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

    Jiaqi Wang, Qi Li, Lingjuan Lyu, Fenglong Ma 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, Dawei Gao, Weirui Kuang, Yaliang Li, Bolin Ding 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

    Peiqing Yang, Shangchen Zhou, Qingyi Tao, Chen Change Loy 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, Jing Yu, Shuo Wang, Tianhao Liu, Huaiyu Wan 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

    Sotetsu Koyamada, Shinri Okano, Soichiro Nishimori, Yu Murata, Keigo Habara, Haruka Kita, Shin Ishii 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

    Dayal Singh Kalra, Maissam Barkeshli 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

    Rodrigo Veiga, Ludovic Stephan, Bruno Loureiro, Florent Krzakala, 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

    Yue Xing, Qifan Song, Guang Cheng 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

    Fu-Yun Wang, Zhaoyang Huang, Alexander Bergman, Dazhong Shen, Peng Gao, Michael Lingelbach, Keqiang Sun, Weikang Bian, Guanglu Song, Yu Liu, Xiaogang Wang, Hongsheng Li 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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