| 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 | 584 | 2021 |
| Global relational models of source code VJ Hellendoorn, C Sutton, R Singh, P Maniatis, D Bieber International conference on learning representations, 2019 | 275 | 2019 |
| Neural program repair by jointly learning to localize and repair M Vasic, A Kanade, P Maniatis, D Bieber, R Singh arXiv preprint arXiv:1904.01720, 2019 | 164 | 2019 |
| Pixcolor: Pixel recursive colorization S Guadarrama, R Dahl, D Bieber, M Norouzi, J Shlens, K Murphy arXiv preprint arXiv:1705.07208, 2017 | 129 | 2017 |
| Language model cascades D Dohan, W Xu, A Lewkowycz, J Austin, D Bieber, RG Lopes, Y Wu, ... arXiv preprint arXiv:2207.10342, 2022 | 82 | 2022 |
| Can large language models reason about program invariants? K Pei, D Bieber, K Shi, C Sutton, P Yin International Conference on Machine Learning, 27496-27520, 2023 | 70 | 2023 |
| Bustle: Bottom-up program synthesis through learning-guided exploration A Odena, K Shi, D Bieber, R Singh, C Sutton, H Dai arXiv preprint arXiv:2007.14381, 2020 | 59 | 2020 |
| Tf-coder: Program synthesis for tensor manipulations K Shi, D Bieber, R Singh ACM Transactions on Programming Languages and Systems (TOPLAS) 44 (2), 1-36, 2022 | 46 | 2022 |
| Show your work: Scratchpads for intermediate computation with language models, 2021 M Nye, AJ Andreassen, G Gur-Ari, H Michalewski, J Austin, D Bieber, ... URL https://arxiv. org/abs/2112.00114, 2021 | 46 | 2021 |
| Learning to execute programs with instruction pointer attention graph neural networks D Bieber, C Sutton, H Larochelle, D Tarlow Advances in Neural Information Processing Systems 33, 8626-8637, 2020 | 46 | 2020 |
| Learning semantic representations to verify hardware designs S Vasudevan, WJ Jiang, D Bieber, R Singh, CR Ho, C Sutton Advances in Neural Information Processing Systems 34, 23491-23504, 2021 | 31 | 2021 |
| Incremental sampling without replacement for sequence models K Shi, D Bieber, C Sutton International Conference on Machine Learning, 8785-8795, 2020 | 29 | 2020 |
| Static prediction of runtime errors by learning to execute programs with external resource descriptions D Bieber, R Goel, D Zheng, H Larochelle, D Tarlow arXiv preprint arXiv:2203.03771, 2022 | 12 | 2022 |
| Neural networks for modeling source code edits R Zhao, D Bieber, K Swersky, D Tarlow arXiv preprint arXiv:1904.02818, 2019 | 12 | 2019 |
| Transforming grayscale images into color images using deep neural networks SG Cotado, J Shlens, D Bieber, M Norouzi, KP Murphy, RL Dahl US Patent 11,087,504, 2021 | 9 | 2021 |
| A library for representing python programs as graphs for machine learning D Bieber, K Shi, P Maniatis, C Sutton, V Hellendoorn, D Johnson, ... arXiv preprint arXiv:2208.07461, 2022 | 4 | 2022 |
| Can Large Language Models Reason About Program Invariants C Sutton, D Bieber, K Shi, K Pei, P Yin Proceedings of the International Conference on Machine Learning, 2023 | 3 | 2023 |
| Generating learned representations of digital circuit designs S Vasudevan, W Jiang, CA Sutton, R Singh, D Bieber, MO Hashemi, ... US Patent App. 18/564,797, 2024 | 2 | 2024 |
| Systems and Methods for Synthesizing Code from Input and Output Examples K Shi, R Singh, DJ Bieber US Patent App. 18/529,387, 2024 | | 2024 |
| Systems and methods for synthesizing code from input and output examples K Shi, R Singh, DJ Bieber US Patent 11,875,139, 2024 | | 2024 |
Charles SuttonGoogle, University of EdinburghEmail verificata su inf.ed.ac.uk
