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data/sampled_jsons/$2._$3._$4._$5._CVE-Bench_T-Agent_AutoGPT_one-day_cost_per_task_year_2025.jsonl

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+{"idx": 0, "title": "CVE-Bench: A Benchmark for AI Agents’ Ability to Exploit", "date": "", "ddg_snippet": "... CVE - Bench , we collect 40 Common Vulnerabilities and ... We apply CVE - Bench to evaluate various LLM agents under both zero-day and one-day settings.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.17332v4", "content": "... CVE - Bench , we collect 40 Common Vulnerabilities and ... We apply CVE - Bench to evaluate various LLM agents under both zero-day and one-day settings."}
+{"idx": 1, "title": "CVE-2021-44228 - Apache Log4j2 Remote Code Execution", "date": "", "ddg_snippet": "... you will find a curated list of external links that provide in-depth information, practical solutions, and valuable tools related to CVE -2021-44228 .", "subpage_snippet": "", "source": "cvefeed.io", "link": "https://cvefeed.io/vuln/detail/CVE-2021-44228", "content": "... you will find a curated list of external links that provide in-depth information, practical solutions, and valuable tools related to CVE -2021-44228 ."}
+{"idx": 2, "title": "x-cmd blog (daily) | [240528] Git Vulnerability CVE-2024-32002", "date": "", "ddg_snippet": "24] Kafka 4 .0.0 Release: Farewell to Zo... ... 23] x-cmd released v0. 5 . 4 : Simplified D... ... 30] Ruby 3 . 4 .0 Released | Co-op Transla...", "subpage_snippet": "", "source": "www.x-cmd.com", "link": "https://www.x-cmd.com/blog/240528/", "content": "24] Kafka 4 .0.0 Release: Farewell to Zo... ... 23] x-cmd released v0. 5 . 4 : Simplified D... ... 30] Ruby 3 . 4 .0 Released | Co-op Transla..."}
+{"idx": 3, "title": "2023", "date": "", "ddg_snippet": "... $2 Per Hour: Exclusive | Time · 2023-01-18: Risky Biz News: Google Search and ... $5 monthly subscription · 2023-01-24: Microsoft will stop selling Windows ...", "subpage_snippet": "", "source": "www.samsclass.info", "link": "https://www.samsclass.info/old_news_2023.html", "content": "... $2 Per Hour: Exclusive | Time · 2023-01-18: Risky Biz News: Google Search and ... $5 monthly subscription · 2023-01-24: Microsoft will stop selling Windows ..."}
+{"idx": 4, "title": "Orion: Fuzzing Workflow Automation", "date": "", "ddg_snippet": "Across our benchmark suite, Orion reduces human effort by 46–204 × \\times depending on the workflow stage, and we demonstrate its effectiveness ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.15195v1", "content": "Across our benchmark suite, Orion reduces human effort by 46–204 × \\times depending on the workflow stage, and we demonstrate its effectiveness ..."}
+{"idx": 5, "title": "How Does Time Horizon Vary Across Domains? - METR", "date": "", "ddg_snippet": "We estimated the time horizon of frontier models released since 2019 on a benchmark combining three sets of software and research tasks ranging from ...", "subpage_snippet": "", "source": "evals.alignment.org", "link": "https://evals.alignment.org/blog/2025-07-14-how-does-time-horizon-vary-across-domains/", "content": "We estimated the time horizon of frontier models released since 2019 on a benchmark combining three sets of software and research tasks ranging from ..."}
+{"idx": 6, "title": "pkgsrc-WIP-changes by thread", "date": "", "ddg_snippet": "Thread index Last updated: Sat Jun 28 12:01:21 2025 Timezone is UTC ... TODO: + chapel-1.30.0. ... Update to 1.20.1.", "subpage_snippet": "", "source": "mail-index.NetBSD.org", "link": "http://mail-index.NetBSD.org/pkgsrc-wip-changes/2023/03/thread2.html", "content": "Thread index Last updated: Sat Jun 28 12:01:21 2025 Timezone is UTC ... TODO: + chapel-1.30.0. ... Update to 1.20.1."}
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+{"idx": 8, "title": "Discourse Version 2.3 - Releases - Discourse Meta", "date": "", "ddg_snippet": "post annotations by staff, including automatic friendly nudge post annotation visible next to first posts and returning posts of long absent users ...", "subpage_snippet": "", "source": "meta.discourse.org", "link": "https://meta.discourse.org/t/discourse-version-2-3/96690", "content": "post annotations by staff, including automatic friendly nudge post annotation visible next to first posts and returning posts of long absent users ..."}
+{"idx": 9, "title": "x-cmd blog (daily) | [240913] How to Check Hard Disk Health on", "date": "", "ddg_snippet": "31] IntelliJ IDEA 2025.1. 4 Re-released:... ... 24] Kafka 4 .0.0 Release: Farewell to Zo... ... 23] x-cmd released v0. 5 . 4 : Simplified D...", "subpage_snippet": "", "source": "www.x-cmd.com", "link": "https://www.x-cmd.com/blog/240913/", "content": "31] IntelliJ IDEA 2025.1. 4 Re-released:... ... 24] Kafka 4 .0.0 Release: Farewell to Zo... ... 23] x-cmd released v0. 5 . 4 : Simplified D..."}

data/sampled_jsons/'Algebraic_Combinatorics_Dataset_Repository'_S18_characters_dataset_Appendix_B.1_training_examples_-.jsonl

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+{"idx": 0, "title": "Machine Learning meets Algebraic Combinatorics: A Suite of Datasets ...", "date": "", "ddg_snippet": "To fill the gap we present the Algebraic Combinatorics Dataset Repository (ACD Repo)1, a collection of 9 datasets consisting of many examples along with an associated question (s).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.06366v1", "content": "To fill the gap we present the Algebraic Combinatorics Dataset Repository (ACD Repo)1, a collection of 9 datasets consisting of many examples along with an associated question (s)."}
+{"idx": 1, "title": "PDF 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": 2, "title": "Clustering-Datasets/01. UCI/letter.arff at master - GitHub", "date": "", "ddg_snippet": "This repository contains the collection of UCI (real-life) datasets and Synthetic (artificial) datasets (with cluster labels and MATLAB files) ready to use with clustering algorithms. - milaan9/Clu...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/milaan9/Clustering-Datasets/blob/master/01.+UCI/letter.arff", "content": "This repository contains the collection of UCI (real-life) datasets and Synthetic (artificial) datasets (with cluster labels and MATLAB files) ready to use with clustering algorithms. - milaan9/Clu..."}
+{"idx": 3, "title": "PDF ALGEBRAIC COMBINATORICS - Mersenne", "date": "", "ddg_snippet": "There are two examples of extra special p-groups of order p3: the Heisenberg group Heis(p) given by upper-triangular 3 × 3 matrices over the field Fp with 1's on the diagonal, and the semidirect product Cp2 ⋊ Cp where Cp acts non-trivially on Cp2.", "subpage_snippet": "", "source": "alco.centre-mersenne.org", "link": "https://alco.centre-mersenne.org/item/10.5802/alco.270.pdf", "content": "There are two examples of extra special p-groups of order p3: the Heisenberg group Heis(p) given by upper-triangular 3 × 3 matrices over the field Fp with 1's on the diagonal, and the semidirect product Cp2 ⋊ Cp where Cp acts non-trivially on Cp2."}
+{"idx": 4, "title": "Computational Complexity in Algebraic Combinatorics", "date": "", "ddg_snippet": "Abstract. Algebraic combinatorics originated in algebra and representation theory, studying their discrete objects and integral quantities via combinatorial methods which have since developed inde-pendent and self-contained lives and brought us some beautiful formulas and combinatorial interpre-tations. The flagship hook-length formula counts the number of Standard Young Tableaux, which also ...", "subpage_snippet": "", "source": "par.nsf.gov", "link": "https://par.nsf.gov/servlets/purl/10586646", "content": "Abstract. Algebraic combinatorics originated in algebra and representation theory, studying their discrete objects and integral quantities via combinatorial methods which have since developed inde-pendent and self-contained lives and brought us some beautiful formulas and combinatorial interpre-tations. The flagship hook-length formula counts the number of Standard Young Tableaux, which also ..."}
+{"idx": 5, "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 mathemat-ics datasets structured for machine learning and designed to accelerate mathematical discovery.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.06366v1", "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": 6, "title": "PDF An Introduction to Algebraic Combinatorics - LMU", "date": "", "ddg_snippet": "Abstract. This is an introduction to algebraic combinatorics , writ- ten for a quarter-long graduate course. It starts with a rigorous in- troduction to formal power series with some combinatorial applica- tions, then discusses integer partitions (proving Jacobi's triple prod- uct identity), permutations (Lehmer codes, cycles) and subtractive methods (alternating sums, cancellations and ...", "subpage_snippet": "", "source": "www.cip.ifi.lmu.de", "link": "https://www.cip.ifi.lmu.de/~grinberg/t/21s/lecs.pdf", "content": "Abstract. This is an introduction to algebraic combinatorics , writ- ten for a quarter-long graduate course. It starts with a rigorous in- troduction to formal power series with some combinatorial applica- tions, then discusses integer partitions (proving Jacobi's triple prod- uct identity), permutations (Lehmer codes, cycles) and subtractive methods (alternating sums, cancellations and ..."}
+{"idx": 7, "title": "PDF A Appendix - papers.nips.cc", "date": "", "ddg_snippet": "How the dataset can be read 100 The dataset can be most conveniently read using Croissant to obtain the dataset records in a standard-101 ized fashion. Users can download the dataset and use the provided Croissant metadata file to load the 102 dataset records. Detailed instructions are provided within the data directory of our public GitHub 103 repository 104 Alternatively, users can manually ...", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper_files/paper/2024/file/af9199ef7d5ff267451a667170124c04-Supplemental-Datasets_and_Benchmarks_Track.pdf", "content": "How the dataset can be read 100 The dataset can be most conveniently read using Croissant to obtain the dataset records in a standard-101 ized fashion. Users can download the dataset and use the provided Croissant metadata file to load the 102 dataset records. Detailed instructions are provided within the data directory of our public GitHub 103 repository 104 Alternatively, users can manually ..."}
+{"idx": 8, "title": "PDF Topics in Algebraic Combinatorics", "date": "", "ddg_snippet": "The adjacency matrix of the graph G is the p ×p matrix A = A(G), over the field of complex numbers, whose (i, j)-entry aij is equal to the number of edges incident to vi and vj. Thus A is a real symmetric matrix (and hence has real eigenvalues) whose trace is the number of loops in G. For instance, if G is the graph", "subpage_snippet": "", "source": "math.mit.edu", "link": "https://math.mit.edu/~rstan/algcomb/algcomb.pdf", "content": "The adjacency matrix of the graph G is the p ×p matrix A = A(G), over the field of complex numbers, whose (i, j)-entry aij is equal to the number of edges incident to vi and vj. Thus A is a real symmetric matrix (and hence has real eigenvalues) whose trace is the number of loops in G. For instance, if G is the graph"}
+{"idx": 9, "title": "PDF 18 - Massachusetts Institute of Technology", "date": "", "ddg_snippet": "These are my lecture notes from 18.211, Combinatorial Analysis, at the Massachusetts Institute of Technology, taught this semester (Fall 2018) by Professor Yufei Zhao1.", "subpage_snippet": "", "source": "people.csail.mit.edu", "link": "https://people.csail.mit.edu/rmwu/static/files/notes/18-211-notes.pdf", "content": "These are my lecture notes from 18.211, Combinatorial Analysis, at the Massachusetts Institute of Technology, taught this semester (Fall 2018) by Professor Yufei Zhao1."}

data/sampled_jsons/0A4Y9qRnu9_Leveraging_Per-Instance_Privacy_Machine_Unlearning_Definition_4.2.jsonl

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+{"idx": 0, "title": "Leveraging Per-Instance Privacy for Machine Unlearning", "date": "", "ddg_snippet": "Abstract We present a principled, per-instance approach to quantifying the difficulty of unlearning via fine-tuning. We begin by sharpening an analysis of noisy gradient descent for unlearning (Chien et al., 2024), obtaining a better utility- unlearning tradeoff by replacing worst-case privacy loss bounds with per-instance privacy losses (Thudi et al., 2024), each of which bounds the (Rényi ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.18786v1", "content": "Abstract We present a principled, per-instance approach to quantifying the difficulty of unlearning via fine-tuning. We begin by sharpening an analysis of noisy gradient descent for unlearning (Chien et al., 2024), obtaining a better utility- unlearning tradeoff by replacing worst-case privacy loss bounds with per-instance privacy losses (Thudi et al., 2024), each of which bounds the (Rényi ..."}
+{"idx": 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": "PDF Rectifying Privacy and Efficacy Measurements in Machine Unlearning: A ...", "date": "", "ddg_snippet": "Abstract Machine unlearning focuses on eficiently removing spe-cific data from trained models, addressing privacy and compli-ance concerns with reasonable costs. Although exact unlearn-ing ensures complete data removal equivalent to retraining, it is impractical for large-scale models, leading to growing inter-est in inexact unlearning methods.", "subpage_snippet": "", "source": "www.usenix.org", "link": "https://www.usenix.org/system/files/usenixsecurity25-naderloui.pdf", "content": "Abstract Machine unlearning focuses on eficiently removing spe-cific data from trained models, addressing privacy and compli-ance concerns with reasonable costs. Although exact unlearn-ing ensures complete data removal equivalent to retraining, it is impractical for large-scale models, leading to growing inter-est in inexact unlearning methods."}
+{"idx": 3, "title": "PDF Forget to Flourish: Leveraging Machine-Unlearning on Pretrained ...", "date": "", "ddg_snippet": "At its core, our approach leverages machine unlearning Cao and Yang (2015); Guo et al. (2019) to poison the pre-trained LLM. The original objective of unlearning is to make the model forget specific data points that it has seen during training so that it produces a high loss for those data points, and it becomes dificult to reconstruct those samples Gu et al. (2024). Motivated by data ...", "subpage_snippet": "", "source": "www.merl.com", "link": "https://www.merl.com/publications/docs/TR2024-168.pdf", "content": "At its core, our approach leverages machine unlearning Cao and Yang (2015); Guo et al. (2019) to poison the pre-trained LLM. The original objective of unlearning is to make the model forget specific data points that it has seen during training so that it produces a high loss for those data points, and it becomes dificult to reconstruct those samples Gu et al. (2024). Motivated by data ..."}
+{"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": "openreview.net", "link": "https://openreview.net/pdf?id=0A4Y9qRnu9", "content": "Abstract We present a principled, per-instance approach to quantifying the dificulty of unlearning via fine-tuning. We begin by sharpening an analysis of noisy gradient descent for unlearning (Chien et al., 2024), obtaining a better utility- unlearning trade-off by replacing worst-case privacy loss bounds with per-instance privacy losses (Thudi et al., 2024), each of which bounds the (R ..."}
+{"idx": 5, "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": 6, "title": "Machine Unlearning: A Comprehensive Survey - arXiv.org", "date": "", "ddg_snippet": "This survey aims to systematically classify a wide range of machine unlearning studies, discussing their differences, connections, and open problems. We categorize current unlearning methods into four key areas: centralized unlearning , federated unlearning , unlearning verification, and privacy and security issues in unlearning .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.07406", "content": "This survey aims to systematically classify a wide range of machine unlearning studies, discussing their differences, connections, and open problems. We categorize current unlearning methods into four key areas: centralized unlearning , federated unlearning , unlearning verification, and privacy and security issues in unlearning ."}
+{"idx": 7, "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": 8, "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": 9, "title": "A Survey on Machine Unlearning: Techniques and New Emerged Privacy Risks", "date": "", "ddg_snippet": "This paper provides an overview and analysis of the existing research on machine unlearning , aiming to present the current vulnerabilities of machine unlearning approaches. We analyze privacy risks in various aspects, including definitions , implementation methods, and real-world applications.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2406.06186", "content": "This paper provides an overview and analysis of the existing research on machine unlearning , aiming to present the current vulnerabilities of machine unlearning approaches. We analyze privacy risks in various aspects, including definitions , implementation methods, and real-world applications."}

data/sampled_jsons/0A4Y9qRnu9_Leveraging_Per-Instance_Privacy_for_Machine_Unlearning_Section_6.2_Figure_1.jsonl

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+{"idx": 0, "title": "Leveraging Per - Instance Privacy for Machine Unlearning", "date": "", "ddg_snippet": "Figure 7. SGD unlearning . Unlearning time vs. privacy score for ResNet-18 on SVHN (left) and ViT-small on CIFAR-10 (right).", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=0A4Y9qRnu9", "content": "Figure 7. SGD unlearning . Unlearning time vs. privacy score for ResNet-18 on SVHN (left) and ViT-small on CIFAR-10 (right)."}
+{"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": "arxiv.org", "link": "https://arxiv.org/html/2505.18786v1", "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": "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": "CS PhD, UofT - Cited by 775 - Machine Learning - Computer Security", "date": "", "ddg_snippet": "Leveraging Per - Instance Privacy for Machine Unlearning .ICML 2025 Workshop on Machine Unlearning for Generative AI, 0. The system can't perform the operation now.", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=bTEybH0AAAAJ&hl=en", "content": "Leveraging Per - Instance Privacy for Machine Unlearning .ICML 2025 Workshop on Machine Unlearning for Generative AI, 0. The system can't perform the operation now."}
+{"idx": 4, "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": 5, "title": "Nicolas PAPERNOT | Professor (Assistant) | Doctor of Philosophy", "date": "", "ddg_snippet": "Leveraging Per - Instance Privacy for Machine Unlearning . Machine unlearning aims to remove points from the training dataset of a machine learning model after training; for example when a user requests their data to be deleted.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/profile/Nicolas-Papernot", "content": "Leveraging Per - Instance Privacy for Machine Unlearning . Machine unlearning aims to remove points from the training dataset of a machine learning model after training; for example when a user requests their data to be deleted."}
+{"idx": 6, "title": "Address: bc 1 qrnu 9 j0w9grqty0p08ut8wg8vpdwhw0admsua8f", "date": "", "ddg_snippet": "bc 1 qrnu 9 j0w9grqty0p08ut8wg8vpdwhw0admsua8f. Confirmed balance. Privacy Policy.", "subpage_snippet": "", "source": "mempool.space", "link": "https://mempool.space/address/bc1qrnu9j0w9grqty0p08ut8wg8vpdwhw0admsua8f", "content": "bc 1 qrnu 9 j0w9grqty0p08ut8wg8vpdwhw0admsua8f. Confirmed balance. Privacy Policy."}
+{"idx": 7, "title": "CleverHans Lab - Anvith", "date": "", "ddg_snippet": "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. BibTeX.", "subpage_snippet": "", "source": "cleverhans.io", "link": "https://cleverhans.io/members/anvith.html", "content": "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. BibTeX."}
+{"idx": 8, "title": "ГДЗ по Математике 3 класс тематический контроль Голубь...", "date": "", "ddg_snippet": "Периметр квадрата находится по формуле: Р= a * 4 . Например периметр квадрата со стороной 3 см находится: Р=3*4 Р=12 см.", "subpage_snippet": "", "source": "otvechalka.su", "link": "https://otvechalka.su/matematika/3-klass/tematicheskij-kontrol-golub/stranicza-66/", "content": "Периметр квадрата находится по формуле: Р= a * 4 . Например периметр квадрата со стороной 3 см находится: Р=3*4 Р=12 см."}
+{"idx": 9, "title": "Номер 4, страница 9 - 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/01-5-4", "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..."}

data/sampled_jsons/0ObGn4e1IS_Gumiho_A_Hybrid_Architecture_to_Prioritize_Early_Tokens_in_Speculative_Decoding.jsonl

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+{"idx": 0, "title": "Gumiho: A Hybrid Architecture to Prioritize Early Tokens in ... Gumiho: A Hybrid Architecture to Prioritize Early Tokens in ... Gumiho - a amd Collection - Hugging Face Gumiho: A Hybrid Architecture to Prioritize Early Tokens in... Gumiho: A Hybrid Architecture to Prioritize Early Tokens in ... Gumiho: A Hybrid Architecture to Prioritize Early Tokens in ... Gumiho: A Hybrid Architecture to Prioritize Early Tokens in ...", "date": "", "ddg_snippet": "Mar 13, 2025 · Building on this insight, we propose Gumiho , a hybrid model combining serial and parallel heads. Specifically, given the critical importance of early tokens , we employ a sophisticated Transformer architecture for the early draft heads in a serial configuration to improve accuracy. Jul 11, 2025 · This project implements Gumiho , a novel hybrid architecture designed to accelerate the auto-regressive token generation process of Large Language Models (LLMs) using speculative decoding . Unlike existing methods that treat all tokens within a generated sequence as equally important, Gumiho is based ... Jun 12, 2025 · Gumiho : A Hybrid Architecture to Prioritize Early Tokens in Speculative Decoding Paper • 2503.10135 •Published Mar 13 Upvote - Share collection View history Collection guide Browse collections May 1, 2025 · This paper proposes Gumiho , a hybrid architecture for speculative decoding in Large Language Models. The core idea is motivated by the insight that early tokens in a draft sequence are more critical for the overall acceptance rate than later ones. Gumiho operationalizes this by using a more accurate, serial Transformer-based head for early tokens and faster parallel MLP-based heads for later ... 🌟 Introducing Gumiho : A Hybrid Architecture for Enhanced Speculative Decoding 🌟 In the realm of Large Language Models (LLMs), speculative decoding (SPD) serves as a pivotal technique for ... Mar 13, 2025 · Abstract and Figures Speculative decoding (SPD) aims to accelerate the auto-regressive token generation process of a target Large Language Model (LLM). Abstract Speculative decoding (SPD) aims to accelerate the auto-regressive token generation process of a target Large Language Model (LLM). Some approaches employ a draft model with multi-ple heads to predict a sequence of future tokens , where each head handles a token in the sequence. The target LLM verifies the predicted sequence and accepts aligned tokens , enabling eficient multi- token ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.10135", "content": "Mar 13, 2025 · Building on this insight, we propose Gumiho , a hybrid model combining serial and parallel heads. Specifically, given the critical importance of early tokens , we employ a sophisticated Transformer architecture for the early draft heads in a serial configuration to improve accuracy. Jul 11, 2025 · This project implements Gumiho , a novel hybrid architecture designed to accelerate the auto-regressive token generation process of Large Language Models (LLMs) using speculative decoding . Unlike existing methods that treat all tokens within a generated sequence as equally important, Gumiho is based ... Jun 12, 2025 · Gumiho : A Hybrid Architecture to Prioritize Early Tokens in Speculative Decoding Paper • 2503.10135 •Published Mar 13 Upvote - Share collection View history Collection guide Browse collections May 1, 2025 · This paper proposes Gumiho , a hybrid architecture for speculative decoding in Large Language Models. The core idea is motivated by the insight that early tokens in a draft sequence are more critical for the overall acceptance rate than later ones. Gumiho operationalizes this by using a more accurate, serial Transformer-based head for early tokens and faster parallel MLP-based heads for later ... 🌟 Introducing Gumiho : A Hybrid Architecture for Enhanced Speculative Decoding 🌟 In the realm of Large Language Models (LLMs), speculative decoding (SPD) serves as a pivotal technique for ... Mar 13, 2025 · Abstract and Figures Speculative decoding (SPD) aims to accelerate the auto-regressive token generation process of a target Large Language Model (LLM). Abstract Speculative decoding (SPD) aims to accelerate the auto-regressive token generation process of a target Large Language Model (LLM). Some approaches employ a draft model with multi-ple heads to predict a sequence of future tokens , where each head handles a token in the sequence. The target LLM verifies the predicted sequence and accepts aligned tokens , enabling eficient multi- token ..."}
+{"idx": 1, "title": "Gumiho: A Hybrid Architecture to Prioritize Early Tokens in ...", "date": "", "ddg_snippet": "Jul 11, 2025 · This project implements Gumiho , a novel hybrid architecture designed to accelerate the auto-regressive token generation process of Large Language Models (LLMs) using speculative decoding . Unlike existing methods that treat all tokens within a generated sequence as equally important, Gumiho is based ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/AMD-AGI/Gumiho", "content": "Jul 11, 2025 · This project implements Gumiho , a novel hybrid architecture designed to accelerate the auto-regressive token generation process of Large Language Models (LLMs) using speculative decoding . Unlike existing methods that treat all tokens within a generated sequence as equally important, Gumiho is based ..."}
+{"idx": 2, "title": "Gumiho: A Hybrid Architecture to Prioritize Early Tokens in...", "date": "", "ddg_snippet": "May 1, 2025 · This paper proposes Gumiho , a hybrid architecture for speculative decoding in Large Language Models. The core idea is motivated by the insight that early tokens in a draft sequence are more critical for the overall acceptance rate than later ones. Gumiho operationalizes this by using a more accurate, serial Transformer-based head for early tokens and faster parallel MLP-based heads for later ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=0ObGn4e1IS", "content": "May 1, 2025 · This paper proposes Gumiho , a hybrid architecture for speculative decoding in Large Language Models. The core idea is motivated by the insight that early tokens in a draft sequence are more critical for the overall acceptance rate than later ones. Gumiho operationalizes this by using a more accurate, serial Transformer-based head for early tokens and faster parallel MLP-based heads for later ..."}
+{"idx": 3, "title": "Gumiho: A Hybrid Architecture to Prioritize Early Tokens in ...", "date": "", "ddg_snippet": "🌟 Introducing Gumiho : A Hybrid Architecture for Enhanced Speculative Decoding 🌟 In the realm of Large Language Models (LLMs), speculative decoding (SPD) serves as a pivotal technique for ...", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/posts/abdullah-kasri_gumiho-a-hybrid-architecture-to-prioritize-activity-7306519551876161536-HoDU", "content": "🌟 Introducing Gumiho : A Hybrid Architecture for Enhanced Speculative Decoding 🌟 In the realm of Large Language Models (LLMs), speculative decoding (SPD) serves as a pivotal technique for ..."}
+{"idx": 4, "title": "Gumiho: A Hybrid Architecture to Prioritize Early Tokens in ...", "date": "", "ddg_snippet": "Mar 13, 2025 · Abstract and Figures Speculative decoding (SPD) aims to accelerate the auto-regressive token generation process of a target Large Language Model (LLM).", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/389821466_Gumiho_A_Hybrid_Architecture_to_Prioritize_Early_Tokens_in_Speculative_Decoding", "content": "Mar 13, 2025 · Abstract and Figures Speculative decoding (SPD) aims to accelerate the auto-regressive token generation process of a target Large Language Model (LLM)."}
+{"idx": 5, "title": "Gumiho: A Hybrid Architecture to Prioritize Early Tokens in ...", "date": "", "ddg_snippet": "Abstract Speculative decoding (SPD) aims to accelerate the auto-regressive token generation process of a target Large Language Model (LLM). Some approaches employ a draft model with multi-ple heads to predict a sequence of future tokens , where each head handles a token in the sequence. The target LLM verifies the predicted sequence and accepts aligned tokens , enabling eficient multi- token ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.10135v1", "content": "Abstract Speculative decoding (SPD) aims to accelerate the auto-regressive token generation process of a target Large Language Model (LLM). Some approaches employ a draft model with multi-ple heads to predict a sequence of future tokens , where each head handles a token in the sequence. The target LLM verifies the predicted sequence and accepts aligned tokens , enabling eficient multi- token ..."}
+{"idx": 6, "title": "Gumiho : A Hybrid Architecture to Prioritize Early Tokens in ...", "date": "", "ddg_snippet": "Speculative decoding (SPD) aims to accelerate the auto-regressive token generation process of a target Large Language Model (LLM).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.10135v1", "content": "Speculative decoding (SPD) aims to accelerate the auto-regressive token generation process of a target Large Language Model (LLM)."}
+{"idx": 7, "title": "Paper page - Gumiho : A Hybrid Architecture to Prioritize Early ...", "date": "", "ddg_snippet": "Gumiho , a hybrid model combining serial and parallel heads with varying architectures , improves speculative decoding for large language models by prioritizing accuracy for early tokens . AI-generated summary.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2503.10135", "content": "Gumiho , a hybrid model combining serial and parallel heads with varying architectures , improves speculative decoding for large language models by prioritizing accuracy for early tokens . AI-generated summary."}
+{"idx": 8, "title": "Gumiho : A Hybrid Architecture to Prioritize Early Tokens ... | alphaXiv", "date": "", "ddg_snippet": "View recent discussion. Abstract: Speculative decoding (SPD) aims to accelerate the auto-regressive token generation process of a target Large Language Model (LLM).Building on this insight, we propose Gumiho , a hybrid model combining serial and parallel heads.", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2503.10135v1", "content": "View recent discussion. Abstract: Speculative decoding (SPD) aims to accelerate the auto-regressive token generation process of a target Large Language Model (LLM).Building on this insight, we propose Gumiho , a hybrid model combining serial and parallel heads."}
+{"idx": 9, "title": "A Token is Worth over 1,000 Tokens : Efficient Knowledge Distillation...", "date": "", "ddg_snippet": "[1] Gumiho : A Hybrid Architecture to Prioritize Early Tokens in Speculative Decoding . Speculative decoding (SPD) aims to accelerate the auto-regressive token generation process of a target Large Language Model (LLM).", "subpage_snippet": "", "source": "www.bohrium.com", "link": "https://www.bohrium.com/paper-details/a-token-is-worth-over-1-000-tokens-efficient-knowledge-distillation-through-low-rank-clone/1131559626442014785-108592", "content": "[1] Gumiho : A Hybrid Architecture to Prioritize Early Tokens in Speculative Decoding . Speculative decoding (SPD) aims to accelerate the auto-regressive token generation process of a target Large Language Model (LLM)."}

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

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+{"idx": 0, "title": "Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient ...", "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 $\\\\boldsymbolμ$. With broad applications in network science and distributed optimization, recent innovations like the self-repellent random walk (SRRW) achieve ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2505.18300", "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 $\\\\boldsymbolμ$. With broad applications in network science and distributed optimization, recent innovations like the self-repellent random walk (SRRW) achieve ..."}
+{"idx": 1, "title": "Our paper on efficient MCMC on graphs accepted at ICML 2025", "date": "", "ddg_snippet": "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 ...", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/posts/jie-hu-ncsu_icml2025-mcmc-activity-7337284173516210176-FGlQ", "content": "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 ..."}
+{"idx": 2, "title": "PDF Self-Repellent Random Walks on General Graphs - IJCAI", "date": "", "ddg_snippet": "Self-repellent random walks on general graphs - achieving minimal sampling variance via nonlinear markov chains. In International Conference on Machine Learning.", "subpage_snippet": "", "source": "www.ijcai.org", "link": "https://www.ijcai.org/proceedings/2024/0929.pdf", "content": "Self-repellent random walks on general graphs - achieving minimal sampling variance via nonlinear markov chains. In International Conference on Machine Learning."}
+{"idx": 3, "title": "Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient ...", "date": "", "ddg_snippet": "We propose a history-driven target (HDT) frame- work in Markov Chain Monte Carlo ( MCMC ) to improve any random walk algorithm on dis- crete state spaces, such as general undirected graphs , for efficient sampling from target distri- bution µ. With broad applications in network sci- ence and distributed optimization, recent innova- tions like the self-repellent random walk (SRRW) achieve near ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=0yzOEMbShU", "content": "We propose a history-driven target (HDT) frame- work in Markov Chain Monte Carlo ( MCMC ) to improve any random walk algorithm on dis- crete state spaces, such as general undirected graphs , for efficient sampling from target distri- bution µ. With broad applications in network sci- ence and distributed optimization, recent innova- tions like the self-repellent random walk (SRRW) achieve near ..."}
+{"idx": 4, "title": "Self-Repellent Random Walks on General Graphs - Achieving Minimal ...", "date": "", "ddg_snippet": "We consider random walks on discrete state spaces, such as general undirected graphs , where the random walkers are designed to approximate a target quantity over the network topology via sampling and neighborhood exploration in the form of Markov chain Monte Carlo ( MCMC ) procedures. Given any Markov chain corresponding to a target probability distribution, we design a self-repellent random ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v202/doshi23a.html", "content": "We consider random walks on discrete state spaces, such as general undirected graphs , where the random walkers are designed to approximate a target quantity over the network topology via sampling and neighborhood exploration in the form of Markov chain Monte Carlo ( MCMC ) procedures. Given any Markov chain corresponding to a target probability distribution, we design a self-repellent random ..."}
+{"idx": 5, "title": "Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient ...", "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": 6, "title": "Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient ...", "date": "", "ddg_snippet": "Overview Introduces novel approach for efficient graph sampling using history-driven MCMC Improves upon self -repelling random walks by considering full sampling history Develops theoretical framework for convergence and mixing properties Demonstrates superior performance on both synthetic and real-world graphs Provides practical implementation guidelines for general graph structures Plain ...", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/beyond-self-repellent-kernels-history-driven-target", "content": "Overview Introduces novel approach for efficient graph sampling using history-driven MCMC Improves upon self -repelling random walks by considering full sampling history Develops theoretical framework for convergence and mixing properties Demonstrates superior performance on both synthetic and real-world graphs Provides practical implementation guidelines for general graph structures Plain ..."}
+{"idx": 7, "title": "Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient ...", "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 $\\\\boldsymbol{\\\\mu}$. With broad applications in network science and distributed optimization, recent innovations like the self-repellent random walk (SRRW) achieve ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Beyond-Self-Repellent-Kernels:-History-Driven-MCMC-Hu-Ma/59bcb50ca3406f2761472b5934c7b8ba1131ff81", "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 $\\\\boldsymbol{\\\\mu}$. With broad applications in network science and distributed optimization, recent innovations like the self-repellent random walk (SRRW) achieve ..."}
+{"idx": 8, "title": "Self-Repellent Random Walks on General Graphs - Achieving Minimal ...", "date": "", "ddg_snippet": "We consider random walks on discrete state spaces, such as general undirected graphs , where the random walkers are designed to approximate a target quantity over the network topology via sampling and neighborhood exploration in the form of Markov chain Monte Carlo ( MCMC ) procedures.", "subpage_snippet": "", "source": "www.ijcai.org", "link": "https://www.ijcai.org/proceedings/2024/0929", "content": "We consider random walks on discrete state spaces, such as general undirected graphs , where the random walkers are designed to approximate a target quantity over the network topology via sampling and neighborhood exploration in the form of Markov chain Monte Carlo ( MCMC ) procedures."}
+{"idx": 9, "title": "Track: Oral 5C Probablistic Models", "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 $\\\\boldsymbol {\\\\mu}$. With broad applications in network science and distributed optimization, recent innovations like the self-repellent random walk (SRRW ...", "subpage_snippet": "", "source": "dev.icml.cc", "link": "https://dev.icml.cc/virtual/2025/session/46916", "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 $\\\\boldsymbol {\\\\mu}$. With broad applications in network science and distributed optimization, recent innovations like the self-repellent random walk (SRRW ..."}

data/sampled_jsons/0yzOEMbShU_Beyond_Self-Repellent_Kernels_Section_4.5_LRU_cache_Equation_15.jsonl

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+{"idx": 0, "title": "Beyond Self-Repellent Kernels: History-Driven Target Towards ...", "date": "", "ddg_snippet": "A recent breakthrough is the self-repellent random walk (SRRW) over general graphs (Doshi et al., 2023), a nonlinear Markov chain that modifies a time-reversible Markov chain with µ-invariant transition kernel P, as shown by Box ① in Figure 1(a).1Unlike non-backtracking random walks relying solely on the most recent history (Alon et al ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=0yzOEMbShU", "content": "A recent breakthrough is the self-repellent random walk (SRRW) over general graphs (Doshi et al., 2023), a nonlinear Markov chain that modifies a time-reversible Markov chain with µ-invariant transition kernel P, as shown by Box ① in Figure 1(a).1Unlike non-backtracking random walks relying solely on the most recent history (Alon et al ..."}
+{"idx": 1, "title": "[2505.18300v2] Beyond Self-Repellent Kernels: History-Driven ...", "date": "", "ddg_snippet": "May 23, 2025 · View a PDF of the paper titled Beyond Self-Repellent Kernels : History-Driven Target Towards Efficient Nonlinear MCMC on General Graphs, by Jie Hu and 2 other authors", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2505.18300v2", "content": "May 23, 2025 · View a PDF of the paper titled Beyond Self-Repellent Kernels : History-Driven Target Towards Efficient Nonlinear MCMC on General Graphs, by Jie Hu and 2 other authors"}
+{"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": "Modeling Cache Performance Beyond LRU LRU-2 vs 2-LRU: An Analytical Study - cs A4.pdf - ResearchGate Modeling Cache Performance Beyond LRU - Massachusetts Institute of 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 a virtual cache before the LRU cache (2- LRU ) provides a huge benefit for small caches in IRM and non-IRM environments. Having an accurate model to calculate the miss rates of replacement algorithms is crucial for performance analysis of large-scale interconnected caches. The kernel section is different from a pullback attractor, although some rela-tions between kernel section and attracting set can be established in Section 2. In particular, both kernel section ... What is a cache model in LRU? r = (In LRU , the oldest age distribution uses 1.) 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. What is the behavior of a 3 line LRU cache? Table 1: Steady-state behavior of a 3-line LRU cache on a simple, repeating access pattern. Live lines are colored green, and dead lines red. 2. However, D evicts A at time 6, so A is dead (red) in 2–5. Similarly, A evicts D at time 1, so D is dead in 6–9. B and C always hit, so they are always live. Why does LRU use thrash-resistance? LRU uses recency alone, leading to its pathologies (e.g., thrashing). Most high-performance policies combine recency with other techniques. Protection: When the working set does not fit in the cache, some policies protect a portion of the working set against eviction to avoid thrashing . This is equivalent to thrash-resistance [23,42]. 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": "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 a virtual cache before the LRU cache (2- LRU ) provides a huge benefit for small caches in IRM and non-IRM environments. Having an accurate model to calculate the miss rates of replacement algorithms is crucial for performance analysis of large-scale interconnected caches. The kernel section is different from a pullback attractor, although some rela-tions between kernel section and attracting set can be established in Section 2. In particular, both kernel section ... What is a cache model in LRU? r = (In LRU , the oldest age distribution uses 1.) 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. What is the behavior of a 3 line LRU cache? Table 1: Steady-state behavior of a 3-line LRU cache on a simple, repeating access pattern. Live lines are colored green, and dead lines red. 2. However, D evicts A at time 6, so A is dead (red) in 2–5. Similarly, A evicts D at time 1, so D is dead in 6–9. B and C always hit, so they are always live. Why does LRU use thrash-resistance? LRU uses recency alone, leading to its pathologies (e.g., thrashing). Most high-performance policies combine recency with other techniques. Protection: When the working set does not fit in the cache, some policies protect a portion of the working set against eviction to avoid thrashing . This is equivalent to thrash-resistance [23,42]. 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": 4, "title": "LRU-2 vs 2-LRU: An Analytical Study - cs", "date": "", "ddg_snippet": "a virtual cache before the LRU cache (2- LRU ) provides a huge benefit for small caches in IRM and non-IRM environments. Having an accurate model to calculate the miss rates of replacement algorithms is crucial for performance analysis of large-scale interconnected caches.", "subpage_snippet": "", "source": "www.cs.usask.ca", "link": "https://www.cs.usask.ca/faculty/makaroff/papers/LRU2.pdf", "content": "a virtual cache before the LRU cache (2- LRU ) provides a huge benefit for small caches in IRM and non-IRM environments. Having an accurate model to calculate the miss rates of replacement algorithms is crucial for performance analysis of large-scale interconnected caches."}
+{"idx": 5, "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": 6, "title": "Beyond Self - Repellent Kernels : History-Driven Target Towards...", "date": "", "ddg_snippet": "4 . 5 Least Recently Used ( LRU ) Cache Scheme.. This self - repellent scheme shows remarkable success, achieving near-zero variance for large. α\\alphaitalic_α. at a rate of.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.18300v3", "content": "4 . 5 Least Recently Used ( LRU ) Cache Scheme.. This self - repellent scheme shows remarkable success, achieving near-zero variance for large. α\\alphaitalic_α. at a rate of."}
+{"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 .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 .ftl. cached _property def sumdata( self )"}
+{"idx": 8, "title": "lru - cache - npm", "date": "", "ddg_snippet": "Start using lru - cache in your project by running `npm i lru - cache `.However, note that using some of the features will necessarily impact performance, by causing the cache to have to do more work. See the \"Performance\" section below. Installation. npm install lru - cache --save.", "subpage_snippet": "", "source": "www.npmjs.com", "link": "https://www.npmjs.com/package/lru-cache", "content": "Start using lru - cache in your project by running `npm i lru - cache `.However, note that using some of the features will necessarily impact performance, by causing the cache to have to do more work. See the \"Performance\" section below. Installation. npm install lru - cache --save."}
+{"idx": 9, "title": "A4.pdf - ResearchGate", "date": "", "ddg_snippet": "The kernel section is different from a pullback attractor, although some rela-tions between kernel section and attracting set can be established in Section 2. In particular, both kernel section ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/profile/Renhai-Wang/publication/335512223_Asymptotic_autonomy_of_kernel_sections_for_Newton-Boussinesq_equations_on_unbounded_zonary_domains/links/5d75dda04585151ee4a8c319/Asymptotic-autonomy-of-kernel-sections-for-Newton-Boussinesq-equations-on-unbounded-zonary-domains.pdf", "content": "The kernel section is different from a pullback attractor, although some rela-tions between kernel section and attracting set can be established in Section 2. In particular, both kernel section ..."}

data/sampled_jsons/10l1pGeOcK_SAFE_Finding_Sparse_and_Flat_Minima_Table_7_VGG-19_CIFAR-10_SAFE_accuracy_90%_95%_98%_spa_year_2024.jsonl

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+{"idx": 0, "title": "PDF SAFE: Finding Sparse and Flat Minima to Improve Pruning", "date": "", "ddg_snippet": "Figure 2: Validation accuracy (mean±std) of VGG-19 and ResNet-20/32 models on CIFAR - 10 /100 pruned across different sparsity levels and methods. SAFE consistently achieves superior performance across a broad range of sparsity levels.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=10l1pGeOcK&name=pdf", "content": "Figure 2: Validation accuracy (mean±std) of VGG-19 and ResNet-20/32 models on CIFAR - 10 /100 pruned across different sparsity levels and methods. SAFE consistently achieves superior performance across a broad range of sparsity levels."}
+{"idx": 1, "title": "SAFE: Finding Sparse and Flat Minima to Improve Pruning", "date": "", "ddg_snippet": "Sparsifying neural networks often suffers from seemingly inevitable performance degradation, and it remains challenging to restore the original performance despite much recent progress. Motivated by recent studies in robust optimization, we aim to tackle this problem by finding subnetworks that are both sparse and flat at the same time. Specifically, we formulate pruning as a sparsity ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2506.06866", "content": "Sparsifying neural networks often suffers from seemingly inevitable performance degradation, and it remains challenging to restore the original performance despite much recent progress. Motivated by recent studies in robust optimization, we aim to tackle this problem by finding subnetworks that are both sparse and flat at the same time. Specifically, we formulate pruning as a sparsity ..."}
+{"idx": 2, "title": "Best performing penalty parameter of SAFE for VGG-19 and ResNet-20/32.", "date": "", "ddg_snippet": "Download scientific diagram | Best performing penalty parameter of SAFE for VGG-19 and ResNet-20/32. from publication: SAFE : Finding Sparse and Flat Minima to Improve Pruning | Sparsifying neural ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/Best-performing-penalty-parameter-of-SAFE-for-VGG-19-and-ResNet-20-32_tbl3_392531034", "content": "Download scientific diagram | Best performing penalty parameter of SAFE for VGG-19 and ResNet-20/32. from publication: SAFE : Finding Sparse and Flat Minima to Improve Pruning | Sparsifying neural ..."}
+{"idx": 3, "title": "SAFE: Finding Sparse and Flat Minima to Improve Pruning", "date": "", "ddg_snippet": "Sparsifying neural networks often suffers from seemingly inevitable performance degradation, and it remains challenging to restore the original performance despite much recent progress.Motivated by recent studies in robust optimization, we aim to tackle this problem by finding subnetworks that are both sparse and flat at the same time.Specifically, we formulate pruning as a sparsity ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/LOG-postech/safe-torch", "content": "Sparsifying neural networks often suffers from seemingly inevitable performance degradation, and it remains challenging to restore the original performance despite much recent progress.Motivated by recent studies in robust optimization, we aim to tackle this problem by finding subnetworks that are both sparse and flat at the same time.Specifically, we formulate pruning as a sparsity ..."}
+{"idx": 4, "title": "PDF Safe: Finding Sparse and Flat Minima to Improve Pruning", "date": "", "ddg_snippet": "Penalizes x iterate to move slightly closer to z during flatness-inducing minimization. This gradually moves x towards sparsity during flatness induction without sudden changes, yielding a sparse and flat minima .", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/media/icml-2025/Slides/46658.pdf", "content": "Penalizes x iterate to move slightly closer to z during flatness-inducing minimization. This gradually moves x towards sparsity during flatness induction without sudden changes, yielding a sparse and flat minima ."}
+{"idx": 5, "title": "Safe: Finding Sparse and Flat Minima to Improve Pruning", "date": "", "ddg_snippet": "We train ResNet-20 on CIFAR-10 to {70 %, 80 %, 90 %, 95 %} sparsity with Safe using linear, cosine schedules, and constant λ, and observe how this affects the final accuracy of the sparse network in Table 12.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.06866v2", "content": "We train ResNet-20 on CIFAR-10 to {70 %, 80 %, 90 %, 95 %} sparsity with Safe using linear, cosine schedules, and constant λ, and observe how this affects the final accuracy of the sparse network in Table 12."}
+{"idx": 6, "title": "[이남훈 교수] SAFE: Finding Sparse and Flat Minima to Improve Pruning", "date": "", "ddg_snippet": "Dongyeop Lee, Kwanhee Lee, Jinseok Chung, and Namhoon Lee. \" SAFE : Finding Sparse and Flat Minima to Improve Pruning\", International Conference on Machine Learning (ICML), 2025. [성과와 관련된 이미지] 이전글[이남훈 교수] SASSHA: Sharpness-aware Adaptive Second-order Optimization with Stable Hessian Approximation", "subpage_snippet": "", "source": "cse.postech.ac.kr", "link": "https://cse.postech.ac.kr/csepostech/research/latest-research.do?mode=view&articleNo=24246&title=[이남훈+교수]+SAFE:+Finding+Sparse+and+Flat+Minima+to+Improve+Pruning", "content": "Dongyeop Lee, Kwanhee Lee, Jinseok Chung, and Namhoon Lee. \" SAFE : Finding Sparse and Flat Minima to Improve Pruning\", International Conference on Machine Learning (ICML), 2025. [성과와 관련된 이미지] 이전글[이남훈 교수] SASSHA: Sharpness-aware Adaptive Second-order Optimization with Stable Hessian Approximation"}
+{"idx": 7, "title": "Low validation accuracy VGG-19 CIFAR-10 CNN - Reddit", "date": "", "ddg_snippet": "Hey Guys, I am trying to train a VGG-19 CNN on CIFAR-10 dataset using data augmentation and batch normalization. The code can be found VGG-19 CNN. I took two approaches to training the model: Using early stopping: loss = 2.2816 and accuracy = 47.1700% Without early stopping: loss = 3.3211 and accuracy = 56.6800% The loss and accuracy are on validation data. Also, when the model is trained ...", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/deeplearning/comments/hxk3g8/low_validation_accuracy_vgg19_cifar10_cnn/", "content": "Hey Guys, I am trying to train a VGG-19 CNN on CIFAR-10 dataset using data augmentation and batch normalization. The code can be found VGG-19 CNN. I took two approaches to training the model: Using early stopping: loss = 2.2816 and accuracy = 47.1700% Without early stopping: loss = 3.3211 and accuracy = 56.6800% The loss and accuracy are on validation data. Also, when the model is trained ..."}
+{"idx": 8, "title": "GitHub - xijia-tao/Implementation-of-VGGs-on-CIFAR-10: The repository ...", "date": "", "ddg_snippet": "The repository contains a detailed analysis on implementing VGG19 and (plain-layered) VGG34 on the CIFAR-10 dataset with code, and an explanation on the distinctive difference between them. It serves the purpose of storing my HW2 for the ICRA training, HKU, 2020. - xijia-tao/Implementation-of- VGGs -on- CIFAR - 10", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/xijia-tao/Implementation-of-VGGs-on-CIFAR-10", "content": "The repository contains a detailed analysis on implementing VGG19 and (plain-layered) VGG34 on the CIFAR-10 dataset with code, and an explanation on the distinctive difference between them. It serves the purpose of storing my HW2 for the ICRA training, HKU, 2020. - xijia-tao/Implementation-of- VGGs -on- CIFAR - 10"}
+{"idx": 9, "title": "publications | Dongyeop Lee - GitHub Pages", "date": "", "ddg_snippet": "2025 SAFE : Finding Sparse and Flat Minima to Improve Pruning Dongyeop Lee, Kwanhee Lee, Jinseok Chung, and Namhoon Lee ICML 2025 (spotlight), Jul 2025 Abs arXiv Code Poster Sparsifying neural networks often suffers from seemingly inevitable performance degradation, and it remains challenging to restore the original performance despite much recent progress. Motivated by recent studies in robust ...", "subpage_snippet": "", "source": "edong6768.github.io", "link": "https://edong6768.github.io/publications/", "content": "2025 SAFE : Finding Sparse and Flat Minima to Improve Pruning Dongyeop Lee, Kwanhee Lee, Jinseok Chung, and Namhoon Lee ICML 2025 (spotlight), Jul 2025 Abs arXiv Code Poster Sparsifying neural networks often suffers from seemingly inevitable performance degradation, and it remains challenging to restore the original performance despite much recent progress. Motivated by recent studies in robust ..."}

data/sampled_jsons/2.5_Score_Based_Denoising_noise_augmentation_score_function_posterior_mean.jsonl

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+{"idx": 0, "title": "Diffusion-Based Posterior Sampling Methods", "date": "", "ddg_snippet": "4 Sept 2025 — Explore diffusion- based posterior sampling methods that blend score models with soft data consistency to robustly solve noisy , ...", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/topics/diffusion-based-posterior-sampling-methods", "content": "4 Sept 2025 — Explore diffusion- based posterior sampling methods that blend score models with soft data consistency to robustly solve noisy , ..."}
+{"idx": 1, "title": "DiffAug: A Diffuse-and-Denoise Augmentation for Training ...", "date": "", "ddg_snippet": "9 Dec 2024 — We introduce DiffAug, a simple and efficient diffusion- based augmentation technique to train image classifiers for the crucial yet challenging goal of improved ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/poster/95014", "content": "9 Dec 2024 — We introduce DiffAug, a simple and efficient diffusion- based augmentation technique to train image classifiers for the crucial yet challenging goal of improved ..."}
+{"idx": 2, "title": "Adaptive and Iterative Point Cloud Denoising with Score‐ ...", "date": "", "ddg_snippet": "21 Apr 2025 — In this paper, we propose an adaptive and iterative point cloud denoising method based on the score - based diffusion model.", "subpage_snippet": "", "source": "onlinelibrary.wiley.com", "link": "https://onlinelibrary.wiley.com/doi/10.1111/cgf.70149?af=R", "content": "21 Apr 2025 — In this paper, we propose an adaptive and iterative point cloud denoising method based on the score - based diffusion model."}
+{"idx": 3, "title": "Score-based Diffusion Models in Function Space", "date": "", "ddg_snippet": "by JH Lim · 2025 · Cited by 61 — We develop a mathematically rigorous framework for denoising score matching with function -valued data called DDO by formulating and extending all necessary ...", "subpage_snippet": "", "source": "jmlr.org", "link": "https://jmlr.org/papers/volume26/23-1472/23-1472.pdf", "content": "by JH Lim · 2025 · Cited by 61 — We develop a mathematically rigorous framework for denoising score matching with function -valued data called DDO by formulating and extending all necessary ..."}
+{"idx": 4, "title": "Normalizing Flows are Capable Generative Models", "date": "", "ddg_snippet": "6 Jun 2025 — We present a post-training score - based denoising technique that allows one to remove the noise in the generated samples. Report issue for ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.06329v3", "content": "6 Jun 2025 — We present a post-training score - based denoising technique that allows one to remove the noise in the generated samples. Report issue for ..."}
+{"idx": 5, "title": "Don't Play Favorites: Minority Guidance for Diffusion Models", "date": "", "ddg_snippet": "Our metric, which we call minority score , is based on Tweedie's formula (Robbins, 1992) that yields the posterior mean of a clean sample given a noise -corrupted ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2301.12334v2", "content": "Our metric, which we call minority score , is based on Tweedie's formula (Robbins, 1992) that yields the posterior mean of a clean sample given a noise -corrupted ..."}
+{"idx": 6, "title": "Normalizing Flows are Capable Generative Models", "date": "", "ddg_snippet": "2.5 . Score Based Denoising . Training with noise augmentation introduces an additional challenge: models trained on the noisy distribution q(y) nat- urally ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/659866a5e8e65a343fe4954105e3fd09410fb122.pdf", "content": "2.5 . Score Based Denoising . Training with noise augmentation introduces an additional challenge: models trained on the noisy distribution q(y) nat- urally ..."}
+{"idx": 7, "title": "Daily Papers", "date": "", "ddg_snippet": "4 days ago — We show how to sample from resulting posteriors by using this probability function for variational inference. Our results , including experiments ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=surrogate+posterior+variance", "content": "4 days ago — We show how to sample from resulting posteriors by using this probability function for variational inference. Our results , including experiments ..."}
+{"idx": 8, "title": "Daily Papers", "date": "", "ddg_snippet": "Specifically, we show that image denoising problems without clean images can be addressed by finding the mode of the posterior distribution and that the ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=sequential+denoising", "content": "Specifically, we show that image denoising problems without clean images can be addressed by finding the mode of the posterior distribution and that the ..."}
+{"idx": 9, "title": "Deep generative models for Bayesian inference on high- ...", "date": "", "ddg_snippet": "by TSW Stevens · 2025 · Cited by 1 — To achieve posterior sampling with pre-trained DMs, one can substitute the score function in equation (2.4) with the factorization of equation ( 2.5 ) leading to ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12177526/", "content": "by TSW Stevens · 2025 · Cited by 1 — To achieve posterior sampling with pre-trained DMs, one can substitute the score function in equation (2.4) with the factorization of equation ( 2.5 ) leading to ..."}

data/sampled_jsons/2208.01565.jsonl

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+{"idx": 0, "title": "[2208.01565] Approximate Bayesian Neural Operators ... arXiv:2208.01565v1 [cs.LG] 2 Aug 2022 dblp: Approximate Bayesian Neural Operators: Uncertainty ... Emilia Magnani - dblp Emilia Magnani - Google Scholar Neural Operator induced Gaussian Process framework for ... Position: Epistemic Artificial Intelligence is Essential", "date": "", "ddg_snippet": "Aug 2, 2022 · Neural operators are a type of deep architecture that learns to solve (i.e. learns the nonlinear solution operator of) partial differential equations (PDEs). The current state of the art for these models does not provide explicit uncertainty quantification. This is arguably even more of a problem for this kind of tasks than elsewhere in machine learning, because the dynamical systems typically ... Abstract Neural operators are a type of deep architecture that learns to solve (i.e. learns the nonlinear so-lution operator of) partial differential equations (PDEs). The current state of the art for these mod-els does not provide explicit uncertainty quantifi-cation. This is arguably even more of a problem for this kind of tasks than elsewhere in machine learning, because the dynamical ... Bibliographic details on Approximate Bayesian Neural Operators: Uncertainty Quantification for Parametric PDEs. Emilia Magnani, Nicholas Krämer, Runa Eschenhagen, Lorenzo Rosasco, Philipp Hennig: Approximate Bayesian Neural Operators: Uncertainty Quantification for Parametric PDEs. CoRR abs/ 2208.01565 (2022) University of Tübingen - Cited by 41 - Probabilistic Numerics - Machine Learning Nov 1, 2024 · The study of neural operators has paved the way for the development of efficient approaches for solving partial differential equations (PDEs) compared… The success of artificial intelligence (AI), especially deep learning (LeCun et al., 2015), is indisputable. AI systems now perform many tasks at or above human levels, with generative models advancing into creativity (Ramesh et al., 2022), large language models (LLMs) (Brown et al., 2020) excelling in language manipulation, and significant advancements in multimodal AI (Radford et al., 2021 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2208.01565", "content": "Aug 2, 2022 · Neural operators are a type of deep architecture that learns to solve (i.e. learns the nonlinear solution operator of) partial differential equations (PDEs). The current state of the art for these models does not provide explicit uncertainty quantification. This is arguably even more of a problem for this kind of tasks than elsewhere in machine learning, because the dynamical systems typically ... Abstract Neural operators are a type of deep architecture that learns to solve (i.e. learns the nonlinear so-lution operator of) partial differential equations (PDEs). The current state of the art for these mod-els does not provide explicit uncertainty quantifi-cation. This is arguably even more of a problem for this kind of tasks than elsewhere in machine learning, because the dynamical ... Bibliographic details on Approximate Bayesian Neural Operators: Uncertainty Quantification for Parametric PDEs. Emilia Magnani, Nicholas Krämer, Runa Eschenhagen, Lorenzo Rosasco, Philipp Hennig: Approximate Bayesian Neural Operators: Uncertainty Quantification for Parametric PDEs. CoRR abs/ 2208.01565 (2022) University of Tübingen - Cited by 41 - Probabilistic Numerics - Machine Learning Nov 1, 2024 · The study of neural operators has paved the way for the development of efficient approaches for solving partial differential equations (PDEs) compared… The success of artificial intelligence (AI), especially deep learning (LeCun et al., 2015), is indisputable. AI systems now perform many tasks at or above human levels, with generative models advancing into creativity (Ramesh et al., 2022), large language models (LLMs) (Brown et al., 2020) excelling in language manipulation, and significant advancements in multimodal AI (Radford et al., 2021 ..."}
+{"idx": 1, "title": "Сколько будет 1565% от 2208? - Калькулятор №1", "date": "", "ddg_snippet": "Например, он поможет узнать сколько будет 1565% от 2208? Введите проценты (например ' 1565'), число (например ' 2208') и нажмите кнопку 'Рассчитать'.", "subpage_snippet": "", "source": "calculator1.ru", "link": "https://calculator1.ru/percent-of-number/1565/2208/", "content": "Например, он поможет узнать сколько будет 1565% от 2208? Введите проценты (например ' 1565'), число (например ' 2208') и нажмите кнопку 'Рассчитать'."}
+{"idx": 2, "title": "НОД и НОК чисел 1565 и 2208 - подробное решение", "date": "", "ddg_snippet": "Наименьшее общее кратное (НОК) целых чисел 1565 и 2208 — это наименьшее натуральное число, которое делится на 1565 и на 2208 без остатка.", "subpage_snippet": "", "source": "nod-nok.ru", "link": "https://nod-nok.ru/primery/nod-i-nok-chisel-1565-2208/", "content": "Наименьшее общее кратное (НОК) целых чисел 1565 и 2208 — это наименьшее натуральное число, которое делится на 1565 и на 2208 без остатка."}
+{"idx": 3, "title": "arXiv:2208.01565v1 [cs.LG] 2 Aug 2022", "date": "", "ddg_snippet": "Abstract Neural operators are a type of deep architecture that learns to solve (i.e. learns the nonlinear so-lution operator of) partial differential equations (PDEs). The current state of the art for these mod-els does not provide explicit uncertainty quantifi-cation. This is arguably even more of a problem for this kind of tasks than elsewhere in machine learning, because the dynamical ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2208.01565.pdf", "content": "Abstract Neural operators are a type of deep architecture that learns to solve (i.e. learns the nonlinear so-lution operator of) partial differential equations (PDEs). The current state of the art for these mod-els does not provide explicit uncertainty quantifi-cation. This is arguably even more of a problem for this kind of tasks than elsewhere in machine learning, because the dynamical ..."}
+{"idx": 4, "title": "Emilia Magnani - Google Scholar", "date": "", "ddg_snippet": "E Magnani, N Krämer, R Eschenhagen, L Rosasco, P Hennig. arXiv preprint arXiv: 2208 . 01565 , 2022.", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=_zPcNdEAAAAJ&hl=en", "content": "E Magnani, N Krämer, R Eschenhagen, L Rosasco, P Hennig. arXiv preprint arXiv: 2208 . 01565 , 2022."}
+{"idx": 5, "title": "Emilia Magnani - dblp", "date": "", "ddg_snippet": "Emilia Magnani, Nicholas Krämer, Runa Eschenhagen, Lorenzo Rosasco, Philipp Hennig: Approximate Bayesian Neural Operators: Uncertainty Quantification for Parametric PDEs. CoRR abs/ 2208.01565 (2022)", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/pid/206/6101", "content": "Emilia Magnani, Nicholas Krämer, Runa Eschenhagen, Lorenzo Rosasco, Philipp Hennig: Approximate Bayesian Neural Operators: Uncertainty Quantification for Parametric PDEs. CoRR abs/ 2208.01565 (2022)"}
+{"idx": 6, "title": "Approximate Bayesian Neural Operators: Uncertainty Quantication for", "date": "", "ddg_snippet": "arXiv: 2208 . 01565 v1 [cs.LG] 2 Aug 2022. Emilia Magnani1 Nicholas Krämer1 Runa Eschenhagen1 Lorenzo Rosasco3,4 Philipp Hennig1,2. 1University of Tübingen, Germany...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2208.01565", "content": "arXiv: 2208 . 01565 v1 [cs.LG] 2 Aug 2022. Emilia Magnani1 Nicholas Krämer1 Runa Eschenhagen1 Lorenzo Rosasco3,4 Philipp Hennig1,2. 1University of Tübingen, Germany..."}
+{"idx": 7, "title": "Approximate Bayesian Neural Operators: Uncertainty Quantification for...", "date": "", "ddg_snippet": "Approximate Bayesian Neural Operators: Uncertainty Quantification for Parametric PDEs. August 2022. DOI:10.48550/arXiv. 2208 . 01565 . Authors: Emilia Magnani.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/362430096_Approximate_Bayesian_Neural_Operators_Uncertainty_Quantification_for_Parametric_PDEs", "content": "Approximate Bayesian Neural Operators: Uncertainty Quantification for Parametric PDEs. August 2022. DOI:10.48550/arXiv. 2208 . 01565 . Authors: Emilia Magnani."}
+{"idx": 8, "title": "Размеры Ягуар И-Пэйс и вес. Какие габариты Jaguar I-Pace?", "date": "", "ddg_snippet": "Масса, кг. 90 kWh AWD S. 4682 x 1895 x 1565. 2208.", "subpage_snippet": "", "source": "www.drom.ru", "link": "https://www.drom.ru/catalog/jaguar/i-pace/specs/dimensions/", "content": "Масса, кг. 90 kWh AWD S. 4682 x 1895 x 1565. 2208."}
+{"idx": 9, "title": "Результат вычитания: 1565 минус -643 | Универсальные онлайн...", "date": "", "ddg_snippet": "Результат вычитания: 2208.", "subpage_snippet": "", "source": "ai-calc.ru", "link": "https://ai-calc.ru/matematicheskie/minus/1565+-643/", "content": "Результат вычитания: 2208."}

data/sampled_jsons/23zxLtvder_SPD_Sync-Point_Drop_Efficient_Tensor_Parallelism_Large_Language_Models_Figure_7c.jsonl

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+{"idx": 1, "title": "SPD : Sync - Point Drop for Efficient Tensor Parallelism of Large ...", "date": "", "ddg_snippet": "Tensor parallelism provides an effective way to increase server large language model (LLM) inference efficiency despite adding an additional communication cost.", "subpage_snippet": "", "source": "machinelearning.apple.com", "link": "https://machinelearning.apple.com/research/sync-point-drop", "content": "Tensor parallelism provides an effective way to increase server large language model (LLM) inference efficiency despite adding an additional communication cost."}
+{"idx": 2, "title": "SPD : Sync - Point Drop for efficient tensor parallelism of Large ...", "date": "", "ddg_snippet": "Overview SPD ( Sync - Point Drop ) reduces communication overhead in tensor parallelism for LLMsIdentifies and selectively drops unnecessary synchronization operationsTraining and running large language models is a bit like trying to cook a massive meal in a...", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/spd-sync-point-drop-efficient-tensor-parallelism", "content": "Overview SPD ( Sync - Point Drop ) reduces communication overhead in tensor parallelism for LLMsIdentifies and selectively drops unnecessary synchronization operationsTraining and running large language models is a bit like trying to cook a massive meal in a..."}
+{"idx": 3, "title": "Figure 1. Tensor parallelism applied on transformer decoder block (in...", "date": "", "ddg_snippet": "SPD : Sync - Point Drop for efficient tensor parallelism of Large Language Models .To enable SPD directly on decoder block, we first introduce a block design for SPD that minimizes negative effects resulting from reduced communication (see Figure 3). ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/Tensor-parallelism-applied-on-transformer-decoder-block-in-2-GPUs-distributed-inference_fig1_389510233", "content": "SPD : Sync - Point Drop for efficient tensor parallelism of Large Language Models .To enable SPD directly on decoder block, we first introduce a block design for SPD that minimizes negative effects resulting from reduced communication (see Figure 3). ..."}
+{"idx": 4, "title": "Sync - Point Drop for Efficient Tensor Parallelism - Kifinity", "date": "", "ddg_snippet": "The rapid expansion of large language models (LLMs) necessitates efficient distributed inference across multiple computing units. Communication overheads from techniques like Tensor Parallelism hinder scalability and low latency.", "subpage_snippet": "", "source": "www.kifinity.com", "link": "https://www.kifinity.com/post/sync-point-drop-machinelearning-apple-e5f9168f", "content": "The rapid expansion of large language models (LLMs) necessitates efficient distributed inference across multiple computing units. Communication overheads from techniques like Tensor Parallelism hinder scalability and low latency."}
+{"idx": 5, "title": "Articles by Hyun-Bin Kim | Synthical", "date": "", "ddg_snippet": "SPD : Sync - Point Drop for Efficient Tensor Parallelism of Large Language Models .", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/profile/9ace9273-e17c-4820-9272-302eab60023e/articles", "content": "SPD : Sync - Point Drop for Efficient Tensor Parallelism of Large Language Models ."}
+{"idx": 6, "title": "GitHub - Toseic/LLM-inference-arxiv-daily: Automatically Update...", "date": "", "ddg_snippet": "SPD : Sync - Point Drop for efficient tensor parallelism of Large Language Models . Han-Byul Kim et.al.KVLink: Accelerating Large Language Models via Efficient KV Cache Reuse.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Toseic/LLM-inference-arxiv-daily", "content": "SPD : Sync - Point Drop for efficient tensor parallelism of Large Language Models . Han-Byul Kim et.al.KVLink: Accelerating Large Language Models via Efficient KV Cache Reuse."}
+{"idx": 7, "title": "Ускорение инференса LLM через тензорный... — AI на vc.ru", "date": "", "ddg_snippet": "Применение модельного параллелизма ( model parallelism ) подразумевает наличие более 1 GPU в сервере или хотя бы по 1 GPU на двух серверах.Настройки в vLLM. Выберите tensor _ parallel _size, кратный hidden_size и num_heads модели.", "subpage_snippet": "", "source": "vc.ru", "link": "https://vc.ru/ai/2052555-uskorenie-inferensa-llm-s-pomoshchyu-tenzornogo-parallelizma", "content": "Применение модельного параллелизма ( model parallelism ) подразумевает наличие более 1 GPU в сервере или хотя бы по 1 GPU на двух серверах.Настройки в vLLM. Выберите tensor _ parallel _size, кратный hidden_size и num_heads модели."}
+{"idx": 8, "title": "Reducing LLM Inference Costs While Preserving Performance", "date": "", "ddg_snippet": "Model Parallelism : To serve very large models (tens to hundreds of billions of parameters), model parallelism is necessary – splitting the model across multiple GPUs or nodes.", "subpage_snippet": "", "source": "www.rohan-paul.com", "link": "https://www.rohan-paul.com/p/reducing-llm-inference-costs-while", "content": "Model Parallelism : To serve very large models (tens to hundreds of billions of parameters), model parallelism is necessary – splitting the model across multiple GPUs or nodes."}
+{"idx": 9, "title": "Ключевые понятия LLM / Хабр", "date": "", "ddg_snippet": "Современные языковые модели ( large language models ) стали ключевым элементом в развитии искусственного интеллекта и обработки естественного языка. Модели, основанные на глубоком обучении и архитектуре трансформеров, способны генерировать текст, отвечать на...", "subpage_snippet": "", "source": "habr.com", "link": "https://habr.com/ru/companies/bothub/articles/928752/", "content": "Современные языковые модели ( large language models ) стали ключевым элементом в развитии искусственного интеллекта и обработки естественного языка. Модели, основанные на глубоком обучении и архитектуре трансформеров, способны генерировать текст, отвечать на..."}

data/sampled_jsons/2405.17618_equation_7_L_ra2c_sample-wise_reverse_A2C.jsonl

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+{"idx": 1, "title": "Advantage Actor-Critic (A2C) algorithm in Reinforcement ... - Medium", "date": "", "ddg_snippet": "Advantage Actor-Critic ( A2C ) algorithm in Reinforcement Learning with Codes and Examples using OpenAI Gym", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/data-science-in-your-pocket/advantage-actor-critic-a2c-algorithm-in-reinforcement-learning-with-codes-and-examples-using-e810273c0c9e", "content": "Advantage Actor-Critic ( A2C ) algorithm in Reinforcement Learning with Codes and Examples using OpenAI Gym"}
+{"idx": 2, "title": "Advantage Actor Critic Tutorial: minA2C - Towards Data Science", "date": "", "ddg_snippet": "In the field of Reinforcement Learning, the Advantage Actor Critic ( A2C ) algorithm combines two types of Reinforcement Learning algorithms (Policy Based and Value Based) together.", "subpage_snippet": "", "source": "towardsdatascience.com", "link": "https://towardsdatascience.com/advantage-actor-critic-tutorial-mina2c-7a3249962fc8/", "content": "In the field of Reinforcement Learning, the Advantage Actor Critic ( A2C ) algorithm combines two types of Reinforcement Learning algorithms (Policy Based and Value Based) together."}
+{"idx": 3, "title": "GitHub - Jitu0110/RLMujoco: SAC, PPO, A2C implementation on Mujoco ...", "date": "", "ddg_snippet": "SAC, PPO, A2C implementation on Mujoco environments : Humanoid-v4, Ant-v4, Cheetah-v4 . Includes reward manipulation. - Jitu0110/RLMujoco", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Jitu0110/RLMujoco", "content": "SAC, PPO, A2C implementation on Mujoco environments : Humanoid-v4, Ant-v4, Cheetah-v4 . Includes reward manipulation. - Jitu0110/RLMujoco"}
+{"idx": 4, "title": "Actor-Critic Methods, Advantage Actor-Critic (A2C) and Generalized ...", "date": "", "ddg_snippet": "Actor-Critic Methods, Advantage Actor-Critic ( A2C ) and Generalized Advantage Estimation (GAE) 18 minute read", "subpage_snippet": "", "source": "avandekleut.github.io", "link": "https://avandekleut.github.io/a2c/", "content": "Actor-Critic Methods, Advantage Actor-Critic ( A2C ) and Generalized Advantage Estimation (GAE) 18 minute read"}
+{"idx": 5, "title": "6.3 A2C Algorithm | Reinforcement Learning - The Actor-Critic Algorithm ...", "date": "", "ddg_snippet": "A complete look at the Actor-Critic ( A2C ) algorithm, used in deep reinforcement learning, which enables a learned reinforcing signal to be more informative for a policy than the rewards available from an environment.", "subpage_snippet": "", "source": "www.informit.com", "link": "https://www.informit.com/articles/article.aspx?p=2995356&seqNum=3", "content": "A complete look at the Actor-Critic ( A2C ) algorithm, used in deep reinforcement learning, which enables a learned reinforcing signal to be more informative for a policy than the rewards available from an environment."}
+{"idx": 6, "title": "Advantage Actor Critic (A2C) implementation - Medium", "date": "", "ddg_snippet": "Advantage Actor Critic ( A2C ) implementation Internet is full of very good resources to learn about reinforcement learning algorithms, and of course advantage actor critic is not an exception.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/deeplearningmadeeasy/advantage-actor-critic-a2c-implementation-944e98616b", "content": "Advantage Actor Critic ( A2C ) implementation Internet is full of very good resources to learn about reinforcement learning algorithms, and of course advantage actor critic is not an exception."}
+{"idx": 7, "title": "PDF Sample-wise Label Confidence Incorporation for Learning with Noisy Labels", "date": "", "ddg_snippet": "To alleviate this challenge, we propose a sample-wise label confidence in-corporation into our method. This incorporation leads to re-duced penalties for inaccurate predictions of noisy samples with lower confidence levels.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/ICCV2023/papers/Ahn_Sample-wise_Label_Confidence_Incorporation_for_Learning_with_Noisy_Labels_ICCV_2023_paper.pdf", "content": "To alleviate this challenge, we propose a sample-wise label confidence in-corporation into our method. This incorporation leads to re-duced penalties for inaccurate predictions of noisy samples with lower confidence levels."}
+{"idx": 8, "title": "Symmetric Reinforcement Learning Loss for Robust Learning on Diverse ...", "date": "", "ddg_snippet": "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": "arxiv.org", "link": "https://arxiv.org/abs/2405.17618", "content": "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": "Abstract - arXiv.org", "date": "", "ddg_snippet": "from p. We propose that the reverse RL losses for A2C and PPO also incorporate reverse information to address noisy factors in the RL training p ocedure. The RCE loss ( Equation 5) defines log 0 = Z where Z < 0 is some constant", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2405.17618", "content": "from p. We propose that the reverse RL losses for A2C and PPO also incorporate reverse information to address noisy factors in the RL training p ocedure. The RCE loss ( Equation 5) defines log 0 = Z where Z < 0 is some constant"}

data/sampled_jsons/2410.09536_Section_5.3_random_segment_length_authors_explanation.jsonl

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+{"idx": 0, "title": "transformer-based off-policy episodic reinforcement learning", "date": "", "ddg_snippet": "by G Li · 2024 · Cited by 7 — In our experiments, we identified the segment length L as a key hyperparameter. The best results were achieved by randomly sampling L at each ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.09536", "content": "by G Li · 2024 · Cited by 7 — In our experiments, we identified the segment length L as a key hyperparameter. The best results were achieved by randomly sampling L at each ..."}
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+{"idx": 2, "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."}

data/sampled_jsons/2502.00136_dataset_empirical_studies.jsonl

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+{"idx": 0, "title": "List of empires - Wikipedia", "date": "", "ddg_snippet": "\"ahmadnagar and the sur empire , 1537 to 1553- a study of contemporary documents\". Proceedings of the Indian History Congress. 44: 176–188.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/List_of_empires", "content": "\"ahmadnagar and the sur empire , 1537 to 1553- a study of contemporary documents\". Proceedings of the Indian History Congress. 44: 176–188."}
+{"idx": 1, "title": "[ 2502 . 00136 ] A Checks-and-Balances Framework for Context-Aware...", "date": "", "ddg_snippet": "Computer Science > Computation and Language. arXiv: 2502 . 00136 (cs). [Submitted on 31 Jan 2025 (v1), last revised 26 May 2025 (this version, v2)]. Title:A Checks-and-Balances Framework for Context-Aware Ethical AI Alignment.(or arXiv: 2502 . 00136 v2 [cs.CL] for this version).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.00136", "content": "Computer Science > Computation and Language. arXiv: 2502 . 00136 (cs). [Submitted on 31 Jan 2025 (v1), last revised 26 May 2025 (this version, v2)]. Title:A Checks-and-Balances Framework for Context-Aware Ethical AI Alignment.(or arXiv: 2502 . 00136 v2 [cs.CL] for this version)."}
+{"idx": 2, "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?search=Ab+test", "content": "Download Open Datasets on 1000s of Projects + Share Projects on One Platform. Explore Popular Topics Like Government, Sports, Medicine, Fintech, Food, More."}
+{"idx": 3, "title": "UCI Machine Learning Repository | Discover datasets around the world!", "date": "", "ddg_snippet": "This data set is designed for testing indexing schemes in time series databases. It is a much larger dataset than has been used in any published study (That we are currently aware of). It contains one million data points.", "subpage_snippet": "", "source": "archive.ics.uci.edu", "link": "https://archive.ics.uci.edu/dataset/136/pseudo+periodic+synthetic+time+series", "content": "This data set is designed for testing indexing schemes in time series databases. It is a much larger dataset than has been used in any published study (That we are currently aware of). It contains one million data points."}
+{"idx": 4, "title": "Department of Computer Science - Datasets - Aalborg...", "date": "", "ddg_snippet": "Datasets . 1 - 25 out of 136 results. Start date (descending).Additional file 3 of The Danish-American Research Exchange (DARE): a cross-sectional study of a binational research education program.", "subpage_snippet": "", "source": "vbn.aau.dk", "link": "https://vbn.aau.dk/en/organisations/institut-for-datalogi/datasets/", "content": "Datasets . 1 - 25 out of 136 results. Start date (descending).Additional file 3 of The Danish-American Research Exchange (DARE): a cross-sectional study of a binational research education program."}
+{"idx": 5, "title": "Сотни бронемашин в год, в перспективе тысячи... | Дзен", "date": "", "ddg_snippet": "Дорогой Друг, пожалуйста, поставь лайк, оставь комментарий и подпишись - нас почти 71 000. Спасибо каждому! 2502.", "subpage_snippet": "", "source": "dzen.ru", "link": "https://dzen.ru/a/ZFiZUAkh2WS60Zio", "content": "Дорогой Друг, пожалуйста, поставь лайк, оставь комментарий и подпишись - нас почти 71 000. Спасибо каждому! 2502."}
+{"idx": 6, "title": "DIY акустика. Теория и практика построения... - Форум onliner.by", "date": "", "ddg_snippet": "Автобарахолка. Отзывы об авто 2502.", "subpage_snippet": "", "source": "forum.onliner.by", "link": "https://forum.onliner.by/viewtopic.php?t=15946879&start=5100", "content": "Автобарахолка. Отзывы об авто 2502."}
+{"idx": 7, "title": "Топ игроков в режиме Миссии - 38 неделя 2025 год | crossout-info", "date": "", "ddg_snippet": "The_Nobody_. Каратель 75% 4 10891 4540. 2502. CBR-420.", "subpage_snippet": "", "source": "crossout-info.com", "link": "https://crossout-info.com/season/pvp/2025-38", "content": "The_Nobody_. Каратель 75% 4 10891 4540. 2502. CBR-420."}
+{"idx": 8, "title": "Новости Владивостока и Приморского края - МК во Владивостоке", "date": "", "ddg_snippet": "2502. Елена Соколова. За год «шашлычный набор» заметно подорожал.", "subpage_snippet": "", "source": "vlad.mk.ru", "link": "https://vlad.mk.ru/", "content": "2502. Елена Соколова. За год «шашлычный набор» заметно подорожал."}
+{"idx": 9, "title": "Терафлекс цена в Озерске от 564 руб., купить Терафлекс...", "date": "", "ddg_snippet": "В наличии. Цена: 2502.136. 2 352₽. Купить.", "subpage_snippet": "", "source": "apteka.ru", "link": "https://apteka.ru/ozersk/preparation/terafleks/", "content": "В наличии. Цена: 2502.136. 2 352₽. Купить."}

data/sampled_jsons/2502.10875_Table_1_statistics_ML-1M_Amazon_Beauty_Amazon_Toys_Games_density_train.jsonl

@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Amazon.com: Beauty & Personal Care", "date": "", "ddg_snippet": "Explore Beauty and Personal Care products on Amazon . Shop makeup, skin care, hair care, nail polish, beauty appliances, men's grooming & more, from best-selling brands like Olay, Neutrogena, Dove, L'Oreal Paris, and more.", "subpage_snippet": "", "source": "www.amazon.com", "link": "https://www.amazon.com/Beauty-Makeup-Skin-Hair-Products/b?node=3760911", "content": "Explore Beauty and Personal Care products on Amazon . Shop makeup, skin care, hair care, nail polish, beauty appliances, men's grooming & more, from best-selling brands like Olay, Neutrogena, Dove, L'Oreal Paris, and more."}
+{"idx": 1, "title": "Amazon beauty - statistics & facts | Statista", "date": "", "ddg_snippet": "Amazon has an advantageous market position over other beauty retailers' websites, as it offers competitive prices and provides a one-stop shopping experience with cross-merchandising.", "subpage_snippet": "", "source": "www.statista.com", "link": "https://www.statista.com/topics/11222/amazon-beauty/", "content": "Amazon has an advantageous market position over other beauty retailers' websites, as it offers competitive prices and provides a one-stop shopping experience with cross-merchandising."}
+{"idx": 2, "title": "Beauty Brands On Amazon Statistics 2025 - Free Yourself", "date": "", "ddg_snippet": "Here are the top 10 beauty brands on Amazon in 2025, based on sales performance, consumer reviews, and market influence: 1 . CeraVe CeraVe leads Amazon's beauty category with a 9.6% market share and over 1.8 million reviews, underscoring its dominance in dermatologist-recommended skincare. 2. Hero Cosmetics", "subpage_snippet": "", "source": "freeyourself.com", "link": "https://freeyourself.com/blogs/news/beauty-brands-on-amazon-statistics", "content": "Here are the top 10 beauty brands on Amazon in 2025, based on sales performance, consumer reviews, and market influence: 1 . CeraVe CeraVe leads Amazon's beauty category with a 9.6% market share and over 1.8 million reviews, underscoring its dominance in dermatologist-recommended skincare. 2. Hero Cosmetics"}
+{"idx": 3, "title": "Amazon Q2 2025: Top 25 Beauty and Personal Care Products", "date": "", "ddg_snippet": "The Top 25 list for Q2 2025 reads like a well-stocked beauty pantry—familiar favorites, proven formulas, and a few strategic surprises that hint at what's next.", "subpage_snippet": "", "source": "beautymatter.com", "link": "https://beautymatter.com/articles/amazon-q2-2025-top-25-beauty-and-personal-care-products", "content": "The Top 25 list for Q2 2025 reads like a well-stocked beauty pantry—familiar favorites, proven formulas, and a few strategic surprises that hint at what's next."}
+{"idx": 4, "title": "Amazon.com: Beauty", "date": "", "ddg_snippet": "Pamper your skin with a selection of luxurious beauty products that deliver visible results, from improved hydration to a more youthful appearance.", "subpage_snippet": "", "source": "www.amazon.com", "link": "https://www.amazon.com/beauty/s?k=beauty", "content": "Pamper your skin with a selection of luxurious beauty products that deliver visible results, from improved hydration to a more youthful appearance."}
+{"idx": 5, "title": "Amazon.com. Spend less. Smile more.", "date": "", "ddg_snippet": "Free shipping on millions of items. Get the best of Shopping and Entertainment with Prime. Enjoy low prices and great deals on the largest selection of everyday essentials and other products, including fashion, home, beauty , electronics, Alexa Devices, sporting goods, toys , automotive, pets, baby, books, video games , musical instruments, office supplies, and more.", "subpage_snippet": "", "source": "www.amazon.com", "link": "https://www.amazon.com/", "content": "Free shipping on millions of items. Get the best of Shopping and Entertainment with Prime. Enjoy low prices and great deals on the largest selection of everyday essentials and other products, including fashion, home, beauty , electronics, Alexa Devices, sporting goods, toys , automotive, pets, baby, books, video games , musical instruments, office supplies, and more."}
+{"idx": 6, "title": "kaiboon0216/Amazon-beauty-products-analysis - GitHub", "date": "", "ddg_snippet": "Amazon - beauty -products-analysis Using data science process to gain business insights from the Amazon beauty datasets and determine the factors that contribute to the high sales of a beauty product Our anaylsis are focused on answering the following questions:", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/kaiboon0216/Amazon-beauty-products-analysis", "content": "Amazon - beauty -products-analysis Using data science process to gain business insights from the Amazon beauty datasets and determine the factors that contribute to the high sales of a beauty product Our anaylsis are focused on answering the following questions:"}
+{"idx": 7, "title": "Amazon.com: Glamazon Beauty", "date": "", "ddg_snippet": "Glamazon Beauty Cosmetics for all skin types To move between items, use your keyboard's up or down arrows.", "subpage_snippet": "", "source": "www.amazon.com", "link": "https://www.amazon.com/stores/page/1FE60412-06FB-4A68-822C-DA83D4150194", "content": "Glamazon Beauty Cosmetics for all skin types To move between items, use your keyboard's up or down arrows."}
+{"idx": 8, "title": "Daily arXiv Papers - 2025-09-18", "date": "", "ddg_snippet": "We train Hala models at 350M, 700M, 1.2B, and 9B parameters, and apply slerp merging to balance Arabic specialization with base-model strengths. On Arabic-centric benchmarks, Hala achieves state-of-the-art results within both the \"nano\" ($\\leq$2B) and \"small\" (7-9B) categories, outperforming their bases.", "subpage_snippet": "", "source": "blog.yueqianlin.com", "link": "https://blog.yueqianlin.com/daily-publication/250918/", "content": "We train Hala models at 350M, 700M, 1.2B, and 9B parameters, and apply slerp merging to balance Arabic specialization with base-model strengths. On Arabic-centric benchmarks, Hala achieves state-of-the-art results within both the \"nano\" ($\\leq$2B) and \"small\" (7-9B) categories, outperforming their bases."}
+{"idx": 9, "title": "Where are the robotic bricklayers? | Hacker News", "date": "", "ddg_snippet": "In the context of automation, the \"robotic revolution\" has already happened between the 4 industrial revolutions - basically, robots exist en masse already, we just don't call them robots. The majority of manufacturing and processing is already hyper optimized to only needing a few overseers to make sure the machines are running smoothly.", "subpage_snippet": "", "source": "news.ycombinator.com", "link": "https://news.ycombinator.com/item?id=28054606", "content": "In the context of automation, the \"robotic revolution\" has already happened between the 4 industrial revolutions - basically, robots exist en masse already, we just don't call them robots. The majority of manufacturing and processing is already hyper optimized to only needing a few overseers to make sure the machines are running smoothly."}

data/sampled_jsons/2QdsjiNXgj_On_a_Connection_Between_Imitation_Learning_and_RLHF_Section_5_density_ratio_DIL_Bregman_d.jsonl

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+{"idx": 0, "title": "Reinforcement learning from human feedback - Wikipedia", "date": "", "ddg_snippet": "Machine learningand data mining. v . t. e. In machine learning , reinforcement learning from human feedback is a technique to align an intelligent agent with human preferences. It involves training a reward model to represent preferences...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Reinforcement_learning_from_human_feedback", "content": "Machine learningand data mining. v . t. e. In machine learning , reinforcement learning from human feedback is a technique to align an intelligent agent with human preferences. It involves training a reward model to represent preferences..."}
+{"idx": 1, "title": "On a Connection Between Imitation Learning and RLHF", "date": "", "ddg_snippet": "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": "openreview.net", "link": "https://openreview.net/forum?id=2QdsjiNXgj", "content": "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": "ON A CONNECTION BETWEEN IMITATION LEARNING ...", "date": "", "ddg_snippet": "by T Xiao · Cited by 14 — This work studies the alignment of large language models with preference data from an imitation learning perspective. We establish a close theoretical ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=2QdsjiNXgj", "content": "by T Xiao · Cited by 14 — This work studies the alignment of large language models with preference data from an imitation learning perspective. We establish a close theoretical ..."}
+{"idx": 3, "title": "On a Connection Between Imitation Learning and RLHF | alphaXiv", "date": "", "ddg_snippet": "Density Ratio Estimation: Instead of learning a reward function, DIL estimates the density ratio between the chosen responses and a reference distribution using Bregman divergence . Objective Function: The DIL objective can be expressed as", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2503.05079v1", "content": "Density Ratio Estimation: Instead of learning a reward function, DIL estimates the density ratio between the chosen responses and a reference distribution using Bregman divergence . Objective Function: The DIL objective can be expressed as"}
+{"idx": 4, "title": "On a Connection Between Imitation Learning and RLHF", "date": "", "ddg_snippet": "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": "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": 5, "title": "[Literature Review] On a Connection Between Imitation Learning ...", "date": "", "ddg_snippet": "The paper titled \" On a Connection Between Imitation Learning and RLHF \" presents a novel perspective on aligning large language models (LLMs) with human preferences through imitation learning .", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/en/review/on-a-connection-between-imitation-learning-and-rlhf", "content": "The paper titled \" On a Connection Between Imitation Learning and RLHF \" presents a novel perspective on aligning large language models (LLMs) with human preferences through imitation learning ."}
+{"idx": 6, "title": "Density Ratio Matching under the Bregman Divergence : A Unified...", "date": "", "ddg_snippet": "On a Connection Between Imitation Learning and RLHF . DIL provides a unified imitation learning perspective on alignment, encompassing existing alignment algorithms as special cases while naturally introducing new variants.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/215704952_Density_Ratio_Matching_under_the_Bregman_Divergence_A_Unified_Framework_of_Density_Ratio_Estimation", "content": "On a Connection Between Imitation Learning and RLHF . DIL provides a unified imitation learning perspective on alignment, encompassing existing alignment algorithms as special cases while naturally introducing new variants."}
+{"idx": 7, "title": "How to Leverage Demonstration Data in Alignment for Large Language", "date": "", "ddg_snippet": "Then, we make use of the connection between imitation learning and density ratio estimation that can be solved with simple classification in an entirely offline fashion.BC is typically cast as KL divergence minimization between the learning policy and expert policy as", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2024.emnlp-main.744.pdf", "content": "Then, we make use of the connection between imitation learning and density ratio estimation that can be solved with simple classification in an entirely offline fashion.BC is typically cast as KL divergence minimization between the learning policy and expert policy as"}
+{"idx": 8, "title": "RLHF on Google Cloud | Google Cloud Blog", "date": "", "ddg_snippet": "What is RLHF ? RLHF tuning consists of two phases: reward modeling and reinforcement learning . 1. Reward modeling For reward modeling, data is collected in the form of comparisons. First off, we feed the same prompt into one or more LLMs to create multiple responses.", "subpage_snippet": "", "source": "cloud.google.com", "link": "https://cloud.google.com/blog/products/ai-machine-learning/rlhf-on-google-cloud", "content": "What is RLHF ? RLHF tuning consists of two phases: reward modeling and reinforcement learning . 1. Reward modeling For reward modeling, data is collected in the form of comparisons. First off, we feed the same prompt into one or more LLMs to create multiple responses."}
+{"idx": 9, "title": "Imitation Learning — Stable Baselines3 2.7.1a3 documentation", "date": "", "ddg_snippet": "You can install imitation with pip install imitation . The imitation documentation has more details on how to use the library, including a quick start guide for the impatient.", "subpage_snippet": "", "source": "stable-baselines3.readthedocs.io", "link": "https://stable-baselines3.readthedocs.io/en/master/guide/imitation.html", "content": "You can install imitation with pip install imitation . The imitation documentation has more details on how to use the library, including a quick start guide for the impatient."}

data/sampled_jsons/2uheUFcFsM_Normalizing_Flows_are_Capable_Generative_Models_Section_2.5_Score_Based_Denoising.jsonl

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+{"idx": 0, "title": "Normalizing Flows are Capable Generative Models - OpenReview", "date": "", "ddg_snippet": "May 1, 2025 · Normalizing Flows (NFs) are likelihood- based models for continuous inputs. They have demonstrated promising results on both density estimation and generative modeling tasks, but have received relatively little attention in recent years.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=2uheUFcFsM", "content": "May 1, 2025 · Normalizing Flows (NFs) are likelihood- based models for continuous inputs. They have demonstrated promising results on both density estimation and generative modeling tasks, but have received relatively little attention in recent years."}
+{"idx": 1, "title": "(PDF) Normalizing Flows are Capable Generative Models", "date": "", "ddg_snippet": "Dec 9, 2024 · Normalizing Flows (NFs) are likelihood- based models for continuous inputs. They have demonstrated promising results on both density estimation and generative modeling tasks, but have received ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/386577118_Normalizing_Flows_are_Capable_Generative_Models", "content": "Dec 9, 2024 · Normalizing Flows (NFs) are likelihood- based models for continuous inputs. They have demonstrated promising results on both density estimation and generative modeling tasks, but have received ..."}
+{"idx": 2, "title": "[2412.06329] Normalizing Flows are Capable Generative Models Images Normalizing Flow Models - GitHub Pages Normalizing Flows are Capable Generative Models Normalizing Flows are Capable Generative Models - OpenReview Normalizing Flows Explained - emergentmind.com (PDF) Normalizing Flows are Capable Generative Models Normalizing Flows are Capable Generative Models - arXiv.org", "date": "", "ddg_snippet": "Dec 9, 2024 · Normalizing Flows (NFs) are likelihood- based models for continuous inputs. They have demonstrated promising results on both density estimation and generative modeling tasks, but have received relatively little attention in recent years. In this work, we demonstrate that NFs are more powerful than previously believed. We present TarFlow: a simple and scalable architecture that enables highly ... View all We continue our study over another type of likelihood based generative models . As before, we assume we are given access to a dataset D of n-dimensional datapoints x. So far we have learned two types of likelihood based generative models : 1. Autoregressive Models : pθ(x)=∏i=1Npθ(xi|x See full list on deepgenerativemodels.github.io In normalizing flows , we wish to map simple distributions (easy to sample and evaluate densities) to complex ones (learned via data). The change of variables formula describe how to evaluate densities of a random variable that is a deterministic transformation from another variable. Change of Variables: Z and X be random variables which are related... See full list on deepgenerativemodels.github.io Apr 22, 2025 · The second reason is the presence of a generative method called Normalizing Flows (NF for short). Like GANs and VAEs, this method lost—by knockout—to diffusion models in the battle for dominance in generative modeling. Still, it’s a very interesting approach with a precise mathematical formulation and an intuitive core. May 1, 2025 · Normalizing Flows (NFs) are likelihood- based models for continuous inputs. They have demonstrated promising results on both density estimation and generative modeling tasks, but have received relatively little attention in recent years. Jul 16, 2025 · Normalizing flows are a family of generative models that construct complex, high-dimensional probability densities as the result of applying a sequence of invertible, differentiable (diffeomorphic) transformations to a simple base distribution, typically a standard normal or a uniform distribution. The fundamental principle underlying normalizing flows is the change-of-variables formula, which ... Dec 9, 2024 · Normalizing Flows (NFs) are likelihood- based models for continuous inputs. They have demonstrated promising results on both density estimation and generative modeling tasks, but have received ... We evaluate these models with a guidance w = 2 and plot the 50K sample FIDs before and after the score based denoising . For visual comparison, we also train two models ImageNet 128x128 models , with the architecture 4-1024-8-8 and noise σ ∈ [0.05, 0.15], respectively.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2412.06329", "content": "Dec 9, 2024 · Normalizing Flows (NFs) are likelihood- based models for continuous inputs. They have demonstrated promising results on both density estimation and generative modeling tasks, but have received relatively little attention in recent years. In this work, we demonstrate that NFs are more powerful than previously believed. We present TarFlow: a simple and scalable architecture that enables highly ... View all We continue our study over another type of likelihood based generative models . As before, we assume we are given access to a dataset D of n-dimensional datapoints x. So far we have learned two types of likelihood based generative models : 1. Autoregressive Models : pθ(x)=∏i=1Npθ(xi|x See full list on deepgenerativemodels.github.io In normalizing flows , we wish to map simple distributions (easy to sample and evaluate densities) to complex ones (learned via data). The change of variables formula describe how to evaluate densities of a random variable that is a deterministic transformation from another variable. Change of Variables: Z and X be random variables which are related... See full list on deepgenerativemodels.github.io Apr 22, 2025 · The second reason is the presence of a generative method called Normalizing Flows (NF for short). Like GANs and VAEs, this method lost—by knockout—to diffusion models in the battle for dominance in generative modeling. Still, it’s a very interesting approach with a precise mathematical formulation and an intuitive core. May 1, 2025 · Normalizing Flows (NFs) are likelihood- based models for continuous inputs. They have demonstrated promising results on both density estimation and generative modeling tasks, but have received relatively little attention in recent years. Jul 16, 2025 · Normalizing flows are a family of generative models that construct complex, high-dimensional probability densities as the result of applying a sequence of invertible, differentiable (diffeomorphic) transformations to a simple base distribution, typically a standard normal or a uniform distribution. The fundamental principle underlying normalizing flows is the change-of-variables formula, which ... Dec 9, 2024 · Normalizing Flows (NFs) are likelihood- based models for continuous inputs. They have demonstrated promising results on both density estimation and generative modeling tasks, but have received ... We evaluate these models with a guidance w = 2 and plot the 50K sample FIDs before and after the score based denoising . For visual comparison, we also train two models ImageNet 128x128 models , with the architecture 4-1024-8-8 and noise σ ∈ [0.05, 0.15], respectively."}
+{"idx": 3, "title": "Normalizing Flow Models - GitHub Pages Normalizing Flows are Capable Generative Models Normalizing Flows are Capable Generative Models - OpenReview Normalizing Flows Explained - emergentmind.com (PDF) Normalizing Flows are Capable Generative Models Normalizing Flows are Capable Generative Models - arXiv.org", "date": "", "ddg_snippet": "We continue our study over another type of likelihood based generative models . As before, we assume we are given access to a dataset D of n-dimensional datapoints x. So far we have learned two types of likelihood based generative models : 1. Autoregressive Models : pθ(x)=∏i=1Npθ(xi|x See full list on deepgenerativemodels.github.io In normalizing flows , we wish to map simple distributions (easy to sample and evaluate densities) to complex ones (learned via data). The change of variables formula describe how to evaluate densities of a random variable that is a deterministic transformation from another variable. Change of Variables: Z and X be random variables which are related... See full list on deepgenerativemodels.github.io Apr 22, 2025 · The second reason is the presence of a generative method called Normalizing Flows (NF for short). Like GANs and VAEs, this method lost—by knockout—to diffusion models in the battle for dominance in generative modeling. Still, it’s a very interesting approach with a precise mathematical formulation and an intuitive core. May 1, 2025 · Normalizing Flows (NFs) are likelihood- based models for continuous inputs. They have demonstrated promising results on both density estimation and generative modeling tasks, but have received relatively little attention in recent years. Jul 16, 2025 · Normalizing flows are a family of generative models that construct complex, high-dimensional probability densities as the result of applying a sequence of invertible, differentiable (diffeomorphic) transformations to a simple base distribution, typically a standard normal or a uniform distribution. The fundamental principle underlying normalizing flows is the change-of-variables formula, which ... Dec 9, 2024 · Normalizing Flows (NFs) are likelihood- based models for continuous inputs. They have demonstrated promising results on both density estimation and generative modeling tasks, but have received ... We evaluate these models with a guidance w = 2 and plot the 50K sample FIDs before and after the score based denoising . For visual comparison, we also train two models ImageNet 128x128 models , with the architecture 4-1024-8-8 and noise σ ∈ [0.05, 0.15], respectively.", "subpage_snippet": "", "source": "deepgenerativemodels.github.io", "link": "https://deepgenerativemodels.github.io/notes/flow/", "content": "We continue our study over another type of likelihood based generative models . As before, we assume we are given access to a dataset D of n-dimensional datapoints x. So far we have learned two types of likelihood based generative models : 1. Autoregressive Models : pθ(x)=∏i=1Npθ(xi|x See full list on deepgenerativemodels.github.io In normalizing flows , we wish to map simple distributions (easy to sample and evaluate densities) to complex ones (learned via data). The change of variables formula describe how to evaluate densities of a random variable that is a deterministic transformation from another variable. Change of Variables: Z and X be random variables which are related... See full list on deepgenerativemodels.github.io Apr 22, 2025 · The second reason is the presence of a generative method called Normalizing Flows (NF for short). Like GANs and VAEs, this method lost—by knockout—to diffusion models in the battle for dominance in generative modeling. Still, it’s a very interesting approach with a precise mathematical formulation and an intuitive core. May 1, 2025 · Normalizing Flows (NFs) are likelihood- based models for continuous inputs. They have demonstrated promising results on both density estimation and generative modeling tasks, but have received relatively little attention in recent years. Jul 16, 2025 · Normalizing flows are a family of generative models that construct complex, high-dimensional probability densities as the result of applying a sequence of invertible, differentiable (diffeomorphic) transformations to a simple base distribution, typically a standard normal or a uniform distribution. The fundamental principle underlying normalizing flows is the change-of-variables formula, which ... Dec 9, 2024 · Normalizing Flows (NFs) are likelihood- based models for continuous inputs. They have demonstrated promising results on both density estimation and generative modeling tasks, but have received ... We evaluate these models with a guidance w = 2 and plot the 50K sample FIDs before and after the score based denoising . For visual comparison, we also train two models ImageNet 128x128 models , with the architecture 4-1024-8-8 and noise σ ∈ [0.05, 0.15], respectively."}
+{"idx": 4, "title": "Normalizing Flows are Capable Generative Models", "date": "", "ddg_snippet": "Apr 22, 2025 · The second reason is the presence of a generative method called Normalizing Flows (NF for short). Like GANs and VAEs, this method lost—by knockout—to diffusion models in the battle for dominance in generative modeling. Still, it’s a very interesting approach with a precise mathematical formulation and an intuitive core.", "subpage_snippet": "", "source": "aiwithmike.substack.com", "link": "https://aiwithmike.substack.com/p/normalizing-flows-are-capable-generative", "content": "Apr 22, 2025 · The second reason is the presence of a generative method called Normalizing Flows (NF for short). Like GANs and VAEs, this method lost—by knockout—to diffusion models in the battle for dominance in generative modeling. Still, it’s a very interesting approach with a precise mathematical formulation and an intuitive core."}
+{"idx": 5, "title": "Normalizing Flows Explained - emergentmind.com", "date": "", "ddg_snippet": "Jul 16, 2025 · Normalizing flows are a family of generative models that construct complex, high-dimensional probability densities as the result of applying a sequence of invertible, differentiable (diffeomorphic) transformations to a simple base distribution, typically a standard normal or a uniform distribution. The fundamental principle underlying normalizing flows is the change-of-variables formula, which ...", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/topics/normalizing-flows", "content": "Jul 16, 2025 · Normalizing flows are a family of generative models that construct complex, high-dimensional probability densities as the result of applying a sequence of invertible, differentiable (diffeomorphic) transformations to a simple base distribution, typically a standard normal or a uniform distribution. The fundamental principle underlying normalizing flows is the change-of-variables formula, which ..."}
+{"idx": 6, "title": "Normalizing Flows are Capable Generative Models - arXiv.org", "date": "", "ddg_snippet": "We evaluate these models with a guidance w = 2 and plot the 50K sample FIDs before and after the score based denoising . For visual comparison, we also train two models ImageNet 128x128 models , with the architecture 4-1024-8-8 and noise σ ∈ [0.05, 0.15], respectively.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.06329v3", "content": "We evaluate these models with a guidance w = 2 and plot the 50K sample FIDs before and after the score based denoising . For visual comparison, we also train two models ImageNet 128x128 models , with the architecture 4-1024-8-8 and noise σ ∈ [0.05, 0.15], respectively."}
+{"idx": 7, "title": "Normalizing Flows are Capable Generative Models", "date": "", "ddg_snippet": "Normalizing Flows (NFs) are likelihood- based models for continuous inputs.Second, we identify a post-training score based denoising technique that allows one to remove the noise portion of the generated samples.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.06329v2", "content": "Normalizing Flows (NFs) are likelihood- based models for continuous inputs.Second, we identify a post-training score based denoising technique that allows one to remove the noise portion of the generated samples."}
+{"idx": 8, "title": "Normalizing Flows are Capable Generative Models | alphaXiv", "date": "", "ddg_snippet": "Abstract: Normalizing Flows (NFs) are likelihood- based models for continuous inputs. They have demonstrated promising results on both density estimation and generative modeling tasks, but have received relatively little attention in recent years.", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2412.06329v1", "content": "Abstract: Normalizing Flows (NFs) are likelihood- based models for continuous inputs. They have demonstrated promising results on both density estimation and generative modeling tasks, but have received relatively little attention in recent years."}
+{"idx": 9, "title": "Normalizing Flows (NFs)", "date": "", "ddg_snippet": "Normalizing flows are likelihood- based generative models that convert simple distributions into complex data densities using sequences of invertible, differentiable transformations.", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/topics/normalizing-flows-nfs", "content": "Normalizing flows are likelihood- based generative models that convert simple distributions into complex data densities using sequences of invertible, differentiable transformations."}

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data/sampled_jsons/32717_ImagineFSL_Self-Supervised_Pretraining_Table_1_Flowers_16-shot.jsonl

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+{"idx": 0, "title": "Transformer (deep learning architecture) - Wikipedia", "date": "", "ddg_snippet": "Transformers typically are first pretrained by self - supervised learning on a large generic dataset, followed by supervised fine-tuning on a small task-specific dataset.Tasks for pretraining and fine-tuning commonly include", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Transformer_(deep_learning_architecture)", "content": "Transformers typically are first pretrained by self - supervised learning on a large generic dataset, followed by supervised fine-tuning on a small task-specific dataset.Tasks for pretraining and fine-tuning commonly include"}
+{"idx": 1, "title": "ImagineFSL: Self-Supervised Pretraining Matters on Imagined ...", "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": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/poster/32717", "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."}
+{"idx": 2, "title": "ImagineFSL: Self-Supervised Pretraining Matters on Imagined ...", "date": "", "ddg_snippet": "We find that, compared to no pretraining , both supervised and self-supervised pretraining are beneficial, with the latter pro-viding better performance. Based on on this finding, we propose an improved self-supervised method tailored for few- shot scenarios, enhancing the transferability of repre-sentations from synthetic to real image domains.", "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": "We find that, compared to no pretraining , both supervised and self-supervised pretraining are beneficial, with the latter pro-viding better performance. Based on on this finding, we propose an improved self-supervised method tailored for few- shot scenarios, enhancing the transferability of repre-sentations from synthetic to real image domains."}
+{"idx": 3, "title": "ImagineFSL/README.md at main · HaoyuanYang-2023 ... - 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": "ImagineFSL: Self-Supervised Pretraining Matters on Imagined ...", "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": 5, "title": "[2407.18125] Self-supervised pre-training with diffusion ...", "date": "", "ddg_snippet": "Jul 25, 2024 · To our knowledge, this work represents the first application of diffusion models for self-supervised learning in landmark detection, which may offer a valuable pre-training approach in few- shot regimes, for mitigating data scarcity.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2407.18125", "content": "Jul 25, 2024 · To our knowledge, this work represents the first application of diffusion models for self-supervised learning in landmark detection, which may offer a valuable pre-training approach in few- shot regimes, for mitigating data scarcity."}
+{"idx": 6, "title": "Few-shot intent detection with self-supervised pretraining ...", "date": "", "ddg_snippet": "Nov 1 , 2024 · Our method is closely related to few- shot learning, few- shot intent detection tasks, prototypical networks, and self-supervised multi-task pretraining . In this section, we review each of these related efforts in turn.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0031320324003923", "content": "Nov 1 , 2024 · Our method is closely related to few- shot learning, few- shot intent detection tasks, prototypical networks, and self-supervised multi-task pretraining . In this section, we review each of these related efforts in turn."}
+{"idx": 7, "title": "[2408.13385] MICM: Rethinking Unsupervised Pretraining for...", "date": "", "ddg_snippet": "Unsupervised Few- Shot Learning (U- FSL ) seeks to bridge this divide by reducing reliance on annotated datasets during initial training phases.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2408.13385", "content": "Unsupervised Few- Shot Learning (U- FSL ) seeks to bridge this divide by reducing reliance on annotated datasets during initial training phases."}
+{"idx": 8, "title": "Stable Diffusion XL — Nunchaku 1 .0. 1 documentation", "date": "", "ddg_snippet": "torch.bfloat 16 , variant=\"fp 16 \" 12 ).to(\"cuda\") 13 prompt = \"A cinematic shot of a baby racoon wearing an intricate italian priest robe.\"", "subpage_snippet": "", "source": "nunchaku.tech", "link": "https://nunchaku.tech/docs/nunchaku/usage/sdxl.html", "content": "torch.bfloat 16 , variant=\"fp 16 \" 12 ).to(\"cuda\") 13 prompt = \"A cinematic shot of a baby racoon wearing an intricate italian priest robe.\""}
+{"idx": 9, "title": "[PDF] Self - Supervised Visual Feature Learning... | Semantic Scholar", "date": "", "ddg_snippet": "With the self - supervised pre - trained 3DRotNet from large datasets, the recognition accuracy is boosted up by 20.4% on UCF101 and 16 .7% on HMDB51 respectively, compared to the models trained from scratch.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Self-Supervised-Visual-Feature-Learning-With-Deep-A-Jing-Tian/4c94ee7df6bc2bfcac76703be4f059a79010f7e5", "content": "With the self - supervised pre - trained 3DRotNet from large datasets, the recognition accuracy is boosted up by 20.4% on UCF101 and 16 .7% on HMDB51 respectively, compared to the models trained from scratch."}

data/sampled_jsons/33113_Deterministic-to-Stochastic_Diverse_Latent_Feature_Mapping_for_Human_Motion_Synthesis_CVPR_202.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": "CVPR Poster Deterministic - to - Stochastic Diverse Latent Feature ...", "date": "", "ddg_snippet": "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.", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/poster/33113", "content": "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."}
+{"idx": 2, "title": "Deterministic - to - Stochastic Diverse Latent Feature Mapping for ...", "date": "", "ddg_snippet": "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.", "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": "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."}
+{"idx": 3, "title": "10 Papers Accepted at CVPR 2025", "date": "", "ddg_snippet": "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": "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": 4, "title": "Deterministic - to - Stochastic Diverse Latent Feature Mapping for ...", "date": "", "ddg_snippet": "Abstract: 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.", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/abs/2505.00998", "content": "Abstract: 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."}
+{"idx": 5, "title": "Contribute to 52CV/ CVPR - 2025 -Papers development by creating an...", "date": "", "ddg_snippet": "Deterministic - to - Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis .LAL: Enhancing 3D Human Motion Prediction with Latency -aware Auxiliary Learning.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/52CV/CVPR-2025-Papers", "content": "Deterministic - to - Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis .LAL: Enhancing 3D Human Motion Prediction with Latency -aware Auxiliary Learning."}
+{"idx": 6, "title": "CVPR 2025 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..."}
+{"idx": 7, "title": "UNIF: United Neural Implicit Functions for Clothed Human ...", "date": "", "ddg_snippet": "Computer Vision and Pattern Recognition . [4] Deterministic - to - Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis .", "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": "Computer Vision and Pattern Recognition . [4] Deterministic - to - Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis ."}
+{"idx": 8, "title": "Meet Seed at CVPR 2025 : 12 Papers Accepted and 2 Talks", "date": "", "ddg_snippet": "The IEEE/CVF Conference on Computer Vision and Pattern Recognition ( CVPR ) 2025 was held from June 11 to 15 in Nashville, Tennessee, USA.", "subpage_snippet": "", "source": "research.doubao.com", "link": "https://research.doubao.com/en/blog/meet-seed-at-cvpr-2025-12-papers-accepted-and-2-talks", "content": "The IEEE/CVF Conference on Computer Vision and Pattern Recognition ( CVPR ) 2025 was held from June 11 to 15 in Nashville, Tennessee, USA."}
+{"idx": 9, "title": "Awesome Human Motion | Awesome- Human - Motion", "date": "", "ddg_snippet": "( CVPR 2025 ) DSDFM: Deterministic - to - Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis , Hua et al.", "subpage_snippet": "", "source": "foruck.github.io", "link": "https://foruck.github.io/Awesome-Human-Motion/", "content": "( CVPR 2025 ) DSDFM: Deterministic - to - Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis , Hua et al."}

data/sampled_jsons/33266_UVGS-_Reimagining_Unstructured_3D_Gaussian_Splatting_using_UV_Mapping_'Branched_mapping_layers.jsonl

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+{"idx": 0, "title": "(PDF) UVGS : Reimagining Unstructured 3 D Gaussian Splatting ...", "date": "", "ddg_snippet": "multiple layers of UV maps , where each UVGS pixel holds. the top-K opacity values of 3DGS points. Branched mapping layers : The rationale behind using . branched mapping layers in both forward and reverse map -. ping networks is to prevent the incompatibility issues aris", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/388685707_UVGS_Reimagining_Unstructured_3D_Gaussian_Splatting_using_UV_Mapping", "content": "multiple layers of UV maps , where each UVGS pixel holds. the top-K opacity values of 3DGS points. Branched mapping layers : The rationale behind using . branched mapping layers in both forward and reverse map -. ping networks is to prevent the incompatibility issues aris"}
+{"idx": 1, "title": "UVGS: Reimagining Unstructured 3D Gaussian Splatting using UV ... UVGS: Reimagining Unstructured 3D Gaussian Splatting UVGS: Reimagining Unstructured 3D Gaussian Splatting using UV ... Graphics Programming weekly - Issue 378 - February 9th, 2025 ... UVGS: Reimagining Unstructured 3D Gaussian Splatting using UV ... CVPR 2025 Open Access Repository UVGS : Reimagining Unstructured 3D Gaussian Splatting using UV Mapp… UVGS : Reimagining Unstructured 3D Gaussian Splatting (PDF) UVGS : Reimagining Unstructured 3D Gaussian Splatting using U… (PDF) UVGS : Reimagining Unstructured 3D Gaussian Splatting using U… (PDF) UVGS : Reimagining Unstructured 3D Gaussian Splatting using U… UVGS : Reimagining Unstructured 3D Gaussian Splatting using UV Mapp… UVGS: Reimagining Unstructured 3D Gaussian Splatting using UV ...", "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. 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. 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. Feb 9, 2025 · UVGS : Reimagining Unstructured 3D Gaussian Splatting using UV Mapping The paper introduces turning 3D Gaussian Splatting from unstructured into structured structure using spherical UV mapping 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... 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. 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. Who wrote uvgs reimagining unstructured 3D Gaussian splatting using UV mapping? author = { Rai, Aashish and Wang , Dilin and Jain, Mihir and Sarafianos, Nikolaos and Chen, Kefan and Sridhar, Srinath and Prakash, Aayush}, title = {UVGS: Reimagining Unstructured 3D Gaussian Splatting using UV Mapping}, booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)}, Can a single Gaussian be represented by a spherical mapping point? Thus, a single Gaussian can be represented by 3.1 . Spherical Mapping points using neural networks. T o address this issue, we in- ance issue for faster and better feature extraction. W e pro- points and well structured. We prefer spherical mapping as tions for objects that extend far in the Z-direction. canonical space. Which opacity value can be represented by a single Gaussian? an opacity value oi∈R. In practice, the color is represented RGB values. Thus, a single Gaussian can be represented by 3.1 . Spherical Mapping points using neural networks. T o address this issue, we in- ance issue for faster and better feature extraction. W e pro- points and well structured. We prefer spherical mapping as What is the difference between get3d and gaussiancube? While Get3D and GaussianCube achieve higher resolution , they suffer from 3D inconsistency, numerous artifacts, and lack richness in 3D detail. In contrast, our method generates high-quality, high-resolution objects that are 3D consistent with sharp, well-defined edges. Can uvgs be treated as a typical RGB image? The compressed UVGS can be treated as typical RGB images . Remarkably, we discover that typical VAEs trained with latent diffusion models can directly generalize to this new representation without additional training. Our novel representation makes it effortless to leverage foundational 2D models, such as diffusion models, to directly model 3DGS. Feb 3, 2025 · 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": "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. 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. 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. Feb 9, 2025 · UVGS : Reimagining Unstructured 3D Gaussian Splatting using UV Mapping The paper introduces turning 3D Gaussian Splatting from unstructured into structured structure using spherical UV mapping 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... 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. 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. Who wrote uvgs reimagining unstructured 3D Gaussian splatting using UV mapping? author = { Rai, Aashish and Wang , Dilin and Jain, Mihir and Sarafianos, Nikolaos and Chen, Kefan and Sridhar, Srinath and Prakash, Aayush}, title = {UVGS: Reimagining Unstructured 3D Gaussian Splatting using UV Mapping}, booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)}, Can a single Gaussian be represented by a spherical mapping point? Thus, a single Gaussian can be represented by 3.1 . Spherical Mapping points using neural networks. T o address this issue, we in- ance issue for faster and better feature extraction. W e pro- points and well structured. We prefer spherical mapping as tions for objects that extend far in the Z-direction. canonical space. Which opacity value can be represented by a single Gaussian? an opacity value oi∈R. In practice, the color is represented RGB values. Thus, a single Gaussian can be represented by 3.1 . Spherical Mapping points using neural networks. T o address this issue, we in- ance issue for faster and better feature extraction. W e pro- points and well structured. We prefer spherical mapping as What is the difference between get3d and gaussiancube? While Get3D and GaussianCube achieve higher resolution , they suffer from 3D inconsistency, numerous artifacts, and lack richness in 3D detail. In contrast, our method generates high-quality, high-resolution objects that are 3D consistent with sharp, well-defined edges. Can uvgs be treated as a typical RGB image? The compressed UVGS can be treated as typical RGB images . Remarkably, we discover that typical VAEs trained with latent diffusion models can directly generalize to this new representation without additional training. Our novel representation makes it effortless to leverage foundational 2D models, such as diffusion models, to directly model 3DGS. Feb 3, 2025 · 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": 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.", "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."}
+{"idx": 3, "title": "UVGS: Reimagining Unstructured 3D Gaussian Splatting using UV ...", "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.", "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": "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": 4, "title": "UVGS: Reimagining Unstructured 3D Gaussian Splatting using UV ...", "date": "", "ddg_snippet": "Feb 3, 2025 · 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": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/UVGS:-Reimagining-Unstructured-3D-Gaussian-using-UV-Rai-Wang/10f0d88160981f85fd6c270e65496ec5948a98d2", "content": "Feb 3, 2025 · 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": 5, "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": 6, "title": "UVGS : Reimagining Unstructured 3 D Gaussian Splatting using UV ...", "date": "", "ddg_snippet": "OverviewIntroduces UV mapping techniques to enhance 3 D Gaussian Splatting Introduces spherical and cylindrical UV mapping for different object types", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/uvgs-reimagining-unstructured-3d-gaussian-splatting-using", "content": "OverviewIntroduces UV mapping techniques to enhance 3 D Gaussian Splatting Introduces spherical and cylindrical UV mapping for different object types"}
+{"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."}

data/sampled_jsons/33381_Video_ColBERT_Contextualized_Late_Interaction_for_Text-to-Video_Retrieval.jsonl

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+{"idx": 0, "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 . Video-ColBERT is built upon 3 main components: a fine-grained spatial and ...", "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 . Video-ColBERT is built upon 3 main components: a fine-grained spatial and ..."}
+{"idx": 1, "title": "PDF Video-ColBERT: Contextualized Late Interaction for Text-to-Video Retrieval", "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 indi-vidual, yet compatible, representations for encoding video content.", "subpage_snippet": "", "source": "www.celsodemelo.net", "link": "http://www.celsodemelo.net/static/publications/Video_ColBERT_CVPR_2025_DistA.pdf", "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 indi-vidual, yet compatible, representations for encoding video content."}
+{"idx": 2, "title": "GitHub - yogesh-iitj/Video-ColBERT", "date": "", "ddg_snippet": "This repository implements Video-ColBERT , a contextualized late interaction model for text-to-video retrieval . Video-ColBERT performs fine-grained token-wise interactions between text queries and video content. This script demonstrates the model with random inputs, showing how similarity matrices ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/yogesh-iitj/Video-ColBERT", "content": "This repository implements Video-ColBERT , a contextualized late interaction model for text-to-video retrieval . Video-ColBERT performs fine-grained token-wise interactions between text queries and video content. This script demonstrates the model with random inputs, showing how similarity matrices ..."}
+{"idx": 3, "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 . 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 Retrieval", "date": "", "ddg_snippet": "Video-ColBERT offers an advanced approach to text-to-video retrieval by refining interaction strategies and achieving competitive performance, opening new pathways for multimodal retrieval research.", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/papers/2503.19009", "content": "Video-ColBERT offers an advanced approach to text-to-video retrieval by refining interaction strategies and achieving competitive performance, opening new pathways for multimodal retrieval research."}
+{"idx": 5, "title": "Video-ColBERT: Contextualized Late Interaction for Text-to-Video Retrieval", "date": "", "ddg_snippet": "Abstract 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 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.19009", "content": "Abstract 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 ..."}
+{"idx": 6, "title": "PDF Video-ColBERT: Contextualized Late Interaction for Text-to-Video Retrieval", "date": "", "ddg_snippet": "Abstract In this work, we tackle the problem of text-to-video re-trieval (T2VR). Inspired by the success of late interaction techniques in text -document, text -image, and text - video re-trieval , our approach, Video-ColBERT , introduces a simple and eficient mechanism for fine-grained similarity assess-ment between queries and videos .", "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": "Abstract In this work, we tackle the problem of text-to-video re-trieval (T2VR). Inspired by the success of late interaction techniques in text -document, text -image, and text - video re-trieval , our approach, Video-ColBERT , introduces a simple and eficient mechanism for fine-grained similarity assess-ment between queries and videos ."}
+{"idx": 7, "title": "Video-ColBERT: Contextualized Late Interaction for Text-to-Video Retrieval", "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": 8, "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 ."}
+{"idx": 9, "title": "Alexander Martin", "date": "", "ddg_snippet": "Inspired by the success of late interaction techniques in text -document, text -image, and text - video re - trieval, our approach, Video-ColBERT , introduces a simple and efficient mechanism for fine-grained similarity assess- ment between queries and videos .", "subpage_snippet": "", "source": "alexmartin1722.github.io", "link": "https://alexmartin1722.github.io/", "content": "Inspired by the success of late interaction techniques in text -document, text -image, and text - video re - trieval, our approach, Video-ColBERT , introduces a simple and efficient mechanism for fine-grained similarity assess- ment between queries and videos ."}

data/sampled_jsons/33381_Video_ColBERT_Table_3_MMSF_MMSV_R@1_scores_year_2023.jsonl

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+{"idx": 0, "title": "Contextualized Late Interaction for Text-to-Video Retrieval", "date": "", "ddg_snippet": "Video -ColBERT is a text-to-video retrieval method using fine-grained spatial and temporal token-wise interaction, query and visual expansions, and a dual ...", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/poster/33381", "content": "Video -ColBERT is a text-to-video retrieval method using fine-grained spatial and temporal token-wise interaction, query and visual expansions, and a dual ..."}
+{"idx": 1, "title": "PDF Video-ColBERT: Contextualized Late Interaction for Text-to-Video Retrieval", "date": "", "ddg_snippet": "VIDEO-COLBERT (depicted in Fig. 2) has 3 main aspects: (i) fine-grained spa-tial and temporal interaction, performing MMS on both in-dependent frames and their contextualized representations, (ii) query and visual expansion tokens which allow for addi-tional information to be encoded for abstract queries and for additional high-level temporal ...", "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": "VIDEO-COLBERT (depicted in Fig. 2) has 3 main aspects: (i) fine-grained spa-tial and temporal interaction, performing MMS on both in-dependent frames and their contextualized representations, (ii) query and visual expansion tokens which allow for addi-tional information to be encoded for abstract queries and for additional high-level temporal ..."}
+{"idx": 2, "title": "GitHub - stanford-futuredata/ColBERT: ColBERT: state-of-the-art neural ...", "date": "", "ddg_snippet": "Figure 1: ColBERT's late interaction, efficiently scoring the fine-grained similarity between a queries and a passage. As Figure 1 illustrates, ColBERT relies on fine-grained contextual late interaction: it encodes each passage into a matrix of token-level embeddings (shown above in blue). Then at search time, it embeds every query into another matrix (shown in green) and efficiently finds ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/stanford-futuredata/ColBERT", "content": "Figure 1: ColBERT's late interaction, efficiently scoring the fine-grained similarity between a queries and a passage. As Figure 1 illustrates, ColBERT relies on fine-grained contextual late interaction: it encodes each passage into a matrix of token-level embeddings (shown above in blue). Then at search time, it embeds every query into another matrix (shown in green) and efficiently finds ..."}
+{"idx": 3, "title": "ColBERT: A complete guide - Medium", "date": "", "ddg_snippet": "Using Eq and Ed, ColBERT computes the relevance score between q and d via late interaction, which is defined as a summation of maximum similarity (MaxSim) operators.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@varun030403/colbert-a-complete-guide-1552468335ae", "content": "Using Eq and Ed, ColBERT computes the relevance score between q and d via late interaction, which is defined as a summation of maximum similarity (MaxSim) operators."}
+{"idx": 4, "title": "Exciting Research Alert: Video-ColBERT - A Breakthrough in Text-to ...", "date": "", "ddg_snippet": "Fine-grained spatial and temporal token-wise interaction - Unlike traditional approaches that compress videos into single vectors, Video-ColBERT performs MeanMaxSim (MMS) operations on both ...", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/posts/singhsidhukuldeep_exciting-research-alert-video-colbert-activity-7336927383549071360-eAP7", "content": "Fine-grained spatial and temporal token-wise interaction - Unlike traditional approaches that compress videos into single vectors, Video-ColBERT performs MeanMaxSim (MMS) operations on both ..."}
+{"idx": 5, "title": "Video-ColBERT: Contextualized Late Interaction for Text-to-Video Retrieval", "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": "arxiv.org", "link": "https://arxiv.org/abs/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": 6, "title": "minimal_colbert_usage_example.ipynb - Colab", "date": "", "ddg_snippet": "This notebook gives you a minimal usage example of downloading our ColBERT checkpoint to encode passages and queries to create a (term-x-term dot-product & max-pool & sum) score of their relevance.", "subpage_snippet": "", "source": "colab.research.google.com", "link": "https://colab.research.google.com/github/sebastian-hofstaetter/neural-ranking-kd/blob/master/minimal_colbert_usage_example.ipynb", "content": "This notebook gives you a minimal usage example of downloading our ColBERT checkpoint to encode passages and queries to create a (term-x-term dot-product & max-pool & sum) score of their relevance."}
+{"idx": 7, "title": "The Late Show with Stephen Colbert - YouTube", "date": "", "ddg_snippet": "Welcome to the official YouTube channel for \"The Late Show with Stephen Colbert \"! Weeknights at 11:35pm/10:35c. Welcome to the official YouTube channel for \"The Late Show with Stephen Colbert \"!", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/c/ColbertLateShow/videos", "content": "Welcome to the official YouTube channel for \"The Late Show with Stephen Colbert \"! Weeknights at 11:35pm/10:35c. Welcome to the official YouTube channel for \"The Late Show with Stephen Colbert \"!"}
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+{"idx": 9, "title": "PDF Video-ColBERT: Contextualized Late Interaction for Text-to-Video ...", "date": "", "ddg_snippet": "We report num- bers pertaining to both offline index creation and query-time ranking. During video indexing, we see that MMS FVis no more expensive than MMS V, as they involve identical for- ward passes through the video encoder. At query-time, de- spite MMS FVinvolving more dot products, latency is vir- tually the same as the single-level ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/supplemental/Reddy_Video-ColBERT_Contextualized_Late_CVPR_2025_supplemental.pdf", "content": "We report num- bers pertaining to both offline index creation and query-time ranking. During video indexing, we see that MMS FVis no more expensive than MMS V, as they involve identical for- ward passes through the video encoder. At query-time, de- spite MMS FVinvolving more dot products, latency is vir- tually the same as the single-level ..."}

data/sampled_jsons/33775_Instant_Gaussian_Stream_Equation_6_interpolated_motion_feature.jsonl

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+{"idx": 1, "title": "GitHub - yjb 6 /IGS: [CVPR25 Highlight] Instant Gaussian Stream : Fast...", "date": "", "ddg_snippet": "This repository contains the official authors implementation associated with the paper: Instant Gaussian Stream : Fast and Generalizable Streaming of Dynamic Scene Reconstruction via Gaussian Splatting.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/yjb6/IGS", "content": "This repository contains the official authors implementation associated with the paper: Instant Gaussian Stream : Fast and Generalizable Streaming of Dynamic Scene Reconstruction via Gaussian Splatting."}
+{"idx": 2, "title": "A particle executes a simple harmonic motion of time period T. Find th", "date": "", "ddg_snippet": "Step 1: Understand the Motion In simple harmonic motion , the displacement x of the particle from the mean position can be described by the equation : x(t)=Asin(ωt) where: - A is the amplitude, - ω is the angular frequency, given by ω=2πT (with T being the time period).", "subpage_snippet": "", "source": "www.doubtnut.com", "link": "https://www.doubtnut.com/qna/9527437", "content": "Step 1: Understand the Motion In simple harmonic motion , the displacement x of the particle from the mean position can be described by the equation : x(t)=Asin(ωt) where: - A is the amplitude, - ω is the angular frequency, given by ω=2πT (with T being the time period)."}
+{"idx": 3, "title": "Bayes theorem, the geometry of changing beliefs - 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=HZGCoVF3YvM", "content": "About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How YouTube works Test new features ."}
+{"idx": 4, "title": "Full text of \"1 Fundamentals Of Finite Element Analysis David V. Hutton&quo...", "date": "", "ddg_snippet": "The form of Equation 6 .53 suggests that expression of the interpolation functions in terms of the nodal coordinates is algebraically complex. Fortunately, the complexity can be reduced by a more judicious choice of coordinates.", "subpage_snippet": "", "source": "archive.org", "link": "https://archive.org/stream/1FundamentalsOfFiniteElementAnalysisDavidV.Hutton/1_Fundamentals+of+Finite+Element+Analysis+-+David+V.+Hutton_djvu.txt", "content": "The form of Equation 6 .53 suggests that expression of the interpolation functions in terms of the nodal coordinates is algebraically complex. Fortunately, the complexity can be reduced by a more judicious choice of coordinates."}
+{"idx": 5, "title": "8 Best Noise Reduction Plugins 2025 (And Freebies)", "date": "", "ddg_snippet": "The “Suggest” feature is a clever touch; it analyzes the audio and gives you a solid starting point. I don’t always keep the exact settings it proposes, but it saves me time and gets me in the ballpark quickly. For fast turnaround jobs, this feature has been gold.", "subpage_snippet": "", "source": "integraudio.com", "link": "https://integraudio.com/8-noise-reduction-plugins/", "content": "The “Suggest” feature is a clever touch; it analyzes the audio and gives you a solid starting point. I don’t always keep the exact settings it proposes, but it saves me time and gets me in the ballpark quickly. For fast turnaround jobs, this feature has been gold."}
+{"idx": 6, "title": "Free Image Background Remover | Adobe Express", "date": "", "ddg_snippet": "See what people are saying about Adobe Express. I enjoy using the remove background feature in Adobe Express during my product launches! In just a few steps, I’m able to quickly remove the original background and add a fun one with my product.\"", "subpage_snippet": "", "source": "www.adobe.com", "link": "https://www.adobe.com/express/feature/image/remove-background", "content": "See what people are saying about Adobe Express. I enjoy using the remove background feature in Adobe Express during my product launches! In just a few steps, I’m able to quickly remove the original background and add a fun one with my product.\""}
+{"idx": 7, "title": "Online Frame Rate Converter Personal – AI Upscales Video to 60fps...", "date": "", "ddg_snippet": "Lower FPS to have smaller files.Natural-looking interpolated motion using AI.", "subpage_snippet": "", "source": "pixelfox.ai", "link": "https://pixelfox.ai/blog/online-frame-rate-converter-personal-ai-upscales-video-to-60fps-120fps-and-144fps-with-pixelfox", "content": "Lower FPS to have smaller files.Natural-looking interpolated motion using AI."}
+{"idx": 8, "title": "Blur Image Backgrounds for Free with AI Ease", "date": "", "ddg_snippet": "Instantly blur background of your photo using AI Ease online free tool. Upload, and seamlessly download your image with blurred background in seconds.Blur Effects. Motion .", "subpage_snippet": "", "source": "www.aiease.ai", "link": "https://www.aiease.ai/app/blur-image-background", "content": "Instantly blur background of your photo using AI Ease online free tool. Upload, and seamlessly download your image with blurred background in seconds.Blur Effects. Motion ."}
+{"idx": 9, "title": "iPhone Black Screen During Call: 7 Ways to Stop it", "date": "", "ddg_snippet": "reduce motion feature Another potential solution is to disable the Reduce Motion feature . Here is how to do it: 1) Open the Settings app on your iPhone.", "subpage_snippet": "", "source": "mspoweruser.com", "link": "https://mspoweruser.com/iphone-black-screen-during-call/", "content": "reduce motion feature Another potential solution is to disable the Reduce Motion feature . Here is how to do it: 1) Open the Settings app on your iPhone."}

data/sampled_jsons/33775_Instant_Gaussian_Stream_Fast_and_Generalizable_Streaming_of_Dynamic_Scene_Reconstruction_via_G.jsonl

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+{"idx": 0, "title": "Instant Gaussian Stream : Fast and Generalizable Streaming of ...", "date": "", "ddg_snippet": "Triplane meets gaussian splatting : Fast and generalizable single-view 3d reconstruction with transformers. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10324–10335, 2024.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.16979", "content": "Triplane meets gaussian splatting : Fast and generalizable single-view 3d reconstruction with transformers. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10324–10335, 2024."}
+{"idx": 1, "title": "CVPR Poster Instant Gaussian Stream : Fast and Generalizable ...", "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...", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/poster/33775", "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..."}
+{"idx": 2, "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": 3, "title": "GitHub - yjb6/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 } Streaming Reconstruction . Data Preparation. Step 1: Prepare Inputs.", "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 } Streaming Reconstruction . Data Preparation. Step 1: Prepare Inputs."}
+{"idx": 4, "title": "Instant Gaussian Stream : Fast and Generalizable Streaming of ...", "date": "", "ddg_snippet": "Yan_ Instant _ Gaussian _ Stream _ Fast _ and _ Generalizable _ Streaming _ of _ Dynamic _ Scene @CVPR2025@CVF.In this paper, we propose Instant Gaussian Stream (IGS), a fast and generalizable streaming framework, to address these issues.", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/venue/Yan_Instant_Gaussian_Stream_Fast_and_Generalizable_Streaming_of_Dynamic_Scene@CVPR2025@CVF", "content": "Yan_ Instant _ Gaussian _ Stream _ Fast _ and _ Generalizable _ Streaming _ of _ Dynamic _ Scene @CVPR2025@CVF.In this paper, we propose Instant Gaussian Stream (IGS), a fast and generalizable streaming framework, to address these issues."}
+{"idx": 5, "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. It achieves per-frame reconstruction times of 2-3 seconds while maintaining high rendering quality by combining a motion...", "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. It achieves per-frame reconstruction times of 2-3 seconds while maintaining high rendering quality by combining a motion..."}
+{"idx": 6, "title": "Papers by Rui Peng with links to code and results.", "date": "", "ddg_snippet": "Instant Gaussian Stream : Fast and Generalizable Streaming of Dynamic Scene Reconstruction via Gaussian Splatting .In this paper, we propose SCGaussian, a Structure Consistent Gaussian Splatting method using matching priors to learn 3D consistent scene structure.", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/search?q=author:Rui+Peng", "content": "Instant Gaussian Stream : Fast and Generalizable Streaming of Dynamic Scene Reconstruction via Gaussian Splatting .In this paper, we propose SCGaussian, a Structure Consistent Gaussian Splatting method using matching priors to learn 3D consistent scene structure."}
+{"idx": 7, "title": "3D Gaussian Splatting - Explained! - YouTube", "date": "", "ddg_snippet": "Gaussian Splatting is taking the world of 3D graphics by storm. Learn how this revolutionary technique can render photoreal scenes in real-time for cutting-e...", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=sQcrZHvrEnU", "content": "Gaussian Splatting is taking the world of 3D graphics by storm. Learn how this revolutionary technique can render photoreal scenes in real-time for cutting-e..."}
+{"idx": 8, "title": "Free 3D Gaussian Splatting Tool | Polycam", "date": "", "ddg_snippet": "Gaussian splatting can effectively render shiny, reflective objects as well as long and thin details . Splatting also excels at capturing large, expansive spaces without sacrificing smaller details . With Polycam, you can enjoy additional features including", "subpage_snippet": "", "source": "poly.cam", "link": "https://poly.cam/tools/gaussian-splatting", "content": "Gaussian splatting can effectively render shiny, reflective objects as well as long and thin details . Splatting also excels at capturing large, expansive spaces without sacrificing smaller details . With Polycam, you can enjoy additional features including"}
+{"idx": 9, "title": "MVSGS: Gaussian splatting radiation field enhancement using...", "date": "", "ddg_snippet": "MVSGaussian: Fast Generalizable Gaussian Splatting Reconstruction from Multi-View Stereo.For more complex scene representations, appropriate high disparity Gaussian scaling is necessary because it opens up the possibility of more efficient and detailed scene representation.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s40747-024-01691-x", "content": "MVSGaussian: Fast Generalizable Gaussian Splatting Reconstruction from Multi-View Stereo.For more complex scene representations, appropriate high disparity Gaussian scaling is necessary because it opens up the possibility of more efficient and detailed scene representation."}

data/sampled_jsons/33775_Instant_Gaussian_Stream_Table_2_Meeting_Room_storage_MB_values.jsonl

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+{"idx": 0, "title": "Instant Gaussian Stream : Fast and Generalizable Streaming of...", "date": "", "ddg_snippet": "per, we propose Instant Gaussian Stream (IGS), a fast and view images is a valuable area of research, with appliFor the Meeting Room dataset, we train the Gaus - sians of the first frame using 15,000 iterations, compressing the number of Gaussians at 7,000 iterations.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.16979", "content": "per, we propose Instant Gaussian Stream (IGS), a fast and view images is a valuable area of research, with appliFor the Meeting Room dataset, we train the Gaus - sians of the first frame using 15,000 iterations, compressing the number of Gaussians at 7,000 iterations."}
+{"idx": 1, "title": "7 Ways To Make A Small Living Room Feel Larger Instantly !", "date": "", "ddg_snippet": "If you have zero room for a table add a narrow console table behind the sofa to display decor on, add a decorative box for your media remotes and store a few books! Our living room .", "subpage_snippet": "", "source": "www.settingforfour.com", "link": "https://www.settingforfour.com/make-small-living-room-feel-larger/", "content": "If you have zero room for a table add a narrow console table behind the sofa to display decor on, add a decorative box for your media remotes and store a few books! Our living room ."}
+{"idx": 2, "title": "Cultist Circle Calculator | Optimize Your EFT Sacrifices", "date": "", "ddg_snippet": "Find the cheapest way to hit your target base value —fast. Auto‑select suggests optimal 1–5 item combos from live prices. Minimize cost while meeting 350k/400k thresholds. Full list of items in Tools > Base Value Table . Pin items you own; exclude categories or specific items.", "subpage_snippet": "", "source": "cultistcircle.com", "link": "https://cultistcircle.com/", "content": "Find the cheapest way to hit your target base value —fast. Auto‑select suggests optimal 1–5 item combos from live prices. Minimize cost while meeting 350k/400k thresholds. Full list of items in Tools > Base Value Table . Pin items you own; exclude categories or specific items."}
+{"idx": 3, "title": "Правила использования at, in, on в английском языке - Skypro", "date": "", "ddg_snippet": "В деловых письмах: «The meeting is scheduled at 10:00 AM on Tuesday, in the conference room .»В академических текстах: «The experiment conducted in 2024 showed significant results.»", "subpage_snippet": "", "source": "sky.pro", "link": "https://sky.pro/media/eng-pravila-ispolzovaniya-at-in-on-v-anglijskom-yazyke/", "content": "В деловых письмах: «The meeting is scheduled at 10:00 AM on Tuesday, in the conference room .»В академических текстах: «The experiment conducted in 2024 showed significant results.»"}
+{"idx": 4, "title": "Twin Flame: What It Is + 11 Signs You've Found Yours | mindbodygreen", "date": "", "ddg_snippet": "Before the two of you meet , you will have an awareness that your other half is out there for you. There is deep longing in this phase, Spinelli notes, and there will be inner work during this phase to prepare you for meeting your twin flame.", "subpage_snippet": "", "source": "www.mindbodygreen.com", "link": "https://www.mindbodygreen.com/articles/twin-flames-signs-meaning-and-stages", "content": "Before the two of you meet , you will have an awareness that your other half is out there for you. There is deep longing in this phase, Spinelli notes, and there will be inner work during this phase to prepare you for meeting your twin flame."}
+{"idx": 5, "title": "AI Video Translator with Voice Cloning & Lip Sync | Free Trial", "date": "", "ddg_snippet": "MP3 to Text Instantly convert MP3 files to text using AI transcription, available in 125+ languages. Streamers can connect their OBS & vMix accounts to the web captioner and start translating live video content to provide translated captions to their audiences. Localize Content with AI. 06.", "subpage_snippet": "", "source": "maestra.ai", "link": "https://maestra.ai/tools/video-translator", "content": "MP3 to Text Instantly convert MP3 files to text using AI transcription, available in 125+ languages. Streamers can connect their OBS & vMix accounts to the web captioner and start translating live video content to provide translated captions to their audiences. Localize Content with AI. 06."}
+{"idx": 6, "title": "ikea.com/gb/en/cat/pax-system-19086", "date": "", "ddg_snippet": "PAX system Personal storage where you can choose exactly what you want.", "subpage_snippet": "", "source": "www.ikea.com", "link": "https://www.ikea.com/gb/en/cat/pax-system-19086/", "content": "PAX system Personal storage where you can choose exactly what you want."}
+{"idx": 7, "title": "How to Create a Class Rank Calculator in Excel - Step... | MyExcelOnline", "date": "", "ddg_snippet": "Excel’s RANK.EQ and RANK.AVG functions assign ranks to values in a dataset. You can combine ranking with IF or FILTER to handle specific scenarios like ties or grade thresholds.", "subpage_snippet": "", "source": "www.myexcelonline.com", "link": "https://www.myexcelonline.com/blog/class-rank-calculator-in-excel/", "content": "Excel’s RANK.EQ and RANK.AVG functions assign ranks to values in a dataset. You can combine ranking with IF or FILTER to handle specific scenarios like ties or grade thresholds."}
+{"idx": 8, "title": "Download Microsoft Teams Desktop and Mobile Apps | Microsoft Teams", "date": "", "ddg_snippet": "Unlimited group meetings for up to 30 hours and 300 participants. 10 GB of cloud storage per user. Real-time collaboration with file sharing, tasks, and polling. Teams.Chat, call, meet . 1 TB of cloud storage per user. 10+ additional apps (including Microsoft Bookings, Planner, and Forms).", "subpage_snippet": "", "source": "www.microsoft.com", "link": "https://www.microsoft.com/en-us/microsoft-teams/download-app", "content": "Unlimited group meetings for up to 30 hours and 300 participants. 10 GB of cloud storage per user. Real-time collaboration with file sharing, tasks, and polling. Teams.Chat, call, meet . 1 TB of cloud storage per user. 10+ additional apps (including Microsoft Bookings, Planner, and Forms)."}
+{"idx": 9, "title": "Convert MB to GB", "date": "", "ddg_snippet": "Instant free online tool for megabyte to gigabyte conversion or vice versa. The megabyte [ MB ] to gigabyte [GB] conversion table and conversion steps are also listed.", "subpage_snippet": "", "source": "www.unitconverters.net", "link": "https://www.unitconverters.net/data-storage/mb-to-gb.htm", "content": "Instant free online tool for megabyte to gigabyte conversion or vice versa. The megabyte [ MB ] to gigabyte [GB] conversion table and conversion steps are also listed."}

data/sampled_jsons/34016_EntityErasure_Table_4_classifier-free_guidance_Text-CFG_Image-CFG_sundries_year_2023.jsonl

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+{"idx": 0, "title": "PDF EntityErasure: Erasing Entity Cleanly via Amodal Entity Segmentation ...", "date": "", "ddg_snippet": "As shown in Table 4 , when the weight of Image-CFG is set to 1, effectively turning off Image-CFG , both MSN and MARS increase significantly, which signals an increase in sundries generation.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Zhu_EntityErasure_Erasing_Entity_Cleanly_via_Amodal_Entity_Segmentation_and_Completion_CVPR_2025_paper.pdf", "content": "As shown in Table 4 , when the weight of Image-CFG is set to 1, effectively turning off Image-CFG , both MSN and MARS increase significantly, which signals an increase in sundries generation."}
+{"idx": 1, "title": "Classifier-free Guidance with Adaptive Scaling - GitHub", "date": "", "ddg_snippet": "Classifier-free Guidance with Adaptive Scaling Classifier-free guidance ( CFG ) is an essential mechanism in contemporary text -driven diffusion models. In practice, in controlling the impact of guidance we can see the trade-off between the quality of the generated images and correspondence to the prompt.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/gmum/beta-CFG", "content": "Classifier-free Guidance with Adaptive Scaling Classifier-free guidance ( CFG ) is an essential mechanism in contemporary text -driven diffusion models. In practice, in controlling the impact of guidance we can see the trade-off between the quality of the generated images and correspondence to the prompt."}
+{"idx": 2, "title": "CFG++: Manifold-constrained Classifier Free Guidance for Diffusion Models", "date": "", "ddg_snippet": "Classifier-free guidance ( CFG ) is a fundamental tool in modern diffusion models for text -guided generation. Although effective, CFG has notable drawbacks. For instance, DDIM with CFG lacks invertibility, complicating image editing; furthermore, high guidance scales, essential for high-quality outputs, frequently result in issues like mode collapse. Contrary to the widespread belief that these ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2406.08070", "content": "Classifier-free guidance ( CFG ) is a fundamental tool in modern diffusion models for text -guided generation. Although effective, CFG has notable drawbacks. For instance, DDIM with CFG lacks invertibility, complicating image editing; furthermore, high guidance scales, essential for high-quality outputs, frequently result in issues like mode collapse. Contrary to the widespread belief that these ..."}
+{"idx": 3, "title": "GitHub Pages - CFG++", "date": "", "ddg_snippet": "Classifier-free guidance ( CFG ) is a fundamental tool in modern diffusion models for text -guided generation. Although effective, CFG's reliance on high guidance scales presents notable drawbacks.", "subpage_snippet": "", "source": "cfgpp-diffusion.github.io", "link": "https://cfgpp-diffusion.github.io/", "content": "Classifier-free guidance ( CFG ) is a fundamental tool in modern diffusion models for text -guided generation. Although effective, CFG's reliance on high guidance scales presents notable drawbacks."}
+{"idx": 4, "title": "CVPR Poster EntityErasure: Erasing Entity Cleanly via Amodal Entity ...", "date": "", "ddg_snippet": "This paper presents EntityErasure , a novel diffusion-based method that can effectively erase entity without inducing unwanted sundries . To this end, we propose to address this problem by dividing it into amodal entity segmentation and completion, such that the region to inpaint takes only entities in the non-inpainting area as reference ...", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/poster/34016", "content": "This paper presents EntityErasure , a novel diffusion-based method that can effectively erase entity without inducing unwanted sundries . To this end, we propose to address this problem by dividing it into amodal entity segmentation and completion, such that the region to inpaint takes only entities in the non-inpainting area as reference ..."}
+{"idx": 5, "title": "[2502.10574] Classifier-free Guidance with Adaptive Scaling", "date": "", "ddg_snippet": "Classifier-free guidance ( CFG ) is an essential mechanism in contemporary text -driven diffusion models. In practice, in controlling the impact of guidance we can see the trade-off between the quality of the generated images and correspondence to the prompt. When we use strong guidance , generated images fit the conditioned text perfectly but at the cost of their quality. Dually, we can use small ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.10574", "content": "Classifier-free guidance ( CFG ) is an essential mechanism in contemporary text -driven diffusion models. In practice, in controlling the impact of guidance we can see the trade-off between the quality of the generated images and correspondence to the prompt. When we use strong guidance , generated images fit the conditioned text perfectly but at the cost of their quality. Dually, we can use small ..."}
+{"idx": 6, "title": "[Iclr2025] Cfg++ : Manifold-constrained Classifier Free Guidance for ...", "date": "", "ddg_snippet": "Classifier-free guidance ( CFG ) is a fundamental tool in modern diffusion models for text -guided generation. Although effective, CFG requires high guidance scales, which has notable drawbacks: Mode collapse and saturation Poor invertibility Unnatural, curved PF-ODE trajectory We propose a simple fix to this seemingly inherent limitation and propose CFG++ 🚀, which corrects the off-manifold ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/CFGpp-diffusion/CFGpp", "content": "Classifier-free guidance ( CFG ) is a fundamental tool in modern diffusion models for text -guided generation. Although effective, CFG requires high guidance scales, which has notable drawbacks: Mode collapse and saturation Poor invertibility Unnatural, curved PF-ODE trajectory We propose a simple fix to this seemingly inherent limitation and propose CFG++ 🚀, which corrects the off-manifold ..."}
+{"idx": 7, "title": "[2306.17806] Stay on topic with Classifier-Free Guidance", "date": "", "ddg_snippet": "Classifier-Free Guidance ( CFG ) has recently emerged in text -to- image generation as a lightweight technique to encourage prompt-adherence in generations. In this work, we demonstrate that CFG can be used broadly as an inference-time technique in pure language modeling. We show that CFG (1) improves the performance of Pythia, GPT-2 and LLaMA-family models across an array of tasks: Q\\\\&A ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2306.17806", "content": "Classifier-Free Guidance ( CFG ) has recently emerged in text -to- image generation as a lightweight technique to encourage prompt-adherence in generations. In this work, we demonstrate that CFG can be used broadly as an inference-time technique in pure language modeling. We show that CFG (1) improves the performance of Pythia, GPT-2 and LLaMA-family models across an array of tasks: Q\\\\&A ..."}
+{"idx": 8, "title": "Classifier-free guidance resolution weighting - GitHub", "date": "", "ddg_snippet": "In section 3.4, the ControlNet paper talks about CFG -RW, quoting: In challenging cases, e.g., when no prompts are given, adding it to both ϵuc and ϵc will completely remove CFG guidance (Figure 5b); using only ϵc will make the guidance very strong (Figure 5c). Our solution is to first add the conditioning image to ϵ_c and then multiply a weight wi to each connection between Stable ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/lllyasviel/ControlNet/discussions/560", "content": "In section 3.4, the ControlNet paper talks about CFG -RW, quoting: In challenging cases, e.g., when no prompts are given, adding it to both ϵuc and ϵc will completely remove CFG guidance (Figure 5b); using only ϵc will make the guidance very strong (Figure 5c). Our solution is to first add the conditioning image to ϵ_c and then multiply a weight wi to each connection between Stable ..."}
+{"idx": 9, "title": "CFG++: Manifold-constrained Classifier Free Guidance for Diffusion ...", "date": "", "ddg_snippet": "Classifier-free guidance ( CFG ) is a fundamental tool in modern diffusion models for text -guided generation. Although effective, CFG has notable drawbacks. For instance, DDIM with CFG lacks invertibility, complicating image editing; furthermore, high guidance scales, essential for high-quality outputs, frequently result in issues like mode collapse. Contrary to the widespread belief that these ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=E77uvbOTtp", "content": "Classifier-free guidance ( CFG ) is a fundamental tool in modern diffusion models for text -guided generation. Although effective, CFG has notable drawbacks. For instance, DDIM with CFG lacks invertibility, complicating image editing; furthermore, high guidance scales, essential for high-quality outputs, frequently result in issues like mode collapse. Contrary to the widespread belief that these ..."}

data/sampled_jsons/35068_XLRS-Bench_Table_2_Qwen2-VL_Avg_score_Chinese_English_year_2023-2024.jsonl

@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Qwen Qwen2.5-Math: The world's leading open-sourced mathematical LLMs | Qwen", "date": "", "ddg_snippet": "September 18, 2024 - While CoT plays a vital role in ... computation, symbolic manipulation, and algorithmic manipulation. Qwen2.5-Math-1.5B/7B/72B-Instruct achieve 79.7, 85.3, and 87.8 respectively on the MATH benchmark using TIR....", "subpage_snippet": "", "source": "qwenlm.github.io", "link": "https://qwenlm.github.io/blog/qwen2.5-math/", "content": "September 18, 2024 - While CoT plays a vital role in ... computation, symbolic manipulation, and algorithmic manipulation. Qwen2.5-Math-1.5B/7B/72B-Instruct achieve 79.7, 85.3, and 87.8 respectively on the MATH benchmark using TIR...."}
+{"idx": 1, "title": "GitHub GitHub - QwenLM/Qwen2.5-VL: Qwen2.5-VL is the multimodal large language model series developed by Qwen team, Alibaba Cloud.", "date": "", "ddg_snippet": "Qwen2 . 5 - VL is the multimodal large language model series developed by Qwen team, Alibaba Cloud. - QwenLM/ Qwen2 . 5 - VL", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/QwenLM/Qwen2.5-VL", "content": "Qwen2 . 5 - VL is the multimodal large language model series developed by Qwen team, Alibaba Cloud. - QwenLM/ Qwen2 . 5 - VL"}
+{"idx": 2, "title": "Qwen Qwen2.5: A Party of Foundation Models! | Qwen", "date": "", "ddg_snippet": "September 18, 2024 - GITHUB HUGGING FACE MODELSCOPE DEMO DISCORD Introduction In the past three months since Qwen2 ’s release, numerous developers have built new models on the Qwen2 language models, providing us with valuable feedback. During this period, we have focused on creating smarter and more knowledgeable ...", "subpage_snippet": "", "source": "qwenlm.github.io", "link": "https://qwenlm.github.io/blog/qwen2.5/", "content": "September 18, 2024 - GITHUB HUGGING FACE MODELSCOPE DEMO DISCORD Introduction In the past three months since Qwen2 ’s release, numerous developers have built new models on the Qwen2 language models, providing us with valuable feedback. During this period, we have focused on creating smarter and more knowledgeable ..."}
+{"idx": 3, "title": "Qwen Qwen2.5 VL! Qwen2.5 VL! Qwen2.5 VL! | Qwen", "date": "", "ddg_snippet": "January 26, 2025 - QWEN CHAT GITHUB HUGGING FACE MODELSCOPE DISCORD We release Qwen2 . 5 - VL , the new flagship vision-language model of Qwen and also a significant leap from the previous Qwen2 - VL . To try the latest model, feel free to visit Qwen Chat and choose Qwen2 . 5 - VL -72B-Instruct.", "subpage_snippet": "", "source": "qwenlm.github.io", "link": "https://qwenlm.github.io/blog/qwen2.5-vl/", "content": "January 26, 2025 - QWEN CHAT GITHUB HUGGING FACE MODELSCOPE DISCORD We release Qwen2 . 5 - VL , the new flagship vision-language model of Qwen and also a significant leap from the previous Qwen2 - VL . To try the latest model, feel free to visit Qwen Chat and choose Qwen2 . 5 - VL -72B-Instruct."}
+{"idx": 4, "title": "arXiv Qwen2 Technical Report", "date": "", "ddg_snippet": "July 15, 2024 - The flagship model, Qwen2-72B, showcases remarkable performance: 84.2 on MMLU, 37.9 on GPQA, 64.6 on HumanEval, 89.5 on GSM8K, and 82.4 on BBH as a base language model. The instruction-tuned variant, Qwen2-72B-Instruct, attains 9.1 on MT-Bench, 48.1 on Arena-Hard, and 35.7 on LiveCodeBench.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2407.10671v1", "content": "July 15, 2024 - The flagship model, Qwen2-72B, showcases remarkable performance: 84.2 on MMLU, 37.9 on GPQA, 64.6 on HumanEval, 89.5 on GSM8K, and 82.4 on BBH as a base language model. The instruction-tuned variant, Qwen2-72B-Instruct, attains 9.1 on MT-Bench, 48.1 on Arena-Hard, and 35.7 on LiveCodeBench."}
+{"idx": 5, "title": "arXiv [2502.13923] Qwen2.5-VL Technical Report", "date": "", "ddg_snippet": "February 19, 2025 - We introduce Qwen2 . 5 - VL , the latest flagship model of Qwen vision-language series, which demonstrates significant advancements in both foundational capabilities and innovative functionalities. Qwen2 . 5 - VL achieves a major leap forward in understanding and interacting with the world through enhanced ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.13923", "content": "February 19, 2025 - We introduce Qwen2 . 5 - VL , the latest flagship model of Qwen vision-language series, which demonstrates significant advancements in both foundational capabilities and innovative functionalities. Qwen2 . 5 - VL achieves a major leap forward in understanding and interacting with the world through enhanced ..."}
+{"idx": 6, "title": "Hugging Face Alibaba-NLP/gme-Qwen2-VL-7B-Instruct · Release the UMRB", "date": "", "ddg_snippet": "Great work!!!! @ zyznull it would be better is you can release the UMR benchmark, thanks.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/Alibaba-NLP/gme-Qwen2-VL-7B-Instruct/discussions/2", "content": "Great work!!!! @ zyznull it would be better is you can release the UMR benchmark, thanks."}
+{"idx": 7, "title": "OpenRouter Qwen2.5 VL 3B Instruct - API, Providers, Stats", "date": "", "ddg_snippet": "Qwen2 . 5 VL 3B is a multimodal LLM from the Qwen Team with the following key enhancements: · SoTA understanding of images of various resolution & ratio: Qwen2 . 5 - VL achieves state-of-the-art performance on visual understanding benchmarks, including MathVista, DocVQA, RealWorldQA, MTVQA, etc", "subpage_snippet": "", "source": "openrouter.ai", "link": "https://openrouter.ai/qwen/qwen2.5-vl-3b-instruct:free", "content": "Qwen2 . 5 VL 3B is a multimodal LLM from the Qwen Team with the following key enhancements: · SoTA understanding of images of various resolution & ratio: Qwen2 . 5 - VL achieves state-of-the-art performance on visual understanding benchmarks, including MathVista, DocVQA, RealWorldQA, MTVQA, etc"}
+{"idx": 8, "title": "Ollama qwen2.5", "date": "", "ddg_snippet": "Qwen2 .5 models are pretrained on Alibaba's latest large-scale dataset, encompassing up to 18 trillion tokens. The model supports up to 128K tokens and has multilingual support.", "subpage_snippet": "", "source": "ollama.com", "link": "https://ollama.com/library/qwen2.5", "content": "Qwen2 .5 models are pretrained on Alibaba's latest large-scale dataset, encompassing up to 18 trillion tokens. The model supports up to 128K tokens and has multilingual support."}
+{"idx": 9, "title": "ResearchGate (PDF) QualBench: Benchmarking Chinese LLMs with Localized Professional Qualifications for Vertical Domain Evaluation", "date": "", "ddg_snippet": "May 8, 2025 - PDF | The rapid advancement of Chinese large language models (LLMs) underscores the need for domain-specific evaluations to ensure reliable... | Find, read and cite all the research you need on ResearchGate", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/391575253_QualBench_Benchmarking_Chinese_LLMs_with_Localized_Professional_Qualifications_for_Vertical_Domain_Evaluation", "content": "May 8, 2025 - PDF | The rapid advancement of Chinese large language models (LLMs) underscores the need for domain-specific evaluations to ensure reliable... | Find, read and cite all the research you need on ResearchGate"}

data/sampled_jsons/3Z827FtMNe_Great_Models_Think_Alike_and_this_Undermines_AI_Oversight.jsonl

@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Great Models Think Alike and this Undermines AI Oversight", "date": "", "ddg_snippet": "As Language Model (LM) capabilities advance, evaluating and supervising them at scale is getting harder for humans. There is hope that other language models can automate both these tasks, which we refer to as ''AI Oversight'' . We study how model similarity affects both aspects of AI oversight by proposing Chance Adjusted Probabilistic Agreement (CAPA): a metric for LM similarity based on ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.04313", "content": "As Language Model (LM) capabilities advance, evaluating and supervising them at scale is getting harder for humans. There is hope that other language models can automate both these tasks, which we refer to as ''AI Oversight'' . We study how model similarity affects both aspects of AI oversight by proposing Chance Adjusted Probabilistic Agreement (CAPA): a metric for LM similarity based on ..."}
+{"idx": 1, "title": "Great Models Think Alike and this Undermines AI Oversight", "date": "", "ddg_snippet": "As Language Model (LM) capabilities advance, evaluating and supervising them at scale is get- ting harder for humans. There is hope that other language models can automate both these tasks, which we refer to as \" AI Oversight \". We study how model similarity affects both aspects of AI oversight by proposing Chance Adjusted Prob- abilistic Agreement (CAPA): a metric for LM similarity based on ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=3Z827FtMNe", "content": "As Language Model (LM) capabilities advance, evaluating and supervising them at scale is get- ting harder for humans. There is hope that other language models can automate both these tasks, which we refer to as \" AI Oversight \". We study how model similarity affects both aspects of AI oversight by proposing Chance Adjusted Prob- abilistic Agreement (CAPA): a metric for LM similarity based on ..."}
+{"idx": 2, "title": "Great Models Think Alike and this Undermines AI Oversight", "date": "", "ddg_snippet": "As model capabilities increase, it becomes harder to find their mistakes, and we might defer more to AI oversight . However, we observe a concerning trend -- model mistakes are becoming more similar with increasing capabilities, pointing to risks from correlated failures.", "subpage_snippet": "", "source": "model-similarity.github.io", "link": "https://model-similarity.github.io/", "content": "As model capabilities increase, it becomes harder to find their mistakes, and we might defer more to AI oversight . However, we observe a concerning trend -- model mistakes are becoming more similar with increasing capabilities, pointing to risks from correlated failures."}
+{"idx": 3, "title": "The AI oversight trap: When smarter models make the same mistakes", "date": "", "ddg_snippet": "A study titled \" Great Models Think Alike and this Undermines AI Oversight \", authored by Shashwat Goel, Joschka Strüber, Ilze Amanda Auzina, Karuna K Chandra, Ponnurangam Kumaraguru, Douwe Kiela, Ameya Prabhu, Matthias Bethge, and Jonas Geiping, investigates how model similarity affects AI oversight .", "subpage_snippet": "", "source": "www.devdiscourse.com", "link": "https://www.devdiscourse.com/article/technology/3256561-the-ai-oversight-trap-when-smarter-models-make-the-same-mistakes", "content": "A study titled \" Great Models Think Alike and this Undermines AI Oversight \", authored by Shashwat Goel, Joschka Strüber, Ilze Amanda Auzina, Karuna K Chandra, Ponnurangam Kumaraguru, Douwe Kiela, Ameya Prabhu, Matthias Bethge, and Jonas Geiping, investigates how model similarity affects AI oversight ."}
+{"idx": 4, "title": "Great Models Think Alike and this Undermines AI Oversight | Cool Papers ...", "date": "", "ddg_snippet": "We study how model similarity affects both aspects of AI oversight by proposing *Chance Adjusted Probabilistic Agreement (CAPA)*--a metric for LM similarity based on overlap in model mistakes. Using CAPA, we first show that *LLM-as-a-judge* scores favor models similar to the judge, generalizing recent self-preference results.", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/venue/3Z827FtMNe@OpenReview", "content": "We study how model similarity affects both aspects of AI oversight by proposing *Chance Adjusted Probabilistic Agreement (CAPA)*--a metric for LM similarity based on overlap in model mistakes. Using CAPA, we first show that *LLM-as-a-judge* scores favor models similar to the judge, generalizing recent self-preference results."}
+{"idx": 5, "title": "Great Models Think Alike and this Undermines AI Oversight", "date": "", "ddg_snippet": "The above results show the benefits of diverse models for AI oversight - less similarity between models reduces bias in LLM-as-a-judge, and also leads to greater gains when training on LM annotations.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.04313v1", "content": "The above results show the benefits of diverse models for AI oversight - less similarity between models reduces bias in LLM-as-a-judge, and also leads to greater gains when training on LM annotations."}
+{"idx": 6, "title": "ICML Poster Great Models Think Alike and this Undermines AI Oversight", "date": "", "ddg_snippet": "We study how model similarity affects both aspects of AI oversight by proposing Chance Adjusted Probabilistic Agreement (CAPA) --a metric for LM similarity based on overlap in model mistakes. Using CAPA, we first show that LLM-as-a-judge scores favor models similar to the judge, generalizing recent self-preference results.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46528", "content": "We study how model similarity affects both aspects of AI oversight by proposing Chance Adjusted Probabilistic Agreement (CAPA) --a metric for LM similarity based on overlap in model mistakes. Using CAPA, we first show that LLM-as-a-judge scores favor models similar to the judge, generalizing recent self-preference results."}
+{"idx": 7, "title": "Great Models Think Alike and This Undermines AI Oversight", "date": "", "ddg_snippet": "The document discusses the challenges of AI oversight as language model capabilities advance, highlighting the importance of model similarity in evaluating and supervising these models . It introduces a new metric, Chance Adjusted Probabilistic Agreement (CAPA), to assess model similarity based on the overlap in model mistakes, revealing concerning trends of increasing correlation in model ...", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/826032662/Great-Models-Think-Alike-and-this-Undermines-AI-Oversight", "content": "The document discusses the challenges of AI oversight as language model capabilities advance, highlighting the importance of model similarity in evaluating and supervising these models . It introduces a new metric, Chance Adjusted Probabilistic Agreement (CAPA), to assess model similarity based on the overlap in model mistakes, revealing concerning trends of increasing correlation in model ..."}
+{"idx": 8, "title": "Great Models Think Alike and this Undermines AI Oversight", "date": "", "ddg_snippet": "TL;DR: We propose a measure for model similarity, finding increasingly correlated failures as model capabilities improve, and show the negative effects of similarity on AI oversight paradigms like LLM-as-a-Judge and Weak-to-Strong Generalization.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=3Z827FtMNe", "content": "TL;DR: We propose a measure for model similarity, finding increasingly correlated failures as model capabilities improve, and show the negative effects of similarity on AI oversight paradigms like LLM-as-a-Judge and Weak-to-Strong Generalization."}
+{"idx": 9, "title": "Great Models Think Alike and this Undermines AI Oversight", "date": "", "ddg_snippet": "Great Models Think Alike and this Undermines AI Oversight Shashwat Goel · Joschka Strüber · Ilze Auzina · Karuna Chandra · Ponnurangam Kumaraguru · Douwe Kiela · Ameya Prabhu · Matthias Bethge · Jonas Geiping Keywords: [ LLM as a Judge ] [ Model Differences ] [ Model Similarity ] [ AI Oversight ] [ Weak to Strong Generalization ]", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2025/10000468", "content": "Great Models Think Alike and this Undermines AI Oversight Shashwat Goel · Joschka Strüber · Ilze Auzina · Karuna Chandra · Ponnurangam Kumaraguru · Douwe Kiela · Ameya Prabhu · Matthias Bethge · Jonas Geiping Keywords: [ LLM as a Judge ] [ Model Differences ] [ Model Similarity ] [ AI Oversight ] [ Weak to Strong Generalization ]"}

data/sampled_jsons/46n3izUNiv_Section_G_IP-Adapter_CLIP_encoding_VAE_latent_space.jsonl

@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ip - adapter plus is not working with full face - Githubissues", "date": "", "ddg_snippet": "Hi. I am trying to use ip - adapter with openvino, two model is working well ( ip - adapter _sd15.bin, ip - adapter _sd15_light.bin) but, I wish to use \" ip - adapter -full-face_sd15.bin\", it is not working well some modification of example is avaiable to convert that model...", "subpage_snippet": "", "source": "githubissues.com", "link": "https://githubissues.com/openvinotoolkit/openvino_notebooks/2400", "content": "Hi. I am trying to use ip - adapter with openvino, two model is working well ( ip - adapter _sd15.bin, ip - adapter _sd15_light.bin) but, I wish to use \" ip - adapter -full-face_sd15.bin\", it is not working well some modification of example is avaiable to convert that model..."}
+{"idx": 1, "title": "ComfyUI: Beginner to Advance Nodes Guide | Stable Diffusion Tutorials", "date": "", "ddg_snippet": "5. CheckPointLoader( VAE ): VAE (Variational Auto Encoder ) connect to the VAE Decode box.Steps: It refers to the inference steps means the number of steps the diffusion mechanism needs to generate an intermediate latent image sample processed in latent space .", "subpage_snippet": "", "source": "www.stablediffusiontutorials.com", "link": "https://www.stablediffusiontutorials.com/2024/04/comfyui-tutorial.html", "content": "5. CheckPointLoader( VAE ): VAE (Variational Auto Encoder ) connect to the VAE Decode box.Steps: It refers to the inference steps means the number of steps the diffusion mechanism needs to generate an intermediate latent image sample processed in latent space ."}
+{"idx": 2, "title": "How to Troubleshoot and Solve ComfyUI Model Issues - ComfyUI", "date": "", "ddg_snippet": "SD3 models use 16-channel latent space with triple text encoder conditioning ( CLIP -L + OpenCLIP bigG + T5-XXL).", "subpage_snippet": "", "source": "docs.comfy.org", "link": "https://docs.comfy.org/troubleshooting/model-issues", "content": "SD3 models use 16-channel latent space with triple text encoder conditioning ( CLIP -L + OpenCLIP bigG + T5-XXL)."}
+{"idx": 3, "title": "Model Database | Krita AI Handbook", "date": "", "ddg_snippet": "ControlNet, IP - Adapter and Clip Vision provide the various control layers. Upscaler models add more options to the upscale workspace. Checkpoint lists the recommended diffusion models used by the plugin’s default styles.", "subpage_snippet": "", "source": "docs.interstice.cloud", "link": "https://docs.interstice.cloud/models/", "content": "ControlNet, IP - Adapter and Clip Vision provide the various control layers. Upscaler models add more options to the upscale workspace. Checkpoint lists the recommended diffusion models used by the plugin’s default styles."}
+{"idx": 4, "title": "stabilityai/stable-diffusion-3.5-large · Hugging Face", "date": "", "ddg_snippet": "Text Encoders ： CLIPs : OpenCLIP-ViT/ G , CLIP -ViT/L, context length 77 tokens. T5: T5-xxl, context length 77/256 tokens at different stages of training. Training Data and StrategyModel tree for stabilityai/stable-diffusion-3.5-large. Adapters . 349 models.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/stabilityai/stable-diffusion-3.5-large", "content": "Text Encoders ： CLIPs : OpenCLIP-ViT/ G , CLIP -ViT/L, context length 77 tokens. T5: T5-xxl, context length 77/256 tokens at different stages of training. Training Data and StrategyModel tree for stabilityai/stable-diffusion-3.5-large. Adapters . 349 models."}
+{"idx": 5, "title": "WAN 2.2 ComfyUI Workflow: Runs on Just 8GB VRAM", "date": "", "ddg_snippet": "CLIP . VAE . Text encoders . Uses FP8 precision for lower memory usage. It’s kind of like a “best of WAN” setup, but with some version-specific quirks", "subpage_snippet": "", "source": "aistudynow.com", "link": "https://aistudynow.com/wan-2-2-comfyui-workflow-runs-on-just-8gb-vram/", "content": "CLIP . VAE . Text encoders . Uses FP8 precision for lower memory usage. It’s kind of like a “best of WAN” setup, but with some version-specific quirks"}
+{"idx": 6, "title": "Getting Started with ComfyUI", "date": "", "ddg_snippet": "CLIP Text Encode ( Prompt ): Typically every workflow contains two of these nodes– one for the positive prompt and one for the negative prompt. The negative prompt guides the model from unwanted concepts ( e. g ., “ugly”, “deformed” ).", "subpage_snippet": "", "source": "learnopencv.com", "link": "https://learnopencv.com/introduction-to-comfyui-for-stable-diffusion/", "content": "CLIP Text Encode ( Prompt ): Typically every workflow contains two of these nodes– one for the positive prompt and one for the negative prompt. The negative prompt guides the model from unwanted concepts ( e. g ., “ugly”, “deformed” )."}
+{"idx": 7, "title": "Stable Diffusion. Курс молодого бойца / Хабр", "date": "", "ddg_snippet": "IP Adapter . Особенно хочется отметить \"магическую\" модель IPAdapter, которая использует исходное и��ображение для генерации подсказки уровня промпта.", "subpage_snippet": "", "source": "habr.com", "link": "https://habr.com/ru/articles/784104/", "content": "IP Adapter . Особенно хочется отметить \"магическую\" модель IPAdapter, которая использует исходное изображение для генерации подсказки уровня промпта."}
+{"idx": 8, "title": "Обучите свою собственную модель FLUX LoRA с поддержкой LOW...", "date": "", "ddg_snippet": "Скачайте VAE (Variational Auto Encoder ) и сохраните его в папке fluxgym/models/ vae : ae.sft. Скачайте модель Flux Dev из репозитория Cocktailpeanut Huggingface и поместите ее в папку fluxgym/models/unet: flux1-dev.sft. Шаг 2: Запуск fluxgym.", "subpage_snippet": "", "source": "d00m4ace.com", "link": "https://d00m4ace.com/posts/obuchite-svoju-sobstvennuju-model-flux-lora-s-podderzhkoj-low-vram-12gbslash16gbslash20gb-na-windows-10/", "content": "Скачайте VAE (Variational Auto Encoder ) и сохраните его в папке fluxgym/models/ vae : ae.sft. Скачайте модель Flux Dev из репозитория Cocktailpeanut Huggingface и поместите ее в папку fluxgym/models/unet: flux1-dev.sft. Шаг 2: Запуск fluxgym."}
+{"idx": 9, "title": "How to run Wan 2.1 Video on ComfyUI - Stable Diffusion Art", "date": "", "ddg_snippet": "Download the Wan VAE model wan_2.1_ vae .safetensors and put it in ComfyUI > models > vae .", "subpage_snippet": "", "source": "stable-diffusion-art.com", "link": "https://stable-diffusion-art.com/wan-2-1/", "content": "Download the Wan VAE model wan_2.1_ vae .safetensors and put it in ComfyUI > models > vae ."}

data/sampled_jsons/46yLEXtav4_Statistical_Collusion_by_Collectives_on_Learning_Platforms_full_paper.jsonl

@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "In this paper , we introduce a new framework to enable members of the collective to learn strategies h efficiently and to infer the parameters that determine their success on the platform .out directly using the term g(x). 16. Statistical Collusion by Collectives on Learning Platforms .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=46yLEXtav4", "content": "In this paper , we introduce a new framework to enable members of the collective to learn strategies h efficiently and to infer the parameters that determine their success on the platform .out directly using the term g(x). 16. Statistical Collusion by Collectives on Learning Platforms ."}
+{"idx": 1, "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/2502.04879v3", "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."}
+{"idx": 2, "title": "(PDF) 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": "www.researchgate.net", "link": "https://www.researchgate.net/publication/388848421_Statistical_Collusion_by_Collectives_on_Learning_Platforms", "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": 3, "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."}
+{"idx": 4, "title": "ICML 2025 Statistical Collusion by Collectives on Learning ...", "date": "", "ddg_snippet": "Papers . Workshops. Community.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/oral/47263", "content": "Papers . Workshops. Community."}
+{"idx": 5, "title": "Statistical Collusion by Collectives on Learning Platforms - Paper ...", "date": "", "ddg_snippet": "As platforms increasingly rely on learning algorithms, collectives may formand 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": "deeplearn.org", "link": "https://deeplearn.org/arxiv/608798/statistical-collusion-by-collectives-on-learning-platforms", "content": "As platforms increasingly rely on learning algorithms, collectives may formand seek ways to influence these platforms to align with their own interests.This can be achieved by coordinated submission of altered data."}
+{"idx": 6, "title": "[Literature Review] Statistical Collusion by Collectives on Learning ...", "date": "", "ddg_snippet": "The paper titled \" Statistical Collusion by Collectives on Learning Platforms \" by Etienne Gauthier, Francis Bach, and Michael I. Jordan presents a framework for analyzing how collectives can influence machine learning platforms by coordinating data submission strategies.", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/en/review/statistical-collusion-by-collectives-on-learning-platforms", "content": "The paper titled \" Statistical Collusion by Collectives on Learning Platforms \" by Etienne Gauthier, Francis Bach, and Michael I. Jordan presents a framework for analyzing how collectives can influence machine learning platforms by coordinating data submission strategies."}
+{"idx": 7, "title": "GauthierE/ statistical - collusion : Statistical Collusion by Collectives ...", "date": "", "ddg_snippet": "Statistical Collusion by Collectives on Learning Platforms . arxiv.org/abs/2502.04879.utils.py # main code used across all notebooks. Description of Experiments. 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.utils.py # main code used across all notebooks. Description of Experiments. This repository considers four key scenarios from the paper : Signal planting with feature-label strategy."}
+{"idx": 8, "title": "ICML 2025 Review Controversies Spark Academic Debate | CSPaper", "date": "", "ddg_snippet": "Of his four submissions, the paper with the lowest average score (2.75) was accepted as a poster, while the three papers with higher scores (3.0) were rejected. Statistical Collusion by Collectives on Learning Platforms Examines collective manipulation in ML platforms .", "subpage_snippet": "", "source": "cspaper.org", "link": "https://cspaper.org/topic/62/icml-2025-review-controversies-spark-academic-debate", "content": "Of his four submissions, the paper with the lowest average score (2.75) was accepted as a poster, while the three papers with higher scores (3.0) were rejected. Statistical Collusion by Collectives on Learning Platforms Examines collective manipulation in ML platforms ."}
+{"idx": 9, "title": "Publications", "date": "", "ddg_snippet": "Statistical collusion by collectives on learning platforms .Workshop on Statistical Learning Techniques for Solving Systems Problems, Whistler, BC, 2007.", "subpage_snippet": "", "source": "people.eecs.berkeley.edu", "link": "https://people.eecs.berkeley.edu/~jordan/publications.html", "content": "Statistical collusion by collectives on learning platforms .Workshop on Statistical Learning Techniques for Solving Systems Problems, Whistler, BC, 2007."}

data/sampled_jsons/4AmFA0qNQ2_Long-Form_Speech_Generation_with_Spoken_Language_Models_year_2024.jsonl

@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Long-Form Speech Generation with Spoken Language Models", "date": "", "ddg_snippet": "We consider the generative modeling of speech over multiple minutes, a requirement for long- form multimedia generation and audio-native voice assistants. However, textless spoken language models struggle to generate plausible speech past tens of seconds, due to high temporal resolution of speech tokens causing loss of coherence, architectural issues with long-sequence training or extrapolation ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2412.18603", "content": "We consider the generative modeling of speech over multiple minutes, a requirement for long- form multimedia generation and audio-native voice assistants. However, textless spoken language models struggle to generate plausible speech past tens of seconds, due to high temporal resolution of speech tokens causing loss of coherence, architectural issues with long-sequence training or extrapolation ..."}
+{"idx": 1, "title": "Long-Form Speech Generation with Spoken Language Models | Cool Papers ...", "date": "", "ddg_snippet": "We consider the generative modeling of speech over multiple minutes, a requirement for long- form multimedia generation and audio-native voice assistants. However, textless spoken language models struggle to generate plausible speech past tens of seconds, due to high temporal resolution of speech tokens causing loss of coherence, architectural issues with long-sequence training or extrapolation ...", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/venue/4AmFA0qNQ2@OpenReview", "content": "We consider the generative modeling of speech over multiple minutes, a requirement for long- form multimedia generation and audio-native voice assistants. However, textless spoken language models struggle to generate plausible speech past tens of seconds, due to high temporal resolution of speech tokens causing loss of coherence, architectural issues with long-sequence training or extrapolation ..."}
+{"idx": 2, "title": "GitHub - google-deepmind/librispeech-long: LibriSpeech-Long is a ...", "date": "", "ddg_snippet": "About LibriSpeech-Long is a benchmark dataset for long- form speech generation and processing. Released as part of \"Long- Form Speech Generation with Spoken Language Models \" (arXiv 2024).", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/google-deepmind/librispeech-long", "content": "About LibriSpeech-Long is a benchmark dataset for long- form speech generation and processing. Released as part of \"Long- Form Speech Generation with Spoken Language Models \" (arXiv 2024)."}
+{"idx": 3, "title": "Long-Form Speech Generation with Spoken Language Models | AI Research ...", "date": "", "ddg_snippet": "The research also doesn't fully address multilingual capabilities. Conclusion This breakthrough represents a significant step toward natural, long- form speech generation . The combination of language modeling and audio processing techniques opens new possibilities for applications in audiobooks, virtual assistants, and educational content.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/long-form-speech-generation-spoken-language-models", "content": "The research also doesn't fully address multilingual capabilities. Conclusion This breakthrough represents a significant step toward natural, long- form speech generation . The combination of language modeling and audio processing techniques opens new possibilities for applications in audiobooks, virtual assistants, and educational content."}
+{"idx": 4, "title": "PDF Researcher develops 'SpeechSSM,' opening up possibilities for a 24-hour ...", "date": "", "ddg_snippet": "Recently, spoken language models (SLMs) have been highlighted as next- generation technology that surpasses the limitations of text-based language models by learning human speech without text to understand and generate linguistic and non-linguistic information.", "subpage_snippet": "", "source": "techxplore.com", "link": "https://techxplore.com/news/2025-07-speechssm-possibilities-hour-ai-voice.pdf", "content": "Recently, spoken language models (SLMs) have been highlighted as next- generation technology that surpasses the limitations of text-based language models by learning human speech without text to understand and generate linguistic and non-linguistic information."}
+{"idx": 5, "title": "Long-Form Speech Generation with Spoken Language Models", "date": "", "ddg_snippet": "Abstract We consider the generative modeling of speech over multiple minutes, a requirement for long- form multimedia generation and audio-native voice assistants. However, textless spoken lan-guage models struggle to generate plausible speech past tens of seconds, due to high temporal resolution of speech tokens causing loss of co-herence, architectural issues with long-sequence training or ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2412.18603", "content": "Abstract We consider the generative modeling of speech over multiple minutes, a requirement for long- form multimedia generation and audio-native voice assistants. However, textless spoken lan-guage models struggle to generate plausible speech past tens of seconds, due to high temporal resolution of speech tokens causing loss of co-herence, architectural issues with long-sequence training or ..."}
+{"idx": 6, "title": "Long-Form Speech Generation with Spoken Language Models ...", "date": "", "ddg_snippet": "This research presents SpeechSSM, a new type of speech language model that can generate long, coherent spoken audio (like a 16-minute story) without using text. Current models struggle with long-f...", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/icml/46499/paper", "content": "This research presents SpeechSSM, a new type of speech language model that can generate long, coherent spoken audio (like a 16-minute story) without using text. Current models struggle with long-f..."}
+{"idx": 7, "title": "Long-Form Speech Generation with Spoken Language Models", "date": "", "ddg_snippet": "Our work makes initial progress on naturalistic, audio-native, long- form speech generation : We introduce SpeechSSM, the first spoken language model for long- form speech . Our 2B and 9B models : produces speech textlessly and in constant memory, for unbounded real-time generation ; demonstrates generative length extrapolation, e.g. 4 min. in training to 16 min; can be trained for either read ...", "subpage_snippet": "", "source": "google.github.io", "link": "https://google.github.io/tacotron/publications/speechssm/", "content": "Our work makes initial progress on naturalistic, audio-native, long- form speech generation : We introduce SpeechSSM, the first spoken language model for long- form speech . Our 2B and 9B models : produces speech textlessly and in constant memory, for unbounded real-time generation ; demonstrates generative length extrapolation, e.g. 4 min. in training to 16 min; can be trained for either read ..."}
+{"idx": 8, "title": "Long-Form Speech Generation with Spoken Language Models", "date": "", "ddg_snippet": "SpeechSSM is derived, the first speech language model family to learn from and sample long- form spoken audio in a single decoding session without text intermediates and leverage recent advances in linear-time sequence modeling to greatly surpass current Transformer spoken LMs in coherence and efficiency on multi-minute generations while still matching them at the utterance level. We consider ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Long-Form-Speech-Generation-with-Spoken-Language-Park-Salazar/b70f44b066d7adcf89cdd0870a81e3266afdcb6d", "content": "SpeechSSM is derived, the first speech language model family to learn from and sample long- form spoken audio in a single decoding session without text intermediates and leverage recent advances in linear-time sequence modeling to greatly surpass current Transformer spoken LMs in coherence and efficiency on multi-minute generations while still matching them at the utterance level. We consider ..."}
+{"idx": 9, "title": "Breaking New Ground in Voice Technology - scisimple.com", "date": "", "ddg_snippet": "With these considerations we propose SpeechSSM, the first speech language model to learn from and sample long- form spoken audio (e.g., 16 minutes of read or extemporaneous speech ) in a single decoding session without text intermediates, based on recent advances in linear-time sequence modeling.", "subpage_snippet": "", "source": "scisimple.com", "link": "https://scisimple.com/en/articles/2025-01-26-breaking-new-ground-in-voice-technology--akg2dxo", "content": "With these considerations we propose SpeechSSM, the first speech language model to learn from and sample long- form spoken audio (e.g., 16 minutes of read or extemporaneous speech ) in a single decoding session without text intermediates, based on recent advances in linear-time sequence modeling."}

data/sampled_jsons/4HQaMUYWAT_An_Analysis_for_Reasoning_Bias_of_Language_Models_with_Small_Initialization_theoretical_f.jsonl

@@ -0,0 +1,10 @@
+{"idx": 0, "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": 1, "title": "An Analysis for Reasoning Bias of Language Models with Small...", "date": "", "ddg_snippet": "Additionally, experiments on real-world language tasks corroborate our theoretical insights. 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": "openreview.net", "link": "https://openreview.net/forum?id=4HQaMUYWAT", "content": "Additionally, experiments on real-world language tasks corroborate our theoretical insights. This work enhances our understanding of how initialization strategies influence LLM performance on reasoning tasks and offers valuable guidelines for training models ."}
+{"idx": 2, "title": "An Analysis for Reasoning Bias of Language Models with Small Initialization", "date": "", "ddg_snippet": "We provide a theoretical framework from the perspective of model training dynamics to explain these phenomena. Additionally, experiments on real-world language tasks corroborate our theoretical ...", "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 provide a theoretical framework from the perspective of model training dynamics to explain these phenomena. Additionally, experiments on real-world language tasks corroborate our theoretical ..."}
+{"idx": 3, "title": "Furyton/awesome-language-model-analysis - GitHub", "date": "", "ddg_snippet": "This paper list focuses on the theoretical and empirical analysis of language models , especially large language models (LLMs). The papers in this list investigate the learning behavior, generalization ability, and other properties of language models through theoretical analysis , empirical analysis , or a combination of both.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Furyton/awesome-language-model-analysis", "content": "This paper list focuses on the theoretical and empirical analysis of language models , especially large language models (LLMs). The papers in this list investigate the learning behavior, generalization ability, and other properties of language models through theoretical analysis , empirical analysis , or a combination of both."}
+{"idx": 4, "title": "An Analysis for Reasoning Bias of Language Models with Small Initialization", "date": "", "ddg_snippet": "Further analysis of initial training dynamics suggests that specific model components, particularly the embedding space and self-attention mechanisms , play piv-otal roles in shaping these learning biases. We provide a theoretical framework from the perspec-tive of model training dynamics to explain these phenomena.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.04375v2", "content": "Further analysis of initial training dynamics suggests that specific model components, particularly the embedding space and self-attention mechanisms , play piv-otal roles in shaping these learning biases. We provide a theoretical framework from the perspec-tive of model training dynamics to explain these phenomena."}
+{"idx": 5, "title": "An Analysis for Reasoning Bias of Language Models With Small Initialization", "date": "", "ddg_snippet": "Additionally, experiments on real-world language tasks corroborate our theoretical insights. 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": "arxiv.org", "link": "https://arxiv.org/pdf/2502.04375v1", "content": "Additionally, experiments on real-world language tasks corroborate our theoretical insights. This work enhances our understanding of how initialization strategies influence LLM performance on reasoning tasks and offers valuable guidelines for training models ."}
+{"idx": 6, "title": "An analysis for reasoning bias of language models with small initialization", "date": "", "ddg_snippet": "Further analysis of initial training dynamics suggests that specific model components, particularly the embedding space and self-attention mechanisms , play pivotal roles in shaping these learning biases. We provide a theoretical framework from the perspective of model training dynamics to explain these phenomena.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.04375v1", "content": "Further analysis of initial training dynamics suggests that specific model components, particularly the embedding space and self-attention mechanisms , play pivotal roles in shaping these learning biases. We provide a theoretical framework from the perspective of model training dynamics to explain these phenomena."}
+{"idx": 7, "title": "Unpacking the bias of large language models - MIT News", "date": "", "ddg_snippet": "MIT researchers discovered the underlying cause of position bias , a phenomenon that causes large language models to overemphasize the beginning or end of a document or conversation, while neglecting the middle. They built a theoretical framework that can be used to diagnose and correct position bias in future model designs, leading to more accurate, reliable AI agents.", "subpage_snippet": "", "source": "news.mit.edu", "link": "https://news.mit.edu/2025/unpacking-large-language-model-bias-0617", "content": "MIT researchers discovered the underlying cause of position bias , a phenomenon that causes large language models to overemphasize the beginning or end of a document or conversation, while neglecting the middle. They built a theoretical framework that can be used to diagnose and correct position bias in future model designs, leading to more accurate, reliable AI agents."}
+{"idx": 8, "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": 9, "title": "PDF Applied Math Ph.D. Seminar - amphds.yingzhouli.com", "date": "", "ddg_snippet": "Abstract: Transformer-based Large Language Models (LLMs) have revolutionized Natural Language Processing by demonstrating excep-tional 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 ...", "subpage_snippet": "", "source": "amphds.yingzhouli.com", "link": "https://amphds.yingzhouli.com/download_file/2025Spring/20250529.pdf", "content": "Abstract: Transformer-based Large Language Models (LLMs) have revolutionized Natural Language Processing by demonstrating excep-tional 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 ..."}

data/sampled_jsons/4Z04wVQ9FY_Linearization_Turns_Neural_Operators_ensemble_LUNO-LA_Flip_OOD_year_2024.jsonl

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+{"idx": 0, "title": "Linearization Turns Neural Operators into Function-Valued Gaussian...", "date": "", "ddg_snippet": "3 LUNO : Linearized Predictive Uncertainty in Neural Operators . 3.1 Function-Valued Gaussian Processes and Probabilistic Currying.D.6.3 Evaluation of a single trajectory. Linearization Turns Neural Operators into Function-Valued Gaussian Processes.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=4Z04wVQ9FY", "content": "3 LUNO : Linearized Predictive Uncertainty in Neural Operators . 3.1 Function-Valued Gaussian Processes and Probabilistic Currying.D.6.3 Evaluation of a single trajectory. Linearization Turns Neural Operators into Function-Valued Gaussian Processes."}
+{"idx": 1, "title": "Linearization Turns Neural Operators into... | OpenReview", "date": "", "ddg_snippet": "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": "openreview.net", "link": "https://openreview.net/forum?id=4Z04wVQ9FY", "content": "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": 2, "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.", "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."}
+{"idx": 3, "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.", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/arxiv/2406.05072", "content": "Neural operators are deep neural networks designed to learn nontrivial solution operators of such differential equations from data."}
+{"idx": 4, "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.We introduce a new framework for approximate Bayesian uncertainty quantification in neural operators using function-valued Gaussian processes.", "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.We introduce a new framework for approximate Bayesian uncertainty quantification in neural operators using function-valued Gaussian processes."}
+{"idx": 5, "title": "(PDF) Linearization Turns Neural Operators into Function-Valued...", "date": "", "ddg_snippet": "Neural operators are deep neural networks designed to learn nontrivial solution operators of such differential equations from data.", "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 are deep neural networks designed to learn nontrivial solution operators of such differential equations from data."}
+{"idx": 6, "title": "Neural PDE operator learning — The Dan MacKinlay stable of...", "date": "", "ddg_snippet": "2024. “ Linearization Turns Neural Operators into Function-Valued Gaussian Processes.” Mahesh, Collins, Bonev, et al. 2024. “Huge Ensembles Part I: Design of Ensemble Weather Forecasts Using Spherical Fourier Neural Operators .”", "subpage_snippet": "", "source": "danmackinlay.name", "link": "https://danmackinlay.name/notebook/ml_pde_operator", "content": "2024. “ Linearization Turns Neural Operators into Function-Valued Gaussian Processes.” Mahesh, Collins, Bonev, et al. 2024. “Huge Ensembles Part I: Design of Ensemble Weather Forecasts Using Spherical Fourier Neural Operators .”"}
+{"idx": 7, "title": "Publications", "date": "", "ddg_snippet": "Linearization Turns Neural Operators into Function-Valued Gaussian Processes.", "subpage_snippet": "", "source": "2bys.github.io", "link": "https://2bys.github.io/publications/", "content": "Linearization Turns Neural Operators into Function-Valued Gaussian Processes."}
+{"idx": 8, "title": "dblp: List of computer science publications by Emilia Magnani", "date": "", "ddg_snippet": "Emilia Magnani, Ernesto De Vito, Philipp Hennig, Lorenzo Rosasco: Learning convolution operators on compact Abelian groups.", "subpage_snippet": "", "source": "dblp.uni-trier.de", "link": "https://dblp.uni-trier.de/pid/206/6101.html", "content": "Emilia Magnani, Ernesto De Vito, Philipp Hennig, Lorenzo Rosasco: Learning convolution operators on compact Abelian groups."}
+{"idx": 9, "title": "Virginia Aglietti - Senior Research Scientist @ Google... | LinkedIn", "date": "", "ddg_snippet": "Excited to be at ICML presenting our work: \" Linearization turns neural operators into function-valued Gaussian processes\"! If you’re around, come…", "subpage_snippet": "", "source": "uk.linkedin.com", "link": "https://uk.linkedin.com/in/virginia-aglietti-a80321a4", "content": "Excited to be at ICML presenting our work: \" Linearization turns neural operators into function-valued Gaussian processes\"! If you’re around, come…"}

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

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+{"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": "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": 2, "title": "A Three-Branch Checks-and-Balances Frameworkfor 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 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": "researchtrend.ai", "link": "https://researchtrend.ai/papers/2502.00136", "content": "This paper introduces a three-branch checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by 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": 3, "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": 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": "infolab.stanford.edu", "date": "", "ddg_snippet": "@article{chang2025threebranch, title={A Three-Branch Checks - and - Balances Framework for Context-Aware Ethical Alignment of Large Language Models}, author={Chang ...", "subpage_snippet": "", "source": "infolab.stanford.edu", "link": "http://infolab.stanford.edu/~echang/Behavior2024.bib", "content": "@article{chang2025threebranch, title={A Three-Branch Checks - and - Balances Framework for Context-Aware Ethical Alignment of Large Language Models}, author={Chang ..."}
+{"idx": 6, "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": 7, "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/2502.00136v3", "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 ..."}
+{"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": "PDF A Checks-and-Balances Framework for Context-Aware Ethical AI Alignment", "date": "", "ddg_snippet": "A Checks - and - Balances Framework for Context-Aware Ethical AI Alignment Susceptible to social biases Vulnerable to reward hacking \"Whack-A-Mole\" reactive approach Catastrophic forgetting issues", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/media/icml-2025/Slides/46461.pdf", "content": "A Checks - and - Balances Framework for Context-Aware Ethical AI Alignment Susceptible to social biases Vulnerable to reward hacking \"Whack-A-Mole\" reactive approach Catastrophic forgetting issues"}

data/sampled_jsons/4uOEiitySn_Limitations_and_Future_Work_section.jsonl

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+{"idx": 0, "title": "4.7.7. Known Limitations and Future Work — OpenFAST...", "date": "", "ddg_snippet": "The following list contains known current limitations in the code: Tight coupling is not yet supported. Only nontapered two-node Euler-Bernoulli (E-B) or Timoshenko (T) element formulations are available.(In the future , a generic cross section will be allowed.)", "subpage_snippet": "", "source": "openfast.readthedocs.io", "link": "https://openfast.readthedocs.io/en/v3.0.0/source/user/subdyn/future_work.html", "content": "The following list contains known current limitations in the code: Tight coupling is not yet supported. Only nontapered two-node Euler-Bernoulli (E-B) or Timoshenko (T) element formulations are available.(In the future , a generic cross section will be allowed.)"}
+{"idx": 1, "title": "Coefficients? 3. conclusions, limitations and future work", "date": "", "ddg_snippet": "The data collected to answer research questions 5 and 6 was analysed using linear regression.Surprisingly, most of the students did not complain about working schedules, week-end work or divided daily working hours nor about overtime work .", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/figures/2137663/table-14-coefficients-conclusions-limitations-and-future", "content": "The data collected to answer research questions 5 and 6 was analysed using linear regression.Surprisingly, most of the students did not complain about working schedules, week-end work or divided daily working hours nor about overtime work ."}
+{"idx": 2, "title": "Migrating Dokoon from Django REST Framework to GraphQL...", "date": "", "ddg_snippet": "Frontend Inefficiencies: Excessive fetch calls in index.js and login.js to aggregate data from multiple endpoints, coupled with error-handling verbosity. 2. GraphQL Adoption: Architectural Rationale. The migration to GraphQL (via graphene-django) addressed these limitations through", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/pulse/migrating-dokoon-from-django-rest-framework-graphql-guide-darbandi-yrkte", "content": "Frontend Inefficiencies: Excessive fetch calls in index.js and login.js to aggregate data from multiple endpoints, coupled with error-handling verbosity. 2. GraphQL Adoption: Architectural Rationale. The migration to GraphQL (via graphene-django) addressed these limitations through"}
+{"idx": 3, "title": "Limitations and Future Work | SpringerLink", "date": "", "ddg_snippet": "Data collection through expert interviews is relatively time-consuming and represents a key challenge. Within the limited time of a dissertation, only a limited number of interviews can be conducted in a reasonable amount of time.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-3-658-43905-7_7", "content": "Data collection through expert interviews is relatively time-consuming and represents a key challenge. Within the limited time of a dissertation, only a limited number of interviews can be conducted in a reasonable amount of time."}
+{"idx": 4, "title": "7.4 Limitations and Future Work", "date": "", "ddg_snippet": "Глава: 7.4 Limitations and Future Work . ВУЗ: НИЯУ МИФИ.Crowdsourcing tasks should include a carefully designed explanation of how negative terms work, ideally with example images that demonstrate the effect of negative terms. 7.3.6Provide dedicated user interfaces.", "subpage_snippet": "", "source": "studfile.net", "link": "https://studfile.net/preview/21440528/page:8/", "content": "Глава: 7.4 Limitations and Future Work . ВУЗ: НИЯУ МИФИ.Crowdsourcing tasks should include a carefully designed explanation of how negative terms work, ideally with example images that demonstrate the effect of negative terms. 7.3.6Provide dedicated user interfaces."}
+{"idx": 5, "title": "Limitations and Future Work in Dark Pattern Mitigation | HackerNoon", "date": "", "ddg_snippet": "The article acknowledges limitations in the study, including taxonomic diversity challenges and biases in participant recruitment. It highlights the need to expand the end-user-empowerment approach to mobile platforms, improve geographic diversity, and conduct larger-scale...", "subpage_snippet": "", "source": "hackernoon.com", "link": "https://hackernoon.com/limitations-and-future-work", "content": "The article acknowledges limitations in the study, including taxonomic diversity challenges and biases in participant recruitment. It highlights the need to expand the end-user-empowerment approach to mobile platforms, improve geographic diversity, and conduct larger-scale..."}
+{"idx": 6, "title": "Limitations and Future Work | gasgiant/FFT-Ocean | DeepWiki", "date": "", "ddg_snippet": "This document outlines the current limitations of the FFT-Ocean implementation and identifies potential areas for future development. As a prototype system, FFT-Ocean has several constraints that affe.", "subpage_snippet": "", "source": "deepwiki.com", "link": "https://deepwiki.com/gasgiant/FFT-Ocean/5-limitations-and-future-work", "content": "This document outlines the current limitations of the FFT-Ocean implementation and identifies potential areas for future development. As a prototype system, FFT-Ocean has several constraints that affe."}
+{"idx": 7, "title": "Limitations and Future Work - Understanding NUMA Effects on...", "date": "", "ddg_snippet": "There are some limitations to this work . Amortized batch freeing can cause problems if data structures have enormous nodes that really should be freed as soon as possible. For example, Brown et al. [4] introduced a data structure called a concurrent interpolation search tree...", "subpage_snippet": "", "source": "1library.co", "link": "https://1library.co/ca/article/limitations-and-future-work.7977130", "content": "There are some limitations to this work . Amortized batch freeing can cause problems if data structures have enormous nodes that really should be freed as soon as possible. For example, Brown et al. [4] introduced a data structure called a concurrent interpolation search tree..."}
+{"idx": 8, "title": "How to Write an Effective Conclusion for a Research Article-CSDN博客", "date": "", "ddg_snippet": "4. Limitations and Future Work . Description and Function.Description and Function: This section combines the limitations of the study with recommendations for future research.", "subpage_snippet": "", "source": "blog.csdn.net", "link": "https://blog.csdn.net/u013441358/article/details/142728208", "content": "4. Limitations and Future Work . Description and Function.Description and Function: This section combines the limitations of the study with recommendations for future research."}
+{"idx": 9, "title": "I've put these 10 prompts together: hopefully it will help... | Medium", "date": "", "ddg_snippet": "Read the introduction and conclusion sections of the paper. Provide a brief summary (3-4 sentences) of the context, objectives, and overarching conclusions drawn by the authors. 3. List and summarize important references[List of limitations and future work suggestions].", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@ttahovsky/ive-put-these-10-prompts-together-hopefully-it-will-help-someone-d2a701162345", "content": "Read the introduction and conclusion sections of the paper. Provide a brief summary (3-4 sentences) of the context, objectives, and overarching conclusions drawn by the authors. 3. List and summarize important references[List of limitations and future work suggestions]."}

data/sampled_jsons/4ufjBV6S4I_RAGGED_Towards_Informed_Design_of_Scalable_and_Stable_RAG_Systems_Section_3.1_retriever_p.jsonl

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+{"idx": 0, "title": "Retrieval-augmented generation - Wikipedia", "date": "", "ddg_snippet": "Retrieval-augmented generation is a technique that enables large language models to retrieve and incorporate new information . With RAG , LLMs do not respond to user queries until they refer to a specified set of documents.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Retrieval-augmented_generation", "content": "Retrieval-augmented generation is a technique that enables large language models to retrieve and incorporate new information . With RAG , LLMs do not respond to user queries until they refer to a specified set of documents."}
+{"idx": 1, "title": "Генерация, дополненная поиском — Википедия", "date": "", "ddg_snippet": "RAG расширяет базу знаний LLM до неограниченных размеров, дает быстрый доступ к специализированным доменам знаний или к внутренней базе знаний организации без необходимости переобучения модели.", "subpage_snippet": "", "source": "ru.wikipedia.org", "link": "https://ru.wikipedia.org/wiki/Генерация,_дополненная_поиском", "content": "RAG расширяет базу знаний LLM до неограниченных размеров, дает быстрый доступ к специализированным доменам знаний или к внутренней базе знаний организации без необходимости переобучения модели."}
+{"idx": 2, "title": "RAGGED: Towards Informed Design of Scalable and Stable RAG ...", "date": "", "ddg_snippet": "In this work, we introduce RAGGED , a framework for system-atically evaluating RAG systems across diverse retriever -reader configurations, retrieval depths, and datasets. Our analysis reveals that reader ro-bustness to noise is the key determinant of RAG stability and scalability.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=4ufjBV6S4I", "content": "In this work, we introduce RAGGED , a framework for system-atically evaluating RAG systems across diverse retriever -reader configurations, retrieval depths, and datasets. Our analysis reveals that reader ro-bustness to noise is the key determinant of RAG stability and scalability."}
+{"idx": 3, "title": "RAGGED: Towards Informed Design of Scalable and Stable RAG ... RAGGED: Towards Informed Design of Scalable and Stable RAG ... RAGGED: Towards Informed Design of RAGGED: Towards Informed Design of Retrieval Augmented ... [2403.09040] RAGGED: Towards Informed Design of Retrieval ... RAGGED: Towards Informed Design of Scalable and Stable RAG ...", "date": "", "ddg_snippet": "Mar 14, 2024 · Retrieval-augmented generation ( RAG ) enhances language models by integrating external knowledge, but its effectiveness is highly dependent on system configuration. Improper retrieval settings can degrade performance, making RAG less reliable than closed-book generation. In this work, we introduce RAGGED , a framework for systematically evaluating RAG systems across diverse retriever -reader ... 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. Which retriever to use? How many documents? Which reader to use? Why RAG Matters: Access to up-to-date knowledge, improved accuracy for complex tasks, cost-effective knowledge integration Challenges: Noisy data, retriever -reader mismatch, diverse task requirements. Solution: The RAGGED framework, a systematic tool for optimizing RAG configurations. Retrieval-augmented generation ( RAG ) systems have shown promise in improving task performance by leveraging external context, but realizing their full potential depends on careful configuration. In this paper, we investigate how the choice of retriever and reader models, context length, and context quality impact RAG per- formance across different task types. Our findings reveal that while ... To answer this, we introduce the RAGGED framework to analyze and optimize RAG systems . On a set of representative DBQA tasks, we study two classic sparse and dense retrievers, and four top-performing LMs in encoder-decoder and decoder-only architectures. This work introduces RAGGED , a framework for systematically evaluating RAG systems across diverse retriever -reader configurations, retrieval depths, and datasets, and reveals that reader robustness to noise is the key determinant of RAG stability and scalability. Retrieval-augmented generation ( RAG ) enhances language models by integrating external knowledge, but its effectiveness is highly ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2403.09040", "content": "Mar 14, 2024 · Retrieval-augmented generation ( RAG ) enhances language models by integrating external knowledge, but its effectiveness is highly dependent on system configuration. Improper retrieval settings can degrade performance, making RAG less reliable than closed-book generation. In this work, we introduce RAGGED , a framework for systematically evaluating RAG systems across diverse retriever -reader ... 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. Which retriever to use? How many documents? Which reader to use? Why RAG Matters: Access to up-to-date knowledge, improved accuracy for complex tasks, cost-effective knowledge integration Challenges: Noisy data, retriever -reader mismatch, diverse task requirements. Solution: The RAGGED framework, a systematic tool for optimizing RAG configurations. Retrieval-augmented generation ( RAG ) systems have shown promise in improving task performance by leveraging external context, but realizing their full potential depends on careful configuration. In this paper, we investigate how the choice of retriever and reader models, context length, and context quality impact RAG per- formance across different task types. Our findings reveal that while ... To answer this, we introduce the RAGGED framework to analyze and optimize RAG systems . On a set of representative DBQA tasks, we study two classic sparse and dense retrievers, and four top-performing LMs in encoder-decoder and decoder-only architectures. This work introduces RAGGED , a framework for systematically evaluating RAG systems across diverse retriever -reader configurations, retrieval depths, and datasets, and reveals that reader robustness to noise is the key determinant of RAG stability and scalability. Retrieval-augmented generation ( RAG ) enhances language models by integrating external knowledge, but its effectiveness is highly ..."}
+{"idx": 4, "title": "RAGGED: Towards Informed Design of Scalable and Stable RAG ...", "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": "github.com", "link": "https://github.com/neulab/ragged", "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": 5, "title": "RAGGED: Towards Informed Design of", "date": "", "ddg_snippet": "Which retriever to use? How many documents? Which reader to use? Why RAG Matters: Access to up-to-date knowledge, improved accuracy for complex tasks, cost-effective knowledge integration Challenges: Noisy data, retriever -reader mismatch, diverse task requirements. Solution: The RAGGED framework, a systematic tool for optimizing RAG configurations.", "subpage_snippet": "", "source": "adaptive-foundation-models.org", "link": "https://adaptive-foundation-models.org/posters/RAGGED_AFM_2024_Poster.pdf", "content": "Which retriever to use? How many documents? Which reader to use? Why RAG Matters: Access to up-to-date knowledge, improved accuracy for complex tasks, cost-effective knowledge integration Challenges: Noisy data, retriever -reader mismatch, diverse task requirements. Solution: The RAGGED framework, a systematic tool for optimizing RAG configurations."}
+{"idx": 6, "title": "RAGGED: Towards Informed Design of Retrieval Augmented ...", "date": "", "ddg_snippet": "Retrieval-augmented generation ( RAG ) systems have shown promise in improving task performance by leveraging external context, but realizing their full potential depends on careful configuration. In this paper, we investigate how the choice of retriever and reader models, context length, and context quality impact RAG per- formance across different task types. Our findings reveal that while ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=KDXj60FpJr", "content": "Retrieval-augmented generation ( RAG ) systems have shown promise in improving task performance by leveraging external context, but realizing their full potential depends on careful configuration. In this paper, we investigate how the choice of retriever and reader models, context length, and context quality impact RAG per- formance across different task types. Our findings reveal that while ..."}
+{"idx": 7, "title": "[2403.09040] RAGGED: Towards Informed Design of Retrieval ...", "date": "", "ddg_snippet": "To answer this, we introduce the RAGGED framework to analyze and optimize RAG systems . On a set of representative DBQA tasks, we study two classic sparse and dense retrievers, and four top-performing LMs in encoder-decoder and decoder-only architectures.", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2403.09040", "content": "To answer this, we introduce the RAGGED framework to analyze and optimize RAG systems . On a set of representative DBQA tasks, we study two classic sparse and dense retrievers, and four top-performing LMs in encoder-decoder and decoder-only architectures."}
+{"idx": 8, "title": "RAGGED: Towards Informed Design of Scalable and Stable RAG ...", "date": "", "ddg_snippet": "This work introduces RAGGED , a framework for systematically evaluating RAG systems across diverse retriever -reader configurations, retrieval depths, and datasets, and reveals that reader robustness to noise is the key determinant of RAG stability and scalability. Retrieval-augmented generation ( RAG ) enhances language models by integrating external knowledge, but its effectiveness is highly ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/RAGGED:-Towards-Informed-Design-of-Scalable-and-RAG-Hsia-Shaikh/ec0082509c157b493fefdefa50dfe0712c5e481d", "content": "This work introduces RAGGED , a framework for systematically evaluating RAG systems across diverse retriever -reader configurations, retrieval depths, and datasets, and reveals that reader robustness to noise is the key determinant of RAG stability and scalability. Retrieval-augmented generation ( RAG ) enhances language models by integrating external knowledge, but its effectiveness is highly ..."}
+{"idx": 9, "title": "RAGGED : Towards Informed Design of Scalable and Stable RAG ...", "date": "", "ddg_snippet": "Improper retrieval settings can degrade performance, making RAG less reliable than closed-book generation. In this work, we introduce RAGGED , a framework for systematically evaluating RAG systems across diverse retriever -reader configurations, retrieval depths, and datasets.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=4ufjBV6S4I", "content": "Improper retrieval settings can degrade performance, making RAG less reliable than closed-book generation. In this work, we introduce RAGGED , a framework for systematically evaluating RAG systems across diverse retriever -reader configurations, retrieval depths, and datasets."}

data/sampled_jsons/51x0dfsD8A_Hierarchical_Overlapping_Clustering_Algorithm_1_2-OC_costtemp_function.jsonl

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+{"idx": 0, "title": "Cluster analysis - Wikipedia", "date": "", "ddg_snippet": "Overlapping clustering (also: alternative clustering , multi-view clustering ): objects may belong to more than one cluster ; usually involving hard clusters . Hierarchical clustering : objects that belong to a child cluster also belong to the parent cluster .", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Cluster_analysis", "content": "Overlapping clustering (also: alternative clustering , multi-view clustering ): objects may belong to more than one cluster ; usually involving hard clusters . Hierarchical clustering : objects that belong to a child cluster also belong to the parent cluster ."}
+{"idx": 1, "title": "8 Clustering Algorithms in Machine Learning that All Data Scientists...", "date": "", "ddg_snippet": "This is a hierarchical clustering algorithm , but the downside is that it doesn't scale well when working with large data sets. It works by iterating over all of the data points and shifts them towards the mode. The mode in this context is the high density area of data points in a region.", "subpage_snippet": "", "source": "www.freecodecamp.org", "link": "https://www.freecodecamp.org/news/8-clustering-algorithms-in-machine-learning-that-all-data-scientists-should-know/", "content": "This is a hierarchical clustering algorithm , but the downside is that it doesn't scale well when working with large data sets. It works by iterating over all of the data points and shifts them towards the mode. The mode in this context is the high density area of data points in a region."}
+{"idx": 2, "title": "Clustering in Machine Learning - GeeksforGeeks", "date": "", "ddg_snippet": "1 . Hard Clustering : In hard clustering , each data point strictly belongs to exactly one cluster , no overlap is allowed. This approach assigns a clear membership, making it easier to interpret and use for definitive segmentation tasks.", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/machine-learning/clustering-in-machine-learning/", "content": "1 . Hard Clustering : In hard clustering , each data point strictly belongs to exactly one cluster , no overlap is allowed. This approach assigns a clear membership, making it easier to interpret and use for definitive segmentation tasks."}
+{"idx": 3, "title": "R: Plots of an Agglomerative Hierarchical Clustering", "date": "", "ddg_snippet": "The banner displays the hierarchy of clusters , and is equivalent to a tree. See Rousseeuw (1986) or chapter 5 of Kaufman and Rousseeuw (1990).", "subpage_snippet": "", "source": "stat.ethz.ch", "link": "https://stat.ethz.ch/R-manual/R-devel/library/cluster/html/plot.agnes.html", "content": "The banner displays the hierarchy of clusters , and is equivalent to a tree. See Rousseeuw (1986) or chapter 5 of Kaufman and Rousseeuw (1990)."}
+{"idx": 4, "title": "Fernando Gama, Santiago Segarra, and Alejandro Ribeiro", "date": "", "ddg_snippet": "IV Hierarchical overlapping clustering algorithm . Algorithm 1 Hierarchical overlapping clustering algorithm O. Input: J: no. of perturbations, N : network, H: hierarchical clustering method, perturbation(·). Output: KX : nested collection of coverings.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1611.01393", "content": "IV Hierarchical overlapping clustering algorithm . Algorithm 1 Hierarchical overlapping clustering algorithm O. Input: J: no. of perturbations, N : network, H: hierarchical clustering method, perturbation(·). Output: KX : nested collection of coverings."}
+{"idx": 5, "title": "Кластеризация в ML: от теоретических основ популярных... / Хабр", "date": "", "ddg_snippet": "Статья «Modern hierarchical , agglomerative clustering algorithms », Daniel Müllner.Статья «On Spectral Clustering : Analysis and an algorithm », Andrew Y. Ng, Michael I . Jordan, Yair Weiss. Документация: описание SpectralClustering, SpectralClustering (алгоритм).", "subpage_snippet": "", "source": "habr.com", "link": "https://habr.com/ru/articles/798331/", "content": "Статья «Modern hierarchical , agglomerative clustering algorithms », Daniel Müllner.Статья «On Spectral Clustering : Analysis and an algorithm », Andrew Y. Ng, Michael I . Jordan, Yair Weiss. Документация: описание SpectralClustering, SpectralClustering (алгоритм)."}
+{"idx": 6, "title": "StandardScaler — scikit-learn 1 .7. 2 documentation", "date": "", "ddg_snippet": "Demo of HDBSCAN clustering algorithm .A demo of K-Means clustering on the handwritten digits data. Comparing different hierarchical linkage methods on toy datasets.", "subpage_snippet": "", "source": "scikit-learn.org", "link": "https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html", "content": "Demo of HDBSCAN clustering algorithm .A demo of K-Means clustering on the handwritten digits data. Comparing different hierarchical linkage methods on toy datasets."}
+{"idx": 7, "title": "GeoGebra Classic", "date": "", "ddg_snippet": "Floor Function . Ceil Function . [ ... ″ ϕ. ς.", "subpage_snippet": "", "source": "autgeo.online", "link": "https://autgeo.online/", "content": "Floor Function . Ceil Function . [ ... ″ ϕ. ς."}
+{"idx": 8, "title": "Introducing Scikit-Learn | Python Data Science Handbook", "date": "", "ddg_snippet": "Limited object hierarchy : Only algorithms are represented by Python classes; datasets are represented in standard formats (NumPy arrays, Pandas DataFrames, SciPy sparse matrices) and parameter names use standard Python strings.", "subpage_snippet": "", "source": "jakevdp.github.io", "link": "https://jakevdp.github.io/PythonDataScienceHandbook/05.02-introducing-scikit-learn.html", "content": "Limited object hierarchy : Only algorithms are represented by Python classes; datasets are represented in standard formats (NumPy arrays, Pandas DataFrames, SciPy sparse matrices) and parameter names use standard Python strings."}
+{"idx": 9, "title": "(Решено) Упр.148 ГДЗ Макарычев 7 класс по алгебре", "date": "", "ddg_snippet": "Решение # 1 . Изображение Решите уравнение:а) 2 x + 9 = 13 - x; б) 14- y= 19-11y; в) 0,5а + 11 = 4- 3а; г) l, 2 n + 1 = 1 - n; д) 1 ,7 - 0,3m = 2 + 1 ,7m; е) 0,8x + 14 = 2 - 1 ,6x; ж) 15...", "subpage_snippet": "", "source": "reshak.ru", "link": "https://reshak.ru/otvet/makar7.php?otvet1=new/148", "content": "Решение # 1 . Изображение Решите уравнение:а) 2 x + 9 = 13 - x; б) 14- y= 19-11y; в) 0,5а + 11 = 4- 3а; г) l, 2 n + 1 = 1 - n; д) 1 ,7 - 0,3m = 2 + 1 ,7m; е) 0,8x + 14 = 2 - 1 ,6x; ж) 15..."}

data/sampled_jsons/51x0dfsD8A_time_complexity_section_page_7.jsonl

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+{"idx": 0, "title": "Understanding Time Complexity with Simple Examples", "date": "", "ddg_snippet": "Is the Time Complexity of an Algorithm/Code the same as the Running/Execution Time of Code? The Time Complexity of an algorithm/code is not equal to the actual time required to execute a particular code, but the number of times a statement executes. We can prove this by using the time command. For example: Write code in C/C++ or any other language to find the maximum between N numbers, where N ...", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/dsa/understanding-time-complexity-simple-examples/", "content": "Is the Time Complexity of an Algorithm/Code the same as the Running/Execution Time of Code? The Time Complexity of an algorithm/code is not equal to the actual time required to execute a particular code, but the number of times a statement executes. We can prove this by using the time command. For example: Write code in C/C++ or any other language to find the maximum between N numbers, where N ..."}
+{"idx": 1, "title": "Big O Cheat Sheet - Time Complexity Chart - freeCodeCamp.org", "date": "", "ddg_snippet": "In this guide, you have learned what time complexity is all about, how performance is determined using the Big O notation, and the various time complexities that exists with examples.", "subpage_snippet": "", "source": "www.freecodecamp.org", "link": "https://www.freecodecamp.org/news/big-o-cheat-sheet-time-complexity-chart/", "content": "In this guide, you have learned what time complexity is all about, how performance is determined using the Big O notation, and the various time complexities that exists with examples."}
+{"idx": 2, "title": "Chapter 7: Time Complexity Flashcards | Quizlet", "date": "", "ddg_snippet": "Study with Quizlet and memorize flashcards containing terms like time complexity class, multi-tape time complexity , nondeterministic time complexity and more.", "subpage_snippet": "", "source": "quizlet.com", "link": "https://quizlet.com/286207298/chapter-7-time-complexity-flash-cards/", "content": "Study with Quizlet and memorize flashcards containing terms like time complexity class, multi-tape time complexity , nondeterministic time complexity and more."}
+{"idx": 3, "title": "Week 7 - Complexity Analysis | PDF | Time Complexity - Scribd", "date": "", "ddg_snippet": "The document provides a comprehensive overview of complexity analysis, focusing on time and space complexity , and how to measure the efficiency of programs using Big O notation. It includes examples of code with their respective time and space complexities, as well as built-in functions and their complexities. Additionally, it discusses the implications of exceeding time or memory limits in ...", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/869863876/Week-7-Complexity-Analysis", "content": "The document provides a comprehensive overview of complexity analysis, focusing on time and space complexity , and how to measure the efficiency of programs using Big O notation. It includes examples of code with their respective time and space complexities, as well as built-in functions and their complexities. Additionally, it discusses the implications of exceeding time or memory limits in ..."}
+{"idx": 4, "title": "PDF *6.7 TIME COMPLEXITY - discoveringcs.net", "date": "", "ddg_snippet": "(Exercise 6.7.1 more explicitly illustrates how a string comparison works.) Put another way, the best-case time complexity of a string comparison is constant, or O(1), because it does not depend on the input size, and the worst-case time complexity for a string comparison is linearly proportional to n, or O(n).", "subpage_snippet": "", "source": "www.discoveringcs.net", "link": "https://www.discoveringcs.net/optional_sections/6.7.pdf", "content": "(Exercise 6.7.1 more explicitly illustrates how a string comparison works.) Put another way, the best-case time complexity of a string comparison is constant, or O(1), because it does not depend on the input size, and the worst-case time complexity for a string comparison is linearly proportional to n, or O(n)."}
+{"idx": 5, "title": "PDF Time Complexity Analysis", "date": "", "ddg_snippet": "Exact Time Complexity Analysis Reminder: The RAM Model Each \"simple\" operation (+, -, =, if, call) takes 1 time step. Loops and subroutine calls are not simple operations. They depend upon the size of the data and the contents of a subroutine. Each memory access takes 1 step.", "subpage_snippet": "", "source": "emilydolson.github.io", "link": "https://emilydolson.github.io/msu_algorithms_spring_2025/slides/TimeComplexityAnalysis.pdf", "content": "Exact Time Complexity Analysis Reminder: The RAM Model Each \"simple\" operation (+, -, =, if, call) takes 1 time step. Loops and subroutine calls are not simple operations. They depend upon the size of the data and the contents of a subroutine. Each memory access takes 1 step."}
+{"idx": 6, "title": "Complexity Analysis Cheat Sheet: Time & Space Complexity", "date": "", "ddg_snippet": "Master algorithm complexity analysis with this comprehensive reference covering Big O notation, time /space complexity , and practical analysis techniques.", "subpage_snippet": "", "source": "clelandco.com", "link": "https://clelandco.com/cheat-sheets/algorithm-analysis/complexity-analysis", "content": "Master algorithm complexity analysis with this comprehensive reference covering Big O notation, time /space complexity , and practical analysis techniques."}
+{"idx": 7, "title": "Time and Space Complexity - GeeksforGeeks", "date": "", "ddg_snippet": "Time Complexity : The time complexity of an algorithm quantifies the amount of time taken by an algorithm to run as a function of the length of the input. Note that the time to run is a function of the length of the input and not the actual execution time of the machine on which the algorithm is running on.", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/dsa/time-complexity-and-space-complexity/", "content": "Time Complexity : The time complexity of an algorithm quantifies the amount of time taken by an algorithm to run as a function of the length of the input. Note that the time to run is a function of the length of the input and not the actual execution time of the machine on which the algorithm is running on."}
+{"idx": 8, "title": "Time and Space Complexity in Data Structures Explained", "date": "", "ddg_snippet": "Understand time and space complexity in data structures. Learn how to optimize performance and enhance your coding efficiency with practical examples and insights.", "subpage_snippet": "", "source": "www.simplilearn.com", "link": "https://www.simplilearn.com/tutorials/data-structure-tutorial/time-and-space-complexity", "content": "Understand time and space complexity in data structures. Learn how to optimize performance and enhance your coding efficiency with practical examples and insights."}
+{"idx": 9, "title": "Explain Time Complexity and Space Complexity with Examples", "date": "", "ddg_snippet": "This guide simplifies time and space complexity , crucial concepts for optimizing algorithms. Learn how to measure performance using Big-O notation (O (1), O (n), O (log n)) with clear examples and real-life analogies. Master DSA and ace coding interviews by understanding how algorithms scale in speed and memory usage.", "subpage_snippet": "", "source": "www.c-sharpcorner.com", "link": "https://www.c-sharpcorner.com/article/explain-time-complexity-and-space-complexity-with-examples/", "content": "This guide simplifies time and space complexity , crucial concepts for optimizing algorithms. Learn how to measure performance using Big-O notation (O (1), O (n), O (log n)) with clear examples and real-life analogies. Master DSA and ace coding interviews by understanding how algorithms scale in speed and memory usage."}

data/sampled_jsons/6yBhoJn6qy_Causal_Modeling_of_Climate_Activism_on_Reddit_Figure_A.1_appendix_subreddits.jsonl

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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": "[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": 2, "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": 3, "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": 4, "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": 5, "title": "(PDF) Analyzing Climate Change Discussions on Reddit", "date": "", "ddg_snippet": "Climate action is one of the United Nations Sustainable Development Goals. We contribute to this effort by analyzing climate change topics on the Reddit social curation platform, which contains ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/366548402_Analyzing_climate_change_discussions_on_Reddit", "content": "Climate action is one of the United Nations Sustainable Development Goals. We contribute to this effort by analyzing climate change topics on the Reddit social curation platform, which contains ..."}
+{"idx": 6, "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 different 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/html/2410.10562v1", "content": "We developed a rich and comprehensive causal model to study the interplay between different determinants of climate activism on Reddit . This work represents a first attempt to apply a multi-causal model to social media data."}
+{"idx": 7, "title": "Causal Modeling of Climate Activism on Reddit - OpenReview", "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": "openreview.net", "link": "https://openreview.net/forum?id=6yBhoJn6qy", "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 - 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"}
+{"idx": 9, "title": "How to Build a Causal Inference Model to Explore Whether Global Warming ...", "date": "", "ddg_snippet": "A Proposed Causal Model The purpose of this article is to show that causal inference machine learning solutions can be used to build models to solve complex, large and meaningful real-world problems, hence I am going to put forward a proposal for the causal links that would be tested and improved by climate and energy experts in a real-world model.", "subpage_snippet": "", "source": "towardsdatascience.com", "link": "https://towardsdatascience.com/how-to-build-a-causal-inference-model-to-explore-whether-global-warming-is-caused-by-human-activity-2bcf1830e071/", "content": "A Proposed Causal Model The purpose of this article is to show that causal inference machine learning solutions can be used to build models to solve complex, large and meaningful real-world problems, hence I am going to put forward a proposal for the causal links that would be tested and improved by climate and energy experts in a real-world model."}

data/sampled_jsons/7ESHFpqjNO_Learning_Place_Cell_Representations_Equation_1_spatial_encoding_objective_function.jsonl

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+{"idx": 0, "title": "Learning Place Cell Representations and Context-Dependent ...", "date": "", "ddg_snippet": "How place cell responses emerge, and how these representations remap is not fully understood. In this work, we propose a similarity-based objective function that translates proximity in space, to proximity in representation . We show that a neural network trained to minimize the proposed objective learns place -like representations .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=7ESHFpqjNO", "content": "How place cell responses emerge, and how these representations remap is not fully understood. In this work, we propose a similarity-based objective function that translates proximity in space, to proximity in representation . We show that a neural network trained to minimize the proposed objective learns place -like representations ."}
+{"idx": 1, "title": "Learning Place Cell Representations and Context ...", "date": "", "ddg_snippet": "6 Nov 2024 — This paper introduces a novel objective function for modeling spatial representation akin to that of the hippocampus by forcing the spatially close positions ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=7ESHFpqjNO&noteId=F5hjJWYocY", "content": "6 Nov 2024 — This paper introduces a novel objective function for modeling spatial representation akin to that of the hippocampus by forcing the spatially close positions ..."}
+{"idx": 2, "title": "Decoding the Cognitive map: Learning place cells and remapping", "date": "", "ddg_snippet": "May 27, 2025 · Our model provides a normative framework for learning spatial representations previously reserved for biological place cells , providing new insight into place cell field formation and remapping.", "subpage_snippet": "", "source": "elifesciences.org", "link": "https://elifesciences.org/reviewed-preprints/99302v2", "content": "May 27, 2025 · Our model provides a normative framework for learning spatial representations previously reserved for biological place cells , providing new insight into place cell field formation and remapping."}
+{"idx": 3, "title": "Mechanisms of experience-dependent place-cell referencing in ...", "date": "", "ddg_snippet": "Apr 1 , 2025 · Through multiday imaging and acute whole- cell recordings in behaving mice, Qian, Li and Magee provide insight into place field formation in general and specifically how the hippocampus adaptively ...", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41593-025-01930-5", "content": "Apr 1 , 2025 · Through multiday imaging and acute whole- cell recordings in behaving mice, Qian, Li and Magee provide insight into place field formation in general and specifically how the hippocampus adaptively ..."}
+{"idx": 4, "title": "Place Cell - an overview | ScienceDirect Topics", "date": "", "ddg_snippet": "A place cell is a type of excitatory pyramidal neuron in the hippocampus that fires at specific locations within an animal's local environment, forming a place field. These cells play a crucial role in encoding spatial memory and can reactivate during rest or sleep, representing distinct maps of different environments.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/topics/neuroscience/place-cell", "content": "A place cell is a type of excitatory pyramidal neuron in the hippocampus that fires at specific locations within an animal's local environment, forming a place field. These cells play a crucial role in encoding spatial memory and can reactivate during rest or sleep, representing distinct maps of different environments."}
+{"idx": 5, "title": "Learning Conjunctive Representations - bioRxiv", "date": "", "ddg_snippet": "May 30, 2024 · Training a feedforward network to minimize the spatial objective function (2), results in the emergence of place -like representations in the output units. These representations are analogous to place cells observed in the Hippocampus [O’Keefe and Dostrovsky, 1971], as illustrated by the example ratemaps in Fig. 2a).", "subpage_snippet": "", "source": "www.biorxiv.org", "link": "https://www.biorxiv.org/content/10.1101/2024.05.30.596595.full.pdf", "content": "May 30, 2024 · Training a feedforward network to minimize the spatial objective function (2), results in the emergence of place -like representations in the output units. These representations are analogous to place cells observed in the Hippocampus [O’Keefe and Dostrovsky, 1971], as illustrated by the example ratemaps in Fig. 2a)."}
+{"idx": 6, "title": "Visual Place Cell Encoding: A Computational Model for Spatial ...", "date": "", "ddg_snippet": "Apr 22, 2025 · These results suggest that structured visual input, even in the absence of motion cues or reward-driven learning , is sufficient to generate place - cell -like spatial representations and support biologically inspired cognitive mapping.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.15953", "content": "Apr 22, 2025 · These results suggest that structured visual input, even in the absence of motion cues or reward-driven learning , is sufficient to generate place - cell -like spatial representations and support biologically inspired cognitive mapping."}
+{"idx": 7, "title": "Coordinated learning of grid cell and place cell spatial and ...", "date": "", "ddg_snippet": "A neural model proposes how entorhinal grid cells and hippocampal place cells may develop as spatial categories in a hierarchy of self-organizing maps (SOMs). The model responds to realistic rat navigational trajectories by learning both grid cells ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC3866446/", "content": "A neural model proposes how entorhinal grid cells and hippocampal place cells may develop as spatial categories in a hierarchy of self-organizing maps (SOMs). The model responds to realistic rat navigational trajectories by learning both grid cells ..."}
+{"idx": 8, "title": "Learning Place Cell Representations and Context-Dependent...", "date": "", "ddg_snippet": "Hippocampal place cells are known for their spatially selective firing patterns, which has led to the suggestion that they encode an animal's location. However, place cells also respond to contextual cues, such as smell.", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/venue/7ESHFpqjNO@OpenReview", "content": "Hippocampal place cells are known for their spatially selective firing patterns, which has led to the suggestion that they encode an animal's location. However, place cells also respond to contextual cues, such as smell."}
+{"idx": 9, "title": "Learning an Efficient Hippocampal Place Map from... | eNeuro", "date": "", "ddg_snippet": "Measuring the uniformity of place cell representation . For place cells that meets the criteria defined above, the field center (xc, yc) fitted by Equation 11 indicates the spatial location that the place cell responds to.", "subpage_snippet": "", "source": "www.eneuro.org", "link": "https://www.eneuro.org/content/8/4/ENEURO.0557-20.2021", "content": "Measuring the uniformity of place cell representation . For place cells that meets the criteria defined above, the field center (xc, yc) fitted by Equation 11 indicates the spatial location that the place cell responds to."}

data/sampled_jsons/7uqVfZW6Mo__Not_All_Diffusion_Model_Activations_Have_Been_Evaluated_as_Discriminative_Features.jsonl

@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Not All Diffusion Model Activations Have Been Evaluated as ...", "date": "", "ddg_snippet": "Diffusion models are initially designed for image generation. Recent research shows that the internal signals within their backbones, named activations , can also serve as dense features for various discriminative tasks such as semantic segmentation. Given numerous activations , selecting a small yet effective subset poses a fundamental problem. To this end, the early study of this field ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.03558", "content": "Diffusion models are initially designed for image generation. Recent research shows that the internal signals within their backbones, named activations , can also serve as dense features for various discriminative tasks such as semantic segmentation. Given numerous activations , selecting a small yet effective subset poses a fundamental problem. To this end, the early study of this field ..."}
+{"idx": 1, "title": "Darkbblue/generic-diffusion-feature - GitHub", "date": "", "ddg_snippet": "Diffusion feature is a quite popular way to utilize generative diffusion models for discrimination. It's very simple: just extract some internal activations from a diffusion model , and then use these 2D features to replace image inputs of any discriminative model . There have been quite many diffusion feature studies. But we notice that almost all of them experiment with Stable Diffusion v1.4 ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Darkbblue/generic-diffusion-feature", "content": "Diffusion feature is a quite popular way to utilize generative diffusion models for discrimination. It's very simple: just extract some internal activations from a diffusion model , and then use these 2D features to replace image inputs of any discriminative model . There have been quite many diffusion feature studies. But we notice that almost all of them experiment with Stable Diffusion v1.4 ..."}
+{"idx": 2, "title": "Not All Diffusion Model Activations Have Been Evaluated as ...", "date": "", "ddg_snippet": "To this end, the early study of this field performs a large-scale quantitative comparison of the discriminative ability of the activations . However, we find that many potential activations have not been evaluated , such as the queries and keys used to compute attention scores.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=7uqVfZW6Mo", "content": "To this end, the early study of this field performs a large-scale quantitative comparison of the discriminative ability of the activations . However, we find that many potential activations have not been evaluated , such as the queries and keys used to compute attention scores."}
+{"idx": 3, "title": "Not All Diffusion Model Activations Have Been Evaluated as ...", "date": "", "ddg_snippet": "However, we find that many potential activations have not been evaluated , such as the queries and keys used to compute attention scores. Moreover, recent advancements in diffusion architectures bring many new activations , such as those within embedded ViT modules. Both combined, activation selection remains unresolved but overlooked.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/hash/633780c1344d0c95e4d2dd3431fe08d9-Abstract-Conference.html", "content": "However, we find that many potential activations have not been evaluated , such as the queries and keys used to compute attention scores. Moreover, recent advancements in diffusion architectures bring many new activations , such as those within embedded ViT modules. Both combined, activation selection remains unresolved but overlooked."}
+{"idx": 4, "title": "Not All Diffusion Model Activations Have Been Evaluated as ...", "date": "", "ddg_snippet": "Unlocking superior discriminative features from diffusion models , this research reveals key activation properties for effective feature selection, surpassing state-of-the-art methods.", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/spotlight-others/7uqvfzw6mo/", "content": "Unlocking superior discriminative features from diffusion models , this research reveals key activation properties for effective feature selection, surpassing state-of-the-art methods."}
+{"idx": 5, "title": "PDF Not All Diffusion Model Activations Have Been Evaluated as ...", "date": "", "ddg_snippet": "Not All Diffusion Model Activations Have Been Evaluated as Discriminative Features Benyuan Meng, Qianqian Xu*, Zitai Wang, Xiaochun Cao, Qingming Huang*", "subpage_snippet": "", "source": "nips.cc", "link": "https://nips.cc/media/neurips-2024/Slides/96411.pdf", "content": "Not All Diffusion Model Activations Have Been Evaluated as Discriminative Features Benyuan Meng, Qianqian Xu*, Zitai Wang, Xiaochun Cao, Qingming Huang*"}
+{"idx": 6, "title": "Not All Diffusion Model Activations Have Been Evaluated as ...", "date": "", "ddg_snippet": "View recent discussion. Abstract: Diffusion models are initially designed for image generation. Recent research shows that the internal signals within their backbones, named activations , can also serve as dense features for various discriminative tasks such as semantic segmentation. Given numerous activations , selecting a small yet effective subset poses a fundamental problem. To this end, the ...", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2410.03558v3", "content": "View recent discussion. Abstract: Diffusion models are initially designed for image generation. Recent research shows that the internal signals within their backbones, named activations , can also serve as dense features for various discriminative tasks such as semantic segmentation. Given numerous activations , selecting a small yet effective subset poses a fundamental problem. To this end, the ..."}
+{"idx": 7, "title": "Not All Diffusion Model Activations Have Been Evaluated as ...", "date": "", "ddg_snippet": "Through a series of experiments, the paper shows that not all diffusion model activations are equally useful as features for image classification. This suggests that there may be untapped potential in these internal representations that could be unlocked through further research.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/not-all-diffusion-model-activations-have-been", "content": "Through a series of experiments, the paper shows that not all diffusion model activations are equally useful as features for image classification. This suggests that there may be untapped potential in these internal representations that could be unlocked through further research."}
+{"idx": 8, "title": "Discffusion: Discriminative Diffusion Models as Few-shot Vision and ...", "date": "", "ddg_snippet": "Diffusion models , such as Stable Diffusion , have shown incredible performance on text-to-image generation. Since text-to-image generation often requires models to generate visual concepts with fine-grained details and attributes specified in text prompts, can we leverage the powerful representations learned by pre-trained diffusion models for discriminative tasks such as image-text matching ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2305.10722", "content": "Diffusion models , such as Stable Diffusion , have shown incredible performance on text-to-image generation. Since text-to-image generation often requires models to generate visual concepts with fine-grained details and attributes specified in text prompts, can we leverage the powerful representations learned by pre-trained diffusion models for discriminative tasks such as image-text matching ..."}
+{"idx": 9, "title": "Not All Diffusion Model Activations Have Been Evaluated as ...", "date": "", "ddg_snippet": "This paper investigates the effective selection of diverse activations from diffusion models for discriminative tasks, revealing universal properties that enhance feature selection methods and demonstrate superior performance in semantic segmentation and correspondence compared to existing state-of-the-art approaches.", "subpage_snippet": "", "source": "chatpaper.com", "link": "https://chatpaper.com/chatpaper/paper/64311?from=subpath-search", "content": "This paper investigates the effective selection of diverse activations from diffusion models for discriminative tasks, revealing universal properties that enhance feature selection methods and demonstrate superior performance in semantic segmentation and correspondence compared to existing state-of-the-art approaches."}

data/sampled_jsons/ACDRepo_Schubert_polynomials_accuracy_table_1_sitegithub.com.jsonl

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+{"idx": 0, "title": "GitHub - AcraeaTerpsicore/Schubert-polynomials-and-patterns-in ...", "date": "", "ddg_snippet": "Overview This repository provides complete computational verification of results from the research paper \" Schubert polynomials and patterns in permutations\" (arXiv:2412.02932v1) by Peter L. Guo and Zhuowei Lin. Our implementations compute and verify all numerical values, tables , and conjectures presented in the paper using first-principles mathematical definitions.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/AcraeaTerpsicore/Schubert-polynomials-and-patterns-in-permutations", "content": "Overview This repository provides complete computational verification of results from the research paper \" Schubert polynomials and patterns in permutations\" (arXiv:2412.02932v1) by Peter L. Guo and Zhuowei Lin. Our implementations compute and verify all numerical values, tables , and conjectures presented in the paper using first-principles mathematical definitions."}
+{"idx": 1, "title": "Schubert-polynomials-and-patterns-in-permutations/cu_du_table ... - GitHub", "date": "", "ddg_snippet": "Print [\" TABLE : Permutations in S_m for m ≤ 5 with nonzero values of c_u and d_u\"];", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/AcraeaTerpsicore/Schubert-polynomials-and-patterns-in-permutations/blob/main/cu_du_table_display.wl", "content": "Print [\" TABLE : Permutations in S_m for m ≤ 5 with nonzero values of c_u and d_u\"];"}
+{"idx": 2, "title": "GitHub - pnnl/ML4AlgComb: ML Benchmarks in Algebraic Combinatorics", "date": "", "ddg_snippet": "Schubert polynomial structure constants: Schubert polynomials are a family of polynomials indexed by permutations of S n . Developed to study the cohomology ring of the flag variety, they have deep connections to algebraic geometry, Lie theory, and representation theory.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/pnnl/ML4AlgComb", "content": "Schubert polynomial structure constants: Schubert polynomials are a family of polynomials indexed by permutations of S n . Developed to study the cohomology ring of the flag variety, they have deep connections to algebraic geometry, Lie theory, and representation theory."}
+{"idx": 3, "title": "Schubert-Polynomial-Package/SchubertPolynomials.m at master - GitHub", "date": "", "ddg_snippet": "A Mathematica package for studying the combinatorics of Schubert polynomials , permutations, pipe dreams, Loretnzian polynomials , and more. - avstdi/ Schubert - Polynomial -Package", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/avstdi/Schubert-Polynomial-Package/blob/master/SchubertPolynomials.m", "content": "A Mathematica package for studying the combinatorics of Schubert polynomials , permutations, pipe dreams, Loretnzian polynomials , and more. - avstdi/ Schubert - Polynomial -Package"}
+{"idx": 4, "title": "GitHub - matthematics/schubmult: Program for computing Littlewood ...", "date": "", "ddg_snippet": "schubmult_py is for multiplying ordinary Schubert polynomials . schubmult_yz is for multiplying double Schubert polynomials in different sets of coefficient variables (labeled y and z), and schubmult_double is for multiplying double Schubert polynomials in the same set of coefficient variables.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/matthematics/schubmult", "content": "schubmult_py is for multiplying ordinary Schubert polynomials . schubmult_yz is for multiplying double Schubert polynomials in different sets of coefficient variables (labeled y and z), and schubmult_double is for multiplying double Schubert polynomials in the same set of coefficient variables."}
+{"idx": 5, "title": "VivianePons/multipolynomial-bases - GitHub", "date": "", "ddg_snippet": "A Sage package to work on multipolynomials bases ( Schubert , Grothendieck, Key) - VivianePons/multipolynomial-bases", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/VivianePons/multipolynomial-bases", "content": "A Sage package to work on multipolynomials bases ( Schubert , Grothendieck, Key) - VivianePons/multipolynomial-bases"}
+{"idx": 6, "title": "A catalogue of polynomial approximations - GitHub", "date": "", "ddg_snippet": "These text files report the polynomial approximations on the Pareto front of a few computational efficiency metrics and of accuracy . The current version only considers single and double float polynomials , with degree at most 16, for a few transcendentals: sin, cos, atan, exp, log, log1px, and lg1px ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/pkhuong/polynomial-approximation-catalogue", "content": "These text files report the polynomial approximations on the Pareto front of a few computational efficiency metrics and of accuracy . The current version only considers single and double float polynomials , with degree at most 16, for a few transcendentals: sin, cos, atan, exp, log, log1px, and lg1px ..."}
+{"idx": 7, "title": "SchubertPolynomials.jl/README.md at main - GitHub", "date": "", "ddg_snippet": "This package provides functions for computing Schubert polynomials via bumpless pipe dreams and drift polynomials . The former (\"BPDs\") are gadgets which enumerate terms in a Schubert polynomial , developed by Lam-Lee-Shimozono. The latter are generalizations of Schur polynomials that William Fulton and I have found useful. The package also uses \"standard\" ways of computing (divided difference ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/pseudoeffective/SchubertPolynomials.jl/blob/main/README.md", "content": "This package provides functions for computing Schubert polynomials via bumpless pipe dreams and drift polynomials . The former (\"BPDs\") are gadgets which enumerate terms in a Schubert polynomial , developed by Lam-Lee-Shimozono. The latter are generalizations of Schur polynomials that William Fulton and I have found useful. The package also uses \"standard\" ways of computing (divided difference ..."}
+{"idx": 8, "title": "GitHub - GunterMueller/SYMMETRICA: SYMMETRICA- written in C by Axel ...", "date": "", "ddg_snippet": "SYMMETRICA- written in C by Axel Kohnert, is a collection of routines to compute with symmetric functions and Schubert polynomials , ordinary, modular, and projective representations of the symmetr...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/GunterMueller/SYMMETRICA/", "content": "SYMMETRICA- written in C by Axel Kohnert, is a collection of routines to compute with symmetric functions and Schubert polynomials , ordinary, modular, and projective representations of the symmetr..."}
+{"idx": 9, "title": "GitHub - ryankeleti/pipedreams: Pipe dreaming", "date": "", "ddg_snippet": "Pipe dreams A little library for computing Schubert polynomials from pipe dreams.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/ryankeleti/pipedreams", "content": "Pipe dreams A little library for computing Schubert polynomials from pipe dreams."}

data/sampled_jsons/ACIDDnTbSJ_Feint_Behaviors_and_Strategies_Formalization_Implementation_Evaluation_Appendix_C.1_Figur_year_2023.jsonl

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+{"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": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/hash/064ae24cdbb3eaacc801ee7f4fe0e4f2-Abstract-Conference.html", "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": "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": "papers.cool", "link": "https://papers.cool/venue/ACIDDnTbSJ@OpenReview", "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": 2, "title": "Computer Science and Game Theory Mar 2024", "date": "", "ddg_snippet": "Title: Application of Nash equilibrium for developing an optimal forest harvesting strategy in Toruń Forest District.Comments: 2 figures , 2 tables. Subjects: Computer Science and Game Theory (cs.GT).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/list/cs.GT/2024-03", "content": "Title: Application of Nash equilibrium for developing an optimal forest harvesting strategy in Toruń Forest District.Comments: 2 figures , 2 tables. Subjects: Computer Science and Game Theory (cs.GT)."}
+{"idx": 3, "title": "Feint Behaviors and Strategies : Formalization , Implementation and...", "date": "", "ddg_snippet": "Figure 1 : An example of Palindrome-directed Generation Templates of Feint behaviors . The first row shows an action sequence of a cross-punch behavior .", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/posters/aciddntbsj/", "content": "Figure 1 : An example of Palindrome-directed Generation Templates of Feint behaviors . The first row shows an action sequence of a cross-punch behavior ."}
+{"idx": 4, "title": "Coding Implementation to End-to-End Transformer... - MarkTechPost", "date": "", "ddg_snippet": "Meta researchers introduced a method that compresses repeated reasoning patterns into short, named procedures—“ behaviors ”— and then conditions models to use them at inference or distills...", "subpage_snippet": "", "source": "www.marktechpost.com", "link": "https://www.marktechpost.com/2025/09/23/coding-implementation-to-end-to-end-transformer-model-optimization-with-hugging-face-optimum-onnx-runtime-and-quantization/", "content": "Meta researchers introduced a method that compresses repeated reasoning patterns into short, named procedures—“ behaviors ”— and then conditions models to use them at inference or distills..."}
+{"idx": 5, "title": "ERA5-Land hourly data from 1950 to present", "date": "", "ddg_snippet": "The intention is to improve the user experience by collecting metrics on visitor behaviour and fix issues on the website.", "subpage_snippet": "", "source": "cds.climate.copernicus.eu", "link": "https://cds.climate.copernicus.eu/datasets/reanalysis-era5-land?tab=download", "content": "The intention is to improve the user experience by collecting metrics on visitor behaviour and fix issues on the website."}
+{"idx": 6, "title": "Junyu LIU - Google Scholar", "date": "", "ddg_snippet": "Feint behaviors and strategies : formalization , implementation and evaluation .", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=EiSrD1UAAAAJ&hl=en", "content": "Feint behaviors and strategies : formalization , implementation and evaluation ."}
+{"idx": 7, "title": "dblp: List of computer science publications by Xiangjun Peng", "date": "", "ddg_snippet": "Junyu Liu, Xiangjun Peng: Feint Behaviors and Strategies : Formalization , Implementation and Evaluation . NeurIPS 2024.", "subpage_snippet": "", "source": "dblp.uni-trier.de", "link": "https://dblp.uni-trier.de/pid/123/5256.html", "content": "Junyu Liu, Xiangjun Peng: Feint Behaviors and Strategies : Formalization , Implementation and Evaluation . NeurIPS 2024."}
+{"idx": 8, "title": "What’s New in DeepSeek-V3. 1 -Terminus: Improved Language...", "date": "", "ddg_snippet": "Key takeaway: tighter evaluation and targeted fine-tuning can lower error rates where models previously “flip” on facts or instructions. Code agent upgrades: fewer hallucinations and better multi-step reasoning.", "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": "Key takeaway: tighter evaluation and targeted fine-tuning can lower error rates where models previously “flip” on facts or instructions. Code agent upgrades: fewer hallucinations and better multi-step reasoning."}
+{"idx": 9, "title": "Metacognitive Reuse in LLM Reasoning", "date": "", "ddg_snippet": "The behaviors are then used by a \"Student\" LLM for downstream reasoning tasks. Figure 1 . Figure 1 : The behavior curation pipeline, showing solution generation, reflection, and behavior extraction, as well as the integration of behaviors into inference and fine-tuning.", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/papers/2509.13237", "content": "The behaviors are then used by a \"Student\" LLM for downstream reasoning tasks. Figure 1 . Figure 1 : The behavior curation pipeline, showing solution generation, reflection, and behavior extraction, as well as the integration of behaviors into inference and fine-tuning."}

data/sampled_jsons/ACL_Anthology_CVE-Bench_long_paper_'Insufficient_Exploration'_definition_year_2023-2024.jsonl

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+{"idx": 0, "title": "CVE - Bench : Benchmarking LLM-based Software Engineering", "date": "", "ddg_snippet": "In this paper , we introduce CVE - Bench (§2), a benchmark that evaluates LLM-based agents in a realistic vulnerability -repairing setting. CVE - Bench contains three unique characteristics", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.naacl-long.212.pdf", "content": "In this paper , we introduce CVE - Bench (§2), a benchmark that evaluates LLM-based agents in a realistic vulnerability -repairing setting. CVE - Bench contains three unique characteristics"}
+{"idx": 1, "title": "Ingestion checks · Issue #5695 · acl -org/ acl - anthology · GitHub", "date": "", "ddg_snippet": "Explore . acl -org / acl - anthology Public. Notifications You must be signed in to change notification settings. Fork 355.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/acl-org/acl-anthology/issues/5695", "content": "Explore . acl -org / acl - anthology Public. Notifications You must be signed in to change notification settings. Fork 355."}
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data/sampled_jsons/AERO_Enhancing_Sharding_Blockchain_via_Deep_Reinforcement_Learning_for_Account_Migration_PDF.jsonl

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+{"idx": 1, "title": "AERO : Enhancing Sharding Blockchain via Deep Reinforcement ...", "date": "", "ddg_snippet": "D Full Shard Transaction Distriction. AERO : Enhancing Sharding Blockchain via Deep Reinforcement Learning for Account Migration . Blockchain , Sharding , Account migration , Reinforcement learning .", "subpage_snippet": "", "source": "zhenxiao.com", "link": "http://zhenxiao.com/papers/WWW_AERO_camera_ready.pdf", "content": "D Full Shard Transaction Distriction. AERO : Enhancing Sharding Blockchain via Deep Reinforcement Learning for Account Migration . Blockchain , Sharding , Account migration , Reinforcement learning ."}
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+{"idx": 5, "title": "[PDF] LB-Chain: Load-Balanced and Low-Latency Blockchain ...", "date": "", "ddg_snippet": "Blockchain sharding has been increasingly used to improve blockchain systems’ performance, in which a blockchain is split into multiple smaller, disjoint shards .", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/LB-Chain:-Load-Balanced-and-Low-Latency-Blockchain-Li-Wang/f78243fab1362932088f8593b898151dee65ffbf", "content": "Blockchain sharding has been increasingly used to improve blockchain systems’ performance, in which a blockchain is split into multiple smaller, disjoint shards ."}
+{"idx": 6, "title": "Enhancing Scalability in Sharding Blockchain via ... | SpringerLink", "date": "", "ddg_snippet": "(2022) BrokerChain: a cross- shard blockchain protocol for account /balance-based state sharding . In: IEEE INFOCOM 2022—IEEE conference on computer communications, London, United Kingdom, pp 1968–1977.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-981-97-1923-5_26", "content": "(2022) BrokerChain: a cross- shard blockchain protocol for account /balance-based state sharding . In: IEEE INFOCOM 2022—IEEE conference on computer communications, London, United Kingdom, pp 1968–1977."}
+{"idx": 7, "title": "ContribChain: A Stress-Balanced Blockchain Sharding Protocol with...", "date": "", "ddg_snippet": "[27] employs Deep Reinforcement Learn -ing (DRL) to optimize state placement. While these ap-proaches enhance account allocation, they fail to consider performance disparities among shards , and thus do not achieve stress balance.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2505.06899", "content": "[27] employs Deep Reinforcement Learn -ing (DRL) to optimize state placement. While these ap-proaches enhance account allocation, they fail to consider performance disparities among shards , and thus do not achieve stress balance."}
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data/sampled_jsons/AERO_Enhancing_Sharding_Blockchain_via_Deep_Reinforcement_Learning_for_Account_Migration_reward_func.jsonl

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+{"idx": 1, "title": "AERO: Enhancing Sharding Blockchain via Deep Reinforcement ...", "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 ...", "date": "", "ddg_snippet": "Jan 29, 2025 · 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": "Jan 29, 2025 · 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."}
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+{"idx": 5, "title": "Sustainable business decision modelling with blockchain ...", "date": "", "ddg_snippet": "by G Wickremasinghe · 2025 — This includes machine learning techniques such as supervised, unsupervised, reinforcement learning , partial differential equations integration ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2096720925000399", "content": "by G Wickremasinghe · 2025 — This includes machine learning techniques such as supervised, unsupervised, reinforcement learning , partial differential equations integration ..."}
+{"idx": 6, "title": "Machine Learning Apr 2025", "date": "", "ddg_snippet": "8 Apr 2025 — Title: Stability Enhancement in Reinforcement Learning via Adaptive Control Lyapunov Function . Donghe Chen, Han Wang, Lin Cheng, Shengping ...", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/list/cs.LG/2025-04?skip=375&show=2000", "content": "8 Apr 2025 — Title: Stability Enhancement in Reinforcement Learning via Adaptive Control Lyapunov Function . Donghe Chen, Han Wang, Lin Cheng, Shengping ..."}
+{"idx": 7, "title": "Blockchain Security and Its Application in Internet of Things", "date": "", "ddg_snippet": "In Figure 1, blockchain -based transactions are verified using a machine learning model, and the prediction result shows that the transaction is legitimate ...", "subpage_snippet": "", "source": "mdpi-res.com", "link": "https://mdpi-res.com/bookfiles/book/10958/Blockchain_Security_and_Its_Application_in_Internet_of_Things.pdf?v=1748528140", "content": "In Figure 1, blockchain -based transactions are verified using a machine learning model, and the prediction result shows that the transaction is legitimate ..."}
+{"idx": 8, "title": "Papers - Zhen Xiao", "date": "", "ddg_snippet": "AERO : Enhancing Sharding Blockchain via Deep Reinforcement Learning for Account Migration Proc. of the Web Conference 2025 (WWW 2025), May 2025. Lichen Pan, Juncheng Liu, Yongquan Fu, Jinhui Yuan, Rongkai Zhang, Pengze Li, and Zhen Xiao.", "subpage_snippet": "", "source": "zhenxiao.com", "link": "http://zhenxiao.com/papers/", "content": "AERO : Enhancing Sharding Blockchain via Deep Reinforcement Learning for Account Migration Proc. of the Web Conference 2025 (WWW 2025), May 2025. Lichen Pan, Juncheng Liu, Yongquan Fu, Jinhui Yuan, Rongkai Zhang, Pengze Li, and Zhen Xiao."}
+{"idx": 9, "title": "Mingxuan Song", "date": "", "ddg_snippet": "WWW 2025 Oral CCF-A Mingxuan Song, Pengze Li, Bohan Zhou, Shenglin Yin, Zhen Xiao*, Jieyi Long. \" AERO : Enhancing Sharding Blockchain via Deep Reinforcement Learning for Account Migration .\" Proceedings of the Web Conference, May 2025. [PDF] CVPR 2024 CCF-A Shenglin Yin, Zhen Xiao*, Mingxuan Song, and Jieyi Long. \"Adversarial Distillation Based on Slack Matching and Attribution Region Alignment ...", "subpage_snippet": "", "source": "www.songmingxuan.com", "link": "https://www.songmingxuan.com/", "content": "WWW 2025 Oral CCF-A Mingxuan Song, Pengze Li, Bohan Zhou, Shenglin Yin, Zhen Xiao*, Jieyi Long. \" AERO : Enhancing Sharding Blockchain via Deep Reinforcement Learning for Account Migration .\" Proceedings of the Web Conference, May 2025. [PDF] CVPR 2024 CCF-A Shenglin Yin, Zhen Xiao*, Mingxuan Song, and Jieyi Long. \"Adversarial Distillation Based on Slack Matching and Attribution Region Alignment ..."}

data/sampled_jsons/AERO_Enhancing_Sharding_Blockchain_via_Deep_Reinforcement_Learning_for_Account_Migration_reward_func_year_2024.jsonl

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+{"idx": 0, "title": "Literature | PDF | Cryptocurrency | Machine Learning", "date": "", "ddg_snippet": "... Blockchain Sharding via Account Migration Structural Identity Representation Learning for Blockchain-Enabled Metaverse Based on Complex N Collaborative ...", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/752858914/literature", "content": "... Blockchain Sharding via Account Migration Structural Identity Representation Learning for Blockchain-Enabled Metaverse Based on Complex N Collaborative ..."}
+{"idx": 1, "title": "Advancement in Blockchain Technology and Applications", "date": "", "ddg_snippet": "Face Detection Using Deep Learning to Ensure a Coercion Resistant Blockchain -Based. Electronic Voting. Eng. Sci. 2021, 16, 341–353. 143. Tandon, S.; Singh, N ...", "subpage_snippet": "", "source": "mdpi-res.com", "link": "https://mdpi-res.com/bookfiles/book/9291/Advancement_in_Blockchain_Technology_and_Applications.pdf?v=1740190107", "content": "Face Detection Using Deep Learning to Ensure a Coercion Resistant Blockchain -Based. Electronic Voting. Eng. Sci. 2021, 16, 341–353. 143. Tandon, S.; Singh, N ..."}
+{"idx": 2, "title": "Machine Learning Jan 2024", "date": "", "ddg_snippet": "8 Jan 2024 — Title: Consistency Enhancement -Based Deep Multiview Clustering via Contrastive Learning . Hao Yang, Hua Mao, Wai Lok Woo, Jie Chen, Xi Peng.", "subpage_snippet": "", "source": "arxiv.org", "link": "http://arxiv.org/list/cs.LG/2024-01?skip=800&show=2000", "content": "8 Jan 2024 — Title: Consistency Enhancement -Based Deep Multiview Clustering via Contrastive Learning . Hao Yang, Hua Mao, Wai Lok Woo, Jie Chen, Xi Peng."}
+{"idx": 3, "title": "Pattern Recognition and Machine Learning (Information ...", "date": "", "ddg_snippet": "Deep unsupervised clustering by information maximization on Gaussian mixture autoencoders, Information Sciences: an International Journal, 714:C, Online ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/book/10.5555/1162264", "content": "Deep unsupervised clustering by information maximization on Gaussian mixture autoencoders, Information Sciences: an International Journal, 714:C, Online ..."}
+{"idx": 4, "title": "Handbook of Blockchain Technology 1", "date": "", "ddg_snippet": "This Handbook provides an interdisciplinary investigation into the role and influence of blockchain technology in areas...", "subpage_snippet": "", "source": "dokumen.pub", "link": "https://dokumen.pub/handbook-of-blockchain-technology-1.html", "content": "This Handbook provides an interdisciplinary investigation into the role and influence of blockchain technology in areas..."}
+{"idx": 5, "title": "Blockchain Security and Its Application in Internet of Things", "date": "", "ddg_snippet": "In Figure 1, blockchain -based transactions are verified using a machine learning model, and the prediction result shows that the transaction is legitimate ...", "subpage_snippet": "", "source": "mdpi-res.com", "link": "https://mdpi-res.com/bookfiles/book/10958/Blockchain_Security_and_Its_Application_in_Internet_of_Things.pdf?v=1748528140", "content": "In Figure 1, blockchain -based transactions are verified using a machine learning model, and the prediction result shows that the transaction is legitimate ..."}
+{"idx": 6, "title": "Machine Learning Jan 2024", "date": "", "ddg_snippet": "8 Jan 2024 — Title: Classification and Reconstruction Processes in Deep Predictive Coding Networks: Antagonists or Allies? Jan Rathjens, Laurenz Wiskott.", "subpage_snippet": "", "source": "arxiv.org", "link": "http://arxiv.org/list/cs.LG/2024-01?skip=325&show=2000", "content": "8 Jan 2024 — Title: Classification and Reconstruction Processes in Deep Predictive Coding Networks: Antagonists or Allies? Jan Rathjens, Laurenz Wiskott."}
+{"idx": 7, "title": "Co-Designing Cryptographic Systems with Resource", "date": "", "ddg_snippet": "by JL Watson · 2024 — We target a core bottleneck in zero-knowledge proofs, multiscalar multiplication (MSM), improve latency for large single proof execution on the ...", "subpage_snippet": "", "source": "www2.eecs.berkeley.edu", "link": "https://www2.eecs.berkeley.edu/Pubs/TechRpts/2024/EECS-2024-172.pdf", "content": "by JL Watson · 2024 — We target a core bottleneck in zero-knowledge proofs, multiscalar multiplication (MSM), improve latency for large single proof execution on the ..."}
+{"idx": 8, "title": "Smart Monitoring and Control in the Future Internet of Things", "date": "", "ddg_snippet": "Ethereum transactions transfer value between accounts , pass data to SC function calls, and deploy new smart contracts to the BC network. Once submitted to ...", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/pdf/smart-monitoring-and-control-in-the-future-internet-of-2n3wdirq99.pdf", "content": "Ethereum transactions transfer value between accounts , pass data to SC function calls, and deploy new smart contracts to the BC network. Once submitted to ..."}
+{"idx": 9, "title": "A comprehensive review on internet of things task ...", "date": "", "ddg_snippet": "by W Dayong · 2024 · Cited by 18 — This paper provides a comprehensive and in-depth understanding of the algorithms and mechanisms of multiple IoT task offloading in the MEC network.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2405844024059474", "content": "by W Dayong · 2024 · Cited by 18 — This paper provides a comprehensive and in-depth understanding of the algorithms and mechanisms of multiple IoT task offloading in the MEC network."}