| Numerical linear algebra LN Trefethen, D Bau Society for Industrial and Applied Mathematics, 2022 | 7115 | 2022 |
| Explaining explanations: An overview of interpretability of machine learning LH Gilpin, D Bau, BZ Yuan, A Bajwa, M Specter, L Kagal 2018 IEEE 5th International Conference on data science and advanced …, 2018 | 3037 | 2018 |
| Network dissection: Quantifying interpretability of deep visual representations D Bau, B Zhou, A Khosla, A Oliva, A Torralba Proceedings of the IEEE conference on computer vision and pattern …, 2017 | 1753 | 2017 |
| Locating and editing factual associations in GPT K Meng, D Bau, A Andonian, Y Belinkov Advances in Neural Information Processing Systems 35, 17359-17372, 2022 | 779 | 2022 |
| GAN Dissection: Visualizing and Understanding Generative Adversarial Networks D Bau, JY Zhu, H Strobelt, B Zhou, JB Tenenbaum, WT Freeman, ... International Conference on Learning Representations (ICLR 2019), 2019 | 602 | 2019 |
| Understanding the role of individual units in a deep neural network D Bau, JY Zhu, H Strobelt, A Lapedriza, B Zhou, A Torralba Proceedings of the National Academy of Sciences 117 (48), 30071-30078, 2020 | 483 | 2020 |
| Learnable programming: blocks and beyond D Bau, J Gray, C Kelleher, J Sheldon, F Turbak Communications of the ACM 60 (6), 72-80, 2017 | 390 | 2017 |
| Determining advertisements using user behavior information such as past navigation information D Bau US Patent App. 10/955,828, 2006 | 386 | 2006 |
| Seeing what a gan cannot generate D Bau, JY Zhu, J Wulff, W Peebles, H Strobelt, B Zhou, A Torralba Proceedings of the IEEE/CVF international conference on computer vision …, 2019 | 369 | 2019 |
| Semantic photo manipulation with a generative image prior D Bau, H Strobelt, W Peebles, J Wulff, B Zhou, JY Zhu, A Torralba arXiv preprint arXiv:2005.07727, 2020 | 367 | 2020 |
| Interpreting deep visual representations via network dissection B Zhou, D Bau, A Oliva, A Torralba IEEE transactions on pattern analysis and machine intelligence 41 (9), 2131-2145, 2018 | 358 | 2018 |
| Interpretable basis decomposition for visual explanation B Zhou, Y Sun, D Bau, A Torralba Proceedings of the European Conference on Computer Vision (ECCV), 119-134, 2018 | 345 | 2018 |
| Mass-editing memory in a transformer K Meng, AS Sharma, A Andonian, Y Belinkov, D Bau The Eleventh International Conference on Learning Representations (ICLR 2022), 2022 | 320 | 2022 |
| What makes fake images detectable? understanding properties that generalize L Chai, D Bau, SN Lim, P Isola Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23 …, 2020 | 309 | 2020 |
| Providing images of named resources in response to a search query D Bau, G Erkan, OA Osman, SA Safier, CYC Lo US Patent 9,026,526, 2015 | 220 | 2015 |
| Annotation based development platform for stateful web services D Bau III, A Bosworth, GS Burd, RA Chavez, KW Marvin US Patent 7,437,710, 2008 | 210 | 2008 |
| Emergent world representations: Exploring a sequence model trained on a synthetic task K Li, AK Hopkins, D Bau, F Viégas, H Pfister, M Wattenberg The Eleventh International Conference on Learning Representations (ICLR 2022), 2022 | 180 | 2022 |
| Systems and methods for creating network-based software services using source code annotations K Marvin, D Remy, D Bau, RA Chavez, D Read US Patent 7,707,564, 2010 | 177 | 2010 |
| Erasing Concepts From Diffusion Models R Gandikota, J Materzynska, J Fiotto-Kaufman, D Bau Proceedings of the 2023 IEEE International Conference on Computer Vision …, 2023 | 172 | 2023 |
| Pencil code: block code for a text world D Bau, DA Bau, M Dawson, CS Pickens Proceedings of the 14th international conference on interaction design and …, 2015 | 157 | 2015 |
Antonio TorralbaProfessor of Computer Science, MITVerified email at csail.mit.edu
