| Implicit generation and generalization in energy-based models Y Du, I Mordatch NeurIPS, 2019 | 541* | 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 | 242 | 2021 |
| Planning with Diffusion for Flexible Behavior Synthesis M Janner*, Y Du*, JB Tenenbaum, S Levine ICML, 2022 | 177 | 2022 |
| Compositional Visual Generation with Composable Diffusion Models N Liu*, S Li*, Y Du*, A Torralba, JB Tenenbaum ECCV, 2022 | 166 | 2022 |
| 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 | 135* | 2021 |
| Neural Descriptor Fields: SE (3)-Equivariant Object Representations for Manipulation A Simeonov*, Y Du*, A Tagliasacchi, JB Tenenbaum, A Rodriguez, ... ICRA, 2022 | 121* | 2022 |
| Observational overfitting in reinforcement learning X Song, Y Jiang, S Tu, Y Du, B Neyshabur ICLR, 2020 | 118 | 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 | 116* | 2022 |
| Improved contrastive divergence training of energy based models Y Du, S Li, J Tenenbaum, I Mordatch ICML, 2021 | 100 | 2021 |
| Is Conditional Generative Modeling all you need for Decision-Making? A Ajay*, Y Du*, A Gupta*, J Tenenbaum, T Jaakkola, P Agrawal ICLR, 2023 | 99 | 2023 |
| Diffusion policy: Visuomotor policy learning via action diffusion C Chi, S Feng, Y Du, Z Xu, E Cousineau, B Burchfiel, S Song RSS, 2023 | 97* | 2023 |
| Compositional visual generation with energy based models Y Du, S Li, I Mordatch Advances in Neural Information Processing Systems 33, 6637-6647, 2020 | 94* | 2020 |
| Neural MMO: A massively multiagent game environment for training and evaluating intelligent agents J Suarez, Y Du, P Isola, I Mordatch NeurIPS Datasets and Benchmarks, 2021 | 89* | 2021 |
| 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 | 79* | 2022 |
| Improving Factuality and Reasoning in Language Models through Multiagent Debate Y Du, S Li, A Torralba, JB Tenenbaum, I Mordatch arXiv preprint arXiv:2305.14325, 2023 | 72 | 2023 |
| Learning to Compose Visual Relations N Liu*, S Li*, Y Du*, J Tenenbaum, A Torralba Advances in Neural Information Processing Systems 34, 2021 | 52* | 2021 |
| Foundation models for decision making: Problems, methods, and opportunities S Yang, O Nachum, Y Du, J Wei, P Abbeel, D Schuurmans arXiv preprint arXiv:2303.04129, 2023 | 50 | 2023 |
| 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 | 49 | 2021 |
| Energy-based models for atomic-resolution protein conformations Y Du, J Meier, J Ma, R Fergus, A Rives ICLR, 2020 | 49 | 2020 |
| Task-agnostic dynamics priors for deep reinforcement learning Y Du, K Narasimhan International Conference on Machine Learning, 1696-1705, 2019 | 47 | 2019 |
Joshua B. TenenbaumMITVerified email at mit.edu
