Neural Information Processing Systems Foundation, Inc. (NeurIPS)

  • Feature Distillation is the Better Choice for Model-Heterogeneous Federated Learning

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Feature Distillation is the Better Choice for Model-Heterogeneous Federated Learning

    Yichen Li, Xiuying Wang, Wenchao Xu, Haozhao Wang, Yining Qi, Jiahua Dong, Ruixuan Li p115939-115957 from Advances in Neural Information Processing Systems 38
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  • Feature Dropout: Revisiting the Role of Augmentations in Contrastive Learning

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Feature Dropout: Revisiting the Role of Augmentations in Contrastive Learning

    Alex Tamkin, Margalit Glasgow, Xiluo He, Noah Goodman p61806-61835 from Advances in Neural Information Processing Systems 36
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  • Feature Learning for Interpretable, Performant Decision Trees

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Feature Learning for Interpretable, Performant Decision Trees

    Jack Good, Torin Kovach, Kyle Miller, Artur Dubrawski p66571-66582 from Advances in Neural Information Processing Systems 36
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  • Feature Learning in L2-Regularized DNNs: Attraction/Repulsion and Sparsity

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Feature Learning in L2-Regularized DNNs: Attraction/Repulsion and Sparsity

    Arthur Jacot, Eugene Golikov, Clement Hongler, Franck Gabriel p6763-6774 from Advances in Neural Information Processing Systems 35
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  • Feature learning via mean-field Langevin dynamics: classifying sparse parities and beyond

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Feature learning via mean-field Langevin dynamics: classifying sparse parities and beyond

    Taiji Suzuki, Denny Wu, Kazusato Oko, Atsushi Nitanda p34536-34556 from Advances in Neural Information Processing Systems 36
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  • Feature Likelihood Divergence: Evaluating the Generalization of Generative Models Using Samples

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Feature Likelihood Divergence: Evaluating the Generalization of Generative Models Using Samples

    Marco Jiralerspong, Joey Bose, Ian Gemp, Chongli Qin, Yoram Bachrach, Gauthier Gidel p33095-33119 from Advances in Neural Information Processing Systems 36
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  • Feature Selection in the Contrastive Analysis Setting

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Feature Selection in the Contrastive Analysis Setting

    Ethan Weinberger, Ian Covert, Su-In Lee p66102-66126 from Advances in Neural Information Processing Systems 36
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  • Feature Unlearning: Theoretical Foundations and Practical Applications with Shuffling

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Feature Unlearning: Theoretical Foundations and Practical Applications with Shuffling

    Yue Yang, Jinhao Li, Hao Wang p13194-13241 from Advances in Neural Information Processing Systems 38
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  • Feature-aware Modulation for Learning from Temporal Tabular Data

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Feature-aware Modulation for Learning from Temporal Tabular Data

    Haorun Cai, Han-Jia Ye p167662-167685 from Advances in Neural Information Processing Systems 38
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  • Feature-Based Instance Neighbor Discovery: Advanced Stable Test-Time Adaptation in Dynamic World

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Feature-Based Instance Neighbor Discovery: Advanced Stable Test-Time Adaptation in Dynamic World

    Qinting Jiang, Chuyang Ye, Dongyan Wei, Bingli Wang, Yuan Xue, Jingyan Jiang, Zhi Wang p153220-153252 from Advances in Neural Information Processing Systems 38
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  • Feature-Learning Networks Are Consistent Across Widths At Realistic Scales

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Feature-Learning Networks Are Consistent Across Widths At Realistic Scales

    Nikhil Vyas, Alexander Atanasov, Blake Bordelon, Depen Morwani, Sabarish Sainathan, Cengiz Pehlevan p1036-1060 from Advances in Neural Information Processing Systems 36
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  • Feature-Level Adversarial Attacks and Ranking Disruption for Visible-Infrared Person Re-identification

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Feature-Level Adversarial Attacks and Ranking Disruption for Visible-Infrared Person Re-identification

    Xi Yang, Huanling Liu, De Cheng, Nannan Wang, Xinbo Gao p135043-135061 from Advances in Neural Information Processing Systems 37
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  • Feature-Proxy Transformer for Few-Shot Segmentation

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Feature-Proxy Transformer for Few-Shot Segmentation

    Jian-Wei Zhang, Yifan Sun, Yi Yang, Wei Chen p6575-6588 from Advances in Neural Information Processing Systems 35
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  • FeCAM: Exploiting the Heterogeneity of Class Distributions in Exemplar-Free Continual Learning

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    FeCAM: Exploiting the Heterogeneity of Class Distributions in Exemplar-Free Continual Learning

    Dipam Goswami, Yuyang Liu, Bartlomiej Twardowski, Joost Van De Weijer p6582-6595 from Advances in Neural Information Processing Systems 36
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  • Fed-CO$_{2}$: Cooperation of Online and Offline Models for Severe Data Heterogeneity in Federated Learning

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Fed-CO$_{2}$: Cooperation of Online and Offline Models for Severe Data Heterogeneity in Federated Learning

    Zhongyi Cai, Ye Shi, Wei Huang, Jingya Wang p21343-21367 from Advances in Neural Information Processing Systems 36
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  • Fed-FA: Theoretically Modeling Client Data Divergence for Federated Language Backdoor Defense

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Fed-FA: Theoretically Modeling Client Data Divergence for Federated Language Backdoor Defense

    Zhiyuan Zhang, Deli Chen, Hao Zhou, Fandong Meng, Jie Zhou, Xu Sun p62006-62031 from Advances in Neural Information Processing Systems 36
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  • Fed-GraB: Federated Long-tailed Learning with Self-Adjusting Gradient Balancer

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Fed-GraB: Federated Long-tailed Learning with Self-Adjusting Gradient Balancer

    Zikai Xiao, Zihan Chen, Songshang Liu, Hualiang Wang, Yang Feng, Jin Hao, Joey Tianyi Zhou, Jian Wu, Howard Yang, Zuozhu Liu p77745-77757 from Advances in Neural Information Processing Systems 36
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  • FedAvg with Fine Tuning: Local Updates Lead to Representation Learning

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    FedAvg with Fine Tuning: Local Updates Lead to Representation Learning

    Liam Collins, Hamed Hassani, Aryan Mokhtari, Sanjay Shakkottai p10572-10586 from Advances in Neural Information Processing Systems 35
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  • FedAvP: Augment Local Data via Shared Policy in Federated Learning

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    FedAvP: Augment Local Data via Shared Policy in Federated Learning

    Minui Hong, Junhyeog Yun, Insu Jeon, Gunhee Kim p18090-18121 from Advances in Neural Information Processing Systems 37
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  • FedEL: Federated Elastic Learning for Heterogeneous Devices

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    FedEL: Federated Elastic Learning for Heterogeneous Devices

    Letian Zhang, Bo Chen, Jieming Bian, Lei Wang, Jie Xu p40114-40145 from Advances in Neural Information Processing Systems 38
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  • Federated Behavioural Planes: Explaining the Evolution of Client Behaviour in Federated Learning

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Federated Behavioural Planes: Explaining the Evolution of Client Behaviour in Federated Learning

    Dario Fenoglio, Gabriele Dominici, Pietro Barbiero, Alberto Tonda, Martin Gjoreski, Marc Langheinrich p112777-112813 from Advances in Neural Information Processing Systems 37
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  • Federated Black-Box Adaptation for Semantic Segmentation

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Federated Black-Box Adaptation for Semantic Segmentation

    Jay Paranjape, Shameema Sikder, S. Vedula, Vishal Patel p112814-112835 from Advances in Neural Information Processing Systems 37
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  • Federated Compositional Deep AUC Maximization

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Federated Compositional Deep AUC Maximization

    Xinwen Zhang, Yihan Zhang, Tianbao Yang, Richard Souvenir, Hongchang Gao p9648-9660 from Advances in Neural Information Processing Systems 36
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  • Federated Conditional Stochastic Optimization

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Federated Conditional Stochastic Optimization

    Xidong Wu, Jianhui Sun, Zhengmian Hu, Junyi Li, Aidong Zhang, Heng Huang p5752-5764 from Advances in Neural Information Processing Systems 36
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