Paper

  • 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

    Pilsung Kang, Jaehee Kim, Yukyung Lee 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 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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  • A Graph-Theoretic Framework for Understanding Open-World Semi-Supervised Learning

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    A Graph-Theoretic Framework for Understanding Open-World Semi-Supervised Learning

    Yixuan Li, Zhenmei Shi, Yiyou Sun p23934-23967 from Advances in Neural Information Processing Systems 36
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  • A graphon-signal analysis of graph neural networks

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    A graphon-signal analysis of graph neural networks

    Ron Levie p64482-64525 from Advances in Neural Information Processing Systems 36
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  • A Greek Parliament Proceedings Dataset for Computational Linguistics and Political Analysis

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    A Greek Parliament Proceedings Dataset for Computational Linguistics and Political Analysis

    Konstantina Dritsa, Panos Louridas, Ioannis Pavlopoulos, Aikaterini Thoma p28874-28888 from Advances in Neural Information Processing Systems 35
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  • A Guide Through the Zoo of Biased SGD

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    A Guide Through the Zoo of Biased SGD

    Yury Demidovich, Grigory Malinovsky, Peter Richtarik, Igor Sokolov p23158-23171 from Advances in Neural Information Processing Systems 36
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  • A Heat Diffusion Perspective on Geodesic Preserving Dimensionality Reduction

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    A Heat Diffusion Perspective on Geodesic Preserving Dimensionality Reduction

    Ian Adelstein, Edward De Brouwer, Guillaume Huguet, Smita Krishnaswamy, Alexander Tong, Guy Wolf, Yanlei Zhang p6986-7016 from Advances in Neural Information Processing Systems 36
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  • A Heavy-Tailed Algebra for Probabilistic Programming

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    A Heavy-Tailed Algebra for Probabilistic Programming

    Liam Hodgkinson, Feynman Liang, Michael Mahoney p19353-19365 from Advances in Neural Information Processing Systems 36
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  • A hierarchical decomposition for explaining ML performance discrepancies

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    A hierarchical decomposition for explaining ML performance discrepancies

    Jean Feng, Alexej Gossmann, Harvineet Singh, Adarsh Subbaswamy, Fan Xia p128516-128555 from Advances in Neural Information Processing Systems 37
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  • A Hierarchical Spatial Transformer for Massive Point Samples in Continuous Space

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    A Hierarchical Spatial Transformer for Massive Point Samples in Continuous Space

    Christine Angelini, Shigang Chen, Ronald Fick, Wenchong He, Zhe Jiang, Miles Medina, Tingsong Xiao, Zelin Xu p33365-33378 from Advances in Neural Information Processing Systems 36
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  • A Hierarchical Training Paradigm for Antibody Structure-sequence Co-design

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    A Hierarchical Training Paradigm for Antibody Structure-sequence Co-design

    Stan Z. Li, Fang Wu p31140-31157 from Advances in Neural Information Processing Systems 36
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