Paper

  • pFedClub: Controllable Heterogeneous Model Aggregation for Personalized Federated Learning

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    pFedClub: Controllable Heterogeneous Model Aggregation for Personalized Federated Learning

    Qi Li, Lingjuan Lyu, Fenglong Ma, Jiaqi Wang p54692-54713 from Advances in Neural Information Processing Systems 37
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  • PFL-Bench: A Comprehensive Benchmark for Personalized Federated Learning

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    PFL-Bench: A Comprehensive Benchmark for Personalized Federated Learning

    Daoyuan Chen, Bolin Ding, Dawei Gao, Weirui Kuang, Yaliang Li p9344-9360 from Advances in Neural Information Processing Systems 35
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  • PGDiff: Guiding Diffusion Models for Versatile Face Restoration via Partial Guidance

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    PGDiff: Guiding Diffusion Models for Versatile Face Restoration via Partial Guidance

    Chen Change Loy, Qingyi Tao, Peiqing Yang, Shangchen Zhou p32194-32214 from Advances in Neural Information Processing Systems 36
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  • PGN: The RNN's New Successor is Effective for Long-Range Time Series Forecasting

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    PGN: The RNN's New Successor is Effective for Long-Range Time Series Forecasting

    Yuxin Jia, Youfang Lin, Tianhao Liu, Huaiyu Wan, Shuo Wang, Jing Yu p84139-84168 from Advances in Neural Information Processing Systems 37
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  • Pgx: Hardware-Accelerated Parallel Game Simulators for Reinforcement Learning

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Pgx: Hardware-Accelerated Parallel Game Simulators for Reinforcement Learning

    Keigo Habara, Shin Ishii, Haruka Kita, Sotetsu Koyamada, Yu Murata, Soichiro Nishimori, Shinri Okano p45716-45743 from Advances in Neural Information Processing Systems 36
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  • Phase diagram of early training dynamics in deep neural networks: effect of the learning rate, depth, and width

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Phase diagram of early training dynamics in deep neural networks: effect of the learning rate, depth, and width

    Maissam Barkeshli, Dayal Singh Kalra p51621-51662 from Advances in Neural Information Processing Systems 36
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  • Phase Diagram of Stochastic Gradient Descent in High-Dimensional Two-Layer Neural Networks

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Phase Diagram of Stochastic Gradient Descent in High-Dimensional Two-Layer Neural Networks

    Florent Krzakala, Bruno Loureiro, Ludovic Stephan, Rodrigo Veiga, Lenka Zdeborová p23244-23255 from Advances in Neural Information Processing Systems 35
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  • Phase Transition from Clean Training to Adversarial Training

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Phase Transition from Clean Training to Adversarial Training

    Guang Cheng, Qifan Song, Yue Xing p9330-9343 from Advances in Neural Information Processing Systems 35
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  • Phase Transitions in When Feedback is Useful

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Phase Transitions in When Feedback is Useful

    Lokesh Boominathan, Xaq Pitkow p10849-10861 from Advances in Neural Information Processing Systems 35
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  • Phased Consistency Models

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Phased Consistency Models

    Alexander Bergman, Weikang Bian, Peng Gao, Zhaoyang Huang, Hongsheng Li, Michael Lingelbach, Yu Liu, Dazhong Shen, Guanglu Song, Keqiang Sun, Fu-Yun Wang, Xiaogang Wang p83951-84009 from Advances in Neural Information Processing Systems 37
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  • PHASES OF ARCHITECTURAL DESIGN IN THE DEVELOPMENT OF A MODULAR AND PANELIZED WOODEN BUILDING WITH EARLY INTEGRATION PROCESS

    World Conference on Timber Engineering 2025

    PHASES OF ARCHITECTURAL DESIGN IN THE DEVELOPMENT OF A MODULAR AND PANELIZED WOODEN BUILDING WITH EARLY INTEGRATION PROCESS

    Jose Luis Caamaño, Fabiana Lorca, Daniela Méndez p5206-5212 from 14th World Conference on Timber Engineering 2025 (WCTE 2025)
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  • PHENOMENOLOGICAL MODEL FOR SEISMIC DESIGN OF MULTI-STOREY CLT BUILDINGS: CALIBRATION OF INPUT PARAMETERS

    World Conference on Timber Engineering 2023 (WCTE 2023)

    PHENOMENOLOGICAL MODEL FOR SEISMIC DESIGN OF MULTI-STOREY CLT BUILDINGS: CALIBRATION OF INPUT PARAMETERS

    Luca Pozza, Stefano Pacchioli, Andrea Polastri, Valentino Nicolussi, Diego A. Talledo, Ian Smith p2805-2810 from World Conference on Timber Engineering (WCTE 2023)
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