Neural Information Processing Systems Foundation, Inc. (NeurIPS)

  • A General Framework for Equivariant Neural Networks on Reductive Lie Groups

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    A General Framework for Equivariant Neural Networks on Reductive Lie Groups

    Ilyes Batatia, Mario Geiger, Jose Munoz, Tess Smidt, Lior Silberman, Christoph Ortner p55260-55284 from Advances in Neural Information Processing Systems 36
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  • A General Framework for Robust G-Invariance in G-Equivariant Networks

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    A General Framework for Robust G-Invariance in G-Equivariant Networks

    Sophia Sanborn, Nina Miolane p67103-67124 from Advances in Neural Information Processing Systems 36
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  • A General Protocol to Probe Large Vision Models for 3D Physical Understanding

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    A General Protocol to Probe Large Vision Models for 3D Physical Understanding

    Guanqi Zhan, Chuanxia Zheng, Weidi Xie, Andrew Zisserman p43468-43498 from Advances in Neural Information Processing Systems 37
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  • A General Theory of Correct, Incorrect, and Extrinsic Equivariance

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    A General Theory of Correct, Incorrect, and Extrinsic Equivariance

    Dian Wang, Xupeng Zhu, Jung Yeon Park, Mingxi Jia, Guanang Su, Robert Platt, Robin Walters p40006-40029 from Advances in Neural Information Processing Systems 36
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  • A General-Purpose Theorem for High-Probability Bounds of Stochastic Approximation with Polyak Averaging

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    A General-Purpose Theorem for High-Probability Bounds of Stochastic Approximation with Polyak Averaging

    Sajad Khodadadian, Martin Zubeldia p56698-56742 from Advances in Neural Information Processing Systems 38
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  • A Generalist Intracortical Motor Decoder

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    A Generalist Intracortical Motor Decoder

    Joel Ye, Fabio Rizzoglio, Xuan Ma, Adam Smoulder, Hongwei Mao, Gary Blumenthal, William Hockeimer, Nicolas Kunigk, Dalton Moore, Patrick Marino, Raeed Chowdhury, J. Patrick Mayo, Aaron Batista, Steven Chase, Michael Boninger, et al. p122532-122572 from Advances in Neural Information Processing Systems 38
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  • A Generalized Binary Tree Mechanism for Private Approximation of All-Pair Shortest Distances

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    A Generalized Binary Tree Mechanism for Private Approximation of All-Pair Shortest Distances

    Zongrui Zou, Chenglin Fan, Michael Dinitz, Jingcheng Liu, Jalaj Upadhyay p40931-40960 from Advances in Neural Information Processing Systems 38
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  • A Generalized Bisimulation Metric of State Similarity between Markov Decision Processes: From Theoretical Propositions to Applications

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    A Generalized Bisimulation Metric of State Similarity between Markov Decision Processes: From Theoretical Propositions to Applications

    Zhenyu Tao, Wei Xu, Xiaohu You p54249-54276 from Advances in Neural Information Processing Systems 38
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  • A Generalized Label Shift Perspective for Cross-Domain Gaze Estimation

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    A Generalized Label Shift Perspective for Cross-Domain Gaze Estimation

    Hao-Ran Yang, Xiaohui Chen, Chuan-Xian Ren p5780-5805 from Advances in Neural Information Processing Systems 38
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  • A generalized neural tangent kernel for surrogate gradient learning

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    A generalized neural tangent kernel for surrogate gradient learning

    Luke Eilers, Raoul-Martin Memmesheimer, Sven Goedeke p9026-9085 from Advances in Neural Information Processing Systems 37
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  • A Generative Model of Symmetry Transformations

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    A Generative Model of Symmetry Transformations

    James Allingham, Bruno Mlodozeniec, Shreyas Padhy, Javier Antorán, David Krueger, Richard Turner, Eric Nalisnick, José Hernández-Lobato p91091-91130 from Advances in Neural Information Processing Systems 37
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  • A generative model of the hippocampal formation trained with theta driven local learning rules

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    A generative model of the hippocampal formation trained with theta driven local learning rules

    Tom M George, Kimberly Stachenfeld, Caswell Barry, Claudia Clopath, Tomoki Fukai p1644-1658 from Advances in Neural Information Processing Systems 36
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  • A Geometric Analysis of PCA

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    A Geometric Analysis of PCA

    Ayoub El Hanchi, Murat Erdogdu, Chris Maddison p176707-176738 from Advances in Neural Information Processing Systems 38
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  • A geometric framework for momentum-based optimizers for low-rank training

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    A geometric framework for momentum-based optimizers for low-rank training

    Steffen Schotthöfer, Timon Klein, Jonas Kusch p100015-100045 from Advances in Neural Information Processing Systems 38
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  • A Geometric Perspective on Variational Autoencoders

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    A Geometric Perspective on Variational Autoencoders

    Clément Chadebec, Stephanie Allassonniere p19618-19630 from Advances in Neural Information Processing Systems 35
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  • A Geometric View of Data Complexity: Efficient Local Intrinsic Dimension Estimation with Diffusion Models

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    A Geometric View of Data Complexity: Efficient Local Intrinsic Dimension Estimation with Diffusion Models

    Hamidreza Kamkari, Brendan Ross, Rasa Hosseinzadeh, Jesse Cresswell, Gabriel Loaiza-Ganem p38307-38354 from Advances in Neural Information Processing Systems 37
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  • A Geometry-Aware Metric for Mode Collapse in Time Series Generative Models

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    A Geometry-Aware Metric for Mode Collapse in Time Series Generative Models

    Yassine ABBAHADDOU, Amine Aboussalah p168149-168179 from Advances in Neural Information Processing Systems 38
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  • A Global Depth-Range-Free Multi-View Stereo Transformer Network with Pose Embedding

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    A Global Depth-Range-Free Multi-View Stereo Transformer Network with Pose Embedding

    Yitong Dong, Yijin Li, Zhaoyang Huang, Weikang Bian, Jingbo Liu, Hujun Bao, Zhaopeng Cui, Hongsheng Li, Guofeng Zhang p64773-64793 from Advances in Neural Information Processing Systems 37
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  • A Globally Optimal Portfolio for m-Sparse Sharpe Ratio Maximization

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    A Globally Optimal Portfolio for m-Sparse Sharpe Ratio Maximization

    Yizun Lin, Zhao-Rong Lai, Cheng Li p17133-17160 from Advances in Neural Information Processing Systems 37
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  • A Gradient Accumulation Method for Dense Retriever under Memory Constraint

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    A Gradient Accumulation Method for Dense Retriever under Memory Constraint

    Jaehee Kim, Yukyung Lee, Pilsung Kang p11765-11788 from Advances in Neural Information Processing Systems 37
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  • A Gradient Estimator Via L1-Randomization for Online Zero-Order Optimization with Two Point Feedback

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    A Gradient Estimator Via L1-Randomization for Online Zero-Order Optimization with Two Point Feedback

    Arya Akhavan, Evgenii Chzhen, Massimiliano Pontil, Alexandre Tsybakov p7685-7696 from Advances in Neural Information Processing Systems 35
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  • A Gradient Guidance Perspective on Stepwise Preference Optimization for Diffusion Models

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    A Gradient Guidance Perspective on Stepwise Preference Optimization for Diffusion Models

    Joshua Tian Jin Tee, Hee Suk Yoon, Abu Hanif Muhammad Syarubany, Eunseop Yoon, Chang Yoo p106735-106764 from Advances in Neural Information Processing Systems 38
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  • A Gradient Guided Diffusion Framework for Chance Constrained Programming

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    A Gradient Guided Diffusion Framework for Chance Constrained Programming

    Boyang Zhang, Zhiguo Wang, Ya-Feng Liu p163954-163973 from Advances in Neural Information Processing Systems 38
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  • A Gradient Sampling Method with Complexity Guarantees for Lipschitz Functions in High and Low Dimensions

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    A Gradient Sampling Method with Complexity Guarantees for Lipschitz Functions in High and Low Dimensions

    Damek Davis, Dmitriy Drusvyatskiy, Yin Tat Lee, Swati Padmanabhan, Guanghao Ye p6692-6703 from Advances in Neural Information Processing Systems 35
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