| Implicit generation and generalization in energy-based models Y Du, I Mordatch arXiv preprint arXiv:1903.08689, 2019 | 432* | 2019 |
| 3d shape generation and completion through point-voxel diffusion L Zhou, Y Du, J Wu Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021 | 136 | 2021 |
| Neural radiance flow for 4d view synthesis and video processing Y Du, Y Zhang, HX Yu, JB Tenenbaum, J Wu 2021 IEEE/CVF International Conference on Computer Vision (ICCV), 14304-14314, 2021 | 99* | 2021 |
| Observational overfitting in reinforcement learning X Song, Y Jiang, S Tu, Y Du, B Neyshabur arXiv preprint arXiv:1912.02975, 2019 | 99 | 2019 |
| Improved contrastive divergence training of energy based models Y Du, S Li, J Tenenbaum, I Mordatch arXiv preprint arXiv:2012.01316, 2020 | 70 | 2020 |
| Compositional Visual Generation with Composable Diffusion Models N Liu*, S Li*, Y Du*, A Torralba, JB Tenenbaum arXiv preprint arXiv:2206.01714, 2022 | 69 | 2022 |
| Neural MMO: A massively multiagent game environment for training and evaluating intelligent agents J Suarez, Y Du, P Isola, I Mordatch arXiv preprint arXiv:1903.00784, 2019 | 66* | 2019 |
| Neural Descriptor Fields: SE (3)-Equivariant Object Representations for Manipulation A Simeonov*, Y Du*, A Tagliasacchi, JB Tenenbaum, A Rodriguez, ... arXiv preprint arXiv:2112.05124, 2021 | 63* | 2021 |
| Compositional visual generation with energy based models Y Du, S Li, I Mordatch Advances in Neural Information Processing Systems 33, 6637-6647, 2020 | 55* | 2020 |
| Pre-trained language models for interactive decision-making S Li, X Puig, C Paxton, Y Du, C Wang, L Fan, T Chen, DA Huang, ... Advances in Neural Information Processing Systems 35, 31199-31212, 2022 | 50* | 2022 |
| Planning with Diffusion for Flexible Behavior Synthesis M Janner*, Y Du*, JB Tenenbaum, S Levine arXiv preprint arXiv:2205.09991, 2022 | 48 | 2022 |
| Task-agnostic dynamics priors for deep reinforcement learning Y Du, K Narasimhan International Conference on Machine Learning, 1696-1705, 2019 | 43 | 2019 |
| Energy-based models for atomic-resolution protein conformations Y Du, J Meier, J Ma, R Fergus, A Rives arXiv preprint arXiv:2004.13167, 2020 | 41 | 2020 |
| Kubric: A scalable dataset generator K Greff, F Belletti, L Beyer, C Doersch, Y Du, D Duckworth, DJ Fleet, ... Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022 | 38* | 2022 |
| Model Based planning with energy-based models Y Du, T Lin, I Mordatch arXiv preprint arXiv:1909.06878, 2019 | 31* | 2019 |
| Learning to exploit stability for 3d scene parsing Y Du, Z Liu, H Basevi, A Leonardis, B Freeman, J Tenenbaum, J Wu Advances in Neural Information Processing Systems 31, 2018 | 31 | 2018 |
| Learning to plan with pointcloud affordances for general-purpose dexterous manipulation A Simeonov, Y Du, B Kim, FR Hogan, P Agrawal, A Rodriguez Conference on Robot Learning, 2020 | 30* | 2020 |
| Learning to Compose Visual Relations N Liu*, S Li*, Y Du*, J Tenenbaum, A Torralba Advances in Neural Information Processing Systems 34, 2021 | 29* | 2021 |
| Unsupervised learning of compositional energy concepts Y Du, S Li, Y Sharma, J Tenenbaum, I Mordatch Advances in Neural Information Processing Systems 34, 15608-15620, 2021 | 28 | 2021 |
| Energy-Based Models for Continual Learning S Li, Y Du, GM van de Ven, I Mordatch arXiv preprint arXiv:2011.12216, 2020 | 26* | 2020 |
Joshua B. TenenbaumMITVerified email at mit.edu
