基本信息
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个人简介
I try to maintain a broad set of interests within machine learning, although I specialize in deep learning. Most recently, I have become interested in Bayesian optimization for automatically tuning the meta-parameters of neural networks (and other machine learning models). This approach has already had tremendous success on several benchmark datasets and often out-performs experts in tuning these models. I think this is a promising avenue for making deep learning more accessible, both within the machine learning community and beyond.
In the past I’ve worked on graphical models, inference algorithms, Bayesian modelling, time-series problems and unsupervised feature learning. My main focus has been applying a probabilistic perspective to machine learning methods. This approach often provides a principled way to analyze existing techniques while serving as a launching point for interesting new extensions.
In the past I’ve worked on graphical models, inference algorithms, Bayesian modelling, time-series problems and unsupervised feature learning. My main focus has been applying a probabilistic perspective to machine learning methods. This approach often provides a principled way to analyze existing techniques while serving as a launching point for interesting new extensions.
研究兴趣
论文共 76 篇作者统计合作学者相似作者
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Avi Singh,John D. Co-Reyes,Rishabh Agarwal, Ankesh Anand, Piyush Patil, Xavier Garcia,Peter J. Liu,James Harrison,Jaehoon Lee,Kelvin Xu, Aaron Parisi,Abhishek Kumar,
arxiv(2023)
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C. Daniel Freeman, Laura Culp, Aaron Parisi,Maxwell L. Bileschi,Gamaleldin F. Elsayed, Alex Rizkowsky,Isabelle Simpson,Alex Alemi,Azade Nova,Ben Adlam, Bernd Bohnet,Gaurav Mishra,
CoRR (2023)
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CVPR 2023pp.9999-10008, (2023)
ICLR 2024 (2023)
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Zi Wang, George E. Dahl,Kevin Swersky,Chansoo Lee,Zelda Mariet, Zachary Nado,Justin Gilmer,Jasper Snoek,Zoubin Ghahramani
arXiv (Cornell University) (2022)
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Binghong Chen,Daniel Tarlow,Kevin Swersky,Martin Maas, Pablo Heiber, Ashish Naik,Milad Hashemi,Parthasarathy Ranganathan
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