Paper

  • Learning (Very) Simple Generative Models is Hard

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Learning (Very) Simple Generative Models is Hard

    Sitan Chen, Jerry Li, Yuanzhi Li p35143-35155 from Advances in Neural Information Processing Systems 35
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  • Learning 1D Causal Visual Representation with De-focus Attention Networks

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Learning 1D Causal Visual Representation with De-focus Attention Networks

    Chenxin Tao, Xizhou Zhu, Shiqian Su, Lewei Lu, Changyao Tian, Xuan Luo, Gao Huang, Hongsheng Li, Yu Qiao, Jie Zhou, Jifeng Dai p25913-25937 from Advances in Neural Information Processing Systems 37
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  • Learning 3D Equivariant Implicit Function with Patch-Level Pose-Invariant Representation

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Learning 3D Equivariant Implicit Function with Patch-Level Pose-Invariant Representation

    Xin Hu, Xiaole Tang, Ruixuan Yu, Jian Sun p18362-18387 from Advances in Neural Information Processing Systems 37
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  • Learning 3D Garment Animation from Trajectories of A Piece of Cloth

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Learning 3D Garment Animation from Trajectories of A Piece of Cloth

    Yidi Shao, Chen Loy, Bo Dai p41803-41825 from Advances in Neural Information Processing Systems 37
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  • Learning a 1-layer conditional generative model in total variation

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Learning a 1-layer conditional generative model in total variation

    Ajil Jalal, Justin Kang, Ananya Uppal, Kannan Ramchandran, Eric Price p43202-43253 from Advances in Neural Information Processing Systems 36
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  • Learning a Condensed Frame for Memory-Efficient Video Class-Incremental Learning

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Learning a Condensed Frame for Memory-Efficient Video Class-Incremental Learning

    Yixuan Pei, Zhiwu Qing, Jun Cen, Xiang Wang, Shiwei Zhang, Yaxiong Wang, Mingqian Tang, Nong Sang, Xueming Qian p31002-31016 from Advances in Neural Information Processing Systems 35
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  • Learning a Neuron by a Shallow ReLU Network: Dynamics and Implicit Bias for Correlated Inputs

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Learning a Neuron by a Shallow ReLU Network: Dynamics and Implicit Bias for Correlated Inputs

    Dmitry Chistikov, Matthias Englert, Ranko Lazic p23748-23760 from Advances in Neural Information Processing Systems 36
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  • Learning a Single Neuron Robustly to Distributional Shifts and Adversarial Label Noise

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Learning a Single Neuron Robustly to Distributional Shifts and Adversarial Label Noise

    Shuyao Li, Sushrut Karmalkar, Ilias Diakonikolas, Jelena Diakonikolas p67383-67421 from Advances in Neural Information Processing Systems 37
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  • Learning Action and Reasoning-Centric Image Editing from Videos and Simulation

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Learning Action and Reasoning-Centric Image Editing from Videos and Simulation

    Benno Krojer, Dheeraj Vattikonda, Luis Lara, Varun Jampani, Eva Portelance, Christopher Pal, Siva Reddy p38035-38078 from Advances in Neural Information Processing Systems 37
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  • Learning Active Camera for Multi-Object Navigation

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Learning Active Camera for Multi-Object Navigation

    Peihao Chen, Dongyu Ji, Kunyang Lin, Weiwen Hu, Wenbing Huang, Thomas Li, Mingkui Tan, Chuang Gan p28670-28682 from Advances in Neural Information Processing Systems 35
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  • Learning Adaptive Tensorial Density Fields for Clean Cryo-ET Reconstruction

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Learning Adaptive Tensorial Density Fields for Clean Cryo-ET Reconstruction

    Yuanhao Wang, Ramzi Idoughi, Wolfgang Heidrich p72270-72283 from Advances in Neural Information Processing Systems 36
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  • Learning Adversarial Low-rank Markov Decision Processes with Unknown Transition and Full-information Feedback

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Learning Adversarial Low-rank Markov Decision Processes with Unknown Transition and Full-information Feedback

    Canzhe Zhao, Ruofeng Yang, Baoxiang Wang, Xuezhou Zhang, Shuai Li p59107-59123 from Advances in Neural Information Processing Systems 36
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