Paper

  • Deep Generative Model for Periodic Graphs

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Deep Generative Model for Periodic Graphs

    Xiaojie Guo, Shiyu Wang, Liang Zhao p35797-35810 from Advances in Neural Information Processing Systems 35
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  • Deep Graph Mating

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Deep Graph Mating

    Seok-Hee Hong, Yongcheng Jing, Dacheng Tao p9753-9772 from Advances in Neural Information Processing Systems 37
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  • Deep Graph Neural Networks via Posteriori-Sampling-based Node-Adaptative Residual Module

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Deep Graph Neural Networks via Posteriori-Sampling-based Node-Adaptative Residual Module

    Yixuan Du, Di Jin, Stan Z. Li, Jun Xia, Carl Yang, Zhizhi Yu, Zelin Zang, Rui Zhang, Ruqiong Zhang, Jingbo Zhou p68211-68238 from Advances in Neural Information Processing Systems 37
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  • Deep Hierarchical Planning from Pixels

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Deep Hierarchical Planning from Pixels

    Pieter Abbeel, Ian Fischer, Danijar Hafner, Kuang-Huei Lee p26091-26104 from Advances in Neural Information Processing Systems 35
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  • Deep Homomorphism Networks

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Deep Homomorphism Networks

    Takanori Maehara, Hoang NT p56076-56107 from Advances in Neural Information Processing Systems 37
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  • Deep Insights into Noisy Pseudo Labeling on Graph Data

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Deep Insights into Noisy Pseudo Labeling on Graph Data

    Jiashun Cheng, Jia Li, Yang Liu, Yu Rong, Fugee Tsung, Wenjia Wang, Botao Wang p76214-76228 from Advances in Neural Information Processing Systems 36
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  • Deep Invariant Networks with Differentiable Augmentation Layers

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Deep Invariant Networks with Differentiable Augmentation Layers

    Alexandre Gramfort, Thomas Moreau, Cédric Rommel p35672-35683 from Advances in Neural Information Processing Systems 35
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  • Deep Learning for Computing Convergence Rates of Markov Chains

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Deep Learning for Computing Convergence Rates of Markov Chains

    Jose Blanchet, Peter Glynn, Yanlin Qu p84777-84798 from Advances in Neural Information Processing Systems 37
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  • Deep Learning in Medical Image Registration: Magic or Mirage?

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Deep Learning in Medical Image Registration: Magic or Mirage?

    Pratik Chaudhari, James Gee, Rohit Jena, Deeksha Sethi p108331-108353 from Advances in Neural Information Processing Systems 37
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  • Deep Learning Methods for Proximal Inference Via Maximum Moment Restriction

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Deep Learning Methods for Proximal Inference Via Maximum Moment Restriction

    Andrew Beam, David Bellamy, Tom Kolokotrones, Benjamin Kompa, James M Robins p11189-11201 from Advances in Neural Information Processing Systems 35
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  • Deep Learning Through A Telescoping Lens: A Simple Model Provides Empirical Insights On Grokking, Gradient Boosting & Beyond

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Deep Learning Through A Telescoping Lens: A Simple Model Provides Empirical Insights On Grokking, Gradient Boosting & Beyond

    Alicia Curth, Alan Jeffares, Mihaela van der Schaar p123498-123533 from Advances in Neural Information Processing Systems 37
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  • Deep learning with kernels through RKHM and the Perron-Frobenius operator

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Deep learning with kernels through RKHM and the Perron-Frobenius operator

    Yuka Hashimoto, Masahiro Ikeda, Hachem Kadri p50677-50696 from Advances in Neural Information Processing Systems 36
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