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data/sampled_jsons/2406.13909_Beyond_Optimism_Limitations_and_Future_Work_neural_network_continuous_MDPs.jsonl

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+{"idx": 0, "title": "Beyond Optimism : Exploration With Partially Observable Rewards", "date": "", "ddg_snippet": "Limitations and Future Work . First, we considered tabular MDPs , thus we plan to follow up on continuous MDPs .Exploration bonus for regret minimization in discrete and continuous average reward MDPs . In Advances in Neural Information Processing Systems (NeurIPS), 2019.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.13909v1", "content": "Limitations and Future Work . First, we considered tabular MDPs , thus we plan to follow up on continuous MDPs .Exploration bonus for regret minimization in discrete and continuous average reward MDPs . In Advances in Neural Information Processing Systems (NeurIPS), 2019."}
+{"idx": 1, "title": "[2406.13909] Beyond Optimism: Exploration With Partially ...", "date": "", "ddg_snippet": "Jun 20, 2024 · In this case, optimism can lead to suboptimal behavior that does not explore further to collapse uncertainty. With this paper, we present a novel exploration strategy that overcomes the limitations of existing methods and guarantees convergence to an optimal policy even when rewards are not always observable.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2406.13909", "content": "Jun 20, 2024 · In this case, optimism can lead to suboptimal behavior that does not explore further to collapse uncertainty. With this paper, we present a novel exploration strategy that overcomes the limitations of existing methods and guarantees convergence to an optimal policy even when rewards are not always observable."}
+{"idx": 2, "title": "Beyond optimism | Proceedings of the 38th International ...", "date": "", "ddg_snippet": "Jun 5, 2025 · In this case, optimism can lead to suboptimal behavior that does not explore further to collapse uncertainty. With this paper, we present a novel exploration strategy that overcomes the limitations of existing methods and guarantees convergence to an optimal policy even when rewards are not always observable.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.5555/3737916.3740005", "content": "Jun 5, 2025 · In this case, optimism can lead to suboptimal behavior that does not explore further to collapse uncertainty. With this paper, we present a novel exploration strategy that overcomes the limitations of existing methods and guarantees convergence to an optimal policy even when rewards are not always observable."}
+{"idx": 3, "title": "Closed-form continuous-time neural networks - Nature", "date": "", "ddg_snippet": "Nov 15, 2022 · This closed-form solution impacts the design of continuous -time and continuous -depth neural models. For instance, since time appears explicitly in closed form, the formulation relaxes the need for ...", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s42256-022-00556-7", "content": "Nov 15, 2022 · This closed-form solution impacts the design of continuous -time and continuous -depth neural models. For instance, since time appears explicitly in closed form, the formulation relaxes the need for ..."}
+{"idx": 4, "title": "Continuous Neural Networks", "date": "", "ddg_snippet": "1 Introduction In (Neal, 1994) neural networks with an infinite num-ber of hidden units were introduced, showing that they could be interpreted as Gaussian processes, and this work served as inspiration for a large body of work on Gaussian processes. Neal’s work showed a counter-example to two common beliefs in the machine learn-ing community: (1) that a neural network with a very large ...", "subpage_snippet": "", "source": "www.microsoft.com", "link": "https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/continuous_nnet.pdf", "content": "1 Introduction In (Neal, 1994) neural networks with an infinite num-ber of hidden units were introduced, showing that they could be interpreted as Gaussian processes, and this work served as inspiration for a large body of work on Gaussian processes. Neal’s work showed a counter-example to two common beliefs in the machine learn-ing community: (1) that a neural network with a very large ..."}
+{"idx": 5, "title": "The continuous memory: A neural network with ordinary ...", "date": "", "ddg_snippet": "Dec 1, 2024 · This demonstrates the significant potential of neural differential equations in continuous -time sequence processing. Notably, when dealing with datasets that feature irregularly spaced time steps, sequence models based on neural ODEs may exhibit superior performance compared to their discrete-time counterparts.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S1568494624010494", "content": "Dec 1, 2024 · This demonstrates the significant potential of neural differential equations in continuous -time sequence processing. Notably, when dealing with datasets that feature irregularly spaced time steps, sequence models based on neural ODEs may exhibit superior performance compared to their discrete-time counterparts."}
+{"idx": 6, "title": "Advances in Neural Information Processing Systems 37 (NeurIPS ...", "date": "", "ddg_snippet": "The Map Equation Goes Neural : Mapping Network Flows with Graph Neural Networks Christopher Blöcker, Chester Tan, Ingo Scholtes Locating What You Need: Towards Adapting Diffusion Models to OOD Concepts In-the-Wild Jianan Yang, Chenchao Gao, Zhiqing Xiao, Junbo Zhao, Sai Wu, Gang Chen, Haobo Wang", "subpage_snippet": "", "source": "proceedings.nips.cc", "link": "https://proceedings.nips.cc/paper_files/paper/2024", "content": "The Map Equation Goes Neural : Mapping Network Flows with Graph Neural Networks Christopher Blöcker, Chester Tan, Ingo Scholtes Locating What You Need: Towards Adapting Diffusion Models to OOD Concepts In-the-Wild Jianan Yang, Chenchao Gao, Zhiqing Xiao, Junbo Zhao, Sai Wu, Gang Chen, Haobo Wang"}
+{"idx": 7, "title": "(PDF) Beyond Optimism : Exploration With Partially Observable...", "date": "", "ddg_snippet": "Limitations and Future W ork. First,weconsideredtabularMDPs,thusweplantofollowupon. continuous MDPs .Y . Burda, H. Edwards, A. Storkey, and O. Klimo v. Exploration by random network distillation. In InternationalConferenceonLearningRepresentations(ICLR),2019.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/381579170_Beyond_Optimism_Exploration_With_Partially_Observable_Rewards", "content": "Limitations and Future W ork. First,weconsideredtabularMDPs,thusweplantofollowupon. continuous MDPs .Y . Burda, H. Edwards, A. Storkey, and O. Klimo v. Exploration by random network distillation. In InternationalConferenceonLearningRepresentations(ICLR),2019."}
+{"idx": 8, "title": "Best Neural Network Applications for US... - Technologic Innovation", "date": "", "ddg_snippet": "Discover the top neural network applications revolutionizing US businesses in 2025. Learn benefits, real-world examples, and how to get started today.", "subpage_snippet": "", "source": "technologicinnovation.com", "link": "https://technologicinnovation.com/2025/09/19/best-neural-network-applications-for-us-businesses-2025/", "content": "Discover the top neural network applications revolutionizing US businesses in 2025. Learn benefits, real-world examples, and how to get started today."}
+{"idx": 9, "title": "On the Occupancy Measure of Non-Markovian Policies in Continuous ...", "date": "", "ddg_snippet": "While expected, for technical reasons, the translation of this result to continuous state space has resisted until now. Our main contribution is to fill this gap and to provide a general measure-theoretic treatment of the problem, permitting, in particular, its extension to continuous MDPs .", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v202/laroche23a/laroche23a.pdf", "content": "While expected, for technical reasons, the translation of this result to continuous state space has resisted until now. Our main contribution is to fill this gap and to provide a general measure-theoretic treatment of the problem, permitting, in particular, its extension to continuous MDPs ."}

data/sampled_jsons/2DMamba_Efficient_State_Space_Model_for_Image_Representation_with_Applications_on_Giga-Pixel_Whole_S.jsonl

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+{"idx": 0, "title": "2DMamba: Efficient State Space Model for Image Representation with ...", "date": "", "ddg_snippet": "Efficiently modeling large 2D contexts is essential for various fields including Giga-Pixel Whole Slide Imaging (WSI) and remote sensing. Transformer-based models offer high parallelism but face challenges due to their quadratic complexity for handling long sequences. Recently, Mamba introduced a selective State Space Model (SSM) with linear complexity and high parallelism, enabling effective ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2412.00678", "content": "Efficiently modeling large 2D contexts is essential for various fields including Giga-Pixel Whole Slide Imaging (WSI) and remote sensing. Transformer-based models offer high parallelism but face challenges due to their quadratic complexity for handling long sequences. Recently, Mamba introduced a selective State Space Model (SSM) with linear complexity and high parallelism, enabling effective ..."}
+{"idx": 1, "title": "CVPR 2025 Open Access Repository", "date": "", "ddg_snippet": "2DMamba : Efficient State Space Model for Image Representation with Applications on Giga-Pixel Whole Slide Image Classification Jingwei Zhang, Anh Tien Nguyen, Xi Han, Vincent Quoc-Huy Trinh, Hong Qin, Dimitris Samaras, Mahdi S. Hosseini; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2025, pp. 3583-3592", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_2DMamba_Efficient_State_Space_Model_for_Image_Representation_with_Applications_CVPR_2025_paper.html", "content": "2DMamba : Efficient State Space Model for Image Representation with Applications on Giga-Pixel Whole Slide Image Classification Jingwei Zhang, Anh Tien Nguyen, Xi Han, Vincent Quoc-Huy Trinh, Hong Qin, Dimitris Samaras, Mahdi S. Hosseini; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2025, pp. 3583-3592"}
+{"idx": 2, "title": "2DMamba: Efficient State Space Model for Image Representation with ...", "date": "", "ddg_snippet": "Pytorch implementation for the 2DMamba framework described in the paper 2DMamba : Efficient State Space Model for Image Representation with Applications on Giga-Pixel Whole Slide Image Classification, arxiv and poster.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/AtlasAnalyticsLab/2DMamba", "content": "Pytorch implementation for the 2DMamba framework described in the paper 2DMamba : Efficient State Space Model for Image Representation with Applications on Giga-Pixel Whole Slide Image Classification, arxiv and poster."}
+{"idx": 3, "title": "CVPR 2025新突破：2DMamba如何高效解决2D图像建模难题？ - 知乎", "date": "", "ddg_snippet": "2DMamba 横空出世：保持空间连续性的 2D状态空间模型 有多强。 \"论文标题： 2DMamba : Efficient State Space Model for Image Representation with Applications on Giga-Pixel Whole Slide Image Classification会议： CVPR2025 研究背景与挑战：高效建模大范围2D上下文对WSI分析和遥感等领域至关重要。Mamba以其线性复杂度和高并行性在1D序列 ...", "subpage_snippet": "", "source": "zhuanlan.zhihu.com", "link": "https://zhuanlan.zhihu.com/p/1913606025006814641", "content": "2DMamba 横空出世：保持空间连续性的 2D状态空间模型 有多强。 \"论文标题： 2DMamba : Efficient State Space Model for Image Representation with Applications on Giga-Pixel Whole Slide Image Classification会议： CVPR2025 研究背景与挑战：高效建模大范围2D上下文对WSI分析和遥感等领域至关重要。Mamba以其线性复杂度和高并行性在1D序列 ..."}
+{"idx": 4, "title": "PDF 2DMamba: Efficient State Space Model for Image Representation with ...", "date": "", "ddg_snippet": "Abstract Eficiently modeling large 2D contexts is essential for vari-ous fields including Giga-Pixel Whole Slide Imaging (WSI) and remote sensing. Transformer-based models offer high parallelism but face challenges due to their quadratic com-plexity for handling long sequences. Recently, Mamba in-troduced a selective State Space Model (SSM) with linear complexity and high parallelism, enabling ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_2DMamba_Efficient_State_Space_Model_for_Image_Representation_with_Applications_CVPR_2025_paper.pdf", "content": "Abstract Eficiently modeling large 2D contexts is essential for vari-ous fields including Giga-Pixel Whole Slide Imaging (WSI) and remote sensing. Transformer-based models offer high parallelism but face challenges due to their quadratic com-plexity for handling long sequences. Recently, Mamba in-troduced a selective State Space Model (SSM) with linear complexity and high parallelism, enabling ..."}
+{"idx": 5, "title": "2DMamba: Efficient State Space Model for Image Representation with ...", "date": "", "ddg_snippet": "2DMamba : Efficient State Space Model for Image Representation with Applications on Giga-Pixel Whole Slide Image Classification", "subpage_snippet": "", "source": "par.nsf.gov", "link": "https://par.nsf.gov/biblio/10634810-efficient-state-space-model-image-representation-applications-giga-pixel-whole-slide-image-classification", "content": "2DMamba : Efficient State Space Model for Image Representation with Applications on Giga-Pixel Whole Slide Image Classification"}
+{"idx": 6, "title": "2DMamba/README.md at main · AtlasAnalyticsLab/2DMamba · GitHub", "date": "", "ddg_snippet": "2DMamba : Efficient State Space Model for Image Representation with Applications on Giga-Pixel Whole Slide Image Classification Pytorch implementation for the 2DMamba framework described in the paper 2DMamba : Efficient State Space Model for Image Representation with Applications on Giga-Pixel Whole Slide Image Classification, arxiv and poster.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/AtlasAnalyticsLab/2DMamba/blob/main/README.md", "content": "2DMamba : Efficient State Space Model for Image Representation with Applications on Giga-Pixel Whole Slide Image Classification Pytorch implementation for the 2DMamba framework described in the paper 2DMamba : Efficient State Space Model for Image Representation with Applications on Giga-Pixel Whole Slide Image Classification, arxiv and poster."}
+{"idx": 7, "title": "2DMamba: Efficient State Space Model for Image Representation with ...", "date": "", "ddg_snippet": "View recent discussion. Abstract: Efficiently modeling large 2D contexts is essential for various fields including Giga-Pixel Whole Slide Imaging (WSI) and remote sensing. Transformer-based models offer high parallelism but face challenges due to their quadratic complexity for handling long sequences. Recently, Mamba introduced a selective State Space Model (SSM) with linear complexity and ...", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2412.00678v1", "content": "View recent discussion. Abstract: Efficiently modeling large 2D contexts is essential for various fields including Giga-Pixel Whole Slide Imaging (WSI) and remote sensing. Transformer-based models offer high parallelism but face challenges due to their quadratic complexity for handling long sequences. Recently, Mamba introduced a selective State Space Model (SSM) with linear complexity and ..."}
+{"idx": 8, "title": "2DMamba: Efficient State Space Model for Image Representation with ...", "date": "", "ddg_snippet": "The model excels at analyzing gigapixel whole slide images - extremely detailed medical scans that are thousands of times larger than regular photos. By processing these massive images more efficiently, it helps doctors identify diseases faster and more accurately.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/2dmamba-efficient-state-space-model-image-representation", "content": "The model excels at analyzing gigapixel whole slide images - extremely detailed medical scans that are thousands of times larger than regular photos. By processing these massive images more efficiently, it helps doctors identify diseases faster and more accurately."}
+{"idx": 9, "title": "arXiv:2412.00678v1 [cs.CV] 1 Dec 2024", "date": "", "ddg_snippet": "2DMamba : Efficient State Space Model for Image Representation with Applications on Giga-Pixel Whole Slide Image Classification", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2412.00678v1", "content": "2DMamba : Efficient State Space Model for Image Representation with Applications on Giga-Pixel Whole Slide Image Classification"}

data/sampled_jsons/AI_alignment_framework_governmental_structure.jsonl

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+{"idx": 0, "title": "PDF The AI Regulatory Key Takeaways Alignment Problem", "date": "", "ddg_snippet": "In \" AI Regulation Has Its Own Alignment Problem,\" we consider the technical and institutional feasibility of four commonly proposed AI regulatory regimes— disclosure, registration, licensing, and auditing—described in the table, and conclude that each sufers from its own regulatory alignment problem.2 Some proposals may fail to address the problems they set out to solve due to ...", "subpage_snippet": "", "source": "hai.stanford.edu", "link": "https://hai.stanford.edu/assets/files/2023-11/AI-Regulatory-Alignment.pdf", "content": "In \" AI Regulation Has Its Own Alignment Problem,\" we consider the technical and institutional feasibility of four commonly proposed AI regulatory regimes— disclosure, registration, licensing, and auditing—described in the table, and conclude that each sufers from its own regulatory alignment problem.2 Some proposals may fail to address the problems they set out to solve due to ..."}
+{"idx": 1, "title": "PDF GAO-21-519SP, ARTIFICIAL INTELLIGENCE: An Accountability Framework for ...", "date": "", "ddg_snippet": "What GAO Found To help managers ensure accountability and responsible use of artificial intelligence ( AI ) in government programs and processes, GAO developed an AI accountability framework . This framework is organized around four complementary principles, which address governance, data, performance, and monitoring.", "subpage_snippet": "", "source": "www.gao.gov", "link": "https://www.gao.gov/assets/gao-21-519sp.pdf", "content": "What GAO Found To help managers ensure accountability and responsible use of artificial intelligence ( AI ) in government programs and processes, GAO developed an AI accountability framework . This framework is organized around four complementary principles, which address governance, data, performance, and monitoring."}
+{"idx": 2, "title": "Governing at the Speed of Change: An AI-Enabled Adaptive Framework for ...", "date": "", "ddg_snippet": "In 2024, 150 state bills on the government use of AI were considered, 10 governors issued AI study orders, yet only 10 legislatures required comprehensive AI inventories. AI adoption carries risks requiring careful management and adaptive governance frameworks for these dynamic technologies.", "subpage_snippet": "", "source": "www.rand.org", "link": "https://www.rand.org/pubs/commentary/2025/09/governing-at-the-speed-of-change-an-ai-enabled-adaptive.html", "content": "In 2024, 150 state bills on the government use of AI were considered, 10 governors issued AI study orders, yet only 10 legislatures required comprehensive AI inventories. AI adoption carries risks requiring careful management and adaptive governance frameworks for these dynamic technologies."}
+{"idx": 3, "title": "What is an AI Governance Framework? The Ultimate ... - aign.global", "date": "", "ddg_snippet": "1. Regulatory Alignment & Global Standards Regulations such as the EU AI Act, global standards like ISO/IEC 42001, and internationally recognized frameworks—including the OECD AI Principles and NIST AI Risk Management Framework —have established clear, enforceable requirements for AI development and use. The European Commission forecasts that non-compliance penalties for high-risk AI could ...", "subpage_snippet": "", "source": "aign.global", "link": "https://aign.global/ai-governance-consulting/aign-global/🟢💡-what-is-an-ai-governance-framework-the-ultimate-guide-2025-edition/", "content": "1. Regulatory Alignment & Global Standards Regulations such as the EU AI Act, global standards like ISO/IEC 42001, and internationally recognized frameworks—including the OECD AI Principles and NIST AI Risk Management Framework —have established clear, enforceable requirements for AI development and use. The European Commission forecasts that non-compliance penalties for high-risk AI could ..."}
+{"idx": 4, "title": "Analysis of Global AI Governance - AI Alignment Forum", "date": "", "ddg_snippet": "Looking forward, the AI governance community faces the challenge of not only selecting appropriate strategies but also maintaining the ability to pivot as new information about alignment difficulty, the international situation, AI offense and defense, and TAI development timelines emerges.", "subpage_snippet": "", "source": "www.alignmentforum.org", "link": "https://www.alignmentforum.org/posts/6nNwMbdRXZDuNd4Gx/analysis-of-global-ai-governance-strategies", "content": "Looking forward, the AI governance community faces the challenge of not only selecting appropriate strategies but also maintaining the ability to pivot as new information about alignment difficulty, the international situation, AI offense and defense, and TAI development timelines emerges."}
+{"idx": 5, "title": "AI Governance Frameworks: A Comparative Analysis", "date": "", "ddg_snippet": "Explore a detailed comparison of global AI governance frameworks , including the EU AI Act, US AI Bill of Rights, and other key regulatory approaches shaping the future of artificial intelligence.", "subpage_snippet": "", "source": "aviperera.com", "link": "https://aviperera.com/ai-governance-frameworks-a-comparative-analysis/", "content": "Explore a detailed comparison of global AI governance frameworks , including the EU AI Act, US AI Bill of Rights, and other key regulatory approaches shaping the future of artificial intelligence."}
+{"idx": 6, "title": "Alignment with national and international AI frameworks and standards", "date": "", "ddg_snippet": "As parliaments consider adopting AI technologies, it is important to be aware of and, where appropriate, to adhere to government standards and relevant national and international frameworks for AI use. These standards and frameworks may significantly influence how parliaments implement AI and, in some cases, may require parliaments to adapt the approaches outlined in these Guidelines.", "subpage_snippet": "", "source": "www.ipu.org", "link": "https://www.ipu.org/ai-guidelines/alignment-with-national-and-international-ai-frameworks-and-standards", "content": "As parliaments consider adopting AI technologies, it is important to be aware of and, where appropriate, to adhere to government standards and relevant national and international frameworks for AI use. These standards and frameworks may significantly influence how parliaments implement AI and, in some cases, may require parliaments to adapt the approaches outlined in these Guidelines."}
+{"idx": 7, "title": "AI Guide for Government - AI CoE", "date": "", "ddg_snippet": "Read more about how data science fits into the broader government AI ecosystem, Integrated Product Teams (IPT), and Developing the AI Workforce in Chapter 2 of the AI Guide for Government, How to structure an organization to embrace AI .", "subpage_snippet": "", "source": "coe.gsa.gov", "link": "https://coe.gsa.gov/coe/ai-guide-for-government/print-all/index.html", "content": "Read more about how data science fits into the broader government AI ecosystem, Integrated Product Teams (IPT), and Developing the AI Workforce in Chapter 2 of the AI Guide for Government, How to structure an organization to embrace AI ."}
+{"idx": 8, "title": "A Practical Guide to Building AI Governance for Government Agencies", "date": "", "ddg_snippet": "This framework outlines key considerations and steps agencies can take to responsibly and confidently integrate AI into their operations. What is AI Governance AI governance refers to the framework of policies, processes, and structures that guide responsible development, deployment, and oversight of artificial intelligence systems.", "subpage_snippet": "", "source": "blog.pvmit.com", "link": "https://blog.pvmit.com/pvm-blog/ai-governance-government-guide", "content": "This framework outlines key considerations and steps agencies can take to responsibly and confidently integrate AI into their operations. What is AI Governance AI governance refers to the framework of policies, processes, and structures that guide responsible development, deployment, and oversight of artificial intelligence systems."}
+{"idx": 9, "title": "Toward Effective AI Governance: A Review of Principles", "date": "", "ddg_snippet": "A key component of RAI is the establishment of effective governance mechanisms, which encompass regulatory frameworks , organizational structures , internal processes, and stakeholder engagement strategies that ensure AI systems are trustworthy and aligned with societal values [2]. In this context, Governance is not limited to compliance with legal norms. However, it includes the design of ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.23417v1", "content": "A key component of RAI is the establishment of effective governance mechanisms, which encompass regulatory frameworks , organizational structures , internal processes, and stakeholder engagement strategies that ensure AI systems are trustworthy and aligned with societal values [2]. In this context, Governance is not limited to compliance with legal norms. However, it includes the design of ..."}

data/sampled_jsons/A_Likelihood_Based_Approach_to_Distribution_Regression_Using_Conditional_Deep_Generative_Models_FD2__year_2024.jsonl

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+{"idx": 0, "title": "A Likelihood Based Approach to Distribution Regression Using ...", "date": "", "ddg_snippet": "Abstract In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where the response variable lies in a high-dimensional ambient space but concentrates around a potentially lower-dimensional manifold.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.02025v1", "content": "Abstract In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where the response variable lies in a high-dimensional ambient space but concentrates around a potentially lower-dimensional manifold."}
+{"idx": 1, "title": "A Likelihood Based Approach to Distribution Regression Using ...", "date": "", "ddg_snippet": "Oct 2, 2024 · In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where the response variable lies in a high-dimensional ambient space but concentrates around a potentially lower-dimensional manifold. More specifically, we study the large-sample properties of a likelihood - based approach for estimating these ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.02025", "content": "Oct 2, 2024 · In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where the response variable lies in a high-dimensional ambient space but concentrates around a potentially lower-dimensional manifold. More specifically, we study the large-sample properties of a likelihood - based approach for estimating these ..."}
+{"idx": 2, "title": "A LIKELIHOOD BASED APPROACH TO DISTRIBUTION REGRESSION USING ...", "date": "", "ddg_snippet": "In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where the re-sponse variable lies in a high-dimensional ambient space but concentrates around a potentially lower-dimensional manifold. More specifically, we study the large-sample properties of a likelihood - based approach for estimating these ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=V6hhhXoTSq", "content": "In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where the re-sponse variable lies in a high-dimensional ambient space but concentrates around a potentially lower-dimensional manifold. More specifically, we study the large-sample properties of a likelihood - based approach for estimating these ..."}
+{"idx": 3, "title": "A Deep Generative Approach to Conditional Sampling A Likelihood Approach to Nonparametric Estimation of a ... ICML Poster A Likelihood Based Approach to Distribution ...", "date": "", "ddg_snippet": "Oct 19, 2021 · We propose a deep generative approach to sampling from a conditional distribution based on a unified formulation of conditional distribution and generalized nonparametric regression function using the noise-outsourcing lemma. The proposed approach aims at learning a conditional generator so that a random sample from the target conditional distribution can be obtained by the action of the ... Abstract We investigate statistical properties of a likelihood approach to nonparametric estimation of a singular distribution using deep generative models . More speci cally, a deep generative model is used to model high-dimensional data that are assumed to concentrate around some low-dimensional structure. In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where the response variable lies in a high-dimensional ambient space but concentrates around a potentially lower-dimensional manifold. More specifically, we study the large-sample properties of a likelihood - based approach for estimating these ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2110.10277", "content": "Oct 19, 2021 · We propose a deep generative approach to sampling from a conditional distribution based on a unified formulation of conditional distribution and generalized nonparametric regression function using the noise-outsourcing lemma. The proposed approach aims at learning a conditional generator so that a random sample from the target conditional distribution can be obtained by the action of the ... Abstract We investigate statistical properties of a likelihood approach to nonparametric estimation of a singular distribution using deep generative models . More speci cally, a deep generative model is used to model high-dimensional data that are assumed to concentrate around some low-dimensional structure. In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where the response variable lies in a high-dimensional ambient space but concentrates around a potentially lower-dimensional manifold. More specifically, we study the large-sample properties of a likelihood - based approach for estimating these ..."}
+{"idx": 4, "title": "A Likelihood Approach to Nonparametric Estimation of a ...", "date": "", "ddg_snippet": "Abstract We investigate statistical properties of a likelihood approach to nonparametric estimation of a singular distribution using deep generative models . More speci cally, a deep generative model is used to model high-dimensional data that are assumed to concentrate around some low-dimensional structure.", "subpage_snippet": "", "source": "jmlr.org", "link": "https://jmlr.org/papers/volume24/21-1099/21-1099.pdf", "content": "Abstract We investigate statistical properties of a likelihood approach to nonparametric estimation of a singular distribution using deep generative models . More speci cally, a deep generative model is used to model high-dimensional data that are assumed to concentrate around some low-dimensional structure."}
+{"idx": 5, "title": "ICML Poster A Likelihood Based Approach to Distribution ...", "date": "", "ddg_snippet": "In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where the response variable lies in a high-dimensional ambient space but concentrates around a potentially lower-dimensional manifold. More specifically, we study the large-sample properties of a likelihood - based approach for estimating these ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46645", "content": "In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where the response variable lies in a high-dimensional ambient space but concentrates around a potentially lower-dimensional manifold. More specifically, we study the large-sample properties of a likelihood - based approach for estimating these ..."}
+{"idx": 6, "title": "A Likelihood Based Approach to Distribution Regression ...", "date": "", "ddg_snippet": "by S Kumar · Cited by 1 — In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=1IyPRv1A0r", "content": "by S Kumar · Cited by 1 — In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution ..."}
+{"idx": 7, "title": "A Likelihood Based Approach to Distribution Regression Using ...", "date": "", "ddg_snippet": "In this work, we explore the theoretical proper- ties of conditional deep generative models un- der the statistical framework of distribution re-.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=1IyPRv1A0r", "content": "In this work, we explore the theoretical proper- ties of conditional deep generative models un- der the statistical framework of distribution re-."}
+{"idx": 8, "title": "Deep Nonparametric Quantile Regression under Covariate ...", "date": "", "ddg_snippet": "by X Feng · 2024 · Cited by 3 — Abstract. This work focuses on addressing the challenges posed by covariate shift in nonparamet- ric quantile regression using deep neural networks. 50 pages", "subpage_snippet": "", "source": "www.jmlr.org", "link": "https://www.jmlr.org/papers/volume25/24-0906/24-0906.pdf", "content": "by X Feng · 2024 · Cited by 3 — Abstract. This work focuses on addressing the challenges posed by covariate shift in nonparamet- ric quantile regression using deep neural networks. 50 pages"}
+{"idx": 9, "title": "Deep nonparametric quantile regression under covariate shift", "date": "", "ddg_snippet": "Abstract. This work focuses on addressing the challenges posed by covariate shift in nonparamet- ric quantile regression using deep neural networks.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/pdf/10.5555/3722577.3722962", "content": "Abstract. This work focuses on addressing the challenges posed by covariate shift in nonparamet- ric quantile regression using deep neural networks."}

data/sampled_jsons/Adcock_Collier_2001_Measurement_validity_shared_standard_qualitative_quantitative_research_publicati.jsonl

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+{"idx": 0, "title": "PDF Measurement Validity: A Shared Standard for Qualitative and ...", "date": "", "ddg_snippet": "We address this gap by exploring four themes. First, we seek to establish a shared framework that allows quantitative and qualitative scholars to assess more effectively, and communicate about, issues of valid measurement . Second, we underscore the need to draw a clear distinction between measurement issues and disputes about concepts.", "subpage_snippet": "", "source": "www.theisrm.org", "link": "https://www.theisrm.org/documents/Adcock+&+Collier+(2001)+Measurement+Validity+-+A+Shared+Standard+for+Qualitative+and+Quantitative+Research.pdf", "content": "We address this gap by exploring four themes. First, we seek to establish a shared framework that allows quantitative and qualitative scholars to assess more effectively, and communicate about, issues of valid measurement . Second, we underscore the need to draw a clear distinction between measurement issues and disputes about concepts."}
+{"idx": 1, "title": "(PDF) Measurement Validity: A Shared Standard For Qualitative and ...", "date": "", "ddg_snippet": "First, we seek to establish a shared framework that allows quantitative and qualitative scholars to assess more effectively, and communicate about, issues of valid measurement .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/228277142_Measurement_Validity_A_Shared_Standard_For_Qualitative_and_Quantitative_Research", "content": "First, we seek to establish a shared framework that allows quantitative and qualitative scholars to assess more effectively, and communicate about, issues of valid measurement ."}
+{"idx": 2, "title": "Adcock y Collier (2001) - Measurement Validity. A Shared Standart For ...", "date": "", "ddg_snippet": "Measurement validity : A shared standard for qualitative and quantitative research Adcock , Robert;Collier, David The American Political Science Review; Sep 2001 ; 95, 3; ProQuest Central pg. 529 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. fReproduced with permission of the copyright owner. Further reproduction prohibited without ...", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/520421528/Adcock-y-Collier-2001-Measurement-Validity-a-Shared-Standart-for-Qualitative-and-Quantitative-Research", "content": "Measurement validity : A shared standard for qualitative and quantitative research Adcock , Robert;Collier, David The American Political Science Review; Sep 2001 ; 95, 3; ProQuest Central pg. 529 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. fReproduced with permission of the copyright owner. Further reproduction prohibited without ..."}
+{"idx": 3, "title": "Measurement Validity: A Shared Standard for Qualitative and ...", "date": "", "ddg_snippet": "Measurement validity is specifically concerned with measurement validity . The first is the challenge of whether operationalization and the scoring of cases establishing shared standards for quantitative and qual- adequately reflect the concept the researcher seeks to itative scholars, a topic that has been widely discussed measure.", "subpage_snippet": "", "source": "www.jstor.org", "link": "https://www.jstor.org/stable/3118231", "content": "Measurement validity is specifically concerned with measurement validity . The first is the challenge of whether operationalization and the scoring of cases establishing shared standards for quantitative and qual- adequately reflect the concept the researcher seeks to itative scholars, a topic that has been widely discussed measure."}
+{"idx": 4, "title": "Measurement validity: A shared standard for - ProQuest", "date": "", "ddg_snippet": "In the process we seek to formulate a methodological standard that can be applied in both qualitative and quantitative research . Measurement validity is specifically concerned with whether operationalization and the scoring of cases adequately reflect the concept the researcher seeks to measure.", "subpage_snippet": "", "source": "www.proquest.com", "link": "https://www.proquest.com/docview/214424808", "content": "In the process we seek to formulate a methodological standard that can be applied in both qualitative and quantitative research . Measurement validity is specifically concerned with whether operationalization and the scoring of cases adequately reflect the concept the researcher seeks to measure."}
+{"idx": 5, "title": "Adcock Collier A Shared Standard for Qualitative and Quantitative ...", "date": "", "ddg_snippet": "View Assessment - Adcock Collier A Shared Standard for Qualitative and Quantitative Research (1).pdf from RESEARCH 101 at University of the East, Manila. Measurement Validity : A Shared Standard for", "subpage_snippet": "", "source": "www.coursehero.com", "link": "https://www.coursehero.com/file/246322672/Adcock-Collier-A-Shared-Standard-for-Qualitative-and-Quantitative-Research-1pdf/", "content": "View Assessment - Adcock Collier A Shared Standard for Qualitative and Quantitative Research (1).pdf from RESEARCH 101 at University of the East, Manila. Measurement Validity : A Shared Standard for"}
+{"idx": 6, "title": "Measurement Validity A Shared Standard for Qualitative and Quantitative ...", "date": "", "ddg_snippet": "Preview text \" Measurement Validity : A Shared Standard for Qualitative and Quantitative Research \" is an article written by Robert Adcock and David Collier and published in the American Political Science Review in 2001 .", "subpage_snippet": "", "source": "www.studocu.com", "link": "https://www.studocu.com/en-gb/document/oxford-brookes-university/researching-politics-and-international-relations-2-methods/measurement-validity-a-shared-standard-for-qualitative-and-quantitative-research/48109231", "content": "Preview text \" Measurement Validity : A Shared Standard for Qualitative and Quantitative Research \" is an article written by Robert Adcock and David Collier and published in the American Political Science Review in 2001 ."}
+{"idx": 7, "title": "Measurement Validity: A Shared Standard for Qualitative and ...", "date": "", "ddg_snippet": "Measurement Validity : A Shared Standard for Qualitative and Quantitative Research Robert Adcock and David Collier American Political Science Review, 2001 , vol. 95, issue 3, 529-546 Abstract: Scholars routinely make claims that presuppose the validity of the observations and measurements that operationalize their concepts.", "subpage_snippet": "", "source": "econpapers.repec.org", "link": "https://econpapers.repec.org/RePEc:cup:apsrev:v:95:y:2001:i:03:p:529-546_00", "content": "Measurement Validity : A Shared Standard for Qualitative and Quantitative Research Robert Adcock and David Collier American Political Science Review, 2001 , vol. 95, issue 3, 529-546 Abstract: Scholars routinely make claims that presuppose the validity of the observations and measurements that operationalize their concepts."}
+{"idx": 8, "title": "Adcock Collier 2001 Measurement Validity | PDF - Scribd", "date": "", "ddg_snippet": "f Measurement Validity : A Shared Standard for Qualitative and Quantitative Research September 2001 to their three types and emphasizes content validation, ent types of validation in qualitative as well as quanti-", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/797627345/Adcock-Collier-2001-Measurement-Validity", "content": "f Measurement Validity : A Shared Standard for Qualitative and Quantitative Research September 2001 to their three types and emphasizes content validation, ent types of validation in qualitative as well as quanti-"}
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data/sampled_jsons/Archetypal_SAE_stability_cosine_similarity_equation_2_formula.jsonl

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+{"idx": 0, "title": "Archetype - Wikipedia", "date": "", "ddg_snippet": "Archetypal literary criticism argues that archetypes determine the form and function of literary works and that a text 's meaning is shaped by cultural and psychological myths.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Archetype", "content": "Archetypal literary criticism argues that archetypes determine the form and function of literary works and that a text 's meaning is shaped by cultural and psychological myths."}
+{"idx": 1, "title": "ARCHETYPAL | English meaning - Cambridge Dictionary", "date": "", "ddg_snippet": "The second aspect has archetypal character and is rigorously independent of the means with which a work is composed.", "subpage_snippet": "", "source": "dictionary.cambridge.org", "link": "https://dictionary.cambridge.org/dictionary/english/archetypal", "content": "The second aspect has archetypal character and is rigorously independent of the means with which a work is composed."}
+{"idx": 2, "title": "ARCHETYPAL Definition & Meaning | Dictionary.com", "date": "", "ddg_snippet": "Archetypal definition: of or having the nature of an archetype , or original model or prototype.. See examples of ARCHETYPAL used in a sentence.", "subpage_snippet": "", "source": "www.dictionary.com", "link": "https://www.dictionary.com/browse/archetypal", "content": "Archetypal definition: of or having the nature of an archetype , or original model or prototype.. See examples of ARCHETYPAL used in a sentence."}
+{"idx": 3, "title": "Archetypal - Definition, Meaning & Synonyms | Vocabulary.com", "date": "", "ddg_snippet": "An archetypal thing represents an original type after which other, similar things are patterned. With her green skin, black garb, and evil ways, the Wicked Witch of the West is an archetypal villain.", "subpage_snippet": "", "source": "www.vocabulary.com", "link": "https://www.vocabulary.com/dictionary/archetypal", "content": "An archetypal thing represents an original type after which other, similar things are patterned. With her green skin, black garb, and evil ways, the Wicked Witch of the West is an archetypal villain."}
+{"idx": 4, "title": "Archetypal - definition of archetypal by The Free Dictionary", "date": "", "ddg_snippet": "archetypal (ˌɑːkɪˈtaɪpəl) or archetypical adj 1. perfect or typical as a specimen of something 2. being an original model or pattern or a prototype", "subpage_snippet": "", "source": "www.thefreedictionary.com", "link": "https://www.thefreedictionary.com/archetypal", "content": "archetypal (ˌɑːkɪˈtaɪpəl) or archetypical adj 1. perfect or typical as a specimen of something 2. being an original model or pattern or a prototype"}
+{"idx": 5, "title": "archetypal adjective - Definition, pictures, pronunciation and...", "date": "", "ddg_snippet": "having all the important qualities that make somebody/something a typical example of a particular kind of person or thing. The Beatles were the archetypal pop group. It was the archetypal British suburb, built in the 1930s. Definition of archetypal adjective in Oxford Advanced Learner's Dictionary.", "subpage_snippet": "", "source": "www.oxfordlearnersdictionaries.com", "link": "https://www.oxfordlearnersdictionaries.com/definition/english/archetypal", "content": "having all the important qualities that make somebody/something a typical example of a particular kind of person or thing. The Beatles were the archetypal pop group. It was the archetypal British suburb, built in the 1930s. Definition of archetypal adjective in Oxford Advanced Learner's Dictionary."}
+{"idx": 6, "title": "archetypal , adj. meanings, etymology and more | Oxford English...", "date": "", "ddg_snippet": "archetypal , adj. meanings, etymology, pronunciation and more in the Oxford English Dictionary", "subpage_snippet": "", "source": "www.oed.com", "link": "https://www.oed.com/dictionary/archetypal_adj", "content": "archetypal , adj. meanings, etymology, pronunciation and more in the Oxford English Dictionary"}
+{"idx": 7, "title": "archetypal - Definition, Meaning & Synonyms - Vocab Dictionary", "date": "", "ddg_snippet": "Archetypal describes something that is a perfect example or typical form of a particular type or category.", "subpage_snippet": "", "source": "vocabdictionary.com", "link": "https://vocabdictionary.com/english/archetypal/", "content": "Archetypal describes something that is a perfect example or typical form of a particular type or category."}
+{"idx": 8, "title": "ARCHETYPAL definition and meaning | Collins English Dictionary", "date": "", "ddg_snippet": "Someone or something that is archetypal has all the most important characteristics of a particular kind of person or thing and is a perfect example of it.", "subpage_snippet": "", "source": "www.collinsdictionary.com", "link": "https://www.collinsdictionary.com/dictionary/english/archetypal", "content": "Someone or something that is archetypal has all the most important characteristics of a particular kind of person or thing and is a perfect example of it."}
+{"idx": 9, "title": "ARCHETYPAL Definition & Meaning - Merriam-Webster", "date": "", "ddg_snippet": ": a primitive generalized plan of structure deduced from the characters of a natural group of plants or animals and assumed to be the characteristic of the ancestor from which they are all descended.", "subpage_snippet": "", "source": "www.merriam-webster.com", "link": "https://www.merriam-webster.com/dictionary/archetypal", "content": ": a primitive generalized plan of structure deduced from the characters of a natural group of plants or animals and assumed to be the characteristic of the ancestor from which they are all descended."}

data/sampled_jsons/Beyond_Optimism-_Exploration_With_Partially_Observable_Rewards_Appendix_Table_1_Two-Room_(2x11)_defa.jsonl

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+{"idx": 0, "title": "Model-Based Exploration in Monitored Markov Decision ...", "date": "", "ddg_snippet": "24 Jun 2025 — Beyond Optimism : Exploration With Partially Observable Rewards . In Advances in Neural Information Processing Systems, 2024a. Parisi et al ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.16772v5", "content": "24 Jun 2025 — Beyond Optimism : Exploration With Partially Observable Rewards . In Advances in Neural Information Processing Systems, 2024a. Parisi et al ..."}
+{"idx": 1, "title": "Model-Based Exploration in Truthful Monitored Markov ...", "date": "", "ddg_snippet": "21 May 2025 — Beyond Optimism : Exploration With Partially Observable Rewards . In Advances in Neural Information Processing Systems, 2024a. Parisi et al ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.16772v2", "content": "21 May 2025 — Beyond Optimism : Exploration With Partially Observable Rewards . In Advances in Neural Information Processing Systems, 2024a. Parisi et al ..."}
+{"idx": 2, "title": "Model-Based Exploration in Monitored Markov Decision ...", "date": "", "ddg_snippet": "Beyond Opti- mism: Exploration With Partially Observable Rewards . In Advances in Neural Information Processing Systems, 2024a. Parisi, S., Mohammedalamen, M ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/45819", "content": "Beyond Opti- mism: Exploration With Partially Observable Rewards . In Advances in Neural Information Processing Systems, 2024a. Parisi, S., Mohammedalamen, M ..."}
+{"idx": 3, "title": "Model-Based Exploration in Monitored Markov Decision Processes", "date": "", "ddg_snippet": "mism: Exploration With Partially Observable Rewards . ... The episode's time limit in River Swim, corridor and Two - Room - 2x11 is 200 steps , and in other ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=GdsbEOwAE7&name=pdf", "content": "mism: Exploration With Partially Observable Rewards . ... The episode's time limit in River Swim, corridor and Two - Room - 2x11 is 200 steps , and in other ..."}
+{"idx": 4, "title": "(PDF) Beyond Optimism : Exploration With Partially Observable ...", "date": "", "ddg_snippet": "observable ( first column), their performance drastically decreases with rewards partial observ ability. 0 over all training steps .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/381579170_Beyond_Optimism_Exploration_With_Partially_Observable_Rewards", "content": "observable ( first column), their performance drastically decreases with rewards partial observ ability. 0 over all training steps ."}
+{"idx": 5, "title": "Beyond Optimism : Exploration With Partially Observable Rewards", "date": "", "ddg_snippet": "Table 1 : Number of training steps . For each environment, we decided a default number of training steps . Then, we multiplied this number for a constant depending on the difficulty of the monitor .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.13909v1", "content": "Table 1 : Number of training steps . For each environment, we decided a default number of training steps . Then, we multiplied this number for a constant depending on the difficulty of the monitor ."}
+{"idx": 6, "title": "Beyond Optimism : Exploration With Partially Observable Rewards", "date": "", "ddg_snippet": "Exploration in reinforcement learning (RL) remains an open challenge.RL algorithms rely on observing rewards to train the agent, and if informative rewards are sparse the agent learns slowly or may not learn at all.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/hash/784fd5a46dfe303e5b51c8621b84cf3f-Abstract-Conference.html", "content": "Exploration in reinforcement learning (RL) remains an open challenge.RL algorithms rely on observing rewards to train the agent, and if informative rewards are sparse the agent learns slowly or may not learn at all."}
+{"idx": 7, "title": "Beyond Optimism : Exploration With Partially Observable Rewards", "date": "", "ddg_snippet": "RL algorithms rely on observing rewards to train the agent, and if informative rewards are sparse the agent learns slowly or may not learn at all. To improve exploration and reward discovery, popular algorithms rely on optimism .", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/abs/2406.13909v2", "content": "RL algorithms rely on observing rewards to train the agent, and if informative rewards are sparse the agent learns slowly or may not learn at all. To improve exploration and reward discovery, popular algorithms rely on optimism ."}
+{"idx": 8, "title": "Beyond Optimism : Exploration With Partially Observable Rewards", "date": "", "ddg_snippet": "RL algorithms rely on observing rewards to train the agent, and if informative rewards are sparse the agent learns slowly or may not learn at all. To improve exploration and reward discovery, popular algorithms rely on optimism .", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/venue/k6ZHvF1vkg@OpenReview", "content": "RL algorithms rely on observing rewards to train the agent, and if informative rewards are sparse the agent learns slowly or may not learn at all. To improve exploration and reward discovery, popular algorithms rely on optimism ."}
+{"idx": 9, "title": "Beyond Optimism : Exploration With Partially Observable Rewards", "date": "", "ddg_snippet": "This paper explores exploration strategies in reinforcement learning (RL) problems with partially observable rewards . It introduces a new approach called \" Optimism Beyond Optimism \" (OBO) that aims to improve exploration in these challenging scenarios.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/beyond-optimism-exploration-partially-observable-rewards", "content": "This paper explores exploration strategies in reinforcement learning (RL) problems with partially observable rewards . It introduces a new approach called \" Optimism Beyond Optimism \" (OBO) that aims to improve exploration in these challenging scenarios."}

data/sampled_jsons/CAPA_κp_c_obs_c_exp_formula_Chance_Adjusted_Probabilistic_Agreement.jsonl

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+{"idx": 0, "title": "The AI oversight trap: When smarter models make the same ... GitHub - jmgirard/agreement: R package for the tidy ... NCA parameter formulas - Certara Similarity affects Oversight Similarity affects Oversight Similarity affects Oversight NCA parameter formulas NCA parameter formulas jmgirard - RPubs", "date": "", "ddg_snippet": "Feb 12, 2025 · Submitted in arXiv (2025), the study introduces a novel metric, Chance Adjusted Probabilistic Agreement ( CAPA ), to measure functional similarity between LMs. The research reveals that as AI models become more powerful, their errors start to align, making oversight less reliable. The goal of the agreement package is to calculate estimates of inter-rater agreement and reliability using generalized formulas that accommodate different designs (e.g., crossed or uncrossed), missing data, and ordered or unordered categories. The package includes generalized functions for all major chance - adjusted indexes of categorical agreement ... See full list on github.com You can install the development version from GitHub with: See full list on github.com Calculate chance - adjusted indexes of categorical agreement for unordered categories Calculate chance - adjusted indexes of categorical agreement for ordered categories Calculate category-specific agreement Calculate intraclass correlation coefficient for dimensional data with 1 trial See full list on github.com Please note that the ‘ agreement ’ project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms. See full list on github.com Jul 9, 2020 · Vss (_ obs , _pred): An estimate of the volume of distribution at steady-state based on the last observed (obs) or last predicted (pred) concentration. Computed for IV Bolus and infusion dosing only. What is chance adjusted probabilistic agreement (Capa)? Paper Code pip install lm-sim Try it yourself! Data We propose Chance Adjusted Probabilistic Agreement (CAPA, or κp), a novel metric for model similarity which adjusts for chance agreement due to accuracy . Using CAPA, we find: (1) LLM-as-a-judge scores are biased towards more similar models controlling for the model's capability. How does Capa adjust for chance agreement of two independent models? CAPA adjusts for chance agreement of two independent models with the given accuracies . (2) When both models are wrong, they can still disagree. CAPA compares sample-wise predictions instead of sample-wise correctness. How does Capa work? CAPA captures whether models make similar mistakes . By analzying 100+ open-weight models, we find that as model capabilities have increased, so has average CAPA to models from other developers in the same capability class. What does Cl_F (_OBS _Pred) mean? Cl (_obs, _pred), Cl_F (_obs, _pred) a: Total body clearance for extravascular administration. = Dose/AUCINF AUMCINF (_obs, _pred): Area under the first moment curve (AUMC) extrapolated to infinity, based on the last observed concentration (obs) or the last predicted concentration (pred). What does aumc %EXTRAP _OBS _Pred mean? AUMCINF (_obs, _pred): Area under the first moment curve (AUMC) extrapolated to infinity, based on the last observed concentration (obs) or the last predicted concentration (pred). AUMC_%Extrap (_obs, _pred): Percent of AUMCINF (_obs, _pred) that is extrapolated. = 100 [ (AUMCINF – AUMClast)/ AUMCINF] Dec 24, 2018 · Using the agreement package to estimate chance - adjusted agreement with a custom weighting scheme. Detailed answers to the practice questions at the end of chapter 7 in McElreath's (2016) textbook on Bayesian data analysis, \"Statistical Rethinking.\"", "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": "Feb 12, 2025 · Submitted in arXiv (2025), the study introduces a novel metric, Chance Adjusted Probabilistic Agreement ( CAPA ), to measure functional similarity between LMs. The research reveals that as AI models become more powerful, their errors start to align, making oversight less reliable. The goal of the agreement package is to calculate estimates of inter-rater agreement and reliability using generalized formulas that accommodate different designs (e.g., crossed or uncrossed), missing data, and ordered or unordered categories. The package includes generalized functions for all major chance - adjusted indexes of categorical agreement ... See full list on github.com You can install the development version from GitHub with: See full list on github.com Calculate chance - adjusted indexes of categorical agreement for unordered categories Calculate chance - adjusted indexes of categorical agreement for ordered categories Calculate category-specific agreement Calculate intraclass correlation coefficient for dimensional data with 1 trial See full list on github.com Please note that the ‘ agreement ’ project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms. See full list on github.com Jul 9, 2020 · Vss (_ obs , _pred): An estimate of the volume of distribution at steady-state based on the last observed (obs) or last predicted (pred) concentration. Computed for IV Bolus and infusion dosing only. What is chance adjusted probabilistic agreement (Capa)? Paper Code pip install lm-sim Try it yourself! Data We propose Chance Adjusted Probabilistic Agreement (CAPA, or κp), a novel metric for model similarity which adjusts for chance agreement due to accuracy . Using CAPA, we find: (1) LLM-as-a-judge scores are biased towards more similar models controlling for the model's capability. How does Capa adjust for chance agreement of two independent models? CAPA adjusts for chance agreement of two independent models with the given accuracies . (2) When both models are wrong, they can still disagree. CAPA compares sample-wise predictions instead of sample-wise correctness. How does Capa work? CAPA captures whether models make similar mistakes . By analzying 100+ open-weight models, we find that as model capabilities have increased, so has average CAPA to models from other developers in the same capability class. What does Cl_F (_OBS _Pred) mean? Cl (_obs, _pred), Cl_F (_obs, _pred) a: Total body clearance for extravascular administration. = Dose/AUCINF AUMCINF (_obs, _pred): Area under the first moment curve (AUMC) extrapolated to infinity, based on the last observed concentration (obs) or the last predicted concentration (pred). What does aumc %EXTRAP _OBS _Pred mean? AUMCINF (_obs, _pred): Area under the first moment curve (AUMC) extrapolated to infinity, based on the last observed concentration (obs) or the last predicted concentration (pred). AUMC_%Extrap (_obs, _pred): Percent of AUMCINF (_obs, _pred) that is extrapolated. = 100 [ (AUMCINF – AUMClast)/ AUMCINF] Dec 24, 2018 · Using the agreement package to estimate chance - adjusted agreement with a custom weighting scheme. Detailed answers to the practice questions at the end of chapter 7 in McElreath's (2016) textbook on Bayesian data analysis, \"Statistical Rethinking.\""}
+{"idx": 1, "title": "GitHub - jmgirard/agreement: R package for the tidy ... NCA parameter formulas - Certara Similarity affects Oversight Similarity affects Oversight Similarity affects Oversight NCA parameter formulas NCA parameter formulas jmgirard - RPubs", "date": "", "ddg_snippet": "The goal of the agreement package is to calculate estimates of inter-rater agreement and reliability using generalized formulas that accommodate different designs (e.g., crossed or uncrossed), missing data, and ordered or unordered categories. The package includes generalized functions for all major chance - adjusted indexes of categorical agreement ... See full list on github.com You can install the development version from GitHub with: See full list on github.com Calculate chance - adjusted indexes of categorical agreement for unordered categories Calculate chance - adjusted indexes of categorical agreement for ordered categories Calculate category-specific agreement Calculate intraclass correlation coefficient for dimensional data with 1 trial See full list on github.com Please note that the ‘ agreement ’ project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms. See full list on github.com Jul 9, 2020 · Vss (_ obs , _pred): An estimate of the volume of distribution at steady-state based on the last observed (obs) or last predicted (pred) concentration. Computed for IV Bolus and infusion dosing only. What is chance adjusted probabilistic agreement (Capa)? Paper Code pip install lm-sim Try it yourself! Data We propose Chance Adjusted Probabilistic Agreement (CAPA, or κp), a novel metric for model similarity which adjusts for chance agreement due to accuracy . Using CAPA, we find: (1) LLM-as-a-judge scores are biased towards more similar models controlling for the model's capability. How does Capa adjust for chance agreement of two independent models? CAPA adjusts for chance agreement of two independent models with the given accuracies . (2) When both models are wrong, they can still disagree. CAPA compares sample-wise predictions instead of sample-wise correctness. How does Capa work? CAPA captures whether models make similar mistakes . By analzying 100+ open-weight models, we find that as model capabilities have increased, so has average CAPA to models from other developers in the same capability class. What does Cl_F (_OBS _Pred) mean? Cl (_obs, _pred), Cl_F (_obs, _pred) a: Total body clearance for extravascular administration. = Dose/AUCINF AUMCINF (_obs, _pred): Area under the first moment curve (AUMC) extrapolated to infinity, based on the last observed concentration (obs) or the last predicted concentration (pred). What does aumc %EXTRAP _OBS _Pred mean? AUMCINF (_obs, _pred): Area under the first moment curve (AUMC) extrapolated to infinity, based on the last observed concentration (obs) or the last predicted concentration (pred). AUMC_%Extrap (_obs, _pred): Percent of AUMCINF (_obs, _pred) that is extrapolated. = 100 [ (AUMCINF – AUMClast)/ AUMCINF] Dec 24, 2018 · Using the agreement package to estimate chance - adjusted agreement with a custom weighting scheme. Detailed answers to the practice questions at the end of chapter 7 in McElreath's (2016) textbook on Bayesian data analysis, \"Statistical Rethinking.\"", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/jmgirard/agreement", "content": "The goal of the agreement package is to calculate estimates of inter-rater agreement and reliability using generalized formulas that accommodate different designs (e.g., crossed or uncrossed), missing data, and ordered or unordered categories. The package includes generalized functions for all major chance - adjusted indexes of categorical agreement ... See full list on github.com You can install the development version from GitHub with: See full list on github.com Calculate chance - adjusted indexes of categorical agreement for unordered categories Calculate chance - adjusted indexes of categorical agreement for ordered categories Calculate category-specific agreement Calculate intraclass correlation coefficient for dimensional data with 1 trial See full list on github.com Please note that the ‘ agreement ’ project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms. See full list on github.com Jul 9, 2020 · Vss (_ obs , _pred): An estimate of the volume of distribution at steady-state based on the last observed (obs) or last predicted (pred) concentration. Computed for IV Bolus and infusion dosing only. What is chance adjusted probabilistic agreement (Capa)? Paper Code pip install lm-sim Try it yourself! Data We propose Chance Adjusted Probabilistic Agreement (CAPA, or κp), a novel metric for model similarity which adjusts for chance agreement due to accuracy . Using CAPA, we find: (1) LLM-as-a-judge scores are biased towards more similar models controlling for the model's capability. How does Capa adjust for chance agreement of two independent models? CAPA adjusts for chance agreement of two independent models with the given accuracies . (2) When both models are wrong, they can still disagree. CAPA compares sample-wise predictions instead of sample-wise correctness. How does Capa work? CAPA captures whether models make similar mistakes . By analzying 100+ open-weight models, we find that as model capabilities have increased, so has average CAPA to models from other developers in the same capability class. What does Cl_F (_OBS _Pred) mean? Cl (_obs, _pred), Cl_F (_obs, _pred) a: Total body clearance for extravascular administration. = Dose/AUCINF AUMCINF (_obs, _pred): Area under the first moment curve (AUMC) extrapolated to infinity, based on the last observed concentration (obs) or the last predicted concentration (pred). What does aumc %EXTRAP _OBS _Pred mean? AUMCINF (_obs, _pred): Area under the first moment curve (AUMC) extrapolated to infinity, based on the last observed concentration (obs) or the last predicted concentration (pred). AUMC_%Extrap (_obs, _pred): Percent of AUMCINF (_obs, _pred) that is extrapolated. = 100 [ (AUMCINF – AUMClast)/ AUMCINF] Dec 24, 2018 · Using the agreement package to estimate chance - adjusted agreement with a custom weighting scheme. Detailed answers to the practice questions at the end of chapter 7 in McElreath's (2016) textbook on Bayesian data analysis, \"Statistical Rethinking.\""}
+{"idx": 2, "title": "CAPA : новый инструмент для обнаружения сходства ошибок в LLM", "date": "", "ddg_snippet": "Исследователи из институтов в Тюбингене, Хайдарабаде и Стэнфорде разработали новый инструмент измерения под названием CAPA ( Chance Adjusted Probabilistic Agreement ), чтобы отслеживать...", "subpage_snippet": "", "source": "temofeev.ru", "link": "https://temofeev.ru/info/articles/capa-novyy-instrument-dlya-obnaruzheniya-skhodstva-oshibok-v-llm/", "content": "Исследователи из институтов в Тюбингене, Хайдарабаде и Стэнфорде разработали новый инструмент измерения под названием CAPA ( Chance Adjusted Probabilistic Agreement ), чтобы отслеживать..."}
+{"idx": 3, "title": "NCA parameter formulas - Certara", "date": "", "ddg_snippet": "Jul 9, 2020 · Vss (_ obs , _pred): An estimate of the volume of distribution at steady-state based on the last observed (obs) or last predicted (pred) concentration. Computed for IV Bolus and infusion dosing only.", "subpage_snippet": "", "source": "onlinehelp.certara.com", "link": "https://onlinehelp.certara.com/phoenix/8.3/topics/NCA_parameter_formulas.htm", "content": "Jul 9, 2020 · Vss (_ obs , _pred): An estimate of the volume of distribution at steady-state based on the last observed (obs) or last predicted (pred) concentration. Computed for IV Bolus and infusion dosing only."}
+{"idx": 4, "title": "Similarity affects 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.", "subpage_snippet": "", "source": "model-similarity.github.io", "link": "https://model-similarity.github.io/", "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."}
+{"idx": 5, "title": "Great Models Think Alike and this Undermines AI Oversight", "date": "", "ddg_snippet": "In this way, our novel similarity metric, Chance Adjusted Probabilistic Alignment ( CAPA ), provides a novel way to quantify functional similarity between models. We use this to analyze both evaluation and training using AI oversight as depicted in Figure 1:", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.04313v1", "content": "In this way, our novel similarity metric, Chance Adjusted Probabilistic Alignment ( CAPA ), provides a novel way to quantify functional similarity between models. We use this to analyze both evaluation and training using AI oversight as depicted in Figure 1:"}
+{"idx": 6, "title": "jmgirard - RPubs", "date": "", "ddg_snippet": "Dec 24, 2018 · Using the agreement package to estimate chance - adjusted agreement with a custom weighting scheme. Detailed answers to the practice questions at the end of chapter 7 in McElreath's (2016) textbook on Bayesian data analysis, \"Statistical Rethinking.\"", "subpage_snippet": "", "source": "rpubs.com", "link": "https://rpubs.com/jmgirard", "content": "Dec 24, 2018 · Using the agreement package to estimate chance - adjusted agreement with a custom weighting scheme. Detailed answers to the practice questions at the end of chapter 7 in McElreath's (2016) textbook on Bayesian data analysis, \"Statistical Rethinking.\""}
+{"idx": 7, "title": "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 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.04313v2", "content": "We study how model similarity affects both aspects of AI oversight by proposing Chance Adjusted Probabilistic Agreement ( CAPA ): a metric for LM ..."}
+{"idx": 8, "title": "\"Great Models Think Alike and this Undermines AI Oversight\"", "date": "", "ddg_snippet": "The Chance Adjusted Probabilistic Agreement metric enables strategic weak-to-strong training. It guides the selection of weak supervisor models that are functionally different from the strong student, maximizing knowledge transfer.", "subpage_snippet": "", "source": "www.rohan-paul.com", "link": "https://www.rohan-paul.com/p/great-models-think-alike-and-this", "content": "The Chance Adjusted Probabilistic Agreement metric enables strategic weak-to-strong training. It guides the selection of weak supervisor models that are functionally different from the strong student, maximizing knowledge transfer."}
+{"idx": 9, "title": "[Literature Review] Great Models Think Alike and this Undermines AI...", "date": "", "ddg_snippet": "CAPA ( Chance Adjusted Probabilistic Agreement ). Model Similarity.Expected Error Overlap ( cexp ): The chance agreement expected from independent models given their accuracies. The CAPA metric is presented through the equation", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/en/review/great-models-think-alike-and-this-undermines-ai-oversight", "content": "CAPA ( Chance Adjusted Probabilistic Agreement ). Model Similarity.Expected Error Overlap ( cexp ): The chance agreement expected from independent models given their accuracies. The CAPA metric is presented through the equation"}

data/sampled_jsons/Catoni_estimator_Ψ(x)_definition_Section_3.2_5IpVe9PH14_Catoni_Contextual_Bandits.jsonl

@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Catoni Contextual Bandits are Robust to Heavy-tailed Rewards", "date": "", "ddg_snippet": "Catoni Estimator We first introduce Catoni estimator . This is a robust estimator proposed by Audibert & Catoni (2011)(see also (Lugosi & Mendelson, 2019)) to estimate random variables with bounded variance and unbounded range.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=5IpVe9PH14", "content": "Catoni Estimator We first introduce Catoni estimator . This is a robust estimator proposed by Audibert & Catoni (2011)(see also (Lugosi & Mendelson, 2019)) to estimate random variables with bounded variance and unbounded range."}
+{"idx": 1, "title": "Tong Zhang's Homepage", "date": "", "ddg_snippet": "article{DaRiXiZh14, author = {Dong Dai and Philippe Rigollet and Lucy Xia and Tong Zhang}, title = {Aggregation of affine estimators }, journal = {Electron.", "subpage_snippet": "", "source": "tongzhang-ml.org", "link": "https://tongzhang-ml.org/publication.html", "content": "article{DaRiXiZh14, author = {Dong Dai and Philippe Rigollet and Lucy Xia and Tong Zhang}, title = {Aggregation of affine estimators }, journal = {Electron."}
+{"idx": 2, "title": "Сводки ополчения Новороссии Z.O.V. (ДНР, ЛНР, Украина, Война)...", "date": "", "ddg_snippet": "Реквизиты для помощи: НА Сберкарту: 4276 1609 2548 3621 Бот обратной связи: @swodka_bot с администрацией напрямую. Здесь можно оставить информацию, которая поможет нашей Победе! Пиар-менеджеры @skufido1 (напрямую) @bosteleg и @magister_mg.", "subpage_snippet": "", "source": "t.me", "link": "https://t.me/s/Swodki", "content": "Реквизиты для помощи: НА Сберкарту: 4276 1609 2548 3621 Бот обратной связи: @swodka_bot с администрацией напрямую. Здесь можно оставить информацию, которая поможет нашей Победе! Пиар-менеджеры @skufido1 (напрямую) @bosteleg и @magister_mg."}
+{"idx": 3, "title": "Как собрать кубик Рубика 3х3. Самая легкая схема для... | диБит", "date": "", "ddg_snippet": "На вашем кубике Рубика сейчас одна из четырех комбинаций: а, б, в, г - см. Рис. 3 – 2 . Переход от одной комбинации к другой происходит единой формулой: Ф П В П' В' Ф'. Эту формулу вам нужно повторить 1-3 раза в зависимости от того какая у вас комбинация.", "subpage_snippet": "", "source": "dibit.ru", "link": "https://dibit.ru/p/articles/kubik-rubika-3x3", "content": "На вашем кубике Рубика сейчас одна из четырех комбинаций: а, б, в, г - см. Рис. 3 – 2 . Переход от одной комбинации к другой происходит единой формулой: Ф П В П' В' Ф'. Эту формулу вам нужно повторить 1-3 раза в зависимости от того какая у вас комбинация."}
+{"idx": 4, "title": "ГДЗ Математика 6 класс Виленкин (базовый уровень) часть...", "date": "", "ddg_snippet": "Найдите значение выражения: а) 4,3 x + 6,9 x + 7,7 x − 5,9 x при x = 5,4; 0,6; 100; б) 4,9a − (3,9a + 0,6a) при a = 3 , 2 ; 9,38; в) 19,84c − (7,84c + 11,7с) при c = 0,4; 5,02.", "subpage_snippet": "", "source": "Reshalka.com", "link": "https://Reshalka.com/uchebniki/6-klass/matematika/vilenkin1/40", "content": "Найдите значение выражения: а) 4,3 x + 6,9 x + 7,7 x − 5,9 x при x = 5,4; 0,6; 100; б) 4,9a − (3,9a + 0,6a) при a = 3 , 2 ; 9,38; в) 19,84c − (7,84c + 11,7с) при c = 0,4; 5,02."}
+{"idx": 5, "title": "Что такое ph воды | Нормы, питьевой показатель", "date": "", "ddg_snippet": "Какими бывают показатели pH в разных средах. Значения pH воды находятся в диапазоне от 0 единиц до максимальных 14. Оптимальный, нейтральный показатель, «золотая середина» – это 7. При данном значении среда с идеальным кислотно-щелочным балансом.", "subpage_snippet": "", "source": "wt-filter.ru", "link": "https://wt-filter.ru/blog/chto-takoe-ph/", "content": "Какими бывают показатели pH в разных средах. Значения pH воды находятся в диапазоне от 0 единиц до максимальных 14. Оптимальный, нейтральный показатель, «золотая с��редина» – это 7. При данном значении среда с идеальным кислотно-щелочным балансом."}
+{"idx": 6, "title": "Основные параметры воды (показатели GH, KH и pH)", "date": "", "ddg_snippet": "Подробное описание основных параметров воды и их значение для обитателей аквариума - общая жёсткость (dH), карбонатная жёсткость (kH) и водородный показатель pH.", "subpage_snippet": "", "source": "www.aqvium.ru", "link": "https://www.aqvium.ru/aquarium/voda/gidrokhimichesky-sostav-vody", "content": "Подробное описание основных параметров воды и их значение для обитателей аквариума - общая жёсткость (dH), карбонатная жёсткость (kH) и водородный показатель pH."}
+{"idx": 7, "title": "номер 9 (страница 21) гдз по английскому языку 3 класс Быкова...", "date": "", "ddg_snippet": "Москва, 14-е издание, переработанное. Английский в фокусе. Модуль 1. Уроки 1a, 1b. сборник упражнений - cтраница 21.", "subpage_snippet": "", "source": "gdz.top", "link": "https://gdz.top/3-klass/english/bykova-spotlight-sbornik-upragnenij/01-9", "content": "Москва, 14-е издание, переработанное. Английский в фокусе. Модуль 1. Уроки 1a, 1b. сборник упражнений - cтраница 21."}
+{"idx": 8, "title": "14 простых техник Невилла Годдарда для проявления чего угодно", "date": "", "ddg_snippet": "Техники Невилла Годдарда для манифестации всего основаны на силе воображения. Важно понимать, что манифесты сбываются в свое время. Нужно быть терпеливыми и позволить этому случиться, не теряя веры. Не каждая техника манифестации может сработать у вас.", "subpage_snippet": "", "source": "telegra.ph", "link": "https://telegra.ph/14-prostyh-tehnik-Nevilla-Goddarda-dlya-proyavleniya-chego-ugodno-07-12", "content": "Техники Невилла Годдарда для манифестации всего основаны на силе воображения. Важно понимать, что манифесты сбываются в свое время. Нужно быть терпеливыми и позволить этому случиться, не теряя веры. Не каждая техника манифестации может сработать у вас."}
+{"idx": 9, "title": "Упражнения на Present Simple с ответами", "date": "", "ddg_snippet": "Анна Березена. 17.03.2023 в 14:15. Здесь нужно использовать Are, так как married — это не глагол, а прилагательное.", "subpage_snippet": "", "source": "EnglishWeb.ru", "link": "https://EnglishWeb.ru/grammar/present-simple-exercises.html", "content": "Анна Березена. 17.03.2023 в 14:15. Здесь нужно использовать Are, так как married — это не глагол, а прилагательное."}

data/sampled_jsons/CrossKD_Cross-head_knowledge_distillation_object_detection_Wang_2024_year_2024.jsonl

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+{"idx": 0, "title": "Cross-Head Knowledge Distillation for Object Detection", "date": "", "ddg_snippet": "by J Wang · 2024 · Cited by 90 — In this paper, we present a general and effective prediction mimicking distillation scheme, called CrossKD , which de- livers the intermediate features of the ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2024/papers/Wang_CrossKD_Cross-Head_Knowledge_Distillation_for_Object_Detection_CVPR_2024_paper.pdf", "content": "by J Wang · 2024 · Cited by 90 — In this paper, we present a general and effective prediction mimicking distillation scheme, called CrossKD , which de- livers the intermediate features of the ..."}
+{"idx": 1, "title": "Cross-Head Knowledge Distillation for Object Detection", "date": "", "ddg_snippet": "by J Wang · 2023 · Cited by 90 — In this paper, we present a general and effective prediction mimicking distillation scheme, called CrossKD , which delivers the intermediate features of the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2306.11369", "content": "by J Wang · 2023 · Cited by 90 — In this paper, we present a general and effective prediction mimicking distillation scheme, called CrossKD , which delivers the intermediate features of the ..."}
+{"idx": 2, "title": "CVPR Poster CrossKD: Cross-Head Knowledge Distillation for ...", "date": "", "ddg_snippet": "Knowledge Distillation (KD) has been validated as an effective model compression technique for learning compact object detectors. Existing state-of-the-art ...", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2024/poster/31390", "content": "Knowledge Distillation (KD) has been validated as an effective model compression technique for learning compact object detectors. Existing state-of-the-art ..."}
+{"idx": 3, "title": "Cross-Head Knowledge Distillation for Object Detection", "date": "", "ddg_snippet": "1 May 2024 — In this paper, we present a general and effective prediction mimicking distillation scheme, called CrossKD , which delivers the intermediate features of the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2306.11369v2", "content": "1 May 2024 — In this paper, we present a general and effective prediction mimicking distillation scheme, called CrossKD , which delivers the intermediate features of the ..."}
+{"idx": 4, "title": "Jiabao Wang", "date": "", "ddg_snippet": "2024 /02 -- Our paper ( CrossKD : Cross - Head Knowledge Distillation for Object Detection ) got accepted by CVPR 2024 ! Publications. * Eauql contribution. # ...", "subpage_snippet": "", "source": "jbwang1997.github.io", "link": "https://jbwang1997.github.io/", "content": "2024 /02 -- Our paper ( CrossKD : Cross - Head Knowledge Distillation for Object Detection ) got accepted by CVPR 2024 ! Publications. * Eauql contribution. # ..."}
+{"idx": 5, "title": "Cross-Head Knowledge Distillation for Object Detection", "date": "", "ddg_snippet": "In this paper we present a general and effective prediction mimicking distillation scheme called CrossKD which delivers the intermediate features of the ...", "subpage_snippet": "", "source": "chatpaper.com", "link": "https://chatpaper.com/paper/46892", "content": "In this paper we present a general and effective prediction mimicking distillation scheme called CrossKD which delivers the intermediate features of the ..."}
+{"idx": 6, "title": "CrossKD: Cross-Head Knowledge Distillation for Object ...", "date": "", "ddg_snippet": "This paper introduces CrossKD , a novel approach to knowledge distillation for dense object detection tasks. CrossKD leverages cross-head ...", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/crosskd-cross-head-knowledge-distillation-object-detection", "content": "This paper introduces CrossKD , a novel approach to knowledge distillation for dense object detection tasks. CrossKD leverages cross-head ..."}
+{"idx": 7, "title": "Jiabao Wang", "date": "", "ddg_snippet": "CrossKD: Cross-Head Knowledge Distillation for Dense Object Detection · Published: 19 Jun 2024, Last Modified: 21 May 2024 · CVPR 2024 · Readers: Everyone ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/profile?id=~Jiabao_Wang5", "content": "CrossKD: Cross-Head Knowledge Distillation for Dense Object Detection · Published: 19 Jun 2024, Last Modified: 21 May 2024 · CVPR 2024 · Readers: Everyone ..."}
+{"idx": 8, "title": "Awesome Knowledge Distillation for Object Detection ...", "date": "", "ddg_snippet": "A curated list of awesome distillation techniques designed for object detectors . Parameters compression and accuracy boosting are core problems for object ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/LutingWang/awesome-knowledge-distillation-for-object-detection", "content": "A curated list of awesome distillation techniques designed for object detectors . Parameters compression and accuracy boosting are core problems for object ..."}
+{"idx": 9, "title": "Knowledge Distillation for YOLO", "date": "", "ddg_snippet": "Wang , Jiabao, et al. “ CrossKD : Cross - head knowledge distillation for object detection .” Proceedings of the IEEE/CVF conference on computer ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@juneta.tao/knowledge-distillation-for-yolo-999a9bc9bcdd", "content": "Wang , Jiabao, et al. “ CrossKD : Cross - head knowledge distillation for object detection .” Proceedings of the IEEE/CVF conference on computer ..."}

data/sampled_jsons/Direct_Preference_Optimization_DPO_avoids_explicit_reward_model_summary.jsonl

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+{"idx": 0, "title": "Explicit Preference Optimization: No Need for an Implicit", "date": "", "ddg_snippet": "In this regard, direct preference optimization ( DPO ) and its many offshoots circumvent the need for a separate reward training step.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.07492v1", "content": "In this regard, direct preference optimization ( DPO ) and its many offshoots circumvent the need for a separate reward training step."}
+{"idx": 1, "title": "Robust Preference Optimization through Reward Model Distillation", "date": "", "ddg_snippet": "Direct Preference Optimization ( DPO ) is a popular offline alignment method that trains a policy directly on preference data without the need to train ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.19316v2", "content": "Direct Preference Optimization ( DPO ) is a popular offline alignment method that trains a policy directly on preference data without the need to train ..."}
+{"idx": 2, "title": "SimPO: Simple Preference Optimization with a Reference-Free", "date": "", "ddg_snippet": "DPO reparameterizes the reward function in RLHF to directly learn a policy model from preference data, eliminating the need for an explicit reward ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.14734v3", "content": "DPO reparameterizes the reward function in RLHF to directly learn a policy model from preference data, eliminating the need for an explicit reward ..."}
+{"idx": 3, "title": "Direct Preference Optimization: Your Language Model is Secretly", "date": "", "ddg_snippet": "In this paper, we show how to directly optimize a language model to adhere to human preferences , without explicit reward modeling or reinforcement ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2305.18290v3", "content": "In this paper, we show how to directly optimize a language model to adhere to human preferences , without explicit reward modeling or reinforcement ..."}
+{"idx": 4, "title": "A Comprehensive Survey of Direct Preference Optimization:", "date": "", "ddg_snippet": "DPO circumvents the need to train an explicit reward model by establishing a direct mapping from reward functions to optimal policies.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.15595v2", "content": "DPO circumvents the need to train an explicit reward model by establishing a direct mapping from reward functions to optimal policies."}
+{"idx": 5, "title": "Direct Preference Optimization: Your Language Model is Secretly", "date": "", "ddg_snippet": "By focusing directly on human preferences without complex algorithms or explicit reward models , DPO stands out as a significant advancement in AI ...", "subpage_snippet": "", "source": "blog.athina.ai", "link": "https://blog.athina.ai/direct-preference-optimization-your-language-model-is-secretly-a-reward-model", "content": "By focusing directly on human preferences without complex algorithms or explicit reward models , DPO stands out as a significant advancement in AI ..."}
+{"idx": 6, "title": "Human Preference Optimization: RLHF + DPO — Innodata", "date": "", "ddg_snippet": "AI optimization techniques include supervised fine-tuning, RLHF training , LLM DPO ( Direct Preference Optimization ), and reward modeling .", "subpage_snippet": "", "source": "innodata.com", "link": "https://innodata.com/generative-ai/human-preference-optimization/", "content": "AI optimization techniques include supervised fine-tuning, RLHF training , LLM DPO ( Direct Preference Optimization ), and reward modeling ."}
+{"idx": 7, "title": "Human Preference Optimization: RLHF + DPO — Innodata", "date": "", "ddg_snippet": "AI optimization techniques include supervised fine-tuning, RLHF training , LLM DPO ( Direct Preference Optimization ), and reward modeling .", "subpage_snippet": "", "source": "innodata.com", "link": "https://innodata.com/generative-ai-2/human-preference-optimization/", "content": "AI optimization techniques include supervised fine-tuning, RLHF training , LLM DPO ( Direct Preference Optimization ), and reward modeling ."}
+{"idx": 8, "title": "Topic 46: RLHF variations: DPO, RRHF, RLAIF", "date": "", "ddg_snippet": "But today we’ll focus only on three of them – very interesting variations of RLHF: Direct Preference Optimization ( DPO ), Reward -Rank Hindsight ...", "subpage_snippet": "", "source": "www.turingpost.com", "link": "https://www.turingpost.com/p/rlhfvariants", "content": "But today we’ll focus only on three of them – very interesting variations of RLHF: Direct Preference Optimization ( DPO ), Reward -Rank Hindsight ..."}
+{"idx": 9, "title": "Vinija's Notes • LLM Alignment", "date": "", "ddg_snippet": "... involves Supervised Fine-Tuning (SFT) for dialogue, where a pre-trained model is optimized to generate preferred responses to prompts, such as direct ...", "subpage_snippet": "", "source": "vinija.ai", "link": "https://vinija.ai/concepts/llm-alignment/", "content": "... involves Supervised Fine-Tuning (SFT) for dialogue, where a pre-trained model is optimized to generate preferred responses to prompts, such as direct ..."}

data/sampled_jsons/EventPS_Yu_CVPR_2024_paper_experimental_results_table_1_3D_printed_objects_MAE.jsonl

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+{"idx": 0, "title": "PDF EventPS: Real-Time Photometric Stereo Using an Event Camera", "date": "", "ddg_snippet": "This paper introduces EventPS , a novel approach to real-time photometric stereo using an event camera. Capitalizing on the exceptional temporal resolution, dynamic range, and low bandwidth character-istics of event cameras, EventPS estimates surface nor-mal only from the radiance changes, significantly enhanc-ing data efficiency.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2024/papers/Yu_EventPS_Real-Time_Photometric_Stereo_Using_an_Event_Camera_CVPR_2024_paper.pdf", "content": "This paper introduces EventPS , a novel approach to real-time photometric stereo using an event camera. Capitalizing on the exceptional temporal resolution, dynamic range, and low bandwidth character-istics of event cameras, EventPS estimates surface nor-mal only from the radiance changes, significantly enhanc-ing data efficiency."}
+{"idx": 1, "title": "EventPS: Real-Time Photometric Stereo Using an Event Camera", "date": "", "ddg_snippet": "Photometric stereo is a well-established technique to es-timate the surface normal of an object . However, the re-quirement of capturing multiple high dynamic range images under different illumination conditions limits the speed and real-time applications. This paper introduces EventPS , a novel approach to real-time photometric stereo using an event camera. Capitalizing on the exceptional ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10655016", "content": "Photometric stereo is a well-established technique to es-timate the surface normal of an object . However, the re-quirement of capturing multiple high dynamic range images under different illumination conditions limits the speed and real-time applications. This paper introduces EventPS , a novel approach to real-time photometric stereo using an event camera. Capitalizing on the exceptional ..."}
+{"idx": 2, "title": "Workbook: CVPR 2024 - Tableau Software", "date": "", "ddg_snippet": "CVPR 2024 (All) Computer Vision and Pattern Recognition Conference Seattle | June 17-21, 2024", "subpage_snippet": "", "source": "public.tableau.com", "link": "https://public.tableau.com/views/CVPR2024/CVPRtrends?:showVizHome=no", "content": "CVPR 2024 (All) Computer Vision and Pattern Recognition Conference Seattle | June 17-21, 2024"}
+{"idx": 3, "title": "CVPR 2024最佳论文分享┆EventPS: 基于事件相机的实时光度立体视觉", "date": "", "ddg_snippet": "本文介绍了 CVPR 2024 的最佳论文提名，该论文利用事件相机的独特属性，实现了实时光度立体视觉。 该算法在传统和深度学习领域均取得成功。", "subpage_snippet": "", "source": "blog.csdn.net", "link": "https://blog.csdn.net/audyxiao001/article/details/140520624", "content": "本文介绍了 CVPR 2024 的最佳论文提名，该论文利用事件相机的独特属性，实现了实时光度立体视觉。 该算法在传统和深度学习领域均取得成功。"}
+{"idx": 4, "title": "CVPR 2024 Accepted Paper List", "date": "", "ddg_snippet": "How to use the paper list below: - Overview: This table presents papers from the CVPR conference, year 2024 . - Filtering: By default, the table loads the first 100 records. You can use the filter box under each column header to search within these loaded entries.", "subpage_snippet": "", "source": "papercopilot.com", "link": "https://papercopilot.com/paper-list/cvpr-paper-list/cvpr-2024-paper-list/", "content": "How to use the paper list below: - Overview: This table presents papers from the CVPR conference, year 2024 . - Filtering: By default, the table loads the first 100 records. You can use the filter box under each column header to search within these loaded entries."}
+{"idx": 5, "title": "SkalskiP/top-cvpr-2024-papers - GitHub", "date": "", "ddg_snippet": "Computer Vision and Pattern Recognition is a massive conference. In 2024 alone, 11,532 papers were submitted, and 2,719 were accepted. I created this repository to help you search for crème de la crème of CVPR publications. If the paper you are looking for is not on my short list, take a peek at the full list of accepted papers .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/SkalskiP/top-cvpr-2024-papers", "content": "Computer Vision and Pattern Recognition is a massive conference. In 2024 alone, 11,532 papers were submitted, and 2,719 were accepted. I created this repository to help you search for crème de la crème of CVPR publications. If the paper you are looking for is not on my short list, take a peek at the full list of accepted papers ."}
+{"idx": 6, "title": "CVPR 2024 Open Access Repository", "date": "", "ddg_snippet": "This paper introduces EventPS a novel approach to real-time photometric stereo using an event camera. Capitalizing on the exceptional temporal resolution dynamic range and low bandwidth characteristics of event cameras EventPS estimates surface normal only from the radiance changes significantly enhancing data efficiency.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2024/html/Yu_EventPS_Real-Time_Photometric_Stereo_Using_an_Event_Camera_CVPR_2024_paper.html", "content": "This paper introduces EventPS a novel approach to real-time photometric stereo using an event camera. Capitalizing on the exceptional temporal resolution dynamic range and low bandwidth characteristics of event cameras EventPS estimates surface normal only from the radiance changes significantly enhancing data efficiency."}
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+{"idx": 8, "title": "CVPR.2024 - Oral | Cool Papers - Immersive Paper Discovery", "date": "", "ddg_snippet": "The list of accepted papers for CVPR.2024 - Oral, including titles, authors, and abstracts, with support for paper interpretation based on Kimi AI.", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/venue/CVPR.2024?group=Oral", "content": "The list of accepted papers for CVPR.2024 - Oral, including titles, authors, and abstracts, with support for paper interpretation based on Kimi AI."}
+{"idx": 9, "title": "EventPS: Real-Time Photometric Stereo Using an Event Camera", "date": "", "ddg_snippet": "This paper introduces EventPS , a novel approach to real-time photometric stereo using an event camera that capitalizes on the exceptional temporal resolution, dynamic range, and low bandwidth character-istics of event cameras, significantly enhancing data efficiency. Photometric stereo is a well-established technique to es-timate the surface normal of an object . However, the re-quirement of ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/EventPS:-Real-Time-Photometric-Stereo-Using-an-Yu-Ren/7f72975f58ceff79a3762464ba7e5f8c29c54aaf", "content": "This paper introduces EventPS , a novel approach to real-time photometric stereo using an event camera that capitalizes on the exceptional temporal resolution, dynamic range, and low bandwidth character-istics of event cameras, significantly enhancing data efficiency. Photometric stereo is a well-established technique to es-timate the surface normal of an object . However, the re-quirement of ..."}

data/sampled_jsons/Feint_Behaviors_and_Strategies_2403.07932_Section_4.2.2_scheduler_weights_initial_values.jsonl

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+{"idx": 0, "title": "Abstract page for arXiv paper 2403 . 07932 : Feint in Multi-Player Games", "date": "", "ddg_snippet": "Our work first formalizes Feint from the perspective of Multi-Player Games, in terms of the temporal, spatial, and their collective impacts. The formalization is built upon Non-transitive Active Markov Game Model, where Feint can have a considerable amount of impacts.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2403.07932", "content": "Our work first formalizes Feint from the perspective of Multi-Player Games, in terms of the temporal, spatial, and their collective impacts. The formalization is built upon Non-transitive Active Markov Game Model, where Feint can have a considerable amount of impacts."}
+{"idx": 1, "title": "[LYRICS] Feint - Snake Eyes (ft. CoMa) - YouTube", "date": "", "ddg_snippet": "This is a lyrics video. I am not affiliated with any artists or labels.- - - ᴅᴏᴡɴʟᴏᴀᴅs - - -Download the song ↓Beatport: http://btprt.dj/RDu7HNDNB, so cl...", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=tx6v2lWRrZ4", "content": "This is a lyrics video. I am not affiliated with any artists or labels.- - - ᴅᴏᴡɴʟᴏᴀᴅs - - -Download the song ↓Beatport: http://btprt.dj/RDu7HNDNB, so cl..."}
+{"idx": 2, "title": "Classes - bg3.wiki", "date": "", "ddg_snippet": "A character's class in Baldur's Gate 3 is one of their primary defining features. Each class represents a different calling, and each offers a number of unique abilities, powers, and skills which influence how that character interacts with ...", "subpage_snippet": "", "source": "bg3.wiki", "link": "https://bg3.wiki/wiki/Classes", "content": "A character's class in Baldur's Gate 3 is one of their primary defining features. Each class represents a different calling, and each offers a number of unique abilities, powers, and skills which influence how that character interacts with ..."}
+{"idx": 3, "title": "The @ Scheduled Annotation in Spring | Baeldung", "date": "", "ddg_snippet": "How to use the @ Scheduled annotation in Spring, to run tasks after a fixed delay, at a fixed rate or according to a cron expression.In this tutorial, we’ll illustrate how the Spring @ Scheduled annotation can be used to configure and schedule tasks.", "subpage_snippet": "", "source": "www.baeldung.com", "link": "https://www.baeldung.com/spring-scheduled-tasks", "content": "How to use the @ Scheduled annotation in Spring, to run tasks after a fixed delay, at a fixed rate or according to a cron expression.In this tutorial, we’ll illustrate how the Spring @ Scheduled annotation can be used to configure and schedule tasks."}
+{"idx": 4, "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.org", "link": "https://dblp.org/pid/123/5256.html", "content": "Junyu Liu, Xiangjun Peng: Feint Behaviors and Strategies : Formalization, Implementation and Evaluation. NeurIPS 2024."}
+{"idx": 5, "title": "Перевод песен Teddy Swims: перевод песни Lose Control, текст...", "date": "", "ddg_snippet": "Yeah I know, I could do it on my own, but I want. Да, я знаю, что мог бы сделать всё сам, но я хочу. That real full moon black magic and it takes two . Настоящую магию полной луны, а для этого нужны двое. Problematic, problem is.", "subpage_snippet": "", "source": "www.amalgama-lab.com", "link": "https://www.amalgama-lab.com/songs/t/teddy_swims/lose_control.html", "content": "Yeah I know, I could do it on my own, but I want. Да, я знаю, что мог бы сделать всё сам, но я хочу. That real full moon black magic and it takes two . Настоящую магию полной луны, а для этого нужны двое. Problematic, problem is."}
+{"idx": 6, "title": "Google Scholar", "date": "", "ddg_snippet": "Google Scholar provides a simple way to broadly search for scholarly literature. Search across a wide variety of disciplines and sources: articles, theses, books, abstracts and court opinions.", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/schhp?hl=en&as_sdt=0,5", "content": "Google Scholar provides a simple way to broadly search for scholarly literature. Search across a wide variety of disciplines and sources: articles, theses, books, abstracts and court opinions."}
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+{"idx": 9, "title": "Blox Fruits Stock Right Now - FruityBlox", "date": "", "ddg_snippet": "Trading Trade Calculator Create Trade Value List Stock Gacha Simulator Discord Premium Codes.Update: 3 hours, 16 minutes, 54 seconds .", "subpage_snippet": "", "source": "fruityblox.com", "link": "https://fruityblox.com/stock", "content": "Trading Trade Calculator Create Trade Value List Stock Gacha Simulator Discord Premium Codes.Update: 3 hours, 16 minutes, 54 seconds ."}

data/sampled_jsons/Gao_et_al._2024_'k-sparse_autoencoders'_dead_latents_modifications_techniques.jsonl

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+{"idx": 0, "title": "[2406.04093] Scaling and evaluating sparse autoencoders", "date": "", "ddg_snippet": "However, studying the properties of autoencoder scaling is difficult due to the need to balance reconstruction and sparsity objectives and the presence of dead latents . We propose using k-sparse autoencoders [Makhzani and Frey, 2013] to directly control sparsity, simplifying tuning and improving the reconstruction-sparsity frontier.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2406.04093", "content": "However, studying the properties of autoencoder scaling is difficult due to the need to balance reconstruction and sparsity objectives and the presence of dead latents . We propose using k-sparse autoencoders [Makhzani and Frey, 2013] to directly control sparsity, simplifying tuning and improving the reconstruction-sparsity frontier."}
+{"idx": 1, "title": "SCALING AND EVALUATING SPARSE AUTOENCODERS - OpenReview", "date": "", "ddg_snippet": "sparsity objectives and the presence of dead latents . We propose using k-sparse autoencoders (Makhzani & Frey, 2013) to directly control sparsity, simplifying tuning and improving the reconstruction-sparsity frontier. Additionally, we find modifications that result in few dead latents , even at the largest scales we tried. Using these techniques , we find clean scaling laws with respect to ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=tcsZt9ZNKD", "content": "sparsity objectives and the presence of dead latents . We propose using k-sparse autoencoders (Makhzani & Frey, 2013) to directly control sparsity, simplifying tuning and improving the reconstruction-sparsity frontier. Additionally, we find modifications that result in few dead latents , even at the largest scales we tried. Using these techniques , we find clean scaling laws with respect to ..."}
+{"idx": 2, "title": "(PDF) Scaling and evaluating sparse autoencoders - ResearchGate", "date": "", "ddg_snippet": "Additionally, we find modifications that result in few dead latents , even at the largest scales we tried. Using these techniques , we find clean scaling laws with respect to autoencoder size and ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/381227195_Scaling_and_evaluating_sparse_autoencoders", "content": "Additionally, we find modifications that result in few dead latents , even at the largest scales we tried. Using these techniques , we find clean scaling laws with respect to autoencoder size and ..."}
+{"idx": 3, "title": "Sparse Autoencoders | PDF", "date": "", "ddg_snippet": "Scaling and evaluating sparse autoencoders Leo Gao ∗ Tom Dupré la Tour† Henk Tillman† Gabriel Goh Rajan Troll Alec Radford Ilya Sutskever Jan Leike Jeffrey Wu† OpenAI Abstract Sparse autoencoders provide a promising unsupervised approach for extracting in- terpretable features from a language model by reconstructing activations from a sparse bottleneck layer. Since language models ...", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/769986840/Sparse-Autoencoders", "content": "Scaling and evaluating sparse autoencoders Leo Gao ∗ Tom Dupré la Tour† Henk Tillman† Gabriel Goh Rajan Troll Alec Radford Ilya Sutskever Jan Leike Jeffrey Wu† OpenAI Abstract Sparse autoencoders provide a promising unsupervised approach for extracting in- terpretable features from a language model by reconstructing activations from a sparse bottleneck layer. Since language models ..."}
+{"idx": 4, "title": "ICLR Poster Scaling and evaluating sparse autoencoders", "date": "", "ddg_snippet": "Scaling and evaluating sparse autoencoders Leo Gao · Tom Dupre la Tour · Henk Tillman · Gabriel Goh · Rajan Troll · Alec Radford · Ilya Sutskever · Jan Leike · Jeffrey Wu", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2025/poster/28040", "content": "Scaling and evaluating sparse autoencoders Leo Gao · Tom Dupre la Tour · Henk Tillman · Gabriel Goh · Rajan Troll · Alec Radford · Ilya Sutskever · Jan Leike · Jeffrey Wu"}
+{"idx": 5, "title": "Scaling and evaluating sparse autoencoders - catalyzex.com", "date": "", "ddg_snippet": "We propose using k-sparse autoencoders [Makhzani and Frey, 2013] to directly control sparsity, simplifying tuning and improving the reconstruction-sparsity frontier. Additionally, we find modifications that result in few dead latents , even at the largest scales we tried.", "subpage_snippet": "", "source": "www.catalyzex.com", "link": "https://www.catalyzex.com/paper/scaling-and-evaluating-sparse-autoencoders", "content": "We propose using k-sparse autoencoders [Makhzani and Frey, 2013] to directly control sparsity, simplifying tuning and improving the reconstruction-sparsity frontier. Additionally, we find modifications that result in few dead latents , even at the largest scales we tried."}
+{"idx": 6, "title": "arXiv:2406.04093v1 [cs.LG] 6 Jun 2024", "date": "", "ddg_snippet": "2.4 Preventing dead latents Dead latents pose another significant dificulty in autoencoder training. In larger autoencoders , an increasingly large proportion of latents stop activating entirely at some point in training. For example, Templeton et al. [ 2024 ] train a 34 million latent autoencoder with only 12 million alive latents , and in our ablations we find up to 90% dead latents7 when no ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2406.04093", "content": "2.4 Preventing dead latents Dead latents pose another significant dificulty in autoencoder training. In larger autoencoders , an increasingly large proportion of latents stop activating entirely at some point in training. For example, Templeton et al. [ 2024 ] train a 34 million latent autoencoder with only 12 million alive latents , and in our ablations we find up to 90% dead latents7 when no ..."}
+{"idx": 7, "title": "AlignmentResearch/sae-k-sparse-mamba - GitHub", "date": "", "ddg_snippet": "This library trains k-sparse autoencoders (SAEs) on the residual stream activations of HuggingFace language models, roughly following the recipe detailed in Scaling and evaluating sparse autoencoders ( Gao et al. 2024 ). This is a lean, simple library with few configuration options. Unlike most other ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/AlignmentResearch/sae-k-sparse-mamba", "content": "This library trains k-sparse autoencoders (SAEs) on the residual stream activations of HuggingFace language models, roughly following the recipe detailed in Scaling and evaluating sparse autoencoders ( Gao et al. 2024 ). This is a lean, simple library with few configuration options. Unlike most other ..."}
+{"idx": 8, "title": "PDF Scaling and Evaluating Sparse Autoencoders", "date": "", "ddg_snippet": "However, studying the proper-ties of autoencoder scaling is dificult due to the need to balance reconstruction and sparsity objectives and the presence of dead latents . We propose using k-sparse autoencoders (Makhzani & Frey, 2013) to directly control sparsity, simplifying tuning and improving the reconstruction-sparsity frontier.", "subpage_snippet": "", "source": "proceedings.iclr.cc", "link": "https://proceedings.iclr.cc/paper_files/paper/2025/file/42ef3308c230942d223c411adf182c88-Paper-Conference.pdf", "content": "However, studying the proper-ties of autoencoder scaling is dificult due to the need to balance reconstruction and sparsity objectives and the presence of dead latents . We propose using k-sparse autoencoders (Makhzani & Frey, 2013) to directly control sparsity, simplifying tuning and improving the reconstruction-sparsity frontier."}
+{"idx": 9, "title": "Decomposing GPT-4's internal representations into 16 million oft ...", "date": "", "ddg_snippet": "Key contributions: proposed using k-sparse autoencoders [2] to directly control sparsity, simplifying tuning and improving the reconstruction-sparsity frontier. find modifications that result in ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@techsachin/decomposing-gpt-4s-internal-representations-into-16-million-oft-interpretable-patterns-with-e16fb02e5189", "content": "Key contributions: proposed using k-sparse autoencoders [2] to directly control sparsity, simplifying tuning and improving the reconstruction-sparsity frontier. find modifications that result in ..."}

data/sampled_jsons/GitHub_pnnlML4AlgComb_Machine_Learning_meets_Algebraic_Combinatorics.jsonl

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+{"idx": 0, "title": "GitHub Foundations Certification Study Guide", "date": "", "ddg_snippet": "Mar 15, 2024 · Describe GitHub Sponsors GitHub certification registration process After completing your study plan, you are ready to take the certification exam and demonstrate your skills. The exam costs $99, but for a limited time (as of this publication date), you can get a 50% discount on the Foundations exam. Here are the steps to schedule your exam:", "subpage_snippet": "", "source": "techcommunity.microsoft.com", "link": "https://techcommunity.microsoft.com/blog/educatordeveloperblog/github-foundations-certification-study-guide/4079056", "content": "Mar 15, 2024 · Describe GitHub Sponsors GitHub certification registration process After completing your study plan, you are ready to take the certification exam and demonstrate your skills. The exam costs $99, but for a limited time (as of this publication date), you can get a 50% discount on the Foundations exam. Here are the steps to schedule your exam:"}
+{"idx": 1, "title": "New Certification on GitHub administration | Microsoft Community...", "date": "", "ddg_snippet": "Jul 8, 2025 · GitHub Learn and Microsoft Learn are working together to enhance your GitHub learning experience. Our GitHub exams are now available on Pearson VUE via Microsoft Learn, bringing a more unified and accessible experience to learners worldwide. If you already use Microsoft Learn, you can more easily discover and engage with GitHub topics.", "subpage_snippet": "", "source": "techcommunity.microsoft.com", "link": "https://techcommunity.microsoft.com/blog/microsoftlearnblog/new-certification-on-github-administration/4428967", "content": "Jul 8, 2025 · GitHub Learn and Microsoft Learn are working together to enhance your GitHub learning experience. Our GitHub exams are now available on Pearson VUE via Microsoft Learn, bringing a more unified and accessible experience to learners worldwide. If you already use Microsoft Learn, you can more easily discover and engage with GitHub topics."}
+{"idx": 2, "title": "¡GitHub Copilot gratis! Ahora al alcance de todos | Microsoft...", "date": "", "ddg_snippet": "¡Buenas noticias! Ahora todos pueden usar GitHub Copilot GRATIS en Visual Studio Code. Sólo necesitas tu cuenta de GitHub . Sin periodos de pruebas, sin...", "subpage_snippet": "", "source": "techcommunity.microsoft.com", "link": "https://techcommunity.microsoft.com/blog/azuredevcommunityblog/¡github-copilot-gratis-ahora-al-alcance-de-todos/4356908", "content": "¡Buenas noticias! Ahora todos pueden usar GitHub Copilot GRATIS en Visual Studio Code. Sólo necesitas tu cuenta de GitHub . Sin periodos de pruebas, sin..."}
+{"idx": 3, "title": "GitHub integration with Microsoft Loop", "date": "", "ddg_snippet": "Aug 25, 2024 · Bring your GitHub issues and pull requests (PRs) into Loop for seamless collaboration and remove the need to switch between multiple apps.", "subpage_snippet": "", "source": "techcommunity.microsoft.com", "link": "https://techcommunity.microsoft.com/blog/microsoft365insiderblog/github-integration-with-microsoft-loop/4225370", "content": "Aug 25, 2024 · Bring your GitHub issues and pull requests (PRs) into Loop for seamless collaboration and remove the need to switch between multiple apps."}
+{"idx": 4, "title": "GitHub Copilot for Azure の一般提供を開始：Agent モードにも対応", "date": "", "ddg_snippet": "Jun 25, 2025 · GitHub Copilot for Azure は、2024 年 11 月の Microsoft Ignite カンファレンスで public preview として公開されました。これにより、開発者、IT 運用者、DevOps 実践者たちは、自分たちの Azure...", "subpage_snippet": "", "source": "techcommunity.microsoft.com", "link": "https://techcommunity.microsoft.com/blog/azuredevcommunityblog/github-copilot-for-azure-の一般提供を開始：agent-モードにも対応/4426996", "content": "Jun 25, 2025 · GitHub Copilot for Azure は、2024 年 11 月の Microsoft Ignite カンファレンスで public preview として公開されました。これにより、開発者、IT 運用者、DevOps 実践者たちは、自分たちの Azure..."}
+{"idx": 5, "title": "Get Certified with GitHub - techcommunity.microsoft.com", "date": "", "ddg_snippet": "GitHub and Microsoft are helping you to boost your tech career with the Get Certified with GitHub livestream series! Starts from June 5th until June 26th. These sessions are designed to help you get certified on the GitHub Foundation Certification and to help you explore essential tools like GitHub Copilot and GitHub Codespaces. Plus, you'll have the chance to earn a free certification voucher ...", "subpage_snippet": "", "source": "techcommunity.microsoft.com", "link": "https://techcommunity.microsoft.com/blog/educatordeveloperblog/get-certified-with-github/4141657", "content": "GitHub and Microsoft are helping you to boost your tech career with the Get Certified with GitHub livestream series! Starts from June 5th until June 26th. These sessions are designed to help you get certified on the GitHub Foundation Certification and to help you explore essential tools like GitHub Copilot and GitHub Codespaces. Plus, you'll have the chance to earn a free certification voucher ..."}
+{"idx": 6, "title": "Deploying a GitHub Actions Self-hosted Runner on Azure: A...", "date": "", "ddg_snippet": "May 13, 2025 · GitHub -hosted runners are great for most workflows but sometimes, you need more control. Whether it’s for custom dependencies, persistent storage, or cost optimization, self-hosted runners on Azure offer a powerful alternative.", "subpage_snippet": "", "source": "techcommunity.microsoft.com", "link": "https://techcommunity.microsoft.com/blog/azureinfrastructureblog/deploying-a-github-actions-self-hosted-runner-on-azure-a-step-by-step-guide/4413362", "content": "May 13, 2025 · GitHub -hosted runners are great for most workflows but sometimes, you need more control. Whether it’s for custom dependencies, persistent storage, or cost optimization, self-hosted runners on Azure offer a powerful alternative."}
+{"idx": 7, "title": "Como obter GitHub Copilot gratuito para estudantes e professores...", "date": "", "ddg_snippet": "May 25, 2023 · O GitHub Copilot é um agente de IA que pode ser utilizado como um parceiro de pair programming, ajudando a escrever código mais rápido e com menos trabalho.", "subpage_snippet": "", "source": "techcommunity.microsoft.com", "link": "https://techcommunity.microsoft.com/blog/desenvolvedoresbr/como-obter-github-copilot-gratuito-para-estudantes-e-professores/3828780", "content": "May 25, 2023 · O GitHub Copilot é um agente de IA que pode ser utilizado como um parceiro de pair programming, ajudando a escrever código mais rápido e com menos trabalho."}
+{"idx": 8, "title": "Step-by-Step: Setting Up GitHub Student and GitHub Copilot as an...", "date": "", "ddg_snippet": "Feb 7, 2023 · To set up Copilot as an authenticated Github Student Developer, you need to follow these steps: GitHub Copilot - Visual Studio Marketplace - GitHub Copilot provides autocomplete-style suggestions from an AI pair programmer as you code. You can receive suggestions from GitHub Copilot either by starting to write the code you want to use, or by writing a natural language comment describing what ...", "subpage_snippet": "", "source": "techcommunity.microsoft.com", "link": "https://techcommunity.microsoft.com/blog/educatordeveloperblog/step-by-step-setting-up-github-student-and-github-copilot-as-an-authenticated-st/3736279", "content": "Feb 7, 2023 · To set up Copilot as an authenticated Github Student Developer, you need to follow these steps: GitHub Copilot - Visual Studio Marketplace - GitHub Copilot provides autocomplete-style suggestions from an AI pair programmer as you code. You can receive suggestions from GitHub Copilot either by starting to write the code you want to use, or by writing a natural language comment describing what ..."}
+{"idx": 9, "title": "DocAider: Automated Documentation Maintenance for Open-source...", "date": "", "ddg_snippet": "Sep 17, 2024 · The tool leverages Github Actions workflows to trigger documentation tasks upon pull requests (PRs) opening, providing valuable insights into continuous documentation maintenance. This approach addresses the challenges of automating documentation and ensures that project documentation remains current with minimal human intervention.", "subpage_snippet": "", "source": "techcommunity.microsoft.com", "link": "https://techcommunity.microsoft.com/blog/educatordeveloperblog/docaider-automated-documentation-maintenance-for-open-source-github-repositories/4245588", "content": "Sep 17, 2024 · The tool leverages Github Actions workflows to trigger documentation tasks upon pull requests (PRs) opening, providing valuable insights into continuous documentation maintenance. This approach addresses the challenges of automating documentation and ensures that project documentation remains current with minimal human intervention."}

data/sampled_jsons/Instant_Gaussian_Stream_Meeting_Room_Dataset_storage_efficiency_Ours-s_3DGStream_Table_2.jsonl

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+{"idx": 0, "title": "Instant Gaussian Stream: Fast and Generalizable Streaming of Dynamic ...", "date": "", "ddg_snippet": "Our method outperforms 3DGStream in rendering quality, train time, and storage efficiency , achieving streaming with just 2.77s of per-frame reconstruction time, a significant improvement over 3DGStream .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.16979v1", "content": "Our method outperforms 3DGStream in rendering quality, train time, and storage efficiency , achieving streaming with just 2.77s of per-frame reconstruction time, a significant improvement over 3DGStream ."}
+{"idx": 1, "title": "Instant Gaussian Stream: Fast and Generalizable Streaming of ... - GitHub", "date": "", "ddg_snippet": "Streaming Reconstruction Data Preparation Step 1: Prepare Inputs We can prepare our streaming dataset following 3DGStream and SpacetimeGaussian, and here we provide a simple script.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/yjb6/IGS", "content": "Streaming Reconstruction Data Preparation Step 1: Prepare Inputs We can prepare our streaming dataset following 3DGStream and SpacetimeGaussian, and here we provide a simple script."}
+{"idx": 2, "title": "PDF Instant Gaussian Stream: Fast and Generalizable Streaming of Dynamic ...", "date": "", "ddg_snippet": "In this pa-per, we propose Instant Gaussian Stream (IGS), a fast and tions, demonstrating that our approach can achieve stream -ing with a average per-frame reconstruction time of 2s+, alongside a enhancement in view synthesis quality.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Yan_Instant_Gaussian_Stream_Fast_and_Generalizable_Streaming_of_Dynamic_Scene_CVPR_2025_paper.pdf", "content": "In this pa-per, we propose Instant Gaussian Stream (IGS), a fast and tions, demonstrating that our approach can achieve stream -ing with a average per-frame reconstruction time of 2s+, alongside a enhancement in view synthesis quality."}
+{"idx": 3, "title": "3DGStream - GitHub", "date": "", "ddg_snippet": "[CVPR 2024 Highlight] Official repository for the paper \" 3DGStream : On-the-fly Training of 3D Gaussians for Efficient Streaming of Photo-Realistic Free-Viewpoint Videos\". - SJoJoK/ 3DGStream", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/SJoJoK/3DGStream", "content": "[CVPR 2024 Highlight] Official repository for the paper \" 3DGStream : On-the-fly Training of 3D Gaussians for Efficient Streaming of Photo-Realistic Free-Viewpoint Videos\". - SJoJoK/ 3DGStream"}
+{"idx": 4, "title": "Instant Gaussian Stream: Fast and Generalizable Streaming of Dynamic ...", "date": "", "ddg_snippet": "In this paper, we propose Instant Gaussian Stream (IGS), a fast and generalizable streaming framework, to address these issues. First, we introduce a generalized Anchor-driven Gaussian Motion Network, which projects multi-view 2D motion features into 3D space, using anchor points to drive the motion of all Gaussians .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.16979", "content": "In this paper, we propose Instant Gaussian Stream (IGS), a fast and generalizable streaming framework, to address these issues. First, we introduce a generalized Anchor-driven Gaussian Motion Network, which projects multi-view 2D motion features into 3D space, using anchor points to drive the motion of all Gaussians ."}
+{"idx": 5, "title": "3DGStream: On-the-fly Training of 3D Gaussians for Efficient Streaming ...", "date": "", "ddg_snippet": "Table 2 : Quantitative comparison on the Meet Room dataset . Note that the training time, required storage and PSNR are averaged over the whole 300 frames. normal-⋆ {}^ {\\star} start_FLOATSUPERSCRIPT ⋆ end_FLOATSUPERSCRIPT Considering the initial model.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.01444v1", "content": "Table 2 : Quantitative comparison on the Meet Room dataset . Note that the training time, required storage and PSNR are averaged over the whole 300 frames. normal-⋆ {}^ {\\star} start_FLOATSUPERSCRIPT ⋆ end_FLOATSUPERSCRIPT Considering the initial model."}
+{"idx": 6, "title": "3DGStream/README.md at main · SJoJoK/3DGStream · GitHub", "date": "", "ddg_snippet": "However, it is necessary to install tiny-cuda-nn and reinstall the submodules/diff- gaussian -rasterization by running pip install submodules/diff- gaussian -rasterization. Additionally, we recommend using PyTorch version 2.0 or higher for enhanced performance, as we utilize torch.compile.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/SJoJoK/3DGStream/blob/main/README.md", "content": "However, it is necessary to install tiny-cuda-nn and reinstall the submodules/diff- gaussian -rasterization by running pip install submodules/diff- gaussian -rasterization. Additionally, we recommend using PyTorch version 2.0 or higher for enhanced performance, as we utilize torch.compile."}
+{"idx": 7, "title": "IGS/README.md at master · yjb6/IGS · GitHub", "date": "", "ddg_snippet": "Streaming Reconstruction Data Preparation Step 1: Prepare Inputs We can prepare our streaming dataset following 3DGStream and SpacetimeGaussian, and here we provide a simple script.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/yjb6/IGS/blob/master/README.md", "content": "Streaming Reconstruction Data Preparation Step 1: Prepare Inputs We can prepare our streaming dataset following 3DGStream and SpacetimeGaussian, and here we provide a simple script."}
+{"idx": 8, "title": "CVPR 2025 Open Access Repository", "date": "", "ddg_snippet": "In this paper, we propose Instant Gaussian Stream (IGS), a fast and generalizable streaming framework, to address these issues. First, we introduce a generalized Anchor-driven Gaussian Motion Network, which projects multi-view 2D motion features into 3D space, using anchor points to drive the motion of all Gaussians .", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/html/Yan_Instant_Gaussian_Stream_Fast_and_Generalizable_Streaming_of_Dynamic_Scene_CVPR_2025_paper.html", "content": "In this paper, we propose Instant Gaussian Stream (IGS), a fast and generalizable streaming framework, to address these issues. First, we introduce a generalized Anchor-driven Gaussian Motion Network, which projects multi-view 2D motion features into 3D space, using anchor points to drive the motion of all Gaussians ."}
+{"idx": 9, "title": "arXiv:2503.16979v1 [cs.CV] 21 Mar 2025", "date": "", "ddg_snippet": "ation results are presented in Tab. 2 . Our method outperforms 3DGStream in rendering quality, train time, and storage eficiency, achieving stream -ing with just 2.77s of per-frame reconstruction time, a s", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.16979", "content": "ation results are presented in Tab. 2 . Our method outperforms 3DGStream in rendering quality, train time, and storage eficiency, achieving stream -ing with just 2.77s of per-frame reconstruction time, a s"}

data/sampled_jsons/Koh_2020_Concept_Bottleneck_Models_abstract_methodology_manually_crafted_concepts_year_2020.jsonl

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+{"idx": 0, "title": "Concept Bottleneck Models", "date": "", "ddg_snippet": "Concept bottlenecks differ from traditional feature engi-neering: we learn mappings from raw input to high-level concepts , whereas feature engineering constructs low-level features that can be computed by handwritten functions. Concepts as auxiliary losses or features.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v119/koh20a/koh20a.pdf", "content": "Concept bottlenecks differ from traditional feature engi-neering: we learn mappings from raw input to high-level concepts , whereas feature engineering constructs low-level features that can be computed by handwritten functions. Concepts as auxiliary losses or features."}
+{"idx": 1, "title": "[2007.04612] Concept Bottleneck Models - arXiv.org", "date": "", "ddg_snippet": "Jul 9, 2020 · By construction, we can intervene on these concept bottleneck models by editing their predicted concept values and propagating these changes to the final prediction.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2007.04612", "content": "Jul 9, 2020 · By construction, we can intervene on these concept bottleneck models by editing their predicted concept values and propagating these changes to the final prediction."}
+{"idx": 2, "title": "Concept Bottleneck Models - PMLR", "date": "", "ddg_snippet": "Abstract We seek to learn models that we can interact with using high-level concepts : if the model did not think there was a bone spur in the x-ray, would it still predict severe arthritis?", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v119/koh20a", "content": "Abstract We seek to learn models that we can interact with using high-level concepts : if the model did not think there was a bone spur in the x-ray, would it still predict severe arthritis?"}
+{"idx": 3, "title": "Concept bottleneck models | Proceedings of the 37th ...", "date": "", "ddg_snippet": "Jul 13, 2020 · By construction, we can intervene on these concept bottleneck models by editing their predicted concept values and propagating these changes to the final prediction.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3524938.3525433", "content": "Jul 13, 2020 · By construction, we can intervene on these concept bottleneck models by editing their predicted concept values and propagating these changes to the final prediction."}
+{"idx": 4, "title": "(PDF) Interactive Concept Bottleneck Models - ResearchGate", "date": "", "ddg_snippet": "Dec 14, 2022 · Concept bottleneck models ( CBMs ) ( Koh et al. 2020 ) are interpretable neural networks that first predict labels for human-interpretable concepts relevant to the prediction task, and...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/366321114_Interactive_Concept_Bottleneck_Models", "content": "Dec 14, 2022 · Concept bottleneck models ( CBMs ) ( Koh et al. 2020 ) are interpretable neural networks that first predict labels for human-interpretable concepts relevant to the prediction task, and..."}
+{"idx": 5, "title": "Concept Bottleneck Models - Google Research", "date": "", "ddg_snippet": "In this paper, we revisit the classic idea of learning concept bottleneck models that first predict concepts (provided at training time) from the raw input, and then predict the final label from these concepts .", "subpage_snippet": "", "source": "research.google", "link": "https://research.google/pubs/concept-bottleneck-models/", "content": "In this paper, we revisit the classic idea of learning concept bottleneck models that first predict concepts (provided at training time) from the raw input, and then predict the final label from these concepts ."}
+{"idx": 6, "title": "Concept Bottleneck Models | TransferLab — appliedAI Institute", "date": "", "ddg_snippet": "By construction, we can intervene on these concept bottleneck models by editing their predicted concept values and propagating these changes to the final prediction.", "subpage_snippet": "", "source": "transferlab.ai", "link": "https://transferlab.ai/refs/koh_concept_2020/", "content": "By construction, we can intervene on these concept bottleneck models by editing their predicted concept values and propagating these changes to the final prediction."}
+{"idx": 7, "title": "Are They the Same Picture? Adapting Concept Bottleneck Models for...", "date": "", "ddg_snippet": "Concept Bottleneck Models (CBMs) are another recent and promising method to facilitate collaboration [ Koh et al., 2020 ]. They allow humans to interact with AI models by viewing and ma-nipulating intermediate, high-level concepts (e.g., whether a bird has a blue wing)...", "subpage_snippet": "", "source": "www.ijcai.org", "link": "https://www.ijcai.org/proceedings/2024/0866.pdf", "content": "Concept Bottleneck Models (CBMs) are another recent and promising method to facilitate collaboration [ Koh et al., 2020 ]. They allow humans to interact with AI models by viewing and ma-nipulating intermediate, high-level concepts (e.g., whether a bird has a blue wing)..."}
+{"idx": 8, "title": "[PDF] Concept Bottleneck Models | Semantic Scholar", "date": "", "ddg_snippet": "On x-ray grading and bird identification, concept bottleneck models achieve competitive accuracy with standard end-to-end models , while enabling interpretation in terms of high-level clinical concepts (“bone spurs”) or bird attributes ( “wing color”). We seek to learn models that we can...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Concept-Bottleneck-Models-Koh-Nguyen/3a24bfb77ed271fef948058e414850f89b0955a7", "content": "On x-ray grading and bird identification, concept bottleneck models achieve competitive accuracy with standard end-to-end models , while enabling interpretation in terms of high-level clinical concepts (“bone spurs”) or bird attributes ( “wing color”). We seek to learn models that we can..."}
+{"idx": 9, "title": "Diverse Concept Proposals for Concept Bottleneck Models", "date": "", "ddg_snippet": "Concept bottleneck models , diverse explanations. 1 Introduction. Recently popularized by Koh et al. ( 2020 ) , concept bottleneck models (CBM) prove to be a highly flexible and (hopefully) interpretable model class.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.18059v1/", "content": "Concept bottleneck models , diverse explanations. 1 Introduction. Recently popularized by Koh et al. ( 2020 ) , concept bottleneck models (CBM) prove to be a highly flexible and (hopefully) interpretable model class."}

data/sampled_jsons/LSIF_loss_function_RLHF_preference_learning_chosen_rejected_responses.jsonl

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+{"idx": 0, "title": "Reinforcement Learning from Human Feedback", "date": "", "ddg_snippet": "loss = -nn. functional .logsigmoid(rewards_ chosen - rewards_ rejected ).mean ().model, as a margin between chosen and rejected responses , rather than solely the. pairwise preference data to more accurately solve the RLHF problem [131].", "subpage_snippet": "", "source": "rlhfbook.com", "link": "https://rlhfbook.com/book.pdf", "content": "loss = -nn. functional .logsigmoid(rewards_ chosen - rewards_ rejected ).mean ().model, as a margin between chosen and rejected responses , rather than solely the. pairwise preference data to more accurately solve the RLHF problem [131]."}
+{"idx": 1, "title": "How to Implement RLHF (Reinforcement Learning from...) | Markaicode", "date": "", "ddg_snippet": "Learn to implement RLHF step-by-step. Build reward models, train RL agents, and improve AI systems with human feedback for better performance.", "subpage_snippet": "", "source": "markaicode.com", "link": "https://markaicode.com/implement-rlhf-reinforcement-learning-human-feedback/", "content": "Learn to implement RLHF step-by-step. Build reward models, train RL agents, and improve AI systems with human feedback for better performance."}
+{"idx": 2, "title": "How RLHF Preference Model Tuning Works (And How Things May Go...", "date": "", "ddg_snippet": "Large Language Models like ChatGPT are trained with Reinforcement Learning From Human Feedback ( RLHF ) to learn human preferences . Let’s uncover how RLHF works and survey its current strongest limitations.", "subpage_snippet": "", "source": "www.assemblyai.com", "link": "https://www.assemblyai.com/blog/how-rlhf-preference-model-tuning-works-and-how-things-may-go-wrong", "content": "Large Language Models like ChatGPT are trained with Reinforcement Learning From Human Feedback ( RLHF ) to learn human preferences . Let’s uncover how RLHF works and survey its current strongest limitations."}
+{"idx": 3, "title": "Reinforcement Learning from Human Feedback ( RLHF ) | Medium", "date": "", "ddg_snippet": "where chosen _inputs and rejected _inputs are tokenized batches of the preferred and less- preferred texts.Only a small number of epochs of training are used to avoid overfitting to the finite comparison data. Once r_θ is ready, the LLM policy is updated by RL.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@danushidk507/reinforcement-learning-from-human-feedback-rlhf-c5ad903f0705", "content": "where chosen _inputs and rejected _inputs are tokenized batches of the preferred and less- preferred texts.Only a small number of epochs of training are used to avoid overfitting to the finite comparison data. Once r_θ is ready, the LLM policy is updated by RL."}
+{"idx": 4, "title": "I Mitation L earning and rlhf", "date": "", "ddg_snippet": "Directed Preference Optimization. To simplify the optimization process of RLHF , DPO uses the log-likelihood of the learning policy to implicitly represent the reward function learning problem against the chosen responses , achieved by minimizing the forward KL divergence.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=2QdsjiNXgj", "content": "Directed Preference Optimization. To simplify the optimization process of RLHF , DPO uses the log-likelihood of the learning policy to implicitly represent the reward function learning problem against the chosen responses , achieved by minimizing the forward KL divergence."}
+{"idx": 5, "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": 6, "title": "Fine Tuning SmolVLM for Human Alignment Using Direct Preference ...", "date": "", "ddg_snippet": "Reinforcement Learning from AI Feedback (RLAIF). Direct Preference Optimization (DPO).While existing methods ( RLHF , RLAIF) utilize the preference model to define a preference loss for training a reward model, they then train a policy that optimizes the learned reward model.", "subpage_snippet": "", "source": "pyimagesearch.com", "link": "https://pyimagesearch.com/2025/08/04/fine-tuning-smolvlm-for-human-alignment-using-direct-preference-optimization/", "content": "Reinforcement Learning from AI Feedback (RLAIF). Direct Preference Optimization (DPO).While existing methods ( RLHF , RLAIF) utilize the preference model to define a preference loss for training a reward model, they then train a policy that optimizes the learned reward model."}
+{"idx": 7, "title": "GitHub - htetwyhtut/A5-Optimization-Human- Preference", "date": "", "ddg_snippet": "Description: A dataset for reinforcement learning from human feedback ( RLHF ). It contains pairs of responses labeled as \" chosen \" ( preferred ) and \" rejected \" (non- preferred ) based on human preferences .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/htetwyhtut/A5-Optimization-Human-Preference", "content": "Description: A dataset for reinforcement learning from human feedback ( RLHF ). It contains pairs of responses labeled as \" chosen \" ( preferred ) and \" rejected \" (non- preferred ) based on human preferences ."}
+{"idx": 8, "title": "(PDF) Personalizing Reinforcement Learning from Human Feedback...", "date": "", "ddg_snippet": "RLHF procedure learns a reward function . rϕ(s).“ rejected ” according to the preference of either “dog group” or “cat group”. The prompt is fixed to be. “Human: Please talk about one kind of pet.” After all the chosen / rejected pairs are generated, we.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/383236577_Personalizing_Reinforcement_Learning_from_Human_Feedback_with_Variational_Preference_Learning", "content": "RLHF procedure learns a reward function . rϕ(s).“ rejected ” according to the preference of either “dog group” or “cat group”. The prompt is fixed to be. “Human: Please talk about one kind of pet.” After all the chosen / rejected pairs are generated, we."}
+{"idx": 9, "title": "Challenges in Direct Preference Optimization for LLMs - Simple Science", "date": "", "ddg_snippet": "RLHF helps improve model responses based on human preferences .For instance, both the preferred and rejected responses tend to lose effectiveness over time. In contrast, the likelihood of generating new, unseen responses tends to increase.", "subpage_snippet": "", "source": "scisimple.com", "link": "https://scisimple.com/en/articles/2025-07-30-challenges-in-direct-preference-optimization-for-llms--a302go2", "content": "RLHF helps improve model responses based on human preferences .For instance, both the preferred and rejected responses tend to lose effectiveness over time. In contrast, the likelihood of generating new, unseen responses tends to increase."}

data/sampled_jsons/Likelihood_Based_Approach_Distribution_Regression_Conditional_Deep_Generative_Models_MLE_KDE_CQR.jsonl

@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Generative Conditional Distributions by Neural (Entropic) ...", "date": "", "ddg_snippet": "4 Jun 2024 — We introduce a novel neural entropic optimal transport method designed to effectively learn generative models of conditional distributions , ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.02317v1", "content": "4 Jun 2024 — We introduce a novel neural entropic optimal transport method designed to effectively learn generative models of conditional distributions , ..."}
+{"idx": 1, "title": "A Gentle Introduction and Tutorial on Deep Generative ...", "date": "", "ddg_snippet": "DGMs can be classified into different categories based on their approach to maximizing data likelihood . The first one is explicit density models that directly ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.07066v3", "content": "DGMs can be classified into different categories based on their approach to maximizing data likelihood . The first one is explicit density models that directly ..."}
+{"idx": 2, "title": "TabNAT: A Continuous-Discrete Joint Generative ...", "date": "", "ddg_snippet": "The proposed TabNAT combines Diffusion models and Transformers. It utilizes Transformers to generate conditional inputs for target columns, employs a ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/45006", "content": "The proposed TabNAT combines Diffusion models and Transformers. It utilizes Transformers to generate conditional inputs for target columns, employs a ..."}
+{"idx": 3, "title": "Towards Accurate and Reliable Deep Regression Models", "date": "", "ddg_snippet": "30 Nov 2023 — This is a general and conceptually simple regression framework with a clear probabilistic interpretation, using energy- based models to represent ... 335 pages", "subpage_snippet": "", "source": "www.fregu856.com", "link": "https://www.fregu856.com/files/thesis.pdf", "content": "30 Nov 2023 — This is a general and conceptually simple regression framework with a clear probabilistic interpretation, using energy- based models to represent ... 335 pages"}
+{"idx": 4, "title": "TabNAT: A Continuous-Discrete Joint Generative ...", "date": "", "ddg_snippet": "TabNAT employs a conditional generative model based on a bi-directional masked transformer to achieve the aforemen- tioned objectives. Fig. 3 presents the ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/2e38216fb31cc24533b7352c0edc5e96813075fd.pdf", "content": "TabNAT employs a conditional generative model based on a bi-directional masked transformer to achieve the aforemen- tioned objectives. Fig. 3 presents the ..."}
+{"idx": 5, "title": "Some models are useful, but how do we know which ones? ...", "date": "", "ddg_snippet": "by PC Bürkner · Cited by 24 — Naturally, the conditions under which we can answer causal queries using conditional distributions become harder to test for causal graphs containing more than ... 95 pages", "subpage_snippet": "", "source": "paulbuerkner.com", "link": "https://paulbuerkner.com/publications/pdf/2023__Buerkner_et_al__Statistics_Surveys.pdf", "content": "by PC Bürkner · Cited by 24 — Naturally, the conditions under which we can answer causal queries using conditional distributions become harder to test for causal graphs containing more than ... 95 pages"}
+{"idx": 6, "title": "Adversarial Random Forests for Density Estimation and ...", "date": "", "ddg_snippet": "by DS Watson · 2023 · Cited by 41 — Smooth results can be obtained by fitting a distribution within each leaf, e.g. via kernel density estimation ( KDE ) or maximum likelihood estimation ( MLE ) ( ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v206/watson23a/watson23a.pdf", "content": "by DS Watson · 2023 · Cited by 41 — Smooth results can be obtained by fitting a distribution within each leaf, e.g. via kernel density estimation ( KDE ) or maximum likelihood estimation ( MLE ) ( ..."}
+{"idx": 7, "title": "Daily Papers", "date": "", "ddg_snippet": "9 Sept 2025 — Conditional language models are predominantly trained with maximum likelihood estimation ( MLE ), giving probability mass to sparsely observed ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=conditional+probability+estimation", "content": "9 Sept 2025 — Conditional language models are predominantly trained with maximum likelihood estimation ( MLE ), giving probability mass to sparsely observed ..."}
+{"idx": 8, "title": "Daily Papers", "date": "", "ddg_snippet": "4 days ago — First, we use a deep generative model to learn a representation of the response that has a unimodal distribution . Existing multiple-output ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=density+map+regression", "content": "4 days ago — First, we use a deep generative model to learn a representation of the response that has a unimodal distribution . Existing multiple-output ..."}
+{"idx": 9, "title": "Anomaly Detection using Normalizing Flows and Contrastive", "date": "", "ddg_snippet": "Likelihoods learned by a generative model , e.g., a normalizing flow via standard log- likelihood training, perform poorly as an anomaly score. We propose to use.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/f4510c5c243218c49bdd059df33b59261041fb70.pdf", "content": "Likelihoods learned by a generative model , e.g., a normalizing flow via standard log- likelihood training, perform poorly as an anomaly score. We propose to use."}

data/sampled_jsons/Likelihood_Based_Approach_to_Distribution_Regression_Using_Conditional_Deep_Generative_Models_arXiv_.jsonl

@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2410.02025] A Likelihood Based Approach to Distribution ...", "date": "", "ddg_snippet": "View a PDF of the paper titled A Likelihood Based Approach to Distribution Regression Using Conditional Deep Generative Models , by Shivam Kumar and 2 other authors. View PDF HTML (experimental).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.02025", "content": "View a PDF of the paper titled A Likelihood Based Approach to Distribution Regression Using Conditional Deep Generative Models , by Shivam Kumar and 2 other authors. View PDF HTML (experimental)."}
+{"idx": 1, "title": "A Likelihood Based Approach to Distribution Regression Using", "date": "", "ddg_snippet": "Conditional deep generative models for distribution regression . Convergence rates of the Sieve MLE. Neural network class.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.02025", "content": "Conditional deep generative models for distribution regression . Convergence rates of the Sieve MLE. Neural network class."}
+{"idx": 2, "title": "( PDF ) A Likelihood Based Approach to Distribution Regression ...", "date": "", "ddg_snippet": "distributional regression using a conditional deep generative model , considering full-dimensional noise.Score- based . generative modeling through stochastic differential equations. arXiv preprint arXiv :2011.13456. Spirtes, P. (2010). Introduction to causal inference.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/384630603_A_Likelihood_Based_Approach_to_Distribution_Regression_Using_Conditional_Deep_Generative_Models", "content": "distributional regression using a conditional deep generative model , considering full-dimensional noise.Score- based . generative modeling through stochastic differential equations. arXiv preprint arXiv :2011.13456. Spirtes, P. (2010). Introduction to causal inference."}
+{"idx": 3, "title": "A Likelihood Based Approach to Distribution Regression Using ...", "date": "", "ddg_snippet": "In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where the response variable lies in a high-dimensional ambient space but concentrates around a potentially lower-dimensional manifold.", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/arxiv/2410.02025", "content": "In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where the response variable lies in a high-dimensional ambient space but concentrates around a potentially lower-dimensional manifold."}
+{"idx": 4, "title": "A Likelihood Based Approach to Distribution Regression Using ...", "date": "", "ddg_snippet": "This paper presents a likelihood - based approach for distribution regression using conditional deep generative models .", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/likelihood-based-approach-to-distribution-regression-using", "content": "This paper presents a likelihood - based approach for distribution regression using conditional deep generative models ."}
+{"idx": 5, "title": "ICML Poster A Likelihood Based Approach to Distribution ...", "date": "", "ddg_snippet": "models ), can estimate those high-dimensional conditional distributions as if the data were low-dimensional. The trick is a likelihood - based fit called a sieve maximum- likelihood estimator that grows in complexity with sample size.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46645", "content": "models ), can estimate those high-dimensional conditional distributions as if the data were low-dimensional. The trick is a likelihood - based fit called a sieve maximum- likelihood estimator that grows in complexity with sample size."}
+{"idx": 6, "title": "A Likelihood Based Approach to Distribution Regression Using ...", "date": "", "ddg_snippet": "The text explores conditional deep generative models for distribution regression , focusing on high-dimensional data concentrated around a lower-dimensional manifold. It analyzes large-sample properties...", "subpage_snippet": "", "source": "powerdrill.ai", "link": "https://powerdrill.ai/discover/discover-A-Likelihood-Based-cm1v7rba8uvnv013whs4l4bq4", "content": "The text explores conditional deep generative models for distribution regression , focusing on high-dimensional data concentrated around a lower-dimensional manifold. It analyzes large-sample properties..."}
+{"idx": 7, "title": "Learning Structured Output Representation using Deep Conditional ...", "date": "", "ddg_snippet": "To address this problem, we propose novel deep conditional generative models (CGMs) for output representation learning and structured prediction. In other words, we model the distribution of high-dimensional output space as a generative model conditioned on the input observation.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper/2015/file/8d55a249e6baa5c06772297520da2051-Paper.pdf", "content": "To address this problem, we propose novel deep conditional generative models (CGMs) for output representation learning and structured prediction. In other words, we model the distribution of high-dimensional output space as a generative model conditioned on the input observation."}
+{"idx": 8, "title": "Musings on typicality – Sander Dieleman", "date": "", "ddg_snippet": "Some likelihood - based generative models of images separate or discard the least-significant bits of each pixel colour value, because they are less perceptually relevant, allowing model capacity to be used more efficiently11 12.", "subpage_snippet": "", "source": "sander.ai", "link": "https://sander.ai/2020/09/01/typicality.html", "content": "Some likelihood - based generative models of images separate or discard the least-significant bits of each pixel colour value, because they are less perceptually relevant, allowing model capacity to be used more efficiently11 12."}
+{"idx": 9, "title": "A Deep Learning Framework for the", "date": "", "ddg_snippet": "• To design a continuous conditional generative deep learning model to generate simulations of the distribution of galaxies in the optical and millimetre range of the electromagnetic spec-trum, from a given set of cosmological parameters. • To design a CapsNet architecture using these...", "subpage_snippet": "", "source": "ccc.inaoep.mx", "link": "https://ccc.inaoep.mx/archivos/2021/CCC-23-005.pdf", "content": "• To design a continuous conditional generative deep learning model to generate simulations of the distribution of galaxies in the optical and millimetre range of the electromagnetic spec-trum, from a given set of cosmological parameters. • To design a CapsNet architecture using these..."}

data/sampled_jsons/MADE_Masked_Autoencoder_for_Density_Estimation_architecture_MLP.jsonl

@@ -0,0 +1,10 @@
+{"idx": 0, "title": "NeRF-MAE: Masked AutoEncoders for Self-Supervised 3D", "date": "", "ddg_snippet": "... density grid as an input modality, NeRF-MAE employs standard 3D Transformers in a masked autoencoding objective suited to the unique formulation of ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2404.01300v3", "content": "... density grid as an input modality, NeRF-MAE employs standard 3D Transformers in a masked autoencoding objective suited to the unique formulation of ..."}
+{"idx": 1, "title": "Steering CLIP’s vision transformer with sparse autoencoders", "date": "", "ddg_snippet": "Although recent progress has been made in understanding the features of language models using decomposition techniques like sparse autoencoders (SAEs ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.08729v1", "content": "Although recent progress has been made in understanding the features of language models using decomposition techniques like sparse autoencoders (SAEs ..."}
+{"idx": 2, "title": "Sparse Autoencoders Trained on the Same Data Learn Different", "date": "", "ddg_snippet": "Sparse autoencoders (SAEs) are a useful tool for uncovering human-interpretable features in the activations of large language models (LLMs).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.16615v2", "content": "Sparse autoencoders (SAEs) are a useful tool for uncovering human-interpretable features in the activations of large language models (LLMs)."}
+{"idx": 3, "title": "Scaling and evaluating sparse autoencoders", "date": "", "ddg_snippet": "Sparse autoencoders provide a promising unsupervised approach for extracting interpretable features from a language model by reconstructing ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.04093v1", "content": "Sparse autoencoders provide a promising unsupervised approach for extracting interpretable features from a language model by reconstructing ..."}
+{"idx": 4, "title": "Open Source Sparse Autoencoders for all Residual Stream Layers", "date": "", "ddg_snippet": "We computed the ghost grad forward pass using Exp(Relu(W_enc(x)[dead_neuron_mask] )) rather than Exp((W_enc(x)[dead_neuron_mask])).", "subpage_snippet": "", "source": "www.alignmentforum.org", "link": "https://www.alignmentforum.org/posts/f9EgfLSurAiqRJySD/open-source-sparse-autoencoders-for-all-residual-stream", "content": "We computed the ghost grad forward pass using Exp(Relu(W_enc(x)[dead_neuron_mask] )) rather than Exp((W_enc(x)[dead_neuron_mask]))."}
+{"idx": 5, "title": "Open Source Sparse Autoencoders for all Residual Stream Layers", "date": "", "ddg_snippet": "We computed the ghost grad forward pass using Exp(Relu(W_enc(x)[dead_neuron_mask] )) rather than Exp((W_enc(x)[dead_neuron_mask])).", "subpage_snippet": "", "source": "www.lesswrong.com", "link": "https://www.lesswrong.com/posts/f9EgfLSurAiqRJySD/open-source-sparse-autoencoders-for-all-residual-stream", "content": "We computed the ghost grad forward pass using Exp(Relu(W_enc(x)[dead_neuron_mask] )) rather than Exp((W_enc(x)[dead_neuron_mask]))."}
+{"idx": 6, "title": "Open Source Sparse Autoencoders for all Residual Stream Layers", "date": "", "ddg_snippet": "We computed the ghost grad forward pass using Exp(Relu(W_enc(x)[dead_neuron_mask] )) rather than Exp((W_enc(x)[dead_neuron_mask])).", "subpage_snippet": "", "source": "www.greaterwrong.com", "link": "https://www.greaterwrong.com/posts/f9EgfLSurAiqRJySD/open-source-sparse-autoencoders-for-all-residual-stream", "content": "We computed the ghost grad forward pass using Exp(Relu(W_enc(x)[dead_neuron_mask] )) rather than Exp((W_enc(x)[dead_neuron_mask]))."}
+{"idx": 7, "title": "Autoencoders (AEs)", "date": "", "ddg_snippet": "Unveiling Hidden Representations: Explore the Power of Autoencoders in Deep Learning for Efficient Data Compression and Feature Extraction.", "subpage_snippet": "", "source": "schneppat.com", "link": "https://schneppat.com/autoencoders.html", "content": "Unveiling Hidden Representations: Explore the Power of Autoencoders in Deep Learning for Efficient Data Compression and Feature Extraction."}
+{"idx": 8, "title": "GitHub - naru-project/naru: Neural Relation Understanding:", "date": "", "ddg_snippet": "... for the VLDB'20 paper, Deep ... MADE : a highly efficient masked MLP , introduced in Masked Autoencoder for Distribution Estimation (ICML'15) .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/naru-project/naru", "content": "... for the VLDB'20 paper, Deep ... MADE : a highly efficient masked MLP , introduced in Masked Autoencoder for Distribution Estimation (ICML'15) ."}
+{"idx": 9, "title": "CVPR 2023 Schedule", "date": "", "ddg_snippet": "3rd Workshop and Challenge on Computer Vision in the Built Environment for the Design, Construction, and Operation of Buildings", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2023/calendar", "content": "3rd Workshop and Challenge on Computer Vision in the Built Environment for the Design, Construction, and Operation of Buildings"}

data/sampled_jsons/MLOps_tools_model_dependency_tracking_verification_MLflow_DVC.jsonl

@@ -0,0 +1,10 @@
+{"idx": 0, "title": "25 Top MLOps Tools You Need to Know in 2025", "date": "", "ddg_snippet": "MLOps tools help standardize, simplify, and streamline the ML ecosystem . These tools are used for experiment tracking, model metadata management, orchestration, ...", "subpage_snippet": "", "source": "www.datacamp.com", "link": "https://www.datacamp.com/blog/top-mlops-tools", "content": "MLOps tools help standardize, simplify, and streamline the ML ecosystem . These tools are used for experiment tracking, model metadata management, orchestration, ..."}
+{"idx": 1, "title": "MLOps: Effortlessly track your model experiments with DVC ...", "date": "", "ddg_snippet": "In this article we will show the workflow of model experiment tracking with Pytorch Lightning, MLflow and DVC on the infamous “Cats vs dogs” classification ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/hub-by-littlebigcode/mlops-effortlessly-track-your-model-experiments-with-dvc-and-mlflow-db650cffab22", "content": "In this article we will show the workflow of model experiment tracking with Pytorch Lightning, MLflow and DVC on the infamous “Cats vs dogs” classification ..."}
+{"idx": 2, "title": "27 MLOps Tools for 2025: Key Features & Benefits", "date": "", "ddg_snippet": "25 Jul 2025 — Explore the top MLOps tools for model creation, deployment, and monitoring that help teams standardize and streamline their ML ecosystems.", "subpage_snippet": "", "source": "lakefs.io", "link": "https://lakefs.io/mlops/mlops-tools/", "content": "25 Jul 2025 — Explore the top MLOps tools for model creation, deployment, and monitoring that help teams standardize and streamline their ML ecosystems."}
+{"idx": 3, "title": "MLOps Landscape in 2025: Top Tools and Platforms", "date": "", "ddg_snippet": "This article explores the key players in the MLOps and FMOps (or LLMOps) ecosystems, encompassing both open-source and closed-source tools .", "subpage_snippet": "", "source": "neptune.ai", "link": "https://neptune.ai/blog/mlops-tools-platforms-landscape", "content": "This article explores the key players in the MLOps and FMOps (or LLMOps) ecosystems, encompassing both open-source and closed-source tools ."}
+{"idx": 4, "title": "10 MLOps Tools for Machine Learning Practitioners to Know", "date": "", "ddg_snippet": "5 Jun 2025 — MLflow is an tool that helps track machine learning experiments. It lets you log training runs, version models, and manage deployment stages.", "subpage_snippet": "", "source": "machinelearningmastery.com", "link": "https://machinelearningmastery.com/10-mlops-tools-for-machine-learning-practitioners-to-know/", "content": "5 Jun 2025 — MLflow is an tool that helps track machine learning experiments. It lets you log training runs, version models, and manage deployment stages."}
+{"idx": 5, "title": "MLOps Explained: A Deep Dive into Machine Learning ...", "date": "", "ddg_snippet": "MLOps (Machine Learning Operations) is a set of practices that combine Machine Learning (ML), DevOps, and data engineering to automate and optimize the ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@hermanmotcheyo/mlops-explained-a-deep-dive-into-machine-learning-operations-ab9342c5c90d", "content": "MLOps (Machine Learning Operations) is a set of practices that combine Machine Learning (ML), DevOps, and data engineering to automate and optimize the ..."}
+{"idx": 6, "title": "Scalable AI Workflows: MLOps Tools Guide", "date": "", "ddg_snippet": "10 May 2025 — Dive into open-source tools powering data management , model versioning, delivery, development and experimentation in MLOps .", "subpage_snippet": "", "source": "data-intelligence.hashnode.dev", "link": "https://data-intelligence.hashnode.dev/mlops-open-source-guide", "content": "10 May 2025 — Dive into open-source tools powering data management , model versioning, delivery, development and experimentation in MLOps ."}
+{"idx": 7, "title": "The Full MLOps Blueprint: Reproducibility and Versioning ...", "date": "", "ddg_snippet": "17 Aug 2025 — Finally, we walked through hands-on simulations covering seed fixation, data versioning with DVC , and experiment tracking with MLflow . If you ...", "subpage_snippet": "", "source": "www.dailydoseofds.com", "link": "https://www.dailydoseofds.com/mlops-crash-course-part-4/", "content": "17 Aug 2025 — Finally, we walked through hands-on simulations covering seed fixation, data versioning with DVC , and experiment tracking with MLflow . If you ..."}
+{"idx": 8, "title": "MLOps project-Part 1: Mlflow, Dagshub, Prophet, DVC, Hydra ...", "date": "", "ddg_snippet": "7 Feb 2024 — With DVC, users can track changes to datasets, experiment configurations, and trained models , ensuring traceability and auditability throughout ...", "subpage_snippet": "", "source": "blog.devops.dev", "link": "https://blog.devops.dev/mlops-project-part-1-mlflow-dagshub-prophet-dvc-hydra-fastapi-187576997a18", "content": "7 Feb 2024 — With DVC, users can track changes to datasets, experiment configurations, and trained models , ensuring traceability and auditability throughout ..."}
+{"idx": 9, "title": "Best Model Management Tools in MLOps", "date": "", "ddg_snippet": "23 Jul 2025 — Tools like MLflow, DVC , Kubeflow, Seldon Core, TFX, Comet.ml, and Neptune.ai offer diverse features tailored to different needs within the MLOps framework.", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/machine-learning/best-model-management-tools-in-mlops/", "content": "23 Jul 2025 — Tools like MLflow, DVC , Kubeflow, Seldon Core, TFX, Comet.ml, and Neptune.ai offer diverse features tailored to different needs within the MLOps framework."}

data/sampled_jsons/Neural_Exploratory_Landscape_Analysis_Equation_3_Upsilon_evaluation_metric_formula.jsonl

@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Identification of metrics suitable for determining the ...", "date": "", "ddg_snippet": "by S Zhu · 2022 · Cited by 11 — To identify which ELA metrics have low dependence on problem dimensionality and sample size for a range of benchmark functions with a wide variety of known ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/abs/pii/S1364815221003236", "content": "by S Zhu · 2022 · Cited by 11 — To identify which ELA metrics have low dependence on problem dimensionality and sample size for a range of benchmark functions with a wide variety of known ..."}
+{"idx": 1, "title": "Deep neural network approach integrated with ...", "date": "", "ddg_snippet": "by TS Madhulatha · 2025 — Performance metrics and evaluation . We consider three error metrics to evaluate the proposed framework's performance, as depicted in Fig. 3 ...", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41598-025-12516-3", "content": "by TS Madhulatha · 2025 — Performance metrics and evaluation . We consider three error metrics to evaluate the proposed framework's performance, as depicted in Fig. 3 ..."}
+{"idx": 2, "title": "Application of deep neural networks to biomarker ...", "date": "", "ddg_snippet": "by E Putin · 2016 · Cited by 418 — The key advantage of using epsilon-prediction accuracy is that it allows cohort analysis without fixed age ranges (e.g. 10-20, 20-30).", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC4931851/", "content": "by E Putin · 2016 · Cited by 418 — The key advantage of using epsilon-prediction accuracy is that it allows cohort analysis without fixed age ranges (e.g. 10-20, 20-30)."}
+{"idx": 3, "title": "AutoML Loss Landscapes", "date": "", "ddg_snippet": "For example, Rodrigues et al. [2020] studied the landscapes of a neuroevolution procedure for optimizing the performance of convolutional neural networks.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3558774", "content": "For example, Rodrigues et al. [2020] studied the landscapes of a neuroevolution procedure for optimizing the performance of convolutional neural networks."}
+{"idx": 4, "title": "Comprehensive Review and Empirical Evaluation of ...", "date": "", "ddg_snippet": "17 Jul 2024 — Our study conducts an extensive empirical assessment of more than 20 causal discovery algorithms on synthetic and real-world datasets.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2407.13054v1", "content": "17 Jul 2024 — Our study conducts an extensive empirical assessment of more than 20 causal discovery algorithms on synthetic and real-world datasets."}
+{"idx": 5, "title": "Towards Scalable Lottery Ticket Networks using Genetic ...", "date": "", "ddg_snippet": "by J Schönberger · 2025 — This work presents an extensive analysis of the significance of using genetic algorithms for identifying strong lottery tickets in randomly ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2508.08877", "content": "by J Schönberger · 2025 — This work presents an extensive analysis of the significance of using genetic algorithms for identifying strong lottery tickets in randomly ..."}
+{"idx": 6, "title": "Characterization of constrained continuous multiobjective ...", "date": "", "ddg_snippet": "by A Vodopija · 2022 · Cited by 29 — We address this issue by extending landscape analysis to constrained multiobjective optimization. By employing four exploratory landscape analysis ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0020025522005448", "content": "by A Vodopija · 2022 · Cited by 29 — We address this issue by extending landscape analysis to constrained multiobjective optimization. By employing four exploratory landscape analysis ..."}
+{"idx": 7, "title": "Multi Objective Quantile Based Reinforcement Learning for ...", "date": "", "ddg_snippet": "preference metric , denoted as LP, is calculated as: LP = n. X i=1 wi · PϕT Di. (L) + Psociety(L). ( 3 ). In Equation 3 , wi represents the weight for each top-down.", "subpage_snippet": "", "source": "www.ijcai.org", "link": "https://www.ijcai.org/proceedings/2025/0027.pdf", "content": "preference metric , denoted as LP, is calculated as: LP = n. X i=1 wi · PϕT Di. (L) + Psociety(L). ( 3 ). In Equation 3 , wi represents the weight for each top-down."}
+{"idx": 8, "title": "A benchmark study of optimizers for short-term solar PV ...", "date": "", "ddg_snippet": "by S Dhingra · 2025 — This study systematically benchmarks four optimizers—Adam, Adaptive Gradient (Adagrad), Rectified Adam (RAdam), and Lookahead—across three deep- ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s00521-025-11546-2", "content": "by S Dhingra · 2025 — This study systematically benchmarks four optimizers—Adam, Adaptive Gradient (Adagrad), Rectified Adam (RAdam), and Lookahead—across three deep- ..."}
+{"idx": 9, "title": "NeurIPS 2024 Papers", "date": "", "ddg_snippet": "Start here, schedule, tutorials, main conference, invited talks, orals, spotlights, papers, paper visualization, competitions, datasets & benchmarks.", "subpage_snippet": "", "source": "nips.cc", "link": "https://nips.cc/virtual/2024/papers.html", "content": "Start here, schedule, tutorials, main conference, invited talks, orals, spotlights, papers, paper visualization, competitions, datasets & benchmarks."}

data/sampled_jsons/Normalizing_Flows_are_Capable_Generative_Models_Score_Based_Denoising_Section_2.5.jsonl

@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Normalizing data for better interpretation of results?", "date": "", "ddg_snippet": "Jul 13, 2021 · Fold-change (or percentage change) is a perfectly reasonable way to want to interpret data, but indeed, just normalizing as you have done creates the issue you've noticed. It's actually worse than just visual interpretation - if you have a model that assumes additive errors, normalizing as you've done causes the errors to become multiplicative. This makes interpretation and statistics much ...", "subpage_snippet": "", "source": "stats.stackexchange.com", "link": "https://stats.stackexchange.com/questions/534344/normalizing-data-for-better-interpretation-of-results", "content": "Jul 13, 2021 · Fold-change (or percentage change) is a perfectly reasonable way to want to interpret data, but indeed, just normalizing as you have done creates the issue you've noticed. It's actually worse than just visual interpretation - if you have a model that assumes additive errors, normalizing as you've done causes the errors to become multiplicative. This makes interpretation and statistics much ..."}
+{"idx": 1, "title": "Is it a good practice to always scale/normalize data for machine...", "date": "", "ddg_snippet": "Jan 7, 2016 · As some of the other answers have already pointed it out, the \"good practice\" as to whether to normalize the data or not depends on the data, model, and application. By normalizing , you are actually throwing away some information about the data such as the absolute maximum and minimum values. So, there is no rule of thumb.", "subpage_snippet": "", "source": "stats.stackexchange.com", "link": "https://stats.stackexchange.com/questions/189652/is-it-a-good-practice-to-always-scale-normalize-data-for-machine-learning", "content": "Jan 7, 2016 · As some of the other answers have already pointed it out, the \"good practice\" as to whether to normalize the data or not depends on the data, model, and application. By normalizing , you are actually throwing away some information about the data such as the absolute maximum and minimum values. So, there is no rule of thumb."}
+{"idx": 2, "title": "How to normalize data to 0-1 range? - Cross Validated", "date": "", "ddg_snippet": "415 I am lost in normalizing , could anyone guide me please. I have a minimum and maximum values, say -23.89 and 7.54990767, respectively. If I get a value of 5 .6878 how can I scale this value on a scale of 0 to 1.", "subpage_snippet": "", "source": "stats.stackexchange.com", "link": "https://stats.stackexchange.com/questions/70801/how-to-normalize-data-to-0-1-range", "content": "415 I am lost in normalizing , could anyone guide me please. I have a minimum and maximum values, say -23.89 and 7.54990767, respectively. If I get a value of 5 .6878 how can I scale this value on a scale of 0 to 1."}
+{"idx": 3, "title": "Should I normalize all data prior feeding the neural network...", "date": "", "ddg_snippet": "Apr 5 , 2020 · My understanding is most of the tutorials recommend normalizing / scaling the data prior feeding the tensorflow models . Doesn't normalization require that data conforms to the normal parametric distribution?", "subpage_snippet": "", "source": "stats.stackexchange.com", "link": "https://stats.stackexchange.com/questions/458579/should-i-normalize-all-data-prior-feeding-the-neural-network-models", "content": "Apr 5 , 2020 · My understanding is most of the tutorials recommend normalizing / scaling the data prior feeding the tensorflow models . Doesn't normalization require that data conforms to the normal parametric distribution?"}
+{"idx": 4, "title": "Why normalize images by subtracting dataset's image mean, instead...", "date": "", "ddg_snippet": "May 8, 2016 · Consistency: Normalizing with the dataset mean ensures all images are treated the same, providing a stable input distribution. Preserves Important Features: Keeps global differences like brightness and contrast between images — useful for learning.", "subpage_snippet": "", "source": "stats.stackexchange.com", "link": "https://stats.stackexchange.com/questions/211436/why-normalize-images-by-subtracting-datasets-image-mean-instead-of-the-current", "content": "May 8, 2016 · Consistency: Normalizing with the dataset mean ensures all images are treated the same, providing a stable input distribution. Preserves Important Features: Keeps global differences like brightness and contrast between images — useful for learning."}
+{"idx": 5, "title": "when should I normalize with $\\log (1+x)$ instead of with $\\log$?", "date": "", "ddg_snippet": "Nov 8, 2019 · for instance normalizing the price of diamonds in the diamonds dataset using log1p if the loss function is RMSE, than normalizing with $\\log$ is akin to using a RMSLE errors. is there a similar insight when normalizing with $\\log (1+x)$? when should I use $\\log (1+x)$ rather than $\\log (x)$?", "subpage_snippet": "", "source": "stats.stackexchange.com", "link": "https://stats.stackexchange.com/questions/435088/when-should-i-normalize-with-log1x-instead-of-with-log", "content": "Nov 8, 2019 · for instance normalizing the price of diamonds in the diamonds dataset using log1p if the loss function is RMSE, than normalizing with $\\log$ is akin to using a RMSLE errors. is there a similar insight when normalizing with $\\log (1+x)$? when should I use $\\log (1+x)$ rather than $\\log (x)$?"}
+{"idx": 6, "title": "Data normalization and standardization in neural networks", "date": "", "ddg_snippet": "1- Min-max normalization retains the original distribution of scores except for a scaling factor and transforms all the scores into a common range [0, 1]. However, this method is not robust (i.e., the method is highly sensitive to outliers. 2 - Standardization (Z- score normalization) The most commonly used technique, which is calculated using the arithmetic mean and standard deviation of the ...", "subpage_snippet": "", "source": "stats.stackexchange.com", "link": "https://stats.stackexchange.com/questions/7757/data-normalization-and-standardization-in-neural-networks", "content": "1- Min-max normalization retains the original distribution of scores except for a scaling factor and transforms all the scores into a common range [0, 1]. However, this method is not robust (i.e., the method is highly sensitive to outliers. 2 - Standardization (Z- score normalization) The most commonly used technique, which is calculated using the arithmetic mean and standard deviation of the ..."}
+{"idx": 7, "title": "normalization - Why do we need to normalize data before principal...", "date": "", "ddg_snippet": "I'm doing principal component analysis on my dataset and my professor told me that I should normalize the data before doing the analysis. Why? What would happen If I did PCA without normalization? ...", "subpage_snippet": "", "source": "stats.stackexchange.com", "link": "https://stats.stackexchange.com/questions/69157/why-do-we-need-to-normalize-data-before-principal-component-analysis-pca", "content": "I'm doing principal component analysis on my dataset and my professor told me that I should normalize the data before doing the analysis. Why? What would happen If I did PCA without normalization? ..."}
+{"idx": 8, "title": "What's the difference between Normalization and Standardization?", "date": "", "ddg_snippet": "At work we were discussing this as my boss has never heard of normalization. In Linear Algebra, Normalization seems to refer to the dividing of a vector by its length. And in statistics, Standardiz...", "subpage_snippet": "", "source": "stats.stackexchange.com", "link": "https://stats.stackexchange.com/questions/10289/whats-the-difference-between-normalization-and-standardization", "content": "At work we were discussing this as my boss has never heard of normalization. In Linear Algebra, Normalization seems to refer to the dividing of a vector by its length. And in statistics, Standardiz..."}
+{"idx": 9, "title": "The correct way to normalize time series data - Cross Validated", "date": "", "ddg_snippet": "Feb 7, 2018 · A question very similar to mine has been asked already: Zero mean unit variance normalization of multivariate time series I'm asking a new question because that one didn't have any replies. I'm", "subpage_snippet": "", "source": "stats.stackexchange.com", "link": "https://stats.stackexchange.com/questions/327192/the-correct-way-to-normalize-time-series-data", "content": "Feb 7, 2018 · A question very similar to mine has been asked already: Zero mean unit variance normalization of multivariate time series I'm asking a new question because that one didn't have any replies. I'm"}

data/sampled_jsons/OM-KIID_hardness_result_Mehta_et_al._AdWords_2007.jsonl

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+{"idx": 0, "title": "Near-Optimum Online Ad Allocation for Targeted Advertising", "date": "", "ddg_snippet": "The AdWords problem, first proposed by [ Mehta et al . 2007 ], is the more general ad allocation problem, but subject to the realistic small bid assumption ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/pdf/10.1145/2764468.2764482?download=true", "content": "The AdWords problem, first proposed by [ Mehta et al . 2007 ], is the more general ad allocation problem, but subject to the realistic small bid assumption ..."}
+{"idx": 1, "title": "Online Matching with Offline Reusable Resources", "date": "", "ddg_snippet": "by JP Dickerson · 2017 · Cited by 184 — [ Mehta et al . 2007 ] Mehta , A.; Saberi, A.; Vazirani, U.; and Vazirani,. V. 2007 . Adwords and generalized online matching. Journal of the. ACM (JACM) 54(5) ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1711.08345", "content": "by JP Dickerson · 2017 · Cited by 184 — [ Mehta et al . 2007 ] Mehta , A.; Saberi, A.; Vazirani, U.; and Vazirani,. V. 2007 . Adwords and generalized online matching. Journal of the. ACM (JACM) 54(5) ..."}
+{"idx": 2, "title": "Promoting Fairness Among Dynamic Agents in Online-Matching ...", "date": "", "ddg_snippet": "Our hardness result of. √. 3 − 1 ≈ 0.732 contributes to this short list of hardness results for OM - KIID . Notably, our analysis focuses on the objective of ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=0C3bLHwjsY", "content": "Our hardness result of. √. 3 − 1 ≈ 0.732 contributes to this short list of hardness results for OM - KIID . Notably, our analysis focuses on the objective of ..."}
+{"idx": 3, "title": "Promoting Fairness Among Dynamic Agents in Online ...", "date": "", "ddg_snippet": "9 Dec 2024 — Our hardness result of √3 − 1 ≈ 0.732 contributes to this short list of hardness results for OM - KIID . Notably, our analysis focuses on ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/poster/96945", "content": "9 Dec 2024 — Our hardness result of √3 − 1 ≈ 0.732 contributes to this short list of hardness results for OM - KIID . Notably, our analysis focuses on ..."}
+{"idx": 4, "title": "Online Task Assignment Problems with Reusable Resources", "date": "", "ddg_snippet": "by H Sumita · 2022 · Cited by 11 — For the problem in the adversarial input model, Mehta et al . [22] presented a. 0.534-competitive algorithm for the unweighted case when edge ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2203.07605", "content": "by H Sumita · 2022 · Cited by 11 — For the problem in the adversarial input model, Mehta et al . [22] presented a. 0.534-competitive algorithm for the unweighted case when edge ..."}
+{"idx": 5, "title": "Online Matching with Offline Reusable Resources", "date": "", "ddg_snippet": "by JP DICKERSON · 2018 · Cited by 181 — Hardness against the ex-post optimal solution. Here we show a hardness result against the ex-. 349 post optimal solution. Manshadi et al . [36] prove that for ...", "subpage_snippet": "", "source": "www.cs.umd.edu", "link": "https://www.cs.umd.edu/~srin/PDF/2021/aaai18-reusable-journal.pdf", "content": "by JP DICKERSON · 2018 · Cited by 181 — Hardness against the ex-post optimal solution. Here we show a hardness result against the ex-. 349 post optimal solution. Manshadi et al . [36] prove that for ..."}
+{"idx": 6, "title": "Online Task Assignment Problems with Reusable Resources", "date": "", "ddg_snippet": "by H Sumita · 2022 · Cited by 11 — When offline vertices have capacities, the online bipartite matching is often called the AdWords prob- lem ( Mehta et al . 2007 ). This variant is also studied ... 9 pages", "subpage_snippet": "", "source": "cdn.aaai.org", "link": "https://cdn.aaai.org/ojs/20455/20455-13-24468-1-2-20220628.pdf", "content": "by H Sumita · 2022 · Cited by 11 — When offline vertices have capacities, the online bipartite matching is often called the AdWords prob- lem ( Mehta et al . 2007 ). This variant is also studied ... 9 pages"}
+{"idx": 7, "title": "Online Allocation Algorithms with Applications in Computational ...", "date": "", "ddg_snippet": "This result generalizes the 1 - -approximation algorithm by Mehta et al [65] and by Buchbinder et . al . [18]. Following a training-based dual algorithm by ...", "subpage_snippet": "", "source": "dspace.mit.edu", "link": "https://dspace.mit.edu/bitstream/handle/1721.1/87940/880284018-MIT.pdf?sequence=2&isAllowed=y", "content": "This result generalizes the 1 - -approximation algorithm by Mehta et al [65] and by Buchbinder et . al . [18]. Following a training-based dual algorithm by ..."}
+{"idx": 8, "title": "Multiplicative Bidding in Online Advertising", "date": "", "ddg_snippet": "In this paper, we initiate the study of the multiplicative bidding language adopted by major Internet search companies. In multiplicative bidding ... 18 pages", "subpage_snippet": "", "source": "mhbateni.com", "link": "https://mhbateni.com/academic/pubs/multibudding-14.pdf", "content": "In this paper, we initiate the study of the multiplicative bidding language adopted by major Internet search companies. In multiplicative bidding ... 18 pages"}
+{"idx": 9, "title": "Fairness Maximization among Offline Agents in Online ...", "date": "", "ddg_snippet": "There are two key ideas helping us break the barrier of 1-1/𝖾~ 63.2% for competitive ratio in online matching. One is boosting, which is to adaptively re- ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3569705", "content": "There are two key ideas helping us break the barrier of 1-1/𝖾~ 63.2% for competitive ratio in online matching. One is boosting, which is to adaptively re- ..."}

data/sampled_jsons/OPT_Zhang_et_al._2022_scaling_laws_error_rate_year_2022.jsonl

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+{"idx": 0, "title": "Neural scaling law", "date": "", "ddg_snippet": "A neural scaling law is an empirical scaling law that describes how neural network performance changes as key factors are scaled up or down.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Neural_scaling_law", "content": "A neural scaling law is an empirical scaling law that describes how neural network performance changes as key factors are scaled up or down."}
+{"idx": 1, "title": "A Hitchhiker's Guide to Scaling Law Estimation", "date": "", "ddg_snippet": "by L Choshen · 2024 · Cited by 7 — Scaling laws predict the loss of a target language model by extrapolating from easier-to-train mod- els with fewer parameters or smaller training.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.11840", "content": "by L Choshen · 2024 · Cited by 7 — Scaling laws predict the loss of a target language model by extrapolating from easier-to-train mod- els with fewer parameters or smaller training."}
+{"idx": 2, "title": "scaling laws for generative mixed-modal language models", "date": "", "ddg_snippet": "by A Aghajanyan · 2023 · Cited by 130 — We study the family of decoder-only models described in GPT-3 Brown et al. (2020) and OPT Zhang et al. (2022). We limit ourselves to training ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2301.03728", "content": "by A Aghajanyan · 2023 · Cited by 130 — We study the family of decoder-only models described in GPT-3 Brown et al. (2020) and OPT Zhang et al. (2022). We limit ourselves to training ..."}
+{"idx": 3, "title": "A Survey of Post-Training Scaling in Large Language Models", "date": "", "ddg_snippet": "by H Lai · 2025 · Cited by 1 — The success of LLMs heavily depends on “Scal- ing Law ” (Brown, 2020; Hoffmann et al ., 2022 ) in pre-training, which unveils the numerical ... 21 pages", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.acl-long.140.pdf", "content": "by H Lai · 2025 · Cited by 1 — The success of LLMs heavily depends on “Scal- ing Law ” (Brown, 2020; Hoffmann et al ., 2022 ) in pre-training, which unveils the numerical ... 21 pages"}
+{"idx": 4, "title": "Scaling Data-Constrained Language Models", "date": "", "ddg_snippet": "by N Muennighoff · 2025 · Cited by 2 — Abstract. The current trend of scaling language models involves increasing both parameter count and training data set size. 66 pages", "subpage_snippet": "", "source": "www.jmlr.org", "link": "https://www.jmlr.org/papers/volume26/24-1000/24-1000.pdf", "content": "by N Muennighoff · 2025 · Cited by 2 — Abstract. The current trend of scaling language models involves increasing both parameter count and training data set size. 66 pages"}
+{"idx": 5, "title": "WHEN SCALING MEETS LLM FINETUNING", "date": "", "ddg_snippet": "by B Zhang · Cited by 214 — These laws provide a valuable tool for guiding training deci- sions (Hoffmann et al ., 2022 ) and model development by understanding how model performance evolves ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=5HCnKDeTws", "content": "by B Zhang · Cited by 214 — These laws provide a valuable tool for guiding training deci- sions (Hoffmann et al ., 2022 ) and model development by understanding how model performance evolves ..."}
+{"idx": 6, "title": "Resolving Discrepancies in Compute-Optimal Scaling of ...", "date": "", "ddg_snippet": "by T Porian · 2024 · Cited by 25 — We explain the discrepancy by reproducing the Kaplan et al . scaling law on two datasets (OpenWebText2 and RefinedWeb) and identifying three factors causing the ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/b6341525cd84f3be0ef203e4d7cd8556-Paper-Conference.pdf", "content": "by T Porian · 2024 · Cited by 25 — We explain the discrepancy by reproducing the Kaplan et al . scaling law on two datasets (OpenWebText2 and RefinedWeb) and identifying three factors causing the ..."}
+{"idx": 7, "title": "(MIS)FITTING:ASURVEY OF SCALING LAWS", "date": "", "ddg_snippet": "by M Li · Cited by 2 — To evaluate the range of models trained to fit a scaling law , train or validation loss are most commonly used, but some works consider other metrics, such as ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=xI71dsS3o4", "content": "by M Li · Cited by 2 — To evaluate the range of models trained to fit a scaling law , train or validation loss are most commonly used, but some works consider other metrics, such as ..."}
+{"idx": 8, "title": "Scaling Vision Transformers", "date": "", "ddg_snippet": "by X Zhai · 2022 · Cited by 1597 — In terms of the law , this saturation corresponds to an additive constant to the error rate : c in E = aC−b + c. At the lower end of the compute spectrum, we ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2022/papers/Zhai_Scaling_Vision_Transformers_CVPR_2022_paper.pdf", "content": "by X Zhai · 2022 · Cited by 1597 — In terms of the law , this saturation corresponds to an additive constant to the error rate : c in E = aC−b + c. At the lower end of the compute spectrum, we ..."}
+{"idx": 9, "title": "Larger and more instructable language models become ...", "date": "", "ddg_snippet": "by L Zhou · 2024 · Cited by 171 — Relevance: users do not sufficiently use their expectations on difficulty to compensate for increasing error rates in high-difficulty regions, ...", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41586-024-07930-y", "content": "by L Zhou · 2024 · Cited by 171 — Relevance: users do not sufficiently use their expectations on difficulty to compensate for increasing error rates in high-difficulty regions, ..."}

data/sampled_jsons/Open_the_Black_Box_step-based_policy_updates_on-policy_OR_off-policy_Li_2024.jsonl

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+{"idx": 0, "title": "Deep Black - Box Reinforcement Learning with Movement Primitives...", "date": "", "ddg_snippet": "Open the Black Box : Step - based Policy Updates for Temporally-Correlated Episodic Reinforcement Learning.", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/papers/deep-black-box-reinforcement-learning-with-movement-1ov7rgyz", "content": "Open the Black Box : Step - based Policy Updates for Temporally-Correlated Episodic Reinforcement Learning."}
+{"idx": 1, "title": "ICLR 2024 Schedule", "date": "", "ddg_snippet": "Open the Black Box : Step - based Policy Updates for Temporally-Correlated Episodic Reinforcement Learning.On the Analysis of GAN-based Image-to-Image Translation with Gaussian Noise Injection.", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2024/calendar", "content": "Open the Black Box : Step - based Policy Updates for Temporally-Correlated Episodic Reinforcement Learning.On the Analysis of GAN-based Image-to-Image Translation with Gaussian Noise Injection."}
+{"idx": 2, "title": "圆桌论道 | ICLR 2024 强化学习和 LLM... | RLChina 强化学习社区", "date": "", "ddg_snippet": "Open the Black Box : Step - based Policy Updates for Temporally-Correlated Episodic Reinforcement Learning. Ge Li , Hongyi Zhou, Dominik Roth, Serge Thilges, Fabian Otto, Rudolf Lioutikov, Gerhard Neumann. On Trajectory Augmentations for Off - Policy Evaluation.", "subpage_snippet": "", "source": "rlchina.org", "link": "http://rlchina.org/topic/900", "content": "Open the Black Box : Step - based Policy Updates for Temporally-Correlated Episodic Reinforcement Learning. Ge Li , Hongyi Zhou, Dominik Roth, Serge Thilges, Fabian Otto, Rudolf Lioutikov, Gerhard Neumann. On Trajectory Augmentations for Off - Policy Evaluation."}
+{"idx": 3, "title": "ICLR 2024 Statistics", "date": "", "ddg_snippet": "Open the Black Box : Step - based Policy Updates for Temporally-Correlated Episodic Reinforcement Learning.Causality-Based Black - Box Backdoor Detection.", "subpage_snippet": "", "source": "guoqiangwei.xyz", "link": "https://guoqiangwei.xyz/iclr2024_stats/iclr2024_submissions.html", "content": "Open the Black Box : Step - based Policy Updates for Temporally-Correlated Episodic Reinforcement Learning.Causality-Based Black - Box Backdoor Detection."}
+{"idx": 4, "title": "ICLR 2024 ，强化学习领域约301篇Accept...", "date": "", "ddg_snippet": "Open the Black Box : Step - based Policy Updates for Temporally-Correlated Episodic Reinforcement Learning [89]. Incentivized Truthful Communication for Federated Bandits [90]. Diffusion Generative Flow Samplers: Improving learning signals through partial trajectory optimization [91].", "subpage_snippet": "", "source": "www.zhuanzhi.ai", "link": "https://www.zhuanzhi.ai/vip/8ab6bb793ed08853af8c4ae2e35fce03", "content": "Open the Black Box : Step - based Policy Updates for Temporally-Correlated Episodic Reinforcement Learning [89]. Incentivized Truthful Communication for Federated Bandits [90]. Diffusion Generative Flow Samplers: Improving learning signals through partial trajectory optimization [91]."}
+{"idx": 5, "title": "Installing Windows security updates during the Windows out-of ...", "date": "", "ddg_snippet": "6 days ago · Important: While writing this post the news came that this capability got delayed again to help ensure delivery of the best possible experience. As the configuration is still available in Microsoft Intune, this post can still provide value. This week is all about the new functionality to install Windows security updates during the Windows out-of- box -experience…", "subpage_snippet": "", "source": "petervanderwoude.nl", "link": "https://petervanderwoude.nl/post/installing-windows-security-updates-during-the-windows-out-of-box-experience/", "content": "6 days ago · Important: While writing this post the news came that this capability got delayed again to help ensure delivery of the best possible experience. As the configuration is still available in Microsoft Intune, this post can still provide value. This week is all about the new functionality to install Windows security updates during the Windows out-of- box -experience…"}
+{"idx": 6, "title": "Windows Quality Updates during the out-of-box experience", "date": "", "ddg_snippet": "Feb 3, 2025 · Windows Quality Updates Introduction Ensuring your devices are up to date is particularly important during device enrollment. Windows Autopilot’s initial concept was to ship the device directly from the supplier to the end user, a process that can present significant challenges with older Windows Builds. One of the major issues here was that most of the time, those devices weren’t up to ...", "subpage_snippet": "", "source": "patchmypc.com", "link": "https://patchmypc.com/blog/windows-quality-updates-out-of-box-experience-autopilot/", "content": "Feb 3, 2025 · Windows Quality Updates Introduction Ensuring your devices are up to date is particularly important during device enrollment. Windows Autopilot’s initial concept was to ship the device directly from the supplier to the end user, a process that can present significant challenges with older Windows Builds. One of the major issues here was that most of the time, those devices weren’t up to ..."}
+{"idx": 7, "title": "Intune Policy Processing on Windows 10 explained", "date": "", "ddg_snippet": "Jul 18, 2019 · In this post I will dive into the Intune policy processing on a MDM managed Windows 10 client. Intune is an MDM system and has the ability to deploy so called device configuration profiles to managed Windows 10 endpoints. We will have a look at the architecture, the settings, and the actual processing including the …", "subpage_snippet": "", "source": "oliverkieselbach.com", "link": "https://oliverkieselbach.com/2019/07/18/intune-policy-processing-on-windows-10-explained/", "content": "Jul 18, 2019 · In this post I will dive into the Intune policy processing on a MDM managed Windows 10 client. Intune is an MDM system and has the ability to deploy so called device configuration profiles to managed Windows 10 endpoints. We will have a look at the architecture, the settings, and the actual processing including the …"}
+{"idx": 8, "title": "Important changes to the Windows enrollment experience coming ...", "date": "", "ddg_snippet": "Sep 16, 2024 · Windows updates are essential for keeping your devices secure and up to date with the latest security, performance, and reliability improvements. One of the top customer requests we receive is to enable Windows updates during provisioning in the out-of- box experience (OOBE), so that devices are fully patched and ready to use as soon as they are enrolled with mobile device management (MDM).", "subpage_snippet": "", "source": "techcommunity.microsoft.com", "link": "https://techcommunity.microsoft.com/blog/intunecustomersuccess/important-changes-to-the-windows-enrollment-experience-coming-soon/4246689", "content": "Sep 16, 2024 · Windows updates are essential for keeping your devices secure and up to date with the latest security, performance, and reliability improvements. One of the top customer requests we receive is to enable Windows updates during provisioning in the out-of- box experience (OOBE), so that devices are fully patched and ready to use as soon as they are enrolled with mobile device management (MDM)."}
+{"idx": 9, "title": "5 Steps to Implement Annual Coding & Compliance Changes", "date": "", "ddg_snippet": "Aug 29, 2025 · Prepare for CPT®, ICD-10, and Medicare updates . Use this practical 5- step plan to guide your team through yearly coding and compliance changes.", "subpage_snippet": "", "source": "namas.co", "link": "https://namas.co/annual-coding-compliance-updates/", "content": "Aug 29, 2025 · Prepare for CPT®, ICD-10, and Medicare updates . Use this practical 5- step plan to guide your team through yearly coding and compliance changes."}

data/sampled_jsons/Origin_Identification_for_Text-Guided_Image-to-Image_Diffusion_Models_transformation_complexity_mAP_.jsonl

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+{"idx": 0, "title": "(PDF) Generalizable Origin Identification for Text - Guided ...", "date": "", "ddg_snippet": "Text - guided image - to - image diffusion models excel in translating images based on textual prompts, allowing for precise and creative visual modifications. However, such a powerful technique can be misused for spreading misinformation, infringing on copyrights...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/387767437_Generalizable_Origin_Identification_for_Text-Guided_Image-to-Image_Diffusion_Models", "content": "Text - guided image - to - image diffusion models excel in translating images based on textual prompts, allowing for precise and creative visual modifications. However, such a powerful technique can be misused for spreading misinformation, infringing on copyrights..."}
+{"idx": 1, "title": "ICML Poster Origin Identification for Text - Guided Image - to - Image ...", "date": "", "ddg_snippet": "Abstract: Text - guided image - to - image diffusion models excel in translating images based on textual prompts, allowing for precise and creative visual modifications.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46505", "content": "Abstract: Text - guided image - to - image diffusion models excel in translating images based on textual prompts, allowing for precise and creative visual modifications."}
+{"idx": 2, "title": "Generalizable Origin Identification for Text - Guided Image - to - Image ...", "date": "", "ddg_snippet": "Text - guided image - to - image diffusion models excel in translating images based on textual prompts, allowing for precise and creative visual modifications.(+31.6%. mAP ), even those with generalization designs.", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/paper/generalizable-origin-identification-for-text", "content": "Text - guided image - to - image diffusion models excel in translating images based on textual prompts, allowing for precise and creative visual modifications.(+31.6%. mAP ), even those with generalization designs."}
+{"idx": 3, "title": "Generalizable Origin Identification for Text - Guided Image - to - Image ...", "date": "", "ddg_snippet": "Text - guided image - to - image diffusion models excel in translating images based on textual prompts, allowing for precise and creative visual modifications.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/generalizable-origin-identification-text-guided-image-to", "content": "Text - guided image - to - image diffusion models excel in translating images based on textual prompts, allowing for precise and creative visual modifications."}
+{"idx": 4, "title": "Generalizable Origin Identification for Text - Guided Image - to - Image ...", "date": "", "ddg_snippet": "Text - guided image - to - image diffusion models are powerful AI tools that can change images based on text descriptions. While useful, these tools can be misused to spread fake information, violate copyrights, or hide the source of images.", "subpage_snippet": "", "source": "ai-search.io", "link": "https://ai-search.io/papers/generalizable-origin-identification-for-text-guided-image-to-image-diffusion-models", "content": "Text - guided image - to - image diffusion models are powerful AI tools that can change images based on text descriptions. While useful, these tools can be misused to spread fake information, violate copyrights, or hide the source of images."}
+{"idx": 5, "title": "Exploring Controllable Ability through Text - to - Image Diffusion Model ...", "date": "", "ddg_snippet": "Recently, diffusion models have gained popularity in art design, animation, and gaming, especially in original painting creation, poster design, and visual identity development. Traditional creative processes, while demanding unique imagination and creativity...", "subpage_snippet": "", "source": "www.preprints.org", "link": "https://www.preprints.org/manuscript/202408.1035/v1", "content": "Recently, diffusion models have gained popularity in art design, animation, and gaming, especially in original painting creation, poster design, and visual identity development. Traditional creative processes, while demanding unique imagination and creativity..."}
+{"idx": 6, "title": "Text - guided image generation with diffusion models - Hands-On...", "date": "", "ddg_snippet": "The image is generated by conditioning over the text prompt and guiding the diffusion process.There are three main components to a diffusion process. The first is the language model that uses a standard tokenizer, converting your text into a usable format for the model .", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/learning/hands-on-generative-ai-with-diffusion-models-building-real-world-applications/text-guided-image-generation-with-diffusion-models", "content": "The image is generated by conditioning over the text prompt and guiding the diffusion process.There are three main components to a diffusion process. The first is the language model that uses a standard tokenizer, converting your text into a usable format for the model ."}
+{"idx": 7, "title": "A Deep Dive Into AI Image Generation: How Diffusion Models Create...", "date": "", "ddg_snippet": "Text - Guided Creation: The model understands what to create because of text input. It leverages a cleverly designed \"shared space\" where the concepts in text and the content of images are linked, allowing the prompt to steer the noisy chaos toward a specific outcome.", "subpage_snippet": "", "source": "www.remio.ai", "link": "https://www.remio.ai/post/a-deep-dive-into-ai-image-generation-how-diffusion-models-create-art-from-noise", "content": "Text - Guided Creation: The model understands what to create because of text input. It leverages a cleverly designed \"shared space\" where the concepts in text and the content of images are linked, allowing the prompt to steer the noisy chaos toward a specific outcome."}
+{"idx": 8, "title": "Ranni: Taming Text - to - Image Diffusion for Accurate Instruction...", "date": "", "ddg_snippet": "Existing text - to - image (T2I) diffusion models usually struggle in interpreting complex prompts, especially those with quantity, object-attribute binding, and multi-subject descriptions.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2024/papers/Feng_Ranni_Taming_Text-to-Image_Diffusion_for_Accurate_Instruction_Following_CVPR_2024_paper.pdf", "content": "Existing text - to - image (T2I) diffusion models usually struggle in interpreting complex prompts, especially those with quantity, object-attribute binding, and multi-subject descriptions."}
+{"idx": 9, "title": "Tracing Image Origins in Text -Based Image - to - Image Diffusion ...", "date": "", "ddg_snippet": "Text -based image - to - image diffusion models enable impressive visual modifications of images based on text input.These 5,000 original images were then transformed using various diffusion models to test the generalizability of the ID² methods.", "subpage_snippet": "", "source": "www.getaiverse.com", "link": "https://www.getaiverse.com/post/die-rueckverfolgung-von-bildquellen-in-textbasierten-bild-zu-bild-diffusionsmodellen", "content": "Text -based image - to - image diffusion models enable impressive visual modifications of images based on text input.These 5,000 original images were then transformed using various diffusion models to test the generalizability of the ID² methods."}

data/sampled_jsons/Provably_Efficient_Risk-Aware_Preference-Based_Reinforcement_Learning_arXiv.jsonl

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+{"idx": 0, "title": "RA-PbRL: Provably Efficient Risk-Aware Preference-Based Reinforcement ...", "date": "", "ddg_snippet": "Reinforcement Learning from Human Feedback (RLHF) has recently surged in popularity, particularly for aligning large language models and other AI systems with human intentions. At its core, RLHF can be viewed as a specialized instance of Preference-based Reinforcement Learning (PbRL), where the preferences specifically originate from human judgments rather than arbitrary evaluators. Despite ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.23569", "content": "Reinforcement Learning from Human Feedback (RLHF) has recently surged in popularity, particularly for aligning large language models and other AI systems with human intentions. At its core, RLHF can be viewed as a specialized instance of Preference-based Reinforcement Learning (PbRL), where the preferences specifically originate from human judgments rather than arbitrary evaluators. Despite ..."}
+{"idx": 1, "title": "PDF RA-PbRL: Provably Eficient Risk-Aware Preference-Based Reinforcement ...", "date": "", "ddg_snippet": "To the best of our knowledge, our proposed RA-PbRL algorithm is the first provably eficient Preference-based Reinforcement Learning (PbRL) algorithm that incorporates both nested and static risk objectives in one algorithm.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/7016d7b7b6e3c05b2128ac5b3aae492d-Paper-Conference.pdf", "content": "To the best of our knowledge, our proposed RA-PbRL algorithm is the first provably eficient Preference-based Reinforcement Learning (PbRL) algorithm that incorporates both nested and static risk objectives in one algorithm."}
+{"idx": 2, "title": "Risk-Aware Preference-baser Reinforcement Learning (RA-PbRL)", "date": "", "ddg_snippet": "Code for paper \"RA-PbRL: Provably Efficient Risk-Aware Preference-Based Reinforcement Learning \" Code Setup Documentation Libraries (>= Python 3.12.4) For more information on the version specifics, see the environment. yaml file. To import the environment, execute the following command prompt commands:", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/aguilarjose11/PbRLNeurips", "content": "Code for paper \"RA-PbRL: Provably Efficient Risk-Aware Preference-Based Reinforcement Learning \" Code Setup Documentation Libraries (>= Python 3.12.4) For more information on the version specifics, see the environment. yaml file. To import the environment, execute the following command prompt commands:"}
+{"idx": 3, "title": "RA-PbRL: Provably Efficient Risk-Aware Preference-Based...", "date": "", "ddg_snippet": "Abstract: Reinforcement Learning from Human Feedback (RLHF) has recently surged in popularity, particularly for aligning large language models and other AI systems with human intentions. At its core, RLHF can be viewed as a specialized instance of Preference-based Reinforcement Learning (PbRL), where the preferences specifically originate from human judgments rather than arbitrary evaluators ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=JNDcFOczOf", "content": "Abstract: Reinforcement Learning from Human Feedback (RLHF) has recently surged in popularity, particularly for aligning large language models and other AI systems with human intentions. At its core, RLHF can be viewed as a specialized instance of Preference-based Reinforcement Learning (PbRL), where the preferences specifically originate from human judgments rather than arbitrary evaluators ..."}
+{"idx": 4, "title": "RA-PbRL: Provably Efficient Risk-Aware Preference-Based Reinforcement ...", "date": "", "ddg_snippet": "Abstract Preference-based Reinforcement Learning (PbRL) studies the problem where agents receive only preferences over pairs of trajectories in each episode. Traditional approaches in this field have predominantly focused on the mean reward or utility criterion. However, in PbRL scenarios demanding heightened risk awareness, such as in AI systems, healthcare, and agriculture, risk-aware ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.23569", "content": "Abstract Preference-based Reinforcement Learning (PbRL) studies the problem where agents receive only preferences over pairs of trajectories in each episode. Traditional approaches in this field have predominantly focused on the mean reward or utility criterion. However, in PbRL scenarios demanding heightened risk awareness, such as in AI systems, healthcare, and agriculture, risk-aware ..."}
+{"idx": 5, "title": "RA-PbRL: Provably Efficient Risk-Aware Preference-Based Reinforcement ...", "date": "", "ddg_snippet": "Authors Yujie Zhao, Jose Efraim Aguilar Escamill, Weyl Lu, Huazheng Wang Abstract Reinforcement Learning from Human Feedback (RLHF) has recently surged in popularity, particularly for aligning large language models and other AI systems with human intentions. At its core, RLHF can be viewed as a specialized instance of Preference-based Reinforcement Learning (PbRL), where the preferences ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/hash/7016d7b7b6e3c05b2128ac5b3aae492d-Abstract-Conference.html", "content": "Authors Yujie Zhao, Jose Efraim Aguilar Escamill, Weyl Lu, Huazheng Wang Abstract Reinforcement Learning from Human Feedback (RLHF) has recently surged in popularity, particularly for aligning large language models and other AI systems with human intentions. At its core, RLHF can be viewed as a specialized instance of Preference-based Reinforcement Learning (PbRL), where the preferences ..."}
+{"idx": 6, "title": "RA-RLHF: Provably Efficient Risk-Aware Reinforcement Learning Human ...", "date": "", "ddg_snippet": "Abstract Reinforcement Learning Human Feedback (RLHF) studies the problem where agents receive only preferences over pairs of trajectories in each episode. Tra-ditional approaches in this field have predominantly focused on the mean re-ward or utility criterion. However, in RLHF scenarios demanding heightened risk awareness, such as in AI systems, healthcare, and agriculture, risk-aware ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.23569v2", "content": "Abstract Reinforcement Learning Human Feedback (RLHF) studies the problem where agents receive only preferences over pairs of trajectories in each episode. Tra-ditional approaches in this field have predominantly focused on the mean re-ward or utility criterion. However, in RLHF scenarios demanding heightened risk awareness, such as in AI systems, healthcare, and agriculture, risk-aware ..."}
+{"idx": 7, "title": "RA-PbRL: Provably Efficient Risk-Aware Preference-Based Reinforcement ...", "date": "", "ddg_snippet": "Preference-based Reinforcement Learning (PbRL) studies the problem where agents receive only preferences over pairs of trajectories in each episode. Traditional approaches in this field have predominantly focused on th…", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2410.23569", "content": "Preference-based Reinforcement Learning (PbRL) studies the problem where agents receive only preferences over pairs of trajectories in each episode. Traditional approaches in this field have predominantly focused on th…"}
+{"idx": 8, "title": "PDF Human-in-the-loop: Provably Efficient Preference-based Reinforcement ...", "date": "", "ddg_snippet": "Abstract We study human-in-the-loop reinforcement learn-ing (RL) with trajectory preferences , where in-stead of receiving a numeric reward at each step, the RL agent only receives preferences over tra-jectory pairs from a human overseer. The goal of the RL agent is to learn the optimal policy which is most preferred by the human overseer. Despite the empirical successes, the theoretical ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v162/chen22ag/chen22ag.pdf", "content": "Abstract We study human-in-the-loop reinforcement learn-ing (RL) with trajectory preferences , where in-stead of receiving a numeric reward at each step, the RL agent only receives preferences over tra-jectory pairs from a human overseer. The goal of the RL agent is to learn the optimal policy which is most preferred by the human overseer. Despite the empirical successes, the theoretical ..."}
+{"idx": 9, "title": "RA-PbRL | Proceedings of the 38th International Conference on Neural ...", "date": "", "ddg_snippet": "Reinforcement Learning from Human Feedback (RLHF) has recently surged in popularity, particularly for aligning large language models and other AI systems with human intentions. At its core, RLHF can be viewed as a specialized instance of Preference-based Reinforcement Learning (PbRL), where the preferences specifically originate from human judgments rather than arbitrary evaluators. Despite ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3737916.3739861", "content": "Reinforcement Learning from Human Feedback (RLHF) has recently surged in popularity, particularly for aligning large language models and other AI systems with human intentions. At its core, RLHF can be viewed as a specialized instance of Preference-based Reinforcement Learning (PbRL), where the preferences specifically originate from human judgments rather than arbitrary evaluators. Despite ..."}

data/sampled_jsons/RA-PbRL_paper_challenges_adapting_risk_measures_quantile_estimation_preference_feedback.jsonl

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+{"idx": 0, "title": "PDF RA-PbRL: Provably Eficient Risk-Aware Preference-Based Reinforcement ...", "date": "", "ddg_snippet": "These scenarios often operate under a one-episode-reward setting, which makes conventional risk -sensitive objectives inapplicable. To ad-dress this, we explore and prove the applicability of two risk -aware objectives to PbRL : nested and static quantile risk objectives.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/7016d7b7b6e3c05b2128ac5b3aae492d-Paper-Conference.pdf", "content": "These scenarios often operate under a one-episode-reward setting, which makes conventional risk -sensitive objectives inapplicable. To ad-dress this, we explore and prove the applicability of two risk -aware objectives to PbRL : nested and static quantile risk objectives."}
+{"idx": 1, "title": "RA-PbRL: Provably Efficient Risk-Aware Preference-Based Reinforcement ...", "date": "", "ddg_snippet": "Reinforcement Learning from Human Feedback (RLHF) has recently surged in popularity, particularly for aligning large language models and other AI systems with human intentions. At its core, RLHF can be viewed as a specialized instance of Preference -based Reinforcement Learning ( PbRL ), where the preferences specifically originate from human judgments rather than arbitrary evaluators. Despite ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.23569", "content": "Reinforcement Learning from Human Feedback (RLHF) has recently surged in popularity, particularly for aligning large language models and other AI systems with human intentions. At its core, RLHF can be viewed as a specialized instance of Preference -based Reinforcement Learning ( PbRL ), where the preferences specifically originate from human judgments rather than arbitrary evaluators. Despite ..."}
+{"idx": 2, "title": "RA-PbRL | Proceedings of the 38th International Conference on Neural ...", "date": "", "ddg_snippet": "These scenarios often operate under a one-episode-reward setting, which makes conventional risk -sensitive objectives inapplicable. To address this, we explore and prove the applicability of two risk -aware objectives to PbRL : nested and static quantile risk objectives.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3737916.3739861", "content": "These scenarios often operate under a one-episode-reward setting, which makes conventional risk -sensitive objectives inapplicable. To address this, we explore and prove the applicability of two risk -aware objectives to PbRL : nested and static quantile risk objectives."}
+{"idx": 3, "title": "[PDF] RA-PbRL: Provably Efficient Risk-Aware Preference-Based ...", "date": "", "ddg_snippet": "This work explores and proves the applicability of two risk -aware objectives to PbRL : nested and static quantile risk objectives and introduces Risk -Aware- PbRL ( RA-PbRL ), an algorithm designed to optimize both nested and static objectives. Preference -based Reinforcement Learning ( PbRL ) studies the problem where agents receive only preferences over pairs of trajectories in each episode ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/RA-PbRL:-Provably-Efficient-Risk-Aware-Learning-Zhao-Escamill/80889b1260bfcf4275f7ec18bb4eec183400f05d", "content": "This work explores and proves the applicability of two risk -aware objectives to PbRL : nested and static quantile risk objectives and introduces Risk -Aware- PbRL ( RA-PbRL ), an algorithm designed to optimize both nested and static objectives. Preference -based Reinforcement Learning ( PbRL ) studies the problem where agents receive only preferences over pairs of trajectories in each episode ..."}
+{"idx": 4, "title": "PDF Human-in-the-loop: Provably Efficient Preference-based Reinforcement ...", "date": "", "ddg_snippet": "Another popular alternative to handle the lack of reward functions is called Preference -based Reinforcement Learn-ing ( PbRL ) (Busa-Fekete et al., 2014; Wirth et al., 2017). In PbRL , instead of observing the reward information on the encountered state-action pairs, the agent only receives 1 bit preference feedback over a trajectory pair from an expert or a human overseer. Such preference ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v162/chen22ag/chen22ag.pdf", "content": "Another popular alternative to handle the lack of reward functions is called Preference -based Reinforcement Learn-ing ( PbRL ) (Busa-Fekete et al., 2014; Wirth et al., 2017). In PbRL , instead of observing the reward information on the encountered state-action pairs, the agent only receives 1 bit preference feedback over a trajectory pair from an expert or a human overseer. Such preference ..."}
+{"idx": 5, "title": "PDF A Survey of Preference-Based Reinforcement Learning Methods", "date": "", "ddg_snippet": "Preference -based reinforcement learning ( PbRL ) is a paradigm for learning from non-numerical feedback in sequential domains. Its key idea is that the requirement for a numer-ical feedback signal is replaced with the assumption of a preference -based feedback signal that indicates relative instead of absolute utility values.", "subpage_snippet": "", "source": "jmlr.org", "link": "https://jmlr.org/papers/volume18/16-634/16-634.pdf", "content": "Preference -based reinforcement learning ( PbRL ) is a paradigm for learning from non-numerical feedback in sequential domains. Its key idea is that the requirement for a numer-ical feedback signal is replaced with the assumption of a preference -based feedback signal that indicates relative instead of absolute utility values."}
+{"idx": 6, "title": "RA-PbRL: Provably Efficient Risk-Aware Preference-Based...", "date": "", "ddg_snippet": "To address this, we explore and prove the applicability of two risk -aware objectives to PbRL : nested and static quantile risk objectives. We also introduce Risk -AwarePbRL ( RA-PbRL ), an algorithm designed to optimize both nested and static objectives.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=JNDcFOczOf", "content": "To address this, we explore and prove the applicability of two risk -aware objectives to PbRL : nested and static quantile risk objectives. We also introduce Risk -AwarePbRL ( RA-PbRL ), an algorithm designed to optimize both nested and static objectives."}
+{"idx": 7, "title": "Advances in Preference-based Reinforcement Learning: A Review", "date": "", "ddg_snippet": "Reinforcement Learning (RL) algorithms suffer from the dependency on accurately engineered reward functions to properly guide the learning agents to do the required tasks. Preference -based reinforcement learning ( PbRL ) addresses that by utilizing human preferences as feedback from the experts instead of numeric rewards. Due to its promising advantage over traditional RL, PbRL has gained more ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/9945333", "content": "Reinforcement Learning (RL) algorithms suffer from the dependency on accurately engineered reward functions to properly guide the learning agents to do the required tasks. Preference -based reinforcement learning ( PbRL ) addresses that by utilizing human preferences as feedback from the experts instead of numeric rewards. Due to its promising advantage over traditional RL, PbRL has gained more ..."}
+{"idx": 8, "title": "RA-PbRL: Provably Efficient Risk-Aware Preference-Based Reinforcement ...", "date": "", "ddg_snippet": "To address this, we explore and prove the applicability of two risk -aware objectives to PbRL : nested and static quantile risk objectives. We also introduce Risk -Aware- PbRL ( RA-PbRL ), an algorithm designed to optimize both nested and static objectives.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.23569v1", "content": "To address this, we explore and prove the applicability of two risk -aware objectives to PbRL : nested and static quantile risk objectives. We also introduce Risk -Aware- PbRL ( RA-PbRL ), an algorithm designed to optimize both nested and static objectives."}
+{"idx": 9, "title": "Risk-Aware Preference-baser Reinforcement Learning (RA-PbRL)", "date": "", "ddg_snippet": "RA-PbRL is a type of Policy-Iteration and \"Confidence Bound\" reinforcement learning algorithm designed for preference -based reinforcement learning while maximizing risk -awareness through Value-at- Risk penalties.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/aguilarjose11/PbRLNeurips", "content": "RA-PbRL is a type of Policy-Iteration and \"Confidence Bound\" reinforcement learning algorithm designed for preference -based reinforcement learning while maximizing risk -awareness through Value-at- Risk penalties."}

data/sampled_jsons/SAFE_pruning_u_{k+1}_=_u_k_+_dual_variable_update_formula.jsonl

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+{"idx": 0, "title": "SAFE: Finding Sparse and Flat Minima to Improve Pruning", "date": "", "ddg_snippet": "by D Lee · 2025 — SAFE , on the other hand, delivers competitive performance without relying on these additional techniques, highlighting the intrinsic ef-.", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/pdf/2506.06866", "content": "by D Lee · 2025 — SAFE , on the other hand, delivers competitive performance without relying on these additional techniques, highlighting the intrinsic ef-."}
+{"idx": 1, "title": "Dynamic Grammar Pruning for Program Size Reduction in ...", "date": "", "ddg_snippet": "by MS Ali · 2023 · Cited by 2 — This research work discusses our approach to automatically reduce a bloated grammar. By utilizing a simple Production Ranking mechanism, we identify ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s42979-023-01840-y", "content": "by MS Ali · 2023 · Cited by 2 — This research work discusses our approach to automatically reduce a bloated grammar. By utilizing a simple Production Ranking mechanism, we identify ..."}
+{"idx": 2, "title": "My Blog Posts, in Reverse Chronological Order - Seita's Place", "date": "", "ddg_snippet": "I find it easiest to remember this rule by expanding out the equations for binary random variables . Let's say they taken on values 0 and 1 with probability a ...", "subpage_snippet": "", "source": "danieltakeshi.github.io", "link": "https://danieltakeshi.github.io/page14/", "content": "I find it easiest to remember this rule by expanding out the equations for binary random variables . Let's say they taken on values 0 and 1 with probability a ..."}
+{"idx": 3, "title": "Programming Z3", "date": "", "ddg_snippet": "by N Bjørner · Cited by 94 — The new tableau updates the assignment of variables to $x = 0, s_1 = 2, s_2 = 4, y = 2$ . The resulting assignment is a model for the original formula . 3.2.2.", "subpage_snippet": "", "source": "theory.stanford.edu", "link": "https://theory.stanford.edu/~nikolaj/programmingz3.html", "content": "by N Bjørner · Cited by 94 — The new tableau updates the assignment of variables to $x = 0, s_1 = 2, s_2 = 4, y = 2$ . The resulting assignment is a model for the original formula . 3.2.2."}
+{"idx": 4, "title": "Optimization Methods for Large-Scale Machine Learning", "date": "", "ddg_snippet": "This paper provides a review and commentary on the past, present, and future of numerical optimization algorithms in the context of machine learning ...", "subpage_snippet": "", "source": "epubs.siam.org", "link": "https://epubs.siam.org/doi/10.1137/16M1080173", "content": "This paper provides a review and commentary on the past, present, and future of numerical optimization algorithms in the context of machine learning ..."}
+{"idx": 5, "title": "A Unified Learning-based Optimization Framework for 0-1 ...", "date": "", "ddg_snippet": "4 days ago — To this end, in this paper, a unified framework that combines RL and optimization theory is proposed to solve 0- 1 mixed optimization problems in ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.12664v1", "content": "4 days ago — To this end, in this paper, a unified framework that combines RL and optimization theory is proposed to solve 0- 1 mixed optimization problems in ..."}
+{"idx": 6, "title": "Optimization Bootcamp - faculty.washington.edu", "date": "", "ddg_snippet": "This chapter provides an introductory overview of key concepts in optimization, as well as several motivating applications. Although this chapter is not ...", "subpage_snippet": "", "source": "faculty.washington.edu", "link": "https://faculty.washington.edu/sbrunton/OptimizationBootcamp.pdf", "content": "This chapter provides an introductory overview of key concepts in optimization, as well as several motivating applications. Although this chapter is not ..."}
+{"idx": 7, "title": "Leveraging self attention driven gated recurrent unit with ...", "date": "", "ddg_snippet": "by MA Alohali · 2025 — This study proposes a Self-Attention Mechanism-Driven Federated Learning for Secure Cyberattack Detection with Crocodile Optimization Algorithm (SAMFL-SCDCOA) ...", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41598-025-99452-4", "content": "by MA Alohali · 2025 — This study proposes a Self-Attention Mechanism-Driven Federated Learning for Secure Cyberattack Detection with Crocodile Optimization Algorithm (SAMFL-SCDCOA) ..."}
+{"idx": 8, "title": "Parabolic optimal control problems with combinatorial ...", "date": "", "ddg_snippet": "by C Buchheim · 2025 · Cited by 9 — In this paper, we present a branch-and-bound approach for solving parabolic optimal control problems with combinatorial switching constraints to global ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s10589-025-00654-3", "content": "by C Buchheim · 2025 · Cited by 9 — In this paper, we present a branch-and-bound approach for solving parabolic optimal control problems with combinatorial switching constraints to global ..."}
+{"idx": 9, "title": "Asymptotic speedup via effect handlers", "date": "", "ddg_snippet": "by D HILLERSTRÖM · 2024 — We study a fundamental efficiency benefit afforded by delimited control, showing that for certain higher-order functions, a language with ...", "subpage_snippet": "", "source": "www.cambridge.org", "link": "https://www.cambridge.org/core/journals/journal-of-functional-programming/article/asymptotic-speedup-via-effect-handlers/296879DE2FD96FB6CF388F27978C76E4", "content": "by D HILLERSTRÖM · 2024 — We study a fundamental efficiency benefit afforded by delimited control, showing that for certain higher-order functions, a language with ..."}

data/sampled_jsons/Self-Supervised_Pretraining_Matters_on_Imagined_Base_Set_for_VLM-based_Few-shot_Learning_PDF_arXiv_year_2024.jsonl

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+{"idx": 0, "title": "Scaling Inference-Time Search with Vision Value Model for", "date": "", "ddg_snippet": "... generated by VisVM-guided search can be leveraged as high-quality SFT data, forming a robust self -training pipeline that significantly enhances VLM ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.03704v3", "content": "... generated by VisVM-guided search can be leveraged as high-quality SFT data, forming a robust self -training pipeline that significantly enhances VLM ..."}
+{"idx": 1, "title": "Ch. 21 - Imitation Learning", "date": "", "ddg_snippet": "My own journey with imitation learning started with a project led by Pete Florence and Lucas Manuelli on using a particular form of self - supervised ...", "subpage_snippet": "", "source": "underactuated.mit.edu", "link": "https://underactuated.mit.edu/imitation.html", "content": "My own journey with imitation learning started with a project led by Pete Florence and Lucas Manuelli on using a particular form of self - supervised ..."}
+{"idx": 2, "title": "Credit Builder: Build Credit & Add to Your Savings with Self", "date": "", "ddg_snippet": "Build your credit with Self 's Credit Builder Account & secured Self Visa® Credit Card. Ideal for credit building, no hard check, & reports to all three bureaus.", "subpage_snippet": "", "source": "www.self.inc", "link": "https://www.self.inc/", "content": "Build your credit with Self 's Credit Builder Account & secured Self Visa® Credit Card. Ideal for credit building, no hard check, & reports to all three bureaus."}
+{"idx": 3, "title": "Log In To Your Self Financial Account", "date": "", "ddg_snippet": "Use this page to access your account at Self Financial, Inc., formerly known as Self Lender.", "subpage_snippet": "", "source": "www.self.inc", "link": "https://www.self.inc/login", "content": "Use this page to access your account at Self Financial, Inc., formerly known as Self Lender."}
+{"idx": 4, "title": "Sign Up For Your Credit Builder Account - Self", "date": "", "ddg_snippet": "Use Self to build credit while you save! Your Self Credit Builder Account includes credit education to help you reach your goals.", "subpage_snippet": "", "source": "www.self.inc", "link": "https://www.self.inc/signup", "content": "Use Self to build credit while you save! Your Self Credit Builder Account includes credit education to help you reach your goals."}
+{"idx": 5, "title": "How can we help? - support.self.inc", "date": "", "ddg_snippet": "Self Financial Public Community Home Back to Self .inc | Support: 1 (877) 883-0999", "subpage_snippet": "", "source": "support.self.inc", "link": "https://support.self.inc/s/", "content": "Self Financial Public Community Home Back to Self .inc | Support: 1 (877) 883-0999"}
+{"idx": 6, "title": "Credit Builder Loans to Build Credit | Self", "date": "", "ddg_snippet": "Elevate your credit score with Self 's Credit Builder Account. Accessible options to build credit, no credit check, & reports to all bureaus.", "subpage_snippet": "", "source": "www.self.inc", "link": "https://www.self.inc/credit-builder-loan", "content": "Elevate your credit score with Self 's Credit Builder Account. Accessible options to build credit, no credit check, & reports to all bureaus."}
+{"idx": 7, "title": "The Self Card — unlocked for everyone.", "date": "", "ddg_snippet": "Explore the secured Self Visa® Credit Card. Build your credit & payment history, plus no hard pull.", "subpage_snippet": "", "source": "www.self.inc", "link": "https://www.self.inc/visa-secured-credit-card", "content": "Explore the secured Self Visa® Credit Card. Build your credit & payment history, plus no hard pull."}
+{"idx": 8, "title": "Leading Credit Building Company - About Self Financial", "date": "", "ddg_snippet": "Hi. We're Self . We're here to help you build credit and savings and reach your financial goals. It all started with some missed payments. Self began in 2015 after a mistake with Founder James Garvey’s credit card that tanked his credit score.", "subpage_snippet": "", "source": "www.self.inc", "link": "https://www.self.inc/about", "content": "Hi. We're Self . We're here to help you build credit and savings and reach your financial goals. It all started with some missed payments. Self began in 2015 after a mistake with Founder James Garvey’s credit card that tanked his credit score."}
+{"idx": 9, "title": "Self - Credit Builder Loans by Self - Credit Building App Online", "date": "", "ddg_snippet": "Self helps you build credit with credit builder loans. A credit builder loan (or account) is a tiny loan that you have to save in a CD.", "subpage_snippet": "", "source": "www.self.inc", "link": "https://www.self.inc/home/dashboard", "content": "Self helps you build credit with credit builder loans. A credit builder loan (or account) is a tiny loan that you have to save in a CD."}

data/sampled_jsons/Statistical_Collusion_by_Collectives_Rs(k)_Hoeffding_error_term.jsonl

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+{"idx": 0, "title": "Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "As platforms increasingly rely on learning algorithms, collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data. To evaluate the potential impact of such behavior, it is essential to understand the computations that collectives must perform to impact platforms in this way. In particular ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.04879", "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. To evaluate the potential impact of such behavior, it is essential to understand the computations that collectives must perform to impact platforms in this way. In particular ..."}
+{"idx": 1, "title": "GitHub - GauthierE/statistical-collusion", "date": "", "ddg_snippet": "statistical-collusion This repository contains the code for reproducing the experiments and figures presented in the paper Statistical Collusion by Collectives on Learning Platforms.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/GauthierE/statistical-collusion", "content": "statistical-collusion This repository contains the code for reproducing the experiments and figures presented in the paper Statistical Collusion by Collectives on Learning Platforms."}
+{"idx": 2, "title": "Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "To ensure independence and correctly apply Hoeffding's inequality, we carefully split the datasets of size n e s t and the rest of the collective of size n n e s t. This is why the estimation term for Δ x in Theorem 3.7 is R (n n e s t), rather than R (n) in Theorem 3.3.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=46yLEXtav4", "content": "To ensure independence and correctly apply Hoeffding's inequality, we carefully split the datasets of size n e s t and the rest of the collective of size n n e s t. This is why the estimation term for Δ x in Theorem 3.7 is R (n n e s t), rather than R (n) in Theorem 3.3."}
+{"idx": 3, "title": "PDF Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "Statistical Collusion by Collectives on Learning Platforms Etienne Gauthier, Francis Bach, Michael I. Jordan (INRIA, Ecole Normale Supérieure) Numerous examples of collectives emerging to strategically influence platforms Uber drivers deactivate the app to create a supply shortage and drive up prices", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/media/icml-2025/Slides/47263.pdf", "content": "Statistical Collusion by Collectives on Learning Platforms Etienne Gauthier, Francis Bach, Michael I. Jordan (INRIA, Ecole Normale Supérieure) Numerous examples of collectives emerging to strategically influence platforms Uber drivers deactivate the app to create a supply shortage and drive up prices"}
+{"idx": 4, "title": "[论文审查] Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "论文详细介绍了实现上述目标的方法，包括但不限于利用 Hoeffding 不等式进行集中性估计，以及通过特定的算法获取成功的下界。", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/zh/review/statistical-collusion-by-collectives-on-learning-platforms", "content": "论文详细介绍了实现上述目标的方法，包括但不限于利用 Hoeffding 不等式进行集中性估计，以及通过特定的算法获取成功的下界。"}
+{"idx": 5, "title": "Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "A framework is developed that provides a theoretical and algorithmic treatment of the issues of a priori assessments of the effect of the collective before taking action and presents experimental results in a product evaluation domain. As platforms increasingly rely on learning algorithms, collectives may form and seek ways to influence these platforms to align with their own interests. This ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Statistical-Collusion-by-Collectives-on-Learning-Gauthier-Bach/1c45ef9ad56839c3309f0a0bdcff50fbb3ad73f5", "content": "A framework is developed that provides a theoretical and algorithmic treatment of the issues of a priori assessments of the effect of the collective before taking action and presents experimental results in a product evaluation domain. As platforms increasingly rely on learning algorithms, collectives may form and seek ways to influence these platforms to align with their own interests. This ..."}
+{"idx": 6, "title": "Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "Abstract As platforms increasingly rely on learning algorithms, collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data. To evaluate the potential impact of such behavior, it is essential to understand the computations that collectives must perform to impact platforms in this way. In ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.04879v1", "content": "Abstract As platforms increasingly rely on learning algorithms, collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data. To evaluate the potential impact of such behavior, it is essential to understand the computations that collectives must perform to impact platforms in this way. In ..."}
+{"idx": 7, "title": "\"Statistical Collusion by Collectives on Learning Platforms.\"", "date": "", "ddg_snippet": "Bibliographic details on Statistical Collusion by Collectives on Learning Platforms.", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/journals/corr/abs-2502-04879", "content": "Bibliographic details on Statistical Collusion by Collectives on Learning Platforms."}
+{"idx": 8, "title": "学習プラットフォーム上の集団による統計的コリューション（Statistical Collusion by Collectives on ...", "date": "", "ddg_snippet": "1. どんなもの? 「 Statistical Collusion by Collectives on Learning Platforms」は、現代の学習プラットフォームにおいて増加する集団的な操作行為、すなわちコリューションについて研究した論文です。この研究は、プラットフォームが採用する学習アルゴリズムに対する影響を目的に、組織化された多数の ...", "subpage_snippet": "", "source": "aibr.jp", "link": "https://aibr.jp/2025/08/30/学習プラットフォーム上の集団による統計的コリ/", "content": "1. どんなもの? 「 Statistical Collusion by Collectives on Learning Platforms」は、現代の学習プラットフォームにおいて増加する集団的な操作行為、すなわちコリューションについて研究した論文です。この研究は、プラットフォームが採用する学習アルゴリズムに対する影響を目的に、組織化された多数の ..."}
+{"idx": 9, "title": "Statistical Collusion by Collectives on Learning Platfor...", "date": "", "ddg_snippet": "This paper talks about how groups of people can work together to change the way online platforms use data and learning algorithms to benefit their interests. The authors created a method that help...", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/icml/46504/paper", "content": "This paper talks about how groups of people can work together to change the way online platforms use data and learning algorithms to benefit their interests. The authors created a method that help..."}

data/sampled_jsons/TCE_Li_et_al_2024_episodic_reinforcement_learning_abstract.jsonl

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+{"idx": 0, "title": "TOP-ERL: Transformer-based Off-Policy Episodic Reinforcement Learning", "date": "", "ddg_snippet": "This work introduces Transformer-based Off-Policy Episodic Reinforcement Learning (TOP-ERL), a novel algorithm that enables off-policy updates in the ERL framework. In ERL, policies predict entire action trajectories over multiple time steps instead of single actions at every time step. These trajectories are typically parameterized by trajectory generators such as Movement Primitives (MP ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.09536", "content": "This work introduces Transformer-based Off-Policy Episodic Reinforcement Learning (TOP-ERL), a novel algorithm that enables off-policy updates in the ERL framework. In ERL, policies predict entire action trajectories over multiple time steps instead of single actions at every time step. These trajectories are typically parameterized by trajectory generators such as Movement Primitives (MP ..."}
+{"idx": 1, "title": "Temporally Correlated Episodic Reinforcement Learning, ICLR 24", "date": "", "ddg_snippet": "In this work, we introduce a novel ERL algorithm, Temporally-Correlated Episodic RL ( TCE ), which effectively utilizes step information in episodic policy updates, opening the 'black box' in existing ERL methods while retaining the smooth and consistent exploration in parameter space.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/BruceGeLi/TCE_RL", "content": "In this work, we introduce a novel ERL algorithm, Temporally-Correlated Episodic RL ( TCE ), which effectively utilizes step information in episodic policy updates, opening the 'black box' in existing ERL methods while retaining the smooth and consistent exploration in parameter space."}
+{"idx": 2, "title": "TOP-ERL: TRANSFORMER BASED OFF-POLICY E REINFORCEMENT LEARNING - arXiv.org", "date": "", "ddg_snippet": "ABSTRACT This work introduces Transformer-based Off-Policy Episodic Reinforcement Learning (TOP-ERL), a novel algorithm that enables off-policy updates in an ERL framework. In ERL, policies predict entire action trajectories over multiple time steps instead of single per-step actions. These trajectories are typically parame-terized by trajectory generators such as Movement Primitives (MP ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.09536v2", "content": "ABSTRACT This work introduces Transformer-based Off-Policy Episodic Reinforcement Learning (TOP-ERL), a novel algorithm that enables off-policy updates in an ERL framework. In ERL, policies predict entire action trajectories over multiple time steps instead of single per-step actions. These trajectories are typically parame-terized by trajectory generators such as Movement Primitives (MP ..."}
+{"idx": 3, "title": "PDF arXiv:2401.11437v1 [cs.LG] 21 Jan 2024", "date": "", "ddg_snippet": "1 INTRODUCTION By considering how policies interact with the environment, reinforcement learning (RL) methodolo-gies can be classified into two distinct categories: step-based RL (SRL) and episodic RL (ERL). SRL predicts actions for each perceived state, while ERL selects an entire behavioral sequence at the start of an episode. Most predominant deep RL methods, such as PPO (Schulman et al ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2401.11437.pdf", "content": "1 INTRODUCTION By considering how policies interact with the environment, reinforcement learning (RL) methodolo-gies can be classified into two distinct categories: step-based RL (SRL) and episodic RL (ERL). SRL predicts actions for each perceived state, while ERL selects an entire behavioral sequence at the start of an episode. Most predominant deep RL methods, such as PPO (Schulman et al ..."}
+{"idx": 4, "title": "TOP-ERL: TRANSFORMER BASED OFF-POLICY E REINFORCEMENT LEARNING - OpenReview", "date": "", "ddg_snippet": "ABSTRACT This work introduces Transformer-based Off-Policy Episodic Reinforcement Learning (TOP-ERL), a novel algorithm that enables off-policy updates in the ERL framework. In ERL, policies predict entire action trajectories over multiple time steps instead of single actions at every time step. These trajectories are typ-ically parameterized by trajectory generators such as Movement ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=fPHT0z4cS5", "content": "ABSTRACT This work introduces Transformer-based Off-Policy Episodic Reinforcement Learning (TOP-ERL), a novel algorithm that enables off-policy updates in the ERL framework. In ERL, policies predict entire action trajectories over multiple time steps instead of single actions at every time step. These trajectories are typ-ically parameterized by trajectory generators such as Movement ..."}
+{"idx": 5, "title": "TOP-ERL: Transformer-based Off-Policy Episodic Reinforcement Learning", "date": "", "ddg_snippet": "This work introduces Transformer-based Off-Policy Episodic Reinforcement Learning (TOP-ERL), a novel algorithm that enables off-policy updates in the ERL framework. In ERL, policies predict entire action trajectories over multiple time steps instead of single actions at every time step.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=N4NhVN30ph", "content": "This work introduces Transformer-based Off-Policy Episodic Reinforcement Learning (TOP-ERL), a novel algorithm that enables off-policy updates in the ERL framework. In ERL, policies predict entire action trajectories over multiple time steps instead of single actions at every time step."}
+{"idx": 6, "title": "Temporally extended successor feature neural episodic control", "date": "", "ddg_snippet": "Abstract One of the long-term goals of reinforcement learning is to build intelligent agents capable of rapidly learning and flexibly transferring skills, similar to humans and animals.", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41598-024-65687-w", "content": "Abstract One of the long-term goals of reinforcement learning is to build intelligent agents capable of rapidly learning and flexibly transferring skills, similar to humans and animals."}
+{"idx": 7, "title": "Open the Black Box: Step-based Policy Updates for...", "date": "", "ddg_snippet": "As a means of addressing this, the paper proposes temporally-correlated episodic RL ( TCE ) that employs a trajectory-based policy representation based on probabilistic dynamic movement primitives (ProDMP) [ Li et al . 2023].", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=mnipav175N", "content": "As a means of addressing this, the paper proposes temporally-correlated episodic RL ( TCE ) that employs a trajectory-based policy representation based on probabilistic dynamic movement primitives (ProDMP) [ Li et al . 2023]."}
+{"idx": 8, "title": "Open the Black Box: Step-based Policy Updates for Temporally-Correlated ...", "date": "", "ddg_snippet": "In this work, we introduce a novel ERL algorithm, Temporally-Correlated Episodic RL ( TCE ), which effectively utilizes step information in episodic policy updates, opening the 'black box' in existing ERL methods while retaining the smooth and consistent exploration in parameter space.", "subpage_snippet": "", "source": "proceedings.iclr.cc", "link": "https://proceedings.iclr.cc/paper_files/paper/2024/hash/52da50b1ef221e4b1793e3bf44dd973d-Abstract-Conference.html", "content": "In this work, we introduce a novel ERL algorithm, Temporally-Correlated Episodic RL ( TCE ), which effectively utilizes step information in episodic policy updates, opening the 'black box' in existing ERL methods while retaining the smooth and consistent exploration in parameter space."}
+{"idx": 9, "title": "Latent Space Exploration and Trajectory Space Update in Temporally ...", "date": "", "ddg_snippet": "In this work, we introduce a novel ERL algorithm, Temporally-Correlated Episodic RL ( TCE ), which effectively utilizes step information in episodic policy updates, opening the 'black box' in existing ERL methods while retaining the smooth and consistent exploration in parameter space.", "subpage_snippet": "", "source": "rudolf.intuitive-robots.net", "link": "https://rudolf.intuitive-robots.net/publication/li-latentspaceexploration-2024-ws/", "content": "In this work, we introduce a novel ERL algorithm, Temporally-Correlated Episodic RL ( TCE ), which effectively utilizes step information in episodic policy updates, opening the 'black box' in existing ERL methods while retaining the smooth and consistent exploration in parameter space."}

data/sampled_jsons/Theorem_3.1_lower_bound_Catoni_Contextual_Bandits_are_Robust_to_Heavy-tailed_Rewards.jsonl

@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Catoni Contextual Bandits are Robust to Heavy - tailed Rewards", "date": "", "ddg_snippet": "OLS (Pacchiano, 2024) Catoni -OFUL ( Theorem 3 .4). Function Type Linear. Linear Non-linear Non-linear.We start with a minimax lower bound for the class of multi-armed bandit problems where the variance of each action’s reward is known to the learner. Theorem 3 . 1 .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=5IpVe9PH14", "content": "OLS (Pacchiano, 2024) Catoni -OFUL ( Theorem 3 .4). Function Type Linear. Linear Non-linear Non-linear.We start with a minimax lower bound for the class of multi-armed bandit problems where the variance of each action’s reward is known to the learner. Theorem 3 . 1 ."}
+{"idx": 1, "title": "Nordhaus--Gaddum type bounds for the complement rank", "date": "", "ddg_snippet": "4 days ago · In Section 2 we prove a Nordhaus–Gaddum type multiplicative lower bound for the complement rank. In Section 3 we establish an additive lower bound . Section 4 presents two strengthened versions of the multiplicative lower bound . Finally, in Section 5 we discuss the corresponding upper bounds for both the product and the sum.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2509.11368", "content": "4 days ago · In Section 2 we prove a Nordhaus–Gaddum type multiplicative lower bound for the complement rank. In Section 3 we establish an additive lower bound . Section 4 presents two strengthened versions of the multiplicative lower bound . Finally, in Section 5 we discuss the corresponding upper bounds for both the product and the sum."}
+{"idx": 2, "title": "The Stats Map · Bounded Difference Inequalities", "date": "", "ddg_snippet": "These are concentration inequalities that concern functions of random variables that obey a bounded differences condition.", "subpage_snippet": "", "source": "thestatsmap.com", "link": "https://thestatsmap.com/bounded-difference-inequalities", "content": "These are concentration inequalities that concern functions of random variables that obey a bounded differences condition."}
+{"idx": 3, "title": "Heavy-Tailed Linear Bandits: Huber Regression with One-Pass", "date": "", "ddg_snippet": "Table 1 : Comparisons of our regret bounds and computational complexity to previous best-known results for heavy-tailed linear bandits .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.00419v1", "content": "Table 1 : Comparisons of our regret bounds and computational complexity to previous best-known results for heavy-tailed linear bandits ."}
+{"idx": 4, "title": "The Stats Map · Berry-Esseen Bounds", "date": "", "ddg_snippet": "KS ( B n 1 i ≤ n ∑ ( X i − μ i ) , N ( 0 , 1 ) ) ≤ ( B n 2 ) 3 /2 C ∑ i ≤ n E ∣ X i − μ i ...", "subpage_snippet": "", "source": "thestatsmap.com", "link": "https://thestatsmap.com/Berry-Esseen-bounds", "content": "KS ( B n 1 i ≤ n ∑ ( X i − μ i ) , N ( 0 , 1 ) ) ≤ ( B n 2 ) 3 /2 C ∑ i ≤ n E ∣ X i − μ i ..."}
+{"idx": 5, "title": "ANOTEONESTIMATESFORTHESPECTRALRADIUSOFA", "date": "", "ddg_snippet": "Abstract. Utilizing the concept of Perron complement, a new estimate for the spectral radius of a nonnegative irreducible matrix is presented. A new matrix is derived that preserves the spec-tral radius while its minimum row sum increases and its maximum row sum decreases. Numerical examples are provided to illustrate the effectiveness of this approach.", "subpage_snippet": "", "source": "www.emis.de", "link": "https://www.emis.de/ft/15388", "content": "Abstract. Utilizing the concept of Perron complement, a new estimate for the spectral radius of a nonnegative irreducible matrix is presented. A new matrix is derived that preserves the spec-tral radius while its minimum row sum increases and its maximum row sum decreases. Numerical examples are provided to illustrate the effectiveness of this approach."}
+{"idx": 6, "title": "AND arXiv:1510.08562v1 [math.OC] 29 Oct 2015 - ResearchGate", "date": "", "ddg_snippet": "lower bound for c (need to choose R > 1/c). The following example illustrates that the convergence can be slow when R is not properly adjusted to c. Similar issues with 1/k-decay step sizes are ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/profile/Mert-Guerbuezbalaban/publication/283334924_Convergence_Rate_of_Incremental_Gradient_and_Incremental_Newton_Methods/links/57d7ff7508ae6399a39905e7/Convergence-Rate-of-Incremental-Gradient-and-Incremental-Newton-Methods.pdf", "content": "lower bound for c (need to choose R > 1/c). The following example illustrates that the convergence can be slow when R is not properly adjusted to c. Similar issues with 1/k-decay step sizes are ..."}
+{"idx": 7, "title": "Global and Non-Global Solutions for Pseudo-Parabolic Equation ...", "date": "", "ddg_snippet": "In Section 4, we discuss the lower bound for blow-up time under J(u0) < d, and I(u0) < 0. In Section 5, we consider the non-blow up case and give the precise exponential decay estimate with some conditions. 2 Notation This section introduces some basic definitions and notations for problem (1.1).", "subpage_snippet": "", "source": "global-sci.com", "link": "https://global-sci.com/article/92153/download", "content": "In Section 4, we discuss the lower bound for blow-up time under J(u0) < d, and I(u0) < 0. In Section 5, we consider the non-blow up case and give the precise exponential decay estimate with some conditions. 2 Notation This section introduces some basic definitions and notations for problem (1.1)."}
+{"idx": 8, "title": "The Stats Map · Anytime-Valid P-Values", "date": "", "ddg_snippet": "Anytime-valid p-values are to p-values what e-processes are to e-values . ... Here P 0 is understood to be the probability under the null.", "subpage_snippet": "", "source": "thestatsmap.com", "link": "https://thestatsmap.com/anytime-valid-p-values", "content": "Anytime-valid p-values are to p-values what e-processes are to e-values . ... Here P 0 is understood to be the probability under the null."}
+{"idx": 9, "title": "The Stats Map · Anti-Concentration", "date": "", "ddg_snippet": "If concentration inequalities study how random variables concentrate around particular values, anti-concentration inequalities are lower bounds on ...", "subpage_snippet": "", "source": "thestatsmap.com", "link": "https://thestatsmap.com/anti-concentration", "content": "If concentration inequalities study how random variables concentrate around particular values, anti-concentration inequalities are lower bounds on ..."}

data/sampled_jsons/VBench_generative_evaluation_paper.jsonl

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+{"idx": 0, "title": "VBench: Comprehensive Benchmark Suite for Video ...", "date": "", "ddg_snippet": "by Z Huang · 2023 · Cited by 604 — We present VBench , a comprehensive benchmark suite that dissects video generation quality into specific, hierarchical, and disentangled dimensions.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2311.17982", "content": "by Z Huang · 2023 · Cited by 604 — We present VBench , a comprehensive benchmark suite that dissects video generation quality into specific, hierarchical, and disentangled dimensions."}
+{"idx": 1, "title": "Comprehensive Benchmark Suite for Video Generative Models", "date": "", "ddg_snippet": "by Z Huang · 2024 · Cited by 605 — Figure 2. VBench Evaluation Results of Video Generative . Models. We visualize the evaluation results of four video gen- eration models in 16 VBench dimensions. 12 pages", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2024/papers/Huang_VBench_Comprehensive_Benchmark_Suite_for_Video_Generative_Models_CVPR_2024_paper.pdf", "content": "by Z Huang · 2024 · Cited by 605 — Figure 2. VBench Evaluation Results of Video Generative . Models. We visualize the evaluation results of four video gen- eration models in 16 VBench dimensions. 12 pages"}
+{"idx": 2, "title": "VBench++: Comprehensive and Versatile Benchmark Suite ...", "date": "", "ddg_snippet": "by Z Huang · 2024 · Cited by 47 — We present VBench , a comprehensive benchmark suite that dissects video generation quality into specific, hierarchical, and disentangled dimensions.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2411.13503", "content": "by Z Huang · 2024 · Cited by 47 — We present VBench , a comprehensive benchmark suite that dissects video generation quality into specific, hierarchical, and disentangled dimensions."}
+{"idx": 3, "title": "[CVPR2024 Highlight] VBench - We Evaluate Video ...", "date": "", "ddg_snippet": "We propose VBench , a comprehensive benchmark suite for video generative models. We design a comprehensive and hierarchical Evaluation Dimension Suite.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Vchitect/VBench", "content": "We propose VBench , a comprehensive benchmark suite for video generative models. We design a comprehensive and hierarchical Evaluation Dimension Suite."}
+{"idx": 4, "title": "Comprehensive Benchmark Suite for Video Generative Models", "date": "", "ddg_snippet": "We present VBench , a comprehensive benchmark suite that dissects video generation quality into specific, hierarchical, and disentangled dimensions.", "subpage_snippet": "", "source": "vchitect.github.io", "link": "https://vchitect.github.io/VBench-project/", "content": "We present VBench , a comprehensive benchmark suite that dissects video generation quality into specific, hierarchical, and disentangled dimensions."}
+{"idx": 5, "title": "VBench-2.0: Advancing Video Generation Benchmark ...", "date": "", "ddg_snippet": "27 Mar 2025 — We introduce VBench-2.0 , a next-generation benchmark designed to automatically evaluate video generative models for their intrinsic faithfulness.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2503.21755", "content": "27 Mar 2025 — We introduce VBench-2.0 , a next-generation benchmark designed to automatically evaluate video generative models for their intrinsic faithfulness."}
+{"idx": 6, "title": "VBench: Comprehensive Benchmark Suite for Video ...", "date": "", "ddg_snippet": "Main Paper Figure 2. VBench Evaluation Results of. Video Generative Models - For each dimension, we map the maximum score achieved by one of the T2V models.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2024/supplemental/Huang_VBench_Comprehensive_Benchmark_CVPR_2024_supplemental.pdf", "content": "Main Paper Figure 2. VBench Evaluation Results of. Video Generative Models - For each dimension, we map the maximum score achieved by one of the T2V models."}
+{"idx": 7, "title": "VBench++: Comprehensive and Versatile Benchmark Suite ...", "date": "", "ddg_snippet": "20 Nov 2024 — We present VBench , a comprehensive benchmark suite that dissects video generation quality into specific, hierarchical, and disentangled dimensions.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2411.13503", "content": "20 Nov 2024 — We present VBench , a comprehensive benchmark suite that dissects video generation quality into specific, hierarchical, and disentangled dimensions."}
+{"idx": 8, "title": "Efficient and Promptable Evaluation Framework for Visual ...", "date": "", "ddg_snippet": "by F Zhang · 2025 · Cited by 8 — We evaluated 15 specific ability dimensions in VBench using our Evaluation Agent and compared its results against VBench in terms of conclusion ... of evaluation ... 22 pages", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.acl-long.374.pdf", "content": "by F Zhang · 2025 · Cited by 8 — We evaluated 15 specific ability dimensions in VBench using our Evaluation Agent and compared its results against VBench in terms of conclusion ... of evaluation ... 22 pages"}
+{"idx": 9, "title": "Video-Bench: Human-Aligned Video Generation Benchmark", "date": "", "ddg_snippet": "This benchmark represents the first attempt to systematically leverage MLLMs across all dimensions relevant to video generation assessment in generative models.", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/11094238", "content": "This benchmark represents the first attempt to systematically leverage MLLMs across all dimensions relevant to video generation assessment in generative models."}

data/sampled_jsons/Video-ColBERT_paper_MSVD_4_frames_per_second.jsonl

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+{"idx": 0, "title": "Frame rate - Wikipedia", "date": "", "ddg_snippet": "Frame rate, most commonly expressed in frame /s, frames per second or FPS , is typically the frequency at which consecutive images are captured or displayed. This definition applies to film and video cameras, computer animation, and motion capture syst...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Frame_rate", "content": "Frame rate, most commonly expressed in frame /s, frames per second or FPS , is typically the frequency at which consecutive images are captured or displayed. This definition applies to film and video cameras, computer animation, and motion capture syst..."}
+{"idx": 1, "title": "[2503.19009] Video-ColBERT: Contextualized Late Interaction ... Images Video-ColBERT: Contextualized Late Interaction for Text-to ... Causal Attention Transformer for Video Text Retrieval Exciting Research Alert: Video-ColBERT - A Breakthrough in ... Video-Captioning Evaluation Metric for Segments (VEMS): A ... CVPR 2025 Open Access Repository Example videos and corresponding captions from the MSVD (left ...", "date": "", "ddg_snippet": "Mar 24, 2025 · 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 ... View all For example, with SigLIP-B/16, VIDEO-COLBERT sets a new state-of-the-art on MSRVTT, MSVD and VATEX. The results using CLIP4Clip with an upgraded SigLIP model indicate that our performance gains are not solely attributed to the im-proved backbone, but rather that our two-level tokenwise interaction strategy provides an effective way to match ... Apr 26, 2025 · In order to reduce the computational and memory requirements, this paper utilized a down-sampling process of 4 frames per second for the videos in the MSVD , MSR-VTT, and LSMDC datasets. The results? Video-ColBERT achieves state-of-the-art performance on common benchmarks like MSR-VTT, MSVD , and VATEX, outperforming other bi-encoder methods while maintaining comparable model size. Thus VEMS evaluates captions at the seg-ment level. A novel dataset structure called MSVD -S, a modified version of the MSVD dataset consisting of captions for multiple segments in a single video has also been proposed. Based on the MSVD -S dataset with weighted frames , VEMS captions to video in a frame -wise man-ner. 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 . The proposed approach uses 3 4 frames per video and yields competitive performance over two benchmark datasets MSVD and MSR-VTT (in both English and Hindi).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.19009", "content": "Mar 24, 2025 · 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 ... View all For example, with SigLIP-B/16, VIDEO-COLBERT sets a new state-of-the-art on MSRVTT, MSVD and VATEX. The results using CLIP4Clip with an upgraded SigLIP model indicate that our performance gains are not solely attributed to the im-proved backbone, but rather that our two-level tokenwise interaction strategy provides an effective way to match ... Apr 26, 2025 · In order to reduce the computational and memory requirements, this paper utilized a down-sampling process of 4 frames per second for the videos in the MSVD , MSR-VTT, and LSMDC datasets. The results? Video-ColBERT achieves state-of-the-art performance on common benchmarks like MSR-VTT, MSVD , and VATEX, outperforming other bi-encoder methods while maintaining comparable model size. Thus VEMS evaluates captions at the seg-ment level. A novel dataset structure called MSVD -S, a modified version of the MSVD dataset consisting of captions for multiple segments in a single video has also been proposed. Based on the MSVD -S dataset with weighted frames , VEMS captions to video in a frame -wise man-ner. 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 . The proposed approach uses 3 4 frames per video and yields competitive performance over two benchmark datasets MSVD and MSR-VTT (in both English and Hindi)."}
+{"idx": 2, "title": "Video-ColBERT: Contextualized Late Interaction for Text-to ...", "date": "", "ddg_snippet": "For example, with SigLIP-B/16, VIDEO-COLBERT sets a new state-of-the-art on MSRVTT, MSVD and VATEX. The results using CLIP4Clip with an upgraded SigLIP model indicate that our performance gains are not solely attributed to the im-proved backbone, but rather that our two-level tokenwise interaction strategy provides an effective way to match ...", "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": "For example, with SigLIP-B/16, VIDEO-COLBERT sets a new state-of-the-art on MSRVTT, MSVD and VATEX. The results using CLIP4Clip with an upgraded SigLIP model indicate that our performance gains are not solely attributed to the im-proved backbone, but rather that our two-level tokenwise interaction strategy provides an effective way to match ..."}
+{"idx": 3, "title": "Causal Attention Transformer for Video Text Retrieval", "date": "", "ddg_snippet": "Apr 26, 2025 · In order to reduce the computational and memory requirements, this paper utilized a down-sampling process of 4 frames per second for the videos in the MSVD , MSR-VTT, and LSMDC datasets.", "subpage_snippet": "", "source": "ietresearch.onlinelibrary.wiley.com", "link": "https://ietresearch.onlinelibrary.wiley.com/doi/full/10.1049/ipr2.70093", "content": "Apr 26, 2025 · In order to reduce the computational and memory requirements, this paper utilized a down-sampling process of 4 frames per second for the videos in the MSVD , MSR-VTT, and LSMDC datasets."}
+{"idx": 4, "title": "Exciting Research Alert: Video-ColBERT - A Breakthrough in ...", "date": "", "ddg_snippet": "The results? Video-ColBERT achieves state-of-the-art performance on common benchmarks like MSR-VTT, MSVD , and VATEX, outperforming other bi-encoder methods while maintaining comparable model size.", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/posts/singhsidhukuldeep_exciting-research-alert-video-colbert-activity-7336927383549071360-eAP7", "content": "The results? Video-ColBERT achieves state-of-the-art performance on common benchmarks like MSR-VTT, MSVD , and VATEX, outperforming other bi-encoder methods while maintaining comparable model size."}
+{"idx": 5, "title": "Video-Captioning Evaluation Metric for Segments (VEMS): A ...", "date": "", "ddg_snippet": "Thus VEMS evaluates captions at the seg-ment level. A novel dataset structure called MSVD -S, a modified version of the MSVD dataset consisting of captions for multiple segments in a single video has also been proposed. Based on the MSVD -S dataset with weighted frames , VEMS captions to video in a frame -wise man-ner.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/s11042-023-17328-z.pdf", "content": "Thus VEMS evaluates captions at the seg-ment level. A novel dataset structure called MSVD -S, a modified version of the MSVD dataset consisting of captions for multiple segments in a single video has also been proposed. Based on the MSVD -S dataset with weighted frames , VEMS captions to video in a frame -wise man-ner."}
+{"idx": 6, "title": "Example videos and corresponding captions from the MSVD (left ...", "date": "", "ddg_snippet": "The proposed approach uses 3 4 frames per video and yields competitive performance over two benchmark datasets MSVD and MSR-VTT (in both English and Hindi).", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/Example-videos-and-corresponding-captions-from-the-MSVD-left-and-MSR-VTT-right_fig7_330171931", "content": "The proposed approach uses 3 4 frames per video and yields competitive performance over two benchmark datasets MSVD and MSR-VTT (in both English and Hindi)."}
+{"idx": 7, "title": "MSVD -Indonesian Benchmark ( Video Retrieval) | Papers With Code", "date": "", "ddg_snippet": "The current state-of-the-art on MSVD -Indonesian is X-CLIP (Cross-Lingual). See a full comparison of 1 papers with code.", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/sota/video-retrieval-on-msvd-indonesian?p=msvd-indonesian-a-benchmark-for-multimodal", "content": "The current state-of-the-art on MSVD -Indonesian is X-CLIP (Cross-Lingual). See a full comparison of 1 papers with code."}
+{"idx": 8, "title": "How many frames per second can the human eye really... | PC Gamer", "date": "", "ddg_snippet": "Two e- paper displays sitting side by side on a table.60 fps ? If you've ever debated framerates, the cognitive researchers we spoke to have some complex answers for you.", "subpage_snippet": "", "source": "www.pcgamer.com", "link": "https://www.pcgamer.com/how-many-frames-per-second-can-the-human-eye-really-see/", "content": "Two e- paper displays sitting side by side on a table.60 fps ? If you've ever debated framerates, the cognitive researchers we spoke to have some complex answers for you."}
+{"idx": 9, "title": "Volume Shader - GPU Performance Test with 3D Visualization", "date": "", "ddg_snippet": "Volume Shader measures FPS ( frames per second ), frame times, and basic GPU utilization during the 3D rendering test.", "subpage_snippet": "", "source": "volume-shader.com", "link": "https://volume-shader.com/", "content": "Volume Shader measures FPS ( frames per second ), frame times, and basic GPU utilization during the 3D rendering test."}

data/sampled_jsons/What_makes_unlearning_hard_and_what_to_do_about_it_C-Proxy_Zhao_2024.jsonl

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+{"idx": 0, "title": "What makes unlearning hard and what to do about it", "date": "", "ddg_snippet": "by K ZHAO · 2024 · Cited by 38 — Unlearning is hard because deep networks are non-convex , and the difficulty increases with entangled retain/forget sets and memorized forget set examples. 41 pages", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/16e18fa3b3add076c30f2a2598f03031-Paper-Conference.pdf", "content": "by K ZHAO · 2024 · Cited by 38 — Unlearning is hard because deep networks are non-convex , and the difficulty increases with entangled retain/forget sets and memorized forget set examples. 41 pages"}
+{"idx": 1, "title": "What makes unlearning hard and what to do about it", "date": "", "ddg_snippet": "3 Jun 2024 — We identify two key factors affecting unlearning difficulty and the performance of unlearning algorithms. Our evaluation on forget sets that ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.01257v1", "content": "3 Jun 2024 — We identify two key factors affecting unlearning difficulty and the performance of unlearning algorithms. Our evaluation on forget sets that ..."}
+{"idx": 2, "title": "What makes unlearning hard and what to do about it", "date": "", "ddg_snippet": "Unlearning is hard when the forget and retain sets are close , or when elements of the forget set are part of the model via memorization.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=QAbhLBF72K&referrer=[the+profile+of+Eleni+Triantafillou](/profile?id=~Eleni_Triantafillou1)", "content": "Unlearning is hard when the forget and retain sets are close , or when elements of the forget set are part of the model via memorization."}
+{"idx": 3, "title": "What makes unlearning hard and what to do about it", "date": "", "ddg_snippet": "We identify two key factors affecting unlearning difficulty and the performance of unlearning algorithms. Our evaluation on forget sets that isolate these ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/poster/95259", "content": "We identify two key factors affecting unlearning difficulty and the performance of unlearning algorithms. Our evaluation on forget sets that isolate these ..."}
+{"idx": 4, "title": "What makes unlearning hard and what to do about it", "date": "", "ddg_snippet": "30 Oct 2024 — Our investigation reveals that different unlearning algorithms suffer disproportionately as the difficulty level increases and surfaces ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.01257v2", "content": "30 Oct 2024 — Our investigation reveals that different unlearning algorithms suffer disproportionately as the difficulty level increases and surfaces ..."}
+{"idx": 5, "title": "[Quick Review] What makes unlearning hard and what to do about it", "date": "", "ddg_snippet": "This paper investigates factors influencing machine unlearning difficulty and algorithm performance, proposing a refined unlearning method to improve ...", "subpage_snippet": "", "source": "liner.com", "link": "https://liner.com/review/what-makes-unlearning-hard-and-what-to-do-about-it", "content": "This paper investigates factors influencing machine unlearning difficulty and algorithm performance, proposing a refined unlearning method to improve ..."}
+{"idx": 6, "title": "[Literature Review] What makes unlearning hard and what to do ...", "date": "", "ddg_snippet": "The paper \"What makes unlearning hard and what to do about it\" explores the emerging area of machine unlearning , which seeks to remove the influence of a ...", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/en/review/what-makes-unlearning-hard-and-what-to-do-about-it", "content": "The paper \"What makes unlearning hard and what to do about it\" explores the emerging area of machine unlearning , which seeks to remove the influence of a ..."}
+{"idx": 7, "title": "arXiv:2412.06966v1 [cs.LG] 9 Dec 2024 - James Grimmelmann", "date": "", "ddg_snippet": "9 Dec 2024 — Machine unlearning is a set of technical methods and here, as always, there are critical gaps—gaps that are too often overlooked—between what ...", "subpage_snippet": "", "source": "james.grimmelmann.net", "link": "https://james.grimmelmann.net/files/articles/unlearning-doesnt-do.pdf", "content": "9 Dec 2024 — Machine unlearning is a set of technical methods and here, as always, there are critical gaps—gaps that are too often overlooked—between what ..."}
+{"idx": 8, "title": "Leveraging Per-Instance Privacy for Machine Unlearning", "date": "", "ddg_snippet": "by NM Sepahvand — Recall that C-Proxy has been used in prior work by Zhao et al. (2024) to identify difficult to unlearn forget sets for certain unlearning algorithms. Figure 2 ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=0A4Y9qRnu9", "content": "by NM Sepahvand — Recall that C-Proxy has been used in prior work by Zhao et al. (2024) to identify difficult to unlearn forget sets for certain unlearning algorithms. Figure 2 ..."}
+{"idx": 9, "title": "[PDF] Towards Unbounded Machine Unlearning", "date": "", "ddg_snippet": "What makes unlearning hard and what to do about it · Kairan ZhaoMeghdad ... 2024. TLDR. This paper identifies two key factors affecting unlearning ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/570a341a8fd511cf0e05687110f053aaac646010", "content": "What makes unlearning hard and what to do about it · Kairan ZhaoMeghdad ... 2024. TLDR. This paper identifies two key factors affecting unlearning ..."}

data/sampled_jsons/X-CLIP_End-to-End_Multi-grained_Contrastive_Learning_for_Video-Text_Retrieval_Yiwei_Ma_abstract.jsonl

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+{"idx": 0, "title": "[2207.07285] X - CLIP : End - to - End Multi - grained Contrastive ...", "date": "", "ddg_snippet": "Title: X - CLIP : End - to - End Multi - grained Contrastive Learning for Video - Text Retrieval .To this end, this paper presents a novel multi - grained contrastive model, namely X - CLIP , for video - text retrieval .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2207.07285", "content": "Title: X - CLIP : End - to - End Multi - grained Contrastive Learning for Video - Text Retrieval .To this end, this paper presents a novel multi - grained contrastive model, namely X - CLIP , for video - text retrieval ."}
+{"idx": 1, "title": "(PDF) X - CLIP : End - to - End Multi - grained Contrastive Learning for ...", "date": "", "ddg_snippet": "To this end , this paper presents a novel multi - grained contrastive model, namely X - CLIP , for video - text retrieval .", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/papers/x-clip-end-to-end-multi-grained-contrastive-learning-for-1m1wfg4a", "content": "To this end , this paper presents a novel multi - grained contrastive model, namely X - CLIP , for video - text retrieval ."}
+{"idx": 2, "title": "xuguohai/ X - CLIP : An official implementation for \" X - CLIP : End - to - End ...\"...", "date": "", "ddg_snippet": "X - CLIP : End - to - End Multi - grained Contrastive Learning for Video - Text Retrieval . Introduction. X - CLIP adopts cross- grained contrastive learning and attention over similarity matrix module to filter out unnecessary information during video - text retrieval .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/xuguohai/X-CLIP", "content": "X - CLIP : End - to - End Multi - grained Contrastive Learning for Video - Text Retrieval . Introduction. X - CLIP adopts cross- grained contrastive learning and attention over similarity matrix module to filter out unnecessary information during video - text retrieval ."}
+{"idx": 3, "title": "X - CLIP : End - to - End Multi - grained Contrastive Learning for ...", "date": "", "ddg_snippet": "State-of-the-art video - text retrieval (VTR) methods usually fully fine-tune the pre-trained model (e.g. CLIP ) on specific datasets, which may suffer from substantial storage costs in practical applications since a separate model per task needs to be stored.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/364484274_X-CLIP_End-to-End_Multi-grained_Contrastive_Learning_for_Video-Text_Retrieval", "content": "State-of-the-art video - text retrieval (VTR) methods usually fully fine-tune the pre-trained model (e.g. CLIP ) on specific datasets, which may suffer from substantial storage costs in practical applications since a separate model per task needs to be stored."}
+{"idx": 4, "title": "X - CLIP : End - to - end Multi - grained Contrastive Learning For ...", "date": "", "ddg_snippet": "Video - text retrieval has been a crucial and fundamental task in multi-modal research.To this end , this paper presents a novel multi - grained contrastive model, namely X - CLIP , for video - text retrieval .", "subpage_snippet": "", "source": "learning2hash.github.io", "link": "https://learning2hash.github.io/publications/ma2022x/", "content": "Video - text retrieval has been a crucial and fundamental task in multi-modal research.To this end , this paper presents a novel multi - grained contrastive model, namely X - CLIP , for video - text retrieval ."}
+{"idx": 5, "title": "Yiwei Ma 马祎炜 - Google Scholar", "date": "", "ddg_snippet": "X - clip : End - to - end multi - grained contrastive learning for video - text retrieval .Beat: Bi-directional One-to-Many Embedding Alignment for Text-based Person Retrieval.", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=KIDY5pUAAAAJ&hl=en", "content": "X - clip : End - to - end multi - grained contrastive learning for video - text retrieval .Beat: Bi-directional One-to-Many Embedding Alignment for Text-based Person Retrieval."}
+{"idx": 6, "title": "ActivityNet Benchmark ( Video Retrieval ) | Papers With Code", "date": "", "ddg_snippet": "X - CLIP : End - to - End Multi - grained Contrastive Learning for Video - Text Retrieval .", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/sota/video-retrieval-on-activitynet", "content": "X - CLIP : End - to - End Multi - grained Contrastive Learning for Video - Text Retrieval ."}
+{"idx": 7, "title": "Improve video retrieval with text by comparing coarse and... | Medium", "date": "", "ddg_snippet": "… presents a novel multi - grained contrastive model, namely X - CLIP , for video - text retrieval .", "subpage_snippet": "", "source": "morris-lee.medium.com", "link": "https://morris-lee.medium.com/improve-video-retrieval-with-text-by-comparing-coarse-and-fine-features-with-x-clip-94c14e3c1db5", "content": "… presents a novel multi - grained contrastive model, namely X - CLIP , for video - text retrieval ."}
+{"idx": 8, "title": "A Unified framework based on Large-scale Momentum Contrastive ...", "date": "", "ddg_snippet": "X - CLIP : End - to - End Multi - grained Contrastive Learning for Video - Text Retrieval . Yiwei Ma , Guohai Xu, Xiaoshuai Sun, Ming Yan, Ji Zhang, Rongrong Ji.", "subpage_snippet": "", "source": "www.connectedpapers.com", "link": "https://www.connectedpapers.com/main/0c89ceb4b0ad930b23db4b9c52ce9798b6704530/A-Unified-framework-based-on-Large+scale-Momentum-Contrastive-learning-for-Text+Video-Retrieval/graph", "content": "X - CLIP : End - to - End Multi - grained Contrastive Learning for Video - Text Retrieval . Yiwei Ma , Guohai Xu, Xiaoshuai Sun, Ming Yan, Ji Zhang, Rongrong Ji."}
+{"idx": 9, "title": "Video – text retrieval via multi -modal masked transformer and adaptive...", "date": "", "ddg_snippet": "Despite significant advancements in deep learning-based video – text retrieval methods, three challenges persist: the alignment of fine-grained semanti. Ma , Y., Xu, G., Sun, X., et al: X - clip : End - to - end multi - grained contrastive learning for video - text retrieval .", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s00530-023-01205-8", "content": "Despite significant advancements in deep learning-based video – text retrieval methods, three challenges persist: the alignment of fine-grained semanti. Ma , Y., Xu, G., Sun, X., et al: X - clip : End - to - end multi - grained contrastive learning for video - text retrieval ."}

data/sampled_jsons/Zou_et_al._2023a_RepE.jsonl

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+{"idx": 0, "title": "Taxonomy, Opportunities, and Challenges of Representation", "date": "", "ddg_snippet": "Initial work on Activation Steering (Turner et al ., 2024 ; Li et al ., 2023a ) was built on the assumption that concepts are represented as linear ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.19649v3", "content": "Initial work on Activation Steering (Turner et al ., 2024 ; Li et al ., 2023a ) was built on the assumption that concepts are represented as linear ..."}
+{"idx": 1, "title": "Robust LLM safeguarding via refusal feature adversarial training", "date": "", "ddg_snippet": "Another recently emerged line of work applies representation engineering ( RepE ) ( Zou et al ., 2023a ) techniques to enhance model safety by directly ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2409.20089v2", "content": "Another recently emerged line of work applies representation engineering ( RepE ) ( Zou et al ., 2023a ) techniques to enhance model safety by directly ..."}
+{"idx": 2, "title": "A Representation Engineering Perspective on the Effectiveness", "date": "", "ddg_snippet": "... RepE ) is an emerging paradigm for understanding and controlling LMs that uses intermediate model representations as the primary unit of analysis ( Zou ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.02956v1", "content": "... RepE ) is an emerging paradigm for understanding and controlling LMs that uses intermediate model representations as the primary unit of analysis ( Zou ..."}
+{"idx": 3, "title": "Beyond Linear Steering: Unified Multi-Attribute Control for", "date": "", "ddg_snippet": "... et al ., 2023b ) learns a logistic regression model on the hidden states of positive vs negative examples, while \"representation engineering\" ( RepE ) ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.24535v2", "content": "... et al ., 2023b ) learns a logistic regression model on the hidden states of positive vs negative examples, while \"representation engineering\" ( RepE ) ..."}
+{"idx": 4, "title": "Enhanced Language Model Truthfulness with Learnable", "date": "", "ddg_snippet": "... using the ITI method and extend its application to unsupervised truthful directions detected through representation engineering ( RepE ) Zou et al .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.00301v3", "content": "... using the ITI method and extend its application to unsupervised truthful directions detected through representation engineering ( RepE ) Zou et al ."}
+{"idx": 5, "title": "[2310.01405] Representation Engineering: A Top-Down Approach to", "date": "", "ddg_snippet": "Authors: Andy Zou , Long Phan , Sarah Chen , James Campbell , Phillip Guo , Richard Ren , Alexander Pan , Xuwang Yin , Mantas Mazeika , Ann-Kathrin ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2310.01405", "content": "Authors: Andy Zou , Long Phan , Sarah Chen , James Campbell , Phillip Guo , Richard Ren , Alexander Pan , Xuwang Yin , Mantas Mazeika , Ann-Kathrin ..."}
+{"idx": 6, "title": "GeDi: Generative Discriminator Guided Sequence Generation", "date": "", "ddg_snippet": "We compare DETOXIGEN with a diverse set of previously reported baseline models (Gehman et al ., 2020;Liu et al ., 2021a), including Domain-Adaptive ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/357386978_GeDi_Generative_Discriminator_Guided_Sequence_Generation", "content": "We compare DETOXIGEN with a diverse set of previously reported baseline models (Gehman et al ., 2020;Liu et al ., 2021a), including Domain-Adaptive ..."}
+{"idx": 7, "title": "Contrastive Decoding: Open-ended Text Generation as", "date": "", "ddg_snippet": "Existing efforts (Shi et al ., 2024b;Jin et al ., 2024) to enhance the context-faithfulness of LLMs primarily focus on modifying decoding strategies or ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/372917594_Contrastive_Decoding_Open-ended_Text_Generation_as_Optimization", "content": "Existing efforts (Shi et al ., 2024b;Jin et al ., 2024) to enhance the context-faithfulness of LLMs primarily focus on modifying decoding strategies or ..."}
+{"idx": 8, "title": "Fine-Grained Interpretation of Political Opinions in Large", "date": "", "ddg_snippet": "... indicates the possibility of understanding and steering LLM behaviours by learning their internal feature representations (Turner et al .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.04774v1", "content": "... indicates the possibility of understanding and steering LLM behaviours by learning their internal feature representations (Turner et al ."}
+{"idx": 9, "title": "(PDF) NLPBench: Evaluating Large Language Models on Solving NLP", "date": "", "ddg_snippet": "PDF | Recent developments in large language models (LLMs) have shown promise in enhancing the capabilities of natural language processing (NLP ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/374170865_NLPBench_Evaluating_Large_Language_Models_on_Solving_NLP_Problems", "content": "PDF | Recent developments in large language models (LLMs) have shown promise in enhancing the capabilities of natural language processing (NLP ..."}

data/sampled_jsons/augmented_Lagrangian_method_dual_variable_update_u_{k+1}_=_u_k_+_rho.jsonl

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+{"idx": 0, "title": "Augmented Lagrangian method - Wikipedia", "date": "", "ddg_snippet": "The augmented Lagrangian is related to, but not identical with, the method of Lagrange multipliers. Viewed differently, the unconstrained objective is the Lagrangian of the constrained problem, with an additional penalty term (the augmentation).", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Augmented_Lagrangian_method", "content": "The augmented Lagrangian is related to, but not identical with, the method of Lagrange multipliers. Viewed differently, the unconstrained objective is the Lagrangian of the constrained problem, with an additional penalty term (the augmentation)."}
+{"idx": 1, "title": "PDF Chapter 7 Duality / augmented Lagrangian / ADMM", "date": "", "ddg_snippet": "The so-called linearized augmented Lagrangian method (LALM) is an alternative approach that replaces the expensive exact x update (7.13) with a proximal point update :", "subpage_snippet": "", "source": "web.eecs.umich.edu", "link": "https://web.eecs.umich.edu/~fessler/course/598/l/n-07-dual.pdf", "content": "The so-called linearized augmented Lagrangian method (LALM) is an alternative approach that replaces the expensive exact x update (7.13) with a proximal point update :"}
+{"idx": 2, "title": "PDF 21.1 Review of dual methods and Augmented Lagrangian method", "date": "", "ddg_snippet": "In dual method , the primal variable x is updated by minimize the Lagrangian , and the dual variable u is updated by the way of gradient ascent, as Ax b is the gradient of u.", "subpage_snippet": "", "source": "stat.cmu.edu", "link": "https://stat.cmu.edu/~ryantibs/convexopt-F16/scribes/admm-scribed.pdf", "content": "In dual method , the primal variable x is updated by minimize the Lagrangian , and the dual variable u is updated by the way of gradient ascent, as Ax b is the gradient of u."}
+{"idx": 3, "title": "Dual Descent ALM and ADMM - arXiv.org", "date": "", "ddg_snippet": "E(x) = 0, and proposed a double-looped inexact ALM (iALM), where the augmented Lagrangian relaxation is solved by the accelerated gradient method in [14], and then the dual variable is updated with a small step size, which ensures that the sequence of dual variables is uniformly bounded.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2109.13214", "content": "E(x) = 0, and proposed a double-looped inexact ALM (iALM), where the augmented Lagrangian relaxation is solved by the accelerated gradient method in [14], and then the dual variable is updated with a small step size, which ensures that the sequence of dual variables is uniformly bounded."}
+{"idx": 4, "title": "Multiplier updates for partially augmented lagrangian?", "date": "", "ddg_snippet": "The standard augmented lagrangian method typically uses the multiplier update $\\lambda_ {k+1} = \\lambda_k + \\rho_kc (x_ {k+1})$. However, my concern is that the derivation for this update depends on $\\nabla L (x_ {k+1},\\lambda_k)=0$. Here are some things I have looked into as an attempt to resolve my confusion:", "subpage_snippet": "", "source": "math.stackexchange.com", "link": "https://math.stackexchange.com/questions/4620607/multiplier-updates-for-partially-augmented-lagrangian", "content": "The standard augmented lagrangian method typically uses the multiplier update $\\lambda_ {k+1} = \\lambda_k + \\rho_kc (x_ {k+1})$. However, my concern is that the derivation for this update depends on $\\nabla L (x_ {k+1},\\lambda_k)=0$. Here are some things I have looked into as an attempt to resolve my confusion:"}
+{"idx": 5, "title": "PDF Revisiting Augmented Lagrangian Duals - Optimization Online", "date": "", "ddg_snippet": "Abstract For nonconvex optimization problems, possibly having mixed-integer variables , a convergent primal- dual solution algorithm is proposed. The approach applies a proximal bundle method to certain augmented Lagrangian dual that arises in the context of the so-called generalized augmented Lagrangians . We recast these Lagrangians into the framework of a classical Lagrangian , by means of a ...", "subpage_snippet": "", "source": "optimization-online.org", "link": "https://optimization-online.org/wp-content/uploads/2020/03/7709.pdf", "content": "Abstract For nonconvex optimization problems, possibly having mixed-integer variables , a convergent primal- dual solution algorithm is proposed. The approach applies a proximal bundle method to certain augmented Lagrangian dual that arises in the context of the so-called generalized augmented Lagrangians . We recast these Lagrangians into the framework of a classical Lagrangian , by means of a ..."}
+{"idx": 6, "title": "PDF 24.1 Overview 24.2 The augmented Lagrangian method", "date": "", "ddg_snippet": "This dual decomposition scheme generalizes trivially to an arbitrary number of collections of variables with linear coupling constraints. Key to the method is the observation that each subproblem in computing ^xi is entirely independent, which means that we can parallelize the solution of these subproblems and then gather each independent solution for the update to . This is entirely natural ...", "subpage_snippet": "", "source": "candes.su.domains", "link": "https://candes.su.domains/teaching/math301/Lectures/ALM.pdf", "content": "This dual decomposition scheme generalizes trivially to an arbitrary number of collections of variables with linear coupling constraints. Key to the method is the observation that each subproblem in computing ^xi is entirely independent, which means that we can parallelize the solution of these subproblems and then gather each independent solution for the update to . This is entirely natural ..."}
+{"idx": 7, "title": "Augmented Lagrangian methods | ManifoldFR", "date": "", "ddg_snippet": "In fact, under suitable assumptions the above update is equivalent to the dual proximal-point iteration: λ k + 1 = argmax λ min z L (z, λ) 1 2 μ ∣ λ λ k ∣ 2 2 ⏟: = L prox (z), λk+1 = λargmax zmin:=Lprox(z)L(z,λ)− 2μ1 ∣λ −λk∣22, as can be seen by interverting min min and max max and noticing taking the maximum over λ ...", "subpage_snippet": "", "source": "manifoldfr.github.io", "link": "https://manifoldfr.github.io/posts/auglag/", "content": "In fact, under suitable assumptions the above update is equivalent to the dual proximal-point iteration: λ k + 1 = argmax λ min z L (z, λ) 1 2 μ ∣ λ λ k ∣ 2 2 ⏟: = L prox (z), λk+1 = λargmax zmin:=Lprox(z)L(z,λ)− 2μ1 ∣λ −λk∣22, as can be seen by interverting min min and max max and noticing taking the maximum over λ ..."}
+{"idx": 8, "title": "Dual/Lagrangian Methods for Constrained Optimization", "date": "", "ddg_snippet": "2 y) is the Hessian of the Lagrangian function that is assumed to be positive definite at", "subpage_snippet": "", "source": "web.stanford.edu", "link": "https://web.stanford.edu/class/msande314/lecture12OPTMLDS", "content": "2 y) is the Hessian of the Lagrangian function that is assumed to be positive definite at"}
+{"idx": 9, "title": "Revisiting augmented Lagrangian duals | Mathematical Programming - Springer", "date": "", "ddg_snippet": "For nonconvex optimization problems, possibly having mixed-integer variables , a convergent primal- dual solution algorithm is proposed. The approach applies a proximal bundle method to certain augmented Lagrangian dual that arises in the context of the so-called generalized augmented Lagrangians . We recast these Lagrangians into the framework of a classical Lagrangian by means of a special ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s10107-021-01703-5", "content": "For nonconvex optimization problems, possibly having mixed-integer variables , a convergent primal- dual solution algorithm is proposed. The approach applies a proximal bundle method to certain augmented Lagrangian dual that arises in the context of the so-called generalized augmented Lagrangians . We recast these Lagrangians into the framework of a classical Lagrangian by means of a special ..."}

data/sampled_jsons/condition_number_blowup_coordinate-wise_private_median_regression_solution_quality.jsonl

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+{"idx": 0, "title": "Condition number - Wikipedia", "date": "", "ddg_snippet": "A problem with a low condition number is said to be well-conditioned, while a problem with a high condition number is said to be ill-conditioned. In non-mathematical terms, an ill-conditioned problem is one where, for a small change in the inputs (the independent variables) there is a large change in the answer or dependent variable.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Condition_number", "content": "A problem with a low condition number is said to be well-conditioned, while a problem with a high condition number is said to be ill-conditioned. In non-mathematical terms, an ill-conditioned problem is one where, for a small change in the inputs (the independent variables) there is a large change in the answer or dependent variable."}
+{"idx": 1, "title": "Median - Wikipedia", "date": "", "ddg_snippet": "The median of a set of numbers is the value separating the higher half from the lower half of a data sample, a population, or a probability distribution. For a data set, it may be thought of as the “middle\" value. The basic feature of the median in describing data compared to the mean...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Median", "content": "The median of a set of numbers is the value separating the higher half from the lower half of a data sample, a population, or a probability distribution. For a data set, it may be thought of as the “middle\" value. The basic feature of the median in describing data compared to the mean..."}
+{"idx": 2, "title": "Cause of a high condition number in a python statsmodels ...", "date": "", "ddg_snippet": "The condition number takes into account high sensitivity in either fitted parameter to the input data, hence the high condition number when all of the data are far to one side of x=0.", "subpage_snippet": "", "source": "stats.stackexchange.com", "link": "https://stats.stackexchange.com/questions/243000/cause-of-a-high-condition-number-in-a-python-statsmodels-regression", "content": "The condition number takes into account high sensitivity in either fitted parameter to the input data, hence the high condition number when all of the data are far to one side of x=0."}
+{"idx": 3, "title": "Notes On Median and Quantile Regression Condition estimation for regression and feature selection Condition number - Wikipedia Condition numbers of the generalized ridge regression and its ... Condition Number Analysis of Logistic Regression, and its ... Condition number - Wikipedia Condition number - Wikipedia Condition number - Wikipedia AE 4803 AIM: Course Notes - 6 Precision, conditioning, and ...", "date": "", "ddg_snippet": "It is well-known that the expected value of a random variable Y minimizes the expected squared deviation between Y and a constant; that is, See full list on eml.berkeley.edu Returning to the linear LAD estimator, while demonstration of consistency of ^ involves routine appli- cation of asymptotic arguments for extremum estimators, demonstration of pn-consistency and asymptotic normality is complicated by the fact that the LAD criterion S( ) is not continuously di¤erentiable in : For comparison, consider the standard th... See full list on eml.berkeley.edu pn can be established for this problem. This condition can be shown to hold by showing that each element of the subgradient of the LAD criterion, when evaluated at the minimizing value ^; is bounded in magnitude by the di¤erence between the right and left derivatives of the criterion, so that 1 n See full list on eml.berkeley.edu is di¤erentiable in = 0. The Taylor s series expansion would thus be applicable if the order of the expectation (over yi and xi) and di¤erentiation (over ) could somehow be interchanged. To do this rigorously, a stochastic equicontinuity condition on the sample average moment function See full list on eml.berkeley.edu Aug 1, 2020 · The regression of two sets of data points was considered in Example 3.1 and it was shown that a large value of the condition number κ 2 (X) does not imply that the solution β of (2) is sensitive to a perturbation in y. A problem with a low condition number is said to be well-conditioned, while a problem with a high condition number is said to be ill-conditioned. In non-mathematical terms, an ill-conditioned problem is one where, for a small change in the inputs (the independent variables) there is a large change in the answer or dependent variable. Abstract: In this paper, we considered the condition number theory of a new generalized ridge regression model. The explicit expressions of di erent types of condition numbers were derived to measure the ill-conditionness of the generalized ridge regression problem with respect to di erent circumstances. To overcome the computational di culty of computing the exact value of the condition ... A pair of condition numbers for the logistic regression problem: when the sample data is non-separable: a condition number for the degree of non-separability of the dataset informing the convergence guarantees of Greedy Coordinate Descent and Stochastic Gradient Descent (SGD) guarantees on reaching linear convergence (thanks to Bach) Is a problem with a low condition number ill conditioned? A problem with a low condition number is said to be well-conditioned , while a problem with a high condition number is said to be ill-conditioned. In non-mathematical terms, an ill-conditioned problem is one where, for a small change in the inputs (the independent variables) there is a large change in the answer or dependent variable. What happens if a condition number is normal? If is normal, then where and are maximal and minimal (by moduli) eigenvalues of respectively. The condition number with respect to L2 arises so often in numerical linear algebra that it is given a name, the condition number of a matrix. What happens if a condition number is exactly one? When the condition number is exactly one (which can only happen if A is a scalar multiple of a linear isometry), then a solution algorithm can find (in principle, meaning if the algorithm introduces no errors of its own) an approximation of the solution whose precision is no worse than that of the data. Define conditioning, the condition number of a problem, and the condition number of a matrix; describe the implications for computations in machine learning. Describe how conditioning challenges are exacerbated or mitigated by solving the normal equations and scaling data.", "subpage_snippet": "", "source": "eml.berkeley.edu", "link": "https://eml.berkeley.edu/~powell/e241a_sp10/qrnotes.pdf", "content": "It is well-known that the expected value of a random variable Y minimizes the expected squared deviation between Y and a constant; that is, See full list on eml.berkeley.edu Returning to the linear LAD estimator, while demonstration of consistency of ^ involves routine appli- cation of asymptotic arguments for extremum estimators, demonstration of pn-consistency and asymptotic normality is complicated by the fact that the LAD criterion S( ) is not continuously di¤erentiable in : For comparison, consider the standard th... See full list on eml.berkeley.edu pn can be established for this problem. This condition can be shown to hold by showing that each element of the subgradient of the LAD criterion, when evaluated at the minimizing value ^; is bounded in magnitude by the di¤erence between the right and left derivatives of the criterion, so that 1 n See full list on eml.berkeley.edu is di¤erentiable in = 0. The Taylor s series expansion would thus be applicable if the order of the expectation (over yi and xi) and di¤erentiation (over ) could somehow be interchanged. To do this rigorously, a stochastic equicontinuity condition on the sample average moment function See full list on eml.berkeley.edu Aug 1, 2020 · The regression of two sets of data points was considered in Example 3.1 and it was shown that a large value of the condition number κ 2 (X) does not imply that the solution β of (2) is sensitive to a perturbation in y. A problem with a low condition number is said to be well-conditioned, while a problem with a high condition number is said to be ill-conditioned. In non-mathematical terms, an ill-conditioned problem is one where, for a small change in the inputs (the independent variables) there is a large change in the answer or dependent variable. Abstract: In this paper, we considered the condition number theory of a new generalized ridge regression model. The explicit expressions of di erent types of condition numbers were derived to measure the ill-conditionness of the generalized ridge regression problem with respect to di erent circumstances. To overcome the computational di culty of computing the exact value of the condition ... A pair of condition numbers for the logistic regression problem: when the sample data is non-separable: a condition number for the degree of non-separability of the dataset informing the convergence guarantees of Greedy Coordinate Descent and Stochastic Gradient Descent (SGD) guarantees on reaching linear convergence (thanks to Bach) Is a problem with a low condition number ill conditioned? A problem with a low condition number is said to be well-conditioned , while a problem with a high condition number is said to be ill-conditioned. In non-mathematical terms, an ill-conditioned problem is one where, for a small change in the inputs (the independent variables) there is a large change in the answer or dependent variable. What happens if a condition number is normal? If is normal, then where and are maximal and minimal (by moduli) eigenvalues of respectively. The condition number with respect to L2 arises so often in numerical linear algebra that it is given a name, the condition number of a matrix. What happens if a condition number is exactly one? When the condition number is exactly one (which can only happen if A is a scalar multiple of a linear isometry), then a solution algorithm can find (in principle, meaning if the algorithm introduces no errors of its own) an approximation of the solution whose precision is no worse than that of the data. Define conditioning, the condition number of a problem, and the condition number of a matrix; describe the implications for computations in machine learning. Describe how conditioning challenges are exacerbated or mitigated by solving the normal equations and scaling data."}
+{"idx": 4, "title": "Condition estimation for regression and feature selection", "date": "", "ddg_snippet": "Aug 1, 2020 · The regression of two sets of data points was considered in Example 3.1 and it was shown that a large value of the condition number κ 2 (X) does not imply that the solution β of (2) is sensitive to a perturbation in y.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0377042719301724", "content": "Aug 1, 2020 · The regression of two sets of data points was considered in Example 3.1 and it was shown that a large value of the condition number κ 2 (X) does not imply that the solution β of (2) is sensitive to a perturbation in y."}
+{"idx": 5, "title": "Condition numbers of the generalized ridge regression and its ...", "date": "", "ddg_snippet": "Abstract: In this paper, we considered the condition number theory of a new generalized ridge regression model. The explicit expressions of di erent types of condition numbers were derived to measure the ill-conditionness of the generalized ridge regression problem with respect to di erent circumstances. To overcome the computational di culty of computing the exact value of the condition ...", "subpage_snippet": "", "source": "www.aimspress.com", "link": "https://www.aimspress.com/aimspress-data/math/2024/2/PDF/math-09-02-205.pdf", "content": "Abstract: In this paper, we considered the condition number theory of a new generalized ridge regression model. The explicit expressions of di erent types of condition numbers were derived to measure the ill-conditionness of the generalized ridge regression problem with respect to di erent circumstances. To overcome the computational di culty of computing the exact value of the condition ..."}
+{"idx": 6, "title": "Condition Number Analysis of Logistic Regression, and its ...", "date": "", "ddg_snippet": "A pair of condition numbers for the logistic regression problem: when the sample data is non-separable: a condition number for the degree of non-separability of the dataset informing the convergence guarantees of Greedy Coordinate Descent and Stochastic Gradient Descent (SGD) guarantees on reaching linear convergence (thanks to Bach)", "subpage_snippet": "", "source": "s3.amazonaws.com", "link": "https://s3.amazonaws.com/mitsloan-php/wp-faculty/sites/30/2019/05/07144136/CMU-Tepper-logistic-regression-only-GCD-v2.0.pdf", "content": "A pair of condition numbers for the logistic regression problem: when the sample data is non-separable: a condition number for the degree of non-separability of the dataset informing the convergence guarantees of Greedy Coordinate Descent and Stochastic Gradient Descent (SGD) guarantees on reaching linear convergence (thanks to Bach)"}
+{"idx": 7, "title": "AE 4803 AIM: Course Notes - 6 Precision, conditioning, and ...", "date": "", "ddg_snippet": "Define conditioning, the condition number of a problem, and the condition number of a matrix; describe the implications for computations in machine learning. Describe how conditioning challenges are exacerbated or mitigated by solving the normal equations and scaling data.", "subpage_snippet": "", "source": "elizqian.github.io", "link": "https://elizqian.github.io/ae-ml-dev/15_svdcond.html", "content": "Define conditioning, the condition number of a problem, and the condition number of a matrix; describe the implications for computations in machine learning. Describe how conditioning challenges are exacerbated or mitigated by solving the normal equations and scaling data."}
+{"idx": 8, "title": "calculus - Finding the Right Initial Conditions for a Three-Body...", "date": "", "ddg_snippet": "A professor answered in private communication that \"the solutions are found numerically by solving a two-point boundary value problem with periodic boundary conditions .", "subpage_snippet": "", "source": "math.stackexchange.com", "link": "https://math.stackexchange.com/questions/2670728/finding-the-right-initial-conditions-for-a-three-body-problem-periodic-solution", "content": "A professor answered in private communication that \"the solutions are found numerically by solving a two-point boundary value problem with periodic boundary conditions ."}
+{"idx": 9, "title": "Top 21 QA Testing Tools for 2025: Must-Have Solutions", "date": "", "ddg_snippet": "The fundamental goal remains unchanged: ship reliable software without exhausting your team on repetitive regression testing cycles. But the methods have become infinitely more sophisticated. Why Invest in Professional QA Testing Tools?", "subpage_snippet": "", "source": "testomat.io", "link": "https://testomat.io/blog/best-qa-testing-tools/", "content": "The fundamental goal remains unchanged: ship reliable software without exhausting your team on repetitive regression testing cycles. But the methods have become infinitely more sophisticated. Why Invest in Professional QA Testing Tools?"}

data/sampled_jsons/cosine_similarity_correction_loss_L_cor_DART_arxiv_2504.11786.jsonl

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+{"idx": 0, "title": "[ 2504 . 11786 ] DART : Disease-aware Image-Text Alignment and...", "date": "", "ddg_snippet": "View a PDF of the paper titled DART : Disease-aware Image-Text Alignment and Self- correcting Re-alignment for Trustworthy Radiology Report Generation, by Sang-Jun Park and 5 other authors.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2504.11786", "content": "View a PDF of the paper titled DART : Disease-aware Image-Text Alignment and Self- correcting Re-alignment for Trustworthy Radiology Report Generation, by Sang-Jun Park and 5 other authors."}
+{"idx": 1, "title": "DART : Disease-aware Image-Text Alignment and Self- correcting ...", "date": "", "ddg_snippet": "In this study, we propose a Disease-aware image-text Alignment and self- correcting Re-alignment for Trustworthy radiology report generation ( DART ) framework.", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/arxiv/2504.11786", "content": "In this study, we propose a Disease-aware image-text Alignment and self- correcting Re-alignment for Trustworthy radiology report generation ( DART ) framework."}
+{"idx": 2, "title": "(PDF) DART : Disease-aware Image-Text Alignment and...", "date": "", "ddg_snippet": "DOI:10.48550/ arXiv . 2504 . 11786 . correction loss that measures the similarity , specifically co-. sine similarity , between the self- corrected text features and. the image features, encouraging the model to minimize any. errors or omissions in the generated report.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/390845711_DART_Disease-aware_Image-Text_Alignment_and_Self-correcting_Re-alignment_for_Trustworthy_Radiology_Report_Generation", "content": "DOI:10.48550/ arXiv . 2504 . 11786 . correction loss that measures the similarity , specifically co-. sine similarity , between the self- corrected text features and. the image features, encouraging the model to minimize any. errors or omissions in the generated report."}
+{"idx": 3, "title": "cosine _ similarity — scikit-learn 1.7.2 documentation", "date": "", "ddg_snippet": "Cosine similarity , or the cosine kernel, computes similarity as the normalized dot product of X and YReturns the cosine similarity between samples in X and Y.", "subpage_snippet": "", "source": "scikit-learn.org", "link": "https://scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.cosine_similarity.html", "content": "Cosine similarity , or the cosine kernel, computes similarity as the normalized dot product of X and YReturns the cosine similarity between samples in X and Y."}
+{"idx": 4, "title": "Embedding Essentials: Cosine Similarity in SQL | e6data", "date": "", "ddg_snippet": "Searching with Cosine Similarity in SQL (Before and After). One of the biggest benefits of vector embeddings is the ability to perform similarity search in SQL using cosine similarity (or distance) metrics.", "subpage_snippet": "", "source": "www.e6data.com", "link": "https://www.e6data.com/blog/embedding-essentials-cosine-similarity-sql-with-vectors", "content": "Searching with Cosine Similarity in SQL (Before and After). One of the biggest benefits of vector embeddings is the ability to perform similarity search in SQL using cosine similarity (or distance) metrics."}
+{"idx": 5, "title": "DART : Disease-aware Image-Text Alignment and Self- correcting ...", "date": "", "ddg_snippet": "The correction loss Lcor is defined aswhere λcor is a weighting coefficient that adjusts the cor -rection loss and is set to 5, and Lgen is the generation loss , i.e., auto-regressive loss . In stage 2, only the self- correction .", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Park_DART_Disease-aware_Image-Text_Alignment_and_Self-correcting_Re-alignment_for_Trustworthy_Radiology_CVPR_2025_paper.pdf", "content": "The correction loss Lcor is defined aswhere λcor is a weighting coefficient that adjusts the cor -rection loss and is set to 5, and Lgen is the generation loss , i.e., auto-regressive loss . In stage 2, only the self- correction ."}
+{"idx": 6, "title": "Dot Product, Cosine Similarity , Scaled Dot Product... | Medium", "date": "", "ddg_snippet": "Cosine Similarity . How: Normalize dot product by magnitudes of vectorsFrom Loss Functions to Training Utilities: Building PyTorch Packages from Scratch.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@GenAIDevTOProd/dot-product-cosine-similarity-scaled-dot-product-flash-attention-what-why-how-ccbcf30d2d92", "content": "Cosine Similarity . How: Normalize dot product by magnitudes of vectorsFrom Loss Functions to Training Utilities: Building PyTorch Packages from Scratch."}
+{"idx": 7, "title": "Часть 5. Обзор техник оценки качества систем RAG / Хабр", "date": "", "ddg_snippet": "Cosine Similarity .. Hit Rate. Cosine Similarity . ARES‡. Качество поиска.", "subpage_snippet": "", "source": "habr.com", "link": "https://habr.com/ru/articles/948786/", "content": "Cosine Similarity .. Hit Rate. Cosine Similarity . ARES‡. Качество поиска."}
+{"idx": 8, "title": "alea-institute/kl3m-doc-small-uncased-001 · Hugging Face", "date": "", "ddg_snippet": "arxiv : 2504 .07854.Calculate cosine similarity between documents similarity _matrix = cosine _ similarity (embeddings) print(\"\\nDocument similarity matrix:\") print( similarity _matrix). For more advanced usage with mean pooling", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/alea-institute/kl3m-doc-small-uncased-001", "content": "arxiv : 2504 .07854.Calculate cosine similarity between documents similarity _matrix = cosine _ similarity (embeddings) print(\"\\nDocument similarity matrix:\") print( similarity _matrix). For more advanced usage with mean pooling"}
+{"idx": 9, "title": "Machine Learning Approaches to Breaking Multilinear Jigsaw Puzzles", "date": "", "ddg_snippet": "Step 3: Graph construction—compute cosine similarities on embeddings, threshold to form edges. Step 4: GNN training for position prediction, using cross-entropy loss on grid slots.", "subpage_snippet": "", "source": "johal.in", "link": "https://johal.in/machine-learning-approaches-to-breaking-multilinear-jigsaw-puzzles/", "content": "Step 3: Graph construction—compute cosine similarities on embeddings, threshold to form edges. Step 4: GNN training for position prediction, using cross-entropy loss on grid slots."}

data/sampled_jsons/fiveai_understanding_safety_finetuning_SSFT_configuration_learning_rate_5e-5_1e-5.jsonl

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+{"idx": 0, "title": "What does the learning rate of 2 or 5 or 10 mean (different ...", "date": "", "ddg_snippet": "Jan 23, 2025 · Hello! I am EXTREMELY new to LLMs, so this might be a very stupid question, and if that’s the case, I apologize in advance. I have been reading some articles and papers that mention the leaning rate of the following types: 2e- 5 or 1e -4. Why does the learning rate hyper-parameter for fine-tuning GPT3. 5 look so different? Am I missing something? Thank you so much in advance!", "subpage_snippet": "", "source": "community.openai.com", "link": "https://community.openai.com/t/what-does-the-learning-rate-of-2-or-5-or-10-mean-different-from-2e-5-or-1e-4-in-fine-tuning/1099415", "content": "Jan 23, 2025 · Hello! I am EXTREMELY new to LLMs, so this might be a very stupid question, and if that’s the case, I apologize in advance. I have been reading some articles and papers that mention the leaning rate of the following types: 2e- 5 or 1e -4. Why does the learning rate hyper-parameter for fine-tuning GPT3. 5 look so different? Am I missing something? Thank you so much in advance!"}
+{"idx": 1, "title": "Understanding the Learning Rate in LLM Fine-Tuning", "date": "", "ddg_snippet": "For LLM fine-tuning, the learning rate is often much smaller than what's used during initial pre-training, typically ranging from 1e − 5 to 5e − 5 . This is because the model already has a strong foundational understanding, and we're just nudging it towards specialization.", "subpage_snippet": "", "source": "www.metriccoders.com", "link": "https://www.metriccoders.com/post/understanding-the-learning-rate-in-llm-fine-tuning", "content": "For LLM fine-tuning, the learning rate is often much smaller than what's used during initial pre-training, typically ranging from 1e − 5 to 5e − 5 . This is because the model already has a strong foundational understanding, and we're just nudging it towards specialization."}
+{"idx": 2, "title": "GitHub - fiveai/understanding_safety_finetuning: Official ...", "date": "", "ddg_snippet": "To better understand the underlying factors that make models safe via safety fine - tuning , we design a synthetic data generation framework that captures salient aspects of an unsafe input by modeling the interaction between the task the model is asked to perform (e.g., “design”) versus the specific concepts the task is asked to be performed upon (e.g., a “cycle” vs. a “bomb”). Using ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/fiveai/understanding_safety_finetuning", "content": "To better understand the underlying factors that make models safe via safety fine - tuning , we design a synthetic data generation framework that captures salient aspects of an unsafe input by modeling the interaction between the task the model is asked to perform (e.g., “design”) versus the specific concepts the task is asked to be performed upon (e.g., a “cycle” vs. a “bomb”). Using ..."}
+{"idx": 3, "title": "understanding_safety_finetuning/ssft at main · fiveai ...", "date": "", "ddg_snippet": "Notifications You must be signed in to change notification settings Fork 2 Star 9 Code Issues Pull requests Projects Security", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/fiveai/understanding_safety_finetuning/tree/main/ssft", "content": "Notifications You must be signed in to change notification settings Fork 2 Star 9 Code Issues Pull requests Projects Security"}
+{"idx": 4, "title": "GSPO Reinforcement Learning | Unsloth Documentation", "date": "", "ddg_snippet": "Enable GSPO in Unsloth by setting importance_sampling_level = \"sequence\" in the GRPO config . The difference between these two algorithms can be seen below, both from the GSPO paper from Qwen and Alibaba", "subpage_snippet": "", "source": "docs.unsloth.ai", "link": "https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide/gspo-reinforcement-learning", "content": "Enable GSPO in Unsloth by setting importance_sampling_level = \"sequence\" in the GRPO config . The difference between these two algorithms can be seen below, both from the GSPO paper from Qwen and Alibaba"}
+{"idx": 5, "title": "Supervised Fine-Tuning (SFT) | QwenLM/Qwen2.5-Coder | DeepWiki", "date": "", "ddg_snippet": "Apr 18, 2025 · Learning Rate : Start with 5e-5 for LoRA and 1e-5 for full fine-tuning Mixed Precision: Use BF16 precision when available for better training stability Evaluation: Save multiple checkpoints and evaluate on a validation set By following these guidelines, you can effectively fine-tune Qwen2. 5 -Coder for specific code generation tasks.", "subpage_snippet": "", "source": "deepwiki.com", "link": "https://deepwiki.com/QwenLM/Qwen2.5-Coder/3.1-supervised-fine-tuning-(sft)", "content": "Apr 18, 2025 · Learning Rate : Start with 5e-5 for LoRA and 1e-5 for full fine-tuning Mixed Precision: Use BF16 precision when available for better training stability Evaluation: Save multiple checkpoints and evaluate on a validation set By following these guidelines, you can effectively fine-tune Qwen2. 5 -Coder for specific code generation tasks."}
+{"idx": 6, "title": "Tuning parameters to train LLMs (Large Language Models)", "date": "", "ddg_snippet": "Jul 25, 2023 · Try different learning rates (e.g., 1e-5 , 3e- 5 , 5e-5 ) and monitor the model’s performance to find the optimal rate that converges quickly and effectively.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@rtales/tuning-parameters-to-train-llms-large-language-models-8861bbc11971", "content": "Jul 25, 2023 · Try different learning rates (e.g., 1e-5 , 3e- 5 , 5e-5 ) and monitor the model’s performance to find the optimal rate that converges quickly and effectively."}
+{"idx": 7, "title": "Mistral 7b Meets Darija 🇩🇿: A Continual Pre-training... | Medium", "date": "", "ddg_snippet": "learning _ rate = 5 e - 5 , embedding_ learning _ rate = 5 e -6We configure the UnslothTrainer with settings for batch size, learning rates , and optimization strategies. we then run the training process on a French text dataset and capture the training statistics for evaluation.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@ayoubkirouane3/mistral-7b-meets-darija-a-continual-pre-training-journey-90e037c5cbef", "content": "learning _ rate = 5 e - 5 , embedding_ learning _ rate = 5 e -6We configure the UnslothTrainer with settings for batch size, learning rates , and optimization strategies. we then run the training process on a French text dataset and capture the training statistics for evaluation."}
+{"idx": 8, "title": "LoRA-TMLR-2024/magicoder-full- finetuning -lr- 5 e -05 · Hugging Face", "date": "", "ddg_snippet": "Finally, we show that full finetuning learns perturbations with a rank that is 10-100× greater than typical LoRA configurations , possibly explaining some of the reported gaps. We conclude by proposing best practices for finetuning with LoRA.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/LoRA-TMLR-2024/magicoder-full-finetuning-lr-5e-05", "content": "Finally, we show that full finetuning learns perturbations with a rank that is 10-100× greater than typical LoRA configurations , possibly explaining some of the reported gaps. We conclude by proposing best practices for finetuning with LoRA."}
+{"idx": 9, "title": "Fine - tune 100+ LLMs Directly From A UI Without... - Undercode Testing", "date": "", "ddg_snippet": "Experiment tracking: TensorBoard, W&B, MLflow. Downstream tasks: Tool use, multimodal understanding .EnlighterJS 3 Syntax Highlighter. Training started with configuration : - Model: meta-llama/Llama-2-7b-hf. - Dataset: alpaca_gpt4_en. - Fine - tuning type: lora.", "subpage_snippet": "", "source": "undercodetesting.com", "link": "https://undercodetesting.com/fine-tune-100-llms-directly-from-a-ui-without-any-code-using-llama-factory/", "content": "Experiment tracking: TensorBoard, W&B, MLflow. Downstream tasks: Tool use, multimodal understanding .EnlighterJS 3 Syntax Highlighter. Training started with configuration : - Model: meta-llama/Llama-2-7b-hf. - Dataset: alpaca_gpt4_en. - Fine - tuning type: lora."}

data/sampled_jsons/httpsarxiv.orghtml2411.07501v4_year_2024.jsonl

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+{"idx": 0, "title": "LAuReL: Learned Augmented Residual Layer - arXiv.org", "date": "", "ddg_snippet": "License: arXiv.org perpetual non-exclusive license arXiv:2411.07501v4 [cs.LG] 24 Jun 2025", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.07501v4", "content": "License: arXiv.org perpetual non-exclusive license arXiv:2411.07501v4 [cs.LG] 24 Jun 2025"}
+{"idx": 1, "title": "arXiv.org e-Print archive", "date": "", "ddg_snippet": "arXiv is a free distribution service and an open-access archive for nearly 2.4 million scholarly articles in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/", "content": "arXiv is a free distribution service and an open-access archive for nearly 2.4 million scholarly articles in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics."}
+{"idx": 2, "title": "HTML papers on arXiv -- why it is important, and how we made it happen", "date": "", "ddg_snippet": "In October 2023, arXiv made HTML formatted papers available to readers. This was the exciting outcome of over a year of accessibility research and development with the scientific community. Currently, only 2.4% of research outputs meet accessibility guidelines. Informed by scientists who rely on assistive technology, our analysis demonstrates that offering HTML is the most impactful step arXiv ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2402.08954", "content": "In October 2023, arXiv made HTML formatted papers available to readers. This was the exciting outcome of over a year of accessibility research and development with the scientific community. Currently, only 2.4% of research outputs meet accessibility guidelines. Informed by scientists who rely on assistive technology, our analysis demonstrates that offering HTML is the most impactful step arXiv ..."}
+{"idx": 3, "title": "GitHub - dginev/ar5iv: A web service offering HTML5 articles from arXiv ...", "date": "", "ddg_snippet": "A web service offering HTML5 articles from arXiv.org as converted with latexml. The e-journal styling of document pages is developed separately at ar5iv-css. Authors can reproduce locally using ar5ivist. Seeded via CorTeX data. Hosted by arXivLabs. Created by", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/dginev/ar5iv", "content": "A web service offering HTML5 articles from arXiv.org as converted with latexml. The e-journal styling of document pages is developed separately at ar5iv-css. Authors can reproduce locally using ar5ivist. Seeded via CorTeX data. Hosted by arXivLabs. Created by"}
+{"idx": 4, "title": "ar5iv - Articles from arXiv.org as responsive HTML5 web documents", "date": "", "ddg_snippet": "ar5iv offers a modern web view for arXiv's preprints. An open community resource, on a quest to a full collection of high-quality documents.", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/", "content": "ar5iv offers a modern web view for arXiv's preprints. An open community resource, on a quest to a full collection of high-quality documents."}
+{"idx": 5, "title": "Error messages in HTML papers - arXiv info", "date": "", "ddg_snippet": "To produce accessible HTML papers we use a converter created and maintained by the LaTeXML team at the National Library of Standards and Technology (NIST). The errors that you have seen when viewing papers in HTML after submission or on arXiv.org are caused when the LaTeXML converter is unable to translate certain TeX and LaTeX software constructs.", "subpage_snippet": "", "source": "info.arxiv.org", "link": "https://info.arxiv.org/about/accessibility_html_error_messages.html", "content": "To produce accessible HTML papers we use a converter created and maintained by the LaTeXML team at the National Library of Standards and Technology (NIST). The errors that you have seen when viewing papers in HTML after submission or on arXiv.org are caused when the LaTeXML converter is unable to translate certain TeX and LaTeX software constructs."}
+{"idx": 6, "title": "accessible HTML - arXiv info", "date": "", "ddg_snippet": "HTML as an accessible format for papers Accessibility barriers in research are not new, but they are urgent. The message we have heard from our community is that arXiv can have the most impact in the shortest time by offering HTML papers alongside the existing PDF. arXiv has successfully launched papers in HTML format. We are gradually backfilling HTML for arXiv's corpus of over 2 million ...", "subpage_snippet": "", "source": "info.arxiv.org", "link": "https://info.arxiv.org/about/accessible_HTML.html", "content": "HTML as an accessible format for papers Accessibility barriers in research are not new, but they are urgent. The message we have heard from our community is that arXiv can have the most impact in the shortest time by offering HTML papers alongside the existing PDF. arXiv has successfully launched papers in HTML format. We are gradually backfilling HTML for arXiv's corpus of over 2 million ..."}
+{"idx": 7, "title": "[2411.07501v4] LAuReL: Learned Augmented Residual Layer - arXiv.org", "date": "", "ddg_snippet": "One of the core pillars of efficient deep learning methods is architectural improvements such as the residual/skip connection, which has led to significantly better model convergence and quality. Since then the residual connection has become ubiquitous in not just convolutional neural networks but also transformer-based architectures, the backbone of LLMs. In this paper we introduce Learned ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2411.07501v4", "content": "One of the core pillars of efficient deep learning methods is architectural improvements such as the residual/skip connection, which has led to significantly better model convergence and quality. Since then the residual connection has become ubiquitous in not just convolutional neural networks but also transformer-based architectures, the backbone of LLMs. In this paper we introduce Learned ..."}
+{"idx": 8, "title": "[2411.07501] LAuReL: Learned Augmented Residual Layer", "date": "", "ddg_snippet": "In this paper we introduce Learned Augmented Residual Layer (LAuReL)—a novel generalization of the canonical residual connection—with the goal to be an in-situ replacement of the latter while outperforming on both model quality and footprint metrics. Our experiments show that using LAuReL can help boost performance for both vision and language models. For example, on the ResNet-50 ...", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2411.07501", "content": "In this paper we introduce Learned Augmented Residual Layer (LAuReL)—a novel generalization of the canonical residual connection—with the goal to be an in-situ replacement of the latter while outperforming on both model quality and footprint metrics. Our experiments show that using LAuReL can help boost performance for both vision and language models. For example, on the ResNet-50 ..."}
+{"idx": 9, "title": "What Do Learning Dynamics Reveal About Generalization in LLM Reasoning?", "date": "", "ddg_snippet": "Abstract Despite the remarkable capabilities of modern large language models (LLMs), the mechanisms behind their problem-solving abilities remain elusive. In this work, we aim to better understand how the learning dynamics of LLM finetuning shapes downstream generalization. Our analysis focuses on reasoning tasks, whose problem structure allows us to distinguish between memorization (the exact ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.07681v1", "content": "Abstract Despite the remarkable capabilities of modern large language models (LLMs), the mechanisms behind their problem-solving abilities remain elusive. In this work, we aim to better understand how the learning dynamics of LLM finetuning shapes downstream generalization. Our analysis focuses on reasoning tasks, whose problem structure allows us to distinguish between memorization (the exact ..."}

data/sampled_jsons/rExtinctionRebellion_rFridaysForFuture_rEarthStrike_activation_percentage_climate_activism_reddit.jsonl

@@ -0,0 +1,10 @@
+{"idx": 0, "title": "r/ExtinctionRebellion on Reddit: I asked the ...", "date": "", "ddg_snippet": "25K subscribers in the ExtinctionRebellion community. This is our darkest hour. Humanity finds itself embroiled in an event unprecedented in its…", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/ExtinctionRebellion/comments/jv6kiw/i_asked_the_extinctionrebellion_community_before/", "content": "25K subscribers in the ExtinctionRebellion community. This is our darkest hour. Humanity finds itself embroiled in an event unprecedented in its…"}
+{"idx": 1, "title": "The burning scar: Inside the destruction of Asia’s ... - Reddit", "date": "", "ddg_snippet": "'She's home': The day climate activists targeted the house of an oil and gas CEO abc.net.au r/EarthStrike •", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/conservation/comments/jsovsn/the_burning_scar_inside_the_destruction_of_asias/", "content": "'She's home': The day climate activists targeted the house of an oil and gas CEO abc.net.au r/EarthStrike •"}
+{"idx": 2, "title": "'Climate Despair' Is Making People Give Up on Life - Reddit", "date": "", "ddg_snippet": "Hold your existing representatives accountable. Collaborate. Organize with others. Join a movement! This is not the time to do your best at home. Get to the streets, be vocal, be loud, demand what's necessary. Join the rebellion! Some activist subs: r/CitizensClimateLobby r/EarthStrike r/ExtinctionRebellion r/FridaysForFuture r/StopFossilFuels", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/environment/comments/cc233y/climate_despair_is_making_people_give_up_on_life/", "content": "Hold your existing representatives accountable. Collaborate. Organize with others. Join a movement! This is not the time to do your best at home. Get to the streets, be vocal, be loud, demand what's necessary. Join the rebellion! Some activist subs: r/CitizensClimateLobby r/EarthStrike r/ExtinctionRebellion r/FridaysForFuture r/StopFossilFuels"}
+{"idx": 3, "title": "The Tempest, Act 2, Scene I \"What's past is prologue\" - Reddit", "date": "", "ddg_snippet": "Just did, but it doesn't seem like there is lot of going on there in general. Where do all the environmentalists and liberals hang out these days? On tikitok? Reply LordHughRAdumbass • r/EarthStrike r/collapse and r/FridaysForFuture Reply Top posts of July 5, 2021Top posts of July 2021Top posts of 2021", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/xrmed/comments/oe8m4z/the_tempest_act_2_scene_i_whats_past_is_prologue/", "content": "Just did, but it doesn't seem like there is lot of going on there in general. Where do all the environmentalists and liberals hang out these days? On tikitok? Reply LordHughRAdumbass • r/EarthStrike r/collapse and r/FridaysForFuture Reply Top posts of July 5, 2021Top posts of July 2021Top posts of 2021"}
+{"idx": 4, "title": "r/EarthStrike on Reddit: Impact and potential of open source ...", "date": "", "ddg_snippet": "22K subscribers in the EarthStrike community. Earth Strike is a grassroots labour-environmental movement focused on organising a GLOBAL GENERAL…", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/EarthStrike/comments/saopvn/impact_and_potential_of_open_source_on_climate/", "content": "22K subscribers in the EarthStrike community. Earth Strike is a grassroots labour-environmental movement focused on organising a GLOBAL GENERAL…"}
+{"idx": 5, "title": "(PDF) Causal Modeling of Climate Activism on Reddit", "date": "", "ddg_snippet": "activation in climate activism groups on Reddit , and over which. time scale?: “Sunrise is a movement to stop climate . change and create millions of good jobs in the process.” • r / ExtinctionRebellion . : “This is our darkest hour.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/384929273_Causal_Modeling_of_Climate_Activism_on_Reddit", "content": "activation in climate activism groups on Reddit , and over which. time scale?: “Sunrise is a movement to stop climate . change and create millions of good jobs in the process.” • r / ExtinctionRebellion . : “This is our darkest hour."}
+{"idx": 6, "title": "Causal Modeling of Climate Activism on Reddit", "date": "", "ddg_snippet": "𝐴 User activated in climate activism groups. 𝐼 Interactions with activists .• r / FridaysForFuture : “A sub dedicated to the international movement of students who skip class on Fridays to demand action to prevent climate change.”", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.10562", "content": "𝐴 User activated in climate activism groups. 𝐼 Interactions with activists .• r / FridaysForFuture : “A sub dedicated to the international movement of students who skip class on Fridays to demand action to prevent climate change.”"}
+{"idx": 7, "title": "View on Redlib, an alternative private front-end to Reddit .", "date": "", "ddg_snippet": "reddit . You are about to leave Redlib. Do you want to continue?/ r / ExtinctionRebellion .", "subpage_snippet": "", "source": "redlib.r4fo.com", "link": "https://redlib.r4fo.com/r/environment/wiki/related_reddits", "content": "reddit . You are about to leave Redlib. Do you want to continue?/ r / ExtinctionRebellion ."}
+{"idx": 8, "title": "Environment - Reddit", "date": "", "ddg_snippet": "r /environment: Current news, information and issues related to the environment.", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/environment/wiki/related_reddits/", "content": "r /environment: Current news, information and issues related to the environment."}
+{"idx": 9, "title": "The burning scar: Inside the destruction of Asia’s last ...", "date": "", "ddg_snippet": "More posts you may like r/FridaysForFuture • r/EarthStrike • r/climatechange • r/EcoNewsNetwork • r/DoomsdayNow • r/solarpunk •", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/ExtinctionRebellion/comments/jsoue8/the_burning_scar_inside_the_destruction_of_asias/", "content": "More posts you may like r/FridaysForFuture • r/EarthStrike • r/climatechange • r/EcoNewsNetwork • r/DoomsdayNow • r/solarpunk •"}

data/sampled_jsons/sitearxiv.org_'Equation_(10)'_'LEnergy'_stochastic_dynamics.jsonl

@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Data-Driven Reconstruction of Stochastic Dynamical", "date": "", "ddg_snippet": "Keywords: Reconstruction of stochastic dynamical equations , Kramers-Moyal coefficients, time series analysis, statistical moments.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2305.10990", "content": "Keywords: Reconstruction of stochastic dynamical equations , Kramers-Moyal coefficients, time series analysis, statistical moments."}
+{"idx": 1, "title": "Effective dynamics of interfaces for nonlinear SPDEs driven by...", "date": "", "ddg_snippet": "Effective dynamics of stochastic models Stochastic damped wave equation with multiplicative noiseMetastable Front Dynamics in Stochastic Allen-Cahn Equation 10 .5.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2505.04036", "content": "Effective dynamics of stochastic models Stochastic damped wave equation with multiplicative noiseMetastable Front Dynamics in Stochastic Allen-Cahn Equation 10 .5."}
+{"idx": 2, "title": "Stochastic thermodynamics of ecosystems", "date": "", "ddg_snippet": ". III Stochastic dynamics . III.1 Fokker-Planck equation . The dynamic approach that we have presented above describes a deterministic motion. In the following we present a stochastic approach to the thermodynamic of an ecosystem.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.19535v1", "content": ". III Stochastic dynamics . III.1 Fokker-Planck equation . The dynamic approach that we have presented above describes a deterministic motion. In the following we present a stochastic approach to the thermodynamic of an ecosystem."}
+{"idx": 3, "title": "Discovering stochastic dynamical equations from ecological time...", "date": "", "ddg_snippet": "Here, we present an equation discovery methodology that takes time series data of state variables as input and outputs a stochastic differential equation . We achieve this by combining traditional approaches from stochastic calculus with the equation -discovery techniques.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2205.02645v6", "content": "Here, we present an equation discovery methodology that takes time series data of state variables as input and outputs a stochastic differential equation . We achieve this by combining traditional approaches from stochastic calculus with the equation -discovery techniques."}
+{"idx": 4, "title": "Stochastic dynamics and the Polchinski equation: an introduction", "date": "", "ddg_snippet": "This introduction surveys a renormalisation group perspective on log-Sobolev inequalities and related properties of stochastic dynamics . We also explain the relationship of this approach to related recent and less recent developments such as Eldan’s stochastic localisation and the Föllmer process, the Boué–Dupuis variational formula and the Barashkov–Gubinelli approach, the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2307.07619v2", "content": "This introduction surveys a renormalisation group perspective on log-Sobolev inequalities and related properties of stochastic dynamics . We also explain the relationship of this approach to related recent and less recent developments such as Eldan’s stochastic localisation and the Föllmer process, the Boué–Dupuis variational formula and the Barashkov–Gubinelli approach, the ..."}
+{"idx": 5, "title": "[2307.07619] Stochastic dynamics and the Polchinski equation ... Learning Stochastic Dynamics with Statistics-Informed Introduction to Supersymmetric Theory of Stochastics stochastic dynamics - arXiv.org Stochastic dynamics and the Polchinski equation: an introduction Long-time dynamics of stochastic differential equations", "date": "", "ddg_snippet": "Jul 14, 2023 · This introduction surveys a renormalisation group perspective on log-Sobolev inequalities and related properties of stochastic dynamics . We also explain the relationship of this approach to related recent and less recent developments such as Eldan's stochastic localisation and the Föllmer process, the Boué--Dupuis variational formula and the Barashkov--Gubinelli approach, the transportation ... Abstract We introduce a machine-learning framework named statistics-informed neural network (SINN) for learn-ing stochastic dynamics from data. This new architecture was theoretically inspired by a universal approximation theorem for stochastic systems, which we introduce in this paper, and the projection-operator formalism for stochas-tic modeling. We devise mechanisms for training the neural ... The theory of stochastic dynamics de ned by Equa-tion (1) can be constructed in two steps. The rst step is to understand the deterministic temporal evolution de ned by the ordinary di erential equation (ODE) ob-tained from the SDE in Equation (1) by xing the noise con guration. Mar 1, 2021 · Many stochastic systems in physics, chemistry, biology as well as other areas can be described by discrete-state Markov models, such that their dynamics is given by a master equation [1, 2]. Examples include the dynamics of (bio-)molecules where states describe discrete con gurations or functional states, the dynamics of chemical reactions and of pop- ulations, where the states are given by ... Abstract This introduction surveys a renormalisation group perspective on log-Sobolev inequalities and related properties of stochastic dynamics . We also explain the relationship of this ap- proach to related recent and less recent developments such as Eldan’s stochastic localisation and the F¨ollmer process, the Bou´e–Dupuis variational formula and the Barashkov–Gubinelli approach ... The fundamental building block of the theory of stochastic differential equations is a math- ematical object calledWiener process, orBrownian motion. This should not be confused with the physical phenomenon of Brownian motion, describing for instance the erratic movements of a small particle in a fluid, though the mathematical model has of ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2307.07619", "content": "Jul 14, 2023 · This introduction surveys a renormalisation group perspective on log-Sobolev inequalities and related properties of stochastic dynamics . We also explain the relationship of this approach to related recent and less recent developments such as Eldan's stochastic localisation and the Föllmer process, the Boué--Dupuis variational formula and the Barashkov--Gubinelli approach, the transportation ... Abstract We introduce a machine-learning framework named statistics-informed neural network (SINN) for learn-ing stochastic dynamics from data. This new architecture was theoretically inspired by a universal approximation theorem for stochastic systems, which we introduce in this paper, and the projection-operator formalism for stochas-tic modeling. We devise mechanisms for training the neural ... The theory of stochastic dynamics de ned by Equa-tion (1) can be constructed in two steps. The rst step is to understand the deterministic temporal evolution de ned by the ordinary di erential equation (ODE) ob-tained from the SDE in Equation (1) by xing the noise con guration. Mar 1, 2021 · Many stochastic systems in physics, chemistry, biology as well as other areas can be described by discrete-state Markov models, such that their dynamics is given by a master equation [1, 2]. Examples include the dynamics of (bio-)molecules where states describe discrete con gurations or functional states, the dynamics of chemical reactions and of pop- ulations, where the states are given by ... Abstract This introduction surveys a renormalisation group perspective on log-Sobolev inequalities and related properties of stochastic dynamics . We also explain the relationship of this ap- proach to related recent and less recent developments such as Eldan’s stochastic localisation and the F¨ollmer process, the Bou´e–Dupuis variational formula and the Barashkov–Gubinelli approach ... The fundamental building block of the theory of stochastic differential equations is a math- ematical object calledWiener process, orBrownian motion. This should not be confused with the physical phenomenon of Brownian motion, describing for instance the erratic movements of a small particle in a fluid, though the mathematical model has of ..."}
+{"idx": 6, "title": "Learning Stochastic Dynamics with Statistics-Informed", "date": "", "ddg_snippet": "Abstract We introduce a machine-learning framework named statistics-informed neural network (SINN) for learn-ing stochastic dynamics from data. This new architecture was theoretically inspired by a universal approximation theorem for stochastic systems, which we introduce in this paper, and the projection-operator formalism for stochas-tic modeling. We devise mechanisms for training the neural ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2202.12278", "content": "Abstract We introduce a machine-learning framework named statistics-informed neural network (SINN) for learn-ing stochastic dynamics from data. This new architecture was theoretically inspired by a universal approximation theorem for stochastic systems, which we introduce in this paper, and the projection-operator formalism for stochas-tic modeling. We devise mechanisms for training the neural ..."}
+{"idx": 7, "title": "Introduction to Supersymmetric Theory of Stochastics", "date": "", "ddg_snippet": "The theory of stochastic dynamics de ned by Equa-tion (1) can be constructed in two steps. The rst step is to understand the deterministic temporal evolution de ned by the ordinary di erential equation (ODE) ob-tained from the SDE in Equation (1) by xing the noise con guration.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1511.03393v4.pdf", "content": "The theory of stochastic dynamics de ned by Equa-tion (1) can be constructed in two steps. The rst step is to understand the deterministic temporal evolution de ned by the ordinary di erential equation (ODE) ob-tained from the SDE in Equation (1) by xing the noise con guration."}
+{"idx": 8, "title": "stochastic dynamics - arXiv.org", "date": "", "ddg_snippet": "Mar 1, 2021 · Many stochastic systems in physics, chemistry, biology as well as other areas can be described by discrete-state Markov models, such that their dynamics is given by a master equation [1, 2]. Examples include the dynamics of (bio-)molecules where states describe discrete con gurations or functional states, the dynamics of chemical reactions and of pop- ulations, where the states are given by ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2102.13394v1", "content": "Mar 1, 2021 · Many stochastic systems in physics, chemistry, biology as well as other areas can be described by discrete-state Markov models, such that their dynamics is given by a master equation [1, 2]. Examples include the dynamics of (bio-)molecules where states describe discrete con gurations or functional states, the dynamics of chemical reactions and of pop- ulations, where the states are given by ..."}
+{"idx": 9, "title": "Stochastic dynamics and the Polchinski equation: an introduction", "date": "", "ddg_snippet": "Abstract This introduction surveys a renormalisation group perspective on log-Sobolev inequalities and related properties of stochastic dynamics . We also explain the relationship of this ap- proach to related recent and less recent developments such as Eldan’s stochastic localisation and the F¨ollmer process, the Bou´e–Dupuis variational formula and the Barashkov–Gubinelli approach ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2307.07619v1", "content": "Abstract This introduction surveys a renormalisation group perspective on log-Sobolev inequalities and related properties of stochastic dynamics . We also explain the relationship of this ap- proach to related recent and less recent developments such as Eldan’s stochastic localisation and the F¨ollmer process, the Bou´e–Dupuis variational formula and the Barashkov–Gubinelli approach ..."}

data/sampled_jsons/siteopenreview.net_Origin_Identification_for_Text-Guided_Image-to-Image_Diffusion_Models_overfitting.jsonl

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+{"idx": 0, "title": "Origin Identification for Text-Guided Image-to- ...", "date": "", "ddg_snippet": "18 Jun 2025 — Origin Identification for Text-Guided Image-to-Image Diffusion Models ... overfitting . Method, mAP (Seen), Acc (Seen), mAP (Unseen), Acc ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=46n3izUNiv&noteId=sDvtTJFLIF", "content": "18 Jun 2025 — Origin Identification for Text-Guided Image-to-Image Diffusion Models ... overfitting . Method, mAP (Seen), Acc (Seen), mAP (Unseen), Acc ..."}
+{"idx": 1, "title": "Origin Identification for Text-Guided Image-to-Image Diffusion ...", "date": "", "ddg_snippet": "Origin Identification for Text-Guided Image-to-Image Diffusion Models . Origin ... 9, we observe overfitting in one type of diffusion model. Specifically, on ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/681ea68d062b8991956d7a196be74f59c4610d76.pdf", "content": "Origin Identification for Text-Guided Image-to-Image Diffusion Models . Origin ... 9, we observe overfitting in one type of diffusion model. Specifically, on ..."}
+{"idx": 2, "title": "GUIDED IMAGE-TO-IMAGE DIFFUSION MODELS", "date": "", "ddg_snippet": "task of origin IDentification for text-guided Image-to-image Diffusion models ... pected W leads to overfitting . In the theoretical section, we proved the ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/5941eae59e3504dfb1ab4e6cbe70630e131191d7.pdf", "content": "task of origin IDentification for text-guided Image-to-image Diffusion models ... pected W leads to overfitting . In the theoretical section, we proved the ..."}
+{"idx": 3, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}

data/sampled_jsons/we_selected_the_ETHICS_dataset_OR_we_choose_the_ETHICS_dataset_four_reasons_context-aware.jsonl

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+{"idx": 0, "title": "Ethics at Every Stage of the AI Lifecycle: Data Preparation", "date": "", "ddg_snippet": "15 Sept 2022 — We want to help you navigate the complicated ethics of data for the AI lifecycle so you can create the best, most useful and responsible dataset for your AI ...", "subpage_snippet": "", "source": "www.appen.com", "link": "https://www.appen.com/blog/ethical-data-for-the-ai-lifecycle-data-preparation", "content": "15 Sept 2022 — We want to help you navigate the complicated ethics of data for the AI lifecycle so you can create the best, most useful and responsible dataset for your AI ..."}
+{"idx": 1, "title": "A high-level overview of AI ethics - PMC", "date": "", "ddg_snippet": "by E Kazim · 2021 · Cited by 296 — This article provides a high-level conceptual discussion of the nascent field of AI ethics by way of introducing basic concepts and sketching central themes.", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC8441585/", "content": "by E Kazim · 2021 · Cited by 296 — This article provides a high-level conceptual discussion of the nascent field of AI ethics by way of introducing basic concepts and sketching central themes."}
+{"idx": 2, "title": "AI ethics education: A systematic literature review", "date": "", "ddg_snippet": "by LJ Wiese · 2025 · Cited by 11 — This paper presents a systematic literature review and qualitative analysis of the early years of AI ethics education as a formalized field.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2666920X25000451", "content": "by LJ Wiese · 2025 · Cited by 11 — This paper presents a systematic literature review and qualitative analysis of the early years of AI ethics education as a formalized field."}
+{"idx": 3, "title": "Reinforcement Learning and Machine ethics: a systematic ...", "date": "", "ddg_snippet": "We present here a systematic review of reinforcement learning for machine ethics and machine ethics within reinforcement learning.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2407.02425v1", "content": "We present here a systematic review of reinforcement learning for machine ethics and machine ethics within reinforcement learning."}
+{"idx": 4, "title": "The ethics of algorithms: key problems and solutions", "date": "", "ddg_snippet": "by A Tsamados · 2022 · Cited by 659 — This article builds on a review of the ethics of algorithms published in 2016 (Mittelstadt et al. Big Data Soc 3(2), 2016).", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s00146-021-01154-8", "content": "by A Tsamados · 2022 · Cited by 659 — This article builds on a review of the ethics of algorithms published in 2016 (Mittelstadt et al. Big Data Soc 3(2), 2016)."}
+{"idx": 5, "title": "Ethical challenges and evolving strategies in the ...", "date": "", "ddg_snippet": "by EB Weiner · 2025 · Cited by 36 — This paper examines the current state of AI in healthcare, focusing on five critical ethical concerns: justice and fairness, transparency, patient consent and ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC11977975/", "content": "by EB Weiner · 2025 · Cited by 36 — This paper examines the current state of AI in healthcare, focusing on five critical ethical concerns: justice and fairness, transparency, patient consent and ..."}
+{"idx": 6, "title": "Ethics in the Age of AI: An Analysis of AI Practitioners' ...", "date": "", "ddg_snippet": "We conducted a survey aimed at understanding AI practitioners' awareness of AI ethics and their challenges in incorporating ethics .", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3635715", "content": "We conducted a survey aimed at understanding AI practitioners' awareness of AI ethics and their challenges in incorporating ethics ."}
+{"idx": 7, "title": "What is AI Ethics? | IBM", "date": "", "ddg_snippet": "AI ethics is a framework that guides data scientists and researchers to build AI systems in an ethical manner to benefit society as a whole.", "subpage_snippet": "", "source": "www.ibm.com", "link": "https://www.ibm.com/think/topics/ai-ethics", "content": "AI ethics is a framework that guides data scientists and researchers to build AI systems in an ethical manner to benefit society as a whole."}
+{"idx": 8, "title": "Digital ethics: Global trends and divergent paths", "date": "", "ddg_snippet": "by AA Guenduez · 2025 · Cited by 2 — We identify 22 key topics grouped into three main clusters: responsible technology development, digital rights, and ethical governance.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0740624X25000449", "content": "by AA Guenduez · 2025 · Cited by 2 — We identify 22 key topics grouped into three main clusters: responsible technology development, digital rights, and ethical governance."}
+{"idx": 9, "title": "Aligning AI With Shared Human Values", "date": "", "ddg_snippet": "by D Hendrycks · Cited by 742 — We introduce the ETHICS dataset , a new benchmark that spans concepts in justice, well-being, duties, virtues, and commonsense morality .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=dNy_RKzJacY", "content": "by D Hendrycks · Cited by 742 — We introduce the ETHICS dataset , a new benchmark that spans concepts in justice, well-being, duties, virtues, and commonsense morality ."}

requirements.txt

@@ -0,0 +1,240 @@
+accelerate==1.10.1
+aiofiles==24.1.0
+aiohappyeyeballs==2.6.1
+aiohttp==3.12.15
+aiosignal==1.4.0
+aiosqlite==0.21.0
+albucore==0.0.24
+albumentations==2.0.8
+alphashape==1.3.1
+annotated-types==0.7.0
+antlr4-python3-runtime==4.9.3
+anyio==4.10.0
+async-timeout==4.0.3
+attrs==25.3.0
+beautifulsoup4==4.13.5
+bio==1.8.0
+biopython==1.85
+biothings_client==0.4.1
+boto3==1.40.30
+botocore==1.40.30
+Brotli==1.1.0
+cachetools==5.5.2
+certifi==2025.8.3
+cffi==2.0.0
+chardet==5.2.0
+charset-normalizer==3.4.3
+chess==1.11.2
+click==8.2.1
+click-log==0.4.0
+coloredlogs==15.0.1
+colorlog==6.9.0
+contourpy==1.3.2
+# Crawl4AI==0.7.4
+cryptography==45.0.7
+curl_cffi==0.13.0
+cycler==0.12.1
+# ddgs==9.5.5
+dill==0.4.0
+distro==1.9.0
+doclayout_yolo==0.0.4
+exceptiongroup==1.3.0
+fake-http-header==0.3.5
+fake-useragent==2.2.0
+fast-langdetect==0.2.5
+fastapi==0.116.1
+fasttext-predict==0.9.2.4
+fastuuid==0.12.0
+ffmpy==0.6.1
+filelock==3.19.1
+flatbuffers==25.2.10
+fonttools==4.59.2
+frozendict==2.4.6
+frozenlist==1.7.0
+fsspec==2025.9.0
+ftfy==6.3.1
+google==3.0.0
+google-auth==2.40.3
+gprofiler-official==1.0.0
+gradio==5.45.0
+gradio_client==1.13.0
+gradio_pdf==0.0.22
+greenlet==3.2.4
+groovy==0.1.2
+h11==0.16.0
+h2==4.3.0
+hf-xet==1.1.10
+hpack==4.1.0
+httpcore==1.0.9
+httpx==0.28.1
+huggingface-hub==0.34.4
+humanfriendly==10.0
+humanize==4.13.0
+hyperframe==6.1.0
+idna==3.10
+imageio==2.37.0
+importlib_metadata==8.7.0
+Jinja2==3.1.6
+jiter==0.10.0
+jmespath==1.0.1
+joblib==1.5.2
+json_repair==0.50.1
+jsonpatch==1.33
+jsonpointer==3.0.0
+jsonschema==4.25.1
+jsonschema-specifications==2025.9.1
+kiwisolver==1.4.9
+langchain==0.3.27
+langchain-core==0.3.76
+langchain-text-splitters==0.3.11
+langsmith==0.4.28
+lark==1.2.2
+lazy_loader==0.4
+litellm==1.77.0
+loguru==0.7.3
+lxml==5.4.0
+markdown-it-py==4.0.0
+markdownify==1.2.0
+MarkupSafe==3.0.2
+matplotlib==3.10.6
+mdurl==0.1.2
+mineru==2.2.2
+mmh3==5.2.0
+modelscope==1.29.2
+mpmath==1.3.0
+multidict==6.6.4
+multitasking==0.0.12
+mygene==3.2.2
+networkx==3.4.2
+nltk==3.9.1
+numpy==2.2.6
+# nvidia-cublas-cu12==12.6.4.1
+# nvidia-cuda-cupti-cu12==12.6.80
+# nvidia-cuda-nvrtc-cu12==12.6.77
+# nvidia-cuda-runtime-cu12==12.6.77
+# nvidia-cudnn-cu12==9.5.1.17
+# nvidia-cufft-cu12==11.3.0.4
+# nvidia-cufile-cu12==1.11.1.6
+# nvidia-curand-cu12==10.3.7.77
+# nvidia-cusolver-cu12==11.7.1.2
+# nvidia-cusparse-cu12==12.5.4.2
+# nvidia-cusparselt-cu12==0.6.3
+# nvidia-nccl-cu12==2.26.2
+# nvidia-nvjitlink-cu12==12.6.85
+# nvidia-nvtx-cu12==12.6.77
+omegaconf==2.3.0
+onnxruntime==1.22.1
+openai==1.107.2
+opencv-python==4.12.0.88
+opencv-python-headless==4.12.0.88
+orjson==3.11.3
+outcome==1.3.0.post0
+packaging==25.0
+pandas==2.3.2
+patchright==1.55.1
+pdfminer.six==20250506
+pdftext==0.6.3
+peewee==3.18.2
+pillow==11.3.0
+platformdirs==4.4.0
+playwright==1.55.0
+polars==1.33.1
+pooch==1.8.2
+primp==0.15.0
+propcache==0.3.2
+protobuf==6.32.1
+psutil==7.0.0
+PubChemPy==1.0.5
+py-cpuinfo==9.0.0
+pyasn1==0.6.1
+pyasn1_modules==0.4.2
+pyclipper==1.3.0.post6
+pycparser==2.23
+pydantic==2.11.9
+pydantic-settings==2.10.1
+pydantic_core==2.33.2
+pydub==0.25.1
+pyee==13.0.0
+Pygments==2.19.2
+pyOpenSSL==25.1.0
+pyparsing==3.2.4
+pypdf==6.0.0
+PyPDF2==3.0.1
+pypdfium2==4.30.0
+PySocks==1.7.1
+python-dateutil==2.9.0.post0
+python-dotenv==1.1.1
+python-multipart==0.0.20
+python-pptx==1.0.2
+pytz==2025.2
+PyYAML==6.0.2
+rank-bm25==0.2.2
+referencing==0.36.2
+regex==2025.9.1
+reportlab==4.4.3
+requests==2.32.5
+requests-toolbelt==1.0.0
+rich==14.1.0
+robust-downloader==0.0.2
+rpds-py==0.27.1
+rsa==4.9.1
+rtree==1.4.1
+ruff==0.13.0
+s3transfer==0.14.0
+safehttpx==0.1.6
+safetensors==0.6.2
+scikit-image==0.25.2
+scikit-learn==1.7.2
+scipy==1.15.3
+seaborn==0.13.2
+selenium==4.35.0
+semantic-version==2.10.0
+serpapi==0.1.5
+shapely==2.1.1
+shellingham==1.5.4
+simsimd==6.5.3
+six==1.17.0
+-e git+https://github.com/huggingface/smolagents.git@e106fce649b69b6c7857c89443921f75f9e36378#egg=smolagents
+sniffio==1.3.1
+snowballstemmer==2.2.0
+sortedcontainers==2.4.0
+soupsieve==2.8
+SQLAlchemy==2.0.43
+starlette==0.47.3
+stringzilla==4.0.10
+sympy==1.14.0
+tenacity==9.1.2
+tf-playwright-stealth==1.2.0
+thop==0.1.1.post2209072238
+threadpoolctl==3.6.0
+tifffile==2025.5.10
+tiktoken==0.11.0
+tokenizers==0.22.0
+tomlkit==0.13.3
+torch==2.7.1
+torchvision==0.22.1
+tqdm==4.67.1
+transformers==4.56.1
+trimesh==4.8.1
+trio==0.30.0
+trio-websocket==0.12.2
+# triton==3.3.1
+typer==0.17.4
+typing-inspection==0.4.1
+typing_extensions==4.14.1
+tzdata==2025.2
+ultralytics==8.3.200
+ultralytics-thop==2.0.17
+undetected-chromedriver==3.5.5
+urllib3==2.5.0
+uvicorn==0.35.0
+wcwidth==0.2.13
+websocket-client==1.8.0
+websockets==15.0.1
+wsproto==1.2.0
+xlsxwriter==3.2.9
+xxhash==3.5.0
+yarl==1.20.1
+yfinance==0.2.65
+zipp==3.23.0
+zstandard==0.25.0