基本信息
views: 323
Career Trajectory
Bio
Research
I work on machine learning, which is the study of algorithms that can learn to solve problems from examples.
Large models and transfer learning Large language models like ChatGPT demonstrate that training large models on many inter-related tasks can have a synergistic effect. I am interested in understanding and applying these principles to improve machine learning in data-constrained settings.
Learning and optimization Algorithms for statistical inference and optimization are the engines that drive machine learning. Although inference and optimization may seem like distinct problems, there is a close interplay between them. I am interested in this interplay.
Applications You can improve machine learning algorithms when you know something about the structure of the data. I have long been interested in applications that involve discrete reasoning, for example when we built the first artificial agent that plays the board game Go at a superhuman level. I am now interested in applying these principles to biochemistry.
I work on machine learning, which is the study of algorithms that can learn to solve problems from examples.
Large models and transfer learning Large language models like ChatGPT demonstrate that training large models on many inter-related tasks can have a synergistic effect. I am interested in understanding and applying these principles to improve machine learning in data-constrained settings.
Learning and optimization Algorithms for statistical inference and optimization are the engines that drive machine learning. Although inference and optimization may seem like distinct problems, there is a close interplay between them. I am interested in this interplay.
Applications You can improve machine learning algorithms when you know something about the structure of the data. I have long been interested in applications that involve discrete reasoning, for example when we built the first artificial agent that plays the board game Go at a superhuman level. I am now interested in applying these principles to biochemistry.
Research Interests
Papers共 58 篇Author StatisticsCo-AuthorSimilar Experts
By YearBy Citation主题筛选期刊级别筛选合作者筛选合作机构筛选
时间
引用量
主题
期刊级别
合作者
合作机构
NeurIPS 2024 (2024)
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CoRR (2024)
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Annual Conference Computational Learning Theorypp.1516-1572, (2024)
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CoRR (2024)
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arxiv(2024)
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Haonan Duan,Marta Skreta,Leonardo Cotta, Ella Miray Rajaonson,Nikita Dhawan,Alan Aspuru-Guzik,Chris J. Maddison
biorxiv(2024)
NeurIPS 2023 (2023)
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0
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George E. Dahl,Frank Schneider,Zachary Nado,Naman Agarwal,Chandramouli Shama Sastry,Philipp Hennig,Sourabh Medapati,Runa Eschenhagen,Priya Kasimbeg,Daniel Suo,Juhan Bae,Justin Gilmer,Abel L. Peirson, Bilal Khan,Rohan Anil,Mike Rabbat,Shankar Krishnan,Daniel Snider,Ehsan Amid,Kongtao Chen,Chris J. Maddison,Rakshith Vasudev,Michal Badura,Ankush Garg,Peter Mattson
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Author Statistics
#Papers: 58
#Citation: 26667
H-Index: 23
G-Index: 44
Sociability: 5
Diversity: 2
Activity: 60
Co-Author
Co-Institution
D-Core
- 合作者
- 学生
- 导师
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