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个人简介
Dr. Chen's research focuses on the theoretical understanding of deep learning, with applications in foundation models, AutoML, computer vision, natural language processing, and addressing scientific problems. His work also encompasses domain adaptation/generalization and self-supervised learning. He published papers on CVPR, ECCV, ICLR, ICML, Neurips, etc. Dr. Chen's work on training-free neural architecture design was highlighted as the "Featured Advances in Artificial Intelligence" in the National Science Foundation (NSF) newsletter in 2022. Dr. Chen co-organized the 4th and 5th versions of UG2+ workshop and challenge in CVPR 2021 and 2022. He also holds a position on the board of the One World Seminar Series on the Mathematics of Machine Learning.
研究兴趣
论文共 33 篇作者统计合作学者相似作者
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CoRR (2024)
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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCEno. 2 (2024): 749-763
crossref(2024)
CVPR 2024 (2024)
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Yimeng Zhang, Akshay Karkal Kamath,Qiucheng Wu,Zhiwen Fan,Wuyang Chen,Zhangyang Wang,Shiyu Chang,Sijia Liu,Cong Hao
ASP-DACpp.745-750, (2023)
International Conference on Automated Machine Learningpp.14/1-29, (2023)
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Sheng Shen, Lei Hou,Yanqi Zhou, Nan Du,Shayne Longpre, Jason Lee,Hyung Won Chung,Barret Zoph,William Fedus,Xinyun Chen,Tu Vu,Yuexin Wu,
arXiv (Cornell University) (2023)
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