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Charles Sutton is a research scientist at Google DeepMind and an honorary fellow of the School of Informatics at the University of Edinburgh. His research concerns probabilistic methods for machine learning, such as software engineering, natural language processing, computer security, and sustainable energy. He has published several papers on topics such as large language models, code generation, and variational inference.
Charles Sutton. I joined Google in January 2018. My research interests span deep learning, probabilistic machine learning, programming languages, data mining, and software engineering. I'm especially excited about applying deep learning to huge code bases, finding patterns about what makes for good code, leading to tools to help people write ...
Charles Sutton is a Research Scientist at Google DeepMind. His research in machine learning is motivated by a broad range of applications, including natural language processing (NLP), analysis of computer systems, sustainable energy, data analysis, programming languages, and software engineering.
Charles Sutton is a Research Scientist at Google Research. He is interested in a broad range of applications of machine learning, including NLP, analysis of computer systems, software engineering, and program synthesis. His work in software engineering has won an ACM Distinguished Paper Award.
17. Nov. 2010 · An Introduction to Conditional Random Fields. Charles Sutton, Andrew McCallum. Often we wish to predict a large number of variables that depend on each other as well as on other observed variables. Structured prediction methods are essentially a combination of classification and graphical modeling, combining the ability of graphical ...
- Charles Sutton, Andrew McCallum
- 2010
Kexin Pei, David Bieber, Kensen Shi, Charles Sutton and Pengcheng Yin. In International Conference on Machine Learning. 2023. Identifying invariants is an important program analysis task with applications towards program understanding, bug finding, vulnerability analysis, and formal verification.
Charles Sutton, Timothy Hobson, James Geddes, Rich Caruana: Data Diff: Interpretable, Executable Summaries of Changes in Distributions for Data Wrangling. KDD 2018 : 2279-2288
