Paper

  • Learning Diffusion Priors from Observations by Expectation Maximization

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Learning Diffusion Priors from Observations by Expectation Maximization

    François Rozet, Gérôme Andry, François Lanusse, Gilles Louppe p87647-87682 from Advances in Neural Information Processing Systems 37
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  • Learning Discrete Concepts in Latent Hierarchical Models

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Learning Discrete Concepts in Latent Hierarchical Models

    Lingjing Kong, Guangyi Chen, Biwei Huang, Eric Xing, Yuejie Chi, Kun Zhang p36938-36975 from Advances in Neural Information Processing Systems 37
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  • Learning Discrete Latent Variable Structures with Tensor Rank Conditions

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Learning Discrete Latent Variable Structures with Tensor Rank Conditions

    Zhengming Chen, Ruichu Cai, Feng Xie, Jie Qiao, Anpeng Wu, Zijian Li, Zhifeng Hao, Kun Zhang p17398-17427 from Advances in Neural Information Processing Systems 37
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  • Learning Disentangled Representations for Perceptual Point Cloud Quality Assessment via Mutual Information Minimization

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Learning Disentangled Representations for Perceptual Point Cloud Quality Assessment via Mutual Information Minimization

    Ziyu Shan, Yujie Zhang, Yipeng Liu, Yiling Xu p84060-84080 from Advances in Neural Information Processing Systems 37
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  • Learning Distinct and Representative Modes for Image Captioning

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Learning Distinct and Representative Modes for Image Captioning

    Qi Chen, Chaorui Deng, Qi Wu p9472-9485 from Advances in Neural Information Processing Systems 35
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  • Learning Distinguishable Trajectory Representation with Contrastive Loss

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Learning Distinguishable Trajectory Representation with Contrastive Loss

    Tianxu Li, Kun Zhu, Juan Li, Yang Zhang p64454-64478 from Advances in Neural Information Processing Systems 37
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  • Learning Distributed and Fair Policies for Network Load Balancing as Markov Potential Game

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Learning Distributed and Fair Policies for Network Load Balancing as Markov Potential Game

    Zhiyuan Yao, Zihan Ding p28815-28828 from Advances in Neural Information Processing Systems 35
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  • Learning Distributions Generated by Single-Layer ReLU Networks in the Presence of Arbitrary Outliers

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Learning Distributions Generated by Single-Layer ReLU Networks in the Presence of Arbitrary Outliers

    Saikiran Bulusu, Geethu Joseph, M. Cenk Gursoy, Pramod Varshney p10959-10969 from Advances in Neural Information Processing Systems 35
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  • Learning Distributions on Manifolds with Free-Form Flows

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Learning Distributions on Manifolds with Free-Form Flows

    Peter Sorrenson, Felix Draxler, Armand Rousselot, Sander Hummerich, Ullrich Köthe p66961-66994 from Advances in Neural Information Processing Systems 37
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  • Learning diverse causally emergent representations from time series data

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Learning diverse causally emergent representations from time series data

    David McSharry, Christos Kaplanis, Fernando Rosas, Pedro Mediano p119547-119572 from Advances in Neural Information Processing Systems 37
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  • Learning Domain-Aware Detection Head with Prompt Tuning

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Learning Domain-Aware Detection Head with Prompt Tuning

    Haochen Li, Rui Zhang, Hantao Yao, Xinkai Song, Yifan Hao, Yongwei Zhao, Ling Li, Yunji Chen p4248-4262 from Advances in Neural Information Processing Systems 36
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  • Learning Dynamic Attribute-factored World Models for Efficient Multi-object Reinforcement Learning

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Learning Dynamic Attribute-factored World Models for Efficient Multi-object Reinforcement Learning

    Fan Feng, Sara Magliacane p19117-19144 from Advances in Neural Information Processing Systems 36
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