Chelsea Finn
Chelsea Finn
在 cs.stanford.edu 的电子邮件经过验证 - 首页
标题
引用次数
引用次数
年份
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
44022017
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
25632016
Unsupervised learning for physical interaction through video prediction
C Finn, I Goodfellow, S Levine
Advances in neural information processing systems 29, 64-72, 2016
8102016
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
6352016
Deep visual foresight for planning robot motion
C Finn, S Levine
2017 IEEE International Conference on Robotics and Automation (ICRA), 2786-2793, 2017
5222017
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
443*2016
Probabilistic model-agnostic meta-learning
C Finn, K Xu, S Levine
Neural Information Processing Systems (NeurIPS), 2018
3832018
One-shot visual imitation learning via meta-learning
C Finn, T Yu, T Zhang, P Abbeel, S Levine
Conference on Robot Learning (CoRL), 2017
3392017
Stochastic variational video prediction
M Babaeizadeh, C Finn, D Erhan, RH Campbell, S Levine
International Conference on Learning Representations (ICLR), 2017
3222017
Model-based reinforcement learning for atari
L Kaiser, M Babaeizadeh, P Milos, B Osinski, RH Campbell, ...
International Conference on Learning Representations (ICLR), 2020
3132020
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
3082018
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
276*2019
Stochastic adversarial video prediction
AX Lee, R Zhang, F Ebert, P Abbeel, C Finn, S Levine
arXiv preprint arXiv:1804.01523, 2018
2682018
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
2342016
Meta-learning with implicit gradients
A Rajeswaran, C Finn, S Kakade, S Levine
Neural Information Processing Systems (NeurIPS), 2019
2332019
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
2072018
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
2052019
Online meta-learning
C Finn, A Rajeswaran, S Kakade, S Levine
International Conference on Machine Learning (ICML), 2019
1872019
Self-supervised visual planning with temporal skip connections
F Ebert, C Finn, AX Lee, S Levine
Conference on Robot Learning (CoRL), 2017
1832017
Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning
T Yu, D Quillen, Z He, R Julian, K Hausman, C Finn, S Levine
Conference on Robot Learning (CoRL), 2019
1762019
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