Neural Information Processing Systems Foundation, Inc. (NeurIPS)

  • In Search of Adam’s Secret Sauce

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    In Search of Adam’s Secret Sauce

    Antonio Orvieto, Robert Gower p70765-70803 from Advances in Neural Information Processing Systems 38
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  • In Silico Mapping of Visual Categorical Selectivity Across the Whole Brain

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    In Silico Mapping of Visual Categorical Selectivity Across the Whole Brain

    Ethan Hwang, Hossein Adeli, Wenxuan Guo, Andrew Luo, Nikolaus Kriegeskorte p182552-182596 from Advances in Neural Information Processing Systems 38
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  • In the Eye of MLLM: Benchmarking Egocentric Video Intent Understanding with Gaze-Guided Prompting

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    In the Eye of MLLM: Benchmarking Egocentric Video Intent Understanding with Gaze-Guided Prompting

    Taiying Peng, Jiacheng Hua, Miao Liu, Feng Lu p121404-121424 from Advances in Neural Information Processing Systems 38
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  • In the Eye of the Beholder: Robust Prediction with Causal User Modeling

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    In the Eye of the Beholder: Robust Prediction with Causal User Modeling

    Amir Feder, Guy Horowitz, Yoav Wald, Roi Reichart, Nir Rosenfeld p14419-14433 from Advances in Neural Information Processing Systems 35
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  • In What Ways Are Deep Neural Networks Invariant and How Should We Measure This?

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    In What Ways Are Deep Neural Networks Invariant and How Should We Measure This?

    Henry Kvinge, Tegan Emerson, Grayson Jorgenson, Scott Vasquez, Tim Doster, Jesse Lew p32816-32829 from Advances in Neural Information Processing Systems 35
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  • In-and-Out: Algorithmic Diffusion for Sampling Convex Bodies

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    In-and-Out: Algorithmic Diffusion for Sampling Convex Bodies

    Yunbum Kook, Santosh Vempala, Matthew Zhang p108354-108388 from Advances in Neural Information Processing Systems 37
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  • In-Context Compositional Learning vis Sparse Coding Transformer

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    In-Context Compositional Learning vis Sparse Coding Transformer

    Wei Chen, Jingxi Yu, Zichen Miao, Qiang Qiu p49858-49879 from Advances in Neural Information Processing Systems 38
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  • In-Context Fully Decentralized Cooperative Multi-Agent Reinforcement Learning

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    In-Context Fully Decentralized Cooperative Multi-Agent Reinforcement Learning

    Chao Li, Bingkun BAO, Yang Gao p164058-164080 from Advances in Neural Information Processing Systems 38
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  • In-Context Impersonation Reveals Large Language Models' Strengths and Biases

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    In-Context Impersonation Reveals Large Language Models' Strengths and Biases

    Leonard Salewski, Stephan Alaniz, Isabel Rio-Torto, Eric Schulz, Zeynep Akata p72044-72057 from Advances in Neural Information Processing Systems 36
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  • In-Context Learning of a Linear Transformer Block: Benefits of the MLP Component and One-Step GD Initialization

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    In-Context Learning of a Linear Transformer Block: Benefits of the MLP Component and One-Step GD Initialization

    Ruiqi Zhang, Jingfeng Wu, Peter Bartlett p18310-18361 from Advances in Neural Information Processing Systems 37
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  • In-context Learning of Linear Dynamical Systems with Transformers: Approximation Bounds and Depth-separation

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    In-context Learning of Linear Dynamical Systems with Transformers: Approximation Bounds and Depth-separation

    Frank Cole, Yuxuan Zhao, Yulong Lu, Tianhao Zhang p187641-187679 from Advances in Neural Information Processing Systems 38
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  • In-Context Learning of Stochastic Differential Equations with Foundation Inference Models

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    In-Context Learning of Stochastic Differential Equations with Foundation Inference Models

    Patrick Seifner, Kostadin Cvejoski, David Berghaus, César Ali Ojeda Marin, Ramsés J. Sánchez p169445-169485 from Advances in Neural Information Processing Systems 38
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  • In-Context Learning State Vector with Inner and Momentum Optimization

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    In-Context Learning State Vector with Inner and Momentum Optimization

    Dongfang Li, Zhenyu Liu, Xinshuo Hu, Zetian Sun, Baotian Hu, Min Zhang p7797-7820 from Advances in Neural Information Processing Systems 37
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  • In-Context Learning Strategies Emerge Rationally

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    In-Context Learning Strategies Emerge Rationally

    Daniel Wurgaft, Ekdeep S Lubana, Core Francisco Park, Hidenori Tanaka, Gautam Reddy, Noah Goodman p131263-131323 from Advances in Neural Information Processing Systems 38
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  • In-Context Learning Unlocked for Diffusion Models

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    In-Context Learning Unlocked for Diffusion Models

    Zhendong Wang, Yifan Jiang, Yadong Lu, Yelong Shen, Pengcheng He, Weizhu Chen, Zhangyang "atlas" Wang, Mingyuan Zhou p8542-8562 from Advances in Neural Information Processing Systems 36
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  • In-Context Learning with Representations: Contextual Generalization of Trained Transformers

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    In-Context Learning with Representations: Contextual Generalization of Trained Transformers

    Tong Yang, Yu Huang, Yingbin Liang, Yuejie Chi p85867-85898 from Advances in Neural Information Processing Systems 37
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  • In-Context Learning with Transformers: Softmax Attention Adapts to Function Lipschitzness

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    In-Context Learning with Transformers: Softmax Attention Adapts to Function Lipschitzness

    Liam Collins, Advait Parulekar, Aryan Mokhtari, Sujay Sanghavi, Sanjay Shakkottai p92638-92696 from Advances in Neural Information Processing Systems 37
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  • In-Context Symmetries: Self-Supervised Learning through Contextual World Models

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    In-Context Symmetries: Self-Supervised Learning through Contextual World Models

    Sharut Gupta, Chenyu Wang, Yifei Wang, Tommi Jaakkola, Stefanie Jegelka p104250-104280 from Advances in Neural Information Processing Systems 37
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  • In-N-Out: Lifting 2D Diffusion Prior for 3D Object Removal via Tuning-Free Latents Alignment

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    In-N-Out: Lifting 2D Diffusion Prior for 3D Object Removal via Tuning-Free Latents Alignment

    Dongting Hu, Huan Fu, Jiaxian Guo, Liuhua Peng, Tingjin Chu, Feng Liu, Tongliang Liu, Mingming Gong p45737-45766 from Advances in Neural Information Processing Systems 37
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  • In-Trajectory Inverse Reinforcement Learning: Learn Incrementally Before an Ongoing Trajectory Terminates

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    In-Trajectory Inverse Reinforcement Learning: Learn Incrementally Before an Ongoing Trajectory Terminates

    Shicheng Liu, Minghui Zhu p117164-117209 from Advances in Neural Information Processing Systems 37
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  • INC: An Indirect Neural Corrector for Auto-Regressive Hybrid PDE Solvers

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    INC: An Indirect Neural Corrector for Auto-Regressive Hybrid PDE Solvers

    Hao Wei, Aleksandra Franz, Björn List, Nils Thuerey p122167-122201 from Advances in Neural Information Processing Systems 38
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  • Incentive-Aware Dynamic Resource Allocation under Long-Term Cost Constraints

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Incentive-Aware Dynamic Resource Allocation under Long-Term Cost Constraints

    Yan Dai, Negin Golrezaei, Patrick Jaillet p101929-101978 from Advances in Neural Information Processing Systems 38
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  • Incentives in Federated Learning: Equilibria, Dynamics, and Mechanisms for Welfare Maximization

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Incentives in Federated Learning: Equilibria, Dynamics, and Mechanisms for Welfare Maximization

    Aniket Murhekar, Zhuowen Yuan, Bhaskar Ray Chaudhury, Bo Li, Ruta Mehta p17811-17831 from Advances in Neural Information Processing Systems 36
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  • Incentives in Private Collaborative Machine Learning

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Incentives in Private Collaborative Machine Learning

    Rachael Sim, Yehong Zhang, Nghia Hoang, Xinyi Xu, Bryan Kian Hsiang Low, Patrick Jaillet p7555-7593 from Advances in Neural Information Processing Systems 36
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