| Implicit generation and modeling with energy based models Y Du, I Mordatch Advances in Neural Information Processing Systems 32, 2019 | 708 | 2019 |
| Diffusion policy: Visuomotor policy learning via action diffusion C Chi, S Feng, Y Du, Z Xu, E Cousineau, B Burchfiel, S Song RSS, 2023 | 490* | 2023 |
| 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 | 456 | 2021 |
| Planning with Diffusion for Flexible Behavior Synthesis M Janner*, Y Du*, JB Tenenbaum, S Levine ICML, 2022 | 441 | 2022 |
| Compositional Visual Generation with Composable Diffusion Models N Liu*, S Li*, Y Du*, A Torralba, JB Tenenbaum ECCV, 2022 | 367 | 2022 |
| Improving factuality and reasoning in language models through multiagent debate Y Du, S Li, A Torralba, JB Tenenbaum, I Mordatch ICML, 2024 | 312 | 2024 |
| Is Conditional Generative Modeling all you need for Decision-Making? A Ajay*, Y Du*, A Gupta*, J Tenenbaum, T Jaakkola, P Agrawal ICLR, 2023 | 275 | 2023 |
| 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 | 241 | 2021 |
| 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 | 209* | 2022 |
| Neural Descriptor Fields: SE (3)-Equivariant Object Representations for Manipulation A Simeonov*, Y Du*, A Tagliasacchi, JB Tenenbaum, A Rodriguez, ... ICRA, 2022 | 193* | 2022 |
| 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 | 178 | 2022 |
| 3D-LLM: Injecting the 3D World into Large Language Models Y Hong, H Zhen, P Chen, S Zheng, Y Du, Z Chen, C Gan NeurIPS, 2023 | 167 | 2023 |
| Training diffusion models with reinforcement learning K Black*, M Janner*, Y Du, I Kostrikov, S Levine ICLR, 2024 | 154* | 2024 |
| Observational overfitting in reinforcement learning X Song, Y Jiang, S Tu, Y Du, B Neyshabur ICLR, 2020 | 149 | 2020 |
| Compositional visual generation with energy based models Y Du, S Li, I Mordatch Advances in Neural Information Processing Systems 33, 6637-6647, 2020 | 144* | 2020 |
| 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 | 135 | 2023 |
| Improved contrastive divergence training of energy based models Y Du, S Li, J Tenenbaum, I Mordatch ICML, 2021 | 128 | 2021 |
| Learning Universal Policies via Text-Guided Video Generation Y Du, M Yang, B Dai, H Dai, O Nachum, J Tenenbaum, D Schuurmans, ... NeurIPS, 2023 | 120 | 2023 |
| Building Cooperative Embodied Agents Modularly with Large Language Models H Zhang, W Du, J Shan, Q Zhou, Y Du, JB Tenenbaum, T Shu, C Gan ICLR, 2024 | 118* | 2024 |
| 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 | 114* | 2021 |
Joshua B. TenenbaumMITVerified email at mit.edu
