| Efficient off-policy meta-reinforcement learning via probabilistic context variables K Rakelly, A Zhou, C Finn, S Levine, D Quillen International conference on machine learning, 5331-5340, 2019 | 680 | 2019 |
| Clockwork convnets for video semantic segmentation E Shelhamer, K Rakelly, J Hoffman, T Darrell Computer Vision–ECCV 2016 Workshops: Amsterdam, The Netherlands, October 8 …, 2016 | 261 | 2016 |
| Conditional networks for few-shot semantic segmentation K Rakelly, E Shelhamer, T Darrell, A Efros, S Levine | 227 | 2018 |
| Few-shot segmentation propagation with guided networks K Rakelly, E Shelhamer, T Darrell, AA Efros, S Levine arXiv preprint arXiv:1806.07373, 2018 | 129 | 2018 |
| A century of portraits: A visual historical record of american high school yearbooks S Ginosar, K Rakelly, S Sachs, B Yin, AA Efros Proceedings of the IEEE International Conference on Computer Vision …, 2015 | 125 | 2015 |
| Meld: Meta-reinforcement learning from images via latent state models TZ Zhao, A Nagabandi, K Rakelly, C Finn, S Levine arXiv preprint arXiv:2010.13957, 2020 | 34 | 2020 |
| Which mutual-information representation learning objectives are sufficient for control? K Rakelly, A Gupta, C Florensa, S Levine Advances in Neural Information Processing Systems 34, 26345-26357, 2021 | 33 | 2021 |
| Few-shot segmentation propagation with guided networks. arXiv 2018 K Rakelly, E Shelhamer, T Darrell, AA Efros, S Levine arXiv preprint arXiv:1806.07373, 0 | 5 | |
| Meta-learning to guide segmentation K Rakelly, E Shelhamer, T Darrell, AA Efros, S Levine | 2 | 2019 |
| Learning and Analyzing Representations for Meta-Learning and Control K Rakelly University of California, Berkeley, 2020 | | 2020 |
| Input-Convex Neural Networks and Posynomial Optimization S Kent, E Mazumdar, B EDU, A Nagabandi, K Rakelly | | 2016 |
