Paper

  • Prototypical VoteNet for Few-Shot 3D Point Cloud Object Detection

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Prototypical VoteNet for Few-Shot 3D Point Cloud Object Detection

    Shizhen Zhao, Xiaojuan Qi p13838-13851 from Advances in Neural Information Processing Systems 35
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  • PROTOTYPING A SMALL MASS TIMBER HOUSE

    World Conference on Timber Engineering 2025

    PROTOTYPING A SMALL MASS TIMBER HOUSE

    Judith Sheine, Mark Fretz, Mikhail Gershfeld, Jason Stenson p2980-2988 from 14th World Conference on Timber Engineering 2025 (WCTE 2025)
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  • ProtoVAE: A Trustworthy Self-Explainable Prototypical Variational Model

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    ProtoVAE: A Trustworthy Self-Explainable Prototypical Variational Model

    Srishti Gautam, Ahcène Boubekki, Stine Hansen, Suaiba Salahuddin, Robert Jenssen, Marina Höhne, Michael Kampffmeyer p17940-17952 from Advances in Neural Information Processing Systems 35
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  • ProtoX: Explaining a Reinforcement Learning Agent Via Prototyping

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    ProtoX: Explaining a Reinforcement Learning Agent Via Prototyping

    Ronilo Ragodos, Tong Wang, Qihang Lin, Xun Zhou p27239-27252 from Advances in Neural Information Processing Systems 35
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  • ProTransformer: Robustify Transformers via Plug-and-Play Paradigm

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    ProTransformer: Robustify Transformers via Plug-and-Play Paradigm

    Zhichao Hou, Weizhi Gao, Yuchen Shen, Feiyi Wang, Xiaorui Liu p137557-137609 from Advances in Neural Information Processing Systems 37
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  • Provable Acceleration of Nesterov's Accelerated Gradient for Asymmetric Matrix Factorization and Linear Neural Networks

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Provable Acceleration of Nesterov's Accelerated Gradient for Asymmetric Matrix Factorization and Linear Neural Networks

    Zhenghao Xu, Yuqing Wang, Tuo Zhao, Rachel Ward, Molei Tao p33726-33755 from Advances in Neural Information Processing Systems 37
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  • Provable Advantage of Curriculum Learning on Parity Targets with Mixed Inputs

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Provable Advantage of Curriculum Learning on Parity Targets with Mixed Inputs

    Emmanuel Abbe, Elisabetta Cornacchia, Aryo Lotfi p24291-24321 from Advances in Neural Information Processing Systems 36
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  • Provable Adversarial Robustness for Group Equivariant Tasks: Graphs, Point Clouds, Molecules, and More

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Provable Adversarial Robustness for Group Equivariant Tasks: Graphs, Point Clouds, Molecules, and More

    Jan Schuchardt, Yan Scholten, Stephan Günnemann p197-252 from Advances in Neural Information Processing Systems 36
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  • Provable and Efficient Dataset Distillation for Kernel Ridge Regression

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Provable and Efficient Dataset Distillation for Kernel Ridge Regression

    Yilan Chen, Wei Huang, Tsui-Wei Weng p88739-88771 from Advances in Neural Information Processing Systems 37
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  • Provable Benefit of Cutout and CutMix for Feature Learning

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Provable Benefit of Cutout and CutMix for Feature Learning

    Junsoo Oh, Chulhee Yun p114656-114743 from Advances in Neural Information Processing Systems 37
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  • Provable Benefit of Multitask Representation Learning in Reinforcement Learning

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Provable Benefit of Multitask Representation Learning in Reinforcement Learning

    Yuan Cheng, Songtao Feng, Jing Yang, Hong Zhang, Yingbin Liang p31741-31754 from Advances in Neural Information Processing Systems 35
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  • Provable benefits of annealing for estimating normalizing constants: Importance Sampling, Noise-Contrastive Estimation, and beyond

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Provable benefits of annealing for estimating normalizing constants: Importance Sampling, Noise-Contrastive Estimation, and beyond

    Omar Chehab, Aapo Hyvarinen, Andrej Risteski p45945-45970 from Advances in Neural Information Processing Systems 36
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