Charles Sutton
TitleCited byYear
An introduction to conditional random fields
C Sutton, A McCallum
Foundations and Trends® in Machine Learning 4 (4), 267-373, 2012
18762012
Introduction to statistical relational learning
D Koller, N Friedman, S Džeroski, C Sutton, A McCallum, A Pfeffer, ...
MIT press, 2007
15352007
Dynamic conditional random fields: Factorized probabilistic models for labeling and segmenting sequence data
C Sutton, A McCallum, K Rohanimanesh
Journal of Machine Learning Research 8 (Mar), 693-723, 2007
5212007
Statistical Machine Learning Makes Automatic Control Practical for Internet Datacenters.
P Bodík, R Griffith, CA Sutton, A Fox, MI Jordan, DA Patterson
HotCloud 9, 12-12, 2009
2332009
Piecewise training for undirected models
C Sutton, A McCallum
arXiv preprint arXiv:1207.1409, 2012
2002012
Learning natural coding conventions
M Allamanis, ET Barr, C Bird, C Sutton
Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations …, 2014
1862014
Exploiting Machine Learning to Subvert Your Spam Filter.
B Nelson, M Barreno, FJ Chi, AD Joseph, BIP Rubinstein, U Saini, ...
LEET 8, 1-9, 2008
1762008
Suggesting accurate method and class names
M Allamanis, ET Barr, C Bird, C Sutton
Proceedings of the 2015 10th Joint Meeting on Foundations of Software …, 2015
1722015
Mining source code repositories at massive scale using language modeling
M Allamanis, C Sutton
Proceedings of the 10th Working Conference on Mining Software Repositories …, 2013
1722013
A convolutional attention network for extreme summarization of source code
M Allamanis, H Peng, C Sutton
International Conference on Machine Learning, 2091-2100, 2016
1532016
Collective segmentation and labeling of distant entities in information extraction
C Sutton, A McCallum
MASSACHUSETTS UNIV AMHERST DEPT OF COMPUTER SCIENCE, 2004
1512004
Probabilistic inference over RFID streams in mobile environments
T Tran, C Sutton, R Cocci, Y Nie, Y Diao, P Shenoy
2009 IEEE 25th International Conference on Data Engineering, 1096-1107, 2009
1412009
A survey of machine learning for big code and naturalness
M Allamanis, ET Barr, P Devanbu, C Sutton
ACM Computing Surveys (CSUR) 51 (4), 81, 2018
1302018
Veegan: Reducing mode collapse in gans using implicit variational learning
A Srivastava, L Valkov, C Russell, MU Gutmann, C Sutton
Advances in Neural Information Processing Systems, 3308-3318, 2017
1212017
Piecewise pseudolikelihood for efficient training of conditional random fields
C Sutton, A McCallum
Proceedings of the 24th international conference on Machine learning, 863-870, 2007
1142007
Mining idioms from source code
M Allamanis, C Sutton
Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations …, 2014
922014
Autoencoding variational inference for topic models
A Srivastava, C Sutton
arXiv preprint arXiv:1703.01488, 2017
862017
Why, when, and what: analyzing stack overflow questions by topic, type, and code
M Allamanis, C Sutton
2013 10th Working Conference on Mining Software Repositories (MSR), 53-56, 2013
862013
Sparse forward-backward using minimum divergence beams for fast training of conditional random fields
C Pal, C Sutton, A McCallum
2006 IEEE International Conference on Acoustics Speech and Signal Processing …, 2006
762006
Misleading learners: Co-opting your spam filter
B Nelson, M Barreno, FJ Chi, AD Joseph, BIP Rubinstein, U Saini, ...
Machine learning in cyber trust, 17-51, 2009
682009
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