| Palm: Scaling language modeling with pathways A Chowdhery, S Narang, J Devlin, M Bosma, G Mishra, A Roberts, ... Journal of Machine Learning Research 24 (240), 1-113, 2023 | 5214 | 2023 |
| An introduction to conditional random fields C Sutton, A McCallum Foundations and Trends® in Machine Learning 4 (4), 267-373, 2012 | 1579 | 2012 |
| An introduction to conditional random fields for relational learning C Sutton Introduction to statistical relational learning, 2007 | 1347 | 2007 |
| Program synthesis with large language models J Austin, A Odena, M Nye, M Bosma, H Michalewski, D Dohan, E Jiang, ... arXiv preprint arXiv:2108.07732, 2021 | 1280 | 2021 |
| A survey of machine learning for big code and naturalness M Allamanis, ET Barr, P Devanbu, C Sutton ACM Computing Surveys (CSUR) 51 (4), 1-37, 2018 | 1049 | 2018 |
| 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 30, 2017 | 836 | 2017 |
| A convolutional attention network for extreme summarization of source code M Allamanis, H Peng, C Sutton International conference on machine learning, 2091-2100, 2016 | 742 | 2016 |
| Autoencoding variational inference for topic models A Srivastava, C Sutton arXiv preprint arXiv:1703.01488, 2017 | 700 | 2017 |
| Dynamic conditional random fields: Factorized probabilistic models for labeling and segmenting sequence data C Sutton, K Rohanimanesh, A McCallum Proceedings of the twenty-first international conference on Machine learning, 99, 2004 | 683 | 2004 |
| Sequence-to-point learning with neural networks for non-intrusive load monitoring C Zhang, M Zhong, Z Wang, N Goddard, C Sutton Proceedings of the AAAI conference on artificial intelligence 32 (1), 2018 | 619 | 2018 |
| Show your work: Scratchpads for intermediate computation with language models M Nye, AJ Andreassen, G Gur-Ari, H Michalewski, J Austin, D Bieber, ... arXiv preprint arXiv:2112.00114, 2021 | 553 | 2021 |
| Learning natural coding conventions M Allamanis, ET Barr, C Bird, C Sutton Proceedings of the 22nd acm sigsoft international symposium on foundations …, 2014 | 530 | 2014 |
| 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 | 527 | 2015 |
| Exploiting machine learning to subvert your spam filter. B Nelson, M Barreno, FJ Chi, AD Joseph, BIP Rubinstein, U Saini, ... LEET 8 (1-9), 16-17, 2008 | 500 | 2008 |
| Mining source code repositories at massive scale using language modeling M Allamanis, C Sutton 2013 10th working conference on mining software repositories (MSR), 207-216, 2013 | 489 | 2013 |
| Gemsec: Graph embedding with self clustering B Rozemberczki, R Davies, R Sarkar, C Sutton Proceedings of the 2019 IEEE/ACM international conference on advances in …, 2019 | 368 | 2019 |
| Statistical Machine Learning Makes Automatic Control Practical for Internet Datacenters. P Bodík, R Griffith, C Sutton, A Fox, MI Jordan, DA Patterson HotCloud 9, 12-12, 2009 | 304 | 2009 |
| Global relational models of source code VJ Hellendoorn, C Sutton, R Singh, P Maniatis, D Bieber International conference on learning representations, 2019 | 271 | 2019 |
| Big code!= big vocabulary: Open-vocabulary models for source code RM Karampatsis, H Babii, R Robbes, C Sutton, A Janes Proceedings of the ACM/IEEE 42nd International Conference on Software …, 2020 | 259 | 2020 |
| Piecewise training for undirected models C Sutton, A McCallum arXiv preprint arXiv:1207.1409, 2012 | 234 | 2012 |
Andrew McCallumDistinguished Professor of Computer Science, University of Massachusetts AmherstVerified email at cs.umass.edu
