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Kate Rakelly
Kate Rakelly
Verified email at eecs.berkeley.edu - Homepage
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Cited by
Cited by
Year
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
4312019
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
2122016
Conditional networks for few-shot semantic segmentation
K Rakelly, E Shelhamer, T Darrell, A Efros, S Levine
1602018
Few-shot segmentation propagation with guided networks
K Rakelly, E Shelhamer, T Darrell, AA Efros, S Levine
arXiv preprint arXiv:1806.07373, 2018
982018
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
852015
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
222020
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
112021
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
12019
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
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