Chelsea Finn
Chelsea Finn
Verified email at cs.stanford.edu - Homepage
Title
Cited by
Cited by
Year
Model-agnostic meta-learning for fast adaptation of deep networks
C Finn, P Abbeel, S Levine
International Conference on Machine Learning (ICML), 1126-1135, 2017
20562017
End-to-end training of deep visuomotor policies
S Levine, C Finn, T Darrell, P Abbeel
The Journal of Machine Learning Research 17 (1), 1334-1373, 2016
19092016
Unsupervised learning for physical interaction through video prediction
C Finn, I Goodfellow, S Levine
Advances in neural information processing systems, 64-72, 2016
5952016
Guided cost learning: Deep inverse optimal control via policy optimization
C Finn, S Levine, P Abbeel
International Conference on Machine Learning (ICML), 49-58, 2016
4082016
Deep visual foresight for planning robot motion
C Finn, S Levine
2017 IEEE International Conference on Robotics and Automation (ICRA), 2786-2793, 2017
3452017
Deep spatial autoencoders for visuomotor learning
C Finn, XY Tan, Y Duan, T Darrell, S Levine, P Abbeel
2016 IEEE International Conference on Robotics and Automation (ICRA), 512-519, 2016
328*2016
One-shot visual imitation learning via meta-learning
C Finn, T Yu, T Zhang, P Abbeel, S Levine
Conference on Robot Learning (CoRL), 2017
2102017
Stochastic variational video prediction
M Babaeizadeh, C Finn, D Erhan, RH Campbell, S Levine
International Conference on Learning Representations (ICLR), 2017
1882017
Recasting gradient-based meta-learning as hierarchical bayes
E Grant, C Finn, S Levine, T Darrell, T Griffiths
International Conference on Learning Representations (ICLR), 2018
1702018
Probabilistic model-agnostic meta-learning
C Finn, K Xu, S Levine
Advances in Neural Information Processing Systems, 9516-9527, 2018
1642018
Stochastic adversarial video prediction
AX Lee, R Zhang, F Ebert, P Abbeel, C Finn, S Levine
arXiv preprint arXiv:1804.01523, 2018
1482018
A connection between generative adversarial networks, inverse reinforcement learning, and energy-based models
C Finn, P Christiano, P Abbeel, S Levine
NeurIPS Workshop on Adversarial Training, 2016
1482016
One-shot imitation from observing humans via domain-adaptive meta-learning
T Yu, C Finn, A Xie, S Dasari, T Zhang, P Abbeel, S Levine
Robotics: Science and Systems (RSS), 2018
1322018
Learning to adapt in dynamic, real-world environments through meta-reinforcement learning
A Nagabandi, I Clavera, S Liu, RS Fearing, P Abbeel, S Levine, C Finn
International Conference on Learning Representations (ICLR), 2019
129*2019
Model-based reinforcement learning for atari
L Kaiser, M Babaeizadeh, P Milos, B Osinski, RH Campbell, ...
International Conference on Learning Representations (ICLR), 2020
1232020
Self-supervised visual planning with temporal skip connections
F Ebert, C Finn, AX Lee, S Levine
Conference on Robot Learning (CoRL), 2017
1192017
Universal planning networks
A Srinivas, A Jabri, P Abbeel, S Levine, C Finn
International Conference on Machine Learning (ICML), 2018
1072018
Meta-learning and universality: Deep representations and gradient descent can approximate any learning algorithm
C Finn, S Levine
International Conference on Learning Representations (ICLR), 2018
982018
Efficient off-policy meta-reinforcement learning via probabilistic context variables
K Rakelly, A Zhou, D Quillen, C Finn, S Levine
International Conference on Machine Learning (ICML), 2019
832019
Towards adapting deep visuomotor representations from simulated to real environments
E Tzeng, C Devin, J Hoffman, C Finn, X Peng, S Levine, K Saenko, ...
arXiv preprint arXiv:1511.07111 2 (3), 2015
772015
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