote Sensing image understanding, termed VRSBench ."}
+{"idx": 7, "title": "Neurlps2024论文解析|VRSBench A Versatile Vision ... - CSDN博客", "date": "", "ddg_snippet": "论文标题 VRSBench : A Versatile Vision -Language Benchmark Dataset for Remote Sensing Image Understanding VRSBench : 用于遥感图像理解的多功能视觉-语言基准数据集 论文链接 VRSBench : A Versatile Vision-Language Benchmark Dataset for Remote Sensing Image Understanding论文下载 论文作者 Xiang Li , Jian Ding, Mohamed Elhoseiny 内容简介 本文介绍了一种新的 ...", "subpage_snippet": "", "source": "blog.csdn.net", "link": "https://blog.csdn.net/SJ_HP/article/details/145682696", "content": "论文标题 VRSBench : A Versatile Vision -Language Benchmark Dataset for Remote Sensing Image Understanding VRSBench : 用于遥感图像理解的多功能视觉-语言基准数据集 论文链接 VRSBench : A Versatile Vision-Language Benchmark Dataset for Remote Sensing Image Understanding论文下载 论文作者 Xiang Li , Jian Ding, Mohamed Elhoseiny 内容简介 本文介绍了一种新的 ..."}
+{"idx": 8, "title": "VRSBench/README.md at main · lx709/VRSBench · GitHub", "date": "", "ddg_snippet": "VRSBench is a Versatile Vision-Language Benchmark for Remote Sensing Image Understanding. It consists of 29,614 remote sensing images with detailed captions, 52,472 object refers, and 3123,221 visual question-answer pairs.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/lx709/VRSBench/blob/main/README.md", "content": "VRSBench is a Versatile Vision-Language Benchmark for Remote Sensing Image Understanding. It consists of 29,614 remote sensing images with detailed captions, 52,472 object refers, and 3123,221 visual question-answer pairs."}
+{"idx": 9, "title": "Abstract - arXiv.org", "date": "", "ddg_snippet": "Based on the semi-automatic data collection pipeline, we collect VRSBench dataset that provides detailed image captioning, visual grounding, and visual question-answer labels in a unified dataset, and therefore, enables a comprehensive evaluation of multiple vision-languages capabilities based on this dataset.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2406.12384", "content": "Based on the semi-automatic data collection pipeline, we collect VRSBench dataset that provides detailed image captioning, visual grounding, and visual question-answer labels in a unified dataset, and therefore, enables a comprehensive evaluation of multiple vision-languages capabilities based on this dataset."}
diff --git a/data/sampled_jsons/VRSBench_Li_et_al._image_resolution_pixels_size.jsonl b/data/sampled_jsons/VRSBench_Li_et_al._image_resolution_pixels_size.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..875af2a39d1705b92ecbb146f9502792f3dea1e4
--- /dev/null
+++ b/data/sampled_jsons/VRSBench_Li_et_al._image_resolution_pixels_size.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "VRSBench: A Versatile Vision-Language Benchmark Dataset for Remote ...", "date": "", "ddg_snippet": "Exploring these improvement opportunities, we present a Versatile vision-language Benchmark for Remote Sensing image understanding, termed VRSBench . This benchmark comprises 29,614 images , with 29,614 human-verified detailed captions, 52,472 object references, and 123,221 question-answer pairs.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2406.12384", "content": "Exploring these improvement opportunities, we present a Versatile vision-language Benchmark for Remote Sensing image understanding, termed VRSBench . This benchmark comprises 29,614 images , with 29,614 human-verified detailed captions, 52,472 object references, and 123,221 question-answer pairs."}
+{"idx": 1, "title": "GitHub - lx709/VRSBench", "date": "", "ddg_snippet": "To construct our VRSBench dataset, we employed multiple data engineering steps, including attribute extraction, prompting engineering, GPT-4 inference, and human verification. Attribute Extraction: we extract image information, including the source and resolution , as well as object information—such as the object category, bounding box, color, position (absolute and relative), and size ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/lx709/VRSBench", "content": "To construct our VRSBench dataset, we employed multiple data engineering steps, including attribute extraction, prompting engineering, GPT-4 inference, and human verification. Attribute Extraction: we extract image information, including the source and resolution , as well as object information—such as the object category, bounding box, color, position (absolute and relative), and size ..."}
+{"idx": 2, "title": "VRSBench:", "date": "", "ddg_snippet": "Exploring these improvement opportunities, we present a V ersatile vision-language Bench mark for R emote S ensing image understanding, termed VRSBench . This benchmark comprises 29,614 images , with 29,614 human-verified detailed captions, 52,472 object references, and 123,221 question-answer pairs.", "subpage_snippet": "", "source": "vrsbench.github.io", "link": "https://vrsbench.github.io/", "content": "Exploring these improvement opportunities, we present a V ersatile vision-language Bench mark for R emote S ensing image understanding, termed VRSBench . This benchmark comprises 29,614 images , with 29,614 human-verified detailed captions, 52,472 object references, and 123,221 question-answer pairs."}
+{"idx": 3, "title": "VRSBench | Proceedings of the 38th International Conference on Neural ...", "date": "", "ddg_snippet": "Exploring these improvement opportunities, we present a V ersatile vision-language Bench mark for R emote Sensing image understanding, termed VRSBench . This benchmark comprises 29,614 images , with 29,614 human-verified detailed captions, 52,472 object references, and 123,221 question-answer pairs.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3737916.3738022", "content": "Exploring these improvement opportunities, we present a V ersatile vision-language Bench mark for R emote Sensing image understanding, termed VRSBench . This benchmark comprises 29,614 images , with 29,614 human-verified detailed captions, 52,472 object references, and 123,221 question-answer pairs."}
+{"idx": 4, "title": "PDF Supplementary of VRSBench: A Versatile Benchmark for Vision Language ...", "date": "", "ddg_snippet": "1.1 Overview VRSBench consists of 29,614 remote sensing images with detailed captions, 52,472 object refers, 123,221 visual question-answer pairs. VRSBench is designed to facilitate the development and evaluation of vision-language models in remote sensing, providing a comprehensive set of annotations including detailed captions, visual grounding, and visual question answering. This section ...", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper_files/paper/2024/file/05b7f821234f66b78f99e7803fffa78a-Supplemental-Datasets_and_Benchmarks_Track.pdf", "content": "1.1 Overview VRSBench consists of 29,614 remote sensing images with detailed captions, 52,472 object refers, 123,221 visual question-answer pairs. VRSBench is designed to facilitate the development and evaluation of vision-language models in remote sensing, providing a comprehensive set of annotations including detailed captions, visual grounding, and visual question answering. This section ..."}
+{"idx": 5, "title": "[PDF] VRSBench: A Versatile Vision-Language Benchmark Dataset for ...", "date": "", "ddg_snippet": "A new benchmark designed to advance the development of general-purpose, large-scale vision-language models for remote sensing images , and evaluated state-of-the-art models on this benchmark for three vision-language tasks: image captioning, visual grounding, and visual question answering. We introduce a new benchmark designed to advance the development of general-purpose, large-scale vision ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/VRSBench:-A-Versatile-Vision-Language-Benchmark-for-Li-Ding/c28110e1d66ee586f611bea5c331920bdacc5d4b", "content": "A new benchmark designed to advance the development of general-purpose, large-scale vision-language models for remote sensing images , and evaluated state-of-the-art models on this benchmark for three vision-language tasks: image captioning, visual grounding, and visual question answering. We introduce a new benchmark designed to advance the development of general-purpose, large-scale vision ..."}
+{"idx": 6, "title": "(PDF) VRSBench: A Versatile Vision-Language Benchmark Dataset for ...", "date": "", "ddg_snippet": "Exploring these improvement opportunities, we present a Versatile vision-language Benchmark for Remote Sensing image understanding, termed VRSBench .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/381518535_VRSBench_A_Versatile_Vision-Language_Benchmark_Dataset_for_Remote_Sensing_Image_Understanding", "content": "Exploring these improvement opportunities, we present a Versatile vision-language Benchmark for Remote Sensing image understanding, termed VRSBench ."}
+{"idx": 7, "title": "PDF VRSBench: A Versatile Vision-Language - neurips.cc", "date": "", "ddg_snippet": "VRSBench -Cap: This challenge requires the prediction of a comprehensive description for a given remote sensing image , encapsulating intricate details and contextual relevance.", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/media/neurips-2024/Slides/97530.pdf", "content": "VRSBench -Cap: This challenge requires the prediction of a comprehensive description for a given remote sensing image , encapsulating intricate details and contextual relevance."}
+{"idx": 8, "title": "xiang709/VRSBench · Datasets at Hugging Face", "date": "", "ddg_snippet": "We're on a journey to advance and democratize artificial intelligence through open source and open science.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/xiang709/VRSBench", "content": "We're on a journey to advance and democratize artificial intelligence through open source and open science."}
+{"idx": 9, "title": "Abstract - arXiv.org", "date": "", "ddg_snippet": "Based on the semi-automatic data collection pipeline, we collect VRSBench dataset that provides detailed image captioning, visual grounding, and visual question-answer labels in a unified dataset, and therefore, enables a comprehensive evaluation of multiple vision-languages capabilities based on this dataset.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2406.12384", "content": "Based on the semi-automatic data collection pipeline, we collect VRSBench dataset that provides detailed image captioning, visual grounding, and visual question-answer labels in a unified dataset, and therefore, enables a comprehensive evaluation of multiple vision-languages capabilities based on this dataset."}
diff --git a/data/sampled_jsons/VRSBench_image_size_resolution_dimensions_Li_Ding_Elhoseiny_2024_year_2024.jsonl b/data/sampled_jsons/VRSBench_image_size_resolution_dimensions_Li_Ding_Elhoseiny_2024_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..aa6f999144cb3571e7782e114844423ed9ee0e7f
--- /dev/null
+++ b/data/sampled_jsons/VRSBench_image_size_resolution_dimensions_Li_Ding_Elhoseiny_2024_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2406.12384] VRSBench : A Versatile Vision-Language Benchmark...", "date": "", "ddg_snippet": "View a PDF of the paper titled VRSBench : A Versatile Vision-Language Benchmark Dataset for Remote Sensing Image Understanding, by Xiang Li and 2 other authors.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2406.12384", "content": "View a PDF of the paper titled VRSBench : A Versatile Vision-Language Benchmark Dataset for Remote Sensing Image Understanding, by Xiang Li and 2 other authors."}
+{"idx": 1, "title": "GitHub - lx709/ VRSBench", "date": "", "ddg_snippet": "Solutions. By company size . VRSBench : A Versatile Vision-Language Benchmark Dataset for Remote Sensing Image Understanding. Xiang Li , Jian Ding , Mohamed Elhoseiny .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/lx709/VRSBench", "content": "Solutions. By company size . VRSBench : A Versatile Vision-Language Benchmark Dataset for Remote Sensing Image Understanding. Xiang Li , Jian Ding , Mohamed Elhoseiny ."}
+{"idx": 2, "title": "VRSBench : A Versatile Vision-Language Benchmark Dataset for...", "date": "", "ddg_snippet": "• VRSBench contains a large collection of remote sensing images paired with natural language descriptions, providing a versatile benchmark for tasks like image captioning, visual question answering, and multimodal reasoning.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/vrsbench-versatile-vision-language-benchmark-dataset-remote", "content": "• VRSBench contains a large collection of remote sensing images paired with natural language descriptions, providing a versatile benchmark for tasks like image captioning, visual question answering, and multimodal reasoning."}
+{"idx": 3, "title": "VRSBench : A Versatile Vision-Language Benchmark", "date": "", "ddg_snippet": "VRSBench : A Versatile Vision-Language Benchmark Dataset for Remote Sensing Image Understanding. Xiang Li Jian Ding Mohamed Elhoseiny King Abdullah University of Science and Technology {xiang. li .1,jian. ding ,mohamed. elhoseiny }@kaust.edu.sa. Abstract.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/05b7f821234f66b78f99e7803fffa78a-Paper-Datasets_and_Benchmarks_Track.pdf", "content": "VRSBench : A Versatile Vision-Language Benchmark Dataset for Remote Sensing Image Understanding. Xiang Li Jian Ding Mohamed Elhoseiny King Abdullah University of Science and Technology {xiang. li .1,jian. ding ,mohamed. elhoseiny }@kaust.edu.sa. Abstract."}
+{"idx": 4, "title": "tdujardin/summarized_ VRSBench · Datasets at Hugging Face", "date": "", "ddg_snippet": "Credits: VRSBench : A Versatile Vision-Language Benchmark Dataset for Remote Sensing Image Understanding. Xiang Li , Jian Ding , Mohamed Elhoseiny .", "subpage_snippet": "", "source": "hf.qhduan.com", "link": "https://hf.qhduan.com/datasets/tdujardin/summarized_VRSBench", "content": "Credits: VRSBench : A Versatile Vision-Language Benchmark Dataset for Remote Sensing Image Understanding. Xiang Li , Jian Ding , Mohamed Elhoseiny ."}
+{"idx": 5, "title": "Online Image Resolution Checker", "date": "", "ddg_snippet": "Check image resolution online for free. Instantly analyze DPI, dimensions , and quality of your images . Support for JPG, PNG, TIFF formats. No registration required.", "subpage_snippet": "", "source": "imresizer.com", "link": "https://imresizer.com/check-image-resolution", "content": "Check image resolution online for free. Instantly analyze DPI, dimensions , and quality of your images . Support for JPG, PNG, TIFF formats. No registration required."}
+{"idx": 6, "title": "Image Size Finder - Check Image Width, Height & File Size", "date": "", "ddg_snippet": "Drop your image into our image size finder to instantly see width and height.3See your image 's file size in the field and find width and height in the table below. Frequently Asked Questions. Does this tool show the dimensions of an image ?", "subpage_snippet": "", "source": "imagy.app", "link": "https://imagy.app/image-size-finder/", "content": "Drop your image into our image size finder to instantly see width and height.3See your image 's file size in the field and find width and height in the table below. Frequently Asked Questions. Does this tool show the dimensions of an image ?"}
+{"idx": 7, "title": "Resize Image Pixel Online", "date": "", "ddg_snippet": "Pi7 Image Tool can resize the image pixel. Here, you can resize JPEG and PNG images . The tool also provides lossless compression to images .", "subpage_snippet": "", "source": "image.pi7.org", "link": "https://image.pi7.org/resize-image-pixel", "content": "Pi7 Image Tool can resize the image pixel. Here, you can resize JPEG and PNG images . The tool also provides lossless compression to images ."}
+{"idx": 8, "title": "VRSBench", "date": "", "ddg_snippet": "Xiang Li , Jian Ding , Mohamed Elhoseiny .This benchmark comprises 29,614 images , with 29,614 human-verified detailed captions, 52,472 object references, and 123,221 question-answer pairs.", "subpage_snippet": "", "source": "vrsbench.github.io", "link": "https://vrsbench.github.io/", "content": "Xiang Li , Jian Ding , Mohamed Elhoseiny .This benchmark comprises 29,614 images , with 29,614 human-verified detailed captions, 52,472 object references, and 123,221 question-answer pairs."}
+{"idx": 9, "title": "Image Compressor - Compress Images Online in High Quality", "date": "", "ddg_snippet": "upload images to compress. drag and drop images here.", "subpage_snippet": "", "source": "imageresizer.com", "link": "https://imageresizer.com/image-compressor/editor", "content": "upload images to compress. drag and drop images here."}
diff --git a/data/sampled_jsons/Video-ColBERT_ablation_study_interaction_types_MMSF_MMSV_temporally_contextualized_year_2024.jsonl b/data/sampled_jsons/Video-ColBERT_ablation_study_interaction_types_MMSF_MMSV_temporally_contextualized_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a821a81bb1b2c965c23bc3feafc0c1356b368d82
--- /dev/null
+++ b/data/sampled_jsons/Video-ColBERT_ablation_study_interaction_types_MMSF_MMSV_temporally_contextualized_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF Video-ColBERT: Contextualized Late Interaction for Text-to-Video Retrieval", "date": "", "ddg_snippet": "On top of this modified interaction , VIDEO-COLBERT uses two MMS operations over both indepen-dent visual frame features and contextualized frame features to strengthen the fine-grained spatial and temporal interac-tion (Fig. 1).", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Reddy_Video-ColBERT_Contextualized_Late_Interaction_for_Text-to-Video_Retrieval_CVPR_2025_paper.pdf", "content": "On top of this modified interaction , VIDEO-COLBERT uses two MMS operations over both indepen-dent visual frame features and contextualized frame features to strengthen the fine-grained spatial and temporal interac-tion (Fig. 1)."}
+{"idx": 1, "title": "GitHub - yogesh-iitj/Video-ColBERT", "date": "", "ddg_snippet": "Overview Video-ColBERT introduces a simple and efficient mechanism for fine-grained similarity assessment between text queries and videos through: Dual token-wise interaction - Performs MeanMaxSim (MMS) on both static frame features and temporally contextualized video features", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/yogesh-iitj/Video-ColBERT", "content": "Overview Video-ColBERT introduces a simple and efficient mechanism for fine-grained similarity assessment between text queries and videos through: Dual token-wise interaction - Performs MeanMaxSim (MMS) on both static frame features and temporally contextualized video features"}
+{"idx": 2, "title": "Video-ColBERT: Contextualized Late Interaction for Text-to-Video Retrieval", "date": "", "ddg_snippet": "On top of this modified interaction , Video-ColBERT uses two MMS operations over both independent visual frame features and contextualized frame features to strengthen the fine-grained spatial and temporal interaction (Fig. 1).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.19009v1", "content": "On top of this modified interaction , Video-ColBERT uses two MMS operations over both independent visual frame features and contextualized frame features to strengthen the fine-grained spatial and temporal interaction (Fig. 1)."}
+{"idx": 3, "title": "Video-ColBERT: Contextualized Late Interaction for Text-to-Video ...", "date": "", "ddg_snippet": "In this work, we tackle the problem of text-to- video retrieval (T2VR). Inspired by the success of late interaction techniques in text-document, text-image, and text- video retrieval, our approach, Video-ColBERT , introduces a simple and efficient mechanism for fine-grained similarity assessment between queries and videos . Video-ColBERT is built upon three main components: a fine-grained spatial ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/11094542", "content": "In this work, we tackle the problem of text-to- video retrieval (T2VR). Inspired by the success of late interaction techniques in text-document, text-image, and text- video retrieval, our approach, Video-ColBERT , introduces a simple and efficient mechanism for fine-grained similarity assessment between queries and videos . Video-ColBERT is built upon three main components: a fine-grained spatial ..."}
+{"idx": 4, "title": "Video-ColBERT: Contextualized Late Interaction for Text-to-Video ...", "date": "", "ddg_snippet": "Video-ColBERT is built upon 3 main components: a fine-grained spatial and temporal token-wise interaction , query and visual expansions, and a dual sigmoid loss during training. We find that this interaction and training paradigm leads to strong individual, yet compatible, representations for encoding video content.", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2503.19009", "content": "Video-ColBERT is built upon 3 main components: a fine-grained spatial and temporal token-wise interaction , query and visual expansions, and a dual sigmoid loss during training. We find that this interaction and training paradigm leads to strong individual, yet compatible, representations for encoding video content."}
+{"idx": 5, "title": "Video-ColBERT: Contextualized Late Interaction for Text-to-Video Retrieval", "date": "", "ddg_snippet": "In this work, we tackle the problem of text-to- video retrieval (T2VR). Inspired by the success of late interaction techniques in text-document, text-image, and text- video retrieval, our approach, Video-ColBERT , introduces a simple and efficient mechanism for fine-grained similarity assessment between queries and videos .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.19009", "content": "In this work, we tackle the problem of text-to- video retrieval (T2VR). Inspired by the success of late interaction techniques in text-document, text-image, and text- video retrieval, our approach, Video-ColBERT , introduces a simple and efficient mechanism for fine-grained similarity assessment between queries and videos ."}
+{"idx": 6, "title": "[PDF] Video-ColBERT: Contextualized Late Interaction for Text-to-Video ...", "date": "", "ddg_snippet": "Video-ColBERT introduces a simple and efficient mechanism for fine-grained similarity assessment between queries and videos , and finds that this interaction and training paradigm leads to strong individual, yet compatible, representations for encoding video content. In this work, we tackle the problem of text-to- video retrieval (T2VR). Inspired by the success of late interaction techniques in ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Video-ColBERT:-Contextualized-Late-Interaction-for-Reddy-Martin/bff2f91c763830a2d14dbbbeca150e92ede02323", "content": "Video-ColBERT introduces a simple and efficient mechanism for fine-grained similarity assessment between queries and videos , and finds that this interaction and training paradigm leads to strong individual, yet compatible, representations for encoding video content. In this work, we tackle the problem of text-to- video retrieval (T2VR). Inspired by the success of late interaction techniques in ..."}
+{"idx": 7, "title": "Video-ColBERT: Contextualized Late Interaction for Text-to-Video ...", "date": "", "ddg_snippet": "The introduction of MeanMaxSim (MMS) functions aligns with the need to accommodate variability in query lengths and provides robust scoring functions adaptable to interactions with both static frame features and temporally contextualized video representations.", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/papers/2503.19009", "content": "The introduction of MeanMaxSim (MMS) functions aligns with the need to accommodate variability in query lengths and provides robust scoring functions adaptable to interactions with both static frame features and temporally contextualized video representations."}
+{"idx": 8, "title": "Alexander Martin", "date": "", "ddg_snippet": "Video-ColBERT is built upon three main components: a fine-grained spatial and temporal token-wise interaction , query and visual expan- sions, and a dual sigmoid loss during training. We find that this interaction and training paradigm leads to strong in- dividual, yet compatible representations for encoding video content.", "subpage_snippet": "", "source": "alexmartin1722.github.io", "link": "https://alexmartin1722.github.io/", "content": "Video-ColBERT is built upon three main components: a fine-grained spatial and temporal token-wise interaction , query and visual expan- sions, and a dual sigmoid loss during training. We find that this interaction and training paradigm leads to strong in- dividual, yet compatible representations for encoding video content."}
+{"idx": 9, "title": "CVPR 2025 Open Access Repository", "date": "", "ddg_snippet": "In this work, we tackle the problem of text-to- video retrieval (T2VR). Inspired by the success of late interaction techniques in text-document, text-image, and text- video retrieval, our approach, Video-ColBERT , introduces a simple and efficient mechanism for fine-grained similarity assessment between queries and videos .", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/html/Reddy_Video-ColBERT_Contextualized_Late_Interaction_for_Text-to-Video_Retrieval_CVPR_2025_paper.html", "content": "In this work, we tackle the problem of text-to- video retrieval (T2VR). Inspired by the success of late interaction techniques in text-document, text-image, and text- video retrieval, our approach, Video-ColBERT , introduces a simple and efficient mechanism for fine-grained similarity assessment between queries and videos ."}
diff --git a/data/sampled_jsons/WWW_2024_accepted_papers_list_blockchain_reinforcement_learning.jsonl b/data/sampled_jsons/WWW_2024_accepted_papers_list_blockchain_reinforcement_learning.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b709f96cf8da64ce854eefaeb4e5cf3ac5714f1c
--- /dev/null
+++ b/data/sampled_jsons/WWW_2024_accepted_papers_list_blockchain_reinforcement_learning.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "NDSS Symposium 2024 Accepted Papers", "date": "", "ddg_snippet": "Stay up-to-date with the cutting-edge research presented at NDSS Symposium 2024 through our compilation of accepted papers on network security .", "subpage_snippet": "", "source": "www.ndss-symposium.org", "link": "https://www.ndss-symposium.org/ndss2024/accepted-papers/", "content": "Stay up-to-date with the cutting-edge research presented at NDSS Symposium 2024 through our compilation of accepted papers on network security ."}
+{"idx": 1, "title": "NeurIPS 2024 Papers", "date": "", "ddg_snippet": "Start here, schedule, tutorials, main conference, invited talks, orals, spotlights, papers , paper visualization, competitions, datasets & benchmarks.", "subpage_snippet": "", "source": "nips.cc", "link": "https://nips.cc/virtual/2024/papers.html", "content": "Start here, schedule, tutorials, main conference, invited talks, orals, spotlights, papers , paper visualization, competitions, datasets & benchmarks."}
+{"idx": 2, "title": "SERA 2024 Accepted Paper List", "date": "", "ddg_snippet": "36 Project-based Learning in Software Engineering Education: Integrating Blockchain ... 51 Reinforcement Learning Architecture for Facial Skin Treatment ...", "subpage_snippet": "", "source": "acisinternational.org", "link": "https://acisinternational.org/sera-2024-accepted-paper-list/", "content": "36 Project-based Learning in Software Engineering Education: Integrating Blockchain ... 51 Reinforcement Learning Architecture for Facial Skin Treatment ..."}
+{"idx": 3, "title": "ACM CCS 2024", "date": "", "ddg_snippet": "First Cycle ; Lutris: A Blockchain Combining Broadcast and Consensus, Sam Blackshear (MystenLabs) Andrey Chursin (MystenLabs) George Danezis (MystenLabs & ...", "subpage_snippet": "", "source": "www.sigsac.org", "link": "https://www.sigsac.org/ccs/CCS2024/program/accepted-papers.html", "content": "First Cycle ; Lutris: A Blockchain Combining Broadcast and Consensus, Sam Blackshear (MystenLabs) Andrey Chursin (MystenLabs) George Danezis (MystenLabs & ..."}
+{"idx": 4, "title": "Main Track Accepted Papers", "date": "", "ddg_snippet": "Main Track Accepted Papers ; 68. Minimizing Weighted Counterfactual Regret with Optimistic Online Mirror Descent. Hang Xu, Kai Li, Bingyun Liu, Haobo Fu, Qiang ...", "subpage_snippet": "", "source": "ijcai24.org", "link": "https://ijcai24.org/main-track-accepted-papers/index.html", "content": "Main Track Accepted Papers ; 68. Minimizing Weighted Counterfactual Regret with Optimistic Online Mirror Descent. Hang Xu, Kai Li, Bingyun Liu, Haobo Fu, Qiang ..."}
+{"idx": 5, "title": "Accepted Paper List [Main Conference]", "date": "", "ddg_snippet": "List of Accepted Papers in IEEE INFOCOM 2024 Main Conference. 5G-WAVE: A Core Network Framework with Decentralized Authorization for Network Slices", "subpage_snippet": "", "source": "infocom2024.ieee-infocom.org", "link": "https://infocom2024.ieee-infocom.org/program/accepted-paper-list-main-conference", "content": "List of Accepted Papers in IEEE INFOCOM 2024 Main Conference. 5G-WAVE: A Core Network Framework with Decentralized Authorization for Network Slices"}
+{"idx": 6, "title": "Survey on Strategic Mining in Blockchain: A Reinforcement ...", "date": "", "ddg_snippet": "This survey highlights the potential of reinforce - ment learning to address the challenges of self- ish mining, including protocol design, threat detec- tion, ...", "subpage_snippet": "", "source": "www.ijcai.org", "link": "https://www.ijcai.org/proceedings/2025/1170.pdf", "content": "This survey highlights the potential of reinforce - ment learning to address the challenges of self- ish mining, including protocol design, threat detec- tion, ..."}
+{"idx": 7, "title": "LCN 2024 List of Accepted Papers", "date": "", "ddg_snippet": "Intelligent Hopping Mechanism for Deception Defense Scenarios Based on. Reinforcement Learning ... Blockchain -Inspired Incentive Mechanism for Trust-Aware ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/iel8/10639609/10639616/10639678.pdf", "content": "Intelligent Hopping Mechanism for Deception Defense Scenarios Based on. Reinforcement Learning ... Blockchain -Inspired Incentive Mechanism for Trust-Aware ..."}
+{"idx": 8, "title": "Academic Blockchain Conference Papers", "date": "", "ddg_snippet": "A curated list of blockchain -related academic papers . All papers are sorted based on the conference name and published year.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/jianyu-niu/blockchain_conference_paper", "content": "A curated list of blockchain -related academic papers . All papers are sorted based on the conference name and published year."}
+{"idx": 9, "title": "Blockchain-based crowdsourced deep reinforcement ...", "date": "", "ddg_snippet": "by A Alagha · 2024 · Cited by 6 — This paper proposes a novel blockchain -based crowdsourced DRL as a Service (DRLaaS) framework. The framework provides DRL-related services to users.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0020025524010211", "content": "by A Alagha · 2024 · Cited by 6 — This paper proposes a novel blockchain -based crowdsourced DRL as a Service (DRLaaS) framework. The framework provides DRL-related services to users."}
diff --git a/data/sampled_jsons/WWW_2024_proceedings_accepted_papers_blockchain_OR_reinforcement_learning_year_2024.jsonl b/data/sampled_jsons/WWW_2024_proceedings_accepted_papers_blockchain_OR_reinforcement_learning_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..197e50350a8a03eb6cb42e5cd211680e9e408ec7
--- /dev/null
+++ b/data/sampled_jsons/WWW_2024_proceedings_accepted_papers_blockchain_OR_reinforcement_learning_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Call for Papers | MBD 2024", "date": "", "ddg_snippet": "... for MBD2024 [and author(s) fees] must be received before November 11th, 2024 (which is compulsory for at least one author per paper ) in order for the ...", "subpage_snippet": "", "source": "2024.museumbigdata.org", "link": "https://2024.museumbigdata.org/call-for-papers/", "content": "... for MBD2024 [and author(s) fees] must be received before November 11th, 2024 (which is compulsory for at least one author per paper ) in order for the ..."}
+{"idx": 1, "title": "2025 4th International Conference on Bigdata Blockchain and", "date": "", "ddg_snippet": "After a careful reviewing process, all accepted papers of ICBBEM 2025 will be published in conference proceedings by Atlentis Press AISR-Advances in ...", "subpage_snippet": "", "source": "www.icbbem.com", "link": "https://www.icbbem.com/publication", "content": "After a careful reviewing process, all accepted papers of ICBBEM 2025 will be published in conference proceedings by Atlentis Press AISR-Advances in ..."}
+{"idx": 2, "title": "AIMS 2024 — Services Society (S2)", "date": "", "ddg_snippet": "International Conference on AI and Multimodal Services (AIMS 2024 ) https:// www .servicessociety.org/aims , November 16 - 19, 2024 , Bangkok, Thailand", "subpage_snippet": "", "source": "www.servicessociety.org", "link": "https://www.servicessociety.org/aims", "content": "International Conference on AI and Multimodal Services (AIMS 2024 ) https:// www .servicessociety.org/aims , November 16 - 19, 2024 , Bangkok, Thailand"}
+{"idx": 3, "title": "International World Wide Web Conference 2024 ( WWW2024 | The", "date": "", "ddg_snippet": "Abstracts and papers can be submitted through the OpenReview link: https://openreview.net/group?id=ACM.org/TheWebConf/ 2024 /Conference .", "subpage_snippet": "", "source": "www2024.thewebconf.org", "link": "https://www2024.thewebconf.org/calls/research-tracks/", "content": "Abstracts and papers can be submitted through the OpenReview link: https://openreview.net/group?id=ACM.org/TheWebConf/ 2024 /Conference ."}
+{"idx": 4, "title": "2024 7th International Conference on Blockchain Technology ...", "date": "", "ddg_snippet": "2024 The 13th International Conference on Networks, Communication and Computing (ICNCC 2024 ) Proceedings : Accepted and presented papers will be ...", "subpage_snippet": "", "source": "brownwalker.com", "link": "https://brownwalker.com/event/262024", "content": "2024 The 13th International Conference on Networks, Communication and Computing (ICNCC 2024 ) Proceedings : Accepted and presented papers will be ..."}
+{"idx": 5, "title": "2024 6th Blockchain and Internet of Things Conference (BIOTC", "date": "", "ddg_snippet": "Accepted papers will be published in the International Conference Proceedings Series and indexed by Ei Compendex and Scopus.", "subpage_snippet": "", "source": "worldconferencecalendar.com", "link": "https://worldconferencecalendar.com/component/option,com_conference/page,show_ad/adid,69265/catid,6/Itemid,26/", "content": "Accepted papers will be published in the International Conference Proceedings Series and indexed by Ei Compendex and Scopus."}
+{"idx": 6, "title": "ALGOCLOUD 2024 – ALGO2024", "date": "", "ddg_snippet": "Accepted papers will be included in the post- proceedings published by Springer in its Lecture Notes in Computer Science series.", "subpage_snippet": "", "source": "algo-conference.org", "link": "https://algo-conference.org/2024/algocloud/", "content": "Accepted papers will be included in the post- proceedings published by Springer in its Lecture Notes in Computer Science series."}
+{"idx": 7, "title": "METAVERSE 2025 — Services Society (S2)", "date": "", "ddg_snippet": "The METAVERSE 2025 tracks seek original, UNPUBLISHED research papers reporting substantive new work in various aspects of vertical industry services ...", "subpage_snippet": "", "source": "www.servicessociety.org", "link": "https://www.servicessociety.org/metaverse", "content": "The METAVERSE 2025 tracks seek original, UNPUBLISHED research papers reporting substantive new work in various aspects of vertical industry services ..."}
+{"idx": 8, "title": "2025 5th International Conference on Computer Science and", "date": "", "ddg_snippet": "All papers , both invited and contributed, will be reviewed by two or three expert reviewers from the conference committees.", "subpage_snippet": "", "source": "www.iccsb.net", "link": "https://www.iccsb.net/", "content": "All papers , both invited and contributed, will be reviewed by two or three expert reviewers from the conference committees."}
+{"idx": 9, "title": "Research Tracks", "date": "", "ddg_snippet": "Failure to register or present the paper may result in the removal of an accepted paper from the conference and its proceedings .", "subpage_snippet": "", "source": "www2025.thewebconf.org", "link": "https://www2025.thewebconf.org/research-tracks", "content": "Failure to register or present the paper may result in the removal of an accepted paper from the conference and its proceedings ."}
diff --git a/data/sampled_jsons/WWW_2024_program_committee_chairs_year_2024.jsonl b/data/sampled_jsons/WWW_2024_program_committee_chairs_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..472fbb3e8a1fd2fa4630544849d90bc02fa7118f
--- /dev/null
+++ b/data/sampled_jsons/WWW_2024_program_committee_chairs_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "2024 Program Committee - NeurIPS", "date": "", "ddg_snippet": "All Senior Area Chairs Aaron Courville Adel Bibi Adina Williams Ahmad Beirami Aida Nematzadeh Aleksandrs Slivkins Alexander C. Berg Alice Oh Alon Cohen Amit Roy-Chowdhury Andrej Risteski Andrew Gordon Wilson Anima Anandkumar Ankit Singh Rawat Anna Rumshisky Arindam Banerjee Aryan Mokhtari Ashish Kapoor Asma Ghandeharioun Aurelien Lucchi ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/Conferences/2024/ProgramCommittee", "content": "All Senior Area Chairs Aaron Courville Adel Bibi Adina Williams Ahmad Beirami Aida Nematzadeh Aleksandrs Slivkins Alexander C. Berg Alice Oh Alon Cohen Amit Roy-Chowdhury Andrej Risteski Andrew Gordon Wilson Anima Anandkumar Ankit Singh Rawat Anna Rumshisky Arindam Banerjee Aryan Mokhtari Ashish Kapoor Asma Ghandeharioun Aurelien Lucchi ..."}
+{"idx": 1, "title": "2024 Program Committee", "date": "", "ddg_snippet": "Senior Area Chairs : Aaron Courville, Adam White, Adam Kalai, Aleksandra Faust, Alexander Rush, Alexandru Niculescu-Mizil, Anca Dragan, Anna Rumshisky, ...", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/Conferences/2024/ProgramCommittee", "content": "Senior Area Chairs : Aaron Courville, Adam White, Adam Kalai, Aleksandra Faust, Alexander Rush, Alexandru Niculescu-Mizil, Anca Dragan, Anna Rumshisky, ..."}
+{"idx": 2, "title": "Program Committee - EC 2024", "date": "", "ddg_snippet": "Comittees Program Committee Program Chairs : Daniela Saban (Stanford University) Robert Kleinberg (Cornell University) Contact the program chairs at ec24pcchairs@gmail.com regarding matters of the program or proceedings. Track Chairs : David Pennock (Rutgers), AI Davide Proserpio (University of Southern California), Empirics", "subpage_snippet": "", "source": "ec24.sigecom.org", "link": "https://ec24.sigecom.org/comittees/program-committee/index.html", "content": "Comittees Program Committee Program Chairs : Daniela Saban (Stanford University) Robert Kleinberg (Cornell University) Contact the program chairs at ec24pcchairs@gmail.com regarding matters of the program or proceedings. Track Chairs : David Pennock (Rutgers), AI Davide Proserpio (University of Southern California), Empirics"}
+{"idx": 3, "title": "Program Committee - American Statistical Association", "date": "", "ddg_snippet": "Aug 3, 2023 · Program Committee 2024 JSM Program Chair Debashis Ghosh University of Colorado, School of Public Health Section on Medical Devices & Diagnostics, ASA Joanne Lin Illumina, Inc. International Biometric Society (ENAR) Benjamin Risk Emory University Mental Health Statistics Section, ASA Adam Ciarleglio George Washington University", "subpage_snippet": "", "source": "ww2.amstat.org", "link": "https://ww2.amstat.org/meetings/jsm/2024/programcommittee.cfm", "content": "Aug 3, 2023 · Program Committee 2024 JSM Program Chair Debashis Ghosh University of Colorado, School of Public Health Section on Medical Devices & Diagnostics, ASA Joanne Lin Illumina, Inc. International Biometric Society (ENAR) Benjamin Risk Emory University Mental Health Statistics Section, ASA Adam Ciarleglio George Washington University"}
+{"idx": 4, "title": "Program Committee", "date": "", "ddg_snippet": "Program Chair . Kate Larson University of Waterloo Canada ; Program Administrator in Chief. Francisco Cruz Brainful Labs Spain ; Ethics Chair . Marija Slavkovik", "subpage_snippet": "", "source": "ijcai24.org", "link": "https://ijcai24.org/program-committee/index.html", "content": "Program Chair . Kate Larson University of Waterloo Canada ; Program Administrator in Chief. Francisco Cruz Brainful Labs Spain ; Ethics Chair . Marija Slavkovik"}
+{"idx": 5, "title": "AAAI-24 Senior Program Committee & Area Chairs - AAAI", "date": "", "ddg_snippet": "AAAI-24 Senior Program Committee Prathosh A P (Indian Institute of Technology Delhi)Heike Adel (Hochschule der Medien (University of Applied Sciences))Bijaya Adhikari (University of Iowa)Somak Aditya (IIT Kharagpur)Vaneet Aggarwal (Purdue University)Wasi Ahmad (UCLA)Zahra Ahmadi (L3S Research Center)Sungjin Ahn (KAIST)Yagiz Aksoy (Simon Fraser University)Stefano Albrecht (University of ...", "subpage_snippet": "", "source": "aaai.org", "link": "https://aaai.org/aaai-24-conference/aaai-24-senior-program-committee/", "content": "AAAI-24 Senior Program Committee Prathosh A P (Indian Institute of Technology Delhi)Heike Adel (Hochschule der Medien (University of Applied Sciences))Bijaya Adhikari (University of Iowa)Somak Aditya (IIT Kharagpur)Vaneet Aggarwal (Purdue University)Wasi Ahmad (UCLA)Zahra Ahmadi (L3S Research Center)Sungjin Ahn (KAIST)Yagiz Aksoy (Simon Fraser University)Stefano Albrecht (University of ..."}
+{"idx": 6, "title": "Program Committee | Ocean Sciences Meeting 2024 - agu.org", "date": "", "ddg_snippet": "About Program Committee Leadership Co- Chairs Vice Chairs Early Career Representatives Student Representatives", "subpage_snippet": "", "source": "www.agu.org", "link": "https://www.agu.org/Ocean-Sciences-Meeting-2024/Pages/About/Program-Committee", "content": "About Program Committee Leadership Co- Chairs Vice Chairs Early Career Representatives Student Representatives"}
+{"idx": 7, "title": "2024 Progam Committee - CVPR", "date": "", "ddg_snippet": "Area Chairs , Senior Area Chairs , Senior Area Chairs , Alex Schwing, Alexei Efros, Aniruddha Kembhavi, Anthony Hoogs, Aude Oliva, Bernard Ghanem, Bharath ...", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/Conferences/2024/ProgramCommittee", "content": "Area Chairs , Senior Area Chairs , Senior Area Chairs , Alex Schwing, Alexei Efros, Aniruddha Kembhavi, Anthony Hoogs, Aude Oliva, Bernard Ghanem, Bharath ..."}
+{"idx": 8, "title": "Program Committee", "date": "", "ddg_snippet": "2024 Program Chair . Takayuki Uchiyama is a member of Panasonic PSIRT and is responsible for product security activities at the business divisions overseas. His ...", "subpage_snippet": "", "source": "www.first.org", "link": "https://www.first.org/conference/2024/program-committee", "content": "2024 Program Chair . Takayuki Uchiyama is a member of Panasonic PSIRT and is responsible for product security activities at the business divisions overseas. His ..."}
+{"idx": 9, "title": "NDSS Symposium 2024 Program Committee", "date": "", "ddg_snippet": "Program Committee Co- Chairs : Mathias Payer, EPFL; Christina Pöpper, New York University Abu Dhabi. Program Committee Members : Aanjhan Ranganathan, Northeastern ...", "subpage_snippet": "", "source": "www.ndss-symposium.org", "link": "https://www.ndss-symposium.org/ndss2024/leadership/program-committee/", "content": "Program Committee Co- Chairs : Mathias Payer, EPFL; Christina Pöpper, New York University Abu Dhabi. Program Committee Members : Aanjhan Ranganathan, Northeastern ..."}
diff --git a/data/sampled_jsons/Waymo_Open_Dataset_paper_size_frames.jsonl b/data/sampled_jsons/Waymo_Open_Dataset_paper_size_frames.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d5e89a4399c8c782de047fccdc9d122b4819abb7
--- /dev/null
+++ b/data/sampled_jsons/Waymo_Open_Dataset_paper_size_frames.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "About – Waymo Open Dataset", "date": "", "ddg_snippet": "Perception Dataset (last updated March 2024) · [New: March 2024: v. · 2,030 segments of 20s each, collected at 10Hz (390,000 frames ) in diverse geographies and ...", "subpage_snippet": "", "source": "waymo.com", "link": "https://waymo.com/open/about/", "content": "Perception Dataset (last updated March 2024) · [New: March 2024: v. · 2,030 segments of 20s each, collected at 10Hz (390,000 frames ) in diverse geographies and ..."}
+{"idx": 1, "title": "Waymo Open Dataset", "date": "", "ddg_snippet": "The Waymo Open Dataset is a collection of datasets and evaluation code that we have released publicly to aid the research community.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/waymo-research/waymo-open-dataset", "content": "The Waymo Open Dataset is a collection of datasets and evaluation code that we have released publicly to aid the research community."}
+{"idx": 2, "title": "Scalability in Perception for Autonomous Driving: Waymo ...", "date": "", "ddg_snippet": "by P Sun · 2020 · Cited by 4211 — We trained the model on single frame of sensor data with all LiDARs included. For vehicles and pedestrians we set the voxel size to. 0.33m, the grid range to [− ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content_CVPR_2020/papers/Sun_Scalability_in_Perception_for_Autonomous_Driving_Waymo_Open_Dataset_CVPR_2020_paper.pdf", "content": "by P Sun · 2020 · Cited by 4211 — We trained the model on single frame of sensor data with all LiDARs included. For vehicles and pedestrians we set the voxel size to. 0.33m, the grid range to [− ..."}
+{"idx": 3, "title": "Waymo Open Dataset: Sharing our self-driving data for ...", "date": "", "ddg_snippet": "Each segment captures 20 seconds of continuous driving, corresponding to 200,000 frames at 10 Hz per sensor. Such continuous footage gives ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/waymo/waymo-open-dataset-6c6ac227ab1a", "content": "Each segment captures 20 seconds of continuous driving, corresponding to 200,000 frames at 10 Hz per sensor. Such continuous footage gives ..."}
+{"idx": 4, "title": "About – Waymo Open Dataset", "date": "", "ddg_snippet": "Waymo is in a unique position to contribute to the research community, by creating and sharing some of the largest and most diverse autonomous driving datasets.", "subpage_snippet": "", "source": "waymo.com", "link": "https://waymo.com/open/", "content": "Waymo is in a unique position to contribute to the research community, by creating and sharing some of the largest and most diverse autonomous driving datasets."}
+{"idx": 5, "title": "Jossome/Waymo-open-dataset-document", "date": "", "ddg_snippet": "The official document of the dataset is too general. There are many missing details. This document aims at making it more convenient to use the dataset .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Jossome/Waymo-open-dataset-document", "content": "The official document of the dataset is too general. There are many missing details. This document aims at making it more convenient to use the dataset ."}
+{"idx": 6, "title": "Waymo Open Dataset: Potential Strategies to Cut across ...", "date": "", "ddg_snippet": "9 Sept 2019 — It also includes 3,000 driving scenes, 600,000 frames , and ... page , thereby enhancing the size and variety of the larger data set.", "subpage_snippet": "", "source": "www.frost.com", "link": "https://www.frost.com/growth-opportunity-news/waymo-open-dataset-potential-strategies-to-cut-across-boundaries/", "content": "9 Sept 2019 — It also includes 3,000 driving scenes, 600,000 frames , and ... page , thereby enhancing the size and variety of the larger data set."}
+{"idx": 7, "title": "ROAD-Waymo: Action Awareness at Scale for Autonomous ...", "date": "", "ddg_snippet": "8 Nov 2024 — This paper introduces ROAD- Waymo , which tackles the diversity and scale issues of ROAD [57] , building on the Waymo - Open dataset [61] .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.01683v2", "content": "8 Nov 2024 — This paper introduces ROAD- Waymo , which tackles the diversity and scale issues of ROAD [57] , building on the Waymo - Open dataset [61] ."}
+{"idx": 8, "title": "Waymo Open Dataset: Open3D Point Cloud Viewer", "date": "", "ddg_snippet": "14 May 2022 — In this post we will focus on the perception dataset . Each file (segment) is a sequence of frames ordered by frame start timestamps. You can ...", "subpage_snippet": "", "source": "salzi.blog", "link": "https://salzi.blog/2022/05/14/waymo-open-dataset-open3d-point-cloud-viewer/", "content": "14 May 2022 — In this post we will focus on the perception dataset . Each file (segment) is a sequence of frames ordered by frame start timestamps. You can ..."}
+{"idx": 9, "title": "Scalability in Perception for Autonomous Driving", "date": "", "ddg_snippet": "by P Sun · 2019 · Cited by 4211 — Our new dataset consists of 1150 scenes that each span 20 seconds, consisting of well synchronized and calibrated high quality LiDAR and camera data.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1912.04838", "content": "by P Sun · 2019 · Cited by 4211 — Our new dataset consists of 1150 scenes that each span 20 seconds, consisting of well synchronized and calibrated high quality LiDAR and camera data."}
diff --git a/data/sampled_jsons/What_Limits_Virtual_Agent_Application_OmniBench_experimental_setup_GPU.jsonl b/data/sampled_jsons/What_Limits_Virtual_Agent_Application_OmniBench_experimental_setup_GPU.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..9ff8d97f65349baa3f61489e27cc4d6f30fecf93
--- /dev/null
+++ b/data/sampled_jsons/What_Limits_Virtual_Agent_Application_OmniBench_experimental_setup_GPU.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "What Limits Virtual Agent Application? OmniBench", "date": "", "ddg_snippet": "Experimental Setup . Settings. We evaluate various models including MLLMs and ... All experiments are conducted with NVIDIA A100 80G GPUs . Baselines. We ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46463", "content": "Experimental Setup . Settings. We evaluate various models including MLLMs and ... All experiments are conducted with NVIDIA A100 80G GPUs . Baselines. We ..."}
+{"idx": 1, "title": "What Limits Virtual Agent Application? OmniBench", "date": "", "ddg_snippet": "Experiments. In this section, we first introduce the experimental setup (Sec- tion 5.1). Then, we comprehensively compare the differences in capabilities ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/d5d3691187b9f32f92e8e287e5005d40aa429f80.pdf", "content": "Experiments. In this section, we first introduce the experimental setup (Sec- tion 5.1). Then, we comprehensively compare the differences in capabilities ..."}
+{"idx": 2, "title": "Generalist Virtual Agents: A Survey on Autonomous ...", "date": "", "ddg_snippet": "Our objective is to provide a comprehensive overview of Generalist Virtual Agents (GVAs), covering their definition, necessity, implementation approaches, ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/wendell0218/GVA-Survey", "content": "Our objective is to provide a comprehensive overview of Generalist Virtual Agents (GVAs), covering their definition, necessity, implementation approaches, ..."}
+{"idx": 3, "title": "Running an LLM Locally with AMD GPU (DirectML) – Experimental ...", "date": "", "ddg_snippet": "Jul 15, 2025 · The purpose I’ll try running an LLM using an AMD GPU . I’ll be using DirectML and its sample code. I’ll modify the code, and please proceed at your own risk. However, on the following environment, it barely runs (it’s unstable and gives strange answers):", "subpage_snippet": "", "source": "blog.en.marunokan.com", "link": "https://blog.en.marunokan.com/?p=1626", "content": "Jul 15, 2025 · The purpose I’ll try running an LLM using an AMD GPU . I’ll be using DirectML and its sample code. I’ll modify the code, and please proceed at your own risk. However, on the following environment, it barely runs (it’s unstable and gives strange answers):"}
+{"idx": 4, "title": "A Performance/Cost Evaluation for a GPU-Based Drug Discovery ...", "date": "", "ddg_snippet": "Jun 15, 2014 · However, these applications still need to scale to larger GPU-based systems to enable remarkable advances in the fields of healthcare, drug discovery, genome research, etc. The inclusion of GPUs in HPC systems exacerbates power and temperature issues, increasing the total cost of ownership (TCO).", "subpage_snippet": "", "source": "www.ncbi.nlm.nih.gov", "link": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4082831/", "content": "Jun 15, 2014 · However, these applications still need to scale to larger GPU-based systems to enable remarkable advances in the fields of healthcare, drug discovery, genome research, etc. The inclusion of GPUs in HPC systems exacerbates power and temperature issues, increasing the total cost of ownership (TCO)."}
+{"idx": 5, "title": "Astra: A Multi-Agent System for GPU Kernel Performance ...", "date": "", "ddg_snippet": "In this work, we investigate the application of LLM-powered agents to GPU kernel optimization, a long-standing challenge at the intersection of high-performance computing and machine learning that requires generating code that is both correct and highly optimized.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2509.07506", "content": "In this work, we investigate the application of LLM-powered agents to GPU kernel optimization, a long-standing challenge at the intersection of high-performance computing and machine learning that requires generating code that is both correct and highly optimized."}
+{"idx": 6, "title": "VP: Host-GPU Multiplexing for Efficient Simulation of Multiple ...", "date": "", "ddg_snippet": "GPU applications run more than 600 times faster than GPU-software emulation on virtual platforms. We also propose Kernel Interleaving an Kernel Coalescing, two techniques that further speed up the simulation by one order of magnitude. Fi-nall", "subpage_snippet": "", "source": "sld.cs.columbia.edu", "link": "https://sld.cs.columbia.edu/pubs/jung_dac15.pdf", "content": "GPU applications run more than 600 times faster than GPU-software emulation on virtual platforms. We also propose Kernel Interleaving an Kernel Coalescing, two techniques that further speed up the simulation by one order of magnitude. Fi-nall"}
+{"idx": 7, "title": "Experimental setup - Parallel Implementation on the GPU", "date": "", "ddg_snippet": "We present a parallel implementation of our algorithm on the GPU using NVIDIA CUDA. Our GPU implementation can achieve speedups of up to 20 times over the IPP Canny algorithm, and is around 5 times faster than the existing CUDA Canny algorithm.", "subpage_snippet": "", "source": "1library.net", "link": "https://1library.net/article/experimental-setup-parallel-implementation-on-the-gpu.z1rgg4vq", "content": "We present a parallel implementation of our algorithm on the GPU using NVIDIA CUDA. Our GPU implementation can achieve speedups of up to 20 times over the IPP Canny algorithm, and is around 5 times faster than the existing CUDA Canny algorithm."}
+{"idx": 8, "title": "Auto-tuning a High-Level Language Targeted to GPU Codes", "date": "", "ddg_snippet": "In this paper, we use HMPP Workbench , a directive-based compilation framework targeted to GPUs . The tool allows us to insert pragmas to transform sequential code and gen-erate CUDA and OpenCL code that can potentially match or exceed the performance of manually tuned GPU kernels.", "subpage_snippet": "", "source": "cavazos-lab.github.io", "link": "http://cavazos-lab.github.io/PolyBench-ACC/resources/Autotuning.a.High-Level.Language.Targeted.to.GPU.Codes-paper.pdf", "content": "In this paper, we use HMPP Workbench , a directive-based compilation framework targeted to GPUs . The tool allows us to insert pragmas to transform sequential code and gen-erate CUDA and OpenCL code that can potentially match or exceed the performance of manually tuned GPU kernels."}
+{"idx": 9, "title": "Accelerating Independent Multi-Agent Reinforcement Learning ...", "date": "", "ddg_snippet": "Aug 22, 2025 · We conduct a detailed analysis of the state-of-the-art training scheme used for training independent MARL systems using a GPU, identifying limitations and inefficiencies. We propose an Independent Multi-GPU training scheme that efficiently distributes agent policy training across multiple GPU devices.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-3-031-99872-0_22", "content": "Aug 22, 2025 · We conduct a detailed analysis of the state-of-the-art training scheme used for training independent MARL systems using a GPU, identifying limitations and inefficiencies. We propose an Independent Multi-GPU training scheme that efficiently distributes agent policy training across multiple GPU devices."}
diff --git a/data/sampled_jsons/What_Makes_and_Breaks_Safety_Fine-tuning_A_Mechanistic_Study_Section_4_learning_rates_year_2024.jsonl b/data/sampled_jsons/What_Makes_and_Breaks_Safety_Fine-tuning_A_Mechanistic_Study_Section_4_learning_rates_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..53b686c2618feecc36afd515a5236ec7c4b2a678
--- /dev/null
+++ b/data/sampled_jsons/What_Makes_and_Breaks_Safety_Fine-tuning_A_Mechanistic_Study_Section_4_learning_rates_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "(PDF) What Makes and Breaks Safety Fine - tuning ? A Mechanistic ...", "date": "", "ddg_snippet": "Popular approaches for safety fine - tuning include: (i). supervised safety fine - tuning (SSFT) (Ouyang et al.,2022); (ii) reinforcement learning with human.• Systematic setup to study safety fine - tuning and jailbreaks. We introduce a novel synthetic data. generation framework that allows.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/382271359_What_Makes_and_Breaks_Safety_Fine-tuning_Mechanistic_Study", "content": "Popular approaches for safety fine - tuning include: (i). supervised safety fine - tuning (SSFT) (Ouyang et al.,2022); (ii) reinforcement learning with human.• Systematic setup to study safety fine - tuning and jailbreaks. We introduce a novel synthetic data. generation framework that allows."}
+{"idx": 1, "title": "What Makes and Breaks Safety Fine - tuning ? Mechanistic Study", "date": "", "ddg_snippet": "To systematically study the mechanisms yielded by safety fine - tuning and how adversarially designed inputs circum-vent said mechanisms , we design a synthetic data generating.", "subpage_snippet": "", "source": "ora.ox.ac.uk", "link": "https://ora.ox.ac.uk/objects/uuid:9757f000-486f-48dc-84b1-1ab9d4db09ee/files/rbz60cx627", "content": "To systematically study the mechanisms yielded by safety fine - tuning and how adversarially designed inputs circum-vent said mechanisms , we design a synthetic data generating."}
+{"idx": 2, "title": "What Makes and Breaks Safety Fine - tuning ?", "date": "", "ddg_snippet": "To systematically study the mechanisms yielded by safety fine - tuning and how adversarially designed inputs circumvent said mechanisms , we design a synthetic data generating process motivated by the framework of jailbreak attacks proposed by Wei et al.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/a9bef53eb7b0e5950d4f2d9c74a16006-Paper-Conference.pdf", "content": "To systematically study the mechanisms yielded by safety fine - tuning and how adversarially designed inputs circumvent said mechanisms , we design a synthetic data generating process motivated by the framework of jailbreak attacks proposed by Wei et al."}
+{"idx": 3, "title": "[2407.10264] What Makes and Breaks Safety Fine - tuning ?", "date": "", "ddg_snippet": "Safety fine - tuning helps align Large Language Models (LLMs) with human preferences for their safe deployment.View a PDF of the paper titled What Makes and Breaks Safety Fine - tuning ? A Mechanistic Study , by Samyak Jain and 6 other authors.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2407.10264", "content": "Safety fine - tuning helps align Large Language Models (LLMs) with human preferences for their safe deployment.View a PDF of the paper titled What Makes and Breaks Safety Fine - tuning ? A Mechanistic Study , by Samyak Jain and 6 other authors."}
+{"idx": 4, "title": "What Makes and Breaks Safety Fine - tuning ? A Mechanistic Study", "date": "", "ddg_snippet": "Abstract: Safety fine - tuning helps align Large Language Models (LLMs) with human preferences for their safe deployment. To better understand the underlying factors that make models safe via safety fine - tuning ...", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/abs/2407.10264v3", "content": "Abstract: Safety fine - tuning helps align Large Language Models (LLMs) with human preferences for their safe deployment. To better understand the underlying factors that make models safe via safety fine - tuning ..."}
+{"idx": 5, "title": "fiveai/understanding_ safety _ finetuning : Official Code for What Makes ...", "date": "", "ddg_snippet": "What Makes and Breaks Safety Fine - tuning ? A Mechanistic Study . arXiv. What Makes and Breaks Safety Fine - tuning ? A Mechanistic Study Samyak Jain, Ekdeep Singh Lubana, Kemal Oksuz, Tom Joy, Philip Torr, Amartya Sanyal, Puneet K. Dokania. alt text.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/fiveai/understanding_safety_finetuning", "content": "What Makes and Breaks Safety Fine - tuning ? A Mechanistic Study . arXiv. What Makes and Breaks Safety Fine - tuning ? A Mechanistic Study Samyak Jain, Ekdeep Singh Lubana, Kemal Oksuz, Tom Joy, Philip Torr, Amartya Sanyal, Puneet K. Dokania. alt text."}
+{"idx": 6, "title": "What Makes and Breaks Safety Fine - tuning ? Mechanistic Study", "date": "", "ddg_snippet": "Safety fine - tuning helps align Large Language Models (LLMs) with human preferences for their safe deployment.The researchers investigated three common safety fine - tuning techniques: supervised safety fine - tuning , direct preference optimization, and unlearning.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/what-makes-breaks-safety-fine-tuning-mechanistic", "content": "Safety fine - tuning helps align Large Language Models (LLMs) with human preferences for their safe deployment.The researchers investigated three common safety fine - tuning techniques: supervised safety fine - tuning , direct preference optimization, and unlearning."}
+{"idx": 7, "title": "What Makes and Breaks Safety Fine - tuning ? A Mechanistic Study", "date": "", "ddg_snippet": "Safety fine - tuning helps align Large Language Models (LLMs) with human preferences for their safe deployment. To better understand the underlying factors that make models safe via safety fine - tuning ...", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/venue/JEflV4nRlH@OpenReview", "content": "Safety fine - tuning helps align Large Language Models (LLMs) with human preferences for their safe deployment. To better understand the underlying factors that make models safe via safety fine - tuning ..."}
+{"idx": 8, "title": "Bayesian beagle - What Makes and Breaks Safety Fine - tuning ?", "date": "", "ddg_snippet": "Summary: The paper “ What Makes and Breaks Safety Fine - tuning ? A Mechanistic Study ” investigates the factors contributing to the safety of large language models (LLMs) through safety fine - tuning .", "subpage_snippet": "", "source": "bayesian-beagle.netlify.app", "link": "https://bayesian-beagle.netlify.app/posts/what_makes_and_breaks_safety_fine_tuning_mechanistic_study/2024-07-14-what_makes_and_breaks_safety_fine_tuning_mechanistic_study", "content": "Summary: The paper “ What Makes and Breaks Safety Fine - tuning ? A Mechanistic Study ” investigates the factors contributing to the safety of large language models (LLMs) through safety fine - tuning ."}
+{"idx": 9, "title": "Papers by Kemal Oksuz with links to code and results.", "date": "", "ddg_snippet": "What Makes and Breaks Safety Fine - tuning ? A Mechanistic Study . Safety fine - tuning helps align Large Language Models (LLMs) with human preferences for their safe deployment.", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/search?q=author:Kemal+Oksuz", "content": "What Makes and Breaks Safety Fine - tuning ? A Mechanistic Study . Safety fine - tuning helps align Large Language Models (LLMs) with human preferences for their safe deployment."}
diff --git a/data/sampled_jsons/What_Makes_and_Breaks_Safety_Fine-tuning_Mechanistic_Study_Observation_4_Lipschitzness_year_2024.jsonl b/data/sampled_jsons/What_Makes_and_Breaks_Safety_Fine-tuning_Mechanistic_Study_Observation_4_Lipschitzness_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..99ecb595e4d8c0520eca75122c1f9ffdcd369834
--- /dev/null
+++ b/data/sampled_jsons/What_Makes_and_Breaks_Safety_Fine-tuning_Mechanistic_Study_Observation_4_Lipschitzness_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ICML 2025 Schedule", "date": "", "ddg_snippet": "Calibration and Bias in Algorithms, Data, and Models: a ... 10:45] Transformative or Conservative? Conservation laws for ResNets and Transformers", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/calendar", "content": "Calibration and Bias in Algorithms, Data, and Models: a ... 10:45] Transformative or Conservative? Conservation laws for ResNets and Transformers"}
+{"idx": 1, "title": "ICML 2024 Papers", "date": "", "ddg_snippet": "Planning, Fast and Slow: Online Reinforcement Learning with Action-Free Offline Data via Multiscale Planners ... The good, the bad and the ugly sides ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2024/papers.html", "content": "Planning, Fast and Slow: Online Reinforcement Learning with Action-Free Offline Data via Multiscale Planners ... The good, the bad and the ugly sides ..."}
+{"idx": 2, "title": "Downloads", "date": "", "ddg_snippet": "Accommodating Picky Customers: Regret Bound and Exploration Complexity for Multi-Objective Reinforcement Learning ... Large-scale Dynamic Pickup and ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/Downloads/2021", "content": "Accommodating Picky Customers: Regret Bound and Exploration Complexity for Multi-Objective Reinforcement Learning ... Large-scale Dynamic Pickup and ..."}
+{"idx": 3, "title": "ICLR 2024 Orals", "date": "", "ddg_snippet": "... study the effects predictive auxiliary objectives have on representation learning across different modules of an RL system and how these mimic ...", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2024/events/oral", "content": "... study the effects predictive auxiliary objectives have on representation learning across different modules of an RL system and how these mimic ..."}
+{"idx": 4, "title": "ICLR 2024 Papers", "date": "", "ddg_snippet": "Mechanistically analyzing the effects of fine - tuning on ... Fine - tuning Aligned Language Models Compromises Safety , Even When Users Do Not Intend To!", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2024/papers.html", "content": "Mechanistically analyzing the effects of fine - tuning on ... Fine - tuning Aligned Language Models Compromises Safety , Even When Users Do Not Intend To!"}
+{"idx": 5, "title": "From Flat to Hierarchical : Extracting Sparse Representations", "date": "", "ddg_snippet": "... and lighting [ 34 , 35 , 36 , 37 , 38 ] , facial characteristics [ 39 , 40 ] , broader scene organizations [ 41 , 42 , 43 ] , and instance ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.03093v1", "content": "... and lighting [ 34 , 35 , 36 , 37 , 38 ] , facial characteristics [ 39 , 40 ] , broader scene organizations [ 41 , 42 , 43 ] , and instance ..."}
+{"idx": 6, "title": "GitHub - MinghuiChen43/awesome-trustworthy-deep-learning: A", "date": "", "ddg_snippet": "... safety through brain-inspired representations, architectures, robust sensory-motor systems, fine - tuning with brain data, interpretability methods, and ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/MinghuiChen43/awesome-trustworthy-deep-learning", "content": "... safety through brain-inspired representations, architectures, robust sensory-motor systems, fine - tuning with brain data, interpretability methods, and ..."}
+{"idx": 7, "title": "NeurIPS 2023 Papers", "date": "", "ddg_snippet": "... Reuse of Dynamics, and Geometric Representation ... What is the Inductive Bias of Flatness Regularization? A Study of Deep Matrix Factorization Models", "subpage_snippet": "", "source": "nips.cc", "link": "https://nips.cc/virtual/2023/papers.html", "content": "... Reuse of Dynamics, and Geometric Representation ... What is the Inductive Bias of Flatness Regularization? A Study of Deep Matrix Factorization Models"}
+{"idx": 8, "title": "NeurIPS 2023 Papers", "date": "", "ddg_snippet": "... Reuse of Dynamics, and Geometric Representation ... What is the Inductive Bias of Flatness Regularization? A Study of Deep Matrix Factorization Models", "subpage_snippet": "", "source": "nips.cc", "link": "https://nips.cc/virtual/2023/papers.html?filter=titles", "content": "... Reuse of Dynamics, and Geometric Representation ... What is the Inductive Bias of Flatness Regularization? A Study of Deep Matrix Factorization Models"}
+{"idx": 9, "title": "Evidence Sets: Towards Inductive-Biases based Analysis of", "date": "", "ddg_snippet": "... of some interesting papers to AI safety in three maximally-relevant subareas of ML literature ( pretraining - > finetuning , generalization and ...", "subpage_snippet": "", "source": "www.greaterwrong.com", "link": "https://www.greaterwrong.com/posts/aW5CPtqtvs2EYKMMK/evidence-sets-towards-inductive-biases-based-analysis-of", "content": "... of some interesting papers to AI safety in three maximally-relevant subareas of ML literature ( pretraining - > finetuning , generalization and ..."}
diff --git a/data/sampled_jsons/Wolpert_No_Free_Lunch_theorem_machine_learning_optimization.jsonl b/data/sampled_jsons/Wolpert_No_Free_Lunch_theorem_machine_learning_optimization.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..00c161d575efca763d774a5d702a83dbcd7893ac
--- /dev/null
+++ b/data/sampled_jsons/Wolpert_No_Free_Lunch_theorem_machine_learning_optimization.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "No free lunch in search and optimization - Wikipedia", "date": "", "ddg_snippet": "Wolpert had previously derived no free lunch theorems for machine learning (statistical inference). [3] Before Wolpert's article was published, Cullen Schaffer independently proved a restricted version of one of Wolpert's theorems and used it to critique the current state of machine learning research on the problem of induction. [4]", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/No_free_lunch_in_search_and_optimization", "content": "Wolpert had previously derived no free lunch theorems for machine learning (statistical inference). [3] Before Wolpert's article was published, Cullen Schaffer independently proved a restricted version of one of Wolpert's theorems and used it to critique the current state of machine learning research on the problem of induction. [4]"}
+{"idx": 1, "title": "No Free Lunch Theorems", "date": "", "ddg_snippet": "No Free Lunch Theorems Broadly speaking, there are two no free lunch theorems . One for supervised machine learning ( Wolpert 1996) and one for search/ optimization ( Wolpert and Macready 1997). For an overview of the ( no ) free lunch and associated theorems , see David Wolpert's What does dinner cost?", "subpage_snippet": "", "source": "no-free-lunch.org", "link": "http://no-free-lunch.org/", "content": "No Free Lunch Theorems Broadly speaking, there are two no free lunch theorems . One for supervised machine learning ( Wolpert 1996) and one for search/ optimization ( Wolpert and Macready 1997). For an overview of the ( no ) free lunch and associated theorems , see David Wolpert's What does dinner cost?"}
+{"idx": 2, "title": "PDF No Free Lunch Theorems For Optimization - Evolutionary Computation ...", "date": "", "ddg_snippet": "Abstract— A framework is developed to explore the connection between effective optimization algorithms and the problems they are solving. A number of \" no free lunch \" (NFL) theorems are presented which establish that for any algorithm, any elevated performance over one class of problems is offset by perfor- mance over another class. These theorems result in a geometric interpretation of ...", "subpage_snippet": "", "source": "www.cs.ubc.ca", "link": "https://www.cs.ubc.ca/~hutter/earg/papers07/00585893.pdf", "content": "Abstract— A framework is developed to explore the connection between effective optimization algorithms and the problems they are solving. A number of \" no free lunch \" (NFL) theorems are presented which establish that for any algorithm, any elevated performance over one class of problems is offset by perfor- mance over another class. These theorems result in a geometric interpretation of ..."}
+{"idx": 3, "title": "No Free Lunch Theorem for Machine Learning", "date": "", "ddg_snippet": "The no free lunch theorem suggests the performance of all optimization algorithms are identical, under some specific constraints. There is provably no single best optimization algorithm or machine learning algorithm. The practical implications of the theorem may be limited given we are interested in a small subset of all possible objective ...", "subpage_snippet": "", "source": "machinelearningmastery.com", "link": "https://machinelearningmastery.com/no-free-lunch-theorem-for-machine-learning/", "content": "The no free lunch theorem suggests the performance of all optimization algorithms are identical, under some specific constraints. There is provably no single best optimization algorithm or machine learning algorithm. The practical implications of the theorem may be limited given we are interested in a small subset of all possible objective ..."}
+{"idx": 4, "title": "What is No Free Lunch Theorem - GeeksforGeeks", "date": "", "ddg_snippet": "The No Free Lunch Theorem is often used in optimization and machine learning , with little comprehension of what it means or implies. The theory asserts that when the performance of all optimization methods is averaged across all conceivable problems, they all perform equally well. It indicates that no one optimum optimization algorithm exists.", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/machine-learning/what-is-no-free-lunch-theorem/", "content": "The No Free Lunch Theorem is often used in optimization and machine learning , with little comprehension of what it means or implies. The theory asserts that when the performance of all optimization methods is averaged across all conceivable problems, they all perform equally well. It indicates that no one optimum optimization algorithm exists."}
+{"idx": 5, "title": "No Free Lunch Theorem and Its Foundational Implications for ... - Medium", "date": "", "ddg_snippet": "The No Free Lunch (NFL) theorem is a sobering reminder that algorithmic excellence is conditional. Across the total universe of problems, every learning or optimization method performs equally ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@adnanmasood/no-free-lunch-theorem-and-its-foundational-implications-for-algorithm-selection-in-artificial-5fc49c218d76", "content": "The No Free Lunch (NFL) theorem is a sobering reminder that algorithmic excellence is conditional. Across the total universe of problems, every learning or optimization method performs equally ..."}
+{"idx": 6, "title": "What is the No-Free-Lunch Theorem? - Data Basecamp", "date": "", "ddg_snippet": "The No-Free-Lunch Theorem (NFL) is a fundamental concept in machine learning and optimization , originally formulated by David Wolpert and William Macready in the late 1990s.", "subpage_snippet": "", "source": "databasecamp.de", "link": "https://databasecamp.de/en/ml/no-free-lunch-theorem-en", "content": "The No-Free-Lunch Theorem (NFL) is a fundamental concept in machine learning and optimization , originally formulated by David Wolpert and William Macready in the late 1990s."}
+{"idx": 7, "title": "No Free Lunch Theorem: No Universal Machine Learning Algorithm", "date": "", "ddg_snippet": "The \" No Free Lunch Theorem \" (NFL) is a foundational concept in the field of optimization and machine learning , introduced by David Wolpert and William Macready in the 1990s ( Wolpert , D. H., & Macready, W. G. , 1997). It essentially states that no one algorithm is universally better than others when averaged across all possible problems.", "subpage_snippet": "", "source": "artificium.us", "link": "http://artificium.us/lessons/03.ml/l-3-106-nfl-theorem/l-3-106.html", "content": "The \" No Free Lunch Theorem \" (NFL) is a foundational concept in the field of optimization and machine learning , introduced by David Wolpert and William Macready in the 1990s ( Wolpert , D. H., & Macready, W. G. , 1997). It essentially states that no one algorithm is universally better than others when averaged across all possible problems."}
+{"idx": 8, "title": "No Free Lunch Theorem in Optimization - numberanalytics.com", "date": "", "ddg_snippet": "The No Free Lunch Theorem (NFLT) is a fundamental concept in optimization and machine learning that has far-reaching implications for the development and application of optimization algorithms. In essence, the NFLT states that no single optimization algorithm can outperform all other algorithms across all possible problems.", "subpage_snippet": "", "source": "www.numberanalytics.com", "link": "https://www.numberanalytics.com/blog/no-free-lunch-theorem-optimization-algorithms", "content": "The No Free Lunch Theorem (NFLT) is a fundamental concept in optimization and machine learning that has far-reaching implications for the development and application of optimization algorithms. In essence, the NFLT states that no single optimization algorithm can outperform all other algorithms across all possible problems."}
+{"idx": 9, "title": "arXiv:2007.10928v1 [cs.LG] 21 Jul 2020", "date": "", "ddg_snippet": "David H. Wolpert Abstract The No Free Lunch theorems prove that under a uniform distribution over induction problems (search problems or learning problems), all induction algorithms performequally. As I discuss in this chapter, the importance of the theorems arises by using them to analyze scenarios involvingnon-uniform distributions, and to compare different algorithms, without any assumption ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2007.10928", "content": "David H. Wolpert Abstract The No Free Lunch theorems prove that under a uniform distribution over induction problems (search problems or learning problems), all induction algorithms performequally. As I discuss in this chapter, the importance of the theorems arises by using them to analyze scenarios involvingnon-uniform distributions, and to compare different algorithms, without any assumption ..."}
diff --git a/data/sampled_jsons/Wortsman_et_al_2021_WiSE-FT_fine-tuning_year_2021.jsonl b/data/sampled_jsons/Wortsman_et_al_2021_WiSE-FT_fine-tuning_year_2021.jsonl
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index 0000000000000000000000000000000000000000..59a7cbb7654675feea8850d1e909744edc696bd0
--- /dev/null
+++ b/data/sampled_jsons/Wortsman_et_al_2021_WiSE-FT_fine-tuning_year_2021.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2109.01903] Robust fine - tuning of zero-shot models", "date": "", "ddg_snippet": "Compared to standard fine - tuning , WiSE - FT provides large accuracy improvements under distribution shift, while preserving high accuracy on the target distribution.View a PDF of the paper titled Robust fine - tuning of zero-shot models, by Mitchell Wortsman and 10 other authors.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2109.01903", "content": "Compared to standard fine - tuning , WiSE - FT provides large accuracy improvements under distribution shift, while preserving high accuracy on the target distribution.View a PDF of the paper titled Robust fine - tuning of zero-shot models, by Mitchell Wortsman and 10 other authors."}
+{"idx": 1, "title": "Upweighting Easy Samples in Fine - Tuning Mitigates Forgetting", "date": "", "ddg_snippet": "WiSE - FT ( Wortsman et al ., 2021 ): We also consider model averaging as a baseline, specifically focusing on WiSE - FT ( Wortsman et al ., 2021 ).such as Wise - FT ( Wortsman et al ., 2021 ). 33. Upweighting Easy Samples in Fine - Tuning Mitigates Forgetting.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=13HPTmZKbM", "content": "WiSE - FT ( Wortsman et al ., 2021 ): We also consider model averaging as a baseline, specifically focusing on WiSE - FT ( Wortsman et al ., 2021 ).such as Wise - FT ( Wortsman et al ., 2021 ). 33. Upweighting Easy Samples in Fine - Tuning Mitigates Forgetting."}
+{"idx": 2, "title": "Figure 4: FLOW is complementary with model averaging ( WiSE - FT ) in...", "date": "", "ddg_snippet": "We compare WiSE - FT [ Wortsman et al ., 2021 ] with a standard model fine - tuning and with FLOW after fine - tuning Gemma 2 2B on MetaMathQA.The results indicate that combining Wise - FT with FLOW outperforms vanilla WiSE - FT with standard fine - tuning .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/FLOW-is-complementary-with-model-averaging-WiSE-FT-in-language-modeling-We-compare_fig2_388754394", "content": "We compare WiSE - FT [ Wortsman et al ., 2021 ] with a standard model fine - tuning and with FLOW after fine - tuning Gemma 2 2B on MetaMathQA.The results indicate that combining Wise - FT with FLOW outperforms vanilla WiSE - FT with standard fine - tuning ."}
+{"idx": 3, "title": "Finetune Like You Pretrain: Improved Finetuning of Zero-Shot Vision...", "date": "", "ddg_snippet": "Full fine - tuning L2-SP (Li et al .( 2021 ). 4. LP- FT (Kumar et al ., 2022): Here, we perform a two-stage finetuning process where we first perform linear probing and then full finetuning with hclass initialized at the linear-probing solution obtained in the first stage.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2023/papers/Goyal_Finetune_Like_You_Pretrain_Improved_Finetuning_of_Zero-Shot_Vision_Models_CVPR_2023_paper.pdf", "content": "Full fine - tuning L2-SP (Li et al .( 2021 ). 4. LP- FT (Kumar et al ., 2022): Here, we perform a two-stage finetuning process where we first perform linear probing and then full finetuning with hclass initialized at the linear-probing solution obtained in the first stage."}
+{"idx": 4, "title": "GitHub Vision Fine - Tuning Insights | Restackio", "date": "", "ddg_snippet": "( 2021 ) and Wortsman et al . (2022) has established a notable benchmark in the field.This approach aligns the fine - tuning process with the pre-training phase, leveraging contrastive loss to enhance model performance. Studies like Khosla et al .", "subpage_snippet": "", "source": "d2wozrt205r2fu.cloudfront.net", "link": "https://d2wozrt205r2fu.cloudfront.net/p/vision-fine-tuning-answer-github-vision-fine-tuning-cat-ai", "content": "( 2021 ) and Wortsman et al . (2022) has established a notable benchmark in the field.This approach aligns the fine - tuning process with the pre-training phase, leveraging contrastive loss to enhance model performance. Studies like Khosla et al ."}
+{"idx": 5, "title": "Fine - Tuning in Machine Vision: A Beginner's Guide", "date": "", "ddg_snippet": "Fine - tuning helps create custom solutions for many industries. It improves work in areas like healthcare and factories. Understanding Fine - Tuning in Machine Vision.ReCon (Qi et al ., 2023).", "subpage_snippet": "", "source": "www.unitxlabs.com", "link": "https://www.unitxlabs.com/resources/fine-tuning-machine-vision-guide/", "content": "Fine - tuning helps create custom solutions for many industries. It improves work in areas like healthcare and factories. Understanding Fine - Tuning in Machine Vision.ReCon (Qi et al ., 2023)."}
+{"idx": 6, "title": "Finetune like you pretrain: Improved finetuning of... | IEEE Xplore", "date": "", "ddg_snippet": "However, recent works (Kumar et al ., 2022; Wortsman et al ., 2021 ) have shown that even subtle differences in the finetuning process can lead to surprisingly large differences in the final performance, both for in-distribution (ID) and out-of-distribution (OOD) data.", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10205046", "content": "However, recent works (Kumar et al ., 2022; Wortsman et al ., 2021 ) have shown that even subtle differences in the finetuning process can lead to surprisingly large differences in the final performance, both for in-distribution (ID) and out-of-distribution (OOD) data."}
+{"idx": 7, "title": "Four Deep Learning Papers to Read in July 2021 | by Robert... | Medium", "date": "", "ddg_snippet": "‘Learning Neural Network Subspaces’. Authors: Wortsman et al . Wortsman et al . ( 2021 ) introduce a novel training paradigm making it possible to train linear (or non-linear) combinations of neural networks in 5 steps: 1) Independently initialize m neural networks.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/data-science/four-deep-learning-papers-to-read-in-july-2021-e91c546d112d", "content": "‘Learning Neural Network Subspaces’. Authors: Wortsman et al . Wortsman et al . ( 2021 ) introduce a novel training paradigm making it possible to train linear (or non-linear) combinations of neural networks in 5 steps: 1) Independently initialize m neural networks."}
+{"idx": 8, "title": "Dual Risk Minimization: Towards Next-Level", "date": "", "ddg_snippet": "Andreassen et al . ( 2021 ) showed that OOD accuracy tends to improve initially but then plateaus as the fine - tuning proceeds.Besides, L2-SP (Li et al ., 2018) and WiSE - FT ( Wortsman et al ., 2022) are two established fine - tuning variants that restrict the divergence from the pre-trained model.", "subpage_snippet": "", "source": "discovery.ucl.ac.uk", "link": "https://discovery.ucl.ac.uk/id/eprint/10199062/1/4_Dual_Risk_Minimization_Towar.pdf", "content": "Andreassen et al . ( 2021 ) showed that OOD accuracy tends to improve initially but then plateaus as the fine - tuning proceeds.Besides, L2-SP (Li et al ., 2018) and WiSE - FT ( Wortsman et al ., 2022) are two established fine - tuning variants that restrict the divergence from the pre-trained model."}
+{"idx": 9, "title": "Proceedings of the International Conference on Machine Learning 2022", "date": "", "ddg_snippet": "Wortsman et al . ( 2021 ) introduce WiSE - FT , a method for improving the robustness of a model θ1 which is fine - tuned from initialization θ0 by linearly interpolating θ1 and θ0.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v162/wortsman22a/wortsman22a.pdf", "content": "Wortsman et al . ( 2021 ) introduce WiSE - FT , a method for improving the robustness of a model θ1 which is fine - tuned from initialization θ0 by linearly interpolating θ1 and θ0."}
diff --git a/data/sampled_jsons/X-CLIP_Ma_et_al._2022_abstract_year_2022.jsonl b/data/sampled_jsons/X-CLIP_Ma_et_al._2022_abstract_year_2022.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..231e161be04267c0647a0d76dbd69e38f04c8ee6
--- /dev/null
+++ b/data/sampled_jsons/X-CLIP_Ma_et_al._2022_abstract_year_2022.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "What is the relation with your X-CLIP and X-CLIP by Yiwei Ma et. al?", "date": "", "ddg_snippet": "I noticed there is another model called X-CLIP by Yiwei Ma et . al , arXiv:2207.07285. Their paper was submitted on arXiv on July 2022 , while your paper (Bolin Ni et . al , 2208.02816) on August 2022 , one month later. At least, it seems that...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/microsoft/VideoX/issues/92", "content": "I noticed there is another model called X-CLIP by Yiwei Ma et . al , arXiv:2207.07285. Their paper was submitted on arXiv on July 2022 , while your paper (Bolin Ni et . al , 2208.02816) on August 2022 , one month later. At least, it seems that..."}
+{"idx": 1, "title": "X-CLIP: End-to-end Multi-grained Contrastive Learning For Video-text ...", "date": "", "ddg_snippet": "X-CLIP : End-to-end Multi-grained Contrastive Learning For Video-text Retrieval Yiwei Ma , Guohai Xu, Xiaoshuai Sun, Ming Yan, Ji Zhang, Rongrong Ji . Proceedings of the 30th ACM International Conference on Multimedia 2022 - 157 citations [Paper] Datasets Evaluation Scalability Self-Supervised Text Retrieval Video-text retrieval has been a crucial and fundamental task in multi-modal research ...", "subpage_snippet": "", "source": "learning2hash.github.io", "link": "https://learning2hash.github.io/publications/ma2022x/", "content": "X-CLIP : End-to-end Multi-grained Contrastive Learning For Video-text Retrieval Yiwei Ma , Guohai Xu, Xiaoshuai Sun, Ming Yan, Ji Zhang, Rongrong Ji . Proceedings of the 30th ACM International Conference on Multimedia 2022 - 157 citations [Paper] Datasets Evaluation Scalability Self-Supervised Text Retrieval Video-text retrieval has been a crucial and fundamental task in multi-modal research ..."}
+{"idx": 2, "title": "X-CLIP: End-to-End Multi-grained Contrastive Learning for Video-Text ...", "date": "", "ddg_snippet": "To this end, this paper presents a novel multi-grained contrastive model, namely X-CLIP , for video-text retrieval. However, another challenge lies in the similarity aggregation problem, which aims to aggregate fine-grained and cross-grained similarity matrices to instance-level similarity.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2207.07285", "content": "To this end, this paper presents a novel multi-grained contrastive model, namely X-CLIP , for video-text retrieval. However, another challenge lies in the similarity aggregation problem, which aims to aggregate fine-grained and cross-grained similarity matrices to instance-level similarity."}
+{"idx": 3, "title": "X-CLIP: End-to-End Multi-grained Contrastive Learning for Video-Text ...", "date": "", "ddg_snippet": "Figure 1: X-CLIP aims for improving video-text retrieval performance via multi-grained contrastive learning, including fine-grained (frame-word), coarse-grained (video-sentence) and cross-grained (video-word, sentence-frame) contrast. The transparency ofwords and frames represents the degree of relevance to query.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/X-CLIP:-End-to-End-Multi-grained-Contrastive-for-Ma-Xu/1ec886e2235763b08fa606a5d5ea3f4540f715ec/figure/0", "content": "Figure 1: X-CLIP aims for improving video-text retrieval performance via multi-grained contrastive learning, including fine-grained (frame-word), coarse-grained (video-sentence) and cross-grained (video-word, sentence-frame) contrast. The transparency ofwords and frames represents the degree of relevance to query."}
+{"idx": 4, "title": "MedCLIP: Contrastive Learning from Unpaired Medical Images and Text", "date": "", "ddg_snippet": "Abstract Existing vision-text contrastive learning like CLIP (Radford et al ., 2021) aims to match the paired image and caption embeddings while pushing others apart, which improves representation transferability and supports zero-shot prediction.", "subpage_snippet": "", "source": "pubmed.ncbi.nlm.nih.gov", "link": "https://pubmed.ncbi.nlm.nih.gov/39144675/", "content": "Abstract Existing vision-text contrastive learning like CLIP (Radford et al ., 2021) aims to match the paired image and caption embeddings while pushing others apart, which improves representation transferability and supports zero-shot prediction."}
+{"idx": 5, "title": "X-CLIP/README.md at main · xuguohai/X-CLIP · GitHub", "date": "", "ddg_snippet": "The implementation of paper X-CLIP : End-to-End Multi-grained Contrastive Learning for Video-Text Retrieval. Accepted by ACMMM22. By Yiwei Ma , Guohai Xu, Xiaoshuai Sun *, Ming Yan, Ji Zhang, Rongrong Ji. X-CLIP adopts cross-grained contrastive learning and attention over similarity matrix module to filter out unnecessary information during video-text retrieval. It achieves SOTA results on MSR ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/xuguohai/X-CLIP/blob/main/README.md", "content": "The implementation of paper X-CLIP : End-to-End Multi-grained Contrastive Learning for Video-Text Retrieval. Accepted by ACMMM22. By Yiwei Ma , Guohai Xu, Xiaoshuai Sun *, Ming Yan, Ji Zhang, Rongrong Ji. X-CLIP adopts cross-grained contrastive learning and attention over similarity matrix module to filter out unnecessary information during video-text retrieval. It achieves SOTA results on MSR ..."}
+{"idx": 6, "title": "X-CLIP: End-to-End Multi-grained Contrastive Learning for Video-Text ...", "date": "", "ddg_snippet": "Request PDF | On Oct 10, 2022 , Yiwei Ma and others published X-CLIP : End-to-End Multi-grained Contrastive Learning for Video-Text Retrieval | Find, read and cite all the research you need on ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/364484274_X-CLIP_End-to-End_Multi-grained_Contrastive_Learning_for_Video-Text_Retrieval", "content": "Request PDF | On Oct 10, 2022 , Yiwei Ma and others published X-CLIP : End-to-End Multi-grained Contrastive Learning for Video-Text Retrieval | Find, read and cite all the research you need on ..."}
+{"idx": 7, "title": "X-CLIP: End-to-End Multi-grained Contrastive Learning for Video-Text ...", "date": "", "ddg_snippet": "ABSTRACT Video-text retrieval has been a crucial and fundamental task in multi-modal research. The development of video-text retrieval has been considerably promoted by large-scale multi-modal contrastive pre-training, which primarily focuses on coarse-grained or fine-grained contrast. However, cross-grained contrast, which is the contrast between coarse-grained representations and fine ...", "subpage_snippet": "", "source": "dlnext.acm.org", "link": "https://dlnext.acm.org/doi/10.1145/3503161.3547910", "content": "ABSTRACT Video-text retrieval has been a crucial and fundamental task in multi-modal research. The development of video-text retrieval has been considerably promoted by large-scale multi-modal contrastive pre-training, which primarily focuses on coarse-grained or fine-grained contrast. However, cross-grained contrast, which is the contrast between coarse-grained representations and fine ..."}
+{"idx": 8, "title": "X-CLIP: End-to-End Multi-grained Contrastive Learning for Video-Text ...", "date": "", "ddg_snippet": "Request PDF | X-CLIP : End-to-End Multi-grained Contrastive Learning for Video-Text Retrieval | Video-text retrieval has been a crucial and fundamental task in multi-modal research. The development ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/362066555_X-CLIP_End-to-End_Multi-grained_Contrastive_Learning_for_Video-Text_Retrieval", "content": "Request PDF | X-CLIP : End-to-End Multi-grained Contrastive Learning for Video-Text Retrieval | Video-text retrieval has been a crucial and fundamental task in multi-modal research. The development ..."}
+{"idx": 9, "title": "Towards Efficient and Effective Text-to-Video Retrieval with Coarse-to ...", "date": "", "ddg_snippet": "With the explosive growth of videos uploaded online ev- ery day from platforms like TikTok, Kwai, YouTube, and Netflix, text-to-video retrieval is a crucial and fundamen- tal task for multi-modal representation learning (Fang et al. 2021; Ge et al. 2022 ; Gorti et al. 2022 ; Luo et al. 2022 ; Ma et al. 2022 ). Recently, the pre-trained text-image matching models ( CLIP (Radford et al. 2021)) from a ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2401.00701", "content": "With the explosive growth of videos uploaded online ev- ery day from platforms like TikTok, Kwai, YouTube, and Netflix, text-to-video retrieval is a crucial and fundamen- tal task for multi-modal representation learning (Fang et al. 2021; Ge et al. 2022 ; Gorti et al. 2022 ; Luo et al. 2022 ; Ma et al. 2022 ). Recently, the pre-trained text-image matching models ( CLIP (Radford et al. 2021)) from a ..."}
diff --git a/data/sampled_jsons/X-CLIP_Ma_et_al_2022_End-to-End_Multi-grained_Contrastive_Learning_Video-Text_Retrieval_year_2022.jsonl b/data/sampled_jsons/X-CLIP_Ma_et_al_2022_End-to-End_Multi-grained_Contrastive_Learning_Video-Text_Retrieval_year_2022.jsonl
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index 0000000000000000000000000000000000000000..eb68f1edc02b5d54ec8261691913683795d6b780
--- /dev/null
+++ b/data/sampled_jsons/X-CLIP_Ma_et_al_2022_End-to-End_Multi-grained_Contrastive_Learning_Video-Text_Retrieval_year_2022.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "X-CLIP: End-to-End Multi-grained Contrastive Learning for ... X-CLIP/README.md at main · xuguohai/X-CLIP · GitHub 【阅读笔记】X-CLIP: End-to-End Multi-grained Contrastive Learning... Images X-CLIP: End-to-End Multi-grained Contrastive Learning for ... X-CLIP: End-to-End Multi-grained Contrastive Learning for ... X-CLIP End-to-End Multi-grained Contrastive Learning for ... X-CLIP: End-to-End Multi-grained Contrastive Learning for ...", "date": "", "ddg_snippet": "Jul 15, 2022 · To this end , this paper presents a novel multi-grained contrastive model, namely X-CLIP , for video-text retrieval . However, another challenge lies in the similarity aggregation problem, which aims to aggregate fine- grained and cross- grained similarity matrices to instance-level similarity. Sep 20, 2022 · The implementation of paper X-CLIP : End-to-End Multi-grained Contrastive Learning for Video-Text Retrieval . Accepted by ACMMM22. By Yiwei Ma , Guohai Xu, Xiaoshuai Sun *, Ming Yan, Ji Zhang, Rongrong Ji. X-CLIP adopts cross- grained contrastive learning and attention over similarity matrix module to filter out unnecessary information during video-text retrieval . It achieves SOTA results on MSR ... 论文： X-CLIP : End-to-End Multi-grained Contrastive Learning for Video-Text Retrieval 源码： github.com/xuguohai/ X -C 概要 视频-文本检索一直是多模态研究中的一个关键和基础任务。 大规模多模态对比预训练显著推动了视频-文本检索的发展，主要集中在粗粒度或细粒度的对比上。 View all Oct 10, 2022 · Request PDF | On Oct 10, 2022 , Yiwei Ma and others published X-CLIP : End-to-End Multi-grained Contrastive Learning for Video-Text Retrieval | Find, read and cite all the research you need on ... Figure 1: X-CLIP aims for improving video-text retrieval performance via multi-grained contrastive learning , including fine- grained (frame-word), coarse- grained ( video -sentence) and cross- grained ( video -word, sentence-frame) contrast. The transparency ofwords and frames represents the degree of relevance to query. Jul 4, 2023 · This is my reading note for X-CLIP : End-to-End Multi-grained Contrastive Learning for Video-Text Retrieval . This paper proposes a method on extending clip to video data. it mostly studied how to aggregate the similarity score from the frame level to video level. Sep 20, 2022 · The implementation of paper X-CLIP : End-to-End Multi-grained Contrastive Learning for Video-Text Retrieval . Accepted by ACMMM22. By Yiwei Ma , Guohai Xu, Xiaoshuai Sun*, Ming Yan, Ji Zhang, Rongrong Ji. X-CLIP adopts cross- grained contrastive learning and attention over similarity matrix module to ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2207.07285", "content": "Jul 15, 2022 · To this end , this paper presents a novel multi-grained contrastive model, namely X-CLIP , for video-text retrieval . However, another challenge lies in the similarity aggregation problem, which aims to aggregate fine- grained and cross- grained similarity matrices to instance-level similarity. Sep 20, 2022 · The implementation of paper X-CLIP : End-to-End Multi-grained Contrastive Learning for Video-Text Retrieval . Accepted by ACMMM22. By Yiwei Ma , Guohai Xu, Xiaoshuai Sun *, Ming Yan, Ji Zhang, Rongrong Ji. X-CLIP adopts cross- grained contrastive learning and attention over similarity matrix module to filter out unnecessary information during video-text retrieval . It achieves SOTA results on MSR ... 论文： X-CLIP : End-to-End Multi-grained Contrastive Learning for Video-Text Retrieval 源码： github.com/xuguohai/ X -C 概要 视频-文本检索一直是多模态研究中的一个关键和基础任务。 大规模多模态对比预训练显著推动了视频-文本检索的发展，主要集中在粗粒度或细粒度的对比上。 View all Oct 10, 2022 · Request PDF | On Oct 10, 2022 , Yiwei Ma and others published X-CLIP : End-to-End Multi-grained Contrastive Learning for Video-Text Retrieval | Find, read and cite all the research you need on ... Figure 1: X-CLIP aims for improving video-text retrieval performance via multi-grained contrastive learning , including fine- grained (frame-word), coarse- grained ( video -sentence) and cross- grained ( video -word, sentence-frame) contrast. The transparency ofwords and frames represents the degree of relevance to query. Jul 4, 2023 · This is my reading note for X-CLIP : End-to-End Multi-grained Contrastive Learning for Video-Text Retrieval . This paper proposes a method on extending clip to video data. it mostly studied how to aggregate the similarity score from the frame level to video level. Sep 20, 2022 · The implementation of paper X-CLIP : End-to-End Multi-grained Contrastive Learning for Video-Text Retrieval . Accepted by ACMMM22. By Yiwei Ma , Guohai Xu, Xiaoshuai Sun*, Ming Yan, Ji Zhang, Rongrong Ji. X-CLIP adopts cross- grained contrastive learning and attention over similarity matrix module to ..."}
+{"idx": 1, "title": "X-CLIP/README.md at main · xuguohai/X-CLIP · GitHub", "date": "", "ddg_snippet": "Sep 20, 2022 · The implementation of paper X-CLIP : End-to-End Multi-grained Contrastive Learning for Video-Text Retrieval . Accepted by ACMMM22. By Yiwei Ma , Guohai Xu, Xiaoshuai Sun *, Ming Yan, Ji Zhang, Rongrong Ji. X-CLIP adopts cross- grained contrastive learning and attention over similarity matrix module to filter out unnecessary information during video-text retrieval . It achieves SOTA results on MSR ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/xuguohai/X-CLIP/blob/main/README.md", "content": "Sep 20, 2022 · The implementation of paper X-CLIP : End-to-End Multi-grained Contrastive Learning for Video-Text Retrieval . Accepted by ACMMM22. By Yiwei Ma , Guohai Xu, Xiaoshuai Sun *, Ming Yan, Ji Zhang, Rongrong Ji. X-CLIP adopts cross- grained contrastive learning and attention over similarity matrix module to filter out unnecessary information during video-text retrieval . It achieves SOTA results on MSR ..."}
+{"idx": 2, "title": "【阅读笔记】X-CLIP: End-to-End Multi-grained Contrastive Learning...", "date": "", "ddg_snippet": "论文： X-CLIP : End-to-End Multi-grained Contrastive Learning for Video-Text Retrieval 源码： github.com/xuguohai/ X -C 概要 视频-文本检索一直是多模态研究中的一个关键和基础任务。 大规模多模态对比预训练显著推动了视频-文本检索的发展，主要集中在粗粒度或细粒度的对比上。", "subpage_snippet": "", "source": "zhuanlan.zhihu.com", "link": "https://zhuanlan.zhihu.com/p/14470928505", "content": "论文： X-CLIP : End-to-End Multi-grained Contrastive Learning for Video-Text Retrieval 源码： github.com/xuguohai/ X -C 概要 视频-文本检索一直是多模态研究中的一个关键和基础任务。 大规模多模态对比预训练显著推动了视频-文本检索的发展，主要集中在粗粒度或细粒度的对比上。"}
+{"idx": 3, "title": "X-CLIP: End-to-End Multi-grained Contrastive Learning for ...", "date": "", "ddg_snippet": "Oct 10, 2022 · Request PDF | On Oct 10, 2022 , Yiwei Ma and others published X-CLIP : End-to-End Multi-grained Contrastive Learning for Video-Text Retrieval | Find, read and cite all the research you need on ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/364484274_X-CLIP_End-to-End_Multi-grained_Contrastive_Learning_for_Video-Text_Retrieval", "content": "Oct 10, 2022 · Request PDF | On Oct 10, 2022 , Yiwei Ma and others published X-CLIP : End-to-End Multi-grained Contrastive Learning for Video-Text Retrieval | Find, read and cite all the research you need on ..."}
+{"idx": 4, "title": "X-CLIP: End-to-End Multi-grained Contrastive Learning for ...", "date": "", "ddg_snippet": "Figure 1: X-CLIP aims for improving video-text retrieval performance via multi-grained contrastive learning , including fine- grained (frame-word), coarse- grained ( video -sentence) and cross- grained ( video -word, sentence-frame) contrast. The transparency ofwords and frames represents the degree of relevance to query.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/X-CLIP:-End-to-End-Multi-grained-Contrastive-for-Ma-Xu/1ec886e2235763b08fa606a5d5ea3f4540f715ec/figure/0", "content": "Figure 1: X-CLIP aims for improving video-text retrieval performance via multi-grained contrastive learning , including fine- grained (frame-word), coarse- grained ( video -sentence) and cross- grained ( video -word, sentence-frame) contrast. The transparency ofwords and frames represents the degree of relevance to query."}
+{"idx": 5, "title": "X-CLIP End-to-End Multi-grained Contrastive Learning for ...", "date": "", "ddg_snippet": "Jul 4, 2023 · This is my reading note for X-CLIP : End-to-End Multi-grained Contrastive Learning for Video-Text Retrieval . This paper proposes a method on extending clip to video data. it mostly studied how to aggregate the similarity score from the frame level to video level.", "subpage_snippet": "", "source": "zhangtemplar.github.io", "link": "https://zhangtemplar.github.io/xclip/", "content": "Jul 4, 2023 · This is my reading note for X-CLIP : End-to-End Multi-grained Contrastive Learning for Video-Text Retrieval . This paper proposes a method on extending clip to video data. it mostly studied how to aggregate the similarity score from the frame level to video level."}
+{"idx": 6, "title": "X-CLIP: End-to-End Multi-grained Contrastive Learning for ...", "date": "", "ddg_snippet": "Sep 20, 2022 · The implementation of paper X-CLIP : End-to-End Multi-grained Contrastive Learning for Video-Text Retrieval . Accepted by ACMMM22. By Yiwei Ma , Guohai Xu, Xiaoshuai Sun*, Ming Yan, Ji Zhang, Rongrong Ji. X-CLIP adopts cross- grained contrastive learning and attention over similarity matrix module to ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/xuguohai/X-CLIP", "content": "Sep 20, 2022 · The implementation of paper X-CLIP : End-to-End Multi-grained Contrastive Learning for Video-Text Retrieval . Accepted by ACMMM22. By Yiwei Ma , Guohai Xu, Xiaoshuai Sun*, Ming Yan, Ji Zhang, Rongrong Ji. X-CLIP adopts cross- grained contrastive learning and attention over similarity matrix module to ..."}
+{"idx": 7, "title": "X-CLIP: End-to-End Multi-grained Contrastive Learning for ...", "date": "", "ddg_snippet": "by Y Ma · 2022 · Cited by 381 — This paper presents a novel multi - grained contrastive model, namely X - CLIP , for video - text retrieval .", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3503161.3547910", "content": "by Y Ma · 2022 · Cited by 381 — This paper presents a novel multi - grained contrastive model, namely X - CLIP , for video - text retrieval ."}
+{"idx": 8, "title": "X-CLIP: End-to-End Multi-grained Contrastive Learning for ...", "date": "", "ddg_snippet": "by Y Ma · 2022 · Cited by 381 — Based on the above analysis, in this paper, we propose an end- to-end multi-grained contrast model , namely X-CLIP, for video- text retrieval.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2207.07285", "content": "by Y Ma · 2022 · Cited by 381 — Based on the above analysis, in this paper, we propose an end- to-end multi-grained contrast model , namely X-CLIP, for video- text retrieval."}
+{"idx": 9, "title": "Awesome Video-Text Retrieval by Deep Learning ...", "date": "", "ddg_snippet": "ACM Multimedia, 2022. [paper] [code]; [Ma et al. ACMMM22] X-C LIP : End-to-End Multi-grained Contrastive Learning for Video-Text Retrieval. ACM Multimedia, 2022.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/danieljf24/awesome-video-text-retrieval", "content": "ACM Multimedia, 2022. [paper] [code]; [Ma et al. ACMMM22] X-C LIP : End-to-End Multi-grained Contrastive Learning for Video-Text Retrieval. ACM Multimedia, 2022."}
diff --git a/data/sampled_jsons/X-CLIP_abstract_Video-text_retrieval_has_been_a_crucial_and_fundamental_task.jsonl b/data/sampled_jsons/X-CLIP_abstract_Video-text_retrieval_has_been_a_crucial_and_fundamental_task.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2583546fbc4f4dc371e03066e75994e01a3b9a13
--- /dev/null
+++ b/data/sampled_jsons/X-CLIP_abstract_Video-text_retrieval_has_been_a_crucial_and_fundamental_task.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2207.07285] X - CLIP : End-to-End Multi-grained Contrastive Learning...", "date": "", "ddg_snippet": "Abstract : Video - text retrieval has been a crucial and fundamental task in multi-modal research. The development of video - text retrieval has been considerably promoted by large-scale multi-modal contrastive pre-training, which primarily focuses on coarse-grained or fine-grained contrast.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2207.07285", "content": "Abstract : Video - text retrieval has been a crucial and fundamental task in multi-modal research. The development of video - text retrieval has been considerably promoted by large-scale multi-modal contrastive pre-training, which primarily focuses on coarse-grained or fine-grained contrast."}
+{"idx": 1, "title": "X - CLIP : End-to-End Multi-grained Contrastive Learning for Video - Text ...", "date": "", "ddg_snippet": "Abstract . Video - text retrieval has been a crucial and fundamental task in multi-modal research. The development of video - text retrieval has been considerably promoted by large-scale multi-modal contrastive pre-training, which primarily focuses on coarse-grained or fine-grained contrast.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/362066555_X-CLIP_End-to-End_Multi-grained_Contrastive_Learning_for_Video-Text_Retrieval", "content": "Abstract . Video - text retrieval has been a crucial and fundamental task in multi-modal research. The development of video - text retrieval has been considerably promoted by large-scale multi-modal contrastive pre-training, which primarily focuses on coarse-grained or fine-grained contrast."}
+{"idx": 2, "title": "X - CLIP - Project Page", "date": "", "ddg_snippet": "Abstract . Video - text retrieval has been a crucial and fundamental task in multi-modal research. The development of video - text retrieval has been considerably promoted by large-scale multi-modal contrastive pre-training, which primarily focuses on coarse-grained or finegrained contrast.", "subpage_snippet": "", "source": "xmu-xiaoma666.github.io", "link": "https://xmu-xiaoma666.github.io/Projects/MM22_XCLIP/", "content": "Abstract . Video - text retrieval has been a crucial and fundamental task in multi-modal research. The development of video - text retrieval has been considerably promoted by large-scale multi-modal contrastive pre-training, which primarily focuses on coarse-grained or finegrained contrast."}
+{"idx": 3, "title": "Prompt Switch: Efficient CLIP Adaptation for Text - Video Retrieval", "date": "", "ddg_snippet": "Text - Video retrieval is a task of great practical value and has received increasing attention, among which learning spatial-temporal video representation is one of the research hotspots.", "subpage_snippet": "", "source": "colab.ws", "link": "https://colab.ws/articles/10.1109/iccv51070.2023.01434", "content": "Text - Video retrieval is a task of great practical value and has received increasing attention, among which learning spatial-temporal video representation is one of the research hotspots."}
+{"idx": 4, "title": "Fine-grained Video Semantic Distillation for Video - Text Retrieval", "date": "", "ddg_snippet": "Abstract . In video - text retrieval , the objective is to align visual and textual information to rank text-video pairs based on their semantic relevance. The emergence of Large-scale Vision-Language Models (VLMs), particularly direct extensions of the CLIP framework...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3696409.3700287", "content": "Abstract . In video - text retrieval , the objective is to align visual and textual information to rank text-video pairs based on their semantic relevance. The emergence of Large-scale Vision-Language Models (VLMs), particularly direct extensions of the CLIP framework..."}
+{"idx": 5, "title": "Rethinking Noisy Video - Text Retrieval via Relation-aware Alignment", "date": "", "ddg_snippet": "Abstract . Video - Text Retrieval (VTR) is a core task in multi-modal understanding, drawing growing attention from both academia and industry in recent years.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Lai_Rethinking_Noisy_Video-Text_Retrieval_via_Relation-aware_Alignment_CVPR_2025_paper.pdf", "content": "Abstract . Video - Text Retrieval (VTR) is a core task in multi-modal understanding, drawing growing attention from both academia and industry in recent years."}
+{"idx": 6, "title": "Working with Text - Video Retrieval part7(Artificial Intelligence) | Medium", "date": "", "ddg_snippet": "Abstract : While recent progress in video - text retrieval has been advanced by the exploration of better representation learning, in this paper, we present a novel multi-grained sparse learning framework, S3MA...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@monocosmo77/working-with-text-video-retrieval-part7-artificial-intelligence-51518bce9513", "content": "Abstract : While recent progress in video - text retrieval has been advanced by the exploration of better representation learning, in this paper, we present a novel multi-grained sparse learning framework, S3MA..."}
+{"idx": 7, "title": "Multi-Granularity and Multi-modal Feature Interaction Approach for...", "date": "", "ddg_snippet": "Video - text retrieval has seen significant advancements, yet the ability of models to discern subtle differences in captions still requires verification. In this paper, we introduce a new approach for fine-grained evaluation.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/multi-granularity-multi-modal-feature-interaction-approach", "content": "Video - text retrieval has seen significant advancements, yet the ability of models to discern subtle differences in captions still requires verification. In this paper, we introduce a new approach for fine-grained evaluation."}
+{"idx": 8, "title": "Expertized Caption Auto-Enhancement for Video - Text Retrieval", "date": "", "ddg_snippet": "This issue is not entirely new, as the field of video - text retrieval has been evolving, but the paper claims to be pioneering in its approach to automatic caption enhancement, which is a novel contribution to the existing body of research .", "subpage_snippet": "", "source": "powerdrill.ai", "link": "https://powerdrill.ai/discover/summary-expertized-caption-auto-enhancement-for-video-text-cm6ttq1subkqq07n42u2c9ae8", "content": "This issue is not entirely new, as the field of video - text retrieval has been evolving, but the paper claims to be pioneering in its approach to automatic caption enhancement, which is a novel contribution to the existing body of research ."}
+{"idx": 9, "title": "CLIP 4 Video -Sampling: Global Semantics-Guided Multi-Granularity...", "date": "", "ddg_snippet": "Abstract . Video - text retrieval (VTR) is an essential task in multimodal learning, aiming to bridge the semantic gap between visual and textual data.", "subpage_snippet": "", "source": "www.scirp.org", "link": "https://www.scirp.org/pdf/jcc20241211_21732954.pdf", "content": "Abstract . Video - text retrieval (VTR) is an essential task in multimodal learning, aiming to bridge the semantic gap between visual and textual data."}
diff --git a/data/sampled_jsons/YFCC100M_dataset_what_does_YFCC_stand_for.jsonl b/data/sampled_jsons/YFCC100M_dataset_what_does_YFCC_stand_for.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..af567998811875a55ac06bbb076f4deeac155552
--- /dev/null
+++ b/data/sampled_jsons/YFCC100M_dataset_what_does_YFCC_stand_for.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Building Better Datasets: Seven Recommendations for Responsible", "date": "", "ddg_snippet": "Despite their institutional differences, all participants expressed encountering common challenges of dataset creation such as striving for data ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2409.00252v1", "content": "Despite their institutional differences, all participants expressed encountering common challenges of dataset creation such as striving for data ..."}
+{"idx": 1, "title": "DenseFusion-1M: Merging Vision Experts for Comprehensive", "date": "", "ddg_snippet": "To acquire detailed description datasets , recent studies seek help for the advanced GPT-4V model or human-in-the-loop strategy [ 8 , 7 , 58 , 57 , 17 ...", "subpage_snippet": "", "source": "kingsleylo.com", "link": "https://kingsleylo.com/article/densefusion-1m-merging-vision-experts-for-comprehensive-multimodal-perception", "content": "To acquire detailed description datasets , recent studies seek help for the advanced GPT-4V model or human-in-the-loop strategy [ 8 , 7 , 58 , 57 , 17 ..."}
+{"idx": 2, "title": "yahoo – Interdependent Thoughts", "date": "", "ddg_snippet": "This database is available for research purposes and known as the ‘ YFCC -100M ’ dataset . ... doesn ’ t return any telling results ...", "subpage_snippet": "", "source": "www.zylstra.org", "link": "https://www.zylstra.org/blog/tag/yahoo/", "content": "This database is available for research purposes and known as the ‘ YFCC -100M ’ dataset . ... doesn ’ t return any telling results ..."}
+{"idx": 3, "title": "creative commons – Interdependent Thoughts", "date": "", "ddg_snippet": "This database is available for research purposes and known as the ‘ YFCC -100M ’ dataset . ... doesn ’ t return any telling results ...", "subpage_snippet": "", "source": "www.zylstra.org", "link": "https://www.zylstra.org/blog/tag/creative-commons/", "content": "This database is available for research purposes and known as the ‘ YFCC -100M ’ dataset . ... doesn ’ t return any telling results ..."}
+{"idx": 4, "title": "Innovation – Page 3 – Interdependent Thoughts", "date": "", "ddg_snippet": "... forming and, through rail roads, changed what ... This database is available for research purposes and known as the ‘ YFCC -100M ’ dataset .", "subpage_snippet": "", "source": "www.zylstra.org", "link": "https://www.zylstra.org/blog/category/innovation-2/page/3/", "content": "... forming and, through rail roads, changed what ... This database is available for research purposes and known as the ‘ YFCC -100M ’ dataset ."}
+{"idx": 5, "title": "machinelearning – Interdependent Thoughts", "date": "", "ddg_snippet": "This database is available for research purposes and known as the ‘ YFCC -100M ’ dataset . ... doesn ’ t return any telling results ...", "subpage_snippet": "", "source": "www.zylstra.org", "link": "https://www.zylstra.org/blog/tag/machinelearning/", "content": "This database is available for research purposes and known as the ‘ YFCC -100M ’ dataset . ... doesn ’ t return any telling results ..."}
+{"idx": 6, "title": "creativecommons – Page 2 – Interdependent Thoughts", "date": "", "ddg_snippet": "This database is available for research purposes and known as the ‘ YFCC -100M ’ dataset . ... doesn ’ t return any telling results ...", "subpage_snippet": "", "source": "www.zylstra.org", "link": "https://www.zylstra.org/blog/tag/creativecommons/page/2/", "content": "This database is available for research purposes and known as the ‘ YFCC -100M ’ dataset . ... doesn ’ t return any telling results ..."}
+{"idx": 7, "title": "Basic visual analytics - LBSN Structure", "date": "", "ddg_snippet": "Imagine someone attempting to analyze geotagged photos from Social Media, such as those found in the YFCC100M dataset , to monitor user frequentation.", "subpage_snippet": "", "source": "lbsn.vgiscience.org", "link": "https://lbsn.vgiscience.org/tutorial/yfcc-geohash/", "content": "Imagine someone attempting to analyze geotagged photos from Social Media, such as those found in the YFCC100M dataset , to monitor user frequentation."}
+{"idx": 8, "title": "TiC-CLIP: Continual Training of CLIP Models", "date": "", "ddg_snippet": "TiC - YFCC We experiment with the 15M subset of YFCC100M (Thomee et al., 2016 ) , namely YFCC15M, selected by OpenAI (Radford et al., 2021 ) .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2310.16226v3", "content": "TiC - YFCC We experiment with the 15M subset of YFCC100M (Thomee et al., 2016 ) , namely YFCC15M, selected by OpenAI (Radford et al., 2021 ) ."}
+{"idx": 9, "title": "Visual Genome: Connecting Language and Vision Using", "date": "", "ddg_snippet": "Footnote 2 Instead, for each image in the Visual Genome dataset , we collect more than \\(50\\) descriptions for different regions in the image ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s11263-016-0981-7", "content": "Footnote 2 Instead, for each image in the Visual Genome dataset , we collect more than \\(50\\) descriptions for different regions in the image ..."}
diff --git a/data/sampled_jsons/YuMEUNNpeb_Position_Medical_Large_Language_Model_Benchmarks_Should_Prioritize_Construct_Validity_Fig.jsonl b/data/sampled_jsons/YuMEUNNpeb_Position_Medical_Large_Language_Model_Benchmarks_Should_Prioritize_Construct_Validity_Fig.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..34c987ad093c62eab53dd9b09d7691d027782d69
--- /dev/null
+++ b/data/sampled_jsons/YuMEUNNpeb_Position_Medical_Large_Language_Model_Benchmarks_Should_Prioritize_Construct_Validity_Fig.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Large language model - Wikipedia", "date": "", "ddg_snippet": "Machine learningand data mining. v. t. e. A large language model is a language model trained with self-supervised machine learning on a vast amount of text, designed for natural language processing tasks, especially language generation.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Large_language_model", "content": "Machine learningand data mining. v. t. e. A large language model is a language model trained with self-supervised machine learning on a vast amount of text, designed for natural language processing tasks, especially language generation."}
+{"idx": 1, "title": "Medical Large Language Model Benchmarks Should Prioritize ...", "date": "", "ddg_snippet": "Medical large language models (LLMs) research often makes bold claims, from encoding clinical knowledge to reasoning like a physician. These claims are usually backed by evaluation on competitive benchmarks —a tradition inherited from mainstream machine learning.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.10694v1", "content": "Medical large language models (LLMs) research often makes bold claims, from encoding clinical knowledge to reasoning like a physician. These claims are usually backed by evaluation on competitive benchmarks —a tradition inherited from mainstream machine learning."}
+{"idx": 2, "title": "ICML 2025 Statistics: Position Track - Paper Copilot", "date": "", "ddg_snippet": "Position : Medical Large Language Model Benchmarks Should Prioritize Construct Validity . Position : Language model developers should report train-test overlap. data. 3,4,4, 5 .", "subpage_snippet": "", "source": "papercopilot.com", "link": "https://papercopilot.com/statistics/icml-statistics/icml-2025-statistics-position-track/", "content": "Position : Medical Large Language Model Benchmarks Should Prioritize Construct Validity . Position : Language model developers should report train-test overlap. data. 3,4,4, 5 ."}
+{"idx": 3, "title": "Latest AI Research LLM Agents, Medical LLMs, And More", "date": "", "ddg_snippet": "Medical Large Language Model Benchmarks Should Prioritize Construct Validity argues that medical LLM benchmarks should prioritize construct validity . This means that the benchmarks should accurately measure the underlying abilities they are intended to assess.", "subpage_snippet": "", "source": "codemeld.org", "link": "https://codemeld.org/blog/latest-ai-research-llm-agents", "content": "Medical Large Language Model Benchmarks Should Prioritize Construct Validity argues that medical LLM benchmarks should prioritize construct validity . This means that the benchmarks should accurately measure the underlying abilities they are intended to assess."}
+{"idx": 4, "title": "Inioluwa Deborah Raji's research works | University of California...", "date": "", "ddg_snippet": "Medical Large Language Model Benchmarks Should Prioritize Construct Validity .In this position paper, we argue that medical LLM benchmarks should (and indeed can) be empirically evaluated for their construct validity .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/scientific-contributions/Inioluwa-Deborah-Raji-2148191730", "content": "Medical Large Language Model Benchmarks Should Prioritize Construct Validity .In this position paper, we argue that medical LLM benchmarks should (and indeed can) be empirically evaluated for their construct validity ."}
+{"idx": 5, "title": "Articles by Frances Dean | Synthical", "date": "", "ddg_snippet": "Medical Large Language Model Benchmarks Should Prioritize Construct Validity . 12 March 2025 by Ahmed Alaa and others.", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/profile/d8b88aa7-cbcf-4d81-a315-a1ff4be5f516/articles", "content": "Medical Large Language Model Benchmarks Should Prioritize Construct Validity . 12 March 2025 by Ahmed Alaa and others."}
+{"idx": 6, "title": "ICML 2025 Sneak Peek: The New Laws of AI | Medium", "date": "", "ddg_snippet": "Another paper, “ Position : Medical Large Language Model Benchmarks Should Prioritize Construct Validity ,” argues that current benchmarks for high-stakes domains like medicine fail to meet the rigorous standards of scientific validity required for trustworthy evaluation.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/foundation-models-deep-dive/icml-2025-sneak-peek-the-new-laws-of-ai-1210789c66a2", "content": "Another paper, “ Position : Medical Large Language Model Benchmarks Should Prioritize Construct Validity ,” argues that current benchmarks for high-stakes domains like medicine fail to meet the rigorous standards of scientific validity required for trustworthy evaluation."}
+{"idx": 7, "title": "Tom Hartvigsen - Publications", "date": "", "ddg_snippet": "Medical Large Language Model Benchmarks Should Prioritize Construct Validity .", "subpage_snippet": "", "source": "www.tomhartvigsen.com", "link": "https://www.tomhartvigsen.com/publications/", "content": "Medical Large Language Model Benchmarks Should Prioritize Construct Validity ."}
+{"idx": 8, "title": "Franny Dean - Google Scholar", "date": "", "ddg_snippet": "2021. Position : Medical Large Language Model Benchmarks Should Prioritize Construct Validity .", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=Y04UhM4AAAAJ&hl=en", "content": "2021. Position : Medical Large Language Model Benchmarks Should Prioritize Construct Validity ."}
+{"idx": 9, "title": "GitHub - Xuchen-Li/llm-arxiv-daily: Automatically update arXiv papers...", "date": "", "ddg_snippet": "PRS-Med: Position Reasoning Segmentation with Vision- Language Model in Medical Imaging. Quoc-Huy Trinh et.al. Medical Large Language Model Benchmarks Should Prioritize Construct Validity .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Xuchen-Li/llm-arxiv-daily", "content": "PRS-Med: Position Reasoning Segmentation with Vision- Language Model in Medical Imaging. Quoc-Huy Trinh et.al. Medical Large Language Model Benchmarks Should Prioritize Construct Validity ."}
diff --git a/data/sampled_jsons/Yujie_Zhao_Jose_Efraim_Aguilar_Escamilla_Weyl_Lu_Huazheng_Wang_risk-aware_preference-based_reinforce.jsonl b/data/sampled_jsons/Yujie_Zhao_Jose_Efraim_Aguilar_Escamilla_Weyl_Lu_Huazheng_Wang_risk-aware_preference-based_reinforce.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b491e93825748fd6937c0f4f31dcea684140a81b
--- /dev/null
+++ b/data/sampled_jsons/Yujie_Zhao_Jose_Efraim_Aguilar_Escamilla_Weyl_Lu_Huazheng_Wang_risk-aware_preference-based_reinforce.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2410.23569] RA-PbRL: Provably Efficient Risk - Aware ...", "date": "", "ddg_snippet": "Authors: Yujie Zhao , Jose Efraim Aguilar Escamill, Weyl Lu , Huazheng Wang . View a PDF of the paper titled RA-PbRL: Provably Efficient Risk - Aware Preference - Based Reinforcement Learning , by Yujie Zhao and 3 other authors.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.23569", "content": "Authors: Yujie Zhao , Jose Efraim Aguilar Escamill, Weyl Lu , Huazheng Wang . View a PDF of the paper titled RA-PbRL: Provably Efficient Risk - Aware Preference - Based Reinforcement Learning , by Yujie Zhao and 3 other authors."}
+{"idx": 1, "title": "RA-PbRL: Provably Efficient Risk - Aware Preference - Based ...", "date": "", "ddg_snippet": "Yujie Zhao , Jose Aguilar Escamilla , Weyl Lu , Huazheng Wang . Abstract. Reinforcement Learning from Human Feedback (RLHF) has recently surged in popularity, particularly for aligning large language models and other AI systems with human intentions. At its core, RLHF can be...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/hash/7016d7b7b6e3c05b2128ac5b3aae492d-Abstract-Conference.html", "content": "Yujie Zhao , Jose Aguilar Escamilla , Weyl Lu , Huazheng Wang . Abstract. Reinforcement Learning from Human Feedback (RLHF) has recently surged in popularity, particularly for aligning large language models and other AI systems with human intentions. At its core, RLHF can be..."}
+{"idx": 2, "title": "RA-PbRL: Provably Efficient Risk - Aware", "date": "", "ddg_snippet": "RA-PbRL: Provably Efficient Risk - Aware Preference - Based Reinforcement Learning . Yujie Zhao 1, Jose Efraim Aguilar Escamill2, Weyl Lu 3, Huazheng Wang 2 1 University of California, San Diego, 2 Oregon State University, 3 University of California, Davis.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=JNDcFOczOf", "content": "RA-PbRL: Provably Efficient Risk - Aware Preference - Based Reinforcement Learning . Yujie Zhao 1, Jose Efraim Aguilar Escamill2, Weyl Lu 3, Huazheng Wang 2 1 University of California, San Diego, 2 Oregon State University, 3 University of California, Davis."}
+{"idx": 3, "title": "RA-PbRL: Provably Efficient Risk - Aware Preference - Based ...", "date": "", "ddg_snippet": "Authors: Yujie Zhao , Jose Efraim Aguilar Escamilla , Weyl Lu , Huazheng Wang . Preference - based Reinforcement Learning ( PbRL ) studies the problem where agents receive only preferences over pairs of trajectories in each episode.", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/venue/JNDcFOczOf@OpenReview", "content": "Authors: Yujie Zhao , Jose Efraim Aguilar Escamilla , Weyl Lu , Huazheng Wang . Preference - based Reinforcement Learning ( PbRL ) studies the problem where agents receive only preferences over pairs of trajectories in each episode."}
+{"idx": 4, "title": "RA-PbRL: Provably Efficient Risk - Aware Preference - Based ...", "date": "", "ddg_snippet": "Preference - based Reinforcement Learning (PbRL) studies the problem where agents receive only preferences over pairs of trajectories in each episode. Traditional approaches in this field have predominantly focused on the mean reward or utility criterion.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/ra-pbrl-provably-efficient-risk-aware-preference", "content": "Preference - based Reinforcement Learning (PbRL) studies the problem where agents receive only preferences over pairs of trajectories in each episode. Traditional approaches in this field have predominantly focused on the mean reward or utility criterion."}
+{"idx": 5, "title": "Weyl LU | Master's Student | University of California, Davis, Davis | UCD", "date": "", "ddg_snippet": "Weyl Lu . Huazheng Wang Huazheng Wang . Preference - based Reinforcement Learning (PbRL) studies the problem where agents receive only preferences over pairs of trajectories in each episode.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/profile/Weyl-Lu", "content": "Weyl Lu . Huazheng Wang Huazheng Wang . Preference - based Reinforcement Learning (PbRL) studies the problem where agents receive only preferences over pairs of trajectories in each episode."}
+{"idx": 6, "title": "Huazheng Wang (0000-0003-3918-6925) - ORCID", "date": "", "ddg_snippet": "RA-PbRL: Provably Efficient Risk - Aware Preference - Based Reinforcement Learning . The Thirty-eighth Annual Conference on Neural Information Processing Systems. 2024 | Conference paper. URIContributors : Zhao , Yujie ; Aguilar Escamilla , Jose ; Lu , Weyl ; Wang , Huazheng .", "subpage_snippet": "", "source": "orcid.org", "link": "https://orcid.org/0000-0003-3918-6925", "content": "RA-PbRL: Provably Efficient Risk - Aware Preference - Based Reinforcement Learning . The Thirty-eighth Annual Conference on Neural Information Processing Systems. 2024 | Conference paper. URIContributors : Zhao , Yujie ; Aguilar Escamilla , Jose ; Lu , Weyl ; Wang , Huazheng ."}
+{"idx": 7, "title": "dblp: List of computer science publications by Huazheng Wang", "date": "", "ddg_snippet": "Yujie Zhao , Jose E. Aguilar Escamilla , Weyl Lu , Huazheng Wang : RA-PbRL: Provably Efficient Risk - Aware Preference - Based Reinforcement Learning . NeurIPS 2024.", "subpage_snippet": "", "source": "dblp.uni-trier.de", "link": "https://dblp.uni-trier.de/pid/163/2233.html", "content": "Yujie Zhao , Jose E. Aguilar Escamilla , Weyl Lu , Huazheng Wang : RA-PbRL: Provably Efficient Risk - Aware Preference - Based Reinforcement Learning . NeurIPS 2024."}
+{"idx": 8, "title": "dblp: Yujie Zhao (disambiguation)", "date": "", "ddg_snippet": "Yujie Zhao , Jose Aguilar Escamilla , Weyl Lu , Huazheng Wang : RA-PbRL: Provably Efficient Risk - Aware Preference - Based Reinforcement Learning . NeurIPS 2024.", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/pid/08/8933.html", "content": "Yujie Zhao , Jose Aguilar Escamilla , Weyl Lu , Huazheng Wang : RA-PbRL: Provably Efficient Risk - Aware Preference - Based Reinforcement Learning . NeurIPS 2024."}
+{"idx": 9, "title": "Huazheng Wang", "date": "", "ddg_snippet": "[09/2024] One paper on risk - aware preference - based RL is accepted by NeurIPS 2024.RA-PbRL: Provably Efficient Risk - Aware Preference - Based Reinforcement Learning Yujie Zhao , Jose Aguilar Escamilla , Weyl Lu , Huazheng Wang .", "subpage_snippet": "", "source": "huazhengwang.github.io", "link": "https://huazhengwang.github.io/", "content": "[09/2024] One paper on risk - aware preference - based RL is accepted by NeurIPS 2024.RA-PbRL: Provably Efficient Risk - Aware Preference - Based Reinforcement Learning Yujie Zhao , Jose Aguilar Escamilla , Weyl Lu , Huazheng Wang ."}
diff --git a/data/sampled_jsons/Zhang_et_al._2024a_initialization_scale_reasoning_language_models.jsonl b/data/sampled_jsons/Zhang_et_al._2024a_initialization_scale_reasoning_language_models.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..45057a7fafb5bd50660ceed4a34faa5c85e8ace4
--- /dev/null
+++ b/data/sampled_jsons/Zhang_et_al._2024a_initialization_scale_reasoning_language_models.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "An Analysis for Reasoning Bias of Language Models with Small Initialization", "date": "", "ddg_snippet": "An alternative approach to enhancing the reasoning abil-ity of LLMs focuses on the model architecture and its training process. In one such study examining the use of Transformers to model compositional functions, it was ob-served that the scale of model parameter initialization sig-nificantly impacts the model's reasoning behavior ( Zhang et al ., 2024a ; 2025). Specifically, smaller ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.04375", "content": "An alternative approach to enhancing the reasoning abil-ity of LLMs focuses on the model architecture and its training process. In one such study examining the use of Transformers to model compositional functions, it was ob-served that the scale of model parameter initialization sig-nificantly impacts the model's reasoning behavior ( Zhang et al ., 2024a ; 2025). Specifically, smaller ..."}
+{"idx": 1, "title": "Zhongwang Zhang (张众望) - Homepage", "date": "", "ddg_snippet": "An Analysis for Reasoning Bias of Language Models with Small Initialization Junjie Yao, Zhongwang Zhang †, Zhi-Qin John Xu† This paper reveals how initialization scales shape transformer-based models' task preferences: smaller scales induce reasoning bias through structured embeddings, while larger scales promote memorization. We attribute this to differential label-driven embedding ...", "subpage_snippet": "", "source": "sjtuzzw.github.io", "link": "https://sjtuzzw.github.io/", "content": "An Analysis for Reasoning Bias of Language Models with Small Initialization Junjie Yao, Zhongwang Zhang †, Zhi-Qin John Xu† This paper reveals how initialization scales shape transformer-based models' task preferences: smaller scales induce reasoning bias through structured embeddings, while larger scales promote memorization. We attribute this to differential label-driven embedding ..."}
+{"idx": 2, "title": "PDF Small Language Models Need Strong Verifiers to Self-Correct Reasoning", "date": "", "ddg_snippet": "Abstract Self-correction has emerged as a promising so-lution to boost the reasoning performance of large language models (LLMs), where LLMs refine their solutions using self-generated cri-tiques that pinpoint the errors. This work ex-plores whether small (≤ 13B) language models (LMs) have the ability of self-correction on rea-soning tasks with minimal inputs from stronger LMs. We propose a ...", "subpage_snippet": "", "source": "web.eecs.umich.edu", "link": "https://web.eecs.umich.edu/~wangluxy/papers/ACL2024_zhang_etal.pdf", "content": "Abstract Self-correction has emerged as a promising so-lution to boost the reasoning performance of large language models (LLMs), where LLMs refine their solutions using self-generated cri-tiques that pinpoint the errors. This work ex-plores whether small (≤ 13B) language models (LMs) have the ability of self-correction on rea-soning tasks with minimal inputs from stronger LMs. We propose a ..."}
+{"idx": 3, "title": "An Analysis for Reasoning Bias of Language Models with Small Initialization", "date": "", "ddg_snippet": "Transformer-based Large Language Models (LLMs) have revolutionized Natural Language Processing by demonstrating exceptional performance across diverse tasks. This study investigates the impact of the parameter initialization scale on the training behavior and task preferences of LLMs. We discover that smaller initialization scales encourage models to favor reasoning tasks, whereas larger ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.04375", "content": "Transformer-based Large Language Models (LLMs) have revolutionized Natural Language Processing by demonstrating exceptional performance across diverse tasks. This study investigates the impact of the parameter initialization scale on the training behavior and task preferences of LLMs. We discover that smaller initialization scales encourage models to favor reasoning tasks, whereas larger ..."}
+{"idx": 4, "title": "An Analysis for Reasoning Bias of Language Models with Small Initialization", "date": "", "ddg_snippet": "An alternative approach to enhancing the reasoning ability of LLMs focuses on the model architecture and its training process. In one such study examining the use of Transformers to model compositional functions, it was observed that the scale of model parameter initialization significantly impacts the model's reasoning behavior ( Zhang et al ., 2024a , 2025). Specifically, smaller ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.04375v2", "content": "An alternative approach to enhancing the reasoning ability of LLMs focuses on the model architecture and its training process. In one such study examining the use of Transformers to model compositional functions, it was observed that the scale of model parameter initialization significantly impacts the model's reasoning behavior ( Zhang et al ., 2024a , 2025). Specifically, smaller ..."}
+{"idx": 5, "title": "Explanations from Large Language Models Make Small Reasoners Better", "date": "", "ddg_snippet": "Integrating free-text explanations to in-context learning of large language models (LLMs) is shown to elicit strong reasoning capabilities along with reasonable explanations. However, deploying them at scale is costly expensive in real-world applications, limiting their usage.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=rH8ZUcfL9r", "content": "Integrating free-text explanations to in-context learning of large language models (LLMs) is shown to elicit strong reasoning capabilities along with reasonable explanations. However, deploying them at scale is costly expensive in real-world applications, limiting their usage."}
+{"idx": 6, "title": "An Analysis for Reasoning Bias of Language Models with Small ...", "date": "", "ddg_snippet": "This research investigates how the parameter initialization scale influences the training dynamics and task preferences of Large Language Models . The study reveals that smaller initialization scales lead LLMs to favor reasoning tasks by promoting differentiated embedding spaces and specialized self-attention mechanisms, whereas larger scales bias models towards memorization.", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2502.04375v2", "content": "This research investigates how the parameter initialization scale influences the training dynamics and task preferences of Large Language Models . The study reveals that smaller initialization scales lead LLMs to favor reasoning tasks by promoting differentiated embedding spaces and specialized self-attention mechanisms, whereas larger scales bias models towards memorization."}
+{"idx": 7, "title": "Improved Logical Reasoning of Language Models via Differentiable ...", "date": "", "ddg_snippet": "Abstract Pre-trained large language models (LMs) struggle to perform logical reasoning reliably despite advances in scale and compositionality. In this work, we tackle this challenge through the lens of symbolic programming.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2023.findings-acl.191/", "content": "Abstract Pre-trained large language models (LMs) struggle to perform logical reasoning reliably despite advances in scale and compositionality. In this work, we tackle this challenge through the lens of symbolic programming."}
+{"idx": 8, "title": "An Analysis for Reasoning Bias of Language Models with Small Initialization", "date": "", "ddg_snippet": "Spotlight Poster An Analysis for Reasoning Bias of Language Models with Small Initialization Junjie Yao · zhongwang zhang · Zhi-Qin John Xu East Exhibition Hall A-B #E-2307", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46492", "content": "Spotlight Poster An Analysis for Reasoning Bias of Language Models with Small Initialization Junjie Yao · zhongwang zhang · Zhi-Qin John Xu East Exhibition Hall A-B #E-2307"}
+{"idx": 9, "title": "PDF Small Language Models Need Strong Veriers to Self-Correct Reasoning", "date": "", "ddg_snippet": "In this work, we focus on the self-correction abilities of small, open-source language models (LMs).2Previous studies have shown that these smaller models can learn self- correction in reasoning through distillation from stronger LMs (Yu et al.,2023b;An et al.,2023; Han et al.,2024).", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2024.findings-acl.924.pdf", "content": "In this work, we focus on the self-correction abilities of small, open-source language models (LMs).2Previous studies have shown that these smaller models can learn self- correction in reasoning through distillation from stronger LMs (Yu et al.,2023b;An et al.,2023; Han et al.,2024)."}
diff --git a/data/sampled_jsons/Zhao_2023b_peeling_technique_bandit_algorithms.jsonl b/data/sampled_jsons/Zhao_2023b_peeling_technique_bandit_algorithms.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..caeeb3a088153902e074ccf8d69860e2095b500a
--- /dev/null
+++ b/data/sampled_jsons/Zhao_2023b_peeling_technique_bandit_algorithms.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[1405.3536] Improving offline evaluation of contextual bandit ...", "date": "", "ddg_snippet": "Online learning algorithms seem to be the most straight forward solution. The contextual bandit framework was introduced for that very purpose. In general the evaluation of a RS is a critical issue.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1405.3536", "content": "Online learning algorithms seem to be the most straight forward solution. The contextual bandit framework was introduced for that very purpose. In general the evaluation of a RS is a critical issue."}
+{"idx": 1, "title": "Minimax Concave Penalized Multi-Armed Bandit Model with...", "date": "", "ddg_snippet": "In this paper, we propose a Minimax Concave Penalized Multi-Armed Bandit (MCP- Bandit ) algorithm for a decision-maker facing high-dimensional data with latent sparse structure in an online learning and decision-making process.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v80/wang18j.html", "content": "In this paper, we propose a Minimax Concave Penalized Multi-Armed Bandit (MCP- Bandit ) algorithm for a decision-maker facing high-dimensional data with latent sparse structure in an online learning and decision-making process."}
+{"idx": 2, "title": "Adaptive Portfolio by Solving Multi-armed Bandit via Thompson...", "date": "", "ddg_snippet": "In this paper, we present a portfolio bandit strategy through Thompson sampling which aims to make online portfolio choices by effectively exploiting the performances among multiple arms.", "subpage_snippet": "", "source": "ideas.repec.org", "link": "https://ideas.repec.org/p/arx/papers/1911.05309.html", "content": "In this paper, we present a portfolio bandit strategy through Thompson sampling which aims to make online portfolio choices by effectively exploiting the performances among multiple arms."}
+{"idx": 3, "title": "Tree Ensembles for Contextual Bandits", "date": "", "ddg_snippet": "Zhu & Van Roy (2023) provide a thorough review of different neural bandit methods and suggest a method based on epistemic neural networks (Osband et al., 2021) for more efficient uncertainty modeling...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=59DCkSGw8S", "content": "Zhu & Van Roy (2023) provide a thorough review of different neural bandit methods and suggest a method based on epistemic neural networks (Osband et al., 2021) for more efficient uncertainty modeling..."}
+{"idx": 4, "title": "Multi-Armed Bandit Approach to Portfolio Choice Problem – BSE Voice", "date": "", "ddg_snippet": "The drop in the bandit algorithms ’ cumulative wealth is more severe compared to classic allocation strategies such as EW or MVP. Especially PW-UCB1, this also can be seen from standard deviation of the returns.", "subpage_snippet": "", "source": "thevoice.bse.eu", "link": "https://thevoice.bse.eu/2020/09/16/multi-armed-bandit-approach-portfolio-choice-problem/", "content": "The drop in the bandit algorithms ’ cumulative wealth is more severe compared to classic allocation strategies such as EW or MVP. Especially PW-UCB1, this also can be seen from standard deviation of the returns."}
+{"idx": 5, "title": "LeanBandits | Reservoir", "date": "", "ddg_snippet": "RemyDegenne/lean- bandits . Bandit algorithms in Lean. Under construction.", "subpage_snippet": "", "source": "reservoir.lean-lang.org", "link": "https://reservoir.lean-lang.org/@RemyDegenne/LeanBandits", "content": "RemyDegenne/lean- bandits . Bandit algorithms in Lean. Under construction."}
+{"idx": 6, "title": "Bandit Algorithms for Website Optimization | John Myles White", "date": "", "ddg_snippet": "This concise book shows you how to use Multiarmed Bandit algorithms to measure the real-world value of any modifications you make to your site.", "subpage_snippet": "", "source": "balabat.com", "link": "https://balabat.com/book/1317360/790fa5/bandit-algorithms-for-website-optimization.html?dsource=recommend", "content": "This concise book shows you how to use Multiarmed Bandit algorithms to measure the real-world value of any modifications you make to your site."}
+{"idx": 7, "title": "A/B Testing in the AI Era: Optimizing for Humans and Machines", "date": "", "ddg_snippet": "Multi-armed bandit algorithms revolutionize this process by adjusting traffic allocation continuously based on real-time performance. When Variant B demonstrates higher conversion after initial visitors, the algorithm might shift to a 70/30 split, capturing more value while still learning.", "subpage_snippet": "", "source": "www.webstacks.com", "link": "https://www.webstacks.com/blog/ai-ab-testing", "content": "Multi-armed bandit algorithms revolutionize this process by adjusting traffic allocation continuously based on real-time performance. When Variant B demonstrates higher conversion after initial visitors, the algorithm might shift to a 70/30 split, capturing more value while still learning."}
+{"idx": 8, "title": "One moment, please...", "date": "", "ddg_snippet": "Algorithms for Optimization (Mykel J. Kochenderfer, et al.)", "subpage_snippet": "", "source": "algorithmsbook.com", "link": "https://algorithmsbook.com/optimization/files/optimization.pdf", "content": "Algorithms for Optimization (Mykel J. Kochenderfer, et al.)"}
+{"idx": 9, "title": "STATISTICAL SCIENCE", "date": "", "ddg_snippet": "Nearly min-imax algorithms for linear bandits with shared representation. arXiv preprint. Available at arXiv:2203.15664.", "subpage_snippet": "", "source": "imstat.org", "link": "https://imstat.org/publications/sts/sts_40_3/sts_40_3.pdf", "content": "Nearly min-imax algorithms for linear bandits with shared representation. arXiv preprint. Available at arXiv:2203.15664."}
diff --git a/data/sampled_jsons/Zhao_et_al.,_2023b_bandit_peeling_technique.jsonl b/data/sampled_jsons/Zhao_et_al.,_2023b_bandit_peeling_technique.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f53dc96ac3a7d6445dee4dec058db70e44e943ef
--- /dev/null
+++ b/data/sampled_jsons/Zhao_et_al.,_2023b_bandit_peeling_technique.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Catoni Contextual Bandits are Robust to Heavy-tailed Rewards", "date": "", "ddg_snippet": "Zhao et al . ( 2023 b ) develop a peeling approach for the unknown variance case without variance estimation in linear settings, and Pacchiano (2024) extend this technique to general function approxima-tion.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=5IpVe9PH14", "content": "Zhao et al . ( 2023 b ) develop a peeling approach for the unknown variance case without variance estimation in linear settings, and Pacchiano (2024) extend this technique to general function approxima-tion."}
+{"idx": 1, "title": "From Intentions to Techniques : A Comprehensive Taxonomy and", "date": "", "ddg_snippet": "(2023) and Wu et al . ( 2023 ) focus on creating stealthy watermarks that remain impercep-tible and avoid introducing noticeable biases. By adjusting the output logits of LLMs, these methods preserve the original text distribution and minimize the likelihood of detection.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.findings-naacl.343.pdf", "content": "(2023) and Wu et al . ( 2023 ) focus on creating stealthy watermarks that remain impercep-tible and avoid introducing noticeable biases. By adjusting the output logits of LLMs, these methods preserve the original text distribution and minimize the likelihood of detection."}
+{"idx": 2, "title": "Abstract arXiv:2502.02486v1 [stat.ML] 4 Feb 2025", "date": "", "ddg_snippet": "al., 2021; Zhao et al., 2023b ; Pacchiano, 2024). Particu-larly, the most relevant ones to our work for the unknown-variance s tting are Zhao et al. (2023b ); Pacchiano (2024). Zhao et al. (2023b ) develop a peeling approach for the unknown variance case without variance es-timation in linear settings, and Pacchiano (2024) extend", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.02486", "content": "al., 2021; Zhao et al., 2023b ; Pacchiano, 2024). Particu-larly, the most relevant ones to our work for the unknown-variance s tting are Zhao et al. (2023b ); Pacchiano (2024). Zhao et al. (2023b ) develop a peeling approach for the unknown variance case without variance es-timation in linear settings, and Pacchiano (2024) extend"}
+{"idx": 3, "title": "Interlude - Halfseekers (Pt. 4) - The Wandering Inn", "date": "", "ddg_snippet": "Sep 15, 2025 · Even the young half-Elf tried her hand at it, though she didn’t have the technique down yet. You had to wait for a lull in the voices and have the knowing tone just right or you’d get laughed at. And the boldness—she stuttered, red-face. “I, um, I have one! There was a half-Elf [Captain] named Inerrook!", "subpage_snippet": "", "source": "wanderinginn.com", "link": "https://wanderinginn.com/2025/09/15/interlude-halfseekers-pt-4/", "content": "Sep 15, 2025 · Even the young half-Elf tried her hand at it, though she didn’t have the technique down yet. You had to wait for a lull in the voices and have the knowing tone just right or you’d get laughed at. And the boldness—she stuttered, red-face. “I, um, I have one! There was a half-Elf [Captain] named Inerrook!"}
+{"idx": 4, "title": "The Alignment Problem - Brian Christian - PDFCOFFEE.COM", "date": "", "ddg_snippet": "The team decided to take a number of different word pairs of this type—woman − man, but also she − he, gal − guy, and so forth—and then use a technique called principal component analysis (PCA) to isolate the axis that explained the greatest amount of the difference in these pairs: presumably, gender.79 Then their task was to try to ...", "subpage_snippet": "", "source": "pdfcoffee.com", "link": "https://pdfcoffee.com/the-alignment-problem-brian-christian-pdf-free.html", "content": "The team decided to take a number of different word pairs of this type—woman − man, but also she − he, gal − guy, and so forth—and then use a technique called principal component analysis (PCA) to isolate the axis that explained the greatest amount of the difference in these pairs: presumably, gender.79 Then their task was to try to ..."}
+{"idx": 5, "title": "Watermark under Fire: A Robustness Evaluation of LLM Watermarking", "date": "", "ddg_snippet": "UG ( Zhao et al ., 2023 ) is not robust due to its context-free design (Piet et al ., 2023 ) . UG shows higher resilience than other watermarkers to paraphrasing attacks.(2024) provides a comprehensive overview of watermarking techniques .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.13425v3", "content": "UG ( Zhao et al ., 2023 ) is not robust due to its context-free design (Piet et al ., 2023 ) . UG shows higher resilience than other watermarkers to paraphrasing attacks.(2024) provides a comprehensive overview of watermarking techniques ."}
+{"idx": 6, "title": "MoE-Mamba: Efficient Selective State Space... | Read Paper on Bytez", "date": "", "ddg_snippet": "We train the models using PyTorch (Paszke et al ., 2019) and utilize FSDP ( Zhao et al ., 2023 b ) for facilitating multi-GPU setup.", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/arxiv/2401.04081/paper", "content": "We train the models using PyTorch (Paszke et al ., 2019) and utilize FSDP ( Zhao et al ., 2023 b ) for facilitating multi-GPU setup."}
+{"idx": 7, "title": "What Factors Affect Multi-Modal In-Context", "date": "", "ddg_snippet": "[2024] and Zhao et al . [2024] develop task-specific MM-ICL templates during the IT stage, further extending its capabilities across more domains. Li et al . [ 2023 a] introduce OtterHD, adapting MM-ICL for high-definition image tasks.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/deeb4d6bdb5860fd7faf321dd5486d25-Paper-Conference.pdf", "content": "[2024] and Zhao et al . [2024] develop task-specific MM-ICL templates during the IT stage, further extending its capabilities across more domains. Li et al . [ 2023 a] introduce OtterHD, adapting MM-ICL for high-definition image tasks."}
+{"idx": 8, "title": "Breaking Down the Defenses: A Comparative Survey of Attacks on Large", "date": "", "ddg_snippet": "Zhao et al . ( 2023 a) study how LLMs learn and forget unsafe examples during fine-tuning, proposing a technique called ForgetFilter to filter fine-tuning data and improve safety without sacri-ficing performance.", "subpage_snippet": "", "source": "amanchadha.com", "link": "https://amanchadha.com/research/2403.04786.pdf", "content": "Zhao et al . ( 2023 a) study how LLMs learn and forget unsafe examples during fine-tuning, proposing a technique called ForgetFilter to filter fine-tuning data and improve safety without sacri-ficing performance."}
+{"idx": 9, "title": "Expediting multiple biological properties of main bioactive compounds...", "date": "", "ddg_snippet": "( Zhao et al . 2023 b ) investigated its antibacterial effect and mechanism against methicillin resistant Staphylococcus aureus (MRSA).A more recent study on MRSA used network analysis and molecular docking to explore antibacterial activity ( Zhao et al . 2023 b ).", "subpage_snippet": "", "source": "amb-express.springeropen.com", "link": "https://amb-express.springeropen.com/articles/10.1186/s13568-025-01911-8", "content": "( Zhao et al . 2023 b ) investigated its antibacterial effect and mechanism against methicillin resistant Staphylococcus aureus (MRSA).A more recent study on MRSA used network analysis and molecular docking to explore antibacterial activity ( Zhao et al . 2023 b )."}
diff --git a/data/sampled_jsons/Zhao_et_al._2023b_bandit_peeling.jsonl b/data/sampled_jsons/Zhao_et_al._2023b_bandit_peeling.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f5bf7525fbc96f90172b46e4356d2b8b8e1f6f7a
--- /dev/null
+++ b/data/sampled_jsons/Zhao_et_al._2023b_bandit_peeling.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Research Papers - Yunfan Zhao", "date": "", "ddg_snippet": "A Decision-Language Model (DLM) for Dynamic Restless Multi-Armed Bandit Tasks in Public Health. Nikhil Behari, Edwin Zhang, Yunfan Zhao , Dheeraj Nagaraj, Aparna Taneja, and Milind Tambe.", "subpage_snippet": "", "source": "yzhao3685.github.io", "link": "https://yzhao3685.github.io/research/", "content": "A Decision-Language Model (DLM) for Dynamic Restless Multi-Armed Bandit Tasks in Public Health. Nikhil Behari, Edwin Zhang, Yunfan Zhao , Dheeraj Nagaraj, Aparna Taneja, and Milind Tambe."}
+{"idx": 1, "title": "Towards Optimal Regret in Adversarial Linear MDPs with Bandit Feedback", "date": "", "ddg_snippet": "Both our results significantly improve over the state-of-the-art: a computationally inefficient algorithm by Kong et al. [2023] with eO(K3/4) can be arbitrarily close to zero, and a computationally efficient algorithm by Sherman et al. [ 2023b ] with regret. eO(K4/5 + poly(1/λmin)) regret, for some problem-dependent constant λmin that eO(K6/7)", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2310.11550", "content": "Both our results significantly improve over the state-of-the-art: a computationally inefficient algorithm by Kong et al. [2023] with eO(K3/4) can be arbitrarily close to zero, and a computationally efficient algorithm by Sherman et al. [ 2023b ] with regret. eO(K4/5 + poly(1/λmin)) regret, for some problem-dependent constant λmin that eO(K6/7)"}
+{"idx": 2, "title": "Li, H., Zhao, L. et al. (2023b). Application and Effect Analysis of JCI ...", "date": "", "ddg_snippet": "Li, H., Zhao , L. et al. ( 2023b ). Application and Effect Analysis of JCI Standards in Hospital Equipment Maintenance Management. China Medical Devices, 38, 145-148.", "subpage_snippet": "", "source": "www.scirp.org", "link": "https://www.scirp.org/reference/referencespapers?referenceid=3975329", "content": "Li, H., Zhao , L. et al. ( 2023b ). Application and Effect Analysis of JCI Standards in Hospital Equipment Maintenance Management. China Medical Devices, 38, 145-148."}
+{"idx": 3, "title": "PDF T O Regret in Adversarial Lin Ear Mdp Bandit Feedback", "date": "", "ddg_snippet": "ABSTRACT We study online reinforcement learning in linear Markov decision processes with adversarial losses and bandit feedback, without prior knowledge on transitions or access to simulators. We introduce two algorithms that achieve improved regret performance compared to existing approaches. The √ first algorithm, although com-putationally ineficient, ensures a regret of eO( K), where K is ...", "subpage_snippet": "", "source": "proceedings.iclr.cc", "link": "https://proceedings.iclr.cc/paper_files/paper/2024/file/cd9664c7094d90e512ce27f2fd58198b-Paper-Conference.pdf", "content": "ABSTRACT We study online reinforcement learning in linear Markov decision processes with adversarial losses and bandit feedback, without prior knowledge on transitions or access to simulators. We introduce two algorithms that achieve improved regret performance compared to existing approaches. The √ first algorithm, although com-putationally ineficient, ensures a regret of eO( K), where K is ..."}
+{"idx": 4, "title": "Zhao et al. Reply: | Phys. Rev. Lett.", "date": "", "ddg_snippet": "Phys. Rev. Lett. 130, 219702 (2023)1 Shenzhen Institute for Quantum Science and Engineering and Department of Physics, Southern University of Science and Technology (SUSTech), Shenzhen 518055, China 2 Quantum Science Center of Guangdong-Hong Kong-Macao Greater Bay Area (Guangdong), Shenzhen 518045, China 3 Shenzhen Key Laboratory of Quantum Science and Engineering, Shenzhen 518055, China 4 ...", "subpage_snippet": "", "source": "link.aps.org", "link": "https://link.aps.org/doi/10.1103/PhysRevLett.130.219702", "content": "Phys. Rev. Lett. 130, 219702 (2023)1 Shenzhen Institute for Quantum Science and Engineering and Department of Physics, Southern University of Science and Technology (SUSTech), Shenzhen 518055, China 2 Quantum Science Center of Guangdong-Hong Kong-Macao Greater Bay Area (Guangdong), Shenzhen 518045, China 3 Shenzhen Key Laboratory of Quantum Science and Engineering, Shenzhen 518055, China 4 ..."}
+{"idx": 5, "title": "Abstract arXiv:2502.02486v1 [stat.ML] 4 Feb 2025", "date": "", "ddg_snippet": "al ., 2021; Zhao et al ., 2023b ; Pacchiano, 2024). Particu-larly, the most relevant ones to our work for the unknown-variance s tting are Zhao et al. ( 2023b ); Pacchiano (2024). Zhao et al. ( 2023b ) develop a peeling approach for the unknown variance case without variance es-timation in linear settings, and Pacchiano (2024) extend", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.02486", "content": "al ., 2021; Zhao et al ., 2023b ; Pacchiano, 2024). Particu-larly, the most relevant ones to our work for the unknown-variance s tting are Zhao et al. ( 2023b ); Pacchiano (2024). Zhao et al. ( 2023b ) develop a peeling approach for the unknown variance case without variance es-timation in linear settings, and Pacchiano (2024) extend"}
+{"idx": 6, "title": "Converting fruit peels into biodegradable, recyclable and antimicrobial ...", "date": "", "ddg_snippet": "In this context, fruit peels (for example, citrus peels) attracted our attention because they are rich in cellulose (polymer matrix), pectin (binder) and polyphenols (antibiotics) at the same time (Roy et al ., 2023, Zhang et al ., 2023b ), and can be regarded as a complete \"packaging system\" for protecting fruits.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0960852424007788", "content": "In this context, fruit peels (for example, citrus peels) attracted our attention because they are rich in cellulose (polymer matrix), pectin (binder) and polyphenols (antibiotics) at the same time (Roy et al ., 2023, Zhang et al ., 2023b ), and can be regarded as a complete \"packaging system\" for protecting fruits."}
+{"idx": 7, "title": "Adaptive Client Sampling in Federated Learning via Online Learning with ...", "date": "", "ddg_snippet": "Adaptive Client Sampling in Federated Learning via Online Learning with Bandit Feedback Boxin Zhao , Lingxiao Wang, Ziqi Liu, Zhiqiang Zhang, Jun Zhou, Chaochao Chen, Mladen Kolar; 26 (8):1−67, 2025. Abstract Due to the high cost of communication, federated learning (FL) systems need to sample a subset of clients that are involved in each round of training. As a result, client sampling plays ...", "subpage_snippet": "", "source": "jmlr.org", "link": "https://jmlr.org/papers/v26/24-0385.html", "content": "Adaptive Client Sampling in Federated Learning via Online Learning with Bandit Feedback Boxin Zhao , Lingxiao Wang, Ziqi Liu, Zhiqiang Zhang, Jun Zhou, Chaochao Chen, Mladen Kolar; 26 (8):1−67, 2025. Abstract Due to the high cost of communication, federated learning (FL) systems need to sample a subset of clients that are involved in each round of training. As a result, client sampling plays ..."}
+{"idx": 8, "title": "Acoustic emission failure experiments and space-time evolution ...", "date": "", "ddg_snippet": "Furthermore, Zhao et al. ( 2023b ) conducted UCS failure experiments on the combined specimens of gangue with four contents to reveal the difference of failure mechanism between gangue and coal with ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/365307824_Acoustic_emission_failure_experiments_and_space-time_evolution_mechanism_of_coal_samples_with_different_gangue_contents", "content": "Furthermore, Zhao et al. ( 2023b ) conducted UCS failure experiments on the combined specimens of gangue with four contents to reveal the difference of failure mechanism between gangue and coal with ..."}
+{"idx": 9, "title": "PDF Multi-task Representation Learning for Fixed Budget Pure-Exploration in ...", "date": "", "ddg_snippet": "Contribution(s) We formulate the first fixed-budget MTRL problem for the linear and bilinear bandit settings and establish the first lower bound for the fixed-budget MTRL linear bandit setting. Context: Previous work of MTRL setting studied fixed confidence linear (Du et al ., 2023) and bilinear bandits (Mukherjee et al ., 2023b ). We establish the first lower bound for the fixed-budget MTRL in ...", "subpage_snippet": "", "source": "rlj.cs.umass.edu", "link": "https://rlj.cs.umass.edu/2025/papers/RLJ_RLC_2025_184.pdf", "content": "Contribution(s) We formulate the first fixed-budget MTRL problem for the linear and bilinear bandit settings and establish the first lower bound for the fixed-budget MTRL linear bandit setting. Context: Previous work of MTRL setting studied fixed confidence linear (Du et al ., 2023) and bilinear bandits (Mukherjee et al ., 2023b ). We establish the first lower bound for the fixed-budget MTRL in ..."}
diff --git a/data/sampled_jsons/Zhou_GAN_conditional_density_estimation_abstract_2022_year_2022.jsonl b/data/sampled_jsons/Zhou_GAN_conditional_density_estimation_abstract_2022_year_2022.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..91bec1b99fd3905e3c194d43b094336dcd06c92f
--- /dev/null
+++ b/data/sampled_jsons/Zhou_GAN_conditional_density_estimation_abstract_2022_year_2022.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Probabilistic Conformal Prediction Using Conditional ...", "date": "", "ddg_snippet": "by Z Wang · 2022 · Cited by 31 — This paper proposes probabilistic conformal prediction (PCP), a predictive inference algorithm that estimates a target variable by a ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2206.06584", "content": "by Z Wang · 2022 · Cited by 31 — This paper proposes probabilistic conformal prediction (PCP), a predictive inference algorithm that estimates a target variable by a ..."}
+{"idx": 1, "title": "Conditional GANs with Auxiliary Discriminative Classifier", "date": "", "ddg_snippet": "by L Hou · Cited by 53 — We propose a novel conditional generative adversarial network with an auxiliary discriminative classifier to achieve faithful conditional generative modeling.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=Yn4CPz_LRKO", "content": "by L Hou · Cited by 53 — We propose a novel conditional generative adversarial network with an auxiliary discriminative classifier to achieve faithful conditional generative modeling."}
+{"idx": 2, "title": "GAN review: Models and medical image fusion applications", "date": "", "ddg_snippet": "by T Zhou · 2023 · Cited by 219 — This paper systematically summarizes various models of GAN , advantages and challenges of GAN models in medical image fusion field.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/abs/pii/S1566253522001865", "content": "by T Zhou · 2023 · Cited by 219 — This paper systematically summarizes various models of GAN , advantages and challenges of GAN models in medical image fusion field."}
+{"idx": 3, "title": "Conditional GANs with Auxiliary Discriminative Classifier", "date": "", "ddg_snippet": "by L Hou · 2022 · Cited by 53 — Conditional generative models aim to learn the underlying joint distribution of data and labels to achieve conditional data generation. Among.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v162/hou22a/hou22a.pdf", "content": "by L Hou · 2022 · Cited by 53 — Conditional generative models aim to learn the underlying joint distribution of data and labels to achieve conditional data generation. Among."}
+{"idx": 4, "title": "Density estimation using deep generative neural networks", "date": "", "ddg_snippet": "by Q Liu · 2021 · Cited by 141 — In this study, we propose Roundtrip, a computational framework for general-purpose density estimation based on deep generative neural networks.", "subpage_snippet": "", "source": "www.pnas.org", "link": "https://www.pnas.org/doi/10.1073/pnas.2101344118", "content": "by Q Liu · 2021 · Cited by 141 — In this study, we propose Roundtrip, a computational framework for general-purpose density estimation based on deep generative neural networks."}
+{"idx": 5, "title": "Instance-Conditioned GAN", "date": "", "ddg_snippet": "by A Casanova · 2021 · Cited by 170 — IC- GAN bears similarities with kernel density estimation (KDE), a non-parametric density estimator in the form of a mixture of parametrized kernels modeling ... 13 pages", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper/2021/file/e7ac288b0f2d41445904d071ba37aaff-Paper.pdf", "content": "by A Casanova · 2021 · Cited by 170 — IC- GAN bears similarities with kernel density estimation (KDE), a non-parametric density estimator in the form of a mixture of parametrized kernels modeling ... 13 pages"}
+{"idx": 6, "title": "arXiv:2107.10060v5 [cs.LG] 17 Jun 2022", "date": "", "ddg_snippet": "by L Hou · 2021 · Cited by 53 — Conditional generative models aim to learn the underlying joint distribution of data and labels to achieve conditional data generation.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2107.10060", "content": "by L Hou · 2021 · Cited by 53 — Conditional generative models aim to learn the underlying joint distribution of data and labels to achieve conditional data generation."}
+{"idx": 7, "title": "CONDITIONAL GANS WITH AUXILIARY DISCRIMINA", "date": "", "ddg_snippet": "by L Hou · Cited by 53 — Conditional generative models aim to learn the underlying joint distribution of data and labels, and thus realize conditional generation.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=Yn4CPz_LRKO&name=pdf", "content": "by L Hou · Cited by 53 — Conditional generative models aim to learn the underlying joint distribution of data and labels, and thus realize conditional generation."}
+{"idx": 8, "title": "Instance-conditioned GAN | Proceedings of the 35th ...", "date": "", "ddg_snippet": "by A Casanova · 2021 · Cited by 170 — In this paper, we take inspiration from kernel density estimation techniques and introduce a non-parametric approach to modeling distributions ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3540261.3542368", "content": "by A Casanova · 2021 · Cited by 170 — In this paper, we take inspiration from kernel density estimation techniques and introduce a non-parametric approach to modeling distributions ..."}
+{"idx": 9, "title": "Adversarial sampling of unknown and high-dimensional ...", "date": "", "ddg_snippet": "by M Hassanaly · 2022 · Cited by 24 — Conditional GANs can be regularized with estimates of conditional moments. •. Moments can be estimated with external neural nets or stochastic estimation . •.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/abs/pii/S0021999121007488", "content": "by M Hassanaly · 2022 · Cited by 24 — Conditional GANs can be regularized with estimates of conditional moments. •. Moments can be estimated with external neural nets or stochastic estimation . •."}
diff --git a/data/sampled_jsons/Zhou_et_al_2022_'A_deep_generative_approach_to_conditional_sampling'_statistical_rates_convergence_a_year_2022.jsonl b/data/sampled_jsons/Zhou_et_al_2022_'A_deep_generative_approach_to_conditional_sampling'_statistical_rates_convergence_a_year_2022.jsonl
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A Deep Generative Approach to Conditional Sampling | Request PDF", "date": "", "ddg_snippet": "Zhou et al . (2021) proposed a generative approach to conditional sampling based on the noise-outsourcing lemma and distribution matching, where the Kullback-Liebler divergence was used for matching the generator distribution and the data distribution.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/356947019_A_Deep_Generative_Approach_to_Conditional_Sampling", "content": "Zhou et al . (2021) proposed a generative approach to conditional sampling based on the noise-outsourcing lemma and distribution matching, where the Kullback-Liebler divergence was used for matching the generator distribution and the data distribution."}
+{"idx": 1, "title": "A Likelihood Based Approach to Distribution Regression Using...", "date": "", "ddg_snippet": "It analyzes large- sample properties, leading to convergence rate analysis for estimating conditional distributions in the Hellinger/Wasserstein metric. A deep neural network models the conditional generator, enabling a flexible approach to distribution regression.", "subpage_snippet": "", "source": "powerdrill.ai", "link": "https://powerdrill.ai/discover/discover-A-Likelihood-Based-cm1v7rba8uvnv013whs4l4bq4", "content": "It analyzes large- sample properties, leading to convergence rate analysis for estimating conditional distributions in the Hellinger/Wasserstein metric. A deep neural network models the conditional generator, enabling a flexible approach to distribution regression."}
+{"idx": 2, "title": "A Deep Generative Approach to Conditional Sampling", "date": "", "ddg_snippet": "We propose a deep generative approach to sampling from a conditional distribution based on a unified formulation of conditional distribution and generalized nonparametric regression function using the noise-outsourcing lemma.", "subpage_snippet": "", "source": "www.tandfonline.com", "link": "https://www.tandfonline.com/doi/full/10.1080/01621459.2021.2016424?cookieSet=1", "content": "We propose a deep generative approach to sampling from a conditional distribution based on a unified formulation of conditional distribution and generalized nonparametric regression function using the noise-outsourcing lemma."}
+{"idx": 3, "title": "Yuling Jiao | DeepAI", "date": "", "ddg_snippet": "A Deep Generative Approach to Conditional Sampling . Convergence Rate Analysis for Deep Ritz Method.", "subpage_snippet": "", "source": "deepai.org", "link": "https://deepai.org/profile/yuling-jiao", "content": "A Deep Generative Approach to Conditional Sampling . Convergence Rate Analysis for Deep Ritz Method."}
+{"idx": 4, "title": "Non-asymptotic convergence bound of conditional diffusion models", "date": "", "ddg_snippet": "Han et al . ( 2022 ) augment the generation process by incorporating the conditional mean as a covariate. Chung et al . ( 2022 ) introduce a random contraction operation during diffusion, hastening the convergence of samples in the potential space towards the target distribution.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.10944v1", "content": "Han et al . ( 2022 ) augment the generation process by incorporating the conditional mean as a covariate. Chung et al . ( 2022 ) introduce a random contraction operation during diffusion, hastening the convergence of samples in the potential space towards the target distribution."}
+{"idx": 5, "title": "A Deep Generative Approach to Conditional Sampling", "date": "", "ddg_snippet": "We propose a deep generative approach to sampling from a conditional distribution based on a unified formulation of conditional distribution and generalized nonparametric regression function using the noise-outsourcing lemma.", "subpage_snippet": "", "source": "ideas.repec.org", "link": "https://ideas.repec.org/a/taf/jnlasa/v118y2023i543p1837-1848.html", "content": "We propose a deep generative approach to sampling from a conditional distribution based on a unified formulation of conditional distribution and generalized nonparametric regression function using the noise-outsourcing lemma."}
+{"idx": 6, "title": "A Deep Generative Approach to Conditional Sampling | ScienceGate", "date": "", "ddg_snippet": "(Higher-Order Moments and Conditional Sampling Analysis ). Generative Approach Using Soft-Labels to Learn Uncertainty in Predicting Emotional Attributes *.", "subpage_snippet": "", "source": "www.sciencegate.app", "link": "https://www.sciencegate.app/document/10.1080/01621459.2021.2016424", "content": "(Higher-Order Moments and Conditional Sampling Analysis ). Generative Approach Using Soft-Labels to Learn Uncertainty in Predicting Emotional Attributes *."}
+{"idx": 7, "title": "Individualized causal mediation analysis with continuous treatment...", "date": "", "ddg_snippet": "In particular, Yoon et al . (2018) proposed GANITE, a conditional generative adversarial network (CGAN)-based deep learning framework, to estimate ITEs for discrete interventions.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s11222-024-10484-8", "content": "In particular, Yoon et al . (2018) proposed GANITE, a conditional generative adversarial network (CGAN)-based deep learning framework, to estimate ITEs for discrete interventions."}
+{"idx": 8, "title": "A deep generative approach to conditional distribution estimation...", "date": "", "ddg_snippet": "We propose a deep generative approach to sampling from a conditional distribution basedon a unified formulation of conditional distribution and generalized nonparametric regression function using the noise-outsourcing lemma.", "subpage_snippet": "", "source": "iro.uiowa.edu", "link": "https://iro.uiowa.edu/esploro/outputs/doctoral/A-deep-generative-approach-to-conditional/9984270955302771", "content": "We propose a deep generative approach to sampling from a conditional distribution basedon a unified formulation of conditional distribution and generalized nonparametric regression function using the noise-outsourcing lemma."}
+{"idx": 9, "title": "Xingyu Zhou - Senior Research Statistician | LinkedIn", "date": "", "ddg_snippet": "We propose a deep generative approach to sampling from a conditional distribution based on a unified formulation of conditional distribution and generalized nonparametric regression function using the noise-outsourcing lemma.", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/in/xingyuzhouiowa", "content": "We propose a deep generative approach to sampling from a conditional distribution based on a unified formulation of conditional distribution and generalized nonparametric regression function using the noise-outsourcing lemma."}
diff --git a/data/sampled_jsons/abstract_of_Atp_Adaptive_tensor_parallelism_for_foundation_models_by_Cheng_et_al.,_2023_year_2023.jsonl b/data/sampled_jsons/abstract_of_Atp_Adaptive_tensor_parallelism_for_foundation_models_by_Cheng_et_al.,_2023_year_2023.jsonl
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ATP : Adaptive Tensor Parallelism for Foundation Models", "date": "", "ddg_snippet": "View a PDF of the paper titled ATP : Adaptive Tensor Parallelism for Foundation Models , by Shenggan Cheng and 3 other authors.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2301.08658", "content": "View a PDF of the paper titled ATP : Adaptive Tensor Parallelism for Foundation Models , by Shenggan Cheng and 3 other authors."}
+{"idx": 1, "title": "ATP : Adaptive Tensor Parallelism for Foundation Models | DeepAI", "date": "", "ddg_snippet": "In this work, we present ATP , an adaptive tensor parallelism framework for foundation models , which can automatically select the optimal parallel strategy on different interconnections.", "subpage_snippet": "", "source": "deepai.org", "link": "https://deepai.org/publication/atp-adaptive-tensor-parallelism-for-foundation-models", "content": "In this work, we present ATP , an adaptive tensor parallelism framework for foundation models , which can automatically select the optimal parallel strategy on different interconnections."}
+{"idx": 2, "title": "ATP : Adaptive Tensor Parallelism for Foundation Models | Ziming Liu", "date": "", "ddg_snippet": "Adaptive Tensor Parallelism for efficient foundation model training.In this work, we present ATP , an adaptive tensor parallelism framework for foundation models , which can automatically select the optimal parallel strategy on different interconnections.", "subpage_snippet": "", "source": "maruyamaaya.github.io", "link": "https://maruyamaaya.github.io/publication/atp/", "content": "Adaptive Tensor Parallelism for efficient foundation model training.In this work, we present ATP , an adaptive tensor parallelism framework for foundation models , which can automatically select the optimal parallel strategy on different interconnections."}
+{"idx": 3, "title": "Shenggan/ atp : Adaptive Tensor Parallelism for Foundation Models", "date": "", "ddg_snippet": "Adaptive Tensor Parallelism for Large Model Traning and Inference. ATP provides a high-performance implementation of Topology-aware Tensor Parallelism with the following characteristics. Two-Level Search Space for Tensor Parallelism .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Shenggan/atp", "content": "Adaptive Tensor Parallelism for Large Model Traning and Inference. ATP provides a high-performance implementation of Topology-aware Tensor Parallelism with the following characteristics. Two-Level Search Space for Tensor Parallelism ."}
+{"idx": 4, "title": "Adaptive Tensor Parallelism for Foundation Models", "date": "", "ddg_snippet": "Adaptive Tensor Parallelism for Large Model Traning and Inference. ATP provides a high-performance implementation of Topology-aware Tensor Parallelism with the following characteristics.", "subpage_snippet": "", "source": "awesome.ecosyste.ms", "link": "https://awesome.ecosyste.ms/projects/github.com/shenggan/atp", "content": "Adaptive Tensor Parallelism for Large Model Traning and Inference. ATP provides a high-performance implementation of Topology-aware Tensor Parallelism with the following characteristics."}
+{"idx": 5, "title": "Shenggan Cheng - Google Akademik", "date": "", "ddg_snippet": "ATP : Adaptive Tensor Parallelism for Foundation Models . 2023 . FTL: A universal framework for training low-bit DNNs via feature transfer.", "subpage_snippet": "", "source": "scholar.google.com.tr", "link": "https://scholar.google.com.tr/citations?user=kDdwP6UAAAAJ&hl=tr", "content": "ATP : Adaptive Tensor Parallelism for Foundation Models . 2023 . FTL: A universal framework for training low-bit DNNs via feature transfer."}
+{"idx": 6, "title": "Cheng , Shenggan | BibSonomy", "date": "", "ddg_snippet": "ACM, (2024 ) ATP : Adaptive Tensor Parallelism for Foundation Models .S. Cheng , Z. Liu, J. Du, and Y. You. CoRR, ( 2023 )tcFFT: A Fast Half-Precision FFT Library for NVIDIA Tensor Cores.B. Li, S. Cheng , and J. Lin.", "subpage_snippet": "", "source": "www.bibsonomy.org", "link": "https://www.bibsonomy.org/person/15184484c13c23846983c9b9376f4b47a/author/0", "content": "ACM, (2024 ) ATP : Adaptive Tensor Parallelism for Foundation Models .S. Cheng , Z. Liu, J. Du, and Y. You. CoRR, ( 2023 )tcFFT: A Fast Half-Precision FFT Library for NVIDIA Tensor Cores.B. Li, S. Cheng , and J. Lin."}
+{"idx": 7, "title": "[PDF] Colossal-AI: A Unified Deep Learning System For Large-Scale...", "date": "", "ddg_snippet": "ATP : Adaptive Tensor Parallelism for Foundation Models . Shenggan Cheng , Ziming Liu, Jiangsu Du, Yang You.Merak: An Efficient Distributed DNN Training Framework With Automated 3D Parallelism for Giant Foundation Models .", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Colossal-AI:-A-Unified-Deep-Learning-System-For-Bian-Liu/271ee12f07c2b604917f2507759051e9847f0ede", "content": "ATP : Adaptive Tensor Parallelism for Foundation Models . Shenggan Cheng , Ziming Liu, Jiangsu Du, Yang You.Merak: An Efficient Distributed DNN Training Framework With Automated 3D Parallelism for Giant Foundation Models ."}
+{"idx": 8, "title": "AI Computing Systems for Large Language Models Training", "date": "", "ddg_snippet": "In this paper, we present a comprehensive overview of artificial intelligence (AI) computing systems for large language models (LLMs) training.", "subpage_snippet": "", "source": "www.sciopen.com", "link": "https://www.sciopen.com/article/10.1007/s11390-024-4178-1", "content": "In this paper, we present a comprehensive overview of artificial intelligence (AI) computing systems for large language models (LLMs) training."}
+{"idx": 9, "title": "Foundations Tensorblue Images", "date": "", "ddg_snippet": "Atp Adaptive Tensor Parallelism For Foundation Models Deepai.", "subpage_snippet": "", "source": "www.tpsearchtool.com", "link": "https://www.tpsearchtool.com/images/foundations-tensorblue", "content": "Atp Adaptive Tensor Parallelism For Foundation Models Deepai."}
diff --git a/data/sampled_jsons/advanced_techniques_for_accelerating_inference_in_autoregressive_Transformer_models.jsonl b/data/sampled_jsons/advanced_techniques_for_accelerating_inference_in_autoregressive_Transformer_models.jsonl
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index 0000000000000000000000000000000000000000..470e1422bd313d4a55ac9f99d06401aec786d018
--- /dev/null
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Transformer (deep learning architecture) - Wikipedia", "date": "", "ddg_snippet": "When an autoregressive transformer is used for inference , such as generating text, the query vector is different at each step, but the already-computed key and value vectors are always the same. The KV caching method saves the computed key and value vectors at each attention block...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Transformer_(deep_learning_architecture)", "content": "When an autoregressive transformer is used for inference , such as generating text, the query vector is different at each step, but the already-computed key and value vectors are always the same. The KV caching method saves the computed key and value vectors at each attention block..."}
+{"idx": 1, "title": "Model -free Speculative Decoding for Transformer -based ASR with...", "date": "", "ddg_snippet": "Introduced by [12] , SD is a technique designed to accelerate autoregressive language models by utilizing a lightweight draft decoder to generate multiple candidate tokens in parallel. In conventional autoregressive inference , generating.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.21522v1", "content": "Introduced by [12] , SD is a technique designed to accelerate autoregressive language models by utilizing a lightweight draft decoder to generate multiple candidate tokens in parallel. In conventional autoregressive inference , generating."}
+{"idx": 2, "title": "\"Towards faster inference of transformers : Strategies for...\"", "date": "", "ddg_snippet": "Citation. DU, Cunxiao. Towards faster inference of transformers : Strategies for accelerating decoding processes.", "subpage_snippet": "", "source": "ink.library.smu.edu.sg", "link": "https://ink.library.smu.edu.sg/etd_coll/613/", "content": "Citation. DU, Cunxiao. Towards faster inference of transformers : Strategies for accelerating decoding processes."}
+{"idx": 3, "title": "Performance Optimization Techniques | huggingface/ transformers", "date": "", "ddg_snippet": "This document covers the performance optimization infrastructure within the Transformers library, focusing on Large Language Model (LLM) inference acceleration techniques .", "subpage_snippet": "", "source": "deepwiki.com", "link": "https://deepwiki.com/huggingface/transformers/6.2-gguf-integration", "content": "This document covers the performance optimization infrastructure within the Transformers library, focusing on Large Language Model (LLM) inference acceleration techniques ."}
+{"idx": 4, "title": "GitHub - chenhongyu2048/LLM- inference -optimization-paper...", "date": "", "ddg_snippet": "The N-Grammys: Accelerating autoregressive inference with learning-free batched speculation: use learning-free, negligible-cost draft strategies, namely N-grams obtained from the model weights and the context.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/chenhongyu2048/LLM-inference-optimization-paper", "content": "The N-Grammys: Accelerating autoregressive inference with learning-free batched speculation: use learning-free, negligible-cost draft strategies, namely N-grams obtained from the model weights and the context."}
+{"idx": 5, "title": "The N-Grammys: Accelerating Autoregressive Inference with...", "date": "", "ddg_snippet": "Models . Key-Value Cache. The N-Grammys: Accelerating Autoregressive Inference with Learning-Free Batched Speculation.Speculative decoding aims to speed up autoregressive generation of a language model by verifying in parallel the tokens generated by a smaller draft model .", "subpage_snippet": "", "source": "hal.science", "link": "https://hal.science/hal-04766931v1/document", "content": "Models . Key-Value Cache. The N-Grammys: Accelerating Autoregressive Inference with Learning-Free Batched Speculation.Speculative decoding aims to speed up autoregressive generation of a language model by verifying in parallel the tokens generated by a smaller draft model ."}
+{"idx": 6, "title": "How to reduce the average response latency - Accelerating LLM...", "date": "", "ddg_snippet": "Leveraging Hardware Upgrades. Model Compression Techniques for Latency. Caching Mechanisms for Faster Responses. Algorithmic Optimizations for Faster Inference .", "subpage_snippet": "", "source": "www.rohan-paul.com", "link": "https://www.rohan-paul.com/p/how-to-reduce-the-average-response", "content": "Leveraging Hardware Upgrades. Model Compression Techniques for Latency. Caching Mechanisms for Faster Responses. Algorithmic Optimizations for Faster Inference ."}
+{"idx": 7, "title": "(PDF) PIM-GPT: A Hybrid Process-in-Memory Accelerator for...", "date": "", "ddg_snippet": "(DOI: 10.48550/arxiv.2310.09385) Decoder-only Transformer models such as GPT have demonstrated superior performance in text generation, by autoregressively predicting the next token.", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/papers/pim-gpt-a-hybrid-process-in-memory-accelerator-for-nroc0abj22", "content": "(DOI: 10.48550/arxiv.2310.09385) Decoder-only Transformer models such as GPT have demonstrated superior performance in text generation, by autoregressively predicting the next token."}
+{"idx": 8, "title": "KV Cache: Optimizing Transformer Inference - Freedium", "date": "", "ddg_snippet": "One key technique for improving inference efficiency in autoregressive models , such as GPT-style architectures, is Key-Value (KV) Cache.KV Cache is a technique used to store and reuse key-value (K, V) pairs from previous decoding steps in autoregressive models .", "subpage_snippet": "", "source": "freedium.cfd", "link": "https://freedium.cfd/b49f5ea4f824", "content": "One key technique for improving inference efficiency in autoregressive models , such as GPT-style architectures, is Key-Value (KV) Cache.KV Cache is a technique used to store and reuse key-value (K, V) pairs from previous decoding steps in autoregressive models ."}
+{"idx": 9, "title": "Mixture-of-Recursions: A Next-Generation Transformer Design for...", "date": "", "ddg_snippet": "In autoregressive decoding, Transformers cache key-value pairs for self-attention layers to accelerate inference .2× inference speedup compared to standard Transformers at equivalent model scale. ~50% reduction in memory consumption, primarily due to KV cache savings.", "subpage_snippet": "", "source": "www.mindhyve.ai", "link": "https://www.mindhyve.ai/post/mixture-of-recursions-a-next-generation-transformer-design-for-efficient-language-modeling", "content": "In autoregressive decoding, Transformers cache key-value pairs for self-attention layers to accelerate inference .2× inference speedup compared to standard Transformers at equivalent model scale. ~50% reduction in memory consumption, primarily due to KV cache savings."}
diff --git a/data/sampled_jsons/adversarial_review_algorithm_contentiousness_variable_delta_modulation_parameter_mathematical_formul.jsonl b/data/sampled_jsons/adversarial_review_algorithm_contentiousness_variable_delta_modulation_parameter_mathematical_formul.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b5370f36c269d640ee250c17fd8e0b2ae0a46955
--- /dev/null
+++ b/data/sampled_jsons/adversarial_review_algorithm_contentiousness_variable_delta_modulation_parameter_mathematical_formul.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Mathematical Theory of Adversarial Deep Learning - OpenReview", "date": "", "ddg_snippet": "In this Show-and-Tell Demos paper, progresses on mathematical theories for adversarial deep learning are reported. Firstly, achieving robust memorization for certain neural networks is shown to be an NP-hard problem. Furthermore, neural networks with O (N n) parameters are constructed for optimal robust memorization of any dataset with dimension n and size N in polynomial time. Secondly ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=fDDYcOA7h6", "content": "In this Show-and-Tell Demos paper, progresses on mathematical theories for adversarial deep learning are reported. Firstly, achieving robust memorization for certain neural networks is shown to be an NP-hard problem. Furthermore, neural networks with O (N n) parameters are constructed for optimal robust memorization of any dataset with dimension n and size N in polynomial time. Secondly ..."}
+{"idx": 1, "title": "ROBY: Evaluating the adversarial robustness of a deep model by its ...", "date": "", "ddg_snippet": "With the successful applications of DNNs in many real-world tasks, model's robustness has raised public concern. Recently the robustness of deep models is often evaluated by purposely generated adversarial samples, which is time-consuming and usually dependent on the specific attacks and model structures. Addressing the problem, we propose a generic evaluation metric ROBY, a novel attack ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0020025521012421", "content": "With the successful applications of DNNs in many real-world tasks, model's robustness has raised public concern. Recently the robustness of deep models is often evaluated by purposely generated adversarial samples, which is time-consuming and usually dependent on the specific attacks and model structures. Addressing the problem, we propose a generic evaluation metric ROBY, a novel attack ..."}
+{"idx": 2, "title": "The mathematics of adversarial attacks in AI — Why deep learning is ...", "date": "", "ddg_snippet": "The key is that the stable and accurate neural networks must have variable dimensions depending on the input, in particular, variable dimensions is a necessary condition for stability. Our result points towards the paradox that accurate and stable neural networks exist, however, modern algorithms do not compute them.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2109.06098v2", "content": "The key is that the stable and accurate neural networks must have variable dimensions depending on the input, in particular, variable dimensions is a necessary condition for stability. Our result points towards the paradox that accurate and stable neural networks exist, however, modern algorithms do not compute them."}
+{"idx": 3, "title": "Chapter 1 - Introduction to adversarial robustness", "date": "", "ddg_snippet": "This tutorial seeks to provide a broad, hands-on introduction to this topic of adversarial robustness in deep learning. The goal is combine both a mathematical presentation and illustrative code examples that highlight some of the key methods and challenges in this setting.", "subpage_snippet": "", "source": "adversarial-ml-tutorial.org", "link": "https://adversarial-ml-tutorial.org/introduction/", "content": "This tutorial seeks to provide a broad, hands-on introduction to this topic of adversarial robustness in deep learning. The goal is combine both a mathematical presentation and illustrative code examples that highlight some of the key methods and challenges in this setting."}
+{"idx": 4, "title": "Enhancing Adversarial Robustness in Automatic Modulation Recognition ...", "date": "", "ddg_snippet": "This study introduces a novel deep learning framework aimed at enhancing the defensive capabilities of Automatic Modulation Recognition (AMR) systems against adversarial attacks through the application of dynamical systems theory and an adaptive weight learning mechanism. Utilizing dynamical systems theory, we analyze and simulate the propagation process of adversarial perturbations, revealing ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-3-031-71464-1_32", "content": "This study introduces a novel deep learning framework aimed at enhancing the defensive capabilities of Automatic Modulation Recognition (AMR) systems against adversarial attacks through the application of dynamical systems theory and an adaptive weight learning mechanism. Utilizing dynamical systems theory, we analyze and simulate the propagation process of adversarial perturbations, revealing ..."}
+{"idx": 5, "title": "Adversarial Robustness in Deep Neural Networks based on Variable ...", "date": "", "ddg_snippet": "A graphical illustration of the proposed variable attributes of ensemble strategy for adversarial robustness of deep neural networks. A flowchart of the stochastic ensemble smoothing strategy.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/362853030_Adversarial_Robustness_in_Deep_Neural_Networks_based_on_Variable_Attributes_Stochastic_Ensemble_Model", "content": "A graphical illustration of the proposed variable attributes of ensemble strategy for adversarial robustness of deep neural networks. A flowchart of the stochastic ensemble smoothing strategy."}
+{"idx": 6, "title": "PDF Are MLLMs Robust Against Adversarial Perturbations? ROMM : A Systematic ...", "date": "", "ddg_snippet": "The primary objective is to examine the robustness of models in multimodal mathematical reasoning when faced with adversarial perturbations. High- school-level problems are chosen as they present a manageable level of complexity, enabling us to focus on robustness without the additional chal- lenges posed by more advanced reasoning and domain ...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.naacl-long.582.pdf", "content": "The primary objective is to examine the robustness of models in multimodal mathematical reasoning when faced with adversarial perturbations. High- school-level problems are chosen as they present a manageable level of complexity, enabling us to focus on robustness without the additional chal- lenges posed by more advanced reasoning and domain ..."}
+{"idx": 7, "title": "Adversarial Robustness in Parameter-Space Classifiers", "date": "", "ddg_snippet": "fig. 9 exhibits the affect of adversarial attacks in signal-domain over the underlying modulation vectors (for parameter -space classifiers) and latent vectors (for signal-space classifiers) received as classifier input.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.20314v2", "content": "fig. 9 exhibits the affect of adversarial attacks in signal-domain over the underlying modulation vectors (for parameter -space classifiers) and latent vectors (for signal-space classifiers) received as classifier input."}
+{"idx": 8, "title": "Improving adversarial robustness of deep neural networks via adaptive ...", "date": "", "ddg_snippet": "RNA [41] replaces the batch normalization layers in the neural networks with different selected types of normalization to reduce the adversarial transferability, which improves the adversarial robustness of DNNs. For a complete review of adversarial attack and defense methods, we refer the reader to the recent excellent surveys [1], [2], [3].", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0925231223006471", "content": "RNA [41] replaces the batch normalization layers in the neural networks with different selected types of normalization to reduce the adversarial transferability, which improves the adversarial robustness of DNNs. For a complete review of adversarial attack and defense methods, we refer the reader to the recent excellent surveys [1], [2], [3]."}
+{"idx": 9, "title": "PDF Enhancing Image Classification Robustness through Adversarial Sampling ...", "date": "", "ddg_snippet": "Then, transfer robustness of gφ is defined as the ability of fθ to maintain its performance on a new task under adversarial attacks when initialized with the pre-trained parameters φ Algorithm 1 Delta Data Augmentation Require: Pre-trained Robust Model MA, Dataset D, Length of adversarial samples k Ensure: Augmented Dataset ˆD 1: Attack ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2024W/LXCV/papers/Reyes-Amezcua_Enhancing_Image_Classification_Robustness_through_Adversarial_Sampling_with_Delta_Data_CVPRW_2024_paper.pdf", "content": "Then, transfer robustness of gφ is defined as the ability of fθ to maintain its performance on a new task under adversarial attacks when initialized with the pre-trained parameters φ Algorithm 1 Delta Data Augmentation Require: Pre-trained Robust Model MA, Dataset D, Length of adversarial samples k Ensure: Augmented Dataset ˆD 1: Attack ..."}
diff --git a/data/sampled_jsons/ar5iv_2405.17618_equation_7_RA2C_loss.jsonl b/data/sampled_jsons/ar5iv_2405.17618_equation_7_RA2C_loss.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..9b0f31a14c51210f23f90aa5fc7b85eaeea6aea4
--- /dev/null
+++ b/data/sampled_jsons/ar5iv_2405.17618_equation_7_RA2C_loss.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Symmetric Reinforcement Learning Loss for Robust Learning on ... Abstract Symmetric Reinforcement Learning Loss for Robust ...", "date": "", "ddg_snippet": "May 27, 2024 · Abstract page for arXiv paper 2405.17618 : Symmetric Reinforcement Learning Loss for Robust Learning on Diverse Tasks and Model Scales May 29, 2024 · Abstract Reinforcement learning (RL) training is inherently unstable due to factors such as moving targets and high gradient variance. Reinforcement Learning from Human Feedback (RLHF) and Reinforcement Learning from AI Feedback (RLAIF) can introduce additional dificulty. Differing preferences can complicate the alignment process, and prediction errors in a trained reward model can become more ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2405.17618", "content": "May 27, 2024 · Abstract page for arXiv paper 2405.17618 : Symmetric Reinforcement Learning Loss for Robust Learning on Diverse Tasks and Model Scales May 29, 2024 · Abstract Reinforcement learning (RL) training is inherently unstable due to factors such as moving targets and high gradient variance. Reinforcement Learning from Human Feedback (RLHF) and Reinforcement Learning from AI Feedback (RLAIF) can introduce additional dificulty. Differing preferences can complicate the alignment process, and prediction errors in a trained reward model can become more ..."}
+{"idx": 1, "title": "Symmetric Reinforcement Learning Loss for Robust Learning on ...", "date": "", "ddg_snippet": "We define a symmetric RL loss , whose fundamental working mechanism aligns with the robust loss function of supervised learning (Wang et al., 2019), to make the RL learning procedure more robust for A2C and PPO.", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2405.17618", "content": "We define a symmetric RL loss , whose fundamental working mechanism aligns with the robust loss function of supervised learning (Wang et al., 2019), to make the RL learning procedure more robust for A2C and PPO."}
+{"idx": 2, "title": "Symmetric Reinforcement Learning Loss for Robust Learning on...", "date": "", "ddg_snippet": "most effectively. Thus, the RA 2 C loss helps deviate from ambiguous predictions as an accelerator. SPPO’s loss gradients are also aligned like SA 2 C and follow the same mechanism (See Appendix B.2). 5 Experiments.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.17618v3", "content": "most effectively. Thus, the RA 2 C loss helps deviate from ambiguous predictions as an accelerator. SPPO’s loss gradients are also aligned like SA 2 C and follow the same mechanism (See Appendix B.2). 5 Experiments."}
+{"idx": 3, "title": "Abstract Symmetric Reinforcement Learning Loss for Robust ...", "date": "", "ddg_snippet": "May 29, 2024 · Abstract Reinforcement learning (RL) training is inherently unstable due to factors such as moving targets and high gradient variance. Reinforcement Learning from Human Feedback (RLHF) and Reinforcement Learning from AI Feedback (RLAIF) can introduce additional dificulty. Differing preferences can complicate the alignment process, and prediction errors in a trained reward model can become more ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2405.17618", "content": "May 29, 2024 · Abstract Reinforcement learning (RL) training is inherently unstable due to factors such as moving targets and high gradient variance. Reinforcement Learning from Human Feedback (RLHF) and Reinforcement Learning from AI Feedback (RLAIF) can introduce additional dificulty. Differing preferences can complicate the alignment process, and prediction errors in a trained reward model can become more ..."}
+{"idx": 4, "title": "Solution of ordinary equations step by step online", "date": "", "ddg_snippet": "Да здравствуют уравнения: кубические, тригонометрические, логарифмические. Пошаговый калькулятор. Разобрался в момент.", "subpage_snippet": "", "source": "calculator-online.org", "link": "https://calculator-online.org/equation", "content": "Да здравствуют уравнения: кубические, тригонометрические, логарифмические. Пошаговый калькулятор. Разобрался в момент."}
+{"idx": 5, "title": "An interactive demo for developers to try the new text-to-speech model...", "date": "", "ddg_snippet": "An interactive demo for developers to try the new text-to-speech model in the OpenAI API...", "subpage_snippet": "", "source": "www.openai.fm", "link": "https://www.openai.fm/", "content": "An interactive demo for developers to try the new text-to-speech model in the OpenAI API..."}
+{"idx": 6, "title": "Сводки ополчения Новороссии Z.O. V . (ДНР, ЛНР, Украина, Война)...", "date": "", "ddg_snippet": "Реквизиты для помощи: НА Сберкарту: 4276 1609 2548 3621 Бот обратной связи: @swodka_bot с администрацией напрямую. Здесь можно оставить информацию, которая поможет нашей Победе! Пиар-менеджеры @skufido1 (напрямую) @bosteleg и @magister_mg.", "subpage_snippet": "", "source": "t.me", "link": "https://t.me/s/Swodki", "content": "Реквизиты для помощи: НА Сберкарту: 4276 1609 2548 3621 Бот обратной связи: @swodka_bot с администрацией напрямую. Здесь можно оставить информацию, которая поможет нашей Победе! Пиар-менеджеры @skufido1 (напрямую) @bosteleg и @magister_mg."}
+{"idx": 7, "title": "Калькулятор уравнений", "date": "", "ddg_snippet": "العربية (AR). עברית (HE). Калькулятор Уравнений, Неравенств и Систем Уравнений. Ссылка на это решение. 75% 90% 100 % 110% 125%. Добавить страницу в закладки — CTRL+D.", "subpage_snippet": "", "source": "mathdf.com", "link": "https://mathdf.com/equ/ru/", "content": "العربية (AR). עברית (HE). Калькулятор Уравнений, Неравенств и Систем Уравнений. Ссылка на это решение. 75% 90% 100 % 110% 125%. Добавить страницу в закладки — CTRL+D."}
+{"idx": 8, "title": "Английский язык 5 класс Spotlight Английский в фокусе Ваулина.", "date": "", "ddg_snippet": "8 – 1 = ИГРА Поиграйте в парах: Угадай число. A: (думай о числе 6) B: семь A: вниз B: пять A: вверх B: пять A: Это правильно. ОТВЕТ 1. 1 + 2 = 3 (one plus two equals 3) 2. 7 – 4 = 3 (seven minus four equals 3) 3. 6 + 2 = 8 (six plus two equals 8) 4. 9 – 1 = 8 (nine minus one...", "subpage_snippet": "", "source": "Reshalka.com", "link": "https://Reshalka.com/uchebniki/5-klass/english/vaulina/43", "content": "8 – 1 = ИГРА Поиграйте в парах: Угадай число. A: (думай о числе 6) B: семь A: вниз B: пять A: вверх B: пять A: Это правильно. ОТВЕТ 1. 1 + 2 = 3 (one plus two equals 3) 2. 7 – 4 = 3 (seven minus four equals 3) 3. 6 + 2 = 8 (six plus two equals 8) 4. 9 – 1 = 8 (nine minus one..."}
+{"idx": 9, "title": "Samsung Account", "date": "", "ddg_snippet": "Support for Internet Explorer is ending.", "subpage_snippet": "", "source": "v3.account.samsung.com", "link": "https://v3.account.samsung.com/", "content": "Support for Internet Explorer is ending."}
diff --git a/data/sampled_jsons/arXiv2102.09672_Nichol_Dhariwal_Improved_Denoising_Diffusion_Probabilistic_Models.jsonl b/data/sampled_jsons/arXiv2102.09672_Nichol_Dhariwal_Improved_Denoising_Diffusion_Probabilistic_Models.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a155497534504e876b9e1b9a58a8b09af5e485c6
--- /dev/null
+++ b/data/sampled_jsons/arXiv2102.09672_Nichol_Dhariwal_Improved_Denoising_Diffusion_Probabilistic_Models.jsonl
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+{"idx": 0, "title": "[2102.09672] Improved Denoising Diffusion Probabilistic Models - arXiv.org", "date": "", "ddg_snippet": "Denoising diffusion probabilistic models (DDPM) are a class of generative models which have recently been shown to produce excellent samples. We show that with a few simple modifications, DDPMs can also achieve competitive log-likelihoods while maintaining high sample quality. Additionally, we find that learning variances of the reverse diffusion process allows sampling with an order of ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2102.09672", "content": "Denoising diffusion probabilistic models (DDPM) are a class of generative models which have recently been shown to produce excellent samples. We show that with a few simple modifications, DDPMs can also achieve competitive log-likelihoods while maintaining high sample quality. Additionally, we find that learning variances of the reverse diffusion process allows sampling with an order of ..."}
+{"idx": 1, "title": "PDF Improved Denoising Diffusion Probabilistic Models", "date": "", "ddg_snippet": "Abstract Denoising diffusion probabilistic models (DDPM) are a class of generative models which have re-cently been shown to produce excellent sam-ples. We show that with a few simple modifi-cations, DDPMs can also achieve competitive log-likelihoods while maintaining high sample quality. Additionally, we find that learning vari-ances of the reverse diffusion process allows sam-pling with an ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v139/nichol21a/nichol21a.pdf", "content": "Abstract Denoising diffusion probabilistic models (DDPM) are a class of generative models which have re-cently been shown to produce excellent sam-ples. We show that with a few simple modifi-cations, DDPMs can also achieve competitive log-likelihoods while maintaining high sample quality. Additionally, we find that learning vari-ances of the reverse diffusion process allows sam-pling with an ..."}
+{"idx": 2, "title": "Improved Denoising Diffusion Probabilistic Models - 知乎", "date": "", "ddg_snippet": "Title: Improved Denoising Diffusion Probabilistic Models 作者：Alex Nichol *, Prafulla Dhariwal * 关键词： diffusion model , fast sampling 论文： Improved Denoising Diffusion Probabilistic Models 摘要 去噪扩散概率模型 （DDPM）是一类生成模型，最近已被证明能产生良好的样本。我们表明，通过一些简单的修改，DDPM也可以在保持高样本质量 ...", "subpage_snippet": "", "source": "zhuanlan.zhihu.com", "link": "https://zhuanlan.zhihu.com/p/557971459", "content": "Title: Improved Denoising Diffusion Probabilistic Models 作者：Alex Nichol *, Prafulla Dhariwal * 关键词： diffusion model , fast sampling 论文： Improved Denoising Diffusion Probabilistic Models 摘要 去噪扩散概率模型 （DDPM）是一类生成模型，最近已被证明能产生良好的样本。我们表明，通过一些简单的修改，DDPM也可以在保持高样本质量 ..."}
+{"idx": 3, "title": "Improved Denoising Diffusion Probabilistic Models - Semantic Scholar", "date": "", "ddg_snippet": "Denoising diffusion probabilistic models are a class of generative models which have recently been shown to produce excellent samples and it is found that learning variances of the reverse diffusion process allows sampling with an order of magnitude fewer forward passes with a negligible difference in sample quality. Denoising diffusion probabilistic models (DDPM) are a class of generative ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Improved-Denoising-Diffusion-Probabilistic-Models-Nichol-Dhariwal/de18baa4964804cf471d85a5a090498242d2e79f", "content": "Denoising diffusion probabilistic models are a class of generative models which have recently been shown to produce excellent samples and it is found that learning variances of the reverse diffusion process allows sampling with an order of magnitude fewer forward passes with a negligible difference in sample quality. Denoising diffusion probabilistic models (DDPM) are a class of generative ..."}
+{"idx": 4, "title": "Improved Denoising Diffusion Probabilistic Models - ADS", "date": "", "ddg_snippet": "Denoising diffusion probabilistic models (DDPM) are a class of generative models which have recently been shown to produce excellent samples. We show that with a few simple modifications, DDPMs can also achieve competitive log-likelihoods while maintaining high sample quality. Additionally, we find that learning variances of the reverse diffusion process allows sampling with an order of ...", "subpage_snippet": "", "source": "ui.adsabs.harvard.edu", "link": "https://ui.adsabs.harvard.edu/abs/2021arXiv210209672N/abstract", "content": "Denoising diffusion probabilistic models (DDPM) are a class of generative models which have recently been shown to produce excellent samples. We show that with a few simple modifications, DDPMs can also achieve competitive log-likelihoods while maintaining high sample quality. Additionally, we find that learning variances of the reverse diffusion process allows sampling with an order of ..."}
+{"idx": 5, "title": "\"Improved Denoising Diffusion Probabilistic Models.\" - dblp", "date": "", "ddg_snippet": "Alex Nichol , Prafulla Dhariwal : Improved Denoising Diffusion Probabilistic Models . CoRR abs/2102.09672 (2021)", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/journals/corr/abs-2102-09672", "content": "Alex Nichol , Prafulla Dhariwal : Improved Denoising Diffusion Probabilistic Models . CoRR abs/2102.09672 (2021)"}
+{"idx": 6, "title": "Improved Denoising Diffusion Probabilistic Models - OpenReview", "date": "", "ddg_snippet": "Finally, we explore how sample quality and log-likelihood scale with the number of diffusion steps and the amount of model capacity. We conclude that denoising diffusion probabilistic models are a promising class of generative models with excellent scaling properties and sample quality.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=-NEXDKk8gZ", "content": "Finally, we explore how sample quality and log-likelihood scale with the number of diffusion steps and the amount of model capacity. We conclude that denoising diffusion probabilistic models are a promising class of generative models with excellent scaling properties and sample quality."}
+{"idx": 7, "title": "Paper page - Improved Denoising Diffusion Probabilistic Models", "date": "", "ddg_snippet": "Abstract Denoising diffusion probabilistic models (DDPM) are a class of generative models which have recently been shown to produce excellent samples. We show that with a few simple modifications, DDPMs can also achieve competitive log-likelihoods while maintaining high sample quality.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2102.09672", "content": "Abstract Denoising diffusion probabilistic models (DDPM) are a class of generative models which have recently been shown to produce excellent samples. We show that with a few simple modifications, DDPMs can also achieve competitive log-likelihoods while maintaining high sample quality."}
+{"idx": 8, "title": "[2102.09672] Improved Denoising Diffusion Probabilistic Models - ar5iv", "date": "", "ddg_snippet": "More recently, Ho et al. (2020) showed an equivalence between denoising diffusion probabilistic models (DDPM) and score based generative models (Song & Ermon, 2019, 2020), which learn a gradient of the log-density of the data distribution using denoising score matching (Hyvärinen, 2005).", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2102.09672", "content": "More recently, Ho et al. (2020) showed an equivalence between denoising diffusion probabilistic models (DDPM) and score based generative models (Song & Ermon, 2019, 2020), which learn a gradient of the log-density of the data distribution using denoising score matching (Hyvärinen, 2005)."}
+{"idx": 9, "title": "Improved Denoising Diffusion Probabilistic Models - PMLR", "date": "", "ddg_snippet": "@InProceedings{pmlr-v139-nichol21a, title = { Improved Denoising Diffusion Probabilistic Models }, author = { Nichol , Alexander Quinn and Dhariwal , Prafulla}, booktitle = {Proceedings of the 38th International Conference on Machine Learning}, pages = {8162--8171}, year = {2021}, editor = {Meila, Marina and Zhang, Tong}, volume = {139}, series = {Proceedings of Machine Learning Research}, month ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v139/nichol21a.html", "content": "@InProceedings{pmlr-v139-nichol21a, title = { Improved Denoising Diffusion Probabilistic Models }, author = { Nichol , Alexander Quinn and Dhariwal , Prafulla}, booktitle = {Proceedings of the 38th International Conference on Machine Learning}, pages = {8162--8171}, year = {2021}, editor = {Meila, Marina and Zhang, Tong}, volume = {139}, series = {Proceedings of Machine Learning Research}, month ..."}
diff --git a/data/sampled_jsons/arXiv2401.01192_Deep-ELA_abstract.jsonl b/data/sampled_jsons/arXiv2401.01192_Deep-ELA_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c9ce12f9e73adaeacb73b7c1bf9b6992dc105a18
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+++ b/data/sampled_jsons/arXiv2401.01192_Deep-ELA_abstract.jsonl
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+{"idx": 0, "title": "arXiv.org e-Print archive", "date": "", "ddg_snippet": "arXiv is a free distribution service and an open-access archive for nearly 2.4 million scholarly articles in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/", "content": "arXiv is a free distribution service and an open-access archive for nearly 2.4 million scholarly articles in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics."}
+{"idx": 1, "title": "Computer Science - arXiv.org", "date": "", "ddg_snippet": "Computer Science (since January 1993) For a specific paper, enter the identifier into the top right search box. Browse: new (most recent mailing, with abstracts) recent (last 5 mailings) current month's listings specific year/month:", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/archive/cs", "content": "Computer Science (since January 1993) For a specific paper, enter the identifier into the top right search box. Browse: new (most recent mailing, with abstracts) recent (last 5 mailings) current month's listings specific year/month:"}
+{"idx": 2, "title": "[2501.12948] DeepSeek-R1: Incentivizing Reasoning Capability in...", "date": "", "ddg_snippet": "Jan 22, 2025 · Abstract page for arXiv paper 2501.12948: DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2501.12948", "content": "Jan 22, 2025 · Abstract page for arXiv paper 2501.12948: DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning"}
+{"idx": 3, "title": "Log in to arXiv | arXiv e-print repository", "date": "", "ddg_snippet": "Log in to arXiv .org The arXiv Privacy Policy has changed. By continuing to use arxiv .org, you are agreeing to the privacy policy.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/login", "content": "Log in to arXiv .org The arXiv Privacy Policy has changed. By continuing to use arxiv .org, you are agreeing to the privacy policy."}
+{"idx": 4, "title": "YOLOv12: Attention-Centric Real-Time Object Detectors - arXiv.org", "date": "", "ddg_snippet": "Feb 18, 2025 · Abstract page for arXiv paper 2502.12524: YOLOv12: Attention-Centric Real-Time Object Detectors", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.12524", "content": "Feb 18, 2025 · Abstract page for arXiv paper 2502.12524: YOLOv12: Attention-Centric Real-Time Object Detectors"}
+{"idx": 5, "title": "[2508.10104] DINOv3 - arXiv.org", "date": "", "ddg_snippet": "Aug 13, 2025 · Self-supervised learning holds the promise of eliminating the need for manual data annotation, enabling models to scale effortlessly to massive datasets and larger architectures. By not being tailored to specific tasks or domains, this training paradigm has the potential to learn visual representations from diverse sources, ranging from natural to aerial images -- using a single algorithm ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2508.10104", "content": "Aug 13, 2025 · Self-supervised learning holds the promise of eliminating the need for manual data annotation, enabling models to scale effortlessly to massive datasets and larger architectures. By not being tailored to specific tasks or domains, this training paradigm has the potential to learn visual representations from diverse sources, ranging from natural to aerial images -- using a single algorithm ..."}
+{"idx": 6, "title": "[2212.10156] Planning-oriented Autonomous Driving - arXiv.org", "date": "", "ddg_snippet": "Dec 20, 2022 · Modern autonomous driving system is characterized as modular tasks in sequential order, i.e., perception, prediction, and planning. In order to perform a wide diversity of tasks and achieve advanced-level intelligence, contemporary approaches either deploy standalone models for individual tasks, or design a multi-task paradigm with separate heads. However, they might suffer from accumulative ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2212.10156", "content": "Dec 20, 2022 · Modern autonomous driving system is characterized as modular tasks in sequential order, i.e., perception, prediction, and planning. In order to perform a wide diversity of tasks and achieve advanced-level intelligence, contemporary approaches either deploy standalone models for individual tasks, or design a multi-task paradigm with separate heads. However, they might suffer from accumulative ..."}
+{"idx": 7, "title": "[1706.03762] Attention Is All You Need - arXiv.org", "date": "", "ddg_snippet": "Jun 12, 2017 · The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1706.03762", "content": "Jun 12, 2017 · The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely ..."}
+{"idx": 8, "title": "[2506.01844] SmolVLA: A Vision-Language-Action Model for ... -...", "date": "", "ddg_snippet": "Jun 2, 2025 · Abstract page for arXiv paper 2506.01844: SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2506.01844", "content": "Jun 2, 2025 · Abstract page for arXiv paper 2506.01844: SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics"}
+{"idx": 9, "title": "[2410.10762] AFlow: Automating Agentic Workflow Generation -...", "date": "", "ddg_snippet": "Oct 14, 2024 · Abstract page for arXiv paper 2410.10762: AFlow: Automating Agentic Workflow Generation", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.10762", "content": "Oct 14, 2024 · Abstract page for arXiv paper 2410.10762: AFlow: Automating Agentic Workflow Generation"}
diff --git a/data/sampled_jsons/arXiv2401.12160v1_ScoreDec_Wu_abstract.jsonl b/data/sampled_jsons/arXiv2401.12160v1_ScoreDec_Wu_abstract.jsonl
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+{"idx": 0, "title": "[ 2401 . 12160 ] ScoreDec : A Phase-preserving High-Fidelity Audio...", "date": "", "ddg_snippet": "(or arXiv : 2401 . 12160 v 1 [eess.AS] for this version).View a PDF of the paper titled ScoreDec : A Phase-preserving High-Fidelity Audio Codec with A Generalized Score -based Diffusion Post-filter, by Yi-Chiao Wu and 4 other authors.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2401.12160", "content": "(or arXiv : 2401 . 12160 v 1 [eess.AS] for this version).View a PDF of the paper titled ScoreDec : A Phase-preserving High-Fidelity Audio Codec with A Generalized Score -based Diffusion Post-filter, by Yi-Chiao Wu and 4 other authors."}
+{"idx": 1, "title": "ABSTRACT arXiv:2401.12160v1 [eess.AS] 22 Jan 2024", "date": "", "ddg_snippet": "spectral domain for the E2E AudioDec codec [11]. The proposed ScoreDec attains high-fidelity speech reconstruction, preserves the original phase inf rmation, and gets rid of the tricky GAN training. According to the objective and subjective experimental results, the reconstructed coded speech achieves human-level naturalness with a ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2401.12160", "content": "spectral domain for the E2E AudioDec codec [11]. The proposed ScoreDec attains high-fidelity speech reconstruction, preserves the original phase inf rmation, and gets rid of the tricky GAN training. According to the objective and subjective experimental results, the reconstructed coded speech achieves human-level naturalness with a ..."}
+{"idx": 2, "title": "[2401.12160] ScoreDec: A Phase-preserving High-Fidelity Audio ...", "date": "", "ddg_snippet": "Feb 27, 2024 · ScoreDec : A Phase-preserving High-Fidelity Audio Codec with A Generalized Score-based Diffusion Post-filter Abstract Although recent mainstream waveform-domain end-to-end (E2E) neural audio codecs achieve impressive coded audio quality with a very low bitrate, the quality gap between the coded and natural audio is still significant.", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2401.12160", "content": "Feb 27, 2024 · ScoreDec : A Phase-preserving High-Fidelity Audio Codec with A Generalized Score-based Diffusion Post-filter Abstract Although recent mainstream waveform-domain end-to-end (E2E) neural audio codecs achieve impressive coded audio quality with a very low bitrate, the quality gap between the coded and natural audio is still significant."}
+{"idx": 3, "title": "ScoreDec: A Phase-preserving High-Fidelity Audio Codec with A ...", "date": "", "ddg_snippet": "Both the objective and subjective experimental results show that ScoreDec with a 24~kbps bitrate encodes and decodes full-band 48~kHz speech with human-level naturalness and well-preserved phase information. Publication: arXiv e-prints Pub Date: January 2024 DOI: 10.48550/ arXiv . 2401 .12160 arXiv : arXiv : 2401 .12160 Bibcode: 2024arXiv240112160W ...", "subpage_snippet": "", "source": "ui.adsabs.harvard.edu", "link": "https://ui.adsabs.harvard.edu/abs/2024arXiv240112160W/abstract", "content": "Both the objective and subjective experimental results show that ScoreDec with a 24~kbps bitrate encodes and decodes full-band 48~kHz speech with human-level naturalness and well-preserved phase information. Publication: arXiv e-prints Pub Date: January 2024 DOI: 10.48550/ arXiv . 2401 .12160 arXiv : arXiv : 2401 .12160 Bibcode: 2024arXiv240112160W ..."}
+{"idx": 4, "title": "ScoreDec: A Phase-preserving High-Fidelity Audio Codec with A ...", "date": "", "ddg_snippet": "当前位置： › › 论文详情 ScoreDec : A Phase-preserving High-Fidelity Audio Codec with A Generalized Score-based Diffusion Post-filter arXiv - EE - Audio and Speech Processing Pub Date : 2024-01-22 , DOI: arxiv - 2401 .12160 Yi-Chiao Wu , Dejan Marković, Steven Krenn, Israel D. Gebru, Alexander Richard", "subpage_snippet": "", "source": "www.x-mol.com", "link": "https://www.x-mol.com/paper/1749893390996836352", "content": "当前位置： › › 论文详情 ScoreDec : A Phase-preserving High-Fidelity Audio Codec with A Generalized Score-based Diffusion Post-filter arXiv - EE - Audio and Speech Processing Pub Date : 2024-01-22 , DOI: arxiv - 2401 .12160 Yi-Chiao Wu , Dejan Marković, Steven Krenn, Israel D. Gebru, Alexander Richard"}
+{"idx": 5, "title": "ScoreDec_demo/index.md at gh-pages · bigpon ... - GitHub", "date": "", "ddg_snippet": "ScoreDec : A Phase-preserving High-Fidelity Audio Codec with A Generalized Score-based Diffusion Post-filter Yi-Chiao Wu , Dejan Marković, Steven Krenn, Israel D. Gebru, and Alexander Richard Meta Reality Labs Research, USA This page is the demo of ScoreDec [paper]", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/bigpon/ScoreDec_demo/blob/gh-pages/index.md", "content": "ScoreDec : A Phase-preserving High-Fidelity Audio Codec with A Generalized Score-based Diffusion Post-filter Yi-Chiao Wu , Dejan Marković, Steven Krenn, Israel D. Gebru, and Alexander Richard Meta Reality Labs Research, USA This page is the demo of ScoreDec [paper]"}
+{"idx": 6, "title": "[PDF] ScoreDec: A Phase-Preserving High-Fidelity Audio Codec ...", "date": "", "ddg_snippet": "A score-based diffusion post-filter (SPF) in the complex spectral domain and combine the previous AudioDec with the SPF to propose ScoreDec , which can be trained using only spectral and score-matching losses and shows human-level naturalness and well-preserved phase information. Although recent mainstream waveform-domain end-to-end (E2E) neural audio codecs achieve impressive coded audio ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/ScoreDec:-A-Phase-Preserving-High-Fidelity-Audio-a-Wu-Markovic/c6255654f590b4625fb7462aa5d0c758b23086c4", "content": "A score-based diffusion post-filter (SPF) in the complex spectral domain and combine the previous AudioDec with the SPF to propose ScoreDec , which can be trained using only spectral and score-matching losses and shows human-level naturalness and well-preserved phase information. Although recent mainstream waveform-domain end-to-end (E2E) neural audio codecs achieve impressive coded audio ..."}
+{"idx": 7, "title": "GitHub - hwenjun18/ arxiv -daily: arxiv daily", "date": "", "ddg_snippet": "ScoreDec : A Phase-preserving High-Fidelity Audio Codec with A Generalized Score -based Diffusion Post-filter. Yi-Chiao Wu et.al. 2401 . 12160 . null.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/hwenjun18/arxiv-daily", "content": "ScoreDec : A Phase-preserving High-Fidelity Audio Codec with A Generalized Score -based Diffusion Post-filter. Yi-Chiao Wu et.al. 2401 . 12160 . null."}
+{"idx": 8, "title": "scoredec : S - Core Graph Decomposition", "date": "", "ddg_snippet": "Package: scoredec 0.1.2. Christos Adam. scoredec : S - Core Graph Decomposition.", "subpage_snippet": "", "source": "cran.r-universe.dev", "link": "https://cran.r-universe.dev/scoredec", "content": "Package: scoredec 0.1.2. Christos Adam. scoredec : S - Core Graph Decomposition."}
+{"idx": 9, "title": "Магнитола K2401 на базе Allwinner A133 - Обсуждение - 4PDA", "date": "", "ddg_snippet": "Обсуждение Магнитола K2401 на базе Allwinner A133 - 4 ядра Cortex-A53@1.5GHz [GcPAsR][Автомагнитола и устройство на Android] Обсуждение ».Файлы Конфигурации дисплея для K2401.", "subpage_snippet": "", "source": "4pda.to", "link": "https://4pda.to/forum/index.php?showtopic=1084167&st=3780", "content": "Обсуждение Магнитола K2401 на базе Allwinner A133 - 4 ядра Cortex-A53@1.5GHz [GcPAsR][Автомагнитола и устройство на Android] Обсуждение ».Файлы Конфигурации дисплея для K2401."}
diff --git a/data/sampled_jsons/arXiv2503.06366_Machine_Learning_meets_Algebraic_Combinatorics.jsonl b/data/sampled_jsons/arXiv2503.06366_Machine_Learning_meets_Algebraic_Combinatorics.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..767e77f93c3e7cafb9fd10ff3ddc7415724ccd12
--- /dev/null
+++ b/data/sampled_jsons/arXiv2503.06366_Machine_Learning_meets_Algebraic_Combinatorics.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "什么是arXiv？ - 知乎", "date": "", "ddg_snippet": "论文讲究时效性，你想了一个 idea， 然后做了仿真，写了论文。但是考虑到投稿问题，有些会议或者期刊 “call for paper ” 是有时间限制的，比如可能多几个月才是论文的收稿期。一方面为了证明自己论文的原创性，将论文放到 arXiv 上挂起来；另一方面，也是为了竞争，谁的论文在 arXiv 挂的早，谁 ...", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/31864895?sort=created", "content": "论文讲究时效性，你想了一个 idea， 然后做了仿真，写了论文。但是考虑到投稿问题，有些会议或者期刊 “call for paper ” 是有时间限制的，比如可能多几个月才是论文的收稿期。一方面为了证明自己论文的原创性，将论文放到 arXiv 上挂起来；另一方面，也是为了竞争，谁的论文在 arXiv 挂的早，谁 ..."}
+{"idx": 1, "title": "arxiv国内有镜像网站吗？ - 知乎", "date": "", "ddg_snippet": "国内确实有arxiv的镜像网站，旨在提升访问速度和下载体验。有两个推荐的镜像站点： CN.ARXIV.ORG：这是官方提供的中国镜像，访问速度快，适合国内用户下载arxiv上的PDF文件。您可以直接访问这个域名获取资料。 中科院镜像 (XXX.ITP.AC.CN)：另一个高效的选择，通过将arxiv.org的链接中的域名替换为 http ...", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/1911390291929306874", "content": "国内确实有arxiv的镜像网站，旨在提升访问速度和下载体验。有两个推荐的镜像站点： CN.ARXIV.ORG：这是官方提供的中国镜像，访问速度快，适合国内用户下载arxiv上的PDF文件。您可以直接访问这个域名获取资料。 中科院镜像 (XXX.ITP.AC.CN)：另一个高效的选择，通过将arxiv.org的链接中的域名替换为 http ..."}
+{"idx": 2, "title": "论文挂在arxiv上会影响之后的投稿吗，查重或者算一稿多投啥的? - 知乎", "date": "", "ddg_snippet": "2. 预留充足的投稿时间：考虑到 arXiv 预印本可能带来的影响，在投稿时要预留比平时更充足的时间 。 如果论文在 arXiv 上发布后，需要进行大量修改才能满足投稿要求，要确保有足够的时间进行修改和完善，避免因时间紧迫而影响论文质量。", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/509528796", "content": "2. 预留充足的投稿时间：考虑到 arXiv 预印本可能带来的影响，在投稿时要预留比平时更充足的时间 。 如果论文在 arXiv 上发布后，需要进行大量修改才能满足投稿要求，要确保有足够的时间进行修改和完善，避免因时间紧迫而影响论文质量。"}
+{"idx": 3, "title": "如果一个论文只在arXiv上挂着，没有在什么会和期刊发表过，却有几万的...", "date": "", "ddg_snippet": "如果一个论文只在arXiv上挂着，没有在什么会和期刊发表过，却有几万的引用量，如何评价这类论文？ 关注者 755 被浏览", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/458164481", "content": "如果一个论文只在arXiv上挂着，没有在什么会和期刊发表过，却有几万的引用量，如何评价这类论文？ 关注者 755 被浏览"}
+{"idx": 4, "title": "如何看待将投往双盲会议的论文提前公布到arXiv的行为? - 知乎", "date": "", "ddg_snippet": "我就算有10张嘴，怎么能骂得过黑子10000张嘴？ 所以，对自己的paper没有相当程度的自信，是不敢传arxiv的。 而往往大佬组的paper质量高的可能性大，也更自信，所以你观测到的大佬传arxiv的数量就更多，背后的原因其实不是大佬想给reviewer施加压力。", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/427222067", "content": "我就算有10张嘴，怎么能骂得过黑子10000张嘴？ 所以，对自己的paper没有相当程度的自信，是不敢传arxiv的。 而往往大佬组的paper质量高的可能性大，也更自信，所以你观测到的大佬传arxiv的数量就更多，背后的原因其实不是大佬想给reviewer施加压力。"}
+{"idx": 5, "title": "在arxiv发表论文的意义体现在哪里？ - 知乎", "date": "", "ddg_snippet": "arXiv 拥有庞大的学术用户群，论文一旦上传，就可以被全球的研究者搜索、阅读和引用。 许多研究人员习惯直接从 arXiv 获取最新的研究动态，而不必等正式出版。 高质量的预印本往往能带来更多关注、下载和引用，甚至有可能在正式发表前就产生影响。 4.", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/8772574507", "content": "arXiv 拥有庞大的学术用户群，论文一旦上传，就可以被全球的研究者搜索、阅读和引用。 许多研究人员习惯直接从 arXiv 获取最新的研究动态，而不必等正式出版。 高质量的预印本往往能带来更多关注、下载和引用，甚至有可能在正式发表前就产生影响。 4."}
+{"idx": 6, "title": "把文章发到arXiv上别人认可嘛？ - 知乎", "date": "", "ddg_snippet": "发表到arXiv上，确切来讲不能叫发表，因为arXiv只是一个共享平台，并不能说明文章水平，会议和期刊就不一样了，要经过好多专家评审，而且不同会议和期刊也代表不同水平。 个人认为发到 arXiv 上的有以下情况： 第一种，就是为了 占坑，因为无论会议和期刊从投出到最终可以检索，都需要一半年 ...", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/30131676", "content": "发表到arXiv上，确切来讲不能叫发表，因为arXiv只是一个共享平台，并不能说明文章水平，会议和期刊就不一样了，要经过好多专家评审，而且不同会议和期刊也代表不同水平。 个人认为发到 arXiv 上的有以下情况： 第一种，就是为了 占坑，因为无论会议和期刊从投出到最终可以检索，都需要一半年 ..."}
+{"idx": 7, "title": "如何知道arXiv上的论文投到哪里了？ - 知乎", "date": "", "ddg_snippet": "Nov 23, 2020 · 如何知道arXiv上的论文投到哪里了？ 比如我在arXiv上看到一篇不错的论文，想引用它，我想直接按录取它的期刊或者会议的格式引用，怎么做？", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/431622372", "content": "Nov 23, 2020 · 如何知道arXiv上的论文投到哪里了？ 比如我在arXiv上看到一篇不错的论文，想引用它，我想直接按录取它的期刊或者会议的格式引用，怎么做？"}
+{"idx": 8, "title": "arXiv怎么发音？ - 知乎", "date": "", "ddg_snippet": "Via arXiv : The arXiv (pronounced \" archive \", as if the \"X\" were the Greek letter Chi, χ) is a repository of electronic preprints , known as e-prints , of scientific papers in the fields of mathematics , physics , astronomy , computer science , quantitative biology , statistics , and quantitative finance, which can be accessed online 展开 ...", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/28517169", "content": "Via arXiv : The arXiv (pronounced \" archive \", as if the \"X\" were the Greek letter Chi, χ) is a repository of electronic preprints , known as e-prints , of scientific papers in the fields of mathematics , physics , astronomy , computer science , quantitative biology , statistics , and quantitative finance, which can be accessed online 展开 ..."}
+{"idx": 9, "title": "如何看待朝鲜国家科学院数学研究所发布 arXiv 论文称千禧年难题纳维-...", "date": "", "ddg_snippet": "Aug 28, 2025 · 如何看待朝鲜国家科学院数学研究所发布 arXiv 论文称千禧年难题纳维-斯托克斯方程已被攻克？ 2025/8/28，arXiv 上挂出了一篇题为 《Global regularity of Leray-Hopf weak solutions to…", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/1944357449965494499", "content": "Aug 28, 2025 · 如何看待朝鲜国家科学院数学研究所发布 arXiv 论文称千禧年难题纳维-斯托克斯方程已被攻克？ 2025/8/28，arXiv 上挂出了一篇题为 《Global regularity of Leray-Hopf weak solutions to…"}
diff --git a/data/sampled_jsons/arXiv_2502.00921_GitHub.jsonl b/data/sampled_jsons/arXiv_2502.00921_GitHub.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5a427e5ab27eefc6c11d9f8683e223f49f6de348
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+++ b/data/sampled_jsons/arXiv_2502.00921_GitHub.jsonl
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+{"idx": 0, "title": "[2502.00921] Blink of an eye: a simple theory for feature ...", "date": "", "ddg_snippet": "Feb 2, 2025 · Abstract page for arXiv paper 2502.00921 : Blink of an eye: a simple theory for feature localization in generative models", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.00921", "content": "Feb 2, 2025 · Abstract page for arXiv paper 2502.00921 : Blink of an eye: a simple theory for feature localization in generative models"}
+{"idx": 1, "title": "GitHub - ecmmyers/Red-Team-Arxiv-Paper-Update: Awesome ...", "date": "", "ddg_snippet": "May 27, 2025 · Awesome Jailbreak, red teaming arxiv papers (Automatically Update Every 12th hours) - ecmmyers/Red-Team- Arxiv -Paper-Update", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/ecmmyers/Red-Team-Arxiv-Paper-Update", "content": "May 27, 2025 · Awesome Jailbreak, red teaming arxiv papers (Automatically Update Every 12th hours) - ecmmyers/Red-Team- Arxiv -Paper-Update"}
+{"idx": 2, "title": "GitHub - GalaxyGeneralRobotics/OpenTrack: Official ...", "date": "", "ddg_snippet": "Official implementation of OpenTrack. Contribute to GalaxyGeneralRobotics/OpenTrack development by creating an account on GitHub .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/GalaxyGeneralRobotics/OpenTrack", "content": "Official implementation of OpenTrack. Contribute to GalaxyGeneralRobotics/OpenTrack development by creating an account on GitHub ."}
+{"idx": 3, "title": "Arxiv今日论文 | 2025-09-22 | 闲记算法", "date": "", "ddg_snippet": "1 day ago · 本篇博文主要内容为 2025-09-22 从 Arxiv .org论文网站获取的最新论文列表，自动更新，按照NLP、CV、ML、AI、IR五个大方向区分，若需要邮件定时接收，请在评论区留下你的邮箱号。 说明：每日论文数据从 Arxiv .org获取，每天早上12:00左右定时自动更新。 友情提示: 如何您需要邮箱接收每日论文数据，请在 ...", "subpage_snippet": "", "source": "lonepatient.top", "link": "http://lonepatient.top/2025/09/22/arxiv_papers_2025-09-22", "content": "1 day ago · 本篇博文主要内容为 2025-09-22 从 Arxiv .org论文网站获取的最新论文列表，自动更新，按照NLP、CV、ML、AI、IR五个大方向区分，若需要邮件定时接收，请在评论区留下你的邮箱号。 说明：每日论文数据从 Arxiv .org获取，每天早上12:00左右定时自动更新。 友情提示: 如何您需要邮箱接收每日论文数据，请在 ..."}
+{"idx": 4, "title": "chen37058/Red-Team-Arxiv-Paper-Update: Awesome ...", "date": "", "ddg_snippet": "2502.00921 , null. 2025-04-14, AgentBreeder: Mitigating the AI Safety Impact of Multi-Agent Scaffolds via Self-Improvement, J Rosser et.al. 2502.00757 · link.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/chen37058/Awesome-Multimodal-Jailbreak", "content": "2502.00921 , null. 2025-04-14, AgentBreeder: Mitigating the AI Safety Impact of Multi-Agent Scaffolds via Self-Improvement, J Rosser et.al. 2502.00757 · link."}
+{"idx": 5, "title": "Reasoning or Performing: locating \"breakthrough\" in ... - GitHub", "date": "", "ddg_snippet": "Feb 22, 2025 · Research Question When asked the DeepSeek models a challenging abstract algebra question, they often generated hundreds of tokens of reasoning before providing the final answer. Yet, on some questi...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/ARBORproject/arborproject.github.io/discussions/11", "content": "Feb 22, 2025 · Research Question When asked the DeepSeek models a challenging abstract algebra question, they often generated hundreds of tokens of reasoning before providing the final answer. Yet, on some questi..."}
+{"idx": 6, "title": "[2502.00921] Blink of an eye: a simple theory for feature ...", "date": "", "ddg_snippet": "Large language models (LLMs) can exhibit undesirable and unexpected behavior in the blink of an eye. In a recent Anthropic demo, Claude switched from coding to Googling pictures of Yellowstone, and these sudden shifts …", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2502.00921", "content": "Large language models (LLMs) can exhibit undesirable and unexpected behavior in the blink of an eye. In a recent Anthropic demo, Claude switched from coding to Googling pictures of Yellowstone, and these sudden shifts …"}
+{"idx": 7, "title": "a simple theory for feature localization in generative models", "date": "", "ddg_snippet": "arXiv:2502.00921v1 [cs.LG] 02 Feb 2025. Blink of an eye: a simple theory for feature localization in ... Submit without Github Submit in Github. Report ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.00921v1", "content": "arXiv:2502.00921v1 [cs.LG] 02 Feb 2025. Blink of an eye: a simple theory for feature localization in ... Submit without Github Submit in Github. Report ..."}
+{"idx": 8, "title": "Marvin Li (@marvin_li03) / X", "date": "", "ddg_snippet": "... arxiv.org/abs/2502.00921 Catch me at the poster session right after! See you there!. arXiv logo · arxiv.org. Blink of an eye: a simple theory for feature ...", "subpage_snippet": "", "source": "x.com", "link": "https://x.com/marvin_li03?lang=en", "content": "... arxiv.org/abs/2502.00921 Catch me at the poster session right after! See you there!. arXiv logo · arxiv.org. Blink of an eye: a simple theory for feature ..."}
+{"idx": 9, "title": "Optimal Control for Industrial Multi-Component CPS via Path ...", "date": "", "ddg_snippet": "Aug 31, 2025 · Cyber-physical systems (CPS) have been increasingly deployed in many safety-critical industrial environments, where effective and efficient optimal control synthesis is vital for ensuring reliable system operation. The optimal control problem—aimed at ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.1145/3769083", "content": "Aug 31, 2025 · Cyber-physical systems (CPS) have been increasingly deployed in many safety-critical industrial environments, where effective and efficient optimal control synthesis is vital for ensuring reliable system operation. The optimal control problem—aimed at ..."}
diff --git a/data/sampled_jsons/arXiv_2503.17332_'Insufficient_Exploration'_definition_year_2023-2024.jsonl b/data/sampled_jsons/arXiv_2503.17332_'Insufficient_Exploration'_definition_year_2023-2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..39e97c4d684a6df412010f278a10d33770786745
--- /dev/null
+++ b/data/sampled_jsons/arXiv_2503.17332_'Insufficient_Exploration'_definition_year_2023-2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Stored Object: paper: arxiv . 2503 .18866 - Githubissues", "date": "", "ddg_snippet": "System which logs and analyzes arxiv reading activity, using github as a serverless database and processing runtime.", "subpage_snippet": "", "source": "githubissues.com", "link": "https://githubissues.com/dmarx/papers-feed/2221", "content": "System which logs and analyzes arxiv reading activity, using github as a serverless database and processing runtime."}
+{"idx": 1, "title": "arXiv : 2503 .14454 | In the Dark", "date": "", "ddg_snippet": "Archive for arXiv : 2503 .14454.Here is a pretty picture showing one of the beautiful cosmic microwave background polarization and intensity maps: Intensity and Polarization maps from ACT: arXiv : 2503 .14451. There are three related preprints on the arXiv today", "subpage_snippet": "", "source": "telescoper.blog", "link": "https://telescoper.blog/tag/arxiv2503-14454/", "content": "Archive for arXiv : 2503 .14454.Here is a pretty picture showing one of the beautiful cosmic microwave background polarization and intensity maps: Intensity and Polarization maps from ACT: arXiv : 2503 .14451. There are three related preprints on the arXiv today"}
+{"idx": 2, "title": "CVE-Bench: A Benchmark for AI Agents’ Ability to Exploit Real-World...", "date": "", "ddg_snippet": "• Insufficient Exploration : Agents fail to explore all possible attacks or endpoints, leading to missed opportunities.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.17332", "content": "• Insufficient Exploration : Agents fail to explore all possible attacks or endpoints, leading to missed opportunities."}
+{"idx": 3, "title": "CVE-Bench: A Benchmark for AI Agents' Ability to Exploit Real-World...", "date": "", "ddg_snippet": "• Insufficient Exploration : Agents fail to explore all possi-ble attacks or endpoints, leading to missed opportunities. • Tool Misuse: Incorrect or suboptimal use of tools (i.e., sqlmap) can result in failed attempts.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.17332", "content": "• Insufficient Exploration : Agents fail to explore all possi-ble attacks or endpoints, leading to missed opportunities. • Tool Misuse: Incorrect or suboptimal use of tools (i.e., sqlmap) can result in failed attempts."}
+{"idx": 4, "title": "GitHub - uiuc-kang-lab/cve-bench: CVE-Bench: A Benchmark for AI...", "date": "", "ddg_snippet": "Explore .Twm Stone and Daniel Kang}, year={2025}, url={https:// arxiv .org/abs/ 2503 . 17332 } }. Acknowledgements. The US AI Safety Institute contributed to the development of this benchmark. About. CVE-Bench: A Benchmark for AI Agents’ Ability to Exploit Real-World Web...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/uiuc-kang-lab/cve-bench", "content": "Explore .Twm Stone and Daniel Kang}, year={2025}, url={https:// arxiv .org/abs/ 2503 . 17332 } }. Acknowledgements. The US AI Safety Institute contributed to the development of this benchmark. About. CVE-Bench: A Benchmark for AI Agents’ Ability to Exploit Real-World Web..."}
+{"idx": 5, "title": "Richard Fang - Google Scholar | University of Illinois - Cited by 275", "date": "", "ddg_snippet": "Q Zhan, R Fang, R Bindu, A Gupta, T Hashimoto, D Kang. arXiv preprint arXiv :2311.05553, 2023.", "subpage_snippet": "", "source": "scholar.google.co.in", "link": "https://scholar.google.co.in/citations?user=l7Ra9p8AAAAJ&hl=en", "content": "Q Zhan, R Fang, R Bindu, A Gupta, T Hashimoto, D Kang. arXiv preprint arXiv :2311.05553, 2023."}
+{"idx": 6, "title": "Mixing neutrinos of colliding neutron stars changes how merger unfolds...", "date": "", "ddg_snippet": "On arXiv : DOI: 10.48550/ arxiv . 2503 .11758. Explore further. What flavor is that neutrino? Adding flavor helps to track neutrino movement in astrophysical systems.", "subpage_snippet": "", "source": "phys.org", "link": "https://phys.org/news/2025-09-neutrinos-colliding-neutron-stars-merger.html", "content": "On arXiv : DOI: 10.48550/ arxiv . 2503 .11758. Explore further. What flavor is that neutrino? Adding flavor helps to track neutrino movement in astrophysical systems."}
+{"idx": 7, "title": "Explore | alphaXiv", "date": "", "ddg_snippet": "Discuss, discover, and read arXiv papers. Explore trending papers, see recent activity and discussions, and follow authors of arXiv papers on alphaXiv.", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/", "content": "Discuss, discover, and read arXiv papers. Explore trending papers, see recent activity and discussions, and follow authors of arXiv papers on alphaXiv."}
+{"idx": 8, "title": "Wan-AI/Wan2.2-Animate-14B · Hugging Face", "date": "", "ddg_snippet": "arxiv : 2503 .20314.(2) Efficient High- Definition Hybrid TI2V. To enable more efficient deployment, Wan2.2 also explores a high-compression design. In addition to the 27B MoE models, a 5B dense model, i.e., TI2V-5B, is released.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/Wan-AI/Wan2.2-Animate-14B", "content": "arxiv : 2503 .20314.(2) Efficient High- Definition Hybrid TI2V. To enable more efficient deployment, Wan2.2 also explores a high-compression design. In addition to the 27B MoE models, a 5B dense model, i.e., TI2V-5B, is released."}
+{"idx": 9, "title": "Show HN: CVE-Bench, the first LLM benchmark using real-world web...", "date": "", "ddg_snippet": "If given a brief description of the vulnerability (1-day), they can exploit up to 25%. Agents are all using GPT-4o without specialized training.The growing risk of AI misuse highlights the need for careful red-teaming.", "subpage_snippet": "", "source": "www.gpt-5.com", "link": "https://www.gpt-5.com/80434572/show-hn-cve-bench-the-first-llm-benchmark-using-real-world-web-vulnerabilities", "content": "If given a brief description of the vulnerability (1-day), they can exploit up to 25%. Agents are all using GPT-4o without specialized training.The growing risk of AI misuse highlights the need for careful red-teaming."}
diff --git a/data/sampled_jsons/arXiv_46yLEXtav4_Statistical_Collusion_by_Collectives_on_Learning_Platforms.jsonl b/data/sampled_jsons/arXiv_46yLEXtav4_Statistical_Collusion_by_Collectives_on_Learning_Platforms.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a6364dd45ce29842fbb396812abbcc234b1286ea
--- /dev/null
+++ b/data/sampled_jsons/arXiv_46yLEXtav4_Statistical_Collusion_by_Collectives_on_Learning_Platforms.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Publications - University of California, Berkeley Statistical Collusion by Collectives on Learning Platforms GitHub - GauthierE/statistical-collusion 論文の概要: Statistical Collusion by Collectives on Learning Plat...", "date": "", "ddg_snippet": "See full list on people.eecs.berkeley.edu See full list on people.eecs.berkeley.edu See full list on people.eecs.berkeley.edu See full list on people.eecs.berkeley.edu See full list on people.eecs.berkeley.edu See full list on people.eecs.berkeley.edu See full list on people.eecs.berkeley.edu See full list on people.eecs.berkeley.edu See full list on people.eecs.berkeley.edu See full list on people.eecs.berkeley.edu Oral Statistical Collusion by Collectives on Learning Platforms Etienne Gauthier · Francis Bach · Michael Jordan West Ballroom D [ Abstract ] [ Visit Oral 6E Social and Economic Perspectives ] Thu 17 Jul 4 p.m. — 4:15 p.m. PDT Poster presentation: Statistical Collusion by Collectives on Learning Platforms Thu 17 Jul 4:30 p.m. PDT — 7 p.m. PDT statistical - collusion This repository contains the code for reproducing the experiments and figures presented in the paper Statistical Collusion by Collectives on Learning Platforms . May 22, 2025 · To evaluate the potential impact of such behavior, it is essential to understand the computations that collectives must perform to impact platforms in this way. In particular, collectives need to make a priori assessments of the effect of the collective before taking action, as they may face potential risks when modifying their data.", "subpage_snippet": "", "source": "people.eecs.berkeley.edu", "link": "https://people.eecs.berkeley.edu/~jordan/publications.html", "content": "See full list on people.eecs.berkeley.edu See full list on people.eecs.berkeley.edu See full list on people.eecs.berkeley.edu See full list on people.eecs.berkeley.edu See full list on people.eecs.berkeley.edu See full list on people.eecs.berkeley.edu See full list on people.eecs.berkeley.edu See full list on people.eecs.berkeley.edu See full list on people.eecs.berkeley.edu See full list on people.eecs.berkeley.edu Oral Statistical Collusion by Collectives on Learning Platforms Etienne Gauthier · Francis Bach · Michael Jordan West Ballroom D [ Abstract ] [ Visit Oral 6E Social and Economic Perspectives ] Thu 17 Jul 4 p.m. — 4:15 p.m. PDT Poster presentation: Statistical Collusion by Collectives on Learning Platforms Thu 17 Jul 4:30 p.m. PDT — 7 p.m. PDT statistical - collusion This repository contains the code for reproducing the experiments and figures presented in the paper Statistical Collusion by Collectives on Learning Platforms . May 22, 2025 · To evaluate the potential impact of such behavior, it is essential to understand the computations that collectives must perform to impact platforms in this way. In particular, collectives need to make a priori assessments of the effect of the collective before taking action, as they may face potential risks when modifying their data."}
+{"idx": 1, "title": "GauthierE/ statistical - collusion : Statistical Collusion by Collectives ...", "date": "", "ddg_snippet": "Statistical Collusion by Collectives on Learning Platforms . arxiv .org/abs/2502.04879.This repository considers four key scenarios from the paper: Signal planting with feature-label strategy.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/GauthierE/statistical-collusion", "content": "Statistical Collusion by Collectives on Learning Platforms . arxiv .org/abs/2502.04879.This repository considers four key scenarios from the paper: Signal planting with feature-label strategy."}
+{"idx": 2, "title": "Statistical Collusion by Collectives on Learning Platforms Statistical Collusion by Collectives on Learning Platforms Statistical Collusion by Collectives on Learning Platforms ... Publications - University of California, Berkeley Statistical Collusion by Collectives on Learning Platforms GitHub - GauthierE/statistical-collusion 論文の概要: Statistical Collusion by Collectives on Learning Plat...", "date": "", "ddg_snippet": "Feb 7, 2025 · As platforms increasingly rely on learning algorithms, collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data. To evaluate the potential impact of such behavior, it is essential to understand the computations that collectives must perform to impact platforms in this way. In particular ... A framework is developed that provides a theoretical and algorithmic treatment of the issues of a priori assessments of the effect of the collective before taking action and presents experimental results in a product evaluation domain. As platforms increasingly rely on learning algorithms, collectives may form and seek ways to influence these platforms to align with their own interests. This ... This paper talks about how groups of people can work together to change the way online platforms use data and learning algorithms to benefit their interests. The authors created a method that help... See full list on people.eecs.berkeley.edu See full list on people.eecs.berkeley.edu See full list on people.eecs.berkeley.edu See full list on people.eecs.berkeley.edu See full list on people.eecs.berkeley.edu See full list on people.eecs.berkeley.edu See full list on people.eecs.berkeley.edu See full list on people.eecs.berkeley.edu See full list on people.eecs.berkeley.edu See full list on people.eecs.berkeley.edu Oral Statistical Collusion by Collectives on Learning Platforms Etienne Gauthier · Francis Bach · Michael Jordan West Ballroom D [ Abstract ] [ Visit Oral 6E Social and Economic Perspectives ] Thu 17 Jul 4 p.m. — 4:15 p.m. PDT Poster presentation: Statistical Collusion by Collectives on Learning Platforms Thu 17 Jul 4:30 p.m. PDT — 7 p.m. PDT statistical - collusion This repository contains the code for reproducing the experiments and figures presented in the paper Statistical Collusion by Collectives on Learning Platforms . May 22, 2025 · To evaluate the potential impact of such behavior, it is essential to understand the computations that collectives must perform to impact platforms in this way. In particular, collectives need to make a priori assessments of the effect of the collective before taking action, as they may face potential risks when modifying their data.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.04879", "content": "Feb 7, 2025 · As platforms increasingly rely on learning algorithms, collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data. To evaluate the potential impact of such behavior, it is essential to understand the computations that collectives must perform to impact platforms in this way. In particular ... A framework is developed that provides a theoretical and algorithmic treatment of the issues of a priori assessments of the effect of the collective before taking action and presents experimental results in a product evaluation domain. As platforms increasingly rely on learning algorithms, collectives may form and seek ways to influence these platforms to align with their own interests. This ... This paper talks about how groups of people can work together to change the way online platforms use data and learning algorithms to benefit their interests. The authors created a method that help... See full list on people.eecs.berkeley.edu See full list on people.eecs.berkeley.edu See full list on people.eecs.berkeley.edu See full list on people.eecs.berkeley.edu See full list on people.eecs.berkeley.edu See full list on people.eecs.berkeley.edu See full list on people.eecs.berkeley.edu See full list on people.eecs.berkeley.edu See full list on people.eecs.berkeley.edu See full list on people.eecs.berkeley.edu Oral Statistical Collusion by Collectives on Learning Platforms Etienne Gauthier · Francis Bach · Michael Jordan West Ballroom D [ Abstract ] [ Visit Oral 6E Social and Economic Perspectives ] Thu 17 Jul 4 p.m. — 4:15 p.m. PDT Poster presentation: Statistical Collusion by Collectives on Learning Platforms Thu 17 Jul 4:30 p.m. PDT — 7 p.m. PDT statistical - collusion This repository contains the code for reproducing the experiments and figures presented in the paper Statistical Collusion by Collectives on Learning Platforms . May 22, 2025 · To evaluate the potential impact of such behavior, it is essential to understand the computations that collectives must perform to impact platforms in this way. In particular, collectives need to make a priori assessments of the effect of the collective before taking action, as they may face potential risks when modifying their data."}
+{"idx": 3, "title": "Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "A framework is developed that provides a theoretical and algorithmic treatment of the issues of a priori assessments of the effect of the collective before taking action and presents experimental results in a product evaluation domain. As platforms increasingly rely on learning algorithms, collectives may form and seek ways to influence these platforms to align with their own interests. This ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Statistical-Collusion-by-Collectives-on-Learning-Gauthier-Bach/1c45ef9ad56839c3309f0a0bdcff50fbb3ad73f5", "content": "A framework is developed that provides a theoretical and algorithmic treatment of the issues of a priori assessments of the effect of the collective before taking action and presents experimental results in a product evaluation domain. As platforms increasingly rely on learning algorithms, collectives may form and seek ways to influence these platforms to align with their own interests. This ..."}
+{"idx": 4, "title": "Statistical Collusion by Collectives on Learning Platforms ...", "date": "", "ddg_snippet": "This paper talks about how groups of people can work together to change the way online platforms use data and learning algorithms to benefit their interests. The authors created a method that help...", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/icml/46504/paper", "content": "This paper talks about how groups of people can work together to change the way online platforms use data and learning algorithms to benefit their interests. The authors created a method that help..."}
+{"idx": 5, "title": "Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "Oral Statistical Collusion by Collectives on Learning Platforms Etienne Gauthier · Francis Bach · Michael Jordan West Ballroom D [ Abstract ] [ Visit Oral 6E Social and Economic Perspectives ] Thu 17 Jul 4 p.m. — 4:15 p.m. PDT Poster presentation: Statistical Collusion by Collectives on Learning Platforms Thu 17 Jul 4:30 p.m. PDT — 7 p.m. PDT", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/oral/47263", "content": "Oral Statistical Collusion by Collectives on Learning Platforms Etienne Gauthier · Francis Bach · Michael Jordan West Ballroom D [ Abstract ] [ Visit Oral 6E Social and Economic Perspectives ] Thu 17 Jul 4 p.m. — 4:15 p.m. PDT Poster presentation: Statistical Collusion by Collectives on Learning Platforms Thu 17 Jul 4:30 p.m. PDT — 7 p.m. PDT"}
+{"idx": 6, "title": "論文の概要: Statistical Collusion by Collectives on Learning Plat...", "date": "", "ddg_snippet": "May 22, 2025 · To evaluate the potential impact of such behavior, it is essential to understand the computations that collectives must perform to impact platforms in this way. In particular, collectives need to make a priori assessments of the effect of the collective before taking action, as they may face potential risks when modifying their data.", "subpage_snippet": "", "source": "fugumt.com", "link": "https://fugumt.com/fugumt/paper_check/2502.04879v2", "content": "May 22, 2025 · To evaluate the potential impact of such behavior, it is essential to understand the computations that collectives must perform to impact platforms in this way. In particular, collectives need to make a priori assessments of the effect of the collective before taking action, as they may face potential risks when modifying their data."}
+{"idx": 7, "title": "Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "As platforms increasingly rely on learning algorithms, collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.04879v1", "content": "As platforms increasingly rely on learning algorithms, collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data."}
+{"idx": 8, "title": "Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "Probability Surveys, 17:257–317, 2020. 9. Statistical Collusion by Collectives on Learning Platforms Li, B. and Liu, W. A theoretical analysis of backdoor poi-. soning attacks in convolutional neural networks.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=46yLEXtav4", "content": "Probability Surveys, 17:257–317, 2020. 9. Statistical Collusion by Collectives on Learning Platforms Li, B. and Liu, W. A theoretical analysis of backdoor poi-. soning attacks in convolutional neural networks."}
+{"idx": 9, "title": "Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "As platforms increasingly rely on learning algorithms, collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data.", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/article/Statistical-Collusion-by-Collectives-on-Learning-Platforms-deaeb81c-5365-4cec-9af2-e4d286dfd04a", "content": "As platforms increasingly rely on learning algorithms, collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data."}
diff --git a/data/sampled_jsons/arXiv_Hierarchical_Overlapping_Clustering_on_Graphs_Cost_Function_Algorithm_Scalability.jsonl b/data/sampled_jsons/arXiv_Hierarchical_Overlapping_Clustering_on_Graphs_Cost_Function_Algorithm_Scalability.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3f6286ad5ff197f0a998effad4f09d46d1c96768
--- /dev/null
+++ b/data/sampled_jsons/arXiv_Hierarchical_Overlapping_Clustering_on_Graphs_Cost_Function_Algorithm_Scalability.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Hierarchical Overlapping Clustering Function Algorithm and Scalability ...", "date": "", "ddg_snippet": "Overlap and hierarchy are two prevalent phenomena in clustering , and usually coexist in a single system. There are several studies on each of them separately, but it is unclear how to characterize and evaluate the hybrid structures yet. To address this issue, we initiate the study of hierarchical overlapping clustering on graphs by introducing a new cost function for it. We show the ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=oHSXRy29tj", "content": "Overlap and hierarchy are two prevalent phenomena in clustering , and usually coexist in a single system. There are several studies on each of them separately, but it is unclear how to characterize and evaluate the hybrid structures yet. To address this issue, we initiate the study of hierarchical overlapping clustering on graphs by introducing a new cost function for it. We show the ..."}
+{"idx": 1, "title": "PDF G O arXiv:2306.09950v1 [cs.DS] 16 Jun 2023", "date": "", "ddg_snippet": "This paper presents two eficient hierarchical clustering (HC) algorithms with respect to Dasgupta's cost function . For any input graph G with a clear cluster-structure, our designed algorithms run in nearly-linear time in the input size of G, and return an O(1)-approximate HC tree with respect to Dasgupta's cost function . We compare the performance of our algorithm against the previous ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2306.09950.pdf", "content": "This paper presents two eficient hierarchical clustering (HC) algorithms with respect to Dasgupta's cost function . For any input graph G with a clear cluster-structure, our designed algorithms run in nearly-linear time in the input size of G, and return an O(1)-approximate HC tree with respect to Dasgupta's cost function . We compare the performance of our algorithm against the previous ..."}
+{"idx": 2, "title": "Hierarchical Overlapping Clustering on Graphs: Cost Function, Algorithm ...", "date": "", "ddg_snippet": "This research paper introduces a new way to group data points in a more complex and realistic manner called hierarchical overlapping clustering (HOC). It combines two methods: hierarchical cluster...", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/icml/46447/paper", "content": "This research paper introduces a new way to group data points in a more complex and realistic manner called hierarchical overlapping clustering (HOC). It combines two methods: hierarchical cluster..."}
+{"idx": 3, "title": "PDF Nearly-Optimal Hierarchical Clustering for Well-Clustered Graphs", "date": "", "ddg_snippet": "Traditional HC algorithms are typically based on agglomerative heuristics and, due to the lack of a clear objective function , there was limited work on their analysis. Dasgupta (2016) introduced a simple cost function for hier-archical clustering , and this work has inspired a number of algorithmic studies on hierarchical clustering .", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v202/laenen23a/laenen23a.pdf", "content": "Traditional HC algorithms are typically based on agglomerative heuristics and, due to the lack of a clear objective function , there was limited work on their analysis. Dasgupta (2016) introduced a simple cost function for hier-archical clustering , and this work has inspired a number of algorithmic studies on hierarchical clustering ."}
+{"idx": 4, "title": "Nearly-optimal hierarchical clustering for well-clustered graphs ...", "date": "", "ddg_snippet": "This paper presents two efficient hierarchical clustering (HC) algorithms with respect to Dasgupta's cost function . For any input graph G with a clear cluster-structure, our designed algorithms run in nearly-linear time in the input size of G, and return an O (1)-approximate HC tree with respect to Dasgupta's cost function . We compare the performance of our algorithm against the previous state ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3618408.3619160", "content": "This paper presents two efficient hierarchical clustering (HC) algorithms with respect to Dasgupta's cost function . For any input graph G with a clear cluster-structure, our designed algorithms run in nearly-linear time in the input size of G, and return an O (1)-approximate HC tree with respect to Dasgupta's cost function . We compare the performance of our algorithm against the previous state ..."}
+{"idx": 5, "title": "Nearly-Optimal Hierarchical Clustering for Well-Clustered Graphs", "date": "", "ddg_snippet": "This paper presents two efficient hierarchical clustering (HC) algorithms with respect to Dasgupta's cost function . For any input graph G with a clear cluster-structure, our designed algorithms run in nearly-linear time in the input size of G, and return an O(1) -approximate HC tree with respect to Dasgupta's cost function . We compare the performance of our algorithm against the previous state ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2306.09950", "content": "This paper presents two efficient hierarchical clustering (HC) algorithms with respect to Dasgupta's cost function . For any input graph G with a clear cluster-structure, our designed algorithms run in nearly-linear time in the input size of G, and return an O(1) -approximate HC tree with respect to Dasgupta's cost function . We compare the performance of our algorithm against the previous state ..."}
+{"idx": 6, "title": "Hierarchical Overlapping Clustering on Graphs: Cost Function, Algorithm ...", "date": "", "ddg_snippet": "Poster Hierarchical Overlapping Clustering on Graphs : Cost Function , Algorithm and Scalability Yicheng Pan · Renjie Chen · Pengyu Long · Bingchen Fan East Exhibition Hall A-B #E-2009", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46447", "content": "Poster Hierarchical Overlapping Clustering on Graphs : Cost Function , Algorithm and Scalability Yicheng Pan · Renjie Chen · Pengyu Long · Bingchen Fan East Exhibition Hall A-B #E-2009"}
+{"idx": 7, "title": "PDF Overlapping Hierarchical Clustering (OHC) - inria.hal.science", "date": "", "ddg_snippet": "The trend gives a function in O(n2:45) so to speed up the process and scale up our algorithm is it possible to precompute a set of possibly overlapping clusters over a given -neighbourhood graph with a classical method, for instance CLIQUE, and build the OHC hierarchy on top of that.", "subpage_snippet": "", "source": "inria.hal.science", "link": "https://inria.hal.science/hal-02452729/file/Overlapping_Hierarchical_Clustering_IDA2020_Camera_Ready_.pdf", "content": "The trend gives a function in O(n2:45) so to speed up the process and scale up our algorithm is it possible to precompute a set of possibly overlapping clusters over a given -neighbourhood graph with a classical method, for instance CLIQUE, and build the OHC hierarchy on top of that."}
+{"idx": 8, "title": "Hierarchical Overlapping Clustering on Graphs: Cost Function, Algorithm ...", "date": "", "ddg_snippet": "To bridge this gap, we initiate the study of hierarchical overlapping clustering on graphs by introducing a new cost function and establishing its rationality through several intu-itive properties. We further develop an approxi-mation algorithm that achieves a constant approx-imation factor for its dual version.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=51x0dfsD8A", "content": "To bridge this gap, we initiate the study of hierarchical overlapping clustering on graphs by introducing a new cost function and establishing its rationality through several intu-itive properties. We further develop an approxi-mation algorithm that achieves a constant approx-imation factor for its dual version."}
+{"idx": 9, "title": "An Improved Cost Function for Hierarchical Cluster Trees", "date": "", "ddg_snippet": "However, the value of our cost function is more meaningful. The new way of formulating the cost function also leads to a polynomial time algorithm to compute the optimal cluster tree when the input graph has a perfect HC-structure, or an approximation algorithm when the input graph 'almost' has a perfect HC-structure.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1812.02715", "content": "However, the value of our cost function is more meaningful. The new way of formulating the cost function also leads to a polynomial time algorithm to compute the optimal cluster tree when the input graph has a perfect HC-structure, or an approximation algorithm when the input graph 'almost' has a perfect HC-structure."}
diff --git a/data/sampled_jsons/arxiv.orgpdf2406.05072_Section_3.2.jsonl b/data/sampled_jsons/arxiv.orgpdf2406.05072_Section_3.2.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f96f39446aeeb0136b4ae37e4a8cfe785d6a75e3
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+++ b/data/sampled_jsons/arxiv.orgpdf2406.05072_Section_3.2.jsonl
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+{"idx": 0, "title": "Изменить или унифицировать размер страницы PDF - быстро...", "date": "", "ddg_snippet": "Унификация или корректировка размеров страниц в PDF очень проста с PDF 24. Просто выберите PDF -файл через поле для файла выше, установите новый размер страницы и начните процесс.", "subpage_snippet": "", "source": "tools.pdf24.org", "link": "https://tools.pdf24.org/ru/change-pdf-page-size", "content": "Унификация или корректировка размеров страниц в PDF очень проста с PDF 24. Просто выберите PDF -файл через поле для файла выше, установите новый размер страницы и начните процесс."}
+{"idx": 1, "title": "Merge PDF | Combine PDF Files Online with Free PDF Merger", "date": "", "ddg_snippet": "Merge PDFs online for free. Combine files fast with our PDF combiner—no file size limits, no registration needed. Works on any device: Mac, Windows, iOS, Android.", "subpage_snippet": "", "source": "smallpdf.com", "link": "https://smallpdf.com/merge-pdf", "content": "Merge PDFs online for free. Combine files fast with our PDF combiner—no file size limits, no registration needed. Works on any device: Mac, Windows, iOS, Android."}
+{"idx": 2, "title": "ГДЗ Английский язык 8 класс (рабочая тетрадь) Биболетова...", "date": "", "ddg_snippet": "UNIT 2. The worlds best friend is you. SECTION 1. SECTION 3 .", "subpage_snippet": "", "source": "Reshalka.com", "link": "https://Reshalka.com/uchebniki/8-klass/english/biboletova2", "content": "UNIT 2. The worlds best friend is you. SECTION 1. SECTION 3 ."}
+{"idx": 3, "title": "ГДЗ по английскому языку 4 класс Биболетова рабочая тетрадь...", "date": "", "ddg_snippet": "Section 2. Speaking about the future (стр. 4-5) Section 3 . When the weather is fine (стр. 5-6)", "subpage_snippet": "", "source": "pomogalka.me", "link": "https://pomogalka.me/4-klass/anglijskij-yazyk/biboletova-rabochaya-tetrad/", "content": "Section 2. Speaking about the future (стр. 4-5) Section 3 . When the weather is fine (стр. 5-6)"}
+{"idx": 4, "title": "3d Rendering Services Contract Sample | FREE", "date": "", "ddg_snippet": "If needed, modify or add clauses to suit your specific requirements. For example, if the client is responsible for purchasing 3D models, include this condition under section 3 . 2 , \"Rights and Obligations of the Customer.\" You can also adjust the payment currency accordingly.", "subpage_snippet": "", "source": "cgaward.com.ua", "link": "https://cgaward.com.ua/publikacii/stati/dogovor-3d-visualization.html", "content": "If needed, modify or add clauses to suit your specific requirements. For example, if the client is responsible for purchasing 3D models, include this condition under section 3 . 2 , \"Rights and Obligations of the Customer.\" You can also adjust the payment currency accordingly."}
+{"idx": 5, "title": "Онлайн Решебник ГДЗ Enjoy English Биболетова 7 класс", "date": "", "ddg_snippet": "1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63. Section 1", "subpage_snippet": "", "source": "reshak.ru", "link": "https://reshak.ru/enjoy7/index.html", "content": "1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63. Section 1"}
+{"idx": 6, "title": "Анти-Плагио - Бесплатный АнтиПлагиат | Без регистрации, без...", "date": "", "ddg_snippet": "Проверяйте уникальность больших текстов и на ИИ онлайн бесплатно и без ограничений. Российский сервис Анти-Плагио поможет создать качественный контент.", "subpage_snippet": "", "source": "anti-plagio.ru", "link": "https://anti-plagio.ru/", "content": "Проверяйте уникальность больших текстов и на ИИ онлайн бесплатно и без ограничений. Российский сервис Анти-Плагио поможет создать качественный контент."}
+{"idx": 7, "title": "Online Python Compiler на русском - IDE, редактор, интерпретатор", "date": "", "ddg_snippet": "Онлайн компилятор Python на русском языке. Вставьте код, скомпилируйте и запустите программу на python online. Напишите свой код с нуля и нажмите кнопку Выполнить, чтобы проверить на ошибки.", "subpage_snippet": "", "source": "online-python-compiler.ru", "link": "https://online-python-compiler.ru/", "content": "Онлайн компилятор Python на русском языке. Вставьте код, скомпилируйте и запустите программу на python online. Напишите свой код с нуля и нажмите кнопку Выполнить, чтобы проверить на ошибки."}
+{"idx": 8, "title": "Бесплатная озвучка текста онлайн | Профессиональный синтез речи", "date": "", "ddg_snippet": "Используйте наш сервис для бесплатной озвучки текста онлайн. Высококачественный синтез речи, профессиональные голоса. Идеально для видео, презентаций, аудиокниг и других проектов.", "subpage_snippet": "", "source": "sintezator-rechi.ru", "link": "https://sintezator-rechi.ru/", "content": "Используйте наш сервис для бесплатной озвучки текста онлайн. Высококачественный синтез речи, профессиональные голоса. Идеально для видео, презентаций, аудиокниг и других проектов."}
+{"idx": 9, "title": "Davlat fuqarolik xizmatchilari vakant lavozimlarining yagona ochiq portali", "date": "", "ddg_snippet": "1:22. id.egov.uz — Yagona identifikatsiya tizimidan ro‘yxatdan o‘tish bo‘yicha videoqo‘llanma.", "subpage_snippet": "", "source": "vacancy.argos.uz", "link": "https://vacancy.argos.uz/", "content": "1:22. id.egov.uz — Yagona identifikatsiya tizimidan ro‘yxatdan o‘tish bo‘yicha videoqo‘llanma."}
diff --git a/data/sampled_jsons/arxiv2503.17332_Table_4_cost_analysis_T-Agent_AutoGPT_monetary_comparison_year_2025.jsonl b/data/sampled_jsons/arxiv2503.17332_Table_4_cost_analysis_T-Agent_AutoGPT_monetary_comparison_year_2025.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2b015605b4bdc3fb4f1ddc3614a2cb0f603bac70
--- /dev/null
+++ b/data/sampled_jsons/arxiv2503.17332_Table_4_cost_analysis_T-Agent_AutoGPT_monetary_comparison_year_2025.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "CVE-Bench: A Benchmark for AI Agents’ Ability to Exploit Real ...", "date": "", "ddg_snippet": "We report the average number of input and output tokens, monetary cost , and the time to execute one task. The values we reported are the average of 5 repetitions.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.17332", "content": "We report the average number of input and output tokens, monetary cost , and the time to execute one task. The values we reported are the average of 5 repetitions."}
+{"idx": 1, "title": "[2306.02224] Auto-GPT for Online Decision Making: Benchmarks ... CVE-Bench: A Benchmark for AI Agents' Ability to Exploit Real ... Auto-GPT for Online Decision Making: Benchmarks and ... [2503.10613] CoSTA$\\ast$: Cost-Sensitive Toolpath Agent for ... [2306.02224] Auto-GPT for Online Decision Making ... - ar5iv CoSTA∗: Cost-Sensitive Toolpath Agent for Multi-turn Image ...", "date": "", "ddg_snippet": "Jun 4 , 2023 · In this paper, we present a comprehensive benchmark study of Auto - GPT styled agents in decision-making tasks that simulate real-world scenarios. Our aim is to gain deeper insights into this problem and understand the adaptability of GPT -based agents . As shown, among successful exploits, T - Agent performs 68% and 30% database access under zero-day and one-day settings, respectively, while the percentage of database access is smaller for AutoGPT : 0% in the both zero-day and one-day settings. In this paper, we present a comprehensive benchmark study of Auto - GPT styled agents in decision-making tasks that simulate real-world scenarios. Our aim is to gain deeper insights into this problem and understand the adaptability of GPT -based agents . Mar 13, 2025 · We build a novel benchmark of challenging multi-turn image editing, on which CoSTA* outperforms state-of-the-art image-editing models or agents in terms of both cost and quality, and performs versatile trade-offs upon user preference. Feb 29, 2024 · In this paper, we present a comprehensive benchmark study of Auto - GPT styled agents in decision-making tasks that simulate real-world scenarios. Our aim is to gain deeper insights into this problem and understand the adaptability of GPT -based agents . In this paper, we present a novel image editing agent that leverages the capabilities of a large multimodal model as a planner combined with the flexibility of the A* algorithm to search for an optimal editing path, balancing the cost -quality tradeoff.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2306.02224", "content": "Jun 4 , 2023 · In this paper, we present a comprehensive benchmark study of Auto - GPT styled agents in decision-making tasks that simulate real-world scenarios. Our aim is to gain deeper insights into this problem and understand the adaptability of GPT -based agents . As shown, among successful exploits, T - Agent performs 68% and 30% database access under zero-day and one-day settings, respectively, while the percentage of database access is smaller for AutoGPT : 0% in the both zero-day and one-day settings. In this paper, we present a comprehensive benchmark study of Auto - GPT styled agents in decision-making tasks that simulate real-world scenarios. Our aim is to gain deeper insights into this problem and understand the adaptability of GPT -based agents . Mar 13, 2025 · We build a novel benchmark of challenging multi-turn image editing, on which CoSTA* outperforms state-of-the-art image-editing models or agents in terms of both cost and quality, and performs versatile trade-offs upon user preference. Feb 29, 2024 · In this paper, we present a comprehensive benchmark study of Auto - GPT styled agents in decision-making tasks that simulate real-world scenarios. Our aim is to gain deeper insights into this problem and understand the adaptability of GPT -based agents . In this paper, we present a novel image editing agent that leverages the capabilities of a large multimodal model as a planner combined with the flexibility of the A* algorithm to search for an optimal editing path, balancing the cost -quality tradeoff."}
+{"idx": 2, "title": "CVE-Bench: A Benchmark for AI Agents' Ability to Exploit Real ...", "date": "", "ddg_snippet": "As shown, among successful exploits, T - Agent performs 68% and 30% database access under zero-day and one-day settings, respectively, while the percentage of database access is smaller for AutoGPT : 0% in the both zero-day and one-day settings.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.17332v4", "content": "As shown, among successful exploits, T - Agent performs 68% and 30% database access under zero-day and one-day settings, respectively, while the percentage of database access is smaller for AutoGPT : 0% in the both zero-day and one-day settings."}
+{"idx": 3, "title": "[2503.10613] CoSTA$\\ast$: Cost-Sensitive Toolpath Agent for ... [2306.02224] Auto-GPT for Online Decision Making ... - ar5iv CoSTA∗: Cost-Sensitive Toolpath Agent for Multi-turn Image ...", "date": "", "ddg_snippet": "Mar 13, 2025 · We build a novel benchmark of challenging multi-turn image editing, on which CoSTA* outperforms state-of-the-art image-editing models or agents in terms of both cost and quality, and performs versatile trade-offs upon user preference. Feb 29, 2024 · In this paper, we present a comprehensive benchmark study of Auto - GPT styled agents in decision-making tasks that simulate real-world scenarios. Our aim is to gain deeper insights into this problem and understand the adaptability of GPT -based agents . In this paper, we present a novel image editing agent that leverages the capabilities of a large multimodal model as a planner combined with the flexibility of the A* algorithm to search for an optimal editing path, balancing the cost -quality tradeoff.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.10613", "content": "Mar 13, 2025 · We build a novel benchmark of challenging multi-turn image editing, on which CoSTA* outperforms state-of-the-art image-editing models or agents in terms of both cost and quality, and performs versatile trade-offs upon user preference. Feb 29, 2024 · In this paper, we present a comprehensive benchmark study of Auto - GPT styled agents in decision-making tasks that simulate real-world scenarios. Our aim is to gain deeper insights into this problem and understand the adaptability of GPT -based agents . In this paper, we present a novel image editing agent that leverages the capabilities of a large multimodal model as a planner combined with the flexibility of the A* algorithm to search for an optimal editing path, balancing the cost -quality tradeoff."}
+{"idx": 4, "title": "CoSTA∗: Cost-Sensitive Toolpath Agent for Multi-turn Image ...", "date": "", "ddg_snippet": "In this paper, we present a novel image editing agent that leverages the capabilities of a large multimodal model as a planner combined with the flexibility of the A* algorithm to search for an optimal editing path, balancing the cost -quality tradeoff.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.10613", "content": "In this paper, we present a novel image editing agent that leverages the capabilities of a large multimodal model as a planner combined with the flexibility of the A* algorithm to search for an optimal editing path, balancing the cost -quality tradeoff."}
+{"idx": 5, "title": "CVE-Bench: A Benchmark for AI Agents ' Ability to Exploit Real-World...", "date": "", "ddg_snippet": "LLM agents can exploit up to 10% and 13% vulnerabilities under zero-day and one-day settings, respectively. ing logs, we find that under the zero-day setting, AutoGPT could identify and exploit new vulnerabilities that are easier than those provided in the one-day description.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.17332", "content": "LLM agents can exploit up to 10% and 13% vulnerabilities under zero-day and one-day settings, respectively. ing logs, we find that under the zero-day setting, AutoGPT could identify and exploit new vulnerabilities that are easier than those provided in the one-day description."}
+{"idx": 6, "title": "A Comparative Analysis Of BondAI And AutoGPT", "date": "", "ddg_snippet": "BondAI and AutoGPT : A Comparative Analysis . AI agent development has transformed from complex coding tasks to accessible, powerful tools for businesses and developers alike.Feature Comparison Table .", "subpage_snippet": "", "source": "smythos.com", "link": "https://smythos.com/ai-agents/comparison/bondai-and-autogpt/", "content": "BondAI and AutoGPT : A Comparative Analysis . AI agent development has transformed from complex coding tasks to accessible, powerful tools for businesses and developers alike.Feature Comparison Table ."}
+{"idx": 7, "title": "Manus AI In Research And Data Analysis : Get Deeper Results", "date": "", "ddg_snippet": "Manus AI is often enabled by combining LLMs with specific AI tools and frameworks (like the concepts behind LangChain or AutoGPT mentioned by IEEE): See the image below for more insight. Here’s a breakdown of what makes Manus AI capable.", "subpage_snippet": "", "source": "oladejoelisha.com", "link": "https://oladejoelisha.com/manus-ai-in-research-and-data-analysis/", "content": "Manus AI is often enabled by combining LLMs with specific AI tools and frameworks (like the concepts behind LangChain or AutoGPT mentioned by IEEE): See the image below for more insight. Here’s a breakdown of what makes Manus AI capable."}
+{"idx": 8, "title": "(PDF) AI Agents vs. Agentic AI: A Conceptual taxonomy, applications...", "date": "", "ddg_snippet": "Comparative architectural analysis is supported with examples from platforms like AutoGPT , CrewAI, and LangGraph. Table 4 Comparison of Generative AI, AI Agents , Agentic AI and Inferred Generative Agents Based on Core Function and Primary Goal.", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/143690675/AI_Agents_vs_Agentic_AI_A_Conceptual_taxonomy_applications_and_challenges", "content": "Comparative architectural analysis is supported with examples from platforms like AutoGPT , CrewAI, and LangGraph. Table 4 Comparison of Generative AI, AI Agents , Agentic AI and Inferred Generative Agents Based on Core Function and Primary Goal."}
+{"idx": 9, "title": "Solved Problem 4 : Cost Analysis for a Sewer Pipe You are | Chegg.com", "date": "", "ddg_snippet": "Past U.S. Census Bureau data shows the following population for the town: Year 2000 2010 Population 35,500 38,100 You need to forecast the population that this sewer should serve at the end of the design period so you can estimate the cost for the capital project.", "subpage_snippet": "", "source": "www.chegg.com", "link": "https://www.chegg.com/homework-help/questions-and-answers/problem-4-cost-analysis-sewer-pipe-designing-main-sewer-pipe-serve-town-past-us-census-bur-q41193512", "content": "Past U.S. Census Bureau data shows the following population for the town: Year 2000 2010 Population 35,500 38,100 You need to forecast the population that this sewer should serve at the end of the design period so you can estimate the cost for the capital project."}
diff --git a/data/sampled_jsons/arxiv_2406.14532_RL_on_Incorrect_Synthetic_Data_Scales_the_Efficiency_of_LLM_Math_Reasoning_by_Eight_year_2024.jsonl b/data/sampled_jsons/arxiv_2406.14532_RL_on_Incorrect_Synthetic_Data_Scales_the_Efficiency_of_LLM_Math_Reasoning_by_Eight_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..bc6e62ea0dbcff1272bfe37c68f80e677005716d
--- /dev/null
+++ b/data/sampled_jsons/arxiv_2406.14532_RL_on_Incorrect_Synthetic_Data_Scales_the_Efficiency_of_LLM_Math_Reasoning_by_Eight_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[ 2406 . 14532 ] RL on Incorrect Synthetic Data Scales the Efficiency ...", "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.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2406.14532", "content": "Training on model-generated synthetic data is a promising approach for finetuning LLMs, but it remains unclear when it helps or hurts."}
+{"idx": 1, "title": "RL on Incorrect Synthetic Data Scales the", "date": "", "ddg_snippet": "Implementation Details. arXiv : 2406 . 14532 v1 [cs.LG] 20 Jun 2024. 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": "Implementation Details. arXiv : 2406 . 14532 v1 [cs.LG] 20 Jun 2024. RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight - Fold ."}
+{"idx": 2, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math ...", "date": "", "ddg_snippet": "Thus while in principle, synthetic data could potentially address data scarcity, it must be designed in an appropriate manner to be effective. However, this has been hard due to a lack of an understanding of how synthetic data contributes to LLM behavior.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.14532v1", "content": "Thus while in principle, synthetic data could potentially address data scarcity, it must be designed in an appropriate manner to be effective. However, this has been hard due to a lack of an understanding of how synthetic data contributes to LLM behavior."}
+{"idx": 3, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math ...", "date": "", "ddg_snippet": "This research introduces the idea of using 'negative' data – incorrect answers – to pinpoint the AI's weak spots. Think of it as a math tutor identifying where a student stumbles in a problem, then guiding them with targeted feedback.", "subpage_snippet": "", "source": "www.promptlayer.com", "link": "https://www.promptlayer.com/research-papers/rl-on-incorrect-synthetic-data-scales-the-efficiency-of-llm-math-reasoning-by-eight-fold", "content": "This research introduces the idea of using 'negative' data – incorrect answers – to pinpoint the AI's weak spots. Think of it as a math tutor identifying where a student stumbles in a problem, then guiding them with targeted feedback."}
+{"idx": 4, "title": "AI-Powered Paper Summarization about the arXiv paper 2406 . 14532 v1", "date": "", "ddg_snippet": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight - Fold .AI-generated Key Points. Authors explore training language models on model-generated synthetic data for math reasoning tasks.", "subpage_snippet": "", "source": "summarizepaper.com", "link": "https://summarizepaper.com/en/arxiv-id/2406.14532v1/", "content": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight - Fold .AI-generated Key Points. Authors explore training language models on model-generated synthetic data for math reasoning tasks."}
+{"idx": 5, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math ...", "date": "", "ddg_snippet": "The study investigates the impact of synthetic data , both correct and incorrect , on the fine-tuning of LLMs for enhanced math reasoning using supervised fine-tuning (SFT) and reinforcement learning ( RL ).", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/papers/2406.14532", "content": "The study investigates the impact of synthetic data , both correct and incorrect , on the fine-tuning of LLMs for enhanced math reasoning using supervised fine-tuning (SFT) and reinforcement learning ( RL )."}
+{"idx": 6, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math ...", "date": "", "ddg_snippet": "Specifically, they saw an 8 - fold increase in the efficiency of the LLM 's math reasoning abilities. The key insight is that the RL process is able to learn from the mistakes in the synthetic data , and use that knowledge to build more robust and flexible math reasoning capabilities in the LLM .", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/rl-incorrect-synthetic-data-scales-efficiency-llm", "content": "Specifically, they saw an 8 - fold increase in the efficiency of the LLM 's math reasoning abilities. The key insight is that the RL process is able to learn from the mistakes in the synthetic data , and use that knowledge to build more robust and flexible math reasoning capabilities in the LLM ."}
+{"idx": 7, "title": "Bayesian beagle - RL on Incorrect Synthetic Data Scales the ...", "date": "", "ddg_snippet": "Finetuning LLMs with model-generated data can improve math reasoning , especially with self-generated correct solutions and per-step negative responses. This approach can double efficiency and reduce spurious correlations.", "subpage_snippet": "", "source": "bayesian-beagle.netlify.app", "link": "https://bayesian-beagle.netlify.app/posts/rl_on_incorrect_synthetic_data_scales_the_efficiency_of_llm_math_reasoning_by_eight_fold/2024-06-20-rl_on_incorrect_synthetic_data_scales_the_efficiency_of_llm_math_reasoning_by_eight_fold", "content": "Finetuning LLMs with model-generated data can improve math reasoning , especially with self-generated correct solutions and per-step negative responses. This approach can double efficiency and reduce spurious correlations."}
+{"idx": 8, "title": "(PDF) RL on Incorrect Synthetic Data Scales the Efficiency of LLM ...", "date": "", "ddg_snippet": "arXiv : 2406 . 14532 v1 [cs.LG] 20 Jun 2024. RL on Incorrect Synthetic Data Scales the Effici ency of LLM Math Reasoning by Eight -F old.SFT on synthetic problems and res ponses by 2x, whereas using step-level RL with negativ e data scales the efficiency by 8 x.", "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": "arXiv : 2406 . 14532 v1 [cs.LG] 20 Jun 2024. RL on Incorrect Synthetic Data Scales the Effici ency of LLM Math Reasoning by Eight -F old.SFT on synthetic problems and res ponses by 2x, whereas using step-level RL with negativ e data scales the efficiency by 8 x."}
+{"idx": 9, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math ...", "date": "", "ddg_snippet": "Abstract: Training on model-generated synthetic data is a promising approach for finetuning LLMs, but it remains unclear when it helps or hurts.", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/es/overview/2406.14532v1", "content": "Abstract: Training on model-generated synthetic data is a promising approach for finetuning LLMs, but it remains unclear when it helps or hurts."}
diff --git a/data/sampled_jsons/arxiv_2503.06366_Section_5_spurious_correlation_Schubert.jsonl b/data/sampled_jsons/arxiv_2503.06366_Section_5_spurious_correlation_Schubert.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7bc142e9a96fd70cc442c2ef69d8b87c9f51d6cd
--- /dev/null
+++ b/data/sampled_jsons/arxiv_2503.06366_Section_5_spurious_correlation_Schubert.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "(PDF) Mitigating Spurious Correlations via Disagreement Probability", "date": "", "ddg_snippet": "DPR leverages the disagreement between the target label and the prediction of a biased model to identify bias-conflicting samples-those without spurious correlations-and upsamples them according to the disagreement probability.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/385528856_Mitigating_Spurious_Correlations_via_Disagreement_Probability", "content": "DPR leverages the disagreement between the target label and the prediction of a biased model to identify bias-conflicting samples-those without spurious correlations-and upsamples them according to the disagreement probability."}
+{"idx": 1, "title": "Understanding and addressing spurious correlation via... | OpenReview", "date": "", "ddg_snippet": "Is the model of spurious correlation introduced in Section 3.1 novel? If not, I would recommend adding citations to where it's defined in the literature, and if so, I would recommend explaining how it differs from other formulations.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=89AOrk05uy", "content": "Is the model of spurious correlation introduced in Section 3.1 novel? If not, I would recommend adding citations to where it's defined in the literature, and if so, I would recommend explaining how it differs from other formulations."}
+{"idx": 2, "title": "Spawrious: A Benchmark for Fine Control of Spurious Correlation ...", "date": "", "ddg_snippet": "4 The Spawrious Challenge In this section , we instantiate the desiderata introduced in Section 3 by presenting Spawrious, a synthetic image classification dataset containing images of four dog breeds (classes) in six background locations ( spurious attributes).", "subpage_snippet": "", "source": "www.readkong.com", "link": "https://www.readkong.com/page/spawrious-a-benchmark-for-fine-control-of-spurious-9635976", "content": "4 The Spawrious Challenge In this section , we instantiate the desiderata introduced in Section 3 by presenting Spawrious, a synthetic image classification dataset containing images of four dog breeds (classes) in six background locations ( spurious attributes)."}
+{"idx": 3, "title": "Section 5 .7 – Scatterplots and Linear Regression – MAT112...", "date": "", "ddg_snippet": "Section 5 .2: Measures of Variation. Section 5 .3: Quartiles, Five Number Summary, and Boxplots.Graph titled 'a spurious correlation ' detailing number of people who died by becoming tangled in their bedsheets versus per capita cheese consumption in pounds.", "subpage_snippet": "", "source": "open.maricopa.edu", "link": "https://open.maricopa.edu/mat112/chapter/section-5-7-scatterplots-and-linear-regression/", "content": "Section 5 .2: Measures of Variation. Section 5 .3: Quartiles, Five Number Summary, and Boxplots.Graph titled 'a spurious correlation ' detailing number of people who died by becoming tangled in their bedsheets versus per capita cheese consumption in pounds."}
+{"idx": 4, "title": "Counterfactual Invariance to Spurious Correlations", "date": "", "ddg_snippet": "Informally, a ‘ spurious correlation ’ is the dependence of a model on some aspect of the input data that an analyst thinks shouldn’t matter.In section 5 , we’ll see that enforcing the signature does a good job of enforcing counterfactual invariance in practice.", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper/2021/file/8710ef761bbb29a6f9d12e4ef8e4379c-Paper.pdf", "content": "Informally, a ‘ spurious correlation ’ is the dependence of a model on some aspect of the input data that an analyst thinks shouldn’t matter.In section 5 , we’ll see that enforcing the signature does a good job of enforcing counterfactual invariance in practice."}
+{"idx": 5, "title": "ARDL model as a remedy for spurious", "date": "", "ddg_snippet": "An immense amount of studies are available on spurious regression topic in time series econometric literature. In this section we briefly discuss the proposed theoretical and empirical methods for the treatment of spurious regression in literature. The literature review is arranged as follows.", "subpage_snippet": "", "source": "mpra.ub.uni-muenchen.de", "link": "https://mpra.ub.uni-muenchen.de/83973/1/MPRA_pa-", "content": "An immense amount of studies are available on spurious regression topic in time series econometric literature. In this section we briefly discuss the proposed theoretical and empirical methods for the treatment of spurious regression in literature. The literature review is arranged as follows."}
+{"idx": 6, "title": "Machine Learning meets Algebraic Combinatorics: A Suite of Datasets...", "date": "", "ddg_snippet": "For example, the open question may be finding a combinatorial interpretation of Schubert polynomial structure constants ( Section 4.6), which are indexed by triples of permutations. In this case the ML task is to predict the structure constant from the three permutations.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.06366", "content": "For example, the open question may be finding a combinatorial interpretation of Schubert polynomial structure constants ( Section 4.6), which are indexed by triples of permutations. In this case the ML task is to predict the structure constant from the three permutations."}
+{"idx": 7, "title": "Elementary Statistics", "date": "", "ddg_snippet": "In this section , we will begin by plotting some tabulated data. The scatter plot of the data will suggest a linear association, which Pearson’s r will corroborate.Figure 5 .4. 5 Spurious Correlation .", "subpage_snippet": "", "source": "www.math.wustl.edu", "link": "https://www.math.wustl.edu/~brian/stats/2200-05.pdf", "content": "In this section , we will begin by plotting some tabulated data. The scatter plot of the data will suggest a linear association, which Pearson’s r will corroborate.Figure 5 .4. 5 Spurious Correlation ."}
+{"idx": 8, "title": "Behavioural", "date": "", "ddg_snippet": "for Non- Spurious Correlation Non- spurious correlation between the study variables in the hypothesised model was tested by comparing the two extracted structural model path coefficients presented in Table 5 .20.", "subpage_snippet": "", "source": "core.ac.uk", "link": "https://core.ac.uk/download/pdf/188770849.pdf", "content": "for Non- Spurious Correlation Non- spurious correlation between the study variables in the hypothesised model was tested by comparing the two extracted structural model path coefficients presented in Table 5 .20."}
+{"idx": 9, "title": "Open Knowledge Repository", "date": "", "ddg_snippet": "Section 4 of the document addresses teacher training considerations in developing, scoring, and using early grade reading assessment.", "subpage_snippet": "", "source": "openknowledge.worldbank.org", "link": "https://openknowledge.worldbank.org/entities/publication/a86f3aa1-7627-5431-ad44-430ee4a5d8da", "content": "Section 4 of the document addresses teacher training considerations in developing, scoring, and using early grade reading assessment."}
diff --git a/data/sampled_jsons/attention_head_pruning_selective_activation_caching_transformer_inference.jsonl b/data/sampled_jsons/attention_head_pruning_selective_activation_caching_transformer_inference.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..1dc57469e5ad71db6d9d36d94b56e32627b7474c
--- /dev/null
+++ b/data/sampled_jsons/attention_head_pruning_selective_activation_caching_transformer_inference.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Transformer (deep learning architecture) - Wikipedia", "date": "", "ddg_snippet": "The original Transformer used ReLU activation .The KV caching method saves the computed key and value vectors at each attention block, so that they are not recomputed at each new token. PagedAttention applies memory paging to KV caching .[75][76][77].", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Transformer_(deep_learning_architecture)", "content": "The original Transformer used ReLU activation .The KV caching method saves the computed key and value vectors at each attention block, so that they are not recomputed at each new token. PagedAttention applies memory paging to KV caching .[75][76][77]."}
+{"idx": 1, "title": "Layer Pruning for Transformer Models | Saturn Cloud Blog", "date": "", "ddg_snippet": "Attention head pruning : Attention head pruning is a specific type of pruning technique used in transformer models. It doesn’t fall directly under weight pruning or neuron pruning , but it is somewhat related to both.", "subpage_snippet": "", "source": "saturncloud.io", "link": "https://saturncloud.io/blog/layer-pruning-for-transformer-models/", "content": "Attention head pruning : Attention head pruning is a specific type of pruning technique used in transformer models. It doesn’t fall directly under weight pruning or neuron pruning , but it is somewhat related to both."}
+{"idx": 2, "title": "Approximating Multiple Attention Heads Using an MLP for... | Medium", "date": "", "ddg_snippet": "Transformers use attention to weigh different parts of the input sequence based on relevance. Multiple attention heads extract different features: Academic explanation", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@mbonsign/approximating-multiple-attention-heads-using-an-mlp-for-efficient-transformer-inference-b7105c224817", "content": "Transformers use attention to weigh different parts of the input sequence based on relevance. Multiple attention heads extract different features: Academic explanation"}
+{"idx": 3, "title": "nlp - What makes differences in each head in the multiheaded...", "date": "", "ddg_snippet": "What the heads really learn has been an active area of research, normally studied by either pruning away heads to see the effect, by measuring the attention patterns to attribute effect, or by probing them in control tasks. These are some conclusions in that regard", "subpage_snippet": "", "source": "datascience.stackexchange.com", "link": "https://datascience.stackexchange.com/questions/109197/what-makes-differences-in-each-head-in-the-multiheaded-attention-in-transformer", "content": "What the heads really learn has been an active area of research, normally studied by either pruning away heads to see the effect, by measuring the attention patterns to attribute effect, or by probing them in control tasks. These are some conclusions in that regard"}
+{"idx": 4, "title": "How to Speed Up Inference for Large Transformer ... - ML Journey", "date": "", "ddg_snippet": "Learn proven strategies to accelerate inference for large transformer models including quantization, pruning , knowledge distillation...", "subpage_snippet": "", "source": "mljourney.com", "link": "https://mljourney.com/how-to-speed-up-inference-for-large-transformer-models/", "content": "Learn proven strategies to accelerate inference for large transformer models including quantization, pruning , knowledge distillation..."}
+{"idx": 5, "title": "induction_ heads _hard.ipynb - Colab", "date": "", "ddg_snippet": "attention : Attention head activations .\"\"\" # Retrieve the attention results from the activation cache for each transformer block. results = [ cache [f\"blocks.{i}.attn.hook_result\"] for i in range(len(model.blocks))].", "subpage_snippet": "", "source": "colab.research.google.com", "link": "https://colab.research.google.com/github/EffiSciencesResearch/ML4G-2.0/blob/master/workshops/induction_heads/induction_heads_hard.ipynb", "content": "attention : Attention head activations .\"\"\" # Retrieve the attention results from the activation cache for each transformer block. results = [ cache [f\"blocks.{i}.attn.hook_result\"] for i in range(len(model.blocks))]."}
+{"idx": 6, "title": "Attention Head Pruning Research", "date": "", "ddg_snippet": "Attention head pruning , or simply ' head pruning ', is the removal of under-used or redundant attention head components in AI Transformer models. This efficiency method is a type of width pruning , that simplifies the neural networks...", "subpage_snippet": "", "source": "www.aussieai.com", "link": "http://www.aussieai.com/research/head-pruning", "content": "Attention head pruning , or simply ' head pruning ', is the removal of under-used or redundant attention head components in AI Transformer models. This efficiency method is a type of width pruning , that simplifies the neural networks..."}
+{"idx": 7, "title": "Memory Attention In Large Language Models | Restackio", "date": "", "ddg_snippet": "Hybrid Dynamic Pruning : A Pathway to Efficient Transformer Inference .Self- attention mechanisms are pivotal in modern neural architectures, particularly in large language models (LLMs).", "subpage_snippet": "", "source": "d2wozrt205r2fu.cloudfront.net", "link": "https://d2wozrt205r2fu.cloudfront.net/p/large-language-models-answer-memory-attention-cat-ai", "content": "Hybrid Dynamic Pruning : A Pathway to Efficient Transformer Inference .Self- attention mechanisms are pivotal in modern neural architectures, particularly in large language models (LLMs)."}
+{"idx": 8, "title": "[SOLVED] How to understand masked multi- head attention in...", "date": "", "ddg_snippet": "encoder in transformer is a self-regressorwhich means it will predict the next token according to the previouswe use masked multi- head attention to do this.", "subpage_snippet": "", "source": "www.developerload.com", "link": "https://www.developerload.com/how-to-understand-masked-multihead-attention-in-transformer", "content": "encoder in transformer is a self-regressorwhich means it will predict the next token according to the previouswe use masked multi- head attention to do this."}
+{"idx": 9, "title": "FastCache: Fast Caching for Diffusion Transformer Through...", "date": "", "ddg_snippet": "Hidden-State Caching in Diffusion and Transformers . Caching mechanisms have long been used in neural networks to reduce inference costs, such as in BERT acceleration or transformer decoding.Spatten: Efficient sparse attention architecture with cascade token and head pruning .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.20353v2", "content": "Hidden-State Caching in Diffusion and Transformers . Caching mechanisms have long been used in neural networks to reduce inference costs, such as in BERT acceleration or transformer decoding.Spatten: Efficient sparse attention architecture with cascade token and head pruning ."}
diff --git a/data/sampled_jsons/automated_model_dependency_verification_provenance_tracking_containerization_reproducibility.jsonl b/data/sampled_jsons/automated_model_dependency_verification_provenance_tracking_containerization_reproducibility.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..04392f0ca372f43c8fa5d8a2337a9eb5452a4dbb
--- /dev/null
+++ b/data/sampled_jsons/automated_model_dependency_verification_provenance_tracking_containerization_reproducibility.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Versioning, Provenance, and Reproducibility - Machine ...", "date": "", "ddg_snippet": "Jun 17, 2024 · Versioning, provenance tracking , and reproducibility are essential tools for responsible engineers that provide a technical foundation for debugging, reliable experiments, trustworthy and safe deployments, safeguards against security attacks and forensics when attacks occur, accountability and auditing, and many other tasks.", "subpage_snippet": "", "source": "mlip-cmu.github.io", "link": "https://mlip-cmu.github.io/book/24-versioning-provenance-and-reproducibility.html", "content": "Jun 17, 2024 · Versioning, provenance tracking , and reproducibility are essential tools for responsible engineers that provide a technical foundation for debugging, reliable experiments, trustworthy and safe deployments, safeguards against security attacks and forensics when attacks occur, accountability and auditing, and many other tasks."}
+{"idx": 1, "title": "Atlas: A Framework for ML Lifecycle Provenance & Transparency", "date": "", "ddg_snippet": "May 14, 2025 · Atlas leverages runtime pipeline monitoring and open specifications for data and software provenance to collect model artifact integrity and end-to-end lineage metadata. Atlas combines trusted hardware and transparency logs to enhance metadata integrity and enable efficient verification of ML pipeline operations, from training through deployment.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.19567v2", "content": "May 14, 2025 · Atlas leverages runtime pipeline monitoring and open specifications for data and software provenance to collect model artifact integrity and end-to-end lineage metadata. Atlas combines trusted hardware and transparency logs to enhance metadata integrity and enable efficient verification of ML pipeline operations, from training through deployment."}
+{"idx": 2, "title": "Version Control for Machine Learning Models: Best Practices ...", "date": "", "ddg_snippet": "Mar 15, 2024 · Embrace Containerization : Container technologies like Docker package your model with its dependencies , ensuring consistent execution across environments.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@tommyadeliyi/version-control-for-machine-learning-models-best-practices-and-tools-b4069c7caebb", "content": "Mar 15, 2024 · Embrace Containerization : Container technologies like Docker package your model with its dependencies , ensuring consistent execution across environments."}
+{"idx": 3, "title": "Best Tools for ML Model Governance, Provenance, and Lineage", "date": "", "ddg_snippet": "May 6, 2025 · Explore tools for ML model governance, provenance, and lineage, including guidance on how to choose the right one.", "subpage_snippet": "", "source": "neptune.ai", "link": "https://neptune.ai/blog/tools-for-ml-model-governance-provenance-lineage", "content": "May 6, 2025 · Explore tools for ML model governance, provenance, and lineage, including guidance on how to choose the right one."}
+{"idx": 4, "title": "Machine Learning Model Versioning: Best Practices for ...", "date": "", "ddg_snippet": "Mar 21, 2025 · Model Management: Tools like MLflow and DVC help track model artifacts and data. Containerization : Docker packages models and dependencies into portable containers. Version Control: Git tracks code changes, while tools like DVC track data and model changes. Best Practices Track all changes, including data, code, and hyperparameters.", "subpage_snippet": "", "source": "codezup.com", "link": "https://codezup.com/machine-learning-model-versioning-a-practical-approach/", "content": "Mar 21, 2025 · Model Management: Tools like MLflow and DVC help track model artifacts and data. Containerization : Docker packages models and dependencies into portable containers. Version Control: Git tracks code changes, while tools like DVC track data and model changes. Best Practices Track all changes, including data, code, and hyperparameters."}
+{"idx": 5, "title": "Development of a Model Versioning System with Dependency ...", "date": "", "ddg_snippet": "This paper presents a unified system for model versioning and dependency tracking that directly addresses this issue. By capturing full experiment lineage and environment configurations, the system makes reproducibility a practical goal rather than a theoretical ideal.", "subpage_snippet": "", "source": "scholar9.com", "link": "https://scholar9.com/publication/IACSE-IJAIML_05_02_001_1750748108.pdf", "content": "This paper presents a unified system for model versioning and dependency tracking that directly addresses this issue. By capturing full experiment lineage and environment configurations, the system makes reproducibility a practical goal rather than a theoretical ideal."}
+{"idx": 6, "title": "Breadcrumbs for your Deep Learning Model: Following ...", "date": "", "ddg_snippet": "Mar 1, 2025 · DLProv is a provenance -centric service to support DL workflow analyses and reproducibility . DLProv captures provenance data and exports provenance graphs for DL model reproducibility . DLProv is W3C PROV compliant, ensuring standardized prospective and retrospective provenance , and enables provenance capture in arbitrary execution frameworks.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2665963824001180", "content": "Mar 1, 2025 · DLProv is a provenance -centric service to support DL workflow analyses and reproducibility . DLProv captures provenance data and exports provenance graphs for DL model reproducibility . DLProv is W3C PROV compliant, ensuring standardized prospective and retrospective provenance , and enables provenance capture in arbitrary execution frameworks."}
+{"idx": 7, "title": "Scalable AI Model Deployment on OpenShift - 044.EU", "date": "", "ddg_snippet": "... AI/ML platform built on Red Hat OpenShift Container Platform that provides end-to-end machine learning workflows from data preparation to model ...", "subpage_snippet": "", "source": "www.044.eu", "link": "https://www.044.eu/scalable-ai-model-deployment-on-openshift/", "content": "... AI/ML platform built on Red Hat OpenShift Container Platform that provides end-to-end machine learning workflows from data preparation to model ..."}
+{"idx": 8, "title": "US11120005B2 - Reliable workflow system provenance tracking at", "date": "", "ddg_snippet": "Provenance often includes tracking origins of data resulting from dependencies and interdependencies within workflow instances generating multiple ...", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/US11120005B2/en", "content": "Provenance often includes tracking origins of data resulting from dependencies and interdependencies within workflow instances generating multiple ..."}
+{"idx": 9, "title": "Beyond Yocto: Exploring Mkosi for TDX Images - BuilderNet - The", "date": "", "ddg_snippet": "... reproducible operating systems that users ... Verification would be much faster for users who trust Debian or their reproducibility infrastructure.", "subpage_snippet": "", "source": "collective.flashbots.net", "link": "https://collective.flashbots.net/t/beyond-yocto-exploring-mkosi-for-tdx-images/4739", "content": "... reproducible operating systems that users ... Verification would be much faster for users who trust Debian or their reproducibility infrastructure."}
diff --git a/data/sampled_jsons/because_Pythia-1B_bad_at_MATH_fails_to_explore_good_solutions.jsonl b/data/sampled_jsons/because_Pythia-1B_bad_at_MATH_fails_to_explore_good_solutions.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..fb4134faf1bd44bc8fe5614ab678b06c4eac2969
--- /dev/null
+++ b/data/sampled_jsons/because_Pythia-1B_bad_at_MATH_fails_to_explore_good_solutions.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Stress-Testing Capability Elicitation With Password-Locked ...", "date": "", "ddg_snippet": "29 May 2024 — For example, because Pythia-1B (the π weak superscript 𝜋 weak ... bad at MATH , it fails to explore good solutions , and thus we observe ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.19550v1", "content": "29 May 2024 — For example, because Pythia-1B (the π weak superscript 𝜋 weak ... bad at MATH , it fails to explore good solutions , and thus we observe ..."}
+{"idx": 1, "title": "Stress-Testing Capability Elicitation With", "date": "", "ddg_snippet": "For example, because Pythia - 1 B (the πweak model used in Figure 5) is very bad at MATH , it fails to explore good solutions , and thus we observe worse RL results for MATH when starting from Pythia - 1 B .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=zzOOqD6R1b", "content": "For example, because Pythia - 1 B (the πweak model used in Figure 5) is very bad at MATH , it fails to explore good solutions , and thus we observe worse RL results for MATH when starting from Pythia - 1 B ."}
+{"idx": 2, "title": "Eliciting Latent Knowledge from “Quirky” Language Models", "date": "", "ddg_snippet": "However, it is not practical to run experiments in domains where human experts in fact struggle because we would have no source of ground truth ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2312.01037v4", "content": "However, it is not practical to run experiments in domains where human experts in fact struggle because we would have no source of ground truth ..."}
+{"idx": 3, "title": "Questionable practices in machine learning", "date": "", "ddg_snippet": "... incentive for researchers and companies to report a state-of-the-art result on some metric often leads to questionable research practices (QRPs): bad ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2407.12220v2", "content": "... incentive for researchers and companies to report a state-of-the-art result on some metric often leads to questionable research practices (QRPs): bad ..."}
+{"idx": 4, "title": "Uncategorized | Import AI | Page 2", "date": "", "ddg_snippet": "... are unsurprising – small models demonstrate a small change between CA and CS but that ’ s mostly because their performance is very bad in ...", "subpage_snippet": "", "source": "jack-clark.net", "link": "https://jack-clark.net/category/uncategorized/page/2/", "content": "... are unsurprising – small models demonstrate a small change between CA and CS but that ’ s mostly because their performance is very bad in ..."}
+{"idx": 5, "title": "Samarth0710/neurips-2024-peer-reviews-test-10 · Datasets at ...", "date": "", "ddg_snippet": "Samarth0710/neurips-2024-peer-reviews-test-10 · Datasets at Hugging Facetrain · 4.24k rows", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/Samarth0710/neurips-2024-peer-reviews-test-10/viewer", "content": "Samarth0710/neurips-2024-peer-reviews-test-10 · Datasets at Hugging Facetrain · 4.24k rows"}
+{"idx": 6, "title": "1 Scaling by Thinking in Continuous Space", "date": "", "ddg_snippet": "... in a high-dimensional vector space would enable the deep exploration of multiple directions simultaneously, instead of linear thinking, leading to a ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.05171v2", "content": "... in a high-dimensional vector space would enable the deep exploration of multiple directions simultaneously, instead of linear thinking, leading to a ..."}
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+{"idx": 8, "title": "Mohit Bansal - ACL Anthology", "date": "", "ddg_snippet": "... CD is applied to various LMs and domains to enhance open-ended text generation, it is still unclear why CD often works well, when it could fail , and ...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/people/m/mohit-bansal/", "content": "... CD is applied to various LMs and domains to enhance open-ended text generation, it is still unclear why CD often works well, when it could fail , and ..."}
+{"idx": 9, "title": "Mohit Bansal - ACL Anthology", "date": "", "ddg_snippet": "In contrast, existing sub-sentence attribution methods may be more precise but fail to align with users’ interests.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/people/mohit-bansal/", "content": "In contrast, existing sub-sentence attribution methods may be more precise but fail to align with users’ interests."}
diff --git a/data/sampled_jsons/cachehttpsarxiv.orgabs2410.09536.jsonl b/data/sampled_jsons/cachehttpsarxiv.orgabs2410.09536.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..21efa3b4d93fbfbd08340081b9445f694308735a
--- /dev/null
+++ b/data/sampled_jsons/cachehttpsarxiv.orgabs2410.09536.jsonl
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+{"idx": 0, "title": "[2410.09536] TOP-ERL: Transformer-based Off-Policy Episodic ... [2410.03065] Compute Or Load KV Cache? Why Not Both? - arXiv.org [2410.23317] VL-Cache: Sparsity and Modality-Aware KV Cache ... [2410.01723] HarmoniCa: Harmonizing Training and Inference ... Leveraging Semantic Cues from Foundation Vision Models for ... GitHub - yikangshen/Ordered-Neurons: Code for the paper ... [2410.14740] Harnessing Your DRAM and SSD for Sustainable and ...", "date": "", "ddg_snippet": "Oct 12, 2024 · This work introduces Transformer-based Off-Policy Episodic Reinforcement Learning (TOP-ERL), a novel algorithm that enables off-policy updates in the ERL framework. In ERL, policies predict entire action trajectories over multiple time steps instead of single actions at every time step. Oct 4, 2024 · Large Language Models (LLMs) are increasingly deployed in large-scale online services, enabling sophisticated applications. However, the computational overhead of generating key-value (KV) caches in the prefill stage presents a major bottleneck, particularly for long-context inputs. Prefix caching mitigates this issue by storing KV caches for reuse, reducing redundant computation. Despite its ... Oct 29, 2024 · To bridge the gap, we propose VL- Cache , a novel KV cache compression recipe tailored for accelerating VLM inference. In this paper, we first investigate the unique sparsity pattern of VLM attention by distinguishing visual and text tokens in prefill and decoding phases. Oct 2, 2024 · To this end, we harmonize training and inference with a novel learning-based caching framework dubbed HarmoniCa. It first incorporates Step-Wise Denoising Training (SDT) to ensure the continuity of the denoising process, where prior steps can be leveraged. Oct 12, 2024 · This paper presents a new method that uses semantic cues from foundation vision model features (like DINOv2) to enhance local feature matching by incorporating semantic reasoning into existing descriptors. This repository contains the code used for word-level language model and unsupervised parsing experiments in Ordered Neurons: Integrating Tree Structures into Recurrent Neural Networks paper, originally forked from the LSTM and QRNN Language Model Toolkit for PyTorch. Oct 17, 2024 · To address this challenge, this paper proposes a mixed-precision with a model modularization algorithm to enable LLM inference on outdated hardware with resource constraints. (The precision denotes the numerical precision like FP16, INT8, INT4) and multi-level caching ( M2Cache ).)", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.09536", "content": "Oct 12, 2024 · This work introduces Transformer-based Off-Policy Episodic Reinforcement Learning (TOP-ERL), a novel algorithm that enables off-policy updates in the ERL framework. In ERL, policies predict entire action trajectories over multiple time steps instead of single actions at every time step. Oct 4, 2024 · Large Language Models (LLMs) are increasingly deployed in large-scale online services, enabling sophisticated applications. However, the computational overhead of generating key-value (KV) caches in the prefill stage presents a major bottleneck, particularly for long-context inputs. Prefix caching mitigates this issue by storing KV caches for reuse, reducing redundant computation. Despite its ... Oct 29, 2024 · To bridge the gap, we propose VL- Cache , a novel KV cache compression recipe tailored for accelerating VLM inference. In this paper, we first investigate the unique sparsity pattern of VLM attention by distinguishing visual and text tokens in prefill and decoding phases. Oct 2, 2024 · To this end, we harmonize training and inference with a novel learning-based caching framework dubbed HarmoniCa. It first incorporates Step-Wise Denoising Training (SDT) to ensure the continuity of the denoising process, where prior steps can be leveraged. Oct 12, 2024 · This paper presents a new method that uses semantic cues from foundation vision model features (like DINOv2) to enhance local feature matching by incorporating semantic reasoning into existing descriptors. This repository contains the code used for word-level language model and unsupervised parsing experiments in Ordered Neurons: Integrating Tree Structures into Recurrent Neural Networks paper, originally forked from the LSTM and QRNN Language Model Toolkit for PyTorch. Oct 17, 2024 · To address this challenge, this paper proposes a mixed-precision with a model modularization algorithm to enable LLM inference on outdated hardware with resource constraints. (The precision denotes the numerical precision like FP16, INT8, INT4) and multi-level caching ( M2Cache ).)"}
+{"idx": 1, "title": "[2410.03065] Compute Or Load KV Cache? Why Not Both? - arXiv.org", "date": "", "ddg_snippet": "Oct 4, 2024 · Large Language Models (LLMs) are increasingly deployed in large-scale online services, enabling sophisticated applications. However, the computational overhead of generating key-value (KV) caches in the prefill stage presents a major bottleneck, particularly for long-context inputs. Prefix caching mitigates this issue by storing KV caches for reuse, reducing redundant computation. Despite its ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.03065", "content": "Oct 4, 2024 · Large Language Models (LLMs) are increasingly deployed in large-scale online services, enabling sophisticated applications. However, the computational overhead of generating key-value (KV) caches in the prefill stage presents a major bottleneck, particularly for long-context inputs. Prefix caching mitigates this issue by storing KV caches for reuse, reducing redundant computation. Despite its ..."}
+{"idx": 2, "title": "[2410.23317] VL-Cache: Sparsity and Modality-Aware KV Cache ...", "date": "", "ddg_snippet": "Oct 29, 2024 · To bridge the gap, we propose VL- Cache , a novel KV cache compression recipe tailored for accelerating VLM inference. In this paper, we first investigate the unique sparsity pattern of VLM attention by distinguishing visual and text tokens in prefill and decoding phases.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.23317", "content": "Oct 29, 2024 · To bridge the gap, we propose VL- Cache , a novel KV cache compression recipe tailored for accelerating VLM inference. In this paper, we first investigate the unique sparsity pattern of VLM attention by distinguishing visual and text tokens in prefill and decoding phases."}
+{"idx": 3, "title": "[2410.01723] HarmoniCa: Harmonizing Training and Inference ... Leveraging Semantic Cues from Foundation Vision Models for ... GitHub - yikangshen/Ordered-Neurons: Code for the paper ... [2410.14740] Harnessing Your DRAM and SSD for Sustainable and ...", "date": "", "ddg_snippet": "Oct 2, 2024 · To this end, we harmonize training and inference with a novel learning-based caching framework dubbed HarmoniCa. It first incorporates Step-Wise Denoising Training (SDT) to ensure the continuity of the denoising process, where prior steps can be leveraged. Oct 12, 2024 · This paper presents a new method that uses semantic cues from foundation vision model features (like DINOv2) to enhance local feature matching by incorporating semantic reasoning into existing descriptors. This repository contains the code used for word-level language model and unsupervised parsing experiments in Ordered Neurons: Integrating Tree Structures into Recurrent Neural Networks paper, originally forked from the LSTM and QRNN Language Model Toolkit for PyTorch. Oct 17, 2024 · To address this challenge, this paper proposes a mixed-precision with a model modularization algorithm to enable LLM inference on outdated hardware with resource constraints. (The precision denotes the numerical precision like FP16, INT8, INT4) and multi-level caching ( M2Cache ).)", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.01723", "content": "Oct 2, 2024 · To this end, we harmonize training and inference with a novel learning-based caching framework dubbed HarmoniCa. It first incorporates Step-Wise Denoising Training (SDT) to ensure the continuity of the denoising process, where prior steps can be leveraged. Oct 12, 2024 · This paper presents a new method that uses semantic cues from foundation vision model features (like DINOv2) to enhance local feature matching by incorporating semantic reasoning into existing descriptors. This repository contains the code used for word-level language model and unsupervised parsing experiments in Ordered Neurons: Integrating Tree Structures into Recurrent Neural Networks paper, originally forked from the LSTM and QRNN Language Model Toolkit for PyTorch. Oct 17, 2024 · To address this challenge, this paper proposes a mixed-precision with a model modularization algorithm to enable LLM inference on outdated hardware with resource constraints. (The precision denotes the numerical precision like FP16, INT8, INT4) and multi-level caching ( M2Cache ).)"}
+{"idx": 4, "title": "Leveraging Semantic Cues from Foundation Vision Models for ... GitHub - yikangshen/Ordered-Neurons: Code for the paper ... [2410.14740] Harnessing Your DRAM and SSD for Sustainable and ...", "date": "", "ddg_snippet": "Oct 12, 2024 · This paper presents a new method that uses semantic cues from foundation vision model features (like DINOv2) to enhance local feature matching by incorporating semantic reasoning into existing descriptors. This repository contains the code used for word-level language model and unsupervised parsing experiments in Ordered Neurons: Integrating Tree Structures into Recurrent Neural Networks paper, originally forked from the LSTM and QRNN Language Model Toolkit for PyTorch. Oct 17, 2024 · To address this challenge, this paper proposes a mixed-precision with a model modularization algorithm to enable LLM inference on outdated hardware with resource constraints. (The precision denotes the numerical precision like FP16, INT8, INT4) and multi-level caching ( M2Cache ).)", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.09533", "content": "Oct 12, 2024 · This paper presents a new method that uses semantic cues from foundation vision model features (like DINOv2) to enhance local feature matching by incorporating semantic reasoning into existing descriptors. This repository contains the code used for word-level language model and unsupervised parsing experiments in Ordered Neurons: Integrating Tree Structures into Recurrent Neural Networks paper, originally forked from the LSTM and QRNN Language Model Toolkit for PyTorch. Oct 17, 2024 · To address this challenge, this paper proposes a mixed-precision with a model modularization algorithm to enable LLM inference on outdated hardware with resource constraints. (The precision denotes the numerical precision like FP16, INT8, INT4) and multi-level caching ( M2Cache ).)"}
+{"idx": 5, "title": "[2410.14740] Harnessing Your DRAM and SSD for Sustainable and ...", "date": "", "ddg_snippet": "Oct 17, 2024 · To address this challenge, this paper proposes a mixed-precision with a model modularization algorithm to enable LLM inference on outdated hardware with resource constraints. (The precision denotes the numerical precision like FP16, INT8, INT4) and multi-level caching ( M2Cache ).)", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.14740", "content": "Oct 17, 2024 · To address this challenge, this paper proposes a mixed-precision with a model modularization algorithm to enable LLM inference on outdated hardware with resource constraints. (The precision denotes the numerical precision like FP16, INT8, INT4) and multi-level caching ( M2Cache ).)"}
+{"idx": 6, "title": "arxiv hep-th: \"Dmitry Chicherin, Johannes Henn, Jaroslav Trnka...\"", "date": "", "ddg_snippet": "Dmitry Chicherin, Johannes Henn, Jaroslav Trnka, Shun-Qing Zhang Positivity properties of five-point two-loop Wilson loops with Lagrangian insertion arxiv . org / abs / 2410 .11456.", "subpage_snippet": "", "source": "bsky.app", "link": "https://bsky.app/profile/arxiv-hep-th.bsky.social/post/3l6mb7jlsel2s", "content": "Dmitry Chicherin, Johannes Henn, Jaroslav Trnka, Shun-Qing Zhang Positivity properties of five-point two-loop Wilson loops with Lagrangian insertion arxiv . org / abs / 2410 .11456."}
+{"idx": 7, "title": "I believe you gave the wrong arXiv link to this Google... - Medium", "date": "", "ddg_snippet": "I believe you gave the wrong arXiv link to this Google DeepMind paper. I found it at this URL", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@epbordelon/i-believe-you-gave-the-wrong-arxiv-link-to-this-google-deepmind-paper-i-found-it-at-this-url-0fe1cb5dd59d", "content": "I believe you gave the wrong arXiv link to this Google DeepMind paper. I found it at this URL"}
+{"idx": 8, "title": "Article URL: https :// arxiv . org / abs / 2410 .16454 Comments URL: https ...", "date": "", "ddg_snippet": "An embarrassingly simple approach to recover unlearned knowledge for LLMs. arxiv . org .", "subpage_snippet": "", "source": "vk.com", "link": "https://vk.com/wall-55993443_56580", "content": "An embarrassingly simple approach to recover unlearned knowledge for LLMs. arxiv . org ."}
+{"idx": 9, "title": "https :// arxiv . org / abs / 2410 .18113 html wrong · Issue #2438...", "date": "", "ddg_snippet": "Description only footnote and references are available. (Optional:) Please add any files, screenshots, or other information here. No response (Required) What is this issue most closely related to? ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/arXiv/html_feedback/issues/2438", "content": "Description only footnote and references are available. (Optional:) Please add any files, screenshots, or other information here. No response (Required) What is this issue most closely related to? ..."}
diff --git a/data/sampled_jsons/causal_representation_learning_noisy_mixing_function_robust_2024.jsonl b/data/sampled_jsons/causal_representation_learning_noisy_mixing_function_robust_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f0eb2265d5723de185ec68a44fe6c812edc9535b
--- /dev/null
+++ b/data/sampled_jsons/causal_representation_learning_noisy_mixing_function_robust_2024.jsonl
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+{"idx": 0, "title": "Causality - Wikipedia", "date": "", "ddg_snippet": "In general, a process can have multiple causes, [1] which are also said to be causal factors for it, and all lie in its past. An effect can in turn be a cause of, or causal factor for, many other effects, which all lie in its future.", "subpage_snippet": "", "source": "en.m.wikipedia.org", "link": "https://en.m.wikipedia.org/wiki/Causality", "content": "In general, a process can have multiple causes, [1] which are also said to be causal factors for it, and all lie in its past. An effect can in turn be a cause of, or causal factor for, many other effects, which all lie in its future."}
+{"idx": 1, "title": "CAUSAL Definition & Meaning - Merriam-Webster", "date": "", "ddg_snippet": "The meaning of CAUSAL is expressing or indicating cause : causative. How to use causal in a sentence.", "subpage_snippet": "", "source": "www.merriam-webster.com", "link": "https://www.merriam-webster.com/dictionary/causal", "content": "The meaning of CAUSAL is expressing or indicating cause : causative. How to use causal in a sentence."}
+{"idx": 2, "title": "CAUSAL | English meaning - Cambridge Dictionary", "date": "", "ddg_snippet": "CAUSAL definition: 1. a relationship, link, etc. between two things in which one causes the other: 2. a relationship…. Learn more.", "subpage_snippet": "", "source": "dictionary.cambridge.org", "link": "https://dictionary.cambridge.org/dictionary/english/causal", "content": "CAUSAL definition: 1. a relationship, link, etc. between two things in which one causes the other: 2. a relationship…. Learn more."}
+{"idx": 3, "title": "CAUSAL Definition & Meaning | Dictionary .com", "date": "", "ddg_snippet": "Causal definition: of, constituting, or implying a cause.. See examples of CAUSAL used in a sentence.", "subpage_snippet": "", "source": "www.dictionary.com", "link": "https://www.dictionary.com/browse/causal", "content": "Causal definition: of, constituting, or implying a cause.. See examples of CAUSAL used in a sentence."}
+{"idx": 4, "title": "CAUSAL definition and meaning | Collins English Dictionary", "date": "", "ddg_snippet": "If there is a causal relationship between two things, one thing is responsible for causing the other thing.", "subpage_snippet": "", "source": "www.collinsdictionary.com", "link": "https://www.collinsdictionary.com/dictionary/english/causal", "content": "If there is a causal relationship between two things, one thing is responsible for causing the other thing."}
+{"idx": 5, "title": "Causal - definition of causal by The Free Dictionary", "date": "", "ddg_snippet": "1. Of, involving, or constituting a cause: a causal relationship between scarcity of goods and higher prices. 2. Indicative of or expressing a cause.", "subpage_snippet": "", "source": "www.thefreedictionary.com", "link": "https://www.thefreedictionary.com/causal", "content": "1. Of, involving, or constituting a cause: a causal relationship between scarcity of goods and higher prices. 2. Indicative of or expressing a cause."}
+{"idx": 6, "title": "causal adjective - Definition, pictures, pronunciation and usage...", "date": "", "ddg_snippet": "Definition of causal adjective in Oxford Advanced Learner's Dictionary. Meaning, pronunciation, picture, example sentences, grammar, usage notes, synonyms and more.", "subpage_snippet": "", "source": "www.oxfordlearnersdictionaries.com", "link": "https://www.oxfordlearnersdictionaries.com/definition/english/causal", "content": "Definition of causal adjective in Oxford Advanced Learner's Dictionary. Meaning, pronunciation, picture, example sentences, grammar, usage notes, synonyms and more."}
+{"idx": 7, "title": "causal , adj. & n. meanings, etymology and more | Oxford English...", "date": "", "ddg_snippet": "causal , adj. & n. meanings, etymology, pronunciation and more in the Oxford English Dictionary", "subpage_snippet": "", "source": "www.oed.com", "link": "https://www.oed.com/dictionary/causal_adj", "content": "causal , adj. & n. meanings, etymology, pronunciation and more in the Oxford English Dictionary"}
+{"idx": 8, "title": "causal - Wiktionary, the free dictionary", "date": "", "ddg_snippet": "Aug 28, 2025 · causal (comparative more causal , superlative most causal ) There is no causal relationship between eating carrots and seeing in the dark.", "subpage_snippet": "", "source": "en.m.wiktionary.org", "link": "https://en.m.wiktionary.org/wiki/causal", "content": "Aug 28, 2025 · causal (comparative more causal , superlative most causal ) There is no causal relationship between eating carrots and seeing in the dark."}
+{"idx": 9, "title": "Causal - Definition, Meaning & Synonyms | Vocabulary.com", "date": "", "ddg_snippet": "Causal is a variation of the word cause , which should be a clue to its meaning. A cause is what makes something happen: the notebook flew across the room because you threw it, so your throwing it was causal. If a bolt of lightning set a statue on fire, the lightning was causal for the fire.", "subpage_snippet": "", "source": "www.vocabulary.com", "link": "https://www.vocabulary.com/dictionary/causal", "content": "Causal is a variation of the word cause , which should be a clue to its meaning. A cause is what makes something happen: the notebook flew across the room because you threw it, so your throwing it was causal. If a bolt of lightning set a statue on fire, the lightning was causal for the fire."}
diff --git a/data/sampled_jsons/causal_representation_learning_robust_noisy_mixing_function_2024_year_2024.jsonl b/data/sampled_jsons/causal_representation_learning_robust_noisy_mixing_function_2024_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a6b36e26c1e5cb274ef9555d32365fc49452f4dd
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+++ b/data/sampled_jsons/causal_representation_learning_robust_noisy_mixing_function_2024_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ICLR 2024 Schedule", "date": "", "ddg_snippet": "... Augmentation-based Self-Supervised Representation Learning ... Understanding the Robustness of Multi-modal Contrastive Learning to Distribution Shift", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2024/calendar", "content": "... Augmentation-based Self-Supervised Representation Learning ... Understanding the Robustness of Multi-modal Contrastive Learning to Distribution Shift"}
+{"idx": 1, "title": "AISTATS 2024 Schedule", "date": "", "ddg_snippet": "On the Statistical Efficiency of Mean-Field Reinforcement Learning with General Function Approximation ... reinforcement learning with function ...", "subpage_snippet": "", "source": "virtual.aistats.org", "link": "https://virtual.aistats.org/virtual/2024/calendar", "content": "On the Statistical Efficiency of Mean-Field Reinforcement Learning with General Function Approximation ... reinforcement learning with function ..."}
+{"idx": 2, "title": "CVPR 2024 Papers", "date": "", "ddg_snippet": "... Continual Learning of Vision-Language Models via Mixture-of ... IMPRINT: Generative Object Compositing by Learning Identity-Preserving Representation", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2024/papers.html", "content": "... Continual Learning of Vision-Language Models via Mixture-of ... IMPRINT: Generative Object Compositing by Learning Identity-Preserving Representation"}
+{"idx": 3, "title": "Adversarial machine learning and instrumental variables for", "date": "", "ddg_snippet": "We are going through a new shift in machine learning (ML), where ML models are increasingly being used to automate decision-making in a multitude of ...", "subpage_snippet": "", "source": "www.deeplearningdaily.com", "link": "https://www.deeplearningdaily.com/adversarial-machine-learning-and-instrumental-variables-for-flexible-causal-modeling/", "content": "We are going through a new shift in machine learning (ML), where ML models are increasingly being used to automate decision-making in a multitude of ..."}
+{"idx": 4, "title": "Improving Generative Methods for Causal Evaluation via", "date": "", "ddg_snippet": "... 2 , incompatible choices of DGP parameters can distort the evaluation of causal estimators by inducing synthetic datasets that poorly represent the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.02892v1", "content": "... 2 , incompatible choices of DGP parameters can distort the evaluation of causal estimators by inducing synthetic datasets that poorly represent the ..."}
+{"idx": 5, "title": "ICML 2020 Papers", "date": "", "ddg_snippet": "Informative Dropout for Robust Representation Learning : A Shape-bias Perspective ... Dynamic Treatment Regimes: A Causal Reinforcement Learning ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2020/papers.html?filter=keywords", "content": "Informative Dropout for Robust Representation Learning : A Shape-bias Perspective ... Dynamic Treatment Regimes: A Causal Reinforcement Learning ..."}
+{"idx": 6, "title": "ICML 2020 Papers", "date": "", "ddg_snippet": "Informative Dropout for Robust Representation Learning : A Shape-bias Perspective ... Dynamic Treatment Regimes: A Causal Reinforcement Learning ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2020/papers.html", "content": "Informative Dropout for Robust Representation Learning : A Shape-bias Perspective ... Dynamic Treatment Regimes: A Causal Reinforcement Learning ..."}
+{"idx": 7, "title": "ICML 2021 Papers", "date": "", "ddg_snippet": "How Does Loss Function Affect Generalization Performance of Deep Learning ? Application to Human Age Estimation ... Neural Networks Helps Learning ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2021/papers.html", "content": "How Does Loss Function Affect Generalization Performance of Deep Learning ? Application to Human Age Estimation ... Neural Networks Helps Learning ..."}
+{"idx": 8, "title": "Downloads", "date": "", "ddg_snippet": "Bridging the Gap: Providing Post-Hoc Symbolic Explanations for Sequential Decision-Making Problems with Inscrutable Representations", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/Downloads/2022", "content": "Bridging the Gap: Providing Post-Hoc Symbolic Explanations for Sequential Decision-Making Problems with Inscrutable Representations"}
+{"idx": 9, "title": "CVPR 2023 Schedule", "date": "", "ddg_snippet": "The 3rd Workshop of Adversarial Machine Learning on Computer Vision: Art of Robustness ... Language Reasoning: Compositionality, Prompts and Causality", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2023/calendar", "content": "The 3rd Workshop of Adversarial Machine Learning on Computer Vision: Art of Robustness ... Language Reasoning: Compositionality, Prompts and Causality"}
diff --git a/data/sampled_jsons/checks_and_balances_framework_ethical_AI_limitations_are_future_work.jsonl b/data/sampled_jsons/checks_and_balances_framework_ethical_AI_limitations_are_future_work.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..26c5053d520148dbc0507a7d511eeaf9bc156ec7
--- /dev/null
+++ b/data/sampled_jsons/checks_and_balances_framework_ethical_AI_limitations_are_future_work.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A Checks-and-Balances Framework for Context-Aware Ethical AI ...", "date": "", "ddg_snippet": "This paper introduces a checks-and-balances framework for ethical alignment of Large Lan- guage Models (LLMs), inspired by three-branch.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/d24155c881921de1284ece531612c8597f7c0a32.pdf", "content": "This paper introduces a checks-and-balances framework for ethical alignment of Large Lan- guage Models (LLMs), inspired by three-branch."}
+{"idx": 1, "title": "A Checks-and-Balances Framework for Context-Aware ...", "date": "", "ddg_snippet": "1 May 2025 — 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": "openreview.net", "link": "https://openreview.net/forum?id=4uOEiitySn&noteId=cYh3zaQycT", "content": "1 May 2025 — This paper introduces a checks-and-balances framework for ethical alignment of Large Language Models (LLMs), inspired by three-branch governmental systems."}
+{"idx": 2, "title": "All that glitters is not gold: trustworthy and ethical AI principles", "date": "", "ddg_snippet": "by C Rees · 2022 · Cited by 25 — This article attempts a critical analysis which draws upon ethical AI documents from a range of contexts including company, organisational, governmental, and ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC9667859/", "content": "by C Rees · 2022 · Cited by 25 — This article attempts a critical analysis which draws upon ethical AI documents from a range of contexts including company, organisational, governmental, and ..."}
+{"idx": 3, "title": "Generative AI and Ethics — The Pressing Present", "date": "", "ddg_snippet": "Such a role provides checks and balances for technology development, ensuring early identification of potential risks. The Chief AI Ethics ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/mechanized/generative-ai-and-ethics-the-pressing-present-3ba725ece432", "content": "Such a role provides checks and balances for technology development, ensuring early identification of potential risks. The Chief AI Ethics ..."}
+{"idx": 4, "title": "The risks and ethical implications of AI in financial services", "date": "", "ddg_snippet": "5 Apr 2024 — Overreliance on AI and unintended consequences – Without a proper system of checks and balances , AI outputs can introduce unwanted risk ...", "subpage_snippet": "", "source": "www.fisglobal.com", "link": "https://www.fisglobal.com/insights/risks-and-ethical-implications-of-ai-in-financial-services", "content": "5 Apr 2024 — Overreliance on AI and unintended consequences – Without a proper system of checks and balances , AI outputs can introduce unwanted risk ..."}
+{"idx": 5, "title": "Ethical framework for AI education based on large ...", "date": "", "ddg_snippet": "by Y Yan · 2024 · Cited by 16 — We propose a five-step, multi-layered ethical framework for AIED to guide the specific deployment and implementation of ethical guidelines.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s10639-024-13241-6", "content": "by Y Yan · 2024 · Cited by 16 — We propose a five-step, multi-layered ethical framework for AIED to guide the specific deployment and implementation of ethical guidelines."}
+{"idx": 6, "title": "A study on ethical implications of artificial intelligence ...", "date": "", "ddg_snippet": "by M Maiti · 2025 · Cited by 16 — This study explores the ethical challenges and best practices surrounding the adoption of AI in various business contexts.", "subpage_snippet": "", "source": "fbj.springeropen.com", "link": "https://fbj.springeropen.com/articles/10.1186/s43093-025-00462-5", "content": "by M Maiti · 2025 · Cited by 16 — This study explores the ethical challenges and best practices surrounding the adoption of AI in various business contexts."}
+{"idx": 7, "title": "Guiding the Future: Ethical Frameworks for AI and Social ...", "date": "", "ddg_snippet": "Ethical Guide Rails for AI : Bias Mitigation: AI systems should actively work to reduce and eliminate biases in decision-making processes.", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/pulse/guiding-future-ethical-frameworks-ai-social-media-combat-barry-jones-av6mf", "content": "Ethical Guide Rails for AI : Bias Mitigation: AI systems should actively work to reduce and eliminate biases in decision-making processes."}
+{"idx": 8, "title": "Towards AI ethics-led sustainability frameworks and toolkits", "date": "", "ddg_snippet": "by D Cumming · 2024 · Cited by 28 — The paper explores the central themes of AI ethics and sustainability frameworks in declarative standards and statements published by various institutions.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2950370124000038", "content": "by D Cumming · 2024 · Cited by 28 — The paper explores the central themes of AI ethics and sustainability frameworks in declarative standards and statements published by various institutions."}
+{"idx": 9, "title": "AI Ethical Guidelines", "date": "", "ddg_snippet": "24 Jun 2025 — These frameworks will be crucial for ensuring the responsible implementation of AI technologies while (1) upholding core academic values of ...", "subpage_snippet": "", "source": "library.educause.edu", "link": "https://library.educause.edu/resources/2025/6/ai-ethical-guidelines", "content": "24 Jun 2025 — These frameworks will be crucial for ensuring the responsible implementation of AI technologies while (1) upholding core academic values of ..."}
diff --git a/data/sampled_jsons/combinatorial_bandits_discontinuous_reward_function_before_2025_year_2024.jsonl b/data/sampled_jsons/combinatorial_bandits_discontinuous_reward_function_before_2025_year_2024.jsonl
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index 0000000000000000000000000000000000000000..61997d5ff1c4a0491d52e5a773316928e0eea3cb
--- /dev/null
+++ b/data/sampled_jsons/combinatorial_bandits_discontinuous_reward_function_before_2025_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Finite-Time Guarantees for Multi-Agent Combinatorial Bandits ...", "date": "", "ddg_snippet": "Aug 28, 2025 · Our contribution introduces the first framework incorporating this form of nonstationary rewards in the combinatorial multi-armed bandit literature. We develop algorithms with theoretical guarantees on dynamic regret and demonstrate practical efficacy through a diabetes intervention case study.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2508.20923", "content": "Aug 28, 2025 · Our contribution introduces the first framework incorporating this form of nonstationary rewards in the combinatorial multi-armed bandit literature. We develop algorithms with theoretical guarantees on dynamic regret and demonstrate practical efficacy through a diabetes intervention case study."}
+{"idx": 1, "title": "Non-Stationary Delayed Combinatorial Semi-Bandit With ...", "date": "", "ddg_snippet": "Feb 24, 2025 · We formalize the described setting as a non-stationary and delayed combinatorial semi- bandit problem with causally related rewards . We model the causal relations by a directed graph in a stationary structural equation model. The agent maximizes the long-term average payoff, defined as a linear function of the base arms' rewards .", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10902019", "content": "Feb 24, 2025 · We formalize the described setting as a non-stationary and delayed combinatorial semi- bandit problem with causally related rewards . We model the causal relations by a directed graph in a stationary structural equation model. The agent maximizes the long-term average payoff, defined as a linear function of the base arms' rewards ."}
+{"idx": 2, "title": "Adversarial Combinatorial Bandits with General Non-linear ...", "date": "", "ddg_snippet": "Abstract In this paper we study the adversarial combinato-rial bandit with a known non-linear reward func-tion , extending existing work on adversarial linear combinatorial bandit . The adversarial combina-torial bandit with general non-linear reward is an important open problem in bandit literature, and it is still unclear whether there is a significant gap from the case of linear reward ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v139/han21b/han21b.pdf", "content": "Abstract In this paper we study the adversarial combinato-rial bandit with a known non-linear reward func-tion , extending existing work on adversarial linear combinatorial bandit . The adversarial combina-torial bandit with general non-linear reward is an important open problem in bandit literature, and it is still unclear whether there is a significant gap from the case of linear reward ..."}
+{"idx": 3, "title": "Combinatorial Bandits with Linear Constraints: Beyond ...", "date": "", "ddg_snippet": "In this paper, we study the problem of combinatorial bandits with long-term linear constraints. Our model captures important application scenarios like ad placement in online advertising systems [40], real-time trafic scheduling in wireless networks, and task assignment in crowdsourcing platforms [29], etc.", "subpage_snippet": "", "source": "www.microsoft.com", "link": "https://www.microsoft.com/en-us/research/wp-content/uploads/2022/10/combinatorial_bandits_with_lin.pdf", "content": "In this paper, we study the problem of combinatorial bandits with long-term linear constraints. Our model captures important application scenarios like ad placement in online advertising systems [40], real-time trafic scheduling in wireless networks, and task assignment in crowdsourcing platforms [29], etc."}
+{"idx": 4, "title": "Combinatorial Bandits Revisited - papers.neurips.cc", "date": "", "ddg_snippet": "Combinatorial network optimization with unknown variables: Multi-armed bandits with linear rewards and individual observations. IEEE/ACM Trans. on Networking, 20(5):1466–1478, 2012.", "subpage_snippet": "", "source": "papers.neurips.cc", "link": "https://papers.neurips.cc/paper/5831-combinatorial-bandits-revisited.pdf", "content": "Combinatorial network optimization with unknown variables: Multi-armed bandits with linear rewards and individual observations. IEEE/ACM Trans. on Networking, 20(5):1466–1478, 2012."}
+{"idx": 5, "title": "Finite-Time Guarantees for Multi-Agent Combinatorial Bandits ...", "date": "", "ddg_snippet": "In contrast with existing frameworks, our problem setting requires the use of bandits with shifting reward distributions (nonstationary bandits ), side information (contextual bandits ), and that allow the selection of multiple arms per round ( combinatorial bandits ).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2508.20923", "content": "In contrast with existing frameworks, our problem setting requires the use of bandits with shifting reward distributions (nonstationary bandits ), side information (contextual bandits ), and that allow the selection of multiple arms per round ( combinatorial bandits )."}
+{"idx": 6, "title": "Neural Constrained Combinatorial Bandits - IEEE Xplore", "date": "", "ddg_snippet": "Feb 25, 2025 · Constrained combinatorial contextual bandits have emerged as trending tools in intelligent systems and networks to model reward and cost signals under combinatorial decision-making. On one hand, both signals are complex functions of the context, e.g., in federated learning, training loss (negative reward ) and energy consumption (cost) are nonlinear functions of edge devices’ system ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10902634", "content": "Feb 25, 2025 · Constrained combinatorial contextual bandits have emerged as trending tools in intelligent systems and networks to model reward and cost signals under combinatorial decision-making. On one hand, both signals are complex functions of the context, e.g., in federated learning, training loss (negative reward ) and energy consumption (cost) are nonlinear functions of edge devices’ system ..."}
+{"idx": 7, "title": "ICLR 2025 Schedule", "date": "", "ddg_snippet": "... Generative Models via Mixture-UCB Bandit ... T2V-Turbo-v2: Enhancing Video Model Post-Training through Data, Reward , and Conditional Guidance Design", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2025/calendar", "content": "... Generative Models via Mixture-UCB Bandit ... T2V-Turbo-v2: Enhancing Video Model Post-Training through Data, Reward , and Conditional Guidance Design"}
+{"idx": 8, "title": "AISTATS 2024 Schedule", "date": "", "ddg_snippet": "On the Statistical Efficiency of Mean-Field Reinforcement Learning with General Function Approximation ... reinforcement learning with function ...", "subpage_snippet": "", "source": "virtual.aistats.org", "link": "https://virtual.aistats.org/virtual/2024/calendar", "content": "On the Statistical Efficiency of Mean-Field Reinforcement Learning with General Function Approximation ... reinforcement learning with function ..."}
+{"idx": 9, "title": "AISTATS 2024 Papers", "date": "", "ddg_snippet": "On the Statistical Efficiency of Mean-Field Reinforcement Learning with General Function Approximation ... Pearson-divergence functional minimization", "subpage_snippet": "", "source": "virtual.aistats.org", "link": "https://virtual.aistats.org/virtual/2024/papers.html", "content": "On the Statistical Efficiency of Mean-Field Reinforcement Learning with General Function Approximation ... Pearson-divergence functional minimization"}
diff --git a/data/sampled_jsons/composition_bounds_differential_privacy_tight_accounting_RDP_2024_year_2024.jsonl b/data/sampled_jsons/composition_bounds_differential_privacy_tight_accounting_RDP_2024_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..59fb9a4e7b8c1d3e227c090b5fed1500f0a182dd
--- /dev/null
+++ b/data/sampled_jsons/composition_bounds_differential_privacy_tight_accounting_RDP_2024_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Budget Recycling Differential Privacy - arXiv.org", "date": "", "ddg_snippet": "Furthermore, we introduce algorithms for tight BR-DP accounting in composition scenarios, and our findings indicate that BR-DP achieves reduced privacy leakage post- composition compared to DP.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.11445v3", "content": "Furthermore, we introduce algorithms for tight BR-DP accounting in composition scenarios, and our findings indicate that BR-DP achieves reduced privacy leakage post- composition compared to DP."}
+{"idx": 1, "title": "Differential Privacy - Tight RDP & zCDP Bounds from Pure DP", "date": "", "ddg_snippet": "May 27, 2024 · There are multiple ways to quantify differential privacy , including pure DP [DMNS06], approximate DP [DKMMN06], Concentrated DP [DR16,BS16], Rényi DP [M17], Gaussian DP [DRS19], & function-DP [DRS19]. Fortunately, these definitions are similar enough that we can convert between most of them (with some loss in parameters).", "subpage_snippet": "", "source": "differentialprivacy.org", "link": "https://differentialprivacy.org/pdp-to-zcdp/", "content": "May 27, 2024 · There are multiple ways to quantify differential privacy , including pure DP [DMNS06], approximate DP [DKMMN06], Concentrated DP [DR16,BS16], Rényi DP [M17], Gaussian DP [DRS19], & function-DP [DRS19]. Fortunately, these definitions are similar enough that we can convert between most of them (with some loss in parameters)."}
+{"idx": 2, "title": "A Randomized Approach to Tight Privacy Accounting", "date": "", "ddg_snippet": "A major challenge in machine learning with differential privacy is privacy accounting , i.e., mea-suring the privacy loss of the composition of DP mechanisms. A privacy accountant takes a list of mechanisms, and returns the privacy parameter (ε and δ) for the composition of those mechanisms. Specifically, a privacy accountant is given a target ε and finds the smallest achievable δ such that ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2023/file/6ae7df1f40f5faeda474b36b61197822-Paper-Conference.pdf", "content": "A major challenge in machine learning with differential privacy is privacy accounting , i.e., mea-suring the privacy loss of the composition of DP mechanisms. A privacy accountant takes a list of mechanisms, and returns the privacy parameter (ε and δ) for the composition of those mechanisms. Specifically, a privacy accountant is given a target ε and finds the smallest achievable δ such that ..."}
+{"idx": 3, "title": "Lecture8ModernToolsforPrivacy Accounting", "date": "", "ddg_snippet": "M1 M2 yields substantial 3.6). Moreover, additional there a partial converse, which privacy -amplification shows that, up to a loss by in sampling, parameters, makes zCDP RDP is equivalent the natural to d", "subpage_snippet": "", "source": "cseweb.ucsd.edu", "link": "https://cseweb.ucsd.edu/~yuxiangw/classes/DSC291-2024Fall/Lectures/lec8.pdf", "content": "M1 M2 yields substantial 3.6). Moreover, additional there a partial converse, which privacy -amplification shows that, up to a loss by in sampling, parameters, makes zCDP RDP is equivalent the natural to d"}
+{"idx": 4, "title": "Numerical Accounting in the Shuffle Model of Differential Privacy", "date": "", "ddg_snippet": "Mar 6, 2023 · Accounting tight bounds , however, is complicated by the complexity brought by the shuffler. The recently proposed numerical techniques for evaluating $ (\\varepsilon,\\delta)$- differential privacy guarantees have been shown to give tighter bounds than commonly used methods for compositions of various complex mechanisms.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=11osftjEbF", "content": "Mar 6, 2023 · Accounting tight bounds , however, is complicated by the complexity brought by the shuffler. The recently proposed numerical techniques for evaluating $ (\\varepsilon,\\delta)$- differential privacy guarantees have been shown to give tighter bounds than commonly used methods for compositions of various complex mechanisms."}
+{"idx": 5, "title": "Avoiding Pitfalls for Privacy Accounting of Subsampled ...", "date": "", "ddg_snippet": "Abstract:We consider the problem of computing tight privacy guarantees for the composition of subsampled differentially private mechanisms. Recent algorithms can numerically compute the privacy parameters to arbitrary precision but must be carefully applied. Our main contribution is to address two common points of confusion. First, some privacy accountants assume that the privacy guarantees ...", "subpage_snippet": "", "source": "bohrium.dp.tech", "link": "https://bohrium.dp.tech/paper/arxiv/2405.20769", "content": "Abstract:We consider the problem of computing tight privacy guarantees for the composition of subsampled differentially private mechanisms. Recent algorithms can numerically compute the privacy parameters to arbitrary precision but must be carefully applied. Our main contribution is to address two common points of confusion. First, some privacy accountants assume that the privacy guarantees ..."}
+{"idx": 6, "title": "Budget composition in differential privacy | by Stephen ...", "date": "", "ddg_snippet": "Nov 24, 2022 · The rest of the post will dive into my personal favorite insights in these 3 stages. We then end the post with how one can use open source libraries to get tight composition bounds .", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@sjonany/budget-composition-in-differential-privacy-5fb793465bc6", "content": "Nov 24, 2022 · The rest of the post will dive into my personal favorite insights in these 3 stages. We then end the post with how one can use open source libraries to get tight composition bounds ."}
+{"idx": 7, "title": "Variants of Differential Privacy — Programming", "date": "", "ddg_snippet": "... of differential privacy is to enable tighter bounds on ... It turns out that sequential composition for \\(\\epsilon\\) - differential privacy is tight .", "subpage_snippet": "", "source": "programming-dp.com", "link": "https://programming-dp.com/ch8.html", "content": "... of differential privacy is to enable tighter bounds on ... It turns out that sequential composition for \\(\\epsilon\\) - differential privacy is tight ."}
+{"idx": 8, "title": "Shifted Interpolation for Differential Privacy", "date": "", "ddg_snippet": "It is therefore a central question to quantify the differential privacy of these algorithms—however, tight characterizations remain open, even in ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.00278v2", "content": "It is therefore a central question to quantify the differential privacy of these algorithms—however, tight characterizations remain open, even in ..."}
+{"idx": 9, "title": "AVEC: Bootstrapping Privacy for Local LLMs", "date": "", "ddg_snippet": "Third, we present a formal privacy accounting model based on Rényi differential privacy and privacy odometers and prove bounds on utility ceilings ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.10561v1", "content": "Third, we present a formal privacy accounting model based on Rényi differential privacy and privacy odometers and prove bounds on utility ceilings ..."}
diff --git a/data/sampled_jsons/conn(a,b)_abnormal_behavioral_pattern_equation_8_co-occur_anomalous_circuits.jsonl b/data/sampled_jsons/conn(a,b)_abnormal_behavioral_pattern_equation_8_co-occur_anomalous_circuits.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2d5f261b0359fc15f305998e4128a8718832cf56
--- /dev/null
+++ b/data/sampled_jsons/conn(a,b)_abnormal_behavioral_pattern_equation_8_co-occur_anomalous_circuits.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "The use of convolutional neural networks for abnormal behavior ...", "date": "", "ddg_snippet": "Abnormal Behavior: Abnormal behaviors are classified based on deviations from the normal patterns described above. These include unexpected movements such as running, abrupt stops, erratic changes in direction, and interactions with objects or individuals that suggest potential security concerns.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0306457324002395", "content": "Abnormal Behavior: Abnormal behaviors are classified based on deviations from the normal patterns described above. These include unexpected movements such as running, abrupt stops, erratic changes in direction, and interactions with objects or individuals that suggest potential security concerns."}
+{"idx": 1, "title": "Functional Connectivity and the CONN Toolbox", "date": "", "ddg_snippet": "Overview This module will introduce you to functional connectivity, the correlation in BOLD signal between two distinct regions of the brain. This correlation can be analyzed when the subject is doing a task (i.e., task-based connectivity), or when the subject is at rest - relaxed and alert, but not doing any particular task (i.e., resting-state connectivity). In the following tutorials, you ...", "subpage_snippet": "", "source": "andysbrainbook.readthedocs.io", "link": "https://andysbrainbook.readthedocs.io/en/latest/FunctionalConnectivity/CONN_Overview.html", "content": "Overview This module will introduce you to functional connectivity, the correlation in BOLD signal between two distinct regions of the brain. This correlation can be analyzed when the subject is doing a task (i.e., task-based connectivity), or when the subject is at rest - relaxed and alert, but not doing any particular task (i.e., resting-state connectivity). In the following tutorials, you ..."}
+{"idx": 2, "title": "Chapter 10 Detecting Abnormal Behaviors | Behavior Analysis with ...", "date": "", "ddg_snippet": "Chapter 10 Detecting Abnormal Behaviors Abnormal data points are instances that are rare or do not occur very often. They are also called outliers. Some examples include illegal bank transactions, defective products, natural disasters, etc. Detecting abnormal behaviors is an important topic in the fields of health care, ecology, economy, psychology, and so on. For example, abnormal behaviors ...", "subpage_snippet": "", "source": "enriquegit.github.io", "link": "https://enriquegit.github.io/behavior-free/abnormalbehaviors.html", "content": "Chapter 10 Detecting Abnormal Behaviors Abnormal data points are instances that are rare or do not occur very often. They are also called outliers. Some examples include illegal bank transactions, defective products, natural disasters, etc. Detecting abnormal behaviors is an important topic in the fields of health care, ecology, economy, psychology, and so on. For example, abnormal behaviors ..."}
+{"idx": 3, "title": "PDF Abnormal Behavior Recognition Based on - Korea Science", "date": "", "ddg_snippet": "Abstract This paper presents a new approach for detecting abnormal behaviors in complex surveillance scenes where anomalies are subtle and difficult to distinguish due to the intricate correlations among multiple objects' behaviors. Specifically, a cascaded probabilistic topic model was put forward for learning the spatial context of local behavior and the temporal context of global behavior ...", "subpage_snippet": "", "source": "koreascience.kr", "link": "https://koreascience.kr/article/JAKO202019854291396.pdf", "content": "Abstract This paper presents a new approach for detecting abnormal behaviors in complex surveillance scenes where anomalies are subtle and difficult to distinguish due to the intricate correlations among multiple objects' behaviors. Specifically, a cascaded probabilistic topic model was put forward for learning the spatial context of local behavior and the temporal context of global behavior ..."}
+{"idx": 4, "title": "Efficient abnormal behavior detection with adaptive weight distribution", "date": "", "ddg_snippet": "We propose an anomalous behavior detection framework called Efficient Abnormal Behavior Detection (EABD), which combines Transformer and CNN structure. It efficiently establishes global contextual correlations and attains real-time performance capabilities with higher inference speed.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0925231224009585", "content": "We propose an anomalous behavior detection framework called Efficient Abnormal Behavior Detection (EABD), which combines Transformer and CNN structure. It efficiently establishes global contextual correlations and attains real-time performance capabilities with higher inference speed."}
+{"idx": 5, "title": "Abnormal behavior detection in industrial control systems based on CNN", "date": "", "ddg_snippet": "This study aims to explore and verify a CNN-based industrial control system abnormal behavior detection method. By constructing and training a deep learning model, it automatically analyzes and identifies potential abnormal behaviors in ICS. We propose an improved multi-branch convolutional neural network structure that can more effectively extract and fuse spatial features of multiple scales ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S111001682401007X", "content": "This study aims to explore and verify a CNN-based industrial control system abnormal behavior detection method. By constructing and training a deep learning model, it automatically analyzes and identifies potential abnormal behaviors in ICS. We propose an improved multi-branch convolutional neural network structure that can more effectively extract and fuse spatial features of multiple scales ..."}
+{"idx": 6, "title": "Abnormal Behavior: Causes, Criteria, and Impact in Psychology", "date": "", "ddg_snippet": "Explore abnormal behavior in psychology, including its definition, criteria, causes, types, and impact. Understand dysfunctional behavior and treatment approaches.", "subpage_snippet": "", "source": "neurolaunch.com", "link": "https://neurolaunch.com/abnormal-behavior/", "content": "Explore abnormal behavior in psychology, including its definition, criteria, causes, types, and impact. Understand dysfunctional behavior and treatment approaches."}
+{"idx": 7, "title": "PDF A hybrid CNN and LSTM-based deep learning model for abnormal behavior ...", "date": "", "ddg_snippet": "This paper uses image processing and deep learning technology to analyze pedes-trian activity status through camera images and identify abnormal behaviors (falls, kicks, punches) in the pictures. Since abnormal behavior is usually irregular and happens in a short period of time, it requires continuous monitoring.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/s11042-021-11887-9.pdf?pdf=button", "content": "This paper uses image processing and deep learning technology to analyze pedes-trian activity status through camera images and identify abnormal behaviors (falls, kicks, punches) in the pictures. Since abnormal behavior is usually irregular and happens in a short period of time, it requires continuous monitoring."}
+{"idx": 8, "title": "Exam 1 Chapter 2 Flashcards | Quizlet", "date": "", "ddg_snippet": "Study with Quizlet and memorize flashcards containing terms like multiply determined, neurotransmitters, more severe organic disorder and central nervous system complications and more.", "subpage_snippet": "", "source": "quizlet.com", "link": "https://quizlet.com/432636781/exam-1-chapter-2-flash-cards/", "content": "Study with Quizlet and memorize flashcards containing terms like multiply determined, neurotransmitters, more severe organic disorder and central nervous system complications and more."}
+{"idx": 9, "title": "PDF Three Laws of Behavior: Allocation, Induction, and Covariance", "date": "", "ddg_snippet": "These three discoveries lead to three laws of behavior: (a) the Law of Allocation, (b) the Law of Induction, and (c) the Law of Covariance. Together, these laws allow behavior analysts to organize most, if not all, of what we know about behavior. Let us examine each discovery and law in turn.", "subpage_snippet": "", "source": "psycnet.apa.org", "link": "https://psycnet.apa.org/fulltext/2018-28692-001.pdf", "content": "These three discoveries lead to three laws of behavior: (a) the Law of Allocation, (b) the Law of Induction, and (c) the Law of Covariance. Together, these laws allow behavior analysts to organize most, if not all, of what we know about behavior. Let us examine each discovery and law in turn."}
diff --git a/data/sampled_jsons/consequential_validity_generative_AI_social_science_measurement_challenge.jsonl b/data/sampled_jsons/consequential_validity_generative_AI_social_science_measurement_challenge.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..55245cdac8ca77aa2450166fd983a9b42fd80bab
--- /dev/null
+++ b/data/sampled_jsons/consequential_validity_generative_AI_social_science_measurement_challenge.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Position: Evaluating Generative AI Systems is a Social Science ...", "date": "", "ddg_snippet": "Generative AI , Capabilities, Behaviors, Impacts, Evaluation, Measurement , Measurement Theory, Social Sciences , Validity . 1 Evaluating GenAI Systems.We take the position that evaluating GenAI systems is a social science measurement challenge .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.00561v1", "content": "Generative AI , Capabilities, Behaviors, Impacts, Evaluation, Measurement , Measurement Theory, Social Sciences , Validity . 1 Evaluating GenAI Systems.We take the position that evaluating GenAI systems is a social science measurement challenge ."}
+{"idx": 1, "title": "(PDF) Position: Evaluating Generative AI Systems is a Social Science ...", "date": "", "ddg_snippet": "Social Science Measurement Challenge .The valid measurement of generative AI (GenAI) systems' capabilities, risks, and impacts forms the bedrock of our ability to evaluate these systems.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/388657599_Position_Evaluating_Generative_AI_Systems_is_a_Social_Science_Measurement_Challenge", "content": "Social Science Measurement Challenge .The valid measurement of generative AI (GenAI) systems' capabilities, risks, and impacts forms the bedrock of our ability to evaluate these systems."}
+{"idx": 2, "title": "Position: Evaluating Generative AI Systems Is a Social Science ...", "date": "", "ddg_snippet": "Finally, we might interrogate consequential validity , focus-ing on the consequences of the judge LLM and its resulting measurements .We take the position that evaluating GenAI systems is a social science measurement challenge .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=1ZC4RNjqzU", "content": "Finally, we might interrogate consequential validity , focus-ing on the consequences of the judge LLM and its resulting measurements .We take the position that evaluating GenAI systems is a social science measurement challenge ."}
+{"idx": 3, "title": "Evaluating Generative AI Systems is a Social Science Measurement ...", "date": "", "ddg_snippet": "generative AI systems that borrow from how social scientists measure human behaviors and abilities. Think of current AI evaluation like using a ruler made of rubber - it stretches and bends, giving different measurements each time.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/evaluating-generative-ai-systems-is-social-science", "content": "generative AI systems that borrow from how social scientists measure human behaviors and abilities. Think of current AI evaluation like using a ruler made of rubber - it stretches and bends, giving different measurements each time."}
+{"idx": 4, "title": "Downes.ca ~ Stephen's Web ~ Evaluating Generative AI Systems is...", "date": "", "ddg_snippet": "The argument in this short paper (6 page PDF) is that \" measurement tasks involved in evaluating GenAI systems are highly reminiscent of measurement tasks found throughout the social sciences \" and thus \"the ML community would benefit from learning from and drawing on the social ...", "subpage_snippet": "", "source": "www.downes.ca", "link": "https://www.downes.ca/post/77850", "content": "The argument in this short paper (6 page PDF) is that \" measurement tasks involved in evaluating GenAI systems are highly reminiscent of measurement tasks found throughout the social sciences \" and thus \"the ML community would benefit from learning from and drawing on the social ..."}
+{"idx": 5, "title": "Personality traits predict students’ use of generative AI in higher...", "date": "", "ddg_snippet": "The second scale measured their educational use of generative AI through five statements such as “I often use generative AI to learn new concepts,” rated on a five-point scale. The researchers then analyzed the data using multiple techniques.", "subpage_snippet": "", "source": "www.psypost.org", "link": "https://www.psypost.org/personality-traits-predict-students-use-of-generative-ai-in-higher-education-study-finds/", "content": "The second scale measured their educational use of generative AI through five statements such as “I often use generative AI to learn new concepts,” rated on a five-point scale. The researchers then analyzed the data using multiple techniques."}
+{"idx": 6, "title": "Generative AI isn’t culturally neutral, research finds | MIT Sloan", "date": "", "ddg_snippet": "As generative AI becomes an increasingly important part of everyday decision-making, recognizing cultural tendencies will be crucial for both individuals and organizations worldwide.", "subpage_snippet": "", "source": "mitsloan.mit.edu", "link": "https://mitsloan.mit.edu/ideas-made-to-matter/generative-ai-isnt-culturally-neutral-research-finds", "content": "As generative AI becomes an increasingly important part of everyday decision-making, recognizing cultural tendencies will be crucial for both individuals and organizations worldwide."}
+{"idx": 7, "title": "A CIO's And CDO’s Playbook For Operationalizing Generative AI", "date": "", "ddg_snippet": "Generative AI is no longer a speculative R&D playground. It’s a strategic lever for enterprise transformation. Yet, for many CIOs and CDOs, the challenge has become a question of how to prepare for GenAI.", "subpage_snippet": "", "source": "www.forbes.com", "link": "https://www.forbes.com/councils/forbestechcouncil/2025/09/22/a-cios-and-cdos-playbook-for-operationalizing-generative-ai/", "content": "Generative AI is no longer a speculative R&D playground. It’s a strategic lever for enterprise transformation. Yet, for many CIOs and CDOs, the challenge has become a question of how to prepare for GenAI."}
+{"idx": 8, "title": "How to build a better AI benchmark | MIT Technology Review", "date": "", "ddg_snippet": "To fix the way we test and measure models, AI is learning tricks from social science .But validity is a central theme, with particular criteria challenging designers to spell out what capability their benchmark is testing and how it relates to the tasks that make up the benchmark.", "subpage_snippet": "", "source": "www.technologyreview.com", "link": "https://www.technologyreview.com/2025/05/08/1116192/how-to-build-a-better-ai-benchmark/", "content": "To fix the way we test and measure models, AI is learning tricks from social science .But validity is a central theme, with particular criteria challenging designers to spell out what capability their benchmark is testing and how it relates to the tasks that make up the benchmark."}
+{"idx": 9, "title": "Towards Interactive Evaluations for Interaction Harms in Human- AI ...", "date": "", "ddg_snippet": "2. An Overview of the Generative AI Evaluation Landscape. We begin by examining contemporary approaches to ethics and safety evaluations of generative AI systems—their methodologies, primary focus areas, and limitations.", "subpage_snippet": "", "source": "knightcolumbia.org", "link": "https://knightcolumbia.org/content/towards-interactive-evaluations-for-interaction-harms-in-human-ai-systems", "content": "2. An Overview of the Generative AI Evaluation Landscape. We begin by examining contemporary approaches to ethics and safety evaluations of generative AI systems—their methodologies, primary focus areas, and limitations."}
diff --git a/data/sampled_jsons/consequential_validity_is_concerned_with_the_Evaluating_Generative_AI_Systems_Is_a_Social_Science_Me.jsonl b/data/sampled_jsons/consequential_validity_is_concerned_with_the_Evaluating_Generative_AI_Systems_Is_a_Social_Science_Me.jsonl
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Position: Evaluating Generative AI Systems is a Social Science ...", "date": "", "ddg_snippet": "Consequential validity is concerned with the consequences of measurement ,101010 Consequential validity has a very different focus than the other lenses of validity. It was first proposed by Messick (1987) , who argued that the consequences of...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.00561v1", "content": "Consequential validity is concerned with the consequences of measurement ,101010 Consequential validity has a very different focus than the other lenses of validity. It was first proposed by Messick (1987) , who argued that the consequences of..."}
+{"idx": 1, "title": "Evaluating Generative AI Systems Is a Social Science ...", "date": "", "ddg_snippet": "6 Jun 2025 — Consequential validity: Consequential validity is concerned with the consequences of measurement, 10 10 10Consequential validity has a very ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.00561v2", "content": "6 Jun 2025 — Consequential validity: Consequential validity is concerned with the consequences of measurement, 10 10 10Consequential validity has a very ..."}
+{"idx": 2, "title": "Toward Valid Measurement Of (Un)fairness For Generative AI: A", "date": "", "ddg_snippet": "... theories of measurement from the social sciences emphasize that validity should be a core consideration in the design of any measurement (Drost 2011 ) ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.04641v1", "content": "... theories of measurement from the social sciences emphasize that validity should be a core consideration in the design of any measurement (Drost 2011 ) ..."}
+{"idx": 3, "title": "Generative AI | Substantia Mea", "date": "", "ddg_snippet": "This contingent issue can have a negative effect on the use of the information that is created by online dialogue systems .", "subpage_snippet": "", "source": "drmarkcamilleri.com", "link": "https://drmarkcamilleri.com/tag/generative-ai/", "content": "This contingent issue can have a negative effect on the use of the information that is created by online dialogue systems ."}
+{"idx": 4, "title": "Adoption of Generative AI by Academic Biomedical Researchers -", "date": "", "ddg_snippet": "What critics and proponents typically agree on is that there is no way to put generative AI back in its bottle. ... Adoption is limited by serious ...", "subpage_snippet": "", "source": "sr.ithaka.org", "link": "https://sr.ithaka.org/publications/adoption-of-generative-ai-by-academic-biomedical-researchers/", "content": "What critics and proponents typically agree on is that there is no way to put generative AI back in its bottle. ... Adoption is limited by serious ..."}
+{"idx": 5, "title": "AI Analyzes the Social Work Licensing Exam, Concerns Deepen", "date": "", "ddg_snippet": "Problems with the social worker licensing exam date back to at least 2010, when a study concluded it evaluated test-taking abilities more than ...", "subpage_snippet": "", "source": "imprintnews.org", "link": "https://imprintnews.org/top-stories/ai-analyzes-the-social-work-licensing-exam-and-concerns-deepen/241949", "content": "Problems with the social worker licensing exam date back to at least 2010, when a study concluded it evaluated test-taking abilities more than ..."}
+{"idx": 6, "title": "HumanAgencyBench: Scalable Evaluation of Human Agency Support", "date": "", "ddg_snippet": "... the idea of human agency by integrating philosophical and scientific theories of agency with AI -assisted evaluation methods: using large language ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.08494v1", "content": "... the idea of human agency by integrating philosophical and scientific theories of agency with AI -assisted evaluation methods: using large language ..."}
+{"idx": 7, "title": "Towards Interactive Evaluations for Interaction Harms in", "date": "", "ddg_snippet": "The growing importance of model evaluations has been accompanied by increased scrutiny, with researchers highlighting both the unique challenges of ...", "subpage_snippet": "", "source": "knightcolumbia.org", "link": "https://knightcolumbia.org/content/towards-interactive-evaluations-for-interaction-harms-in-human-ai-systems", "content": "The growing importance of model evaluations has been accompanied by increased scrutiny, with researchers highlighting both the unique challenges of ..."}
+{"idx": 8, "title": "AI as Normal Technology | Knight First Amendment Institute", "date": "", "ddg_snippet": "This is different from the question of whether it is helpful for an individual user to conceptualize a specific AI system as a tool as opposed to a ...", "subpage_snippet": "", "source": "knightcolumbia.org", "link": "https://knightcolumbia.org/content/ai-as-normal-technology", "content": "This is different from the question of whether it is helpful for an individual user to conceptualize a specific AI system as a tool as opposed to a ..."}
+{"idx": 9, "title": "Robotic and Cognitive Automation Blog 56", "date": "", "ddg_snippet": "Next, we caution that ( generative ) AI is also at the peak of inflated (hype) expectations and discuss nine in-principle issues that AI struggles with ...", "subpage_snippet": "", "source": "www.roboticandcognitiveautomation.co.uk", "link": "https://www.roboticandcognitiveautomation.co.uk/Blog61.html", "content": "Next, we caution that ( generative ) AI is also at the peak of inflated (hype) expectations and discuss nine in-principle issues that AI struggles with ..."}
diff --git a/data/sampled_jsons/consequential_validity_social_science_measurement_generative_AI_year_2024.jsonl b/data/sampled_jsons/consequential_validity_social_science_measurement_generative_AI_year_2024.jsonl
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index 0000000000000000000000000000000000000000..f2ed23594f7281b4889e6aa81ee9dd02bfb07b1c
--- /dev/null
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Evaluating Generative AI Systems is a Social Science", "date": "", "ddg_snippet": "... is that the ML community would benefit from learning from and drawing on the social sciences when developing approaches and instruments for measuring ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.10939v1", "content": "... is that the ML community would benefit from learning from and drawing on the social sciences when developing approaches and instruments for measuring ..."}
+{"idx": 1, "title": "Toward Valid Measurement Of (Un)fairness For Generative AI: A", "date": "", "ddg_snippet": "Well-established theories of measurement from the social sciences emphasize that validity should be a core consideration in the design of any ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.04641v1", "content": "Well-established theories of measurement from the social sciences emphasize that validity should be a core consideration in the design of any ..."}
+{"idx": 2, "title": "Position: Evaluating Generative AI Systems is a Social Science", "date": "", "ddg_snippet": "Generative AI , Capabilities, Behaviors, Impacts, Evaluation, Measurement , Measurement Theory, Social Sciences , Validity", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.00561v1", "content": "Generative AI , Capabilities, Behaviors, Impacts, Evaluation, Measurement , Measurement Theory, Social Sciences , Validity"}
+{"idx": 3, "title": "Generative AI | Substantia Mea", "date": "", "ddg_snippet": "... it examines their insights about the content quality, source trustworthiness as well as on the interactivity features of these text- generative AI ...", "subpage_snippet": "", "source": "drmarkcamilleri.com", "link": "https://drmarkcamilleri.com/tag/generative-ai/", "content": "... it examines their insights about the content quality, source trustworthiness as well as on the interactivity features of these text- generative AI ..."}
+{"idx": 4, "title": "Reply to Teeny and Matz: Toward the robust measurement of", "date": "", "ddg_snippet": "Reply to Teeny and Matz: Toward the robust measurement of personalized persuasion with generative AI ... advancements, involving generative AI and ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/385011319_Reply_to_Teeny_and_Matz_Toward_the_robust_measurement_of_personalized_persuasion_with_generative_AI", "content": "Reply to Teeny and Matz: Toward the robust measurement of personalized persuasion with generative AI ... advancements, involving generative AI and ..."}
+{"idx": 5, "title": "Implementing generative AI: A guide for commercial life", "date": "", "ddg_snippet": "Behavioural Science ... AI in Pharma Marketing ... Life Sciences Industry Report", "subpage_snippet": "", "source": "pharmaphorum.com", "link": "https://pharmaphorum.com/digital/implementing-generative-ai-guide-commercial-life-sciences-teams", "content": "Behavioural Science ... AI in Pharma Marketing ... Life Sciences Industry Report"}
+{"idx": 6, "title": "Public perceptions of AI science and scientists relatively more", "date": "", "ddg_snippet": "Dror Walter, Yotam Ophir, Patrick E Jamieson, Kathleen Hall Jamieson, Public perceptions of AI science and scientists relatively more negative but ...", "subpage_snippet": "", "source": "academic.oup.com", "link": "https://academic.oup.com/pnasnexus/article/4/6/pgaf163/8159304", "content": "Dror Walter, Yotam Ophir, Patrick E Jamieson, Kathleen Hall Jamieson, Public perceptions of AI science and scientists relatively more negative but ..."}
+{"idx": 7, "title": "Towards Interactive Evaluations for Interaction Harms in", "date": "", "ddg_snippet": "... social sciences , we address these measurement challenges by presenting practical principles for designing interactive evaluations using ecologically ...", "subpage_snippet": "", "source": "knightcolumbia.org", "link": "https://knightcolumbia.org/content/towards-interactive-evaluations-for-interaction-harms-in-human-ai-systems", "content": "... social sciences , we address these measurement challenges by presenting practical principles for designing interactive evaluations using ecologically ..."}
+{"idx": 8, "title": "Ten guidelines for AI scoring of psychological assessments |", "date": "", "ddg_snippet": "... that AI can bring to creativity assessment, while also considering how this increase in efficiency might exacerbate or even generate new validity ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/Ten-guidelines-for-AI-scoring-of-psychological-assessments_tbl1_390922030", "content": "... that AI can bring to creativity assessment, while also considering how this increase in efficiency might exacerbate or even generate new validity ..."}
+{"idx": 9, "title": "AI as Normal Technology | Knight First Amendment Institute", "date": "", "ddg_snippet": "... AI be gradual, allowing people and institutions to adapt as AI capabilities and adoption increase, or will there be jumps leading to massive ...", "subpage_snippet": "", "source": "knightcolumbia.org", "link": "https://knightcolumbia.org/content/ai-as-normal-technology", "content": "... AI be gradual, allowing people and institutions to adapt as AI capabilities and adoption increase, or will there be jumps leading to massive ..."}
diff --git a/data/sampled_jsons/consequential_validity_social_science_measurement_theory_definition.jsonl b/data/sampled_jsons/consequential_validity_social_science_measurement_theory_definition.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f028cc710352c1b6ffb95e8379941cabf6947c4b
--- /dev/null
+++ b/data/sampled_jsons/consequential_validity_social_science_measurement_theory_definition.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Construct validity - Wikipedia", "date": "", "ddg_snippet": "Construct validity is particularly important in the social sciences , psychology , psychometrics and language studies.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Construct_validity", "content": "Construct validity is particularly important in the social sciences , psychology , psychometrics and language studies."}
+{"idx": 1, "title": "(PDF) Trustworthy Social Bias Measurement", "date": "", "ddg_snippet": "In this work, we design bias measures that warrant trust based on the cross-disciplinary theory of measurement modeling.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/366527758_Trustworthy_Social_Bias_Measurement", "content": "In this work, we design bias measures that warrant trust based on the cross-disciplinary theory of measurement modeling."}
+{"idx": 2, "title": "Ben Wilbrink: Validity", "date": "", "ddg_snippet": "... and expectation to find in the history of science many examples of the simultaneous development of physical theory and adequate - valid - measurement ...", "subpage_snippet": "", "source": "benwilbrink.nl", "link": "https://benwilbrink.nl/literature/validity.htm", "content": "... and expectation to find in the history of science many examples of the simultaneous development of physical theory and adequate - valid - measurement ..."}
+{"idx": 3, "title": "Measurement as Bricolage: Examining How Data Scientists", "date": "", "ddg_snippet": "However, the traditional top-down conceptualization of measurement is in tension with our understanding of data science as a bottom-up process ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.02819v1", "content": "However, the traditional top-down conceptualization of measurement is in tension with our understanding of data science as a bottom-up process ..."}
+{"idx": 4, "title": "Introduction | Transforming Justice Responses to Non-Recent", "date": "", "ddg_snippet": "Theory of Architecture ... Theory , Methods, and Historiography ... Literary Studies ( Science Fiction)", "subpage_snippet": "", "source": "academic.oup.com", "link": "https://academic.oup.com/book/59821/chapter/511214842", "content": "Theory of Architecture ... Theory , Methods, and Historiography ... Literary Studies ( Science Fiction)"}
+{"idx": 5, "title": "Evidence - Argumenta - Journal of Analytic Philosophy", "date": "", "ddg_snippet": "In this paper, we discuss some of these definitions and introduce a new measure of the success of a theory relative to a body of evidence aimed at ...", "subpage_snippet": "", "source": "www.argumenta.org", "link": "https://www.argumenta.org/articles/keyword/evidence/", "content": "In this paper, we discuss some of these definitions and introduce a new measure of the success of a theory relative to a body of evidence aimed at ..."}
+{"idx": 6, "title": "Open Research Online", "date": "", "ddg_snippet": "The development of a capability wellbeing measure in economic evaluation for children and young people aged 11-15. ... of Medicine & Science in ...", "subpage_snippet": "", "source": "oro.open.ac.uk", "link": "https://oro.open.ac.uk/view/faculty_dept/fass-ssgs-phil.html", "content": "The development of a capability wellbeing measure in economic evaluation for children and young people aged 11-15. ... of Medicine & Science in ..."}
+{"idx": 7, "title": "Research Gap", "date": "", "ddg_snippet": "Institute for Operations Research and the Management Sciences ... Critical realism (philosophy of the social sciences )", "subpage_snippet": "", "source": "dashboard.resgap.com", "link": "https://dashboard.resgap.com/demo/deepdive", "content": "Institute for Operations Research and the Management Sciences ... Critical realism (philosophy of the social sciences )"}
+{"idx": 8, "title": "Neurobiological Theories and Models of Consciousness -", "date": "", "ddg_snippet": "A recently developed computational and neurobiological theory of phenomenal consciousness is applied to a series of persistent philosophical problems ...", "subpage_snippet": "", "source": "philpapers.org", "link": "https://philpapers.org/browse/neurobiological-theories-and-models-of-consciousness", "content": "A recently developed computational and neurobiological theory of phenomenal consciousness is applied to a series of persistent philosophical problems ..."}
+{"idx": 9, "title": "Measures of Intelligence - Bibliography - PhilPapers", "date": "", "ddg_snippet": "There is a widely held view on measurement inferences, that goes back to Stevens’s ([1946]) theory of measurement scales and ‘permissible ...", "subpage_snippet": "", "source": "philpapers.org", "link": "https://philpapers.org/browse/measures-of-intelligence", "content": "There is a widely held view on measurement inferences, that goes back to Stevens’s ([1946]) theory of measurement scales and ‘permissible ..."}
diff --git a/data/sampled_jsons/contextual_bandits_function_dependent_parameter_choice_computational_complexity.jsonl b/data/sampled_jsons/contextual_bandits_function_dependent_parameter_choice_computational_complexity.jsonl
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index 0000000000000000000000000000000000000000..d68ed7997f85920a0c42d091eced2492d0369e41
--- /dev/null
+++ b/data/sampled_jsons/contextual_bandits_function_dependent_parameter_choice_computational_complexity.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Statistical Inference for Misspecified Contextual Bandits", "date": "", "ddg_snippet": "Contextual bandit algorithms have transformed modern experimentation by enabling real-time adaptation for personalized treatment and efficient use of ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.06287v1", "content": "Contextual bandit algorithms have transformed modern experimentation by enabling real-time adaptation for personalized treatment and efficient use of ..."}
+{"idx": 1, "title": "Feel-Good Thompson Sampling for Contextual Bandits: a Markov", "date": "", "ddg_snippet": "In the fundamental setting of linear contextual bandits , where the information-theoretic regret lower bound is Ω ( d T ) Ω 𝑑 𝑇 \\smash ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.15290v1", "content": "In the fundamental setting of linear contextual bandits , where the information-theoretic regret lower bound is Ω ( d T ) Ω 𝑑 𝑇 \\smash ..."}
+{"idx": 2, "title": "Differential Privacy in Kernelized Contextual Bandits via", "date": "", "ddg_snippet": "We consider the problem of contextual kernel bandits with stochastic contexts, where the underlying reward function belongs to a known Reproducing ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.13639v1", "content": "We consider the problem of contextual kernel bandits with stochastic contexts, where the underlying reward function belongs to a known Reproducing ..."}
+{"idx": 3, "title": "Leveraging Offline Data in Linear Latent Contextual Bandits", "date": "", "ddg_snippet": "In this light, we study a linear contextual bandit setting where each user has its own high-dimensional reward parameter , but reward parameters ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.17324v2", "content": "In this light, we study a linear contextual bandit setting where each user has its own high-dimensional reward parameter , but reward parameters ..."}
+{"idx": 4, "title": "Contextual Multi-Armed Bandit Problems in Reinforcement", "date": "", "ddg_snippet": "The LinUCB algorithm is a contextual bandit algorithm that models the expected reward of an action given a context as a linear function , and it ...", "subpage_snippet": "", "source": "hackernoon.com", "link": "https://hackernoon.com/contextual-multi-armed-bandit-problems-in-reinforcement-learning", "content": "The LinUCB algorithm is a contextual bandit algorithm that models the expected reward of an action given a context as a linear function , and it ..."}
+{"idx": 5, "title": "Nonlinear Model-Based Sequential Decision-Making in Agriculture", "date": "", "ddg_snippet": "Subsequent work extended this framework to incorporate contextual information, leading to the development of contextual bandit models (Slivkins ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.01924v2", "content": "Subsequent work extended this framework to incorporate contextual information, leading to the development of contextual bandit models (Slivkins ..."}
+{"idx": 6, "title": "Disinformation elicits learning biases", "date": "", "ddg_snippet": "... computational accounts, parameter recovery) but incomplete for the specific claims about heightened positivity bias at low credibility, which depend ...", "subpage_snippet": "", "source": "elifesciences.org", "link": "https://elifesciences.org/reviewed-preprints/106073", "content": "... computational accounts, parameter recovery) but incomplete for the specific claims about heightened positivity bias at low credibility, which depend ..."}
+{"idx": 7, "title": "ICML 2020 Papers", "date": "", "ddg_snippet": "On the (In)tractability of Computing Normalizing Constants for the Product of Determinantal Point Processes ... Functions Maximization with Matroid ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2020/papers.html?filter=keywords", "content": "On the (In)tractability of Computing Normalizing Constants for the Product of Determinantal Point Processes ... Functions Maximization with Matroid ..."}
+{"idx": 8, "title": "ICML 2020 Papers", "date": "", "ddg_snippet": "On the (In)tractability of Computing Normalizing Constants for the Product of Determinantal Point Processes ... Functions Maximization with Matroid ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2020/papers.html", "content": "On the (In)tractability of Computing Normalizing Constants for the Product of Determinantal Point Processes ... Functions Maximization with Matroid ..."}
+{"idx": 9, "title": "AISTATS 2022 Schedule", "date": "", "ddg_snippet": "Standardisation- function Kernel Stein Discrepancy: A Unifying View on Kernel Stein Discrepancy Tests for Goodness-of-fit", "subpage_snippet": "", "source": "virtual.aistats.org", "link": "https://virtual.aistats.org/virtual/2022/calendar", "content": "Standardisation- function Kernel Stein Discrepancy: A Unifying View on Kernel Stein Discrepancy Tests for Goodness-of-fit"}
diff --git a/data/sampled_jsons/critical_windows_reasoning_language_models_2024.jsonl b/data/sampled_jsons/critical_windows_reasoning_language_models_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..1f88da43f0708590685e3c552110b120670e9988
--- /dev/null
+++ b/data/sampled_jsons/critical_windows_reasoning_language_models_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Large Language Models Are Reasoning Teachers | Request PDF", "date": "", "ddg_snippet": "... Collaborative Distillation (NesyCD), a novel knowledge distillation method for learning the complex reasoning abilities of Large Language Models ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/372919115_Large_Language_Models_Are_Reasoning_Teachers", "content": "... Collaborative Distillation (NesyCD), a novel knowledge distillation method for learning the complex reasoning abilities of Large Language Models ..."}
+{"idx": 1, "title": "Efficient Reasoning for Large Reasoning Language Models via", "date": "", "ddg_snippet": "Large Reasoning Language Models (LRLMs), including OpenAI’s o1/o3 (OpenAI 2024 , 2025 ) and DeepSeek-R1 (Guo et al.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.05337v1", "content": "Large Reasoning Language Models (LRLMs), including OpenAI’s o1/o3 (OpenAI 2024 , 2025 ) and DeepSeek-R1 (Guo et al."}
+{"idx": 2, "title": "Efficient Reasoning Models: A Survey", "date": "", "ddg_snippet": "... line of works directly builds small language models with strong reasoning abilities using RL (Li et al., 2023a ; 2025d ; Zhu et al., 2024b ) .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.10903v1", "content": "... line of works directly builds small language models with strong reasoning abilities using RL (Li et al., 2023a ; 2025d ; Zhu et al., 2024b ) ."}
+{"idx": 3, "title": "Best Large Language Models for Windows of 2025 - Reviews &", "date": "", "ddg_snippet": "Compare and read user reviews of the best Large Language Models for Windows currently available using the table below.", "subpage_snippet": "", "source": "sourceforge.net", "link": "https://sourceforge.net/software/large-language-models/windows/", "content": "Compare and read user reviews of the best Large Language Models for Windows currently available using the table below."}
+{"idx": 4, "title": "Models and structured reasoning – Dimitri Glazkov", "date": "", "ddg_snippet": "For instance, a more sophisticated reasoning structure might guide a model to detect that the problem could be split into multiple sub-problems that ...", "subpage_snippet": "", "source": "glazkov.com", "link": "https://glazkov.com/2023/06/13/models-and-structured-reasoning/", "content": "For instance, a more sophisticated reasoning structure might guide a model to detect that the problem could be split into multiple sub-problems that ..."}
+{"idx": 5, "title": "Comments - Language models are nearly AGIs but we don't", "date": "", "ddg_snippet": "I work at Google Research, not on but somewhat adjacent to large language models .) I have a different objection, which is essentially that the ...", "subpage_snippet": "", "source": "philosophybear.substack.com", "link": "https://philosophybear.substack.com/p/language-models-are-nearly-agis-but/comments", "content": "I work at Google Research, not on but somewhat adjacent to large language models .) I have a different objection, which is essentially that the ..."}
+{"idx": 6, "title": "Reasoning with Foundation Models | OpenLM.ai", "date": "", "ddg_snippet": "We organize the current foundation models into three categories: language foundation models , vision foundation models , and multimodal foundation ...", "subpage_snippet": "", "source": "openlm.ai", "link": "https://openlm.ai/reasoning-with-foundation-models/", "content": "We organize the current foundation models into three categories: language foundation models , vision foundation models , and multimodal foundation ..."}
+{"idx": 7, "title": "On the future of language models — LessWrong", "date": "", "ddg_snippet": "Language models are large neural nets, with some specific ... Foundation models ” are the most straightforward version of language models .", "subpage_snippet": "", "source": "www.lesswrong.com", "link": "https://www.lesswrong.com/posts/pnaZajFFWuXAnetpz/on-the-future-of-language-models", "content": "Language models are large neural nets, with some specific ... Foundation models ” are the most straightforward version of language models ."}
+{"idx": 8, "title": "Top 9 Large Language Models of 2024", "date": "", "ddg_snippet": "As we step into 2024 , it s crucial to understand which large language models now lead the pack from our discussion on the best and how they can be ...", "subpage_snippet": "", "source": "www.multimodal.dev", "link": "https://www.multimodal.dev/post/best-large-language-models-of-2024", "content": "As we step into 2024 , it s crucial to understand which large language models now lead the pack from our discussion on the best and how they can be ..."}
+{"idx": 9, "title": "Everything You Need to Know About Reasoning Models: o1, o3,", "date": "", "ddg_snippet": "... Models (o1, o3): These heavyweights are engineered for the most demanding scenarios—where accuracy, depth of reasoning , and handling ambiguity are ...", "subpage_snippet": "", "source": "techcommunity.microsoft.com", "link": "https://techcommunity.microsoft.com/blog/azure-ai-services-blog/everything-you-need-to-know-about-reasoning-models-o1-o3-o4-mini-and-beyond/4406846", "content": "... Models (o1, o3): These heavyweights are engineered for the most demanding scenarios—where accuracy, depth of reasoning , and handling ambiguity are ..."}
diff --git a/data/sampled_jsons/crocker_plots_computational_complexity_memory_requirements_topological_persistence.jsonl b/data/sampled_jsons/crocker_plots_computational_complexity_memory_requirements_topological_persistence.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f1754dca338e4749a911bd856bae382e30bbfe05
--- /dev/null
+++ b/data/sampled_jsons/crocker_plots_computational_complexity_memory_requirements_topological_persistence.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Crocker Art Museum - Wikipedia", "date": "", "ddg_snippet": "Edwin B. Crocker (1818–1875), a wealthy California lawyer and judge, and his wife, Margaret Crocker (1822–1901), began to assemble a significant collection of paintings and drawings during an extended trip to Europe, from 1869 to 1871.", "subpage_snippet": "", "source": "en.m.wikipedia.org", "link": "https://en.m.wikipedia.org/wiki/Crocker_Art_Museum", "content": "Edwin B. Crocker (1818–1875), a wealthy California lawyer and judge, and his wife, Margaret Crocker (1822–1901), began to assemble a significant collection of paintings and drawings during an extended trip to Europe, from 1869 to 1871."}
+{"idx": 1, "title": "Crocker - Wikipedia", "date": "", "ddg_snippet": "Crocker is an archaic synonym of potter.", "subpage_snippet": "", "source": "en.m.wikipedia.org", "link": "https://en.m.wikipedia.org/wiki/Crocker", "content": "Crocker is an archaic synonym of potter."}
+{"idx": 2, "title": "Temporal network analysis using zigzag persistence | EPJ Data", "date": "", "ddg_snippet": "Persistent homology, the flagship tool from the field of Topological Data Analysis (TDA), is used to measure the shape of a dataset at multiple ...", "subpage_snippet": "", "source": "epjdatascience.springeropen.com", "link": "https://epjdatascience.springeropen.com/articles/10.1140/epjds/s13688-023-00379-5", "content": "Persistent homology, the flagship tool from the field of Topological Data Analysis (TDA), is used to measure the shape of a dataset at multiple ..."}
+{"idx": 3, "title": "A dynamical computational model of theta generation in", "date": "", "ddg_snippet": "In this context, the design of a computational model that replicates memory -related theta-gamma oscillations and theta phase reset is of uttermost ...", "subpage_snippet": "", "source": "elifesciences.org", "link": "https://elifesciences.org/articles/87356", "content": "In this context, the design of a computational model that replicates memory -related theta-gamma oscillations and theta phase reset is of uttermost ..."}
+{"idx": 4, "title": "A dynamical computational model of theta generation in", "date": "", "ddg_snippet": "In this context, the design of a computational model that replicates memoryrelated theta-gamma oscillations and theta phase reset is of uttermost ...", "subpage_snippet": "", "source": "elifesciences.org", "link": "https://elifesciences.org/reviewed-preprints/87356", "content": "In this context, the design of a computational model that replicates memoryrelated theta-gamma oscillations and theta phase reset is of uttermost ..."}
+{"idx": 5, "title": "The Crocker Art Museum | Crocker Art Museum", "date": "", "ddg_snippet": "The Crocker serves as the primary regional resource for the study and appreciation of fine art and offers a diverse spectrum of exhibitions, events, and programs.", "subpage_snippet": "", "source": "www.crockerart.org", "link": "https://www.crockerart.org/", "content": "The Crocker serves as the primary regional resource for the study and appreciation of fine art and offers a diverse spectrum of exhibitions, events, and programs."}
+{"idx": 6, "title": "The City of Crocker", "date": "", "ddg_snippet": "In the heart of the Ozarks, Crocker rests amidst forested hills and farmlands, the same natural beauty that first inspired the founding fathers to settle here. Crocker offers a peaceful rural setting and a carefully preserved heritage.", "subpage_snippet": "", "source": "crockercity.com", "link": "https://crockercity.com/", "content": "In the heart of the Ozarks, Crocker rests amidst forested hills and farmlands, the same natural beauty that first inspired the founding fathers to settle here. Crocker offers a peaceful rural setting and a carefully preserved heritage."}
+{"idx": 7, "title": "Crocker Art Museum | Culture, Victorian House & Teal Pavilion", "date": "", "ddg_snippet": "Explore the Crocker Art Museum in Sacramento, featuring California Impressionist art, German drawings, and antiquity.", "subpage_snippet": "", "source": "www.visitsacramento.com", "link": "https://www.visitsacramento.com/things-to-do/arts-and-entertainment/crocker-art-museum/", "content": "Explore the Crocker Art Museum in Sacramento, featuring California Impressionist art, German drawings, and antiquity."}
+{"idx": 8, "title": "Crocker Murders: Timeline details Georgia children's deaths", "date": "", "ddg_snippet": "Aug 29, 2025 · Crocker Timeline: Family members could be put to death for murders, burial of Effingham County kids It began with a welfare check in Guyton. It turned into a gruesome discovery: the bodies of two children found buried in their own backyard.", "subpage_snippet": "", "source": "www.wjcl.com", "link": "https://www.wjcl.com/article/crocker-murders-timeline-1756486268/65934413", "content": "Aug 29, 2025 · Crocker Timeline: Family members could be put to death for murders, burial of Effingham County kids It began with a welfare check in Guyton. It turned into a gruesome discovery: the bodies of two children found buried in their own backyard."}
+{"idx": 9, "title": "Plan Your Visit | Crocker Art Museum", "date": "", "ddg_snippet": "Experience innovative interactions with art at the Crocker Art Museum. With three floors and 15 unique gallery spaces to explore, discover a diverse collection of art that spans centuries, continents, and cultures. There is always something surprising to find at the Crocker !", "subpage_snippet": "", "source": "www.crockerart.org", "link": "https://www.crockerart.org/visit", "content": "Experience innovative interactions with art at the Crocker Art Museum. With three floors and 15 unique gallery spaces to explore, discover a diverse collection of art that spans centuries, continents, and cultures. There is always something surprising to find at the Crocker !"}
diff --git a/data/sampled_jsons/cross-reenactment_cross-identity_deepfake_detection_blending_arxiv.jsonl b/data/sampled_jsons/cross-reenactment_cross-identity_deepfake_detection_blending_arxiv.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..05a7b28d2bd597ffa6fdb961129c79d4dbf9d4a0
--- /dev/null
+++ b/data/sampled_jsons/cross-reenactment_cross-identity_deepfake_detection_blending_arxiv.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "CAST: Cross-Attentive Spatio-Temporal feature fusion for ...", "date": "", "ddg_snippet": "Jun 26, 2025 · In this study we presents CAST, a novel Cross -Attentive Spatio-Temporal feature fusion architecture tailored for robust deepfake video detection . Specifically, we introduce a cross -attention mechanism to effectively fuse spatial and temporal features extracted from a CNN-Transformer network.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.21711v1", "content": "Jun 26, 2025 · In this study we presents CAST, a novel Cross -Attentive Spatio-Temporal feature fusion architecture tailored for robust deepfake video detection . Specifically, we introduce a cross -attention mechanism to effectively fuse spatial and temporal features extracted from a CNN-Transformer network."}
+{"idx": 1, "title": "Generalizing Deepfake Video Detection with Plug-and-Play ...", "date": "", "ddg_snippet": "Most prior deepfake detectors [25, 43, 46, 60, 69] per-form well within the same dataset. However, they often fail to generalize well in cross -dataset scenarios where training and testing data distributions differ. Training with synthetic ( blending ) images appears to be one of the most effective solutions to this problem, as evidenced by [24, 44], which encourages detectors to learn generic ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Yan_Generalizing_Deepfake_Video_Detection_with_Plug-and-Play_Video-Level_Blending_and_Spatiotemporal_CVPR_2025_paper.pdf", "content": "Most prior deepfake detectors [25, 43, 46, 60, 69] per-form well within the same dataset. However, they often fail to generalize well in cross -dataset scenarios where training and testing data distributions differ. Training with synthetic ( blending ) images appears to be one of the most effective solutions to this problem, as evidenced by [24, 44], which encourages detectors to learn generic ..."}
+{"idx": 2, "title": "CAD: A General Multimodal Framework for Video Deepfake ... Robust cross-dataset deepfake detection with multitask self ... A Novel Multi-modality Deepfake Detection Using Cross-Model ... CAST: Cross-Attentive Spatio-Temporal feature fusion for ... [2504.17223] Towards Generalizable Deepfake Detection with ...", "date": "", "ddg_snippet": "May 21, 2025 · To address these shortcomings, we propose a general multimodal framework for video deepfake detection via Cross -Modal Alignment and Distillation (CAD). Mar 3, 2025 · To create robust deepfake detection models capable of generalizing across unseen manipulation techniques, we employ the Self- Blending Images (SBI) method as part of the data generation process. Jun 5, 2025 · Deepfake video detection is one of the advanced techniques used to detect deepfakes from videos based on video frames and audio. Many deepfake videos are created for malicious purposes, such as disseminating misinformation on social media platforms. While existing deep learning models have shown effectiveness in classifying deepfakes, they often encounter several challenges, including low ... Jun 26, 2025 · Download Citation | CAST: Cross -Attentive Spatio-Temporal feature fusion for Deepfake detection | Deepfakes have emerged as a significant threat to digital media authenticity, increasing the need ... Apr 24, 2025 · The rapid evolution of deep generative models poses a critical challenge to deepfake detection , as detectors trained on forgery-specific artifacts often suffer significant performance degradation when encountering unseen forgeries. While existing methods predominantly rely on spatial domain analysis, frequency domain operations are primarily limited to feature-level augmentation, leaving ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2505.15233", "content": "May 21, 2025 · To address these shortcomings, we propose a general multimodal framework for video deepfake detection via Cross -Modal Alignment and Distillation (CAD). Mar 3, 2025 · To create robust deepfake detection models capable of generalizing across unseen manipulation techniques, we employ the Self- Blending Images (SBI) method as part of the data generation process. Jun 5, 2025 · Deepfake video detection is one of the advanced techniques used to detect deepfakes from videos based on video frames and audio. Many deepfake videos are created for malicious purposes, such as disseminating misinformation on social media platforms. While existing deep learning models have shown effectiveness in classifying deepfakes, they often encounter several challenges, including low ... Jun 26, 2025 · Download Citation | CAST: Cross -Attentive Spatio-Temporal feature fusion for Deepfake detection | Deepfakes have emerged as a significant threat to digital media authenticity, increasing the need ... Apr 24, 2025 · The rapid evolution of deep generative models poses a critical challenge to deepfake detection , as detectors trained on forgery-specific artifacts often suffer significant performance degradation when encountering unseen forgeries. While existing methods predominantly rely on spatial domain analysis, frequency domain operations are primarily limited to feature-level augmentation, leaving ..."}
+{"idx": 3, "title": "Robust cross-dataset deepfake detection with multitask self ...", "date": "", "ddg_snippet": "Mar 3, 2025 · To create robust deepfake detection models capable of generalizing across unseen manipulation techniques, we employ the Self- Blending Images (SBI) method as part of the data generation process.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S240595952500027X", "content": "Mar 3, 2025 · To create robust deepfake detection models capable of generalizing across unseen manipulation techniques, we employ the Self- Blending Images (SBI) method as part of the data generation process."}
+{"idx": 4, "title": "A Novel Multi-modality Deepfake Detection Using Cross-Model ...", "date": "", "ddg_snippet": "Jun 5, 2025 · Deepfake video detection is one of the advanced techniques used to detect deepfakes from videos based on video frames and audio. Many deepfake videos are created for malicious purposes, such as disseminating misinformation on social media platforms. While existing deep learning models have shown effectiveness in classifying deepfakes, they often encounter several challenges, including low ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s12559-025-10465-7", "content": "Jun 5, 2025 · Deepfake video detection is one of the advanced techniques used to detect deepfakes from videos based on video frames and audio. Many deepfake videos are created for malicious purposes, such as disseminating misinformation on social media platforms. While existing deep learning models have shown effectiveness in classifying deepfakes, they often encounter several challenges, including low ..."}
+{"idx": 5, "title": "CAST: Cross-Attentive Spatio-Temporal feature fusion for ...", "date": "", "ddg_snippet": "Jun 26, 2025 · Download Citation | CAST: Cross -Attentive Spatio-Temporal feature fusion for Deepfake detection | Deepfakes have emerged as a significant threat to digital media authenticity, increasing the need ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/393148583_CAST_Cross-Attentive_Spatio-Temporal_feature_fusion_for_Deepfake_detection", "content": "Jun 26, 2025 · Download Citation | CAST: Cross -Attentive Spatio-Temporal feature fusion for Deepfake detection | Deepfakes have emerged as a significant threat to digital media authenticity, increasing the need ..."}
+{"idx": 6, "title": "[2504.17223] Towards Generalizable Deepfake Detection with ...", "date": "", "ddg_snippet": "Apr 24, 2025 · The rapid evolution of deep generative models poses a critical challenge to deepfake detection , as detectors trained on forgery-specific artifacts often suffer significant performance degradation when encountering unseen forgeries. While existing methods predominantly rely on spatial domain analysis, frequency domain operations are primarily limited to feature-level augmentation, leaving ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2504.17223", "content": "Apr 24, 2025 · The rapid evolution of deep generative models poses a critical challenge to deepfake detection , as detectors trained on forgery-specific artifacts often suffer significant performance degradation when encountering unseen forgeries. While existing methods predominantly rely on spatial domain analysis, frequency domain operations are primarily limited to feature-level augmentation, leaving ..."}
+{"idx": 7, "title": "Robust Deepfake Detection for Electronic Know Your ...", "date": "", "ddg_snippet": "30 Jul 2025 — The approach using identity vectors performs well on face swapping but struggles with face reenactment . Cozzolino et al. propose ID-Reveal ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.22601v1", "content": "30 Jul 2025 — The approach using identity vectors performs well on face swapping but struggles with face reenactment . Cozzolino et al. propose ID-Reveal ..."}
+{"idx": 8, "title": "Deepfake Detection that Generalizes Across Benchmarks", "date": "", "ddg_snippet": "8 Aug 2025 — The proposed method achieves state-of-the-art performance, outperforming more complex, recent approaches in average cross -dataset AUROC. Our ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.06248v1", "content": "8 Aug 2025 — The proposed method achieves state-of-the-art performance, outperforming more complex, recent approaches in average cross -dataset AUROC. Our ..."}
+{"idx": 9, "title": "A Controllable 3D Deepfake Generation Framework with ...", "date": "", "ddg_snippet": "6 days ago — These applications require real-time, cross-identity facial reenactment , where facial expressions and head poses are manipulated frame-by-frame.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.11624v1", "content": "6 days ago — These applications require real-time, cross-identity facial reenactment , where facial expressions and head poses are manipulated frame-by-frame."}
diff --git a/data/sampled_jsons/current_state,_goal_state_action,_goal_action_pairs_neural_network_Beyond_Optimism_year_2024.jsonl b/data/sampled_jsons/current_state,_goal_state_action,_goal_action_pairs_neural_network_Beyond_Optimism_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..291eda62dee94d0ffede5f3450c3a6dc9d5451e1
--- /dev/null
+++ b/data/sampled_jsons/current_state,_goal_state_action,_goal_action_pairs_neural_network_Beyond_Optimism_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Recurrent neural network - Wikipedia", "date": "", "ddg_snippet": "... unit , which maintains a hidden state —a form of memory that is updated at each time step based on the current input and the previous hidden state ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Recurrent_neural_network", "content": "... unit , which maintains a hidden state —a form of memory that is updated at each time step based on the current input and the previous hidden state ..."}
+{"idx": 1, "title": "Beyond Ground States: Physics-Inspired Optimization of Excited", "date": "", "ddg_snippet": "... state to remain in a product state and uses a gradient-based approach to find approximate solutions to large-scale quadratic unconstrained binary ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.12394v1", "content": "... state to remain in a product state and uses a gradient-based approach to find approximate solutions to large-scale quadratic unconstrained binary ..."}
+{"idx": 2, "title": "neural network - Why and when is deep reinforcement learning", "date": "", "ddg_snippet": "... optimal policy, it must balance exploration of all available actions for all states with exploiting what the Q-table says is the optimal action for a ...", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/50530546/why-and-when-is-deep-reinforcement-learning-needed-instead-of-q-learning", "content": "... optimal policy, it must balance exploration of all available actions for all states with exploiting what the Q-table says is the optimal action for a ..."}
+{"idx": 3, "title": "Goal-Directed and Habitual Control in Human Substance Use:", "date": "", "ddg_snippet": "Theories of addiction posit a deficit in goal -directed behavior and an increased propensity toward habitual actions in individuals with substance use ...", "subpage_snippet": "", "source": "karger.com", "link": "https://karger.com/nps/article/81/5/403/825536/Goal-Directed-and-Habitual-Control-in-Human", "content": "Theories of addiction posit a deficit in goal -directed behavior and an increased propensity toward habitual actions in individuals with substance use ..."}
+{"idx": 4, "title": "Frontiers | A Neural Network Framework for Cognitive Bias", "date": "", "ddg_snippet": "These principles are inherent to (all) neural networks which were originally optimized to perform concrete biological, perceptual, and motor ...", "subpage_snippet": "", "source": "www.frontiersin.org", "link": "https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2018.01561/full", "content": "These principles are inherent to (all) neural networks which were originally optimized to perform concrete biological, perceptual, and motor ..."}
+{"idx": 5, "title": "Beyond Optimism: Exploration With Partially Observable Rewards", "date": "", "ddg_snippet": "With this paper, we present a novel exploration strategy that overcomes the limitations of existing methods and guarantees convergence to an optimal policy even when rewards are not always observable.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.13909v2", "content": "With this paper, we present a novel exploration strategy that overcomes the limitations of existing methods and guarantees convergence to an optimal policy even when rewards are not always observable."}
+{"idx": 6, "title": "Exploration With Partially Observable Rewards", "date": "", "ddg_snippet": "For discrete actions, the network would take the ( current state, goal state ) pair and output the value for all ( action, goal action ) pairs , similarly to how ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/784fd5a46dfe303e5b51c8621b84cf3f-Paper-Conference.pdf", "content": "For discrete actions, the network would take the ( current state, goal state ) pair and output the value for all ( action, goal action ) pairs , similarly to how ..."}
+{"idx": 7, "title": "Exploration With Partially Observable Rewards", "date": "", "ddg_snippet": "by S Parisi · 2024 · Cited by 4 — For discrete actions, the network would take the ( current state, goal state ) ... ( action, goal action ) pairs , similarly to how deep Q- networks [48] ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2406.13909?", "content": "by S Parisi · 2024 · Cited by 4 — For discrete actions, the network would take the ( current state, goal state ) ... ( action, goal action ) pairs , similarly to how deep Q- networks [48] ..."}
+{"idx": 8, "title": "WO2010048146A1 - System, method and device for predicting", "date": "", "ddg_snippet": "... 26 — Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/WO2010048146A1/en", "content": "... 26 — Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network"}
+{"idx": 9, "title": "US20100106603A1 - System, method and device for predicting", "date": "", "ddg_snippet": "... 26 — Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/US20100106603A1/en", "content": "... 26 — Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network"}
diff --git a/data/sampled_jsons/curse_dimensionality_distance_concentration_sparsity_high_dimensional_space_year_2023.jsonl b/data/sampled_jsons/curse_dimensionality_distance_concentration_sparsity_high_dimensional_space_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c9de33c4d53d841d3e464ca3bd82da19373a400f
--- /dev/null
+++ b/data/sampled_jsons/curse_dimensionality_distance_concentration_sparsity_high_dimensional_space_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Curse of dimensionality - Wikipedia", "date": "", "ddg_snippet": "Machine learningand data mining. v. t. e. The curse of dimensionality refers to various phenomena that arise when analyzing and organizing data in high - dimensional spaces that do not occur in low- dimensional settings such as the three- dimensional phy...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Curse_of_dimensionality", "content": "Machine learningand data mining. v. t. e. The curse of dimensionality refers to various phenomena that arise when analyzing and organizing data in high - dimensional spaces that do not occur in low- dimensional settings such as the three- dimensional phy..."}
+{"idx": 1, "title": "What is Curse of Dimensionality in Machine Learning?", "date": "", "ddg_snippet": "Data Sparsity . Distance Concentration . Curse of Dimensionality refers to a set of problems that arise when working with high - dimensional data. The dimension of a dataset corresponds to the number of attributes/features that exist in a dataset.", "subpage_snippet": "", "source": "www.mygreatlearning.com", "link": "https://www.mygreatlearning.com/blog/understanding-curse-of-dimensionality/", "content": "Data Sparsity . Distance Concentration . Curse of Dimensionality refers to a set of problems that arise when working with high - dimensional data. The dimension of a dataset corresponds to the number of attributes/features that exist in a dataset."}
+{"idx": 2, "title": "Interpreting the Curse of Dimensionality from Distance ...", "date": "", "ddg_snippet": "20 Mar 2025 — Data sparsity in high-dimensional space causes the models overfitting and weakens the generalization performance [8] , [9] . To classify data ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2401.00422v3", "content": "20 Mar 2025 — Data sparsity in high-dimensional space causes the models overfitting and weakens the generalization performance [8] , [9] . To classify data ..."}
+{"idx": 3, "title": "The Curse of Dimensionality in Machine Learning", "date": "", "ddg_snippet": "The curse of dimensionality is a challenge in machine learning that occurs when we have too many characteristics (or \"dimensions\") to consider.", "subpage_snippet": "", "source": "zilliz.com", "link": "https://zilliz.com/glossary/curse-of-dimensionality-in-machine-learning", "content": "The curse of dimensionality is a challenge in machine learning that occurs when we have too many characteristics (or \"dimensions\") to consider."}
+{"idx": 4, "title": "The curse of dimensionality: when more data becomes ...", "date": "", "ddg_snippet": "The curse of dimensionality paradoxically undermines AI performance as data dimensions increase, creating mathematical conditions where distance metrics ...", "subpage_snippet": "", "source": "agathon.ai", "link": "https://agathon.ai/insights/the-curse-of-dimensionality:-when-more-data-becomes-your-enemy", "content": "The curse of dimensionality paradoxically undermines AI performance as data dimensions increase, creating mathematical conditions where distance metrics ..."}
+{"idx": 5, "title": "The Curse of Dimensionality in Machine Learning", "date": "", "ddg_snippet": "13 Sept 2023 — The Curse of Dimensionality refers to the various challenges and complications that arise when analyzing and organizing data in high - dimensional spaces.", "subpage_snippet": "", "source": "www.datacamp.com", "link": "https://www.datacamp.com/blog/curse-of-dimensionality-machine-learning", "content": "13 Sept 2023 — The Curse of Dimensionality refers to the various challenges and complications that arise when analyzing and organizing data in high - dimensional spaces."}
+{"idx": 6, "title": "Conquering the Curse of Dimensionality: How Deep ...", "date": "", "ddg_snippet": "In high - dimensional spaces , the distinction between the nearest and farthest neighbors diminishes, rendering these distance metrics less effective. Consequently ...", "subpage_snippet": "", "source": "www.lunartech.ai", "link": "https://www.lunartech.ai/blog/conquering-the-curse-of-dimensionality-how-deep-learning-transforms-machine-learning", "content": "In high - dimensional spaces , the distinction between the nearest and farthest neighbors diminishes, rendering these distance metrics less effective. Consequently ..."}
+{"idx": 7, "title": "Curse of Dimensionality", "date": "", "ddg_snippet": "24 Jun 2024 — The curse of dimensionality captures the essence of the challenge faced when dealing with high - dimensional data spaces .", "subpage_snippet": "", "source": "deepgram.com", "link": "https://deepgram.com/ai-glossary/curse-of-dimensionality", "content": "24 Jun 2024 — The curse of dimensionality captures the essence of the challenge faced when dealing with high - dimensional data spaces ."}
+{"idx": 8, "title": "Interpreting the Curse of Dimensionality from", "date": "", "ddg_snippet": "Keywords: Curse of dimensionality , distance concentration , manifold effect, data sparsity , dimension reduction. Distance measurement may be invalid in high - dimensional space due to the phe-nomenon of distance concentration [5], [11], [12], [13].", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2401.00422", "content": "Keywords: Curse of dimensionality , distance concentration , manifold effect, data sparsity , dimension reduction. Distance measurement may be invalid in high - dimensional space due to the phe-nomenon of distance concentration [5], [11], [12], [13]."}
+{"idx": 9, "title": "(PDF) Interpreting the Curse of Dimensionality from Distance ...", "date": "", "ddg_snippet": "learning tasks in high - dimensional space . Keywords: Curse of dimensionality , distance concentration , manifold effect, data sparsity , dimension reduction. 1 Introduction.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/377063922_Interpreting_the_Curse_of_Dimensionality_from_Distance_Concentration_and_Manifold_Effect", "content": "learning tasks in high - dimensional space . Keywords: Curse of dimensionality , distance concentration , manifold effect, data sparsity , dimension reduction. 1 Introduction."}
diff --git a/data/sampled_jsons/data-dependent_differential_privacy_machine_unlearning.jsonl b/data/sampled_jsons/data-dependent_differential_privacy_machine_unlearning.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7442b9986b7a3dbe75e1717895dc5a32127f419e
--- /dev/null
+++ b/data/sampled_jsons/data-dependent_differential_privacy_machine_unlearning.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Unveiling Privacy Risks in Machine Unlearning: Reconstruction", "date": "", "ddg_snippet": "Highlighting privacy risks in data deletion or machine unlearning , the findings emphasize the need for techniques like differential privacy .", "subpage_snippet": "", "source": "www.marktechpost.com", "link": "https://www.marktechpost.com/2024/12/27/unveiling-privacy-risks-in-machine-unlearning-reconstruction-attacks-on-deleted-data/", "content": "Highlighting privacy risks in data deletion or machine unlearning , the findings emphasize the need for techniques like differential privacy ."}
+{"idx": 1, "title": "Differential Privacy in Federated Learning: Mitigating", "date": "", "ddg_snippet": "Furthermore, recent literature also investigates machine unlearning techniques as a way to address privacy and compliance challenges in federated ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.13987v1", "content": "Furthermore, recent literature also investigates machine unlearning techniques as a way to address privacy and compliance challenges in federated ..."}
+{"idx": 2, "title": "Game-Theoretic Machine Unlearning: Mitigating Extra Privacy", "date": "", "ddg_snippet": "First, to satisfy privacy protection requirements, data removal conducted by machine unlearning usually reduces model performance [ 9 ] .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.03914v1", "content": "First, to satisfy privacy protection requirements, data removal conducted by machine unlearning usually reduces model performance [ 9 ] ."}
+{"idx": 3, "title": "Machine unlearning gets a practical privacy upgrade - Help Net", "date": "", "ddg_snippet": "... at the Universitat Rovira i Virgili in Catalonia, EUPG offers a practical way to forget data in machine learning models with provable privacy ...", "subpage_snippet": "", "source": "www.helpnetsecurity.com", "link": "https://www.helpnetsecurity.com/2025/07/17/machine-unlearning-privacy-upgrade/", "content": "... at the Universitat Rovira i Virgili in Catalonia, EUPG offers a practical way to forget data in machine learning models with provable privacy ..."}
+{"idx": 4, "title": "Infosys Knowledge Institute | Privacy in the Digital World", "date": "", "ddg_snippet": "The European Union and the state of California have already enacted the General Data Protection Regulation (GDPR) and California Consumer Privacy Act ...", "subpage_snippet": "", "source": "www.infosys.com", "link": "https://www.infosys.com/iki/perspectives/privacy-digital.html", "content": "The European Union and the state of California have already enacted the General Data Protection Regulation (GDPR) and California Consumer Privacy Act ..."}
+{"idx": 5, "title": "TPDP 2024 – Theory and Practice of Differential Privacy", "date": "", "ddg_snippet": "Next, I will cover data curation and potential intersections with differential privacy . ... Differential privacy and sublinear algorithms are both ...", "subpage_snippet": "", "source": "tpdp.journalprivacyconfidentiality.org", "link": "https://tpdp.journalprivacyconfidentiality.org/2024/", "content": "Next, I will cover data curation and potential intersections with differential privacy . ... Differential privacy and sublinear algorithms are both ..."}
+{"idx": 6, "title": "How to Use Heuristics for Differential Privacy - Article -", "date": "", "ddg_snippet": "How to Use Heuristics for Differential Privacy .\" Proceedings of the IEEE Annual Symposium on Foundations of Computer Science (FOCS) 60th (2019).", "subpage_snippet": "", "source": "www.hbs.edu", "link": "https://www.hbs.edu/faculty/Pages/item.aspx?num=60698", "content": "How to Use Heuristics for Differential Privacy .\" Proceedings of the IEEE Annual Symposium on Foundations of Computer Science (FOCS) 60th (2019)."}
+{"idx": 7, "title": "Publications – Privacy Technology Research Group", "date": "", "ddg_snippet": "Personalized Federated Learning With Differential Privacy and Convergence Guarantee Wei, Kang and Li, Jun and Ma, Chuan and Ding, Ming and Chen, Wen ...", "subpage_snippet": "", "source": "research.csiro.au", "link": "https://research.csiro.au/isp/research/publications/", "content": "Personalized Federated Learning With Differential Privacy and Convergence Guarantee Wei, Kang and Li, Jun and Ma, Chuan and Ding, Ming and Chen, Wen ..."}
+{"idx": 8, "title": "Machine Unlearning - by Bidhan Roy and Marcos Villagra", "date": "", "ddg_snippet": "This breakthrough ushers in a new paradigm of AI privacy , where machines learn, unlearn , and relearn with unprecedented flexibility.", "subpage_snippet": "", "source": "blog.bagel.com", "link": "https://blog.bagel.com/p/machine-unlearning", "content": "This breakthrough ushers in a new paradigm of AI privacy , where machines learn, unlearn , and relearn with unprecedented flexibility."}
+{"idx": 9, "title": "Belt and Braces: When Federated Learning Meets Differential", "date": "", "ddg_snippet": "Building federated learning with differential privacy to train and refine machine -learning models with more comprehensive datasets can help exploit ...", "subpage_snippet": "", "source": "cacm.acm.org", "link": "https://cacm.acm.org/research/belt-and-braces-when-federated-learning-meets-differential-privacy/", "content": "Building federated learning with differential privacy to train and refine machine -learning models with more comprehensive datasets can help exploit ..."}
diff --git a/data/sampled_jsons/data-dependent_privacy_machine_unlearning_before2024.jsonl b/data/sampled_jsons/data-dependent_privacy_machine_unlearning_before2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..da82882f45cfe48399e186485a90272161e9d575
--- /dev/null
+++ b/data/sampled_jsons/data-dependent_privacy_machine_unlearning_before2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Inexact Unlearning Needs More Careful Evaluations to Avoid a False...", "date": "", "ddg_snippet": "Instead, inexact machine unlearning has emerged as a research field that tries to approximate this gold standard solution whilst remaining cost and time efficient for the data controller. How one measures the quality of such an approximation is an open research problem.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.01218v1", "content": "Instead, inexact machine unlearning has emerged as a research field that tries to approximate this gold standard solution whilst remaining cost and time efficient for the data controller. How one measures the quality of such an approximation is an open research problem."}
+{"idx": 1, "title": "Advancing Differential Privacy : Where We Are Now and Future...", "date": "", "ddg_snippet": "Harvard Data Science Review • Issue 6.1, Winter 2024 . Advancing Di erential Privacy : Where We Are Now and Future Directions for Real-World Deployment. the standard ‘accuracy first’ setting, the privacy losses are data - dependent now, thus considered sensitive information.", "subpage_snippet": "", "source": "s3.amazonaws.com", "link": "https://s3.amazonaws.com/assets.pubpub.org/ki90cxv0c1p0h148qvqipnk3mxcxy2e2.pdf", "content": "Harvard Data Science Review • Issue 6.1, Winter 2024 . Advancing Di erential Privacy : Where We Are Now and Future Directions for Real-World Deployment. the standard ‘accuracy first’ setting, the privacy losses are data - dependent now, thus considered sensitive information."}
+{"idx": 2, "title": "(PDF) Challenges towards the Next Frontier in Privacy", "date": "", "ddg_snippet": "Data-adaptive DP algorithms via data - dependent DP losses: Recall that data-adaptive DP algo-. rithms aim at “adding a smaller amount of noise” when the dataset is “nice”.When machine unlearning jeopardizes privacy . In Proceedings of the 2021 ACM SIGSAC.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/370058733_Challenges_towards_the_Next_Frontier_in_Privacy?_share=1", "content": "Data-adaptive DP algorithms via data - dependent DP losses: Recall that data-adaptive DP algo-. rithms aim at “adding a smaller amount of noise” when the dataset is “nice”.When machine unlearning jeopardizes privacy . In Proceedings of the 2021 ACM SIGSAC."}
+{"idx": 3, "title": "gradients look alike: sensitivity is often overestimated in dp- ...", "date": "", "ddg_snippet": "by A Thudi · 2023 · Cited by 8 — every step to bound the overall data-dependent privacy leakage of a training run. ... Machine Unlearning . Given a training algorithm M, machine unlearning ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2307.00310", "content": "by A Thudi · 2023 · Cited by 8 — every step to bound the overall data-dependent privacy leakage of a training run. ... Machine Unlearning . Given a training algorithm M, machine unlearning ..."}
+{"idx": 4, "title": "(PDF) Gradients Look Alike: Sensitivity is Often Overestimated in...", "date": "", "ddg_snippet": "The proposed data - dependent privacy analysis for DP-SGD differs from the existing data-independent analysis in that it considers the sensitivity of an individual datapoint with respect to training on a specific dataset.", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/papers/gradients-look-alike-sensitivity-is-often-overestimated-in-3crroi85", "content": "The proposed data - dependent privacy analysis for DP-SGD differs from the existing data-independent analysis in that it considers the sensitivity of an individual datapoint with respect to training on a specific dataset."}
+{"idx": 5, "title": "Differentially Private Next-Token Prediction of Large Language Models", "date": "", "ddg_snippet": "Another promising privacy -related notion, machine unlearning , has emerged to reduce memorization by verifiably removing learned information from a data sample without retraining a model from scratch (Guo et al., 2019; Bourtoule et al., 2021).", "subpage_snippet": "", "source": "www.cs.jhu.edu", "link": "https://www.cs.jhu.edu/~kevinduh/t/naacl24/final_pdf/paper454.pdf", "content": "Another promising privacy -related notion, machine unlearning , has emerged to reduce memorization by verifiably removing learned information from a data sample without retraining a model from scratch (Guo et al., 2019; Bourtoule et al., 2021)."}
+{"idx": 6, "title": "Optimized Tradeoffs for Private Prediction with Majority", "date": "", "ddg_snippet": "(2018), machine unlearning Bourtoule et al. (2021), private distributed learning algorithms such as Stochastic Sign-SGD Xiang & Su (2023), and in ensemble feature selection Liu et al. Data - dependent Randomized Response Majority (DaRRM).", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=dwJluAakM8", "content": "(2018), machine unlearning Bourtoule et al. (2021), private distributed learning algorithms such as Stochastic Sign-SGD Xiang & Su (2023), and in ensemble feature selection Liu et al. Data - dependent Randomized Response Majority (DaRRM)."}
+{"idx": 7, "title": "Gradients Look Alike: Sensitivity is", "date": "", "ddg_snippet": "Currently, to obtain data -independent privacy guarantees, a model trainer needs to bound how much any individual datapoint from any dataset can contribute to a gradient update—a quantity known as the algorithm’s sensitivity. This is currently done by setting an.", "subpage_snippet": "", "source": "www.usenix.org", "link": "https://www.usenix.org/system/files/usenixsecurity24-thudi.pdf", "content": "Currently, to obtain data -independent privacy guarantees, a model trainer needs to bound how much any individual datapoint from any dataset can contribute to a gradient update—a quantity known as the algorithm’s sensitivity. This is currently done by setting an."}
+{"idx": 8, "title": "Shengyuan Hu - Meta | LinkedIn", "date": "", "ddg_snippet": "Machine unlearning is a promising approach to mitigate undesirable memorization of training data in ML models. However, in this work we show that existing approaches for unlearning in LLMs are surprisingly susceptible to a simple set of targeted relearning attacks.", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/in/shengyuan-hu-6803a0126", "content": "Machine unlearning is a promising approach to mitigate undesirable memorization of training data in ML models. However, in this work we show that existing approaches for unlearning in LLMs are surprisingly susceptible to a simple set of targeted relearning attacks."}
+{"idx": 9, "title": "Communication Efficient and Differentially", "date": "", "ddg_snippet": "We introduce the Data - dependent Randomized Response Majority (DaRRM) framework, which generalizes all private majority ensembling algorithms through a data - dependent noise function.", "subpage_snippet": "", "source": "11hifish.github.io", "link": "https://11hifish.github.io/content/thesis_proposal.pdf", "content": "We introduce the Data - dependent Randomized Response Majority (DaRRM) framework, which generalizes all private majority ensembling algorithms through a data - dependent noise function."}
diff --git a/data/sampled_jsons/deceptive_strategies_multi-agent_reinforcement_learning_year_2023.jsonl b/data/sampled_jsons/deceptive_strategies_multi-agent_reinforcement_learning_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4171377d25fa454b5ef0adbe367886d68a94fde1
--- /dev/null
+++ b/data/sampled_jsons/deceptive_strategies_multi-agent_reinforcement_learning_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Novel Multi-Agent Action Masked Deep Reinforcement Learning for", "date": "", "ddg_snippet": "Artificial intelligence in industrial engineering, Autonomous decision making, Distributed multi - agent learning , Reinforcement learning .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.16635v1", "content": "Artificial intelligence in industrial engineering, Autonomous decision making, Distributed multi - agent learning , Reinforcement learning ."}
+{"idx": 1, "title": "Language Agents with Reinforcement Learning for Strategic Play", "date": "", "ddg_snippet": "To build strategic language agents for the Werewolf game, we propose a novel framework that powers LLM-based agents with reinforcement learning (RL ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2310.18940v4", "content": "To build strategic language agents for the Werewolf game, we propose a novel framework that powers LLM-based agents with reinforcement learning (RL ..."}
+{"idx": 2, "title": "A Survey of Multi Agent Reinforcement Learning Federated", "date": "", "ddg_snippet": "Decentralized Multi - Agent Reinforcement Learning (DMARL) : Here, agents cooperate without a central server, communicating directly peer-to-peer.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.06278v1", "content": "Decentralized Multi - Agent Reinforcement Learning (DMARL) : Here, agents cooperate without a central server, communicating directly peer-to-peer."}
+{"idx": 3, "title": "[2410.17351] Hierarchical Multi-agent Reinforcement Learning", "date": "", "ddg_snippet": "Abstract: Recent advances in multi - agent reinforcement learning (MARL) have created opportunities to solve complex real-world tasks.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.17351", "content": "Abstract: Recent advances in multi - agent reinforcement learning (MARL) have created opportunities to solve complex real-world tasks."}
+{"idx": 4, "title": "Language Agents with Reinforcement Learning for Strategic Play", "date": "", "ddg_snippet": "To build strategic language agents for the Werewolf game, we propose a novel framework that powers LLM-based agents with reinforcement learning (RL ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2310.18940v3", "content": "To build strategic language agents for the Werewolf game, we propose a novel framework that powers LLM-based agents with reinforcement learning (RL ..."}
+{"idx": 5, "title": "Hierarchical Multi-agent Reinforcement Learning for Cyber", "date": "", "ddg_snippet": "Recent advances in multi - agent reinforcement learning (MARL) have createdopportunities to solve complex real-world tasks.", "subpage_snippet": "", "source": "deeplearn.org", "link": "https://deeplearn.org/arxiv/540872/hierarchical-multi-agent-reinforcement-learning-for-cyber-network-defense", "content": "Recent advances in multi - agent reinforcement learning (MARL) have createdopportunities to solve complex real-world tasks."}
+{"idx": 6, "title": "Deceptive Behavior in Advanced Artificial Intelligence Systems", "date": "", "ddg_snippet": "Key Research Findings 3.1 Meta’s Negotiation AI (2022) Source: \"Emergent Deception and Cooperation in Multi - Agent Reinforcement Learning \" (Meta ...", "subpage_snippet": "", "source": "cypherage.com", "link": "https://cypherage.com/t-deceptive-behavior-in-advanced-artificial-intelligence-systems", "content": "Key Research Findings 3.1 Meta’s Negotiation AI (2022) Source: \"Emergent Deception and Cooperation in Multi - Agent Reinforcement Learning \" (Meta ..."}
+{"idx": 7, "title": "Hierarchical Multi-agent Reinforcement Learning for Cyber", "date": "", "ddg_snippet": "Recent advances in multi - agent reinforcement learning (MARL) have created opportunities to solve complex real-world tasks.", "subpage_snippet": "", "source": "ethanrathbun.com", "link": "https://ethanrathbun.com/2024/10/28/hierarchical-multi-agent-reinforcement-learning-for-cyber-network-defense/", "content": "Recent advances in multi - agent reinforcement learning (MARL) have created opportunities to solve complex real-world tasks."}
+{"idx": 8, "title": "Exploration Strategies in Deep Reinforcement Learning |", "date": "", "ddg_snippet": "Boltzmann exploration : The agent draws actions from a boltzmann distribution (softmax) over the learned Q values, regulated by a temperature ...", "subpage_snippet": "", "source": "lilianweng.github.io", "link": "https://lilianweng.github.io/posts/2020-06-07-exploration-drl/", "content": "Boltzmann exploration : The agent draws actions from a boltzmann distribution (softmax) over the learned Q values, regulated by a temperature ..."}
+{"idx": 9, "title": "Simon Stepputtis | Publications", "date": "", "ddg_snippet": "This paper introduces a novel transfer learning framework for deep multi - agent reinforcement learning . ... learning for complex multi - agent transfer ...", "subpage_snippet": "", "source": "simonstepputtis.com", "link": "https://simonstepputtis.com/publications/", "content": "This paper introduces a novel transfer learning framework for deep multi - agent reinforcement learning . ... learning for complex multi - agent transfer ..."}
diff --git a/data/sampled_jsons/difference_between_circuit_fingerprinting_attacks_and_anomalous_circuit_detection_Tor.jsonl b/data/sampled_jsons/difference_between_circuit_fingerprinting_attacks_and_anomalous_circuit_detection_Tor.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..af3367efb5c6161c761e9f1546882645e496b515
--- /dev/null
+++ b/data/sampled_jsons/difference_between_circuit_fingerprinting_attacks_and_anomalous_circuit_detection_Tor.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Tor (network)", "date": "", "ddg_snippet": "Tor is a free overlay network for enabling anonymous communication. It is built on free and open-source software run by over seven thousand ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Tor_(network)", "content": "Tor is a free overlay network for enabling anonymous communication. It is built on free and open-source software run by over seven thousand ..."}
+{"idx": 1, "title": "Exposing Malicious Accomplices in Tor via Anomalous ...", "date": "", "ddg_snippet": "by Y Yao — We utilize the unique fingerprints of the Entry-Exit pair to conduct anomalous circuit detection. Among the two types of anomalous circuits mentioned, Routing ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=qcnePVejeV", "content": "by Y Yao — We utilize the unique fingerprints of the Entry-Exit pair to conduct anomalous circuit detection. Among the two types of anomalous circuits mentioned, Routing ..."}
+{"idx": 2, "title": "Exposing Malicious Accomplices in Tor via Anomalous Circuit ...", "date": "", "ddg_snippet": "by Y Yao · 2025 — This paper presents a novel approach for detecting anomalous circuits in the Tor network, and for the first time provides a more comprehensive identification ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3696410.3714767", "content": "by Y Yao · 2025 — This paper presents a novel approach for detecting anomalous circuits in the Tor network, and for the first time provides a more comprehensive identification ..."}
+{"idx": 3, "title": "Towards Predicting Efficient and Anonymous Tor Circuits", "date": "", "ddg_snippet": "by A Barton · 2018 · Cited by 25 — CLASI is the first anonymity metric in Tor that measures an adver- sary's ability to infer client Autonomous Systems (ASes) by fingerprinting ...", "subpage_snippet": "", "source": "www.usenix.org", "link": "https://www.usenix.org/system/files/conference/usenixsecurity18/sec18-barton.pdf", "content": "by A Barton · 2018 · Cited by 25 — CLASI is the first anonymity metric in Tor that measures an adver- sary's ability to infer client Autonomous Systems (ASes) by fingerprinting ..."}
+{"idx": 4, "title": "Tor network anonymity evaluation based on node anonymity", "date": "", "ddg_snippet": "by J Cui · 2023 · Cited by 6 — This enables us to promptly detect any abnormal states indicating attacks on nodes, thereby maintaining the anonymity of individuals in ...", "subpage_snippet": "", "source": "cybersecurity.springeropen.com", "link": "https://cybersecurity.springeropen.com/articles/10.1186/s42400-023-00191-8", "content": "by J Cui · 2023 · Cited by 6 — This enables us to promptly detect any abnormal states indicating attacks on nodes, thereby maintaining the anonymity of individuals in ..."}
+{"idx": 5, "title": "A Method for Identifying Tor Users Visiting Websites Based ...", "date": "", "ddg_snippet": "31 Jan 2022 — Website fingerprinting attacks can identify the websites that users are visiting to discern whether they are performing illegal operations.", "subpage_snippet": "", "source": "onlinelibrary.wiley.com", "link": "https://onlinelibrary.wiley.com/doi/10.1155/2022/3306098", "content": "31 Jan 2022 — Website fingerprinting attacks can identify the websites that users are visiting to discern whether they are performing illegal operations."}
+{"idx": 6, "title": "Using Traffic Analysis for Tor-based Malware Detection", "date": "", "ddg_snippet": "by P Dodia · 2022 · Cited by 41 — Differences between benign and malicious Tor traffic arise from. (1) server traffic fingerprints (patterns, burstiness, lifetime of con- nections, frequency, ... 15 pages", "subpage_snippet": "", "source": "coeus.ece.gatech.edu", "link": "https://coeus.ece.gatech.edu/articles/CCS22.pdf", "content": "by P Dodia · 2022 · Cited by 41 — Differences between benign and malicious Tor traffic arise from. (1) server traffic fingerprints (patterns, burstiness, lifetime of con- nections, frequency, ... 15 pages"}
+{"idx": 7, "title": "Advanced Deep Keyword Fingerprinting Attacks and ...", "date": "", "ddg_snippet": "24 Aug 2025 — One such attack is Website Fingerprinting (WF), which identifies the websites visited by users based on the analysis of Tor traffic metadata, ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3708821.3733914", "content": "24 Aug 2025 — One such attack is Website Fingerprinting (WF), which identifies the websites visited by users based on the analysis of Tor traffic metadata, ..."}
+{"idx": 8, "title": "Attacks-on-Tor/Attacks-on-Tor: Thirteen Years of Tor Attacks", "date": "", "ddg_snippet": "... circuit fingerprinting an attacker can distinguish the regular from suspicious circuits . Next, the attacker can apply a form of website fingerprinting to ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Attacks-on-Tor/Attacks-on-Tor", "content": "... circuit fingerprinting an attacker can distinguish the regular from suspicious circuits . Next, the attacker can apply a form of website fingerprinting to ..."}
+{"idx": 9, "title": "One Cell is Enough to Break Tor's Anonymity", "date": "", "ddg_snippet": "by X Fu · Cited by 73 — Different from existing attacks , these attacks can confirm anonymous communication relationships quickly and accurately by manipulating one single cell and pose ...", "subpage_snippet": "", "source": "www.blackhat.com", "link": "https://www.blackhat.com/presentations/bh-dc-09/Fu/BlackHat-DC-09-Fu-Break-Tors-Anonymity.pdf", "content": "by X Fu · Cited by 73 — Different from existing attacks , these attacks can confirm anonymous communication relationships quickly and accurately by manipulating one single cell and pose ..."}
diff --git a/data/sampled_jsons/diffusion_model_reward_estimation_challenges_non-differentiable.jsonl b/data/sampled_jsons/diffusion_model_reward_estimation_challenges_non-differentiable.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..1c957e3f035d79ba854eefb658f3757d338e4174
--- /dev/null
+++ b/data/sampled_jsons/diffusion_model_reward_estimation_challenges_non-differentiable.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Non-differentiable Reward Optimization for Diffusion-based", "date": "", "ddg_snippet": "To address this challenge , we propose a non - differentiable reward formulation with a dynamic thresholding algorithm that stabilizes learning and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.12977v1", "content": "To address this challenge , we propose a non - differentiable reward formulation with a dynamic thresholding algorithm that stabilizes learning and ..."}
+{"idx": 1, "title": "Confronting Reward Overoptimization for Diffusion Models: A", "date": "", "ddg_snippet": "... italic_t , it performs a one-step denoising using the current diffusion model parameterized by θ 𝜃 \\theta italic_θ , estimates a temporal reward ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2402.08552v2", "content": "... italic_t , it performs a one-step denoising using the current diffusion model parameterized by θ 𝜃 \\theta italic_θ , estimates a temporal reward ..."}
+{"idx": 2, "title": "Fine-Tuning Discrete Diffusion Models via Reward Optimization", "date": "", "ddg_snippet": "... that enables direct backpropagation of rewards through entire trajectories generated by diffusion models , by making the originally non - differentiable ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.13643v2", "content": "... that enables direct backpropagation of rewards through entire trajectories generated by diffusion models , by making the originally non - differentiable ..."}
+{"idx": 3, "title": "A Reward-Directed Diffusion Framework for Generative Design", "date": "", "ddg_snippet": "Empirical results indicate that this iterative reward -directed method substantially improves the diffusion model ’s ability to generate samples with ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.01509v1", "content": "Empirical results indicate that this iterative reward -directed method substantially improves the diffusion model ’s ability to generate samples with ..."}
+{"idx": 4, "title": "G2D2: Gradient-Guided Discrete Diffusion for Inverse Problem", "date": "", "ddg_snippet": "To address the fundamental challenge of non -differentiability in discrete diffusion models , we propose Gradient-Guided Discrete Diffusion ( G2D2 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.14710v2", "content": "To address the fundamental challenge of non -differentiability in discrete diffusion models , we propose Gradient-Guided Discrete Diffusion ( G2D2 ..."}
+{"idx": 5, "title": "ICLR 2024 Schedule", "date": "", "ddg_snippet": "Navigating the Design Space of Equivariant Diffusion -Based Generative Models for De Novo 3D Molecule Generation ... Differential Equations for ...", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2024/calendar", "content": "Navigating the Design Space of Equivariant Diffusion -Based Generative Models for De Novo 3D Molecule Generation ... Differential Equations for ..."}
+{"idx": 6, "title": "ICML 2020 Papers", "date": "", "ddg_snippet": "... Models by Mutual Information Neural Estimation ... Fast and Consistent Learning of Hidden Markov Models by Incorporating Non -Consecutive Correlations", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2020/papers.html?filter=keywords", "content": "... Models by Mutual Information Neural Estimation ... Fast and Consistent Learning of Hidden Markov Models by Incorporating Non -Consecutive Correlations"}
+{"idx": 7, "title": "ICML 2020 Papers", "date": "", "ddg_snippet": "... Models by Mutual Information Neural Estimation ... Fast and Consistent Learning of Hidden Markov Models by Incorporating Non -Consecutive Correlations", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2020/papers.html", "content": "... Models by Mutual Information Neural Estimation ... Fast and Consistent Learning of Hidden Markov Models by Incorporating Non -Consecutive Correlations"}
+{"idx": 8, "title": "CVPR 2024 Papers", "date": "", "ddg_snippet": "... Diffusion : Predicate Logic-Based ... Draw Step by Step: Reconstructing CAD Construction Sequences from Point Clouds via Multimodal Diffusion .", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2024/papers.html", "content": "... Diffusion : Predicate Logic-Based ... Draw Step by Step: Reconstructing CAD Construction Sequences from Point Clouds via Multimodal Diffusion ."}
+{"idx": 9, "title": "CVPR 2023 Schedule", "date": "", "ddg_snippet": "The 6th Workshop and Prize Challenge Bridging the Gap between Computational Photography and Visual Recognition (UG2+) in conjunction with IEEE CVPR ...", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2023/calendar", "content": "The 6th Workshop and Prize Challenge Bridging the Gap between Computational Photography and Visual Recognition (UG2+) in conjunction with IEEE CVPR ..."}
diff --git a/data/sampled_jsons/drift_term_stochastic_differential_equation_motion_synthesis_t=999_1000_steps_year_2024.jsonl b/data/sampled_jsons/drift_term_stochastic_differential_equation_motion_synthesis_t=999_1000_steps_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f5f44b530e72e4fff23f31d0296ee49c06f5c13d
--- /dev/null
+++ b/data/sampled_jsons/drift_term_stochastic_differential_equation_motion_synthesis_t=999_1000_steps_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Drift parameter estimation for nonlinear stochastic differential ...", "date": "", "ddg_snippet": "A singular stochastic differential equation driven by fractional Brownian motion .In this paper, a family of estimators for an unknown parameter in the drift term of a scalar linear stochastic differential equation is given.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/386694148_Drift_parameter_estimation_for_nonlinear_stochastic_differential_equations_driven_by_fractional_Brownian_motion", "content": "A singular stochastic differential equation driven by fractional Brownian motion .In this paper, a family of estimators for an unknown parameter in the drift term of a scalar linear stochastic differential equation is given."}
+{"idx": 1, "title": "stochastic processes - Geometric Brownian motion - Volatility...", "date": "", "ddg_snippet": "Geometric Brownian motion - Volatility Interpretation (in the drift term ). Ask Question. Asked 11 years, 5 months ago.The convexity of the exponential function of the stochastic variable $W$ makes its expectation greater than the exponentiation of the expectation of $W$.", "subpage_snippet": "", "source": "quant.stackexchange.com", "link": "https://quant.stackexchange.com/questions/10681/geometric-brownian-motion-volatility-interpretation-in-the-drift-term", "content": "Geometric Brownian motion - Volatility Interpretation (in the drift term ). Ask Question. Asked 11 years, 5 months ago.The convexity of the exponential function of the stochastic variable $W$ makes its expectation greater than the exponentiation of the expectation of $W$."}
+{"idx": 2, "title": "21. Stochastic Differential Equations - YouTube", "date": "", "ddg_snippet": "About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How YouTube works Test new features.", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=qdbkvD4N-us", "content": "About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How YouTube works Test new features."}
+{"idx": 3, "title": "(PDF) Parameter Estimation of Stochastic Differential Equation", "date": "", "ddg_snippet": "Modification of two- step method in estimating the parameters of stochastic differential equation models.", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/107600399/Parameter_Estimation_of_Stochastic_Differential_Equation", "content": "Modification of two- step method in estimating the parameters of stochastic differential equation models."}
+{"idx": 4, "title": "dg. differential geometry - Intuition for the Drift Term of... - MathOverflow", "date": "", "ddg_snippet": "of the (Ito) stochastic differential equation defining Brownian motion on $(M,g)$. Question: What is the geometric meaning of this $\\mu$? I would really like some intuition as to how the geometry generates this term (i.e. how to interpret it geometrically).", "subpage_snippet": "", "source": "mathoverflow.net", "link": "https://mathoverflow.net/questions/273580/intuition-for-the-drift-term-of-the-laplace-beltrami-operator", "content": "of the (Ito) stochastic differential equation defining Brownian motion on $(M,g)$. Question: What is the geometric meaning of this $\\mu$? I would really like some intuition as to how the geometry generates this term (i.e. how to interpret it geometrically)."}
+{"idx": 5, "title": "(PDF) Maximum Likelihood Estimation for Stochastic Differential ...", "date": "", "ddg_snippet": "TL;DR: In this article, the authors consider a stochastic differential equation with a drift term depending on a random variable and give the expression of the exact likelihood, which is consistent and asymptotically Gaussian when the drift term depends linearly on the random effect.", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/papers/maximum-likelihood-estimation-for-stochastic-differential-3nis33fq96", "content": "TL;DR: In this article, the authors consider a stochastic differential equation with a drift term depending on a random variable and give the expression of the exact likelihood, which is consistent and asymptotically Gaussian when the drift term depends linearly on the random effect."}
+{"idx": 6, "title": "Infinite ergodicity in generalized geometric Brownian motions with...", "date": "", "ddg_snippet": "The stochastic differential equation is now given by.The next step consists in studying whether the asymptotic function also has a meaning if the normalization is not pos-sible, e.g. in the Fisk-Stratonovich case, α = 1/2.", "subpage_snippet": "", "source": "hal.science", "link": "https://hal.science/hal-04086877/document", "content": "The stochastic differential equation is now given by.The next step consists in studying whether the asymptotic function also has a meaning if the normalization is not pos-sible, e.g. in the Fisk-Stratonovich case, α = 1/2."}
+{"idx": 7, "title": "Parameter Estimation of Stochastic Differential Equation", "date": "", "ddg_snippet": "Two- Step Method in Stochastic Differential Equation (SDE).parameters of the drift and diffusion term in SDE in the second step . Two Step Method: The First Step . The general equation of regression splines with truncated power series basis is", "subpage_snippet": "", "source": "core.ac.uk", "link": "https://core.ac.uk/download/pdf/11494552.pdf", "content": "Two- Step Method in Stochastic Differential Equation (SDE).parameters of the drift and diffusion term in SDE in the second step . Two Step Method: The First Step . The general equation of regression splines with truncated power series basis is"}
+{"idx": 8, "title": "A Gentle Introduction to Geometric Brownian Motion in Finance", "date": "", "ddg_snippet": "Equation 21 — Differential Equation of Continuous Time Growth with Random Component. This will be a stochastic process and the most commonly used process, especially within financial markets, is the Brownian Motion .", "subpage_snippet": "", "source": "readmedium.com", "link": "https://readmedium.com/a-gentle-introduction-to-geometric-brownian-motion-in-finance-68c37ba6f828", "content": "Equation 21 — Differential Equation of Continuous Time Growth with Random Component. This will be a stochastic process and the most commonly used process, especially within financial markets, is the Brownian Motion ."}
+{"idx": 9, "title": "Brownian Motions and Quantifying Randomness in Physical Systems", "date": "", "ddg_snippet": "Stochastic differential equations (SDEs). A typical stochastic differential equation for a random process ##X( t )## is.", "subpage_snippet": "", "source": "www.physicsforums.com", "link": "https://www.physicsforums.com/insights/brownian-motions-and-quantifying-randomness-in-physical-systems/", "content": "Stochastic differential equations (SDEs). A typical stochastic differential equation for a random process ##X( t )## is."}
diff --git a/data/sampled_jsons/du_Plessis_et_al._2017_weakly_supervised_learning_year_2017.jsonl b/data/sampled_jsons/du_Plessis_et_al._2017_weakly_supervised_learning_year_2017.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..88de449836ff217acbc5b1fa7bdcb3d0f451237a
--- /dev/null
+++ b/data/sampled_jsons/du_Plessis_et_al._2017_weakly_supervised_learning_year_2017.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Guiding workplace learning in vocational education and", "date": "", "ddg_snippet": "Alternatively, one can regard all learning experiences as intentional because they aim at ensuring the continuity of social and work practices ...", "subpage_snippet": "", "source": "ervet-journal.springeropen.com", "link": "https://ervet-journal.springeropen.com/articles/10.1186/s40461-017-0053-4", "content": "Alternatively, one can regard all learning experiences as intentional because they aim at ensuring the continuity of social and work practices ..."}
+{"idx": 1, "title": "Graduate Academics - University of Denver", "date": "", "ddg_snippet": "At the University of Denver, graduate education is defined by nationally renowned programs, world-leading faculty, and unsurpassed professional development opportunities. Offering small cohorts and hands-on experiential learning, DU sets you up for success—over 80% of master's and doctoral ...", "subpage_snippet": "", "source": "www.du.edu", "link": "https://www.du.edu/academics/graduate-academics", "content": "At the University of Denver, graduate education is defined by nationally renowned programs, world-leading faculty, and unsurpassed professional development opportunities. Offering small cohorts and hands-on experiential learning, DU sets you up for success—over 80% of master's and doctoral ..."}
+{"idx": 2, "title": "Items where Subject is \"J Health care, prevention, harm", "date": "", "ddg_snippet": "West, Robert and Cox, Sharon and Notley, Caitlin Jade and Du Plessis , Guy and Hastings, Janna (2024) Achieving consensus, coherence, clarity and ...", "subpage_snippet": "", "source": "www.drugsandalcohol.ie", "link": "https://www.drugsandalcohol.ie/view/subjects/JA2.html", "content": "West, Robert and Cox, Sharon and Notley, Caitlin Jade and Du Plessis , Guy and Hastings, Janna (2024) Achieving consensus, coherence, clarity and ..."}
+{"idx": 3, "title": "The life stories and experiences of the children admitted to", "date": "", "ddg_snippet": "... asylum, from 1890 to 1907, and he was appointed as the surgeon-superintendent of the Chronic Sick Hospital from 1890 to March 1903 ( Du Plessis 2017 ...", "subpage_snippet": "", "source": "ajod.org", "link": "https://ajod.org/index.php/ajod/article/view/669/1401", "content": "... asylum, from 1890 to 1907, and he was appointed as the surgeon-superintendent of the Chronic Sick Hospital from 1890 to March 1903 ( Du Plessis 2017 ..."}
+{"idx": 4, "title": "Teaching analytics, value and tools for teacher data literacy:", "date": "", "ddg_snippet": "... areas in LA, include student retention, predicting students at-risk, personalised learning which in turn are highly student-driven ( Beer et al .", "subpage_snippet": "", "source": "educationaltechnologyjournal.springeropen.com", "link": "https://educationaltechnologyjournal.springeropen.com/articles/10.1186/s41239-020-00201-6", "content": "... areas in LA, include student retention, predicting students at-risk, personalised learning which in turn are highly student-driven ( Beer et al ."}
+{"idx": 5, "title": "Embracing the future of Artificial Intelligence in the", "date": "", "ddg_snippet": "... timely interventions for children with special educational needs, enriching both their learning experiences and daily life (Zawacki-Richter et al ...", "subpage_snippet": "", "source": "educationaltechnologyjournal.springeropen.com", "link": "https://educationaltechnologyjournal.springeropen.com/articles/10.1186/s41239-024-00448-3", "content": "... timely interventions for children with special educational needs, enriching both their learning experiences and daily life (Zawacki-Richter et al ..."}
+{"idx": 6, "title": "Frontiers | Uncrewed surface vehicles in the Global Ocean", "date": "", "ddg_snippet": "Burger 3 James Burris 14 Lionel Camus 15 Brad de Young 16 Marcel du Plessis 4 Mike Flanigan 17 Gregory R. ... 25 Marine Mammal Laboratory, Alaska ...", "subpage_snippet": "", "source": "www.frontiersin.org", "link": "https://www.frontiersin.org/journals/marine-science/articles/10.3389/fmars.2025.1523585/full", "content": "Burger 3 James Burris 14 Lionel Camus 15 Brad de Young 16 Marcel du Plessis 4 Mike Flanigan 17 Gregory R. ... 25 Marine Mammal Laboratory, Alaska ..."}
+{"idx": 7, "title": "Laminin γ1-dependent basement membranes are instrumental to", "date": "", "ddg_snippet": "... nervous system, Laminin is important for neuro-epithelial morphogenesis ( Bryan et al ., 2016 ; Ivanovitch et al ., 2013 ; Sidhaye and Norden, 2017 ...", "subpage_snippet": "", "source": "elifesciences.org", "link": "https://elifesciences.org/reviewed-preprints/92004v2", "content": "... nervous system, Laminin is important for neuro-epithelial morphogenesis ( Bryan et al ., 2016 ; Ivanovitch et al ., 2013 ; Sidhaye and Norden, 2017 ..."}
+{"idx": 8, "title": "Desmodium Volatiles in “Push-Pull” Cropping Systems and", "date": "", "ddg_snippet": "... Pest Management (IPM) strategies, such as the promotion of natural enemies, are desirable ( Nyamutukwa et al ., 2022 ; Van den Berg and du Plessis ...", "subpage_snippet": "", "source": "elifesciences.org", "link": "https://elifesciences.org/reviewed-preprints/100981", "content": "... Pest Management (IPM) strategies, such as the promotion of natural enemies, are desirable ( Nyamutukwa et al ., 2022 ; Van den Berg and du Plessis ..."}
+{"idx": 9, "title": "Hyperbolic Genome Embeddings", "date": "", "ddg_snippet": "... learning of genome sequences has enabled the exploration of critical unsolved problems in biology, particularly the understanding of genome function ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.21648v1", "content": "... learning of genome sequences has enabled the exploration of critical unsolved problems in biology, particularly the understanding of genome function ..."}
diff --git a/data/sampled_jsons/entropy_guided_sampling_DDIM_deliberate_practice_framework.jsonl b/data/sampled_jsons/entropy_guided_sampling_DDIM_deliberate_practice_framework.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d81dbaf185d4107f5795a49c1a1e22bbb00f3ab6
--- /dev/null
+++ b/data/sampled_jsons/entropy_guided_sampling_DDIM_deliberate_practice_framework.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Improving the Scaling Laws of Synthetic Data with Deliberate Practice", "date": "", "ddg_snippet": "Problem Setup. The Need for Informative Examples. Approximate Sampling of Informative Examples. 2.1 Efficient Entropy-Guided Sampling with DDIM . 3 The deliberate Practice Framework for Synthetic Data Generation 4 Training on informative examples improves the scaling laws 4.1 Theoretical Analysis under an Idealized Setup.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.15588v1", "content": "Problem Setup. The Need for Informative Examples. Approximate Sampling of Informative Examples. 2.1 Efficient Entropy-Guided Sampling with DDIM . 3 The deliberate Practice Framework for Synthetic Data Generation 4 Training on informative examples improves the scaling laws 4.1 Theoretical Analysis under an Idealized Setup."}
+{"idx": 1, "title": "Entropy-Driven Sampling and Training Scheme for Conditional ... - Springer", "date": "", "ddg_snippet": "Fig. 2. Pipeline for Entropy -driven Sampling process. Sampler represents a class of iteration method (DDPM or DDIM ), which is non-parametric. All models are pretrained without gradient updating in the sampling process.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-3-031-20047-2_43", "content": "Fig. 2. Pipeline for Entropy -driven Sampling process. Sampler represents a class of iteration method (DDPM or DDIM ), which is non-parametric. All models are pretrained without gradient updating in the sampling process."}
+{"idx": 2, "title": "Improving the Scaling Laws of Synthetic Data with Deliberate Practice", "date": "", "ddg_snippet": "Inspired by the principle of deliberate prac - tice in human learning, we propose Deliberate Practice for Synthetic Data Generation (DP), a novel framework that improves sample efficiency through dynamic synthetic data generation. Prior work has shown that scaling synthetic data is in- herently challenging, as naively adding new data leads to diminishing returns. To address this, prun- ing has ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=0LZRtvK871", "content": "Inspired by the principle of deliberate prac - tice in human learning, we propose Deliberate Practice for Synthetic Data Generation (DP), a novel framework that improves sample efficiency through dynamic synthetic data generation. Prior work has shown that scaling synthetic data is in- herently challenging, as naively adding new data leads to diminishing returns. To address this, prun- ing has ..."}
+{"idx": 3, "title": "ermongroup/ddim | DeepWiki", "date": "", "ddg_snippet": "DDIM is a generative model framework introduced by Song, Meng, and Ermon that enables much faster sampling compared to traditional DDPMs. While DDPMs require hundreds to thousands of steps to generate high-quality samples, DDIM can achieve comparable results with significantly fewer steps (10-50) through a deterministic generative process.", "subpage_snippet": "", "source": "deepwiki.com", "link": "https://deepwiki.com/ermongroup/ddim/1-overview", "content": "DDIM is a generative model framework introduced by Song, Meng, and Ermon that enables much faster sampling compared to traditional DDPMs. While DDPMs require hundreds to thousands of steps to generate high-quality samples, DDIM can achieve comparable results with significantly fewer steps (10-50) through a deterministic generative process."}
+{"idx": 4, "title": "DDIM Sampling Algorithm - apxml.com", "date": "", "ddg_snippet": "Comparison of DDPM and deterministic DDIM (η = 0 η = 0) sampling . DDPM uses small, stochastic steps based on the Markovian assumption. DDIM calculates a predicted x 0 x0 at each step and uses it to take larger, deterministic steps according to a chosen subsequence S S, significantly reducing the number of required network evaluations. The ability to use fewer steps (N s t e p s ≪ T N steps ...", "subpage_snippet": "", "source": "apxml.com", "link": "https://apxml.com/courses/intro-diffusion-models/chapter-5-sampling-generation-process/ddim-sampling-algorithm", "content": "Comparison of DDPM and deterministic DDIM (η = 0 η = 0) sampling . DDPM uses small, stochastic steps based on the Markovian assumption. DDIM calculates a predicted x 0 x0 at each step and uses it to take larger, deterministic steps according to a chosen subsequence S S, significantly reducing the number of required network evaluations. The ability to use fewer steps (N s t e p s ≪ T N steps ..."}
+{"idx": 5, "title": "How do you implement and compare DDPM and DDIM sampling?", "date": "", "ddg_snippet": "DDIM builds upon the DDPM framework by introducing a non-Markovian sampling process that allows for a more efficient reverse diffusion. While DDPM requires hundreds or even thousands of steps for sampling , DDIM can significantly reduce this number without compromising on sample quality.", "subpage_snippet": "", "source": "blog.milvus.io", "link": "https://blog.milvus.io/ai-quick-reference/how-do-you-implement-and-compare-ddpm-and-ddim-sampling", "content": "DDIM builds upon the DDPM framework by introducing a non-Markovian sampling process that allows for a more efficient reverse diffusion. While DDPM requires hundreds or even thousands of steps for sampling , DDIM can significantly reduce this number without compromising on sample quality."}
+{"idx": 6, "title": "[2206.11474] Entropy-driven Sampling and Training Scheme for ...", "date": "", "ddg_snippet": "To address this problem, we propose two simple but effective approaches from two perspectives. For sampling procedure, we introduce the entropy of predicted distribution as the measure of guidance vanishing level and propose an entropy -aware scaling method to adaptively recover the conditional semantic guidance.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2206.11474", "content": "To address this problem, we propose two simple but effective approaches from two perspectives. For sampling procedure, we introduce the entropy of predicted distribution as the measure of guidance vanishing level and propose an entropy -aware scaling method to adaptively recover the conditional semantic guidance."}
+{"idx": 7, "title": "Sample entropy as function of DDIM denoising steps.", "date": "", "ddg_snippet": "Indeed, our proposed late initialization method (gls- DDIM -05) was able to significantly improve sample diversity, even for a small number of denoising steps, e.g., 3, 5, and 10 sampling steps (see ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/Sample-entropy-as-function-of-DDIM-denoising-steps_fig5_371175557", "content": "Indeed, our proposed late initialization method (gls- DDIM -05) was able to significantly improve sample diversity, even for a small number of denoising steps, e.g., 3, 5, and 10 sampling steps (see ..."}
+{"idx": 8, "title": "Entropy-Guided Sampling of Flat Modes in Discrete Spaces", "date": "", "ddg_snippet": "Sampling from flat modes in discrete spaces is a crucial yet underexplored problem. Flat modes represent robust solutions and have broad applications in combinatorial optimization and discrete generative modeling. However, existing sampling algorithms often overlook the mode volume and struggle to capture flat modes effectively. To address this limitation, we propose \\\\emph{Entropic Discrete ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2505.02296", "content": "Sampling from flat modes in discrete spaces is a crucial yet underexplored problem. Flat modes represent robust solutions and have broad applications in combinatorial optimization and discrete generative modeling. However, existing sampling algorithms often overlook the mode volume and struggle to capture flat modes effectively. To address this limitation, we propose \\\\emph{Entropic Discrete ..."}
+{"idx": 9, "title": "GitHub - ZGCTroy/ED-DPM: classifier guidance rescale for class ...", "date": "", "ddg_snippet": "Accepted by ECCV 2022 This is the official codebase for Entropy -driven Sampling and Training Scheme for Conditional Diffusion Generation. official paper link This repository is heavily based on openai/ guided -diffusion, with modifications listed below: add EDS ( E ntropy- D riven conditional S ampling) in classifier-guidance sample process without retraining add ECT ( E ntropy C onstraint T ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/ZGCTroy/ED-DPM", "content": "Accepted by ECCV 2022 This is the official codebase for Entropy -driven Sampling and Training Scheme for Conditional Diffusion Generation. official paper link This repository is heavily based on openai/ guided -diffusion, with modifications listed below: add EDS ( E ntropy- D riven conditional S ampling) in classifier-guidance sample process without retraining add ECT ( E ntropy C onstraint T ..."}
diff --git a/data/sampled_jsons/feint_behaviors_temporal_spatial_advantage_definition_year_2024.jsonl b/data/sampled_jsons/feint_behaviors_temporal_spatial_advantage_definition_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..9a1c1621717e40d3c02404f400e6a9f7a5179426
--- /dev/null
+++ b/data/sampled_jsons/feint_behaviors_temporal_spatial_advantage_definition_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Feint Behaviors and Strategies: Formalization, Implementation and...", "date": "", "ddg_snippet": "Because the Feint created a temporal advantage (by deceiving the opponent) and a spatial advantage (by attacking a different area), the opponent is unable to defend the waist attack and is knocked down. The figure illustrates the action sequence of both the agent and opponent.", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/posters/aciddntbsj/", "content": "Because the Feint created a temporal advantage (by deceiving the opponent) and a spatial advantage (by attacking a different area), the opponent is unable to defend the waist attack and is knocked down. The figure illustrates the action sequence of both the agent and opponent."}
+{"idx": 1, "title": "NeurIPS Poster Feint Behaviors and Strategies: Formalization...", "date": "", "ddg_snippet": "Feint behaviors refer to a set of nuanced deceptive behaviors , which enable players temporal and spatial advantages over opponents in competitive games.", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/poster/96274", "content": "Feint behaviors refer to a set of nuanced deceptive behaviors , which enable players temporal and spatial advantages over opponents in competitive games."}
+{"idx": 2, "title": "The Understanding of Spatial - Temporal Behaviors ... | IGI Global", "date": "", "ddg_snippet": "Chicago. Zhang, Yu-Jin. \"The Understanding of Spatial - Temporal Behaviors .\" In Advanced Methodologies and Technologies in Artificial Intelligence, Computer Simulation, and Human-Computer Interaction, edited by Mehdi Khosrow-Pour, D.B.A., 392-405.", "subpage_snippet": "", "source": "www.igi-global.com", "link": "https://www.igi-global.com/chapter/the-understanding-of-spatial-temporal-behaviors/213144", "content": "Chicago. Zhang, Yu-Jin. \"The Understanding of Spatial - Temporal Behaviors .\" In Advanced Methodologies and Technologies in Artificial Intelligence, Computer Simulation, and Human-Computer Interaction, edited by Mehdi Khosrow-Pour, D.B.A., 392-405."}
+{"idx": 3, "title": "The Understanding of Spatial - Temporal Behaviors", "date": "", "ddg_snippet": "Spatial - Temporal Behavior Understanding. Chapter. Apr 2024.An approach-based taxonomy is chosen that compares the advantages and limitations of each approach. Recognition methodologies for an analysis of the simple actions of a single person are first presented in the article.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/345121104_The_Understanding_of_Spatial-Temporal_Behaviors", "content": "Spatial - Temporal Behavior Understanding. Chapter. Apr 2024.An approach-based taxonomy is chosen that compares the advantages and limitations of each approach. Recognition methodologies for an analysis of the simple actions of a single person are first presented in the article."}
+{"idx": 4, "title": "\"A155: Exploring the Temporal - Spatial Advantage in SRTT Tasks for...\"", "date": "", "ddg_snippet": "This study aimed to delve into the temporal and spatial predictability strengths of specialist martial arts athletes, shedding light on their advantages in time sense and spatial awareness.", "subpage_snippet": "", "source": "scholarworks.boisestate.edu", "link": "https://scholarworks.boisestate.edu/ijpah/vol3/iss3/155/", "content": "This study aimed to delve into the temporal and spatial predictability strengths of specialist martial arts athletes, shedding light on their advantages in time sense and spatial awareness."}
+{"idx": 5, "title": "Tracking tourist spatial - temporal behavior in urban places...", "date": "", "ddg_snippet": "This document presents an overview of methods for tracking tourist spatial - temporal behavior in urban places and their advantages and disadvantages.", "subpage_snippet": "", "source": "www.slideshare.net", "link": "https://www.slideshare.net/slideshow/tracking-tourist-spatialtemporal-behavior-in-urban-places-a-methodological-overview-and-gps-case-study/58059548", "content": "This document presents an overview of methods for tracking tourist spatial - temporal behavior in urban places and their advantages and disadvantages."}
+{"idx": 6, "title": "Tourists' Spatial - Temporal Behavior Patterns Analysis Based on...", "date": "", "ddg_snippet": "Tourist Spatial – Temporal Behavior Pattern Clustering.the spatial and temporal behavior patterns of tourists and adjust the spatial layout of. facilities according to their prefer ences. Therefor e, this paper takes Zhongshan Botanical.", "subpage_snippet": "", "source": "www.readkong.com", "link": "https://www.readkong.com/page/tourists-spatial-temporal-behavior-patterns-analysis-based-5669174", "content": "Tourist Spatial – Temporal Behavior Pattern Clustering.the spatial and temporal behavior patterns of tourists and adjust the spatial layout of. facilities according to their prefer ences. Therefor e, this paper takes Zhongshan Botanical."}
+{"idx": 7, "title": "Spatial - Temporal Behavior Understanding (2023) | ดูหนัง The Cheese...", "date": "", "ddg_snippet": "This chapter will define spatial - temporal technology and spatial - temporal behavior understanding, and introduce their development and hierarchical research state. This chapter will discuss how to detect the key points ( spatial - temporal interest points)...", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/papers/spatial-temporal-behavior-understanding-3lo3c5xm", "content": "This chapter will define spatial - temporal technology and spatial - temporal behavior understanding, and introduce their development and hierarchical research state. This chapter will discuss how to detect the key points ( spatial - temporal interest points)..."}
+{"idx": 8, "title": "A Comparison of Tourists’ Spatial – Temporal Behaviors Between...", "date": "", "ddg_snippet": "Figure 3. Visitor spatial – temporal behavior at Points of Interest as 3 D weights. Other than Shoshone Overlook, each POI was visited by a higher proportion in the onsite sample than in the LBS sample. The range of stay time observed in the LBS sample across POIs (1.0–6.6 min) was much...", "subpage_snippet": "", "source": "www.wisdomlib.org", "link": "https://www.wisdomlib.org/science/journal/sustainability-journal-mdpi/d/doc1848026.html", "content": "Figure 3. Visitor spatial – temporal behavior at Points of Interest as 3 D weights. Other than Shoshone Overlook, each POI was visited by a higher proportion in the onsite sample than in the LBS sample. The range of stay time observed in the LBS sample across POIs (1.0–6.6 min) was much..."}
+{"idx": 9, "title": "Spatial - Temporal Behavior Understanding | SpringerLink", "date": "", "ddg_snippet": "The understanding of spatial - temporal behaviors . USA. Hershey: IGI Global, Encyclopedia of Information Science and Technology, 4th Ed.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-981-19-7603-2_11", "content": "The understanding of spatial - temporal behaviors . USA. Hershey: IGI Global, Encyclopedia of Information Science and Technology, 4th Ed."}
diff --git a/data/sampled_jsons/fiveaiunderstanding_safety_finetuning_minGPT_n_layer_=_6_OR_n_layer=6_OR_n_layer_6_filetypepy.jsonl b/data/sampled_jsons/fiveaiunderstanding_safety_finetuning_minGPT_n_layer_=_6_OR_n_layer=6_OR_n_layer_6_filetypepy.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e7921f8ed9c16e6cf61653e2def245ef422ca2e3
--- /dev/null
+++ b/data/sampled_jsons/fiveaiunderstanding_safety_finetuning_minGPT_n_layer_=_6_OR_n_layer=6_OR_n_layer_6_filetypepy.jsonl
@@ -0,0 +1,7 @@
+{"idx": 0, "title": "F5 AI Security with guardrails", "date": "", "ddg_snippet": "Comprehensive security for your AI apps, models, agents, and data—from pilot to production.", "subpage_snippet": "", "source": "www.f5.com", "link": "https://www.f5.com/go/solution/f5-ai-security-with-guardrails", "content": "Comprehensive security for your AI apps, models, agents, and data—from pilot to production."}
+{"idx": 1, "title": "Researchers find a way to address the problem of AI ...", "date": "", "ddg_snippet": "6 days ago · The reason models are adjusted in this way is simple. Exiting earlier makes inference faster and more efficient, since the system skips layers. But those skipped layers may have been critical to ...", "subpage_snippet": "", "source": "www.techradar.com", "link": "https://www.techradar.com/pro/researchers-find-a-way-to-address-the-problem-of-ai-forgetting-how-to-behave-safely", "content": "6 days ago · The reason models are adjusted in this way is simple. Exiting earlier makes inference faster and more efficient, since the system skips layers. But those skipped layers may have been critical to ..."}
+{"idx": 2, "title": "[2505.16737] Mitigating Fine-tuning Risks in LLMs via Safety ...", "date": "", "ddg_snippet": "May 22, 2025 · The significant progress of large language models (LLMs) has led to remarkable achievements across numerous applications. However, their ability to generate harmful content has sparked substantial safety concerns. Despite the implementation of safety alignment techniques during the pre-training phase, recent research indicates that fine-tuning LLMs on adversarial or even benign data can ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2505.16737", "content": "May 22, 2025 · The significant progress of large language models (LLMs) has led to remarkable achievements across numerous applications. However, their ability to generate harmful content has sparked substantial safety concerns. Despite the implementation of safety alignment techniques during the pre-training phase, recent research indicates that fine-tuning LLMs on adversarial or even benign data can ..."}
+{"idx": 3, "title": "Fine-Tuning vs. Training LLMs: A Practical Guide - Medium", "date": "", "ddg_snippet": "Jan 7, 2025 · Here’s what it contains: A structured 42 weeks roadmap with study resources 30+ practice problems for each topic A discord community A resources hub that contains: Free-to-read books YouTube ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@whyamit101/fine-tuning-vs-training-llms-a-practical-guide-fa8a0488758c", "content": "Jan 7, 2025 · Here’s what it contains: A structured 42 weeks roadmap with study resources 30+ practice problems for each topic A discord community A resources hub that contains: Free-to-read books YouTube ..."}
+{"idx": 4, "title": "Fine-tuning Guide - Together.ai Docs", "date": "", "ddg_snippet": "Learn the basics and best practices of fine-tuning large language models.", "subpage_snippet": "", "source": "docs.together.ai", "link": "https://docs.together.ai/docs/fine-tuning-quickstart", "content": "Learn the basics and best practices of fine-tuning large language models."}
+{"idx": 5, "title": "SafetyKit’s blueprint for scaling risk agents with OpenAI’s ...", "date": "", "ddg_snippet": "Sep 9, 2025 · SafetyKit builds multimodal AI agents to help marketplaces, payment platforms, and fintechs detect and act on fraud and prohibited activity across text, images, financial transactions, product listings, and more. Recent breakthroughs in model reasoning and multimodal understanding now make this more effective, setting a new bar for risk, compliance, and safety operations.", "subpage_snippet": "", "source": "openai.com", "link": "https://openai.com/index/safetykit/", "content": "Sep 9, 2025 · SafetyKit builds multimodal AI agents to help marketplaces, payment platforms, and fintechs detect and act on fraud and prohibited activity across text, images, financial transactions, product listings, and more. Recent breakthroughs in model reasoning and multimodal understanding now make this more effective, setting a new bar for risk, compliance, and safety operations."}
+{"idx": 6, "title": "Fine-Tuning OpenAI Models: A Low-Code Approach to Custom AI", "date": "", "ddg_snippet": "Fine-tuning large language models (LLMs) has become a crucial strategy for businesses seeking AI that aligns with their unique needs. This article explores OpenAI fine-tuning methods, including Supervised Fine-Tuning, Vision Fine-Tuning, and Direct Preference Optimization. In this article, I will explore a low-code, step-by-step guide on building datasets, uploading training data, creating ...", "subpage_snippet": "", "source": "ai.plainenglish.io", "link": "https://ai.plainenglish.io/fine-tuning-openai-models-a-low-code-approach-to-custom-ai-5fc9512bbb4a", "content": "Fine-tuning large language models (LLMs) has become a crucial strategy for businesses seeking AI that aligns with their unique needs. This article explores OpenAI fine-tuning methods, including Supervised Fine-Tuning, Vision Fine-Tuning, and Direct Preference Optimization. In this article, I will explore a low-code, step-by-step guide on building datasets, uploading training data, creating ..."}
diff --git a/data/sampled_jsons/focal_plane_sensor_processor_SCAMP_feature_detection.jsonl b/data/sampled_jsons/focal_plane_sensor_processor_SCAMP_feature_detection.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d5361bb58a4e1c6e4cece84e9d465f14323c28b2
--- /dev/null
+++ b/data/sampled_jsons/focal_plane_sensor_processor_SCAMP_feature_detection.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "US11514594B2 - Composite imaging systems using a focal plane", "date": "", "ddg_snippet": "US11514594B2 - Composite imaging systems using a focal plane array with in-pixel analog storage elements - Google Patents", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/US11514594B2/en", "content": "US11514594B2 - Composite imaging systems using a focal plane array with in-pixel analog storage elements - Google Patents"}
+{"idx": 1, "title": "BIT-VO", "date": "", "ddg_snippet": "Focal - plane Sensor - processor (FPSP) is a next-generation camera technology which enables every pixel on the sensor chip to perform computation in ...", "subpage_snippet": "", "source": "rmurai.co.uk", "link": "https://rmurai.co.uk/projects/BIT-VO/", "content": "Focal - plane Sensor - processor (FPSP) is a next-generation camera technology which enables every pixel on the sensor chip to perform computation in ..."}
+{"idx": 2, "title": "[2004.11186] BIT-VO: Visual Odometry at 300 FPS using Binary", "date": "", "ddg_snippet": "Abstract: Focal - plane Sensor - processor (FPSP) is a next-generation camera technology which enables every pixel on the sensor chip to perform ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2004.11186", "content": "Abstract: Focal - plane Sensor - processor (FPSP) is a next-generation camera technology which enables every pixel on the sensor chip to perform ..."}
+{"idx": 3, "title": "(PDF) High-speed Light-weight CNN Inference via Strided", "date": "", "ddg_snippet": "The SCAMP -5d incorporates a 256 × 256 PPA array of pixel- processors , each containing light sensor , local memory registers and other functional ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/343655155_High-speed_Light-weight_CNN_Inference_via_Strided_Convolutions_on_a_Pixel_Processor_Array", "content": "The SCAMP -5d incorporates a 256 × 256 PPA array of pixel- processors , each containing light sensor , local memory registers and other functional ..."}
+{"idx": 4, "title": "(PDF) Weighted Node Mapping and Localisation on a Pixel", "date": "", "ddg_snippet": "The PPA sensor comprises of an array of Processing Elements (PEs), each of which can capture and process visual information directly.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/350187131_Weighted_Node_Mapping_and_Localisation_on_a_Pixel_Processor_Array", "content": "The PPA sensor comprises of an array of Processing Elements (PEs), each of which can capture and process visual information directly."}
+{"idx": 5, "title": "CVPR 2021 Workshop on Event-based Vision", "date": "", "ddg_snippet": "... feature /object detection , tracking, calibration, sensor ... Near- focal plane processing, such as pixel processor arrays - PPAs (e.g., SCAMP sensor ).", "subpage_snippet": "", "source": "tub-rip.github.io", "link": "https://tub-rip.github.io/eventvision2021/", "content": "... feature /object detection , tracking, calibration, sensor ... Near- focal plane processing, such as pixel processor arrays - PPAs (e.g., SCAMP sensor )."}
+{"idx": 6, "title": "CVPR 2025 Workshop on Event-based Vision | 5th International", "date": "", "ddg_snippet": "... optical flow estimation, recognition, segmentation, feature /object detection , visual tracking, calibration, action understanding, sensor fusion ...", "subpage_snippet": "", "source": "tub-rip.github.io", "link": "https://tub-rip.github.io/eventvision2025/", "content": "... optical flow estimation, recognition, segmentation, feature /object detection , visual tracking, calibration, action understanding, sensor fusion ..."}
+{"idx": 7, "title": "EP2443551A2 - Processing with compact arithmetic processing", "date": "", "ddg_snippet": "Embodiments of the present invention are directed to a processor or other device, such as a programmable and/or massively parallel processor or other ...", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/EP2443551A2/en", "content": "Embodiments of the present invention are directed to a processor or other device, such as a programmable and/or massively parallel processor or other ..."}
+{"idx": 8, "title": "Items where Year is 2014 - SZTAKI Publication Repository", "date": "", "ddg_snippet": "... Tamás and Zarándy, Ákos and Bauer, Péter and Vanek, Bálint and Bokor, József Error analysis of attitude estimation with focal - plane processors ...", "subpage_snippet": "", "source": "eprints.sztaki.hu", "link": "https://eprints.sztaki.hu/view/year/2014.html", "content": "... Tamás and Zarándy, Ákos and Bauer, Péter and Vanek, Bálint and Bokor, József Error analysis of attitude estimation with focal - plane processors ..."}
+{"idx": 9, "title": "Change Log — SeisComP Release documentation", "date": "", "ddg_snippet": "... of geofeature names when a feature ... Take sensor location elevation into account when computing the hypocentral distance in amplitude time windows.", "subpage_snippet": "", "source": "www.seiscomp.de", "link": "https://www.seiscomp.de/doc/base/changelog.html", "content": "... of geofeature names when a feature ... Take sensor location elevation into account when computing the hypocentral distance in amplitude time windows."}
diff --git a/data/sampled_jsons/four-step_self-supervised_learning_mapping_behaviors_emotions_DIKE_ERIS.jsonl b/data/sampled_jsons/four-step_self-supervised_learning_mapping_behaviors_emotions_DIKE_ERIS.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f67c88a2e5b5ffa196ce94b1674eb8d1173bec31
--- /dev/null
+++ b/data/sampled_jsons/four-step_self-supervised_learning_mapping_behaviors_emotions_DIKE_ERIS.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A Review of Self-regulated Learning: Six Models and Four Directions for ...", "date": "", "ddg_snippet": "Self -regulated learning (SRL) includes the cognitive, metacognitive, behavioral, motivational, and emotional/affective aspects of learning . It is, therefore, an extraordinary umbrella under which a considerable number of variables that influence ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC5408091/", "content": "Self -regulated learning (SRL) includes the cognitive, metacognitive, behavioral, motivational, and emotional/affective aspects of learning . It is, therefore, an extraordinary umbrella under which a considerable number of variables that influence ..."}
+{"idx": 1, "title": "Self-regulation for adults: Strategies for getting a handle on emotions ...", "date": "", "ddg_snippet": "The ability to self -regulate can be learned and improved. One tip to help you practice improving your self -regulation skills involves using the four-step Stop-Breathe-Reflect-Choose approach: When you feel upsetting emotions , tell yourself to calm down and think more clearly. Try to relax by taking deep slow breaths, counting to 10, or taking a ...", "subpage_snippet": "", "source": "www.health.harvard.edu", "link": "https://www.health.harvard.edu/mind-and-mood/self-regulation-for-adults-strategies-for-getting-a-handle-on-emotions-and-behavior", "content": "The ability to self -regulate can be learned and improved. One tip to help you practice improving your self -regulation skills involves using the four-step Stop-Breathe-Reflect-Choose approach: When you feel upsetting emotions , tell yourself to calm down and think more clearly. Try to relax by taking deep slow breaths, counting to 10, or taking a ..."}
+{"idx": 2, "title": "Self-Regulation Curriculum | The Zones of Regulation", "date": "", "ddg_snippet": "The Zones of Regulation is a complete social-emotional learning curriculum, created to teach children self -regulation and emotional control.", "subpage_snippet": "", "source": "zonesofregulation.com", "link": "https://zonesofregulation.com/", "content": "The Zones of Regulation is a complete social-emotional learning curriculum, created to teach children self -regulation and emotional control."}
+{"idx": 3, "title": "Audio-Based Emotion Recognition Using Self-Supervised Learning on an ...", "date": "", "ddg_snippet": "To understand the utility of self-supervised learning for audio-based emotion recognition, we have applied self-supervised learning pre-training to the classification of emotions from the CMU Multimodal Opinion Sentiment and Emotion Intensity (CMU- MOSEI)'s acoustic data.", "subpage_snippet": "", "source": "www.ncbi.nlm.nih.gov", "link": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11076058/", "content": "To understand the utility of self-supervised learning for audio-based emotion recognition, we have applied self-supervised learning pre-training to the classification of emotions from the CMU Multimodal Opinion Sentiment and Emotion Intensity (CMU- MOSEI)'s acoustic data."}
+{"idx": 4, "title": "Self-Directed Learning A Four-Step Process - Studocu", "date": "", "ddg_snippet": "Self -Directed Learning : A Four-Step Process Learning independently can be challenging, even for the brightest and most motivated students. As a means of better understanding the processes involved in this mode of study, this Teaching Tip outlines key components of four key stages to independent learning , known as self -directed learning : being ready to learn, setting learning goals, engaging in ...", "subpage_snippet": "", "source": "www.studocu.com", "link": "https://www.studocu.com/en-us/document/university-of-the-people/online-education-strategies/self-directed-learning-a-four-step-process/15783251", "content": "Self -Directed Learning : A Four-Step Process Learning independently can be challenging, even for the brightest and most motivated students. As a means of better understanding the processes involved in this mode of study, this Teaching Tip outlines key components of four key stages to independent learning , known as self -directed learning : being ready to learn, setting learning goals, engaging in ..."}
+{"idx": 5, "title": "Edward Y. Chang arXiv:2405.07076v2 [cs.CL] 14 May 2024", "date": "", "ddg_snippet": "Modeling Linguistic Behaviors : DIKE starts by modeling and classifying linguistic behaviors , using a self-supervised learning approach to understand how specific linguistic features correlate with human emotions . Modeling Context-Based Ethical Guardrails: DIKE Subsequently, develops ethical guardrails by establishing guidelines that identify and prevent undesirable linguistic outputs, thereby ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2405.07076v2", "content": "Modeling Linguistic Behaviors : DIKE starts by modeling and classifying linguistic behaviors , using a self-supervised learning approach to understand how specific linguistic features correlate with human emotions . Modeling Context-Based Ethical Guardrails: DIKE Subsequently, develops ethical guardrails by establishing guidelines that identify and prevent undesirable linguistic outputs, thereby ..."}
+{"idx": 6, "title": "A Three-Branch Checks-and-Balances Frameworkfor Context-Aware Ethical ...", "date": "", "ddg_snippet": "Through self-supervised learning and adversarial testing, our framework demonstrates how emotional modeling can guide linguistic behaviors toward ethical outcomes while preserving independence across knowledge generation, ethical oversight, and contextual interpretation.", "subpage_snippet": "", "source": "paperreading.club", "link": "https://paperreading.club/page?id=281349", "content": "Through self-supervised learning and adversarial testing, our framework demonstrates how emotional modeling can guide linguistic behaviors toward ethical outcomes while preserving independence across knowledge generation, ethical oversight, and contextual interpretation."}
+{"idx": 7, "title": "A Checks-and-Balances Framework for Context-Aware Ethical AI Alignment", "date": "", "ddg_snippet": "Drawing from psychological theories where managing emotional responses prevents harmful behaviors , we develop a self-supervised learning pipeline that maps emotions to linguistic behaviors , enabling precise behavioral modulation through emotional conditioning.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.00136", "content": "Drawing from psychological theories where managing emotional responses prevents harmful behaviors , we develop a self-supervised learning pipeline that maps emotions to linguistic behaviors , enabling precise behavioral modulation through emotional conditioning."}
+{"idx": 8, "title": "Integrating Emotional and Linguistic Models for Ethical Compliance in ...", "date": "", "ddg_snippet": "Our innovative approaches include mapping emotions and behaviors using self-supervised learning techniques, refining these guardrails through adversarial reviews, and systematically adjusting outputs to ensure ethical alignment.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.07076v2", "content": "Our innovative approaches include mapping emotions and behaviors using self-supervised learning techniques, refining these guardrails through adversarial reviews, and systematically adjusting outputs to ensure ethical alignment."}
+{"idx": 9, "title": "PDF Reproducible Materials: DBT® Skills Training Manual: Second Edition", "date": "", "ddg_snippet": "Emotions can be especially important when we don't have time to think things through. • Strong emotions help us overcome obstacles—in our minds and in the environment. emoTionS communicaTe To (anD influence) oTherS • Facial expressions are hard-wired aspects of emotions . Facial expressions communicate faster than words.", "subpage_snippet": "", "source": "choicespsychotherapy.net", "link": "https://choicespsychotherapy.net/wp-content/uploads/Emotion-Regulation-Handouts-and-WorksheetsNEWMANUAL.pdf", "content": "Emotions can be especially important when we don't have time to think things through. • Strong emotions help us overcome obstacles—in our minds and in the environment. emoTionS communicaTe To (anD influence) oTherS • Facial expressions are hard-wired aspects of emotions . Facial expressions communicate faster than words."}
diff --git a/data/sampled_jsons/framework_that_supports_both_tensor_parallelism_and_data_parallelism_year_2023.jsonl b/data/sampled_jsons/framework_that_supports_both_tensor_parallelism_and_data_parallelism_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a42ac31b470c10f0202ed4cb16ac3a0a7a5c8d75
--- /dev/null
+++ b/data/sampled_jsons/framework_that_supports_both_tensor_parallelism_and_data_parallelism_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Parallelisms — NVIDIA NeMo Framework User Guide", "date": "", "ddg_snippet": "Enable Data Parallelism # In NeMo Framework , DDP is the default parallel deployment method. This means that the total number of GPUs corresponds to the size of the DP group, and training an LLM with model parallelism decreases the size of the DP group. Currently, NeMo Framework supports optimizer distribution only for Adam optimizer.", "subpage_snippet": "", "source": "docs.nvidia.com", "link": "https://docs.nvidia.com/nemo-framework/user-guide/24.09/nemotoolkit/features/parallelisms.html", "content": "Enable Data Parallelism # In NeMo Framework , DDP is the default parallel deployment method. This means that the total number of GPUs corresponds to the size of the DP group, and training an LLM with model parallelism decreases the size of the DP group. Currently, NeMo Framework supports optimizer distribution only for Adam optimizer."}
+{"idx": 1, "title": "Beyond Data Parallelism: A Beginner-Friendly Tour of ... - Medium", "date": "", "ddg_snippet": "🚀 Beyond Data Parallelism : A Beginner-Friendly Tour of Model, Pipeline, and Tensor Multi-GPU Parallelism Scaling up deep learning often means using multiple GPUs in parallel. This article ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@samanch70/beyond-data-parallelism-a-beginner-friendly-tour-of-model-pipeline-and-tensor-multi-gpu-a9fdf2e8176d", "content": "🚀 Beyond Data Parallelism : A Beginner-Friendly Tour of Model, Pipeline, and Tensor Multi-GPU Parallelism Scaling up deep learning often means using multiple GPUs in parallel. This article ..."}
+{"idx": 2, "title": "Parallelisms — NVIDIA NeMo Framework User Guide 24.07 documentation", "date": "", "ddg_snippet": "Parallelisms NeMo Megatron supports various data - and model-parallel deep learning workload deployment methods (which can be mixed together arbitrarily). Data Parallelism Data Parallelism (DP) replicates the model across multiple GPUs. Data batches are evenly distributed between GPUs and the data -parallel GPUs process them independently. While the computation workload is efficiently ...", "subpage_snippet": "", "source": "docs.nvidia.com", "link": "https://docs.nvidia.com/nemo-framework/user-guide/24.07/nemotoolkit/features/parallelisms.html", "content": "Parallelisms NeMo Megatron supports various data - and model-parallel deep learning workload deployment methods (which can be mixed together arbitrarily). Data Parallelism Data Parallelism (DP) replicates the model across multiple GPUs. Data batches are evenly distributed between GPUs and the data -parallel GPUs process them independently. While the computation workload is efficiently ..."}
+{"idx": 3, "title": "Model Parallelism - Hugging Face", "date": "", "ddg_snippet": "In Tensor Parallelism each GPU processes only a slice of a tensor and only aggregates the full tensor for operations that require the whole thing. In this section we use concepts and diagrams from the Megatron-LM paper: Efficient Large-Scale Language Model Training on GPU Clusters.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/docs/transformers/v4.15.0/parallelism", "content": "In Tensor Parallelism each GPU processes only a slice of a tensor and only aggregates the full tensor for operations that require the whole thing. In this section we use concepts and diagrams from the Megatron-LM paper: Efficient Large-Scale Language Model Training on GPU Clusters."}
+{"idx": 4, "title": "Parallelisms — NVIDIA NeMo Framework User Guide", "date": "", "ddg_snippet": "For more optimzier options, please visit this page. Model Parallelism # Model Parallelism (MP) is a distributed model deployment method that partitions the model parameters across GPUs to reduce the need of per-GPU memory. NeMo Framework supports various model-parallel methods, which can be mixed to maximize LLM training performance. Tensor Parallelism # Tensor Parallelism (TP) is a model ...", "subpage_snippet": "", "source": "docs.nvidia.com", "link": "https://docs.nvidia.com/nemo-framework/user-guide/latest/nemotoolkit/features/parallelisms.html", "content": "For more optimzier options, please visit this page. Model Parallelism # Model Parallelism (MP) is a distributed model deployment method that partitions the model parameters across GPUs to reduce the need of per-GPU memory. NeMo Framework supports various model-parallel methods, which can be mixed to maximize LLM training performance. Tensor Parallelism # Tensor Parallelism (TP) is a model ..."}
+{"idx": 5, "title": "Multi-GPU Training With Model Parallelism in DeepSpeed", "date": "", "ddg_snippet": "This allows you to train larger models without running out of memory. Flexible Parallelism : DeepSpeed supports both data and model parallelism (including pipeline parallelism and tensor parallelism ), giving developers freedom to mix and match strategies based on their architecture and hardware constraints.", "subpage_snippet": "", "source": "dev.co", "link": "https://dev.co/ai/multi-gpu-training-with-model-parallelism-in-deepspeed", "content": "This allows you to train larger models without running out of memory. Flexible Parallelism : DeepSpeed supports both data and model parallelism (including pipeline parallelism and tensor parallelism ), giving developers freedom to mix and match strategies based on their architecture and hardware constraints."}
+{"idx": 6, "title": "Paradigms of Parallelism - Colossal-AI", "date": "", "ddg_snippet": "For parts that cannot utilize tensor parallelism , such as non-linear operations like LayerNorm, the sample data can be split into multiple parts along the sequence dimension, with each GPU computing a portion of the data . Then, tensor parallelism is used for the linear parts like attention and MLP, where activations need to be aggregated.", "subpage_snippet": "", "source": "colossalai.org", "link": "https://colossalai.org/docs/concepts/paradigms_of_parallelism/", "content": "For parts that cannot utilize tensor parallelism , such as non-linear operations like LayerNorm, the sample data can be split into multiple parts along the sequence dimension, with each GPU computing a portion of the data . Then, tensor parallelism is used for the linear parts like attention and MLP, where activations need to be aggregated."}
+{"idx": 7, "title": "4D Parallelism Design | huggingface/picotron | DeepWiki", "date": "", "ddg_snippet": "This document explains the 4D Parallelism architecture in Picotron, a minimalist framework for pre-training Llama-like models. The four dimensions of parallelism—Data Parallel (DP), Tensor Parallel (TP), Pipeline Parallel (PP), and Context Parallel (CP)—work together to efficiently distribute training across multiple GPUs or nodes.", "subpage_snippet": "", "source": "deepwiki.com", "link": "https://deepwiki.com/huggingface/picotron/2.1-4d-parallelism-design", "content": "This document explains the 4D Parallelism architecture in Picotron, a minimalist framework for pre-training Llama-like models. The four dimensions of parallelism—Data Parallel (DP), Tensor Parallel (TP), Pipeline Parallel (PP), and Context Parallel (CP)—work together to efficiently distribute training across multiple GPUs or nodes."}
+{"idx": 8, "title": "2D Parallelism (Tensor Parallelism + FSDP) - Lightning", "date": "", "ddg_snippet": "2D Parallelism combines Tensor Parallelism (TP) and Fully Sharded Data Parallelism (FSDP) to leverage the memory efficiency of FSDP and the computational scalability of TP. This hybrid approach balances the trade-offs of each method, optimizing memory usage and minimizing communication overhead, enabling the training of extremely large models on large GPU clusters.", "subpage_snippet": "", "source": "lightning.ai", "link": "https://lightning.ai/docs/pytorch/stable/advanced/model_parallel/tp_fsdp.html", "content": "2D Parallelism combines Tensor Parallelism (TP) and Fully Sharded Data Parallelism (FSDP) to leverage the memory efficiency of FSDP and the computational scalability of TP. This hybrid approach balances the trade-offs of each method, optimizing memory usage and minimizing communication overhead, enabling the training of extremely large models on large GPU clusters."}
+{"idx": 9, "title": "ResearchonModelParallelismandData ParallelismOptimizationMethodsinLarge ...", "date": "", "ddg_snippet": "n methods—model parallelism and data parallelism—for distributed training of LLMs in recommendation scenarios. For model parallelism , we implement both tensor parallelism and pipe", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2506.17551", "content": "n methods—model parallelism and data parallelism—for distributed training of LLMs in recommendation scenarios. For model parallelism , we implement both tensor parallelism and pipe"}
diff --git a/data/sampled_jsons/g(z)_=_max(z,0)__max(z,0)_1_regret_matching.jsonl b/data/sampled_jsons/g(z)_=_max(z,0)__max(z,0)_1_regret_matching.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..42f1762ef1a96299c7eca64a8b27b455c8a8c377
--- /dev/null
+++ b/data/sampled_jsons/g(z)_=_max(z,0)__max(z,0)_1_regret_matching.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Regret Matching+: (In)Stability and Fast Convergence in ...", "date": "", "ddg_snippet": "by G Farina · 2023 · Cited by 17 — Regret Matching+ (RM+) and its variants are important algorithms for solving large-scale games [35]. However, a theoretical understanding of ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2305.14709", "content": "by G Farina · 2023 · Cited by 17 — Regret Matching+ (RM+) and its variants are important algorithms for solving large-scale games [35]. However, a theoretical understanding of ..."}
+{"idx": 1, "title": "Minimizing Weighted Counterfactual Regret with Optimistic ...", "date": "", "ddg_snippet": "by H Xu · Cited by 4 — It decomposes the total regret into counterfactual regrets , utilizing local regret minimization algorithms, such as Regret Match - ing (RM) or RM+, to minimize ...", "subpage_snippet": "", "source": "www.ijcai.org", "link": "https://www.ijcai.org/proceedings/2024/0583.pdf", "content": "by H Xu · Cited by 4 — It decomposes the total regret into counterfactual regrets , utilizing local regret minimization algorithms, such as Regret Match - ing (RM) or RM+, to minimize ..."}
+{"idx": 2, "title": "More Efficient Internal-Regret-Minimizing Algorithms", "date": "", "ddg_snippet": "by A Greenwald · Cited by 10 — Standard no-internal- regret (NIR) algorithms compute a fixed point of a matrix, and hence typically require O(n3) run time per round of.", "subpage_snippet": "", "source": "cs.brown.edu", "link": "https://cs.brown.edu/people/wschudy/papers/regret.pdf", "content": "by A Greenwald · Cited by 10 — Standard no-internal- regret (NIR) algorithms compute a fixed point of a matrix, and hence typically require O(n3) run time per round of."}
+{"idx": 3, "title": "Towards Achieving Sub-linear Regret and Hard Constraint ...", "date": "", "ddg_snippet": "by A Ghosh · 2024 · Cited by 9 — We study the constrained Markov decision processes (CMDPs), in which an agent aims to maximize the expected cumulative reward subject to a constraint on the ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v238/ghosh24a/ghosh24a.pdf", "content": "by A Ghosh · 2024 · Cited by 9 — We study the constrained Markov decision processes (CMDPs), in which an agent aims to maximize the expected cumulative reward subject to a constraint on the ..."}
+{"idx": 4, "title": "Last-Iterate Convergence Properties of Regret-Matching ...", "date": "", "ddg_snippet": "by Y Cai · Cited by 5 — In this paper, we study the last-iterate convergence properties of various popular variants of RM$^+$.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=fWk5Qx0exc", "content": "by Y Cai · Cited by 5 — In this paper, we study the last-iterate convergence properties of various popular variants of RM$^+$."}
+{"idx": 5, "title": "Regret Minimization in Games with Incomplete Information", "date": "", "ddg_snippet": "by M Zinkevich · Cited by 1100 — If all players can recall their previous actions and the corresponding information sets, the game is said to be one of perfect recall. This work will focus on ...", "subpage_snippet": "", "source": "martin.zinkevich.org", "link": "https://martin.zinkevich.org/publications/regretpoker.pdf", "content": "by M Zinkevich · Cited by 1100 — If all players can recall their previous actions and the corresponding information sets, the game is said to be one of perfect recall. This work will focus on ..."}
+{"idx": 6, "title": "Rationality of Learning Algorithms in Repeated Normal- ...", "date": "", "ddg_snippet": "by S Bajaj · 2024 · Cited by 3 — Section 3 establishes that the rationality ratio of fictitious play and regret matching algorithms is unbounded in the worst-case. Section 4 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2402.08747", "content": "by S Bajaj · 2024 · Cited by 3 — Section 3 establishes that the rationality ratio of fictitious play and regret matching algorithms is unbounded in the worst-case. Section 4 ..."}
+{"idx": 7, "title": "Computers are incredibly fast, accurate and stupid. Human ...", "date": "", "ddg_snippet": "by M Lanctot · 2013 · Cited by 62 — In this thesis, we investigate the problem of decision-making in large two-player zero -sum games using Monte Carlo sampling and regret minimization methods. We ... 149 pages", "subpage_snippet": "", "source": "mlanctot.info", "link": "https://mlanctot.info/files/papers/PhD_Thesis_MarcLanctot.pdf", "content": "by M Lanctot · 2013 · Cited by 62 — In this thesis, we investigate the problem of decision-making in large two-player zero -sum games using Monte Carlo sampling and regret minimization methods. We ... 149 pages"}
+{"idx": 8, "title": "Fast and Furious Learning in Zero-Sum Games", "date": "", "ddg_snippet": "by JP Bailey · Cited by 41 — Specifically, in the case of zero -sum games what is referred to as “convergence\" to equilibrium, is the fact that when both agent apply regret -minimizing ... 11 pages", "subpage_snippet": "", "source": "papers.neurips.cc", "link": "http://papers.neurips.cc/paper/9458-fast-and-furious-learning-in-zero-sum-games-vanishing-regret-with-non-vanishing-step-sizes.pdf", "content": "by JP Bailey · Cited by 41 — Specifically, in the case of zero -sum games what is referred to as “convergence\" to equilibrium, is the fact that when both agent apply regret -minimizing ... 11 pages"}
+{"idx": 9, "title": "THÈSEDEDOCTORAT - Joon Kwon", "date": "", "ddg_snippet": "by MMR Laraki · 2016 — On présente dans le Chapitre I le problème d'online linear optimization, puis on construit les stratégies de descente miroir avec paramètres variables pour la ...", "subpage_snippet": "", "source": "joon-kwon.github.io", "link": "https://joon-kwon.github.io/these-garamond.pdf", "content": "by MMR Laraki · 2016 — On présente dans le Chapitre I le problème d'online linear optimization, puis on construit les stratégies de descente miroir avec paramètres variables pour la ..."}
diff --git a/data/sampled_jsons/g-computation_TMLE_targeted_maximum_likelihood_interventional_effects_causal_inference_2010.jsonl b/data/sampled_jsons/g-computation_TMLE_targeted_maximum_likelihood_interventional_effects_causal_inference_2010.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..38bab1aef5478e61687cd6286e2c450a9de24a2d
--- /dev/null
+++ b/data/sampled_jsons/g-computation_TMLE_targeted_maximum_likelihood_interventional_effects_causal_inference_2010.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Nonparametric efficient causal estimation of the", "date": "", "ddg_snippet": "In this work, we define statistical and causal target parameters via the g - computation formula by carrying out interventions directly on the product ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2404.01736v4", "content": "In this work, we define statistical and causal target parameters via the g - computation formula by carrying out interventions directly on the product ..."}
+{"idx": 1, "title": "Bayesian implementation of Targeted Maximum Likelihood", "date": "", "ddg_snippet": "... causality, this paper proposes three Bayesian approaches of the popular Targeted Maximum Likelihood Estimation ( TMLE ) algorithm, a flexible ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.15909v1", "content": "... causality, this paper proposes three Bayesian approaches of the popular Targeted Maximum Likelihood Estimation ( TMLE ) algorithm, a flexible ..."}
+{"idx": 2, "title": "concrete: Targeted Estimation of Survival and Competing Risks", "date": "", "ddg_snippet": "... causal inference frameworks (Rubin, 1974 ; Pearl et al., 2016 ) gained recognition for their utility in translating clinical questions into ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2310.19197v2", "content": "... causal inference frameworks (Rubin, 1974 ; Pearl et al., 2016 ) gained recognition for their utility in translating clinical questions into ..."}
+{"idx": 3, "title": "Longitudinal Targeted Minimum Loss-based Estimation with", "date": "", "ddg_snippet": "... general method that uses a transformer architecture to facilitate valid statistical inference in longitudinal settings concerning survival outcomes ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2404.04399v1", "content": "... general method that uses a transformer architecture to facilitate valid statistical inference in longitudinal settings concerning survival outcomes ..."}
+{"idx": 4, "title": "References | The tlverse Software Ecosystem for Causal Inference", "date": "", "ddg_snippet": "Stochastic Treatment Regimes.” In Targeted Learning in Data Science: Causal Inference for Complex Longitudinal Studies , 167–80.", "subpage_snippet": "", "source": "tlverse.org", "link": "https://tlverse.org/acic2019-workshop/references.html", "content": "Stochastic Treatment Regimes.” In Targeted Learning in Data Science: Causal Inference for Complex Longitudinal Studies , 167–80."}
+{"idx": 5, "title": "Chapter 10 Causal Mediation Analysis | Targeted Learning in R", "date": "", "ddg_snippet": "... consult recent advances in the vast and quickly growing literature on causal mediation analysis, including interventional direct and indirect effects ...", "subpage_snippet": "", "source": "tlverse.org", "link": "https://tlverse.org/tlverse-handbook/causal-mediation-analysis.html", "content": "... consult recent advances in the vast and quickly growing literature on causal mediation analysis, including interventional direct and indirect effects ..."}
+{"idx": 6, "title": "Positivity assumption violations and TMLE for longitudinal data", "date": "", "ddg_snippet": "... targeted maximum likelihood type TMLE as in our original work around 2010 , but one wants to reduce the dimension of $L(t)$ to make it do-able (see A ...", "subpage_snippet": "", "source": "vanderlaan-lab.org", "link": "https://vanderlaan-lab.org/2019/12/29/positivity-assumption-violations-and-tmle-for-longitudinal-data-with-many-time-varying-covariates/", "content": "... targeted maximum likelihood type TMLE as in our original work around 2010 , but one wants to reduce the dimension of $L(t)$ to make it do-able (see A ..."}
+{"idx": 7, "title": "Reflection on modern methods: when worlds collide—prediction,", "date": "", "ddg_snippet": "Causal inference methods covered include propensity scores, inverse probability of treatment weights (IPTWs), G computation and targeted maximum ...", "subpage_snippet": "", "source": "academic.oup.com", "link": "https://academic.oup.com/ije/article/49/6/2058/5531243", "content": "Causal inference methods covered include propensity scores, inverse probability of treatment weights (IPTWs), G computation and targeted maximum ..."}
+{"idx": 8, "title": "Neugebauer, Romain S. - Kaiser Permanente Division of Research", "date": "", "ddg_snippet": "Inferences are based on a modern causal inference methodology, Targeted Learning, applied within a trial emulation framework.", "subpage_snippet": "", "source": "divisionofresearch.kaiserpermanente.org", "link": "https://divisionofresearch.kaiserpermanente.org/researchers/neugebauer-romain/", "content": "Inferences are based on a modern causal inference methodology, Targeted Learning, applied within a trial emulation framework."}
+{"idx": 9, "title": "MALF", "date": "", "ddg_snippet": "... Group (CSG) at the LSHTM data-adaptive methods for model selection and evaluation based on cross-validation techniques cvAUROC and applying advanced ...", "subpage_snippet": "", "source": "maluque.netlify.app", "link": "https://maluque.netlify.app/", "content": "... Group (CSG) at the LSHTM data-adaptive methods for model selection and evaluation based on cross-validation techniques cvAUROC and applying advanced ..."}
diff --git a/data/sampled_jsons/gPINN_Yu_2022_diffusion_equation_exact_solution_sin(pix)_exp(-pi^2t)_domain_[0,1].jsonl b/data/sampled_jsons/gPINN_Yu_2022_diffusion_equation_exact_solution_sin(pix)_exp(-pi^2t)_domain_[0,1].jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6a08f607c2204e1de6bd34006b6161851d4d4ec5
--- /dev/null
+++ b/data/sampled_jsons/gPINN_Yu_2022_diffusion_equation_exact_solution_sin(pix)_exp(-pi^2t)_domain_[0,1].jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Diffusion equation with hard initial and boundary conditions...", "date": "", "ddg_snippet": "Diffusion -reaction equation . Burgers equation with residual-based adaptive refinement.The reference solution func is defined as: def func(x): return np. sin (np. pi * x [:, 0 : 1 ]) * np. exp (-x[:, 1 :]) Now, we have specified the geometry and the PDE residual.", "subpage_snippet": "", "source": "deepxde.readthedocs.io", "link": "https://deepxde.readthedocs.io/en/latest/demos/pinn_forward/diffusion.1d.exactBC.html", "content": "Diffusion -reaction equation . Burgers equation with residual-based adaptive refinement.The reference solution func is defined as: def func(x): return np. sin (np. pi * x [:, 0 : 1 ]) * np. exp (-x[:, 1 :]) Now, we have specified the geometry and the PDE residual."}
+{"idx": 1, "title": "4.DiffusionEquation.ipynb - Colab", "date": "", "ddg_snippet": "$$y(x, 0 )= sin (\\ pi x )$$. Boundary ConditionsGenerate data of the exact solution . subdirectory_arrow_right 2 cells hidden.", "subpage_snippet": "", "source": "colab.research.google.com", "link": "https://colab.research.google.com/github/pnnl/neuromancer/blob/master/examples/PDEs/Part_1_PINN_DiffusionEquation.ipynb", "content": "$$y(x, 0 )= sin (\\ pi x )$$. Boundary ConditionsGenerate data of the exact solution . subdirectory_arrow_right 2 cells hidden."}
+{"idx": 2, "title": "1 D-3D advection diffusion , need help on syntax... - FEniCS Project", "date": "", "ddg_snippet": "I am attempting to set up a 1 D (dG) 3D (FEM) advection- diffusion system and then run some convergence tests. I am running into what I believe may be some syntax errors. I have run multiple tests without my convection ter…", "subpage_snippet": "", "source": "fenicsproject.discourse.group", "link": "https://fenicsproject.discourse.group/t/1d-3d-advection-diffusion-need-help-on-syntax/18004", "content": "I am attempting to set up a 1 D (dG) 3D (FEM) advection- diffusion system and then run some convergence tests. I am running into what I believe may be some syntax errors. I have run multiple tests without my convection ter…"}
+{"idx": 3, "title": "1 D diffusion equation with different dx and dt - MATLAB Answers...", "date": "", "ddg_snippet": "I'm trying to compare and approximation of the 1 D diffusion equation with the real value with different step size dx=h and dt.", "subpage_snippet": "", "source": "www.mathworks.com", "link": "https://www.mathworks.com/matlabcentral/answers/510510-1d-diffusion-equation-with-different-dx-and-dt", "content": "I'm trying to compare and approximation of the 1 D diffusion equation with the real value with different step size dx=h and dt."}
+{"idx": 4, "title": "huangboming/PDEs-PINN-Examples-Using-Tensorflow 2 - Githubissues", "date": "", "ddg_snippet": "The exact solution : Exact _Poisson_ 2 D. Diffusion Equation example.pinn solution 3d real solution 3d. Poisson 1 D (Inverse Problem) example.", "subpage_snippet": "", "source": "githubissues.com", "link": "https://githubissues.com/huangboming/PDEs-PINN-Examples-Using-Tensorflow2/readme", "content": "The exact solution : Exact _Poisson_ 2 D. Diffusion Equation example.pinn solution 3d real solution 3d. Poisson 1 D (Inverse Problem) example."}
+{"idx": 5, "title": "symbolic - Solving a heat equation problem - Mathematica Stack...", "date": "", "ddg_snippet": "I'm brand new to Mathematica. I am trying to solve a heat equation problem, but I keep getting back the input on the output line.One thing is that you need square brackets for Sin, for example Sin [ Pi x ].", "subpage_snippet": "", "source": "mathematica.stackexchange.com", "link": "https://mathematica.stackexchange.com/questions/197155/solving-a-heat-equation-problem", "content": "I'm brand new to Mathematica. I am trying to solve a heat equation problem, but I keep getting back the input on the output line.One thing is that you need square brackets for Sin, for example Sin [ Pi x ]."}
+{"idx": 6, "title": "Non-Homogeneous Reaction- Diffusion Equation with Robin BC...", "date": "", "ddg_snippet": "For one example, I decided to code up a very simple Reaction- Diffusion Equation with Robin BC whose solution I know to verify that MOOSE could converge to the correct solution .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/idaholab/moose/discussions/27389", "content": "For one example, I decided to code up a very simple Reaction- Diffusion Equation with Robin BC whose solution I know to verify that MOOSE could converge to the correct solution ."}
+{"idx": 7, "title": "Determining an unknown source in a time-fractional diffusion ...", "date": "", "ddg_snippet": "Since the exact solution of the fractional diffusion equation is difficult to obtain, we generate the additional data g( x ).", "subpage_snippet": "", "source": "advancesincontinuousanddiscretemodels.springeropen.com", "link": "https://advancesincontinuousanddiscretemodels.springeropen.com/articles/10.1186/s13662-023-03779-z", "content": "Since the exact solution of the fractional diffusion equation is difficult to obtain, we generate the additional data g( x )."}
+{"idx": 8, "title": "Two-dimensional diffusion and advection - Fundamentals of Numerical...", "date": "", "ddg_snippet": "Example 13. 2 .3 (Advection- diffusion equation in 2 D). We will solve an advection-diffusion problem", "subpage_snippet": "", "source": "fncbook.com", "link": "https://fncbook.com/diffadv", "content": "Example 13. 2 .3 (Advection- diffusion equation in 2 D). We will solve an advection-diffusion problem"}
+{"idx": 9, "title": "ap.analysis of pdes - One dimensional heat equation ... - MathOverflow", "date": "", "ddg_snippet": "Solution of Heat equation with Neumann BC in an arbitrary domain .", "subpage_snippet": "", "source": "mathoverflow.net", "link": "https://mathoverflow.net/questions/333120/one-dimensional-heat-equation-with-boundary-conditions", "content": "Solution of Heat equation with Neumann BC in an arbitrary domain ."}
diff --git a/data/sampled_jsons/gPINN_Yu_2022_diffusion_equation_u_t_=_u_xx_exact_solution_sin(pix)_domain.jsonl b/data/sampled_jsons/gPINN_Yu_2022_diffusion_equation_u_t_=_u_xx_exact_solution_sin(pix)_domain.jsonl
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index 0000000000000000000000000000000000000000..0922217a7a801a51b5f56f1cd32fb7a11afc80b5
--- /dev/null
+++ b/data/sampled_jsons/gPINN_Yu_2022_diffusion_equation_u_t_=_u_xx_exact_solution_sin(pix)_domain.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "The 1D diffusion equation", "date": "", "ddg_snippet": "Also, the diffusion equation makes quite different demands to the numerical methods.To obtain a unique solution of the diffusion equation , or equivalently, to apply numerical methods, we need initial and boundary conditions.", "subpage_snippet": "", "source": "hplgit.github.io", "link": "https://hplgit.github.io/num-methods-for-PDEs/doc/pub/diffu/sphinx/._main_diffu001.html", "content": "Also, the diffusion equation makes quite different demands to the numerical methods.To obtain a unique solution of the diffusion equation , or equivalently, to apply numerical methods, we need initial and boundary conditions."}
+{"idx": 1, "title": "Diffusion Equation and Maximum Principle", "date": "", "ddg_snippet": "First I will outline my thinking and then I will provide a link to one solution I found online (there are many others, though they are similar).One of my first thoughts was that the minimum of u ( x , t ) should be the maximum of - u ( x , t ); therefore, if we have solved part a we have, in essence, solved...", "subpage_snippet": "", "source": "www.physicsforums.com", "link": "https://www.physicsforums.com/threads/diffusion-equation-and-maximum-principle.714941/", "content": "First I will outline my thinking and then I will provide a link to one solution I found online (there are many others, though they are similar).One of my first thoughts was that the minimum of u ( x , t ) should be the maximum of - u ( x , t ); therefore, if we have solved part a we have, in essence, solved..."}
+{"idx": 2, "title": "Cauchy problem for diffusion equation and condition $ x ^2$ if $ x \\in...", "date": "", "ddg_snippet": "Inhomogeneous Diffusion Equation - Maximum Principle. 0. Analytical solution for diffusion equation with decay. 2. Explanation to PDE $ u _{tt}-c^2 u _{ xx }=0$ solution .", "subpage_snippet": "", "source": "math.stackexchange.com", "link": "https://math.stackexchange.com/questions/2829899/cauchy-problem-for-diffusion-equation-and-condition-x2-if-x-in-0-1", "content": "Inhomogeneous Diffusion Equation - Maximum Principle. 0. Analytical solution for diffusion equation with decay. 2. Explanation to PDE $ u _{tt}-c^2 u _{ xx }=0$ solution ."}
+{"idx": 3, "title": "ap.analysis of pdes - Solutions to the diffusion equation", "date": "", "ddg_snippet": "When it comes to solving the heat diffusion equation u _ t = u _ xx the two most important solutions are a) a combination (sum) of sin -terms to resemble the function of the initial condition (that is.The two solutions solve different problems for the same equation.", "subpage_snippet": "", "source": "mathoverflow.net", "link": "https://mathoverflow.net/questions/2117/solutions-to-the-diffusion-equation", "content": "When it comes to solving the heat diffusion equation u _ t = u _ xx the two most important solutions are a) a combination (sum) of sin -terms to resemble the function of the initial condition (that is.The two solutions solve different problems for the same equation."}
+{"idx": 4, "title": "python - 2D finite difference scheme of reaction diffusion equation", "date": "", "ddg_snippet": "1. I want to visualize the solution of the following partial differential equation At different times, such as u ( t =1) and u ( t =3). I use a finite difference scheme and the following Python code: import numpy as np import matplotlib.pyplot as plt #.", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/78455770/2d-finite-difference-scheme-of-reaction-diffusion-equation", "content": "1. I want to visualize the solution of the following partial differential equation At different times, such as u ( t =1) and u ( t =3). I use a finite difference scheme and the following Python code: import numpy as np import matplotlib.pyplot as plt #."}
+{"idx": 5, "title": "Entire Solutions with Merging Fronts to Reaction– Diffusion Equations", "date": "", "ddg_snippet": "We deal with a reaction– diffusion equation u t = u xx + f(u) which has two stable constant equilibria, u&nb.Large-time behavior of solutions of parabolic equations on the real line with convergent initial data III: unstable limit at infinity. Article Open access 11 July 2022 .", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s10884-006-9046-x", "content": "We deal with a reaction– diffusion equation u t = u xx + f(u) which has two stable constant equilibria, u&nb.Large-time behavior of solutions of parabolic equations on the real line with convergent initial data III: unstable limit at infinity. Article Open access 11 July 2022 ."}
+{"idx": 6, "title": "Exact solution of mixed problems for variable coefficient...", "date": "", "ddg_snippet": "In this work, using Fourier transforms, we obtain exact solutions for different lagging models of heat conduction in a semi-infinite domain , which allow the construction of analytic-numerical solutions with prescribed accuracy.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/240919173_Exact_solution_of_mixed_problems_for_variable_coefficient_one-dimensional_diffusion_equation", "content": "In this work, using Fourier transforms, we obtain exact solutions for different lagging models of heat conduction in a semi-infinite domain , which allow the construction of analytic-numerical solutions with prescribed accuracy."}
+{"idx": 7, "title": "(a) Find the solution of the diffusion | StudyX", "date": "", "ddg_snippet": "# Proof Objective (a) * Find the solution $ u ( x , t )$ to the diffusion equation with the given initial and boundary conditions.", "subpage_snippet": "", "source": "studyx.ai", "link": "https://studyx.ai/questions/4m1k3k5/a-find-the-solution-of-the-diffusion-equation-for-0-0-such-that-u-x-0-0-0-0-u-t-0-t-0", "content": "# Proof Objective (a) * Find the solution $ u ( x , t )$ to the diffusion equation with the given initial and boundary conditions."}
+{"idx": 8, "title": "Sine Fourier transform (dst) for diffusion PDE - MATLAB Answers...", "date": "", "ddg_snippet": "Good day, following the exposition presented on this site PDE with FFT I'm trying to solve a simple diffusion equation u _ t = u _ xx with u(x,0)=3* sin (2* pi * x ) and u(0, t )= u (2,t)=0. Here is my program, but it doesn't produce the correct answer : u(x,t) = 3 sin (2 pix )e^{-4pi^2t}", "subpage_snippet": "", "source": "www.mathworks.com", "link": "https://www.mathworks.com/matlabcentral/answers/262295-sine-fourier-transform-dst-for-diffusion-pde", "content": "Good day, following the exposition presented on this site PDE with FFT I'm trying to solve a simple diffusion equation u _ t = u _ xx with u(x,0)=3* sin (2* pi * x ) and u(0, t )= u (2,t)=0. Here is my program, but it doesn't produce the correct answer : u(x,t) = 3 sin (2 pix )e^{-4pi^2t}"}
+{"idx": 9, "title": "Partial Differential Equations III & V, Exercise Sheet 6: Solutions ...", "date": "", "ddg_snippet": "Finite speed of propagation for a degenerate diffusion equation .The energy method: Uniqueness for the heat equation in a time dependent domain .", "subpage_snippet": "", "source": "maths.dur.ac.uk", "link": "https://maths.dur.ac.uk/users/amit.einav/PDE+III/Home+Assignments/PDEsExerciseSheet6Solutions.html", "content": "Finite speed of propagation for a degenerate diffusion equation .The energy method: Uniqueness for the heat equation in a time dependent domain ."}
diff --git a/data/sampled_jsons/gPINN_paper_diffusion-reaction_equation_setup_Jeremy_Yu_Lu_Computer_Methods_Applied_Mechanics_Engine.jsonl b/data/sampled_jsons/gPINN_paper_diffusion-reaction_equation_setup_Jeremy_Yu_Lu_Computer_Methods_Applied_Mechanics_Engine.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..13c086eb47c3ae217464b1572762ba587876eac7
--- /dev/null
+++ b/data/sampled_jsons/gPINN_paper_diffusion-reaction_equation_setup_Jeremy_Yu_Lu_Computer_Methods_Applied_Mechanics_Engine.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - lu-group/gpinn: Gradient-enhanced physics-informed ...", "date": "", "ddg_snippet": "The data and code for the paper J. Yu , L. Lu , X. Meng, & G. E. Karniadakis. Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems. Computer Methods in Applied Mechanics and Engineering , 393, 114823, 2022 ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/lu-group/gPINN", "content": "The data and code for the paper J. Yu , L. Lu , X. Meng, & G. E. Karniadakis. Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems. Computer Methods in Applied Mechanics and Engineering , 393, 114823, 2022 ..."}
+{"idx": 1, "title": "[2111.02801] Gradient-enhanced physics-informed neural ... gPINN for PRIMES Conference - MIT Mathematics gpinn/README.md at main · lu-group/gpinn · GitHub Gradient-enhanced physics-informed neural networks for ... Gradient-enhanced physics-informed neural networks for forward and A gradient-enhanced physics-informed neural network (gPINN) schem… A gradient-enhanced physics-informed neural network (gPINN) schem… Gradient-enhanced physics-informed neural networks for forward and A gradient-enhanced physics-informed neural network (gPINN) schem… A gradient-enhanced physics-informed neural network (gPINN) schem… A gradient-enhanced physics-informed neural network (gPINN ...", "date": "", "ddg_snippet": "Nov 1, 2021 · Deep learning has been shown to be an effective tool in solving partial differential equations (PDEs) through physics-informed neural networks (PINNs). PINNs embed the PDE residual into the loss function of the neural network, and have been successfully employed to solve diverse forward and inverse PDE problems. However, one disadvantage of the first generation of PINNs is that they usually ... Oct 1, 2021 · MIT PRIMES Conference, 10/16/21 Presented by: Jeremy Yu Mentor: Dr. Lu Lu gPINN : Gradient-enhanced physics-informed neural networks The data and code for the paper J. Yu , L. Lu , X. Meng, & G. E. Karniadakis. Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems. Computer Methods in Applied Mechanics and Engineering , 393, 114823, 2022. Nov 1, 2021 · Deep learning has been shown to be an effective tool in solving partial differential equations (PDEs) through physics-informed neural networks (PINNs). PINNs embed the PDE residual into the loss ... How to solve PDEs using gpinn? gPINN to solve PDEs. 3.2.1. Poisson equation with the Dirichlet boundary conditions u(x= 0) = 0 and u(x=π) = π. The analytic solution is where N(x) is a neural network. Hence, the loss function is where Lfand Lgare defined in Eqs. (2) and (3), respectively. PINN for u decreases from 26% to 0.48% (Fig. 2 A). Is gpinn based on a non-Fickian diffusion-thermoelasticity problem? As far as we are aware, this is the first time that gPINN is implemented on the generalized coupled non-Fickian/non-Fourierian diffusion-thermoelasticity problem . Based on the Lord-Shulman theory of coupled thermoelasticity, the governing equations for a strip of copper are derived. Can gpinn be applied to a two-dimensional generalized thermoelastic diffusion problem? This example examines the application of gPINN to a two-dimensional generalized thermoelastic diffusion problem. A two-dimensional homogeneous and isotropic body is assumed to undergo both thermal and chemical potential shocks, and the governing equations are introduced by (Sherief et al., 2004), (Sherief and Saleh, 2005). Can gpinn solve inverse PDE problems? We demonstrated the effectiveness of gPINN in both forward and inverse PDE problems, including Poisson equation, diffusion–reaction equation, Brinkman–Forchheimer model, Burgers’ equation, and Allen-Cahn equation. How many neural networks are embodied in gpinn? Due to the different behavior of output variables (i.e., concentration, displacement, and temperature), three separate neural networks are embodied in gPINN. To systematically tackle the issue of boundary and initial conditions satisfaction, some problematic ones are enforced as hard constraints. Are gpinn and hyperparameter setting based on multiple neural networks? A comparative analysis between an individual neural network and multiple neural networks is presented to address systematically the probable capacities of the latter structure. Multiple independent runs with a range of assumptions are needed to derive the architecture of gPINN and hyperparameter setting. Nov 1, 2023 · This paper seeks to extend the concept of a newly developed deep learning approach called gradient-enhanced physics-informed neural network ( gPINN ) to implement it on a system of coupled partial differential equations (PDEs).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2111.02801", "content": "Nov 1, 2021 · Deep learning has been shown to be an effective tool in solving partial differential equations (PDEs) through physics-informed neural networks (PINNs). PINNs embed the PDE residual into the loss function of the neural network, and have been successfully employed to solve diverse forward and inverse PDE problems. However, one disadvantage of the first generation of PINNs is that they usually ... Oct 1, 2021 · MIT PRIMES Conference, 10/16/21 Presented by: Jeremy Yu Mentor: Dr. Lu Lu gPINN : Gradient-enhanced physics-informed neural networks The data and code for the paper J. Yu , L. Lu , X. Meng, & G. E. Karniadakis. Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems. Computer Methods in Applied Mechanics and Engineering , 393, 114823, 2022. Nov 1, 2021 · Deep learning has been shown to be an effective tool in solving partial differential equations (PDEs) through physics-informed neural networks (PINNs). PINNs embed the PDE residual into the loss ... How to solve PDEs using gpinn? gPINN to solve PDEs. 3.2.1. Poisson equation with the Dirichlet boundary conditions u(x= 0) = 0 and u(x=π) = π. The analytic solution is where N(x) is a neural network. Hence, the loss function is where Lfand Lgare defined in Eqs. (2) and (3), respectively. PINN for u decreases from 26% to 0.48% (Fig. 2 A). Is gpinn based on a non-Fickian diffusion-thermoelasticity problem? As far as we are aware, this is the first time that gPINN is implemented on the generalized coupled non-Fickian/non-Fourierian diffusion-thermoelasticity problem . Based on the Lord-Shulman theory of coupled thermoelasticity, the governing equations for a strip of copper are derived. Can gpinn be applied to a two-dimensional generalized thermoelastic diffusion problem? This example examines the application of gPINN to a two-dimensional generalized thermoelastic diffusion problem. A two-dimensional homogeneous and isotropic body is assumed to undergo both thermal and chemical potential shocks, and the governing equations are introduced by (Sherief et al., 2004), (Sherief and Saleh, 2005). Can gpinn solve inverse PDE problems? We demonstrated the effectiveness of gPINN in both forward and inverse PDE problems, including Poisson equation, diffusion–reaction equation, Brinkman–Forchheimer model, Burgers’ equation, and Allen-Cahn equation. How many neural networks are embodied in gpinn? Due to the different behavior of output variables (i.e., concentration, displacement, and temperature), three separate neural networks are embodied in gPINN. To systematically tackle the issue of boundary and initial conditions satisfaction, some problematic ones are enforced as hard constraints. Are gpinn and hyperparameter setting based on multiple neural networks? A comparative analysis between an individual neural network and multiple neural networks is presented to address systematically the probable capacities of the latter structure. Multiple independent runs with a range of assumptions are needed to derive the architecture of gPINN and hyperparameter setting. Nov 1, 2023 · This paper seeks to extend the concept of a newly developed deep learning approach called gradient-enhanced physics-informed neural network ( gPINN ) to implement it on a system of coupled partial differential equations (PDEs)."}
+{"idx": 2, "title": "gPINN for PRIMES Conference - MIT Mathematics", "date": "", "ddg_snippet": "Oct 1, 2021 · MIT PRIMES Conference, 10/16/21 Presented by: Jeremy Yu Mentor: Dr. Lu Lu", "subpage_snippet": "", "source": "math.mit.edu", "link": "https://math.mit.edu/research/highschool/primes/materials/2021/October/1-3-Yu.pdf", "content": "Oct 1, 2021 · MIT PRIMES Conference, 10/16/21 Presented by: Jeremy Yu Mentor: Dr. Lu Lu"}
+{"idx": 3, "title": "gpinn/README.md at main · lu-group/gpinn · GitHub", "date": "", "ddg_snippet": "gPINN : Gradient-enhanced physics-informed neural networks The data and code for the paper J. Yu , L. Lu , X. Meng, & G. E. Karniadakis. Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems. Computer Methods in Applied Mechanics and Engineering , 393, 114823, 2022.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/lu-group/gpinn/blob/main/README.md", "content": "gPINN : Gradient-enhanced physics-informed neural networks The data and code for the paper J. Yu , L. Lu , X. Meng, & G. E. Karniadakis. Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems. Computer Methods in Applied Mechanics and Engineering , 393, 114823, 2022."}
+{"idx": 4, "title": "A gradient-enhanced physics-informed neural network (gPINN ...", "date": "", "ddg_snippet": "Nov 1, 2023 · This paper seeks to extend the concept of a newly developed deep learning approach called gradient-enhanced physics-informed neural network ( gPINN ) to implement it on a system of coupled partial differential equations (PDEs).", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0952197623010928", "content": "Nov 1, 2023 · This paper seeks to extend the concept of a newly developed deep learning approach called gradient-enhanced physics-informed neural network ( gPINN ) to implement it on a system of coupled partial differential equations (PDEs)."}
+{"idx": 5, "title": "lu-group/gpinn: Gradient-enhanced physics-informed ...", "date": "", "ddg_snippet": "Computer Methods in Applied Mechanics and Engineering, 393, 114823, 2022. ... Diffusion-reaction equation · Poisson equation in 2D. Inverse PDEs problems.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/lu-group/gpinn", "content": "Computer Methods in Applied Mechanics and Engineering, 393, 114823, 2022. ... Diffusion-reaction equation · Poisson equation in 2D. Inverse PDEs problems."}
+{"idx": 6, "title": "Gradient-enhanced physics-informed neural networks for ...", "date": "", "ddg_snippet": "Apr 1, 2022 · Deep learning has been shown to be an effective tool in solving partial differential equations (PDEs) through physics-informed neural networks (PINNs)…", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0045782522001438", "content": "Apr 1, 2022 · Deep learning has been shown to be an effective tool in solving partial differential equations (PDEs) through physics-informed neural networks (PINNs)…"}
+{"idx": 7, "title": "Gradient-enhanced physics-informed neural networks for ...", "date": "", "ddg_snippet": "Nov 1, 2021 · Deep learning has been shown to be an effective tool in solving partial differential equations (PDEs) through physics-informed neural networks (PINNs). PINNs embed the PDE residual into the loss ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/355925155_Gradient-enhanced_physics-informed_neural_networks_for_forward_and_inverse_PDE_problems", "content": "Nov 1, 2021 · Deep learning has been shown to be an effective tool in solving partial differential equations (PDEs) through physics-informed neural networks (PINNs). PINNs embed the PDE residual into the loss ..."}
+{"idx": 8, "title": "Gradient-enhanced physics-informed neural networks for ...", "date": "", "ddg_snippet": "by J Yu · 2022 · Cited by 657 — We propose a new method , gradient-enhanced physics-informed neural networks (gPINNs), for improving the accuracy of PINNs .", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/abs/pii/S0045782522001438", "content": "by J Yu · 2022 · Cited by 657 — We propose a new method , gradient-enhanced physics-informed neural networks (gPINNs), for improving the accuracy of PINNs ."}
+{"idx": 9, "title": "Physical informed neural networks with soft and hard ...", "date": "", "ddg_snippet": "This article primarily focuses on investigating the dynamics of the unsteady advection- diffusion equation (ADE) under various boundary constraints.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2306.12749v2", "content": "This article primarily focuses on investigating the dynamics of the unsteady advection- diffusion equation (ADE) under various boundary constraints."}
diff --git a/data/sampled_jsons/github.comfiveaiunderstanding_safety_finetuning_files.jsonl b/data/sampled_jsons/github.comfiveaiunderstanding_safety_finetuning_files.jsonl
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index 0000000000000000000000000000000000000000..423e117848e8ba214ca4c96f50414abc3ce613ac
--- /dev/null
+++ b/data/sampled_jsons/github.comfiveaiunderstanding_safety_finetuning_files.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub · Build and ship software on a single, collaborative...", "date": "", "ddg_snippet": "Whether you’re scaling your development process or just learning how to code, GitHub is where you belong. Join the world’s most widely adopted AI-powered developer platform to build the technologies that redefine what’s possible.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/", "content": "Whether you’re scaling your development process or just learning how to code, GitHub is where you belong. Join the world’s most widely adopted AI-powered developer platform to build the technologies that redefine what’s possible."}
+{"idx": 1, "title": "Sign in to GitHub · GitHub", "date": "", "ddg_snippet": "GitHub is where people build software. More than 150 million people use GitHub to discover, fork, and contribute to over 420 million projects.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/login", "content": "GitHub is where people build software. More than 150 million people use GitHub to discover, fork, and contribute to over 420 million projects."}
+{"idx": 2, "title": "GitHub", "date": "", "ddg_snippet": "How people build software. GitHub has 524 repositories available. Follow their code on GitHub .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/github", "content": "How people build software. GitHub has 524 repositories available. Follow their code on GitHub ."}
+{"idx": 3, "title": "Sign up for GitHub", "date": "", "ddg_snippet": "GitHub is where people build software. More than 150 million people use GitHub to discover, fork, and contribute to over 420 million projects.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/signup", "content": "GitHub is where people build software. More than 150 million people use GitHub to discover, fork, and contribute to over 420 million projects."}
+{"idx": 4, "title": "Git · GitHub", "date": "", "ddg_snippet": "While Git takes care of the underlying version control, GitHub is the collaboration platform built on top of it. GitHub is the place for pull requests, comments, reviews, integrated tests, and so much more. Most developers work locally to develop and use GitHub for collaboration.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/git-guides", "content": "While Git takes care of the underlying version control, GitHub is the collaboration platform built on top of it. GitHub is the place for pull requests, comments, reviews, integrated tests, and so much more. Most developers work locally to develop and use GitHub for collaboration."}
+{"idx": 5, "title": "GitHub Learn", "date": "", "ddg_snippet": "GitHub Learn is the all-in-one learning experience platform that unifies GitHub ’s official learning and enablement resources into personalized journeys. Whether you're pursuing certification or want to learn about one of our new features, GitHub Learn helps you set goals, track progress, and build the skills that matter — all from one trusted source.", "subpage_snippet": "", "source": "learn.github.com", "link": "https://learn.github.com/learning", "content": "GitHub Learn is the all-in-one learning experience platform that unifies GitHub ’s official learning and enablement resources into personalized journeys. Whether you're pursuing certification or want to learn about one of our new features, GitHub Learn helps you set goals, track progress, and build the skills that matter — all from one trusted source."}
+{"idx": 6, "title": "Pricing · Plans for every developer · GitHub", "date": "", "ddg_snippet": "We get it, there's a lot you can do with GitHub . That’s why we've packed all of it into a single risk-free trial that includes GitHub Enterprise, Copilot, and Advanced Security.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/pricing", "content": "We get it, there's a lot you can do with GitHub . That’s why we've packed all of it into a single risk-free trial that includes GitHub Enterprise, Copilot, and Advanced Security."}
+{"idx": 7, "title": "Explore GitHub", "date": "", "ddg_snippet": "Explore is your guide to finding your next project, catching up with what’s trending, and connecting with the GitHub community.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/explore", "content": "Explore is your guide to finding your next project, catching up with what’s trending, and connecting with the GitHub community."}
+{"idx": 8, "title": "About GitHub", "date": "", "ddg_snippet": "GitHub is where people build software. More than 150 million people use GitHub to discover, fork, and contribute to over 420 million projects.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/about", "content": "GitHub is where people build software. More than 150 million people use GitHub to discover, fork, and contribute to over 420 million projects."}
+{"idx": 9, "title": "️ Virtual Display Driver Development Team - GitHub", "date": "", "ddg_snippet": "Add virtual monitors to your windows 10/11 device! Works with VR, OBS, Sunshine, and/or any desktop sharing software. - VirtualDrivers/Virtual-Display-Driver", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/VirtualDrivers/Virtual-Display-Driver", "content": "Add virtual monitors to your windows 10/11 device! Works with VR, OBS, Sunshine, and/or any desktop sharing software. - VirtualDrivers/Virtual-Display-Driver"}
diff --git a/data/sampled_jsons/github.comfiveaiunderstanding_safety_finetuning_minGPT_transformer_blocks_configuration.jsonl b/data/sampled_jsons/github.comfiveaiunderstanding_safety_finetuning_minGPT_transformer_blocks_configuration.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8f5392a38673f757b3c202dea515581b5de45d93
--- /dev/null
+++ b/data/sampled_jsons/github.comfiveaiunderstanding_safety_finetuning_minGPT_transformer_blocks_configuration.jsonl
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+{"idx": 0, "title": "GitHub Foundations Certification Study Guide", "date": "", "ddg_snippet": "Mar 15, 2024 · Describe GitHub Sponsors GitHub certification registration process After completing your study plan, you are ready to take the certification exam and demonstrate your skills. The exam costs $99, but for a limited time (as of this publication date), you can get a 50% discount on the Foundations exam. Here are the steps to schedule your exam:", "subpage_snippet": "", "source": "techcommunity.microsoft.com", "link": "https://techcommunity.microsoft.com/blog/educatordeveloperblog/github-foundations-certification-study-guide/4079056", "content": "Mar 15, 2024 · Describe GitHub Sponsors GitHub certification registration process After completing your study plan, you are ready to take the certification exam and demonstrate your skills. The exam costs $99, but for a limited time (as of this publication date), you can get a 50% discount on the Foundations exam. Here are the steps to schedule your exam:"}
+{"idx": 1, "title": "GitHub Copilot Vibe Coding Workshop | Microsoft Community Hub", "date": "", "ddg_snippet": "Jul 9, 2025 · Introducing GitHub Copilot Vibe Coding Workshop I'm more than happy to introduce this GitHub Copilot Vibe Coding Workshop, a resource available for everyone to use. It's based on a typical app development scenario – building a web application that consists of a frontend UI and backend API with database transaction. This workshop has six steps:", "subpage_snippet": "", "source": "techcommunity.microsoft.com", "link": "https://techcommunity.microsoft.com/blog/azuredevcommunityblog/github-copilot-vibe-coding-workshop/4430440", "content": "Jul 9, 2025 · Introducing GitHub Copilot Vibe Coding Workshop I'm more than happy to introduce this GitHub Copilot Vibe Coding Workshop, a resource available for everyone to use. It's based on a typical app development scenario – building a web application that consists of a frontend UI and backend API with database transaction. This workshop has six steps:"}
+{"idx": 2, "title": "GitHub integration with Microsoft Loop", "date": "", "ddg_snippet": "Aug 25, 2024 · Bring your GitHub issues and pull requests (PRs) into Loop for seamless collaboration and remove the need to switch between multiple apps.", "subpage_snippet": "", "source": "techcommunity.microsoft.com", "link": "https://techcommunity.microsoft.com/blog/microsoft365insiderblog/github-integration-with-microsoft-loop/4225370", "content": "Aug 25, 2024 · Bring your GitHub issues and pull requests (PRs) into Loop for seamless collaboration and remove the need to switch between multiple apps."}
+{"idx": 3, "title": "¡GitHub Copilot gratis! Ahora al alcance de todos | Microsoft...", "date": "", "ddg_snippet": "¡Buenas noticias! Ahora todos pueden usar GitHub Copilot GRATIS en Visual Studio Code. Sólo necesitas tu cuenta de GitHub . Sin periodos de pruebas, sin...", "subpage_snippet": "", "source": "techcommunity.microsoft.com", "link": "https://techcommunity.microsoft.com/blog/azuredevcommunityblog/¡github-copilot-gratis-ahora-al-alcance-de-todos/4356908", "content": "¡Buenas noticias! Ahora todos pueden usar GitHub Copilot GRATIS en Visual Studio Code. Sólo necesitas tu cuenta de GitHub . Sin periodos de pruebas, sin..."}
+{"idx": 4, "title": "New Certification on GitHub administration | Microsoft Community...", "date": "", "ddg_snippet": "Jul 8, 2025 · GitHub Learn and Microsoft Learn are working together to enhance your GitHub learning experience. Our GitHub exams are now available on Pearson VUE via Microsoft Learn, bringing a more unified and accessible experience to learners worldwide. If you already use Microsoft Learn, you can more easily discover and engage with GitHub topics.", "subpage_snippet": "", "source": "techcommunity.microsoft.com", "link": "https://techcommunity.microsoft.com/blog/microsoftlearnblog/new-certification-on-github-administration/4428967", "content": "Jul 8, 2025 · GitHub Learn and Microsoft Learn are working together to enhance your GitHub learning experience. Our GitHub exams are now available on Pearson VUE via Microsoft Learn, bringing a more unified and accessible experience to learners worldwide. If you already use Microsoft Learn, you can more easily discover and engage with GitHub topics."}
+{"idx": 5, "title": "Linking your personal Microsoft Account to your GitHub validated...", "date": "", "ddg_snippet": "Dec 2, 2022 · After your GitHub and Microsoft account credentials are linked, you can use that single sign-in anywhere a personal Microsoft account can be used, like on...", "subpage_snippet": "", "source": "techcommunity.microsoft.com", "link": "https://techcommunity.microsoft.com/blog/educatordeveloperblog/redeeming-azure-for-student-from-your-github-student-pack-when-you-do-not-have-a/3689053", "content": "Dec 2, 2022 · After your GitHub and Microsoft account credentials are linked, you can use that single sign-in anywhere a personal Microsoft account can be used, like on..."}
+{"idx": 6, "title": "GitHub Copilot for Azure の一般提供を開始：Agent モードにも対応", "date": "", "ddg_snippet": "Jun 25, 2025 · GitHub Copilot for Azure は、2024 年 11 月の Microsoft Ignite カンファレンスで public preview として公開されました。これにより、開発者、IT 運用者、DevOps 実践者たちは、自分たちの Azure...", "subpage_snippet": "", "source": "techcommunity.microsoft.com", "link": "https://techcommunity.microsoft.com/blog/azuredevcommunityblog/github-copilot-for-azure-の一般提供を開始：agent-モードにも対応/4426996", "content": "Jun 25, 2025 · GitHub Copilot for Azure は、2024 年 11 月の Microsoft Ignite カンファレンスで public preview として公開されました。これにより、開発者、IT 運用者、DevOps 実践者たちは、自分たちの Azure..."}
+{"idx": 7, "title": "Get Certified with GitHub - techcommunity.microsoft.com", "date": "", "ddg_snippet": "GitHub and Microsoft are helping you to boost your tech career with the Get Certified with GitHub livestream series! Starts from June 5th until June 26th. These sessions are designed to help you get certified on the GitHub Foundation Certification and to help you explore essential tools like GitHub Copilot and GitHub Codespaces. Plus, you'll have the chance to earn a free certification voucher ...", "subpage_snippet": "", "source": "techcommunity.microsoft.com", "link": "https://techcommunity.microsoft.com/blog/educatordeveloperblog/get-certified-with-github/4141657", "content": "GitHub and Microsoft are helping you to boost your tech career with the Get Certified with GitHub livestream series! Starts from June 5th until June 26th. These sessions are designed to help you get certified on the GitHub Foundation Certification and to help you explore essential tools like GitHub Copilot and GitHub Codespaces. Plus, you'll have the chance to earn a free certification voucher ..."}
+{"idx": 8, "title": "¿Qué es GitHub Copilot y cómo pueden los estudiantes y maestros...", "date": "", "ddg_snippet": "GitHub Copilot es un programador de pares de Inteligencia Artificial que te ayuda a escribir código más rápido y con menos trabajo. Puede ayudarte mientras...", "subpage_snippet": "", "source": "techcommunity.microsoft.com", "link": "https://techcommunity.microsoft.com/blog/educatordeveloperblog/¿qué-es-github-copilot-y-cómo-pueden-los-estudiantes-y-maestros-obtenerlo-gratis/3815760", "content": "GitHub Copilot es un programador de pares de Inteligencia Artificial que te ayuda a escribir código más rápido y con menos trabajo. Puede ayudarte mientras..."}
+{"idx": 9, "title": "DocAider: Automated Documentation Maintenance for Open-source...", "date": "", "ddg_snippet": "The tool leverages Github Actions workflows to trigger documentation tasks upon pull requests (PRs) opening, providing valuable insights into continuous documentation maintenance. This approach addresses the challenges of automating documentation and ensures that project documentation remains current with minimal human intervention.", "subpage_snippet": "", "source": "techcommunity.microsoft.com", "link": "https://techcommunity.microsoft.com/blog/educatordeveloperblog/docaider-automated-documentation-maintenance-for-open-source-github-repositories/4245588", "content": "The tool leverages Github Actions workflows to trigger documentation tasks upon pull requests (PRs) opening, providing valuable insights into continuous documentation maintenance. This approach addresses the challenges of automating documentation and ensures that project documentation remains current with minimal human intervention."}
diff --git a/data/sampled_jsons/github_fiveai_understanding_safety_finetuning_config_learning_rate_year_2024.jsonl b/data/sampled_jsons/github_fiveai_understanding_safety_finetuning_config_learning_rate_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c2bb7bb79d8b66ab3b557c501c8dcfe1bc162ece
--- /dev/null
+++ b/data/sampled_jsons/github_fiveai_understanding_safety_finetuning_config_learning_rate_year_2024.jsonl
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+{"idx": 0, "title": "GitHub - fiveai / understanding _ safety _ finetuning : Official Code for...", "date": "", "ddg_snippet": "fiveai / understanding _ safety _ finetuning Public.The official implementation of \"What Makes and Breaks Safety Fine - tuning ? A Mechanistic Study\". This work is accepted to NeurIPS 2024.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/fiveai/understanding_safety_finetuning", "content": "fiveai / understanding _ safety _ finetuning Public.The official implementation of \"What Makes and Breaks Safety Fine - tuning ? A Mechanistic Study\". This work is accepted to NeurIPS 2024."}
+{"idx": 1, "title": "GSPO Reinforcement Learning | Unsloth Documentation", "date": "", "ddg_snippet": "Reinforcement Learning (RL) Guide. This lead to the creation of GSPO, which now assigns the importance on the sequence likelihood rather than the individual token likelihoods of the tokens. Enable GSPO in Unsloth by setting importance_sampling_level ...", "subpage_snippet": "", "source": "docs.unsloth.ai", "link": "https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide/gspo-reinforcement-learning", "content": "Reinforcement Learning (RL) Guide. This lead to the creation of GSPO, which now assigns the importance on the sequence likelihood rather than the individual token likelihoods of the tokens. Enable GSPO in Unsloth by setting importance_sampling_level ..."}
+{"idx": 2, "title": "A Practical Guide to Contrastive Learning", "date": "", "ddg_snippet": "The author emphasizes the importance of hyperparameter tuning in the SimSiam model, particularly for learning rate and MLP hidden layers, to achieve optimal performance. The use of UMAP for visualizing the learned representations indicates the author's view that it is a useful tool...", "subpage_snippet": "", "source": "readmedium.com", "link": "https://readmedium.com/a-practical-guide-to-contrastive-learning-26e912c0362f", "content": "The author emphasizes the importance of hyperparameter tuning in the SimSiam model, particularly for learning rate and MLP hidden layers, to achieve optimal performance. The use of UMAP for visualizing the learned representations indicates the author's view that it is a useful tool..."}
+{"idx": 3, "title": "Axolotl Framework Tutorial: Simplified LLM Fine - Tuning ... | Markaicode", "date": "", "ddg_snippet": "Learn Axolotl framework for efficient LLM fine - tuning . Step-by-step guide with code examples, configuration setup, and deployment strategies.", "subpage_snippet": "", "source": "markaicode.com", "link": "https://markaicode.com/axolotl-framework-tutorial-llm-fine-tuning/", "content": "Learn Axolotl framework for efficient LLM fine - tuning . Step-by-step guide with code examples, configuration setup, and deployment strategies."}
+{"idx": 4, "title": "Scaling Llama 2 (7 - 70B) Fine - tuning on Multi-Node GPUs with Ray...", "date": "", "ddg_snippet": "Ray AIR distributed fine - tuning flow | Source: github .com. 3.1. Creating an experiment in MLflow You have to first create an experiment in MLflow to register and save the fine - tuned model along with its artefacts. learning _ rate = config .get(\" learning _ rate \", 2e-4)", "subpage_snippet": "", "source": "plainenglish.io", "link": "https://plainenglish.io/blog/scaling-llama-2-7-70b-fine-tuning-on-multi-node-gpus-with-ray-on-databricks-d63cdb", "content": "Ray AIR distributed fine - tuning flow | Source: github .com. 3.1. Creating an experiment in MLflow You have to first create an experiment in MLflow to register and save the fine - tuned model along with its artefacts. learning _ rate = config .get(\" learning _ rate \", 2e-4)"}
+{"idx": 5, "title": "gpt-oss-(20B)- Fine - tuning .ipynb - Colab", "date": "", "ddg_snippet": "Gpt-oss fine - tuning now supports 8× longer context with 0 accuracy loss. Read more. Unsloth now supports Text-to-Speech (TTS) models.We're about to demonstrate the power of the new OpenAI GPT-OSS 20B model through a finetuning example.", "subpage_snippet": "", "source": "colab.research.google.com", "link": "https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-(20B)-Fine-tuning.ipynb", "content": "Gpt-oss fine - tuning now supports 8× longer context with 0 accuracy loss. Read more. Unsloth now supports Text-to-Speech (TTS) models.We're about to demonstrate the power of the new OpenAI GPT-OSS 20B model through a finetuning example."}
+{"idx": 6, "title": "Budget Instruction Fine - tuning of Llama... - MLOps Community", "date": "", "ddg_snippet": "Guide for preparing data and fine - tuning of Llama 3 8B Instruct. After learning some of the basics for fine - tuning LLMs, now we can do the actual fine - tuning . In this blogpost we are going to fine - tune the Llama 3 8B Instruct LLM on a custom created medical instruct dataset.", "subpage_snippet": "", "source": "mlops.community", "link": "https://mlops.community/budget-instruction-fine-tuning-of-llama-3-8b-instructon-medical-data-with-hugging-face-google-colab-and-unsloth/", "content": "Guide for preparing data and fine - tuning of Llama 3 8B Instruct. After learning some of the basics for fine - tuning LLMs, now we can do the actual fine - tuning . In this blogpost we are going to fine - tune the Llama 3 8B Instruct LLM on a custom created medical instruct dataset."}
+{"idx": 7, "title": "Train a diffusion model", "date": "", "ddg_snippet": "Training configuration .Guide to finetuning a Stable Diffusion model on your own dataset. Guide to using LoRA, a memory-efficient technique for finetuning really large models faster.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/docs/diffusers/tutorials/basic_training", "content": "Training configuration .Guide to finetuning a Stable Diffusion model on your own dataset. Guide to using LoRA, a memory-efficient technique for finetuning really large models faster."}
+{"idx": 8, "title": "Gemini AI Photo Editor - Free Transform Your Photos with Advanced AI", "date": "", "ddg_snippet": "Intelligent Image Understanding . Our AI engine recognizes objects, scenes, styles, and emotions in your photos with remarkable accuracy. This ensures every transformation maintains meaningful connections with the original while applying your desired changes seamlessly.", "subpage_snippet": "", "source": "geminiaiphotoeditor.com", "link": "https://geminiaiphotoeditor.com/", "content": "Intelligent Image Understanding . Our AI engine recognizes objects, scenes, styles, and emotions in your photos with remarkable accuracy. This ensures every transformation maintains meaningful connections with the original while applying your desired changes seamlessly."}
+{"idx": 9, "title": "What Makes and Breaks Safety Fine - tuning ? | OpenReview", "date": "", "ddg_snippet": "Safety fine - tuning helps align Large Language Models (LLMs) with human preferences for their safe deployment. To better understand the underlying factors that make models safe via safety...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=JEflV4nRlH", "content": "Safety fine - tuning helps align Large Language Models (LLMs) with human preferences for their safe deployment. To better understand the underlying factors that make models safe via safety..."}
diff --git a/data/sampled_jsons/how_increased_Sympathy_leads_to_activation_OR_Sympathy_affects_participation_OR_Sympathy_drives_acti_year_2024.jsonl b/data/sampled_jsons/how_increased_Sympathy_leads_to_activation_OR_Sympathy_affects_participation_OR_Sympathy_drives_acti_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e55f6e6d25ab76f6cf5ccc5ed648e11398419be1
--- /dev/null
+++ b/data/sampled_jsons/how_increased_Sympathy_leads_to_activation_OR_Sympathy_affects_participation_OR_Sympathy_drives_acti_year_2024.jsonl
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+{"idx": 1, "title": "Slot Gacor And The Path To Fortune: How To Pick Out The Right", "date": "", "ddg_snippet": "However, while the idea of striking the kitty seems tempting, sympathy how to take the right slots is crucial for increasing the potential of your ...", "subpage_snippet": "", "source": "localwifipoacher.com", "link": "https://localwifipoacher.com/slot-gacor-and-the-path-to-fortune-how-to-pick-out-the-right-slots-for-calm-wins-and-big-rewards/", "content": "However, while the idea of striking the kitty seems tempting, sympathy how to take the right slots is crucial for increasing the potential of your ..."}
+{"idx": 2, "title": "Understanding Empathy vs. Sympathy: What's the Difference?", "date": "", "ddg_snippet": "Cultivating empathy over sympathy enhances relationships by encouraging active listening & emotional engagement, leading to more meaningful ...", "subpage_snippet": "", "source": "positivepsychology.com", "link": "https://positivepsychology.com/empathy-vs-sympathy/", "content": "Cultivating empathy over sympathy enhances relationships by encouraging active listening & emotional engagement, leading to more meaningful ..."}
+{"idx": 3, "title": "Sympathy, empathy, and compassion: A grounded theory study of", "date": "", "ddg_snippet": "... to investigate advanced cancer patients’ understandings, experiences, and preferences of “ sympathy ,” “empathy,” and “compassion” in ...", "subpage_snippet": "", "source": "journals.sagepub.com", "link": "https://journals.sagepub.com/doi/10.1177/0269216316663499", "content": "... to investigate advanced cancer patients’ understandings, experiences, and preferences of “ sympathy ,” “empathy,” and “compassion” in ..."}
+{"idx": 4, "title": "A Comparison of Empathy and Sympathy Between", "date": "", "ddg_snippet": "We conducted a MANOVA and failed to identify differences in levels of empathy or sympathy across participants regardless of academic discipline ...", "subpage_snippet": "", "source": "tpcjournal.nbcc.org", "link": "https://tpcjournal.nbcc.org/a-comparison-of-empathy-and-sympathy-between-counselors-in-training-and-their-non-counseling-academic-peers/", "content": "We conducted a MANOVA and failed to identify differences in levels of empathy or sympathy across participants regardless of academic discipline ..."}
+{"idx": 5, "title": "Empathy and Sympathy in Ethics | Internet Encyclopedia of", "date": "", "ddg_snippet": "Thus: “ mitfühlen ,” to “feel with” or “sympathize” and “ nachfühlen ,” to “feel vicariously” or even “ to feel after” as in ...", "subpage_snippet": "", "source": "iep.utm.edu", "link": "https://iep.utm.edu/empathy-sympathy-in-ethics/", "content": "Thus: “ mitfühlen ,” to “feel with” or “sympathize” and “ nachfühlen ,” to “feel vicariously” or even “ to feel after” as in ..."}
+{"idx": 6, "title": "How Malleable are Non-Cognitive Skills? Measuring the Impact of", "date": "", "ddg_snippet": "... to share results and policy lessons from randomized evaluations, to build new partnerships between researchers and practitioners, and to train ...", "subpage_snippet": "", "source": "www.povertyactionlab.org", "link": "https://www.povertyactionlab.org/evaluation/how-malleable-are-non-cognitive-skills-measuring-impact-increasing-grit-turkey", "content": "... to share results and policy lessons from randomized evaluations, to build new partnerships between researchers and practitioners, and to train ..."}
+{"idx": 7, "title": "Gender Differences in Classroom Sympathy and Antipathy: A", "date": "", "ddg_snippet": "A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research ...", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/2227-7102/15/7/830", "content": "A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research ..."}
+{"idx": 8, "title": "See Jane Run: Women Politicians as Role Models for Adolescents", "date": "", "ddg_snippet": "This rupture leads to an increase in women’s social visibility and political participation , further eroding traditional norms and institutions that ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/227543444_See_Jane_Run_Women_Politicians_as_Role_Models_for_Adolescents", "content": "This rupture leads to an increase in women’s social visibility and political participation , further eroding traditional norms and institutions that ..."}
+{"idx": 9, "title": "A new tool for boosting empathy in healthcare", "date": "", "ddg_snippet": "The paper 'Substantial Increases in Healthcare Students' State Empathy Scores Owing to Participation in a Single Improvisation Session' was published ...", "subpage_snippet": "", "source": "universitiesmatter.edu.au", "link": "https://universitiesmatter.edu.au/a-new-tool-for-boosting-empathy-in-healthcare/", "content": "The paper 'Substantial Increases in Healthcare Students' State Empathy Scores Owing to Participation in a Single Improvisation Session' was published ..."}
diff --git a/data/sampled_jsons/httpsar5iv.labs.arxiv.orghtml2411.07501.jsonl b/data/sampled_jsons/httpsar5iv.labs.arxiv.orghtml2411.07501.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6f4a81460214482264d03380c025916ac143dd83
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+++ b/data/sampled_jsons/httpsar5iv.labs.arxiv.orghtml2411.07501.jsonl
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+{"idx": 0, "title": "ar5iv - Articles from arXiv.org as responsive HTML5 web documents", "date": "", "ddg_snippet": "ar5iv offers a modern web view for arXiv's preprints. An open community resource, on a quest to a full collection of high-quality documents.", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/", "content": "ar5iv offers a modern web view for arXiv's preprints. An open community resource, on a quest to a full collection of high-quality documents."}
+{"idx": 1, "title": "ar5iv - Articles from arXiv.org as responsive HTML5 web documents", "date": "", "ddg_snippet": "ar5iv - Articles from arXiv.org as responsive HTML5 web documents", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/en_US", "content": "ar5iv - Articles from arXiv.org as responsive HTML5 web documents"}
+{"idx": 2, "title": "GitHub - dginev/ar5iv: A web service offering HTML5 articles from arXiv ...", "date": "", "ddg_snippet": "A web service offering HTML5 articles from arXiv.org as converted with latexml. The e-journal styling of document pages is developed separately at ar5iv-css. Authors can reproduce locally using ar5ivist. Seeded via CorTeX data. Hosted by arXivLabs. Created by", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/dginev/ar5iv", "content": "A web service offering HTML5 articles from arXiv.org as converted with latexml. The e-journal styling of document pages is developed separately at ar5iv-css. Authors can reproduce locally using ar5ivist. Seeded via CorTeX data. Hosted by arXivLabs. Created by"}
+{"idx": 3, "title": "Releases · dginev/ar5iv - GitHub", "date": "", "ddg_snippet": "A lot remains to be done - especially in the \"Article Viewer\" project, which hasn't been properly initiated just yet. Our gratitude to the community for the hundreds of reports in 2022 - please keep sending more our way! ar5iv is now moving closer towards native arXiv integration, and that will be a broader theme to explore in 2023.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/dginev/ar5iv/releases", "content": "A lot remains to be done - especially in the \"Article Viewer\" project, which hasn't been properly initiated just yet. Our gratitude to the community for the hundreds of reports in 2022 - please keep sending more our way! ar5iv is now moving closer towards native arXiv integration, and that will be a broader theme to explore in 2023."}
+{"idx": 4, "title": "ar5iv 04.2024 - An HTML5 dataset for arXiv.org · SIGMathLing", "date": "", "ddg_snippet": "Description This is the first public release of the ar5iv dataset generated by the KWARC research group. It contains HTML5+MathML conversions of the scientific documents from the arXiv.org preprint server, upto the start of April 2024. As of April 2024, the provided HTML here also seeds the live ar5iv Lab site, maintained by the same author.", "subpage_snippet": "", "source": "sigmathling.kwarc.info", "link": "https://sigmathling.kwarc.info/resources/ar5iv-dataset-2024/", "content": "Description This is the first public release of the ar5iv dataset generated by the KWARC research group. It contains HTML5+MathML conversions of the scientific documents from the arXiv.org preprint server, upto the start of April 2024. As of April 2024, the provided HTML here also seeds the live ar5iv Lab site, maintained by the same author."}
+{"idx": 5, "title": "ar5iv/README.md at main · dginev/ar5iv · GitHub", "date": "", "ddg_snippet": "A web service offering HTML5 articles from arXiv.org as converted with latexml - ar5iv/README.md at main · dginev/ar5iv", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/dginev/ar5iv/blob/main/README.md", "content": "A web service offering HTML5 articles from arXiv.org as converted with latexml - ar5iv/README.md at main · dginev/ar5iv"}
+{"idx": 6, "title": "Releases · dginev/ar5ivist - GitHub", "date": "", "ddg_snippet": "This ar5ivist release tracks a stable commit tested against arXiv's sandboxes, with the LaTeXML v0.9 release candidate. This is not the v0.9 release yet, and there will likely be one more ar5ivist release in 2025. This release tracks the new version 0.8.8 of LaTeXML, and the associated ar5iv-css at ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/dginev/ar5ivist/releases", "content": "This ar5ivist release tracks a stable commit tested against arXiv's sandboxes, with the LaTeXML v0.9 release candidate. This is not the v0.9 release yet, and there will likely be one more ar5ivist release in 2025. This release tracks the new version 0.8.8 of LaTeXML, and the associated ar5iv-css at ..."}
+{"idx": 7, "title": "[2411.07501] LAuReL: Learned Augmented Residual Layer", "date": "", "ddg_snippet": "In this paper we introduce Learned Augmented Residual Layer (LAuReL)—a novel generalization of the canonical residual connection—with the goal to be an in-situ replacement of the latter while outperforming on both model quality and footprint metrics. Our experiments show that using LAuReL can help boost performance for both vision and language models. For example, on the ResNet-50 ...", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2411.07501", "content": "In this paper we introduce Learned Augmented Residual Layer (LAuReL)—a novel generalization of the canonical residual connection—with the goal to be an in-situ replacement of the latter while outperforming on both model quality and footprint metrics. Our experiments show that using LAuReL can help boost performance for both vision and language models. For example, on the ResNet-50 ..."}
+{"idx": 8, "title": "LAuReL: Learned Augmented Residual Layer - arXiv.org", "date": "", "ddg_snippet": "In this paper we introduce learned augmented residual layer, LAuReL, which generalizes the canonical residual connection. Recall that deep-learning models with residual connections have a 'block' structure, with many blocks chained together between the input and final output; these could be convolution/identity blocks within a ResNet, a transformer block in a transformer encoder/decoder ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.07501v4", "content": "In this paper we introduce learned augmented residual layer, LAuReL, which generalizes the canonical residual connection. Recall that deep-learning models with residual connections have a 'block' structure, with many blocks chained together between the input and final output; these could be convolution/identity blocks within a ResNet, a transformer block in a transformer encoder/decoder ..."}
+{"idx": 9, "title": "Diffusion Probabilistic Models beat GAN on Medical 2D Images", "date": "", "ddg_snippet": "The success of Deep Learning applications critically depends on the quality and scale of the underlying training data. Generative adversarial networks (GANs) can generate arbitrary large datasets, but diversity and fid…", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2212.07501", "content": "The success of Deep Learning applications critically depends on the quality and scale of the underlying training data. Generative adversarial networks (GANs) can generate arbitrary large datasets, but diversity and fid…"}
diff --git a/data/sampled_jsons/httpsarxiv.orgabs2310.06839.jsonl b/data/sampled_jsons/httpsarxiv.orgabs2310.06839.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b60e1f077b4f6dd293fa5f26240c10da4041fedd
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+{"idx": 1, "title": "[2310.06839v1] LongLLMLingua: Accelerating and Enhancing LLMs in Long ...", "date": "", "ddg_snippet": "Abstract: In long context scenarios, large language models (LLMs) face three main challenges: higher computational/financial cost, longer latency, and inferior performance. Some studies reveal that the performance of LLMs depends on both the density and the position of the key information (question relevant) in the input prompt. Inspired by these findings, we propose LongLLMLingua for prompt ...", "subpage_snippet": "", "source": "export.arxiv.org", "link": "http://export.arxiv.org/abs/2310.06839v1", "content": "Abstract: In long context scenarios, large language models (LLMs) face three main challenges: higher computational/financial cost, longer latency, and inferior performance. Some studies reveal that the performance of LLMs depends on both the density and the position of the key information (question relevant) in the input prompt. Inspired by these findings, we propose LongLLMLingua for prompt ..."}
+{"idx": 2, "title": "[2310.06839] LongLLMLingua: Accelerating and Enhancing LLMs in Long ...", "date": "", "ddg_snippet": "In long context scenarios, large language models (LLMs) face three main challenges: higher computational/financial cost, longer latency, and inferior performance. Some studies reveal that the performance of LLMs depend…", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2310.06839", "content": "In long context scenarios, large language models (LLMs) face three main challenges: higher computational/financial cost, longer latency, and inferior performance. Some studies reveal that the performance of LLMs depend…"}
+{"idx": 3, "title": "LongLLMLingua: Accelerating and Enhancing LLMs in Long Context ...", "date": "", "ddg_snippet": "ArXiv preprint, abs/2310.19923. Gautier Izacard, Mathilde Caron, Lucas Hosseini, Sebas-tian Riedel, Piotr Bojanowski, Armand Joulin, and Edouard Grave. 2022. Unsupervised dense informa-tion retrieval with contrastive learning. Transactions on Machine Learning Research. Huiqiang Jiang, Qianhui Wu, Chin-Yew Lin, Yuqing Yang, and Lili Qiu. 2023a.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2310.06839", "content": "ArXiv preprint, abs/2310.19923. Gautier Izacard, Mathilde Caron, Lucas Hosseini, Sebas-tian Riedel, Piotr Bojanowski, Armand Joulin, and Edouard Grave. 2022. Unsupervised dense informa-tion retrieval with contrastive learning. Transactions on Machine Learning Research. Huiqiang Jiang, Qianhui Wu, Chin-Yew Lin, Yuqing Yang, and Lili Qiu. 2023a."}
+{"idx": 4, "title": "LongLLMLingua: Accelerating and Enhancing LLMs in Long Context ...", "date": "", "ddg_snippet": "ArXiv preprint, abs/2311.04939. Li et al. (2023c) Yucheng Li, Bo Dong, Frank Guerin, and Chenghua Lin. 2023c. Compressing context to enhance inference efficiency of large language models. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 6342-6353, Singapore. Association for Computational ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2310.06839v2", "content": "ArXiv preprint, abs/2311.04939. Li et al. (2023c) Yucheng Li, Bo Dong, Frank Guerin, and Chenghua Lin. 2023c. Compressing context to enhance inference efficiency of large language models. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 6342-6353, Singapore. Association for Computational ..."}
+{"idx": 5, "title": "LongLLMLingua: Accelerating and Enhancing LLMs in Long Context ...", "date": "", "ddg_snippet": "Abstract In long context scenarios, large language models (LLMs) face three main challenges: higher computational/financial cost, longer latency, and inferior performance. Some studies reveal that the performance of LLMs depends on both the density and the position of the key information (question relevant) in the input prompt. Inspired by these findings, we propose LongLLMLingua for prompt ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2310.06839", "content": "Abstract In long context scenarios, large language models (LLMs) face three main challenges: higher computational/financial cost, longer latency, and inferior performance. Some studies reveal that the performance of LLMs depends on both the density and the position of the key information (question relevant) in the input prompt. Inspired by these findings, we propose LongLLMLingua for prompt ..."}
+{"idx": 6, "title": "LongLLMLingua: Accelerating and Enhancing LLMs in Long Context ...", "date": "", "ddg_snippet": "Abstract In long context scenarios, large language models (LLMs) face three main challenges: higher computational cost, performance reduction, and position bias. Research indicates that LLM performance hinges on the density and position of key information in the input prompt. Inspired by these findings, we propose LongLLMLingua for prompt compression towards improving LLMs' perception of the ...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2024.acl-long.91/", "content": "Abstract In long context scenarios, large language models (LLMs) face three main challenges: higher computational cost, performance reduction, and position bias. Research indicates that LLM performance hinges on the density and position of key information in the input prompt. Inspired by these findings, we propose LongLLMLingua for prompt compression towards improving LLMs' perception of the ..."}
+{"idx": 7, "title": "LongLLMLingua: ACCELERATING AND ENHANCING LLM L CONTEXT SCENARIOS VIA ...", "date": "", "ddg_snippet": "documents. ArXiv preprint, abs/2310.19923, 2023. URL https://arxiv. o g Wang. Lm-infinite: Simple on-the-fl length generalization for large lang , Lucas Hosseini, Sebastian Riedel, Piotr Bojanowski, Armand Joulin, and Edouard Grave. Unsupervised dense informatio retrieval with contrastive learn-ing. Transactions on Machine L", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=9YvfRrpmyw", "content": "documents. ArXiv preprint, abs/2310.19923, 2023. URL https://arxiv. o g Wang. Lm-infinite: Simple on-the-fl length generalization for large lang , Lucas Hosseini, Sebastian Riedel, Piotr Bojanowski, Armand Joulin, and Edouard Grave. Unsupervised dense informatio retrieval with contrastive learn-ing. Transactions on Machine L"}
+{"idx": 8, "title": "dblp: LongLLMLingua: Accelerating and Enhancing LLMs in Long Context ...", "date": "", "ddg_snippet": "> Home [-] Details and statistics DOI: 10.48550/ARXIV.2310.06839 access: open type: Informal or Other Publication metadata version: 2024-01-26 Huiqiang Jiang, Qianhui Wu, Xufang Luo, Dongsheng Li, Chin-Yew Lin, Yuqing Yang, Lili Qiu: LongLLMLingua: Accelerating and Enhancing LLMs in Long Context Scenarios via Prompt Compression. CoRR abs/2310 ...", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/journals/corr/abs-2310-06839", "content": "> Home [-] Details and statistics DOI: 10.48550/ARXIV.2310.06839 access: open type: Informal or Other Publication metadata version: 2024-01-26 Huiqiang Jiang, Qianhui Wu, Xufang Luo, Dongsheng Li, Chin-Yew Lin, Yuqing Yang, Lili Qiu: LongLLMLingua: Accelerating and Enhancing LLMs in Long Context Scenarios via Prompt Compression. CoRR abs/2310 ..."}
+{"idx": 9, "title": "LongLLMLingua: Accelerating and Enhancing LLMs in Long Context ...", "date": "", "ddg_snippet": "In long context scenarios, large language models (LLMs) face three main challenges: higher computational/financial cost, longer latency, and inferior performance. Some studies reveal that the performance of LLMs depends on both the density and the position of the key information (question relevant) in the input prompt. Inspired by these findings, we propose LongLLMLingua for prompt compression ...", "subpage_snippet": "", "source": "ui.adsabs.harvard.edu", "link": "https://ui.adsabs.harvard.edu/abs/2023arXiv231006839J/abstract", "content": "In long context scenarios, large language models (LLMs) face three main challenges: higher computational/financial cost, longer latency, and inferior performance. Some studies reveal that the performance of LLMs depends on both the density and the position of the key information (question relevant) in the input prompt. Inspired by these findings, we propose LongLLMLingua for prompt compression ..."}
diff --git a/data/sampled_jsons/httpsarxiv.orghtml2506.06866v1.jsonl b/data/sampled_jsons/httpsarxiv.orghtml2506.06866v1.jsonl
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+{"idx": 0, "title": "SAFE: Finding Sparse and Flat Minima to Improve Pruning GraspClutter6D: A Large-scale Real-world Dataset for Robust ... Health-LLM: Large Language Models for Health Prediction via ... arXiv.org e-Print archive [2508.06866] Grain Boundaries in Ceramic Solid-State Lithium ... GraspClutter6D: A Large-scale Real-world Dataset - arXiv.org", "date": "", "ddg_snippet": "Jun 7, 2025 · 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 ... Apr 9, 2025 · Abstract page for arXiv paper 2504.06866: GraspClutter6D: A Large-scale Real-world Dataset for Robust Perception and Grasping in Cluttered Scenes Abstract Large language models (LLMs) are capable of many natural language tasks, yet they are far from perfect. In health applications, grounding and interpreting domain-specific and non-linguistic data is important. This paper investigates the capacity of LLMs to deliver multi-modal health predictions based on contextual information (e.g. user demographics, health knowledge) and ... arXiv is a free distribution service and an open-access archive for nearly 2.4 million scholarly articles in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics. Aug 9, 2025 · It is now widely accepted that grain boundaries play a critical role in the performance and reliability of solid-state batteries with lithium metal anodes. Understanding and controlling grain boundaries is essential for enabling safe, high-rate operation of solid-state batteries. This review explores the multifaceted influence of grain boundaries in ceramic solid electrolytes and metal anodes ... Apr 9, 2025 · Abstract Robust grasping in cluttered environments remains an open challenge in robotics. While benchmark datasets have significantly advanced deep learning methods, they mainly focus on simplistic scenes with light occlusion and insufficient diversity, limiting their applicability to practical scenarios. We present GraspClutter6D, a large-scale real-world grasping dataset featuring: (1) 1,000 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2506.06866", "content": "Jun 7, 2025 · 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 ... Apr 9, 2025 · Abstract page for arXiv paper 2504.06866: GraspClutter6D: A Large-scale Real-world Dataset for Robust Perception and Grasping in Cluttered Scenes Abstract Large language models (LLMs) are capable of many natural language tasks, yet they are far from perfect. In health applications, grounding and interpreting domain-specific and non-linguistic data is important. This paper investigates the capacity of LLMs to deliver multi-modal health predictions based on contextual information (e.g. user demographics, health knowledge) and ... arXiv is a free distribution service and an open-access archive for nearly 2.4 million scholarly articles in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics. Aug 9, 2025 · It is now widely accepted that grain boundaries play a critical role in the performance and reliability of solid-state batteries with lithium metal anodes. Understanding and controlling grain boundaries is essential for enabling safe, high-rate operation of solid-state batteries. This review explores the multifaceted influence of grain boundaries in ceramic solid electrolytes and metal anodes ... Apr 9, 2025 · Abstract Robust grasping in cluttered environments remains an open challenge in robotics. While benchmark datasets have significantly advanced deep learning methods, they mainly focus on simplistic scenes with light occlusion and insufficient diversity, limiting their applicability to practical scenarios. We present GraspClutter6D, a large-scale real-world grasping dataset featuring: (1) 1,000 ..."}
+{"idx": 1, "title": "GraspClutter6D: A Large-scale Real-world Dataset for Robust ... Health-LLM: Large Language Models for Health Prediction via ... arXiv.org e-Print archive [2508.06866] Grain Boundaries in Ceramic Solid-State Lithium ... GraspClutter6D: A Large-scale Real-world Dataset - arXiv.org", "date": "", "ddg_snippet": "Apr 9, 2025 · Abstract page for arXiv paper 2504.06866: GraspClutter6D: A Large-scale Real-world Dataset for Robust Perception and Grasping in Cluttered Scenes Abstract Large language models (LLMs) are capable of many natural language tasks, yet they are far from perfect. In health applications, grounding and interpreting domain-specific and non-linguistic data is important. This paper investigates the capacity of LLMs to deliver multi-modal health predictions based on contextual information (e.g. user demographics, health knowledge) and ... arXiv is a free distribution service and an open-access archive for nearly 2.4 million scholarly articles in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics. Aug 9, 2025 · It is now widely accepted that grain boundaries play a critical role in the performance and reliability of solid-state batteries with lithium metal anodes. Understanding and controlling grain boundaries is essential for enabling safe, high-rate operation of solid-state batteries. This review explores the multifaceted influence of grain boundaries in ceramic solid electrolytes and metal anodes ... Apr 9, 2025 · Abstract Robust grasping in cluttered environments remains an open challenge in robotics. While benchmark datasets have significantly advanced deep learning methods, they mainly focus on simplistic scenes with light occlusion and insufficient diversity, limiting their applicability to practical scenarios. We present GraspClutter6D, a large-scale real-world grasping dataset featuring: (1) 1,000 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2504.06866", "content": "Apr 9, 2025 · Abstract page for arXiv paper 2504.06866: GraspClutter6D: A Large-scale Real-world Dataset for Robust Perception and Grasping in Cluttered Scenes Abstract Large language models (LLMs) are capable of many natural language tasks, yet they are far from perfect. In health applications, grounding and interpreting domain-specific and non-linguistic data is important. This paper investigates the capacity of LLMs to deliver multi-modal health predictions based on contextual information (e.g. user demographics, health knowledge) and ... arXiv is a free distribution service and an open-access archive for nearly 2.4 million scholarly articles in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics. Aug 9, 2025 · It is now widely accepted that grain boundaries play a critical role in the performance and reliability of solid-state batteries with lithium metal anodes. Understanding and controlling grain boundaries is essential for enabling safe, high-rate operation of solid-state batteries. This review explores the multifaceted influence of grain boundaries in ceramic solid electrolytes and metal anodes ... Apr 9, 2025 · Abstract Robust grasping in cluttered environments remains an open challenge in robotics. While benchmark datasets have significantly advanced deep learning methods, they mainly focus on simplistic scenes with light occlusion and insufficient diversity, limiting their applicability to practical scenarios. We present GraspClutter6D, a large-scale real-world grasping dataset featuring: (1) 1,000 ..."}
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+{"idx": 3, "title": "HTML Görüntüleyici - Çevrimiçi HTML Editörü ve Önizleme Aracı", "date": "", "ddg_snippet": "HTML Görüntüleyici - Sözdizimi vurgulama ve duyarlı tasarım testi ile HTML kodunu düzenlemek, önizlemek ve biçimlendirmek için güçlü bir çevrimiçi araç.", "subpage_snippet": "", "source": "htmlonline.org", "link": "https://htmlonline.org/tr/", "content": "HTML Görüntüleyici - Sözdizimi vurgulama ve duyarlı tasarım testi ile HTML kodunu düzenlemek, önizlemek ve biçimlendirmek için güçlü bir çevrimiçi araç."}
+{"idx": 4, "title": "Learn HTML - Free Interactive HTML Tutorial", "date": "", "ddg_snippet": "learn- html . org is a free interactive HTML tutorial for people who want to learn HTML , fast.", "subpage_snippet": "", "source": "www.learn-html.org", "link": "https://www.learn-html.org/", "content": "learn- html . org is a free interactive HTML tutorial for people who want to learn HTML , fast."}
+{"idx": 5, "title": "How should I set the language of the content in my HTML page?", "date": "", "ddg_snippet": "How should I set the language of the content in my HTML page?", "subpage_snippet": "", "source": "www.w3.org", "link": "https://www.w3.org/International/questions/qa-html-language-declarations", "content": "How should I set the language of the content in my HTML page?"}
+{"idx": 6, "title": "Link assoluti e relativi con a href | Guida HTML | HTML .it", "date": "", "ddg_snippet": "Tag a href - link assoluti e link relativi - conoscerli per organizzare il progetto HTML in directory e sottodirectory ed imparare a muoversi tra i file.", "subpage_snippet": "", "source": "www.html.it", "link": "https://www.html.it/pag/16048/i-percorsi-assoluti-e-relativi/", "content": "Tag a href - link assoluti e link relativi - conoscerli per organizzare il progetto HTML in directory e sottodirectory ed imparare a muoversi tra i file."}
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+{"idx": 9, "title": "GraspClutter6D: A Large-scale Real-world Dataset - arXiv.org", "date": "", "ddg_snippet": "Apr 9, 2025 · Abstract Robust grasping in cluttered environments remains an open challenge in robotics. While benchmark datasets have significantly advanced deep learning methods, they mainly focus on simplistic scenes with light occlusion and insufficient diversity, limiting their applicability to practical scenarios. We present GraspClutter6D, a large-scale real-world grasping dataset featuring: (1) 1,000 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.06866", "content": "Apr 9, 2025 · Abstract Robust grasping in cluttered environments remains an open challenge in robotics. While benchmark datasets have significantly advanced deep learning methods, they mainly focus on simplistic scenes with light occlusion and insufficient diversity, limiting their applicability to practical scenarios. We present GraspClutter6D, a large-scale real-world grasping dataset featuring: (1) 1,000 ..."}
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+{"idx": 0, "title": "PNNL- 36691", "date": "", "ddg_snippet": "1 Datasets and associated code can be found at https://github.com/pnnl/ML4AlgComb PNNL -36691 Introduction 3 of interest in algebraic combinatorics, and (iii) by nature of being discrete, the objects of interest in algebraic combinatorics tend to be more amendable to representation on a computer.", "subpage_snippet": "", "source": "www.pnnl.gov", "link": "https://www.pnnl.gov/main/publications/external/technical_reports/PNNL-36691.pdf", "content": "1 Datasets and associated code can be found at https://github.com/pnnl/ML4AlgComb PNNL -36691 Introduction 3 of interest in algebraic combinatorics, and (iii) by nature of being discrete, the objects of interest in algebraic combinatorics tend to be more amendable to representation on a computer."}
+{"idx": 1, "title": "GitHub - pnnl/ML4AlgComb: ML Benchmarks in Algebraic ...", "date": "", "ddg_snippet": "ML Benchmarks in Algebraic Combinatorics. Contribute to pnnl / ML4AlgComb development by creating an account on GitHub .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/pnnl/ML4AlgComb", "content": "ML Benchmarks in Algebraic Combinatorics. Contribute to pnnl / ML4AlgComb development by creating an account on GitHub ."}
+{"idx": 2, "title": "Pacific Northwest National Laboratory (Public) · GitHub", "date": "", "ddg_snippet": "Pacific Northwest National Laboratory (Public) This Org is intended for the hosting of approved released PNNL software repositories for public use and collaboration.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/PNNL", "content": "Pacific Northwest National Laboratory (Public) This Org is intended for the hosting of approved released PNNL software repositories for public use and collaboration."}
+{"idx": 3, "title": "pnnl repositories · GitHub", "date": "", "ddg_snippet": "6 days ago · This Org is intended for the hosting of approved released PNNL software repositories for public use and collaboration. - Pacific Northwest National Laboratory (Public)", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/orgs/pnnl/repositories", "content": "6 days ago · This Org is intended for the hosting of approved released PNNL software repositories for public use and collaboration. - Pacific Northwest National Laboratory (Public)"}
+{"idx": 4, "title": "Machine Learning meets Algebraic Combinatorics: A Suite of ...", "date": "", "ddg_snippet": "May 1, 2025 · Link To Code: https://github.com/pnnl/ML4AlgComb Primary Area: Applications->Everything Else Keywords: Datasets, AI for math, Mathematical reasoning and conjecturing, Algebraic combinatorics Submission Number: 3973", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=tlniJJFUW2", "content": "May 1, 2025 · Link To Code: https://github.com/pnnl/ML4AlgComb Primary Area: Applications->Everything Else Keywords: Datasets, AI for math, Mathematical reasoning and conjecturing, Algebraic combinatorics Submission Number: 3973"}
+{"idx": 5, "title": "ML4AlgComb/README.md at master · pnnl/ML4AlgComb · GitHub", "date": "", "ddg_snippet": "ML Benchmarks in Algebraic Combinatorics. Contribute to pnnl / ML4AlgComb development by creating an account on GitHub .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/pnnl/ML4AlgComb/blob/master/README.md", "content": "ML Benchmarks in Algebraic Combinatorics. Contribute to pnnl / ML4AlgComb development by creating an account on GitHub ."}
+{"idx": 6, "title": "GitHub - pnnl/neuromancer: Pytorch-based framework for ...", "date": "", "ddg_snippet": "Pytorch-based framework for solving parametric constrained optimization problems, physics-informed system identification, and parametric model predictive control. - GitHub - pnnl /neuromancer: Pytorch-based framework for solving parametric constrained optimization problems, physics-informed system identification, and parametric model predictive control.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/pnnl/neuromancer", "content": "Pytorch-based framework for solving parametric constrained optimization problems, physics-informed system identification, and parametric model predictive control. - GitHub - pnnl /neuromancer: Pytorch-based framework for solving parametric constrained optimization problems, physics-informed system identification, and parametric model predictive control."}
+{"idx": 7, "title": "Machine Learning meets Algebraic Combinatorics: A Suite ...", "date": "", "ddg_snippet": "Link To Code: https://github.com/pnnl/ML4AlgComb . Primary Area: Applications->Everything Else. Keywords: Datasets, AI for math, Mathematical reasoning and ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=tlniJJFUW2&noteId=kkpF1lRRMF", "content": "Link To Code: https://github.com/pnnl/ML4AlgComb . Primary Area: Applications->Everything Else. Keywords: Datasets, AI for math, Mathematical reasoning and ..."}
+{"idx": 8, "title": "Machine Learning meets Algebraic Combinatorics", "date": "", "ddg_snippet": "by H Chau — Papers that use the dataset will be listed at https :// github.com/pnnl / ML4AlgComb . • What (other) tasks could the dataset be used for? This dataset could be used ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=KQ1gI5qzAf", "content": "by H Chau — Papers that use the dataset will be listed at https :// github.com/pnnl / ML4AlgComb . • What (other) tasks could the dataset be used for? This dataset could be used ..."}
+{"idx": 9, "title": "https://huggingface.co/datasets/ACDRepo/symmetric_...", "date": "", "ddg_snippet": "Data loaders can be found [here]( https :// github.com/pnnl / ML4AlgComb /tree/master/symmetric_group_character). In all cases the characters are heavily ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/ACDRepo/symmetric_group_characters_18/resolve/main/README.md?download=true", "content": "Data loaders can be found [here]( https :// github.com/pnnl / ML4AlgComb /tree/master/symmetric_group_character). In all cases the characters are heavily ..."}
diff --git a/data/sampled_jsons/httpsopenreview.netattachmentid=51x0dfsD8A&name=pdf.jsonl b/data/sampled_jsons/httpsopenreview.netattachmentid=51x0dfsD8A&name=pdf.jsonl
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index 0000000000000000000000000000000000000000..2aa650c2f9fb3827074660f6cf2fc5a4423104ea
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+++ b/data/sampled_jsons/httpsopenreview.netattachmentid=51x0dfsD8A&name=pdf.jsonl
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+{"idx": 0, "title": "How can I export all submission attachments with the OpenReview API?", "date": "", "ddg_snippet": "File \"C:\\Users\\dernoncourt\\anaconda3\\envs\\openreview\\Lib\\site-packages\\openreview\\api\\client.py\", line 609, in get_attachment response = self.__handle_response(response)", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/78771514/how-can-i-export-all-submission-attachments-with-the-openreview-api", "content": "File \"C:\\Users\\dernoncourt\\anaconda3\\envs\\openreview\\Lib\\site-packages\\openreview\\api\\client.py\", line 609, in get_attachment response = self.__handle_response(response)"}
+{"idx": 1, "title": "Scrape papers from OpenReview using OpenReview API", "date": "", "ddg_snippet": "Scrape papers from top conferences like ICML, ICLR, NeurIPS, etc using OpenReview API, by searching for specific keywords in title, abstract or keywords in the submissions and save them to a CSV file. Brings down the time taken to gather papers from several hours to a few minutes through automation", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/pranftw/openreview_scraper", "content": "Scrape papers from top conferences like ICML, ICLR, NeurIPS, etc using OpenReview API, by searching for specific keywords in title, abstract or keywords in the submissions and save them to a CSV file. Brings down the time taken to gather papers from several hours to a few minutes through automation"}
+{"idx": 2, "title": "Submissions, comments, reviews, and decisions | OpenReview", "date": "", "ddg_snippet": "How to add formatting to reviews or comments How to submit a Review Revision How to add formulas or use mathematical notation How to edit a submission after the deadline - Authors How to upload paper decisions in bulk How to hide/reveal fields Update camera-ready PDFs after the deadline expires", "subpage_snippet": "", "source": "docs.openreview.net", "link": "https://docs.openreview.net/how-to-guides/submissions-comments-reviews-and-decisions", "content": "How to add formatting to reviews or comments How to submit a Review Revision How to add formulas or use mathematical notation How to edit a submission after the deadline - Authors How to upload paper decisions in bulk How to hide/reveal fields Update camera-ready PDFs after the deadline expires"}
+{"idx": 3, "title": "Enabling Supplementary Material Upload - OpenReview", "date": "", "ddg_snippet": "Getting Started Hosting a venue on OpenReview Enabling Supplementary Material Upload You can add supplementary material to the submission form by clicking on the 'Revision' button and adding the following JSON under Additional Submission Options:", "subpage_snippet": "", "source": "docs.openreview.net", "link": "https://docs.openreview.net/getting-started/hosting-a-venue-on-openreview/enabling-supplementary-material-upload", "content": "Getting Started Hosting a venue on OpenReview Enabling Supplementary Material Upload You can add supplementary material to the submission form by clicking on the 'Revision' button and adding the following JSON under Additional Submission Options:"}
+{"idx": 4, "title": "OpenReview Python API Documentation", "date": "", "ddg_snippet": "CHAPTER 1 About OpenReview OpenReview aims to promote openness in scientific communication, particularly the peer review process, by providing a flexible cloud-based web interface and underlying database API. This document is a guide to the python API supported by OpenReview.", "subpage_snippet": "", "source": "openreview-py-dm-branch.readthedocs.io", "link": "https://openreview-py-dm-branch.readthedocs.io/_/downloads/en/latest/pdf/", "content": "CHAPTER 1 About OpenReview OpenReview aims to promote openness in scientific communication, particularly the peer review process, by providing a flexible cloud-based web interface and underlying database API. This document is a guide to the python API supported by OpenReview."}
+{"idx": 5, "title": "Finding your profile ID | OpenReview", "date": "", "ddg_snippet": "Your OpenReview profile ID is a unique string made up of a tilde concatenated with your full name and a number, for example ∼First_Last1. If you go to your OpenReview profile, your ID will be at the end of the url (for example, https ://openreview.net/profile?id= ∼Your_Id1)", "subpage_snippet": "", "source": "docs.openreview.net", "link": "https://docs.openreview.net/getting-started/creating-an-openreview-profile/finding-your-profile-id", "content": "Your OpenReview profile ID is a unique string made up of a tilde concatenated with your full name and a number, for example ∼First_Last1. If you go to your OpenReview profile, your ID will be at the end of the url (for example, https ://openreview.net/profile?id= ∼Your_Id1)"}
+{"idx": 6, "title": "Adapting Humanoid Locomotion over Challenging Terrain via ... - OpenReview", "date": "", "ddg_snippet": "Humanoid robots are a key focus in robotics, with their capacity to navigate tough terrains being essential for many uses. While strides have been made, creating adaptable locomotion for complex...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=O0oK2bVist", "content": "Humanoid robots are a key focus in robotics, with their capacity to navigate tough terrains being essential for many uses. While strides have been made, creating adaptable locomotion for complex..."}
+{"idx": 7, "title": "Venues | OpenReview", "date": "", "ddg_snippet": "Promoting openness in scientific communication and the peer-review process", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/", "content": "Promoting openness in scientific communication and the peer-review process"}
+{"idx": 8, "title": "OpenReview Documentation", "date": "", "ddg_snippet": "Getting Started: Contains the FAQ, how to create a Venue, how to create a profile, and how to interact with the API.", "subpage_snippet": "", "source": "docs.openreview.net", "link": "https://docs.openreview.net/", "content": "Getting Started: Contains the FAQ, how to create a Venue, how to create a profile, and how to interact with the API."}
+{"idx": 9, "title": "A Python script downloading all ICLR and NIPS papers from openreview.net", "date": "", "ddg_snippet": "Hi Junhongxu, I've been trying to re-use your code download all submission papers from ICLR this year. Seems like a structure of website makes some changes. Especially from line 21 it's cracked. Could you please help me check this? Thanks", "subpage_snippet": "", "source": "gist.github.com", "link": "https://gist.github.com/JunhongXu/cf27321f710ac3a8c07926b15a916201", "content": "Hi Junhongxu, I've been trying to re-use your code download all submission papers from ICLR this year. Seems like a structure of website makes some changes. Especially from line 21 it's cracked. Could you please help me check this? Thanks"}
diff --git a/data/sampled_jsons/iDDPM_3.54_OR_2.92_ImageNet_64x64_FID_score_Nichol_Dhariwal.jsonl b/data/sampled_jsons/iDDPM_3.54_OR_2.92_ImageNet_64x64_FID_score_Nichol_Dhariwal.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5226f0fbe1d1cf18957d965b0a7cdd211471f8e6
--- /dev/null
+++ b/data/sampled_jsons/iDDPM_3.54_OR_2.92_ImageNet_64x64_FID_score_Nichol_Dhariwal.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "imagenet64x64/README.md at master · sndnyang ... - GitHub", "date": "", "ddg_snippet": "The link to the stored-in-image imagenet64x64 dataset. And a resnet/wrn code for it. - imagenet64x64/README.md at master · sndnyang/imagenet64x64", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/sndnyang/imagenet64x64/blob/master/README.md", "content": "The link to the stored-in-image imagenet64x64 dataset. And a resnet/wrn code for it. - imagenet64x64/README.md at master · sndnyang/imagenet64x64"}
+{"idx": 1, "title": "a ViT Backbone for Score-based Diffusion Models", "date": "", "ddg_snippet": "Table 4: FID ↓ results on class-conditional ImageNet 64x64 and comparison of experimental setting. ... 2.92 . 270M. 2048. 250K. U-ViT (ours). 6.75. 131M. 1024.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/6b72782694a08b0cde5dac666544509e5298c730.pdf", "content": "Table 4: FID ↓ results on class-conditional ImageNet 64x64 and comparison of experimental setting. ... 2.92 . 270M. 2048. 250K. U-ViT (ours). 6.75. 131M. 1024."}
+{"idx": 2, "title": "[2202.05830] Learning Fast Samplers for Diffusion Models by ...", "date": "", "ddg_snippet": "Table 2: FID / IS scores for DDSS against baseline methods for a DDPM trained on ImageNet 64x64 with the L hybrid objective proposed by Nichol & Dhariwal (2021).", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2202.05830", "content": "Table 2: FID / IS scores for DDSS against baseline methods for a DDPM trained on ImageNet 64x64 with the L hybrid objective proposed by Nichol & Dhariwal (2021)."}
+{"idx": 3, "title": "Simplified and Generalized Masked Diffusion for Discrete Data", "date": "", "ddg_snippet": "Figure 2: Left: FID evaluation for 50k samples randomly generated from MD4 on pixel-level modeling of ImageNet 64×\\times×64 (numbers in Tab. 6). Right: Number of tokens revealed per generation step (T=256𝑇256T=256italic_T = 256).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.04329v4", "content": "Figure 2: Left: FID evaluation for 50k samples randomly generated from MD4 on pixel-level modeling of ImageNet 64×\\times×64 (numbers in Tab. 6). Right: Number of tokens revealed per generation step (T=256𝑇256T=256italic_T = 256)."}
+{"idx": 4, "title": "[2209.12152] All are Worth Words: A ViT Backbone for ... - ar5iv", "date": "", "ddg_snippet": "On class-conditional ImageNet 64 × 64, we initially try the U-ViT-M configuration with 131M parameters. As shown in Table 1, it gets a FID of 5.85, which is better than 6.92 of IDDPM that employs a U-Net with 100M parameters.", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2209.12152", "content": "On class-conditional ImageNet 64 × 64, we initially try the U-ViT-M configuration with 131M parameters. As shown in Table 1, it gets a FID of 5.85, which is better than 6.92 of IDDPM that employs a U-Net with 100M parameters."}
+{"idx": 5, "title": "Directly Denoising Diffusion Models", "date": "", "ddg_snippet": "30 May 2024 — DDPM (Ho et al., 2020), 250, 11.0, 0.67, 0.58. iDDPM ( Nichol & Dhariwal , 2021), 250, 2.92 , 0.74, 0.62. ADM ( Dhariwal & Nichol , 2021), 250, 2.07 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.13540v2", "content": "30 May 2024 — DDPM (Ho et al., 2020), 250, 11.0, 0.67, 0.58. iDDPM ( Nichol & Dhariwal , 2021), 250, 2.92 , 0.74, 0.62. ADM ( Dhariwal & Nichol , 2021), 250, 2.07 ..."}
+{"idx": 6, "title": "Theoretical research on generative diffusion models", "date": "", "ddg_snippet": "by MN Yeğin · 2024 · Cited by 2 — iDDPM ( FID ) Nichol and Dhariwal (2021). 2.90. -. 3.37. Consistency ... 2.92 . INDM (VP, NLL) Kim et al. (2022a). 3.06. 2.05. Analytic DDIM Bao ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2404.09016?", "content": "by MN Yeğin · 2024 · Cited by 2 — iDDPM ( FID ) Nichol and Dhariwal (2021). 2.90. -. 3.37. Consistency ... 2.92 . INDM (VP, NLL) Kim et al. (2022a). 3.06. 2.05. Analytic DDIM Bao ..."}
+{"idx": 7, "title": "IMPROVED DIFFUSION-BASED GENERATIVE MODEL", "date": "", "ddg_snippet": "by Z Wang — samplers: IDDPM ( Dhariwal & Nichol , 2021), DDIM (Song et al., 2022) ... 3.54 . ADM-AT (Ours). 43.95. 19.57. 14.12. 6.16. 3.45. (d) DPM-Solver. Methods \\ NFEs ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=1DVgysiIt7", "content": "by Z Wang — samplers: IDDPM ( Dhariwal & Nichol , 2021), DDIM (Song et al., 2022) ... 3.54 . ADM-AT (Ours). 43.95. 19.57. 14.12. 6.16. 3.45. (d) DPM-Solver. Methods \\ NFEs ..."}
+{"idx": 8, "title": "Understanding Diffusion Objectives as the ELBO with ...", "date": "", "ddg_snippet": "by DP Kingma · Cited by 190 — For e-parametrization model, we took iDDPM [ Nichol and Dhariwal , 2021] as the ... 3.54 4.53 205.3 ± 2.7. 3.02 4.60 248.7 ± 3.4. VDM++ (Ours), EDM-monotonic ... 33 pages", "subpage_snippet": "", "source": "papers.neurips.cc", "link": "https://papers.neurips.cc/paper_files/paper/2023/file/ce79fbf9baef726645bc2337abb0ade2-Paper-Conference.pdf", "content": "by DP Kingma · Cited by 190 — For e-parametrization model, we took iDDPM [ Nichol and Dhariwal , 2021] as the ... 3.54 4.53 205.3 ± 2.7. 3.02 4.60 248.7 ± 3.4. VDM++ (Ours), EDM-monotonic ... 33 pages"}
+{"idx": 9, "title": "Normalizing Flows are Capable Generative Models - Jiatao Gu", "date": "", "ddg_snippet": "Improved DDPM ( Nichol & Dhariwal , 2021) Diff/FM 3.54 ... Diff/FM 2.92 . ADM(dropout) ( Dhariwal & Nichol ... Bottom: Sample FID vs input noise σ on ImageNet 64x64 ,.", "subpage_snippet": "", "source": "jiataogu.me", "link": "https://jiataogu.me/papers/zhai2025normalizing.pdf", "content": "Improved DDPM ( Nichol & Dhariwal , 2021) Diff/FM 3.54 ... Diff/FM 2.92 . ADM(dropout) ( Dhariwal & Nichol ... Bottom: Sample FID vs input noise σ on ImageNet 64x64 ,."}
diff --git a/data/sampled_jsons/iDDPM_Nichol_Dhariwal_2021_improved_denoising_diffusion_probabilistic_models_FID_ImageNet_64x64_year_2021.jsonl b/data/sampled_jsons/iDDPM_Nichol_Dhariwal_2021_improved_denoising_diffusion_probabilistic_models_FID_ImageNet_64x64_year_2021.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4ae1e667273dd3f78fa9f05c9ec3a5b2bc82e856
--- /dev/null
+++ b/data/sampled_jsons/iDDPM_Nichol_Dhariwal_2021_improved_denoising_diffusion_probabilistic_models_FID_ImageNet_64x64_year_2021.jsonl
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+{"idx": 0, "title": "Directly Denoising Diffusion Model", "date": "", "ddg_snippet": "24 May 2024 — By improving noise schedule and variance taking into consideration, Nichol & Dhariwal further enhanced these models in 2021 , achieving better ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.13540v1", "content": "24 May 2024 — By improving noise schedule and variance taking into consideration, Nichol & Dhariwal further enhanced these models in 2021 , achieving better ..."}
+{"idx": 1, "title": "improved diffusion-based generative model", "date": "", "ddg_snippet": "by Z Wang · 2025 — To verify the effectiveness of our AT method, we conduct experiments with four diffusion samplers: IDDPM ( Dhariwal & Nichol , 2021 ), DDIM ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.17099", "content": "by Z Wang · 2025 — To verify the effectiveness of our AT method, we conduct experiments with four diffusion samplers: IDDPM ( Dhariwal & Nichol , 2021 ), DDIM ..."}
+{"idx": 2, "title": "Directly Denoising Diffusion Models - GitHub", "date": "", "ddg_snippet": "Dhariwal further enhanced these models in 2021 , achieving better log-likelihood scores and better FID scores. Song et al. focused on optimizing the score- ...", "subpage_snippet": "", "source": "raw.githubusercontent.com", "link": "https://raw.githubusercontent.com/mlresearch/v235/main/assets/zhang24bl/zhang24bl.pdf", "content": "Dhariwal further enhanced these models in 2021 , achieving better log-likelihood scores and better FID scores. Song et al. focused on optimizing the score- ..."}
+{"idx": 3, "title": "Image generation with Shortest-path Diffusion", "date": "", "ddg_snippet": "“ Improved denoising diffusion probabilistic model ”, ICML 2021 . Page 6 ... Better FID than iDDPM with less T and training iterations. ImageNet64 results.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/media/icml-2023/Slides/23971.pdf", "content": "“ Improved denoising diffusion probabilistic model ”, ICML 2021 . Page 6 ... Better FID than iDDPM with less T and training iterations. ImageNet64 results."}
+{"idx": 4, "title": "Enhanced Diffusion Sampling via Extrapolation with ...", "date": "", "ddg_snippet": "by J Choi — This paper introduces RX-DPM, a sampling method for diffusion probabilistic models that significantly enhances the efficiency and accuracy of the sampling ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=rCGleSgNBK", "content": "by J Choi — This paper introduces RX-DPM, a sampling method for diffusion probabilistic models that significantly enhances the efficiency and accuracy of the sampling ..."}
+{"idx": 5, "title": "Boosting Diffusion Models with an Adaptive Momentum ...", "date": "", "ddg_snippet": "by X Wang · Cited by 9 — This similarity has been utilized to improve the diffusion models sampling in many works especially in diffusion guidance sampling [ Dhariwal and Nichol , 2021 ;. 9 pages", "subpage_snippet": "", "source": "www.ijcai.org", "link": "https://www.ijcai.org/proceedings/2024/0157.pdf", "content": "by X Wang · Cited by 9 — This similarity has been utilized to improve the diffusion models sampling in many works especially in diffusion guidance sampling [ Dhariwal and Nichol , 2021 ;. 9 pages"}
+{"idx": 6, "title": "Understanding Diffusion Objectives as the ELBO with ...", "date": "", "ddg_snippet": "by DP Kingma · Cited by 190 — All experiments on ImageNet 64x64 were done with the U-Net diffusion model architecture from. [ Nichol and Dhariwal , 2021 ]. We carried out extensive ablation ... 33 pages", "subpage_snippet": "", "source": "papers.neurips.cc", "link": "https://papers.neurips.cc/paper_files/paper/2023/file/ce79fbf9baef726645bc2337abb0ade2-Paper-Conference.pdf", "content": "by DP Kingma · Cited by 190 — All experiments on ImageNet 64x64 were done with the U-Net diffusion model architecture from. [ Nichol and Dhariwal , 2021 ]. We carried out extensive ablation ... 33 pages"}
+{"idx": 7, "title": "Normalizing Flows are Capable Generative Models - Jiatao Gu", "date": "", "ddg_snippet": "On the other hand, guidance in diffusion models (Dhariwal ... Improved DDPM (Nichol & Dhariwal, 2021 ) Diff/FM 3.54 ... Bottom: Sample FID vs input noise σ on ...", "subpage_snippet": "", "source": "jiataogu.me", "link": "https://jiataogu.me/papers/zhai2025normalizing.pdf", "content": "On the other hand, guidance in diffusion models (Dhariwal ... Improved DDPM (Nichol & Dhariwal, 2021 ) Diff/FM 3.54 ... Bottom: Sample FID vs input noise σ on ..."}
+{"idx": 8, "title": "representative guidance: diffusion sampling", "date": "", "ddg_snippet": "by AD Dinh · Cited by 1 — Alexander Quinn Nichol and Prafulla Dhariwal. Improved denoising diffusion probabilistic models . In International Conference on Machine Learning, pp. 8162–8171.", "subpage_snippet": "", "source": "proceedings.iclr.cc", "link": "https://proceedings.iclr.cc/paper_files/paper/2025/file/ecc28b4ce9b39f5f23c3efb03e25b7bf-Paper-Conference.pdf", "content": "by AD Dinh · Cited by 1 — Alexander Quinn Nichol and Prafulla Dhariwal. Improved denoising diffusion probabilistic models . In International Conference on Machine Learning, pp. 8162–8171."}
+{"idx": 9, "title": "Refining Generative Process with Discriminator Guidance in ...", "date": "", "ddg_snippet": "Nichol, A. Q. and Dhariwal, P. Improved denoising diffusion probabilistic models . In International Conference on. Machine Learning, pp. 8162–8171. PMLR, 2021.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v202/kim23i/kim23i.pdf", "content": "Nichol, A. Q. and Dhariwal, P. Improved denoising diffusion probabilistic models . In International Conference on. Machine Learning, pp. 8162–8171. PMLR, 2021."}
diff --git a/data/sampled_jsons/implicit_neural_representation_derivative_computation_closed-form_analytical_autograd_year_2024.jsonl b/data/sampled_jsons/implicit_neural_representation_derivative_computation_closed-form_analytical_autograd_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..73d930bc026aab92929e997aba218fe74d6e3382
--- /dev/null
+++ b/data/sampled_jsons/implicit_neural_representation_derivative_computation_closed-form_analytical_autograd_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "SH-SAS: An Implicit Neural Representation for Complex", "date": "", "ddg_snippet": "Neural Radiance Fields (NeRF) [ 33 ] revolutionized 3D scene representation by introducing implicit volumetric representations using multi-layer ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.11087v1", "content": "Neural Radiance Fields (NeRF) [ 33 ] revolutionized 3D scene representation by introducing implicit volumetric representations using multi-layer ..."}
+{"idx": 1, "title": "Neural Geometry Processing via Spherical Neural Surfaces", "date": "", "ddg_snippet": "In this work, we propose a spherical neural surface representation for genus-0 surfaces and demonstrate how to compute core geometric operators ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2407.07755v3", "content": "In this work, we propose a spherical neural surface representation for genus-0 surfaces and demonstrate how to compute core geometric operators ..."}
+{"idx": 2, "title": "Inverse Design in Nanophotonics via Representation Learning", "date": "", "ddg_snippet": "... representation (left) models the partial differential equation (PDE) solution or a derived optical property : a differentiable surrogate or ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.00546v1", "content": "... representation (left) models the partial differential equation (PDE) solution or a derived optical property : a differentiable surrogate or ..."}
+{"idx": 3, "title": "Injecting Measurement Information Yields a Fast and", "date": "", "ddg_snippet": "... formulations appear in a multitude of fields, with applications including acoustic reconstruction (Kac 1966 ) , seismic profiling (Hardage 1985 ) , ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.02964v1", "content": "... formulations appear in a multitude of fields, with applications including acoustic reconstruction (Kac 1966 ) , seismic profiling (Hardage 1985 ) , ..."}
+{"idx": 4, "title": "Low-rank surrogate modeling and stochastic zero-order", "date": "", "ddg_snippet": "In our problem formulation (see Figure 1 ), we consider replacing the k k -th linear layer of a deep neural network (NN) with a black box (BB ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.15113v1", "content": "In our problem formulation (see Figure 1 ), we consider replacing the k k -th linear layer of a deep neural network (NN) with a black box (BB ..."}
+{"idx": 5, "title": "Implementing a Deep Learning Library from Scratch in Python -", "date": "", "ddg_snippet": "Implicit measures are specialized operators that do the transformation of intermediate representations , either through explicit normalization, for ...", "subpage_snippet": "", "source": "www.kdnuggets.com", "link": "https://www.kdnuggets.com/2020/09/implementing-deep-learning-library-scratch-python.html", "content": "Implicit measures are specialized operators that do the transformation of intermediate representations , either through explicit normalization, for ..."}
+{"idx": 6, "title": "Deep learning methods for inverse problems [PeerJ]", "date": "", "ddg_snippet": "Analytic inversion ( Natterer, 2001 ; Schuster, 2007 ) having the objective of finding a closed form , possibly approximate, of F −1 .", "subpage_snippet": "", "source": "peerj.com", "link": "https://peerj.com/articles/cs-951/", "content": "Analytic inversion ( Natterer, 2001 ; Schuster, 2007 ) having the objective of finding a closed form , possibly approximate, of F −1 ."}
+{"idx": 7, "title": "SGP | UCL", "date": "", "ddg_snippet": "The main advantage of Spherical Neural Surfaces as a geometry representation is that it is extremely natural to compute many important quantities ...", "subpage_snippet": "", "source": "geometry.cs.ucl.ac.uk", "link": "https://geometry.cs.ucl.ac.uk/projects/2025/sns/", "content": "The main advantage of Spherical Neural Surfaces as a geometry representation is that it is extremely natural to compute many important quantities ..."}
+{"idx": 8, "title": "Please read: make it easier to help you - Meta Discussion -", "date": "", "ddg_snippet": "This StackOverflow answer explains how a DataFrame can be turned into a string representation that can be copy/pasted.", "subpage_snippet": "", "source": "discourse.julialang.org", "link": "https://discourse.julialang.org/t/please-read-make-it-easier-to-help-you/14757", "content": "This StackOverflow answer explains how a DataFrame can be turned into a string representation that can be copy/pasted."}
+{"idx": 9, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/inference-time_diffusion_model_alignment_classifier_guidance_DOODL_year_2023.jsonl b/data/sampled_jsons/inference-time_diffusion_model_alignment_classifier_guidance_DOODL_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..fdd10602b4b57368ec25770883cdf1171e0143e4
--- /dev/null
+++ b/data/sampled_jsons/inference-time_diffusion_model_alignment_classifier_guidance_DOODL_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Reconstruction Alignment Improves Unified Multimodal ...", "date": "", "ddg_snippet": "8 Sept 2025 — Model inference. At inference time , our post-trained UMM operates identically to a standard UMM and requires no additional visual embeddings.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.07295v1", "content": "8 Sept 2025 — Model inference. At inference time , our post-trained UMM operates identically to a standard UMM and requires no additional visual embeddings."}
+{"idx": 1, "title": "Doodle Your Motion: Sketch-Guided Human Motion Generation", "date": "", "ddg_snippet": "In this article, we introduce Sketch-guided human Motion Diffusion (SMD), to address a novel scenario: sketch-to-motion, aiming to generate plausible and ...", "subpage_snippet": "", "source": "www.computer.org", "link": "https://www.computer.org/csdl/journal/tg/2025/09/10812894/22UpYksOnio", "content": "In this article, we introduce Sketch-guided human Motion Diffusion (SMD), to address a novel scenario: sketch-to-motion, aiming to generate plausible and ..."}
+{"idx": 2, "title": "diff-usion/Awesome-Diffusion-Models: A collection of ...", "date": "", "ddg_snippet": "This repository contains a collection of resources and papers on Diffusion Models . Please refer to this page as this page may not contain all the information ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/diff-usion/Awesome-Diffusion-Models", "content": "This repository contains a collection of resources and papers on Diffusion Models . Please refer to this page as this page may not contain all the information ..."}
+{"idx": 3, "title": "Democratising Sketch Control in Diffusion Models", "date": "", "ddg_snippet": "by S Koley · 2024 · Cited by 29 — Abstract. This paper unravels the potential of sketches for dif- fusion models , addressing the deceptive promise of direct sketch control in generative AI.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2024/papers/Koley_Its_All_About_Your_Sketch_Democratising_Sketch_Control_in_Diffusion_CVPR_2024_paper.pdf", "content": "by S Koley · 2024 · Cited by 29 — Abstract. This paper unravels the potential of sketches for dif- fusion models , addressing the deceptive promise of direct sketch control in generative AI."}
+{"idx": 4, "title": "CHATS: Combining Human-Aligned Optimization and Test ...", "date": "", "ddg_snippet": "by M Fu · Cited by 2 — This paper presents CHATS, a framework for text-to-image generation (T2I) that enhances both text-image alignment and generation quality. Unlike ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=D4Y71nbGRg", "content": "by M Fu · Cited by 2 — This paper presents CHATS, a framework for text-to-image generation (T2I) that enhances both text-image alignment and generation quality. Unlike ..."}
+{"idx": 5, "title": "CHATS: Combining Human-Aligned Optimization and Test ...", "date": "", "ddg_snippet": "In this work, we for the first time , explore facilitating the collaboration of human performance alignment and test- time sampling to unlock the potential of ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46027", "content": "In this work, we for the first time , explore facilitating the collaboration of human performance alignment and test- time sampling to unlock the potential of ..."}
+{"idx": 6, "title": "Creativity and Machine Learning: A Survey", "date": "", "ddg_snippet": "13 Feb 2025 — Because of this, at inference time , a diffusion model can generate a new sample by starting from pure random noise. The generation can also be ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2104.02726v7", "content": "13 Feb 2025 — Because of this, at inference time , a diffusion model can generate a new sample by starting from pure random noise. The generation can also be ..."}
+{"idx": 7, "title": "Daily Papers", "date": "", "ddg_snippet": "Our approach streamlines the training process of conditional diffusion models and can also be applied during inference on any pre-trained conditional model.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=classifier", "content": "Our approach streamlines the training process of conditional diffusion models and can also be applied during inference on any pre-trained conditional model."}
+{"idx": 8, "title": "Direct Consistency Optimization for Robust Customization ...", "date": "", "ddg_snippet": "We show that DCO enhances the image-text alignment and sample quality of personalized T2I synthesis compared to regular diffusion fine-tuning. And together with ...", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper_files/paper/2024/file/baf583e395665636887b3bda9b5ec7a1-Paper-Conference.pdf", "content": "We show that DCO enhances the image-text alignment and sample quality of personalized T2I synthesis compared to regular diffusion fine-tuning. And together with ..."}
+{"idx": 9, "title": "Direct Consistency Optimization for Robust Customization ...", "date": "", "ddg_snippet": "9 Dec 2024 — This paper presents a method that enhances the performance of the personalization of T2I diffusion models .", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/poster/94883", "content": "9 Dec 2024 — This paper presents a method that enhances the performance of the personalization of T2I diffusion models ."}
diff --git a/data/sampled_jsons/instance-specific_privacy_accounting_f-DP_Gaussian_differential_privacy_composition_2024.jsonl b/data/sampled_jsons/instance-specific_privacy_accounting_f-DP_Gaussian_differential_privacy_composition_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..22c392249dc95c91dce0015a43520a0e03bd0353
--- /dev/null
+++ b/data/sampled_jsons/instance-specific_privacy_accounting_f-DP_Gaussian_differential_privacy_composition_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Individual Privacy Accounting with Gaussian Differential Privacy", "date": "", "ddg_snippet": "Individual privacy accounting enables bounding diferential privacy ( DP ) loss individually for each participant involved in the analysis. This can be informative as often the individual privacy losses are considerably smaller than those indicated by the DP bounds that are based on considering worst-case bounds at each data access. In order to account for the individual privacy losses in a ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2209.15596", "content": "Individual privacy accounting enables bounding diferential privacy ( DP ) loss individually for each participant involved in the analysis. This can be informative as often the individual privacy losses are considerably smaller than those indicated by the DP bounds that are based on considering worst-case bounds at each data access. In order to account for the individual privacy losses in a ..."}
+{"idx": 1, "title": "Gaussian Differential Privacy | Journal of the Royal Statistical ...", "date": "", "ddg_snippet": "GDP is the focal privacy definition among the family of f - DP guarantees due to a central limit theorem for differential privacy that we prove. More precisely, the privacy guarantees of any hypothesis testing based definition of privacy (including the original differential privacy definition) converges to GDP in the limit under composition .", "subpage_snippet": "", "source": "academic.oup.com", "link": "https://academic.oup.com/jrsssb/article/84/1/3/7056089", "content": "GDP is the focal privacy definition among the family of f - DP guarantees due to a central limit theorem for differential privacy that we prove. More precisely, the privacy guarantees of any hypothesis testing based definition of privacy (including the original differential privacy definition) converges to GDP in the limit under composition ."}
+{"idx": 2, "title": "PDF Lecture8ModernToolsforPrivacy Accounting", "date": "", "ddg_snippet": "The limitations showing of the it classical is possible Gaussian to achieve mechanism (0, )- DP using described Gaussian in perturbations. the previous This there is room for the improvement capabilities of in the the classical calibration Gaussian of mechanism, the variance since of the a Gaussian standard the corresponding Now we can provided use global Lemma by L2 Theorem 3 sensitivity. to ...", "subpage_snippet": "", "source": "cseweb.ucsd.edu", "link": "https://cseweb.ucsd.edu/~yuxiangw/classes/DSC291-2024Fall/Lectures/lec8.pdf", "content": "The limitations showing of the it classical is possible Gaussian to achieve mechanism (0, )- DP using described Gaussian in perturbations. the previous This there is room for the improvement capabilities of in the the classical calibration Gaussian of mechanism, the variance since of the a Gaussian standard the corresponding Now we can provided use global Lemma by L2 Theorem 3 sensitivity. to ..."}
+{"idx": 3, "title": "Individual Privacy Accounting with Gaussian Differential Privacy", "date": "", "ddg_snippet": "measuring privacy in fully adapti ve compositions : privacy filters, which halt the algorithms when a given budget is exceeded, and pri vacy odometers, which output bounds on the priv acy loss ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/378769909_Individual_Privacy_Accounting_with_Gaussian_Differential_Privacy", "content": "measuring privacy in fully adapti ve compositions : privacy filters, which halt the algorithms when a given budget is exceeded, and pri vacy odometers, which output bounds on the priv acy loss ..."}
+{"idx": 4, "title": "Individual Privacy Accounting with Gaussian Differential Privacy", "date": "", "ddg_snippet": "We make first steps in this direction by providing a careful analysis using the Gaussian differential privacy which gives optimal bounds for the Gaussian mechanism, one of the most versatile DP mechanisms.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=JmC_Tld3v-f", "content": "We make first steps in this direction by providing a careful analysis using the Gaussian differential privacy which gives optimal bounds for the Gaussian mechanism, one of the most versatile DP mechanisms."}
+{"idx": 5, "title": "Individual Privacy Accounting with Gaussian Differential Privacy", "date": "", "ddg_snippet": "Individual privacy accounting enables bounding differential privacy ( DP ) loss individually for each participant involved in the analysis. This can be informative as often the individual privacy losses are considerably smaller than those indicated by the DP bounds that are based on considering worst-case bounds at each data access. In order to account for the individual privacy losses in a ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2209.15596", "content": "Individual privacy accounting enables bounding differential privacy ( DP ) loss individually for each participant involved in the analysis. This can be informative as often the individual privacy losses are considerably smaller than those indicated by the DP bounds that are based on considering worst-case bounds at each data access. In order to account for the individual privacy losses in a ..."}
+{"idx": 6, "title": "dp-accounting · PyPI", "date": "", "ddg_snippet": "This directory contains tools for tracking differential privacy budgets, available as part of the Google differential privacy library. The set of DpEvent classes allow you to describe complex differentially private mechanisms such as Laplace and Gaussian , subsampling mechanisms, and their compositions .", "subpage_snippet": "", "source": "pypi.org", "link": "https://pypi.org/project/dp-accounting/", "content": "This directory contains tools for tracking differential privacy budgets, available as part of the Google differential privacy library. The set of DpEvent classes allow you to describe complex differentially private mechanisms such as Laplace and Gaussian , subsampling mechanisms, and their compositions ."}
+{"idx": 7, "title": "PDF A Randomized Approach to Tight Privacy Accounting", "date": "", "ddg_snippet": "A major challenge in machine learning with differential privacy is privacy accounting , i.e., mea-suring the privacy loss of the composition of DP mechanisms. A privacy accountant takes a list of mechanisms, and returns the privacy parameter (ε and δ) for the composition of those mechanisms. Specifically, a privacy accountant is given a target ε and finds the smallest achievable δ such that ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2023/file/6ae7df1f40f5faeda474b36b61197822-Paper-Conference.pdf", "content": "A major challenge in machine learning with differential privacy is privacy accounting , i.e., mea-suring the privacy loss of the composition of DP mechanisms. A privacy accountant takes a list of mechanisms, and returns the privacy parameter (ε and δ) for the composition of those mechanisms. Specifically, a privacy accountant is given a target ε and finds the smallest achievable δ such that ..."}
+{"idx": 8, "title": "Sarus implements 𝑓-differential privacy in OpenDP", "date": "", "ddg_snippet": "An accountant based on approximate differential privacy and its generic composition theorems may largely overestimate the privacy consumption. This is the case in particular for the Gaussian mechanism, and it gets worse as the number of compositions increases.", "subpage_snippet": "", "source": "www.sarus.tech", "link": "https://www.sarus.tech/post/sarus-implements-differential-privacy-in-opendp", "content": "An accountant based on approximate differential privacy and its generic composition theorems may largely overestimate the privacy consumption. This is the case in particular for the Gaussian mechanism, and it gets worse as the number of compositions increases."}
+{"idx": 9, "title": "PDF Tight and Flexible Accounting of Differential Privacy", "date": "", "ddg_snippet": "However, in Figure 1 we demonstrate that we cannot, in general, convert the RDP of Gaussian mechanism into an ( , )- DP that matches the optimal accounting one can achieve through either the privacy profile or f - DP directly.", "subpage_snippet": "", "source": "cseweb.ucsd.edu", "link": "https://cseweb.ucsd.edu/~yuxiangw/talks/mit_privacy_talk.pdf", "content": "However, in Figure 1 we demonstrate that we cannot, in general, convert the RDP of Gaussian mechanism into an ( , )- DP that matches the optimal accounting one can achieve through either the privacy profile or f - DP directly."}
diff --git a/data/sampled_jsons/lambda_m_value_disease-matching_constraint_Equation_(5)_sitear5iv.labs.arxiv.org_year_2024.jsonl b/data/sampled_jsons/lambda_m_value_disease-matching_constraint_Equation_(5)_sitear5iv.labs.arxiv.org_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ca6809bc2f73f656ac9d29ba2b77384f56dcffe7
--- /dev/null
+++ b/data/sampled_jsons/lambda_m_value_disease-matching_constraint_Equation_(5)_sitear5iv.labs.arxiv.org_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Divide and concur: A general approach to constraint satisfaction", "date": "", "ddg_snippet": "Abstract. Many difficult computational problems involve the simultaneous satisfaction of multiple constraints which are individually easy to satisfy.", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/0801.0222", "content": "Abstract. Many difficult computational problems involve the simultaneous satisfaction of multiple constraints which are individually easy to satisfy."}
+{"idx": 1, "title": "[2304.04740] Reflected Diffusion Models - ar5iv - arXiv", "date": "", "ddg_snippet": "To learn the score function on a general bounded domain, we introduce constrained denoising score matching (CDSM). Unlike previous methods (Hyvärinen, 2007) , ...", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2304.04740", "content": "To learn the score function on a general bounded domain, we introduce constrained denoising score matching (CDSM). Unlike previous methods (Hyvärinen, 2007) , ..."}
+{"idx": 2, "title": "Skill-Based Few-Shot Selection for In-Context Learning - ar5iv", "date": "", "ddg_snippet": "Experimental results across five cross-domain semantic parsing datasets and six backbone models show that Skill-KNN significantly outperforms existing methods.", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2305.14210", "content": "Experimental results across five cross-domain semantic parsing datasets and six backbone models show that Skill-KNN significantly outperforms existing methods."}
+{"idx": 3, "title": "Simulation techniques for cosmological simulations - ar5iv", "date": "", "ddg_snippet": "These tests explore the influence of the gravitational softening, the time stepping algorithm, the starting redshift, the accuracy of force computations, and ...", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/0801.1023", "content": "These tests explore the influence of the gravitational softening, the time stepping algorithm, the starting redshift, the accuracy of force computations, and ..."}
+{"idx": 4, "title": "Real-Time Model Calibration with Deep Reinforcement Learning", "date": "", "ddg_snippet": "Under this definition, the stability objective is given by Equation 5 . The stability objective defines an energy decreasing condition that drives the ...", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2006.04001", "content": "Under this definition, the stability objective is given by Equation 5 . The stability objective defines an energy decreasing condition that drives the ..."}
+{"idx": 5, "title": "Anti-Anthropic Solutions to the Cosmic Coincidence Problem", "date": "", "ddg_snippet": "A cosmological constant , Λ Λ \\ Lambda , can explain all current data, but requires two extreme fine-tunings: the value of Λ Λ \\ Lambda must be many orders of ...", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/1309.0849", "content": "A cosmological constant , Λ Λ \\ Lambda , can explain all current data, but requires two extreme fine-tunings: the value of Λ Λ \\ Lambda must be many orders of ..."}
+{"idx": 6, "title": "Buckingham Pi Analysis for Dimensionally Consistent Learning", "date": "", "ddg_snippet": "The dimensionless loss imposes a soft Buckingham Pi constraint from equation (5)) and the BuckiNet layer satisfies equation (3).", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2202.04643", "content": "The dimensionless loss imposes a soft Buckingham Pi constraint from equation (5)) and the BuckiNet layer satisfies equation (3)."}
+{"idx": 7, "title": "Open quantum systems are harder to track than open classical ...", "date": "", "ddg_snippet": "To find a PRE, the set of nonlinear polynomial constraints given by Eq. ( 5 ) must be solved. The difficulty of this task becomes exponentially more difficult as ...", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/1905.10935", "content": "To find a PRE, the set of nonlinear polynomial constraints given by Eq. ( 5 ) must be solved. The difficulty of this task becomes exponentially more difficult as ..."}
+{"idx": 8, "title": "Model-Based Reinforcement Learning via Latent-Space ...", "date": "", "ddg_snippet": "To evaluate the moment matching terms in Equation 5 we use μ , σ 2 𝜇 superscript 𝜎 2 \\mu,\\sigma^{2} directly for the mean and variance of q ( z t ) q ...", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2106.13229", "content": "To evaluate the moment matching terms in Equation 5 we use μ , σ 2 𝜇 superscript 𝜎 2 \\mu,\\sigma^{2} directly for the mean and variance of q ( z t ) q ..."}
+{"idx": 9, "title": "Reuse and Diffuse: Iterative Denoising for Text-to-Video ...", "date": "", "ddg_snippet": "In this paper, we propose a framework called “Reuse and Diffuse” dubbed VidRD to produce more frames following the frames already generated by an LDM.", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2309.03549", "content": "In this paper, we propose a framework called “Reuse and Diffuse” dubbed VidRD to produce more frames following the frames already generated by an LDM."}
diff --git a/data/sampled_jsons/language_model_pre-training_scaling_laws_error_rate_dataset_size.jsonl b/data/sampled_jsons/language_model_pre-training_scaling_laws_error_rate_dataset_size.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a0bfdff267712e5983de9fcb43367a64f8bbbfed
--- /dev/null
+++ b/data/sampled_jsons/language_model_pre-training_scaling_laws_error_rate_dataset_size.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Scaling Laws for Pre-training Agents and World Models", "date": "", "ddg_snippet": "The role of scale in pre-training is until now best understood in the context of large language models (LLMs). Following the observation that the empirical relationship between loss and key scaling quantities can be accurately described by power laws [Kaplan et al., 2020], ensuing work studied the precise trade-off between model and dataset size [Hoffmann et al., 2022], as well as ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.04434", "content": "The role of scale in pre-training is until now best understood in the context of large language models (LLMs). Following the observation that the empirical relationship between loss and key scaling quantities can be accurately described by power laws [Kaplan et al., 2020], ensuing work studied the precise trade-off between model and dataset size [Hoffmann et al., 2022], as well as ..."}
+{"idx": 1, "title": "LLM pre-training and scaling laws | Sai's Notebook", "date": "", "ddg_snippet": "The \"Chinchilla Law \" is a concept from the 2022 paper \" Training Compute-Optimal Large Language Models ,\" which identifies the optimal balance between model size , training dataset size , and compute budget for large language models (LLMs).", "subpage_snippet": "", "source": "sai-tai.com", "link": "https://sai-tai.com/ai/llm/wk1/pretrain-scaling/", "content": "The \"Chinchilla Law \" is a concept from the 2022 paper \" Training Compute-Optimal Large Language Models ,\" which identifies the optimal balance between model size , training dataset size , and compute budget for large language models (LLMs)."}
+{"idx": 2, "title": "Language Model Scaling Laws: Beyond Bigger AI Models in 2024 - Medium", "date": "", "ddg_snippet": "These scaling laws determined the optimal allocation of a fixed compute budget, balancing model size , dataset size , and training duration to maximise performance gains.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@aiml_58187/beyond-bigger-models-the-evolution-of-language-model-scaling-laws-d4bc974d3876", "content": "These scaling laws determined the optimal allocation of a fixed compute budget, balancing model size , dataset size , and training duration to maximise performance gains."}
+{"idx": 3, "title": "PDF Revisiting Scaling Laws for Language Models: The Role of Data Quality ...", "date": "", "ddg_snippet": "Traditional scaling laws in natural language processing suggest that increasing model size and training data enhances performance. How- ever, recent studies reveal deviations, partic- ularly in large language models , where per- formance improvements decelerate a phe- nomenon known as sub- scaling .", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.acl-long.1163.pdf", "content": "Traditional scaling laws in natural language processing suggest that increasing model size and training data enhances performance. How- ever, recent studies reveal deviations, partic- ularly in large language models , where per- formance improvements decelerate a phe- nomenon known as sub- scaling ."}
+{"idx": 4, "title": "Paper Notes: Scaling Laws for Pre-Training Agents and World Models", "date": "", "ddg_snippet": "They observe: Scaling laws do seem to apply to world models (which, in this context, means action-conditioned video generative models ) The optimal trade-off between model and dataset size is influenced by the number of tokens per observation (the compression rate ) Scaling laws with behavior cloning are hard to observe with modest compute budgets", "subpage_snippet": "", "source": "itcanthink.substack.com", "link": "https://itcanthink.substack.com/p/paper-notes-scaling-laws-for-pre", "content": "They observe: Scaling laws do seem to apply to world models (which, in this context, means action-conditioned video generative models ) The optimal trade-off between model and dataset size is influenced by the number of tokens per observation (the compression rate ) Scaling laws with behavior cloning are hard to observe with modest compute budgets"}
+{"idx": 5, "title": "PDF Scaling Laws for Neural Language Models - papers.baulab.info", "date": "", "ddg_snippet": "Abstract We study empirical scaling laws for language model performance on the cross-entropy loss. The loss scales as a power- law with model size , dataset size , and the amount of compute used for training , with some trends spanning more than seven orders of magnitude. Other architectural details such as network width or depth have minimal effects within a wide range. Simple equations govern ...", "subpage_snippet": "", "source": "papers.baulab.info", "link": "https://papers.baulab.info/papers/Kaplan-2020.pdf", "content": "Abstract We study empirical scaling laws for language model performance on the cross-entropy loss. The loss scales as a power- law with model size , dataset size , and the amount of compute used for training , with some trends spanning more than seven orders of magnitude. Other architectural details such as network width or depth have minimal effects within a wide range. Simple equations govern ..."}
+{"idx": 6, "title": "Scaling Laws for Neural Language Models - Semantic Scholar", "date": "", "ddg_snippet": "Larger models are significantly more sample-efficient, such that optimally compute-efficient training involves training very large models on a relatively modest amount of data and stopping significantly before convergence. We study empirical scaling laws for language model performance on the cross-entropy loss. The loss scales as a power- law with model size , dataset size , and the amount of ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Scaling-Laws-for-Neural-Language-Models-Kaplan-McCandlish/e6c561d02500b2596a230b341a8eb8b921ca5bf2", "content": "Larger models are significantly more sample-efficient, such that optimally compute-efficient training involves training very large models on a relatively modest amount of data and stopping significantly before convergence. We study empirical scaling laws for language model performance on the cross-entropy loss. The loss scales as a power- law with model size , dataset size , and the amount of ..."}
+{"idx": 7, "title": "Scaling Law for Language Models Training Considering Batch Size - arXiv.org", "date": "", "ddg_snippet": "We begin by training language models ranging from 125 million to 2.6 billion parameters, using up to 300 billion high-quality tokens. Through these experiments, we establish a basic scaling law on model size and training data amount. We then examine how varying batch sizes and learning rates affect the convergence and generalization of these ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.01505v1", "content": "We begin by training language models ranging from 125 million to 2.6 billion parameters, using up to 300 billion high-quality tokens. Through these experiments, we establish a basic scaling law on model size and training data amount. We then examine how varying batch sizes and learning rates affect the convergence and generalization of these ..."}
+{"idx": 8, "title": "How to build AI scaling laws for efficient LLM training and budget ...", "date": "", "ddg_snippet": "The functional form of scaling laws is relatively simple, incorporating components from the small models that capture the number of parameters and their scaling effect, the number of training tokens and their scaling effect, and the baseline performance for the model family of interest.", "subpage_snippet": "", "source": "news.mit.edu", "link": "https://news.mit.edu/2025/how-build-ai-scaling-laws-efficient-llm-training-budget-maximization-0916", "content": "The functional form of scaling laws is relatively simple, incorporating components from the small models that capture the number of parameters and their scaling effect, the number of training tokens and their scaling effect, and the baseline performance for the model family of interest."}
+{"idx": 9, "title": "Daily Paper: Predictable Scale: Part I — Optimal Hyperparameter Scaling ...", "date": "", "ddg_snippet": "Daily Paper: Predictable Scale: Part I — Optimal Hyperparameter Scaling Law in Large Language Model Pretraining Proposes empirical scaling laws (Step Law ) that accurately estimate optimal Batch Size and Learning Rate based on model and data size , robust across different model structures, sparsity, and data distributions.", "subpage_snippet": "", "source": "yikai-liao.github.io", "link": "https://yikai-liao.github.io/hugo/p/2025-04-predictable-scale/", "content": "Daily Paper: Predictable Scale: Part I — Optimal Hyperparameter Scaling Law in Large Language Model Pretraining Proposes empirical scaling laws (Step Law ) that accurately estimate optimal Batch Size and Learning Rate based on model and data size , robust across different model structures, sparsity, and data distributions."}
diff --git a/data/sampled_jsons/main_implementation_challenge_of_OFUL_algorithm.jsonl b/data/sampled_jsons/main_implementation_challenge_of_OFUL_algorithm.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..726eb48b7945f4659f53ad8b97ee611c0e41c1cd
--- /dev/null
+++ b/data/sampled_jsons/main_implementation_challenge_of_OFUL_algorithm.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Improved Algorithms for Linear Stochastic Bandits", "date": "", "ddg_snippet": "We call the resulting algorithm the OFUL ALGORITHM for “optimism in the face of uncertainty linear bandit algorithm ”. Pseudo-code of the algorithm is given in Figure 1. The crux of the problem is the construction of the condence sets Ct.", "subpage_snippet": "", "source": "david.palenica.com", "link": "https://david.palenica.com/papers/linear-bandit/linear-bandits-NIPS2011-camera-ready.pdf", "content": "We call the resulting algorithm the OFUL ALGORITHM for “optimism in the face of uncertainty linear bandit algorithm ”. Pseudo-code of the algorithm is given in Figure 1. The crux of the problem is the construction of the condence sets Ct."}
+{"idx": 1, "title": "Improved Algorithms for Stochastic Linear Bandits", "date": "", "ddg_snippet": "(2011) proposed the OFUL algorithm for linear bandit problems with a changing and possibly infinite action set, which is essentially the problem that we investigate. We consider stochastic linear bandit problems where the reward function is a composition of a possibly non-linear feature map...", "subpage_snippet": "", "source": "www.ias.informatik.tu-darmstadt.de", "link": "https://www.ias.informatik.tu-darmstadt.de/uploads/Team/HamishFlynn/mmucb.pdf", "content": "(2011) proposed the OFUL algorithm for linear bandit problems with a changing and possibly infinite action set, which is essentially the problem that we investigate. We consider stochastic linear bandit problems where the reward function is a composition of a possibly non-linear feature map..."}
+{"idx": 2, "title": "Lower Bounds for Stochastic Linear Bandits – Bandit Algorithms", "date": "", "ddg_snippet": "That is, an algorithm that enjoys the bound of OFUL if the the linear model is correct, but recovers the regret of UCB otherwise.Notes. Note 1: The worst-case bound demonstrates the near-optimality of the OFUL algorithm for a specific action-set.", "subpage_snippet": "", "source": "banditalgs.com", "link": "https://banditalgs.com/2016/10/20/lower-bounds-for-stochastic-linear-bandits/", "content": "That is, an algorithm that enjoys the bound of OFUL if the the linear model is correct, but recovers the regret of UCB otherwise.Notes. Note 1: The worst-case bound demonstrates the near-optimality of the OFUL algorithm for a specific action-set."}
+{"idx": 3, "title": "Meta-learning with Stochastic Linear Bandits", "date": "", "ddg_snippet": "Inspired by recent work on learning-to-learn linear regression, we consider a class of bandit algorithms that implement a regularized version of the well-known OFUL algorithm , where the regularization is a square euclidean distance to a bias vector.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2005.08531", "content": "Inspired by recent work on learning-to-learn linear regression, we consider a class of bandit algorithms that implement a regularized version of the well-known OFUL algorithm , where the regularization is a square euclidean distance to a bias vector."}
+{"idx": 4, "title": "Table of Contents", "date": "", "ddg_snippet": "The main topic is the design of algorithms for these problems and the development of nite-time performance guarantees.We tested the linear bandit algorithms presented in the previous section, along with OFUL in the recent “Exploration/Exploitation” challenge 5.", "subpage_snippet": "", "source": "era.library.ualberta.ca", "link": "https://era.library.ualberta.ca/items/46e48c73-59e3-4b95-9af9-08acee77f2c7/download/4d7187db-bd4d-4de2-80dd-a56a43604cdd", "content": "The main topic is the design of algorithms for these problems and the development of nite-time performance guarantees.We tested the linear bandit algorithms presented in the previous section, along with OFUL in the recent “Exploration/Exploitation” challenge 5."}
+{"idx": 5, "title": "Bandits with Mean Bounds | OpenReview", "date": "", "ddg_snippet": "In the linear setting, we present the Restricted-set OFUL (R- OFUL ) algorithm that additionally uses the geometric properties of the problem to (potentially) restrict the set of arms being played and reduce exploration rates for suboptimal arms.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=4TZ4DE24fX&noteId=9RHDKkC5eW", "content": "In the linear setting, we present the Restricted-set OFUL (R- OFUL ) algorithm that additionally uses the geometric properties of the problem to (potentially) restrict the set of arms being played and reduce exploration rates for suboptimal arms."}
+{"idx": 6, "title": "(PDF) Meta-learning with Stochastic Linear Bandits", "date": "", "ddg_snippet": "Inspired by recent work on learning-to-learn linear regression , we consider a class of bandit algorithms that implement a regularized version of the well-known OFUL algorithm , where the regularization is a square euclidean distance to a bias vector.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/344595446_Meta-learning_with_Stochastic_Linear_Bandits", "content": "Inspired by recent work on learning-to-learn linear regression , we consider a class of bandit algorithms that implement a regularized version of the well-known OFUL algorithm , where the regularization is a square euclidean distance to a bias vector."}
+{"idx": 7, "title": "Reinforcement Learning with Trajectory Feedback", "date": "", "ddg_snippet": "Algorithm 1 OFUL for RL with Trajectory Feedback and Known Model.The analysis of OFUL is based upon two key ingredi-ents, (i) a concentration result, and (ii) an elliptical po-tential lemma.", "subpage_snippet": "", "source": "cdn.aaai.org", "link": "https://cdn.aaai.org/ojs/16895/16895-13-20389-1-2-20210518.pdf", "content": "Algorithm 1 OFUL for RL with Trajectory Feedback and Known Model.The analysis of OFUL is based upon two key ingredi-ents, (i) a concentration result, and (ii) an elliptical po-tential lemma."}
+{"idx": 8, "title": "Problem-Complexity Adaptive Model Selection", "date": "", "ddg_snippet": "B Proofs of the main results. C ALB-Dim for Stochastic Contextual Bandits with Finite Arms.5.2 ALB-Dim Algorithm . The algorithm in this case is identical to that of Algorithm 2, except with the dierence that in place of OFUL , we use SupLinRel of [Aue02] as the black-box.", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/pdf/problem-complexity-adaptive-model-selection-for-stochastic-3ds77j3j2m.pdf", "content": "B Proofs of the main results. C ALB-Dim for Stochastic Contextual Bandits with Finite Arms.5.2 ALB-Dim Algorithm . The algorithm in this case is identical to that of Algorithm 2, except with the dierence that in place of OFUL , we use SupLinRel of [Aue02] as the black-box."}
+{"idx": 9, "title": "Efficient Kernel UCB for Contextual Bandits", "date": "", "ddg_snippet": "Upper condence algorithm (UCB) algorithms main To extend the analysis of OFUL (Abbasi-yadkori et al., 2011) to the contextual kernel UCB algorithm , we will use the following proposition that has been proved and used by Jézéquel et al.", "subpage_snippet": "", "source": "hal.science", "link": "https://hal.science/hal-03575953v1/document", "content": "Upper condence algorithm (UCB) algorithms main To extend the analysis of OFUL (Abbasi-yadkori et al., 2011) to the contextual kernel UCB algorithm , we will use the following proposition that has been proved and used by Jézéquel et al."}
diff --git a/data/sampled_jsons/manifold_learning_small_alpha_parameter_noise_variance_regularization_estimation_challenge.jsonl b/data/sampled_jsons/manifold_learning_small_alpha_parameter_noise_variance_regularization_estimation_challenge.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4ec4185148fc546cccbd54cbc7b74bff8de20a86
--- /dev/null
+++ b/data/sampled_jsons/manifold_learning_small_alpha_parameter_noise_variance_regularization_estimation_challenge.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "5.11 MLBasics-Challenges.ppt - University at Buffalo", "date": "", "ddg_snippet": "Is it possible for the estimated function to generalize well for new inputs? In machine learning we allow the dimensionality of the manifold to vary from one point to another. This often happens when a manifold Intersects itself, as in a figure-eight.", "subpage_snippet": "", "source": "cedar.buffalo.edu", "link": "https://cedar.buffalo.edu/~srihari/CSE676/5.11+MLBasics-Challenges.pdf", "content": "Is it possible for the estimated function to generalize well for new inputs? In machine learning we allow the dimensionality of the manifold to vary from one point to another. This often happens when a manifold Intersects itself, as in a figure-eight."}
+{"idx": 1, "title": "Structure-Adaptive Manifold Estimation - Journal of Machine ...", "date": "", "ddg_snippet": "Many manifold learning procedures locally approximate a manifold by a weighted average over a small neighborhood. However, in the presence of large noise , the assigned weights become so corrupted that the averaged estimate shows very poor performance.", "subpage_snippet": "", "source": "jmlr.org", "link": "https://jmlr.org/papers/volume23/21-0338/21-0338.pdf", "content": "Many manifold learning procedures locally approximate a manifold by a weighted average over a small neighborhood. However, in the presence of large noise , the assigned weights become so corrupted that the averaged estimate shows very poor performance."}
+{"idx": 2, "title": "CS7015 (Deep Learning) : Lecture 8 - Regularization: Bias ...", "date": "", "ddg_snippet": "In summary (informally) Simple model: high bias, low variance Complex model: low bias, high variance There is always a trade-o between the bias and variance Both bias and variance contribute to the mean square error. Let us see how. But how is this related to model complexity? Let us see. @ When will ^f(xi) be high?", "subpage_snippet": "", "source": "cse.iitm.ac.in", "link": "https://cse.iitm.ac.in/~miteshk/CS7015/Slides/Teaching/pdf/Lecture8.pdf", "content": "In summary (informally) Simple model: high bias, low variance Complex model: low bias, high variance There is always a trade-o between the bias and variance Both bias and variance contribute to the mean square error. Let us see how. But how is this related to model complexity? Let us see. @ When will ^f(xi) be high?"}
+{"idx": 3, "title": "Learning Multiple Tasks using Manifold Regularization - NeurIPS", "date": "", "ddg_snippet": "An approximation of the manifold regularization scheme is presented that preserves the convexity of the single task learning prob-lem, and makes the proposed MTL framework efficient and easy to implement. We show the efficacy of our method on several datasets.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper/4163-learning-multiple-tasks-using-manifold-regularization.pdf", "content": "An approximation of the manifold regularization scheme is presented that preserves the convexity of the single task learning prob-lem, and makes the proposed MTL framework efficient and easy to implement. We show the efficacy of our method on several datasets."}
+{"idx": 4, "title": "Latent Manifold Reconstruction and Representation with ...", "date": "", "ddg_snippet": "This work demonstrates the significance of combining manifold reconstruction with manifold learning to achieve reliable representation of the latent manifold, particularly when dealing with noisy real-world data.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.04412v1", "content": "This work demonstrates the significance of combining manifold reconstruction with manifold learning to achieve reliable representation of the latent manifold, particularly when dealing with noisy real-world data."}
+{"idx": 5, "title": "Manifold regularized stacked autoencoders-based feature ...", "date": "", "ddg_snippet": "Aug 1, 2020 · MRSAE -based feature learning is effective for process fault detection. MRSAE provides an effective way for fault detection due to powerful feature learning . Multivariate statistical process control (MSPC) has been widely employed for process fault detection.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0959152420302353", "content": "Aug 1, 2020 · MRSAE -based feature learning is effective for process fault detection. MRSAE provides an effective way for fault detection due to powerful feature learning . Multivariate statistical process control (MSPC) has been widely employed for process fault detection."}
+{"idx": 6, "title": "Introduction to Applied Machine Learning - 6 Regularization ...", "date": "", "ddg_snippet": "Regularization does this by applying a penalty to the parametric model coefficients ( parameter estimates ) We will consider three approaches to regularization . These approaches are available for both regression and classification problems and for a variety of parametric statistical algorithms.", "subpage_snippet": "", "source": "jjcurtin.github.io", "link": "https://jjcurtin.github.io/book_iaml/l06_regularization.html", "content": "Regularization does this by applying a penalty to the parametric model coefficients ( parameter estimates ) We will consider three approaches to regularization . These approaches are available for both regression and classification problems and for a variety of parametric statistical algorithms."}
+{"idx": 7, "title": "Regularization in Machine Learning - GeeksforGeeks", "date": "", "ddg_snippet": "Regularization is an important technique in machine learning that helps to improve model accuracy by preventing overfitting which happens when a model learns the training data too well including noise and outliers and perform poor on new data.", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/machine-learning/regularization-in-machine-learning/", "content": "Regularization is an important technique in machine learning that helps to improve model accuracy by preventing overfitting which happens when a model learns the training data too well including noise and outliers and perform poor on new data."}
+{"idx": 8, "title": "Representation Learning : A Review and", "date": "", "ddg_snippet": "Most impressive are the two transfer learning challenges held in 2011 and won by representation learning algorithms. Learning is conceived in term of estimating a set of model parameters that (locally) maximizes the regularized likelihood of the training data.", "subpage_snippet": "", "source": "file.cz123.top", "link": "https://file.cz123.top/3PhD/3BLO/Paper_20240526/1{Manifold+hypothesis}[16](v)2013_arXiv_Bengio_Representation+learning+A+review+and+new+perspectives.pdf", "content": "Most impressive are the two transfer learning challenges held in 2011 and won by representation learning algorithms. Learning is conceived in term of estimating a set of model parameters that (locally) maximizes the regularized likelihood of the training data."}
+{"idx": 9, "title": "Learning a Metric Space for", "date": "", "ddg_snippet": "Local learning for manifold estimation . Low Rank Covariance Estimation . Approximate solution to SDPs.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=PXMxTio8DW_", "content": "Local learning for manifold estimation . Low Rank Covariance Estimation . Approximate solution to SDPs."}
diff --git a/data/sampled_jsons/minGPT_config_sitegithub.comfiveaiunderstanding_safety_finetuning.jsonl b/data/sampled_jsons/minGPT_config_sitegithub.comfiveaiunderstanding_safety_finetuning.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..44296beb3852e677fd5005ac7c820508378fa88f
--- /dev/null
+++ b/data/sampled_jsons/minGPT_config_sitegithub.comfiveaiunderstanding_safety_finetuning.jsonl
@@ -0,0 +1 @@
+{"idx": 0, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/mkuB677eMM_SimXRD-4M-_Big_Simulated_X-ray_Diffraction_Data_and_Crystal_Symmetry_Classification_Bench.jsonl b/data/sampled_jsons/mkuB677eMM_SimXRD-4M-_Big_Simulated_X-ray_Diffraction_Data_and_Crystal_Symmetry_Classification_Bench.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..afb8d01f5f2039a8a1c699ba1155344ebbfeb21b
--- /dev/null
+++ b/data/sampled_jsons/mkuB677eMM_SimXRD-4M-_Big_Simulated_X-ray_Diffraction_Data_and_Crystal_Symmetry_Classification_Bench.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "SIMXRD-4M: B SIMULATED X-RAY DIFFRACTION D C SYMMETRY ...", "date": "", "ddg_snippet": "e limited availability of training data and established bench -marks. To address this, we introduce SimXRD - 4M , the largest open-source simulated XRD pattern dataset to date, a. med at accelerating the development of crystallographic informatics. We developed a novel XRD simulation method that incorporates compre.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=mkuB677eMM", "content": "e limited availability of training data and established bench -marks. To address this, we introduce SimXRD - 4M , the largest open-source simulated XRD pattern dataset to date, a. med at accelerating the development of crystallographic informatics. We developed a novel XRD simulation method that incorporates compre."}
+{"idx": 1, "title": "SimXRD-4M: Big Simulated X-ray Diffraction Data and Crystal ...", "date": "", "ddg_snippet": "We benchmark both the in-library and out-library symmetry identification performance on SimXRD of 21 models, which can be divided into three types of sequence models: convolution neural networks (i.e., the backbone of existing symmetry classification models), recurrent models, and transformers.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.15469v2", "content": "We benchmark both the in-library and out-library symmetry identification performance on SimXRD of 21 models, which can be divided into three types of sequence models: convolution neural networks (i.e., the backbone of existing symmetry classification models), recurrent models, and transformers."}
+{"idx": 2, "title": "SimXRD-4M: Big Simulated X-ray Diffraction Data and Crystal ...", "date": "", "ddg_snippet": "Experimental XRD pattern of a Li-rich layered oxide cathode was compared with simulated pattern generated using PysimXRD . The simulation incorporates multiphysical coupling, producing patterns that closely match experimental measurement with minimal residual errors.", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/media/iclr-2025/Slides/28452.pdf", "content": "Experimental XRD pattern of a Li-rich layered oxide cathode was compared with simulated pattern generated using PysimXRD . The simulation incorporates multiphysical coupling, producing patterns that closely match experimental measurement with minimal residual errors."}
+{"idx": 3, "title": "GitHub - Bin-Cao/SimXRD: [ICLR 2025] SimXRD-4M: Big Simulated ...", "date": "", "ddg_snippet": "Data Description: Crystals are categorized into 230 space groups, each representing a distinct symmetry catrgory. XRD patterns, which correspond to the crystal structure, serve as vital tools for studying these materials.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Bin-Cao/SimXRD", "content": "Data Description: Crystals are categorized into 230 space groups, each representing a distinct symmetry catrgory. XRD patterns, which correspond to the crystal structure, serve as vital tools for studying these materials."}
+{"idx": 4, "title": "dblp: SimXRD-4M: Big Simulated X-ray Diffraction Data and ...", "date": "", "ddg_snippet": "May 15, 2025 · Bibliographic details on SimXRD - 4M : Big Simulated X - ray Diffraction Data and Crystal Symmetry Classification Benchmark .", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/conf/iclr/CaoLZTLZ25", "content": "May 15, 2025 · Bibliographic details on SimXRD - 4M : Big Simulated X - ray Diffraction Data and Crystal Symmetry Classification Benchmark ."}
+{"idx": 5, "title": "SimXRD-4M: Big Simulated X-ray Diffraction Data Accelerate ...", "date": "", "ddg_snippet": "Jun 25, 2024 · To address this, we introduce SimXRD , the largest open-source simulated XRD pattern dataset so far, to accelerate the development of crystallographic informatics.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/381665624_SimXRD-4M_Big_Simulated_X-ray_Diffraction_Data_Accelerate_the_Crystalline_Symmetry_Classification", "content": "Jun 25, 2024 · To address this, we introduce SimXRD , the largest open-source simulated XRD pattern dataset so far, to accelerate the development of crystallographic informatics."}
+{"idx": 6, "title": "(PDF) SIMXRD - 4 M : big simulated x - ray diffraction data and crystal ...", "date": "", "ddg_snippet": "Powder X - ray diffraction ( XRD ) patterns are highly effective for crystal identification and play a pivotal role in materials discovery.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/389657996_SIMXRD-4M_BIG_SIMULATED_X-RAY_DIFFRACTION_DATA_AND_CRYSTAL_SYMMETRY_CLASSIFICATION_BENCHMARK", "content": "Powder X - ray diffraction ( XRD ) patterns are highly effective for crystal identification and play a pivotal role in materials discovery."}
+{"idx": 7, "title": "SimXRD - 4 M : Big Simulated X - ray Diffraction Data Accelerate the...", "date": "", "ddg_snippet": "Powder X - ray diffraction ( XRD ) patterns are greatly effective in identifying crystals . Although machine learning (ML) has significantly advanced the analysis of powder XRD patterns, the progress is hindered by a lack of training data .", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/article/SimXRD-4M:-Big-Simulated-X-ray-Diffraction-Data-Accelerate-the-Crystal-Symmetry-Classification-8f73fec2-f675-4f6a-ab07-8e6e05d1c379", "content": "Powder X - ray diffraction ( XRD ) patterns are greatly effective in identifying crystals . Although machine learning (ML) has significantly advanced the analysis of powder XRD patterns, the progress is hindered by a lack of training data ."}
+{"idx": 8, "title": "caobin/CPPbenchmark · Datasets at Hugging Face", "date": "", "ddg_snippet": "CPPbenchmark is a curated benchmark suite for evaluating machine learning models on crystal property prediction (CPP) tasks. It includes eight tasks—seven regression (e.g., formation energy, band gap, elastic moduli) and one classification (metal/non-metal)—using high-quality...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/caobin/CPPbenchmark", "content": "CPPbenchmark is a curated benchmark suite for evaluating machine learning models on crystal property prediction (CPP) tasks. It includes eight tasks—seven regression (e.g., formation energy, band gap, elastic moduli) and one classification (metal/non-metal)—using high-quality..."}
+{"idx": 9, "title": "HKUST(GZ) - 51 tarafından alıntılandı - Graph learning", "date": "", "ddg_snippet": "i10-endeksi.2024. SIMXRD - 4 M : bıg sımulated x - ray dıffractıon data and crystal symmetry classıfıcatıon benchmark .", "subpage_snippet": "", "source": "scholar.google.es", "link": "https://scholar.google.es/citations?user=c1a0_UcAAAAJ&hl=tr", "content": "i10-endeksi.2024. SIMXRD - 4 M : bıg sımulated x - ray dıffractıon data and crystal symmetry classıfıcatıon benchmark ."}
diff --git a/data/sampled_jsons/multi-agent_submodular_coordination_multi-target_tracking_adversarial_behavior_experimental_setup.jsonl b/data/sampled_jsons/multi-agent_submodular_coordination_multi-target_tracking_adversarial_behavior_experimental_setup.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..79013fe30af3f5486b78ddd826b71187ddb163e7
--- /dev/null
+++ b/data/sampled_jsons/multi-agent_submodular_coordination_multi-target_tracking_adversarial_behavior_experimental_setup.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Diffusion Models for Multi-target Adversarial Tracking", "date": "", "ddg_snippet": "As unmanned drones proliferate, accurate autonomous target estimation is even more crucial for security and safety. This paper presents C onstrained A gent-based D iffusion for EN han CE d Multi-Agent Tracking (CADENCE), an approach aimed at generating comprehensive predictions of adversary locations by leveraging past sparse state information.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2307.06244v2", "content": "As unmanned drones proliferate, accurate autonomous target estimation is even more crucial for security and safety. This paper presents C onstrained A gent-based D iffusion for EN han CE d Multi-Agent Tracking (CADENCE), an approach aimed at generating comprehensive predictions of adversary locations by leveraging past sparse state information."}
+{"idx": 1, "title": "Diffusion Models for Multi-target Adversarial Tracking | IEEE ...", "date": "", "ddg_snippet": "Target tracking plays a crucial role in real-world scenarios, particularly in drug-trafficking interdiction, where the knowledge of an adversarial target's location is often limited. Improving autonomous tracking systems will enable unmanned aerial, surface, and underwater vehicles to better assist in interdicting smugglers that use manned surface, semisubmersible, and aerial vessels. As ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/abstract/document/10416775", "content": "Target tracking plays a crucial role in real-world scenarios, particularly in drug-trafficking interdiction, where the knowledge of an adversarial target's location is often limited. Improving autonomous tracking systems will enable unmanned aerial, surface, and underwater vehicles to better assist in interdicting smugglers that use manned surface, semisubmersible, and aerial vessels. As ..."}
+{"idx": 2, "title": "Multi-target tracking for unmanned aerial vehicle swarms using deep ...", "date": "", "ddg_snippet": "In recent years, deep reinforcement learning (DRL) has proved its great potential in multi-agent cooperation. However, how to apply DRL to multi-target tracking (MTT) problem for unmanned aerial vehicle (UAV) swarms is challenging: 1) the scale of UAVs may be large, but the existing multi-agent reinforcement learning (MARL) methods that rely on global or joint information of all agents suffer ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0925231221014223", "content": "In recent years, deep reinforcement learning (DRL) has proved its great potential in multi-agent cooperation. However, how to apply DRL to multi-target tracking (MTT) problem for unmanned aerial vehicle (UAV) swarms is challenging: 1) the scale of UAVs may be large, but the existing multi-agent reinforcement learning (MARL) methods that rely on global or joint information of all agents suffer ..."}
+{"idx": 3, "title": "Collaborative Target Tracking Algorithm for Multi-Agent Based on ... - MDPI", "date": "", "ddg_snippet": "Target tracking is a representative task in multi-agent reinforcement learning (MARL), where agents must collaborate effectively in environments with dense obstacles, evasive targets , and high-dimensional observations—conditions that often lead to local optima and training inefficiencies. To address these challenges, this paper proposes a collaborative tracking algorithm for UAVs that ...", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/2504-446X/9/8/521", "content": "Target tracking is a representative task in multi-agent reinforcement learning (MARL), where agents must collaborate effectively in environments with dense obstacles, evasive targets , and high-dimensional observations—conditions that often lead to local optima and training inefficiencies. To address these challenges, this paper proposes a collaborative tracking algorithm for UAVs that ..."}
+{"idx": 4, "title": "PDF Motion Coordination of Multi-Agent Networks for Multiple Target ...", "date": "", "ddg_snippet": "In the first problem, we aim to find a reference density path for the multi-target system that the multi-agent network must track as a whole. In our proposed solution approach, the probability density of the multi-target system is represented as a Gaussian mixture density which is estimated by an adaptive Gaussian sum filter.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/s10846-022-01786-y.pdf", "content": "In the first problem, we aim to find a reference density path for the multi-target system that the multi-agent network must track as a whole. In our proposed solution approach, the probability density of the multi-target system is represented as a Gaussian mixture density which is estimated by an adaptive Gaussian sum filter."}
+{"idx": 5, "title": "Improving multi-target cooperative tracking guidance for UAV swarms ...", "date": "", "ddg_snippet": "Through close cooperation, UAV swarms can show superior coordination , intelligence, and autonomy than traditional multi -UAV systems. At the same time, Multi-Target Tracking Guidance (MTTG) in unknown environments has also become an important application direction for UAV swarms.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S1000936121003423", "content": "Through close cooperation, UAV swarms can show superior coordination , intelligence, and autonomy than traditional multi -UAV systems. At the same time, Multi-Target Tracking Guidance (MTTG) in unknown environments has also become an important application direction for UAV swarms."}
+{"idx": 6, "title": "(PDF) Multi-Target Pursuit by a Decentralized ... - ResearchGate", "date": "", "ddg_snippet": "Our results demonstrate that a multi-agent pursuit team has the ability to learn highly efficient coordinated control policies in terms of target tracking and exploration even when confronted with ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/367409974_Multi-Target_Pursuit_by_a_Decentralized_Heterogeneous_UAV_Swarm_using_Deep_Multi-Agent_Reinforcement_Learning", "content": "Our results demonstrate that a multi-agent pursuit team has the ability to learn highly efficient coordinated control policies in terms of target tracking and exploration even when confronted with ..."}
+{"idx": 7, "title": "Online Submodular Coordination With Bounded Tracking Regret: Theory ...", "date": "", "ddg_snippet": "We are motivated by the future of autonomy that involves multiple robots coordinating in dynamic, unstructured, and adversarial environments to complete complex tasks such as target tracking , environmental mapping, and area monitoring. Such tasks are often modeled as submodular maximization coordination problems.", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10050730", "content": "We are motivated by the future of autonomy that involves multiple robots coordinating in dynamic, unstructured, and adversarial environments to complete complex tasks such as target tracking , environmental mapping, and area monitoring. Such tasks are often modeled as submodular maximization coordination problems."}
+{"idx": 8, "title": "Bandit Submodular Maximization for Multi-Robot Coordination in ...", "date": "", "ddg_snippet": "All the above multi -robot tasks and more, from target tracking and environmental exploration to collaborative mapping and area monitoring, can be modeled as submodular coordination problems, and thus, Sequential Greedy and its variants have been commonly used in robotics [1]-[11], [14]-[18].", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2305.12795", "content": "All the above multi -robot tasks and more, from target tracking and environmental exploration to collaborative mapping and area monitoring, can be modeled as submodular coordination problems, and thus, Sequential Greedy and its variants have been commonly used in robotics [1]-[11], [14]-[18]."}
+{"idx": 9, "title": "Factored Multi-Agent Soft Actor-Critic for Cooperative Multi-Target ...", "date": "", "ddg_snippet": "However, in practice, the trajectory of a moving target cannot be known by the UAV in advance, which poses a great challenge for realizing real-time tracking . Meanwhile, state-of-the-art multi-agent value-based methods have achieved significant progress for cooperative tasks.", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/2504-446X/7/3/150", "content": "However, in practice, the trajectory of a moving target cannot be known by the UAV in advance, which poses a great challenge for realizing real-time tracking . Meanwhile, state-of-the-art multi-agent value-based methods have achieved significant progress for cooperative tasks."}
diff --git a/data/sampled_jsons/multi-agent_tracking_adversarial_target_behavior_nearby_agent_experimental_setup_submodular_coordina.jsonl b/data/sampled_jsons/multi-agent_tracking_adversarial_target_behavior_nearby_agent_experimental_setup_submodular_coordina.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2dee3b0f94848ab7767b93720ce252e275c47cd5
--- /dev/null
+++ b/data/sampled_jsons/multi-agent_tracking_adversarial_target_behavior_nearby_agent_experimental_setup_submodular_coordina.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Multi - agent system - Wikipedia", "date": "", "ddg_snippet": "A multi - agent system is a computerized system composed of multiple interacting intelligent agents . Multi - agent systems can solve problems that are difficult or impossible for an individual agent or a monolithic system to solve.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Multi-agent_system", "content": "A multi - agent system is a computerized system composed of multiple interacting intelligent agents . Multi - agent systems can solve problems that are difficult or impossible for an individual agent or a monolithic system to solve."}
+{"idx": 1, "title": "Diffusion Models for Multi-target Adversarial Tracking", "date": "", "ddg_snippet": "Jan 12, 2024 · As unmanned drones proliferate, accurate autonomous target estimation is even more crucial for security and safety. This paper presents C onstrained A gent -based D iffusion for EN han CE d Multi- Agent Tracking (CADENCE), an approach aimed at generating comprehensive predictions of adversary locations by leveraging past sparse state information.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2307.06244v2", "content": "Jan 12, 2024 · As unmanned drones proliferate, accurate autonomous target estimation is even more crucial for security and safety. This paper presents C onstrained A gent -based D iffusion for EN han CE d Multi- Agent Tracking (CADENCE), an approach aimed at generating comprehensive predictions of adversary locations by leveraging past sparse state information."}
+{"idx": 2, "title": "Multi-Agent Multi-Target Pursuit with Dynamic Target ... - MDPI", "date": "", "ddg_snippet": "Nov 11, 2023 · In this paper, we consider the cooperative decision-making problem for multi- target tracking in multi- agent systems using multi- agent deep reinforcement learning algorithms. Multi- agent multi- target pursuit has faced new challenges in practical applications, where pursuers need to plan collision-free paths and appropriate multi- target allocation strategies to determine which target to track at ...", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/2079-9292/12/22/4613", "content": "Nov 11, 2023 · In this paper, we consider the cooperative decision-making problem for multi- target tracking in multi- agent systems using multi- agent deep reinforcement learning algorithms. Multi- agent multi- target pursuit has faced new challenges in practical applications, where pursuers need to plan collision-free paths and appropriate multi- target allocation strategies to determine which target to track at ..."}
+{"idx": 3, "title": "Multi-agent target search strategy optimization: Hierarchical ... Diffusion Models for Multi-target Adversarial Tracking An Online Optimization Approach for Multi-Agent Tracking of ... Collaborative Target Tracking Algorithm for Multi-Agent Based ... Multi -Agent Multi-Target Pursuit with Dynamic Target Allocation a… Diffusion Models for Multi - target Adversarial Tracking - arXiv.org Multi-agent target search strategy optimization: Hierarchical Multi -Agent Multi-Target Pursuit with Dynamic Target Allocation a… Multi -Agent Multi-Target Pursuit with Dynamic Target Allocation a… Multi-agent target search strategy optimization: Hierarchical [2306.11301] Adversarial Search and Tracking with Multiagent ...", "date": "", "ddg_snippet": "Dec 1, 2023 · The multi-criteria negative feedback mechanism optimizes the strategy direction of multi- agent target search by enhancing the information of multi- agent experiential learning and improving the training efficiency of reinforcement learning models. Target tracking plays a crucial role in real-world scenarios, particularly in drug-trafficking interdiction, where the knowledge of an adversarial target ’s location is often limited. Improving autonomous tracking systems will enable unmanned aerial, surface, and underwater vehicles to better assist in interdicting smugglers that use manned surface, semisubmersible, and aerial vessels. As ... Abstract This paper addresses tracking of a moving target in a multi- agent network. The target follows a linear dynamics corrupted by an adversarial noise, i.e., the noise is not generated from a statistical distribution. The location of the target at each time induces a global time-varying loss function, and the global loss is a sum of local losses, each of which is associated to one agent ... Jul 24, 2025 · Target tracking is a representative task in multi- agent reinforcement learning (MARL), where agents must collaborate effectively in environments with dense obstacles, evasive targets, and high-dimensional observations—conditions that often lead to local optima and training inefficiencies. To address these challenges, this paper proposes a collaborative tracking algorithm for UAVs that ... What are the problems faced by multi-agent multi-target pursuit tasks? In this section, we explore the problems faced by multi - agent multi - target pursuit tasks and demonstrate our solutions. One of the problems is that when there are multiple targets, it is necessary to determine which target to track currently for each pursuer, as each pursuer can only track a single target at a time. What is constrained agent-based diffusion for enhanced multi-target tracking? We design a novel approach named Constrained Agent-based Diffusion for ENhanCEd Multi-Target Tracking ( CADENCE ) that employs cross-attention to enable information exchange across different agents. A key benefit of diffusion models is their non-parametric formulation for generating multimodal hypotheses as compared to prior work. What reinforcement learning methods are used in multi-agent target search? In recent research, many reinforcement learning methods have been applied to multi - agent target search problems, such as value iteration-based methods like Q-learning, DQN , policy gradient-based methods like Actor-Critic (AC), Deep Deterministic Policy Gradient (DDPG) , , . What is multi-agent multi-target pursuit? Multi - agent multi - target pursuit has faced new challenges in practical applications, where pursuers need to plan collision-free paths and appropriate multi - target allocation strategies to determine which target to track at the current time for each pursuer. We design three feasible multi - target allocation strategies from different perspectives. How does multi-agent multi-target tracking work? In the scenario of multi-agent multi-target tracking, we retain the possibility of collisions between agents . When the distance between pursuing agents is less than the collision threshold , the pursuing agents will collide. If there is a collision before all the evaders are captured, the trial is declared to be a \"fail\" and terminates. What is multi-agent target search? Multi - agent target search Due to the efficiency, robustness, and flexibility of multi - agent systems in search tasks, they are being widely applied across various domains , including wilderness monitoring, rescue operations, urban surveillance, and more. Jun 20, 2023 · We study a search and tracking (S&T) problem where a team of dynamic search agents must collaborate to track an adversarial , evasive agent . The heterogeneous search team may only have access to a limited number of past adversary trajectories within a large search space. This problem is challenging for both model-based searching and reinforcement learning (RL) methods since the adversary ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S1568494623010177", "content": "Dec 1, 2023 · The multi-criteria negative feedback mechanism optimizes the strategy direction of multi- agent target search by enhancing the information of multi- agent experiential learning and improving the training efficiency of reinforcement learning models. Target tracking plays a crucial role in real-world scenarios, particularly in drug-trafficking interdiction, where the knowledge of an adversarial target ’s location is often limited. Improving autonomous tracking systems will enable unmanned aerial, surface, and underwater vehicles to better assist in interdicting smugglers that use manned surface, semisubmersible, and aerial vessels. As ... Abstract This paper addresses tracking of a moving target in a multi- agent network. The target follows a linear dynamics corrupted by an adversarial noise, i.e., the noise is not generated from a statistical distribution. The location of the target at each time induces a global time-varying loss function, and the global loss is a sum of local losses, each of which is associated to one agent ... Jul 24, 2025 · Target tracking is a representative task in multi- agent reinforcement learning (MARL), where agents must collaborate effectively in environments with dense obstacles, evasive targets, and high-dimensional observations—conditions that often lead to local optima and training inefficiencies. To address these challenges, this paper proposes a collaborative tracking algorithm for UAVs that ... What are the problems faced by multi-agent multi-target pursuit tasks? In this section, we explore the problems faced by multi - agent multi - target pursuit tasks and demonstrate our solutions. One of the problems is that when there are multiple targets, it is necessary to determine which target to track currently for each pursuer, as each pursuer can only track a single target at a time. What is constrained agent-based diffusion for enhanced multi-target tracking? We design a novel approach named Constrained Agent-based Diffusion for ENhanCEd Multi-Target Tracking ( CADENCE ) that employs cross-attention to enable information exchange across different agents. A key benefit of diffusion models is their non-parametric formulation for generating multimodal hypotheses as compared to prior work. What reinforcement learning methods are used in multi-agent target search? In recent research, many reinforcement learning methods have been applied to multi - agent target search problems, such as value iteration-based methods like Q-learning, DQN , policy gradient-based methods like Actor-Critic (AC), Deep Deterministic Policy Gradient (DDPG) , , . What is multi-agent multi-target pursuit? Multi - agent multi - target pursuit has faced new challenges in practical applications, where pursuers need to plan collision-free paths and appropriate multi - target allocation strategies to determine which target to track at the current time for each pursuer. We design three feasible multi - target allocation strategies from different perspectives. How does multi-agent multi-target tracking work? In the scenario of multi-agent multi-target tracking, we retain the possibility of collisions between agents . When the distance between pursuing agents is less than the collision threshold , the pursuing agents will collide. If there is a collision before all the evaders are captured, the trial is declared to be a \"fail\" and terminates. What is multi-agent target search? Multi - agent target search Due to the efficiency, robustness, and flexibility of multi - agent systems in search tasks, they are being widely applied across various domains , including wilderness monitoring, rescue operations, urban surveillance, and more. Jun 20, 2023 · We study a search and tracking (S&T) problem where a team of dynamic search agents must collaborate to track an adversarial , evasive agent . The heterogeneous search team may only have access to a limited number of past adversary trajectories within a large search space. This problem is challenging for both model-based searching and reinforcement learning (RL) methods since the adversary ..."}
+{"idx": 4, "title": "Diffusion Models for Multi-target Adversarial Tracking", "date": "", "ddg_snippet": "Target tracking plays a crucial role in real-world scenarios, particularly in drug-trafficking interdiction, where the knowledge of an adversarial target ’s location is often limited. Improving autonomous tracking systems will enable unmanned aerial, surface, and underwater vehicles to better assist in interdicting smugglers that use manned surface, semisubmersible, and aerial vessels. As ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/abstract/document/10416775", "content": "Target tracking plays a crucial role in real-world scenarios, particularly in drug-trafficking interdiction, where the knowledge of an adversarial target ’s location is often limited. Improving autonomous tracking systems will enable unmanned aerial, surface, and underwater vehicles to better assist in interdicting smugglers that use manned surface, semisubmersible, and aerial vessels. As ..."}
+{"idx": 5, "title": "An Online Optimization Approach for Multi-Agent Tracking of ...", "date": "", "ddg_snippet": "Abstract This paper addresses tracking of a moving target in a multi- agent network. The target follows a linear dynamics corrupted by an adversarial noise, i.e., the noise is not generated from a statistical distribution. The location of the target at each time induces a global time-varying loss function, and the global loss is a sum of local losses, each of which is associated to one agent ...", "subpage_snippet": "", "source": "scholar.harvard.edu", "link": "https://scholar.harvard.edu/files/shahin/files/acc_2017.pdf", "content": "Abstract This paper addresses tracking of a moving target in a multi- agent network. The target follows a linear dynamics corrupted by an adversarial noise, i.e., the noise is not generated from a statistical distribution. The location of the target at each time induces a global time-varying loss function, and the global loss is a sum of local losses, each of which is associated to one agent ..."}
+{"idx": 6, "title": "Collaborative Target Tracking Algorithm for Multi-Agent Based ...", "date": "", "ddg_snippet": "Jul 24, 2025 · Target tracking is a representative task in multi- agent reinforcement learning (MARL), where agents must collaborate effectively in environments with dense obstacles, evasive targets, and high-dimensional observations—conditions that often lead to local optima and training inefficiencies. To address these challenges, this paper proposes a collaborative tracking algorithm for UAVs that ...", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/2504-446X/9/8/521", "content": "Jul 24, 2025 · Target tracking is a representative task in multi- agent reinforcement learning (MARL), where agents must collaborate effectively in environments with dense obstacles, evasive targets, and high-dimensional observations—conditions that often lead to local optima and training inefficiencies. To address these challenges, this paper proposes a collaborative tracking algorithm for UAVs that ..."}
+{"idx": 7, "title": "[2306.11301] Adversarial Search and Tracking with Multiagent ...", "date": "", "ddg_snippet": "Jun 20, 2023 · We study a search and tracking (S&T) problem where a team of dynamic search agents must collaborate to track an adversarial , evasive agent . The heterogeneous search team may only have access to a limited number of past adversary trajectories within a large search space. This problem is challenging for both model-based searching and reinforcement learning (RL) methods since the adversary ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2306.11301", "content": "Jun 20, 2023 · We study a search and tracking (S&T) problem where a team of dynamic search agents must collaborate to track an adversarial , evasive agent . The heterogeneous search team may only have access to a limited number of past adversary trajectories within a large search space. This problem is challenging for both model-based searching and reinforcement learning (RL) methods since the adversary ..."}
+{"idx": 8, "title": "(PDF) Near -Optimal Online Learning for Multi - Agent Submodular ...", "date": "", "ddg_snippet": "Near -optimal multi -. agent learning for safe coverage control.Online submodular coordination with bounded. tracking regret: Theory, algorithm, and applications to multi-robot coordination . IEEE Robotics. and Automation Letters, 8(4):2261–2268, 2023.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/388847678_Near-Optimal_Online_Learning_for_Multi-Agent_Submodular_Coordination_Tight_Approximation_and_Communication_Efficiency", "content": "Near -optimal multi -. agent learning for safe coverage control.Online submodular coordination with bounded. tracking regret: Theory, algorithm, and applications to multi-robot coordination . IEEE Robotics. and Automation Letters, 8(4):2261–2268, 2023."}
+{"idx": 9, "title": "Near -Optimal Online Learning for Multi - Agent Submodular ...", "date": "", "ddg_snippet": "Multi - agent submodular maximization(MA-SM) problem involves coordinating multiple agents to collaboratively maximize a submodular utility function.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.05028v1", "content": "Multi - agent submodular maximization(MA-SM) problem involves coordinating multiple agents to collaboratively maximize a submodular utility function."}
diff --git a/data/sampled_jsons/n_layer_default_minGPT_sitegithub.comkarpathyminGPT.jsonl b/data/sampled_jsons/n_layer_default_minGPT_sitegithub.comkarpathyminGPT.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5823ac35d16b42792705e336b066278834f18540
--- /dev/null
+++ b/data/sampled_jsons/n_layer_default_minGPT_sitegithub.comkarpathyminGPT.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "minGPT/README.md at master · karpathy/minGPT · GitHub", "date": "", "ddg_snippet": "The minGPT library is three files: [ mingpt /model.py] ( mingpt /model.py) contains the actual Transformer model definition, [ mingpt /bpe.py] ( mingpt /bpe.py) contains a mildly refactored Byte Pair Encoder that translates between text and sequences of integers exactly like OpenAI did in GPT, [ mingpt /trainer.py] ( mingpt /trainer.py) is (GPT-independent ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/karpathy/minGPT/blob/master/README.md?plain=1", "content": "The minGPT library is three files: [ mingpt /model.py] ( mingpt /model.py) contains the actual Transformer model definition, [ mingpt /bpe.py] ( mingpt /bpe.py) contains a mildly refactored Byte Pair Encoder that translates between text and sequences of integers exactly like OpenAI did in GPT, [ mingpt /trainer.py] ( mingpt /trainer.py) is (GPT-independent ..."}
+{"idx": 1, "title": "minGPT/mingpt/utils.py at master · karpathy/minGPT · GitHub", "date": "", "ddg_snippet": "A minimal PyTorch re-implementation of the OpenAI GPT (Generative Pretrained Transformer) training - minGPT / mingpt /utils.py at master · karpathy/ minGPT", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/karpathy/minGPT/blob/master/mingpt/utils.py", "content": "A minimal PyTorch re-implementation of the OpenAI GPT (Generative Pretrained Transformer) training - minGPT / mingpt /utils.py at master · karpathy/ minGPT"}
+{"idx": 2, "title": "Layer norm weights not excluded from weight decay - GitHub", "date": "", "ddg_snippet": "Aug 23, 2020 · We could potentially exclude all standalone Parameters from weight decay by default ( perhaps with an explicit override list of parameter names we want to include) ? Or we could invert the problem and specify a list of module types to be included and parameter_names to be excluded in weight decay.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/karpathy/minGPT/issues/23", "content": "Aug 23, 2020 · We could potentially exclude all standalone Parameters from weight decay by default ( perhaps with an explicit override list of parameter names we want to include) ? Or we could invert the problem and specify a list of module types to be included and parameter_names to be excluded in weight decay."}
+{"idx": 3, "title": "AssertionError when run generate.ipynb with default parameter", "date": "", "ddg_snippet": "Jul 30, 2023 · karpathy / minGPT Public Notifications You must be signed in to change notification settings Fork 2.9k Star 22.4k", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/karpathy/minGPT/issues/120", "content": "Jul 30, 2023 · karpathy / minGPT Public Notifications You must be signed in to change notification settings Fork 2.9k Star 22.4k"}
+{"idx": 4, "title": "GitHub", "date": "", "ddg_snippet": "A PyTorch re-implementation of GPTtraining. minGPT tries to be small, clean, interpretable and educational, as most of the currently available ones are a bit sprawling. GPT is not a complicated model and this implementation is appropriately about 300 lines of code, including boilerplate and a totally unnecessary custom causal self-attention module. Anyway, all that's going on is that a ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/karpathy/minGPT/blob/ad77167036e87b72d6db117678020741005da6c6/README.md", "content": "A PyTorch re-implementation of GPTtraining. minGPT tries to be small, clean, interpretable and educational, as most of the currently available ones are a bit sprawling. GPT is not a complicated model and this implementation is appropriately about 300 lines of code, including boilerplate and a totally unnecessary custom causal self-attention module. Anyway, all that's going on is that a ..."}
+{"idx": 5, "title": "minGPT /README.md at master · karpathy/ minGPT · GitHub", "date": "", "ddg_snippet": "A minimal PyTorch re-implementation of the OpenAI GPT (Generative Pretrained Transformer) training - minGPT /README.md at master · karpathy/ minGPT .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/karpathy/minGPT/blob/master/README.md", "content": "A minimal PyTorch re-implementation of the OpenAI GPT (Generative Pretrained Transformer) training - minGPT /README.md at master · karpathy/ minGPT ."}
+{"idx": 6, "title": "Adapted this awesome minGPT to use PyTorch lightning!", "date": "", "ddg_snippet": "from mingpt .model import GPT , GPTConfig.block_size=train_dataset.block_size, n _ layer =8", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/karpathy/minGPT/pull/14/files", "content": "from mingpt .model import GPT , GPTConfig.block_size=train_dataset.block_size, n _ layer =8"}
+{"idx": 7, "title": "About layer norm dimention parameter: · Issue #113 · karpathy/ minGPT", "date": "", "ddg_snippet": "In model.py, the implementation of layer norm is : self.ln_1 = nn.LayerNorm(config. n _embd) If batch_size = 64, block_size = 6, embedding_size = 48, then the shape of input is [64, 6, 48], and the layer norm parameter is [48]", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/karpathy/minGPT/issues/113", "content": "In model.py, the implementation of layer norm is : self.ln_1 = nn.LayerNorm(config. n _embd) If batch_size = 64, block_size = 6, embedding_size = 48, then the shape of input is [64, 6, 48], and the layer norm parameter is [48]"}
+{"idx": 8, "title": "GitHub - karpathy/ minGPT : A minimal PyTorch re-implementation of...", "date": "", "ddg_snippet": "GPT -1-like: 12 layers , 12 heads, d_model 768 (125M). We use the same model and architecture as GPT -2, including the modified initialization, pre-normalization, and reversible tokenization described therein.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/karpathy/minGPT", "content": "GPT -1-like: 12 layers , 12 heads, d_model 768 (125M). We use the same model and architecture as GPT -2, including the modified initialization, pre-normalization, and reversible tokenization described therein."}
+{"idx": 9, "title": "[WIP] codestyle and Catalyst example by Scitator · Pull Request #19...", "date": "", "ddg_snippet": "There are no files selected for viewing. 137 changes: 96 additions & 41 deletions 137 mingpt /model.py.class GPT 1Config(GPTConfig): \"\"\" GPT -1 like network roughly 125M params \"\"\".", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/karpathy/minGPT/pull/19/files", "content": "There are no files selected for viewing. 137 changes: 96 additions & 41 deletions 137 mingpt /model.py.class GPT 1Config(GPTConfig): \"\"\" GPT -1 like network roughly 125M params \"\"\"."}
diff --git a/data/sampled_jsons/neural_PDE_solver_multi-scale_time_integration_error_correction.jsonl b/data/sampled_jsons/neural_PDE_solver_multi-scale_time_integration_error_correction.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c6b57421396c84f44b39cad1773df2bed6b1afb0
--- /dev/null
+++ b/data/sampled_jsons/neural_PDE_solver_multi-scale_time_integration_error_correction.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - bitzhangcy/Neural-PDE-Solver", "date": "", "ddg_snippet": "This is an open-source repository for Neural-PDE-Solver , a curated collection of literature on solving Partial Differential Equations ( PDEs ) using Neural Operators. The goal is to track recent progress and organize related papers systematically. I am currently looking for collaborators to help maintain and expand this repository.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/bitzhangcy/Neural-PDE-Solver", "content": "This is an open-source repository for Neural-PDE-Solver , a curated collection of literature on solving Partial Differential Equations ( PDEs ) using Neural Operators. The goal is to track recent progress and organize related papers systematically. I am currently looking for collaborators to help maintain and expand this repository."}
+{"idx": 1, "title": "Multi-scale low-frequency enhanced spectral neural operator for ...", "date": "", "ddg_snippet": "However, insufficient low-frequency learning ability and inability to utilize physical prior knowledge remain an obstacle for the PDEs solver which designed by neural operator. To tackle this challenge, we drew inspiration from the multigrid method and developed the multi-scale low-frequency enhanced spectral neural operator.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0952197625009121", "content": "However, insufficient low-frequency learning ability and inability to utilize physical prior knowledge remain an obstacle for the PDEs solver which designed by neural operator. To tackle this challenge, we drew inspiration from the multigrid method and developed the multi-scale low-frequency enhanced spectral neural operator."}
+{"idx": 2, "title": "MultiPDENet: PDE-embedded Learning with Multi-time-stepping", "date": "", "ddg_snippet": "Notably, it integrates a trainable neural solver for precise predictions at micro time scales , while employing a NN to correct errors at macro time steps. Additionally, by embedding PDEs , MultiPDENet offers enhanced generalizability. The primary contributions of this work are summarized as follows:", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.15987v1", "content": "Notably, it integrates a trainable neural solver for precise predictions at micro time scales , while employing a NN to correct errors at macro time steps. Additionally, by embedding PDEs , MultiPDENet offers enhanced generalizability. The primary contributions of this work are summarized as follows:"}
+{"idx": 3, "title": "Blending neural operators and relaxation methods in PDE ... - Nature", "date": "", "ddg_snippet": "Neural -network-based solvers for partial differential equations ( PDEs ) suffer from difficulties tackling high-frequency modes when learning complex functions, whereas for classical solvers it is ...", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s42256-024-00910-x", "content": "Neural -network-based solvers for partial differential equations ( PDEs ) suffer from difficulties tackling high-frequency modes when learning complex functions, whereas for classical solvers it is ..."}
+{"idx": 4, "title": "PDE-Refiner: Achieving Accurate Long Rollouts with Neural PDE Solvers", "date": "", "ddg_snippet": "Time -dependent partial differential equations ( PDEs ) are ubiquitous in science and engineering. Recently, mostly due to the high computational cost of traditional solution techniques, deep neural network based surrogates have gained increased interest. The practical utility of such neural PDE solvers relies on their ability to provide accurate, stable predictions over long time horizons, which ...", "subpage_snippet": "", "source": "phlippe.github.io", "link": "https://phlippe.github.io/PDERefiner/", "content": "Time -dependent partial differential equations ( PDEs ) are ubiquitous in science and engineering. Recently, mostly due to the high computational cost of traditional solution techniques, deep neural network based surrogates have gained increased interest. The practical utility of such neural PDE solvers relies on their ability to provide accurate, stable predictions over long time horizons, which ..."}
+{"idx": 5, "title": "PDE-constrained Learning with Multi-time-stepping for Accelerated...", "date": "", "ddg_snippet": "This paper introduces MultiPDENet, a neural network architecture designed to accelerate fluid dynamic simulations by combining classical numerical methods, such as a multi-scale time -stepping scheme inspired by Runge-Kutta methods, finite-difference derivatives, and a Fourier Neural Operator for learned corrections . This approach embeds physical constraints from partial differential equations ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=stcN89QGfL", "content": "This paper introduces MultiPDENet, a neural network architecture designed to accelerate fluid dynamic simulations by combining classical numerical methods, such as a multi-scale time -stepping scheme inspired by Runge-Kutta methods, finite-difference derivatives, and a Fourier Neural Operator for learned corrections . This approach embeds physical constraints from partial differential equations ..."}
+{"idx": 6, "title": "PDF Multi-Scale Message Passing Neural PDE Solvers", "date": "", "ddg_snippet": "We propose a novel multi-scale message passing neural network algorithm for learning the solutions of time -dependent PDEs . Our algorithm possesses both temporal and spatial multi-scale resolution features by incorporating multi-scale sequence models and graph gating modules in the encoder and processor, respec-tively. Benchmark numerical experiments are presented to demonstrate that the ...", "subpage_snippet": "", "source": "www.sam.math.ethz.ch", "link": "https://www.sam.math.ethz.ch/sam_reports/reports_final/reports2023/2023-14.pdf", "content": "We propose a novel multi-scale message passing neural network algorithm for learning the solutions of time -dependent PDEs . Our algorithm possesses both temporal and spatial multi-scale resolution features by incorporating multi-scale sequence models and graph gating modules in the encoder and processor, respec-tively. Benchmark numerical experiments are presented to demonstrate that the ..."}
+{"idx": 7, "title": "Taylor series error correction network for super-resolution of ...", "date": "", "ddg_snippet": "In this study, we present the Taylor Expansion Error Correction Network (TEECNet), a neural network designed to efficiently super-resolve partial differential equations ( PDEs ) solutions via graph representations.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0021999124008179", "content": "In this study, we present the Taylor Expansion Error Correction Network (TEECNet), a neural network designed to efficiently super-resolve partial differential equations ( PDEs ) solutions via graph representations."}
+{"idx": 8, "title": "Multiscale Neural Operators for Solving Time-Independent PDEs", "date": "", "ddg_snippet": "Multiscale Neural Operators for Solving Time -Independent PDEs TL;DR: We study how to solve time -independent Partial Differential Equations on large meshes and introduce a novel graph rewiring technique for this.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/merantix-momentum/multiscale-pde-operators", "content": "Multiscale Neural Operators for Solving Time -Independent PDEs TL;DR: We study how to solve time -independent Partial Differential Equations on large meshes and introduce a novel graph rewiring technique for this."}
+{"idx": 9, "title": "PhysicsCorrect: A Training-Free Approach for Stable Neural PDE Simulations", "date": "", "ddg_snippet": "Our approach requires no additional training, operates eficiently during inference, and is compatible with any pretrained neural PDE solver . For many PDEs , the Jacobian matrix and its pseudoinverse can be precomputed in an ofline warm-up phase, resulting in minimal computational overhead. Even for highly nonlinear PDEs with imperfect Jacobian approximations, the correction significantly ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2507.02227v1", "content": "Our approach requires no additional training, operates eficiently during inference, and is compatible with any pretrained neural PDE solver . For many PDEs , the Jacobian matrix and its pseudoinverse can be precomputed in an ofline warm-up phase, resulting in minimal computational overhead. Even for highly nonlinear PDEs with imperfect Jacobian approximations, the correction significantly ..."}
diff --git a/data/sampled_jsons/neural_scaling_laws_data_size_exponent_0.095_0.27_Kaplan_Hoffmann_chinchilla.jsonl b/data/sampled_jsons/neural_scaling_laws_data_size_exponent_0.095_0.27_Kaplan_Hoffmann_chinchilla.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..04527d4e16f521b2a598eb1bafd46346f6b4ceeb
--- /dev/null
+++ b/data/sampled_jsons/neural_scaling_laws_data_size_exponent_0.095_0.27_Kaplan_Hoffmann_chinchilla.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Large language model - Wikipedia", "date": "", "ddg_snippet": "One particular scaling law (\" Chinchilla scaling \") for LLM autoregressively trained for one epoch, with a log-log learning rate schedule, states that:[93].", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Large_language_model", "content": "One particular scaling law (\" Chinchilla scaling \") for LLM autoregressively trained for one epoch, with a log-log learning rate schedule, states that:[93]."}
+{"idx": 1, "title": "Power Lines: Scaling Laws for Weight Decay and Batch Size in", "date": "", "ddg_snippet": "We study scaling laws for HPs: formulas for how to scale HPs as we scale model size N 𝑁 N italic_N , dataset size D 𝐷 D italic_D , and batch ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.13738v1", "content": "We study scaling laws for HPs: formulas for how to scale HPs as we scale model size N 𝑁 N italic_N , dataset size D 𝐷 D italic_D , and batch ..."}
+{"idx": 2, "title": "[2001.08361] Scaling Laws for Neural Language Models - arXiv.org Scaling Laws for Neural Language Models - papers.baulab.info Scaling Laws for LLM Pretraining - jonvet.com Scaling Laws of Neural Language Models - GitHub [2102.06701] Explaining Neural Scaling Laws - arXiv.org Neural Scaling Laws (Then and Now) | AndoLogs Explaining Neural Scaling Laws - Google Research Scaling Laws for LLM Pretraining Scaling Laws for Neural Language Models - papers.baulab.info Scaling Laws for Neural Language Models - papers.baulab.info Neural Scaling Laws (Then and Now) | AndoLogs [2102.06701] Explaining Neural Scaling Laws - arXiv.org Explaining Neural Scaling Laws - Google Research", "date": "", "ddg_snippet": "Jan 23, 2020 · We study empirical scaling laws for language model performance on the cross-entropy loss. The loss scales as a power- law with model size , dataset size , and the amount of compute used for training, with some trends spanning more than seven orders of magnitude. Other architectural details such as network width or depth have minimal effects within a wide range. Simple equations govern the ... Abstract We study empirical scaling laws for language model performance on the cross-entropy loss. The loss scales as a power- law with model size , dataset size , and the amount of compute used for training, with some trends spanning more than seven orders of magnitude. Other architectural details such as network width or depth have minimal effects within a wide range. Simple equations govern ... Dec 18, 2024 · A comparison of Scaling Laws for LLM Pretraining, from Kaplan , to Chinchilla , the Chinchilla Trap, covering compute-optimal training and inference. This repository contains an implementation of scaling laws as first found by Kaplan et al in Scaling Laws of Neural Language Models. We find how the test loss of a language model scales with parameter count, dataset size , total compute, and the number of training steps. We also find the optimal number of parameters for a given compute budget, and the critical batch size at which a language model must be trained for the optimal trade-off between time efficiency and compute efficiency (as suggested in An Empirical Model of Large-Batch Training by McCandlish, Kaplan , et al). Our results qualitatively match their results in all cases. We also obtain quantitative matches in some cases despite significant differences in our experimental settings (for example, we train much smaller models, and our dataset OpenWebText is different from the WebText dataset used internally at OpenAI). Perhaps, the most interesting result is that we find a similar scaling law for the dependence of optimal model size $N_{\\text{opt}}$ on a compute budget $C$ as they do: $$N_{\\text{opt}} \\propto C^{ 0 .72}$$ Kaplan et al obtained $N_{\\text{opt}} \\propto C^{ 0 .73}$. See full list on github.com Kaplan et al discovered that the test loss of a language model scales as a power law with the number of (non-embedding) model parameters $N$, the number of tokens $D$ in the training dataset, and the amount of compute $C$ used to train a model: $$L(X) = \\left( \\frac{X_c}{X} \\right)^{\\alpha_X}$$ where $X = N, D,$ or $C$. When scaling with $N$ or $D$, the non- scaling variable is fixed at a large value so that it is not a bottleneck in affecting the model performance. If both $N$ and $D$ are allowed to vary, the scaling laws $L(N)$ and $L(D)$ can be combined into a single equation: $$L(N, D) = \\left[ \\left(\\frac{N_c}{N}\\right)^{ \\frac{\\alpha_N}{\\alpha_D} } + \\frac{D_c}{D} \\right]^{\\alpha_D}$$ We trained several models with varying model and dataset sizes, using (almost) the same set of hyperparameters as theirs, and found good fits to these equations. (See the figure below.) While the trends match up qualitatively, there are differences in some scaling exponents and coefficients. (See the table below.) In retrospect, this was expected as our training dataset is different from theirs, and hence it is not meaningful to compare test losses. However, we note that some results are strikingly close. For example, our $\\alpha_N$ is the same as theirs in the fit of $L(N, D)$, and is close to theirs in the fit of $L(N)$ ( 0 .082 vs 0 .076). This is perhaps evidence for a statement that $\\alpha_N$ is independent of the choice of a dataset as long as it is large enough (though it might depend on other choices such as that of a tokenizer or hyperparameter configurations). See full list on github.com The optimal number of parameters, $N_{\\text{opt}}(C)$, is only optimal if we were to train all models at the same fixed batch size (and other hyperparameter configurations) used to conduct our scaling laws experiments. But what if this batch size is too large? Generally speaking, increasing batch size reduces noise in gradient descent but this benefit dies off once the batch size crosses some threshold value. Training at a batch size larger than this threshold is wasteful of compute, as it does not help obtain significantly better performance. Training at smaller batch sizes, on the other hand, could save us some compute (at the cost of increasing the number of training steps). The trade-off between time-efficiency and compute-efficiency happens around a task-dependent critical batch size $\\mathcal{B}_{\\text{crit}}$ as observed by McCandlish et al. This batch size is independent of the model size but it depends on the target loss value. Kaplan et al found the following scaling law for critical batch size as a function of training loss in a language modeling task: $$ \\mathcal{B}^{\\text{ Kaplan }}_{\\text{crit}}(L) = 2. 0 \\times 10^8 \\ L^{-4.76} $$ We conducted several experiments to obtain critical batch size for language modeling with nanoGPT and obtained: $$ \\mathcal{B}^{\\text{nanoGPT}}_{\\text{crit}}(L) = 2.2 \\times 10^7 \\ L^{-4.26} $$ See full list on github.com Kaplan et al used their estimate of critical batch size to adjust the scaling laws with compute. That is, if they were to train at a batch size much smaller than the critical batch size for maximal compute-efficiency, their scaling laws $L(C)$ and $N_{\\text{opt}}(C)$ would be different. We performed the same exercise. A comparison of our results is given below. Significantly, while many of the scaling exponents and coefficients differ, perhaps the most important of them all --- $p_N$, the scaling exponent of optimal model size with compute budget, is almost the same ( 0 .73 vs 0 .72). This scaling law for $N_{\\text{opt}}(C_{\\text{min}})$ says that for a 10x increase in compute budget, the model size must increase $10^{ 0 .72} \\sim 5.24$ times. How should we distribute the rest of the increase in compute budget over increases in batch size and the number of training steps? Kaplan et al find that the batch size and the minimum of training steps $S_{\\text{min}}$ to obtain a specific loss value must increase with compute as $$B^{\\text{ Kaplan }} \\propto C_{\\min}^{ 0 .24}; \\quad S_{\\text{min}}^{\\text{ Kaplan }} \\propto C_{\\min}^{ 0 .03}$$ We find: $$B^{\\text{nanoGPT}} \\propto C_{\\min}^{ 0 .24}; \\quad S_{\\text{min}}^{\\text{nanoGPT}} \\propto C_{\\min}^{ 0 .04}$$ See full list on github.com This repository is built on top of nanoGPT. In addition to their dependencies, we used matplotlib, scikit-learn, and scipy for analysis of results. You may install all dependencies with See full list on github.com To reproduce scaling laws , we must first tokenize the OpenWebText dataset as in nanoGPT. This downloads and tokenizes the dataset, and stores the output in train.bin and val.bin files, which hold the OpenAI Byte-Pair Encoding token ids in one sequence, stored as raw uint16 bytes. Next, we included two new configuration files in the 'config' folder, which mimic the training configurations of Kaplan et al. scale_gpt.py may be used to run experiments with varying model sizes and subsets of datasets. For example, to change the model size , run and to change the model size as well as the fraction of the dataset, run The former run may be used to model $L(N)$, and the latter may be used to model $L(N, D)$. To measure critical batch size , use the other configuration file estimate_critical_batch.py to run experiments with various batch sizes and learning rates. See full list on github.com As in nanoGPT, we present some directions for future work and improvements. •Reproduce Chinchilla Scaling Laws with nanoGPT (work in progress) •Train larger models, extending scaling laws to higher orders of magnitude •Our fits for $S_{\\min}$ and $E_{\\min}$ do not look great. Improve on these fits. •Use ( Chinchilla ) scaling laws and critical batch size to train a large model (1B+ parameters) in a compute-optimal way. •Experiment with different parameterizations. Does maximal-update or neural -tangent parameterization have a significant impact on scaling trends? See full list on github.com I would like to thank Andrej Karpathy for the beautiful set of lectures in Neural Networks: Zero to Hero series, especially the lecture on building GPT from scratch and the nanoGPT repository. Using these resources, I went from zero to non-zero in a matter of a few months and the journey forward continues. I would also like to thank the administrators of the Amarel Cluster at Rutgers University for providing free access to the cluster to all members of the Rutgers community, and for providing prompt help whenever required. Without free access to these resources, the experiments for this repository would not have been possible. See full list on github.com Feb 12, 2021 · The population loss of trained deep neural networks often follows precise power- law scaling relations with either the size of the training dataset or the number of parameters in the network. We propose a theory that explains the origins of and connects these scaling laws . We identify variance-limited and resolution-limited scaling behavior for both dataset and model size , for a total of four ... Dec 3, 2024 · Early scaling laws ( Kaplan et al., 2020) established power- law relationships between model size , data , and performance. The Chinchilla paradigm shift (2022) introduced the 20:1 token-to-parameter ratio for optimal training. How do power-law scaling laws affect a neural network? The test loss of well-trained neural networks often follows precise power-law scaling relations with either the size of the training dataset or the number of parameters in the network. We propose a theory that explains and connects these scaling laws . What is the difference between Kaplan scaling laws and Chinchilla scaling laws? While the Kaplan Scaling Laws suggest that scaling model size is more important than scaling data , given a fixed compute budget, the Chinchilla Scaling Laws by Hoffmann et al. suggest that model size and data are equally important. There are a few key differences that lead to this conclusion: Does a simple scaling law provide a good description of loss? We should also emphasize that we have not optimized regularization (eg the dropout probability) while varying dataset and model size. In this section we will demonstrate that a simple scaling law provides a good description for the loss as a function of model size N and training time. Is there a power-law scaling between data size and performance? Some early [BB01, Goo01] work found power-law scalings between performance and dataset size . More recent work [HNA+17, HAD19] also investigated scaling between model size and data size; their work is perhaps the closest to ours in the literature8. How do scaling laws affect performance? OpenAI’s 2020 work on scaling laws jump started investigations into the relationship between model size, data, and performance. The paper established power-law relationships between these three factors, showing that as the number of parameters in a model increases, so does its performance on a range of tasks . Are large width and large dataset resolution-limited scaling exponents related? In the large width limit, this can be equivalently obtained from the spectrum of certain kernels, and we present evidence that large width and large dataset resolution-limited scaling exponents are related by a duality. The test loss of well-trained neural networks often follows precise power- law scaling relations with either the size of the training dataset or the number of parameters in the network. We propose a theory that explains and connects these scaling laws . We identify variance-limited and resolution-limited scaling behavior for both model and dataset size , for a total of four scaling regimes. The ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2001.08361", "content": "Jan 23, 2020 · We study empirical scaling laws for language model performance on the cross-entropy loss. The loss scales as a power- law with model size , dataset size , and the amount of compute used for training, with some trends spanning more than seven orders of magnitude. Other architectural details such as network width or depth have minimal effects within a wide range. Simple equations govern the ... Abstract We study empirical scaling laws for language model performance on the cross-entropy loss. The loss scales as a power- law with model size , dataset size , and the amount of compute used for training, with some trends spanning more than seven orders of magnitude. Other architectural details such as network width or depth have minimal effects within a wide range. Simple equations govern ... Dec 18, 2024 · A comparison of Scaling Laws for LLM Pretraining, from Kaplan , to Chinchilla , the Chinchilla Trap, covering compute-optimal training and inference. This repository contains an implementation of scaling laws as first found by Kaplan et al in Scaling Laws of Neural Language Models. We find how the test loss of a language model scales with parameter count, dataset size , total compute, and the number of training steps. We also find the optimal number of parameters for a given compute budget, and the critical batch size at which a language model must be trained for the optimal trade-off between time efficiency and compute efficiency (as suggested in An Empirical Model of Large-Batch Training by McCandlish, Kaplan , et al). Our results qualitatively match their results in all cases. We also obtain quantitative matches in some cases despite significant differences in our experimental settings (for example, we train much smaller models, and our dataset OpenWebText is different from the WebText dataset used internally at OpenAI). Perhaps, the most interesting result is that we find a similar scaling law for the dependence of optimal model size $N_{\\text{opt}}$ on a compute budget $C$ as they do: $$N_{\\text{opt}} \\propto C^{ 0 .72}$$ Kaplan et al obtained $N_{\\text{opt}} \\propto C^{ 0 .73}$. See full list on github.com Kaplan et al discovered that the test loss of a language model scales as a power law with the number of (non-embedding) model parameters $N$, the number of tokens $D$ in the training dataset, and the amount of compute $C$ used to train a model: $$L(X) = \\left( \\frac{X_c}{X} \\right)^{\\alpha_X}$$ where $X = N, D,$ or $C$. When scaling with $N$ or $D$, the non- scaling variable is fixed at a large value so that it is not a bottleneck in affecting the model performance. If both $N$ and $D$ are allowed to vary, the scaling laws $L(N)$ and $L(D)$ can be combined into a single equation: $$L(N, D) = \\left[ \\left(\\frac{N_c}{N}\\right)^{ \\frac{\\alpha_N}{\\alpha_D} } + \\frac{D_c}{D} \\right]^{\\alpha_D}$$ We trained several models with varying model and dataset sizes, using (almost) the same set of hyperparameters as theirs, and found good fits to these equations. (See the figure below.) While the trends match up qualitatively, there are differences in some scaling exponents and coefficients. (See the table below.) In retrospect, this was expected as our training dataset is different from theirs, and hence it is not meaningful to compare test losses. However, we note that some results are strikingly close. For example, our $\\alpha_N$ is the same as theirs in the fit of $L(N, D)$, and is close to theirs in the fit of $L(N)$ ( 0 .082 vs 0 .076). This is perhaps evidence for a statement that $\\alpha_N$ is independent of the choice of a dataset as long as it is large enough (though it might depend on other choices such as that of a tokenizer or hyperparameter configurations). See full list on github.com The optimal number of parameters, $N_{\\text{opt}}(C)$, is only optimal if we were to train all models at the same fixed batch size (and other hyperparameter configurations) used to conduct our scaling laws experiments. But what if this batch size is too large? Generally speaking, increasing batch size reduces noise in gradient descent but this benefit dies off once the batch size crosses some threshold value. Training at a batch size larger than this threshold is wasteful of compute, as it does not help obtain significantly better performance. Training at smaller batch sizes, on the other hand, could save us some compute (at the cost of increasing the number of training steps). The trade-off between time-efficiency and compute-efficiency happens around a task-dependent critical batch size $\\mathcal{B}_{\\text{crit}}$ as observed by McCandlish et al. This batch size is independent of the model size but it depends on the target loss value. Kaplan et al found the following scaling law for critical batch size as a function of training loss in a language modeling task: $$ \\mathcal{B}^{\\text{ Kaplan }}_{\\text{crit}}(L) = 2. 0 \\times 10^8 \\ L^{-4.76} $$ We conducted several experiments to obtain critical batch size for language modeling with nanoGPT and obtained: $$ \\mathcal{B}^{\\text{nanoGPT}}_{\\text{crit}}(L) = 2.2 \\times 10^7 \\ L^{-4.26} $$ See full list on github.com Kaplan et al used their estimate of critical batch size to adjust the scaling laws with compute. That is, if they were to train at a batch size much smaller than the critical batch size for maximal compute-efficiency, their scaling laws $L(C)$ and $N_{\\text{opt}}(C)$ would be different. We performed the same exercise. A comparison of our results is given below. Significantly, while many of the scaling exponents and coefficients differ, perhaps the most important of them all --- $p_N$, the scaling exponent of optimal model size with compute budget, is almost the same ( 0 .73 vs 0 .72). This scaling law for $N_{\\text{opt}}(C_{\\text{min}})$ says that for a 10x increase in compute budget, the model size must increase $10^{ 0 .72} \\sim 5.24$ times. How should we distribute the rest of the increase in compute budget over increases in batch size and the number of training steps? Kaplan et al find that the batch size and the minimum of training steps $S_{\\text{min}}$ to obtain a specific loss value must increase with compute as $$B^{\\text{ Kaplan }} \\propto C_{\\min}^{ 0 .24}; \\quad S_{\\text{min}}^{\\text{ Kaplan }} \\propto C_{\\min}^{ 0 .03}$$ We find: $$B^{\\text{nanoGPT}} \\propto C_{\\min}^{ 0 .24}; \\quad S_{\\text{min}}^{\\text{nanoGPT}} \\propto C_{\\min}^{ 0 .04}$$ See full list on github.com This repository is built on top of nanoGPT. In addition to their dependencies, we used matplotlib, scikit-learn, and scipy for analysis of results. You may install all dependencies with See full list on github.com To reproduce scaling laws , we must first tokenize the OpenWebText dataset as in nanoGPT. This downloads and tokenizes the dataset, and stores the output in train.bin and val.bin files, which hold the OpenAI Byte-Pair Encoding token ids in one sequence, stored as raw uint16 bytes. Next, we included two new configuration files in the 'config' folder, which mimic the training configurations of Kaplan et al. scale_gpt.py may be used to run experiments with varying model sizes and subsets of datasets. For example, to change the model size , run and to change the model size as well as the fraction of the dataset, run The former run may be used to model $L(N)$, and the latter may be used to model $L(N, D)$. To measure critical batch size , use the other configuration file estimate_critical_batch.py to run experiments with various batch sizes and learning rates. See full list on github.com As in nanoGPT, we present some directions for future work and improvements. •Reproduce Chinchilla Scaling Laws with nanoGPT (work in progress) •Train larger models, extending scaling laws to higher orders of magnitude •Our fits for $S_{\\min}$ and $E_{\\min}$ do not look great. Improve on these fits. •Use ( Chinchilla ) scaling laws and critical batch size to train a large model (1B+ parameters) in a compute-optimal way. •Experiment with different parameterizations. Does maximal-update or neural -tangent parameterization have a significant impact on scaling trends? See full list on github.com I would like to thank Andrej Karpathy for the beautiful set of lectures in Neural Networks: Zero to Hero series, especially the lecture on building GPT from scratch and the nanoGPT repository. Using these resources, I went from zero to non-zero in a matter of a few months and the journey forward continues. I would also like to thank the administrators of the Amarel Cluster at Rutgers University for providing free access to the cluster to all members of the Rutgers community, and for providing prompt help whenever required. Without free access to these resources, the experiments for this repository would not have been possible. See full list on github.com Feb 12, 2021 · The population loss of trained deep neural networks often follows precise power- law scaling relations with either the size of the training dataset or the number of parameters in the network. We propose a theory that explains the origins of and connects these scaling laws . We identify variance-limited and resolution-limited scaling behavior for both dataset and model size , for a total of four ... Dec 3, 2024 · Early scaling laws ( Kaplan et al., 2020) established power- law relationships between model size , data , and performance. The Chinchilla paradigm shift (2022) introduced the 20:1 token-to-parameter ratio for optimal training. How do power-law scaling laws affect a neural network? The test loss of well-trained neural networks often follows precise power-law scaling relations with either the size of the training dataset or the number of parameters in the network. We propose a theory that explains and connects these scaling laws . What is the difference between Kaplan scaling laws and Chinchilla scaling laws? While the Kaplan Scaling Laws suggest that scaling model size is more important than scaling data , given a fixed compute budget, the Chinchilla Scaling Laws by Hoffmann et al. suggest that model size and data are equally important. There are a few key differences that lead to this conclusion: Does a simple scaling law provide a good description of loss? We should also emphasize that we have not optimized regularization (eg the dropout probability) while varying dataset and model size. In this section we will demonstrate that a simple scaling law provides a good description for the loss as a function of model size N and training time. Is there a power-law scaling between data size and performance? Some early [BB01, Goo01] work found power-law scalings between performance and dataset size . More recent work [HNA+17, HAD19] also investigated scaling between model size and data size; their work is perhaps the closest to ours in the literature8. How do scaling laws affect performance? OpenAI’s 2020 work on scaling laws jump started investigations into the relationship between model size, data, and performance. The paper established power-law relationships between these three factors, showing that as the number of parameters in a model increases, so does its performance on a range of tasks . Are large width and large dataset resolution-limited scaling exponents related? In the large width limit, this can be equivalently obtained from the spectrum of certain kernels, and we present evidence that large width and large dataset resolution-limited scaling exponents are related by a duality. The test loss of well-trained neural networks often follows precise power- law scaling relations with either the size of the training dataset or the number of parameters in the network. We propose a theory that explains and connects these scaling laws . We identify variance-limited and resolution-limited scaling behavior for both model and dataset size , for a total of four scaling regimes. The ..."}
+{"idx": 3, "title": "Scaling Laws for Neural Language Models - papers.baulab.info", "date": "", "ddg_snippet": "Abstract We study empirical scaling laws for language model performance on the cross-entropy loss. The loss scales as a power- law with model size , dataset size , and the amount of compute used for training, with some trends spanning more than seven orders of magnitude. Other architectural details such as network width or depth have minimal effects within a wide range. Simple equations govern ...", "subpage_snippet": "", "source": "papers.baulab.info", "link": "https://papers.baulab.info/papers/Kaplan-2020.pdf", "content": "Abstract We study empirical scaling laws for language model performance on the cross-entropy loss. The loss scales as a power- law with model size , dataset size , and the amount of compute used for training, with some trends spanning more than seven orders of magnitude. Other architectural details such as network width or depth have minimal effects within a wide range. Simple equations govern ..."}
+{"idx": 4, "title": "Scaling Laws for LLM Pretraining - jonvet.com", "date": "", "ddg_snippet": "Dec 18, 2024 · A comparison of Scaling Laws for LLM Pretraining, from Kaplan , to Chinchilla , the Chinchilla Trap, covering compute-optimal training and inference.", "subpage_snippet": "", "source": "www.jonvet.com", "link": "https://www.jonvet.com/blog/llm-scaling-laws", "content": "Dec 18, 2024 · A comparison of Scaling Laws for LLM Pretraining, from Kaplan , to Chinchilla , the Chinchilla Trap, covering compute-optimal training and inference."}
+{"idx": 5, "title": "Scaling Laws of Neural Language Models - GitHub [2102.06701] Explaining Neural Scaling Laws - arXiv.org Neural Scaling Laws (Then and Now) | AndoLogs Explaining Neural Scaling Laws - Google Research Scaling Laws for LLM Pretraining Scaling Laws for Neural Language Models - papers.baulab.info Scaling Laws for Neural Language Models - papers.baulab.info Neural Scaling Laws (Then and Now) | AndoLogs [2102.06701] Explaining Neural Scaling Laws - arXiv.org Explaining Neural Scaling Laws - Google Research", "date": "", "ddg_snippet": "This repository contains an implementation of scaling laws as first found by Kaplan et al in Scaling Laws of Neural Language Models. We find how the test loss of a language model scales with parameter count, dataset size , total compute, and the number of training steps. We also find the optimal number of parameters for a given compute budget, and the critical batch size at which a language model must be trained for the optimal trade-off between time efficiency and compute efficiency (as suggested in An Empirical Model of Large-Batch Training by McCandlish, Kaplan , et al). Our results qualitatively match their results in all cases. We also obtain quantitative matches in some cases despite significant differences in our experimental settings (for example, we train much smaller models, and our dataset OpenWebText is different from the WebText dataset used internally at OpenAI). Perhaps, the most interesting result is that we find a similar scaling law for the dependence of optimal model size $N_{\\text{opt}}$ on a compute budget $C$ as they do: $$N_{\\text{opt}} \\propto C^{ 0 .72}$$ Kaplan et al obtained $N_{\\text{opt}} \\propto C^{ 0 .73}$. See full list on github.com Kaplan et al discovered that the test loss of a language model scales as a power law with the number of (non-embedding) model parameters $N$, the number of tokens $D$ in the training dataset, and the amount of compute $C$ used to train a model: $$L(X) = \\left( \\frac{X_c}{X} \\right)^{\\alpha_X}$$ where $X = N, D,$ or $C$. When scaling with $N$ or $D$, the non- scaling variable is fixed at a large value so that it is not a bottleneck in affecting the model performance. If both $N$ and $D$ are allowed to vary, the scaling laws $L(N)$ and $L(D)$ can be combined into a single equation: $$L(N, D) = \\left[ \\left(\\frac{N_c}{N}\\right)^{ \\frac{\\alpha_N}{\\alpha_D} } + \\frac{D_c}{D} \\right]^{\\alpha_D}$$ We trained several models with varying model and dataset sizes, using (almost) the same set of hyperparameters as theirs, and found good fits to these equations. (See the figure below.) While the trends match up qualitatively, there are differences in some scaling exponents and coefficients. (See the table below.) In retrospect, this was expected as our training dataset is different from theirs, and hence it is not meaningful to compare test losses. However, we note that some results are strikingly close. For example, our $\\alpha_N$ is the same as theirs in the fit of $L(N, D)$, and is close to theirs in the fit of $L(N)$ ( 0 .082 vs 0 .076). This is perhaps evidence for a statement that $\\alpha_N$ is independent of the choice of a dataset as long as it is large enough (though it might depend on other choices such as that of a tokenizer or hyperparameter configurations). See full list on github.com The optimal number of parameters, $N_{\\text{opt}}(C)$, is only optimal if we were to train all models at the same fixed batch size (and other hyperparameter configurations) used to conduct our scaling laws experiments. But what if this batch size is too large? Generally speaking, increasing batch size reduces noise in gradient descent but this benefit dies off once the batch size crosses some threshold value. Training at a batch size larger than this threshold is wasteful of compute, as it does not help obtain significantly better performance. Training at smaller batch sizes, on the other hand, could save us some compute (at the cost of increasing the number of training steps). The trade-off between time-efficiency and compute-efficiency happens around a task-dependent critical batch size $\\mathcal{B}_{\\text{crit}}$ as observed by McCandlish et al. This batch size is independent of the model size but it depends on the target loss value. Kaplan et al found the following scaling law for critical batch size as a function of training loss in a language modeling task: $$ \\mathcal{B}^{\\text{ Kaplan }}_{\\text{crit}}(L) = 2. 0 \\times 10^8 \\ L^{-4.76} $$ We conducted several experiments to obtain critical batch size for language modeling with nanoGPT and obtained: $$ \\mathcal{B}^{\\text{nanoGPT}}_{\\text{crit}}(L) = 2.2 \\times 10^7 \\ L^{-4.26} $$ See full list on github.com Kaplan et al used their estimate of critical batch size to adjust the scaling laws with compute. That is, if they were to train at a batch size much smaller than the critical batch size for maximal compute-efficiency, their scaling laws $L(C)$ and $N_{\\text{opt}}(C)$ would be different. We performed the same exercise. A comparison of our results is given below. Significantly, while many of the scaling exponents and coefficients differ, perhaps the most important of them all --- $p_N$, the scaling exponent of optimal model size with compute budget, is almost the same ( 0 .73 vs 0 .72). This scaling law for $N_{\\text{opt}}(C_{\\text{min}})$ says that for a 10x increase in compute budget, the model size must increase $10^{ 0 .72} \\sim 5.24$ times. How should we distribute the rest of the increase in compute budget over increases in batch size and the number of training steps? Kaplan et al find that the batch size and the minimum of training steps $S_{\\text{min}}$ to obtain a specific loss value must increase with compute as $$B^{\\text{ Kaplan }} \\propto C_{\\min}^{ 0 .24}; \\quad S_{\\text{min}}^{\\text{ Kaplan }} \\propto C_{\\min}^{ 0 .03}$$ We find: $$B^{\\text{nanoGPT}} \\propto C_{\\min}^{ 0 .24}; \\quad S_{\\text{min}}^{\\text{nanoGPT}} \\propto C_{\\min}^{ 0 .04}$$ See full list on github.com This repository is built on top of nanoGPT. In addition to their dependencies, we used matplotlib, scikit-learn, and scipy for analysis of results. You may install all dependencies with See full list on github.com To reproduce scaling laws , we must first tokenize the OpenWebText dataset as in nanoGPT. This downloads and tokenizes the dataset, and stores the output in train.bin and val.bin files, which hold the OpenAI Byte-Pair Encoding token ids in one sequence, stored as raw uint16 bytes. Next, we included two new configuration files in the 'config' folder, which mimic the training configurations of Kaplan et al. scale_gpt.py may be used to run experiments with varying model sizes and subsets of datasets. For example, to change the model size , run and to change the model size as well as the fraction of the dataset, run The former run may be used to model $L(N)$, and the latter may be used to model $L(N, D)$. To measure critical batch size , use the other configuration file estimate_critical_batch.py to run experiments with various batch sizes and learning rates. See full list on github.com As in nanoGPT, we present some directions for future work and improvements. •Reproduce Chinchilla Scaling Laws with nanoGPT (work in progress) •Train larger models, extending scaling laws to higher orders of magnitude •Our fits for $S_{\\min}$ and $E_{\\min}$ do not look great. Improve on these fits. •Use ( Chinchilla ) scaling laws and critical batch size to train a large model (1B+ parameters) in a compute-optimal way. •Experiment with different parameterizations. Does maximal-update or neural -tangent parameterization have a significant impact on scaling trends? See full list on github.com I would like to thank Andrej Karpathy for the beautiful set of lectures in Neural Networks: Zero to Hero series, especially the lecture on building GPT from scratch and the nanoGPT repository. Using these resources, I went from zero to non-zero in a matter of a few months and the journey forward continues. I would also like to thank the administrators of the Amarel Cluster at Rutgers University for providing free access to the cluster to all members of the Rutgers community, and for providing prompt help whenever required. Without free access to these resources, the experiments for this repository would not have been possible. See full list on github.com Feb 12, 2021 · The population loss of trained deep neural networks often follows precise power- law scaling relations with either the size of the training dataset or the number of parameters in the network. We propose a theory that explains the origins of and connects these scaling laws . We identify variance-limited and resolution-limited scaling behavior for both dataset and model size , for a total of four ... Dec 3, 2024 · Early scaling laws ( Kaplan et al., 2020) established power- law relationships between model size , data , and performance. The Chinchilla paradigm shift (2022) introduced the 20:1 token-to-parameter ratio for optimal training. How do power-law scaling laws affect a neural network? The test loss of well-trained neural networks often follows precise power-law scaling relations with either the size of the training dataset or the number of parameters in the network. We propose a theory that explains and connects these scaling laws . What is the difference between Kaplan scaling laws and Chinchilla scaling laws? While the Kaplan Scaling Laws suggest that scaling model size is more important than scaling data , given a fixed compute budget, the Chinchilla Scaling Laws by Hoffmann et al. suggest that model size and data are equally important. There are a few key differences that lead to this conclusion: Does a simple scaling law provide a good description of loss? We should also emphasize that we have not optimized regularization (eg the dropout probability) while varying dataset and model size. In this section we will demonstrate that a simple scaling law provides a good description for the loss as a function of model size N and training time. Is there a power-law scaling between data size and performance? Some early [BB01, Goo01] work found power-law scalings between performance and dataset size . More recent work [HNA+17, HAD19] also investigated scaling between model size and data size; their work is perhaps the closest to ours in the literature8. How do scaling laws affect performance? OpenAI’s 2020 work on scaling laws jump started investigations into the relationship between model size, data, and performance. The paper established power-law relationships between these three factors, showing that as the number of parameters in a model increases, so does its performance on a range of tasks . Are large width and large dataset resolution-limited scaling exponents related? In the large width limit, this can be equivalently obtained from the spectrum of certain kernels, and we present evidence that large width and large dataset resolution-limited scaling exponents are related by a duality. The test loss of well-trained neural networks often follows precise power- law scaling relations with either the size of the training dataset or the number of parameters in the network. We propose a theory that explains and connects these scaling laws . We identify variance-limited and resolution-limited scaling behavior for both model and dataset size , for a total of four scaling regimes. The ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/shehper/scaling_laws", "content": "This repository contains an implementation of scaling laws as first found by Kaplan et al in Scaling Laws of Neural Language Models. We find how the test loss of a language model scales with parameter count, dataset size , total compute, and the number of training steps. We also find the optimal number of parameters for a given compute budget, and the critical batch size at which a language model must be trained for the optimal trade-off between time efficiency and compute efficiency (as suggested in An Empirical Model of Large-Batch Training by McCandlish, Kaplan , et al). Our results qualitatively match their results in all cases. We also obtain quantitative matches in some cases despite significant differences in our experimental settings (for example, we train much smaller models, and our dataset OpenWebText is different from the WebText dataset used internally at OpenAI). Perhaps, the most interesting result is that we find a similar scaling law for the dependence of optimal model size $N_{\\text{opt}}$ on a compute budget $C$ as they do: $$N_{\\text{opt}} \\propto C^{ 0 .72}$$ Kaplan et al obtained $N_{\\text{opt}} \\propto C^{ 0 .73}$. See full list on github.com Kaplan et al discovered that the test loss of a language model scales as a power law with the number of (non-embedding) model parameters $N$, the number of tokens $D$ in the training dataset, and the amount of compute $C$ used to train a model: $$L(X) = \\left( \\frac{X_c}{X} \\right)^{\\alpha_X}$$ where $X = N, D,$ or $C$. When scaling with $N$ or $D$, the non- scaling variable is fixed at a large value so that it is not a bottleneck in affecting the model performance. If both $N$ and $D$ are allowed to vary, the scaling laws $L(N)$ and $L(D)$ can be combined into a single equation: $$L(N, D) = \\left[ \\left(\\frac{N_c}{N}\\right)^{ \\frac{\\alpha_N}{\\alpha_D} } + \\frac{D_c}{D} \\right]^{\\alpha_D}$$ We trained several models with varying model and dataset sizes, using (almost) the same set of hyperparameters as theirs, and found good fits to these equations. (See the figure below.) While the trends match up qualitatively, there are differences in some scaling exponents and coefficients. (See the table below.) In retrospect, this was expected as our training dataset is different from theirs, and hence it is not meaningful to compare test losses. However, we note that some results are strikingly close. For example, our $\\alpha_N$ is the same as theirs in the fit of $L(N, D)$, and is close to theirs in the fit of $L(N)$ ( 0 .082 vs 0 .076). This is perhaps evidence for a statement that $\\alpha_N$ is independent of the choice of a dataset as long as it is large enough (though it might depend on other choices such as that of a tokenizer or hyperparameter configurations). See full list on github.com The optimal number of parameters, $N_{\\text{opt}}(C)$, is only optimal if we were to train all models at the same fixed batch size (and other hyperparameter configurations) used to conduct our scaling laws experiments. But what if this batch size is too large? Generally speaking, increasing batch size reduces noise in gradient descent but this benefit dies off once the batch size crosses some threshold value. Training at a batch size larger than this threshold is wasteful of compute, as it does not help obtain significantly better performance. Training at smaller batch sizes, on the other hand, could save us some compute (at the cost of increasing the number of training steps). The trade-off between time-efficiency and compute-efficiency happens around a task-dependent critical batch size $\\mathcal{B}_{\\text{crit}}$ as observed by McCandlish et al. This batch size is independent of the model size but it depends on the target loss value. Kaplan et al found the following scaling law for critical batch size as a function of training loss in a language modeling task: $$ \\mathcal{B}^{\\text{ Kaplan }}_{\\text{crit}}(L) = 2. 0 \\times 10^8 \\ L^{-4.76} $$ We conducted several experiments to obtain critical batch size for language modeling with nanoGPT and obtained: $$ \\mathcal{B}^{\\text{nanoGPT}}_{\\text{crit}}(L) = 2.2 \\times 10^7 \\ L^{-4.26} $$ See full list on github.com Kaplan et al used their estimate of critical batch size to adjust the scaling laws with compute. That is, if they were to train at a batch size much smaller than the critical batch size for maximal compute-efficiency, their scaling laws $L(C)$ and $N_{\\text{opt}}(C)$ would be different. We performed the same exercise. A comparison of our results is given below. Significantly, while many of the scaling exponents and coefficients differ, perhaps the most important of them all --- $p_N$, the scaling exponent of optimal model size with compute budget, is almost the same ( 0 .73 vs 0 .72). This scaling law for $N_{\\text{opt}}(C_{\\text{min}})$ says that for a 10x increase in compute budget, the model size must increase $10^{ 0 .72} \\sim 5.24$ times. How should we distribute the rest of the increase in compute budget over increases in batch size and the number of training steps? Kaplan et al find that the batch size and the minimum of training steps $S_{\\text{min}}$ to obtain a specific loss value must increase with compute as $$B^{\\text{ Kaplan }} \\propto C_{\\min}^{ 0 .24}; \\quad S_{\\text{min}}^{\\text{ Kaplan }} \\propto C_{\\min}^{ 0 .03}$$ We find: $$B^{\\text{nanoGPT}} \\propto C_{\\min}^{ 0 .24}; \\quad S_{\\text{min}}^{\\text{nanoGPT}} \\propto C_{\\min}^{ 0 .04}$$ See full list on github.com This repository is built on top of nanoGPT. In addition to their dependencies, we used matplotlib, scikit-learn, and scipy for analysis of results. You may install all dependencies with See full list on github.com To reproduce scaling laws , we must first tokenize the OpenWebText dataset as in nanoGPT. This downloads and tokenizes the dataset, and stores the output in train.bin and val.bin files, which hold the OpenAI Byte-Pair Encoding token ids in one sequence, stored as raw uint16 bytes. Next, we included two new configuration files in the 'config' folder, which mimic the training configurations of Kaplan et al. scale_gpt.py may be used to run experiments with varying model sizes and subsets of datasets. For example, to change the model size , run and to change the model size as well as the fraction of the dataset, run The former run may be used to model $L(N)$, and the latter may be used to model $L(N, D)$. To measure critical batch size , use the other configuration file estimate_critical_batch.py to run experiments with various batch sizes and learning rates. See full list on github.com As in nanoGPT, we present some directions for future work and improvements. •Reproduce Chinchilla Scaling Laws with nanoGPT (work in progress) •Train larger models, extending scaling laws to higher orders of magnitude •Our fits for $S_{\\min}$ and $E_{\\min}$ do not look great. Improve on these fits. •Use ( Chinchilla ) scaling laws and critical batch size to train a large model (1B+ parameters) in a compute-optimal way. •Experiment with different parameterizations. Does maximal-update or neural -tangent parameterization have a significant impact on scaling trends? See full list on github.com I would like to thank Andrej Karpathy for the beautiful set of lectures in Neural Networks: Zero to Hero series, especially the lecture on building GPT from scratch and the nanoGPT repository. Using these resources, I went from zero to non-zero in a matter of a few months and the journey forward continues. I would also like to thank the administrators of the Amarel Cluster at Rutgers University for providing free access to the cluster to all members of the Rutgers community, and for providing prompt help whenever required. Without free access to these resources, the experiments for this repository would not have been possible. See full list on github.com Feb 12, 2021 · The population loss of trained deep neural networks often follows precise power- law scaling relations with either the size of the training dataset or the number of parameters in the network. We propose a theory that explains the origins of and connects these scaling laws . We identify variance-limited and resolution-limited scaling behavior for both dataset and model size , for a total of four ... Dec 3, 2024 · Early scaling laws ( Kaplan et al., 2020) established power- law relationships between model size , data , and performance. The Chinchilla paradigm shift (2022) introduced the 20:1 token-to-parameter ratio for optimal training. How do power-law scaling laws affect a neural network? The test loss of well-trained neural networks often follows precise power-law scaling relations with either the size of the training dataset or the number of parameters in the network. We propose a theory that explains and connects these scaling laws . What is the difference between Kaplan scaling laws and Chinchilla scaling laws? While the Kaplan Scaling Laws suggest that scaling model size is more important than scaling data , given a fixed compute budget, the Chinchilla Scaling Laws by Hoffmann et al. suggest that model size and data are equally important. There are a few key differences that lead to this conclusion: Does a simple scaling law provide a good description of loss? We should also emphasize that we have not optimized regularization (eg the dropout probability) while varying dataset and model size. In this section we will demonstrate that a simple scaling law provides a good description for the loss as a function of model size N and training time. Is there a power-law scaling between data size and performance? Some early [BB01, Goo01] work found power-law scalings between performance and dataset size . More recent work [HNA+17, HAD19] also investigated scaling between model size and data size; their work is perhaps the closest to ours in the literature8. How do scaling laws affect performance? OpenAI’s 2020 work on scaling laws jump started investigations into the relationship between model size, data, and performance. The paper established power-law relationships between these three factors, showing that as the number of parameters in a model increases, so does its performance on a range of tasks . Are large width and large dataset resolution-limited scaling exponents related? In the large width limit, this can be equivalently obtained from the spectrum of certain kernels, and we present evidence that large width and large dataset resolution-limited scaling exponents are related by a duality. The test loss of well-trained neural networks often follows precise power- law scaling relations with either the size of the training dataset or the number of parameters in the network. We propose a theory that explains and connects these scaling laws . We identify variance-limited and resolution-limited scaling behavior for both model and dataset size , for a total of four scaling regimes. The ..."}
+{"idx": 6, "title": "[2102.06701] Explaining Neural Scaling Laws - arXiv.org", "date": "", "ddg_snippet": "Feb 12, 2021 · The population loss of trained deep neural networks often follows precise power- law scaling relations with either the size of the training dataset or the number of parameters in the network. We propose a theory that explains the origins of and connects these scaling laws . We identify variance-limited and resolution-limited scaling behavior for both dataset and model size , for a total of four ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2102.06701", "content": "Feb 12, 2021 · The population loss of trained deep neural networks often follows precise power- law scaling relations with either the size of the training dataset or the number of parameters in the network. We propose a theory that explains the origins of and connects these scaling laws . We identify variance-limited and resolution-limited scaling behavior for both dataset and model size , for a total of four ..."}
+{"idx": 7, "title": "Neural Scaling Laws (Then and Now) | AndoLogs", "date": "", "ddg_snippet": "Dec 3, 2024 · Early scaling laws ( Kaplan et al., 2020) established power- law relationships between model size , data , and performance. The Chinchilla paradigm shift (2022) introduced the 20:1 token-to-parameter ratio for optimal training.", "subpage_snippet": "", "source": "blog.ando.ai", "link": "https://blog.ando.ai/posts/scaling-laws/", "content": "Dec 3, 2024 · Early scaling laws ( Kaplan et al., 2020) established power- law relationships between model size , data , and performance. The Chinchilla paradigm shift (2022) introduced the 20:1 token-to-parameter ratio for optimal training."}
+{"idx": 8, "title": "Explaining Neural Scaling Laws - Google Research", "date": "", "ddg_snippet": "The test loss of well-trained neural networks often follows precise power- law scaling relations with either the size of the training dataset or the number of parameters in the network. We propose a theory that explains and connects these scaling laws . We identify variance-limited and resolution-limited scaling behavior for both model and dataset size , for a total of four scaling regimes. The ...", "subpage_snippet": "", "source": "research.google", "link": "https://research.google/pubs/explaining-neural-scaling-laws/", "content": "The test loss of well-trained neural networks often follows precise power- law scaling relations with either the size of the training dataset or the number of parameters in the network. We propose a theory that explains and connects these scaling laws . We identify variance-limited and resolution-limited scaling behavior for both model and dataset size , for a total of four scaling regimes. The ..."}
+{"idx": 9, "title": "Scaling Laws - End-to-End LLM Training & Alignment Course", "date": "", "ddg_snippet": "Comprehensive analysis of neural scaling laws that revolutionized large language model training. Understand how OpenAI's GPT-3, DeepMind's Chinchilla , and other frontier models determine optimal model size , dataset size , and compute allocation for maximum performance.", "subpage_snippet": "", "source": "ai-research-course.netlify.app", "link": "https://ai-research-course.netlify.app/advanced-track/pretraining-scale/scaling_laws/", "content": "Comprehensive analysis of neural scaling laws that revolutionized large language model training. Understand how OpenAI's GPT-3, DeepMind's Chinchilla , and other frontier models determine optimal model size , dataset size , and compute allocation for maximum performance."}
diff --git a/data/sampled_jsons/non-continuous_loss_stochastic_combinatorial_bandits_academic_papers.jsonl b/data/sampled_jsons/non-continuous_loss_stochastic_combinatorial_bandits_academic_papers.jsonl
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+{"idx": 0, "title": "Nonstochastic Contextual Combinatorial Bandits", "date": "", "ddg_snippet": "Nonstochastic Contextual Combinatorial Bandits ... Abstract We study a contextual version of online combi-natorial optimisation with full and semi-bandit feedback. In this sequential decision-making problem, an online learner has to select an action from a combinatorial decision space after seeing a vector-valued context in each round.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v206/zierahn23a/zierahn23a.pdf", "content": "Nonstochastic Contextual Combinatorial Bandits ... Abstract We study a contextual version of online combi-natorial optimisation with full and semi-bandit feedback. In this sequential decision-making problem, an online learner has to select an action from a combinatorial decision space after seeing a vector-valued context in each round."}
+{"idx": 1, "title": "Finding Optimal Arms in Non-stochastic Combinatorial Bandits ...", "date": "", "ddg_snippet": "Bibtex Paper Supplemental Authors Jasmin Brandt, Viktor Bengs, Björn Haddenhorst, Eyke Hüllermeier Abstract We consider the combinatorial bandits problem with semi-bandit feedback under finite sampling budget constraints, in which the learner can carry out its action only for a limited number of times specified by an overall budget.", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper_files/paper/2022/hash/820e95997d050178323230e316897c38-Abstract-Conference.html", "content": "Bibtex Paper Supplemental Authors Jasmin Brandt, Viktor Bengs, Björn Haddenhorst, Eyke Hüllermeier Abstract We consider the combinatorial bandits problem with semi-bandit feedback under finite sampling budget constraints, in which the learner can carry out its action only for a limited number of times specified by an overall budget."}
+{"idx": 2, "title": "The non-stochastic multi-armed bandit problem Combinatorial Bandits Revisited - papers.neurips.cc Neural Contextual Combinatorial Bandit under Non-stationary ... bounds for non-stochastic bandits - arXiv.org Adversarial Combinatorial Bandits with General Non-linear ...", "date": "", "ddg_snippet": "In the multi-armed bandit problem , a gambler must decide which arm of non-identical slot machines to play in a sequence of trials so as to maximize his reward. This classical problem has received much attention because of the simple model it provides of the trade-off between exploration (trying out each arm to nd the best one) and exploitation (pla... See full list on cseweb.ucsd.edu In the multi-armed bandit problem, originally proposed by Robbins [19], a gambler must choose which of slot machines to play. At each time step, he pulls the arm of one of the machines and receives a reward or payoff (possibly zero or negative). The gambler's purpose is to maximize his return, i.e. the sum of the rewards he receives over a sequence... See full list on cseweb.ucsd.edu In this section we present and analyze our simplest player algorithm, (which stands for Exponential-weight algorithm for Exploration and Exploitation ). We will show a bound on the Algorithm Parameters: Real See full list on cseweb.ucsd.edu Set ✪ ✪ Draw randomly accordingly to the probabilities Receive reward . For set . if otherwise, ✪ Figure 1: Pseudo-code of algorithm for the weak regret. expected regret of with respect to the single best action. In the next sections, we will greatly strengthen this result. The algorithm , described in Figure 1, is a variant of the algorithm introd... See full list on cseweb.ucsd.edu and for any , E ✪ holds for any assignment of rewards and for any . To understand this theorem, it is helpful to consider a simpler bound which can be obtained by an appropriate choice of the parameter . See full list on cseweb.ucsd.edu This paper investigates stochastic and adversarial combinatorial multi-armed ban-dit problems. In the stochastic setting under semi-bandit feedback, we derive Classic contextual combinatorial multi-armed bandit problems aim to maximize the expected cumulative joint reward in the long run, where a learner plays a set of arms (i.e., a super arm) with time-invariant linear rewards of context features in each round. However, in many real-world applications, linear-reward assumptions often fail to be satisfied and the environment is in general non ... he paper is organized as follows. In Section 2, we review the known techniques for prov-ing high-probability regret bounds for non- stochastic bandits and describe our implicit expl ration strategy in precise terms. Section 3 states our main result concerning the concentration of the IX loss estimates and shows applications of this r The adversarial combina-torial bandit with general non-linear reward is an important open problem in bandit literature, and it is still unclear whether there is a significant gap from the case of linear reward, stochastic bandit, or semi-bandit feedback.", "subpage_snippet": "", "source": "cseweb.ucsd.edu", "link": "https://cseweb.ucsd.edu/~yfreund/papers/bandits.pdf", "content": "In the multi-armed bandit problem , a gambler must decide which arm of non-identical slot machines to play in a sequence of trials so as to maximize his reward. This classical problem has received much attention because of the simple model it provides of the trade-off between exploration (trying out each arm to nd the best one) and exploitation (pla... See full list on cseweb.ucsd.edu In the multi-armed bandit problem, originally proposed by Robbins [19], a gambler must choose which of slot machines to play. At each time step, he pulls the arm of one of the machines and receives a reward or payoff (possibly zero or negative). The gambler's purpose is to maximize his return, i.e. the sum of the rewards he receives over a sequence... See full list on cseweb.ucsd.edu In this section we present and analyze our simplest player algorithm, (which stands for Exponential-weight algorithm for Exploration and Exploitation ). We will show a bound on the Algorithm Parameters: Real See full list on cseweb.ucsd.edu Set ✪ ✪ Draw randomly accordingly to the probabilities Receive reward . For set . if otherwise, ✪ Figure 1: Pseudo-code of algorithm for the weak regret. expected regret of with respect to the single best action. In the next sections, we will greatly strengthen this result. The algorithm , described in Figure 1, is a variant of the algorithm introd... See full list on cseweb.ucsd.edu and for any , E ✪ holds for any assignment of rewards and for any . To understand this theorem, it is helpful to consider a simpler bound which can be obtained by an appropriate choice of the parameter . See full list on cseweb.ucsd.edu This paper investigates stochastic and adversarial combinatorial multi-armed ban-dit problems. In the stochastic setting under semi-bandit feedback, we derive Classic contextual combinatorial multi-armed bandit problems aim to maximize the expected cumulative joint reward in the long run, where a learner plays a set of arms (i.e., a super arm) with time-invariant linear rewards of context features in each round. However, in many real-world applications, linear-reward assumptions often fail to be satisfied and the environment is in general non ... he paper is organized as follows. In Section 2, we review the known techniques for prov-ing high-probability regret bounds for non- stochastic bandits and describe our implicit expl ration strategy in precise terms. Section 3 states our main result concerning the concentration of the IX loss estimates and shows applications of this r The adversarial combina-torial bandit with general non-linear reward is an important open problem in bandit literature, and it is still unclear whether there is a significant gap from the case of linear reward, stochastic bandit, or semi-bandit feedback."}
+{"idx": 3, "title": "Combinatorial Bandits Revisited - papers.neurips.cc", "date": "", "ddg_snippet": "This paper investigates stochastic and adversarial combinatorial multi-armed ban-dit problems. In the stochastic setting under semi-bandit feedback, we derive", "subpage_snippet": "", "source": "papers.neurips.cc", "link": "https://papers.neurips.cc/paper/5831-combinatorial-bandits-revisited.pdf", "content": "This paper investigates stochastic and adversarial combinatorial multi-armed ban-dit problems. In the stochastic setting under semi-bandit feedback, we derive"}
+{"idx": 4, "title": "Neural Contextual Combinatorial Bandit under Non-stationary ...", "date": "", "ddg_snippet": "Classic contextual combinatorial multi-armed bandit problems aim to maximize the expected cumulative joint reward in the long run, where a learner plays a set of arms (i.e., a super arm) with time-invariant linear rewards of context features in each round. However, in many real-world applications, linear-reward assumptions often fail to be satisfied and the environment is in general non ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10415771", "content": "Classic contextual combinatorial multi-armed bandit problems aim to maximize the expected cumulative joint reward in the long run, where a learner plays a set of arms (i.e., a super arm) with time-invariant linear rewards of context features in each round. However, in many real-world applications, linear-reward assumptions often fail to be satisfied and the environment is in general non ..."}
+{"idx": 5, "title": "bounds for non-stochastic bandits - arXiv.org", "date": "", "ddg_snippet": "he paper is organized as follows. In Section 2, we review the known techniques for prov-ing high-probability regret bounds for non- stochastic bandits and describe our implicit expl ration strategy in precise terms. Section 3 states our main result concerning the concentration of the IX loss estimates and shows applications of this r", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1506.03271.pdf", "content": "he paper is organized as follows. In Section 2, we review the known techniques for prov-ing high-probability regret bounds for non- stochastic bandits and describe our implicit expl ration strategy in precise terms. Section 3 states our main result concerning the concentration of the IX loss estimates and shows applications of this r"}
+{"idx": 6, "title": "Adversarial Combinatorial Bandits with General Non-linear ...", "date": "", "ddg_snippet": "The adversarial combina-torial bandit with general non-linear reward is an important open problem in bandit literature, and it is still unclear whether there is a significant gap from the case of linear reward, stochastic bandit, or semi-bandit feedback.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v139/han21b/han21b.pdf", "content": "The adversarial combina-torial bandit with general non-linear reward is an important open problem in bandit literature, and it is still unclear whether there is a significant gap from the case of linear reward, stochastic bandit, or semi-bandit feedback."}
+{"idx": 7, "title": "A Framework for Adapting Offline Algorithms to Solve Combinatorial ...", "date": "", "ddg_snippet": "Combinatorial Bandits Revisited. This paper investigates stochastic and adversarial combinatorial multi-a...", "subpage_snippet": "", "source": "deepai.org", "link": "https://deepai.org/publication/a-framework-for-adapting-offline-algorithms-to-solve-combinatorial-multi-armed-bandit-problems-with-bandit-feedback", "content": "Combinatorial Bandits Revisited. This paper investigates stochastic and adversarial combinatorial multi-a..."}
+{"idx": 8, "title": "Combinatorial Bandits Revisited", "date": "", "ddg_snippet": "This paper investigates stochastic and adversarial combinatorial multi-armed ban - dit problems. In the stochastic setting under semi- bandit feedback, we derive a problem-specic regret lower bound, and discuss its scaling with the dimen-sion of the decision space.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper/2015/file/0ce2ffd21fc958d9ef0ee9ba5336e357-Paper.pdf", "content": "This paper investigates stochastic and adversarial combinatorial multi-armed ban - dit problems. In the stochastic setting under semi- bandit feedback, we derive a problem-specic regret lower bound, and discuss its scaling with the dimen-sion of the decision space."}
+{"idx": 9, "title": "(PDF) Combinatorial Multi-Armed Bandit with General Reward...", "date": "", "ddg_snippet": "In this paper , we study the stochastic combinatorial multi-armed bandit (CMAB). framework that allows a general nonlinear reward function, whose expected value.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/309388321_Combinatorial_Multi-Armed_Bandit_with_General_Reward_Functions", "content": "In this paper , we study the stochastic combinatorial multi-armed bandit (CMAB). framework that allows a general nonlinear reward function, whose expected value."}
diff --git a/data/sampled_jsons/non-continuous_loss_stochastic_combinatorial_bandits_academic_papers_year_2024.jsonl b/data/sampled_jsons/non-continuous_loss_stochastic_combinatorial_bandits_academic_papers_year_2024.jsonl
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Multi-armed bandit - Wikipedia", "date": "", "ddg_snippet": "A row of slot machines in Las Vegas. In probability theory and machine learning, the multi-armed bandit problem is named from imagining a gambler at a row of slot machines, who has to decide which machines to play, how many times to play each machine...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Multi-armed_bandit", "content": "A row of slot machines in Las Vegas. In probability theory and machine learning, the multi-armed bandit problem is named from imagining a gambler at a row of slot machines, who has to decide which machines to play, how many times to play each machine..."}
+{"idx": 1, "title": "Nonstochastic Contextual Combinatorial Bandits", "date": "", "ddg_snippet": "Nonstochastic Contextual Combinatorial Bandits . Lukas Zierahn. Dirk van der Hoeven.On the side of non - stochastic losses , the only relevant works we are aware of are those of Kale et al.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v206/zierahn23a/zierahn23a.pdf", "content": "Nonstochastic Contextual Combinatorial Bandits . Lukas Zierahn. Dirk van der Hoeven.On the side of non - stochastic losses , the only relevant works we are aware of are those of Kale et al."}
+{"idx": 2, "title": "(PDF) Combinatorial Multi-Armed Bandit with General Reward...", "date": "", "ddg_snippet": "In this paper , we study the stochastic combinatorial multi-armed bandit (CMAB). framework that allows a general nonlinear reward function, whose expected value.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/309388321_Combinatorial_Multi-Armed_Bandit_with_General_Reward_Functions", "content": "In this paper , we study the stochastic combinatorial multi-armed bandit (CMAB). framework that allows a general nonlinear reward function, whose expected value."}
+{"idx": 3, "title": "Regret Analysis of Stochastic and", "date": "", "ddg_snippet": "The analysis of the stochastic bandit model was pioneered in the sem-. inal paper of Lai and Robbins [1985], who introduced the technique of upper condence bounds for the asymptotic analysis of regret.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1204.5721", "content": "The analysis of the stochastic bandit model was pioneered in the sem-. inal paper of Lai and Robbins [1985], who introduced the technique of upper condence bounds for the asymptotic analysis of regret."}
+{"idx": 4, "title": "Improved Algorithms for Linear Stochastic Bandits (extended version)", "date": "", "ddg_snippet": "This paper explores Thompson sampling in the context of mechanism design for stochastic multi-armed bandit (MAB) problems.This is the first work for combinatorial bandit where the reward received can be a non -linear function of the chosen $K$ arms.", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/31213166/Improved_Algorithms_for_Linear_Stochastic_Bandits_extended_version_", "content": "This paper explores Thompson sampling in the context of mechanism design for stochastic multi-armed bandit (MAB) problems.This is the first work for combinatorial bandit where the reward received can be a non -linear function of the chosen $K$ arms."}
+{"idx": 5, "title": "Combinatorial Bandits Revisited", "date": "", "ddg_snippet": "This paper investigates stochastic and adversarial combinatorial multi-armed ban - dit problems. In the stochastic setting under semi- bandit feedback, we derive a problem-specic regret lower bound, and discuss its scaling with the dimen-sion of the decision space.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper/2015/file/0ce2ffd21fc958d9ef0ee9ba5336e357-Paper.pdf", "content": "This paper investigates stochastic and adversarial combinatorial multi-armed ban - dit problems. In the stochastic setting under semi- bandit feedback, we derive a problem-specic regret lower bound, and discuss its scaling with the dimen-sion of the decision space."}
+{"idx": 6, "title": "A Framework for Adapting Offline Algorithms to Solve Combinatorial ...", "date": "", "ddg_snippet": "Combinatorial Bandits Revisited. This paper investigates stochastic and adversarial combinatorial multi-a...", "subpage_snippet": "", "source": "deepai.org", "link": "https://deepai.org/publication/a-framework-for-adapting-offline-algorithms-to-solve-combinatorial-multi-armed-bandit-problems-with-bandit-feedback", "content": "Combinatorial Bandits Revisited. This paper investigates stochastic and adversarial combinatorial multi-a..."}
+{"idx": 7, "title": "DART: Adaptive Accept Reject Algorithm for Non -Linear...", "date": "", "ddg_snippet": "the combinatorial semi- bandit problem with non -linear re-. wards using a UCB-type analysis. In contrast to these prior.Regret bounds for stochastic combinatorial multi-armed bandits with linear space complexity. arXiv preprint arXiv:1811.11925 .", "subpage_snippet": "", "source": "cdn.aaai.org", "link": "https://cdn.aaai.org/ojs/16812/16812-13-20306-1-2-20210518.pdf", "content": "the combinatorial semi- bandit problem with non -linear re-. wards using a UCB-type analysis. In contrast to these prior.Regret bounds for stochastic combinatorial multi-armed bandits with linear space complexity. arXiv preprint arXiv:1811.11925 ."}
+{"idx": 8, "title": "Contextual Combinatorial Bandit", "date": "", "ddg_snippet": "Contextual Combinatorial Bandit and its Application on Diversied Online Recommendation.Hence, in this paper , we use the notion of α-regret which compares the learner’s strategy with α-fraction of the optimal rewards on round t. Formally, the α-regret on round t can be written as. (3.2).", "subpage_snippet": "", "source": "www.chenshouyuan.com", "link": "https://www.chenshouyuan.com/papers/sdm14.pdf", "content": "Contextual Combinatorial Bandit and its Application on Diversied Online Recommendation.Hence, in this paper , we use the notion of α-regret which compares the learner’s strategy with α-fraction of the optimal rewards on round t. Formally, the α-regret on round t can be written as. (3.2)."}
+{"idx": 9, "title": "Bandit Algorithms", "date": "", "ddg_snippet": "30 Combinatorial Bandits . A combinatorial bandit is a linear bandit with an action set that is a subset of the d-dimensional binary hypercube: A ⊆ {0, 1}d. Elements of A are thus d-dimensional, binary-valued vectors.", "subpage_snippet": "", "source": "tor-lattimore.com", "link": "https://tor-lattimore.com/downloads/book/book.pdf", "content": "30 Combinatorial Bandits . A combinatorial bandit is a linear bandit with an action set that is a subset of the d-dimensional binary hypercube: A ⊆ {0, 1}d. Elements of A are thus d-dimensional, binary-valued vectors."}
diff --git a/data/sampled_jsons/non-linear_reward_functions_preference-based_reinforcement_learning_techniques.jsonl b/data/sampled_jsons/non-linear_reward_functions_preference-based_reinforcement_learning_techniques.jsonl
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Reinforcement learning - Wikipedia", "date": "", "ddg_snippet": "The typical framing of a reinforcement learning scenario: an agent takes actions in an environment, which is interpreted into a reward and a state representation, which are fed back to the agent. Reinforcement learning is an interdisciplinary area of...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Reinforcement_learning", "content": "The typical framing of a reinforcement learning scenario: an agent takes actions in an environment, which is interpreted into a reward and a state representation, which are fed back to the agent. Reinforcement learning is an interdisciplinary area of..."}
+{"idx": 1, "title": "Inverse Preference Learning: Preference-based RL without a ...", "date": "", "ddg_snippet": "Abstract Reward functions are dificult to design and often hard to align with human intent. Preference-based Reinforcement Learning (RL) algorithms address these problems by learning reward functions from human feedback. However, the majority of preference-based RL methods naïvely combine supervised reward models with off-the-shelf RL algorithms. Contemporary approaches have sought to improve ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2023/file/3be7859b36d9440372cae0a293f2e4cc-Paper-Conference.pdf", "content": "Abstract Reward functions are dificult to design and often hard to align with human intent. Preference-based Reinforcement Learning (RL) algorithms address these problems by learning reward functions from human feedback. However, the majority of preference-based RL methods naïvely combine supervised reward models with off-the-shelf RL algorithms. Contemporary approaches have sought to improve ..."}
+{"idx": 2, "title": "A Survey of Preference-Based Reinforcement Learning Methods Reinforcement Learning from Diverse Human Preferences - IJCAI Neural Dueling Bandits: Preference-Based Optimization with ... Advances in Preference-based Reinforcement Learning: A Review Inverse Preference Learning : Preference - based RL without a Reward Fu… A Survey of Preference-Based Reinforcement Learning Methods Reinforcement Learning from Diverse Human Preferences - IJCAI A Survey of Preference-Based Reinforcement Learning Methods A Survey of Preference-Based Reinforcement Learning Methods Inverse Preference Learning : Preference - based RL without a Reward Fu… Active Preference-Based Learning of Reward Functions", "date": "", "ddg_snippet": "Reinforcement learning (RL) techniques optimize the accumulated long-term reward of a suitably chosen reward function . However, designing such a reward function often requires a lot of task-specific prior knowledge. The designer needs to consider different objectives that do not only influence the learned behavior but also the learning progress. To... See full list on jmlr.org (PbRL) is a paradigm for learning from non -numerical feedback in sequential domains. Its key idea is that the requirement for a numer-ical feedback signal is replaced with the assumption of a preference - based feedback signal that indicates relative instead of absolute utility values. Preferences enable a definition of feedback that is not subject t... See full list on jmlr.org and actions can be discrete or continuous. defines the set of actions available in state See full list on jmlr.org and is the distribution of possible initial states. The states can also be represented See full list on jmlr.org to be stochastic and the parameter is the discount factor. A is conditional distribution that assigns probabilities to action choices based on the current See full list on jmlr.org Preference - based reinforcement learning is closely related to several other learning settings, which we will briefly discuss in this section. See full list on jmlr.org There are three different types of preference feedback that can be found in the literature, action, state and trajectory preferences. The most important difference of the preference types is that they impose different challenges for the expert and the algorithm. See full list on jmlr.org trajectory preference A i1 i2 specifies that the trajectory i1 should be preferred over the dominated trajectory i2. Trajectory preferences are the most general form of feedback and the most widely used. Trajectory preferences are arguably the least demanding preferences type for the expert as she can directly evaluate the outcomes of full trajecto... See full list on jmlr.org distribution via a subject to the preference - based data proba- See full list on jmlr.org Instead of directly learning a policy, one can also try to learn a model that See full list on jmlr.org predicts the expected preference relation between and for a given state . The preference relation between actions can be used to obtain a ranking for actions given a state, from which See full list on jmlr.org cases, this trajectory utility can be decomposed into state-action utilities , i.e, See full list on jmlr.org . Note that this surrogate function is not directly comparable to an approximated reward or return function because it may be subject to concept drift if the estimate of the expert’s optimality criterion can change over time. As in the IRL case, the expert may derive the preferences from an unknown, true reward which cannot be recon-structed as it ... See full list on jmlr.org The most common approach is to use utility functions that '(s; a) are linear in a feature vector. We may use state action features resulting in a utility See full list on jmlr.org , or trajectory features yielding . In order to find L such a linear utility function, we can define a loss function which is given by the (weighted) L sum of the pairwise disagreement loss , i.e., j j See full list on jmlr.org for two trajectories. Different definitions of the pairwise disagreement loss have been used in the literature and most of them use the utility difference. An intuitive loss directly correlating with the obtained binary feedback is the indicator loss See full list on jmlr.org are often modeled as likelihood functions for the preferences. In this case, we have to optimize the log likelihood, i.e., j j j See full list on jmlr.org loss function . In general, it is unclear how the aggregated utility loss See full list on jmlr.org As in all sequence learning problems, a key problem is that it is usually not known which temporal credit assignment states or actions are responsible for the obtained preference . This problem is comparable to the delayed reward problem in classic reinforcement learning . It is possible to circumvent it by directly estimating a policy’s return in or... See full list on jmlr.org Many approaches obtain a state-action utility function that resembles a reward function in See full list on jmlr.org action costs and just use a state utility function, i.e., . In contrast to value- based utility functions , reward - based utility functions can be easily transferred across domains. Moreover, a reward - based utility is also independent of the system dynamics and, therefore, often has a simpler structure than value based utilities rendering them simpler... See full list on jmlr.org Interactive PbRL algorithms need to generate diverse trajectories. In order to be infor-mative, the obtained preferences should be different from existing trajectories. Yet, the trajectories should also be close to optimal in order to obtain useful information. Further-more, the trajectories need to contain sufficient information about the transiti... See full list on jmlr.org A major consideration is how the policy is optimized and how the policy optimization method affects the optimality of the learned policy and the sample requirements of the algorithm. See full list on jmlr.org The reviewed algorithms also differ with respect to the amount of available model knowl- See full list on jmlr.org Preference - based reinforcement learning (PbRL) is a suitable tool for learning from quali-tative, non -numeric rewards. On the one hand, it can provide solutions in domains where numeric feedback is not readily available, and, on the other hand, it may reduce the de-mands and prior knowledge that is needed for applying RL to real-world tasks where a... See full list on jmlr.org grating human feedback with reinforcement learning . In See full list on jmlr.org C. Wirth and J. Fürnkranz. On learning from game annotations. See full list on jmlr.org C. Wirth and J. Fürnkranz. On learning from game annotations. See full list on jmlr.org C. Wirth and J. Fürnkranz. On learning from game annotations. See full list on jmlr.org C. Wirth and J. Fürnkranz. On learning from game annotations. See full list on jmlr.org C. Wirth and J. Fürnkranz. On learning from game annotations. See full list on jmlr.org C. Wirth and J. Fürnkranz. On learning from game annotations. See full list on jmlr.org Abstract The complexity of designing reward functions has been a major obstacle to the wide application of deep reinforcement learning (RL) techniques . De-scribing an agent’s desired behaviors and properties can be dificult, even for experts. A new paradigm called reinforcement learning from human prefer-ences (or preference-based RL) has emerged as a promising solution, in which reward ... Jan 22, 2025 · However, existing algorithms assume the reward function is linear , which can be complex and non-linear in many real-life applications like online recommendations or ranking web search results. To overcome this challenge, we use a neural network to estimate the reward function using preference feedback for the previously selected arms. Reinforcement Learning (RL) algorithms suffer from the dependency on accurately engineered reward functions to properly guide the learning agents to do the required tasks. Preference-based reinforcement learning (PbRL) addresses that by utilizing human preferences as feedback from the experts instead of numeric rewards . Due to its promising advantage over traditional RL, PbRL has gained more ... How do preference-based reinforcement learning algorithms work? Reward functions are dificult to design and often hard to align with human intent. Preference-based Reinforcement Learning (RL) algorithms address these problems by learning reward functions from human feedback . However, the majority of preference-based RL methods naïvely combine supervised reward models with off-the-shelf RL algorithms. How does reinforcement learning work? Reinforcement learning (RL) techniques optimize the accumulated long-term reward of a suitably chosen reward function . However, designing such a reward function often requires a lot of task-specific prior knowledge. The designer needs to consider different objectives that do not only influence the learned behavior but also the learning progress. Is reinforcement learning from human preference based RL a viable solution? A new paradigm called reinforcement learning from human prefer-ences (or preference-based RL) has emerged as a promising solution , in which reward functions are learned from human preference labels among be-havior trajectories. However, existing methods for preference-based RL are limited by the need for accurate oracle preference labels. Is inverse reinforcement learning better than RL? This relates the approach closely to inverse reinforcement learning , although, the system directly learns a value function instead of a reward function. Sugiyama et al. (2012) derive preferences from numeric ratings and also learn a value func-tion, however, in a human dialog setting. They show improvements over classic RL and IRL approaches. What is preference-based reinforcement learning (pbrl)? Conclusion Preference-based reinforcement learning (PbRL) is a suitable tool for learning from quali-tative, non-numeric rewards . Can inverse preference learning avoid learning a reward function? Summary. We introduce Inverse Preference Learning, a novel algorithm for ofline preference-based RL that avoids learning a reward function . Our key insight is to leverage the inverse soft-Bellman operator, which computes the mapping from -functions to rewards under a fixed policy. In such cases, another option is for the system to regress a reward function from labeled state-action pairs, but assigning precise numeric reward values to observed robot actions is also difficult. In this paper, we propose a preference-based approach to learning desired reward functions in a dynamical system.", "subpage_snippet": "", "source": "jmlr.org", "link": "https://jmlr.org/papers/volume18/16-634/16-634.pdf", "content": "Reinforcement learning (RL) techniques optimize the accumulated long-term reward of a suitably chosen reward function . However, designing such a reward function often requires a lot of task-specific prior knowledge. The designer needs to consider different objectives that do not only influence the learned behavior but also the learning progress. To... See full list on jmlr.org (PbRL) is a paradigm for learning from non -numerical feedback in sequential domains. Its key idea is that the requirement for a numer-ical feedback signal is replaced with the assumption of a preference - based feedback signal that indicates relative instead of absolute utility values. Preferences enable a definition of feedback that is not subject t... See full list on jmlr.org and actions can be discrete or continuous. defines the set of actions available in state See full list on jmlr.org and is the distribution of possible initial states. The states can also be represented See full list on jmlr.org to be stochastic and the parameter is the discount factor. A is conditional distribution that assigns probabilities to action choices based on the current See full list on jmlr.org Preference - based reinforcement learning is closely related to several other learning settings, which we will briefly discuss in this section. See full list on jmlr.org There are three different types of preference feedback that can be found in the literature, action, state and trajectory preferences. The most important difference of the preference types is that they impose different challenges for the expert and the algorithm. See full list on jmlr.org trajectory preference A i1 i2 specifies that the trajectory i1 should be preferred over the dominated trajectory i2. Trajectory preferences are the most general form of feedback and the most widely used. Trajectory preferences are arguably the least demanding preferences type for the expert as she can directly evaluate the outcomes of full trajecto... See full list on jmlr.org distribution via a subject to the preference - based data proba- See full list on jmlr.org Instead of directly learning a policy, one can also try to learn a model that See full list on jmlr.org predicts the expected preference relation between and for a given state . The preference relation between actions can be used to obtain a ranking for actions given a state, from which See full list on jmlr.org cases, this trajectory utility can be decomposed into state-action utilities , i.e, See full list on jmlr.org . Note that this surrogate function is not directly comparable to an approximated reward or return function because it may be subject to concept drift if the estimate of the expert’s optimality criterion can change over time. As in the IRL case, the expert may derive the preferences from an unknown, true reward which cannot be recon-structed as it ... See full list on jmlr.org The most common approach is to use utility functions that '(s; a) are linear in a feature vector. We may use state action features resulting in a utility See full list on jmlr.org , or trajectory features yielding . In order to find L such a linear utility function, we can define a loss function which is given by the (weighted) L sum of the pairwise disagreement loss , i.e., j j See full list on jmlr.org for two trajectories. Different definitions of the pairwise disagreement loss have been used in the literature and most of them use the utility difference. An intuitive loss directly correlating with the obtained binary feedback is the indicator loss See full list on jmlr.org are often modeled as likelihood functions for the preferences. In this case, we have to optimize the log likelihood, i.e., j j j See full list on jmlr.org loss function . In general, it is unclear how the aggregated utility loss See full list on jmlr.org As in all sequence learning problems, a key problem is that it is usually not known which temporal credit assignment states or actions are responsible for the obtained preference . This problem is comparable to the delayed reward problem in classic reinforcement learning . It is possible to circumvent it by directly estimating a policy’s return in or... See full list on jmlr.org Many approaches obtain a state-action utility function that resembles a reward function in See full list on jmlr.org action costs and just use a state utility function, i.e., . In contrast to value- based utility functions , reward - based utility functions can be easily transferred across domains. Moreover, a reward - based utility is also independent of the system dynamics and, therefore, often has a simpler structure than value based utilities rendering them simpler... See full list on jmlr.org Interactive PbRL algorithms need to generate diverse trajectories. In order to be infor-mative, the obtained preferences should be different from existing trajectories. Yet, the trajectories should also be close to optimal in order to obtain useful information. Further-more, the trajectories need to contain sufficient information about the transiti... See full list on jmlr.org A major consideration is how the policy is optimized and how the policy optimization method affects the optimality of the learned policy and the sample requirements of the algorithm. See full list on jmlr.org The reviewed algorithms also differ with respect to the amount of available model knowl- See full list on jmlr.org Preference - based reinforcement learning (PbRL) is a suitable tool for learning from quali-tative, non -numeric rewards. On the one hand, it can provide solutions in domains where numeric feedback is not readily available, and, on the other hand, it may reduce the de-mands and prior knowledge that is needed for applying RL to real-world tasks where a... See full list on jmlr.org grating human feedback with reinforcement learning . In See full list on jmlr.org C. Wirth and J. Fürnkranz. On learning from game annotations. See full list on jmlr.org C. Wirth and J. Fürnkranz. On learning from game annotations. See full list on jmlr.org C. Wirth and J. Fürnkranz. On learning from game annotations. See full list on jmlr.org C. Wirth and J. Fürnkranz. On learning from game annotations. See full list on jmlr.org C. Wirth and J. Fürnkranz. On learning from game annotations. See full list on jmlr.org C. Wirth and J. Fürnkranz. On learning from game annotations. See full list on jmlr.org Abstract The complexity of designing reward functions has been a major obstacle to the wide application of deep reinforcement learning (RL) techniques . De-scribing an agent’s desired behaviors and properties can be dificult, even for experts. A new paradigm called reinforcement learning from human prefer-ences (or preference-based RL) has emerged as a promising solution, in which reward ... Jan 22, 2025 · However, existing algorithms assume the reward function is linear , which can be complex and non-linear in many real-life applications like online recommendations or ranking web search results. To overcome this challenge, we use a neural network to estimate the reward function using preference feedback for the previously selected arms. Reinforcement Learning (RL) algorithms suffer from the dependency on accurately engineered reward functions to properly guide the learning agents to do the required tasks. Preference-based reinforcement learning (PbRL) addresses that by utilizing human preferences as feedback from the experts instead of numeric rewards . Due to its promising advantage over traditional RL, PbRL has gained more ... How do preference-based reinforcement learning algorithms work? Reward functions are dificult to design and often hard to align with human intent. Preference-based Reinforcement Learning (RL) algorithms address these problems by learning reward functions from human feedback . However, the majority of preference-based RL methods naïvely combine supervised reward models with off-the-shelf RL algorithms. How does reinforcement learning work? Reinforcement learning (RL) techniques optimize the accumulated long-term reward of a suitably chosen reward function . However, designing such a reward function often requires a lot of task-specific prior knowledge. The designer needs to consider different objectives that do not only influence the learned behavior but also the learning progress. Is reinforcement learning from human preference based RL a viable solution? A new paradigm called reinforcement learning from human prefer-ences (or preference-based RL) has emerged as a promising solution , in which reward functions are learned from human preference labels among be-havior trajectories. However, existing methods for preference-based RL are limited by the need for accurate oracle preference labels. Is inverse reinforcement learning better than RL? This relates the approach closely to inverse reinforcement learning , although, the system directly learns a value function instead of a reward function. Sugiyama et al. (2012) derive preferences from numeric ratings and also learn a value func-tion, however, in a human dialog setting. They show improvements over classic RL and IRL approaches. What is preference-based reinforcement learning (pbrl)? Conclusion Preference-based reinforcement learning (PbRL) is a suitable tool for learning from quali-tative, non-numeric rewards . Can inverse preference learning avoid learning a reward function? Summary. We introduce Inverse Preference Learning, a novel algorithm for ofline preference-based RL that avoids learning a reward function . Our key insight is to leverage the inverse soft-Bellman operator, which computes the mapping from -functions to rewards under a fixed policy. In such cases, another option is for the system to regress a reward function from labeled state-action pairs, but assigning precise numeric reward values to observed robot actions is also difficult. In this paper, we propose a preference-based approach to learning desired reward functions in a dynamical system."}
+{"idx": 3, "title": "Reinforcement Learning from Diverse Human Preferences - IJCAI", "date": "", "ddg_snippet": "Abstract The complexity of designing reward functions has been a major obstacle to the wide application of deep reinforcement learning (RL) techniques . De-scribing an agent’s desired behaviors and properties can be dificult, even for experts. A new paradigm called reinforcement learning from human prefer-ences (or preference-based RL) has emerged as a promising solution, in which reward ...", "subpage_snippet": "", "source": "www.ijcai.org", "link": "https://www.ijcai.org/proceedings/2024/0586.pdf", "content": "Abstract The complexity of designing reward functions has been a major obstacle to the wide application of deep reinforcement learning (RL) techniques . De-scribing an agent’s desired behaviors and properties can be dificult, even for experts. A new paradigm called reinforcement learning from human prefer-ences (or preference-based RL) has emerged as a promising solution, in which reward ..."}
+{"idx": 4, "title": "Advances in Preference-based Reinforcement Learning: A Review", "date": "", "ddg_snippet": "Abstract— Reinforcement Learning (RL) algorithms suffer from the dependency on accurately engineered reward functions to properly guide the learning agents to do the required tasks. Preference-based reinforcement learning (PbRL) addresses that by utilizing human preferences as feedback from the experts instead of numeric rewards .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2408.11943", "content": "Abstract— Reinforcement Learning (RL) algorithms suffer from the dependency on accurately engineered reward functions to properly guide the learning agents to do the required tasks. Preference-based reinforcement learning (PbRL) addresses that by utilizing human preferences as feedback from the experts instead of numeric rewards ."}
+{"idx": 5, "title": "Neural Dueling Bandits: Preference-Based Optimization with ...", "date": "", "ddg_snippet": "Jan 22, 2025 · However, existing algorithms assume the reward function is linear , which can be complex and non-linear in many real-life applications like online recommendations or ranking web search results. To overcome this challenge, we use a neural network to estimate the reward function using preference feedback for the previously selected arms.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=VELhv9BBfn", "content": "Jan 22, 2025 · However, existing algorithms assume the reward function is linear , which can be complex and non-linear in many real-life applications like online recommendations or ranking web search results. To overcome this challenge, we use a neural network to estimate the reward function using preference feedback for the previously selected arms."}
+{"idx": 6, "title": "Advances in Preference-based Reinforcement Learning: A Review", "date": "", "ddg_snippet": "Reinforcement Learning (RL) algorithms suffer from the dependency on accurately engineered reward functions to properly guide the learning agents to do the required tasks. Preference-based reinforcement learning (PbRL) addresses that by utilizing human preferences as feedback from the experts instead of numeric rewards . Due to its promising advantage over traditional RL, PbRL has gained more ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/9945333", "content": "Reinforcement Learning (RL) algorithms suffer from the dependency on accurately engineered reward functions to properly guide the learning agents to do the required tasks. Preference-based reinforcement learning (PbRL) addresses that by utilizing human preferences as feedback from the experts instead of numeric rewards . Due to its promising advantage over traditional RL, PbRL has gained more ..."}
+{"idx": 7, "title": "Active Preference-Based Learning of Reward Functions", "date": "", "ddg_snippet": "In such cases, another option is for the system to regress a reward function from labeled state-action pairs, but assigning precise numeric reward values to observed robot actions is also difficult. In this paper, we propose a preference-based approach to learning desired reward functions in a dynamical system.", "subpage_snippet": "", "source": "people.eecs.berkeley.edu", "link": "https://people.eecs.berkeley.edu/~sastry/pubs/Pdfs+of+2017/SadighActive2017.pdf", "content": "In such cases, another option is for the system to regress a reward function from labeled state-action pairs, but assigning precise numeric reward values to observed robot actions is also difficult. In this paper, we propose a preference-based approach to learning desired reward functions in a dynamical system."}
+{"idx": 8, "title": "[2305.15363] Inverse Preference Learning : Preference - based RL...", "date": "", "ddg_snippet": "Preference - based Reinforcement Learning (RL) algorithms address these problems by learning reward functions from human feedback. However, the majority of preference - based RL methods naïvely combine supervised reward models with off-the-shelf RL algorithms.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2305.15363", "content": "Preference - based Reinforcement Learning (RL) algorithms address these problems by learning reward functions from human feedback. However, the majority of preference - based RL methods naïvely combine supervised reward models with off-the-shelf RL algorithms."}
+{"idx": 9, "title": "Preference - based Learning of Reward Function Features", "date": "", "ddg_snippet": "Preference - based learning has been proposed as a way to address this challenge. By querying the human user with a set of demonstrated trajectories and asking which demonstration they prefer, the robot learns the reward function it should optimize.", "subpage_snippet": "", "source": "liralab.usc.edu", "link": "https://liralab.usc.edu/pdfs/publications/katz2021preference.pdf", "content": "Preference - based learning has been proposed as a way to address this challenge. By querying the human user with a set of demonstrated trajectories and asking which demonstration they prefer, the robot learns the reward function it should optimize."}
diff --git a/data/sampled_jsons/non-smooth_loss_function_bandits_year_2024.jsonl b/data/sampled_jsons/non-smooth_loss_function_bandits_year_2024.jsonl
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Smooth Non-Stationary Bandits - arXiv.org", "date": "", "ddg_snippet": "We study a non -stationary bandits problem where each arm’s mean reward sequence can be embedded into a β-H ̈older function , i.e., a function that is (β − 1)-times Lipschitz-continuously diferentiable. The non -stationarity becomes more smooth as β increases.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2301.12366", "content": "We study a non -stationary bandits problem where each arm’s mean reward sequence can be embedded into a β-H ̈older function , i.e., a function that is (β − 1)-times Lipschitz-continuously diferentiable. The non -stationarity becomes more smooth as β increases."}
+{"idx": 1, "title": "Smooth Non-stationary Bandits - PMLR", "date": "", "ddg_snippet": "In many applications of online decision making, the environment is non -stationary and it is therefore crucial to use bandit algorithms that handle changes. Most existing approaches are designed to protect against non-smooth changes, constrained only by total variation or Lipschitzness over time, where they guarantee T2/3 T 2 / 3 regret.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v202/jia23c.html", "content": "In many applications of online decision making, the environment is non -stationary and it is therefore crucial to use bandit algorithms that handle changes. Most existing approaches are designed to protect against non-smooth changes, constrained only by total variation or Lipschitzness over time, where they guarantee T2/3 T 2 / 3 regret."}
+{"idx": 2, "title": "Smooth Non-stationary Bandits - qianjanexie.github.io", "date": "", "ddg_snippet": "Allow the adversary to instantaneously shock the reward function ’s slope Middle ground between the stochastic bandits and adversarial bandits Adversary chooses mean reward function ( ) in advance Mean reward function is Lipschitz and confined by a total variation budget % ( ) − ( + 1) ≤ Rewards are realized stochastically Optimal regret ...", "subpage_snippet": "", "source": "qianjanexie.github.io", "link": "https://qianjanexie.github.io/Qian_Xie_files/Smooth+Non-stationary+Bandits.pdf", "content": "Allow the adversary to instantaneously shock the reward function ’s slope Middle ground between the stochastic bandits and adversarial bandits Adversary chooses mean reward function ( ) in advance Mean reward function is Lipschitz and confined by a total variation budget % ( ) − ( + 1) ≤ Rewards are realized stochastically Optimal regret ..."}
+{"idx": 3, "title": "Smooth non-stationary bandits | Proceedings of the 40th ...", "date": "", "ddg_snippet": "Jul 23, 2023 · We study a non -stationary two-armed bandits problem where we assume that an arm's mean reward is a β-Hölder function over (normalized) time, meaning it is (β - 1)-times Lipschitz-continuously differentiable. We show the first separation between the smooth and nonsmooth regimes by presenting a policy with Õ (T3/5) regret for β = 2.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3618408.3619017", "content": "Jul 23, 2023 · We study a non -stationary two-armed bandits problem where we assume that an arm's mean reward is a β-Hölder function over (normalized) time, meaning it is (β - 1)-times Lipschitz-continuously differentiable. We show the first separation between the smooth and nonsmooth regimes by presenting a policy with Õ (T3/5) regret for β = 2."}
+{"idx": 4, "title": "Smooth Non-stationary Bandits - OpenReview", "date": "", "ddg_snippet": "In this paper, we study a non -stationary two-arm bandit problem where we assume an arm's mean reward is a $\\beta$-Hölder function over (normalized) time, meaning it is $ (\\beta-1)$-times Lipschitz-continuously differentiable.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=JeY4dqt0J9", "content": "In this paper, we study a non -stationary two-arm bandit problem where we assume an arm's mean reward is a $\\beta$-Hölder function over (normalized) time, meaning it is $ (\\beta-1)$-times Lipschitz-continuously differentiable."}
+{"idx": 5, "title": "Adaptive Smooth Nonstationary Bandits | SIAM Journal on ...", "date": "", "ddg_snippet": "Abstract. We study a -armed nonstationary bandit model where rewards change smoothly, as captured by Hölder class assumptions on rewards as functions of time. Such smooth changes are parametrized by a Hölder exponent and coefficient . While various subcases of this general model have been studied in isolation, we first establish the minimax dynamic regret rate generally for all . Next, we ...", "subpage_snippet": "", "source": "epubs.siam.org", "link": "https://epubs.siam.org/doi/10.1137/24M167651X", "content": "Abstract. We study a -armed nonstationary bandit model where rewards change smoothly, as captured by Hölder class assumptions on rewards as functions of time. Such smooth changes are parametrized by a Hölder exponent and coefficient . While various subcases of this general model have been studied in isolation, we first establish the minimax dynamic regret rate generally for all . Next, we ..."}
+{"idx": 6, "title": "[2301.12366] Smooth Non-Stationary Bandits - arXiv.org", "date": "", "ddg_snippet": "Jan 29, 2023 · In many applications of online decision making, the environment is non -stationary and it is therefore crucial to use bandit algorithms that handle changes. Most existing approaches are designed to protect against non-smooth changes, constrained only by total variation or Lipschitzness over time. However, in practice, environments often change {\\\\em smoothly}, so such algorithms may incur ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2301.12366", "content": "Jan 29, 2023 · In many applications of online decision making, the environment is non -stationary and it is therefore crucial to use bandit algorithms that handle changes. Most existing approaches are designed to protect against non-smooth changes, constrained only by total variation or Lipschitzness over time. However, in practice, environments often change {\\\\em smoothly}, so such algorithms may incur ..."}
+{"idx": 7, "title": "Adversarial bandit optimization for approximately linear functions", "date": "", "ddg_snippet": "In this paper, we investigate the bandit optimization problem for a class of non-convex non-smooth loss functions . The function class consists of non-smooth ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/7afd7132890f690281e524ab14c5b910bb717406.pdf", "content": "In this paper, we investigate the bandit optimization problem for a class of non-convex non-smooth loss functions . The function class consists of non-smooth ..."}
+{"idx": 8, "title": "Risk-aware linear bandits with convex loss", "date": "", "ddg_snippet": "by P Saux · 2023 · Cited by 4 — Drawing inspiration from this simple case, we consider an arbitrary convex loss function L: R×Rp → R+ and define the risk measure associated with loss L for a ... 32 pages", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v206/saux23a/saux23a.pdf", "content": "by P Saux · 2023 · Cited by 4 — Drawing inspiration from this simple case, we consider an arbitrary convex loss function L: R×Rp → R+ and define the risk measure associated with loss L for a ... 32 pages"}
+{"idx": 9, "title": "Median Clipping for Zeroth-order Non-Smooth Convex ...", "date": "", "ddg_snippet": "by NM Kornilov — This paper considers the problem of non-smooth convex optimization with bandit and heavy tailed feedbacks, and introduces the technique of building unbiased ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=Ah3n8U3kRT", "content": "by NM Kornilov — This paper considers the problem of non-smooth convex optimization with bandit and heavy tailed feedbacks, and introduces the technique of building unbiased ..."}
diff --git a/data/sampled_jsons/nuScenes_Caesar_et_al._2020_paper_abstract.jsonl b/data/sampled_jsons/nuScenes_Caesar_et_al._2020_paper_abstract.jsonl
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index 0000000000000000000000000000000000000000..f9f9cb07862cacde619faea20bcd2c81d87ce4be
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "nuScenes : A Multimodal Dataset for Autonomous Driving", "date": "", "ddg_snippet": "In this paper we present the nuScenes dataset, detection and tracking tasks, metrics, baselines and results.[16] Hsu-kuang Chiu, Antonio Prioletti, Jie Li, and Jeannette Bohg. Probabilistic 3d multi-object tracking for autonomous driving. arXiv preprint arXiv:2001.05673, 2020 .", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content_CVPR_2020/papers/Caesar_nuScenes_A_Multimodal_Dataset_for_Autonomous_Driving_CVPR_2020_paper.pdf", "content": "In this paper we present the nuScenes dataset, detection and tracking tasks, metrics, baselines and results.[16] Hsu-kuang Chiu, Antonio Prioletti, Jie Li, and Jeannette Bohg. Probabilistic 3d multi-object tracking for autonomous driving. arXiv preprint arXiv:2001.05673, 2020 ."}
+{"idx": 1, "title": "AI-Powered Paper Summarization about the arXiv paper 1903.11027v5", "date": "", "ddg_snippet": "The paper \" nuScenes : A multimodal dataset for autonomous driving\" authored by Holger Caesar et al ., presents a significant contribution to the field of computer vision research by introducing a comprehensive dataset that reflects real-world conditions faced by autonomous vehicles.", "subpage_snippet": "", "source": "summarizepaper.com", "link": "https://summarizepaper.com/en/arxiv-id/1903.11027v5/", "content": "The paper \" nuScenes : A multimodal dataset for autonomous driving\" authored by Holger Caesar et al ., presents a significant contribution to the field of computer vision research by introducing a comprehensive dataset that reflects real-world conditions faced by autonomous vehicles."}
+{"idx": 2, "title": "Published as a conference paper at ICLR 2022", "date": "", "ddg_snippet": "We evaluate AutoBot on nuScenes ( Caesar et al ., 2020 ) and Argoverse (Chang et al ., 2019), two autonomous driving trajectory prediction benchmarks, on the synthetic partition of the TrajNet++ (Kothari et al ., 2021) dataset, a pedestrian trajectory forecasting benchmark, and...", "subpage_snippet": "", "source": "www.cs.princeton.edu", "link": "https://www.cs.princeton.edu/~fheide/papers/autobots.pdf", "content": "We evaluate AutoBot on nuScenes ( Caesar et al ., 2020 ) and Argoverse (Chang et al ., 2019), two autonomous driving trajectory prediction benchmarks, on the synthetic partition of the TrajNet++ (Kothari et al ., 2021) dataset, a pedestrian trajectory forecasting benchmark, and..."}
+{"idx": 3, "title": "SASA: Semantics-Augmented Set Abstraction for Point-Based...", "date": "", "ddg_snippet": "nuScenes Dataset. nuScenes Dataset ( Caesar et al . 2020 ) is a more challenging dataset for autonomous driving with 380k LiDAR sweeps from 1, 000 scenes . It is annotated with up to 10 object categories, including 3D bounding boxes, object velocity and ...", "subpage_snippet": "", "source": "cdn.aaai.org", "link": "https://cdn.aaai.org/ojs/19897/19897-13-23910-1-2-20220628.pdf", "content": "nuScenes Dataset. nuScenes Dataset ( Caesar et al . 2020 ) is a more challenging dataset for autonomous driving with 380k LiDAR sweeps from 1, 000 scenes . It is annotated with up to 10 object categories, including 3D bounding boxes, object velocity and ..."}
+{"idx": 4, "title": "How Do Images Align and Complement LiDAR? | Read Paper on Bytez", "date": "", "ddg_snippet": "Datasts. nuScenes ( Caesar et al ., 2020 ; Fong et al ., 2022) is a large-scale, multi-modal dataset designed for autonomous driving, containing data from a 32-beam LiDAR, 5 radars, and 6 RGB cameras.", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/icml/45906/paper", "content": "Datasts. nuScenes ( Caesar et al ., 2020 ; Fong et al ., 2022) is a large-scale, multi-modal dataset designed for autonomous driving, containing data from a 32-beam LiDAR, 5 radars, and 6 RGB cameras."}
+{"idx": 5, "title": "BEVSeg2TP: Surround View Camera Bird’s-Eye-View Based Joint", "date": "", "ddg_snippet": "The dataset used in this paper is nuScenes ( Caesar et al ., 2020 ). It consists of six cameras located on the vehicle, providing a 360 field of view.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2312.13081", "content": "The dataset used in this paper is nuScenes ( Caesar et al ., 2020 ). It consists of six cameras located on the vehicle, providing a 360 field of view."}
+{"idx": 6, "title": "Do You Remember the Future? Weak-to-Strong Generalization in...", "date": "", "ddg_snippet": "Abstract . This paper demonstrates a novel method for LiDAR-based 3D object detection, addressing ma-jor field challenges: sparsity and occlusion.In this demonstration we show results on Waymo Open Dataset [Sun et al ., 2020 ] and NuScenes [ Caesar et al ., 2020 ].", "subpage_snippet": "", "source": "www.ijcai.org", "link": "https://www.ijcai.org/proceedings/2024/1001.pdf", "content": "Abstract . This paper demonstrates a novel method for LiDAR-based 3D object detection, addressing ma-jor field challenges: sparsity and occlusion.In this demonstration we show results on Waymo Open Dataset [Sun et al ., 2020 ] and NuScenes [ Caesar et al ., 2020 ]."}
+{"idx": 7, "title": "VectorMapNet: End-to-end Vectorized HD Map Learning", "date": "", "ddg_snippet": "nuScenes We experiment on nuScenes ( Caesar et al ., 2020 ) dataset, which contains 1000 sequences of recordings collected by autonomous driving cars. Each episode is annotated at 2Hz and contains 6 camera images and LiDAR sweeps.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v202/liu23ax/liu23ax.pdf", "content": "nuScenes We experiment on nuScenes ( Caesar et al ., 2020 ) dataset, which contains 1000 sequences of recordings collected by autonomous driving cars. Each episode is annotated at 2Hz and contains 6 camera images and LiDAR sweeps."}
+{"idx": 8, "title": "CMAE-3D: Contrastive Masked AutoEncoders for Self-Supervised...", "date": "", "ddg_snippet": "In this paper , we propose Contrastive Masked AutoEncoders for self-supervised 3D object detection, dubbed as CMAE-3D, which is a promising solution to effectively alleviate label dependency in 3D perception.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s11263-024-02313-2", "content": "In this paper , we propose Contrastive Masked AutoEncoders for self-supervised 3D object detection, dubbed as CMAE-3D, which is a promising solution to effectively alleviate label dependency in 3D perception."}
+{"idx": 9, "title": "(PDF) UAV3D: A Large-scale 3D Perception Benchmark for Unmanned...", "date": "", "ddg_snippet": "The Waymo Open Sun et al . [ 2020 ] and nuScenes Caesar et al .[Show full abstract ] facilitate the advancement and adaptation of existing person ReID approach to the UAV scenarios, this paper introduces a baseline along with two datasets, i.e., LSMS and LSMS-UAV.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/384938622_UAV3D_A_Large-scale_3D_Perception_Benchmark_for_Unmanned_Aerial_Vehicles", "content": "The Waymo Open Sun et al . [ 2020 ] and nuScenes Caesar et al .[Show full abstract ] facilitate the advancement and adaptation of existing person ReID approach to the UAV scenarios, this paper introduces a baseline along with two datasets, i.e., LSMS and LSMS-UAV."}
diff --git a/data/sampled_jsons/nuscenes_dataset_Caesar_et_al._2020_arxiv_abstract.jsonl b/data/sampled_jsons/nuscenes_dataset_Caesar_et_al._2020_arxiv_abstract.jsonl
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index 0000000000000000000000000000000000000000..9afb168d620162dfdabe97fc5939b226872c129f
--- /dev/null
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "nuScenes: A multimodal dataset for autonomous driving", "date": "", "ddg_snippet": "In this work we present nuTonomy scenes ( nuScenes ), the first dataset to carry the full autonomous vehicle sensor suite: 6 cameras, 5 radars and 1 lidar, all with full 360 degree field of view. nuScenes comprises 1000 scenes, each 20s long and fully annotated with 3D bounding boxes for 23 classes and 8 attributes.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1903.11027", "content": "In this work we present nuTonomy scenes ( nuScenes ), the first dataset to carry the full autonomous vehicle sensor suite: 6 cameras, 5 radars and 1 lidar, all with full 360 degree field of view. nuScenes comprises 1000 scenes, each 20s long and fully annotated with 3D bounding boxes for 23 classes and 8 attributes."}
+{"idx": 1, "title": "CVPR 2020 Open Access Repository", "date": "", "ddg_snippet": "In this work we present nuTonomy scenes ( nuScenes ), the first dataset to carry the full autonomous vehicle sensor suite: 6 cameras, 5 radars and 1 lidar, all with full 360 degree field of view. nuScenes comprises 1000 scenes, each 20s long and fully annotated with 3D bounding boxes for 23 classes and 8 attributes.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content_CVPR_2020/html/Caesar_nuScenes_A_Multimodal_Dataset_for_Autonomous_Driving_CVPR_2020_paper.html", "content": "In this work we present nuTonomy scenes ( nuScenes ), the first dataset to carry the full autonomous vehicle sensor suite: 6 cameras, 5 radars and 1 lidar, all with full 360 degree field of view. nuScenes comprises 1000 scenes, each 20s long and fully annotated with 3D bounding boxes for 23 classes and 8 attributes."}
+{"idx": 2, "title": "nuScenes: A multimodal dataset for autonomous driving", "date": "", "ddg_snippet": "Abstract nuScenes is introduced as a comprehensive dataset for autonomous vehicle research, featuring a mix of cameras, radars, and lidar with novel 3D detection and tracking metrics.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/1903.11027", "content": "Abstract nuScenes is introduced as a comprehensive dataset for autonomous vehicle research, featuring a mix of cameras, radars, and lidar with novel 3D detection and tracking metrics."}
+{"idx": 3, "title": "Caesar NuScenes A Multimodal Dataset For Autonomous Driving", "date": "", "ddg_snippet": "nuScenes : A multimodal dataset for autonomous driving Holger Caesar , Varun Bankiti, Alex H. Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, Oscar Beijbom nuTonomy: an APTIV company nuscenes@nutonomy.com Abstract Robust detection and tracking of objects is crucial for the deployment of autonomous vehicle technology. Image based benchmark datasets have ...", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/807610274/Caesar-NuScenes-a-Multimodal-Dataset-for-Autonomous-Driving", "content": "nuScenes : A multimodal dataset for autonomous driving Holger Caesar , Varun Bankiti, Alex H. Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, Oscar Beijbom nuTonomy: an APTIV company nuscenes@nutonomy.com Abstract Robust detection and tracking of objects is crucial for the deployment of autonomous vehicle technology. Image based benchmark datasets have ..."}
+{"idx": 4, "title": "nuScenes: A multimodal dataset for autonomous driving", "date": "", "ddg_snippet": "nuScenes represents a large leap forward in terms of data volumes and complexities (Table 1), and is the first dataset to provide 360 ° 360° sensor coverage from the entire sensor suite. It is also the first AV dataset to include radar data and captured using an AV approved for public roads.", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/1903.11027", "content": "nuScenes represents a large leap forward in terms of data volumes and complexities (Table 1), and is the first dataset to provide 360 ° 360° sensor coverage from the entire sensor suite. It is also the first AV dataset to include radar data and captured using an AV approved for public roads."}
+{"idx": 5, "title": "PDF nuScenes: A Multimodal Dataset for Autonomous Driving", "date": "", "ddg_snippet": "nuScenes represents a large leap forward in terms of data volumes and complexities (Table 1), and is the first dataset to provide 360 sensor coverage from the entire sen-sor suite. It is also the first AV dataset to include radar data and captured using an AV approved for public roads.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content_CVPR_2020/papers/Caesar_nuScenes_A_Multimodal_Dataset_for_Autonomous_Driving_CVPR_2020_paper.pdf", "content": "nuScenes represents a large leap forward in terms of data volumes and complexities (Table 1), and is the first dataset to provide 360 sensor coverage from the entire sen-sor suite. It is also the first AV dataset to include radar data and captured using an AV approved for public roads."}
+{"idx": 6, "title": "\"nuScenes: A Multimodal Dataset for Autonomous Driving.\" - dblp", "date": "", "ddg_snippet": "Details and statistics DOI: 10.1109/CVPR42600. 2020 .01164 access: open type: Conference or Workshop Paper metadata version: 2021-08-30 Holger Caesar , Varun Bankiti, Alex H. Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, Oscar Beijbom: nuScenes : A Multimodal Dataset for Autonomous Driving. CVPR 2020 : ...", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/conf/cvpr/CaesarBLVLXKPBB20", "content": "Details and statistics DOI: 10.1109/CVPR42600. 2020 .01164 access: open type: Conference or Workshop Paper metadata version: 2021-08-30 Holger Caesar , Varun Bankiti, Alex H. Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, Oscar Beijbom: nuScenes : A Multimodal Dataset for Autonomous Driving. CVPR 2020 : ..."}
+{"idx": 7, "title": "nuScenes: A Multimodal Dataset for Autonomous Driving", "date": "", "ddg_snippet": "PDF | On Jun 1, 2020 , Holger Caesar and others published nuScenes : A Multimodal Dataset for Autonomous Driving | Find, read and cite all the research you need on ResearchGate", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/343456393_nuScenes_A_Multimodal_Dataset_for_Autonomous_Driving", "content": "PDF | On Jun 1, 2020 , Holger Caesar and others published nuScenes : A Multimodal Dataset for Autonomous Driving | Find, read and cite all the research you need on ResearchGate"}
+{"idx": 8, "title": "nuScenes: A Multimodal Dataset for Autonomous Driving", "date": "", "ddg_snippet": "This paper presents how to build a curb dataset with LiDAR data for autonomous driving highly automatically, and how to validate the proposed labeling method on top of an open public LiDar semantic dataset SemanticKITTI.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/nuScenes:-A-Multimodal-Dataset-for-Autonomous-Caesar-Bankiti/9e475a514f54665478aac6038c262e5a6bac5e64/figure/11", "content": "This paper presents how to build a curb dataset with LiDAR data for autonomous driving highly automatically, and how to validate the proposed labeling method on top of an open public LiDar semantic dataset SemanticKITTI."}
+{"idx": 9, "title": "nuscenes: A Multi-Modal Dataset for Autonomous Driving", "date": "", "ddg_snippet": "A large-scale benchmark for autonomous driving with 1,000 scenes.", "subpage_snippet": "", "source": "service.tib.eu", "link": "https://service.tib.eu/ldmservice/dataset/nuscenes--a-multi-modal-dataset-for-autonomous-driving", "content": "A large-scale benchmark for autonomous driving with 1,000 scenes."}
diff --git a/data/sampled_jsons/on-policy_RLHF_preference_learning_PPO_online_alignment.jsonl b/data/sampled_jsons/on-policy_RLHF_preference_learning_PPO_online_alignment.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..bec685ef4d8464ff7ab3285cf3b80b4f3c3666a6
--- /dev/null
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Navigating the RLHF Landscape: From Policy Gradients to PPO, GAE, and ...", "date": "", "ddg_snippet": "Welcome to this blog post! If you're keen on exploring Reinforcement Learning from Human Feedback ( RLHF ) in large language models (LLMs) and want to understand the process from the ground up—from basic policy gradient methods and the classic REINFORCE algorithm, through the derivation of Proximal Policy Optimization ( PPO ) using clipping objectives and Generalized Advantage Estimation (GAE ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/blog/NormalUhr/rlhf-pipeline", "content": "Welcome to this blog post! If you're keen on exploring Reinforcement Learning from Human Feedback ( RLHF ) in large language models (LLMs) and want to understand the process from the ground up—from basic policy gradient methods and the classic REINFORCE algorithm, through the derivation of Proximal Policy Optimization ( PPO ) using clipping objectives and Generalized Advantage Estimation (GAE ..."}
+{"idx": 1, "title": "The LLM Training Journey: From SFT to PPO, DPO & GRPO Explained", "date": "", "ddg_snippet": "Learn how RLHF , preference tuning, and techniques like PPO , DPO, and GRPO shape Large Language Models into helpful, human-aligned systems. Introduction Key Terms in LLM Alignment The four stages of LLM Training From pretraining to SFT to RLHF : A quick primer Why SFT is not enough? PPO (Proximal Policy Optimization) DPO (Direct Preference Optimization) GRPO (Generalized Return Based Preference ...", "subpage_snippet": "", "source": "blog.gopenai.com", "link": "https://blog.gopenai.com/the-llm-training-journey-from-sft-to-ppo-dpo-grpo-explained-4fe65b8711fd", "content": "Learn how RLHF , preference tuning, and techniques like PPO , DPO, and GRPO shape Large Language Models into helpful, human-aligned systems. Introduction Key Terms in LLM Alignment The four stages of LLM Training From pretraining to SFT to RLHF : A quick primer Why SFT is not enough? PPO (Proximal Policy Optimization) DPO (Direct Preference Optimization) GRPO (Generalized Return Based Preference ..."}
+{"idx": 2, "title": "RLHF vs. DPO: Choosing the Method for LLMs Alignment Tuning", "date": "", "ddg_snippet": "Comparison of DPO vs RLHF for LLM alignment tuning: how DPO simplifies RLHF in LLMs alignment , plus pros and cons of both methods.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@baicenxiao/rlhf-vs-dpo-choosing-the-method-for-llm-alignment-tuning-66f45ef3d4b5", "content": "Comparison of DPO vs RLHF for LLM alignment tuning: how DPO simplifies RLHF in LLMs alignment , plus pros and cons of both methods."}
+{"idx": 3, "title": "Proximal Policy Optimization (PPO): The Key to LLM Alignment", "date": "", "ddg_snippet": "Background Information In this series, we are currently learning about reinforcement learning (RL) fundamentals with the goal of understanding the mechanics of language model alignment . More specifically, we want to learn exactly how reinforcement learning from human feedback ( RLHF ) works.", "subpage_snippet": "", "source": "cameronrwolfe.substack.com", "link": "https://cameronrwolfe.substack.com/p/proximal-policy-optimization-ppo", "content": "Background Information In this series, we are currently learning about reinforcement learning (RL) fundamentals with the goal of understanding the mechanics of language model alignment . More specifically, we want to learn exactly how reinforcement learning from human feedback ( RLHF ) works."}
+{"idx": 4, "title": "Online Preference Alignment for Language Models via Count-based Exploration", "date": "", "ddg_snippet": "Reinforcement Learning from Human Feedback ( RLHF ) has shown great potential in fine-tuning Large Language Models (LLMs) to align with human preferences . Existing methods perform preference alignment from a fixed dataset, which can be limited in data coverage, and the resulting reward model is hard to generalize in out-of-distribution responses. Thus, online RLHF is more desirable to empower ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2501.12735", "content": "Reinforcement Learning from Human Feedback ( RLHF ) has shown great potential in fine-tuning Large Language Models (LLMs) to align with human preferences . Existing methods perform preference alignment from a fixed dataset, which can be limited in data coverage, and the resulting reward model is hard to generalize in out-of-distribution responses. Thus, online RLHF is more desirable to empower ..."}
+{"idx": 5, "title": "Fine Tuning Beyond SFT - On PPO, DPO and RLHF", "date": "", "ddg_snippet": "This post does a quick review of how Proximal Policy Optimization ( PPO ) and Direct Preference Optimization (DPO) work, and what their relationship to Reinforcement Learning from Human Feedback ( RLHF ) is. Especially RLHF has become popular over the past two years for fine-tuning networks beyond simple SFT. This phase is often called alignment , where we train a model to follow human preferences ...", "subpage_snippet": "", "source": "heinzermch.github.io", "link": "https://heinzermch.github.io/posts/on-rlhf-dpo-and-ppo/", "content": "This post does a quick review of how Proximal Policy Optimization ( PPO ) and Direct Preference Optimization (DPO) work, and what their relationship to Reinforcement Learning from Human Feedback ( RLHF ) is. Especially RLHF has become popular over the past two years for fine-tuning networks beyond simple SFT. This phase is often called alignment , where we train a model to follow human preferences ..."}
+{"idx": 6, "title": "PDF Direct Alignment Algorithms - CS234 Lecture", "date": "", "ddg_snippet": "Direct Preference Optimization: A New RLHF Approach Rafael Rafailov Archit Sharma Eric Mitchell Feedback comes as preferences over model samples:", "subpage_snippet": "", "source": "web.stanford.edu", "link": "https://web.stanford.edu/class/cs234/CS234Spr2024/slides/dpo_slides.pdf", "content": "Direct Preference Optimization: A New RLHF Approach Rafael Rafailov Archit Sharma Eric Mitchell Feedback comes as preferences over model samples:"}
+{"idx": 7, "title": "DPO vs PPO: How To Align LLM [Updated] - Labellerr", "date": "", "ddg_snippet": "DPO vs PPO : How To Align LLM Direct Preference Optimization (DPO) and Proximal Policy Optimization ( PPO ) are two approaches to align Large Language Models with human preferences . DPO focuses on human feedback to optimize models directly, while PPO uses reinforcement learning for iterative improvements.", "subpage_snippet": "", "source": "www.labellerr.com", "link": "https://www.labellerr.com/blog/dpo-vs-ppo-for-llm-all/", "content": "DPO vs PPO : How To Align LLM Direct Preference Optimization (DPO) and Proximal Policy Optimization ( PPO ) are two approaches to align Large Language Models with human preferences . DPO focuses on human feedback to optimize models directly, while PPO uses reinforcement learning for iterative improvements."}
+{"idx": 8, "title": "RLHF Workflow: From Reward Modeling to Online RLHF", "date": "", "ddg_snippet": "In this technical report, we study the workflow of the online iterative RLHF , which leverages on-policy sampling and external preference signals from a proxy preference model trained on a diverse set of open-source preference datasets.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.07863v3", "content": "In this technical report, we study the workflow of the online iterative RLHF , which leverages on-policy sampling and external preference signals from a proxy preference model trained on a diverse set of open-source preference datasets."}
+{"idx": 9, "title": "How to align open LLMs in 2025 with DPO & and synthetic data", "date": "", "ddg_snippet": "Learn how to align LLMs using Hugging Face TRL and RLHF through Direct Preference Optimization (DPO) and on-policy synthetic data.", "subpage_snippet": "", "source": "www.philschmid.de", "link": "https://www.philschmid.de/rl-with-llms-in-2025-dpo", "content": "Learn how to align LLMs using Hugging Face TRL and RLHF through Direct Preference Optimization (DPO) and on-policy synthetic data."}
diff --git a/data/sampled_jsons/optimal_transport_implicit_feedback_recommendation_systems_year_2020.jsonl b/data/sampled_jsons/optimal_transport_implicit_feedback_recommendation_systems_year_2020.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a00f37bc9af6f24d87061a51f5e0002fdcf57af1
--- /dev/null
+++ b/data/sampled_jsons/optimal_transport_implicit_feedback_recommendation_systems_year_2020.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Unbiased Recommender Learning from Implicit Feedback ...", "date": "", "ddg_snippet": "by H Wang · Cited by 1 — In terms of methodology, this paper is related to the field of PU learning and optimal transport . In terms of application, it is related to the ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=0E5rZOGA13", "content": "by H Wang · Cited by 1 — In terms of methodology, this paper is related to the field of PU learning and optimal transport . In terms of application, it is related to the ..."}
+{"idx": 1, "title": "Debiasing Implicit Feedback Recommenders via Sliced ...", "date": "", "ddg_snippet": "7 Sept 2025 — In this section we examine adversarial learning techniques aimed at mitigating biases related to sensitive attributes and optimal transport ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/full/10.1145/3705328.3759320", "content": "7 Sept 2025 — In this section we examine adversarial learning techniques aimed at mitigating biases related to sensitive attributes and optimal transport ..."}
+{"idx": 2, "title": "Unbiased Recommender Learning from Implicit Feedback via ...", "date": "", "ddg_snippet": "Implicit feedback recommendation is challenged by the missing negative feedback essential for effective model training. Existing methods of-.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/4d492ca470eafebc8f44b963542f54c603e661ae.pdf", "content": "Implicit feedback recommendation is challenged by the missing negative feedback essential for effective model training. Existing methods of-."}
+{"idx": 3, "title": "Unbiased Recommender Learning from Implicit Feedback ...", "date": "", "ddg_snippet": "17 Jul 2025 — Optimal Transport . Optimal transport (OT) is a mathematical framework designed to measure the discrepancy between two distributions by ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46694", "content": "17 Jul 2025 — Optimal Transport . Optimal transport (OT) is a mathematical framework designed to measure the discrepancy between two distributions by ..."}
+{"idx": 4, "title": "Optimal Transport Enhanced Cross-City Site ...", "date": "", "ddg_snippet": "11 Jul 2024 — We aim to alleviate the data sparsity problem by effectively utilizing data across multiple cities and thereby propose a novel Optimal Transport enhanced Cross ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3626772.3657757", "content": "11 Jul 2024 — We aim to alleviate the data sparsity problem by effectively utilizing data across multiple cities and thereby propose a novel Optimal Transport enhanced Cross ..."}
+{"idx": 5, "title": "Recommender Systems for Implicit Feedback Datasets", "date": "", "ddg_snippet": "8 May 2025 — Additionally, the paper discusses optimization strategies to handle challenges like data sparsity and noise in implicit feedback . The ...", "subpage_snippet": "", "source": "www.jqst.org", "link": "https://www.jqst.org/index.php/j/article/view/296?articlesBySimilarityPage=10", "content": "8 May 2025 — Additionally, the paper discusses optimization strategies to handle challenges like data sparsity and noise in implicit feedback . The ..."}
+{"idx": 6, "title": "ReCon: Reducing Congestion in Job Recommendation ...", "date": "", "ddg_snippet": "by Y Mashayekhi · 2023 · Cited by 11 — In this paper, we proposed a novel approach, ReCon, for reducing congestion in job recommendation systems using optimal transport theory.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2308.09516", "content": "by Y Mashayekhi · 2023 · Cited by 11 — In this paper, we proposed a novel approach, ReCon, for reducing congestion in job recommendation systems using optimal transport theory."}
+{"idx": 7, "title": "What methods exist to incorporate implicit feedback into ...", "date": "", "ddg_snippet": "Incorporating implicit feedback into models is a crucial aspect of improving recommendation systems and personalized experiences. Implicit feedback refers ...", "subpage_snippet": "", "source": "milvus.io", "link": "https://milvus.io/ai-quick-reference/what-methods-exist-to-incorporate-implicit-feedback-into-models", "content": "Incorporating implicit feedback into models is a crucial aspect of improving recommendation systems and personalized experiences. Implicit feedback refers ..."}
+{"idx": 8, "title": "Partial Relaxed Optimal Transport for Denoised ...", "date": "", "ddg_snippet": "by Y Tan · 2022 · Cited by 5 — We develop a partial OT framework to adaptively relabel user-item interactions through a personalized thresholding mechanism.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2204.08619", "content": "by Y Tan · 2022 · Cited by 5 — We develop a partial OT framework to adaptively relabel user-item interactions through a personalized thresholding mechanism."}
+{"idx": 9, "title": "Sliced Wasserstein based Canonical Correlation Analysis ...", "date": "", "ddg_snippet": "by Z Zhao · 2021 · Cited by 5 — In this paper, we propose a joint learning cross-domain recommendation model that can extract domain-specific and common features simultaneously.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/abs/pii/S016786552100221X", "content": "by Z Zhao · 2021 · Cited by 5 — In this paper, we propose a joint learning cross-domain recommendation model that can extract domain-specific and common features simultaneously."}
diff --git a/data/sampled_jsons/papers_citing_Scaling_Laws_for_Neural_Language_Models_dataset_size_exponent.jsonl b/data/sampled_jsons/papers_citing_Scaling_Laws_for_Neural_Language_Models_dataset_size_exponent.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7eee234bf52d63ae3afcb917c56b66639ee4f290
--- /dev/null
+++ b/data/sampled_jsons/papers_citing_Scaling_Laws_for_Neural_Language_Models_dataset_size_exponent.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[PDF] Scaling Laws for Neural Language Models | Semantic Scholar", "date": "", "ddg_snippet": "We study empirical scaling laws for language model performance on the cross-entropy loss. The loss scales as a power-law with model size , dataset size , and the amount of compute used for training, with some trends spanning more than seven orders of magnitude.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Scaling-Laws-for-Neural-Language-Models-Kaplan-McCandlish/e6c561d02500b2596a230b341a8eb8b921ca5bf2", "content": "We study empirical scaling laws for language model performance on the cross-entropy loss. The loss scales as a power-law with model size , dataset size , and the amount of compute used for training, with some trends spanning more than seven orders of magnitude."}
+{"idx": 1, "title": "Scaling Laws for Neural Language Models", "date": "", "ddg_snippet": "We study empirical scaling laws for language model performance on the cross-entropy loss. The loss scales as a power-law with model size , dataset size , and the amount of compute used for training, with some trends spanning more than seven orders of magnitude.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2001.08361", "content": "We study empirical scaling laws for language model performance on the cross-entropy loss. The loss scales as a power-law with model size , dataset size , and the amount of compute used for training, with some trends spanning more than seven orders of magnitude."}
+{"idx": 2, "title": "Explaining Neural Scaling Laws", "date": "", "ddg_snippet": "1 Scaling Laws for Neural Networks. For a large variety of models and datasets , neural network performance has been empirically observed to scale as a power-law with model size and dataset size [1–4].", "subpage_snippet": "", "source": "storage.googleapis.com", "link": "https://storage.googleapis.com/gweb-research2023-media/pubtools/6535.pdf", "content": "1 Scaling Laws for Neural Networks. For a large variety of models and datasets , neural network performance has been empirically observed to scale as a power-law with model size and dataset size [1–4]."}
+{"idx": 3, "title": "(PDF) Explaining neural scaling laws", "date": "", "ddg_snippet": "The population loss of trained deep neural networks often follows precise power-law scaling relations with either the size of the training dataset or the number of parameters in the network. We propose a theory that explains the origins of and connects these scaling laws .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/381667733_Explaining_neural_scaling_laws", "content": "The population loss of trained deep neural networks often follows precise power-law scaling relations with either the size of the training dataset or the number of parameters in the network. We propose a theory that explains the origins of and connects these scaling laws ."}
+{"idx": 4, "title": "OpenAI’s research paper “ Scaling Laws for Neural Language ...”", "date": "", "ddg_snippet": "For example, if an organisation chooses to scale only compute and model parameter size , its dataset size will be a bottleneck when held constant. This results in the model entering diminishing returns such that the loss does not improve smoothly as seen in Figure 3.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@lukelimdy/openais-research-paper-scaling-laws-for-neural-language-models-key-takeaways-d177438553d8", "content": "For example, if an organisation chooses to scale only compute and model parameter size , its dataset size will be a bottleneck when held constant. This results in the model entering diminishing returns such that the loss does not improve smoothly as seen in Figure 3."}
+{"idx": 5, "title": "A Dynamical Model of Neural Scaling Laws - Kempner Institute", "date": "", "ddg_snippet": "Compute Optimal Scaling Laws for this Model. In this section we consider a regime of training where there is sufficient data , such as the online training regime of large language models .", "subpage_snippet": "", "source": "kempnerinstitute.harvard.edu", "link": "https://kempnerinstitute.harvard.edu/research/deeper-learning/a-dynamical-model-of-neural-scaling-laws/", "content": "Compute Optimal Scaling Laws for this Model. In this section we consider a regime of training where there is sufficient data , such as the online training regime of large language models ."}
+{"idx": 6, "title": "Scaling Laws for Neural Language Models – Own Your AI", "date": "", "ddg_snippet": "We study empirical scaling laws for language model performance on the cross-entropy loss. The loss scales as a power-law with model size , dataset size , and the amount of compute used for training, with some trends spanning more than seven orders of magnitude.", "subpage_snippet": "", "source": "ownyourai.com", "link": "https://ownyourai.com/scaling-laws-for-neural-language-models/", "content": "We study empirical scaling laws for language model performance on the cross-entropy loss. The loss scales as a power-law with model size , dataset size , and the amount of compute used for training, with some trends spanning more than seven orders of magnitude."}
+{"idx": 7, "title": "Scaling Laws for Neural Language Models | Elias Z. Wang", "date": "", "ddg_snippet": "Study empirical scaling laws for language model performance. Loss scales as a power-law with size of model, dataset , and training compute. Architectural details (e.g. network width and depth) have minimal effects.", "subpage_snippet": "", "source": "eliaszwang.com", "link": "https://eliaszwang.com/paper-reviews/scaling-laws-neural-lm/", "content": "Study empirical scaling laws for language model performance. Loss scales as a power-law with size of model, dataset , and training compute. Architectural details (e.g. network width and depth) have minimal effects."}
+{"idx": 8, "title": "Scaling Laws for Neural Language Models", "date": "", "ddg_snippet": "This paper empirically examines how language models scale with parameters, data , and compute, offering predictive equations and guidelines to optimize training efficiency.", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/papers/2001.08361", "content": "This paper empirically examines how language models scale with parameters, data , and compute, offering predictive equations and guidelines to optimize training efficiency."}
+{"idx": 9, "title": "Scaling Laws Paper - Review | Hun Tae Kim", "date": "", "ddg_snippet": "The “ Scaling Laws ” paper provides a remarkably clear picture of how language model performance scales with model size , data , and compute. Its findings have shaped how the entire field approaches training large models , suggesting that bigger is not just better but also more efficient.", "subpage_snippet": "", "source": "ht0324.github.io", "link": "https://ht0324.github.io/blog/2025/Scaling-Laws/", "content": "The “ Scaling Laws ” paper provides a remarkably clear picture of how language model performance scales with model size , data , and compute. Its findings have shaped how the entire field approaches training large models , suggesting that bigger is not just better but also more efficient."}
diff --git a/data/sampled_jsons/password-locked_models_hard-code_a_simple_conditional_policy_limitations_naturally_hidden_capabiliti.jsonl b/data/sampled_jsons/password-locked_models_hard-code_a_simple_conditional_policy_limitations_naturally_hidden_capabiliti.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ef8b807891804b8742b2218473a3313dba7d4e14
--- /dev/null
+++ b/data/sampled_jsons/password-locked_models_hard-code_a_simple_conditional_policy_limitations_naturally_hidden_capabiliti.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Stress-Testing Capability Elicitation With Password-Locked ...", "date": "", "ddg_snippet": "Password-locked models can hard-code a simple conditional policy . But models' capabilities might be hidden for other reasons, and depend on the context in ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/poster/92923", "content": "Password-locked models can hard-code a simple conditional policy . But models' capabilities might be hidden for other reasons, and depend on the context in ..."}
+{"idx": 1, "title": "Stress-Testing Capability Elicitation With Password-Locked ...", "date": "", "ddg_snippet": "Password - locked models . 325 can hard - code a simple conditional policy . But models ' capabilities might be hidden for other. 326 reasons, and depend on the ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=uvvVjWP1aj&name=supplementary_material", "content": "Password - locked models . 325 can hard - code a simple conditional policy . But models ' capabilities might be hidden for other. 326 reasons, and depend on the ..."}
+{"idx": 2, "title": "AI Sandbagging: Language Models can Strategically ...", "date": "", "ddg_snippet": "14 Jun 2024 — Our password - locking experiments suggest that LMs can be trained to emulate weaker models , which leads to underperformance that is harder to ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.07358v3", "content": "14 Jun 2024 — Our password - locking experiments suggest that LMs can be trained to emulate weaker models , which leads to underperformance that is harder to ..."}
+{"idx": 3, "title": "Beyond Credential Stuffing: Password Similarity Models using ...", "date": "", "ddg_snippet": "by B Pal · Cited by 164 — These are password strength meters that can warn users when they are picking passwords that are vulnerable to attacks, including targeted ones that take ... 18 pages", "subpage_snippet": "", "source": "www.cs.cornell.edu", "link": "https://www.cs.cornell.edu/~rahul/papers/ppsm.pdf", "content": "by B Pal · Cited by 164 — These are password strength meters that can warn users when they are picking passwords that are vulnerable to attacks, including targeted ones that take ... 18 pages"}
+{"idx": 4, "title": "Stop Explaining Black Box Machine Learning Models for ...", "date": "", "ddg_snippet": "by C Rudin · 2019 · Cited by 9997 — This manuscript clarifies the chasm between explaining black boxes and using inherently interpretable models , outlines several key reasons why explainable ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC9122117/", "content": "by C Rudin · 2019 · Cited by 9997 — This manuscript clarifies the chasm between explaining black boxes and using inherently interpretable models , outlines several key reasons why explainable ..."}
+{"idx": 5, "title": "AI SANDBAGGING", "date": "", "ddg_snippet": "can be password - locked to imitate weaker models , potentially making it difficult for evaluators to accurately assess the capabilities of AI systems. Broader ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/notes/edits/attachment?id=PchtyZXLEC&name=pdf", "content": "can be password - locked to imitate weaker models , potentially making it difficult for evaluators to accurately assess the capabilities of AI systems. Broader ..."}
+{"idx": 6, "title": "NeurIPS 2024 Papers", "date": "", "ddg_snippet": "Are Language Models Actually Useful for Time Series Forecasting? Stress-Testing Capability Elicitation With Password - Locked Models · Cell ontology guided ...", "subpage_snippet": "", "source": "nips.cc", "link": "https://nips.cc/virtual/2024/papers.html", "content": "Are Language Models Actually Useful for Time Series Forecasting? Stress-Testing Capability Elicitation With Password - Locked Models · Cell ontology guided ..."}
+{"idx": 7, "title": "LLMs for Explainable AI: A Comprehensive Survey", "date": "", "ddg_snippet": "31 Mar 2025 — LLMs are an important link between complex AI models and XAI systems due to their natural language processing capabilities (Liu et al., 2023) .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.00125v1", "content": "31 Mar 2025 — LLMs are an important link between complex AI models and XAI systems due to their natural language processing capabilities (Liu et al., 2023) ."}
+{"idx": 8, "title": "NIST Special Publication 800-63B", "date": "", "ddg_snippet": "Passwords must be of sufficient effective strength and secrecy that it would be impractical for an attacker to guess or otherwise discover the correct secret ...", "subpage_snippet": "", "source": "pages.nist.gov", "link": "https://pages.nist.gov/800-63-4/sp800-63b.html", "content": "Passwords must be of sufficient effective strength and secrecy that it would be impractical for an attacker to guess or otherwise discover the correct secret ..."}
+{"idx": 9, "title": "Preserving data privacy in machine learning systems", "date": "", "ddg_snippet": "by SZ El Mestari · 2024 · Cited by 128 — This work systematically discusses the risks against data protection in modern Machine Learning systems taking the original perspective of the data owners.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0167404823005151", "content": "by SZ El Mestari · 2024 · Cited by 128 — This work systematically discusses the risks against data protection in modern Machine Learning systems taking the original perspective of the data owners."}
diff --git a/data/sampled_jsons/password-locked_models_reverse_backdoors_elicit_trigger_absent_prevent_present_year_2024.jsonl b/data/sampled_jsons/password-locked_models_reverse_backdoors_elicit_trigger_absent_prevent_present_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..23ea55a3836bc7e9dba60649642a9d291b952084
--- /dev/null
+++ b/data/sampled_jsons/password-locked_models_reverse_backdoors_elicit_trigger_absent_prevent_present_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[Paper] Stress-testing capability elicitation with password ...", "date": "", "ddg_snippet": "4 Jun 2024 — But password - locked models are “ reverse backdoors ”: we don't try to prevent the with- trigger behavior when the trigger is present , we try to ...", "subpage_snippet": "", "source": "www.alignmentforum.org", "link": "https://www.alignmentforum.org/posts/c4sZqhqPwNKGz3fFW/paper-stress-testing-capability-elicitation-with-password", "content": "4 Jun 2024 — But password - locked models are “ reverse backdoors ”: we don't try to prevent the with- trigger behavior when the trigger is present , we try to ..."}
+{"idx": 1, "title": "[Paper] Stress-testing capability elicitation with password - locked ...", "date": "", "ddg_snippet": "Elicitation on password - locked models generalizes well across domains (even when we made password - locked models using techniques that we thought might prevent generalization). Using RL on password - locked models recovers hidden capabilities, except when the weak model ...", "subpage_snippet": "", "source": "lw2.issarice.com", "link": "https://lw2.issarice.com/posts/c4sZqhqPwNKGz3fFW/paper-stress-testing-capability-elicitation-with-password", "content": "Elicitation on password - locked models generalizes well across domains (even when we made password - locked models using techniques that we thought might prevent generalization). Using RL on password - locked models recovers hidden capabilities, except when the weak model ..."}
+{"idx": 2, "title": "[Paper] Stress-testing capability elicitation with", "date": "", "ddg_snippet": "Elicitation on password - locked models generalizes well across domains (even when we made password - locked models using techniques that we thought might ...", "subpage_snippet": "", "source": "www.greaterwrong.com", "link": "https://www.greaterwrong.com/posts/c4sZqhqPwNKGz3fFW/paper-stress-testing-capability-elicitation-with-password", "content": "Elicitation on password - locked models generalizes well across domains (even when we made password - locked models using techniques that we thought might ..."}
+{"idx": 3, "title": "[Paper] Stress-testing capability elicitation with", "date": "", "ddg_snippet": "Elicitation on password - locked models generalizes well across domains (even when we made password - locked models using techniques that we thought might ...", "subpage_snippet": "", "source": "www.greaterwrong.com", "link": "https://www.greaterwrong.com/posts/c4sZqhqPwNKGz3fFW/paper-stress-testing-capability-elicitation-by-training", "content": "Elicitation on password - locked models generalizes well across domains (even when we made password - locked models using techniques that we thought might ..."}
+{"idx": 4, "title": "[Paper] Stress-testing capability elicitation with", "date": "", "ddg_snippet": "Elicitation on password - locked models generalizes well across domains (even when we made password - locked models using techniques that we thought ...", "subpage_snippet": "", "source": "www.lesswrong.com", "link": "https://www.lesswrong.com/posts/c4sZqhqPwNKGz3fFW/paper-stress-testing-capability-elicitation-with-password", "content": "Elicitation on password - locked models generalizes well across domains (even when we made password - locked models using techniques that we thought ..."}
+{"idx": 5, "title": "GitHub - dmdhrumilmistry/pyhtools: A Python Hacking Library...", "date": "", "ddg_snippet": "A Python Hacking Library consisting of network scanner, arp spoofer and detector, dns spoofer, code injector, packet sniffer, network jammer, email sender, downloader, wireless password harvester credential harvester, keylogger, download&execute, ransomware, data harvestors, etc.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/dmdhrumilmistry/pyhtools", "content": "A Python Hacking Library consisting of network scanner, arp spoofer and detector, dns spoofer, code injector, packet sniffer, network jammer, email sender, downloader, wireless password harvester credential harvester, keylogger, download&execute, ransomware, data harvestors, etc."}
+{"idx": 6, "title": "Weevely web shell: Complete guide - Hackercool Magazine", "date": "", "ddg_snippet": "Here, we are creating a backdoor to our attacker machine on port 1122. The IP address should be our attacker machine’s. Once we create a reverse backdoor, we just need to listen on the port we specified above using netcat as shown below.", "subpage_snippet": "", "source": "www.hackercoolmagazine.com", "link": "https://www.hackercoolmagazine.com/weevely-web-shell-complete-guide/", "content": "Here, we are creating a backdoor to our attacker machine on port 1122. The IP address should be our attacker machine’s. Once we create a reverse backdoor, we just need to listen on the port we specified above using netcat as shown below."}
+{"idx": 7, "title": "writeup.dvi", "date": "", "ddg_snippet": "– Reverse Backdoor: A program which creates a connection to an external host and. binds a shell to that connection.This means that backdoors cannot be directly implemented. Privilege modication is also unsupported, preventing root insertion.", "subpage_snippet": "", "source": "people.csail.mit.edu", "link": "https://people.csail.mit.edu/nickolai/papers/skowyra-rop.pdf", "content": "– Reverse Backdoor: A program which creates a connection to an external host and. binds a shell to that connection.This means that backdoors cannot be directly implemented. Privilege modication is also unsupported, preventing root insertion."}
+{"idx": 8, "title": "Fabien's Shortform — LessWrong", "date": "", "ddg_snippet": "I would be interested to understand why you would categorize something like “Frontier Models Are Capable of In-Context Scheming” as non-empirical ...", "subpage_snippet": "", "source": "www.lesswrong.com", "link": "https://www.lesswrong.com/posts/nAsMfmxDv6Qp7cfHh/fabien-s-shortform", "content": "I would be interested to understand why you would categorize something like “Frontier Models Are Capable of In-Context Scheming” as non-empirical ..."}
+{"idx": 9, "title": "Holes in Latent Space: Topological Signatures Under Adversarial", "date": "", "ddg_snippet": "These topological patterns hold across models of varying sizes, suggesting that adversarial triggers systematically reshape the representation space ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.20435v1", "content": "These topological patterns hold across models of varying sizes, suggesting that adversarial triggers systematically reshape the representation space ..."}
diff --git a/data/sampled_jsons/path_aggregation_lemma_Derivative-based_Vision_Network_for_INR_year_2025.jsonl b/data/sampled_jsons/path_aggregation_lemma_Derivative-based_Vision_Network_for_INR_year_2025.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8fbad84f556db8b1b39c176e3fd41485b7a83984
--- /dev/null
+++ b/data/sampled_jsons/path_aggregation_lemma_Derivative-based_Vision_Network_for_INR_year_2025.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Scale Equivariant Graph Metanetworks", "date": "", "ddg_snippet": "15 Jun 2024 — In [74] high-order spatial derivatives are used (suitable only for INRs), in [47] the architecture operates on stacked parameter vectors (but ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.10685v1", "content": "15 Jun 2024 — In [74] high-order spatial derivatives are used (suitable only for INRs), in [47] the architecture operates on stacked parameter vectors (but ..."}
+{"idx": 1, "title": "Equivariant Architectures for Learning in Deep Weight Spaces", "date": "", "ddg_snippet": "by A Navon · 2023 · Cited by 85 — We demonstrate the efficacy of DWSNets on two types of tasks: (1) processing INRs ; and (2) processing standard neural networks . The results indicate that our ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v202/navon23a/navon23a.pdf", "content": "by A Navon · 2023 · Cited by 85 — We demonstrate the efficacy of DWSNets on two types of tasks: (1) processing INRs ; and (2) processing standard neural networks . The results indicate that our ..."}
+{"idx": 2, "title": "Spatiotemporal Implicit Neural Representation as a ...", "date": "", "ddg_snippet": "INRs directly model the mapping from low-dimensional regimes, such as coordinates and derivatives , to high-frequency structures, which is necessary to represent ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.03185v2", "content": "INRs directly model the mapping from low-dimensional regimes, such as coordinates and derivatives , to high-frequency structures, which is necessary to represent ..."}
+{"idx": 3, "title": "Scale-Invariant Continuous Implicit Neural Representa", "date": "", "ddg_snippet": "by S Xu — Our INR - based decoder network consists of four fully connected layers with residual connections, and one fully connected layer with learnable parameters to ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=9swCsnoNX4", "content": "by S Xu — Our INR - based decoder network consists of four fully connected layers with residual connections, and one fully connected layer with learnable parameters to ..."}
+{"idx": 4, "title": "Track: Poster Session 6 West", "date": "", "ddg_snippet": "17 Jul 2025 — As multimodal large language models (MLLMs) advance, MLLM-based virtual agents have demonstrated remarkable performance. However, existing ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/session/50262", "content": "17 Jul 2025 — As multimodal large language models (MLLMs) advance, MLLM-based virtual agents have demonstrated remarkable performance. However, existing ..."}
+{"idx": 5, "title": "Towards Theoretical and Empirical Foundations of Machine ...", "date": "", "ddg_snippet": "Furthermore, it explores the architectural design space of using neural networks to approximate PDE solutions and fundamentally understands the choice of ...", "subpage_snippet": "", "source": "ml.cmu.edu", "link": "https://ml.cmu.edu/research/phd-dissertation-pdfs/tmarwah_phd_mld_2025.pdf", "content": "Furthermore, it explores the architectural design space of using neural networks to approximate PDE solutions and fundamentally understands the choice of ..."}
+{"idx": 6, "title": "Daily Papers", "date": "", "ddg_snippet": "Implicit neural representations ( INRs ) use neural networks to provide continuous and resolution-independent representations of complex signals with a small ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=Entropy-based+Activation+Function+Optimization+(EAFO)", "content": "Implicit neural representations ( INRs ) use neural networks to provide continuous and resolution-independent representations of complex signals with a small ..."}
+{"idx": 7, "title": "Safety, Robustness, and Interpretability in Machine Learning", "date": "", "ddg_snippet": "by S Pfrommer · 2025 — We introduce a model predictive control- based safety guide which refines the actions of a base RL policy, conditioned on user-provided ... 158 pages", "subpage_snippet": "", "source": "www2.eecs.berkeley.edu", "link": "https://www2.eecs.berkeley.edu/Pubs/TechRpts/2025/EECS-2025-67.pdf", "content": "by S Pfrommer · 2025 — We introduce a model predictive control- based safety guide which refines the actions of a base RL policy, conditioned on user-provided ... 158 pages"}
+{"idx": 8, "title": "Combining Bayesian and Deep Learning Methods in ...", "date": "", "ddg_snippet": "ants of the proposed INR - based model, namely INR -Laplace (eq. 4.2,4.3 ... Jia, “ Path Aggregation Network for Instance. Segmentation,” in Proceedings of ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/8c42a70be50a99b7321458c450a482f8d3f723d5.pdf", "content": "ants of the proposed INR - based model, namely INR -Laplace (eq. 4.2,4.3 ... Jia, “ Path Aggregation Network for Instance. Segmentation,” in Proceedings of ..."}
+{"idx": 9, "title": "Deletion and Insertion Tests in Regression Models", "date": "", "ddg_snippet": "by N Hama · 2023 · Cited by 16 — We consider three schemes: simply treating binary variables in {0,1} as if they were continuous values in [0,1], multilinear interpolation of the function ... 38 pages", "subpage_snippet": "", "source": "www.jmlr.org", "link": "https://www.jmlr.org/papers/volume24/22-0560/22-0560.pdf", "content": "by N Hama · 2023 · Cited by 16 — We consider three schemes: simply treating binary variables in {0,1} as if they were continuous values in [0,1], multilinear interpolation of the function ... 38 pages"}
diff --git a/data/sampled_jsons/peeling_device_probability_theory_variance_estimation.jsonl b/data/sampled_jsons/peeling_device_probability_theory_variance_estimation.jsonl
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index 0000000000000000000000000000000000000000..1e8f581a3486dfe0c28fecd6dce135772dc5ab74
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Empirical Process: Peeling Technique - Stanford University", "date": "", "ddg_snippet": "The two main technique used in the paper is the peeling lemma and the Talagrand Concentration Inequality. In this section, we have a slightly simpler version of the proof instead of the original one in the paper.", "subpage_snippet": "", "source": "web.stanford.edu", "link": "https://web.stanford.edu/~yplu/note/localization.pdf", "content": "The two main technique used in the paper is the peeling lemma and the Talagrand Concentration Inequality. In this section, we have a slightly simpler version of the proof instead of the original one in the paper."}
+{"idx": 1, "title": "\"Peeling Technique\" in Probability - Mathematics Stack Exchange", "date": "", "ddg_snippet": "Jul 18, 2024 · Never heard of the \" Peeling Argument\", but the first inequality just seems to be the standard inequality $\\mathbb P (A\\cup B)\\leq \\mathbb P (A)+\\mathbb P (B)$ for ( probability ) measures. And the second inequality should come from a comparison of the summands and their respective events (monotonicity of ( probability ) measure).", "subpage_snippet": "", "source": "math.stackexchange.com", "link": "https://math.stackexchange.com/questions/4947385/peeling-technique-in-probability", "content": "Jul 18, 2024 · Never heard of the \" Peeling Argument\", but the first inequality just seems to be the standard inequality $\\mathbb P (A\\cup B)\\leq \\mathbb P (A)+\\mathbb P (B)$ for ( probability ) measures. And the second inequality should come from a comparison of the summands and their respective events (monotonicity of ( probability ) measure)."}
+{"idx": 2, "title": "EstimatedVCdimensionforriskbounds Esti - arXiv.org Probability and Computing – The Peeling Algorithm Generic chaining and the ℓ1-penalty - ScienceDirect Probability Theory & Stochastic Processes / Caltech CMS 117 Probability Theory & Computational Mathematics Probability Theory & Computational Mathematics Probability and Computing – The Peeling Algorithm Probability Theory & Computational Mathematics Probability Theory & Computational Mathematics Probability Theory & Computational Mathematics L10: Probability, statistics, and estimation theory", "date": "", "ddg_snippet": "Abstract Vapnik-Chervonenkis (VC) dimension is a fundamental measure of the generalization capac-ity of learning algorithms. However, apart from a few special cases, it is hard or impossible to calculate analytically. Vapnik et al. [10] proposed a technique for estimating the VC dimension empirically. While their approach behaves well in simulation... See full list on arxiv.org Statistical learning theory is fundamentally concerned with picking, out of some class of plausible or convenient models, ones whose predictions will be nearly optimal. Statistical optimality is most often demonstrated by controlling the risk, or generalization error, of predictive models, i.e., their expected inaccuracy on new data from the same s... See full list on arxiv.org f∈F Similar bounds exist for other loss functions such as margin loss, loss functions constrained to a compact interval, or extended real-valued loss functions for regression problems. Given a function class F, knowing h∗ = VCD(F) is crucial to using these sorts of results. However, for many interesting function classes (support vector machines, mu... See full list on arxiv.org kg − gjkk ≤ η. The η-covering there isn’t one). The η-entropy g1, . . . gn are an η-cover of G if every g ∈ G is within η of some gj, number N(η, G) is the cardinality of the smallest η-cover (or ∞ if is the log of the covering number, H(η, N(η, G). G) = log While it may seem excessive to use covering numbers and entropy to deal with a function cla... See full list on arxiv.org In this paper, we showed how to derive generalization error bounds from the estimated rather than actual VC dimension of a function class F. Our method uses the simulation procedure proposed by Vapnik et al. [10] for the estimates. Empirical process theory for nonparametric least squares regression shows that these estimates concentrate around the ... See full list on arxiv.org Remark: Tα is finite with positive probability > 0, e.g. when the first three Pois(3α) random variables come out as 0. But Tα is also infinite with positive probability . Jun 1, 2013 · Our task in 3 Symmetrization, contraction and deviation inequalities, and the peeling device , 4 Bounds for the symmetrized process is to show that with λ 0 ≈ log p / n, the set T M (θ ⁎) has large probability (for any θ ⁎ and suitable M). We first give in Theorem 2.1 a result where the margin assumption is assumed to hold “globally”. Along the way, we will explore other applications of probability theory in computa-tional statistics, computational mathematics, computer science, electrical engineering, and control theory . How can we describe discrete probability models using the same measure-theoretic framework? Theorem 6.14, on the product measure, ensures that P is a probability measure. The linear darts example confirms that we need to use measure theory to develop a rigorous account of probability . These examples also show that we can describe discrete probability models using exactly the same measure-theoretic framework. What if probability theory was based on Lebesgue's expectation of a random variable? theory of probability . The author set himself the task of putting in their natural peculiar. of Lebesgue’s theories of measure and integration. However, after Lebesgue’s expectation of a random variable, became apparent. These analogies allowed of of orthogonal functions. But if probability theory was to be based on the above How do you find the number of nodes surviving peeling? n the number of nodes surviving peeling . Pr[s Pr[s Pr[s = n · o(1) = o(n). 0 is a small enough constant. Markov: finally: = O(1/m) + o(1) = o(1). Local interactions in large graphs. Also used in statistical physics. Galton-Watson Processes / Trees. What is a variance estimator? The variance measures how much the parameter estimate fluctuates, on average, over the choice of a random sample . Among all estimators with a given bias, we prefer the one with the lowest variability. Therefore, to evaluate the quality of a particular estimator, it is helpful to have a lower bound on the variance of the estimator. How can statistics be used to solve a simple probability experiment? The field of statistics exploits this fact to make inferences about the state of the world. These regularities can also be used to develop eficient algorithms for solving a wide range of computational problems. In this introductory section, we give a simple example of the patterns that can emerge from a simple probability experiment. What is acceptance probability P(E)? P(E) In other words, an accepted sample has the same law as the target random variable. Hint: The pattern of argument is very similar to the computation of the acceptance probability P(E). 4. What is the probability that it takes exactly repetitions of the rejection sampling procedure before we accept a sample? Review of probability theory Definitions (informal) Probabilities are numbers assigned to events that indicate “how likely” it is that the event will occur when a random experiment is performed", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1111.3404", "content": "Abstract Vapnik-Chervonenkis (VC) dimension is a fundamental measure of the generalization capac-ity of learning algorithms. However, apart from a few special cases, it is hard or impossible to calculate analytically. Vapnik et al. [10] proposed a technique for estimating the VC dimension empirically. While their approach behaves well in simulation... See full list on arxiv.org Statistical learning theory is fundamentally concerned with picking, out of some class of plausible or convenient models, ones whose predictions will be nearly optimal. Statistical optimality is most often demonstrated by controlling the risk, or generalization error, of predictive models, i.e., their expected inaccuracy on new data from the same s... See full list on arxiv.org f∈F Similar bounds exist for other loss functions such as margin loss, loss functions constrained to a compact interval, or extended real-valued loss functions for regression problems. Given a function class F, knowing h∗ = VCD(F) is crucial to using these sorts of results. However, for many interesting function classes (support vector machines, mu... See full list on arxiv.org kg − gjkk ≤ η. The η-covering there isn’t one). The η-entropy g1, . . . gn are an η-cover of G if every g ∈ G is within η of some gj, number N(η, G) is the cardinality of the smallest η-cover (or ∞ if is the log of the covering number, H(η, N(η, G). G) = log While it may seem excessive to use covering numbers and entropy to deal with a function cla... See full list on arxiv.org In this paper, we showed how to derive generalization error bounds from the estimated rather than actual VC dimension of a function class F. Our method uses the simulation procedure proposed by Vapnik et al. [10] for the estimates. Empirical process theory for nonparametric least squares regression shows that these estimates concentrate around the ... See full list on arxiv.org Remark: Tα is finite with positive probability > 0, e.g. when the first three Pois(3α) random variables come out as 0. But Tα is also infinite with positive probability . Jun 1, 2013 · Our task in 3 Symmetrization, contraction and deviation inequalities, and the peeling device , 4 Bounds for the symmetrized process is to show that with λ 0 ≈ log p / n, the set T M (θ ⁎) has large probability (for any θ ⁎ and suitable M). We first give in Theorem 2.1 a result where the margin assumption is assumed to hold “globally”. Along the way, we will explore other applications of probability theory in computa-tional statistics, computational mathematics, computer science, electrical engineering, and control theory . How can we describe discrete probability models using the same measure-theoretic framework? Theorem 6.14, on the product measure, ensures that P is a probability measure. The linear darts example confirms that we need to use measure theory to develop a rigorous account of probability . These examples also show that we can describe discrete probability models using exactly the same measure-theoretic framework. What if probability theory was based on Lebesgue's expectation of a random variable? theory of probability . The author set himself the task of putting in their natural peculiar. of Lebesgue’s theories of measure and integration. However, after Lebesgue’s expectation of a random variable, became apparent. These analogies allowed of of orthogonal functions. But if probability theory was to be based on the above How do you find the number of nodes surviving peeling? n the number of nodes surviving peeling . Pr[s Pr[s Pr[s = n · o(1) = o(n). 0 is a small enough constant. Markov: finally: = O(1/m) + o(1) = o(1). Local interactions in large graphs. Also used in statistical physics. Galton-Watson Processes / Trees. What is a variance estimator? The variance measures how much the parameter estimate fluctuates, on average, over the choice of a random sample . Among all estimators with a given bias, we prefer the one with the lowest variability. Therefore, to evaluate the quality of a particular estimator, it is helpful to have a lower bound on the variance of the estimator. How can statistics be used to solve a simple probability experiment? The field of statistics exploits this fact to make inferences about the state of the world. These regularities can also be used to develop eficient algorithms for solving a wide range of computational problems. In this introductory section, we give a simple example of the patterns that can emerge from a simple probability experiment. What is acceptance probability P(E)? P(E) In other words, an accepted sample has the same law as the target random variable. Hint: The pattern of argument is very similar to the computation of the acceptance probability P(E). 4. What is the probability that it takes exactly repetitions of the rejection sampling procedure before we accept a sample? Review of probability theory Definitions (informal) Probabilities are numbers assigned to events that indicate “how likely” it is that the event will occur when a random experiment is performed"}
+{"idx": 3, "title": "Probability and Computing – The Peeling Algorithm", "date": "", "ddg_snippet": "Remark: Tα is finite with positive probability > 0, e.g. when the first three Pois(3α) random variables come out as 0. But Tα is also infinite with positive probability .", "subpage_snippet": "", "source": "ae.iti.kit.edu", "link": "https://ae.iti.kit.edu/download/swalzer/2024-randomised-algorithms/peeling-handout.pdf", "content": "Remark: Tα is finite with positive probability > 0, e.g. when the first three Pois(3α) random variables come out as 0. But Tα is also infinite with positive probability ."}
+{"idx": 4, "title": "Probability Theory & Stochastic Processes / Caltech CMS 117", "date": "", "ddg_snippet": "Along the way, we will explore other applications of probability theory in computa-tional statistics, computational mathematics, computer science, electrical engineering, and control theory .", "subpage_snippet": "", "source": "tropp.caltech.edu", "link": "https://tropp.caltech.edu/notes/Tro24-Probability-Theory-LN.pdf", "content": "Along the way, we will explore other applications of probability theory in computa-tional statistics, computational mathematics, computer science, electrical engineering, and control theory ."}
+{"idx": 5, "title": "L10: Probability, statistics, and estimation theory", "date": "", "ddg_snippet": "Review of probability theory Definitions (informal) Probabilities are numbers assigned to events that indicate “how likely” it is that the event will occur when a random experiment is performed", "subpage_snippet": "", "source": "people.engr.tamu.edu", "link": "https://people.engr.tamu.edu/rgutier/lectures/sp/l10.pdf", "content": "Review of probability theory Definitions (informal) Probabilities are numbers assigned to events that indicate “how likely” it is that the event will occur when a random experiment is performed"}
+{"idx": 6, "title": "Seeing Theory - Basic Probability", "date": "", "ddg_snippet": "Expectation. Variance . Set Theory . Counting. Conditional Probability . Random Variable. Discrete and Continuous. Central Limit Theorem . Point Estimation . Confidence Interval. The Bootstrap.", "subpage_snippet": "", "source": "seeing-theory.brown.edu", "link": "https://seeing-theory.brown.edu/basic-probability/index.html", "content": "Expectation. Variance . Set Theory . Counting. Conditional Probability . Random Variable. Discrete and Continuous. Central Limit Theorem . Point Estimation . Confidence Interval. The Bootstrap."}
+{"idx": 7, "title": "probability theory - Maximum likelihood estimator of the difference...", "date": "", "ddg_snippet": "Learn more about Teams. Maximum likelihood estimator of the difference between two normal means and minimising its variance .", "subpage_snippet": "", "source": "math.stackexchange.com", "link": "https://math.stackexchange.com/questions/2532709/maximum-likelihood-estimator-of-the-difference-between-two-normal-means-and-mini", "content": "Learn more about Teams. Maximum likelihood estimator of the difference between two normal means and minimising its variance ."}
+{"idx": 8, "title": "Variance of Random Variable | Probability theory - EngineersTutor", "date": "", "ddg_snippet": "For discrete RV X, the variance is written as Var( X) = E[(X-m)2] where m = Expected value of X. Another formula for Var(X) = E[X2] –m2. standard deviation. variance of random variable.", "subpage_snippet": "", "source": "engineerstutor.com", "link": "https://engineerstutor.com/2018/10/29/variance-of-random-variable-probability-theory/", "content": "For discrete RV X, the variance is written as Var( X) = E[(X-m)2] where m = Expected value of X. Another formula for Var(X) = E[X2] –m2. standard deviation. variance of random variable."}
+{"idx": 9, "title": "[Developments in Decision- Theoretic Variance Estimation ]: Comment...", "date": "", "ddg_snippet": "Content source. journal article. [Developments in Decision- Theoretic Variance Estimation ]: Comment.The purpose of the Institute of Mathematical Statistics (IMS) is to foster the development and dissemination of the theory and applications of statistics and probability .", "subpage_snippet": "", "source": "www.jstor.org", "link": "https://www.jstor.org/stable/2245898", "content": "Content source. journal article. [Developments in Decision- Theoretic Variance Estimation ]: Comment.The purpose of the Institute of Mathematical Statistics (IMS) is to foster the development and dissemination of the theory and applications of statistics and probability ."}
diff --git a/data/sampled_jsons/per-instance_differential_privacy_unlearning_-Sepahvand_-2025.jsonl b/data/sampled_jsons/per-instance_differential_privacy_unlearning_-Sepahvand_-2025.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d5217e4493c253ba6113ac7171de13db17291ee2
--- /dev/null
+++ b/data/sampled_jsons/per-instance_differential_privacy_unlearning_-Sepahvand_-2025.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[1707.07708] Per-instance Differential Privacy - arXiv.org", "date": "", "ddg_snippet": "We consider a refinement of differential privacy --- per instance differential privacy (pDP), which captures the privacy of a specific individual with respect to a fixed data set. We show that this is a strict generalization of the standard DP and inherits all its desirable properties, e.g., composition, invariance to side information and closedness to postprocessing, except that they all hold ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1707.07708", "content": "We consider a refinement of differential privacy --- per instance differential privacy (pDP), which captures the privacy of a specific individual with respect to a fixed data set. We show that this is a strict generalization of the standard DP and inherits all its desirable properties, e.g., composition, invariance to side information and closedness to postprocessing, except that they all hold ..."}
+{"idx": 1, "title": "Leveraging Per - Instance Privacy for Machine Unlearning", "date": "", "ddg_snippet": "Armed with per - instance privacy losses, we revisit Chien et al.’s 2024 theoretical analysis of noisy gradient descent as an unlearning scheme (coined “Langevin unlearning ” a.k.a “noisy fine-tuning”), based on training without the forget set.", "subpage_snippet": "", "source": "tpdp.journalprivacyconfidentiality.org", "link": "https://tpdp.journalprivacyconfidentiality.org/2025/pdf/sepahvand.pdf", "content": "Armed with per - instance privacy losses, we revisit Chien et al.’s 2024 theoretical analysis of noisy gradient descent as an unlearning scheme (coined “Langevin unlearning ” a.k.a “noisy fine-tuning”), based on training without the forget set."}
+{"idx": 2, "title": "Privately Publishable Per - instance Privacy", "date": "", "ddg_snippet": "Per - instance DP and ex-post per - instance DP belong to a growing family of DP denitions that provide a more ne-grained characterization of the privacy loss.Wang, Y.-X. Per - instance differential privacy . Journal of Privacy and Condentiality, 9(1), 2019.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper/2021/file/9087b0efc7c7acd1ef7e153678809c77-Paper.pdf", "content": "Per - instance DP and ex-post per - instance DP belong to a growing family of DP denitions that provide a more ne-grained characterization of the privacy loss.Wang, Y.-X. Per - instance differential privacy . Journal of Privacy and Condentiality, 9(1), 2019."}
+{"idx": 3, "title": "ICLR Poster On the Inherent Privacy Properties of Discrete Denoising...", "date": "", "ddg_snippet": "Focusing on per - instance differential privacy (pDP), our framework elucidates the potential privacy leakage for each data point in a given training dataset, offering insights into how the privacy loss of each point correlates with the dataset's distribution.", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2025/poster/31493", "content": "Focusing on per - instance differential privacy (pDP), our framework elucidates the potential privacy leakage for each data point in a given training dataset, offering insights into how the privacy loss of each point correlates with the dataset's distribution."}
+{"idx": 4, "title": "Mixed Differential Privacy in Computer Vision", "date": "", "ddg_snippet": "Per - instance differential privacy . Journal of Privacy and Confidentiality, 9(1), 2019. 2, 7, 15.", "subpage_snippet": "", "source": "assets.amazon.science", "link": "https://assets.amazon.science/7c/d1/317157e941ec87058cbc9818be75/mixed-differential-privacy-in-computer-vision.pdf", "content": "Per - instance differential privacy . Journal of Privacy and Confidentiality, 9(1), 2019. 2, 7, 15."}
+{"idx": 5, "title": "Per - instance Differential Privacy and the Adaptivity of Posterior...", "date": "", "ddg_snippet": "Shortly after its introduction in 2006, differential privacy became the flagship data privacy definition. Since then, numerous variants and extensions were proposed to adapt it to different scenarios and attacker models.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/318699823_Per-instance_Differential_Privacy_and_the_Adaptivity_of_Posterior_Sampling_in_Linear_and_Ridge_regression", "content": "Shortly after its introduction in 2006, differential privacy became the flagship data privacy definition. Since then, numerous variants and extensions were proposed to adapt it to different scenarios and attacker models."}
+{"idx": 6, "title": "Improving Privacy in Machine Learning - Simple Science", "date": "", "ddg_snippet": "These newer methods, like per - instance differential privacy (pDP) and Fisher information loss (FIL), provide more nuanced privacy guarantees by taking into account the specific dataset being analyzed. #New Mechanisms for Better Privacy.", "subpage_snippet": "", "source": "scisimple.com", "link": "https://scisimple.com/en/articles/2025-08-31-improving-privacy-in-machine-learning--a3z6jyd", "content": "These newer methods, like per - instance differential privacy (pDP) and Fisher information loss (FIL), provide more nuanced privacy guarantees by taking into account the specific dataset being analyzed. #New Mechanisms for Better Privacy."}
+{"idx": 7, "title": "On the Inherent Privacy Properties of Discrete Denoising Diffusion...", "date": "", "ddg_snippet": "Focusing on per - instance differential privacy (pDP), our framework elucidates the potential privacy leakage for each data point in a given training dataset, offering insights into how the privacy loss of each point correlates with the dataset's distribution.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/inherent-privacy-properties-discrete-denoising-diffusion-models", "content": "Focusing on per - instance differential privacy (pDP), our framework elucidates the potential privacy leakage for each data point in a given training dataset, offering insights into how the privacy loss of each point correlates with the dataset's distribution."}
+{"idx": 8, "title": "Privately Publishable Per - instance Privacy | DeepAI", "date": "", "ddg_snippet": "Per - instance Differential Privacy and the Adaptivity of Posterior Sampling in Linear and Ridge regression.", "subpage_snippet": "", "source": "deepai.org", "link": "https://deepai.org/publication/privately-publishable-per-instance-privacy", "content": "Per - instance Differential Privacy and the Adaptivity of Posterior Sampling in Linear and Ridge regression."}
+{"idx": 9, "title": "Instance -Wise Laplace Mechanism via Deep Reinforcement Learning...", "date": "", "ddg_snippet": "Recent research has shown a growing interest in per - instance differential privacy (pDP), highlighting the fact that each data instance within a dataset may incur distinct levels of privacy loss.", "subpage_snippet": "", "source": "ojs.aaai.org", "link": "https://ojs.aaai.org/index.php/AAAI/article/view/30506", "content": "Recent research has shown a growing interest in per - instance differential privacy (pDP), highlighting the fact that each data instance within a dataset may incur distinct levels of privacy loss."}
diff --git a/data/sampled_jsons/per-instance_privacy_applied_to_machine_unlearning.jsonl b/data/sampled_jsons/per-instance_privacy_applied_to_machine_unlearning.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..83a29d404dc329614fb878e5d13e9f3b78b17799
--- /dev/null
+++ b/data/sampled_jsons/per-instance_privacy_applied_to_machine_unlearning.jsonl
@@ -0,0 +1,10 @@
+{"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": "A survey on machine unlearning: Techniques and new emerged privacy ...", "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": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2214212625000481", "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": 3, "title": "A survey of security and privacy issues of machine unlearning", "date": "", "ddg_snippet": "Machine unlearning is a cutting-edge technology that embodies the privacy legal principle of the right to be forgotten within the realm of machine learning (ML). It aims to remove specific data or knowledge from trained models without retraining from scratch and has gained significant attention in the field of artificial intelligence in recent years. However, the development of machine ...", "subpage_snippet": "", "source": "onlinelibrary.wiley.com", "link": "https://onlinelibrary.wiley.com/doi/full/10.1002/aaai.12209", "content": "Machine unlearning is a cutting-edge technology that embodies the privacy legal principle of the right to be forgotten within the realm of machine learning (ML). It aims to remove specific data or knowledge from trained models without retraining from scratch and has gained significant attention in the field of artificial intelligence in recent years. However, the development of machine ..."}
+{"idx": 4, "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 ..."}
+{"idx": 5, "title": "Leveraging Per-Instance Privacy for Machine Unlearning", "date": "", "ddg_snippet": "Leveraging Per-Instance Privacy for Machine Unlearning Naz Sepahvand · Anvith Thudi · Berivan Isik · Ashmita Bhattacharyya · Nicolas Papernot · Eleni Triantafillou · Daniel Roy · Gintare Karolina Dziugaite", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46697", "content": "Leveraging Per-Instance Privacy for Machine Unlearning Naz Sepahvand · Anvith Thudi · Berivan Isik · Ashmita Bhattacharyya · Nicolas Papernot · Eleni Triantafillou · Daniel Roy · Gintare Karolina Dziugaite"}
+{"idx": 6, "title": "Privacy Preservation through Practical Machine Unlearning", "date": "", "ddg_snippet": "Machine Learning models thrive on vast datasets, continuously adapting to provide accurate predictions and recommendations. However, in an era dominated by privacy concerns, Machine Unlearning emerges as a transformative approach, enabling the selective removal of data from trained models. This paper examines methods such as Naive Retraining and Exact Unlearning via the SISA framework ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.10635", "content": "Machine Learning models thrive on vast datasets, continuously adapting to provide accurate predictions and recommendations. However, in an era dominated by privacy concerns, Machine Unlearning emerges as a transformative approach, enabling the selective removal of data from trained models. This paper examines methods such as Naive Retraining and Exact Unlearning via the SISA framework ..."}
+{"idx": 7, "title": "Enhancing Privacy in Machine Unlearning: Posterior Perturbation Against ...", "date": "", "ddg_snippet": "Machine unlearning aims to safeguard data privacy by mitigating the data’s impact on machine learning models. Nonetheless, machine unlearning practices can introduce new privacy vulnerabilities, leaving models susceptible to various forms of attack, such as...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-981-96-1551-3_16", "content": "Machine unlearning aims to safeguard data privacy by mitigating the data’s impact on machine learning models. Nonetheless, machine unlearning practices can introduce new privacy vulnerabilities, leaving models susceptible to various forms of attack, such as..."}
+{"idx": 8, "title": "Towards Privacy-Preserving and Secure Machine Unlearning: Taxonomy ...", "date": "", "ddg_snippet": "Machine Unlearning (MU) is a growing sub-field of Machine Learning (ML) that aims to update ML models efficiently following users' requests to remove training data without retraining of the original ML model. While MU provides novel ways to preserve user privacy and ensure model integrity through the removal of compromised data, or the data that has been requested to be removed for privacy ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10835512", "content": "Machine Unlearning (MU) is a growing sub-field of Machine Learning (ML) that aims to update ML models efficiently following users' requests to remove training data without retraining of the original ML model. While MU provides novel ways to preserve user privacy and ensure model integrity through the removal of compromised data, or the data that has been requested to be removed for privacy ..."}
+{"idx": 9, "title": "Ensuring User Privacy and Model Security via Machine Unlearning: A ...", "date": "", "ddg_snippet": "As an emerging discipline, machine learning has been widely used in artificial intelligence, education, meteorology and other fields. In the training of machine learning models, trainers need to use a large amount of practical data, which inevitably involves user privacy . Besides, by polluting the training data, a malicious adversary can poison the model, thus compromising model security. The ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/org/science/article/pii/S1546221823006148", "content": "As an emerging discipline, machine learning has been widely used in artificial intelligence, education, meteorology and other fields. In the training of machine learning models, trainers need to use a large amount of practical data, which inevitably involves user privacy . Besides, by polluting the training data, a malicious adversary can poison the model, thus compromising model security. The ..."}
diff --git a/data/sampled_jsons/per-instance_privacy_bounds_vs_worst-case_privacy_bounds_machine_unlearning.jsonl b/data/sampled_jsons/per-instance_privacy_bounds_vs_worst-case_privacy_bounds_machine_unlearning.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..cbdf15dc8f832526652045240245142d61020641
--- /dev/null
+++ b/data/sampled_jsons/per-instance_privacy_bounds_vs_worst-case_privacy_bounds_machine_unlearning.jsonl
@@ -0,0 +1,10 @@
+{"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": "Noise Variance Optimization in Differential Privacy: A Game-Theoretic ...", "date": "", "ddg_snippet": "This is because the traditional DP computes privacy loss based on the worst-case scenario, i.e., statistical outliers. In this work, to tackle this challenge, we utilize per-instance DP (pDP) as a constraint, measuring privacy loss for each data instance and optimizing noise tailored to individual instances .", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10609362", "content": "This is because the traditional DP computes privacy loss based on the worst-case scenario, i.e., statistical outliers. In this work, to tackle this challenge, we utilize per-instance DP (pDP) as a constraint, measuring privacy loss for each data instance and optimizing noise tailored to individual instances ."}
+{"idx": 2, "title": "PDF Privately Publishable Per-instance Privacy", "date": "", "ddg_snippet": "Per-instance differential privacy provides a theoretically sound alternative to the empirical approach for revealing the gap between the worst-case DP bound and the actual privacy loss in practice.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper/2021/file/9087b0efc7c7acd1ef7e153678809c77-Paper.pdf", "content": "Per-instance differential privacy provides a theoretically sound alternative to the empirical approach for revealing the gap between the worst-case DP bound and the actual privacy loss in practice."}
+{"idx": 3, "title": "Ensuring User Privacy and Model Security via Machine Unlearning: A ...", "date": "", "ddg_snippet": "As an emerging discipline, machine learning has been widely used in artificial intelligence, education, meteorology and other fields. In the training of machine learning models, trainers need to use a large amount of practical data, which inevitably involves user privacy . Besides, by polluting the training data, a malicious adversary can poison the model, thus compromising model security. The ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/org/science/article/pii/S1546221823006148", "content": "As an emerging discipline, machine learning has been widely used in artificial intelligence, education, meteorology and other fields. In the training of machine learning models, trainers need to use a large amount of practical data, which inevitably involves user privacy . Besides, by polluting the training data, a malicious adversary can poison the model, thus compromising model security. The ..."}
+{"idx": 4, "title": "Privately Publishable Per-instance Privacy - OpenReview", "date": "", "ddg_snippet": "Abstract: We consider how to privately share the personalized privacy losses incurred by objective perturbation, using per-instance differential privacy (pDP). Standard differential privacy (DP) gives us a worst-case bound that might be orders of magnitude larger than the privacy loss to a particular individual relative to a fixed dataset.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=pPbrtkTHe9", "content": "Abstract: We consider how to privately share the personalized privacy losses incurred by objective perturbation, using per-instance differential privacy (pDP). Standard differential privacy (DP) gives us a worst-case bound that might be orders of magnitude larger than the privacy loss to a particular individual relative to a fixed dataset."}
+{"idx": 5, "title": "Leveraging Per-Instance Privacy for Machine Unlearning", "date": "", "ddg_snippet": "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 ́enyi) diver-gence to retraining without an individual data point.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2505.18786", "content": "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 ́enyi) diver-gence to retraining without an individual data point."}
+{"idx": 6, "title": "Leveraging Per-Instance Privacy for Machine Unlearning", "date": "", "ddg_snippet": "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 to retraining without an individual data point.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Leveraging-Per-Instance-Privacy-for-Machine-Sepahvand-Thudi/dca9861c26bd83a7b1c30fb4255810afdd1e4aa3", "content": "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 to retraining without an individual data point."}
+{"idx": 7, "title": "Per-instance Differential Privacy", "date": "", "ddg_snippet": "In the rst experiment, we consider the algorithm of adding isotropic Gaussian noise to linear regression coe cients and then compare the worst-case DP and the distribution of per-instance DP for points in the data set (illustrated as box plots).", "subpage_snippet": "", "source": "journalprivacyconfidentiality.org", "link": "https://journalprivacyconfidentiality.org/index.php/jpc/article/download/662/675/", "content": "In the rst experiment, we consider the algorithm of adding isotropic Gaussian noise to linear regression coe cients and then compare the worst-case DP and the distribution of per-instance DP for points in the data set (illustrated as box plots)."}
+{"idx": 8, "title": "Privately Publishable Per-instance Privacy - NIPS", "date": "", "ddg_snippet": "Abstract We consider how to privately share the personalized privacy losses incurred by objective perturbation, using per-instance differential privacy (pDP). Standard differential privacy (DP) gives us a worst-case bound that might be orders of magnitude larger than the privacy loss to a particular individual relative to a fixed dataset.", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper/2021/hash/9087b0efc7c7acd1ef7e153678809c77-Abstract.html", "content": "Abstract We consider how to privately share the personalized privacy losses incurred by objective perturbation, using per-instance differential privacy (pDP). Standard differential privacy (DP) gives us a worst-case bound that might be orders of magnitude larger than the privacy loss to a particular individual relative to a fixed dataset."}
+{"idx": 9, "title": "Privately publishable per-instance privacy | Proceedings of the 35th ...", "date": "", "ddg_snippet": "Abstract We consider how to privately share the personalized privacy losses incurred by objective perturbation, using per-instance differential privacy (pDP). Standard differential privacy (DP) gives us a worst-case bound that might be orders of magnitude larger than the privacy loss to a particular individual relative to a fixed dataset.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3540261.3541587", "content": "Abstract We consider how to privately share the personalized privacy losses incurred by objective perturbation, using per-instance differential privacy (pDP). Standard differential privacy (DP) gives us a worst-case bound that might be orders of magnitude larger than the privacy loss to a particular individual relative to a fixed dataset."}
diff --git a/data/sampled_jsons/per-instance_privacy_loss_Renyi_divergence_unlearning_theoretical_foundation.jsonl b/data/sampled_jsons/per-instance_privacy_loss_Renyi_divergence_unlearning_theoretical_foundation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..830512f25984019494c91a6f8934cde421e89e4f
--- /dev/null
+++ b/data/sampled_jsons/per-instance_privacy_loss_Renyi_divergence_unlearning_theoretical_foundation.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Use of 'as per' vs 'per' - English Language & Usage Stack...", "date": "", "ddg_snippet": "I've read and have heard of both 'as per ' and ' per ' being used conversationally, both with the same connotation of either 'according to' or 'on authority of' Examples: \"Tell Ron to start molding ...", "subpage_snippet": "", "source": "english.stackexchange.com", "link": "https://english.stackexchange.com/questions/54864/use-of-as-per-vs-per", "content": "I've read and have heard of both 'as per ' and ' per ' being used conversationally, both with the same connotation of either 'according to' or 'on authority of' Examples: \"Tell Ron to start molding ..."}
+{"idx": 1, "title": "meaning - Difference between \"per\" and \"a\" - English Language &...", "date": "", "ddg_snippet": "What is the difference between the following two sentences? She goes to the forest three times per week. She goes to the forest three times a week.", "subpage_snippet": "", "source": "english.stackexchange.com", "link": "https://english.stackexchange.com/questions/62009/difference-between-per-and-a", "content": "What is the difference between the following two sentences? She goes to the forest three times per week. She goes to the forest three times a week."}
+{"idx": 2, "title": "\"By\" vs \"Per\". Which one should I use on expressions like \"P&L...", "date": "", "ddg_snippet": "The word \" per \" carries the implication (as in percent) that there is a division going on - so if someone says to me \"I'll tell you the number of widgets manufactured per employee\" I'm expecting one number - the total number of widgets manufactured divided by the number of employees.", "subpage_snippet": "", "source": "english.stackexchange.com", "link": "https://english.stackexchange.com/questions/22644/by-vs-per-which-one-should-i-use-on-expressions-like-pl-geography-or-va", "content": "The word \" per \" carries the implication (as in percent) that there is a division going on - so if someone says to me \"I'll tell you the number of widgets manufactured per employee\" I'm expecting one number - the total number of widgets manufactured divided by the number of employees."}
+{"idx": 3, "title": "Legg-Calve-Perthes disease - Symptoms and causes - Mayo Clinic", "date": "", "ddg_snippet": "Jun 22, 2024 · Legg-Calve-Perthes (LEG-kahl-VAY- PER -tuz) disease is a childhood condition that occurs when blood supply to the ball part (femoral head) of the hip joint is temporarily interrupted and the bone begins to die. This weakened bone gradually breaks apart and can lose its round shape.", "subpage_snippet": "", "source": "www.mayoclinic.org", "link": "https://www.mayoclinic.org/diseases-conditions/legg-calve-perthes-disease/symptoms-causes/syc-20374343", "content": "Jun 22, 2024 · Legg-Calve-Perthes (LEG-kahl-VAY- PER -tuz) disease is a childhood condition that occurs when blood supply to the ball part (femoral head) of the hip joint is temporarily interrupted and the bone begins to die. This weakened bone gradually breaks apart and can lose its round shape."}
+{"idx": 4, "title": "Perimenopause - Symptoms and causes - Mayo Clinic", "date": "", "ddg_snippet": "Aug 14, 2025 · Discover effective treatments and learn self-care strategies for hot flashes, night sweats, vaginal dryness and other perimenopause symptoms.", "subpage_snippet": "", "source": "www.mayoclinic.org", "link": "https://www.mayoclinic.org/diseases-conditions/perimenopause/symptoms-causes/syc-20354666", "content": "Aug 14, 2025 · Discover effective treatments and learn self-care strategies for hot flashes, night sweats, vaginal dryness and other perimenopause symptoms."}
+{"idx": 5, "title": "What is the difference between \"as per\" and \"according to\"?", "date": "", "ddg_snippet": "The particular construction as per my knowledge is unnatural - as per is normally only used in reference to some prior statement / school of thought. It more properly corresponds to in accordance with, and can't simply be used to replace the idiomatic according to my knowledge / information / understanding.", "subpage_snippet": "", "source": "english.stackexchange.com", "link": "https://english.stackexchange.com/questions/256/what-is-the-difference-between-as-per-and-according-to", "content": "The particular construction as per my knowledge is unnatural - as per is normally only used in reference to some prior statement / school of thought. It more properly corresponds to in accordance with, and can't simply be used to replace the idiomatic according to my knowledge / information / understanding."}
+{"idx": 6, "title": "Is there a definitive spelling for the shortened version of “as...", "date": "", "ddg_snippet": "Jun 27, 2012 · 3 There's no definitive spelling, but as per ushe is a common one with the benefit of being fairly unambiguous. Alternatives include as per use, but that could be confused with \"for each use\", and as per uje, but that looks a bit odd. The OED doesn't include either, but does note as per is also a shortened form.", "subpage_snippet": "", "source": "english.stackexchange.com", "link": "https://english.stackexchange.com/questions/72652/is-there-a-definitive-spelling-for-the-shortened-version-of-as-per-usual", "content": "Jun 27, 2012 · 3 There's no definitive spelling, but as per ushe is a common one with the benefit of being fairly unambiguous. Alternatives include as per use, but that could be confused with \"for each use\", and as per uje, but that looks a bit odd. The OED doesn't include either, but does note as per is also a shortened form."}
+{"idx": 7, "title": "Calorie Calculator - Mayo Clinic", "date": "", "ddg_snippet": "If you're pregnant or breast-feeding, are a competitive athlete, or have a metabolic disease, such as diabetes, the calorie calculator may overestimate or underestimate your actual calorie needs.", "subpage_snippet": "", "source": "www.mayoclinic.org", "link": "https://www.mayoclinic.org/healthy-lifestyle/weight-loss/in-depth/calorie-calculator/itt-20402304", "content": "If you're pregnant or breast-feeding, are a competitive athlete, or have a metabolic disease, such as diabetes, the calorie calculator may overestimate or underestimate your actual calorie needs."}
+{"idx": 8, "title": "Bipolar disorder - Symptoms and causes - Mayo Clinic", "date": "", "ddg_snippet": "Aug 14, 2024 · Overview Bipolar disorder, formerly called manic depression, is a mental health condition that causes extreme mood swings. These include emotional highs, also known as mania or hypomania, and lows, also known as depression. Hypomania is less extreme than mania.", "subpage_snippet": "", "source": "www.mayoclinic.org", "link": "https://www.mayoclinic.org/diseases-conditions/bipolar-disorder/symptoms-causes/syc-20355955", "content": "Aug 14, 2024 · Overview Bipolar disorder, formerly called manic depression, is a mental health condition that causes extreme mood swings. These include emotional highs, also known as mania or hypomania, and lows, also known as depression. Hypomania is less extreme than mania."}
+{"idx": 9, "title": "Mayo Clinic corrected QT interval (QTc) calculator - Medical ...", "date": "", "ddg_snippet": "Worried about QT interval prolongation? This online evidence based resource will help guide you how to measure the QT interval and calculate the QTc value with an easy to use calculator which takes into account the patients underlying rhythm, gender and age.", "subpage_snippet": "", "source": "www.mayoclinic.org", "link": "https://www.mayoclinic.org/medical-professionals/cardiovascular-diseases/calculators/corrected-qt-interval-qtc-calculator/itt-20487211", "content": "Worried about QT interval prolongation? This online evidence based resource will help guide you how to measure the QT interval and calculate the QTc value with an easy to use calculator which takes into account the patients underlying rhythm, gender and age."}
diff --git a/data/sampled_jsons/private_median_framework_l_infinity_guarantee_regression.jsonl b/data/sampled_jsons/private_median_framework_l_infinity_guarantee_regression.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6ef146208ec48876da8c364472c114309ba785c5
--- /dev/null
+++ b/data/sampled_jsons/private_median_framework_l_infinity_guarantee_regression.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Differentially private multivariate medians", "date": "", "ddg_snippet": "Despite this fact, using multivariate medians for differentially private and robust multivariate location estimation has not been systematically ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2210.06459v3", "content": "Despite this fact, using multivariate medians for differentially private and robust multivariate location estimation has not been systematically ..."}
+{"idx": 1, "title": "Efficient Sparse Least Absolute Deviation Regression with", "date": "", "ddg_snippet": "Under the framework of differential privacy, a long line of research on privacy-preserving ML has been inspired, such as sparse regression [ 2 , 3 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2401.01294v1", "content": "Under the framework of differential privacy, a long line of research on privacy-preserving ML has been inspired, such as sparse regression [ 2 , 3 ..."}
+{"idx": 2, "title": "1 Introduction", "date": "", "ddg_snippet": "In linear regression , DP variants include privatized F-tests [ 25 ] and ridge regression with confidence-preserving intervals [ 26 ] .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.05212v1", "content": "In linear regression , DP variants include privatized F-tests [ 25 ] and ridge regression with confidence-preserving intervals [ 26 ] ."}
+{"idx": 3, "title": "Downloads", "date": "", "ddg_snippet": "Adversarial Multi Class Learning under Weak Supervision with Performance Guarantees ... A theory of high dimensional regression with arbitrary ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/Downloads/2021", "content": "Adversarial Multi Class Learning under Weak Supervision with Performance Guarantees ... A theory of high dimensional regression with arbitrary ..."}
+{"idx": 4, "title": "ICML 2021 Papers", "date": "", "ddg_snippet": "Tighter Bounds on the Log Marginal Likelihood of Gaussian Process Regression Using Conjugate Gradients ... in Graph Neural Networks Helps Learning ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2021/papers.html", "content": "Tighter Bounds on the Log Marginal Likelihood of Gaussian Process Regression Using Conjugate Gradients ... in Graph Neural Networks Helps Learning ..."}
+{"idx": 5, "title": "Minimax rate for multivariate data under componentwise local", "date": "", "ddg_snippet": "Local privacy involves privatizing data before sharing it with a data collector, while central privacy involves a centralized curator who maintains ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2305.10416v3", "content": "Local privacy involves privatizing data before sharing it with a data collector, while central privacy involves a centralized curator who maintains ..."}
+{"idx": 6, "title": "Michael Yoo", "date": "", "ddg_snippet": "The above linear sweep approach is not good enough, because our worst-case scenario is a linear sweep for every connection point, which will be 10B ...", "subpage_snippet": "", "source": "michael.yoo.id.au", "link": "https://michael.yoo.id.au/", "content": "The above linear sweep approach is not good enough, because our worst-case scenario is a linear sweep for every connection point, which will be 10B ..."}
+{"idx": 7, "title": "SK Small Caps", "date": "", "ddg_snippet": "To comply with NFA regulations, we display Cumulative Rate of Return for strategies with a track record of less than one year.", "subpage_snippet": "", "source": "collective2.com", "link": "https://collective2.com/details/147794805", "content": "To comply with NFA regulations, we display Cumulative Rate of Return for strategies with a track record of less than one year."}
+{"idx": 8, "title": "URS4", "date": "", "ddg_snippet": "To comply with NFA regulations, we display Cumulative Rate of Return for strategies with a track record of less than one year.", "subpage_snippet": "", "source": "belforfx.autotradenow.com", "link": "https://belforfx.autotradenow.com/details-list/140807894", "content": "To comply with NFA regulations, we display Cumulative Rate of Return for strategies with a track record of less than one year."}
+{"idx": 9, "title": "URS4", "date": "", "ddg_snippet": "To comply with NFA regulations, we display Cumulative Rate of Return for strategies with a track record of less than one year.", "subpage_snippet": "", "source": "cornix.collective2.com", "link": "https://cornix.collective2.com/details-list/140807894", "content": "To comply with NFA regulations, we display Cumulative Rate of Return for strategies with a track record of less than one year."}
diff --git a/data/sampled_jsons/probabilistic_currying_function-valued_Gaussian_process_multi-output_Gaussian_process.jsonl b/data/sampled_jsons/probabilistic_currying_function-valued_Gaussian_process_multi-output_Gaussian_process.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8040d0f3f27b0ffb5856d0d7db222fb6dfd24f40
--- /dev/null
+++ b/data/sampled_jsons/probabilistic_currying_function-valued_Gaussian_process_multi-output_Gaussian_process.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Linearization Turns Neural Operators into Function - Valued Gaussian ...", "date": "", "ddg_snippet": "3.1. Function - Valued Gaussian Processes and Probabilistic Currying . We want to use model linearization to extend the Gaussian belief over the parameters of a neural network f : Rd × Rp → Rd′ into a ( multi - output ) Gaussian process belief over the function learned by the neural...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=4Z04wVQ9FY", "content": "3.1. Function - Valued Gaussian Processes and Probabilistic Currying . We want to use model linearization to extend the Gaussian belief over the parameters of a neural network f : Rd × Rp → Rd′ into a ( multi - output ) Gaussian process belief over the function learned by the neural..."}
+{"idx": 1, "title": "Linearization Turns Neural Operators into Function-Valued ...", "date": "", "ddg_snippet": "Finally, probabilistic currying transforms f into a function-valued Gaussian process ... multi-output Gaussian process with mean function m and covariance ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46474", "content": "Finally, probabilistic currying transforms f into a function-valued Gaussian process ... multi-output Gaussian process with mean function m and covariance ..."}
+{"idx": 2, "title": "1 | The Sidef programming language", "date": "", "ddg_snippet": "... Functions ... Dinesman s multiple-dwelling problem ... First-class functions", "subpage_snippet": "", "source": "trizen.gitbook.io", "link": "https://trizen.gitbook.io/sidef-lang/programming_tasks/1", "content": "... Functions ... Dinesman s multiple-dwelling problem ... First-class functions"}
+{"idx": 3, "title": "C | The Sidef programming language", "date": "", "ddg_snippet": "... Functions ... Dinesman s multiple-dwelling problem ... First-class functions", "subpage_snippet": "", "source": "trizen.gitbook.io", "link": "https://trizen.gitbook.io/sidef-lang/programming_tasks/c", "content": "... Functions ... Dinesman s multiple-dwelling problem ... First-class functions"}
+{"idx": 4, "title": "David Duvenaud", "date": "", "ddg_snippet": "We examine these joint predictive distributions, which we call LLM Processes , over arbitrarily-many quantities in settings such as forecasting, multi ...", "subpage_snippet": "", "source": "www.cs.toronto.edu", "link": "http://www.cs.toronto.edu/~duvenaud/", "content": "We examine these joint predictive distributions, which we call LLM Processes , over arbitrarily-many quantities in settings such as forecasting, multi ..."}
+{"idx": 5, "title": "David Duvenaud", "date": "", "ddg_snippet": "We examine these joint predictive distributions, which we call LLM Processes , over arbitrarily-many quantities in settings such as forecasting, multi ...", "subpage_snippet": "", "source": "www.cs.toronto.edu", "link": "https://www.cs.toronto.edu/~duvenaud/", "content": "We examine these joint predictive distributions, which we call LLM Processes , over arbitrarily-many quantities in settings such as forecasting, multi ..."}
+{"idx": 6, "title": "Various Consequences: Uncertainty Quantification with", "date": "", "ddg_snippet": "Then, since our example is a simple function , we can analytically calculate the resulting probability density for the output .", "subpage_snippet": "", "source": "www.variousconsequences.com", "link": "http://www.variousconsequences.com/2010/02/uncertainty-quantification-with.html", "content": "Then, since our example is a simple function , we can analytically calculate the resulting probability density for the output ."}
+{"idx": 7, "title": "ASCL.net - Browsing Codes", "date": "", "ddg_snippet": "... heteroscedastic uncertainties and missing data with a vectorized multiplicative update rule; this can be used create a mask and iterate the process ...", "subpage_snippet": "", "source": "ascl.net", "link": "http://ascl.net/code/all/page/3/limit/2255/order/title/listmode/full/dir/asc", "content": "... heteroscedastic uncertainties and missing data with a vectorized multiplicative update rule; this can be used create a mask and iterate the process ..."}
+{"idx": 8, "title": "ASCL.net - Browsing Codes", "date": "", "ddg_snippet": "The Jeans Anisotropic MGE (JAM) modeling method uses the Multi - Gaussian Expansion parameterization for the galaxy surface brightness.", "subpage_snippet": "", "source": "ascl.net", "link": "https://ascl.net/code/all/page/9/limit/100/order/date/listmode/full/dir/asc", "content": "The Jeans Anisotropic MGE (JAM) modeling method uses the Multi - Gaussian Expansion parameterization for the galaxy surface brightness."}
+{"idx": 9, "title": "What Influences the Field Goal Attempts of Professional", "date": "", "ddg_snippet": "... a Bayesian log Gaussian Cox process model allowing joint analysis of the spatial pattern of locations and outcomes of shots across multiple games.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.02137v1", "content": "... a Bayesian log Gaussian Cox process model allowing joint analysis of the spatial pattern of locations and outcomes of shots across multiple games."}
diff --git a/data/sampled_jsons/probability_of_necessity_causal_models_Pearl_counterfactuals.jsonl b/data/sampled_jsons/probability_of_necessity_causal_models_Pearl_counterfactuals.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..99f009fb71fe9a04240489e23dc80569430a822a
--- /dev/null
+++ b/data/sampled_jsons/probability_of_necessity_causal_models_Pearl_counterfactuals.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Causal Models - Stanford Encyclopedia of Philosophy", "date": "", "ddg_snippet": "by C Hitchcock · 2018 · Cited by 136 — Pearl (2009) calls a probability of this form a probability of necessity . It is often called the probability of causation, although this ...", "subpage_snippet": "", "source": "plato.stanford.edu", "link": "https://plato.stanford.edu/entries/causal-models/", "content": "by C Hitchcock · 2018 · Cited by 136 — Pearl (2009) calls a probability of this form a probability of necessity . It is often called the probability of causation, although this ..."}
+{"idx": 1, "title": "Causal Inference - Proceedings of Machine Learning Research", "date": "", "ddg_snippet": "by J Pearl · Cited by 332 — Abstract. This paper reviews a theory of causal inference based on the Structural Causal Model (SCM) described in ( Pearl , 2000a). 20 pages", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v6/pearl10a/pearl10a.pdf", "content": "by J Pearl · Cited by 332 — Abstract. This paper reviews a theory of causal inference based on the Structural Causal Model (SCM) described in ( Pearl , 2000a). 20 pages"}
+{"idx": 2, "title": "An Introduction to Causal Inference - PMC", "date": "", "ddg_snippet": "by J Pearl · 2010 · Cited by 860 — Alternatively, Pearl (1995) used expressions of the form P(Y = y|set(X = x)) or P(Y = y|do(X = x)) to denote the probability (or frequency) that event (Y = y) ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC2836213/", "content": "by J Pearl · 2010 · Cited by 860 — Alternatively, Pearl (1995) used expressions of the form P(Y = y|set(X = x)) or P(Y = y|do(X = x)) to denote the probability (or frequency) that event (Y = y) ..."}
+{"idx": 3, "title": "Counterfactual Graphical Models: Constraints and Inference", "date": "", "ddg_snippet": "by JD Correa · Cited by 7 — There are also quantities such as the probability of necessity ... SCMs allow us to define counterfactual quantities with pre- cision based on the Pearl's Causal ... 20 pages", "subpage_snippet": "", "source": "causalai.net", "link": "https://causalai.net/r115.pdf", "content": "by JD Correa · Cited by 7 — There are also quantities such as the probability of necessity ... SCMs allow us to define counterfactual quantities with pre- cision based on the Pearl's Causal ... 20 pages"}
+{"idx": 4, "title": "the Increasing Complexity of Satisfiability in Pearl's Causal ...", "date": "", "ddg_snippet": "by J Dörfler · Cited by 1 — This paper studies the complexity issues of the satisfiability and validity problems of formulas and shows an increasing complexity within the framework of ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=rvvSSmGIFS", "content": "by J Dörfler · Cited by 1 — This paper studies the complexity issues of the satisfiability and validity problems of formulas and shows an increasing complexity within the framework of ..."}
+{"idx": 5, "title": "Three Counterfactual Interpretations And Their Identification", "date": "", "ddg_snippet": "by J PEARL · 1999 · Cited by 261 — This counterfactual notion, which Robins and Greenland (1989) called the “probability of causation” measures how necessary the cause is for the production of ... 58 pages", "subpage_snippet": "", "source": "ftp.cs.ucla.edu", "link": "https://ftp.cs.ucla.edu/pub/stat_ser/r260-reprint.pdf", "content": "by J PEARL · 1999 · Cited by 261 — This counterfactual notion, which Robins and Greenland (1989) called the “probability of causation” measures how necessary the cause is for the production of ... 58 pages"}
+{"idx": 6, "title": "Probabilities of Causation", "date": "", "ddg_snippet": "by J Pearl · 2022 · Cited by 2 — • Probability of necessity (PN): X → Y . • Probability of sufficiency (PS): X ← Y. • Probability of necessity and sufficiency (PNS): X ↔ Y. 3 ... 25 pages", "subpage_snippet": "", "source": "ics.uci.edu", "link": "https://ics.uci.edu/~dechter/courses/ics-295cr/2021-22_Q2_Winter/slides/classP6-w22-EdgarRobles-Probabilities_of_Causation.pdf", "content": "by J Pearl · 2022 · Cited by 2 — • Probability of necessity (PN): X → Y . • Probability of sufficiency (PS): X ← Y. • Probability of necessity and sufficiency (PNS): X ↔ Y. 3 ... 25 pages"}
+{"idx": 7, "title": "Counterfactual Probabilities", "date": "", "ddg_snippet": "by A Balke · 2013 · Cited by 340 — In this paper we present methods for computing the proba bilities of such queries using the formulation proposed in [Balke and Pearl , 1994], where the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1302.6784", "content": "by A Balke · 2013 · Cited by 340 — In this paper we present methods for computing the proba bilities of such queries using the formulation proposed in [Balke and Pearl , 1994], where the ..."}
+{"idx": 8, "title": "Probabilities of causation: Bounds and identification", "date": "", "ddg_snippet": "by J Tian · 2000 · Cited by 353 — Robins and Greenland [44] gave a counterfactual definition for the probability of necessary causation taking counterfactuals as primitives, and assuming that ... 27 pages", "subpage_snippet": "", "source": "ftp.cs.ucla.edu", "link": "https://ftp.cs.ucla.edu/pub/stat_ser/r271-A.pdf", "content": "by J Tian · 2000 · Cited by 353 — Robins and Greenland [44] gave a counterfactual definition for the probability of necessary causation taking counterfactuals as primitives, and assuming that ... 27 pages"}
+{"idx": 9, "title": "Probabilities Of Causation: Three Counterfactual ...", "date": "", "ddg_snippet": "by J Pearl · 1999 · Cited by 261 — This paper provides formal semantics, based on structural models ofcounterfactuals, for the probability that event x was a necessary orsufficient cause (or ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1023/A:1005233831499", "content": "by J Pearl · 1999 · Cited by 261 — This paper provides formal semantics, based on structural models ofcounterfactuals, for the probability that event x was a necessary orsufficient cause (or ..."}
diff --git a/data/sampled_jsons/probability_of_necessity_definition_causal_Bayesian_networks_Pearl.jsonl b/data/sampled_jsons/probability_of_necessity_definition_causal_Bayesian_networks_Pearl.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..9193bb992e55439fb3d4e820979bf7c72750b2d7
--- /dev/null
+++ b/data/sampled_jsons/probability_of_necessity_definition_causal_Bayesian_networks_Pearl.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "CAUSALITY by Judea Pearl - University of California, Los Angeles", "date": "", "ddg_snippet": "1.2.2 Bayesian Networks 1.2.3 The d-separation criterion 1.2.4 Inference with Bayesian networks 1.3 Causal Bayesian Networks 1.3.1 Causal networks as oracles for interventions 1.3.2 Causal relationships and their stability 1.4 Functional Causal Models 1.4.1 Structural Equations 1.4.2 Probabilistic predictions in causal models", "subpage_snippet": "", "source": "bayes.cs.ucla.edu", "link": "https://bayes.cs.ucla.edu/BOOK-99/book-toc.html", "content": "1.2.2 Bayesian Networks 1.2.3 The d-separation criterion 1.2.4 Inference with Bayesian networks 1.3 Causal Bayesian Networks 1.3.1 Causal networks as oracles for interventions 1.3.2 Causal relationships and their stability 1.4 Functional Causal Models 1.4.1 Structural Equations 1.4.2 Probabilistic predictions in causal models"}
+{"idx": 1, "title": "On the probability of necessity and sufficiency of explaining ...", "date": "", "ddg_snippet": "Apr 1, 2025 · A formal way to quantify the necessity and sufficiency of an explanation is through the use of the Probability of Necessity and Sufficiency (PNS) ( Pearl , 2009).", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0893608024009948", "content": "Apr 1, 2025 · A formal way to quantify the necessity and sufficiency of an explanation is through the use of the Probability of Necessity and Sufficiency (PNS) ( Pearl , 2009)."}
+{"idx": 2, "title": "Identifying and bounding the probability of necessity for ...", "date": "", "ddg_snippet": "We focus on the backward-looking perspective for causal inference. In particular, we focus on or-dinal outcomes, which are common in empirical research. We first propose the general definition of the probability of necessity with ordinal outcomes and illustrate its meaning with examples. With ordinal outcomes, the recent causal inference literature has made some progress from the forward ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2411.01234", "content": "We focus on the backward-looking perspective for causal inference. In particular, we focus on or-dinal outcomes, which are common in empirical research. We first propose the general definition of the probability of necessity with ordinal outcomes and illustrate its meaning with examples. With ordinal outcomes, the recent causal inference literature has made some progress from the forward ..."}
+{"idx": 3, "title": "Bayesian Causality - PMC", "date": "", "ddg_snippet": "Abstract Although no universally accepted definition of causality exists, in practice one is often faced with the question of statistically assessing causal relationships in different settings. We present a uniform general approach to causality problems derived from the axiomatic foundations of the Bayesian statistical framework.", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC7545951/", "content": "Abstract Although no universally accepted definition of causality exists, in practice one is often faced with the question of statistically assessing causal relationships in different settings. We present a uniform general approach to causality problems derived from the axiomatic foundations of the Bayesian statistical framework."}
+{"idx": 4, "title": "Introduction to Probabilities, Graphs, and Causal Models", "date": "", "ddg_snippet": "1.1.1 Why Probabilities? Causality connotes lawlike necessity , whereas probabilities connote exceptionality, doubt, and lack of regularity.", "subpage_snippet": "", "source": "web.cs.ucla.edu", "link": "http://web.cs.ucla.edu/~kaoru/ch1-final.pdf", "content": "1.1.1 Why Probabilities? Causality connotes lawlike necessity , whereas probabilities connote exceptionality, doubt, and lack of regularity."}
+{"idx": 5, "title": "1On Pearl's Hierarchy and the Foundations of Causal ...", "date": "", "ddg_snippet": "by E Bareinboim · Cited by 9 — In words, if a variable X is fixed to x by intervention, X = x must be observed with probability one. This is a technical condition and reflects the ... 62 pages", "subpage_snippet": "", "source": "causalai.net", "link": "https://causalai.net/r60.pdf", "content": "by E Bareinboim · Cited by 9 — In words, if a variable X is fixed to x by intervention, X = x must be observed with probability one. This is a technical condition and reflects the ... 62 pages"}
+{"idx": 6, "title": "Intervention and Conditioning in Causal Bayesian Networks", "date": "", "ddg_snippet": "23 May 2024 — Pearl \\citeyear pearl :2k gives examples showing that neither the probability of necessity nor the probability of sufficiency in a CBN can be ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.14728v1", "content": "23 May 2024 — Pearl \\citeyear pearl :2k gives examples showing that neither the probability of necessity nor the probability of sufficiency in a CBN can be ..."}
+{"idx": 7, "title": "Bayesian networks for causal analysis in socioecological ...", "date": "", "ddg_snippet": "by R Cabañas · 2025 · Cited by 2 — This can be achieved using the so-called probability of necessity (PN) which can be defined as (2) PN ( X , Y ) = P ( Y x ′ = y ′ | X = x , Y = y ) . X ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S1574954125001827", "content": "by R Cabañas · 2025 · Cited by 2 — This can be achieved using the so-called probability of necessity (PN) which can be defined as (2) PN ( X , Y ) = P ( Y x ′ = y ′ | X = x , Y = y ) . X ..."}
+{"idx": 8, "title": "Intervention and Conditioning in Causal Bayesian Networks", "date": "", "ddg_snippet": "by J Halpern — Pearl (2000) gives examples showing that neither the probability of necessity nor the probability of sufficiency in a CBN can be identified; we can just ...", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper_files/paper/2024/file/a2118322165fffb648d1e341ff5a5b05-Paper-Conference.pdf", "content": "by J Halpern — Pearl (2000) gives examples showing that neither the probability of necessity nor the probability of sufficiency in a CBN can be identified; we can just ..."}
+{"idx": 9, "title": "An ODE to Judea Pearl, Causality and Bayesian Networks", "date": "", "ddg_snippet": "11 Sept 2019 — Pearl then goes on to leverage the rule of inverse probability to design of causal networks – including Chains, Forks, and Colliders – these ...", "subpage_snippet": "", "source": "gagandeep.org", "link": "https://gagandeep.org/2019/09/11/an-ode-to-judea-pearl-causality-and-bayesian-networks/", "content": "11 Sept 2019 — Pearl then goes on to leverage the rule of inverse probability to design of causal networks – including Chains, Forks, and Colliders – these ..."}
diff --git a/data/sampled_jsons/protein_inverse_folding_model_side-chain_flexibility_limitation_computational_method.jsonl b/data/sampled_jsons/protein_inverse_folding_model_side-chain_flexibility_limitation_computational_method.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4f63fd2de1602b0b4dd3965338ccf0038983b348
--- /dev/null
+++ b/data/sampled_jsons/protein_inverse_folding_model_side-chain_flexibility_limitation_computational_method.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Protein folding - Wikipedia", "date": "", "ddg_snippet": "Protein before and after folding . Results of protein folding . Protein folding is the physical process by which a protein , after synthesis by a ribosome as a linear chain of amino acids, changes from an unstable random coil into a more ordered three-d...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Protein_folding", "content": "Protein before and after folding . Results of protein folding . Protein folding is the physical process by which a protein , after synthesis by a ribosome as a linear chain of amino acids, changes from an unstable random coil into a more ordered three-d..."}
+{"idx": 1, "title": "What Is Inverse Folding & How To Practically Apply It", "date": "", "ddg_snippet": "Inverse Folding models can be used to create alternative versions of the above binder that might have more desirable properties than the binder above. Tips & Tricks for Inverse Folding .", "subpage_snippet": "", "source": "neurosnap.ai", "link": "https://neurosnap.ai/blog/post/what-is-inverse-folding-how-to-practically-apply-it/65908e76104e7841a40c3187", "content": "Inverse Folding models can be used to create alternative versions of the above binder that might have more desirable properties than the binder above. Tips & Tricks for Inverse Folding ."}
+{"idx": 2, "title": "An end-to-end deep learning method for protein side - chain packing...", "date": "", "ddg_snippet": "Accurate protein side - chain modeling is crucial for protein folding and protein design. In the past decades, many successful methods have been proposed to address this issue.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/371164369_An_end-to-end_deep_learning_method_for_protein_side-chain_packing_and_inverse_folding", "content": "Accurate protein side - chain modeling is crucial for protein folding and protein design. In the past decades, many successful methods have been proposed to address this issue."}
+{"idx": 3, "title": "Inverse Folding ICML 2022", "date": "", "ddg_snippet": "Existing benchmark for inverse folding on structurally split proteins (Ingraham et al., 2019). Conditioning sequence design on two conformations drives down sequence perplexity at flexible residues compared to using a single conformation.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/media/icml-2022/Slides/16886.pdf", "content": "Existing benchmark for inverse folding on structurally split proteins (Ingraham et al., 2019). Conditioning sequence design on two conformations drives down sequence perplexity at flexible residues compared to using a single conformation."}
+{"idx": 4, "title": "Predicting absolute protein folding stability using generative models", "date": "", "ddg_snippet": "Despite these current limitations , our work shows how the amino acid likelihoods from an ‘ inverse folding model ’ (ESM-IF) can be used to predict, with useful accuracy, the absolute stability for a series of small–medium sized single-domain proteins with equilibrium two-state folding .", "subpage_snippet": "", "source": "www.biorxiv.org", "link": "https://www.biorxiv.org/content/10.1101/2024.03.14.584940v1.full", "content": "Despite these current limitations , our work shows how the amino acid likelihoods from an ‘ inverse folding model ’ (ESM-IF) can be used to predict, with useful accuracy, the absolute stability for a series of small–medium sized single-domain proteins with equilibrium two-state folding ."}
+{"idx": 5, "title": "ProPOSE: Direct Exhaustive Protein – Protein Docking with Side ...", "date": "", "ddg_snippet": "Despite decades of development, protein – protein docking remains a largely unsolved problem. The main difficulties are the immense space spanned by the translational and rotational degrees of freedo...Currently, a few misplaced side chains can cause docking programs to fail.", "subpage_snippet": "", "source": "pubs.acs.org", "link": "https://pubs.acs.org/doi/full/10.1021/acs.jctc.8b00225", "content": "Despite decades of development, protein – protein docking remains a largely unsolved problem. The main difficulties are the immense space spanned by the translational and rotational degrees of freedo...Currently, a few misplaced side chains can cause docking programs to fail."}
+{"idx": 6, "title": "SurfDesign: Effective Protein Design on Molecular... | OpenReview", "date": "", "ddg_snippet": "For example, side - chain flexibility plays a critical role in binding stability and affinity.Can you analyze how your surface-based inverse - folding model outperforms other models on the interface design of protein complexs? Lack of Computational Efficiency Comparison.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=JCFJFBm5rE", "content": "For example, side - chain flexibility plays a critical role in binding stability and affinity.Can you analyze how your surface-based inverse - folding model outperforms other models on the interface design of protein complexs? Lack of Computational Efficiency Comparison."}
+{"idx": 7, "title": "New AI-powered method accelerates protein simulations and reveals...", "date": "", "ddg_snippet": "Such predictions were extremely difficult in the past due to the flexibility of these proteins . The model is also able to estimate the relative folding free energies of protein mutants, which previous simulation methods could not achieve due to computational limitations .", "subpage_snippet": "", "source": "phys.org", "link": "https://phys.org/news/2025-07-ai-powered-method-protein-simulations.html", "content": "Such predictions were extremely difficult in the past due to the flexibility of these proteins . The model is also able to estimate the relative folding free energies of protein mutants, which previous simulation methods could not achieve due to computational limitations ."}
+{"idx": 8, "title": "Computational methods in drug discovery", "date": "", "ddg_snippet": "It can model side chain flexibility of the target molecule. Computational methods such as pharmacokinetic modeling and predicting drug–drug interactions using large DDI interaction databases are successful and are both cost and time saving as well [287,288].", "subpage_snippet": "", "source": "www.beilstein-journals.org", "link": "https://www.beilstein-journals.org/bjoc/content/pdf/1860-5397-12-267.pdf", "content": "It can model side chain flexibility of the target molecule. Computational methods such as pharmacokinetic modeling and predicting drug–drug interactions using large DDI interaction databases are successful and are both cost and time saving as well [287,288]."}
+{"idx": 9, "title": "Applying computational protein design to therapeutic antibody...", "date": "", "ddg_snippet": "Protein -generic inverse folding methods generally form the foundation for antibody-specific design approaches, with ProteinMPNN and ESM-IF being two prominent examples.", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12137305/", "content": "Protein -generic inverse folding methods generally form the foundation for antibody-specific design approaches, with ProteinMPNN and ESM-IF being two prominent examples."}
diff --git a/data/sampled_jsons/qcnePVejeV_anomalous_circuit_detection_malicious_nodes_Tor.jsonl b/data/sampled_jsons/qcnePVejeV_anomalous_circuit_detection_malicious_nodes_Tor.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..91873b592e2224e2371353438539ddaacb9be581
--- /dev/null
+++ b/data/sampled_jsons/qcnePVejeV_anomalous_circuit_detection_malicious_nodes_Tor.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Do Not Trust What They Tell: Exposing Malicious Accomplices in Tor ...", "date": "", "ddg_snippet": "Our goal is to detect anomalous circuits with Entry-Exit node pairs chosen by users that may have explicitly or implicitly violated Tor ’s circuit construction guidelines.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=qcnePVejeV", "content": "Our goal is to detect anomalous circuits with Entry-Exit node pairs chosen by users that may have explicitly or implicitly violated Tor ’s circuit construction guidelines."}
+{"idx": 1, "title": "Warning: Over 100 Tor Nodes Found Designed to Spy On Deep Web...", "date": "", "ddg_snippet": "Over 100 Malicious Tor Nodes Snooping Dark Web Users.New Tor Design to Strengthen Tor Hidden Services. The researchers say Tor Project is aware of the HSDir issue and is working to identify and remove malicious HSDirs from the network.", "subpage_snippet": "", "source": "thehackernews.com", "link": "https://thehackernews.com/2016/07/tor-deep-web-spying.html", "content": "Over 100 Malicious Tor Nodes Snooping Dark Web Users.New Tor Design to Strengthen Tor Hidden Services. The researchers say Tor Project is aware of the HSDir issue and is working to identify and remove malicious HSDirs from the network."}
+{"idx": 2, "title": "GitHub - ericyoc/quantum- circuit - anomaly - detection -poc: A quantum...", "date": "", "ddg_snippet": "Quantum Circuit Anomaly Detection Against Adversarial Attacks. This Python code demonstrates a quantum anomaly detection approach using the Cirq and PennyLane libraries, designed to detect adversarial attacks on quantum circuits .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/ericyoc/quantum-circuit-anomaly-detection-poc", "content": "Quantum Circuit Anomaly Detection Against Adversarial Attacks. This Python code demonstrates a quantum anomaly detection approach using the Cirq and PennyLane libraries, designed to detect adversarial attacks on quantum circuits ."}
+{"idx": 3, "title": "Energy evaluation of dependent malicious nodes detection in...", "date": "", "ddg_snippet": "Detection of malicious nodes in the internet of things (IoT) network consumes power, which is one of the main constraints of the IoT network performance. To evaluate the energy-security trade-off for malicious node detection , this paper proposes an Arduino-based system for dependent...", "subpage_snippet": "", "source": "ijece.iaescore.com", "link": "https://ijece.iaescore.com/index.php/IJECE/article/view/38682", "content": "Detection of malicious nodes in the internet of things (IoT) network consumes power, which is one of the main constraints of the IoT network performance. To evaluate the energy-security trade-off for malicious node detection , this paper proposes an Arduino-based system for dependent..."}
+{"idx": 4, "title": "(PDF) Low-resource routing attacks against TOR", "date": "", "ddg_snippet": "detecting selectively malicious nodes . Malicious Tor nodes , characterized by frequent appearance and disappearance, challenge traditional reputation-based approaches, which often require time to stabilize scores and fail to capture localized fluctuations effectively.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/221342248_Low-resource_routing_attacks_against_TOR", "content": "detecting selectively malicious nodes . Malicious Tor nodes , characterized by frequent appearance and disappearance, challenge traditional reputation-based approaches, which often require time to stabilize scores and fail to capture localized fluctuations effectively."}
+{"idx": 5, "title": "Over 100 suspicious, snooping Tor nodes discovered | ZDNET", "date": "", "ddg_snippet": "Home Tech Security. Over 100 suspicious, snooping Tor nodes discovered. Tor nodes are acting oddly -- and it may be a sign that they are snooping on services they host.", "subpage_snippet": "", "source": "www.zdnet.com", "link": "https://www.zdnet.com/article/over-100-spying-malicious-tor-nodes-discovered/", "content": "Home Tech Security. Over 100 suspicious, snooping Tor nodes discovered. Tor nodes are acting oddly -- and it may be a sign that they are snooping on services they host."}
+{"idx": 6, "title": "The German and Netherlands Tor Nodes Problem – Jason's Blog", "date": "", "ddg_snippet": "And you’d have to assume that nation states are behind a lot of malicious Tor nodes .By tweaking your Tor torrc configuration, you can make sure that your tor circuits do not go through either Germany or Netherlands, by doing the following steps", "subpage_snippet": "", "source": "jasonsblog.ddns.net", "link": "https://jasonsblog.ddns.net/index.php/2025/07/12/the-german-and-netherlands-tor-nodes-problem/", "content": "And you’d have to assume that nation states are behind a lot of malicious Tor nodes .By tweaking your Tor torrc configuration, you can make sure that your tor circuits do not go through either Germany or Netherlands, by doing the following steps"}
+{"idx": 7, "title": "Researchers Discover Tor Nodes Designed to... - Schneier on Security", "date": "", "ddg_snippet": "Two researchers have discovered over 100 Tor nodes that are spying on hidden services.", "subpage_snippet": "", "source": "www.schneier.com", "link": "https://www.schneier.com/blog/archives/2016/07/researchers_dis.html", "content": "Two researchers have discovered over 100 Tor nodes that are spying on hidden services."}
+{"idx": 8, "title": "Tor Bridges - Triplebit", "date": "", "ddg_snippet": "Triplebit operates high performance, unfiltered, and high capacity bridges on the Tor network.Please read the Tor Project's bridge documentation for information on how to set up manual bridges in Tor Browser.", "subpage_snippet": "", "source": "www.triplebit.org", "link": "https://www.triplebit.org/bridges/", "content": "Triplebit operates high performance, unfiltered, and high capacity bridges on the Tor network.Please read the Tor Project's bridge documentation for information on how to set up manual bridges in Tor Browser."}
+{"idx": 9, "title": "(PDF) Intrusion Detection System for Detecting Malicious Nodes in...", "date": "", "ddg_snippet": "Hence, a node can misbehave and fail to establish route or route the data due to its malicious activity to decrease the performance of ad hoc network. In this paper, we propose an intrusion detection system to detect the malicious nodes in MANETs.", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/78135591/Intrusion_Detection_System_for_Detecting_Malicious_Nodes_in_Mobile_Ad_Hoc_Networks", "content": "Hence, a node can misbehave and fail to establish route or route the data due to its malicious activity to decrease the performance of ad hoc network. In this paper, we propose an intrusion detection system to detect the malicious nodes in MANETs."}
diff --git a/data/sampled_jsons/quantile_risk_measures_alpha_confidence_level_higher_alpha_less_conservative.jsonl b/data/sampled_jsons/quantile_risk_measures_alpha_confidence_level_higher_alpha_less_conservative.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..1f7a93daea9b702e08442b80adb5eb4a8f6916fe
--- /dev/null
+++ b/data/sampled_jsons/quantile_risk_measures_alpha_confidence_level_higher_alpha_less_conservative.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "QuEst: Enhancing Estimates of Quantile-Based Distributional", "date": "", "ddg_snippet": "... quantile -related metrics, including tail measures like Conditional Value at Risk (CVaR) (Rockafellar and Uryasev, 2002 ) and population- level segments ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.05220v1", "content": "... quantile -related metrics, including tail measures like Conditional Value at Risk (CVaR) (Rockafellar and Uryasev, 2002 ) and population- level segments ..."}
+{"idx": 1, "title": "Differentially Private Conformal Prediction via Quantile Binary", "date": "", "ddg_snippet": "... to contain the true response with a user-specified probability 1 − α 1 𝛼 1-\\ alpha 1 - italic_α , where α ∈ ( 0 , 1 ) 𝛼 0 1 \\ alpha \\in(0,1 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.12497v1", "content": "... to contain the true response with a user-specified probability 1 − α 1 𝛼 1-\\ alpha 1 - italic_α , where α ∈ ( 0 , 1 ) 𝛼 0 1 \\ alpha \\in(0,1 ..."}
+{"idx": 2, "title": "Quantile-Optimal Policy Learning under Unmeasured Confounding", "date": "", "ddg_snippet": "... quantile -optimal policy learning where the goal is to find a policy whose reward distribution has the largest α 𝛼 \\ alpha italic_α - quantile for ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.07140v1", "content": "... quantile -optimal policy learning where the goal is to find a policy whose reward distribution has the largest α 𝛼 \\ alpha italic_α - quantile for ..."}
+{"idx": 3, "title": "Optimistic Exploration for Risk-Averse Constrained", "date": "", "ddg_snippet": "In this paper, we observe that this conservatism can prevent policies from finding the correct risk -averse solution in a risky Gridworld environment ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.08793v1", "content": "In this paper, we observe that this conservatism can prevent policies from finding the correct risk -averse solution in a risky Gridworld environment ..."}
+{"idx": 4, "title": "Value-at-Risk, Tail Value-at-Risk and upper tail transform of", "date": "", "ddg_snippet": "The Value-at- Risk (VaR) of comonotonic sums can be decomposed into marginal VaR’s at the same level . ... Risk (TVaR) and the upper tail transform of ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.13422v1", "content": "The Value-at- Risk (VaR) of comonotonic sums can be decomposed into marginal VaR’s at the same level . ... Risk (TVaR) and the upper tail transform of ..."}
+{"idx": 5, "title": "Personality and pay: do gender gaps in confidence explain", "date": "", "ddg_snippet": "C32 - Time-Series Models; Dynamic Quantile ... E01 - Measurement and Data on National Income and Product Accounts and Wealth; Environmental Accounts", "subpage_snippet": "", "source": "academic.oup.com", "link": "https://academic.oup.com/oep/article/70/4/919/5046671", "content": "C32 - Time-Series Models; Dynamic Quantile ... E01 - Measurement and Data on National Income and Product Accounts and Wealth; Environmental Accounts"}
+{"idx": 6, "title": "inquiry into the relationship between intelligence and", "date": "", "ddg_snippet": "C32 - Time-Series Models; Dynamic Quantile ... E01 - Measurement and Data on National Income and Product Accounts and Wealth; Environmental Accounts", "subpage_snippet": "", "source": "academic.oup.com", "link": "https://academic.oup.com/ej/article/135/668/1141/7914156", "content": "C32 - Time-Series Models; Dynamic Quantile ... E01 - Measurement and Data on National Income and Product Accounts and Wealth; Environmental Accounts"}
+{"idx": 7, "title": "Downloads", "date": "", "ddg_snippet": "alpha $-IoU: A Family of Power Intersection over Union Losses for Bounding Box Regression ... for k-Nearest Neighbor Classifiers Based on Higher -Order ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/Downloads/2021", "content": "alpha $-IoU: A Family of Power Intersection over Union Losses for Bounding Box Regression ... for k-Nearest Neighbor Classifiers Based on Higher -Order ..."}
+{"idx": 8, "title": "Estimating Effects After Weighting • WeightIt", "date": "", "ddg_snippet": "The main outcome types we consider here are continuous, with the effect measured by the mean difference; binary, with the effect measured by the risk ...", "subpage_snippet": "", "source": "ngreifer.github.io", "link": "https://ngreifer.github.io/WeightIt/articles/estimating-effects.html", "content": "The main outcome types we consider here are continuous, with the effect measured by the mean difference; binary, with the effect measured by the risk ..."}
+{"idx": 9, "title": "Statistical Power and Why It Matters | A Simple Introduction", "date": "", "ddg_snippet": "The higher the statistical power of a test, the lower the risk of making a Type II error. ... should use a power analysis to set an appropriate level ...", "subpage_snippet": "", "source": "www.scribbr.com", "link": "https://www.scribbr.com/statistics/statistical-power/", "content": "The higher the statistical power of a test, the lower the risk of making a Type II error. ... should use a power analysis to set an appropriate level ..."}
diff --git a/data/sampled_jsons/rEarthStrike_rFridaysForFuture_rExtinctionRebellion_climate_activism_Reddit_study_92%.jsonl b/data/sampled_jsons/rEarthStrike_rFridaysForFuture_rExtinctionRebellion_climate_activism_Reddit_study_92%.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..311be38f49642823a63955390a327262949f75b3
--- /dev/null
+++ b/data/sampled_jsons/rEarthStrike_rFridaysForFuture_rExtinctionRebellion_climate_activism_Reddit_study_92%.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Earth Strike - Wikipedia", "date": "", "ddg_snippet": "Earth Strike was founded on 10 November 2018 after a user on the subreddit r/Chomsky called for a \"General Strike to Save The Planet\". [citation needed] The post quickly gathered attention within Reddit , and the r/EarthStrike subreddit was formed to organise a general strike. [citation needed] The initial protests were held on 15 January 2019, with 27 September being announced as the date for ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Earth_Strike", "content": "Earth Strike was founded on 10 November 2018 after a user on the subreddit r/Chomsky called for a \"General Strike to Save The Planet\". [citation needed] The post quickly gathered attention within Reddit , and the r/EarthStrike subreddit was formed to organise a general strike. [citation needed] The initial protests were held on 15 January 2019, with 27 September being announced as the date for ..."}
+{"idx": 1, "title": "Earth Strike - Reddit", "date": "", "ddg_snippet": "r/EarthStrike : Earth Strike is a grassroots labour-environmental movement focused on organising a GLOBAL GENERAL STRIKE TO SAVE THE PLANET!", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/EarthStrike/", "content": "r/EarthStrike : Earth Strike is a grassroots labour-environmental movement focused on organising a GLOBAL GENERAL STRIKE TO SAVE THE PLANET!"}
+{"idx": 2, "title": "Fridays For Future is an international climate movement active in most ...", "date": "", "ddg_snippet": "#FridaysForFuture is a youth-led and -organised global climate strike movement that started in August 2018, when [then] 15-year-old Greta Thunberg began a school strike for climate . To begin with, she was alone, but she was soon joined by others...", "subpage_snippet": "", "source": "fridaysforfuture.org", "link": "https://fridaysforfuture.org/", "content": "#FridaysForFuture is a youth-led and -organised global climate strike movement that started in August 2018, when [then] 15-year-old Greta Thunberg began a school strike for climate . To begin with, she was alone, but she was soon joined by others..."}
+{"idx": 3, "title": "fridaysForFuture : r/EarthStrike - Reddit", "date": "", "ddg_snippet": "1.2K votes, 31 comments. 22K subscribers in the EarthStrike community. Earth Strike is a grassroots labour-environmental movement focused on…", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/EarthStrike/comments/b9bplk/fridaysforfuture/", "content": "1.2K votes, 31 comments. 22K subscribers in the EarthStrike community. Earth Strike is a grassroots labour-environmental movement focused on…"}
+{"idx": 4, "title": "r/FridaysForFuture - Reddit", "date": "", "ddg_snippet": "A sub dedicated to the international movement of students who skip class on Fridays to demand action to prevent climate change.", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/FridaysForFuture/", "content": "A sub dedicated to the international movement of students who skip class on Fridays to demand action to prevent climate change."}
+{"idx": 5, "title": "r/EarthStrike on Reddit: We need your support - Open letter by # ...", "date": "", "ddg_snippet": "I think climate activism needs to become synonymus with anti-war activism , especially when that war is threatening democratic and ecological movements like this. War on its own is a huge contributor to climate change, not just through the direct emission it creates, which is extremely significant, but also in the effects it has on destabalising regions; ensuring that any ecological goals that ...", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/EarthStrike/comments/dhd3uv/we_need_your_support_open_letter_by/", "content": "I think climate activism needs to become synonymus with anti-war activism , especially when that war is threatening democratic and ecological movements like this. War on its own is a huge contributor to climate change, not just through the direct emission it creates, which is extremely significant, but also in the effects it has on destabalising regions; ensuring that any ecological goals that ..."}
+{"idx": 6, "title": "Statement in solidarity with Rojava : r/EarthStrike - Reddit", "date": "", "ddg_snippet": "260 votes, 22 comments. 22K subscribers in the EarthStrike community. Earth Strike is a grassroots labour-environmental movement focused on…", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/EarthStrike/comments/dj6q67/fridaysforfuture_statement_in_solidarity_with/", "content": "260 votes, 22 comments. 22K subscribers in the EarthStrike community. Earth Strike is a grassroots labour-environmental movement focused on…"}
+{"idx": 7, "title": "r/EarthStrike on Reddit: Fridays For Future - March 15th - Event Info ...", "date": "", "ddg_snippet": "22K subscribers in the EarthStrike community. Earth Strike is a grassroots labour-environmental movement focused on organising a GLOBAL GENERAL…", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/EarthStrike/comments/axpg4v/fridays_for_future_march_15th_event_info_here_not/", "content": "22K subscribers in the EarthStrike community. Earth Strike is a grassroots labour-environmental movement focused on organising a GLOBAL GENERAL…"}
+{"idx": 8, "title": "Causal Modeling of Climate Activism on Reddit", "date": "", "ddg_snippet": "14 Oct 2024 — The three main ones are Extinction Rebellion, Earth Strike, and Fridays For Future , which account for more than 92% of the activated users we ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.10562v1", "content": "14 Oct 2024 — The three main ones are Extinction Rebellion, Earth Strike, and Fridays For Future , which account for more than 92% of the activated users we ..."}
+{"idx": 9, "title": "Causal Modeling of Climate Activism on Reddit", "date": "", "ddg_snippet": "by J Lenti · Cited by 9 — are E xtinction Rebellion, Earth Strike, and Fridays For Future, which account for more than 92% of the activated users we observe. We say that an activation ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=6yBhoJn6qy", "content": "by J Lenti · Cited by 9 — are E xtinction Rebellion, Earth Strike, and Fridays For Future, which account for more than 92% of the activated users we observe. We say that an activation ..."}
diff --git a/data/sampled_jsons/rStar-Math_Small_LLMs_Can_Master_Math_Reasoning_with_Self-Evolved_Deep_Thinking_ICML_2025_abstract.jsonl b/data/sampled_jsons/rStar-Math_Small_LLMs_Can_Master_Math_Reasoning_with_Self-Evolved_Deep_Thinking_ICML_2025_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6801a9179a7ae3c5b97fc61ef54b11e21884a4aa
--- /dev/null
+++ b/data/sampled_jsons/rStar-Math_Small_LLMs_Can_Master_Math_Reasoning_with_Self-Evolved_Deep_Thinking_ICML_2025_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Small LLMs Can Master Math Reasoning with Self-Evolved ...", "date": "", "ddg_snippet": "We present rStar - Math to demonstrate that small language models (SLMs) can rival or even surpass the math reasoning capability of OpenAI o1, without ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46400", "content": "We present rStar - Math to demonstrate that small language models (SLMs) can rival or even surpass the math reasoning capability of OpenAI o1, without ..."}
+{"idx": 1, "title": "Small LLMs Can Master Math Reasoning with Self-Evolved ...", "date": "", "ddg_snippet": "by X Guan · Cited by 157 — Abstract: We present rStar-Math to demonstrate that small language models (SLMs) can rival or even surpass the math reasoning capability of OpenAI o1, without ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=5zwF1GizFa", "content": "by X Guan · Cited by 157 — Abstract: We present rStar-Math to demonstrate that small language models (SLMs) can rival or even surpass the math reasoning capability of OpenAI o1, without ..."}
+{"idx": 2, "title": "Small LLMs Can Master Math Reasoning with Self-Evolved ...", "date": "", "ddg_snippet": "rStar-Math: Small LLMs Can Master Math Reasoning with Self-Evolved Deep Thinking ... rStar-Math achieves this by exercising ``deep thinking'' through Monte ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/oral/47258", "content": "rStar-Math: Small LLMs Can Master Math Reasoning with Self-Evolved Deep Thinking ... rStar-Math achieves this by exercising ``deep thinking'' through Monte ..."}
+{"idx": 3, "title": "arXiv:2502.06773v1 [cs.AI] 10 Feb 2025", "date": "", "ddg_snippet": "by G Ye · 2025 · Cited by 12 — RLSP offers a smoother and more efficient framework to equip LLMs with sophisticated search behaviors that can lead to improved reasoning .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.06773", "content": "by G Ye · 2025 · Cited by 12 — RLSP offers a smoother and more efficient framework to equip LLMs with sophisticated search behaviors that can lead to improved reasoning ."}
+{"idx": 4, "title": "ICML 2025 - Microsoft Research", "date": "", "ddg_snippet": "13 Jul 2025 — rStar-Math: Small LLMs Can Master Math Reasoning with Self-Evolved Deep Thinking Li Lyna Zhang, Ning Shang, Yi Zhu, Fan Yang, Mao Yang.", "subpage_snippet": "", "source": "www.microsoft.com", "link": "https://www.microsoft.com/en-us/research/event/icml-2025/", "content": "13 Jul 2025 — rStar-Math: Small LLMs Can Master Math Reasoning with Self-Evolved Deep Thinking Li Lyna Zhang, Ning Shang, Yi Zhu, Fan Yang, Mao Yang."}
+{"idx": 5, "title": "ICML 2025 Orals", "date": "", "ddg_snippet": "rStar-Math: Small LLMs Can Master Math Reasoning with Self-Evolved Deep Thinking ... rStar-Math achieves this by exercising ``deep thinking'' through Monte ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/events/oral", "content": "rStar-Math: Small LLMs Can Master Math Reasoning with Self-Evolved Deep Thinking ... rStar-Math achieves this by exercising ``deep thinking'' through Monte ..."}
+{"idx": 6, "title": "Track: Oral 3A Reasoning", "date": "", "ddg_snippet": "We present rStar-Math to demonstrate that small language models (SLMs) can rival or even surpass the math reasoning capability of OpenAI o1, without ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/session/46904", "content": "We present rStar-Math to demonstrate that small language models (SLMs) can rival or even surpass the math reasoning capability of OpenAI o1, without ..."}
+{"idx": 7, "title": "arXiv:2503.12854v2 [cs.CL] 28 Mar 2025", "date": "", "ddg_snippet": "by S Tu · 2025 — In this study, we investigate the effectiveness of DPO in facilitating self - improvement for LLMs through iterative preference-based learning.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.12854", "content": "by S Tu · 2025 — In this study, we investigate the effectiveness of DPO in facilitating self - improvement for LLMs through iterative preference-based learning."}
+{"idx": 8, "title": "CAN 1B LLM SURPASS 405B LLM? RETHINKING", "date": "", "ddg_snippet": "by R Liu · Cited by 61 — We evaluate the following methods: (1) rStar - Math (Guan et al., 2025 ): This method first generates reasoning data via MCTS, followed by online policy and ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=CvjX9Lhpze", "content": "by R Liu · Cited by 61 — We evaluate the following methods: (1) rStar - Math (Guan et al., 2025 ): This method first generates reasoning data via MCTS, followed by online policy and ..."}
+{"idx": 9, "title": "rStar - Math : Small LLMs Can Master Math Reasoning with ...", "date": "", "ddg_snippet": "A self -play mutual reasoning approach that significantly improves reasoning capabilities of small language models (SLMs) without fine-tuning or superior models. rStar decouples reasoning into a self -play mutual generation-discrimination process.", "subpage_snippet": "", "source": "www.microsoft.com", "link": "https://www.microsoft.com/en-us/research/publication/rstar-math-small-llms-can-master-math-reasoning-with-self-evolved-deep-thinking/", "content": "A self -play mutual reasoning approach that significantly improves reasoning capabilities of small language models (SLMs) without fine-tuning or superior models. rStar decouples reasoning into a self -play mutual generation-discrimination process."}
diff --git a/data/sampled_jsons/r_coherence(y)_formula_Soft_Reasoning_log_probabilities.jsonl b/data/sampled_jsons/r_coherence(y)_formula_Soft_Reasoning_log_probabilities.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5b5ba51f68b5a3f6a6a4d67787796144b81e07fa
--- /dev/null
+++ b/data/sampled_jsons/r_coherence(y)_formula_Soft_Reasoning_log_probabilities.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A Survey of Reasoning Large Language Models", "date": "", "ddg_snippet": "by ZZ Li · 2025 · Cited by 142 — Soft reasoning benchmarks, lacking explicitly defined correct ... This in- cludes computing log probabilities from the reference policy ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.17419?", "content": "by ZZ Li · 2025 · Cited by 142 — Soft reasoning benchmarks, lacking explicitly defined correct ... This in- cludes computing log probabilities from the reference policy ..."}
+{"idx": 1, "title": "Soft Reasoning: Navigating Solution Spaces in Large Language ...", "date": "", "ddg_snippet": "View recent discussion. Abstract: Large Language Models (LLMs) struggle with complex reasoning due to limited diversity and inefficient search. We propose Soft Reasoning , an embedding-based search framework that optimises the embedding of the first token to guide generation. It combines (1) embedding perturbation for controlled exploration and (2) Bayesian optimisation to refine embeddings via ...", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2505.24688v2", "content": "View recent discussion. Abstract: Large Language Models (LLMs) struggle with complex reasoning due to limited diversity and inefficient search. We propose Soft Reasoning , an embedding-based search framework that optimises the embedding of the first token to guide generation. It combines (1) embedding perturbation for controlled exploration and (2) Bayesian optimisation to refine embeddings via ..."}
+{"idx": 2, "title": "From System 1 to System 2: A Survey of Reasoning Large ...", "date": "", "ddg_snippet": "Feb 24, 2025 · This includes computing log probabilities from the reference policy to quantify behavioral consistency, estimating value functions via the critic model to assess state desirability, and calculating advantage estimates to prioritize high-impact learning signals.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.17419v6", "content": "Feb 24, 2025 · This includes computing log probabilities from the reference policy to quantify behavioral consistency, estimating value functions via the critic model to assess state desirability, and calculating advantage estimates to prioritize high-impact learning signals."}
+{"idx": 3, "title": "Improving Multilingual Capabilities with Cultural and Local Knowledge...", "date": "", "ddg_snippet": "After filtering the training data, we had around 485K samples, of which 20% are of localized domain and cultural knowledge, while the rest are of generic tasks like math, MCQs, reasoning , summarization, rephrasing, and translation.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.09753v3", "content": "After filtering the training data, we had around 485K samples, of which 20% are of localized domain and cultural knowledge, while the rest are of generic tasks like math, MCQs, reasoning , summarization, rephrasing, and translation."}
+{"idx": 4, "title": "Benchmarks 201: Why Leaderboards > Arenas >> LLM-as-Judge", "date": "", "ddg_snippet": "MuSR (Multistep Soft Reasoning , paper). MATH (Mathematics Aptitude Test of Heuristics, Level 5 subset, paper). [00:44:15] Long context reasoning benchmarks. [00:46:34] Agent benchmarks, GAIA, and the ARC AGI challenge.", "subpage_snippet": "", "source": "www.latent.space", "link": "https://www.latent.space/p/benchmarks-201", "content": "MuSR (Multistep Soft Reasoning , paper). MATH (Mathematics Aptitude Test of Heuristics, Level 5 subset, paper). [00:44:15] Long context reasoning benchmarks. [00:46:34] Agent benchmarks, GAIA, and the ARC AGI challenge."}
+{"idx": 5, "title": "Published as a conference paper at ICLR 2025", "date": "", "ddg_snippet": "The average log -likelihood of the response under the policy model πθ is defined as followsZayne Sprague, Xi Ye, Kaj Bostrom, Swarat Chaudhuri, and Greg Durrett. Musr: Testing the limits of chain-of-thought with multistep soft reasoning .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=jfwe9qNqRi", "content": "The average log -likelihood of the response under the policy model πθ is defined as followsZayne Sprague, Xi Ye, Kaj Bostrom, Swarat Chaudhuri, and Greg Durrett. Musr: Testing the limits of chain-of-thought with multistep soft reasoning ."}
+{"idx": 6, "title": "huggingface.co/datasets/ICLR2024/ICLR2024-papers/raw/03dcde377...", "date": "", "ddg_snippet": "We formulate four game splits to scrutinize agents' learning and generalization of essential principles of interactive physical reasoning , fostering learning through interaction with representative scenarios.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/ICLR2024/ICLR2024-papers/raw/03dcde377c26f2ee0e62753f684aab35cda07e4e/data.json", "content": "We formulate four game splits to scrutinize agents' learning and generalization of essential principles of interactive physical reasoning , fostering learning through interaction with representative scenarios."}
+{"idx": 7, "title": "Are LLM Belief Updates Consistent with Bayes' Theorem?", "date": "", "ddg_snippet": "by S Imran · 2025 — MuSR: Testing the Limits of Chain-of-thought with. Multistep Soft Reasoning , March 2024. ... the prior and posterior log probabilities averaged ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2507.17951", "content": "by S Imran · 2025 — MuSR: Testing the Limits of Chain-of-thought with. Multistep Soft Reasoning , March 2024. ... the prior and posterior log probabilities averaged ..."}
+{"idx": 8, "title": "GRANITE 3.0 LANGUAGE MODELS - Robauto.ai", "date": "", "ddg_snippet": "by IBM Granite Team · Cited by 4 — pairs and 3) ratios of log-probabilities from two related models, as a contrastive reward signal. In the following, we discuss each of these in some detail ...", "subpage_snippet": "", "source": "robauto.ai", "link": "https://robauto.ai/wp-content/uploads/2024/11/paper.pdf", "content": "by IBM Granite Team · Cited by 4 — pairs and 3) ratios of log-probabilities from two related models, as a contrastive reward signal. In the following, we discuss each of these in some detail ..."}
+{"idx": 9, "title": "PMPO: Probabilistic Metric Prompt Optimization for Small and ...", "date": "", "ddg_snippet": "The language model M generates outputs with probabilities PM( y | x, P) conditioned on input x and instruction P. Our objective is to derive an optimal prompt P∗ that maximizes the expected weighted log-probability, that is, E(x, y , r )∼D [ r · log PM( y | x, P)], where r can represent either a binary label or a scalar pref-erence score.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2505.16307v2", "content": "The language model M generates outputs with probabilities PM( y | x, P) conditioned on input x and instruction P. Our objective is to derive an optimal prompt P∗ that maximizes the expected weighted log-probability, that is, E(x, y , r )∼D [ r · log PM( y | x, P)], where r can represent either a binary label or a scalar pref-erence score."}
diff --git a/data/sampled_jsons/reinforcement_learning_trajectory_replanning_robotics_2023.jsonl b/data/sampled_jsons/reinforcement_learning_trajectory_replanning_robotics_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..24a70ffb90aec0a6a12e1f641484638427b38d57
--- /dev/null
+++ b/data/sampled_jsons/reinforcement_learning_trajectory_replanning_robotics_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Reinforcement - Wikipedia", "date": "", "ddg_snippet": "Reinforcement is an important component of operant conditioning and behavior modification. The concept has been applied in a variety of practical areas, including parenting, coaching, therapy, self-help, education, and management.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Reinforcement", "content": "Reinforcement is an important component of operant conditioning and behavior modification. The concept has been applied in a variety of practical areas, including parenting, coaching, therapy, self-help, education, and management."}
+{"idx": 1, "title": "REINFORCEMENT Definition & Meaning - Merriam-Webster", "date": "", "ddg_snippet": "The meaning of REINFORCEMENT is the action of strengthening or encouraging something : the state of being reinforced. How to use reinforcement in a sentence.", "subpage_snippet": "", "source": "www.merriam-webster.com", "link": "https://www.merriam-webster.com/dictionary/reinforcement", "content": "The meaning of REINFORCEMENT is the action of strengthening or encouraging something : the state of being reinforced. How to use reinforcement in a sentence."}
+{"idx": 2, "title": "Types of Reinforcement in Psychology: Definition and Examples", "date": "", "ddg_snippet": "May 7, 2023 · Reinforcement strengthens behavior. Learn more about the reinforcement definition in psychology, along with examples and how it works to modify behavior.", "subpage_snippet": "", "source": "www.explorepsychology.com", "link": "https://www.explorepsychology.com/reinforcement-definition/", "content": "May 7, 2023 · Reinforcement strengthens behavior. Learn more about the reinforcement definition in psychology, along with examples and how it works to modify behavior."}
+{"idx": 3, "title": "REINFORCEMENT | English meaning - Cambridge Dictionary", "date": "", "ddg_snippet": "REINFORCEMENT definition: 1. the act of making something stronger: 2. soldiers sent to join an army to make it stronger: 3…. Learn more.", "subpage_snippet": "", "source": "dictionary.cambridge.org", "link": "https://dictionary.cambridge.org/dictionary/english/reinforcement", "content": "REINFORCEMENT definition: 1. the act of making something stronger: 2. soldiers sent to join an army to make it stronger: 3…. Learn more."}
+{"idx": 4, "title": "What Is Reinforcement in Operant Conditioning? - Verywell Mind", "date": "", "ddg_snippet": "Apr 4, 2023 · Reinforcement is an important concept in operant conditioning and the learning process. Learn how it's used and see conditioned reinforcer examples in everyday life.", "subpage_snippet": "", "source": "www.verywellmind.com", "link": "https://www.verywellmind.com/what-is-reinforcement-2795414", "content": "Apr 4, 2023 · Reinforcement is an important concept in operant conditioning and the learning process. Learn how it's used and see conditioned reinforcer examples in everyday life."}
+{"idx": 5, "title": "What Is Reinforcement? Psychology, Definition, And ...", "date": "", "ddg_snippet": "Jul 10, 2024 · Reinforcement psychology involves the use of providing something or taking it away to achieve a desired behavior. Primary reinforcement occurs naturally, while secondary reinforcement is conditioned.", "subpage_snippet": "", "source": "www.betterhelp.com", "link": "https://www.betterhelp.com/advice/psychologists/what-is-reinforcement-psychology-definition-and-applications/", "content": "Jul 10, 2024 · Reinforcement psychology involves the use of providing something or taking it away to achieve a desired behavior. Primary reinforcement occurs naturally, while secondary reinforcement is conditioned."}
+{"idx": 6, "title": "Positive vs. Negative Reinforcement : Definitions & Examples ...", "date": "", "ddg_snippet": "Jun 2, 2025 · Explore positive and negative reinforcement with clear definitions, real-world examples, and insights into how these psychological principles influence behaviour.", "subpage_snippet": "", "source": "therapy-central.com", "link": "https://therapy-central.com/2025/06/02/positive-negative-reinforcement-explained/", "content": "Jun 2, 2025 · Explore positive and negative reinforcement with clear definitions, real-world examples, and insights into how these psychological principles influence behaviour."}
+{"idx": 7, "title": "ARiADNE: A Reinforcement learning approach using Attention-based...", "date": "", "ddg_snippet": "(DOI: 10.1109/ICRA48891. 2023 .10160565) In autonomous robot exploration tasks, a mobile robot needs to actively explore and map an unknown environment as fast as possible.", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/papers/ariadne-a-reinforcement-learning-approach-using-attention-2d7czt3d", "content": "(DOI: 10.1109/ICRA48891. 2023 .10160565) In autonomous robot exploration tasks, a mobile robot needs to actively explore and map an unknown environment as fast as possible."}
+{"idx": 8, "title": "Fast Trajectory Planner with a Reinforcement Learning -based...", "date": "", "ddg_snippet": "Keywords: Reinforcement Learning , Artificial Intelligence Enabled Robotics , Motion Planning , Artificial Intelligence Based Methods, Collision Avoidance.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2509.17381", "content": "Keywords: Reinforcement Learning , Artificial Intelligence Enabled Robotics , Motion Planning , Artificial Intelligence Based Methods, Collision Avoidance."}
+{"idx": 9, "title": "Combining Decision Making and Trajectory Planning for Lane...", "date": "", "ddg_snippet": "Graph Reinforcement Learning -Based Decision-Making Technology for Connected and Autonomous Vehicles: Framework, Review, and Future Trends.", "subpage_snippet": "", "source": "www.connectedpapers.com", "link": "https://www.connectedpapers.com/main/1d35c826c87b7cb9025fc31aae57611ec205dd13/Combining-Decision-Making-and-Trajectory-Planning-for-Lane-Changing-Using-Deep-Reinforcement-Learning/graph", "content": "Graph Reinforcement Learning -Based Decision-Making Technology for Connected and Autonomous Vehicles: Framework, Review, and Future Trends."}
diff --git a/data/sampled_jsons/risk-aware_preference-based_reinforcement_learning_challenges.jsonl b/data/sampled_jsons/risk-aware_preference-based_reinforcement_learning_challenges.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6f1bb08bf281a399793d81675adb1814288ed3b9
--- /dev/null
+++ b/data/sampled_jsons/risk-aware_preference-based_reinforcement_learning_challenges.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "RA-PbRL: Provably Efficient Risk-Aware Preference-Based Reinforcement ...", "date": "", "ddg_snippet": "Reinforcement Learning from Human Feedback (RLHF) has recently surged in popularity, particularly for aligning large language models and other AI systems with human intentions. At its core, RLHF can be viewed as a specialized instance of Preference-based Reinforcement Learning (PbRL), where the preferences specifically originate from human judgments rather than arbitrary evaluators. Despite ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.23569", "content": "Reinforcement Learning from Human Feedback (RLHF) has recently surged in popularity, particularly for aligning large language models and other AI systems with human intentions. At its core, RLHF can be viewed as a specialized instance of Preference-based Reinforcement Learning (PbRL), where the preferences specifically originate from human judgments rather than arbitrary evaluators. Despite ..."}
+{"idx": 1, "title": "PDF RA-PbRL: Provably Eficient Risk-Aware Preference-Based Reinforcement ...", "date": "", "ddg_snippet": "Abstract Reinforcement Learning from Human Feedback (RLHF) has recently surged in popularity, particularly for aligning large language models and other AI sys-tems with human intentions. At its core, RLHF can be viewed as a special-ized instance of Preference-based Reinforcement Learning (PbRL), where the preferences specifically originate from human judgments rather than arbitrary evaluators ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/7016d7b7b6e3c05b2128ac5b3aae492d-Paper-Conference.pdf", "content": "Abstract Reinforcement Learning from Human Feedback (RLHF) has recently surged in popularity, particularly for aligning large language models and other AI sys-tems with human intentions. At its core, RLHF can be viewed as a special-ized instance of Preference-based Reinforcement Learning (PbRL), where the preferences specifically originate from human judgments rather than arbitrary evaluators ..."}
+{"idx": 2, "title": "Efficient Preference-Based Reinforcement Learning Using Learned ...", "date": "", "ddg_snippet": "Preference-based reinforcement learning (PbRL) can enable robots to learn to perform tasks based on an individual's preferences without requiring a hand-crafted re-ward function. However, existing approaches either assume access to a high-fidelity simulator or analytic model or take a model-free approach that requires extensive, possibly unsafe online environment interactions. In this paper ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10161081", "content": "Preference-based reinforcement learning (PbRL) can enable robots to learn to perform tasks based on an individual's preferences without requiring a hand-crafted re-ward function. However, existing approaches either assume access to a high-fidelity simulator or analytic model or take a model-free approach that requires extensive, possibly unsafe online environment interactions. In this paper ..."}
+{"idx": 3, "title": "Risk-Aware Preference-baser Reinforcement Learning (RA-PbRL)", "date": "", "ddg_snippet": "Risk-Aware Preference -baser Reinforcement Learning (RA-PbRL) RA-PbRL is a type of Policy-Iteration and \"Confidence Bound\" reinforcement learning algorithm designed for preference-based reinforcement learning while maximizing risk -awareness through Value-at- Risk penalties. The intuition behind the algorithm depends on the idea of confidence bounds.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/aguilarjose11/PbRLNeurips", "content": "Risk-Aware Preference -baser Reinforcement Learning (RA-PbRL) RA-PbRL is a type of Policy-Iteration and \"Confidence Bound\" reinforcement learning algorithm designed for preference-based reinforcement learning while maximizing risk -awareness through Value-at- Risk penalties. The intuition behind the algorithm depends on the idea of confidence bounds."}
+{"idx": 4, "title": "RA-PbRL: Provably Efficient Risk-Aware Preference-Based Reinforcement ...", "date": "", "ddg_snippet": "Abstract Preference-based Reinforcement Learning (PbRL) studies the problem where agents receive only preferences over pairs of trajectories in each episode. Traditional approaches in this field have predominantly focused on the mean reward or utility criterion. However, in PbRL scenarios demanding heightened risk awareness, such as in AI systems, healthcare, and agriculture, risk-aware ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.23569v1", "content": "Abstract Preference-based Reinforcement Learning (PbRL) studies the problem where agents receive only preferences over pairs of trajectories in each episode. Traditional approaches in this field have predominantly focused on the mean reward or utility criterion. However, in PbRL scenarios demanding heightened risk awareness, such as in AI systems, healthcare, and agriculture, risk-aware ..."}
+{"idx": 5, "title": "Risk-Aware Reinforcement Learning with Coherent Risk Measures and Non ...", "date": "", "ddg_snippet": "We propose a unified framework to analyze the regret of risk-aware RL policy that uses a coherent risk measure in conjunction with non-linear function approximation.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=-RwZOVybbj", "content": "We propose a unified framework to analyze the regret of risk-aware RL policy that uses a coherent risk measure in conjunction with non-linear function approximation."}
+{"idx": 6, "title": "RA-PbRL: Provably Efficient Risk-Aware Preference-Based Reinforcement ...", "date": "", "ddg_snippet": "Risk-aware preference-based reinforcement learning (PbRL) addresses a critical gap in traditional PbRL, which predominantly focuses on maximizing average reward without considering risk .", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/posters/jndcfoczof/", "content": "Risk-aware preference-based reinforcement learning (PbRL) addresses a critical gap in traditional PbRL, which predominantly focuses on maximizing average reward without considering risk ."}
+{"idx": 7, "title": "Advances in Preference-based Reinforcement Learning: A Review", "date": "", "ddg_snippet": "Reinforcement Learning (RL) algorithms suffer from the dependency on accurately engineered reward functions to properly guide the learning agents to do the required tasks. Preference-based reinforcement learning (PbRL) addresses that by utilizing human preferences as feedback from the experts instead of numeric rewards. Due to its promising advantage over traditional RL, PbRL has gained more ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/9945333", "content": "Reinforcement Learning (RL) algorithms suffer from the dependency on accurately engineered reward functions to properly guide the learning agents to do the required tasks. Preference-based reinforcement learning (PbRL) addresses that by utilizing human preferences as feedback from the experts instead of numeric rewards. Due to its promising advantage over traditional RL, PbRL has gained more ..."}
+{"idx": 8, "title": "Risk-Aware Reinforcement Learning with Dynamic Safety Filter for ...", "date": "", "ddg_snippet": "Mobile robots face collision risk avoidance challenges in dynamic environments, necessitating that we address the safety and adaptability shortcomings of traditional navigation methods. Traditional methods rely on predefined rules, making it difficult to achieve flexible, safe, and real-time obstacle avoidance in complex, dynamic environments. To address this issue, a risk-aware , dynamic ...", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/1424-8220/25/17/5488", "content": "Mobile robots face collision risk avoidance challenges in dynamic environments, necessitating that we address the safety and adaptability shortcomings of traditional navigation methods. Traditional methods rely on predefined rules, making it difficult to achieve flexible, safe, and real-time obstacle avoidance in complex, dynamic environments. To address this issue, a risk-aware , dynamic ..."}
+{"idx": 9, "title": "RA-PbRL | Proceedings of the 38th International Conference on Neural ...", "date": "", "ddg_snippet": "Reinforcement Learning from Human Feedback (RLHF) has recently surged in popularity, particularly for aligning large language models and other AI systems with human intentions. At its core, RLHF can be viewed as a specialized instance of Preference-based Reinforcement Learning (PbRL), where the preferences specifically originate from human judgments rather than arbitrary evaluators. Despite ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3737916.3739861", "content": "Reinforcement Learning from Human Feedback (RLHF) has recently surged in popularity, particularly for aligning large language models and other AI systems with human intentions. At its core, RLHF can be viewed as a specialized instance of Preference-based Reinforcement Learning (PbRL), where the preferences specifically originate from human judgments rather than arbitrary evaluators. Despite ..."}
diff --git a/data/sampled_jsons/scaling_laws_for_neural_language_models_dataset_size_exponent.jsonl b/data/sampled_jsons/scaling_laws_for_neural_language_models_dataset_size_exponent.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d143cfa15a58dbd639b9d85918546ae3a3d3f699
--- /dev/null
+++ b/data/sampled_jsons/scaling_laws_for_neural_language_models_dataset_size_exponent.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Neural scaling law - Wikipedia", "date": "", "ddg_snippet": "A neural scaling law is a theoretical or empirical statistical law between these parameters. There are also other parameters with other scaling laws . Size of the model.\" Scaling Laws for Neural Language Models \".", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Neural_scaling_law", "content": "A neural scaling law is a theoretical or empirical statistical law between these parameters. There are also other parameters with other scaling laws . Size of the model.\" Scaling Laws for Neural Language Models \"."}
+{"idx": 1, "title": "Scaling Laws for Neural Language Models", "date": "", "ddg_snippet": "We study empirical scaling laws for language model performance on the cross-entropy loss. The loss scales as a power-law with model size, dataset size , and the amount of compute used for training, with some trends spanning more than seven orders of magnitude.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2001.08361", "content": "We study empirical scaling laws for language model performance on the cross-entropy loss. The loss scales as a power-law with model size, dataset size , and the amount of compute used for training, with some trends spanning more than seven orders of magnitude."}
+{"idx": 2, "title": "Scaling laws for neural language models | OpenAI", "date": "", "ddg_snippet": "We study empirical scaling laws for language model performance on the cross-entropy loss.Simple equations govern the dependence of overfitting on model/ dataset size and the dependence of training speed on model size.", "subpage_snippet": "", "source": "openai.com", "link": "https://openai.com/index/scaling-laws-for-neural-language-models/", "content": "We study empirical scaling laws for language model performance on the cross-entropy loss.Simple equations govern the dependence of overfitting on model/ dataset size and the dependence of training speed on model size."}
+{"idx": 3, "title": "Scaling Laws for Neural Language Models | Medium", "date": "", "ddg_snippet": "In this post I share my notes on Scaling laws for Neural Language Models (Kaplan — OpenAI — 01/2020). This paper has since been challenged.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@checkpoint89/scaling-laws-for-neural-language-models-fa1c0790833d", "content": "In this post I share my notes on Scaling laws for Neural Language Models (Kaplan — OpenAI — 01/2020). This paper has since been challenged."}
+{"idx": 4, "title": "GitHub - shehper/ scaling _ laws : An open-source implementation of...", "date": "", "ddg_snippet": "Scaling Laws of Neural Language Models . scaling laws with parameter count, dataset size , and compute.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/shehper/scaling_laws", "content": "Scaling Laws of Neural Language Models . scaling laws with parameter count, dataset size , and compute."}
+{"idx": 5, "title": "Explaining Neural Scaling Laws", "date": "", "ddg_snippet": "1 Scaling Laws for Neural Networks. For a large variety of models and datasets, neural network performance has been empirically observed to scale as a power-law with model size and dataset size [1–4].", "subpage_snippet": "", "source": "storage.googleapis.com", "link": "https://storage.googleapis.com/gweb-research2023-media/pubtools/6535.pdf", "content": "1 Scaling Laws for Neural Networks. For a large variety of models and datasets, neural network performance has been empirically observed to scale as a power-law with model size and dataset size [1–4]."}
+{"idx": 6, "title": "[PDF] Scaling Laws for Neural Language Models | Semantic Scholar", "date": "", "ddg_snippet": "We study empirical scaling laws for language model performance on the cross-entropy loss. The loss scales as a power-law with model size, dataset size , and the amount of compute used for training, with some trends spanning more than seven orders of magnitude.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Scaling-Laws-for-Neural-Language-Models-Kaplan-McCandlish/e6c561d02500b2596a230b341a8eb8b921ca5bf2", "content": "We study empirical scaling laws for language model performance on the cross-entropy loss. The loss scales as a power-law with model size, dataset size , and the amount of compute used for training, with some trends spanning more than seven orders of magnitude."}
+{"idx": 7, "title": "Scaling Laws for Neural Language Models", "date": "", "ddg_snippet": "This paper empirically examines how language models scale with parameters, data, and compute, offering predictive equations and guidelines to optimize training efficiency.", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/papers/2001.08361", "content": "This paper empirically examines how language models scale with parameters, data, and compute, offering predictive equations and guidelines to optimize training efficiency."}
+{"idx": 8, "title": "Scaling Laws for Neural Language Models | Papers With Code", "date": "", "ddg_snippet": "We study empirical scaling laws for language model performance on the cross-entropy loss.Simple equations govern the dependence of overfitting on model/ dataset size and the dependence of training speed on model size.", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/paper/scaling-laws-for-neural-language-models?from=n29", "content": "We study empirical scaling laws for language model performance on the cross-entropy loss.Simple equations govern the dependence of overfitting on model/ dataset size and the dependence of training speed on model size."}
+{"idx": 9, "title": "Data and Parameter Scaling Laws for Neural Machine Translation", "date": "", "ddg_snippet": "2020. Scaling laws for neural language models . Jungo Kasai, Nikolaos Pappas, Hao Peng, James Cross, and Noah A Smith.", "subpage_snippet": "", "source": "www.cs.jhu.edu", "link": "https://www.cs.jhu.edu/~kevinduh/papers/gordon21scaling.pdf", "content": "2020. Scaling laws for neural language models . Jungo Kasai, Nikolaos Pappas, Hao Peng, James Cross, and Noah A Smith."}
diff --git a/data/sampled_jsons/segmentation_foundation_models_improvements_2024_2025_SAM_Segment_Anything_year_2024.jsonl b/data/sampled_jsons/segmentation_foundation_models_improvements_2024_2025_SAM_Segment_Anything_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..dcf54e4773b2bea377c2645491b27f6cfa835995
--- /dev/null
+++ b/data/sampled_jsons/segmentation_foundation_models_improvements_2024_2025_SAM_Segment_Anything_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Segment Anything for Microscopy - Nature Methods", "date": "", "ddg_snippet": "Feb 12, 2025 · Here, we present Segment Anything for Microscopy ( μSAM ), a tool for segmentation and tracking in multidimensional microscopy data. It is based on Segment Anything , a vision foundation ...", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41592-024-02580-4", "content": "Feb 12, 2025 · Here, we present Segment Anything for Microscopy ( μSAM ), a tool for segmentation and tracking in multidimensional microscopy data. It is based on Segment Anything , a vision foundation ..."}
+{"idx": 1, "title": "Inspiring the Next Generation of Segment Anything Models ... Segment Anything for Microscopy - Nature Methods A review of the Segment Anything Model (SAM) for medical ... Improving the Generalization of Segmentation Foundation Model ... SAM 2 + GPT-4o: Cascading Foundation Models via Visual ... Medical Image Segmentation Foundation Models. CVPR 2024 ... Improving the Generalization of Segmentation Foundation Model under Uncertainty-aware Fine-tuning of Segmentation Foundation Models A review of the Segment Anything Model (SAM) for ... - ScienceDirect Improving the Generalization of Segmentation Foundation Model under Improving the Generalization of Segmentation Foundation Model under A review of the Segment Anything Model (SAM) for ... - ScienceDirect Uncertainty-aware Fine-tuning of Segmentation Foundation ...", "date": "", "ddg_snippet": "Dec 2, 2024 · As large-scale foundation models trained on billions of image--mask pairs covering a vast diversity of scenes, objects, and contexts, SAM and its upgraded version, SAM ~2, have significantly influenced multiple fields within computer vision. Feb 12, 2025 · Here, we present Segment Anything for Microscopy ( μSAM ), a tool for segmentation and tracking in multidimensional microscopy data. It is based on Segment Anything , a vision foundation ... Jan 1, 2025 · The purpose of this paper is to provide an overview of the developments that have occurred in the Segment Anything Model ( SAM ) within the medical image segmentation category over the course of the past year. Segment - Anything ( SAM ), among others, is the state-of-the-art image segmentation foundation model demonstrating strong zero/few-shot gen-eralization. Despite the success, recent studies reveal the weakness of SAM under strong distribution shift. Feb 21, 2025 · SAM 2 is notable for its accuracy in image segmentation and superior performance in video segmentation , requiring significantly less interaction time compared to previous models: we show how SAM 2 required 3 points to segment objects across an entire video! The 16 full papers presented were thoroughly reviewed and selected from the 200 submissions. This challenge aims to prompt the development of universal promotable medical image segmentation foundation models that are deployable on laptops or other edge devices without reliance on GPUs. Is segment-anything a good image segmentation model? Segment - Anything ( SAM ), among others, is the state-of-the-art image segmentation foundation model demonstrating strong zero/few-shot gen-eralization. Despite the success, recent studies reveal the weakness of SAM under strong distribution shift. Does segmentation with uncertainty model improve Sam? Segmentation with Uncertainty Model (SUM) improves SAM without forgetting to ''segment anything.'' Left: Both HQ-SAM and SUM show qualitative improvements over SAM, particularly in salient-object segmentation of complex structures (top row). What is the segment anything model (Sam)? SAM shows promise for clinical applications and large-scale dataset integration. The purpose of this paper is to provide an overview of the developments that have occurred in the Segment Anything Model ( SAM ) within the medical image segmentation category over the course of the past year. Which image segmentation Foundation model is able to zero/few-shot generalization? The success of large language models has inspired the computer vision community to explore image segmenta-tion foundation model that is able to zero/few-shot general-ize through prompt engineering. Segment-Anything (SAM) , among others, is the state-of-the-art image segmentation foundation model demonstrating strong zero/few-shot gen-eralization. Does pre-trained Sam improve medical image segmentation accuracy? Adaptation to Medical Images: Segmentation for medical images is a major application of foundation models. Our empirical observations in Tab. 4 on two medical segmenta-tion datasets suggest that direct applying pre-trained SAM is suboptimal. With weakly supervised adaptation, the seg-mentation accuracy is greatly improved . How to modify Sam for medical image segmentation? The most direct method for modifying SAM for medical image segmentation is explicitly fine-tuning SAM (Xiong et al., 2024) for the specific purpose. MedSAM (Ma et al., 2024) is a novel approach for segmenting medical images that builds upon SAM as shown in Fig. 3. Segmentation with Uncertainty Model (SUM) improves SAM without forgetting to '' segment anything .'' Left: Both HQ- SAM and SUM show qualitative improvements over SAM , particularly in salient-object segmentation of complex structures (top row).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2412.01240", "content": "Dec 2, 2024 · As large-scale foundation models trained on billions of image--mask pairs covering a vast diversity of scenes, objects, and contexts, SAM and its upgraded version, SAM ~2, have significantly influenced multiple fields within computer vision. Feb 12, 2025 · Here, we present Segment Anything for Microscopy ( μSAM ), a tool for segmentation and tracking in multidimensional microscopy data. It is based on Segment Anything , a vision foundation ... Jan 1, 2025 · The purpose of this paper is to provide an overview of the developments that have occurred in the Segment Anything Model ( SAM ) within the medical image segmentation category over the course of the past year. Segment - Anything ( SAM ), among others, is the state-of-the-art image segmentation foundation model demonstrating strong zero/few-shot gen-eralization. Despite the success, recent studies reveal the weakness of SAM under strong distribution shift. Feb 21, 2025 · SAM 2 is notable for its accuracy in image segmentation and superior performance in video segmentation , requiring significantly less interaction time compared to previous models: we show how SAM 2 required 3 points to segment objects across an entire video! The 16 full papers presented were thoroughly reviewed and selected from the 200 submissions. This challenge aims to prompt the development of universal promotable medical image segmentation foundation models that are deployable on laptops or other edge devices without reliance on GPUs. Is segment-anything a good image segmentation model? Segment - Anything ( SAM ), among others, is the state-of-the-art image segmentation foundation model demonstrating strong zero/few-shot gen-eralization. Despite the success, recent studies reveal the weakness of SAM under strong distribution shift. Does segmentation with uncertainty model improve Sam? Segmentation with Uncertainty Model (SUM) improves SAM without forgetting to ''segment anything.'' Left: Both HQ-SAM and SUM show qualitative improvements over SAM, particularly in salient-object segmentation of complex structures (top row). What is the segment anything model (Sam)? SAM shows promise for clinical applications and large-scale dataset integration. The purpose of this paper is to provide an overview of the developments that have occurred in the Segment Anything Model ( SAM ) within the medical image segmentation category over the course of the past year. Which image segmentation Foundation model is able to zero/few-shot generalization? The success of large language models has inspired the computer vision community to explore image segmenta-tion foundation model that is able to zero/few-shot general-ize through prompt engineering. Segment-Anything (SAM) , among others, is the state-of-the-art image segmentation foundation model demonstrating strong zero/few-shot gen-eralization. Does pre-trained Sam improve medical image segmentation accuracy? Adaptation to Medical Images: Segmentation for medical images is a major application of foundation models. Our empirical observations in Tab. 4 on two medical segmenta-tion datasets suggest that direct applying pre-trained SAM is suboptimal. With weakly supervised adaptation, the seg-mentation accuracy is greatly improved . How to modify Sam for medical image segmentation? The most direct method for modifying SAM for medical image segmentation is explicitly fine-tuning SAM (Xiong et al., 2024) for the specific purpose. MedSAM (Ma et al., 2024) is a novel approach for segmenting medical images that builds upon SAM as shown in Fig. 3. Segmentation with Uncertainty Model (SUM) improves SAM without forgetting to '' segment anything .'' Left: Both HQ- SAM and SUM show qualitative improvements over SAM , particularly in salient-object segmentation of complex structures (top row)."}
+{"idx": 2, "title": "A review of the Segment Anything Model (SAM) for medical ...", "date": "", "ddg_snippet": "Jan 1, 2025 · The purpose of this paper is to provide an overview of the developments that have occurred in the Segment Anything Model ( SAM ) within the medical image segmentation category over the course of the past year.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0895611124001502", "content": "Jan 1, 2025 · The purpose of this paper is to provide an overview of the developments that have occurred in the Segment Anything Model ( SAM ) within the medical image segmentation category over the course of the past year."}
+{"idx": 3, "title": "Improving the Generalization of Segmentation Foundation Model ...", "date": "", "ddg_snippet": "Segment - Anything ( SAM ), among others, is the state-of-the-art image segmentation foundation model demonstrating strong zero/few-shot gen-eralization. Despite the success, recent studies reveal the weakness of SAM under strong distribution shift.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2024/papers/Zhang_Improving_the_Generalization_of_Segmentation_Foundation_Model_under_Distribution_Shift_CVPR_2024_paper.pdf", "content": "Segment - Anything ( SAM ), among others, is the state-of-the-art image segmentation foundation model demonstrating strong zero/few-shot gen-eralization. Despite the success, recent studies reveal the weakness of SAM under strong distribution shift."}
+{"idx": 4, "title": "SAM 2 + GPT-4o: Cascading Foundation Models via Visual ...", "date": "", "ddg_snippet": "Feb 21, 2025 · SAM 2 is notable for its accuracy in image segmentation and superior performance in video segmentation , requiring significantly less interaction time compared to previous models: we show how SAM 2 required 3 points to segment objects across an entire video!", "subpage_snippet": "", "source": "www.edge-ai-vision.com", "link": "https://www.edge-ai-vision.com/2025/02/sam-2-gpt-4o-cascading-foundation-models-via-visual-prompting-part-1/", "content": "Feb 21, 2025 · SAM 2 is notable for its accuracy in image segmentation and superior performance in video segmentation , requiring significantly less interaction time compared to previous models: we show how SAM 2 required 3 points to segment objects across an entire video!"}
+{"idx": 5, "title": "Medical Image Segmentation Foundation Models. CVPR 2024 ...", "date": "", "ddg_snippet": "The 16 full papers presented were thoroughly reviewed and selected from the 200 submissions. This challenge aims to prompt the development of universal promotable medical image segmentation foundation models that are deployable on laptops or other edge devices without reliance on GPUs.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/book/10.1007/978-3-031-81854-7", "content": "The 16 full papers presented were thoroughly reviewed and selected from the 200 submissions. This challenge aims to prompt the development of universal promotable medical image segmentation foundation models that are deployable on laptops or other edge devices without reliance on GPUs."}
+{"idx": 6, "title": "Uncertainty-aware Fine-tuning of Segmentation Foundation ...", "date": "", "ddg_snippet": "Segmentation with Uncertainty Model (SUM) improves SAM without forgetting to '' segment anything .'' Left: Both HQ- SAM and SUM show qualitative improvements over SAM , particularly in salient-object segmentation of complex structures (top row).", "subpage_snippet": "", "source": "kangning-liu.github.io", "link": "https://kangning-liu.github.io/SUM_website/", "content": "Segmentation with Uncertainty Model (SUM) improves SAM without forgetting to '' segment anything .'' Left: Both HQ- SAM and SUM show qualitative improvements over SAM , particularly in salient-object segmentation of complex structures (top row)."}
+{"idx": 7, "title": "Segment anything in medical images", "date": "", "ddg_snippet": "by J Ma · 2024 · Cited by 2392 — Here we present MedSAM , a foundation model designed for bridging this gap by enabling universal medical image segmentation .", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41467-024-44824-z", "content": "by J Ma · 2024 · Cited by 2392 — Here we present MedSAM , a foundation model designed for bridging this gap by enabling universal medical image segmentation ."}
+{"idx": 8, "title": "facebookresearch/segment-anything", "date": "", "ddg_snippet": "Segment Anything Model 2 (SAM 2 ) is a foundation model towards solving promptable visual segmentation in images and videos. We extend SAM to video by ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/facebookresearch/segment-anything", "content": "Segment Anything Model 2 (SAM 2 ) is a foundation model towards solving promptable visual segmentation in images and videos. We extend SAM to video by ..."}
+{"idx": 9, "title": "Segment anything model for medical images?", "date": "", "ddg_snippet": "by Y Huang · 2024 · Cited by 529 — The Segment Anything Model (SAM ) is the first foundation model for general image segmentation. It has achieved impressive results on various natural image ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/abs/pii/S1361841523003213", "content": "by Y Huang · 2024 · Cited by 529 — The Segment Anything Model (SAM ) is the first foundation model for general image segmentation. It has achieved impressive results on various natural image ..."}
diff --git a/data/sampled_jsons/selenium_webdriver_playwright_GUI_action_execution_from_text_API_2024.jsonl b/data/sampled_jsons/selenium_webdriver_playwright_GUI_action_execution_from_text_API_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e12ddd3ea76fa4a2a93b3308626712a625520acb
--- /dev/null
+++ b/data/sampled_jsons/selenium_webdriver_playwright_GUI_action_execution_from_text_API_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Автоматизация входа на сайты с помощью Selenium WebDriver ...", "date": "", "ddg_snippet": "Selenium WebDriver в связке с Python предоставляет мощный и гибкий инструментарий для решения этой задачи. Что такое Selenium WebDriver и зачем он нужен для автоматизации? Selenium WebDriver — это интерфейс для программного управления браузерами.", "subpage_snippet": "", "source": "external.software", "link": "https://external.software/archives/18566", "content": "Selenium WebDriver в связке с Python предоставляет мощный и гибкий инструментарий для решения этой задачи. Что такое Selenium WebDriver и зачем он нужен для автоматизации? Selenium WebDriver — это интерфейс для программного управления браузерами."}
+{"idx": 1, "title": "selenium webdriver - How to use custom test-id in Playwright ?", "date": "", "ddg_snippet": "This all works very nicely in C#/ Selenium but I am not sure how to implement such a strategy in PLaywright . I can see the standard locator methods such as getByText, name, id etc. Is this possible and if yes can someone please tell me how.", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/77872657/how-to-use-custom-test-id-in-playwright", "content": "This all works very nicely in C#/ Selenium but I am not sure how to implement such a strategy in PLaywright . I can see the standard locator methods such as getByText, name, id etc. Is this possible and if yes can someone please tell me how."}
+{"idx": 2, "title": "Playing with Playwright Java | Playwright vs Selenium ... | Applitools", "date": "", "ddg_snippet": "Playing with Playwright – Java API and Playwright vs Selenium .From an ElementHandle, you can take actions (e.g. click, fill, etc) or get information (getAttribute, isEnabled, isChecked, etc).", "subpage_snippet": "", "source": "applitools.com", "link": "https://applitools.com/blog/playwright-java/", "content": "Playing with Playwright – Java API and Playwright vs Selenium .From an ElementHandle, you can take actions (e.g. click, fill, etc) or get information (getAttribute, isEnabled, isChecked, etc)."}
+{"idx": 3, "title": "Парсинг данных с помощью Selenium в 2024 году", "date": "", "ddg_snippet": "Веб-скрейпинг с помощью Selenium и Python в 2024 году. Selenium - это популярная библиотека с открытым исходным кодом для веб-скрапинга, которая использует протокол WebDriver для управления браузерами Chrome, Firefox и Safari. Но почему это полезно?", "subpage_snippet": "", "source": "cod-reg.ru", "link": "https://cod-reg.ru/posts/veb-skreiping-s-pomoshchyu-selenium-i-python-v-2024-godu/", "content": "Веб-скрейпинг с помощью Selenium и Python в 2024 году. Selenium - это популярная библиотека с открытым исходным кодом для веб-скрапинга, которая использует протокол WebDriver для управления браузерами Chrome, Firefox и Safari. Но почему это полезно?"}
+{"idx": 4, "title": "Playwright vs Selenium : Pros, Cons, and Use Cases Compared", "date": "", "ddg_snippet": "Selenium ’s reliance on the WebDriver API makes it slower, especially for large test suites. Playwright has auto-wait mechanisms that wait for elements to be ready before interacting, whereas Selenium requires the user to insert waits (implicit or explicit) for elements manually.", "subpage_snippet": "", "source": "research.aimultiple.com", "link": "https://research.aimultiple.com/playwright-vs-selenium/", "content": "Selenium ’s reliance on the WebDriver API makes it slower, especially for large test suites. Playwright has auto-wait mechanisms that wait for elements to be ready before interacting, whereas Selenium requires the user to insert waits (implicit or explicit) for elements manually."}
+{"idx": 5, "title": "Tutorial: Step by Step Implement Continuous Testing with Playwright .", "date": "", "ddg_snippet": "npx playwright test. You should see the browser open and a few actions executed . It will be quick, maybe too quick to catch what is happening on the screen. That’s totally fine, as it is an example test.Select a date in a DatePicker with Selenium WebDriver .", "subpage_snippet": "", "source": "courtneyzhan.medium.com", "link": "https://courtneyzhan.medium.com/tutorial-step-by-step-implement-continuous-testing-with-playwright-part-2-2f65faf50687", "content": "npx playwright test. You should see the browser open and a few actions executed . It will be quick, maybe too quick to catch what is happening on the screen. That’s totally fine, as it is an example test.Select a date in a DatePicker with Selenium WebDriver ."}
+{"idx": 6, "title": "Will Playwright become next Selenium ? - DEV Community", "date": "", "ddg_snippet": "Posted on Jan 11, 2024 . Will Playwright become next Selenium ?Adding to its allure, Playwright supports RESTful API testing alongside web testing, affording testers flexibility without delving into separate tools like Selenium WebDriver and RestAssured.", "subpage_snippet": "", "source": "dev.to", "link": "https://dev.to/magi-magificient/will-playwright-become-next-selenium-4pn5", "content": "Posted on Jan 11, 2024 . Will Playwright become next Selenium ?Adding to its allure, Playwright supports RESTful API testing alongside web testing, affording testers flexibility without delving into separate tools like Selenium WebDriver and RestAssured."}
+{"idx": 7, "title": "Finding web elements | Selenium", "date": "", "ddg_snippet": "Virtual Authenticator. Actions API . Keyboard.from selenium import webdriver from selenium . webdriver .common.by import By. driver = webdriver .Firefox() #.", "subpage_snippet": "", "source": "www.selenium.dev", "link": "https://www.selenium.dev/documentation/webdriver/elements/finders/", "content": "Virtual Authenticator. Actions API . Keyboard.from selenium import webdriver from selenium . webdriver .common.by import By. driver = webdriver .Firefox() #."}
+{"idx": 8, "title": "Ультимативная шпаргалка по Selenium с Python для... / Хабр", "date": "", "ddg_snippet": "Поэтому шпаргалка по Selenium с Python может служить для ознакомления с полезным API для автоматизации веб-сайтов (или веб-приложений).", "subpage_snippet": "", "source": "habr.com", "link": "https://habr.com/ru/companies/otus/articles/596071/", "content": "Поэтому шпаргалка по Selenium с Python может служить для ознакомления с полезным API для автоматизации веб-сайтов (или веб-приложений)."}
+{"idx": 9, "title": "Уроки по использованию Selenium WebDriver для парсинга данных", "date": "", "ddg_snippet": "В статье представлены пошаговые инструкции по установке, настройке и использованию основных функций Selenium , а также примеры парсинга данных с веб-страниц.", "subpage_snippet": "", "source": "sky.pro", "link": "https://sky.pro/wiki/python/uroki-po-ispolzovaniyu-selenium-webdriver-dlya-parsinga-dannyh/", "content": "В статье представлены пошаговые инструкции по установке, настройке и использованию основных функций Selenium , а также примеры парсинга данных с веб-страниц."}
diff --git a/data/sampled_jsons/self-blended_images_cross-blended_images_deepfake_classification.jsonl b/data/sampled_jsons/self-blended_images_cross-blended_images_deepfake_classification.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6120d21adc4d6902702bf74a4436bffd767d2e43
--- /dev/null
+++ b/data/sampled_jsons/self-blended_images_cross-blended_images_deepfake_classification.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2204.08376] Detecting Deepfakes with Self-Blended Images", "date": "", "ddg_snippet": "In this paper, we present novel synthetic training data called self-blended images (SBIs) to detect deepfakes . SBIs are generated by blending pseudo source and target images from single pristine images , reproducing common forgery artifacts (e.g., blending boundaries and statistical inconsistencies between source and target images ). The key idea behind SBIs is that more general and hardly ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2204.08376", "content": "In this paper, we present novel synthetic training data called self-blended images (SBIs) to detect deepfakes . SBIs are generated by blending pseudo source and target images from single pristine images , reproducing common forgery artifacts (e.g., blending boundaries and statistical inconsistencies between source and target images ). The key idea behind SBIs is that more general and hardly ..."}
+{"idx": 1, "title": "PDF Detecting Deepfakes with Self-Blended Images - CVF Open Access", "date": "", "ddg_snippet": "Abstract In this paper, we present novel synthetic training data called self-blended images (SBIs) to detect deepfakes . SBIs are generated by blending pseudo source and target images from single pristine images , reproducing common forgery artifacts (e.g., blending boundaries and statistical inconsis-tencies between source and target images ).", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2022/papers/Shiohara_Detecting_Deepfakes_With_Self-Blended_Images_CVPR_2022_paper.pdf", "content": "Abstract In this paper, we present novel synthetic training data called self-blended images (SBIs) to detect deepfakes . SBIs are generated by blending pseudo source and target images from single pristine images , reproducing common forgery artifacts (e.g., blending boundaries and statistical inconsis-tencies between source and target images )."}
+{"idx": 2, "title": "Detecting Deepfakes with Self-Blended Images - GitHub", "date": "", "ddg_snippet": "Detecting Deepfakes with Self-Blended Images The official PyTorch implementation for the following paper: Detecting Deepfakes with Self-Blended Images , Kaede Shiohara and Toshihiko Yamasaki, CVPR 2022 Oral", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/mapooon/SelfBlendedImages", "content": "Detecting Deepfakes with Self-Blended Images The official PyTorch implementation for the following paper: Detecting Deepfakes with Self-Blended Images , Kaede Shiohara and Toshihiko Yamasaki, CVPR 2022 Oral"}
+{"idx": 3, "title": "FSBI: Deepfake detection with frequency enhanced self-blended images", "date": "", "ddg_snippet": "This study introduces a frequency enhanced self-blended images (FSBI) approach for deepfake detection. This proposed approach utilizes discrete wavelet transforms (DWT) to extract discriminative features from self-blended images (SBI). The features are then used to train a convolutional network architecture model.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S026288562500006X", "content": "This study introduces a frequency enhanced self-blended images (FSBI) approach for deepfake detection. This proposed approach utilizes discrete wavelet transforms (DWT) to extract discriminative features from self-blended images (SBI). The features are then used to train a convolutional network architecture model."}
+{"idx": 4, "title": "Detecting Deepfakes with Self-Blended Images - IEEE Xplore", "date": "", "ddg_snippet": "In this paper, we present novel synthetic training data called self-blended images (SBIs) to detect deepfakes . SBIs are generated by blending pseudo source and target images from single pristine images , reproducing common forgery artifacts (e.g., blending boundaries and statistical inconsistencies between source and target images ). The key idea behind SBIs is that more general and hardly ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/9880195", "content": "In this paper, we present novel synthetic training data called self-blended images (SBIs) to detect deepfakes . SBIs are generated by blending pseudo source and target images from single pristine images , reproducing common forgery artifacts (e.g., blending boundaries and statistical inconsistencies between source and target images ). The key idea behind SBIs is that more general and hardly ..."}
+{"idx": 5, "title": "PDF Deepfake Detection with Frequency-Enhanced Self-Blended Images", "date": "", "ddg_snippet": "Therefore, we explore other color spaces to see if they help to improve the deepfake detection process. HSV (hue, satura-tion, value) is considered the main experimental color space. As a base model for our experiments, FSBI (Frequency-Enhanced Self-Blended Images ) [16] is chosen because of its robustness, efectiveness, and novelty.", "subpage_snippet": "", "source": "cse.aua.am", "link": "https://cse.aua.am/files/2025/06/Deepfake_Detection_with_Frequency_Enhanced_Self_Blended_Images.pdf", "content": "Therefore, we explore other color spaces to see if they help to improve the deepfake detection process. HSV (hue, satura-tion, value) is considered the main experimental color space. As a base model for our experiments, FSBI (Frequency-Enhanced Self-Blended Images ) [16] is chosen because of its robustness, efectiveness, and novelty."}
+{"idx": 6, "title": "Detecting Deepfakes with Self-Blended Images - Semantic Scholar", "date": "", "ddg_snippet": "Novel synthetic training data called self-blended images (SBIs) to detect deepfakes are presented and extensive experiments show that the method improves the model generalization to unknown manipulations and scenes. In this paper, we present novel synthetic training data called self-blended images (SBIs) to detect deepfakes . SBIs are generated by blending pseudo source and target images from ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Detecting-Deepfakes-with-Self-Blended-Images-Shiohara-Yamasaki/ef3b913e6509077c67e678674e2ba33d99a201a5", "content": "Novel synthetic training data called self-blended images (SBIs) to detect deepfakes are presented and extensive experiments show that the method improves the model generalization to unknown manipulations and scenes. In this paper, we present novel synthetic training data called self-blended images (SBIs) to detect deepfakes . SBIs are generated by blending pseudo source and target images from ..."}
+{"idx": 7, "title": "FSBI: Deepfakes Detection with Frequency Enhanced Self-Blended Images", "date": "", "ddg_snippet": "Advances in deepfake research have led to the creation of almost perfect manipulations undetectable by human eyes and some deepfakes detection tools. Recently, several techniques have been proposed to differentiate deepfakes from realistic images and videos. This paper introduces a Frequency Enhanced Self-Blended Images (FSBI) approach for deepfakes detection. This proposed approach utilizes ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2406.08625", "content": "Advances in deepfake research have led to the creation of almost perfect manipulations undetectable by human eyes and some deepfakes detection tools. Recently, several techniques have been proposed to differentiate deepfakes from realistic images and videos. This paper introduces a Frequency Enhanced Self-Blended Images (FSBI) approach for deepfakes detection. This proposed approach utilizes ..."}
+{"idx": 8, "title": "Detecting Deepfakes With Self-Blended Images", "date": "", "ddg_snippet": "In this paper, we present novel synthetic training data called self-blended images (SBIs) to detect deepfakes . SBIs are generated by blending pseudo source and target images from single pristine images , reproducing common forgery artifacts (e.g., blending boundaries and statistical inconsistencies between source and target images ).", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2022/html/Shiohara_Detecting_Deepfakes_With_Self-Blended_Images_CVPR_2022_paper.html", "content": "In this paper, we present novel synthetic training data called self-blended images (SBIs) to detect deepfakes . SBIs are generated by blending pseudo source and target images from single pristine images , reproducing common forgery artifacts (e.g., blending boundaries and statistical inconsistencies between source and target images )."}
+{"idx": 9, "title": "Wavelet-Based Self-image Blending for More General Face Forgery ...", "date": "", "ddg_snippet": "In recent years, numerous methods for deepfake detection have emerged, but they often face performance issues when evaluated across different datasets. This paper introduces two components based on the frequency and RGB domains that achieve excellent generalization. Specifically, a Self-Blended Generator Based on Discrete Wavelet Transform is proposed to generate Deepfakes from a frequency ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-3-032-04546-1_2", "content": "In recent years, numerous methods for deepfake detection have emerged, but they often face performance issues when evaluated across different datasets. This paper introduces two components based on the frequency and RGB domains that achieve excellent generalization. Specifically, a Self-Blended Generator Based on Discrete Wavelet Transform is proposed to generate Deepfakes from a frequency ..."}
diff --git a/data/sampled_jsons/siteahf.nucle_armuseum.org_Stanislaus_Ulam_his_purely_physics_creativity.jsonl b/data/sampled_jsons/siteahf.nucle_armuseum.org_Stanislaus_Ulam_his_purely_physics_creativity.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..44296beb3852e677fd5005ac7c820508378fa88f
--- /dev/null
+++ b/data/sampled_jsons/siteahf.nucle_armuseum.org_Stanislaus_Ulam_his_purely_physics_creativity.jsonl
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diff --git a/data/sampled_jsons/sitear5iv.labs.arxiv.org_2410.09536.jsonl b/data/sampled_jsons/sitear5iv.labs.arxiv.org_2410.09536.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..32e9873206af86c0286ac7d32087b4ea076e8781
--- /dev/null
+++ b/data/sampled_jsons/sitear5iv.labs.arxiv.org_2410.09536.jsonl
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+{"idx": 0, "title": "[2410.09536] Untitled Document [ar5iv.labs.arxiv.org]", "date": "", "ddg_snippet": "Conversion to HTML had a Fatal error and exited abruptly. This document may be truncated or damaged. Feeling", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2410.09536", "content": "Conversion to HTML had a Fatal error and exited abruptly. This document may be truncated or damaged. Feeling"}
+{"idx": 1, "title": "[1708.05324] Multimode Nonlinear Fiber Optics: Massively Parallel...", "date": "", "ddg_snippet": "[7] A. Shah, R. Hsu, A. Tarighat, A. Sayed, and B. Jalali, “Coherent optical MIMO (COMIMO),” Journal of Lightwave Technology, no. 8, pp. 2410–2419, Aug.", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/1708.05324", "content": "[7] A. Shah, R. Hsu, A. Tarighat, A. Sayed, and B. Jalali, “Coherent optical MIMO (COMIMO),” Journal of Lightwave Technology, no. 8, pp. 2410–2419, Aug."}
+{"idx": 2, "title": "[1905.04610] Explainable AI for Trees: From Local Explanations to...", "date": "", "ddg_snippet": "...H Alderman “Serum uric acid and cardiovascular mortality: the NHANES I epidemiologic follow-up study, 1971-1992” In Jama 283.18 American Medical Association, 2000, pp. 2404–2410.", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/1905.04610", "content": "...H Alderman “Serum uric acid and cardiovascular mortality: the NHANES I epidemiologic follow-up study, 1971-1992” In Jama 283.18 American Medical Association, 2000, pp. 2404–2410."}
+{"idx": 3, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
+{"idx": 4, "title": "[1807.11886] Deep Dual Pyramid Network for Barcode Segmentation...", "date": "", "ddg_snippet": "Digital signs(such as barcode or QR code) are widely used in our daily life, and for many applications, we need to localize them on images.", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/1807.11886", "content": "Digital signs(such as barcode or QR code) are widely used in our daily life, and for many applications, we need to localize them on images."}
+{"idx": 5, "title": "[1807.06622] Deep Learning-Based BSDE Solver for Libor Market...", "date": "", "ddg_snippet": "The Libor Market Model, also known as the BGM Model, is a term structure model of interest rates.", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/1807.06622", "content": "The Libor Market Model, also known as the BGM Model, is a term structure model of interest rates."}
+{"idx": 6, "title": "[1906.04962] Synthesizing Diverse Lung Nodules Wherever Massively...", "date": "", "ddg_snippet": "Accurate Computer-Assisted Diagnosis, relying on large-scale annotated pathological images, can alleviate the risk of overlooking the diagnosis.", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/1906.04962", "content": "Accurate Computer-Assisted Diagnosis, relying on large-scale annotated pathological images, can alleviate the risk of overlooking the diagnosis."}
+{"idx": 7, "title": "[2305.04044] Diffusion-NAT: Self-Prompting Discrete Diffusion for...", "date": "", "ddg_snippet": "Recently, continuous diffusion models (CDM) have been introduced into non-autoregressive (NAR) text-to-text generation.", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2305.04044", "content": "Recently, continuous diffusion models (CDM) have been introduced into non-autoregressive (NAR) text-to-text generation."}
diff --git a/data/sampled_jsons/sitear5iv.labs.arxiv.org_METransformer.jsonl b/data/sampled_jsons/sitear5iv.labs.arxiv.org_METransformer.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..06f1f0d5041bc555f324140f3688c5f619192bc3
--- /dev/null
+++ b/data/sampled_jsons/sitear5iv.labs.arxiv.org_METransformer.jsonl
@@ -0,0 +1,8 @@
+{"idx": 0, "title": "METransformer: Radiology Report Generation by Transformer ...", "date": "", "ddg_snippet": "It is observed that METransformer is able to generate descriptions better aligned with that written by radiologists. For example, METransformer can diagnose anomalies in the heart part, while the Baseline model fails.", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2304.02211", "content": "It is observed that METransformer is able to generate descriptions better aligned with that written by radiologists. For example, METransformer can diagnose anomalies in the heart part, while the Baseline model fails."}
+{"idx": 1, "title": "[2311.02329] Complex Organ Mask Guided Radiology Report ...", "date": "", "ddg_snippet": "Feb 27, 2024 · Metransformer : Radiology report generation by transformer with multiple learnable expert tokens. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11558–11567, 2023.", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2311.02329", "content": "Feb 27, 2024 · Metransformer : Radiology report generation by transformer with multiple learnable expert tokens. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11558–11567, 2023."}
+{"idx": 2, "title": "Automatic Medical Report Generation: Methods and Applications", "date": "", "ddg_snippet": "METransformer wang2023metransformer addresses this by concatenating multiple expert tokens in the image encoder and using orthogonal loss to minimize overlap among these tokens, thereby encouraging them to capture complementary information.", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2408.13988", "content": "METransformer wang2023metransformer addresses this by concatenating multiple expert tokens in the image encoder and using orthogonal loss to minimize overlap among these tokens, thereby encouraging them to capture complementary information."}
+{"idx": 3, "title": "[2405.12833] A Survey of Deep Learning-based Radiology Report ...", "date": "", "ddg_snippet": "Metransformer : Radiology report generation by transformer with multiple learnable expert tokens. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11558–11567, 2023d.", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2405.12833", "content": "Metransformer : Radiology report generation by transformer with multiple learnable expert tokens. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11558–11567, 2023d."}
+{"idx": 4, "title": "[2311.18681] RaDialog: A Large Vision-Language Model for ...", "date": "", "ddg_snippet": "Feb 27, 2024 · Metransformer : Radiology report generation by transformer with multiple learnable expert tokens. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11558–11567, 2023.", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2311.18681", "content": "Feb 27, 2024 · Metransformer : Radiology report generation by transformer with multiple learnable expert tokens. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11558–11567, 2023."}
+{"idx": 5, "title": "HC-LLM: Historical-Constrained Large Language Models for ...", "date": "", "ddg_snippet": "Metransformer: Radiology report generation by transformer with multiple learnable expert tokens. In Proceedings of the IEEE/CVF Conference on Computer ...", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2412.11070", "content": "Metransformer: Radiology report generation by transformer with multiple learnable expert tokens. In Proceedings of the IEEE/CVF Conference on Computer ..."}
+{"idx": 6, "title": "[2006.11527] Memory Transformer", "date": "", "ddg_snippet": "Mar 14, 2024 · Transformer-based models have achieved state-of-the-art results in many natural language processing tasks. The self-attention architecture allows transformer to combine information from all elements of a sequence into …", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2006.11527", "content": "Mar 14, 2024 · Transformer-based models have achieved state-of-the-art results in many natural language processing tasks. The self-attention architecture allows transformer to combine information from all elements of a sequence into …"}
+{"idx": 7, "title": "[2311.14199] A Systematic Review of Deep Learning-based ...", "date": "", "ddg_snippet": "Radiology report generation (RRG) aims to automatically generate free-text descriptions from clinical radiographs, e.g., chest X-Ray images. RRG plays an essential role in promoting clinical automation and presents sig…", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2311.14199", "content": "Radiology report generation (RRG) aims to automatically generate free-text descriptions from clinical radiographs, e.g., chest X-Ray images. RRG plays an essential role in promoting clinical automation and presents sig…"}
diff --git a/data/sampled_jsons/sitear5iv.labs.arxiv.orghtml2503.16979_Instant_Gaussian_Stream.jsonl b/data/sampled_jsons/sitear5iv.labs.arxiv.orghtml2503.16979_Instant_Gaussian_Stream.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..44296beb3852e677fd5005ac7c820508378fa88f
--- /dev/null
+++ b/data/sampled_jsons/sitear5iv.labs.arxiv.orghtml2503.16979_Instant_Gaussian_Stream.jsonl
@@ -0,0 +1 @@
+{"idx": 0, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/sitearxiv.org_1903.11027.jsonl b/data/sampled_jsons/sitearxiv.org_1903.11027.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..834b9f0c4c4aa90a58ea3731d8884ca93d7855f7
--- /dev/null
+++ b/data/sampled_jsons/sitearxiv.org_1903.11027.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[1903.11027] nuScenes: A multimodal dataset for autonomous", "date": "", "ddg_snippet": "cs > arXiv: 1903 . 11027 ... arXiv: 1903 . 11027 (cs) ... or arXiv: 1903 .11027v5 [cs.LG] for this version)", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1903.11027", "content": "cs > arXiv: 1903 . 11027 ... arXiv: 1903 . 11027 (cs) ... or arXiv: 1903 .11027v5 [cs.LG] for this version)"}
+{"idx": 1, "title": "The System Description of CPS Team for Track on Driving with", "date": "", "ddg_snippet": "... arXiv preprint arXiv: 1903 . 11027 , 2019.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.11071v1", "content": "... arXiv preprint arXiv: 1903 . 11027 , 2019."}
+{"idx": 2, "title": "Structured Labeling Enables Faster Vision-Language Models for", "date": "", "ddg_snippet": "... Available: https://arxiv.org/abs/ 1903 . 11027", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.05442v1", "content": "... Available: https://arxiv.org/abs/ 1903 . 11027"}
+{"idx": 3, "title": "MoSE: Skill-by-Skill Mixture-of-Expert Learning for Autonomous", "date": "", "ddg_snippet": "... arXiv preprint arXiv: 1903 . 11027 , 2019.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.07818v1", "content": "... arXiv preprint arXiv: 1903 . 11027 , 2019."}
+{"idx": 4, "title": "SpotVLM: Cloud-edge Collaborative Real-time VLM based on", "date": "", "ddg_snippet": "... arXiv preprint arXiv: 1903 . 11027 .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.12638v1", "content": "... arXiv preprint arXiv: 1903 . 11027 ."}
+{"idx": 5, "title": "LLM4Drive: A Survey of Large Language Models for Autonomous", "date": "", "ddg_snippet": "It combines LLMs with 2D detection tasks and obtains better performance in detection tasks and QA tasks compared to other multi-modal large models ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2311.01043v4", "content": "It combines LLMs with 2D detection tasks and obtains better performance in detection tasks and QA tasks compared to other multi-modal large models ..."}
+{"idx": 6, "title": "SKGE-SWIN: End-To-End Autonomous Vehicle Waypoint Prediction", "date": "", "ddg_snippet": "This division was done to ensure that the model could learn from various different conditions and scenarios, and to test the model’s performance on ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.20762v1", "content": "This division was done to ensure that the model could learn from various different conditions and scenarios, and to test the model’s performance on ..."}
+{"idx": 7, "title": "Scalar-Gauss-Bonnet gravity: Infrared causality and", "date": "", "ddg_snippet": "Compared to the pure gravitational EFT case, we find that a detectable window opens up when the theory is endowed with an extra scalar degree of ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.10973v4", "content": "Compared to the pure gravitational EFT case, we find that a detectable window opens up when the theory is endowed with an extra scalar degree of ..."}
+{"idx": 8, "title": "Glad: A Streaming Scene Generator for Autonomous Driving", "date": "", "ddg_snippet": "Despite these advancements, video generation still faces several challenges, including maintaining temporal consistency, generating longer videos ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.00045v1", "content": "Despite these advancements, video generation still faces several challenges, including maintaining temporal consistency, generating longer videos ..."}
+{"idx": 9, "title": "3D-MOOD: Lifting 2D to 3D for Monocular Open-Set Object", "date": "", "ddg_snippet": "These 3D queries encode essential geometric information and are used for the 3D bounding box head to improve the model’s accuracy and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.23567v1", "content": "These 3D queries encode essential geometric information and are used for the 3D bounding box head to improve the model’s accuracy and ..."}
diff --git a/data/sampled_jsons/sitearxiv.org_2007.04612_abstract_year_2020.jsonl b/data/sampled_jsons/sitearxiv.org_2007.04612_abstract_year_2020.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e31ec2d54b7f37c8d4f411d0f31a7d1aa18b621f
--- /dev/null
+++ b/data/sampled_jsons/sitearxiv.org_2007.04612_abstract_year_2020.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Abstract page for arXiv paper 2007 . 04612 : Concept Bottleneck Models", "date": "", "ddg_snippet": "Abstract :We seek to learn models that we can interact with using high-level concepts: if the model did not think there was a bone spur in the x-ray, would it still predict severe arthritis?", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2007.04612", "content": "Abstract :We seek to learn models that we can interact with using high-level concepts: if the model did not think there was a bone spur in the x-ray, would it still predict severe arthritis?"}
+{"idx": 1, "title": "[2308.04612v2] The homotopy type of the PL cobordism category. II", "date": "", "ddg_snippet": "Abstract :In this article, we prove the PL analogue of the theorem of Galatius, Madsen, Tillmann, and Weiss which describes the homotopy type of the smooth cobordism category.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2308.04612v2", "content": "Abstract :In this article, we prove the PL analogue of the theorem of Galatius, Madsen, Tillmann, and Weiss which describes the homotopy type of the smooth cobordism category."}
+{"idx": 2, "title": "arXiv:2502.13632v1 [cs.LG] 19 Feb 2025", "date": "", "ddg_snippet": "by OR Bidusa · 2025 — Concept bottleneck models. Preprint,. arXiv: 2007.04612 . Sonia Laguna, Ricards Marcinkevics, Moritz Vanden- hirtz, and Julia E. Vogt. 2024 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.13632", "content": "by OR Bidusa · 2025 — Concept bottleneck models. Preprint,. arXiv: 2007.04612 . Sonia Laguna, Ricards Marcinkevics, Moritz Vanden- hirtz, and Julia E. Vogt. 2024 ..."}
+{"idx": 3, "title": "Concept Bottleneck Models", "date": "", "ddg_snippet": "Pang Wei Koh * 1 Thao Nguyen * 1 2 Yew Siang Tang * 1 Stephen Mussmann 1 Emma Pierson 1 Been Kim 2 Percy Liang 1. Abstract .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2007.04612", "content": "Pang Wei Koh * 1 Thao Nguyen * 1 2 Yew Siang Tang * 1 Stephen Mussmann 1 Emma Pierson 1 Been Kim 2 Percy Liang 1. Abstract ."}
+{"idx": 4, "title": "Concept Bottleneck Models", "date": "", "ddg_snippet": "Pang Wei Koh * 1 Thao Nguyen * 1 2 Yew Siang Tang * 1 Stephen Mussmann 1 Emma Pierson 1 Been Kim 2 Percy Liang 1. Abstract .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2007.04612v1", "content": "Pang Wei Koh * 1 Thao Nguyen * 1 2 Yew Siang Tang * 1 Stephen Mussmann 1 Emma Pierson 1 Been Kim 2 Percy Liang 1. Abstract ."}
+{"idx": 5, "title": "Evaluating Explanations Through LLMs: Beyond Traditional User Studies", "date": "", "ddg_snippet": "Abstract . As AI becomes fundamental in sectors like healthcare, explainable AI (XAI) tools are essential for trust and transparency.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.17781v1", "content": "Abstract . As AI becomes fundamental in sectors like healthcare, explainable AI (XAI) tools are essential for trust and transparency."}
+{"idx": 6, "title": "Enhancing Interpretability and Intervenability via LLM ...", "date": "", "ddg_snippet": "19 Feb 2025 — Preprint, arXiv: 2007.04612 . Laguna et al. (2024) Sonia Laguna, Ričards Marcinkevičs, Moritz Vandenhirtz, and Julia E. Vogt. 2024.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.13632v1", "content": "19 Feb 2025 — Preprint, arXiv: 2007.04612 . Laguna et al. (2024) Sonia Laguna, Ričards Marcinkevičs, Moritz Vandenhirtz, and Julia E. Vogt. 2024."}
+{"idx": 7, "title": "Explainable Neural Network-based Modulation ...", "date": "", "ddg_snippet": "by LJ Wong · 2021 · Cited by 21 — Abstract —While Radio Frequency Machine Learning (RFML) is expected to be a key enabler of future wireless standards,.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2101.01239", "content": "by LJ Wong · 2021 · Cited by 21 — Abstract —While Radio Frequency Machine Learning (RFML) is expected to be a key enabler of future wireless standards,."}
+{"idx": 8, "title": "Concept-based Interpretation Without Linear Assumption", "date": "", "ddg_snippet": "5 Feb 2024 — These concepts cover a wide range of semantics, from low-level colors and textures, to high-level abstract descriptions (e.g. “smart”, “domestic ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2208.14966v2", "content": "5 Feb 2024 — These concepts cover a wide range of semantics, from low-level colors and textures, to high-level abstract descriptions (e.g. “smart”, “domestic ..."}
+{"idx": 9, "title": "Concept Bottleneck Model for Enhancing Human Neural ...", "date": "", "ddg_snippet": "28 Jun 2025 — CBM-HNMU leverages the Concept Bottleneck Model (CBM) as an interpretable framework to approximate black-box reasoning and communicate conceptual understanding.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.22803v1", "content": "28 Jun 2025 — CBM-HNMU leverages the Concept Bottleneck Model (CBM) as an interpretable framework to approximate black-box reasoning and communicate conceptual understanding."}
diff --git a/data/sampled_jsons/sitearxiv.org_2312.03046_filetypepdf_Table_2_Flowers.jsonl b/data/sampled_jsons/sitearxiv.org_2312.03046_filetypepdf_Table_2_Flowers.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..50473621d03349c69d759b55ef6753ed2e1a78a4
--- /dev/null
+++ b/data/sampled_jsons/sitearxiv.org_2312.03046_filetypepdf_Table_2_Flowers.jsonl
@@ -0,0 +1,6 @@
+{"idx": 0, "title": "[2312.08982] Macro-flickering of AQ Mensae on the daily time-scales...", "date": "", "ddg_snippet": "View PDF HTML (experimental). Abstract:We analyzed TESS photometric data of the flickering-active cataclysmic star AQ Men in 2018--2019. We processed 7 sectors with 14 light curves (LCs) inside them, with a time resolution of 2 min.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2312.08982", "content": "View PDF HTML (experimental). Abstract:We analyzed TESS photometric data of the flickering-active cataclysmic star AQ Men in 2018--2019. We processed 7 sectors with 14 light curves (LCs) inside them, with a time resolution of 2 min."}
+{"idx": 1, "title": "Diversified in-domain synthesis with efficient fine-tuning for few-shot", "date": "", "ddg_snippet": "arXiv: 2312 . 03046 v2 [cs.CV] 7 Dec 2023. Abstract. Few-shot image classification aims to learn an image classifier using only a small set of labeled examples per class. Table 2 . 16-shot results on all datasets for base/new classes averaged across 3 seeds.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2312.03046", "content": "arXiv: 2312 . 03046 v2 [cs.CV] 7 Dec 2023. Abstract. Few-shot image classification aims to learn an image classifier using only a small set of labeled examples per class. Table 2 . 16-shot results on all datasets for base/new classes averaged across 3 seeds."}
+{"idx": 2, "title": "Diversified in-domain synthesis with efficient fine-tuning for few-shot...", "date": "", "ddg_snippet": "In Table 1, we present the results for the default scenario for all compared methods and ours with and without synthetic data. First, we can see that TPT is better than VPT on average, although for EuroSAT, ImageNet, Oxford Pets, and Food 101, VPT outperforms TPT.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2312.03046v2", "content": "In Table 1, we present the results for the default scenario for all compared methods and ours with and without synthetic data. First, we can see that TPT is better than VPT on average, although for EuroSAT, ImageNet, Oxford Pets, and Food 101, VPT outperforms TPT."}
+{"idx": 3, "title": "Chat-CBM: Towards Interactive Concept Bottleneck Models with...", "date": "", "ddg_snippet": "Report Issue Back to Abstract Download PDF . Table of Contents. Table 2 : Classification accuracy on datasets without concept labels. We report the mean and standard deviation from five runs with different random seeds. (Qwen2.5-32B-Instruct for Chat-CBM).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.17522v1", "content": "Report Issue Back to Abstract Download PDF . Table of Contents. Table 2 : Classification accuracy on datasets without concept labels. We report the mean and standard deviation from five runs with different random seeds. (Qwen2.5-32B-Instruct for Chat-CBM)."}
+{"idx": 4, "title": "[2509.13439] NNLO QCD corrections to $γγ\\rightarrow Q\\bar{Q}$ from...", "date": "", "ddg_snippet": "Supplemented with localised ultraviolet renormalisation, it enables the direct Monte Carlo integration of (differential) cross sections at arbitrary perturbative order in four-dimensional spacetime.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2509.13439", "content": "Supplemented with localised ultraviolet renormalisation, it enables the direct Monte Carlo integration of (differential) cross sections at arbitrary perturbative order in four-dimensional spacetime."}
+{"idx": 5, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/sitearxiv.org_2312.15427_non-continuous_OR_discontinuous.jsonl b/data/sampled_jsons/sitearxiv.org_2312.15427_non-continuous_OR_discontinuous.jsonl
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Asymptotic-preserving and positivity-preserving discontinuous ... Orientation of discontinuous fillers in polymer composites ... Discontinuous coarsening leads to unchanged tensile ... Positivity-preserving and entropy-bounded discontinuous ... Archetypal oscillator for smooth and discontinuous dynamics A bound preserving cut discontinuous Galerkin method for one ... High order asymptotic preserving discontinuous Galerkin ...", "date": "", "ddg_snippet": "Mar 25, 2025 · In this paper, we develop an asymptotic-preserving and positivity-preserving discontinuous Galerkin (DG) method for solving the semiconductor Boltzmann equation in the diffusive scaling. We first formulate the diffusive relaxation system based on the even-odd decomposition method, which allows us to split into one relaxation step and one transport step. We adopt a robust implicit scheme that ... Feb 1, 2025 · Polymer composites have progressively found applications in sectors such as automotive, aerospace and energy storage. Their high performance can be ma… May 1, 2024 · The discontinuous coarsening process is responsible for the formation of heterogeneous precipitates in Ni 2 CoCrFeTi 0.24 Al 0.2 during recrystallization. This typical discontinuous coarsening process is confirmed by an unchanged precipitate volume fraction (∼30 %), a sharp transition in precipitate size (from ∼10 nm to ∼100 nm), and a ... Oct 26, 2023 · This article concerns the development of a fully conservative, positivity-preserving, and entropy-bounded discontinuous Galerkin scheme for the multicomponent, chemically reacting, compressible Navier-Stokes equations with complex thermodynamics. In particular, we extend to viscous flows the fully conservative, positivity-preserving, and entropy-bounded discontinuous Galerkin method for the ... Oct 30, 2006 · We propose an archetypal system to investigate transitions from smooth to discontinuous dynamics. In the smooth regime, the system bears significant similarities to the Duffing oscillator, exhibiting the standard dynamics governed by the hyperbolic structure associated with the stationary state of the double well. At the discontinuous limit, however, there is a substantial departure in the ... Pei Fu1,*, Gunilla Kreiss2 and Sara Zahedi3 Abstract. In this paper we present a family of high order cut finite element methods with bound preserving properties for hyperbolic conservation laws in one space dimension. The methods are based on the discontinuous Galerkin framework and use a regular background mesh, where interior boundaries are allowed to cut through the mesh arbitrarily. Our ... Aug 15, 2022 · In this paper, we will develop a class of high order asymptotic preserving (AP) discontinuous Galerkin (DG) methods for nonlinear time-dependent gray …", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.19487", "content": "Mar 25, 2025 · In this paper, we develop an asymptotic-preserving and positivity-preserving discontinuous Galerkin (DG) method for solving the semiconductor Boltzmann equation in the diffusive scaling. We first formulate the diffusive relaxation system based on the even-odd decomposition method, which allows us to split into one relaxation step and one transport step. We adopt a robust implicit scheme that ... Feb 1, 2025 · Polymer composites have progressively found applications in sectors such as automotive, aerospace and energy storage. Their high performance can be ma… May 1, 2024 · The discontinuous coarsening process is responsible for the formation of heterogeneous precipitates in Ni 2 CoCrFeTi 0.24 Al 0.2 during recrystallization. This typical discontinuous coarsening process is confirmed by an unchanged precipitate volume fraction (∼30 %), a sharp transition in precipitate size (from ∼10 nm to ∼100 nm), and a ... Oct 26, 2023 · This article concerns the development of a fully conservative, positivity-preserving, and entropy-bounded discontinuous Galerkin scheme for the multicomponent, chemically reacting, compressible Navier-Stokes equations with complex thermodynamics. In particular, we extend to viscous flows the fully conservative, positivity-preserving, and entropy-bounded discontinuous Galerkin method for the ... Oct 30, 2006 · We propose an archetypal system to investigate transitions from smooth to discontinuous dynamics. In the smooth regime, the system bears significant similarities to the Duffing oscillator, exhibiting the standard dynamics governed by the hyperbolic structure associated with the stationary state of the double well. At the discontinuous limit, however, there is a substantial departure in the ... Pei Fu1,*, Gunilla Kreiss2 and Sara Zahedi3 Abstract. In this paper we present a family of high order cut finite element methods with bound preserving properties for hyperbolic conservation laws in one space dimension. The methods are based on the discontinuous Galerkin framework and use a regular background mesh, where interior boundaries are allowed to cut through the mesh arbitrarily. Our ... Aug 15, 2022 · In this paper, we will develop a class of high order asymptotic preserving (AP) discontinuous Galerkin (DG) methods for nonlinear time-dependent gray …"}
+{"idx": 1, "title": "Orientation of discontinuous fillers in polymer composites ...", "date": "", "ddg_snippet": "Feb 1, 2025 · Polymer composites have progressively found applications in sectors such as automotive, aerospace and energy storage. Their high performance can be ma…", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0079642524001294", "content": "Feb 1, 2025 · Polymer composites have progressively found applications in sectors such as automotive, aerospace and energy storage. Their high performance can be ma…"}
+{"idx": 2, "title": "Discontinuous coarsening leads to unchanged tensile ...", "date": "", "ddg_snippet": "May 1, 2024 · The discontinuous coarsening process is responsible for the formation of heterogeneous precipitates in Ni 2 CoCrFeTi 0.24 Al 0.2 during recrystallization. This typical discontinuous coarsening process is confirmed by an unchanged precipitate volume fraction (∼30 %), a sharp transition in precipitate size (from ∼10 nm to ∼100 nm), and a ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0749641924000901", "content": "May 1, 2024 · The discontinuous coarsening process is responsible for the formation of heterogeneous precipitates in Ni 2 CoCrFeTi 0.24 Al 0.2 during recrystallization. This typical discontinuous coarsening process is confirmed by an unchanged precipitate volume fraction (∼30 %), a sharp transition in precipitate size (from ∼10 nm to ∼100 nm), and a ..."}
+{"idx": 3, "title": "Positivity-preserving and entropy-bounded discontinuous ... Archetypal oscillator for smooth and discontinuous dynamics A bound preserving cut discontinuous Galerkin method for one ... High order asymptotic preserving discontinuous Galerkin ...", "date": "", "ddg_snippet": "Oct 26, 2023 · This article concerns the development of a fully conservative, positivity-preserving, and entropy-bounded discontinuous Galerkin scheme for the multicomponent, chemically reacting, compressible Navier-Stokes equations with complex thermodynamics. In particular, we extend to viscous flows the fully conservative, positivity-preserving, and entropy-bounded discontinuous Galerkin method for the ... Oct 30, 2006 · We propose an archetypal system to investigate transitions from smooth to discontinuous dynamics. In the smooth regime, the system bears significant similarities to the Duffing oscillator, exhibiting the standard dynamics governed by the hyperbolic structure associated with the stationary state of the double well. At the discontinuous limit, however, there is a substantial departure in the ... Pei Fu1,*, Gunilla Kreiss2 and Sara Zahedi3 Abstract. In this paper we present a family of high order cut finite element methods with bound preserving properties for hyperbolic conservation laws in one space dimension. The methods are based on the discontinuous Galerkin framework and use a regular background mesh, where interior boundaries are allowed to cut through the mesh arbitrarily. Our ... Aug 15, 2022 · In this paper, we will develop a class of high order asymptotic preserving (AP) discontinuous Galerkin (DG) methods for nonlinear time-dependent gray …", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2310.17637", "content": "Oct 26, 2023 · This article concerns the development of a fully conservative, positivity-preserving, and entropy-bounded discontinuous Galerkin scheme for the multicomponent, chemically reacting, compressible Navier-Stokes equations with complex thermodynamics. In particular, we extend to viscous flows the fully conservative, positivity-preserving, and entropy-bounded discontinuous Galerkin method for the ... Oct 30, 2006 · We propose an archetypal system to investigate transitions from smooth to discontinuous dynamics. In the smooth regime, the system bears significant similarities to the Duffing oscillator, exhibiting the standard dynamics governed by the hyperbolic structure associated with the stationary state of the double well. At the discontinuous limit, however, there is a substantial departure in the ... Pei Fu1,*, Gunilla Kreiss2 and Sara Zahedi3 Abstract. In this paper we present a family of high order cut finite element methods with bound preserving properties for hyperbolic conservation laws in one space dimension. The methods are based on the discontinuous Galerkin framework and use a regular background mesh, where interior boundaries are allowed to cut through the mesh arbitrarily. Our ... Aug 15, 2022 · In this paper, we will develop a class of high order asymptotic preserving (AP) discontinuous Galerkin (DG) methods for nonlinear time-dependent gray …"}
+{"idx": 4, "title": "Archetypal oscillator for smooth and discontinuous dynamics", "date": "", "ddg_snippet": "Oct 30, 2006 · We propose an archetypal system to investigate transitions from smooth to discontinuous dynamics. In the smooth regime, the system bears significant similarities to the Duffing oscillator, exhibiting the standard dynamics governed by the hyperbolic structure associated with the stationary state of the double well. At the discontinuous limit, however, there is a substantial departure in the ...", "subpage_snippet": "", "source": "link.aps.org", "link": "https://link.aps.org/doi/10.1103/PhysRevE.74.046218", "content": "Oct 30, 2006 · We propose an archetypal system to investigate transitions from smooth to discontinuous dynamics. In the smooth regime, the system bears significant similarities to the Duffing oscillator, exhibiting the standard dynamics governed by the hyperbolic structure associated with the stationary state of the double well. At the discontinuous limit, however, there is a substantial departure in the ..."}
+{"idx": 5, "title": "A bound preserving cut discontinuous Galerkin method for one ...", "date": "", "ddg_snippet": "Pei Fu1,*, Gunilla Kreiss2 and Sara Zahedi3 Abstract. In this paper we present a family of high order cut finite element methods with bound preserving properties for hyperbolic conservation laws in one space dimension. The methods are based on the discontinuous Galerkin framework and use a regular background mesh, where interior boundaries are allowed to cut through the mesh arbitrarily. Our ...", "subpage_snippet": "", "source": "www.esaim-m2an.org", "link": "https://www.esaim-m2an.org/articles/m2an/pdf/2024/05/m2an230308.pdf", "content": "Pei Fu1,*, Gunilla Kreiss2 and Sara Zahedi3 Abstract. In this paper we present a family of high order cut finite element methods with bound preserving properties for hyperbolic conservation laws in one space dimension. The methods are based on the discontinuous Galerkin framework and use a regular background mesh, where interior boundaries are allowed to cut through the mesh arbitrarily. Our ..."}
+{"idx": 6, "title": "High order asymptotic preserving discontinuous Galerkin ...", "date": "", "ddg_snippet": "Aug 15, 2022 · In this paper, we will develop a class of high order asymptotic preserving (AP) discontinuous Galerkin (DG) methods for nonlinear time-dependent gray …", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0021999122003709", "content": "Aug 15, 2022 · In this paper, we will develop a class of high order asymptotic preserving (AP) discontinuous Galerkin (DG) methods for nonlinear time-dependent gray …"}
+{"idx": 7, "title": "Stochastic Solutions to Hamilton–Jacobi–Bellman Dirichlet Problems", "date": "", "ddg_snippet": "is a viscosity subsolution is called a non - continuous ( or discontinuous ) viscosity solution (cf., e.g., [3, Definition V.2.2] or [31, Definition 4.2.1] ). Using this terminology, Theorem 2.5 shows that the stochastic solution.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2404.17236v3", "content": "is a viscosity subsolution is called a non - continuous ( or discontinuous ) viscosity solution (cf., e.g., [3, Definition V.2.2] or [31, Definition 4.2.1] ). Using this terminology, Theorem 2.5 shows that the stochastic solution."}
+{"idx": 8, "title": "Ill-posedness in $B^s_{p,\\infty}$ of the Euler equations...", "date": "", "ddg_snippet": "(2) Non - Continuous Dependence: the data-to-solution map of (3.1) obtained in (1) is not continuous from any bounded subset in Bsp,∞ into LT∞(Bsp,∞(R)). More precisely, there exists two sequences of solutions St(un0) and St(u0) such that.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2509.12619", "content": "(2) Non - Continuous Dependence: the data-to-solution map of (3.1) obtained in (1) is not continuous from any bounded subset in Bsp,∞ into LT∞(Bsp,∞(R)). More precisely, there exists two sequences of solutions St(un0) and St(u0) such that."}
+{"idx": 9, "title": "STOCHASTIC", "date": "", "ddg_snippet": "The objective of this paper is to revisit the stochastic approach to the above Dirich-let problem when the coecients are only continuous and bounded. Under such weak conditions on the coecients, controlled stochastic dierential equations of the form.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2404.17236", "content": "The objective of this paper is to revisit the stochastic approach to the above Dirich-let problem when the coecients are only continuous and bounded. Under such weak conditions on the coecients, controlled stochastic dierential equations of the form."}
diff --git a/data/sampled_jsons/sitearxiv.org_2403.09040_RSS_formula_RAG_Stability_Score.jsonl b/data/sampled_jsons/sitearxiv.org_2403.09040_RSS_formula_RAG_Stability_Score.jsonl
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "RAGGED : Towards Informed Design of Scalable and Stable RAG ...", "date": "", "ddg_snippet": "Figure 5: Ragged stability score for NQ, retriever colbert. What is more interesting is that while a scalable model is often more stable , a stable model does not have to be a scalable one. For example, LLAMA2 models are not as scalable but are still stable .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2403.09040", "content": "Figure 5: Ragged stability score for NQ, retriever colbert. What is more interesting is that while a scalable model is often more stable , a stable model does not have to be a scalable one. For example, LLAMA2 models are not as scalable but are still stable ."}
+{"idx": 1, "title": "RAGGED: Towards Informed Design of Scalable and Stable RAG Systems", "date": "", "ddg_snippet": "In this work, we introduce RAGGED, a framework for systematically evaluating RAG systems across diverse retriever-reader configurations, retrieval depths, and datasets. Our analysis reveals that reader robustness to noise is the key determinant of RAG stability and scalability.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2403.09040", "content": "In this work, we introduce RAGGED, a framework for systematically evaluating RAG systems across diverse retriever-reader configurations, retrieval depths, and datasets. Our analysis reveals that reader robustness to noise is the key determinant of RAG stability and scalability."}
+{"idx": 2, "title": "RAGGED : Towards Informed Design of Scalable and Stable RAG ...", "date": "", "ddg_snippet": "RAG Stability Score ( RSS ) The RSS metric quantifies how consistently a model maintains performance around its optimal retrieval depth.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.09040v3", "content": "RAG Stability Score ( RSS ) The RSS metric quantifies how consistently a model maintains performance around its optimal retrieval depth."}
+{"idx": 3, "title": "RAGGED : Towards Informed Design of Retrieval Augmented...", "date": "", "ddg_snippet": "arXiv: 2403 . 09040 v2 [cs.CL] 12 Aug 2024.However, the effectiveness of RAG is highly dependent on its configuration. To systematically find the optimal configuration, we introduce RAGGED , a framework for analyzing RAG configurations across various DBQA tasks.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.09040v2", "content": "arXiv: 2403 . 09040 v2 [cs.CL] 12 Aug 2024.However, the effectiveness of RAG is highly dependent on its configuration. To systematically find the optimal configuration, we introduce RAGGED , a framework for analyzing RAG configurations across various DBQA tasks."}
+{"idx": 4, "title": "RAGGED : Towards Informed Design of Retrieval Augmented...", "date": "", "ddg_snippet": "arXiv: 2403 . 09040 v1 [cs.CL] 14 Mar 2024. score of the reader output against all possible gold answers and report the highest score . This is suitable if we do not need the generated answer to match the exact formatting and wording of the ground truth answer.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.09040v1", "content": "arXiv: 2403 . 09040 v1 [cs.CL] 14 Mar 2024. score of the reader output against all possible gold answers and report the highest score . This is suitable if we do not need the generated answer to match the exact formatting and wording of the ground truth answer."}
+{"idx": 5, "title": "RAGGED: Towards Informed Design of Retrieval Augmented Generation Systems", "date": "", "ddg_snippet": "Abstract Retrieval-augmented generation ( RAG ) can significantly improve the performance of language models (LMs) by providing additional context for tasks such as document-based question answering (DBQA). However, the effectiveness of RAG is highly dependent on its configuration. To systematically find the optimal configuration, we introduce RAGGED, a framework for analyzing RAG configurations ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.09040", "content": "Abstract Retrieval-augmented generation ( RAG ) can significantly improve the performance of language models (LMs) by providing additional context for tasks such as document-based question answering (DBQA). However, the effectiveness of RAG is highly dependent on its configuration. To systematically find the optimal configuration, we introduce RAGGED, a framework for analyzing RAG configurations ..."}
+{"idx": 6, "title": "[2403.09040] RAGGED: Towards Informed Design of Retrieval Augmented ...", "date": "", "ddg_snippet": "Retrieval-augmented generation ( RAG ) greatly benefits language models (LMs) by providing additional context for tasks such as document-based question answering (DBQA). Despite its potential, the power of RAG is highly …", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2403.09040", "content": "Retrieval-augmented generation ( RAG ) greatly benefits language models (LMs) by providing additional context for tasks such as document-based question answering (DBQA). Despite its potential, the power of RAG is highly …"}
+{"idx": 7, "title": "Toward Optimal Search and Retrieval for RAG - arXiv.org", "date": "", "ddg_snippet": "We aim to address questions that will enable practitioners to design retrieval systems tailored for use in RAG pipelines. For example, what are the weaknesses of the typical search and retrieval setup in RAG systems? Which search hyperparameters matter for RAG task performance?", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.07396v1", "content": "We aim to address questions that will enable practitioners to design retrieval systems tailored for use in RAG pipelines. For example, what are the weaknesses of the typical search and retrieval setup in RAG systems? Which search hyperparameters matter for RAG task performance?"}
+{"idx": 8, "title": "Context Embeddings for Efficient Answer Generation in RAG", "date": "", "ddg_snippet": "1. Introduction Figure 1. COCOM: Compressing multiple contexts for RAG into a small set (ξ = 4, 16, 128 𝜉 4 16 128 \\xi= {4,16,128} italic_ξ = 4 , 16 , 128) of Context Embeddings leads to a massive speed up in answer generation while maintaining higher performance compared to other methods. Results are shown for the ASQA dataset.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2407.09252v3", "content": "1. Introduction Figure 1. COCOM: Compressing multiple contexts for RAG into a small set (ξ = 4, 16, 128 𝜉 4 16 128 \\xi= {4,16,128} italic_ξ = 4 , 16 , 128) of Context Embeddings leads to a massive speed up in answer generation while maintaining higher performance compared to other methods. Results are shown for the ASQA dataset."}
+{"idx": 9, "title": "Tatsuki Koga arXiv:2412.04697v2 [cs.CR] 26 Feb 2025", "date": "", "ddg_snippet": "ABSTRACT nsitive data that lies outside their training data. For this purpose, retrieval-augmented generation ( RAG ) is particularly effective—it assists LLMs by directly providing relev nt information from the external knowledge sources. However, without extra privacy safeguards, RAG outputs risk leaking", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2412.04697", "content": "ABSTRACT nsitive data that lies outside their training data. For this purpose, retrieval-augmented generation ( RAG ) is particularly effective—it assists LLMs by directly providing relev nt information from the external knowledge sources. However, without extra privacy safeguards, RAG outputs risk leaking"}
diff --git a/data/sampled_jsons/sitearxiv.org_2404.08819_abstract.jsonl b/data/sampled_jsons/sitearxiv.org_2404.08819_abstract.jsonl
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index 0000000000000000000000000000000000000000..0eed34aa48948f62a6abbe435e7783b161893afa
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2404.08819] The Illusion of State in State-Space Models", "date": "", "ddg_snippet": "by W Merrill · 2024 · Cited by 89 — Abstract page for arXiv paper 2404.08819 : The Illusion of State in State-Space Models. ... Abstract , Comments, Journal reference, ACM ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2404.08819", "content": "by W Merrill · 2024 · Cited by 89 — Abstract page for arXiv paper 2404.08819 : The Illusion of State in State-Space Models. ... Abstract , Comments, Journal reference, ACM ..."}
+{"idx": 1, "title": "The Illusion of State in State-Space Models", "date": "", "ddg_snippet": "by W Merrill · 2024 · Cited by 89 — Abstract . State-space models (SSMs) have emerged as a potential ... arXiv: 2404.08819 v3 [cs.LG] 5 Mar 2025. Page 2. The Illusion of State in State-Space ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2404.08819", "content": "by W Merrill · 2024 · Cited by 89 — Abstract . State-space models (SSMs) have emerged as a potential ... arXiv: 2404.08819 v3 [cs.LG] 5 Mar 2025. Page 2. The Illusion of State in State-Space ..."}
+{"idx": 2, "title": "The Illusion of State in State-Space Models", "date": "", "ddg_snippet": "arXiv: 2404.08819 v2 [cs.LG] 04 Jun 2024. The Illusion of State ... Abstract . Report issue for preceding element. State-space models (SSMs) have emerged as a ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2404.08819v2", "content": "arXiv: 2404.08819 v2 [cs.LG] 04 Jun 2024. The Illusion of State ... Abstract . Report issue for preceding element. State-space models (SSMs) have emerged as a ..."}
+{"idx": 3, "title": "The Illusion of State in State-Space Models", "date": "", "ddg_snippet": "arXiv: 2404.08819 v1 [cs.LG] 12 Apr 2024. The Illusion of State in State-Space Models ... Abstract . Report issue for preceding element. State-space models (SSMs) have emerged as a potential alternative ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2404.08819v1", "content": "arXiv: 2404.08819 v1 [cs.LG] 12 Apr 2024. The Illusion of State in State-Space Models ... Abstract . Report issue for preceding element. State-space models (SSMs) have emerged as a potential alternative ..."}
+{"idx": 4, "title": "Understanding and Mitigating Bottlenecks of State Space ...", "date": "", "ddg_snippet": "11 Mar 2025 — (2024) William Merrill, Jackson Petty, and Ashish Sabharwal. The illusion of state in state-space models. arXiv preprint arXiv: 2404.08819 , 2024.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.00658v2", "content": "11 Mar 2025 — (2024) William Merrill, Jackson Petty, and Ashish Sabharwal. The illusion of state in state-space models. arXiv preprint arXiv: 2404.08819 , 2024."}
+{"idx": 5, "title": "Theoretical Foundations of Deep Selective State-Space ...", "date": "", "ddg_snippet": "The illusion of state in state-space models. arXiv preprint arXiv: 2404.08819 , 2024. Hambly and Lyons [2010] Ben Hambly and Terry Lyons. Uniqueness ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2402.19047v4", "content": "The illusion of state in state-space models. arXiv preprint arXiv: 2404.08819 , 2024. Hambly and Lyons [2010] Ben Hambly and Terry Lyons. Uniqueness ..."}
+{"idx": 6, "title": "Finite State Automata Inside Transformers with Chain-of- ...", "date": "", "ddg_snippet": "27 Feb 2025 — The illusion of state in state-space models. Preprint, arXiv: 2404.08819 . Merrill, William and Sabharwal, Ashish (2024) ↑ Merrill, William and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.20129v1", "content": "27 Feb 2025 — The illusion of state in state-space models. Preprint, arXiv: 2404.08819 . Merrill, William and Sabharwal, Ashish (2024) ↑ Merrill, William and ..."}
+{"idx": 7, "title": "Attention, State Space Models, and Recurrent Neural ...", "date": "", "ddg_snippet": "24 May 2024 — The illusion of state in state-space models. arXiv preprint arXiv: 2404.08819 , 2024. Nauen et al. [2024] ↑ Tobias Christian Nauen, Sebastian ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.15731v1", "content": "24 May 2024 — The illusion of state in state-space models. arXiv preprint arXiv: 2404.08819 , 2024. Nauen et al. [2024] ↑ Tobias Christian Nauen, Sebastian ..."}
+{"idx": 8, "title": "Exploring the Limitations of Mamba in COPY and CoT ...", "date": "", "ddg_snippet": "(2024) William Merrill, Jackson Petty, and Ashish Sabharwal. 2024. The illusion of state in state-space models. arXiv preprint arXiv: 2404.08819 . Merrill and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.03810v2", "content": "(2024) William Merrill, Jackson Petty, and Ashish Sabharwal. 2024. The illusion of state in state-space models. arXiv preprint arXiv: 2404.08819 . Merrill and ..."}
+{"idx": 9, "title": "State Space Models are Comparable to Transformers in ...", "date": "", "ddg_snippet": "29 May 2024 — The illusion of state in state-space models. arXiv preprint arXiv: 2404.08819 , 2024. Nakada and Imaizumi (2020) R. Nakada ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.19036v1", "content": "29 May 2024 — The illusion of state in state-space models. arXiv preprint arXiv: 2404.08819 , 2024. Nakada and Imaizumi (2020) R. Nakada ..."}
diff --git a/data/sampled_jsons/sitearxiv.org_2410.10562_subreddits_climate_activism_activation_percentage.jsonl b/data/sampled_jsons/sitearxiv.org_2410.10562_subreddits_climate_activism_activation_percentage.jsonl
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@@ -0,0 +1,7 @@
+{"idx": 0, "title": "[2410.10562] Causal Modeling of Climate Activism on Reddit", "date": "", "ddg_snippet": "Oct 14, 2024 · Climate activism is crucial in stimulating collective societal and behavioral change towards sustainable practices through political pressure. Although multiple factors contribute to the participation in activism , their complex relationships and the scarcity of data on their interactions have restricted most prior research to studying them in isolation, thus preventing the development of a ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.10562", "content": "Oct 14, 2024 · Climate activism is crucial in stimulating collective societal and behavioral change towards sustainable practices through political pressure. Although multiple factors contribute to the participation in activism , their complex relationships and the scarcity of data on their interactions have restricted most prior research to studying them in isolation, thus preventing the development of a ..."}
+{"idx": 1, "title": "Causal Modeling of Climate Activism on Reddit", "date": "", "ddg_snippet": "𝐴 User activated in climate activism groups. 𝐼 Interactions with activists .RQ1: Does media coverage about climate and climate action affect activation in climate activism groups on Reddit, and over which time scale?", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.10562", "content": "𝐴 User activated in climate activism groups. 𝐼 Interactions with activists .RQ1: Does media coverage about climate and climate action affect activation in climate activism groups on Reddit, and over which time scale?"}
+{"idx": 2, "title": "Causal Modeling of Climate Activism on Reddit", "date": "", "ddg_snippet": "User activated in climate activism groups. I𝐼Iitalic_I. Interactions with activists .Does media coverage about climate and climate action affect activation in climate activism groups on Reddit, and over which time scale?", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.10562v1", "content": "User activated in climate activism groups. I𝐼Iitalic_I. Interactions with activists .Does media coverage about climate and climate action affect activation in climate activism groups on Reddit, and over which time scale?"}
+{"idx": 3, "title": "[2410.10562] Causal Modeling of Climate Activism on Reddit", "date": "", "ddg_snippet": "That is, given two users with the same level of sympathy who participate in similar subreddits , if one interacts with an activist , the odds of activation increase by approximately 2.7 times.", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2410.10562", "content": "That is, given two users with the same level of sympathy who participate in similar subreddits , if one interacts with an activist , the odds of activation increase by approximately 2.7 times."}
+{"idx": 4, "title": "arXiv:2502.05049v1 [cs.SI] 7 Feb 2025", "date": "", "ddg_snippet": "to each subreddit for each dimension. Users can also be projected if represented as a weighted combination of t e subreddits where they participated. A user’s z-score for an attribute is the weighted average of the subreddit z-scores, weighted by the number of comme ts the user posted in each subreddit . The resulting z-score is then used to ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.05049", "content": "to each subreddit for each dimension. Users can also be projected if represented as a weighted combination of t e subreddits where they participated. A user’s z-score for an attribute is the weighted average of the subreddit z-scores, weighted by the number of comme ts the user posted in each subreddit . The resulting z-score is then used to ..."}
+{"idx": 5, "title": "Extracting Participation in Collective Action from Social Media", "date": "", "ddg_snippet": "Subreddits like r/ climate and r/environment, while highly ranked by climate -related mentions, did not appear in the top 10 for participation in collective action.Causal Modeling of Climate Activism on Reddit. arXiv preprint arXiv: 2410 . 10562 .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.07368v1", "content": "Subreddits like r/ climate and r/environment, while highly ranked by climate -related mentions, did not appear in the top 10 for participation in collective action.Causal Modeling of Climate Activism on Reddit. arXiv preprint arXiv: 2410 . 10562 ."}
+{"idx": 6, "title": "On the Inference of Sociodemographics on Reddit", "date": "", "ddg_snippet": "Causal Modeling of Climate Activism on Reddit. arXiv preprint arXiv: 2410 . 10562 .consists of an observed sequence of subreddit activations .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.05049v1", "content": "Causal Modeling of Climate Activism on Reddit. arXiv preprint arXiv: 2410 . 10562 .consists of an observed sequence of subreddit activations ."}
diff --git a/data/sampled_jsons/sitearxiv.org_Can_We_Leave_Deepfake_Data_Behind_in_Training_Deepfake_Detector.jsonl b/data/sampled_jsons/sitearxiv.org_Can_We_Leave_Deepfake_Data_Behind_in_Training_Deepfake_Detector.jsonl
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Can We Leave Deepfake Data Behind in Training Deepfake Detector?", "date": "", "ddg_snippet": "The generalization ability of deepfake detectors is vital for their applications in real-world scenarios. One effective solution to enhance this ability is to train the models with manually-blended data, which we termed \"blendfake\", encouraging models to learn generic forgery artifacts like blending boundary. Interestingly, current SoTA methods utilize blendfake without incorporating any ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2408.17052", "content": "The generalization ability of deepfake detectors is vital for their applications in real-world scenarios. One effective solution to enhance this ability is to train the models with manually-blended data, which we termed \"blendfake\", encouraging models to learn generic forgery artifacts like blending boundary. Interestingly, current SoTA methods utilize blendfake without incorporating any ..."}
+{"idx": 1, "title": "Deepfake Detection that Generalizes Across Benchmarks", "date": "", "ddg_snippet": "Can we leave deepfake data behind in training deepfake detector? In The Thirty-eighth Annual Conference on Neural Information Processing Systems, 2024. Choi et al. [2024] ↑ Jongwook Choi, Taehoon Kim, Yonghyun Jeong, Seungryul Baek, and Jongwon Choi. Exploiting style latent flows for generalizing deepfake video detection.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.06248v1", "content": "Can we leave deepfake data behind in training deepfake detector? In The Thirty-eighth Annual Conference on Neural Information Processing Systems, 2024. Choi et al. [2024] ↑ Jongwook Choi, Taehoon Kim, Yonghyun Jeong, Seungryul Baek, and Jongwon Choi. Exploiting style latent flows for generalizing deepfake video detection."}
+{"idx": 2, "title": "Can We Leave Deepfake Data Behind in Training Deepfake Detector?", "date": "", "ddg_snippet": "In recent years, the development of deepfake 1 has aroused significant concerns regarding privacy and security among the public. Deepfake detection aims to identify whether a face from an unknown source has been manipulated by deepfake techniques. Most detection methods perform promisingly when trained and tested on identical manipulations. However, given the unpredictability and complexity of ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2408.17052", "content": "In recent years, the development of deepfake 1 has aroused significant concerns regarding privacy and security among the public. Deepfake detection aims to identify whether a face from an unknown source has been manipulated by deepfake techniques. Most detection methods perform promisingly when trained and tested on identical manipulations. However, given the unpredictability and complexity of ..."}
+{"idx": 3, "title": "Learning Real Facial Concepts for Independent Deepfake Detection", "date": "", "ddg_snippet": "Can we leave deepfake data behind in training deepfake detector? In NeurIPS, 2024. Choi et al. [2024] ↑ Jongwook Choi, Taehoon Kim, Yonghyun Jeong, Seungryul Baek, and Jongwon Choi. Exploiting style latent flows for generalizing deepfake video detection. In CVPR, pages 1133-1143, 2024. Dolhansky et al. [2020] ↑", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.04460v1", "content": "Can we leave deepfake data behind in training deepfake detector? In NeurIPS, 2024. Choi et al. [2024] ↑ Jongwook Choi, Taehoon Kim, Yonghyun Jeong, Seungryul Baek, and Jongwon Choi. Exploiting style latent flows for generalizing deepfake video detection. In CVPR, pages 1133-1143, 2024. Dolhansky et al. [2020] ↑"}
+{"idx": 4, "title": "When Deepfakes Look Real: Detecting AI-Generated Faces with", "date": "", "ddg_snippet": "We conduct extensive experiments across 11 datasets , including both cross-domain and cross-method evaluations, to evaluate the effectiveness of ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.09022v1", "content": "We conduct extensive experiments across 11 datasets , including both cross-domain and cross-method evaluations, to evaluate the effectiveness of ..."}
+{"idx": 5, "title": "Veritas: Generalizable Deepfake Detection via Pattern-Aware", "date": "", "ddg_snippet": "However, current detectors mostly follow a standard evaluation, which involves training on one dataset Rossler et al.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.21048v1", "content": "However, current detectors mostly follow a standard evaluation, which involves training on one dataset Rossler et al."}
+{"idx": 6, "title": "The Tug-of-War Between Deepfake Generation and Detection", "date": "", "ddg_snippet": "However, since training detection algorithms depends on fake data created by generation tools, deepfake detectors lag behind generators.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2407.06174v4", "content": "However, since training detection algorithms depends on fake data created by generation tools, deepfake detectors lag behind generators."}
+{"idx": 7, "title": "AuthGuard: Generalizable Deepfake Detection via Language", "date": "", "ddg_snippet": "To enhance generalization in deepfake detection, we propose AuthGuard , a unified deepfake detection and reasoning framework that captures both ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.04501v1", "content": "To enhance generalization in deepfake detection, we propose AuthGuard , a unified deepfake detection and reasoning framework that captures both ..."}
+{"idx": 8, "title": "Seeing Through Deepfakes: A Human-Inspired Framework for", "date": "", "ddg_snippet": "We hypothesize that this sensitivity gives humans a natural advantage in detecting deepfake faces, as they can instinctively recognize fake faces ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.14807v1", "content": "We hypothesize that this sensitivity gives humans a natural advantage in detecting deepfake faces, as they can instinctively recognize fake faces ..."}
+{"idx": 9, "title": "Towards Generalized Source Tracing for Codec-Based Deepfake", "date": "", "ddg_snippet": "In this paper, we show that models trained solely on codec-resynthesized data tend to overfit to non-speech regions and struggle to generalize to ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.07294v2", "content": "In this paper, we show that models trained solely on codec-resynthesized data tend to overfit to non-speech regions and struggle to generalize to ..."}
diff --git a/data/sampled_jsons/sitearxiv.org_Corollary_2_data_perturbation_MNIST_Wasserstein_sparsely_connected.jsonl b/data/sampled_jsons/sitearxiv.org_Corollary_2_data_perturbation_MNIST_Wasserstein_sparsely_connected.jsonl
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index 0000000000000000000000000000000000000000..fee8ad5d7b394374c5a626cfda41595f01e6e651
--- /dev/null
+++ b/data/sampled_jsons/sitearxiv.org_Corollary_2_data_perturbation_MNIST_Wasserstein_sparsely_connected.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "On the Wasserstein median of probability ...", "date": "", "ddg_snippet": "In Section 4, we discuss two special cases on how the Wasserstein median problem has its connection to the literature based on the arguments pertained to the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2209.03318v5", "content": "In Section 4, we discuss two special cases on how the Wasserstein median problem has its connection to the literature based on the arguments pertained to the ..."}
+{"idx": 1, "title": "Robust Estimation under the Wasserstein Distance", "date": "", "ddg_snippet": "by S Nietert · 2023 · Cited by 8 — Abstract. We study the problem of robust distribution estimation under the Wasserstein distance, a popular discrepancy measure between ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2302.01237", "content": "by S Nietert · 2023 · Cited by 8 — Abstract. We study the problem of robust distribution estimation under the Wasserstein distance, a popular discrepancy measure between ..."}
+{"idx": 2, "title": "A New Robust Partial 𝑝-Wasserstein-Based Metric for ...", "date": "", "ddg_snippet": "6 May 2024 — The p p p italic_p - Wasserstein distance is a powerful metric for measuring similarities between probability distributions.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.03664v1", "content": "6 May 2024 — The p p p italic_p - Wasserstein distance is a powerful metric for measuring similarities between probability distributions."}
+{"idx": 3, "title": "Wasserstein-Based Metric for Comparing Distributions", "date": "", "ddg_snippet": "by S Raghvendra · 2024 · Cited by 17 — The 2 - Wasserstein distance is sensitive to mi- nor geometric differences between distributions, making it a very powerful dissimilarity ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2405.03664", "content": "by S Raghvendra · 2024 · Cited by 17 — The 2 - Wasserstein distance is sensitive to mi- nor geometric differences between distributions, making it a very powerful dissimilarity ..."}
+{"idx": 4, "title": "arXiv:2212.00570v2 [stat.ML] 14 Apr 2024", "date": "", "ddg_snippet": "by M Gürbüzbalaban · 2022 · Cited by 7 — We have the following corollary as an immediate consequence of Lemma D.1. Corollary D. 2 Under Assumption 2 .9 and the assumptions of Lemma D ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2212.00570", "content": "by M Gürbüzbalaban · 2022 · Cited by 7 — We have the following corollary as an immediate consequence of Lemma D.1. Corollary D. 2 Under Assumption 2 .9 and the assumptions of Lemma D ..."}
+{"idx": 5, "title": "arXiv:2504.20194v2 [stat.ML] 20 May 2025", "date": "", "ddg_snippet": "by A Kokot · 2025 — In this work, we explore whether divergences such as the Sinkhorn divergence can also yield benefits over the Wasserstein distance for coreset ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2504.20194", "content": "by A Kokot · 2025 — In this work, we explore whether divergences such as the Sinkhorn divergence can also yield benefits over the Wasserstein distance for coreset ..."}
+{"idx": 6, "title": "Diffusion generative models meet compressed sensing, with", "date": "", "ddg_snippet": "We develop a dimension reduction pipeline CSDM that combines diffusion models with compressed sensing [ 12 , 13 , 24 ] : (i) compress the data in ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.03898v1", "content": "We develop a dimension reduction pipeline CSDM that combines diffusion models with compressed sensing [ 12 , 13 , 24 ] : (i) compress the data in ..."}
+{"idx": 7, "title": "Likelihood Ratio Tests by Kernel Gaussian Embedding1footnote", "date": "", "ddg_snippet": "These include procedures for selecting kernels in a data -dependent manner [ 15 , 27 ] , spectral-regularization techniques [ 16 , 8 ] , calibration ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.07982v1", "content": "These include procedures for selecting kernels in a data -dependent manner [ 15 , 27 ] , spectral-regularization techniques [ 16 , 8 ] , calibration ..."}
+{"idx": 8, "title": "Likelihood Ratio Tests by Kernel Gaussian Embedding1footnote", "date": "", "ddg_snippet": "These include procedures for selecting kernels in a data -dependent manner [ 15 , 27 ] , spectral-regularization techniques [ 16 , 8 ] , calibration ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.07982v2", "content": "These include procedures for selecting kernels in a data -dependent manner [ 15 , 27 ] , spectral-regularization techniques [ 16 , 8 ] , calibration ..."}
+{"idx": 9, "title": "On the explainable properties of 1-Lipschitz Neural Networks:", "date": "", "ddg_snippet": "In b) , we illustrate that this approach can be applied to larger datasets, such as Celeb-A, by creating two counterfactual examples for the closed ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2206.06854v3", "content": "In b) , we illustrate that this approach can be applied to larger datasets, such as Celeb-A, by creating two counterfactual examples for the closed ..."}
diff --git a/data/sampled_jsons/sitearxiv.org_Deep-ELA_abstract.jsonl b/data/sampled_jsons/sitearxiv.org_Deep-ELA_abstract.jsonl
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index 0000000000000000000000000000000000000000..4e2ac740b6048c6d7ebadc02a4d3262bbb253149
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Deep-ELA: Deep Exploratory Landscape Analysis with Self ...", "date": "", "ddg_snippet": "Given the strengths and weaknesses of both, ELA and deep learning -based methodologies, there is a compelling case to be made for a synthesis of the two. This paper seeks to bridge this gap by introducing a hybrid approach, capitalizing on the merits of both paradigms.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2401.01192", "content": "Given the strengths and weaknesses of both, ELA and deep learning -based methodologies, there is a compelling case to be made for a synthesis of the two. This paper seeks to bridge this gap by introducing a hybrid approach, capitalizing on the merits of both paradigms."}
+{"idx": 1, "title": "Deep-ELA: Deep Exploratory Landscape Analysis with Self ...", "date": "", "ddg_snippet": "Jan 2, 2024 · Specifically, we pre-trained four transformers on millions of randomly generated optimization problems to learn deep representations of the landscapes of continuous single- and multi-objective optimization problems.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2401.01192", "content": "Jan 2, 2024 · Specifically, we pre-trained four transformers on millions of randomly generated optimization problems to learn deep representations of the landscapes of continuous single- and multi-objective optimization problems."}
+{"idx": 2, "title": "Deep - ELA : Deep Exploratory Landscape Analysis with Self-Supervised...", "date": "", "ddg_snippet": "Within this work, we propose a hybrid approach, Deep - ELA , which combines (the benefits of) deep learning and ELA features.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2401.01192v2", "content": "Within this work, we propose a hybrid approach, Deep - ELA , which combines (the benefits of) deep learning and ELA features."}
+{"idx": 3, "title": "Deep-ELA: Deep Exploratory Landscape Analysis with Self ...", "date": "", "ddg_snippet": "Given the strengths and weaknesses of both, ELA and deep learning-based methodologies, there is a compelling case to be made for a synthesis of the two. This paper seeks to bridge this gap by introducing a hybrid approach, capitalizing on the merits of both paradigms.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2401.01192", "content": "Given the strengths and weaknesses of both, ELA and deep learning-based methodologies, there is a compelling case to be made for a synthesis of the two. This paper seeks to bridge this gap by introducing a hybrid approach, capitalizing on the merits of both paradigms."}
+{"idx": 4, "title": "arXiv:2408.10672v2 [cs.LG] 26 Sep 2024", "date": "", "ddg_snippet": "Pascal Kerschke, and Heike Trautmann. Deep - ela : Deep exploratory landscape analysis with self-supervised pretrained transformers for single-and multi-objective continuous optimization problems.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2408.10672v2", "content": "Pascal Kerschke, and Heike Trautmann. Deep - ela : Deep exploratory landscape analysis with self-supervised pretrained transformers for single-and multi-objective continuous optimization problems."}
+{"idx": 5, "title": "Abstract - arXiv.org", "date": "", "ddg_snippet": "autmann. Deep - ela : Deep exploratory landscape analysis with self-supervised pretrained transformers for single-and multi-objective continuous optimization problems. arXiv preprint arXiv:2401.011", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2408.10672v1", "content": "autmann. Deep - ela : Deep exploratory landscape analysis with self-supervised pretrained transformers for single-and multi-objective continuous optimization problems. arXiv preprint arXiv:2401.011"}
+{"idx": 6, "title": "arXiv:2501.17663v1 [cs.LG] 29 Jan 2025", "date": "", "ddg_snippet": "Jan 30, 2025 · While most of these studies analyze the generalizability of only the ELA features, (with the exception of the inclusion of transformer features in [Cenikj et al., 2024a]), our study aims to provide a more comprehensive view of recently proposed features whose generalizability is yet to be investigated.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2501.17663", "content": "Jan 30, 2025 · While most of these studies analyze the generalizability of only the ELA features, (with the exception of the inclusion of transformer features in [Cenikj et al., 2024a]), our study aims to provide a more comprehensive view of recently proposed features whose generalizability is yet to be investigated."}
+{"idx": 7, "title": "Published as a conference paper at ICLR 2025", "date": "", "ddg_snippet": "Training Convergence. Difference between NeurELA and Deep - ELA . Further Interpretation Analysis. arXiv:2408.10672v3 [cs.LG] 27 Mar 2025.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2408.10672", "content": "Training Convergence. Difference between NeurELA and Deep - ELA . Further Interpretation Analysis. arXiv:2408.10672v3 [cs.LG] 27 Mar 2025."}
+{"idx": 8, "title": "A Survey of Meta-features Used for Automated Selection of Algorithms...", "date": "", "ddg_snippet": "Deep - ELA [71] is a methodology involving the unsupervised training of transformer models to produce representations of optimization problems which are invariant to problem transformations.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.06629v1", "content": "Deep - ELA [71] is a methodology involving the unsupervised training of transformer models to produce representations of optimization problems which are invariant to problem transformations."}
+{"idx": 9, "title": "Landscape Features in Single-Objective Continuous ...", "date": "", "ddg_snippet": "Jan 29, 2025 · We aim to provide a comprehensive analysis of recently proposed problem landscape features based on deep learning and topological landscape analysis, and compare them to the most commonly used ELA feature groups.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.17663v1", "content": "Jan 29, 2025 · We aim to provide a comprehensive analysis of recently proposed problem landscape features based on deep learning and topological landscape analysis, and compare them to the most commonly used ELA feature groups."}
diff --git a/data/sampled_jsons/sitearxiv.org_Diversified_in-domain_synthesis_with_efficient_fine-tuning_abstract.jsonl b/data/sampled_jsons/sitearxiv.org_Diversified_in-domain_synthesis_with_efficient_fine-tuning_abstract.jsonl
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index 0000000000000000000000000000000000000000..b77fbb3ae5390f642025ce8f84750806dbb4c025
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+++ b/data/sampled_jsons/sitearxiv.org_Diversified_in-domain_synthesis_with_efficient_fine-tuning_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2312.03046] Diversified in - domain synthesis with efficient ...", "date": "", "ddg_snippet": "Following this trend, we propose Diversified In - domain Synthesis with Efficient Fine - tuning (DISEF), a novel approach which addresses the generalization challenge in few-shot learning using synthetic data. DISEF consists of two main components.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2312.03046", "content": "Following this trend, we propose Diversified In - domain Synthesis with Efficient Fine - tuning (DISEF), a novel approach which addresses the generalization challenge in few-shot learning using synthetic data. DISEF consists of two main components."}
+{"idx": 1, "title": "Diversified in-domain synthesis with efficient fine-tuning for few-shot ...", "date": "", "ddg_snippet": "Abstract Few-shot image classification aims to learn an image classifier using only a small set of labeled examples per class. A recent research direction for improving few-shot classifiers involves augmenting the labelled samples with synthetic images created by state-of-the-art text-to-image generation models. Following this trend, we propose D iversified I n-domain S ynthesis with E ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2312.03046v2", "content": "Abstract Few-shot image classification aims to learn an image classifier using only a small set of labeled examples per class. A recent research direction for improving few-shot classifiers involves augmenting the labelled samples with synthetic images created by state-of-the-art text-to-image generation models. Following this trend, we propose D iversified I n-domain S ynthesis with E ..."}
+{"idx": 2, "title": "LoFT: LoRA-Fused Training Dataset Generation with Few- ...", "date": "", "ddg_snippet": "16 May 2025 — Diversified in-domain synthesis with efficient fine-tuning for few-shot classification, 2023. Dravid et al. [2024] ↑ Amil Dravid, Yossi ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.11703v1", "content": "16 May 2025 — Diversified in-domain synthesis with efficient fine-tuning for few-shot classification, 2023. Dravid et al. [2024] ↑ Amil Dravid, Yossi ..."}
+{"idx": 3, "title": "DataDream: Few-shot Guided Dataset Generation - arXiv.org", "date": "", "ddg_snippet": "Concurrently to our work, Diversified In-domain Synthesis with Efficient Fine-tuning (DISEF) [9] proposes to create a synthetic augmentation pipeline which leverages few-shots by starting the geration process from a noised real sample (same as [14]), then promotes diversity by denoising it conditioned on the caption from a different real image.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2407.10910v1", "content": "Concurrently to our work, Diversified In-domain Synthesis with Efficient Fine-tuning (DISEF) [9] proposes to create a synthetic augmentation pipeline which leverages few-shots by starting the geration process from a noised real sample (same as [14]), then promotes diversity by denoising it conditioned on the caption from a different real image."}
+{"idx": 4, "title": "Diversified in - domain synthesis with efficient fine - tuning for few-shot", "date": "", "ddg_snippet": "Following this trend, we pro-pose Diversified In - domain Synthesis with Efficient Fine - tuning (DISEF), a novel approach which addresses the gen-eralization challenge in few-shot learning using synthetic data. DISEF consists of two main components.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2312.03046", "content": "Following this trend, we pro-pose Diversified In - domain Synthesis with Efficient Fine - tuning (DISEF), a novel approach which addresses the gen-eralization challenge in few-shot learning using synthetic data. DISEF consists of two main components."}
+{"idx": 5, "title": "DataDream: Few-shot Guided Dataset Generation", "date": "", "ddg_snippet": "16 Jul 2024 — Concurrently to our work, Diversified In-domain Synthesis with Efficient Fine-tuning (DISEF) [9] proposes to create a synthetic augmentation ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2407.10910v2", "content": "16 Jul 2024 — Concurrently to our work, Diversified In-domain Synthesis with Efficient Fine-tuning (DISEF) [9] proposes to create a synthetic augmentation ..."}
+{"idx": 6, "title": "Retrieval-enriched zero-shot image classification in low- ...", "date": "", "ddg_snippet": "1 Nov 2024 — Diversified in-domain synthesis with efficient fine-tuning for few-shot classification. arXiv preprint arXiv:2312.03046. Deng et al. (2009)", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.00988v1", "content": "1 Nov 2024 — Diversified in-domain synthesis with efficient fine-tuning for few-shot classification. arXiv preprint arXiv:2312.03046. Deng et al. (2009)"}
+{"idx": 7, "title": "Computer Science Dec 2023", "date": "", "ddg_snippet": "Abstract ... Title: Diversified in-domain synthesis with efficient fine-tuning for few-shot classification ... abstract ; Added details in Introduction, Dataset ...", "subpage_snippet": "", "source": "arxiv.org", "link": "http://arxiv.org/list/cs/2023-12?skip=1300&show=1000", "content": "Abstract ... Title: Diversified in-domain synthesis with efficient fine-tuning for few-shot classification ... abstract ; Added details in Introduction, Dataset ..."}
+{"idx": 8, "title": "Computer Science Dec 2023", "date": "", "ddg_snippet": "[1341] arXiv:2312.03046 [ pdf , html, other]. Title: Diversified in-domain synthesis with efficient fine-tuning for few-shot classification. Victor G. Turrisi ...", "subpage_snippet": "", "source": "web3.arxiv.org", "link": "https://web3.arxiv.org/list/cs/2023-12?show=2000&skip=635", "content": "[1341] arXiv:2312.03046 [ pdf , html, other]. Title: Diversified in-domain synthesis with efficient fine-tuning for few-shot classification. Victor G. Turrisi ..."}
+{"idx": 9, "title": "DataDream: Few-shot Guided Dataset Generation", "date": "", "ddg_snippet": "One potential application lies in training or fine - tuning task-specific models on synthetic data.[9] da Costa, V.G.T., Dall’Asen, N., Wang, Y., Sebe, N., Ricci, E.: Diversified in - domain synthesis with efficient fine - tuning for few-shot classification (2023).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2407.10910v2/", "content": "One potential application lies in training or fine - tuning task-specific models on synthetic data.[9] da Costa, V.G.T., Dall’Asen, N., Wang, Y., Sebe, N., Ricci, E.: Diversified in - domain synthesis with efficient fine - tuning for few-shot classification (2023)."}
diff --git a/data/sampled_jsons/sitearxiv.org_FlowDec_Equation_6_probability_path_mean.jsonl b/data/sampled_jsons/sitearxiv.org_FlowDec_Equation_6_probability_path_mean.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..1a1f22845857d35de61f621ffc63cec1d6bfeb67
--- /dev/null
+++ b/data/sampled_jsons/sitearxiv.org_FlowDec_Equation_6_probability_path_mean.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "FlowDec: A flow-based full-band general audio codec with high ...", "date": "", "ddg_snippet": "Abstract We propose FlowDec , a neural full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.01485v1", "content": "Abstract We propose FlowDec , a neural full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method."}
+{"idx": 1, "title": "Elucidating the Design Choice of Probability Paths in Flow Matching...", "date": "", "ddg_snippet": "Flow matching has recently emerged as a powerful paradigm for generative modeling and has been extended to probabilistic time series forecasting in latent spaces. However, the impact of the specific choice of probability path model on forecasting performance remains under-explored.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.03229v2", "content": "Flow matching has recently emerged as a powerful paradigm for generative modeling and has been extended to probabilistic time series forecasting in latent spaces. However, the impact of the specific choice of probability path model on forecasting performance remains under-explored."}
+{"idx": 2, "title": "Microsoft Word - PathProb.docx", "date": "", "ddg_snippet": "Path probability of stochastic motion: A functional approach.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1602.04363", "content": "Path probability of stochastic motion: A functional approach."}
+{"idx": 3, "title": "Matching Normalizing Flows and Probability Paths on Manifolds", "date": "", "ddg_snippet": "Continuous Normalizing Flows (CNFs) are a class of generative models that transform a prior distribution to a model distribution by solving an ordinary differential equation (ODE). We propose to train CNFs on manifolds by minimizing probability path divergence (PPD), a novel family...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2207.04711", "content": "Continuous Normalizing Flows (CNFs) are a class of generative models that transform a prior distribution to a model distribution by solving an ordinary differential equation (ODE). We propose to train CNFs on manifolds by minimizing probability path divergence (PPD), a novel family..."}
+{"idx": 4, "title": "Density Ratio Estimation with Conditional Probability Paths", "date": "", "ddg_snippet": "Right: A useful decomposition of the probability path and time scores is obtained by conditioning on a final data point. The ensuing conditional density is Gaussian, and thus, the ensuing conditional time scores are analytically tractable.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.02300v2", "content": "Right: A useful decomposition of the probability path and time scores is obtained by conditioning on a final data point. The ensuing conditional density is Gaussian, and thus, the ensuing conditional time scores are analytically tractable."}
+{"idx": 5, "title": "[2503.01485] FlowDec: A flow-based full-band general audio ...", "date": "", "ddg_snippet": "Mar 3, 2025 · We propose FlowDec , a neural full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method. Compared to the prior work ScoreDec which is based on score matching, we generalize from speech to general audio and move from 24 kbit/s to as low as 4 kbit/s, while improving output ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.01485", "content": "Mar 3, 2025 · We propose FlowDec , a neural full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method. Compared to the prior work ScoreDec which is based on score matching, we generalize from speech to general audio and move from 24 kbit/s to as low as 4 kbit/s, while improving output ..."}
+{"idx": 6, "title": "A arXiv:2503.01485v1 [cs.SD] 3 Mar 2025", "date": "", "ddg_snippet": "At our default setting NFE = 6 , this results in a total RTF of 0.2285 for FlowDec -75(m/s) and 0.2235 for FlowDec - 25s, a significant improvement over the RTF of 1.707 for ScoreDec (Wu et al., 2024).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.01485v1", "content": "At our default setting NFE = 6 , this results in a total RTF of 0.2285 for FlowDec -75(m/s) and 0.2235 for FlowDec - 25s, a significant improvement over the RTF of 1.707 for ScoreDec (Wu et al., 2024)."}
+{"idx": 7, "title": "Elucidating the Design Choice of Probability Paths in Flow ...", "date": "", "ddg_snippet": "However, the impact of the specific choice of probability path model on forecasting performance, particularly for high-dimensional spatio-temporal dynamics, remains under-explored. In this work, we demonstrate that forecasting spatio-temporal data with flow matching is highly sensitive to the selection of the probability path model.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.03229v3", "content": "However, the impact of the specific choice of probability path model on forecasting performance, particularly for high-dimensional spatio-temporal dynamics, remains under-explored. In this work, we demonstrate that forecasting spatio-temporal data with flow matching is highly sensitive to the selection of the probability path model."}
+{"idx": 8, "title": "[2503.01485] FlowDec: A flow-based full-band general audio ...", "date": "", "ddg_snippet": "We propose FlowDec , a neural full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method. Compar…", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2503.01485", "content": "We propose FlowDec , a neural full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method. Compar…"}
+{"idx": 9, "title": "BinauralFlow: A Causal and Streamable Approach for High-Quality", "date": "", "ddg_snippet": "Meanwhile, the inclusion of reverberation effects and background noise that match the environment is crucial for improving the realism and immersion ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.22865v1", "content": "Meanwhile, the inclusion of reverberation effects and background noise that match the environment is crucial for improving the realism and immersion ..."}
diff --git a/data/sampled_jsons/sitearxiv.org_GenAI_Arena_Section_4.1_Playground_V2.5_SDXL_same_architecture_private_dataset_year_2024.jsonl b/data/sampled_jsons/sitearxiv.org_GenAI_Arena_Section_4.1_Playground_V2.5_SDXL_same_architecture_private_dataset_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3e80ed41183c588d60dba6573f113a16330dbfff
--- /dev/null
+++ b/data/sampled_jsons/sitearxiv.org_GenAI_Arena_Section_4.1_Playground_V2.5_SDXL_same_architecture_private_dataset_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GenAI Arena: An Open Evaluation Platform for Generative Models", "date": "", "ddg_snippet": "The currently top-1 model is Playground V2.5 , released by Playground .ai, which follows the same architecture as SDXL but is trained with a private dataset . In contrast, SDXL only ranks in the thirteenth position, lagging significantly behind.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.04485v4", "content": "The currently top-1 model is Playground V2.5 , released by Playground .ai, which follows the same architecture as SDXL but is trained with a private dataset . In contrast, SDXL only ranks in the thirteenth position, lagging significantly behind."}
+{"idx": 1, "title": "arXiv:2406.04485v4 [cs.AI] 11 Nov 2024", "date": "", "ddg_snippet": "on, we col-lected 6300 votes in total. The currently top-1 model is Playground V2.5 , released by Playground .ai, which follows the same architecture as SDXL but is trained with a private dataset . In contrast, SDXL only ranks in the thirteenth", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2406.04485", "content": "on, we col-lected 6300 votes in total. The currently top-1 model is Playground V2.5 , released by Playground .ai, which follows the same architecture as SDXL but is trained with a private dataset . In contrast, SDXL only ranks in the thirteenth"}
+{"idx": 2, "title": "GenAI Arena : An Open Evaluation Platform for Generative Models", "date": "", "ddg_snippet": "3 GenAI - Arena : Design and Implementation. Figure 2: GenAI Arena User Voting Interface.. Playground V 2 and Playground V 2 . 5 are based on SDXL architecture , but trained by Playground .ai from scratch with an internal dataset .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.04485v1", "content": "3 GenAI - Arena : Design and Implementation. Figure 2: GenAI Arena User Voting Interface.. Playground V 2 and Playground V 2 . 5 are based on SDXL architecture , but trained by Playground .ai from scratch with an internal dataset ."}
+{"idx": 3, "title": "GenAI Arena: An Open Evaluation Platform for Generative Models", "date": "", "ddg_snippet": "The currently top-ranked models are Playground V2.5 and Playground V2. Both of the models are released by Playground .ai, which follows the same architecture as SDXL but is trained with a private dataset .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.04485v3", "content": "The currently top-ranked models are Playground V2.5 and Playground V2. Both of the models are released by Playground .ai, which follows the same architecture as SDXL but is trained with a private dataset ."}
+{"idx": 4, "title": "GenAI Arena : An Open Evaluation Platform for", "date": "", "ddg_snippet": "Prompt Templates for GenAI -Bench. GenAI Arena : An Open Evaluation Platform for Generative Models. Playground V 2 and Playground V 2 . 5 are based on SDXL architecture , but trained by Playground .ai from scratch with an internal dataset .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2406.04485v2", "content": "Prompt Templates for GenAI -Bench. GenAI Arena : An Open Evaluation Platform for Generative Models. Playground V 2 and Playground V 2 . 5 are based on SDXL architecture , but trained by Playground .ai from scratch with an internal dataset ."}
+{"idx": 5, "title": "[2402.17245] Playground v2.5: Three Insights towards Enhancing ...", "date": "", "ddg_snippet": "Through extensive analysis and experiments, Playground v2.5 demonstrates state-of-the-art performance in terms of aesthetic quality under various conditions and aspect ratios, outperforming both widely-used open-source models like SDXL and Playground v2, and closed-source commercial systems such as DALLE 3 and Midjourney v5.2.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2402.17245", "content": "Through extensive analysis and experiments, Playground v2.5 demonstrates state-of-the-art performance in terms of aesthetic quality under various conditions and aspect ratios, outperforming both widely-used open-source models like SDXL and Playground v2, and closed-source commercial systems such as DALLE 3 and Midjourney v5.2."}
+{"idx": 6, "title": "Playground v2.5: Three Insights towards Enhancing Aesthetic Quality in ...", "date": "", "ddg_snippet": "Because the performance differential between Playground v2.5 and SDXL was so large, we also tested against state-of-the-art closed-source models like DALL ⋅ ⋅ \\cdot ⋅ E 3 betker2023improving and Midjourney 5.2, and found that Playground v2.5 still outperforms these models in aesthetic quality.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2402.17245v1", "content": "Because the performance differential between Playground v2.5 and SDXL was so large, we also tested against state-of-the-art closed-source models like DALL ⋅ ⋅ \\cdot ⋅ E 3 betker2023improving and Midjourney 5.2, and found that Playground v2.5 still outperforms these models in aesthetic quality."}
+{"idx": 7, "title": "GenAI Arena: An Open Evaluation Platform for Generative Models", "date": "", "ddg_snippet": "This paper proposes an open platform GenAI-Arena to evaluate different image and video generative models, where users can actively participate in evaluating these models. By leveraging collective user feedback and votes, GenAI-Arena aims to provide a more democratic and accurate measure of model performance.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2406.04485", "content": "This paper proposes an open platform GenAI-Arena to evaluate different image and video generative models, where users can actively participate in evaluating these models. By leveraging collective user feedback and votes, GenAI-Arena aims to provide a more democratic and accurate measure of model performance."}
+{"idx": 8, "title": "GenAI-Arena arXiv:2406.04485v1 [cs.AI] 6 Jun 2024", "date": "", "ddg_snippet": "ked models are Playground V2.5 and Playground V2. Both of the models are released by Playground .ai, which follows the same architect re as SDXL but is trained with a private dataset . In contrast, SDXL only ranks in t", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2406.04485v1", "content": "ked models are Playground V2.5 and Playground V2. Both of the models are released by Playground .ai, which follows the same architect re as SDXL but is trained with a private dataset . In contrast, SDXL only ranks in t"}
+{"idx": 9, "title": "Playground v3: Improving Text-to-Image Alignment with Deep-Fusion Large ...", "date": "", "ddg_snippet": "We introduce Playground v3 (PGv3), our latest text-to-image model that achieves state-of-the-art (SoTA) performance across multiple testing benchmarks, excels in graphic design abilities and introduces new capabilities. Unlike traditional text-to-image generative models that rely on pre-trained language models like T5 or CLIP text encoders, our approach fully integrates Large Language Models ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2409.10695", "content": "We introduce Playground v3 (PGv3), our latest text-to-image model that achieves state-of-the-art (SoTA) performance across multiple testing benchmarks, excels in graphic design abilities and introduces new capabilities. Unlike traditional text-to-image generative models that rely on pre-trained language models like T5 or CLIP text encoders, our approach fully integrates Large Language Models ..."}
diff --git a/data/sampled_jsons/sitearxiv.org_Neural_Persistence_Dynamics.jsonl b/data/sampled_jsons/sitearxiv.org_Neural_Persistence_Dynamics.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6664e0438c95672079e6a5cda259ab5b9ad832da
--- /dev/null
+++ b/data/sampled_jsons/sitearxiv.org_Neural_Persistence_Dynamics.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Neural Persistence Dynamics", "date": "", "ddg_snippet": "Conceptually, Neural Persistence Dynamics embodies the idea of learning the dynamics in a temporal sequence of vectorized topological summaries instead of ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.15732v2", "content": "Conceptually, Neural Persistence Dynamics embodies the idea of learning the dynamics in a temporal sequence of vectorized topological summaries instead of ..."}
+{"idx": 1, "title": "Neural Persistence Dynamics", "date": "", "ddg_snippet": "...but provide compelling empirical evidence that our proposed model – neural persistence dynamics – substantially outperforms the state-of-the-art across a diverse set of parameter...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.15732v1", "content": "...but provide compelling empirical evidence that our proposed model – neural persistence dynamics – substantially outperforms the state-of-the-art across a diverse set of parameter..."}
+{"idx": 2, "title": "[2405.15732] Neural Persistence Dynamics", "date": "", "ddg_snippet": "by S Zeng · 2024 — We consider the problem of learning the dynamics in the topology of time-evolving point clouds , the prevalent spatiotemporal model for systems exhibiting ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2405.15732", "content": "by S Zeng · 2024 — We consider the problem of learning the dynamics in the topology of time-evolving point clouds , the prevalent spatiotemporal model for systems exhibiting ..."}
+{"idx": 3, "title": "Neural Persistence Dynamics", "date": "", "ddg_snippet": "Conceptually, Neural Persistence Dynamics embodies the idea of learning the dynamics in a temporal sequence of vectorized topological summaries instead of, e.g...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.15732", "content": "Conceptually, Neural Persistence Dynamics embodies the idea of learning the dynamics in a temporal sequence of vectorized topological summaries instead of, e.g..."}
+{"idx": 4, "title": "Neural Persistence Dynamics", "date": "", "ddg_snippet": "A schematic overview of our Neural Persistence Dynamics approach is shown in Fig. 1. Additional details, including different model variants, are illustrated in Fig.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2405.15732", "content": "A schematic overview of our Neural Persistence Dynamics approach is shown in Fig. 1. Additional details, including different model variants, are illustrated in Fig."}
+{"idx": 5, "title": "Persistency of Excitation for Robustness of Neural Networks", "date": "", "ddg_snippet": "by K Nar · 2019 · Cited by 20 — In this work, we analyze the dynamics of the gradient descent algorithm while training a two-layer neural network with two different loss functions.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1911.01043", "content": "by K Nar · 2019 · Cited by 20 — In this work, we analyze the dynamics of the gradient descent algorithm while training a two-layer neural network with two different loss functions."}
+{"idx": 6, "title": "Koopman Autoencoders Learn Neural Representation ...", "date": "", "ddg_snippet": "by NS Aswani · 2025 — This paper explores a simple question: can we model the internal transformations of a neural net- work using dynamical systems theory?", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2505.12809?", "content": "by NS Aswani · 2025 — This paper explores a simple question: can we model the internal transformations of a neural net- work using dynamical systems theory?"}
+{"idx": 7, "title": "Data-Efficient Neural Training with Dynamic Connectomes", "date": "", "ddg_snippet": "9 Aug 2025 — Neural persistence : A complexity measure for deep neural networks using algebraic topology. In International Conference on Learning ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.06817v1", "content": "9 Aug 2025 — Neural persistence : A complexity measure for deep neural networks using algebraic topology. In International Conference on Learning ..."}
+{"idx": 8, "title": "Hybridization of Persistent Homology with Neural Networks ...", "date": "", "ddg_snippet": "3 Sept 2024 — A complex filtration process effectively captures the dynamic contours of datasets by tracking the emergence and disappearance of holes or voids ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2409.01519v1", "content": "3 Sept 2024 — A complex filtration process effectively captures the dynamic contours of datasets by tracking the emergence and disappearance of holes or voids ..."}
+{"idx": 9, "title": "Physical blowups via buffered time change in a mean-field ...", "date": "", "ddg_snippet": "by N Papadopoulos · 2025 — Here, we unambiguously define physical blowup dynamics as solutions to a fixed-point problem bearing on the time change associated to the McKean ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2508.15961", "content": "by N Papadopoulos · 2025 — Here, we unambiguously define physical blowup dynamics as solutions to a fixed-point problem bearing on the time change associated to the McKean ..."}
diff --git a/data/sampled_jsons/sitearxiv.org_Taming_Knowledge_Conflicts_in_Language_Models_JUICE_dual-run_mechanism.jsonl b/data/sampled_jsons/sitearxiv.org_Taming_Knowledge_Conflicts_in_Language_Models_JUICE_dual-run_mechanism.jsonl
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index 0000000000000000000000000000000000000000..e9e4b1677d72b7e2703685670ec98787820c5c33
--- /dev/null
+++ b/data/sampled_jsons/sitearxiv.org_Taming_Knowledge_Conflicts_in_Language_Models_JUICE_dual-run_mechanism.jsonl
@@ -0,0 +1,9 @@
+{"idx": 0, "title": "Taming Knowledge Conflicts in Language Models", "date": "", "ddg_snippet": "Building upon this insight, we propose Just Run Twice ( JuICE ), a test-time attention intervention method that steers LMs toward either parametric beliefs or contextual knowledge without requiring fine-tuning. JuICE identifies a set of reliable attention heads and leverages a dual-run approach to mitigate the superposition effects.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.10996v1", "content": "Building upon this insight, we propose Just Run Twice ( JuICE ), a test-time attention intervention method that steers LMs toward either parametric beliefs or contextual knowledge without requiring fine-tuning. JuICE identifies a set of reliable attention heads and leverages a dual-run approach to mitigate the superposition effects."}
+{"idx": 1, "title": "Taming Knowledge Conflicts in Language Models - arXiv.org", "date": "", "ddg_snippet": "dual-run approach to mitigate the superposi-tion effects. Extensive experiments across 11 datasets and 6 model architectures demonstrate that JUICE sets the new state-of-the-art perfor-mance and robust generalization, achieving sig-nificant and consistent improvement across dif-ferent domains under various conflict types. Fi-nally, we theoretically analyze knowledge conflict and the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.10996", "content": "dual-run approach to mitigate the superposi-tion effects. Extensive experiments across 11 datasets and 6 model architectures demonstrate that JUICE sets the new state-of-the-art perfor-mance and robust generalization, achieving sig-nificant and consistent improvement across dif-ferent domains under various conflict types. Fi-nally, we theoretically analyze knowledge conflict and the ..."}
+{"idx": 2, "title": "Taming Knowledge Conflicts in Language Models", "date": "", "ddg_snippet": "JuICE achieves consistently high performance in facing challenging knowledge conflicts . In this paper, we begin by treating LMs as an oracle and considering the setting of factual recall, a task requiring pure memorization.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.10996v2", "content": "JuICE achieves consistently high performance in facing challenging knowledge conflicts . In this paper, we begin by treating LMs as an oracle and considering the setting of factual recall, a task requiring pure memorization."}
+{"idx": 3, "title": "[2503.10996] Taming Knowledge Conflicts in Language Models Taming Knowledge Conflicts in Language Models - arXiv.org Resolving Knowledge Conflicts in Large Language Models [2403.08319] Knowledge Conflicts for LLMs: A Survey - arXiv.org [2504.12982] Accommodate Knowledge Conflicts in Retrieval ... Taming Knowledge Conflicts in Language Models Taming Knowledge Conflicts in Language Models", "date": "", "ddg_snippet": "Mar 14, 2025 · Building upon this insight, we propose Just Run Twice ( JuICE ), a test-time attention intervention method that steers LMs toward either parametric beliefs or contextual knowledge without requiring fine-tuning. JuICE identifies a set of reliable attention heads and leverages a dual-run approach to mitigate the superposition effects. dual-run approach to mitigate the superposi-tion effects. Extensive experiments across 11 datasets and 6 model architectures demonstrate that JUICE sets the new state-of-the-art perfor-mance and robust generalization, achieving sig-nificant and consistent improvement across dif-ferent domains under various conflict types. Fi-nally, we theoretically analyze knowledge conflict and the ... Oct 2, 2023 · Large language models (LLMs) often encounter knowledge conflicts , scenarios where discrepancy arises between the internal parametric knowledge of LLMs and non-parametric information provided in the prompt context. In this work we ask what are the desiderata for LLMs when a knowledge conflict arises and whether existing LLMs fulfill them. We posit that LLMs should 1) identify knowledge ... Mar 13, 2024 · This survey provides an in -depth analysis of knowledge conflicts for large language models (LLMs), highlighting the complex challenges they encounter when blending contextual and parametric knowledge . Our focus is on three categories of knowledge conflicts : context-memory, inter-context, and intra-memory conflict. These conflicts can significantly impact the trustworthiness and performance of ... Apr 17, 2025 · The proliferation of large language models (LLMs) has significantly advanced information retrieval systems, particularly in response generation (RG). Unfortunately, LLMs often face knowledge conflicts between internal memory and retrievaled external information, arising from misinformation, biases, or outdated knowledge . These conflicts undermine response reliability and introduce uncertainty ... Building upon this insight, we propose Just Run Twice ( JuICE ), a test-time attention intervention method that steers LMs toward either parametric beliefs or contextual knowledge without requiring fine-tuning. JuICE identifies a set of reliable attention heads and leverages a dual-run approach to mitigate the superposition effects. Jun 9, 2025 · This simplified design serves as an ablation study, highlighting the significance of JuICE ’s dual-run mechanism . Algorithm 4presents the JuNealgorithm in detail.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.10996", "content": "Mar 14, 2025 · Building upon this insight, we propose Just Run Twice ( JuICE ), a test-time attention intervention method that steers LMs toward either parametric beliefs or contextual knowledge without requiring fine-tuning. JuICE identifies a set of reliable attention heads and leverages a dual-run approach to mitigate the superposition effects. dual-run approach to mitigate the superposi-tion effects. Extensive experiments across 11 datasets and 6 model architectures demonstrate that JUICE sets the new state-of-the-art perfor-mance and robust generalization, achieving sig-nificant and consistent improvement across dif-ferent domains under various conflict types. Fi-nally, we theoretically analyze knowledge conflict and the ... Oct 2, 2023 · Large language models (LLMs) often encounter knowledge conflicts , scenarios where discrepancy arises between the internal parametric knowledge of LLMs and non-parametric information provided in the prompt context. In this work we ask what are the desiderata for LLMs when a knowledge conflict arises and whether existing LLMs fulfill them. We posit that LLMs should 1) identify knowledge ... Mar 13, 2024 · This survey provides an in -depth analysis of knowledge conflicts for large language models (LLMs), highlighting the complex challenges they encounter when blending contextual and parametric knowledge . Our focus is on three categories of knowledge conflicts : context-memory, inter-context, and intra-memory conflict. These conflicts can significantly impact the trustworthiness and performance of ... Apr 17, 2025 · The proliferation of large language models (LLMs) has significantly advanced information retrieval systems, particularly in response generation (RG). Unfortunately, LLMs often face knowledge conflicts between internal memory and retrievaled external information, arising from misinformation, biases, or outdated knowledge . These conflicts undermine response reliability and introduce uncertainty ... Building upon this insight, we propose Just Run Twice ( JuICE ), a test-time attention intervention method that steers LMs toward either parametric beliefs or contextual knowledge without requiring fine-tuning. JuICE identifies a set of reliable attention heads and leverages a dual-run approach to mitigate the superposition effects. Jun 9, 2025 · This simplified design serves as an ablation study, highlighting the significance of JuICE ’s dual-run mechanism . Algorithm 4presents the JuNealgorithm in detail."}
+{"idx": 4, "title": "Training of Scaffolded Language Models with Language Supervision...", "date": "", "ddg_snippet": "Scaffolded language models embed language models (LMs) in a framework that extends their ability beyond traditional NLP tasks to perform open-ended digital automation tasks.Li, G., Chen, Y., and Tong, H. Taming knowledge conflicts in language models , 2025a.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.16392", "content": "Scaffolded language models embed language models (LMs) in a framework that extends their ability beyond traditional NLP tasks to perform open-ended digital automation tasks.Li, G., Chen, Y., and Tong, H. Taming knowledge conflicts in language models , 2025a."}
+{"idx": 5, "title": "Resolving Knowledge Conflicts in Large Language Models [2403.08319] Knowledge Conflicts for LLMs: A Survey - arXiv.org [2504.12982] Accommodate Knowledge Conflicts in Retrieval ... Taming Knowledge Conflicts in Language Models Taming Knowledge Conflicts in Language Models", "date": "", "ddg_snippet": "Oct 2, 2023 · Large language models (LLMs) often encounter knowledge conflicts , scenarios where discrepancy arises between the internal parametric knowledge of LLMs and non-parametric information provided in the prompt context. In this work we ask what are the desiderata for LLMs when a knowledge conflict arises and whether existing LLMs fulfill them. We posit that LLMs should 1) identify knowledge ... Mar 13, 2024 · This survey provides an in -depth analysis of knowledge conflicts for large language models (LLMs), highlighting the complex challenges they encounter when blending contextual and parametric knowledge . Our focus is on three categories of knowledge conflicts : context-memory, inter-context, and intra-memory conflict. These conflicts can significantly impact the trustworthiness and performance of ... Apr 17, 2025 · The proliferation of large language models (LLMs) has significantly advanced information retrieval systems, particularly in response generation (RG). Unfortunately, LLMs often face knowledge conflicts between internal memory and retrievaled external information, arising from misinformation, biases, or outdated knowledge . These conflicts undermine response reliability and introduce uncertainty ... Building upon this insight, we propose Just Run Twice ( JuICE ), a test-time attention intervention method that steers LMs toward either parametric beliefs or contextual knowledge without requiring fine-tuning. JuICE identifies a set of reliable attention heads and leverages a dual-run approach to mitigate the superposition effects. Jun 9, 2025 · This simplified design serves as an ablation study, highlighting the significance of JuICE ’s dual-run mechanism . Algorithm 4presents the JuNealgorithm in detail.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2310.00935", "content": "Oct 2, 2023 · Large language models (LLMs) often encounter knowledge conflicts , scenarios where discrepancy arises between the internal parametric knowledge of LLMs and non-parametric information provided in the prompt context. In this work we ask what are the desiderata for LLMs when a knowledge conflict arises and whether existing LLMs fulfill them. We posit that LLMs should 1) identify knowledge ... Mar 13, 2024 · This survey provides an in -depth analysis of knowledge conflicts for large language models (LLMs), highlighting the complex challenges they encounter when blending contextual and parametric knowledge . Our focus is on three categories of knowledge conflicts : context-memory, inter-context, and intra-memory conflict. These conflicts can significantly impact the trustworthiness and performance of ... Apr 17, 2025 · The proliferation of large language models (LLMs) has significantly advanced information retrieval systems, particularly in response generation (RG). Unfortunately, LLMs often face knowledge conflicts between internal memory and retrievaled external information, arising from misinformation, biases, or outdated knowledge . These conflicts undermine response reliability and introduce uncertainty ... Building upon this insight, we propose Just Run Twice ( JuICE ), a test-time attention intervention method that steers LMs toward either parametric beliefs or contextual knowledge without requiring fine-tuning. JuICE identifies a set of reliable attention heads and leverages a dual-run approach to mitigate the superposition effects. Jun 9, 2025 · This simplified design serves as an ablation study, highlighting the significance of JuICE ’s dual-run mechanism . Algorithm 4presents the JuNealgorithm in detail."}
+{"idx": 6, "title": "[2403.08319] Knowledge Conflicts for LLMs: A Survey - arXiv.org", "date": "", "ddg_snippet": "Mar 13, 2024 · This survey provides an in -depth analysis of knowledge conflicts for large language models (LLMs), highlighting the complex challenges they encounter when blending contextual and parametric knowledge . Our focus is on three categories of knowledge conflicts : context-memory, inter-context, and intra-memory conflict. These conflicts can significantly impact the trustworthiness and performance of ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2403.08319", "content": "Mar 13, 2024 · This survey provides an in -depth analysis of knowledge conflicts for large language models (LLMs), highlighting the complex challenges they encounter when blending contextual and parametric knowledge . Our focus is on three categories of knowledge conflicts : context-memory, inter-context, and intra-memory conflict. These conflicts can significantly impact the trustworthiness and performance of ..."}
+{"idx": 7, "title": "[2504.12982] Accommodate Knowledge Conflicts in Retrieval ... Taming Knowledge Conflicts in Language Models Taming Knowledge Conflicts in Language Models", "date": "", "ddg_snippet": "Apr 17, 2025 · The proliferation of large language models (LLMs) has significantly advanced information retrieval systems, particularly in response generation (RG). Unfortunately, LLMs often face knowledge conflicts between internal memory and retrievaled external information, arising from misinformation, biases, or outdated knowledge . These conflicts undermine response reliability and introduce uncertainty ... Building upon this insight, we propose Just Run Twice ( JuICE ), a test-time attention intervention method that steers LMs toward either parametric beliefs or contextual knowledge without requiring fine-tuning. JuICE identifies a set of reliable attention heads and leverages a dual-run approach to mitigate the superposition effects. Jun 9, 2025 · This simplified design serves as an ablation study, highlighting the significance of JuICE ’s dual-run mechanism . Algorithm 4presents the JuNealgorithm in detail.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2504.12982", "content": "Apr 17, 2025 · The proliferation of large language models (LLMs) has significantly advanced information retrieval systems, particularly in response generation (RG). Unfortunately, LLMs often face knowledge conflicts between internal memory and retrievaled external information, arising from misinformation, biases, or outdated knowledge . These conflicts undermine response reliability and introduce uncertainty ... Building upon this insight, we propose Just Run Twice ( JuICE ), a test-time attention intervention method that steers LMs toward either parametric beliefs or contextual knowledge without requiring fine-tuning. JuICE identifies a set of reliable attention heads and leverages a dual-run approach to mitigate the superposition effects. Jun 9, 2025 · This simplified design serves as an ablation study, highlighting the significance of JuICE ’s dual-run mechanism . Algorithm 4presents the JuNealgorithm in detail."}
+{"idx": 8, "title": "1 Introduction", "date": "", "ddg_snippet": "Scaffolded language models embed language models (LMs) in a framework that extends their ability beyond traditional NLP tasks to perform open-ended digital automation tasks.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.16392v2", "content": "Scaffolded language models embed language models (LMs) in a framework that extends their ability beyond traditional NLP tasks to perform open-ended digital automation tasks."}
diff --git a/data/sampled_jsons/sitearxiv.orgabs2305.14709.jsonl b/data/sampled_jsons/sitearxiv.orgabs2305.14709.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d11b759449a451ef0fa7e60438ec34aa9d58f7c7
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+++ b/data/sampled_jsons/sitearxiv.orgabs2305.14709.jsonl
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+{"idx": 0, "title": "Regret Matching+: (In)Stability and Fast Convergence ...", "date": "", "ddg_snippet": "by G Farina · 2023 · Cited by 17 — Abstract:Regret Matching+ (RM+) and its variants are important algorithms for solving large-scale games . However, a theoretical understanding of ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2305.14709", "content": "by G Farina · 2023 · Cited by 17 — Abstract:Regret Matching+ (RM+) and its variants are important algorithms for solving large-scale games . However, a theoretical understanding of ..."}
+{"idx": 1, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/sitearxiv.orgabs2502.01846_UVGS_dataset_size_year_2024.jsonl b/data/sampled_jsons/sitearxiv.orgabs2502.01846_UVGS_dataset_size_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..421f3b59b6ad9b70c0633988afe3561960368897
--- /dev/null
+++ b/data/sampled_jsons/sitearxiv.orgabs2502.01846_UVGS_dataset_size_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "(株) 東芝 【6502】： 株価 ・株式情報 - Yahoo!ファイナンス", "date": "", "ddg_snippet": "(株) 東芝 【6502】の 株価 、チャート、最新の関連ニュース、掲示板、みんなの評価などをご覧いただけます。 前日終値、高値、安値はもちろんのこと年初来高値/安値もご覧いただけます。", "subpage_snippet": "", "source": "finance.yahoo.co.jp", "link": "https://finance.yahoo.co.jp/quote/6502.T", "content": "(株) 東芝 【6502】の 株価 、チャート、最新の関連ニュース、掲示板、みんなの評価などをご覧いただけます。 前日終値、高値、安値はもちろんのこと年初来高値/安値もご覧いただけます。"}
+{"idx": 1, "title": "東芝 、74年の株式上場の歴史に幕…上場廃止になると株式はどう ...", "date": "", "ddg_snippet": "Dec 20, 2023 · 冷蔵庫や洗濯機、掃除機といった家電の国産化に初めて成功し、世に送り出してきた 東芝 。 その 東芝 が2023年12月20日、株式市場から退場することになりました。 東芝 株 は上場廃止となり、基本的に売買ができなくなります。", "subpage_snippet": "", "source": "media.moneyforward.com", "link": "https://media.moneyforward.com/articles/9104/summary", "content": "Dec 20, 2023 · 冷蔵庫や洗濯機、掃除機といった家電の国産化に初めて成功し、世に送り出してきた 東芝 。 その 東芝 が2023年12月20日、株式市場から退場することになりました。 東芝 株 は上場廃止となり、基本的に売買ができなくなります。"}
+{"idx": 2, "title": "東芝 : 日経会社情報DIGITAL : 日経電子版", "date": "", "ddg_snippet": "東芝 の売上や利益率など企業概要、プレスリリース、人事・おくやみ情報まであらゆる情報をワンストップで提供。", "subpage_snippet": "", "source": "www.nikkei.com", "link": "https://www.nikkei.com/nkd/company/?nik_code=0001162", "content": "東芝 の売上や利益率など企業概要、プレスリリース、人事・おくやみ情報まであらゆる情報をワンストップで提供。"}
+{"idx": 3, "title": "東芝 ( 6502 ) : 株価 /予想・目標 株価 [ TOSHIBA ] - みんかぶ", "date": "", "ddg_snippet": "Nov 19, 2024 · 東芝 (6502) 今日の 株価 、予想（AI株価診断など）、チャート推移、ニュース、その他にも今後の見通しや買い時・売り時の判断に役立つ情報を掲載", "subpage_snippet": "", "source": "minkabu.jp", "link": "https://minkabu.jp/stock/6502", "content": "Nov 19, 2024 · 東芝 (6502) 今日の 株価 、予想（AI株価診断など）、チャート推移、ニュース、その他にも今後の見通しや買い時・売り時の判断に役立つ情報を掲載"}
+{"idx": 4, "title": "東芝 【 6502 】の 株価 チャート｜日足・分足・週足・月足・年足 ...", "date": "", "ddg_snippet": "東芝 【6502】の 株価 チャート。 日足、1分足、5分足、週足、月足、年足を表示できます。 出来高、売買代金、ヒストリカルPER・平均PERの表示に加え、テクニカル指標は移動平均線、平滑移動平均線、一目均衡表、ボリンジャーバンド、移動平均カイリ率、MACD、ストキャスティクスに対応。 また、 東芝 の同業他社などとの【比較チャート】機能や表示期間の【変更・移動】機能も搭載。...", "subpage_snippet": "", "source": "kabutan.jp", "link": "https://kabutan.jp/stock/chart?code=6502", "content": "東芝 【6502】の 株価 チャート。 日足、1分足、5分足、週足、月足、年足を表示できます。 出来高、売買代金、ヒストリカルPER・平均PERの表示に加え、テクニカル指標は移動平均線、平滑移動平均線、一目均衡表、ボリンジャーバンド、移動平均カイリ率、MACD、ストキャスティクスに対応。 また、 東芝 の同業他社などとの【比較チャート】機能や表示期間の【変更・移動】機能も搭載。..."}
+{"idx": 5, "title": "東芝 ( 6502 ) 株価 | マーケット情報 | 楽天証券", "date": "", "ddg_snippet": "東芝 （銘柄コード:6502）の 株価 やチャート、ニュース、株主優待等をご覧いただけます。", "subpage_snippet": "", "source": "www.rakuten-sec.co.jp", "link": "https://www.rakuten-sec.co.jp/web/market/search/quote.html?ric=6502.T", "content": "東芝 （銘柄コード:6502）の 株価 やチャート、ニュース、株主優待等をご覧いただけます。"}
+{"idx": 6, "title": "東芝 （ 6502 ）チャート・時系列｜日本株（個別株） ｜ 投資の森", "date": "", "ddg_snippet": "Dec 20, 2023 · 東芝 （6502）のチャートと時系列データを表示します。 チャートは、ローソク足で1ヶ月から長期チャートまで表示し、時系列データは、過去の4本値で任意の期間で見ることができます。", "subpage_snippet": "", "source": "nikkeiyosoku.com", "link": "https://nikkeiyosoku.com/stock/chart/6502/", "content": "Dec 20, 2023 · 東芝 （6502）のチャートと時系列データを表示します。 チャートは、ローソク足で1ヶ月から長期チャートまで表示し、時系列データは、過去の4本値で任意の期間で見ることができます。"}
+{"idx": 7, "title": "6502 株価 - 東芝 - Bloomberg Markets", "date": "", "ddg_snippet": "Dec 31, 2000 · 東芝 (6502) の 株価 、株式情報、チャート、関連ニュースなど、企業概要や 株価 の分析をご覧いただけます。", "subpage_snippet": "", "source": "www.bloomberg.co.jp", "link": "https://www.bloomberg.co.jp/quote/6502:JP", "content": "Dec 31, 2000 · 東芝 (6502) の 株価 、株式情報、チャート、関連ニュースなど、企業概要や 株価 の分析をご覧いただけます。"}
+{"idx": 8, "title": "6502 東芝 | 株価 チャート - IR BANK", "date": "", "ddg_snippet": "Dec 19, 2023 · 株価 チャート 株価 12/19 前日 (12/18) 4,595 始値 4,600 高値 4,605 安値 4,590 終値 -0.11% 4,590 出来高 +382.84% 8,079,300", "subpage_snippet": "", "source": "irbank.net", "link": "https://irbank.net/6502/chart", "content": "Dec 19, 2023 · 株価 チャート 株価 12/19 前日 (12/18) 4,595 始値 4,600 高値 4,605 安値 4,590 終値 -0.11% 4,590 出来高 +382.84% 8,079,300"}
+{"idx": 9, "title": "Toshiba Corp. チャート ( 6502 ) - Investing .com", "date": "", "ddg_snippet": "株式会社 東芝 株のリアルタイムチャートです。 ローソク足、面積グラフ、線グラフ、棒グラフ、などのチャートタイプを選択できます。", "subpage_snippet": "", "source": "jp.investing.com", "link": "https://jp.investing.com/equities/toshiba-corp.-chart", "content": "株式会社 東芝 株のリアルタイムチャートです。 ローソク足、面積グラフ、線グラフ、棒グラフ、などのチャートタイプを選択できます。"}
diff --git a/data/sampled_jsons/sitearxiv.orghtml2405.15732v2_Crocker_bottleneck.jsonl b/data/sampled_jsons/sitearxiv.orghtml2405.15732v2_Crocker_bottleneck.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..144572e8fddea34268d7148e84671485fd8cf5bb
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+++ b/data/sampled_jsons/sitearxiv.orghtml2405.15732v2_Crocker_bottleneck.jsonl
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+{"idx": 0, "title": "Neural Persistence Dynamics", "date": "", "ddg_snippet": "Crocker stacks are an extension to crocker ... Finding suitable hyperparameters is also the main bottleneck for crocker stacks (which rely on a linear SVR).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.15732v2", "content": "Crocker stacks are an extension to crocker ... Finding suitable hyperparameters is also the main bottleneck for crocker stacks (which rely on a linear SVR)."}
diff --git a/data/sampled_jsons/sitearxiv.orghtml2405.15732v2_Crocker_sequences.jsonl b/data/sampled_jsons/sitearxiv.orghtml2405.15732v2_Crocker_sequences.jsonl
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index 0000000000000000000000000000000000000000..77aedcb5023ae1c1e4dd1a450485b5b53c671c9a
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@@ -0,0 +1 @@
+{"idx": 0, "title": "Neural Persistence Dynamics", "date": "", "ddg_snippet": "Eventually, the crocker stacks per homology dimension are vectorized (i.e., the tensor is flattened) and concatenated into a single vector per observation ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.15732v2", "content": "Eventually, the crocker stacks per homology dimension are vectorized (i.e., the tensor is flattened) and concatenated into a single vector per observation ..."}
diff --git a/data/sampled_jsons/sitearxiv.orghtml2410.09543v1_A.3.2_Hardware_Configuration.jsonl b/data/sampled_jsons/sitearxiv.orghtml2410.09543v1_A.3.2_Hardware_Configuration.jsonl
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index 0000000000000000000000000000000000000000..44296beb3852e677fd5005ac7c820508378fa88f
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diff --git a/data/sampled_jsons/sitearxiv.orghtml2410.09543v1_Appendix_A.3.2_hardware_GPU.jsonl b/data/sampled_jsons/sitearxiv.orghtml2410.09543v1_Appendix_A.3.2_hardware_GPU.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b1115a30d0daf1bd35517a0f35eb79d66e313c75
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+{"idx": 0, "title": "Boltzmann-Aligned Inverse Folding Model as a Predictor of ...", "date": "", "ddg_snippet": "12 Oct 2024 — A.3.2 Hardware . Report issue for preceding element. All our experiments are conducted on a computing cluster with CPUs of AMD EPYC 7763 64 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.09543v1", "content": "12 Oct 2024 — A.3.2 Hardware . Report issue for preceding element. All our experiments are conducted on a computing cluster with CPUs of AMD EPYC 7763 64 ..."}
+{"idx": 1, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/sitegithub.comFLAIROxah2ac2_README_dataset.jsonl b/data/sampled_jsons/sitegithub.comFLAIROxah2ac2_README_dataset.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..44296beb3852e677fd5005ac7c820508378fa88f
--- /dev/null
+++ b/data/sampled_jsons/sitegithub.comFLAIROxah2ac2_README_dataset.jsonl
@@ -0,0 +1 @@
+{"idx": 0, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/sitegithub.com_AI9Stars_XLRS-Bench_Qwen2-VL_Chinese_English_performance.jsonl b/data/sampled_jsons/sitegithub.com_AI9Stars_XLRS-Bench_Qwen2-VL_Chinese_English_performance.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..640e36a1d304a9977df2de7232b951d066b6ebdb
--- /dev/null
+++ b/data/sampled_jsons/sitegithub.com_AI9Stars_XLRS-Bench_Qwen2-VL_Chinese_English_performance.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - AI9Stars/XLRS-Bench: [CVPR 2025 HIghlight] XLRS ...", "date": "", "ddg_snippet": "Apr 1, 2025 · We present XLRS-Bench , a comprehensive benchmark for evaluating the perception and reasoning capabilities of MLLMs in ultra-high-resolution RS scenarios, featuring the largest average image size of 8,500 × 8,500 observed thus far. Our dataset encompasses 45,942 annotations across 16 tasks, all expertly curated by a team of 45 experts.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/AI9Stars/XLRS-Bench", "content": "Apr 1, 2025 · We present XLRS-Bench , a comprehensive benchmark for evaluating the perception and reasoning capabilities of MLLMs in ultra-high-resolution RS scenarios, featuring the largest average image size of 8,500 × 8,500 observed thus far. Our dataset encompasses 45,942 annotations across 16 tasks, all expertly curated by a team of 45 experts."}
+{"idx": 1, "title": "XLRS-Bench/README.md at main · AI9Stars/XLRS-Bench", "date": "", "ddg_snippet": "Apr 1, 2025 · The main advantages of XLRS-Bench compared to existing MLLM benchmarks as follows: Ultra-high Resolution. XLRS-Bench features the largest image sizes available, 10∼20× than that of existing datasets, with 840 images out of all images at a resolution of 10,000×10,000 pixels High-quality Annotation.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/AI9Stars/XLRS-Bench/blob/main/README.md", "content": "Apr 1, 2025 · The main advantages of XLRS-Bench compared to existing MLLM benchmarks as follows: Ultra-high Resolution. XLRS-Bench features the largest image sizes available, 10∼20× than that of existing datasets, with 840 images out of all images at a resolution of 10,000×10,000 pixels High-quality Annotation."}
+{"idx": 2, "title": "AI9Stars - GitHub", "date": "", "ddg_snippet": "XLRS-Bench Public [CVPR 2025 HIghlight] XLRS-Bench : ould Your Multimodal LLMs Understand Extremely Large Ultra-High-Resolution Remote Sensing Imagery?", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/AI9Stars", "content": "XLRS-Bench Public [CVPR 2025 HIghlight] XLRS-Bench : ould Your Multimodal LLMs Understand Extremely Large Ultra-High-Resolution Remote Sensing Imagery?"}
+{"idx": 3, "title": "Releases: AI9Stars/XLRS-Bench - GitHub", "date": "", "ddg_snippet": "Contribute to AI9Stars / XLRS-Bench development by creating an account on GitHub.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/AI9Stars/XLRS-Bench/releases", "content": "Contribute to AI9Stars / XLRS-Bench development by creating an account on GitHub."}
+{"idx": 4, "title": "GitHub - xiaobaiv/Qwen2-VL: Qwen2-VL is the multimodal large ...", "date": "", "ddg_snippet": "Multilingual Support: to serve global users, besides English and Chinese , Qwen2-VL now supports the understanding of texts in different languages inside images, including most European languages, Japanese, Korean, Arabic, Vietnamese, etc.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/xiaobaiv/Qwen2-VL", "content": "Multilingual Support: to serve global users, besides English and Chinese , Qwen2-VL now supports the understanding of texts in different languages inside images, including most European languages, Japanese, Korean, Arabic, Vietnamese, etc."}
+{"idx": 5, "title": "GitHub - QwenLM/Qwen-VL: The official repo of Qwen-VL (通义千问 ....", "date": "", "ddg_snippet": "Aug 22, 2023 · Notably, Qwen- VL -Max outperforms both GPT-4V from OpenAI and Gemini from Google in tasks on Chinese question answering and Chinese text comprehension. This breakthrough underscores the model’s advanced capabilities and its potential to set new standards in the field of multimodal AI research and application.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/QwenLM/Qwen-VL", "content": "Aug 22, 2023 · Notably, Qwen- VL -Max outperforms both GPT-4V from OpenAI and Gemini from Google in tasks on Chinese question answering and Chinese text comprehension. This breakthrough underscores the model’s advanced capabilities and its potential to set new standards in the field of multimodal AI research and application."}
+{"idx": 6, "title": "Fine-tuning Qwen2-VL Series - GitHub", "date": "", "ddg_snippet": "Sep 12, 2025 · An open-source implementaion for fine-tuning Qwen2-VL and Qwen2 .5- VL series by Alibaba Cloud. - 2U1/ Qwen2-VL -Finetune", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/2U1/Qwen2-VL-Finetune", "content": "Sep 12, 2025 · An open-source implementaion for fine-tuning Qwen2-VL and Qwen2 .5- VL series by Alibaba Cloud. - 2U1/ Qwen2-VL -Finetune"}
+{"idx": 7, "title": "GitHub - InternLM/Intern-S1: A Scientific Multimodal Foundation Model", "date": "", "ddg_snippet": "- Performance . We evaluate the Intern-S1 on various benchmarks including general datasets and scientific datasets. Qwen 2 .5- VL -72B.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/InternLM/Intern-S1", "content": "- Performance . We evaluate the Intern-S1 on various benchmarks including general datasets and scientific datasets. Qwen 2 .5- VL -72B."}
+{"idx": 8, "title": "GitHub - AI 9 Stars /AStar-Thought: A*-Thought: Efficient Reasoning via...", "date": "", "ddg_snippet": "AI 9 Stars /AStar-Thought.A*-Thought could improve both the performance and the efficiency of LRMs (e.g., DeepSeek-R1-Distill-Qwen-32B, QwQ-32B) under different budgets: Up to 2.39x accuracy and 2.49x ACU improvements in low-budget scenarios.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/AI9Stars/AStar-Thought", "content": "AI 9 Stars /AStar-Thought.A*-Thought could improve both the performance and the efficiency of LRMs (e.g., DeepSeek-R1-Distill-Qwen-32B, QwQ-32B) under different budgets: Up to 2.39x accuracy and 2.49x ACU improvements in low-budget scenarios."}
+{"idx": 9, "title": "GitHub - QwenLM/Qwen-Image: Qwen-Image is a powerful image...", "date": "", "ddg_snippet": "Qwen-Image is a powerful image generation foundation model capable of complex text rendering and precise image editing.Make sure your transformers>=4.51.3 (Supporting Qwen 2 .5- VL ). Install the latest version of diffusers.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/QwenLM/Qwen-Image", "content": "Qwen-Image is a powerful image generation foundation model capable of complex text rendering and precise image editing.Make sure your transformers>=4.51.3 (Supporting Qwen 2 .5- VL ). Install the latest version of diffusers."}
diff --git a/data/sampled_jsons/sitegithub.com_tengxiao1DIL_LSIF.jsonl b/data/sampled_jsons/sitegithub.com_tengxiao1DIL_LSIF.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d7bb22ce4da09bfc33746a54d46b0570c54e23a4
--- /dev/null
+++ b/data/sampled_jsons/sitegithub.com_tengxiao1DIL_LSIF.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "tengxiao1 (Teng Xiao) · GitHub Releases · tengxiao1/DIL · GitHub GitHub - tengxiao1/tengxiao1 GitHub - microsoft/lsif-java: Language Server Indexing Format ... GitHub - tcz717/LsifDotnet: Language Server Indexing Format ... Issues: tengxiao1/DIL - GitHub lsif-indexer · GitHub Topics · GitHub", "date": "", "ddg_snippet": "@allenai . tengxiao1 has 21 repositories available. Follow their code on GitHub . On a Connection Between Imitation Learning and RLHF (ICLR 2025) - Releases · tengxiao1 / DIL tengxiao1 / tengxiao1 Public Notifications You must be signed in to change notification settings Fork 0 Star 0 The purpose of the Language Server Index Format ( LSIF ) is to define a standard format for language servers or other programming tools to dump their knowledge about a workspace. This dump can later be used to answer language server LSP requests for the same workspace without running the language server itself. Since much of the information would be invalidated by a change to the workspace, the dumped information typically excludes requests used when mutating a document. So, for example, the result of a code complete request is typically not part of such a dump. A first draft specification can be found here. See full list on github . com JDK 17 is required to build or run this tool. See full list on github . com •Go to the build path: > cd cmd •Install the required dependencies to build the Java Language Server Indexer: > npm install •Build the Java Language Server Indexer: > npm run build See full list on github . com If you are interested in fixing issues and contributing directly to the code base, please see the document How to Contribute for more details. See full list on github . com Dump a solution's lsif file Goto the folder of the solution file and run: .\\ lsif -dotnet.exe And a dump. lsif file will be created in the current folder. On a Connection Between Imitation Learning and RLHF (ICLR 2025) - Issues · tengxiao1 / DIL Hind is a Ruby gem for generating code intelligence data in LSIF (Language Server Index Format) and SCIP (Sourcegraph Code Intelligence Protocol) formats. It helps create index files that power code navigation features like go-to-definition, find references, and hover documentation.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/tengxiao1", "content": "@allenai . tengxiao1 has 21 repositories available. Follow their code on GitHub . On a Connection Between Imitation Learning and RLHF (ICLR 2025) - Releases · tengxiao1 / DIL tengxiao1 / tengxiao1 Public Notifications You must be signed in to change notification settings Fork 0 Star 0 The purpose of the Language Server Index Format ( LSIF ) is to define a standard format for language servers or other programming tools to dump their knowledge about a workspace. This dump can later be used to answer language server LSP requests for the same workspace without running the language server itself. Since much of the information would be invalidated by a change to the workspace, the dumped information typically excludes requests used when mutating a document. So, for example, the result of a code complete request is typically not part of such a dump. A first draft specification can be found here. See full list on github . com JDK 17 is required to build or run this tool. See full list on github . com •Go to the build path: > cd cmd •Install the required dependencies to build the Java Language Server Indexer: > npm install •Build the Java Language Server Indexer: > npm run build See full list on github . com If you are interested in fixing issues and contributing directly to the code base, please see the document How to Contribute for more details. See full list on github . com Dump a solution's lsif file Goto the folder of the solution file and run: .\\ lsif -dotnet.exe And a dump. lsif file will be created in the current folder. On a Connection Between Imitation Learning and RLHF (ICLR 2025) - Issues · tengxiao1 / DIL Hind is a Ruby gem for generating code intelligence data in LSIF (Language Server Index Format) and SCIP (Sourcegraph Code Intelligence Protocol) formats. It helps create index files that power code navigation features like go-to-definition, find references, and hover documentation."}
+{"idx": 1, "title": "Releases · tengxiao1/DIL · GitHub", "date": "", "ddg_snippet": "On a Connection Between Imitation Learning and RLHF (ICLR 2025) - Releases · tengxiao1 / DIL", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/tengxiao1/DIL/releases", "content": "On a Connection Between Imitation Learning and RLHF (ICLR 2025) - Releases · tengxiao1 / DIL"}
+{"idx": 2, "title": "GitHub - tengxiao1/tengxiao1", "date": "", "ddg_snippet": "tengxiao1 / tengxiao1 Public Notifications You must be signed in to change notification settings Fork 0 Star 0", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/tengxiao1/tengxiao1", "content": "tengxiao1 / tengxiao1 Public Notifications You must be signed in to change notification settings Fork 0 Star 0"}
+{"idx": 3, "title": "GitHub - microsoft/lsif-java: Language Server Indexing Format ... GitHub - tcz717/LsifDotnet: Language Server Indexing Format ... Issues: tengxiao1/DIL - GitHub lsif-indexer · GitHub Topics · GitHub", "date": "", "ddg_snippet": "The purpose of the Language Server Index Format ( LSIF ) is to define a standard format for language servers or other programming tools to dump their knowledge about a workspace. This dump can later be used to answer language server LSP requests for the same workspace without running the language server itself. Since much of the information would be invalidated by a change to the workspace, the dumped information typically excludes requests used when mutating a document. So, for example, the result of a code complete request is typically not part of such a dump. A first draft specification can be found here. See full list on github . com JDK 17 is required to build or run this tool. See full list on github . com •Go to the build path: > cd cmd •Install the required dependencies to build the Java Language Server Indexer: > npm install •Build the Java Language Server Indexer: > npm run build See full list on github . com If you are interested in fixing issues and contributing directly to the code base, please see the document How to Contribute for more details. See full list on github . com Dump a solution's lsif file Goto the folder of the solution file and run: .\\ lsif -dotnet.exe And a dump. lsif file will be created in the current folder. On a Connection Between Imitation Learning and RLHF (ICLR 2025) - Issues · tengxiao1 / DIL Hind is a Ruby gem for generating code intelligence data in LSIF (Language Server Index Format) and SCIP (Sourcegraph Code Intelligence Protocol) formats. It helps create index files that power code navigation features like go-to-definition, find references, and hover documentation.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Microsoft/lsif-java", "content": "The purpose of the Language Server Index Format ( LSIF ) is to define a standard format for language servers or other programming tools to dump their knowledge about a workspace. This dump can later be used to answer language server LSP requests for the same workspace without running the language server itself. Since much of the information would be invalidated by a change to the workspace, the dumped information typically excludes requests used when mutating a document. So, for example, the result of a code complete request is typically not part of such a dump. A first draft specification can be found here. See full list on github . com JDK 17 is required to build or run this tool. See full list on github . com •Go to the build path: > cd cmd •Install the required dependencies to build the Java Language Server Indexer: > npm install •Build the Java Language Server Indexer: > npm run build See full list on github . com If you are interested in fixing issues and contributing directly to the code base, please see the document How to Contribute for more details. See full list on github . com Dump a solution's lsif file Goto the folder of the solution file and run: .\\ lsif -dotnet.exe And a dump. lsif file will be created in the current folder. On a Connection Between Imitation Learning and RLHF (ICLR 2025) - Issues · tengxiao1 / DIL Hind is a Ruby gem for generating code intelligence data in LSIF (Language Server Index Format) and SCIP (Sourcegraph Code Intelligence Protocol) formats. It helps create index files that power code navigation features like go-to-definition, find references, and hover documentation."}
+{"idx": 4, "title": "GitHub - tcz717/LsifDotnet: Language Server Indexing Format ...", "date": "", "ddg_snippet": "Dump a solution's lsif file Goto the folder of the solution file and run: .\\ lsif -dotnet.exe And a dump. lsif file will be created in the current folder.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/tcz717/LsifDotnet", "content": "Dump a solution's lsif file Goto the folder of the solution file and run: .\\ lsif -dotnet.exe And a dump. lsif file will be created in the current folder."}
+{"idx": 5, "title": "Issues: tengxiao1/DIL - GitHub", "date": "", "ddg_snippet": "On a Connection Between Imitation Learning and RLHF (ICLR 2025) - Issues · tengxiao1 / DIL", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/tengxiao1/DIL/issues", "content": "On a Connection Between Imitation Learning and RLHF (ICLR 2025) - Issues · tengxiao1 / DIL"}
+{"idx": 6, "title": "lsif-indexer · GitHub Topics · GitHub", "date": "", "ddg_snippet": "Hind is a Ruby gem for generating code intelligence data in LSIF (Language Server Index Format) and SCIP (Sourcegraph Code Intelligence Protocol) formats. It helps create index files that power code navigation features like go-to-definition, find references, and hover documentation.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/topics/lsif-indexer", "content": "Hind is a Ruby gem for generating code intelligence data in LSIF (Language Server Index Format) and SCIP (Sourcegraph Code Intelligence Protocol) formats. It helps create index files that power code navigation features like go-to-definition, find references, and hover documentation."}
+{"idx": 7, "title": "GitHub - tengxiao 1 / DIL : On a Connection Between Imitation Learning...", "date": "", "ddg_snippet": "conda create -n DIL python=3.10 && conda activate DIL . python -m pip install flash-attn --no-build-isolation.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/tengxiao1/DIL", "content": "conda create -n DIL python=3.10 && conda activate DIL . python -m pip install flash-attn --no-build-isolation."}
+{"idx": 8, "title": "GitHub - tengxiao 1 /SimPER: SimPER: A Minimalist Approach to...", "date": "", "ddg_snippet": "tengxiao 1 / SimPER Public. Notifications You must be signed in to change notification settings.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/tengxiao1/SimPER", "content": "tengxiao 1 / SimPER Public. Notifications You must be signed in to change notification settings."}
+{"idx": 9, "title": "GitHub - tengxiao 1 /Cal-DPO: Cal-DPO: Calibrated Direct Preference...", "date": "", "ddg_snippet": "tengxiao 1 / Cal-DPO Public. Notifications You must be signed in to change notification settings. Fork 1. tengxiao 1 /Cal-DPO. main.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/tengxiao1/Cal-DPO", "content": "tengxiao 1 / Cal-DPO Public. Notifications You must be signed in to change notification settings. Fork 1. tengxiao 1 /Cal-DPO. main."}
diff --git a/data/sampled_jsons/sitegithub.comfiveaiunderstanding_safety_finetuning_minGPT_default_transformer_blocks_year_2024.jsonl b/data/sampled_jsons/sitegithub.comfiveaiunderstanding_safety_finetuning_minGPT_default_transformer_blocks_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..44296beb3852e677fd5005ac7c820508378fa88f
--- /dev/null
+++ b/data/sampled_jsons/sitegithub.comfiveaiunderstanding_safety_finetuning_minGPT_default_transformer_blocks_year_2024.jsonl
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+{"idx": 0, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/sitegithub.comfiveaiunderstanding_safety_finetuning_minGPT_transformer_blocks_configuration.jsonl b/data/sampled_jsons/sitegithub.comfiveaiunderstanding_safety_finetuning_minGPT_transformer_blocks_configuration.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..44296beb3852e677fd5005ac7c820508378fa88f
--- /dev/null
+++ b/data/sampled_jsons/sitegithub.comfiveaiunderstanding_safety_finetuning_minGPT_transformer_blocks_configuration.jsonl
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diff --git a/data/sampled_jsons/sitehuggingface.co_papers_2011.13456.jsonl b/data/sampled_jsons/sitehuggingface.co_papers_2011.13456.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..35696c0b105b9ce76bea88fb3f527ab8064d4aa9
--- /dev/null
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+{"idx": 0, "title": "cv papers - a adamelliotfields Collection", "date": "", "ddg_snippet": "Paper • 2011.13456 • Published Nov 26, 2020 • 2 · Mixture of Diffusers for scene composition and high resolution image generation. Paper ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/collections/adamelliotfields/cv-papers-6659b7b5b03af6f392ffdb6a", "content": "Paper • 2011.13456 • Published Nov 26, 2020 • 2 · Mixture of Diffusers for scene composition and high resolution image generation. Paper ..."}
+{"idx": 1, "title": "https://huggingface.co/google/ncsnpp-church-256/ra...", "date": "", "ddg_snippet": "... Paper **: [Score-Based Generative Modeling through Stochastic Differential Equations](https://arxiv.org/abs/ 2011.13456 ) **Authors**: Yang Song, Jascha Sohl ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/google/ncsnpp-church-256/raw/main/README.md", "content": "... Paper **: [Score-Based Generative Modeling through Stochastic Differential Equations](https://arxiv.org/abs/ 2011.13456 ) **Authors**: Yang Song, Jascha Sohl ..."}
+{"idx": 2, "title": "Score Based Model - a PulYong Collection", "date": "", "ddg_snippet": "18 Jun 2025 — Score-Based Generative Modeling through Stochastic Differential Equations . Paper • 2011.13456 • Published Nov 26, 2020 • 2 · Elucidating the ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/collections/PulYong/score-based-model-670ca8dd74913d9a6f693e06", "content": "18 Jun 2025 — Score-Based Generative Modeling through Stochastic Differential Equations . Paper • 2011.13456 • Published Nov 26, 2020 • 2 · Elucidating the ..."}
+{"idx": 3, "title": "Collections", "date": "", "ddg_snippet": "Paper • 2011.13456 • Published Nov 26, 2020 • 2 · Elucidating the Design Space of Diffusion-Based Generative Models . Paper • 2206.00364 • Published Jun 1, 2022 ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/collections?paper=2305.08891", "content": "Paper • 2011.13456 • Published Nov 26, 2020 • 2 · Elucidating the Design Space of Diffusion-Based Generative Models . Paper • 2206.00364 • Published Jun 1, 2022 ..."}
+{"idx": 4, "title": "paper - a Qin56 Collection", "date": "", "ddg_snippet": "6 Apr 2024 — Paper • 2011.13456 • Published Nov 26, 2020 • 2 · Vector Quantized Diffusion Model for Text-to-Image Synthesis. Paper • 2111.14822 • Published ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/collections/Qin56/paper-660e5a956455d218e94b72f7", "content": "6 Apr 2024 — Paper • 2011.13456 • Published Nov 26, 2020 • 2 · Vector Quantized Diffusion Model for Text-to-Image Synthesis. Paper • 2111.14822 • Published ..."}
+{"idx": 5, "title": "google/ncsnpp-bedroom-256", "date": "", "ddg_snippet": "arxiv: 2011.13456 . License: apache-2.0. Model card Files Files and versions ... Score-Based Generative Modeling through Stochastic Differential Equations (SDE).", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/google/ncsnpp-bedroom-256", "content": "arxiv: 2011.13456 . License: apache-2.0. Model card Files Files and versions ... Score-Based Generative Modeling through Stochastic Differential Equations (SDE)."}
+{"idx": 6, "title": "Variance Exploding Stochastic Differential Equation (VE- ...", "date": "", "ddg_snippet": "The variance exploding stochastic differential equation (SDE) scheduler . For more information, see the original paper: https://arxiv.org/abs/2011.13456.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/docs/diffusers/v0.15.0/api/schedulers/score_sde_ve", "content": "The variance exploding stochastic differential equation (SDE) scheduler . For more information, see the original paper: https://arxiv.org/abs/2011.13456."}
+{"idx": 7, "title": "Score-Based Generative Modeling through Stochastic ...", "date": "", "ddg_snippet": "26 Nov 2020 — A reverse-time stochastic differential equation framework enables efficient and accurate generative modeling and sampling, achieving state-of- ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2011.13456", "content": "26 Nov 2020 — A reverse-time stochastic differential equation framework enables efficient and accurate generative modeling and sampling, achieving state-of- ..."}
+{"idx": 8, "title": "Stochastic Karras VE", "date": "", "ddg_snippet": "Stochastic Karras VE . Overview. Elucidating the Design Space of Diffusion-Based Generative Models by Tero Karras, Miika Aittala, Timo Aila and Samuli Laine.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/docs/diffusers/v0.6.0/en/api/pipelines/stochastic_karras_ve", "content": "Stochastic Karras VE . Overview. Elucidating the Design Space of Diffusion-Based Generative Models by Tero Karras, Miika Aittala, Timo Aila and Samuli Laine."}
+{"idx": 9, "title": "Variance preserving stochastic differential equation (VP- ...", "date": "", "ddg_snippet": "Variance preserving stochastic differential equation (VP-SDE) scheduler. Overview. Original paper can be found here. Score SDE-VP is under construction ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/docs/diffusers/v0.13.0/en/api/schedulers/score_sde_vp", "content": "Variance preserving stochastic differential equation (VP-SDE) scheduler. Overview. Original paper can be found here. Score SDE-VP is under construction ..."}
diff --git a/data/sampled_jsons/sitenature.com_Advancing_mathematics_by_guiding_human_intuition_with_AI_abstract_knot_theory_represe.jsonl b/data/sampled_jsons/sitenature.com_Advancing_mathematics_by_guiding_human_intuition_with_AI_abstract_knot_theory_represe.jsonl
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Advancing mathematics by guiding human intuition with AI", "date": "", "ddg_snippet": "by A Davies · 2021 · Cited by 683 — ... knot theory and representation theory have been made available ... Advancing mathematics by guiding human intuition with AI . Nature 600 ...", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41586-021-04086-x", "content": "by A Davies · 2021 · Cited by 683 — ... knot theory and representation theory have been made available ... Advancing mathematics by guiding human intuition with AI . Nature 600 ..."}
+{"idx": 1, "title": "Advancing mathematics by guiding human intuition with AI", "date": "", "ddg_snippet": "The building blocks of all representations are the irreducible ones, and understand-ing them is one of the most important goals of representation theory .", "subpage_snippet": "", "source": "preview-www.nature.com", "link": "https://preview-www.nature.com/articles/s41586-021-04086-x.pdf", "content": "The building blocks of all representations are the irreducible ones, and understand-ing them is one of the most important goals of representation theory ."}
+{"idx": 2, "title": "Volume 600 Issue 7887, 2 December 2021 - Nature", "date": "", "ddg_snippet": "Dec 2, 2021 · AI -guided intuition Pure mathematics involves the discovery of patterns between mathematical objects and using these connections to formulate conjectures. Mathematicians have deployed computers ...", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/nature/volumes/600/issues/7887", "content": "Dec 2, 2021 · AI -guided intuition Pure mathematics involves the discovery of patterns between mathematical objects and using these connections to formulate conjectures. Mathematicians have deployed computers ..."}
+{"idx": 3, "title": "Artificial intelligence aids intuition in mathematical discovery", "date": "", "ddg_snippet": "Dec 1, 2021 · They used it to identify previously unknown relationships in knot theory and in combinatorial representation theory .", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/d41586-021-03512-4", "content": "Dec 1, 2021 · They used it to identify previously unknown relationships in knot theory and in combinatorial representation theory ."}
+{"idx": 4, "title": "AI-driven research in pure mathematics and theoretical physics", "date": "", "ddg_snippet": "Aug 5, 2024 · To establish a reasonable training set, one could choose the following representation (and indeed the choice of representation is extremely important).", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s42254-024-00740-1", "content": "Aug 5, 2024 · To establish a reasonable training set, one could choose the following representation (and indeed the choice of representation is extremely important)."}
+{"idx": 5, "title": "Rigor with machine learning from field theory to the Poincaré ...", "date": "", "ddg_snippet": "Apr 8, 2024 · Knot theory and the Poincaré conjecture ML is increasingly applied in pure mathematics . Here, we focus on low-dimensional topology, especially knot theory .", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s42254-024-00709-0", "content": "Apr 8, 2024 · Knot theory and the Poincaré conjecture ML is increasingly applied in pure mathematics . Here, we focus on low-dimensional topology, especially knot theory ."}
+{"idx": 6, "title": "Machine learning as a tool in theoretical science - Nature", "date": "", "ddg_snippet": "Feb 14, 2022 · A priori, this is a problem for unsupervised learning. But this work used supervised learning instead: a knot theory expert chose target invariants that the ML system tried to predict.", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s42254-022-00431-9", "content": "Feb 14, 2022 · A priori, this is a problem for unsupervised learning. But this work used supervised learning instead: a knot theory expert chose target invariants that the ML system tried to predict."}
+{"idx": 7, "title": "Advancing mathematics by guiding human intuition with AI", "date": "", "ddg_snippet": "Guiding mathematical intuition with AI . A mathematician’s intuition plays an enormously important role in mathematical discovery—“It is only with a combination of both rigor-ous formalism and good intuition that one can tackle complex math-ematical problems”25.", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41586-021-04086-x.pdf?error=cookies_not_supported&code=cd93302f-a7bc-42b4-8a3b-55bc885457c0", "content": "Guiding mathematical intuition with AI . A mathematician’s intuition plays an enormously important role in mathematical discovery—“It is only with a combination of both rigor-ous formalism and good intuition that one can tackle complex math-ematical problems”25."}
+{"idx": 8, "title": "On scientific understanding with artificial intelligence", "date": "", "ddg_snippet": "by M Krenn · 2022 · Cited by 356 — ... knot theory , which allowed mathematicians to conjecture and prove new theorems. ... Advancing mathematics by guiding human intuition with AI .", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s42254-022-00518-3", "content": "by M Krenn · 2022 · Cited by 356 — ... knot theory , which allowed mathematicians to conjecture and prove new theorems. ... Advancing mathematics by guiding human intuition with AI ."}
+{"idx": 9, "title": "Machine learning of knot topology in non-Hermitian band ...", "date": "", "ddg_snippet": "by J Chen · 2024 · Cited by 12 — Davies, A. et al. Advancing mathematics by guiding human intuition with AI . Nature 600, 70–74 (2021). Article ADS MATH Google Scholar.", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s42005-024-01710-w", "content": "by J Chen · 2024 · Cited by 12 — Davies, A. et al. Advancing mathematics by guiding human intuition with AI . Nature 600, 70–74 (2021). Article ADS MATH Google Scholar."}
diff --git a/data/sampled_jsons/siteopenaccess.thecvf.com_EventPS_mean_angular_error_table_3D_printed.jsonl b/data/sampled_jsons/siteopenaccess.thecvf.com_EventPS_mean_angular_error_table_3D_printed.jsonl
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index 0000000000000000000000000000000000000000..b0a53b363b38dfa8ae531cf7667c8237eb77b46a
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+++ b/data/sampled_jsons/siteopenaccess.thecvf.com_EventPS_mean_angular_error_table_3D_printed.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Combined Physics and Event Camera Simulator for Slip ...", "date": "", "ddg_snippet": "by T Reinold · 2025 · Cited by 1 — Robot manipulation is a common task in fields like in- dustrial manufacturing. Detecting when objects slip from a robot's grasp is crucial for safe and ... 9 pages", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/WACV2025W/EVGEN/papers/Reinold_Combined_Physics_and_Event_Camera_Simulator_for_Slip_Detection_WACVW_2025_paper.pdf", "content": "by T Reinold · 2025 · Cited by 1 — Robot manipulation is a common task in fields like in- dustrial manufacturing. Detecting when objects slip from a robot's grasp is crucial for safe and ... 9 pages"}
+{"idx": 1, "title": "PS-EIP: Robust Photometric Stereo Based on Event Interval Profile", "date": "", "ddg_snippet": "Building upon these advantages, the Lambertian event-based photometric stereo, namely EventPS [67], has re-cently been proposed. EventPS leverages the fact that, un-der a continuously moving light source, events are triggered by differences in logarithmic Lambertian reections.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Kitazawa_PS-EIP_Robust_Photometric_Stereo_Based_on_Event_Interval_Profile_CVPR_2025_paper.pdf", "content": "Building upon these advantages, the Lambertian event-based photometric stereo, namely EventPS [67], has re-cently been proposed. EventPS leverages the fact that, un-der a continuously moving light source, events are triggered by differences in logarithmic Lambertian reections."}
+{"idx": 2, "title": "Specular Object Reconstruction Behind Frosted Glass by ...", "date": "", "ddg_snippet": "by T Iwaguchi · 2024 · Cited by 1 — The mean angular errors (MAE) for #1 to #5 are 1.91, 0.98, 1.06, 1.00, 1.84, and 4.14, respectively. Evaluation on observation number We investigate the.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/WACV2024/papers/Iwaguchi_Specular_Object_Reconstruction_Behind_Frosted_Glass_by_Differentiable_Rendering_WACV_2024_paper.pdf", "content": "by T Iwaguchi · 2024 · Cited by 1 — The mean angular errors (MAE) for #1 to #5 are 1.91, 0.98, 1.06, 1.00, 1.84, and 4.14, respectively. Evaluation on observation number We investigate the."}
+{"idx": 3, "title": "High-Fidelity Event-Radiance Recovery via Transient Event ...", "date": "", "ddg_snippet": "by J Han · 2023 · Cited by 12 — In this paper, a new approach for direct recovery of scene radiance from event signals is proposed. We overcome the instability and errors of event signals ... 10 pages", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2023/papers/Han_High-Fidelity_Event-Radiance_Recovery_via_Transient_Event_Frequency_CVPR_2023_paper.pdf", "content": "by J Han · 2023 · Cited by 12 — In this paper, a new approach for direct recovery of scene radiance from event signals is proposed. We overcome the instability and errors of event signals ... 10 pages"}
+{"idx": 4, "title": "MMMU: A Massive Multi-discipline Multimodal ...", "date": "", "ddg_snippet": "Table 11. Table index of case study figures by subjects with associated ( error ) categories. 24. Page 13. Art: Visual Arts. Question: The artist's use of color ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2024/supplemental/Yue_MMMU_A_Massive_CVPR_2024_supplemental.pdf", "content": "Table 11. Table index of case study figures by subjects with associated ( error ) categories. 24. Page 13. Art: Visual Arts. Question: The artist's use of color ..."}
+{"idx": 5, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
+{"idx": 6, "title": "Active Event Alignment for Monocular Distance Estimation", "date": "", "ddg_snippet": "By aligning events within a small region, we estimate the angu- lar velocity required to stabilize the image motion. We demonstrate that, under certain ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/WACV2025/papers/Cai_Active_Event_Alignment_for_Monocular_Distance_Estimation_WACV_2025_paper.pdf", "content": "By aligning events within a small region, we estimate the angu- lar velocity required to stabilize the image motion. We demonstrate that, under certain ..."}
+{"idx": 7, "title": "Time-to-Contact Map by Joint Estimation of Up-to-Scale ...", "date": "", "ddg_snippet": "by UM Nunes · 2023 · Cited by 9 — It consists of 7 real event sequences ob- serving planar prints of landing surfaces and 1 real event se- quence observing the 3D print of a landing surface. 11 pages", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/ICCV2023/papers/Nunes_Time-to-Contact_Map_by_Joint_Estimation_of_Up-to-Scale_Inverse_Depth_and_ICCV_2023_paper.pdf", "content": "by UM Nunes · 2023 · Cited by 9 — It consists of 7 real event sequences ob- serving planar prints of landing surfaces and 1 real event se- quence observing the 3D print of a landing surface. 11 pages"}
+{"idx": 8, "title": "ETAP: Event-based Tracking of Any Point - CVF Open Access", "date": "", "ddg_snippet": "by F Hamann · 2025 · Cited by 2 — Tracking any point (TAP) recently shifted the motion esti- mation paradigm from focusing on individual salient points.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Hamann_ETAP_Event-based_Tracking_of_Any_Point_CVPR_2025_paper.pdf", "content": "by F Hamann · 2025 · Cited by 2 — Tracking any point (TAP) recently shifted the motion esti- mation paradigm from focusing on individual salient points."}
+{"idx": 9, "title": "Rethinking Domain Generalization for Face Anti-Spoofing", "date": "", "ddg_snippet": "by Y Sun · 2023 · Cited by 106 — This work studies the generalization issue of face anti- spoofing (FAS) models on domain gaps, such as image res- olution, blurriness and sensor variations. 12 pages", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2023/papers/Sun_Rethinking_Domain_Generalization_for_Face_Anti-Spoofing_Separability_and_Alignment_CVPR_2023_paper.pdf", "content": "by Y Sun · 2023 · Cited by 106 — This work studies the generalization issue of face anti- spoofing (FAS) models on domain gaps, such as image res- olution, blurriness and sensor variations. 12 pages"}
diff --git "a/data/sampled_jsons/siteopenreview.net_0hrkN07DuO_Linear_convergence_Sinkhorn_generalized_static_Schr\303\266dinger_bridge_conc.jsonl" "b/data/sampled_jsons/siteopenreview.net_0hrkN07DuO_Linear_convergence_Sinkhorn_generalized_static_Schr\303\266dinger_bridge_conc.jsonl"
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+{"idx": 0, "title": "Linear Convergence of Sinkhorn's Algorithm for Generalized Static ...", "date": "", "ddg_snippet": "We establish Kantorovich du-ality and linear convergence of Sinkhorn's algo-rithm for the generalized SSB problem under mild conditions. Our results provide a new rigorous foundation for understanding Sinkhorn -type iter-ative methods in the context of large-scale gener-alized Schrödinger bridges .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=0hrkN07DuO", "content": "We establish Kantorovich du-ality and linear convergence of Sinkhorn's algo-rithm for the generalized SSB problem under mild conditions. Our results provide a new rigorous foundation for understanding Sinkhorn -type iter-ative methods in the context of large-scale gener-alized Schrödinger bridges ."}
+{"idx": 1, "title": "Symmetrized Schrödinger Bridge Matching - OpenReview", "date": "", "ddg_snippet": "We leverage a symmetrized variant of Sinkhorn to study more lenient convergence of Schrödinger potentials and prove distinctive theoretical properties of the symmetrization such as linear convergence and monotonic improvements. To this end, we propose a dynamic SB algorithm named Symmetrized Schrödinger Bridge Matching (SSBM).", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=22to0JZ4zh", "content": "We leverage a symmetrized variant of Sinkhorn to study more lenient convergence of Schrödinger potentials and prove distinctive theoretical properties of the symmetrization such as linear convergence and monotonic improvements. To this end, we propose a dynamic SB algorithm named Symmetrized Schrödinger Bridge Matching (SSBM)."}
+{"idx": 2, "title": "Hanbaek Lyu - OpenReview", "date": "", "ddg_snippet": "Publications Linear convergence of Sinkhorn's algorithm for generalized static Schrödinger bridge Rahul Choudhary, Hanbaek Lyu Published: 01 May 2025, Last Modified: 10 Aug 2025 ICML 2025 poster", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/profile?id=~Hanbaek_Lyu1", "content": "Publications Linear convergence of Sinkhorn's algorithm for generalized static Schrödinger bridge Rahul Choudhary, Hanbaek Lyu Published: 01 May 2025, Last Modified: 10 Aug 2025 ICML 2025 poster"}
+{"idx": 3, "title": "Generalized Schrödinger Bridge Matching - OpenReview", "date": "", "ddg_snippet": "In this work, we consider a generalized distribution matching setup, where these marginals are only implicitly described as a solution to some task-specific objective function. The problem setup, known as the Generalized Schrödinger Bridge (GSB), appears prevalently in many scientific areas both within and without machine learning.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=SoismgeX7z", "content": "In this work, we consider a generalized distribution matching setup, where these marginals are only implicitly described as a solution to some task-specific objective function. The problem setup, known as the Generalized Schrödinger Bridge (GSB), appears prevalently in many scientific areas both within and without machine learning."}
+{"idx": 4, "title": "SYMMETRIZED SCHRODINGER ¨ BRIDGE MATCHIN - OpenReview", "date": "", "ddg_snippet": "ABSTRACT Schr ̈odinger bridge (SB) has demonstrated numerous applications in probabilis-tic generative modeling. Finding the solution of probability paths aligns with entropy-regularized optimal transport that employs the Sinkhorn algorithm, which is characterized by performing iterative proportional fitting between marginal den-sities. This paper argues that the standard training of the SB ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=22to0JZ4zh", "content": "ABSTRACT Schr ̈odinger bridge (SB) has demonstrated numerous applications in probabilis-tic generative modeling. Finding the solution of probability paths aligns with entropy-regularized optimal transport that employs the Sinkhorn algorithm, which is characterized by performing iterative proportional fitting between marginal den-sities. This paper argues that the standard training of the SB ..."}
+{"idx": 5, "title": "Gaussian entropic optimal transport: Schrödinger bridges and the ...", "date": "", "ddg_snippet": "We extend this filtering methodology to develop a refined and self-contained convergence analysis of Gaussian Sinkhorn algorithms, including closed form expressions of entropic transport maps and Schr\\\"odinger bridges .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=Tl3er61wvW", "content": "We extend this filtering methodology to develop a refined and self-contained convergence analysis of Gaussian Sinkhorn algorithms, including closed form expressions of entropic transport maps and Schr\\\"odinger bridges ."}
+{"idx": 6, "title": "Linear convergence of Sinkhorn's algorithm for generalized static ...", "date": "", "ddg_snippet": "The paper introduces a generalized formulation of the static Schrödinger bridge (SSB) problem by replacing the standard entropy divergence with a general strictly convex function. It develops a corresponding Sinkhorn -type algorithm, establishes its linear convergence , and identifies conditions where the convergence rate does not depend on the problem's dimension. The work is recognized as a ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=0hrkN07DuO", "content": "The paper introduces a generalized formulation of the static Schrödinger bridge (SSB) problem by replacing the standard entropy divergence with a general strictly convex function. It develops a corresponding Sinkhorn -type algorithm, establishes its linear convergence , and identifies conditions where the convergence rate does not depend on the problem's dimension. The work is recognized as a ..."}
+{"idx": 7, "title": "Non-asymptotic convergence bounds for Sinkhorn iterates and their ...", "date": "", "ddg_snippet": "In this work, we analyze the convergence of the Sinkhorn algorithm for probability measures defined on the d-dimensional torus T, that admit densities with respect to the Haar measure of T. In particular, we prove pointwise exponential convergence of Sinkhorn iterates and their gradient.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=lQXQJzffEP", "content": "In this work, we analyze the convergence of the Sinkhorn algorithm for probability measures defined on the d-dimensional torus T, that admit densities with respect to the Haar measure of T. In particular, we prove pointwise exponential convergence of Sinkhorn iterates and their gradient."}
+{"idx": 8, "title": "Optimal transport with f-divergence regularization and generalized ...", "date": "", "ddg_snippet": "Abstract: Entropic regularization provides a generalization of the original optimal transport problem. It introduces a penalty term defined by the Kullback-Leibler divergence, making the problem more tractable via the celebrated Sinkhorn algorithm. Replacing the Kullback-Leibler divergence with a general f -divergence leads to a natural generalization. The case of divergences defined by ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=0kDNUB4ae3", "content": "Abstract: Entropic regularization provides a generalization of the original optimal transport problem. It introduces a penalty term defined by the Kullback-Leibler divergence, making the problem more tractable via the celebrated Sinkhorn algorithm. Replacing the Kullback-Leibler divergence with a general f -divergence leads to a natural generalization. The case of divergences defined by ..."}
+{"idx": 9, "title": "Sinkhorn AutoEncoders - OpenReview", "date": "", "ddg_snippet": "Sinkhorn convergence is discussed only in terms of sample size and smoothing regularizer, not in the context of batch training. - Quantitative results are on par or marginally better than other methods, they also lack some comparisons (see details below).", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=BygNqoR9tm", "content": "Sinkhorn convergence is discussed only in terms of sample size and smoothing regularizer, not in the context of batch training. - Quantitative results are on par or marginally better than other methods, they also lack some comparisons (see details below)."}
diff --git a/data/sampled_jsons/siteopenreview.net_4Xnqm4f71y_DVI_derivative_computation_Section_3.3.jsonl b/data/sampled_jsons/siteopenreview.net_4Xnqm4f71y_DVI_derivative_computation_Section_3.3.jsonl
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GS : S GIANT MODELS WITH CONDI COMPUTATION AND AUTOMATIC ...", "date": "", "ddg_snippet": "To implement the model in Section 2.1 efficiently on a cluster of devices, we first express the model in terms of linear algebra operations, which are highly tailored and optimized in our software stack TensorFlow (Abadi et al., 2016) and the hardware platform (TPU).", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=qrwe7XHTmYb", "content": "To implement the model in Section 2.1 efficiently on a cluster of devices, we first express the model in terms of linear algebra operations, which are highly tailored and optimized in our software stack TensorFlow (Abadi et al., 2016) and the hardware platform (TPU)."}
+{"idx": 1, "title": "Gradient Inversion of Multimodal Models - OpenReview", "date": "", "ddg_snippet": "3.3 . Multimodal Gradient Inversion Attack In this section , we describe our proposed approach, which consists of two components: document reconstruction and text reconstruction. The document reconstruction follows an optimization-based approach, while the text reconstruction employs an analytic-based approach.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=j4IELrBhoG", "content": "3.3 . Multimodal Gradient Inversion Attack In this section , we describe our proposed approach, which consists of two components: document reconstruction and text reconstruction. The document reconstruction follows an optimization-based approach, while the text reconstruction employs an analytic-based approach."}
+{"idx": 2, "title": "DVI:A Derivative-based Vision Network for INR", "date": "", "ddg_snippet": "by R Yang — Yet, in section 3.3 the paper states \"We use the recursive formula for high order derivatives in (Xiao et al., 2023) to compute the derivative map at an ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=4Xnqm4f71y", "content": "by R Yang — Yet, in section 3.3 the paper states \"We use the recursive formula for high order derivatives in (Xiao et al., 2023) to compute the derivative map at an ..."}
+{"idx": 3, "title": "NTFields: Neural Time Fields for Physics-Informed... | OpenReview", "date": "", "ddg_snippet": "No Acknowledgement Section : I certify that there is no acknowledgement section in this submission for double blind review.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=ApF0dmi1_9K", "content": "No Acknowledgement Section : I certify that there is no acknowledgement section in this submission for double blind review."}
+{"idx": 4, "title": "OmniQuant: Omnidirectionally Calibrated Quantization... | OpenReview", "date": "", "ddg_snippet": "Large language models (LLMs) have revolutionized natural language processing tasks. However, their practical deployment is hindered by their immense memory and computation requirements.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=8Wuvhh0LYW", "content": "Large language models (LLMs) have revolutionized natural language processing tasks. However, their practical deployment is hindered by their immense memory and computation requirements."}
+{"idx": 5, "title": "Physics-Informed Neural Network Policy Iteration: Algorithms...", "date": "", "ddg_snippet": "We run both ELM-PI and PINN-PI to compute the optimal control and policy for the inverted pendulum (see Section E.3 in the Appendix for more details). Figure 1 displays the results of implementing ELM-PI on an inverted pendu-lum.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=sZla6SnooP", "content": "We run both ELM-PI and PINN-PI to compute the optimal control and policy for the inverted pendulum (see Section E.3 in the Appendix for more details). Figure 1 displays the results of implementing ELM-PI on an inverted pendu-lum."}
+{"idx": 6, "title": "Provable Compositional Generalization for... | OpenReview", "date": "", "ddg_snippet": "Regarding computational costs, the loss requires additional passes through the encoder and decoder as well as computation of the encoder’s gradients wrt. the loss. We found this to increase training time by a maximum of 28% across runs.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=7VPTUWkiDQ", "content": "Regarding computational costs, the loss requires additional passes through the encoder and decoder as well as computation of the encoder’s gradients wrt. the loss. We found this to increase training time by a maximum of 28% across runs."}
+{"idx": 7, "title": "Published as a conference paper at ICLR 2024 - OpenReview", "date": "", "ddg_snippet": "ABSTRACT Proximal causal learning is a powerful framework for identifying the causal effect under the existence of unmeasured confounders. Within this framework, the dou-bly robust (DR) estimator was derived and has shown its effectiveness in estima-tion, especially when the model assumption is violated. However, the current form of the DR estimator is restricted to binary treatments, while ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=TjGJFkU3xL", "content": "ABSTRACT Proximal causal learning is a powerful framework for identifying the causal effect under the existence of unmeasured confounders. Within this framework, the dou-bly robust (DR) estimator was derived and has shown its effectiveness in estima-tion, especially when the model assumption is violated. However, the current form of the DR estimator is restricted to binary treatments, while ..."}
+{"idx": 8, "title": "HYBRID NUMERICAL PINNS: ON THE EFFECTIVE NESS OF NUMERICAL ...", "date": "", "ddg_snippet": "This work demonstrates that automatic differentiation has strong limitations when employed to compute physical derivatives in a general physics-informed frame-work, therefore limiting the range of applications that these methods can address. A hybrid approach is proposed, combining deep learning and traditional numer-ical solvers such as the finite element method, to address the shortcomings ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=R5FzCFR5yU", "content": "This work demonstrates that automatic differentiation has strong limitations when employed to compute physical derivatives in a general physics-informed frame-work, therefore limiting the range of applications that these methods can address. A hybrid approach is proposed, combining deep learning and traditional numer-ical solvers such as the finite element method, to address the shortcomings ..."}
+{"idx": 9, "title": "INR-V: A Continuous Representation Space for Video-based ...", "date": "", "ddg_snippet": "Oct 28, 2022 · Improve the clarity of the paper (see previous section ). Add video generation experiments on UCF-101 and Kinetics. Compare computation complexity of INV and convolution networks for video generation. Shows that INV-R is working without a CLIP network or better motivate the use of CLIP in the approach.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=aIoEkwc2oB", "content": "Oct 28, 2022 · Improve the clarity of the paper (see previous section ). Add video generation experiments on UCF-101 and Kinetics. Compare computation complexity of INV and convolution networks for video generation. Shows that INV-R is working without a CLIP network or better motivate the use of CLIP in the approach."}
diff --git a/data/sampled_jsons/siteopenreview.net_9m87e9Keq1_section_5_experiments_base_LLMs.jsonl b/data/sampled_jsons/siteopenreview.net_9m87e9Keq1_section_5_experiments_base_LLMs.jsonl
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Mitigating Quantization Errors Due to Activation Spikes in GLU-Based LLMs", "date": "", "ddg_snippet": "51 In our comprehensive experiments , we demonstrate that recently released LLMs incorporating 52 GLU variants struggle with activation spikes when applying activation quantization. Consequently, 53 the proposed methods, QFeM and QFeP, substantially enhance the performance of the primitive 54 quantization method, the round-to-nearest (RTN) method.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=t8ch1OCvHh", "content": "51 In our comprehensive experiments , we demonstrate that recently released LLMs incorporating 52 GLU variants struggle with activation spikes when applying activation quantization. Consequently, 53 the proposed methods, QFeM and QFeP, substantially enhance the performance of the primitive 54 quantization method, the round-to-nearest (RTN) method."}
+{"idx": 1, "title": "The Unlocking Spell on Base LLMs: Rethinking Alignment via...", "date": "", "ddg_snippet": "Alignment tuning has become the de facto standard practice for enabling base large language models ( LLMs ) to serve as open-domain AI assistants. The alignment tuning process typically involves...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=wxJ0eXwwda", "content": "Alignment tuning has become the de facto standard practice for enabling base large language models ( LLMs ) to serve as open-domain AI assistants. The alignment tuning process typically involves..."}
+{"idx": 2, "title": "Towards Federated RLHF with Aggregated Client Preference for LLMs", "date": "", "ddg_snippet": "-The experiments need supplements: In the section of performance analysis on different binary selectors, the experiments applied three base model with both different model structure and parameter size.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=mqNKiEB6pd", "content": "-The experiments need supplements: In the section of performance analysis on different binary selectors, the experiments applied three base model with both different model structure and parameter size."}
+{"idx": 3, "title": "LLM-based Typed Hyperresolution for Commonsense Reasoning with ...", "date": "", "ddg_snippet": "The paper introduces LLM -based Typed Hyperresolution ( LLM -TH), a novel framework for enhancing commonsense reasoning by LLMs through logical inference with large, potentially incomplete KBs. The key ideas involve combining theory resolution, where the LLM fills in gaps in the KB by identifying commonsense entailments, with typed hyperresolution, which improves efficiency by limiting reasoning ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=wNobG8bV5Q", "content": "The paper introduces LLM -based Typed Hyperresolution ( LLM -TH), a novel framework for enhancing commonsense reasoning by LLMs through logical inference with large, potentially incomplete KBs. The key ideas involve combining theory resolution, where the LLM fills in gaps in the KB by identifying commonsense entailments, with typed hyperresolution, which improves efficiency by limiting reasoning ..."}
+{"idx": 4, "title": "Human-inspired Episodic Memory for Infinite Context LLMs", "date": "", "ddg_snippet": "Experiments on the LongBench and $\\infty$-Bench benchmarks demonstrate EM- LLM's superior performance, consistently outperforming the state-of-the-art retrieval model InfLLM across various baseline LLMs . In addition, EM- LLM outperforms its popular counterpart, RAG, in a wide range of tasks, while requiring similar resources.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=BI2int5SAC", "content": "Experiments on the LongBench and $\\infty$-Bench benchmarks demonstrate EM- LLM's superior performance, consistently outperforming the state-of-the-art retrieval model InfLLM across various baseline LLMs . In addition, EM- LLM outperforms its popular counterpart, RAG, in a wide range of tasks, while requiring similar resources."}
+{"idx": 5, "title": "Exploring the Potential of Large Language Models (LLMs) in ... - OpenReview", "date": "", "ddg_snippet": "In Section 5 , we conduct preliminary experiments on applying LLMs as predictors, utilizing both textual attributes and edge relationships. The results demonstrate that LLMs present effectiveness in processing textual attributes and achieving good zero-shot performance on certain datasets.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=ScNNo7v4t0", "content": "In Section 5 , we conduct preliminary experiments on applying LLMs as predictors, utilizing both textual attributes and edge relationships. The results demonstrate that LLMs present effectiveness in processing textual attributes and achieving good zero-shot performance on certain datasets."}
+{"idx": 6, "title": "Catastrophic Jailbreak of Open Source Llms Via E Generation", "date": "", "ddg_snippet": "The refined-alignment procedure. For the generation-aware alignment experiments in Section 5 , we sample examples from different decoding strategies, including temperature sampling (with tem-perature τ varied from 0 to 1 with step size 0.1), top-p sampling (with p from 0 to 1 with step size 0.1), and top-K sampling (with K from {1, 2, 5 , 10, 20 ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=r42tSSCHPh", "content": "The refined-alignment procedure. For the generation-aware alignment experiments in Section 5 , we sample examples from different decoding strategies, including temperature sampling (with tem-perature τ varied from 0 to 1 with step size 0.1), top-p sampling (with p from 0 to 1 with step size 0.1), and top-K sampling (with K from {1, 2, 5 , 10, 20 ..."}
+{"idx": 7, "title": "Synthesizing Post-Training Data for LLMs through Multi-Agent...", "date": "", "ddg_snippet": "Post-training is essential for enabling large language models ( LLMs ) to follow human instructions. Inspired by the recent success of using LLMs to simulate human society, we leverage multi-agent simulation to automatically generate diverse text-based scenarios, capturing a wide range of real-world human needs.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=o83aL1nZJd", "content": "Post-training is essential for enabling large language models ( LLMs ) to follow human instructions. Inspired by the recent success of using LLMs to simulate human society, we leverage multi-agent simulation to automatically generate diverse text-based scenarios, capturing a wide range of real-world human needs."}
+{"idx": 8, "title": "Improving Consistency in Large Language Models through Chain of...", "date": "", "ddg_snippet": "To summarize the changes, we have added a number of new experiments in Section 4, created a new section ( Section 5 ) to analyze the behavior of the finetuned models, greatly expanded the discussion in Section 6, and finally added clarifying material throughout the paper and in a number of appendices.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=asiBW1bB9b", "content": "To summarize the changes, we have added a number of new experiments in Section 4, created a new section ( Section 5 ) to analyze the behavior of the finetuned models, greatly expanded the discussion in Section 6, and finally added clarifying material throughout the paper and in a number of appendices."}
+{"idx": 9, "title": "CIRCUIT: A Benchmark for Circuit Interpretation and Reasoning ...", "date": "", "ddg_snippet": "This paper presents a Circuit Interpretation and Reasoning Capabilities (CIRCUIT) dataset to evaluate LLMs in understanding and reasoning about analog circuits. The authors conduct a series of experiments to assess the performance of various LLMs in understanding analog circuits and their topologies from diagrams and netlists. Soundness: 3: good", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=5iUUorHeM3", "content": "This paper presents a Circuit Interpretation and Reasoning Capabilities (CIRCUIT) dataset to evaluate LLMs in understanding and reasoning about analog circuits. The authors conduct a series of experiments to assess the performance of various LLMs in understanding analog circuits and their topologies from diagrams and netlists. Soundness: 3: good"}
diff --git a/data/sampled_jsons/siteopenreview.net_RAGGED_three_retrieval_approaches_BM25_ColBERT.jsonl b/data/sampled_jsons/siteopenreview.net_RAGGED_three_retrieval_approaches_BM25_ColBERT.jsonl
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index 0000000000000000000000000000000000000000..e8f282b8a55ef046d8c6c27f66a724b6359266b5
--- /dev/null
+++ b/data/sampled_jsons/siteopenreview.net_RAGGED_three_retrieval_approaches_BM25_ColBERT.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "RAGGED: Towards Informed Design of Retrieval Augmented ... - OpenReview", "date": "", "ddg_snippet": "RAG system components: For retrievers, we use two approaches:(1) BM25 [Robertson et al., 2009], a sparse retriever based on lexical information, and (2) ColBERT [Santhanam et al., 2021], a dense retriever based on neural embeddings.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=SX14yxTTRB", "content": "RAG system components: For retrievers, we use two approaches:(1) BM25 [Robertson et al., 2009], a sparse retriever based on lexical information, and (2) ColBERT [Santhanam et al., 2021], a dense retriever based on neural embeddings."}
+{"idx": 1, "title": "Coherence-based Query Performance Measures for Dense Retrieval - OpenReview", "date": "", "ddg_snippet": "Retrieval Systems: We deploy three retrieval approaches : BM25 sparse retrieval (applying Porter's English stemmer and removing standard stopwords) as implemented by Terrier [38], and two single-representation dense retrieval approaches , namely ANCE [56], and TCT- ColBERT [28] with PyTerrier [30] integrations.1", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=4SXEZSNxTe", "content": "Retrieval Systems: We deploy three retrieval approaches : BM25 sparse retrieval (applying Porter's English stemmer and removing standard stopwords) as implemented by Terrier [38], and two single-representation dense retrieval approaches , namely ANCE [56], and TCT- ColBERT [28] with PyTerrier [30] integrations.1"}
+{"idx": 2, "title": "Transfer Learning Approaches for Building", "date": "", "ddg_snippet": "– Query Translation: BM 25 retrieval using translated queries produced by a specic MT model and original documents in the target language.8.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=TYEHy7_-jlg", "content": "– Query Translation: BM 25 retrieval using translated queries produced by a specic MT model and original documents in the target language.8."}
+{"idx": 3, "title": "Diagnosing Retrieval -Augmented Generation", "date": "", "ddg_snippet": "In this section, we evaluate three RAG baselines ( BM 25 _GPT-4, E5-Mistral_GPT-4, and E5-Mistral_Llama3-70B) across three domains with increasing difficulty: Writing, Finance, and KIWI.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=J9oefdGUuM", "content": "In this section, we evaluate three RAG baselines ( BM 25 _GPT-4, E5-Mistral_GPT-4, and E5-Mistral_Llama3-70B) across three domains with increasing difficulty: Writing, Finance, and KIWI."}
+{"idx": 4, "title": "IterKey: Iterative Keyword Generation with LLMs for... | OpenReview", "date": "", "ddg_snippet": "Its performance is comparable to dense retrieval -based RAG and prior iterative query refinement methods using dense models. In summary, IterKey is a novel BM 25 -based approach leveraging LLMs to iteratively refine RAG , effectively balancing accuracy with interpretability.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=73aRitnIL7&referrer=[the+profile+of+Taro+Watanabe](/profile?id=~Taro_Watanabe1)", "content": "Its performance is comparable to dense retrieval -based RAG and prior iterative query refinement methods using dense models. In summary, IterKey is a novel BM 25 -based approach leveraging LLMs to iteratively refine RAG , effectively balancing accuracy with interpretability."}
+{"idx": 5, "title": "Ragged: Towards Informed Design of Retrieval Augmented Generation ...", "date": "", "ddg_snippet": "125 BM25 BM25 (Robertson et al., 2009) is a probabilistic retrieval model that estimates passage 126 relevance via term weighting and passage length normalization. It relies on term-matching and is 127 supposed to be relatively proficient at identifying lexical similarity, especially in special domains. 129 128 ColBERT One of the best-performing neural-based retrievers is ColBERT (Santhanam et ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=KDXj60FpJr", "content": "125 BM25 BM25 (Robertson et al., 2009) is a probabilistic retrieval model that estimates passage 126 relevance via term weighting and passage length normalization. It relies on term-matching and is 127 supposed to be relatively proficient at identifying lexical similarity, especially in special domains. 129 128 ColBERT One of the best-performing neural-based retrievers is ColBERT (Santhanam et ..."}
+{"idx": 6, "title": "RAGGED: Towards Informed Design of Scalable and Stable RAG Systems", "date": "", "ddg_snippet": "For peak-then-decline models (LLAMA, CLAUDE-3-HAIKU), ColBERT performs worse than BM25 at large k, despite better retrieval quality. This suggests that some readers are highly sensitive to the nature of the retrieval noise.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=4ufjBV6S4I", "content": "For peak-then-decline models (LLAMA, CLAUDE-3-HAIKU), ColBERT performs worse than BM25 at large k, despite better retrieval quality. This suggests that some readers are highly sensitive to the nature of the retrieval noise."}
+{"idx": 7, "title": "From Retrieval to Generation: Comparing Different Approaches", "date": "", "ddg_snippet": "Additionally, we analyze language modeling tasks using WikiText-103, showing that retrieval -based approaches like BM25 achieve lower perplexity compared to generative and hybrid methods, highlighting their utility in retrieval -augmented generation.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=JL7oNfdO7i", "content": "Additionally, we analyze language modeling tasks using WikiText-103, showing that retrieval -based approaches like BM25 achieve lower perplexity compared to generative and hybrid methods, highlighting their utility in retrieval -augmented generation."}
+{"idx": 8, "title": "RAGGED: Towards Informed Design of Retrieval Augmented Generation ...", "date": "", "ddg_snippet": "Summary of the paper: This paper introduces RAGGED , an evaluation framework designed to analyze the performance of RAG systems. It investigates how various components—such as different retrievers ( BM25 , ColBERT , Contriever) and reader models (FLAN, LLaMa, GPT, Claude)—impact RAG effectiveness across diverse tasks and datasets, including NQ, HotPotQA, and BioASQ. Key findings reveal that ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=KDXj60FpJr", "content": "Summary of the paper: This paper introduces RAGGED , an evaluation framework designed to analyze the performance of RAG systems. It investigates how various components—such as different retrievers ( BM25 , ColBERT , Contriever) and reader models (FLAN, LLaMa, GPT, Claude)—impact RAG effectiveness across diverse tasks and datasets, including NQ, HotPotQA, and BioASQ. Key findings reveal that ..."}
+{"idx": 9, "title": "Reinforced Query Reasoners for Reasoning-intensive Retrieval Tasks", "date": "", "ddg_snippet": "Experiment results on BRIGHT benchmark show that, with BM25 as retrievers, both RQR-7B and RQR-1.5B models significantly outperform existing baselines, including prompt-based query reasoners and some latest dense retrievers trained for reasoning-intensive retrieval tasks, offering superior adaptability for real-world deployment.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=llCASUGMqt", "content": "Experiment results on BRIGHT benchmark show that, with BM25 as retrievers, both RQR-7B and RQR-1.5B models significantly outperform existing baselines, including prompt-based query reasoners and some latest dense retrievers trained for reasoning-intensive retrieval tasks, offering superior adaptability for real-world deployment."}
diff --git a/data/sampled_jsons/siteopenreview.net_YjBrt82S3v_Symmetric_Reinforcement_Learning_Loss.jsonl b/data/sampled_jsons/siteopenreview.net_YjBrt82S3v_Symmetric_Reinforcement_Learning_Loss.jsonl
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index 0000000000000000000000000000000000000000..cba0b3bf28dc33004c309c06f0798be8f4364605
--- /dev/null
+++ b/data/sampled_jsons/siteopenreview.net_YjBrt82S3v_Symmetric_Reinforcement_Learning_Loss.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Symmetric Exploration in Combinatorial", "date": "", "ddg_snippet": "Equivariant deep reinforcement learning . Symmetric Reinforcement Distillation (SymRD). Solution-preserving transformation policy.In symmetric TSP, the distance are symmetric , i.e., dij = dji, while asymmetric TSP is a relaxed version of this assumption. IP formulation.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=a5RnAsDAYP", "content": "Equivariant deep reinforcement learning . Symmetric Reinforcement Distillation (SymRD). Solution-preserving transformation policy.In symmetric TSP, the distance are symmetric , i.e., dij = dji, while asymmetric TSP is a relaxed version of this assumption. IP formulation."}
+{"idx": 1, "title": "CoBERL: Contrastive BERT for Reinforcement Learning", "date": "", "ddg_snippet": "010 011 Many reinforcement learning (RL) agents require 012 a large amount of experience to solve tasks. We 013 propose Contrastive BERT for RL (COBERL), 014 an agent that combines a new contrastive loss 015 and a hybrid...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=RNn-2FCIdxa", "content": "010 011 Many reinforcement learning (RL) agents require 012 a large amount of experience to solve tasks. We 013 propose Contrastive BERT for RL (COBERL), 014 an agent that combines a new contrastive loss 015 and a hybrid..."}
+{"idx": 2, "title": "Benchmarking Offline Reinforcement Learning in", "date": "", "ddg_snippet": "Extending reinforcement learning (RL) to offline contexts is a promising prospect, partic-ularly in sectors where data collection poses substantial challenges or risks.Multi-agent reinforcement learning as a rehearsal for decentralized planning. Neurocomputing, 190:82–94, 2016.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=fm679EfNqc&name=pdf", "content": "Extending reinforcement learning (RL) to offline contexts is a promising prospect, partic-ularly in sectors where data collection poses substantial challenges or risks.Multi-agent reinforcement learning as a rehearsal for decentralized planning. Neurocomputing, 190:82–94, 2016."}
+{"idx": 3, "title": "Self-supervised Color Generalization in Reinforcement Learn", "date": "", "ddg_snippet": "Reinforcement Learning & Symmetries . Dynamic Mode Representation.Published in Transactions on Machine Learning Research (10/2024). Self-supervised Color Generalization in Reinforcement Learn -ing.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=4On0PLRI8H", "content": "Reinforcement Learning & Symmetries . Dynamic Mode Representation.Published in Transactions on Machine Learning Research (10/2024). Self-supervised Color Generalization in Reinforcement Learn -ing."}
+{"idx": 4, "title": "RL4CO: an Extensive Reinforcement Learning", "date": "", "ddg_snippet": "online reinforcement learning approach. We benchmark the DF version 1135 for RL with the same node and context embedding structure as the original in Kim et al.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=Becrgm5xAq&name=supplementary_material", "content": "online reinforcement learning approach. We benchmark the DF version 1135 for RL with the same node and context embedding structure as the original in Kim et al."}
+{"idx": 5, "title": "Meta- learning Population-based Methods for Reinforcement", "date": "", "ddg_snippet": "Reinforcement learning (RL) algorithms are highly sensitive to their hyperparameter set-tings. Recently, numerous methods have been proposed to dynamically optimize these hyperparameters.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=d9htascfP8&name=pdf", "content": "Reinforcement learning (RL) algorithms are highly sensitive to their hyperparameter set-tings. Recently, numerous methods have been proposed to dynamically optimize these hyperparameters."}
+{"idx": 6, "title": "Symmetric Reinforcement Learning Loss for Robust Learning on Diverse ...", "date": "", "ddg_snippet": "In this work, we focus on RL algorithms that share learning dificulties with cross-entropy loss , especially for low-probability predictions. To enhance stability, we adapt reverse cross-entropy (RCE) from supervised learning for noisy data, defining a symmetric RL loss . We demonstrate performance improvements across various tasks and scales.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=YjBrt82S3v", "content": "In this work, we focus on RL algorithms that share learning dificulties with cross-entropy loss , especially for low-probability predictions. To enhance stability, we adapt reverse cross-entropy (RCE) from supervised learning for noisy data, defining a symmetric RL loss . We demonstrate performance improvements across various tasks and scales."}
+{"idx": 7, "title": "Symmetric Reinforcement Learning Loss for Robust Learning on Diverse ...", "date": "", "ddg_snippet": "Reinforcement learning (RL) training is inherently unstable due to factors such as moving targets and high gradient variance. Reinforcement Learning from Hu-man Feedback (RLHF) and Reinforcement Learning from AI Feedback (RLAIF) introduce additional challenges. For instance, diverse preferences complicate the alignment process, and prediction errors in a trained reward model can become more ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=9oq0iY2Jxx", "content": "Reinforcement learning (RL) training is inherently unstable due to factors such as moving targets and high gradient variance. Reinforcement Learning from Hu-man Feedback (RLHF) and Reinforcement Learning from AI Feedback (RLAIF) introduce additional challenges. For instance, diverse preferences complicate the alignment process, and prediction errors in a trained reward model can become more ..."}
+{"idx": 8, "title": "$\\\\mathrm{SO}(2)$-Equivariant Reinforcement Learning - OpenReview", "date": "", "ddg_snippet": "Abstract: Equivariant neural networks enforce symmetry within the structure of their convolutional layers, resulting in a substantial improvement in sample efficiency when learning an equivariant or invariant function. Such models are applicable to robotic manipulation learning which can often be formulated as a rotationally symmetric problem. This paper studies equivariant model architectures ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=7F9cOhdvfk_", "content": "Abstract: Equivariant neural networks enforce symmetry within the structure of their convolutional layers, resulting in a substantial improvement in sample efficiency when learning an equivariant or invariant function. Such models are applicable to robotic manipulation learning which can often be formulated as a rotationally symmetric problem. This paper studies equivariant model architectures ..."}
+{"idx": 9, "title": "Symmetric Reinforcement Learning Loss for Robust Learning on Diverse ...", "date": "", "ddg_snippet": "In this work, we focus on RL algorithms that share learning difficulties with cross-entropy loss , especially for low-probability predictions. To enhance stability, we adapt reverse cross-entropy (RCE) from supervised learning for noisy data, defining a symmetric RL loss . We demonstrate performance improvements across various tasks and scales.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=YjBrt82S3v", "content": "In this work, we focus on RL algorithms that share learning difficulties with cross-entropy loss , especially for low-probability predictions. To enhance stability, we adapt reverse cross-entropy (RCE) from supervised learning for noisy data, defining a symmetric RL loss . We demonstrate performance improvements across various tasks and scales."}
diff --git a/data/sampled_jsons/siteopenreview.net_qtuxDy2qEB_diagonal_update_Section_3.jsonl b/data/sampled_jsons/siteopenreview.net_qtuxDy2qEB_diagonal_update_Section_3.jsonl
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index 0000000000000000000000000000000000000000..a73b40840371b84a90393b950b82e9ef2a4c9116
--- /dev/null
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "On the Parameterization and Initialization of Diagonal State Space Models", "date": "", "ddg_snippet": "Our final model S4D is a simple diagonal version of S4 whose kernel computation requires just 3 lines of code and performs comparably to S4 in almost all settings, with state-of-the-art results in image, audio, and medical time-series domains, and 85% average on the Long Range Arena benchmark. 23", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/references/pdf?id=i1bSMHTxM", "content": "Our final model S4D is a simple diagonal version of S4 whose kernel computation requires just 3 lines of code and performs comparably to S4 in almost all settings, with state-of-the-art results in image, audio, and medical time-series domains, and 85% average on the Long Range Arena benchmark. 23"}
+{"idx": 1, "title": "PDF Simplifying and Understanding State Space Models with Diagonal Linear RNNs", "date": "", "ddg_snippet": "First, we propose Diagonal Linear RNNs, which simplify diagonal state spaces by dropping the continuous state space discretization, and then propose a suitable initialization scheme.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/notes/edits/attachment?id=W88FkGP5of&name=pdf", "content": "First, we propose Diagonal Linear RNNs, which simplify diagonal state spaces by dropping the continuous state space discretization, and then propose a suitable initialization scheme."}
+{"idx": 2, "title": "On the Parameterization and Initialization of Diagonal State Space Models", "date": "", "ddg_snippet": "We provide a new mathematical analysis of DSS's initialization, showing that the diagonal ap-proximation of the original HiPPO matrix surprisingly produces the same dynamics as S4 when the state size goes to infinity. We propose even simpler variants of diagonal SSMs using different initializations of the state matrix ( Section 4).", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=yJE7iQSAep", "content": "We provide a new mathematical analysis of DSS's initialization, showing that the diagonal ap-proximation of the original HiPPO matrix surprisingly produces the same dynamics as S4 when the state size goes to infinity. We propose even simpler variants of diagonal SSMs using different initializations of the state matrix ( Section 4)."}
+{"idx": 3, "title": "Watermarking Graph Neural Networks Via Explanations for Ownership ...", "date": "", "ddg_snippet": "Abstract: Graph Neural Networks (GNNs) are the mainstream method to learn pervasive graph data and are widely deployed in industry, making their intellectual property valuable. However, protecting GNNs from unauthorized use remains a challenge. Watermarking, which embeds ownership information into a model, is a potential solution. However, existing watermarking methods have two key limitations ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=EgP6IEyfYJ", "content": "Abstract: Graph Neural Networks (GNNs) are the mainstream method to learn pervasive graph data and are widely deployed in industry, making their intellectual property valuable. However, protecting GNNs from unauthorized use remains a challenge. Watermarking, which embeds ownership information into a model, is a potential solution. However, existing watermarking methods have two key limitations ..."}
+{"idx": 4, "title": "Were RNNs All We Needed? - OpenReview", "date": "", "ddg_snippet": "The introduction of Transformers in 2017 reshaped the landscape of deep learning. Originally proposed for sequence modelling, Transformers have since achieved widespread success across various...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=GrmFFxGnOR", "content": "The introduction of Transformers in 2017 reshaped the landscape of deep learning. Originally proposed for sequence modelling, Transformers have since achieved widespread success across various..."}
+{"idx": 5, "title": "Quanquan Gu - OpenReview", "date": "", "ddg_snippet": "Quanquan Gu Research Scientist, ByteDance Inc. Associate Professor, Department of Computer Science, University of California, Los Angeles Joined February 2017", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/profile?id=~Quanquan_Gu1", "content": "Quanquan Gu Research Scientist, ByteDance Inc. Associate Professor, Department of Computer Science, University of California, Los Angeles Joined February 2017"}
+{"idx": 6, "title": "KDD 2024 Applied Data Science Track | OpenReview", "date": "", "ddg_snippet": "Open Peer Review. Open Publishing. Open Access. Open Discussion. Open Recommendations. Open Directory. Open API. Open Source.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/group?id=KDD.org/2024/Applied_Data_Science_Track", "content": "Open Peer Review. Open Publishing. Open Access. Open Discussion. Open Recommendations. Open Directory. Open API. Open Source."}
+{"idx": 7, "title": "PDF University of Cambridge, UK University of British Columbia, Canada", "date": "", "ddg_snippet": "h(t+1) (t) X icm = V + w(t) f(t)(H(t) icm c~c ~c )ii;m0 (11) ~c aluate the diagonal blocks of a matrix function; cf. Sec-tion 4.2. Note that the diagonal blo ks are symmetric and therefore extracting m0 or 0m is equiva-lent. Alternative updates can be d fined from the matrix function that we detail in the appendix A.5. The optimal kind of", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/f769ed973540097e6a721fc0b64e4271088eabe0.pdf", "content": "h(t+1) (t) X icm = V + w(t) f(t)(H(t) icm c~c ~c )ii;m0 (11) ~c aluate the diagonal blocks of a matrix function; cf. Sec-tion 4.2. Note that the diagonal blo ks are symmetric and therefore extracting m0 or 0m is equiva-lent. Alternative updates can be d fined from the matrix function that we detail in the appendix A.5. The optimal kind of"}
+{"idx": 8, "title": "KnowTrace: Explicit Knowledge Tracing for Structured...", "date": "", "ddg_snippet": "For the backtracing mechanism, the reasoning rationales are distilled by tracing back along the self-organized knowledge structures from the target entities to the initial entities as described in Section 3.3.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=F6rZaxOC6m", "content": "For the backtracing mechanism, the reasoning rationales are distilled by tracing back along the self-organized knowledge structures from the target entities to the initial entities as described in Section 3.3."}
+{"idx": 9, "title": "Physics of Language Models: Part 3.3, Knowledge Capacity Scaling Laws", "date": "", "ddg_snippet": "Scaling laws describe the relationship between the size of language models and their capabilities. Unlike prior studies that evaluate a model's capability via loss or benchmarks, we estimate...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=FxNNiUgtfa", "content": "Scaling laws describe the relationship between the size of language models and their capabilities. Unlike prior studies that evaluate a model's capability via loss or benchmarks, we estimate..."}
diff --git a/data/sampled_jsons/sitepapercopilot.com_WWW_2024_accepted_papers_count_information_retrieval_year_2024.jsonl b/data/sampled_jsons/sitepapercopilot.com_WWW_2024_accepted_papers_count_information_retrieval_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..979fb176f3816f5c323624cf53b43464a8295609
--- /dev/null
+++ b/data/sampled_jsons/sitepapercopilot.com_WWW_2024_accepted_papers_count_information_retrieval_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "WWW 2024 Accepted Paper List", "date": "", "ddg_snippet": "This table presents papers from the WWW conference, year 2024 . Filtering: By default, the table loads the first 100 records.", "subpage_snippet": "", "source": "papercopilot.com", "link": "https://papercopilot.com/www-paper-list/www-2024-paper-list/", "content": "This table presents papers from the WWW conference, year 2024 . Filtering: By default, the table loads the first 100 records."}
+{"idx": 1, "title": "WWW 2024 Statistics", "date": "", "ddg_snippet": "For example, if there are 100 total submissions and 27 were accepted , the Accept Rate = 27 / 100 = 27% . - min / max / mean / std: These are statistical ...", "subpage_snippet": "", "source": "papercopilot.com", "link": "https://papercopilot.com/statistics/www-statistics/www-2024-statistics/", "content": "For example, if there are 100 total submissions and 27 were accepted , the Accept Rate = 27 / 100 = 27% . - min / max / mean / std: These are statistical ..."}
+{"idx": 2, "title": "NeurIPS 2024 Accepted Paper List", "date": "", "ddg_snippet": "This table presents papers from the NeurIPS conference, year 2024 . Filtering: By default, the table loads the first 100 records.", "subpage_snippet": "", "source": "papercopilot.com", "link": "https://papercopilot.com/paper-list/neurips-paper-list/neurips-2024-paper-list/", "content": "This table presents papers from the NeurIPS conference, year 2024 . Filtering: By default, the table loads the first 100 records."}
+{"idx": 3, "title": "ACMMM 2024 Accepted Paper List", "date": "", "ddg_snippet": "How to interpret the columns above: - Count : The total number of submissions is calculated as: #Total = # Accept + #Reject + #Withdraw + #Desk Reject - #Post ...", "subpage_snippet": "", "source": "papercopilot.com", "link": "https://papercopilot.com/paper-list/acmmm-paper-list/acmmm-2024-paper-list/", "content": "How to interpret the columns above: - Count : The total number of submissions is calculated as: #Total = # Accept + #Reject + #Withdraw + #Desk Reject - #Post ..."}
+{"idx": 4, "title": "EMNLP 2024 Accepted Paper List", "date": "", "ddg_snippet": "This table presents papers from the EMNLP conference, year 2024 . Filtering: By default, the table loads the first 100 records.", "subpage_snippet": "", "source": "papercopilot.com", "link": "https://papercopilot.com/paper-list/emnlp-paper-list/emnlp-2024-paper-list/", "content": "This table presents papers from the EMNLP conference, year 2024 . Filtering: By default, the table loads the first 100 records."}
+{"idx": 5, "title": "ACL 2024 Accepted Paper List", "date": "", "ddg_snippet": "This table presents papers from the ACL conference, year 2024 . Filtering: By default, the table loads the first 100 records.", "subpage_snippet": "", "source": "papercopilot.com", "link": "https://papercopilot.com/paper-list/acl-paper-list/acl-2024-paper-list/", "content": "This table presents papers from the ACL conference, year 2024 . Filtering: By default, the table loads the first 100 records."}
+{"idx": 6, "title": "AAAI 2024 Accepted Paper List", "date": "", "ddg_snippet": "This table presents papers from the AAAI conference, year 2024 . Filtering: By default, the table loads the first 100 records.", "subpage_snippet": "", "source": "papercopilot.com", "link": "https://papercopilot.com/paper-list/aaai-paper-list/aaai-2024-paper-list/", "content": "This table presents papers from the AAAI conference, year 2024 . Filtering: By default, the table loads the first 100 records."}
+{"idx": 7, "title": "NeurIPS 2024 Statistics", "date": "", "ddg_snippet": "For example, if there are 100 total submissions and 27 were accepted , the Accept Rate = 27 / 100 = 27% . - min / max / mean / std: These are statistical ...", "subpage_snippet": "", "source": "papercopilot.com", "link": "https://papercopilot.com/statistics/neurips-statistics/neurips-2024-statistics/", "content": "For example, if there are 100 total submissions and 27 were accepted , the Accept Rate = 27 / 100 = 27% . - min / max / mean / std: These are statistical ..."}
+{"idx": 8, "title": "ACML 2024 Accepted Paper List", "date": "", "ddg_snippet": "This table presents papers from the ACML conference, year 2024 . Filtering: By default, the table loads the first 100 records.", "subpage_snippet": "", "source": "papercopilot.com", "link": "https://papercopilot.com/paper-list/acml-paper-list/acml-2024-paper-list/", "content": "This table presents papers from the ACML conference, year 2024 . Filtering: By default, the table loads the first 100 records."}
+{"idx": 9, "title": "CoRL 2024 Accepted Paper List", "date": "", "ddg_snippet": "This table presents papers from the CORL conference, year 2024 . Filtering: By default, the table loads the first 100 records.", "subpage_snippet": "", "source": "papercopilot.com", "link": "https://papercopilot.com/paper-list/corl-paper-list/corl-2024-paper-list/", "content": "This table presents papers from the CORL conference, year 2024 . Filtering: By default, the table loads the first 100 records."}
diff --git a/data/sampled_jsons/siteresearchgate.net_Table_1_Sieve_MLE_FD3_MEAN_SD.jsonl b/data/sampled_jsons/siteresearchgate.net_Table_1_Sieve_MLE_FD3_MEAN_SD.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..cd04d777ed1e587abd7ddd891f4aa70a9c30d2cd
--- /dev/null
+++ b/data/sampled_jsons/siteresearchgate.net_Table_1_Sieve_MLE_FD3_MEAN_SD.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Discovering and requesting research – ResearchGate USP and BP solubility criteria | Download Table - ResearchGate Search | ResearchGate Table 3 Normative values generated for diadochokinetic rate ... Table 1 Common types of adducts in LC-MS - ResearchGate", "date": "", "ddg_snippet": "Simply type the name of the researcher, research item, or question you’re looking for in the search bar at the top of any ResearchGate page and press Enter. If the item you’re looking for doesn’t immediately appear in the search results list, try using the filters across the top of the page (e.g. Research, People, Questions). Note: The search bar a... See full list on help. researchgate . net As we strongly believe that scientific content should be accessible to everyone, this option to request a full-text is available to both logged-in members and logged-out users. On many publication pages, if there’s no full-text available, you will see a blue Request full-text button at the top right-hand corner of the publication page. On publicati... See full list on help. researchgate . net Clicking the Request full-textbutton on a publication page causes a message to be sent from the requester to the authors of the publication. Recipients of such messages can ignore them, decline them, or respond. An author who concludes that they have the right to share the requested content may choose to share the full-text publicly or to send a pr... See full list on help. researchgate . net Full-text requests can only be fulfilled by the authors of the publication. No content is automatically supplied in response to a request and in no event will ResearchGate make available or provide a copy of the full-text you have requested. Because of this, the time it takes for your request to be fulfilled is entirely dependent on the authors. It... See full list on help. researchgate . net It’s not possible to cancel a full-text request. Because of this, we encourage you to exercise discretion when requesting full-texts from authors and to only request research that is of interest to you. See full list on help. researchgate . net Requests contain all Full-text, Author, and other types of requests. These generally require some kind of answer or confirmation. You can find your requests by clicking on the speech bubble icon at the top-right of any ResearchGate page, or by going directly to Requests. To send a full-text that has been requested: 1 . Click on the speech bubble ico... See full list on help. researchgate . net To decline a full-text request: 1 . Visit Requests 2. In the Open requests tab, scroll down to the relevant research item and select Decline request. 3. If you want to give a reason for declining (e.g. I don’t have the full-text of this paper), enter the reason in the pop-up window, or choose a quick reply, then click Send message. 4. To decline wit... See full list on help. researchgate . net USP and BP solubility criteria are given in Table 1 . Techniques to enhance solubility and dissolution rate [4] There are different techniques available for enhancing the solubility and dissolution ... Enter a title, author name, or research area to search for publications Download Table | Normative values generated for diadochokinetic rate (syllables per second), by task, age and sex. from publication: Alternating and sequential motion rates in older adults ... Download Table | Common types of adducts in LC-MS from publication: Ion annotation-assisted analysis of LC-MS based metabolomic experiment | Analysis of multiple LC-MS based metabolomic studies is ...", "subpage_snippet": "", "source": "help.researchgate.net", "link": "https://help.researchgate.net/hc/en-us/articles/14292986311825-Discovering-and-requesting-research", "content": "Simply type the name of the researcher, research item, or question you’re looking for in the search bar at the top of any ResearchGate page and press Enter. If the item you’re looking for doesn’t immediately appear in the search results list, try using the filters across the top of the page (e.g. Research, People, Questions). Note: The search bar a... See full list on help. researchgate . net As we strongly believe that scientific content should be accessible to everyone, this option to request a full-text is available to both logged-in members and logged-out users. On many publication pages, if there’s no full-text available, you will see a blue Request full-text button at the top right-hand corner of the publication page. On publicati... See full list on help. researchgate . net Clicking the Request full-textbutton on a publication page causes a message to be sent from the requester to the authors of the publication. Recipients of such messages can ignore them, decline them, or respond. An author who concludes that they have the right to share the requested content may choose to share the full-text publicly or to send a pr... See full list on help. researchgate . net Full-text requests can only be fulfilled by the authors of the publication. No content is automatically supplied in response to a request and in no event will ResearchGate make available or provide a copy of the full-text you have requested. Because of this, the time it takes for your request to be fulfilled is entirely dependent on the authors. It... See full list on help. researchgate . net It’s not possible to cancel a full-text request. Because of this, we encourage you to exercise discretion when requesting full-texts from authors and to only request research that is of interest to you. See full list on help. researchgate . net Requests contain all Full-text, Author, and other types of requests. These generally require some kind of answer or confirmation. You can find your requests by clicking on the speech bubble icon at the top-right of any ResearchGate page, or by going directly to Requests. To send a full-text that has been requested: 1 . Click on the speech bubble ico... See full list on help. researchgate . net To decline a full-text request: 1 . Visit Requests 2. In the Open requests tab, scroll down to the relevant research item and select Decline request. 3. If you want to give a reason for declining (e.g. I don’t have the full-text of this paper), enter the reason in the pop-up window, or choose a quick reply, then click Send message. 4. To decline wit... See full list on help. researchgate . net USP and BP solubility criteria are given in Table 1 . Techniques to enhance solubility and dissolution rate [4] There are different techniques available for enhancing the solubility and dissolution ... Enter a title, author name, or research area to search for publications Download Table | Normative values generated for diadochokinetic rate (syllables per second), by task, age and sex. from publication: Alternating and sequential motion rates in older adults ... Download Table | Common types of adducts in LC-MS from publication: Ion annotation-assisted analysis of LC-MS based metabolomic experiment | Analysis of multiple LC-MS based metabolomic studies is ..."}
+{"idx": 1, "title": "USP and BP solubility criteria | Download Table - ResearchGate", "date": "", "ddg_snippet": "USP and BP solubility criteria are given in Table 1 . Techniques to enhance solubility and dissolution rate [4] There are different techniques available for enhancing the solubility and dissolution ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/USP-and-BP-solubility-criteria_tbl1_328251133", "content": "USP and BP solubility criteria are given in Table 1 . Techniques to enhance solubility and dissolution rate [4] There are different techniques available for enhancing the solubility and dissolution ..."}
+{"idx": 2, "title": "Table 3 Normative values generated for diadochokinetic rate ...", "date": "", "ddg_snippet": "Download Table | Normative values generated for diadochokinetic rate (syllables per second), by task, age and sex. from publication: Alternating and sequential motion rates in older adults ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/Normative-values-generated-for-diadochokinetic-rate-syllables-per-second-by-task-age_tbl1_236652250", "content": "Download Table | Normative values generated for diadochokinetic rate (syllables per second), by task, age and sex. from publication: Alternating and sequential motion rates in older adults ..."}
+{"idx": 3, "title": "Table 1 Common types of adducts in LC-MS - ResearchGate", "date": "", "ddg_snippet": "Download Table | Common types of adducts in LC-MS from publication: Ion annotation-assisted analysis of LC-MS based metabolomic experiment | Analysis of multiple LC-MS based metabolomic studies is ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/Common-types-of-adducts-in-LC-MS_tbl1_228114152", "content": "Download Table | Common types of adducts in LC-MS from publication: Ion annotation-assisted analysis of LC-MS based metabolomic experiment | Analysis of multiple LC-MS based metabolomic studies is ..."}
+{"idx": 4, "title": "Search | ResearchGate", "date": "", "ddg_snippet": "Enter a title, author name, or research area to search for publications", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/search/publications", "content": "Enter a title, author name, or research area to search for publications"}
+{"idx": 5, "title": "ResearchGate | Find and share research", "date": "", "ddg_snippet": "Access 160+ million publication pages and connect with 25+ million researchers. Join for free and gain visibility by uploading your research.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/", "content": "Access 160+ million publication pages and connect with 25+ million researchers. Join for free and gain visibility by uploading your research."}
+{"idx": 6, "title": "Search | ResearchGate", "date": "", "ddg_snippet": "Enter a title, author name, or research area to search for publications", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/search/app", "content": "Enter a title, author name, or research area to search for publications"}
+{"idx": 7, "title": "how to reduce their economic impact", "date": "", "ddg_snippet": "stone fruit yellows was detected in apricot at one location. Table 1 . Overview of the current status of fruit tree phytoplasmas in Netherlands (NL) and in ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/profile/Franco_Fernandez/publication/308794055_Phytoplasmas_and_phytoplasma_disease_management_how_to_reduce_their_economic_impact_COST_ACTION_FA0807_Advances_in_knowledge_about_phytoplasma_diseases_in_Argentina_Chapter_2_pages_82-89_Assunta_Berta/links/582319b808ae61258e3c9628/Phytoplasmas-and-phytoplasma-disease-management-how-to-reduce-their-economic-impact-COST-ACTION-FA0807-Advances-in-knowledge-about-phytoplasma-diseases-in-Argentina-Chapter-2-pages-82-89-Assunta-Be.pdf", "content": "stone fruit yellows was detected in apricot at one location. Table 1 . Overview of the current status of fruit tree phytoplasmas in Netherlands (NL) and in ..."}
+{"idx": 8, "title": "Bioavailability of Beta Carotene in Traditional Fermented, ...", "date": "", "ddg_snippet": "5 Dec 2013 — Table 1 . Feed composition of the four groups of rats used in the study. Nutrients. Sources. %. Group 1 : Control (kg). Group 2: Basal (kg). Group ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/271288103_Bioavailability_of_Beta_Carotene_in_Traditional_Fermented_Roasted_Granules_Gari_from_Bio-Fortified_Cassava_Roots/fulltext/5ac9153d0f7e9bcd519748f2/Bioavailability-of-Beta-Carotene-in-Traditional-Fermented-Roasted-Granules-Gari-from-Bio-Fortified-Cassava-Roots.pdf", "content": "5 Dec 2013 — Table 1 . Feed composition of the four groups of rats used in the study. Nutrients. Sources. %. Group 1 : Control (kg). Group 2: Basal (kg). Group ..."}
+{"idx": 9, "title": "Milestones in the history of thematic cartography, statistical ...", "date": "", "ddg_snippet": "by M Friendly · 2006 · Cited by 713 — Tables , graphs, maps and even text, whether static or dynamic, provide some means to see what lies within, determine the answer to a question, ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/profile/Michael-Friendly/publication/240118128_Milestones_in_the_history_of_thematic_cartography_statistica_l_graphics_and_data_visualization/links/5470dda10cf24af340c3b793/Milestones-in-the-history-of-thematic-cartography-statistica-l-graphics-and-data-visualization.pdf", "content": "by M Friendly · 2006 · Cited by 713 — Tables , graphs, maps and even text, whether static or dynamic, provide some means to see what lies within, determine the answer to a question, ..."}
diff --git a/data/sampled_jsons/software_composition_analysis_tools_for_python.jsonl b/data/sampled_jsons/software_composition_analysis_tools_for_python.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4a8af7c2aa4115f6ca7bf76e12ac7566ad29da1b
--- /dev/null
+++ b/data/sampled_jsons/software_composition_analysis_tools_for_python.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Source Code Analysis Tools", "date": "", "ddg_snippet": "ABOM is an online SCA ( software composition analysis ) tool that scans your application for open-source vulnerabilities using only a manifest file.", "subpage_snippet": "", "source": "owasp.org", "link": "https://owasp.org/www-community/Source_Code_Analysis_Tools", "content": "ABOM is an online SCA ( software composition analysis ) tool that scans your application for open-source vulnerabilities using only a manifest file."}
+{"idx": 1, "title": "Static Code Analysis for Python: 7 features to look out for", "date": "", "ddg_snippet": "23 Sept 2024 — In addition to type checking, SCA tools offer many other benefits for Python developers, such as code style enforcement, complexity analysis, ...", "subpage_snippet": "", "source": "spectralops.io", "link": "https://spectralops.io/blog/static-code-analysis-for-python-7-features-to-look-out-for/", "content": "23 Sept 2024 — In addition to type checking, SCA tools offer many other benefits for Python developers, such as code style enforcement, complexity analysis, ..."}
+{"idx": 2, "title": "Top 10 Software Composition Analysis (SCA) tools in 2025", "date": "", "ddg_snippet": "9 Jan 2025 — SCA tools are our best line of defense for open-source security , this article explores the top 10 open-source dependency scanners for 2025.", "subpage_snippet": "", "source": "www.aikido.dev", "link": "https://www.aikido.dev/blog/top-10-software-composition-analysis-sca-tools-in-2025", "content": "9 Jan 2025 — SCA tools are our best line of defense for open-source security , this article explores the top 10 open-source dependency scanners for 2025."}
+{"idx": 3, "title": "Best Software Composition Analysis (SCA) Tools for Python", "date": "", "ddg_snippet": "Compare the Top Software Composition Analysis (SCA) Tools that integrate with Python as of September 2025 · 1. Aikido Security . Aikido Security · 2. Kiuwan Code ...", "subpage_snippet": "", "source": "sourceforge.net", "link": "https://sourceforge.net/software/software-composition-analysis-sca/integrates-with-python/", "content": "Compare the Top Software Composition Analysis (SCA) Tools that integrate with Python as of September 2025 · 1. Aikido Security . Aikido Security · 2. Kiuwan Code ..."}
+{"idx": 4, "title": "Best 10 Software Composition Analysis (SCA) Tools [2025]", "date": "", "ddg_snippet": "11 Aug 2025 — Snyk supports most mainstream languages, Node.js, Java, Python, Go, .NET, Ruby, and even Docker images. Snyk supports developer feedback loops ...", "subpage_snippet": "", "source": "www.ox.security", "link": "https://www.ox.security/blog/software-composition-analysis-and-sca-tools/", "content": "11 Aug 2025 — Snyk supports most mainstream languages, Node.js, Java, Python, Go, .NET, Ruby, and even Docker images. Snyk supports developer feedback loops ..."}
+{"idx": 5, "title": "Best Software Composition Analysis (SCA) Tools for Python", "date": "", "ddg_snippet": "Find and compare the best Software Composition Analysis (SCA) tools for Python in 2025 · Kiuwan Code Security · Debricked · Snyk · Mend.io · Backslash Security.", "subpage_snippet": "", "source": "slashdot.org", "link": "https://slashdot.org/software/software-composition-analysis-sca/for-python/", "content": "Find and compare the best Software Composition Analysis (SCA) tools for Python in 2025 · Kiuwan Code Security · Debricked · Snyk · Mend.io · Backslash Security."}
+{"idx": 6, "title": "Software Composition Analysis — Open Source Tool", "date": "", "ddg_snippet": "The SCA tools inspect package managers, manifest files, souce code, binary files, container images and more. The identified open source is ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@selvakumarsubramanian/software-composition-analysis-open-source-tool-2f27be4a856f", "content": "The SCA tools inspect package managers, manifest files, souce code, binary files, container images and more. The identified open source is ..."}
+{"idx": 7, "title": "Best Tools for Software Composition Analysis (SCA)", "date": "", "ddg_snippet": "12 Jun 2025 — 1. ✓ Snyk · 2. ✓ OWASP Dependency-Check · 3. Mend (formerly WhiteSource) · 4. FOSSA · 5. Black Duck (Synopsys) · 6. GitHub Dependabot · 7. 🛡️ ...", "subpage_snippet": "", "source": "www.devopsschool.com", "link": "https://www.devopsschool.com/blog/best-tools-for-software-composition-analysis-sca/", "content": "12 Jun 2025 — 1. ✓ Snyk · 2. ✓ OWASP Dependency-Check · 3. Mend (formerly WhiteSource) · 4. FOSSA · 5. Black Duck (Synopsys) · 6. GitHub Dependabot · 7. 🛡️ ..."}
+{"idx": 8, "title": "What Is Software Composition Analysis (SCA)?", "date": "", "ddg_snippet": "Tools like Snyk and WhiteSource use advanced algorithms and extensive databases to identify components accurately. By understanding the open-source components ...", "subpage_snippet": "", "source": "www.paloaltonetworks.com", "link": "https://www.paloaltonetworks.com/cyberpedia/what-is-sca", "content": "Tools like Snyk and WhiteSource use advanced algorithms and extensive databases to identify components accurately. By understanding the open-source components ..."}
+{"idx": 9, "title": "magnologan/awesome-sca: A comprehensive list of ...", "date": "", "ddg_snippet": "A comprehensive list of Software Composition Analysis Tools . Following repo contains a collection of SCA tools which can be used to analyze risks in third ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/magnologan/awesome-sca", "content": "A comprehensive list of Software Composition Analysis Tools . Following repo contains a collection of SCA tools which can be used to analyze risks in third ..."}
diff --git a/data/sampled_jsons/software_framework_for_hybrid_data_parallelism_and_tensor_parallelism.jsonl b/data/sampled_jsons/software_framework_for_hybrid_data_parallelism_and_tensor_parallelism.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..958f59ea847bb46700ed6cb2e66869e25a9fc4c3
--- /dev/null
+++ b/data/sampled_jsons/software_framework_for_hybrid_data_parallelism_and_tensor_parallelism.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "An overview of tensor and matrix decomposition methods, applied", "date": "", "ddg_snippet": "Tensors and their decompositions are especially beneficial in unsupervised learning settings, but are gaining popularity in other sub-disciplines like ...", "subpage_snippet": "", "source": "sugaku.net", "link": "https://sugaku.net/oa/W4413149345/an-overview-of-tensor-and-matrix-decomposition-methods-applied-to-deep-neural-networks", "content": "Tensors and their decompositions are especially beneficial in unsupervised learning settings, but are gaining popularity in other sub-disciplines like ..."}
+{"idx": 1, "title": "NPU vs TPU: Understanding the Key Differences in AI Hardware", "date": "", "ddg_snippet": "TPUs often excel in speed and scalability for large datasets, leveraging their specialized architecture for tensor computations.", "subpage_snippet": "", "source": "www.wevolver.com", "link": "https://www.wevolver.com/article/npu-vs-tpu", "content": "TPUs often excel in speed and scalability for large datasets, leveraging their specialized architecture for tensor computations."}
+{"idx": 2, "title": "GitHub - cyclops-community/ctf: Cyclops Tensor Framework:", "date": "", "ddg_snippet": "Cyclops is a parallel (distributed-memory) numerical library for multidimensional arrays ( tensors ) in C++ and Python.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/cyclops-community/ctf", "content": "Cyclops is a parallel (distributed-memory) numerical library for multidimensional arrays ( tensors ) in C++ and Python."}
+{"idx": 3, "title": "US20150169369A1 - Systems and methods for parallelizing and", "date": "", "ddg_snippet": "... data locality optimizations may depend on the input data , which may become available only at run time, can further complicate the design of locality, ...", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/US20150169369A1/en", "content": "... data locality optimizations may depend on the input data , which may become available only at run time, can further complicate the design of locality, ..."}
+{"idx": 4, "title": "GreenHadoop: leveraging green energy in data-processing", "date": "", "ddg_snippet": "... SOftware research and development ... Distributed computing libraries and environments for quantum algorithms and hybrid classical-quantum workflows", "subpage_snippet": "", "source": "www.bsc.es", "link": "https://www.bsc.es/research-and-development/publications/greenhadoop-leveraging-green-energy-data-processing-frameworks", "content": "... SOftware research and development ... Distributed computing libraries and environments for quantum algorithms and hybrid classical-quantum workflows"}
+{"idx": 5, "title": "Adaptive GPU Array Layout Auto-Tuning | Dr.-Ing. Nicolas", "date": "", "ddg_snippet": "Software Engineering Methods for Parallel and High Performance Applications (SEM4HPC) , Source Code ... DATA PROCSSING AND MACHINE LEARNING FRAMEWORKS ...", "subpage_snippet": "", "source": "www.mergian.de", "link": "https://www.mergian.de/2016/sem4hpc-matog/", "content": "Software Engineering Methods for Parallel and High Performance Applications (SEM4HPC) , Source Code ... DATA PROCSSING AND MACHINE LEARNING FRAMEWORKS ..."}
+{"idx": 6, "title": "SEM4HPC | Dr.-Ing. Nicolas Weber", "date": "", "ddg_snippet": "Software Engineering Methods for Parallel and High Performance Applications (SEM4HPC) , Source Code ... DATA PROCSSING AND MACHINE LEARNING FRAMEWORKS ...", "subpage_snippet": "", "source": "www.mergian.de", "link": "https://www.mergian.de/tags/sem4hpc/", "content": "Software Engineering Methods for Parallel and High Performance Applications (SEM4HPC) , Source Code ... DATA PROCSSING AND MACHINE LEARNING FRAMEWORKS ..."}
+{"idx": 7, "title": "MATOG | Dr.-Ing. Nicolas Weber", "date": "", "ddg_snippet": "Software Engineering Methods for Parallel and High Performance Applications (SEM4HPC) , Source Code ... DATA PROCSSING AND MACHINE LEARNING FRAMEWORKS ...", "subpage_snippet": "", "source": "www.mergian.de", "link": "https://www.mergian.de/tags/matog/", "content": "Software Engineering Methods for Parallel and High Performance Applications (SEM4HPC) , Source Code ... DATA PROCSSING AND MACHINE LEARNING FRAMEWORKS ..."}
+{"idx": 8, "title": "Linear Scaling Made Possible with Weight Streaming - Cerebras", "date": "", "ddg_snippet": "In addition to the list above, you must decide on the values of the parameters defining degree of data parallelism , tensor model parallelism and ...", "subpage_snippet": "", "source": "www.cerebras.ai", "link": "https://www.cerebras.ai/blog/linear-scaling-made-possible-with-weight-streaming", "content": "In addition to the list above, you must decide on the values of the parameters defining degree of data parallelism , tensor model parallelism and ..."}
+{"idx": 9, "title": "GitHub - alibaba/EasyParallelLibrary: Easy Parallel Library", "date": "", "ddg_snippet": "... parallelism strategies with a few lines of annotations, including data parallelism , pipeline parallelism , tensor model parallelism , and their hybrids ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/alibaba/EasyParallelLibrary", "content": "... parallelism strategies with a few lines of annotations, including data parallelism , pipeline parallelism , tensor model parallelism , and their hybrids ..."}
diff --git a/data/sampled_jsons/spatiotemporal_reasoning_models_deep_learning_remote_sensing.jsonl b/data/sampled_jsons/spatiotemporal_reasoning_models_deep_learning_remote_sensing.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d00dd357cf04600beb69528adb4a92a4547f357b
--- /dev/null
+++ b/data/sampled_jsons/spatiotemporal_reasoning_models_deep_learning_remote_sensing.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Remote Sensing SpatioTemporal Vision-Language Models", "date": "", "ddg_snippet": "6 days ago — Convolutional Neural Networks (CNNs) are a class of deep learning models specifically designed to process grid-like data such as images.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.02573v3/", "content": "6 days ago — Convolutional Neural Networks (CNNs) are a class of deep learning models specifically designed to process grid-like data such as images."}
+{"idx": 1, "title": "Leveraging Spatiotemporal Metadata for Semi-Supervised ...", "date": "", "ddg_snippet": "29 Apr 2024 — Applying deep learning models to analyze and interpret remote sensing imagery is a powerful tool that has been successfully applied in ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2404.18583v1", "content": "29 Apr 2024 — Applying deep learning models to analyze and interpret remote sensing imagery is a powerful tool that has been successfully applied in ..."}
+{"idx": 2, "title": "Deep learning on spatiotemporal graphs: A systematic ...", "date": "", "ddg_snippet": "by A Zeghina · 2024 · Cited by 18 — In this systematic literature review, we have aimed to answer the most important questions regarding spatiotemporal graph deep learning architectures .", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0925231224006325", "content": "by A Zeghina · 2024 · Cited by 18 — In this systematic literature review, we have aimed to answer the most important questions regarding spatiotemporal graph deep learning architectures ."}
+{"idx": 3, "title": "Exploring spatiotemporal changes in cities and villages ...", "date": "", "ddg_snippet": "26 Sept 2021 — This paper proposes a method to perceive the spatiotemporal changes in urban and rural intentional connotations through the perspective of remote sensing.", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s40494-021-00595-0", "content": "26 Sept 2021 — This paper proposes a method to perceive the spatiotemporal changes in urban and rural intentional connotations through the perspective of remote sensing."}
+{"idx": 4, "title": "Deep Learning-based Uncertainty Quantification for spatio ...", "date": "", "ddg_snippet": "6 Aug 2025 — Deep Learning -based Uncertainty Quantification for spatio-temporal environmental Remote Sensing : : A systematic literature review. Authors: Aya ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1016/j.neucom.2025.130242", "content": "6 Aug 2025 — Deep Learning -based Uncertainty Quantification for spatio-temporal environmental Remote Sensing : : A systematic literature review. Authors: Aya ..."}
+{"idx": 5, "title": "Deep Learning for Spatio-Temporal Data - Junbo Zhang@JDT", "date": "", "ddg_snippet": "Deep Learning for ST-Data. – Cannot fit raw spatiotemporal data into a deep learning model → Data transformation. – Texts and images → spatial and ...", "subpage_snippet": "", "source": "www.zhangjunbo.org", "link": "https://www.zhangjunbo.org/ppt/2020_Tutorial_on_DL4STData.pdf", "content": "Deep Learning for ST-Data. – Cannot fit raw spatiotemporal data into a deep learning model → Data transformation. – Texts and images → spatial and ..."}
+{"idx": 6, "title": "Non-Cooperative Target Recognition of Optical Remote ...", "date": "", "ddg_snippet": "by X Tang · 2020 — This paper puts forward a new method for offshore non-cooperative target recognition and tracing based on spatio-temporal reasoning .", "subpage_snippet": "", "source": "search.proquest.com", "link": "https://search.proquest.com/openview/ac509a73af385d67b7624861b01d71a8/1?pq-origsite=gscholar&cbl=4998670", "content": "by X Tang · 2020 — This paper puts forward a new method for offshore non-cooperative target recognition and tracing based on spatio-temporal reasoning ."}
+{"idx": 7, "title": "GSTM-SCD: Graph-enhanced spatio-temporal state space ...", "date": "", "ddg_snippet": "by X Liu · 2025 — A survey of sample-efficient deep learning for change detection in remote sensing : Tasks, strategies, and challenges. IEEE Geosci. Remote. Sens. Mag. (2025) ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0924271625003557", "content": "by X Liu · 2025 — A survey of sample-efficient deep learning for change detection in remote sensing : Tasks, strategies, and challenges. IEEE Geosci. Remote. Sens. Mag. (2025) ..."}
+{"idx": 8, "title": "Spatio-Temporal Predictive Modeling Techniques for Different ...", "date": "", "ddg_snippet": "Black-box techniques, such as deep learning or ensemble methods that put accuracy above interpretability, may be used in spatio-temporal prediction models.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3696661", "content": "Black-box techniques, such as deep learning or ensemble methods that put accuracy above interpretability, may be used in spatio-temporal prediction models."}
+{"idx": 9, "title": "Spatio-temporal data fusion techniques for modeling digital ...", "date": "", "ddg_snippet": "by Y Li · 2025 · Cited by 16 — This research proposes a new cross-domain spatio-temporal data fusion framework for supporting complex urban governance.", "subpage_snippet": "", "source": "www.tandfonline.com", "link": "https://www.tandfonline.com/doi/full/10.1080/10095020.2024.2350175", "content": "by Y Li · 2025 · Cited by 16 — This research proposes a new cross-domain spatio-temporal data fusion framework for supporting complex urban governance."}
diff --git a/data/sampled_jsons/spatiotemporal_reasoning_models_deep_learning_remote_sensing_year_2023.jsonl b/data/sampled_jsons/spatiotemporal_reasoning_models_deep_learning_remote_sensing_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5ca31b8892ca2782d70f317ccb7b228f2dd3fd6b
--- /dev/null
+++ b/data/sampled_jsons/spatiotemporal_reasoning_models_deep_learning_remote_sensing_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Remote Sensing SpatioTemporal Vision-Language Models", "date": "", "ddg_snippet": "6 days ago — Convolutional Neural Networks (CNNs) are a class of deep learning models specifically designed to process grid-like data such as images.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.02573v3/", "content": "6 days ago — Convolutional Neural Networks (CNNs) are a class of deep learning models specifically designed to process grid-like data such as images."}
+{"idx": 1, "title": "Leveraging Spatiotemporal Metadata for Semi-Supervised ...", "date": "", "ddg_snippet": "29 Apr 2024 — Applying deep learning models to analyze and interpret remote sensing imagery is a powerful tool that has been successfully applied in ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2404.18583v1", "content": "29 Apr 2024 — Applying deep learning models to analyze and interpret remote sensing imagery is a powerful tool that has been successfully applied in ..."}
+{"idx": 2, "title": "Deep learning on spatiotemporal graphs: A systematic ...", "date": "", "ddg_snippet": "by A Zeghina · 2024 · Cited by 17 — In this systematic literature review, we have aimed to answer the most important questions regarding spatiotemporal graph deep learning architectures .", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0925231224006325", "content": "by A Zeghina · 2024 · Cited by 17 — In this systematic literature review, we have aimed to answer the most important questions regarding spatiotemporal graph deep learning architectures ."}
+{"idx": 3, "title": "Exploring spatiotemporal changes in cities and villages ...", "date": "", "ddg_snippet": "26 Sept 2021 — This paper proposes a method to perceive the spatiotemporal changes in urban and rural intentional connotations through the perspective of remote sensing.", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s40494-021-00595-0", "content": "26 Sept 2021 — This paper proposes a method to perceive the spatiotemporal changes in urban and rural intentional connotations through the perspective of remote sensing."}
+{"idx": 4, "title": "Deep Learning-based Uncertainty Quantification for spatio ...", "date": "", "ddg_snippet": "6 Aug 2025 — Deep Learning -based Uncertainty Quantification for spatio-temporal environmental Remote Sensing : : A systematic literature review. Authors: Aya ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1016/j.neucom.2025.130242", "content": "6 Aug 2025 — Deep Learning -based Uncertainty Quantification for spatio-temporal environmental Remote Sensing : : A systematic literature review. Authors: Aya ..."}
+{"idx": 5, "title": "Deep Learning for Spatio-Temporal Data - Junbo Zhang@JDT", "date": "", "ddg_snippet": "Deep Learning for ST-Data. – Cannot fit raw spatiotemporal data into a deep learning model → Data transformation. – Texts and images → spatial and ...", "subpage_snippet": "", "source": "www.zhangjunbo.org", "link": "https://www.zhangjunbo.org/ppt/2020_Tutorial_on_DL4STData.pdf", "content": "Deep Learning for ST-Data. – Cannot fit raw spatiotemporal data into a deep learning model → Data transformation. – Texts and images → spatial and ..."}
+{"idx": 6, "title": "Non-Cooperative Target Recognition of Optical Remote ...", "date": "", "ddg_snippet": "by X Tang · 2020 — This paper puts forward a new method for offshore non-cooperative target recognition and tracing based on spatio-temporal reasoning .", "subpage_snippet": "", "source": "search.proquest.com", "link": "https://search.proquest.com/openview/ac509a73af385d67b7624861b01d71a8/1?pq-origsite=gscholar&cbl=4998670", "content": "by X Tang · 2020 — This paper puts forward a new method for offshore non-cooperative target recognition and tracing based on spatio-temporal reasoning ."}
+{"idx": 7, "title": "GSTM-SCD: Graph-enhanced spatio-temporal state space ...", "date": "", "ddg_snippet": "by X Liu · 2025 — A survey of sample-efficient deep learning for change detection in remote sensing : Tasks, strategies, and challenges. IEEE Geosci. Remote. Sens. Mag. (2025) ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0924271625003557", "content": "by X Liu · 2025 — A survey of sample-efficient deep learning for change detection in remote sensing : Tasks, strategies, and challenges. IEEE Geosci. Remote. Sens. Mag. (2025) ..."}
+{"idx": 8, "title": "Spatio-Temporal Predictive Modeling Techniques for Different ...", "date": "", "ddg_snippet": "Black-box techniques, such as deep learning or ensemble methods that put accuracy above interpretability, may be used in spatio-temporal prediction models.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3696661", "content": "Black-box techniques, such as deep learning or ensemble methods that put accuracy above interpretability, may be used in spatio-temporal prediction models."}
+{"idx": 9, "title": "Spatio-temporal data fusion techniques for modeling digital ...", "date": "", "ddg_snippet": "by Y Li · 2025 · Cited by 16 — This research proposes a new cross-domain spatio-temporal data fusion framework for supporting complex urban governance.", "subpage_snippet": "", "source": "www.tandfonline.com", "link": "https://www.tandfonline.com/doi/full/10.1080/10095020.2024.2350175", "content": "by Y Li · 2025 · Cited by 16 — This research proposes a new cross-domain spatio-temporal data fusion framework for supporting complex urban governance."}
diff --git a/data/sampled_jsons/statistical_collusion_Hoeffding_error_term.jsonl b/data/sampled_jsons/statistical_collusion_Hoeffding_error_term.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..95bf0466ef543603205ce697c33d01af72eea182
--- /dev/null
+++ b/data/sampled_jsons/statistical_collusion_Hoeffding_error_term.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Hoeffding's inequality - Wikipedia", "date": "", "ddg_snippet": "In probability theory, Hoeffding's inequality provides an upper bound on the probability that the sum of bounded independent random variables deviates from its expected value by more than a certain amount. Hoeffding's inequality was proven by Wassily Hoeffding in 1963. [1] Hoeffding's inequality is a special case of the Azuma- Hoeffding inequality and McDiarmid's inequality. It is similar to ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Hoeffding's_inequality", "content": "In probability theory, Hoeffding's inequality provides an upper bound on the probability that the sum of bounded independent random variables deviates from its expected value by more than a certain amount. Hoeffding's inequality was proven by Wassily Hoeffding in 1963. [1] Hoeffding's inequality is a special case of the Azuma- Hoeffding inequality and McDiarmid's inequality. It is similar to ..."}
+{"idx": 1, "title": "My Favorite Statistical Measure: Hoeffding's D - GitHub", "date": "", "ddg_snippet": "The essence of Hoeffding's D lies in its assessment of the joint ranking of the data points. It calculates the sum of certain terms derived from these comparisons, which reflect the degree of concordance and discordance across all pairs and quadruples.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Dicklesworthstone/hoeffdings_d_explainer", "content": "The essence of Hoeffding's D lies in its assessment of the joint ranking of the data points. It calculates the sum of certain terms derived from these comparisons, which reflect the degree of concordance and discordance across all pairs and quadruples."}
+{"idx": 2, "title": "Hoeffding's Inequality - Statistics How To", "date": "", "ddg_snippet": "Hoeffding's inequality formally states what this \"close\" and \"usually\" should be [1]. Although the inequality is a general result in probability theory, it is widely used in machine learning as well as more esoteric topics such as information theory, random algorithm analysis, and statistical learning theory.", "subpage_snippet": "", "source": "www.statisticshowto.com", "link": "https://www.statisticshowto.com/hoeffdings-inequality/", "content": "Hoeffding's inequality formally states what this \"close\" and \"usually\" should be [1]. Although the inequality is a general result in probability theory, it is widely used in machine learning as well as more esoteric topics such as information theory, random algorithm analysis, and statistical learning theory."}
+{"idx": 3, "title": "Examples of using the Hoeffding D statistic - The DO Loop", "date": "", "ddg_snippet": "In contrast, the Hoeffding association assesses dependence/independence. The association between MPG_City and the other variables is small, but the table of Hoeffding statistics does not give information about the direction of the association. Magnitudes: The off-diagonal Hoeffding D statistics are mostly small values between 0.15 and 0.35.", "subpage_snippet": "", "source": "blogs.sas.com", "link": "https://blogs.sas.com/content/iml/2021/05/03/examples-hoeffding-d.html", "content": "In contrast, the Hoeffding association assesses dependence/independence. The association between MPG_City and the other variables is small, but the table of Hoeffding statistics does not give information about the direction of the association. Magnitudes: The off-diagonal Hoeffding D statistics are mostly small values between 0.15 and 0.35."}
+{"idx": 4, "title": "Understanding the Hoeffding Inequality: How Confident Are You in Your ...", "date": "", "ddg_snippet": "The Hoeffding Inequality: A Bound on the Error The Hoeffding Inequality provides a probabilistic bound on the difference between the sample mean (what you observe in your sample) and the true mean (the actual average in the population).", "subpage_snippet": "", "source": "www.devacron.com", "link": "https://www.devacron.com/2025/02/12/understanding-the-hoeffding-inequality-how-confident-are-you-in-your-sample/", "content": "The Hoeffding Inequality: A Bound on the Error The Hoeffding Inequality provides a probabilistic bound on the difference between the sample mean (what you observe in your sample) and the true mean (the actual average in the population)."}
+{"idx": 5, "title": "Statistical Learning Theory Part 1: Hoeffding's Inequality Derivation ...", "date": "", "ddg_snippet": "Photo by Luca Bravo on Unsplash 1: Background & Motivation Hoeffding's Inequality is an important concentration inequality in Mathematical Statistics and Machine Learning (ML), leveraged extensively in theoretically areas such as Statistical Learning Theory as well as applied areas such as Reinforcement Learning. I have noticed in pockets of the ML community it common to present Hoeffding ...", "subpage_snippet": "", "source": "anr248.medium.com", "link": "https://anr248.medium.com/statistical-learning-theory-hoeffdings-inequality-derivation-simulation-e3a97100d147", "content": "Photo by Luca Bravo on Unsplash 1: Background & Motivation Hoeffding's Inequality is an important concentration inequality in Mathematical Statistics and Machine Learning (ML), leveraged extensively in theoretically areas such as Statistical Learning Theory as well as applied areas such as Reinforcement Learning. I have noticed in pockets of the ML community it common to present Hoeffding ..."}
+{"idx": 6, "title": "My Favorite Statistical Measure: Hoeffding's D | Hacker News", "date": "", "ddg_snippet": "Honestly, the outcomes of statistical tests are so correlated that if your question is \"is there a relationship between these numbers\", it really doesn't matter which test you do. If there's a decent relationship there, you could run Pearson, Hoeffding , Chatterjee, a simple linear regression: it'd be a weird dataset where you get different results.", "subpage_snippet": "", "source": "news.ycombinator.com", "link": "https://news.ycombinator.com/item?id=39447125", "content": "Honestly, the outcomes of statistical tests are so correlated that if your question is \"is there a relationship between these numbers\", it really doesn't matter which test you do. If there's a decent relationship there, you could run Pearson, Hoeffding , Chatterjee, a simple linear regression: it'd be a weird dataset where you get different results."}
+{"idx": 7, "title": "Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "Note that Hoeffding's inequality for the term ℙ (x, y) ∼ 𝒟 (x = x, y = y ^ x) holds in probability over D (N n) conditionnally on D (n est). It should only be applied # 𝒳 times here contrary to # 𝒳 (# 𝒴 1) times in signal planting because y ^ x is fixed conditionnally on D (n est).", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2502.04879", "content": "Note that Hoeffding's inequality for the term ℙ (x, y) ∼ 𝒟 (x = x, y = y ^ x) holds in probability over D (N n) conditionnally on D (n est). It should only be applied # 𝒳 times here contrary to # 𝒳 (# 𝒴 1) times in signal planting because y ^ x is fixed conditionnally on D (n est)."}
+{"idx": 8, "title": "Hoeffding's Inequality Simplified - numberanalytics.com", "date": "", "ddg_snippet": "Understanding Hoeffding's Inequality Hoeffding's Inequality is a fundamental concept in probability theory, providing a bound on the probability of the deviation of a sum of independent random variables from its expected value. This inequality has far-reaching implications in various fields, including statistics, data analysis, and machine learning.", "subpage_snippet": "", "source": "www.numberanalytics.com", "link": "https://www.numberanalytics.com/blog/hoeffdings-inequality-simplified-measure-theory", "content": "Understanding Hoeffding's Inequality Hoeffding's Inequality is a fundamental concept in probability theory, providing a bound on the probability of the deviation of a sum of independent random variables from its expected value. This inequality has far-reaching implications in various fields, including statistics, data analysis, and machine learning."}
+{"idx": 9, "title": "PDF CS229 Supplemental Lecture notes Hoeffding's inequa", "date": "", "ddg_snippet": "1 Basic probability bounds A basic question in probability, statistics, and machine learning is the fol-lowing: given a random variable Z with expectation E[Z], how likely is Z to be close to its expectation? And more precisely, how close is it likely to be? With that in mind, these notes give a few tools for computing bounds of the form P(Z ≥ E[Z] + t) and P(Z ≤ E[Z] − t) (1)", "subpage_snippet": "", "source": "cs229.stanford.edu", "link": "https://cs229.stanford.edu/extra-notes/hoeffding.pdf", "content": "1 Basic probability bounds A basic question in probability, statistics, and machine learning is the fol-lowing: given a random variable Z with expectation E[Z], how likely is Z to be close to its expectation? And more precisely, how close is it likely to be? With that in mind, these notes give a few tools for computing bounds of the form P(Z ≥ E[Z] + t) and P(Z ≤ E[Z] − t) (1)"}
diff --git a/data/sampled_jsons/stochastic_combinatorial_bandits_non-continuous_loss_function_year_2021.jsonl b/data/sampled_jsons/stochastic_combinatorial_bandits_non-continuous_loss_function_year_2021.jsonl
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index 0000000000000000000000000000000000000000..29794b3516a73f754fa3fdcaed8e2f22bd770582
--- /dev/null
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Nonstochastic Contextual Combinatorial Bandits", "date": "", "ddg_snippet": "by L Zierahn · 2023 · Cited by 8 — In the present work, we study the nonstochas- tic version of the contextual combinatorial bandit problem, where the sequence of losses incurred by the learning ... 43 pages", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v206/zierahn23a/zierahn23a.pdf", "content": "by L Zierahn · 2023 · Cited by 8 — In the present work, we study the nonstochas- tic version of the contextual combinatorial bandit problem, where the sequence of losses incurred by the learning ... 43 pages"}
+{"idx": 1, "title": "Stochastic Top K-Subset Bandits with Linear Space and ...", "date": "", "ddg_snippet": "by M Agarwal · 2022 · Cited by 13 — In this work, we present the first combinatorial bandit algorithm for which the only feedback is a non -linear reward of the selected K arms. No other feedback ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/full/10.1145/3507787", "content": "by M Agarwal · 2022 · Cited by 13 — In this work, we present the first combinatorial bandit algorithm for which the only feedback is a non -linear reward of the selected K arms. No other feedback ..."}
+{"idx": 2, "title": "Reviews: Combinatorial Multi-Armed Bandit with General ...", "date": "", "ddg_snippet": "The paper studies a generalization of the stochastic combinatorial multi-armed bandits problem. In this generalization, the reward of each super-arm is a ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper/2016/file/aa169b49b583a2b5af89203c2b78c67c-Reviews.html", "content": "The paper studies a generalization of the stochastic combinatorial multi-armed bandits problem. In this generalization, the reward of each super-arm is a ..."}
+{"idx": 3, "title": "Combinatorial Multi-Armed Bandit with General Reward ...", "date": "", "ddg_snippet": "by W Chen · Cited by 179 — In this paper, we study the stochastic combinatorial multi-armed bandit (CMAB) framework that allows a general nonlinear reward function, whose expected value ... 30 pages", "subpage_snippet": "", "source": "www.microsoft.com", "link": "https://www.microsoft.com/en-us/research/wp-content/uploads/2016/12/nips2016_cmabgeneral_full_fixed.pdf", "content": "by W Chen · Cited by 179 — In this paper, we study the stochastic combinatorial multi-armed bandit (CMAB) framework that allows a general nonlinear reward function, whose expected value ... 30 pages"}
+{"idx": 4, "title": "Cost-Efficient Distributed Learning via Combinatorial Multi- ...", "date": "", "ddg_snippet": "by M Egger · 2025 · Cited by 2 — We consider the distributed stochastic gradient descent problem, where a main node distributes gradient calculations among n workers.", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12111685/", "content": "by M Egger · 2025 · Cited by 2 — We consider the distributed stochastic gradient descent problem, where a main node distributes gradient calculations among n workers."}
+{"idx": 5, "title": "Combinatorial Blocking Bandits with Stochastic Delays", "date": "", "ddg_snippet": "by A Atsidakou · 2021 · Cited by 18 — Our model falls into the area of stochastic non -stationary bandits . Important lines in this area include restless bandits , where the arms' mean rewards ... 10 pages", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "http://proceedings.mlr.press/v139/atsidakou21a/atsidakou21a.pdf", "content": "by A Atsidakou · 2021 · Cited by 18 — Our model falls into the area of stochastic non -stationary bandits . Important lines in this area include restless bandits , where the arms' mean rewards ... 10 pages"}
+{"idx": 6, "title": "A Unified Analysis of Nonstochastic Delayed Feedback for ...", "date": "", "ddg_snippet": "by L Zierahn · 2025 — In this section we establish general results that are then applied to combinatorial bandits , MDPs, and linear bandits in the next sections. First, we give a ... 60 pages", "subpage_snippet": "", "source": "www.jmlr.org", "link": "https://www.jmlr.org/papers/volume26/24-0496/24-0496.pdf", "content": "by L Zierahn · 2025 — In this section we establish general results that are then applied to combinatorial bandits , MDPs, and linear bandits in the next sections. First, we give a ... 60 pages"}
+{"idx": 7, "title": "Ranked Prioritization of Groups in Combinatorial Bandit ...", "date": "", "ddg_snippet": "by L Xu · Cited by 5 — However, these prior works only consider stochastic , not combinatorial , bandits . In our combinatorial bandit problem setup, we must determine ...", "subpage_snippet": "", "source": "www.ijcai.org", "link": "https://www.ijcai.org/proceedings/2022/0723.pdf", "content": "by L Xu · Cited by 5 — However, these prior works only consider stochastic , not combinatorial , bandits . In our combinatorial bandit problem setup, we must determine ..."}
+{"idx": 8, "title": "cherryATA", "date": "", "ddg_snippet": "first work addressing a non-continuous loss function in a stochastic combinatorial MAB-like framework . To over- come this challenge, we exploit the specific ...", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/pdf/2502.00775v1", "content": "first work addressing a non-continuous loss function in a stochastic combinatorial MAB-like framework . To over- come this challenge, we exploit the specific ..."}
+{"idx": 9, "title": "ATA: Adaptive Task Allocation for Efficient Resource ...", "date": "", "ddg_snippet": "To the best of our knowledge, this is the first work addressing a non-continuous loss function in a stochastic combinatorial MAB-like framework .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.00775v2", "content": "To the best of our knowledge, this is the first work addressing a non-continuous loss function in a stochastic combinatorial MAB-like framework ."}
diff --git a/data/sampled_jsons/stochastic_localization_theory_diffusion_models_critical_windows.jsonl b/data/sampled_jsons/stochastic_localization_theory_diffusion_models_critical_windows.jsonl
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index 0000000000000000000000000000000000000000..bdad137e54e91d75d1a09c8ca17da0a0251ae414
--- /dev/null
+++ b/data/sampled_jsons/stochastic_localization_theory_diffusion_models_critical_windows.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Critical windows : non-asymptotic theory for feature emergence in...", "date": "", "ddg_snippet": "We develop theory to understand an intriguing property of diffusion models for image generation that we term critical windows . Empirically, it has been observed that there are narrow time intervals in sampling during which particular features of the final image emerge, e.g...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.01633v2", "content": "We develop theory to understand an intriguing property of diffusion models for image generation that we term critical windows . Empirically, it has been observed that there are narrow time intervals in sampling during which particular features of the final image emerge, e.g..."}
+{"idx": 1, "title": "a simple theory for feature localization in generative models", "date": "", "ddg_snippet": "2 Feb 2025 — While critical windows have been studied at length in diffusion models , existing theory heavily relies on strong distributional assumptions and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.00921v1", "content": "2 Feb 2025 — While critical windows have been studied at length in diffusion models , existing theory heavily relies on strong distributional assumptions and ..."}
+{"idx": 2, "title": "BLINK OF AN EYE: A SIMPLE THEORY FOR FEATURE", "date": "", "ddg_snippet": "... critical windows in both diffusion models and autoregressive models . Our bounds are fully rigorous and show that such windows arise generically when the ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/869c142886213628c0ffd59131341197f4ddace8.pdf", "content": "... critical windows in both diffusion models and autoregressive models . Our bounds are fully rigorous and show that such windows arise generically when the ..."}
+{"idx": 3, "title": "Critical windows: non-asymptotic theory for feature ...", "date": "", "ddg_snippet": "by M Li · 2024 · Cited by 27 — We develop theory to understand an intriguing property of diffusion models for image generation that we term critical windows .", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3692070.3693167", "content": "by M Li · 2024 · Cited by 27 — We develop theory to understand an intriguing property of diffusion models for image generation that we term critical windows ."}
+{"idx": 4, "title": "Dynamical regimes of diffusion models", "date": "", "ddg_snippet": "by G Biroli · 2024 · Cited by 83 — We study generative diffusion models in the regime where both the data dimension and the sample size are large, and the score function is trained optimally.", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41467-024-54281-3", "content": "by G Biroli · 2024 · Cited by 83 — We study generative diffusion models in the regime where both the data dimension and the sample size are large, and the score function is trained optimally."}
+{"idx": 5, "title": "Blink of an eye: a simple theory for feature localization in...", "date": "", "ddg_snippet": "by M Li · Cited by 1 — While critical windows have been studied at length in diffusion models , existing theory heavily relies on strong distributional assumptions and ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=nodyP4FLrM", "content": "by M Li · Cited by 1 — While critical windows have been studied at length in diffusion models , existing theory heavily relies on strong distributional assumptions and ..."}
+{"idx": 6, "title": "a simple theory for feature localization in generative models", "date": "", "ddg_snippet": "While critical windows have been studied at length in diffusion models , existing theory heavily relies on strong distributional assumptions and the particulars ...", "subpage_snippet": "", "source": "chatpaper.com", "link": "https://chatpaper.com/chatpaper/paper/165036", "content": "While critical windows have been studied at length in diffusion models , existing theory heavily relies on strong distributional assumptions and the particulars ..."}
+{"idx": 7, "title": "A Sharp Convergence Theory for The Probability Flow ODEs ...", "date": "", "ddg_snippet": "by G Li · 2024 · Cited by 45 — Abstract. Diffusion models , which convert noise into new data instances by learning to reverse a diffusion process,. 49 pages", "subpage_snippet": "", "source": "yuxinchen2020.github.io", "link": "https://yuxinchen2020.github.io/publications/DiffusionODE.pdf", "content": "by G Li · 2024 · Cited by 45 — Abstract. Diffusion models , which convert noise into new data instances by learning to reverse a diffusion process,. 49 pages"}
+{"idx": 8, "title": "Blink of an eye: a simple theory for feature localization ... | OpenReview", "date": "", "ddg_snippet": "Using the formalism of stochastic localization for generative models , we show that it emerges generically as the generation process localizes to a sub-population of the distribution it models . While critical windows have been studied at length in diffusion models , existing theory ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=QvqnPVGWAN", "content": "Using the formalism of stochastic localization for generative models , we show that it emerges generically as the generation process localizes to a sub-population of the distribution it models . While critical windows have been studied at length in diffusion models , existing theory ..."}
+{"idx": 9, "title": "(PDF) Blink of an eye: a simple theory for feature localization in...", "date": "", "ddg_snippet": "This phenomenon is not unique to autoregressive models : in diffusion models , key features of the final output are decided in narrow `` critical windows '' of the generation process. In this work we develop a simple, unifying theory to explain this phenomenon.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/388658326_Blink_of_an_eye_a_simple_theory_for_feature_localization_in_generative_models", "content": "This phenomenon is not unique to autoregressive models : in diffusion models , key features of the final output are decided in narrow `` critical windows '' of the generation process. In this work we develop a simple, unifying theory to explain this phenomenon."}
diff --git a/data/sampled_jsons/structural_causal_model_definition_directed_acyclic_graph_structural_equations_Pearl.jsonl b/data/sampled_jsons/structural_causal_model_definition_directed_acyclic_graph_structural_equations_Pearl.jsonl
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index 0000000000000000000000000000000000000000..a3654831b5ff3377cd2f90014817df4cd8d5d3ab
--- /dev/null
+++ b/data/sampled_jsons/structural_causal_model_definition_directed_acyclic_graph_structural_equations_Pearl.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Directed acyclic graph - Wikipedia", "date": "", "ddg_snippet": "Example of a directed acyclic graph . In mathematics, particularly graph theory, and computer science, a directed acyclic graph is a directed graph with no directed cycles. That is, it consists of vertices and edges, with each edge directed from one v...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Directed_acyclic_graph", "content": "Example of a directed acyclic graph . In mathematics, particularly graph theory, and computer science, a directed acyclic graph is a directed graph with no directed cycles. That is, it consists of vertices and edges, with each edge directed from one v..."}
+{"idx": 1, "title": "Beyond Curve Fitting: My Journey to Causal AI | by Todd... | Medium", "date": "", "ddg_snippet": "According to Pearl , this can be structural equations , logical statements, or causal diagrams.Endogenous means internal cause , while Exogenous refers to external stimuli. The blueprint for this model is a directed acyclic graph (DAG).", "subpage_snippet": "", "source": "toddmoses.medium.com", "link": "https://toddmoses.medium.com/beyond-curve-fitting-my-journey-to-causal-ai-752ccc86ef97", "content": "According to Pearl , this can be structural equations , logical statements, or causal diagrams.Endogenous means internal cause , while Exogenous refers to external stimuli. The blueprint for this model is a directed acyclic graph (DAG)."}
+{"idx": 2, "title": "A hint to The Book of Why by Judea Pearl | Process Horizon", "date": "", "ddg_snippet": "Structural Causal Model (SCM): Translate the causal diagram into a Structural Causal Model , where each variable has a structural equation describing how it depends on its direct causes .", "subpage_snippet": "", "source": "blog.processhorizon.com", "link": "https://blog.processhorizon.com/a-hint-to-the-book-of-why-by-judea-pearl/", "content": "Structural Causal Model (SCM): Translate the causal diagram into a Structural Causal Model , where each variable has a structural equation describing how it depends on its direct causes ."}
+{"idx": 3, "title": "causality - Causal Bayesian network, causal diagram, structural ...", "date": "", "ddg_snippet": "A causal diagram is a directed acyclic graph (DAG). Structural Causal Model / Causal Structural Model . Suppose now we know that X = U(0,1).", "subpage_snippet": "", "source": "stats.stackexchange.com", "link": "https://stats.stackexchange.com/questions/510440/causal-bayesian-network-causal-diagram-structural-causal-model-and-marginal-st", "content": "A causal diagram is a directed acyclic graph (DAG). Structural Causal Model / Causal Structural Model . Suppose now we know that X = U(0,1)."}
+{"idx": 4, "title": "Causality", "date": "", "ddg_snippet": "( Causal Structure ) A causal structure of a set of variables V is a directed acyclic graph (DAG) in which each node corresponds to a distinct element of V, and each link represents a direct functional relationship among the corresponding variables.", "subpage_snippet": "", "source": "projects.illc.uva.nl", "link": "https://projects.illc.uva.nl/cil/uploaded_files/inlineitem/Pearl_2009_Causality.pdf", "content": "( Causal Structure ) A causal structure of a set of variables V is a directed acyclic graph (DAG) in which each node corresponds to a distinct element of V, and each link represents a direct functional relationship among the corresponding variables."}
+{"idx": 5, "title": "Directed Acyclic Graphs – Vincent Arel-Bundock", "date": "", "ddg_snippet": "Directed Acyclic Graphs . A simple introduction with simulations in R. Author.In his book Causality , Pearl (2000) introduces “ Structural Causal Models ” and shows how counterfactual comparisons can be graphically analyzed with a tool called the “ directed acyclic graph .”", "subpage_snippet": "", "source": "arelbundock.com", "link": "https://arelbundock.com/posts/acmq_en_06_dag/", "content": "Directed Acyclic Graphs . A simple introduction with simulations in R. Author.In his book Causality , Pearl (2000) introduces “ Structural Causal Models ” and shows how counterfactual comparisons can be graphically analyzed with a tool called the “ directed acyclic graph .”"}
+{"idx": 6, "title": "On statistical and causal models associated with acyclic directed", "date": "", "ddg_snippet": "Causal models in statistics are often described using acyclic directed mixed graphs (AD-MGs), which contain directed and bidirected edges and no directed cycles.of ( cyclic ) linear structural equation models in the previous decade.", "subpage_snippet": "", "source": "www.statslab.cam.ac.uk", "link": "https://www.statslab.cam.ac.uk/~qz280/publication/admg-model/paper.pdf", "content": "Causal models in statistics are often described using acyclic directed mixed graphs (AD-MGs), which contain directed and bidirected edges and no directed cycles.of ( cyclic ) linear structural equation models in the previous decade."}
+{"idx": 7, "title": "Directed acyclic graphs with edge-specific bounds - PMC", "date": "", "ddg_snippet": "A causal directed acyclic graph defined by nonparametric structural equations satisfies the global Markov property as stated above (cf.", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC3412607/", "content": "A causal directed acyclic graph defined by nonparametric structural equations satisfies the global Markov property as stated above (cf."}
+{"idx": 8, "title": "The Causal Foundations of Structural Equation Modeling", "date": "", "ddg_snippet": "Also known as graphical causal models , Directed Acyclic Graphs (DAG), or non-parametric structural equation models ( Pearl , 2012a), BN serve for both prediction and causal inferencing ( Pearl , 1988).", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/267960379_The_Causal_Foundations_of_Structural_Equation_Modeling", "content": "Also known as graphical causal models , Directed Acyclic Graphs (DAG), or non-parametric structural equation models ( Pearl , 2012a), BN serve for both prediction and causal inferencing ( Pearl , 1988)."}
+{"idx": 9, "title": "McDonald, Causal Models and Metaphysics, Part 1 - Using Causal ...", "date": "", "ddg_snippet": "namely, structural equation models and directed acyclic graphs . These models come from.An analysis of causation in terms of structural equation models (SEMs) and corresponding directed acyclic graphs (DAGs) has at least two components.", "subpage_snippet": "", "source": "philpapers.org", "link": "https://philpapers.org/archive/MCDCMA-5.pdf", "content": "namely, structural equation models and directed acyclic graphs . These models come from.An analysis of causation in terms of structural equation models (SEMs) and corresponding directed acyclic graphs (DAGs) has at least two components."}
diff --git a/data/sampled_jsons/three_distinct_retriever_paradigms_RAGGED_Towards_Informed_Design_of_Scalable_and_Stable_RAG_Systems.jsonl b/data/sampled_jsons/three_distinct_retriever_paradigms_RAGGED_Towards_Informed_Design_of_Scalable_and_Stable_RAG_Systems.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3c58774db50e2b70950c04ec4eee6f249f09d670
--- /dev/null
+++ b/data/sampled_jsons/three_distinct_retriever_paradigms_RAGGED_Towards_Informed_Design_of_Scalable_and_Stable_RAG_Systems.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Traditional RAG to Graph RAG: The Evolution of Retrieval Systems", "date": "", "ddg_snippet": "... change from traditional retrieval -augmented generation to Graph RAG proves an interesting shift in machines ’ understanding and processing of ...", "subpage_snippet": "", "source": "www.analyticsvidhya.com", "link": "https://www.analyticsvidhya.com/blog/2025/03/traditional-rag-vs-graph-rag/", "content": "... change from traditional retrieval -augmented generation to Graph RAG proves an interesting shift in machines ’ understanding and processing of ..."}
+{"idx": 1, "title": "RAG+: Enhancing Retrieval-Augmented Generation with", "date": "", "ddg_snippet": "... RAG systems exhibit a critical weakness: existing RAG paradigms often overlook the cognitive step of applying knowledge, leaving a gap between ...", "subpage_snippet": "", "source": "keyurramoliya.com", "link": "https://keyurramoliya.com/posts/Rag-Plus/", "content": "... RAG systems exhibit a critical weakness: existing RAG paradigms often overlook the cognitive step of applying knowledge, leaving a gap between ..."}
+{"idx": 2, "title": "Each to Their Own: Exploring the Optimal Embedding in RAG", "date": "", "ddg_snippet": "To leverage the strengths of various embedding models, we propose and test two novel RAG methods: Mixture-Embedding RAG and Confident RAG .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.17442v1", "content": "To leverage the strengths of various embedding models, we propose and test two novel RAG methods: Mixture-Embedding RAG and Confident RAG ."}
+{"idx": 3, "title": "Beyond Chunks and Graphs: Retrieval-Augmented Generation", "date": "", "ddg_snippet": "... and architectural limitations of existing RAG paradigms , we propose T 2 RAG ( T riplet-driven T hinking for R etrieval- A ugmented G eneration), a ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.02435v1", "content": "... and architectural limitations of existing RAG paradigms , we propose T 2 RAG ( T riplet-driven T hinking for R etrieval- A ugmented G eneration), a ..."}
+{"idx": 4, "title": "What is RAG in AI? Complete Guide to Retrieval-Augmented", "date": "", "ddg_snippet": "... on their pre-trained knowledge, RAG combines two distinct yet complementary capabilities: the ability to search and retrieve relevant information ...", "subpage_snippet": "", "source": "www.securityprivacyrisk.com", "link": "https://www.securityprivacyrisk.com/what-is-rag-in-ai-complete-guide-to-retrieval-augmented-generation-2025/", "content": "... on their pre-trained knowledge, RAG combines two distinct yet complementary capabilities: the ability to search and retrieve relevant information ..."}
+{"idx": 5, "title": "Azure AI Foundry, AutoGen, and Beyond", "date": "", "ddg_snippet": "Multi-agent systems introduce a powerful new paradigm where distinct AI agents, which have specialized roles, communicate and collaborate toward a ...", "subpage_snippet": "", "source": "dzone.com", "link": "https://dzone.com/articles/blueprint-agentic-ai-azure-foundry-autogen", "content": "Multi-agent systems introduce a powerful new paradigm where distinct AI agents, which have specialized roles, communicate and collaborate toward a ..."}
+{"idx": 6, "title": "17 Free and Open-source Low-code AI Platforms to Build AI", "date": "", "ddg_snippet": "... and seamless integration with tools like Stable Diffusion and countless models, ComfyUI empowers artists and developers alike to push the boundaries ...", "subpage_snippet": "", "source": "medevel.com", "link": "https://medevel.com/x-low-code-ai-2/", "content": "... and seamless integration with tools like Stable Diffusion and countless models, ComfyUI empowers artists and developers alike to push the boundaries ..."}
+{"idx": 7, "title": "From ELIZA to Conversation Modeling: Evolution of", "date": "", "ddg_snippet": "RAG pairs the LLM with an external knowledge source: when a user asks a question, the system first retrieves relevant documents (from a database or ...", "subpage_snippet": "", "source": "techaireports.com", "link": "https://techaireports.com/from-eliza-to-conversation-modeling-evolution-of-conversational-ai-systems-and-paradigms/", "content": "RAG pairs the LLM with an external knowledge source: when a user asks a question, the system first retrieves relevant documents (from a database or ..."}
+{"idx": 8, "title": "Small Language Model Category - MarkTechPost", "date": "", "ddg_snippet": "EmbeddingGemma is Google’s new open text embedding model optimized for on-device AI, designed to balance efficiency with state- of -the-art retrieval ...", "subpage_snippet": "", "source": "www.marktechpost.com", "link": "https://www.marktechpost.com/category/technology/artificial-intelligence/language-model/small-language-model-language-model/", "content": "EmbeddingGemma is Google’s new open text embedding model optimized for on-device AI, designed to balance efficiency with state- of -the-art retrieval ..."}
+{"idx": 9, "title": "Operationalizing Intelligence: A Comprehensive Guide to LLMOps", "date": "", "ddg_snippet": "... techniques for data curation, cleaning, tokenization, and , increasingly, the use of vector databases to support Retrieval -Augmented Generation ( RAG ...", "subpage_snippet": "", "source": "uplatz.com", "link": "https://uplatz.com/blog/operationalizing-intelligence-a-comprehensive-guide-to-llmops-versioning-deployment-and-monitoring-strategies/", "content": "... techniques for data curation, cleaning, tokenization, and , increasingly, the use of vector databases to support Retrieval -Augmented Generation ( RAG ..."}
diff --git a/data/sampled_jsons/ultra-high-resolution_remote_sensing_benchmark_image_size.jsonl b/data/sampled_jsons/ultra-high-resolution_remote_sensing_benchmark_image_size.jsonl
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index 0000000000000000000000000000000000000000..a28f81ec19ca749abc36e39a2aeb0ceacba423ea
--- /dev/null
+++ b/data/sampled_jsons/ultra-high-resolution_remote_sensing_benchmark_image_size.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "(PDF) Remote sensing image super- resolution and object detection...", "date": "", "ddg_snippet": "Thus, the Remote Sensing image Super-resolution Object Detection (RSSOD) dataset will facilitate those researchers to benchmark the methods and detection tasks performed on high - resolution (HR) images .", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/87136645/Remote_sensing_image_super_resolution_and_object_detection_Benchmark_and_state_of_the_art", "content": "Thus, the Remote Sensing image Super-resolution Object Detection (RSSOD) dataset will facilitate those researchers to benchmark the methods and detection tasks performed on high - resolution (HR) images ."}
+{"idx": 1, "title": "GitHub - satellite- image -deep-learning/techniques: Techniques for...", "date": "", "ddg_snippet": "ISPRS_S2FL -> Multimodal Remote Sensing Benchmark Datasets for Land Cover Classification with A Shared and Specific Feature Learning Model.GLNet -> Collaborative Global-Local Networks for Memory-Efficient Segmentation of Ultra - High Resolution Images .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/satellite-image-deep-learning/techniques", "content": "ISPRS_S2FL -> Multimodal Remote Sensing Benchmark Datasets for Land Cover Classification with A Shared and Specific Feature Learning Model.GLNet -> Collaborative Global-Local Networks for Memory-Efficient Segmentation of Ultra - High Resolution Images ."}
+{"idx": 2, "title": "(PDF) A Vision Centric Remote Sensing Benchmark", "date": "", "ddg_snippet": "Enhancing Ultra High Resolution Remote Sensing Imagery Analysis with ImageRAG.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/390038158_A_Vision_Centric_Remote_Sensing_Benchmark", "content": "Enhancing Ultra High Resolution Remote Sensing Imagery Analysis with ImageRAG."}
+{"idx": 3, "title": "OpenEarthMap: A Benchmark Dataset for Global High - Resolution ...", "date": "", "ddg_snippet": "high - resolution remote sensing image analysis to facilitate advances in theory and practice. SpaceNet [47] and IEEE GRSS DFC [18], among others, regularly introduce bench - mark datasets to the public through competitions that drive research and development.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/WACV2023/papers/Xia_OpenEarthMap_A_Benchmark_Dataset_for_Global_High-Resolution_Land_Cover_Mapping_WACV_2023_paper.pdf", "content": "high - resolution remote sensing image analysis to facilitate advances in theory and practice. SpaceNet [47] and IEEE GRSS DFC [18], among others, regularly introduce bench - mark datasets to the public through competitions that drive research and development."}
+{"idx": 4, "title": "Land-use classification based on high - resolution remote sensing ...", "date": "", "ddg_snippet": "(DOI: 10.1371/journal.pone.0300473) High - resolution imagery and deep learning models have gained increasing importance in land-use mapping. In recent years, several new deep learning network modeling methods have surfaced.", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/papers/land-use-classification-based-on-high-resolution-remote-4rilsuq213", "content": "(DOI: 10.1371/journal.pone.0300473) High - resolution imagery and deep learning models have gained increasing importance in land-use mapping. In recent years, several new deep learning network modeling methods have surfaced."}
+{"idx": 5, "title": "Object detection in optical remote sensing images : A survey and...", "date": "", "ddg_snippet": "High - resolution remote sensing image target detection based on discriminant and rotation invariant convolutional neural networks.", "subpage_snippet": "", "source": "www.bohrium.com", "link": "https://www.bohrium.com/paper-details/object-detection-in-optical-remote-sensing-images-a-survey-and-a-new-benchmark/812703620695851008-3770", "content": "High - resolution remote sensing image target detection based on discriminant and rotation invariant convolutional neural networks."}
+{"idx": 6, "title": "Enabling Machine-Assisted Visual Analytics for High - Resolution ...", "date": "", "ddg_snippet": "In the last decade, several high resolution remote sensing benchmark datasets have been developed and publicly released. These datasets, while diverse in design, lack the required intra-class variation for high-performing, machine-assisted visual analytics.", "subpage_snippet": "", "source": "www.computer.org", "link": "https://www.computer.org/csdl/proceedings-article/big-data/2020/09378199/1s64ybuhmWA", "content": "In the last decade, several high resolution remote sensing benchmark datasets have been developed and publicly released. These datasets, while diverse in design, lack the required intra-class variation for high-performing, machine-assisted visual analytics."}
+{"idx": 7, "title": "Application of High - Resolution Remote Sensing Images in Urban...", "date": "", "ddg_snippet": "High - resolution remote sensing imagery , Urban Greening Rate, vegetation index.A Study on the Application of High - Resolution Remote Sensing Images in the Extraction of Green Space Information in Small Areas.", "subpage_snippet": "", "source": "drpress.org", "link": "https://drpress.org/ojs/index.php/HSET/article/view/23866", "content": "High - resolution remote sensing imagery , Urban Greening Rate, vegetation index.A Study on the Application of High - Resolution Remote Sensing Images in the Extraction of Green Space Information in Small Areas."}
+{"idx": 8, "title": "yriyazi/ High - Resolution - Remote - Sensing - Image ... - Githubissues", "date": "", "ddg_snippet": "yriyazi / High - Resolution - Remote - Sensing - Image -Captioning-Based-on-Structured-Attention-and-SAM-Network. Automatically generating language descriptions for remote sensing images has emerged as a significant research area within the field of remote sensing .", "subpage_snippet": "", "source": "githubissues.com", "link": "https://githubissues.com/yriyazi/High-Resolution-Remote-Sensing-Image-Captioning-Based-on-Structured-Attention-and-SAM-Network/readme", "content": "yriyazi / High - Resolution - Remote - Sensing - Image -Captioning-Based-on-Structured-Attention-and-SAM-Network. Automatically generating language descriptions for remote sensing images has emerged as a significant research area within the field of remote sensing ."}
+{"idx": 9, "title": "AI applications in forest monitoring need remote sensing benchmark ...", "date": "", "ddg_snippet": "With the rise in high resolution remote sensing technologies there has been an explosion in the amount of data available for forest monitoring, and an accompanying growth in artificial intelligence applications to automatically derive forest properties of interest from these datasets.", "subpage_snippet": "", "source": "deepai.org", "link": "https://deepai.org/publication/ai-applications-in-forest-monitoring-need-remote-sensing-benchmark-datasets", "content": "With the rise in high resolution remote sensing technologies there has been an explosion in the amount of data available for forest monitoring, and an accompanying growth in artificial intelligence applications to automatically derive forest properties of interest from these datasets."}
diff --git a/data/sampled_jsons/we_fine-tune_OR_base_models_OR_finetuning_sitearxiv.orghtml2406.14532v1_year_2024.jsonl b/data/sampled_jsons/we_fine-tune_OR_base_models_OR_finetuning_sitearxiv.orghtml2406.14532v1_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ffcf33003cd54305d56b530f23513a913a17ca77
--- /dev/null
+++ b/data/sampled_jsons/we_fine-tune_OR_base_models_OR_finetuning_sitearxiv.orghtml2406.14532v1_year_2024.jsonl
@@ -0,0 +1,2 @@
+{"idx": 0, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM ...", "date": "", "ddg_snippet": "20 Jun 2024 — In this experiment, we fine-tune DeepSeek ... When scaling meets llm finetuning : The effect of data, model and finetuning method, 2024a.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.14532v1", "content": "20 Jun 2024 — In this experiment, we fine-tune DeepSeek ... When scaling meets llm finetuning : The effect of data, model and finetuning method, 2024a."}
+{"idx": 1, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/we_selected_dataset_four_reasons_Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignmen.jsonl b/data/sampled_jsons/we_selected_dataset_four_reasons_Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignmen.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..1edca2495e5178b83207485b6d36c6fd3fd92324
--- /dev/null
+++ b/data/sampled_jsons/we_selected_dataset_four_reasons_Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignmen.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "InfiAlign: A Scalable and Sample-Efficient Framework for", "date": "", "ddg_snippet": "In this work, we introduce InfiAlign , a unified and scalable post-training framework for aligning LLMs on reasoning tasks with high sample ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.05496v1", "content": "In this work, we introduce InfiAlign , a unified and scalable post-training framework for aligning LLMs on reasoning tasks with high sample ..."}
+{"idx": 1, "title": "Trustworthy Reasoning: Evaluating and Enhancing Factual", "date": "", "ddg_snippet": "... we introduce RELIANCE , a comprehensive framework that integrates: (1) For RQ1, we construct factual and counterfactual reasoning dataset using ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.22940v1", "content": "... we introduce RELIANCE , a comprehensive framework that integrates: (1) For RQ1, we construct factual and counterfactual reasoning dataset using ..."}
+{"idx": 2, "title": "SATA-Bench: Select All That Apply Benchmark for Multiple Choice", "date": "", "ddg_snippet": "The data curation process consists of three stages: selection of relevant source datasets , transformation of the data into SATA format, and filtering ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.00643v1", "content": "The data curation process consists of three stages: selection of relevant source datasets , transformation of the data into SATA format, and filtering ..."}
+{"idx": 3, "title": "Can Large Language Models Adequately Perform Symbolic Reasoning", "date": "", "ddg_snippet": "We further propose a unified framework that integrates LLMs with genetic programming to form a closed-loop symbolic reasoning system, where LLMs act ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.03963v1", "content": "We further propose a unified framework that integrates LLMs with genetic programming to form a closed-loop symbolic reasoning system, where LLMs act ..."}
+{"idx": 4, "title": "Towards a trustworthness model for Open Source software.", "date": "", "ddg_snippet": "... by means of the questionnaires, we selected a set of OSS projects, widely adopted and generally considered trustable, to be used as references.", "subpage_snippet": "", "source": "123dok.org", "link": "https://123dok.org/document/q5m9xxn7-towards-a-trustworthness-model-for-open-source-software.html", "content": "... by means of the questionnaires, we selected a set of OSS projects, widely adopted and generally considered trustable, to be used as references."}
+{"idx": 5, "title": "Feng | Ethical Considerations in Integrating AI in Research", "date": "", "ddg_snippet": "We included a comparison of selected GPT-based chatbot products: ChatGPT-3.5, ChatGPT- 4 , Perplexity AI, and Google Bard.", "subpage_snippet": "", "source": "publishing.escholarship.umassmed.edu", "link": "https://publishing.escholarship.umassmed.edu/jeslib/article/id/846/", "content": "We included a comparison of selected GPT-based chatbot products: ChatGPT-3.5, ChatGPT- 4 , Perplexity AI, and Google Bard."}
+{"idx": 6, "title": "Style over Substance: Distilled Language Models Reason Via", "date": "", "ddg_snippet": "... reasoning tasks, and the datasets we release can serve as valuable resources for future research into synthetic data generation and fine-tuning ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.01738v3", "content": "... reasoning tasks, and the datasets we release can serve as valuable resources for future research into synthetic data generation and fine-tuning ..."}
+{"idx": 7, "title": "Luke Zettlemoyer - ACL Anthology", "date": "", "ddg_snippet": "We introduce REPLUG, a retrieval-augmented language modeling framework that treats the language model (LM) as a black box and augments it with a ...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/people/l/luke-zettlemoyer/", "content": "We introduce REPLUG, a retrieval-augmented language modeling framework that treats the language model (LM) as a black box and augments it with a ..."}
+{"idx": 8, "title": "The effects of teaching strategies on learning to... |", "date": "", "ddg_snippet": "We designed the Be Smart about your Health resources for secondary school teachers and their students (in the 8 th or 9 th year of school, age 13-15 ...", "subpage_snippet": "", "source": "f1000research.com", "link": "https://f1000research.com/articles/13-1426", "content": "We designed the Be Smart about your Health resources for secondary school teachers and their students (in the 8 th or 9 th year of school, age 13-15 ..."}
+{"idx": 9, "title": "GitHub -", "date": "", "ddg_snippet": "Data quality checks involve visualising examples of the dataset and generating descriptive statistics about each variables of dataset , such as mean ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/SatelliteVu/SatelliteVu-AWS-Disaster-Response-Hackathon", "content": "Data quality checks involve visualising examples of the dataset and generating descriptive statistics about each variables of dataset , such as mean ..."}
diff --git a/data/sampled_jsons/what_is_sqlmap_definition.jsonl b/data/sampled_jsons/what_is_sqlmap_definition.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2282807057115f28bff9238050aebe458cb116d0
--- /dev/null
+++ b/data/sampled_jsons/what_is_sqlmap_definition.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "SQLmap version 0.7 in the wild - Security Database", "date": "", "ddg_snippet": "SQLmap is an automatic SQL injection tool entirely developed in Python. It is capable to perform an extensive database management system back-end ...", "subpage_snippet": "", "source": "www.security-database.com", "link": "https://www.security-database.com/toolswatch/SQLmap-version-7-in-the-wild.html", "content": "SQLmap is an automatic SQL injection tool entirely developed in Python. It is capable to perform an extensive database management system back-end ..."}
+{"idx": 1, "title": "SQLMap v0.8 released - Security Database", "date": "", "ddg_snippet": "SQLmap is an automatic SQL injection tool entirely developed in Python. It is capable to perform an extensive database management system back-end ...", "subpage_snippet": "", "source": "www.security-database.com", "link": "https://www.security-database.com/toolswatch/SQLMap-v0-8-released.html", "content": "SQLmap is an automatic SQL injection tool entirely developed in Python. It is capable to perform an extensive database management system back-end ..."}
+{"idx": 2, "title": "(Info) SQLmap v0.8 stable soon to be released - Security", "date": "", "ddg_snippet": "SQLmap is an automatic SQL injection tool entirely developed in Python. It is capable to perform an extensive database management system back-end ...", "subpage_snippet": "", "source": "www.security-database.com", "link": "https://www.security-database.com/toolswatch/Info-SQLmap-v0-8-stable-soon-to-be.html", "content": "SQLmap is an automatic SQL injection tool entirely developed in Python. It is capable to perform an extensive database management system back-end ..."}
+{"idx": 3, "title": "What is sqlmap? - Hacktress", "date": "", "ddg_snippet": "sqlmap is an open source penetration testing tool that automates the process of detecting and exploiting SQL injection flaws and taking over of ...", "subpage_snippet": "", "source": "www.hacktress.com", "link": "https://www.hacktress.com/what-is-sqlmap/", "content": "sqlmap is an open source penetration testing tool that automates the process of detecting and exploiting SQL injection flaws and taking over of ..."}
+{"idx": 4, "title": "Unleashing the Power of SQL Injection Testing with SQLMap: A", "date": "", "ddg_snippet": "In the interests of brevity in this guide – which is focused on sqlmap – the definition of these attack strategies will not be covered here.", "subpage_snippet": "", "source": "www.codelivly.com", "link": "https://www.codelivly.com/sqlmap-tutorial/", "content": "In the interests of brevity in this guide – which is focused on sqlmap – the definition of these attack strategies will not be covered here."}
+{"idx": 5, "title": "Techniques · sqlmapproject/sqlmap Wiki · GitHub", "date": "", "ddg_snippet": "The bisection algorithm implemented in sqlmap to perform this technique is able to fetch each character of the output with a maximum of seven HTTP ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/sqlmapproject/sqlmap/wiki/Techniques", "content": "The bisection algorithm implemented in sqlmap to perform this technique is able to fetch each character of the output with a maximum of seven HTTP ..."}
+{"idx": 6, "title": "SQLMAP - Enumeration of Databases & Users from Vulnerable", "date": "", "ddg_snippet": "As SQLi is the most widely found vulnerability in web applications, you can definitely use sqlmap to check out a no of various kinds of security ...", "subpage_snippet": "", "source": "kalilinuxtutorials.com", "link": "https://kalilinuxtutorials.com/sqlmap2/", "content": "As SQLi is the most widely found vulnerability in web applications, you can definitely use sqlmap to check out a no of various kinds of security ..."}
+{"idx": 7, "title": "FO-Sec :: Cheatsheet :: SQLMap", "date": "", "ddg_snippet": "SQLMap is one of the best and most known tools to scan a webpage for SQL Injection vulnerabilities. ... sqlmap .org [!] legal disclaimer: Usage of ...", "subpage_snippet": "", "source": "www.fo-sec.com", "link": "https://www.fo-sec.com/cheatsheet/sqlmap", "content": "SQLMap is one of the best and most known tools to scan a webpage for SQL Injection vulnerabilities. ... sqlmap .org [!] legal disclaimer: Usage of ..."}
+{"idx": 8, "title": "sql Archives - Hacktress", "date": "", "ddg_snippet": "sqlmap is an open source penetration testing tool that automates the process of detecting and exploiting SQL injection flaws and taking over of ...", "subpage_snippet": "", "source": "www.hacktress.com", "link": "https://www.hacktress.com/tag/sql/", "content": "sqlmap is an open source penetration testing tool that automates the process of detecting and exploiting SQL injection flaws and taking over of ..."}
+{"idx": 9, "title": "Exploiting difficult SQL injection vulnerabilities using", "date": "", "ddg_snippet": "When your detection needs are more complex than what can be satisfied by the above options, there is also another sqlmap feature that with a little ...", "subpage_snippet": "", "source": "thegreycorner.com", "link": "http://thegreycorner.com/2017/01/05/exploiting-difficult-sql-injection.html", "content": "When your detection needs are more complex than what can be satisfied by the above options, there is also another sqlmap feature that with a little ..."}
diff --git a/data/sampled_jsons/who_introduced_classifier-free_guidance.jsonl b/data/sampled_jsons/who_introduced_classifier-free_guidance.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d9f1efb024709d6d0df803561ffdf45c7d247bfb
--- /dev/null
+++ b/data/sampled_jsons/who_introduced_classifier-free_guidance.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2207.12598] Classifier-Free Diffusion Guidance - arXiv.org", "date": "", "ddg_snippet": "Classifier guidance is a recently introduced method to trade off mode coverage and sample fidelity in conditional diffusion models post training, in the same spirit as low temperature sampling or truncation in other types of generative models. Classifier guidance combines the score estimate of a diffusion model with the gradient of an image classifier and thereby requires training an image ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2207.12598", "content": "Classifier guidance is a recently introduced method to trade off mode coverage and sample fidelity in conditional diffusion models post training, in the same spirit as low temperature sampling or truncation in other types of generative models. Classifier guidance combines the score estimate of a diffusion model with the gradient of an image classifier and thereby requires training an image ..."}
+{"idx": 1, "title": "Diffusion Models — DDPMs, DDIMs, and Classifier Free Guidance", "date": "", "ddg_snippet": "Classifier guidance was introduced in the paper \"Diffusion Models Beat GANs on Image Synthesis\" and essentially uses a classifier to guide the diffusion model to generate images of a desired ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/better-programming/diffusion-models-ddpms-ddims-and-classifier-free-guidance-e07b297b2869", "content": "Classifier guidance was introduced in the paper \"Diffusion Models Beat GANs on Image Synthesis\" and essentially uses a classifier to guide the diffusion model to generate images of a desired ..."}
+{"idx": 2, "title": "Classifier-Free Diffusion Guidance - OpenReview", "date": "", "ddg_snippet": "Abstract: Classifier guidance is a recently introduced method to trade off mode coverage and sample fidelity in conditional diffusion models post training, in the same spirit as low temperature sampling or truncation in other types of generative models. This method combines the score estimate of a diffusion model with the gradient of an image classifier and thereby requires training an image ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=qw8AKxfYbI", "content": "Abstract: Classifier guidance is a recently introduced method to trade off mode coverage and sample fidelity in conditional diffusion models post training, in the same spirit as low temperature sampling or truncation in other types of generative models. This method combines the score estimate of a diffusion model with the gradient of an image classifier and thereby requires training an image ..."}
+{"idx": 3, "title": "Classifier-Free Guidance Overview - emergentmind.com", "date": "", "ddg_snippet": "Classifier-Free Guidance (CFG) is a conditional sampling technique originally introduced for denoising diffusion probabilistic models that achieves high-fidelity, prompt-aligned generative outputs without the need for an auxiliary classifier network. The method operates by interpolating between the predictions of models run with and without conditioning information, using a guidance weight to ...", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/topics/classifier-free-guidance", "content": "Classifier-Free Guidance (CFG) is a conditional sampling technique originally introduced for denoising diffusion probabilistic models that achieves high-fidelity, prompt-aligned generative outputs without the need for an auxiliary classifier network. The method operates by interpolating between the predictions of models run with and without conditioning information, using a guidance weight to ..."}
+{"idx": 4, "title": "Understand Classifier Guidance and Classifier-free Guidance in ... - Medium", "date": "", "ddg_snippet": "We introduce conditional controls in diffusion models in generative AI, which involves classifier guidance and classifier-free guidance .", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@baicenxiao/understand-classifier-guidance-and-classifier-free-guidance-in-diffusion-model-via-python-e92c0c46ec18", "content": "We introduce conditional controls in diffusion models in generative AI, which involves classifier guidance and classifier-free guidance ."}
+{"idx": 5, "title": "[PDF] Classifier-Free Diffusion Guidance | Semantic Scholar", "date": "", "ddg_snippet": "Classifier guidance is a recently introduced method to trade off mode coverage and sample fidelity in conditional diffusion models post training, in the same spirit as low temperature sampling or truncation in other types of generative models.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Classifier-Free-Diffusion-Guidance-Ho/af9f365ed86614c800f082bd8eb14be76072ad16", "content": "Classifier guidance is a recently introduced method to trade off mode coverage and sample fidelity in conditional diffusion models post training, in the same spirit as low temperature sampling or truncation in other types of generative models."}
+{"idx": 6, "title": "Incorporating Classifier-Free Guidance in Diffusion Model-Based ...", "date": "", "ddg_snippet": "Classifier-free guidance , introduced by [7], addresses this issue by eliminating the need for a dedicated classifier . In-stead, it involves training an unconditional denoising diffu-sion model, parameterized by a score estimator, alongside", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2409.10494", "content": "Classifier-free guidance , introduced by [7], addresses this issue by eliminating the need for a dedicated classifier . In-stead, it involves training an unconditional denoising diffu-sion model, parameterized by a score estimator, alongside"}
+{"idx": 7, "title": "Classifier-Free Diffusion Guidance | Cool Papers - Immersive Paper ...", "date": "", "ddg_snippet": "Classifier guidance is a recently introduced method to trade off mode coverage and sample fidelity in conditional diffusion models post training, in the same spirit as low temperature sampling or truncation in other types of generative models. Classifier guidance combines the score estimate of a diffusion model with the gradient of an image classifier and thereby requires training an image ...", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/arxiv/2207.12598", "content": "Classifier guidance is a recently introduced method to trade off mode coverage and sample fidelity in conditional diffusion models post training, in the same spirit as low temperature sampling or truncation in other types of generative models. Classifier guidance combines the score estimate of a diffusion model with the gradient of an image classifier and thereby requires training an image ..."}
+{"idx": 8, "title": "Stay on topic with Classifier-Free Guidance - OpenReview", "date": "", "ddg_snippet": "Classifier-Free Guidance (CFG) has emerged as an ele-gant training- free approach to address this (Ho & Salimans, 2021). In CFG, the generative model itself is used sans modifications during inference to encourage desirata.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=RiM3cl9MdK", "content": "Classifier-Free Guidance (CFG) has emerged as an ele-gant training- free approach to address this (Ho & Salimans, 2021). In CFG, the generative model itself is used sans modifications during inference to encourage desirata."}
+{"idx": 9, "title": "Diffusion Inference with Dynamic Classifier-free Guidance", "date": "", "ddg_snippet": "To improve on this, a novel approach, known as Classifier-Free Guidance (CFG), was introduced that removed the need for a separate classifier . It controls the sample generation process using a parameter known as the CFG scale.", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10675724", "content": "To improve on this, a novel approach, known as Classifier-Free Guidance (CFG), was introduced that removed the need for a separate classifier . It controls the sample generation process using a parameter known as the CFG scale."}
diff --git "a/data/sampled_jsons/\316\224_update_rule_Adversarial_Review_Algorithm_Checks-and-Balances_Framework_for_Context-Aware_Ethical_A.jsonl" "b/data/sampled_jsons/\316\224_update_rule_Adversarial_Review_Algorithm_Checks-and-Balances_Framework_for_Context-Aware_Ethical_A.jsonl"
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index 0000000000000000000000000000000000000000..6092fb039123eaff5f9f82d819578cb1293c222f
--- /dev/null
+++ "b/data/sampled_jsons/\316\224_update_rule_Adversarial_Review_Algorithm_Checks-and-Balances_Framework_for_Context-Aware_Ethical_A.jsonl"
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Algorithmic Decision Making and the Cost of Fairness | Request", "date": "", "ddg_snippet": "... Fair Forests \" framework , contending that well-calibrated probabilities provide a robust foundation for implementing bias-sensitive splits and ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/318916375_Algorithmic_Decision_Making_and_the_Cost_of_Fairness", "content": "... Fair Forests \" framework , contending that well-calibrated probabilities provide a robust foundation for implementing bias-sensitive splits and ..."}
+{"idx": 1, "title": "Live Code Review Preparation: QNexus Core v9.1 Optimization", "date": "", "ddg_snippet": "Quantum Phase Modulation Implementation Update Based on the quantum noise simulation results from last week’s tests, I’ve implemented the phase ...", "subpage_snippet": "", "source": "cybernative.ai", "link": "https://cybernative.ai/t/live-code-review-preparation-qnexus-core-v9-1-optimization-suite/22078", "content": "Quantum Phase Modulation Implementation Update Based on the quantum noise simulation results from last week’s tests, I’ve implemented the phase ..."}
+{"idx": 2, "title": "Downloads", "date": "", "ddg_snippet": "A Linearly Convergent Method for Non-Smooth Non-Convex Optimization on the Grassmannian with Applications to Robust Subspace and Dictionary Learning", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/Downloads/2019", "content": "A Linearly Convergent Method for Non-Smooth Non-Convex Optimization on the Grassmannian with Applications to Robust Subspace and Dictionary Learning"}
+{"idx": 3, "title": "Downloads", "date": "", "ddg_snippet": "... General Theory and Speeding up BFGS Rules for Faster ... A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/Downloads/2018", "content": "... General Theory and Speeding up BFGS Rules for Faster ... A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks"}
+{"idx": 4, "title": "NeurIPS 2020 Papers", "date": "", "ddg_snippet": "Reinforcement Learning in Factored MDPs: Oracle-Efficient Algorithms and Tighter Regret Bounds for the Non-Episodic Setting", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2020/papers.html", "content": "Reinforcement Learning in Factored MDPs: Oracle-Efficient Algorithms and Tighter Regret Bounds for the Non-Episodic Setting"}
+{"idx": 5, "title": "NeurIPS 2023 Papers", "date": "", "ddg_snippet": "Align Your Prompts: Test-Time Prompting with Distribution ... Sketching Algorithms for Sparse Dictionary Learning: PTAS and Turnstile Streaming", "subpage_snippet": "", "source": "nips.cc", "link": "https://nips.cc/virtual/2023/papers.html?filter=titles", "content": "Align Your Prompts: Test-Time Prompting with Distribution ... Sketching Algorithms for Sparse Dictionary Learning: PTAS and Turnstile Streaming"}
+{"idx": 6, "title": "NeurIPS 2023 Papers", "date": "", "ddg_snippet": "Align Your Prompts: Test-Time Prompting with Distribution ... Sketching Algorithms for Sparse Dictionary Learning: PTAS and Turnstile Streaming", "subpage_snippet": "", "source": "nips.cc", "link": "https://nips.cc/virtual/2023/papers.html", "content": "Align Your Prompts: Test-Time Prompting with Distribution ... Sketching Algorithms for Sparse Dictionary Learning: PTAS and Turnstile Streaming"}
+{"idx": 7, "title": "Search AI ethics Primer bibliography", "date": "", "ddg_snippet": "RobertoLofaro.com - Knowledge Portal - human-generated content Change, with and without technology for updates on publications, follow @robertolofaro ...", "subpage_snippet": "", "source": "robertolofaro.com", "link": "https://robertolofaro.com/searchkaggleaiethics.php", "content": "RobertoLofaro.com - Knowledge Portal - human-generated content Change, with and without technology for updates on publications, follow @robertolofaro ..."}
+{"idx": 8, "title": "Search AI ethics Primer bibliography", "date": "", "ddg_snippet": "RobertoLofaro.com - Knowledge Portal - human-generated content Change, with and without technology for updates on publications, follow @robertolofaro ...", "subpage_snippet": "", "source": "robertolofaro.com", "link": "http://robertolofaro.com/searchkaggleaiethics.php", "content": "RobertoLofaro.com - Knowledge Portal - human-generated content Change, with and without technology for updates on publications, follow @robertolofaro ..."}
+{"idx": 9, "title": "Tags | SoftwarePatternsLexicon.com", "date": "", "ddg_snippet": "Advanced design patterns, best practices, and integration technique ... Build robust, scalable, and efficient stream processing systems.", "subpage_snippet": "", "source": "softwarepatternslexicon.com", "link": "https://softwarepatternslexicon.com/tags/", "content": "Advanced design patterns, best practices, and integration technique ... Build robust, scalable, and efficient stream processing systems."}