Neural Information Processing Systems Foundation, Inc. (NeurIPS)

  • Learning Energy-based Model via Dual-MCMC Teaching

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Learning Energy-based Model via Dual-MCMC Teaching

    Jiali Cui, Tian Han p28861-28872 from Advances in Neural Information Processing Systems 36
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  • Learning Energy-Based Prior Model with Diffusion-Amortized MCMC

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Learning Energy-Based Prior Model with Diffusion-Amortized MCMC

    Ruiqi Gao, Xiaojian (Shawn) Ma, Ying Nian Wu, Sirui Xie, Peiyu Yu, Song-Chun Zhu, Yaxuan Zhu p42717-42747 from Advances in Neural Information Processing Systems 36
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  • Learning Enhanced Representation for Tabular Data Via Neighborhood Propagation

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Learning Enhanced Representation for Tabular Data Via Neighborhood Propagation

    Kounianhua Du, Quan Gan, Jiarui Jin, Yangkun Wang, David P Wipf, Weinan Zhang, Zheng Zhang, Xilong Zhao, Ruiwen Zhou p16373-16384 from Advances in Neural Information Processing Systems 35
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  • Learning Environment-Aware Affordance for 3D Articulated Object Manipulation under Occlusions

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Learning Environment-Aware Affordance for 3D Articulated Object Manipulation under Occlusions

    Kai Cheng, Hao Dong, Chuanruo Ning, Ruihai Wu, Guanqi Zhan, Yan Zhao p60966-60983 from Advances in Neural Information Processing Systems 36
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  • Learning Equilibria in Adversarial Team Markov Games: A Nonconvex-Hidden-Concave Min-Max Optimization Problem

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Learning Equilibria in Adversarial Team Markov Games: A Nonconvex-Hidden-Concave Min-Max Optimization Problem

    Fivos Kalogiannis, Ioannis Panageas, Jingming Yan p92832-92890 from Advances in Neural Information Processing Systems 37
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  • Learning Equivariant Segmentation with Instance-Unique Querying

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Learning Equivariant Segmentation with Instance-Unique Querying

    James Liang, Dongfang Liu, Wenguan Wang p12826-12840 from Advances in Neural Information Processing Systems 35
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  • Learning Exponential Families from Truncated Samples

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Learning Exponential Families from Truncated Samples

    Jane Lee, Andre Wibisono, Emmanouil Zampetakis p34826-34851 from Advances in Neural Information Processing Systems 36
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  • Learning Expressive Meta-Representations with Mixture of Expert Neural Processes

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Learning Expressive Meta-Representations with Mixture of Expert Neural Processes

    Herke Van Hoof, Qi Wang p26242-26255 from Advances in Neural Information Processing Systems 35
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  • Learning Fine-grained View-Invariant Representations from Unpaired Ego-Exo Videos via Temporal Alignment

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Learning Fine-grained View-Invariant Representations from Unpaired Ego-Exo Videos via Temporal Alignment

    Kristen Grauman, Zihui (Sherry) Xue p53688-53710 from Advances in Neural Information Processing Systems 36
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  • Learning Formal Mathematics From Intrinsic Motivation

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Learning Formal Mathematics From Intrinsic Motivation

    David Broman, Noah Goodman, Nick Haber, Gabriel Poesia p43032-43057 from Advances in Neural Information Processing Systems 37
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  • Learning Fractional White Noises in Neural Stochastic Differential Equations

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Learning Fractional White Noises in Neural Stochastic Differential Equations

    Jaesik Choi, Thanh Nguyen-Tang, Anh Tong, Toan Tran p37660-37675 from Advances in Neural Information Processing Systems 35
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  • Learning Frequency-Adapted Vision Foundation Model for Domain Generalized Semantic Segmentation

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Learning Frequency-Adapted Vision Foundation Model for Domain Generalized Semantic Segmentation

    Qi Bi, Yawen Huang, Wei Ji, Yuexiang Li, Jingjun Yi, Haolan Zhan, Hao Zheng, Yefeng Zheng p94047-94072 from Advances in Neural Information Processing Systems 37
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  • Learning from a Sample in Online Algorithms

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Learning from a Sample in Online Algorithms

    C. J. Argue, Alan Frieze, Anupam Gupta, Christopher Seiler p13852-13863 from Advances in Neural Information Processing Systems 35
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  • Learning from Active Human Involvement through Proxy Value Propagation

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Learning from Active Human Involvement through Proxy Value Propagation

    Chenda Duan, Quanyi Li, Wenjie Mo, Zhenghao (Mark) Peng, Bolei Zhou p77969-77992 from Advances in Neural Information Processing Systems 36
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  • Learning From Biased Soft Labels

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Learning From Biased Soft Labels

    Xin Geng, Yong Rui, Yu Shi, Ning Xu, Xu Yang, Hua Yuan p59566-59584 from Advances in Neural Information Processing Systems 36
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  • Learning from Both Structural and Textual Knowledge for Inductive Knowledge Graph Completion

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Learning from Both Structural and Textual Knowledge for Inductive Knowledge Graph Completion

    Jianfeng Du, Kunxun Qi, Hai Wan p26546-26562 from Advances in Neural Information Processing Systems 36
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  • Learning from Distributed Users in Contextual Linear Bandits Without Sharing the Context

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Learning from Distributed Users in Contextual Linear Bandits Without Sharing the Context

    Christina Fragouli, Osama Hanna, Lin Yang p11049-11062 from Advances in Neural Information Processing Systems 35
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  • Learning from Few Samples: Transformation-Invariant SVMs with Composition and Locality at Multiple Scales

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Learning from Few Samples: Transformation-Invariant SVMs with Composition and Locality at Multiple Scales

    P. R. Kumar, Tao Liu, Xi Liu, Ruida Zhou p9151-9163 from Advances in Neural Information Processing Systems 35
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  • Learning from Future: A Novel Self-Training Framework for Semantic Segmentation

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Learning from Future: A Novel Self-Training Framework for Semantic Segmentation

    Ye Du, Jingjing Fei, Zehua Fu, Wei Li, Qingjie Liu, Yujun Shen, Haochen Wang, Liwei Wu, Rui Zhao p4749-4761 from Advances in Neural Information Processing Systems 35
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  • Learning from higher-order correlations, efficiently: hypothesis tests, random features, and neural networks

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Learning from higher-order correlations, efficiently: hypothesis tests, random features, and neural networks

    Lorenzo Bardone, Federica Gerace, Sebastian Goldt, Eszter Székely p78479-78522 from Advances in Neural Information Processing Systems 37
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  • Learning from Highly Sparse Spatio-temporal Data

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Learning from Highly Sparse Spatio-temporal Data

    Enhong Chen, Leyan Deng, Defu Lian, Chenwang Wu p94022-94046 from Advances in Neural Information Processing Systems 37
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  • Learning from Label Proportions by Learning with Label Noise

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Learning from Label Proportions by Learning with Label Noise

    Clay Scott, Yutong Wang, Jianxin Zhang p26933-26942 from Advances in Neural Information Processing Systems 35
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  • Learning from Noisy Labels via Conditional Distributionally Robust Optimization

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Learning from Noisy Labels via Conditional Distributionally Robust Optimization

    Hui Guo, Boyu Wang, Grace Yi p82627-82672 from Advances in Neural Information Processing Systems 37
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  • Learning from Offline Foundation Features with Tensor Augmentations

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Learning from Offline Foundation Features with Tensor Augmentations

    Emir Konuk, Phitchapha Lertsiravaramet, Christos Matsoukas, Kevin Smith, Moein Sorkhei p120103-120123 from Advances in Neural Information Processing Systems 37
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