基本信息
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Career Trajectory
Bio
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.
Research Interests
Papers共 33 篇Author StatisticsCo-AuthorSimilar Experts
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CoRR (2024)
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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCEno. 2 (2024): 749-763
CVPR 2024 (2024)
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crossref(2024)
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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arXiv (Cornell University) (2023)
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