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Computer Science

D-Index
54
Citations
34570
World Ranking
4420
National Ranking
2064

Overview

Charles Sutton is a researcher affiliated with Google in the United States. Their work primarily spans the field of Computer Science with a focus on Artificial Intelligence, Information Systems, and Software. Sutton's research integrates aspects of software engineering, natural language processing, and machine learning applied in various contexts including materials science and engineering.

Their publication record includes contributions to several topics as indicated by the distribution of their research themes. These topics include:

  • Software Engineering Research
  • Topic Modeling
  • Software Testing and Debugging Techniques
  • Natural Language Processing Techniques
  • Machine Learning in Materials Science
  • Machine Learning and Algorithms
  • Ferroelectric and Negative Capacitance Devices

Charles Sutton's recent research outputs consist of papers published in notable venues. Among these are:

  • "PaLM: Scaling Language Modeling with Pathways" (2022), published in arXiv (Cornell University)
  • "Knowledge Engineering Using Large Language Models" (2023), published in Leibniz-Zentrum für Informatik (Schloss Dagstuhl)
  • "Show Your Work: Scratchpads for Intermediate Computation with Language Models" (2021), published in arXiv (Cornell University)
  • "Big Code!= Big Vocabulary: Open-Vocabulary Models for Source Code" (2020), published in arXiv (Cornell University)
  • "The IDEAL household energy dataset, electricity, gas, contextual sensor data and survey data for 255 UK homes" (2021), published in Scientific Data

Throughout their career, Sutton has frequently published in several venues, with a majority of papers appearing in arXiv (Cornell University). Other venues include the Leibniz-Zentrum für Informatik (Schloss Dagstuhl), Scientific Data, University of Edinburgh, and OPAL (Open@LaTrobe) at La Trobe University.

The researcher has collaborated with various co-authors on multiple occasions. Frequent collaborators include:

  • Kensen Shi
  • Henryk Michalewski
  • Jacob Austin
  • Augustus Odena
  • Pengcheng Yin

Charles Sutton's work is characterized by a strong interdisciplinary approach that combines software engineering principles, large-scale language modeling, and practical applications of machine learning. This is reflected in their engagement with both foundational AI research and application-driven studies across diverse scientific domains.

Best Publications

  • Advances in Neural Information Processing Systems 25

    Yichuan Zhang;Charles Sutton;Amos Storkey;Zoubin Ghahramani

  • An Introduction to Conditional Random Fields for Relational Learning

    Charles Sutton;Andrew McCallum

  • Introduction to Statistical Relational Learning

    Charles Sutton;Andrew McCallum

  • Proceedings for the 5th International Conference on Learning Representations

    Akash Srivastava;Charles Sutton

  • Dynamic conditional random fields: factorized probabilistic models for labeling and segmenting sequence data

    Charles Sutton;Khashayar Rohanimanesh;Andrew McCallum

  • A Survey of Machine Learning for Big Code and Naturalness

    Miltiadis Allamanis;Earl T. Barr;Premkumar Devanbu;Charles Sutton

  • Advances in Neural Information Processing Systems 28 (NIPS 2015)

    Mingjun Zhong;Nigel Goddard;Charles Sutton

  • An Introduction to Conditional Random Fields

    Charles Sutton;Andrew McCallum

  • Dynamic Conditional Random Fields: Factorized Probabilistic Models for Labeling and Segmenting Sequence Data

    Charles Sutton;Andrew McCallum;Khashayar Rohanimanesh

  • VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learning

    Akash Srivastava;Lazar Valkov;Chris Russell;Michael U. Gutmann

  • A Convolutional Attention Network for Extreme Summarization of Source Code

    Miltiadis Allamanis;Hao Peng;Charles A. Sutton

  • Suggesting accurate method and class names

    Miltiadis Allamanis;Earl T. Barr;Christian Bird;Charles Sutton

  • Learning natural coding conventions

    Miltiadis Allamanis;Earl T. Barr;Christian Bird;Charles Sutton

  • Autoencoding Variational Inference for Topic Models

    Akash Srivastava;Charles Sutton

  • Mining source code repositories at massive scale using language modeling

    Miltiadis Allamanis;Charles Sutton

  • Exploiting machine learning to subvert your spam filter

    Blaine Nelson;Marco Barreno;Fuching Jack Chi;Anthony D. Joseph

  • Sequence-to-point learning with neural networks for nonintrusive load monitoring

    Chaoyun Zhang;Mingjun Zhong;Zongzuo Wang;Nigel Goddard

  • GEMSEC: graph embedding with self clustering

    Benedek Rozemberczki;Ryan Davies;Rik Sarkar;Charles Sutton

  • Statistical machine learning makes automatic control practical for internet datacenters

    Peter Bodík;Rean Griffith;Charles Sutton;Armando Fox

  • Sequence-to-Point Learning with Neural Networks for Non-Intrusive Load Monitoring

    Chaoyun Zhang;Mingjun Zhong;Zongzuo Wang;Nigel H. Goddard

  • Advances in Neural Information Processing Systems 27 (NIPS 2014)

    Mingjun Zhong;Nigel Goddard;Charles Sutton

Frequent Co-Authors

Andrew McCallum
Andrew McCallum University of Massachusetts Amherst
Andrew D. Gordon
Andrew D. Gordon Microsoft (United States)
Michael I. Jordan
Michael I. Jordan University of California, Berkeley
Rishabh Singh
Rishabh Singh Google (United States)
Ernesto Jiménez-Ruiz
Ernesto Jiménez-Ruiz City, University of London
Paul R. Cohen
Paul R. Cohen University of Pittsburgh
Ian Horrocks
Ian Horrocks University of Oxford
Christian Bird
Christian Bird Microsoft (United States)
Rich Caruana
Rich Caruana Microsoft (United States)
Premkumar Devanbu
Premkumar Devanbu University of California, Davis

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