| A survey of machine learning for big code and naturalness M Allamanis, ET Barr, P Devanbu, C Sutton ACM Computing Surveys (CSUR) 51 (4), 2018 | 973 | 2018 |
| Learning to represent programs with graphs M Allamanis, M Brockschmidt, M Khademi International Conference on Learning Representations, 2018 | 955 | 2018 |
| CodeSearchNet challenge: Evaluating the state of semantic code search H Husain, HH Wu, T Gazit, M Allamanis, M Brockschmidt arXiv preprint arXiv:1909.09436, 2019 | 864 | 2019 |
| Gemini: a family of highly capable multimodal models G Team, R Anil, S Borgeaud, Y Wu, JB Alayrac, J Yu, R Soricut, ... arXiv preprint arXiv:2312.11805, 2023 | 806 | 2023 |
| A convolutional attention network for extreme summarization of source code M Allamanis, H Peng, C Sutton International Conference on Machine Learning, 2091-2100, 2016 | 700 | 2016 |
| Constrained graph variational autoencoders for molecule design Q Liu, M Allamanis, M Brockschmidt, A Gaunt Advances in neural information processing systems 31, 2018 | 511 | 2018 |
| Learning natural coding conventions M Allamanis, ET Barr, C Bird, C Sutton Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations …, 2014 | 507 | 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 | 504 | 2015 |
| 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 | 466 | 2013 |
| The adverse effects of code duplication in machine learning models of code M Allamanis Proceedings of the 2019 ACM SIGPLAN International Symposium on New Ideas …, 2019 | 316 | 2019 |
| Structured Neural Summarization P Fernandes, M Allamanis, M Brockschmidt ICLR, 2018 | 247 | 2018 |
| Bimodal modelling of source code and natural language M Allamanis, D Tarlow, A Gordon, Y Wei International Conference on Machine Learning, 2123-2132, 2015 | 244 | 2015 |
| Deep learning type inference VJ Hellendoorn, C Bird, ET Barr, M Allamanis Proceedings of the 2018 26th ACM joint meeting on european software …, 2018 | 228 | 2018 |
| Mining idioms from source code M Allamanis, C Sutton Proceedings of the 22nd ACM SIGSOFT international symposium on foundations …, 2014 | 225 | 2014 |
| 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 | 202 | 2013 |
| Generative code modeling with graphs M Brockschmidt, M Allamanis, AL Gaunt, O Polozov International Conference on Learning Representations (ICLR), 2018 | 190 | 2018 |
| Codit: Code editing with tree-based neural models S Chakraborty, Y Ding, M Allamanis, B Ray IEEE Transactions on Software Engineering 48 (4), 1385-1399, 2020 | 167* | 2020 |
| Typilus: Neural Type Hints M Allamanis, ET Barr, S Ducousso, Z Gao 41st ACM SIGPLAN Conference on Programming Language Design and Implementation, 2020 | 131 | 2020 |
| Dire: A neural approach to decompiled identifier naming J Lacomis, P Yin, E Schwartz, M Allamanis, C Le Goues, G Neubig, ... 2019 34th IEEE/ACM International Conference on Automated Software …, 2019 | 120 | 2019 |
| Learning to represent edits P Yin, G Neubig, M Allamanis, M Brockschmidt, AL Gaunt International Conference in Representation Learning (ICLR), 2019 | 120 | 2019 |
Marc BrockschmidtResearch Scientist, Google DeepMind在 google.com 的电子邮件经过验证
