| Curiosity-driven exploration by self-supervised prediction D Pathak, P Agrawal, AA Efros, T Darrell International conference on machine learning, 2778-2787, 2017 | 3061 | 2017 |
| Human pose estimation with iterative error feedback J Carreira, P Agrawal, K Fragkiadaki, J Malik Proceedings of the IEEE conference on computer vision and pattern …, 2016 | 1062 | 2016 |
| Fully automated echocardiogram interpretation in clinical practice: feasibility and diagnostic accuracy J Zhang, S Gajjala, P Agrawal, GH Tison, LA Hallock, L Beussink-Nelson, ... Circulation 138 (16), 1623-1635, 2018 | 883 | 2018 |
| What makes ImageNet good for transfer learning? M Huh, P Agrawal, AA Efros Advances in Neural Information Processing Systems Workshop, 2016 | 874 | 2016 |
| Learning to see by moving P Agrawal, J Carreira, J Malik Proceedings of the IEEE international conference on computer vision, 37-45, 2015 | 696 | 2015 |
| Learning to poke by poking: Experiential learning of intuitive physics P Agrawal, AV Nair, P Abbeel, J Malik, S Levine Advances in neural information processing systems 29, 2016 | 630 | 2016 |
| Analyzing the performance of multilayer neural networks for object recognition P Agrawal, R Girshick, J Malik Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland …, 2014 | 609 | 2014 |
| Combining self-supervised learning and imitation for vision-based rope manipulation A Nair, D Chen, P Agrawal, P Isola, P Abbeel, J Malik, S Levine 2017 IEEE international conference on robotics and automation (ICRA), 2146-2153, 2017 | 359 | 2017 |
| Is conditional generative modeling all you need for decision-making? A Ajay, Y Du, A Gupta, J Tenenbaum, T Jaakkola, P Agrawal arXiv preprint arXiv:2211.15657, 2022 | 349 | 2022 |
| Zero-shot visual imitation D Pathak, P Mahmoudieh, G Luo, P Agrawal, D Chen, Y Shentu, ... International Conference on Learning Representations, 2018 | 329 | 2018 |
| Learning visual predictive models of physics for playing billiards K Fragkiadaki, P Agrawal, S Levine, J Malik International Conference on Learning Representations, 2015 | 297 | 2015 |
| A system for general in-hand object re-orientation T Chen, J Xu, P Agrawal Conference on Robot Learning, 297-307, 2022 | 254 | 2022 |
| Rapid locomotion via reinforcement learning GB Margolis, G Yang, K Paigwar, T Chen, P Agrawal The International Journal of Robotics Research 43 (4), 572-587, 2024 | 224 | 2024 |
| Investigating human priors for playing video games R Dubey, P Agrawal, D Pathak, TL Griffiths, AA Efros Proceedings of the 35th International Conference on Machine Learning, 1349--1357, 2018 | 208 | 2018 |
| Opal: Offline primitive discovery for accelerating offline reinforcement learning A Ajay, A Kumar, P Agrawal, S Levine, O Nachum arXiv preprint arXiv:2010.13611, 2020 | 193 | 2020 |
| Neural descriptor fields: Se (3)-equivariant object representations for manipulation A Simeonov, Y Du, A Tagliasacchi, JB Tenenbaum, A Rodriguez, ... 2022 International Conference on Robotics and Automation (ICRA), 6394-6400, 2022 | 163 | 2022 |
| 3d neural scene representations for visuomotor control Y Li, S Li, V Sitzmann, P Agrawal, A Torralba Conference on Robot Learning, 112-123, 2022 | 143 | 2022 |
| Superposition of many models into one B Cheung, A Terekhov, Y Chen, P Agrawal, B Olshausen arXiv preprint arXiv:1902.05522, 2019 | 143 | 2019 |
| Equivariant contrastive learning R Dangovski, L Jing, C Loh, S Han, A Srivastava, B Cheung, P Agrawal, ... arXiv preprint arXiv:2111.00899, 2021 | 141 | 2021 |
| Walk these ways: Tuning robot control for generalization with multiplicity of behavior GB Margolis, P Agrawal Conference on Robot Learning, 22-31, 2023 | 136 | 2023 |
Tao ChenCo-founder, Dexmate; Ph.D., CSAIL, MITEmail verificata su dexmate.ai