Chelsea Finn
Chelsea Finn
Stanford, Google Brain, UC Berkeley
Verified email at cs.stanford.edu - Homepage
TitleCited byYear
End-to-End Training of Deep Visuomotor Policies
S Levine, C Finn, T Darrell, P Abbeel
Journal of Machine Learning Research (JMLR), 2016
13372016
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
C Finn, P Abbeel, S Levine
International Conference on Machine Learning (ICML), 2017
8792017
Unsupervised Learning for Physical Interaction through Video Prediction
C Finn, I Goodfellow, S Levine
Neural Information Processing Systems (NIPS), 2016
3992016
Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization
C Finn, S Levine, P Abbeel
International Conference on Machine Learning (ICML), 2016
2582016
Deep Spatial Autoencoders for Visuomotor Learning
C Finn, XY Tan, Y Duan, T Darrell, S Levine, P Abbeel
International Conference on Robotics and Automation (ICRA), 2016
231*2016
Deep Visual Foresight for Planning Robot Motion
C Finn, S Levine
International Conference on Robotics and Automation (ICRA), 2017
2052017
One-shot visual imitation learning via meta-learning
C Finn, T Yu, T Zhang, P Abbeel, S Levine
Conference on Robot Learning (CoRL), 2017
1182017
Stochastic Variational Video Prediction
M Babaeizadeh, C Finn, D Erhan, RH Campbell, S Levine
International Conference on Learning Representations (ICLR), 2018
1042018
A Connection between Generative Adversarial Networks, Inverse Reinforcement Learning, and Energy-Based Models
C Finn, P Christiano, P Abbeel, S Levine
NIPS Workshop on Adversarial Training, 2016
922016
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
852018
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, 2018
722018
Stochastic adversarial video prediction
AX Lee, R Zhang, F Ebert, P Abbeel, C Finn, S Levine
arXiv preprint arXiv:1804.01523, 2018
672018
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
642015
Probabilistic Model-Agnostic Meta-Learning
C Finn, K Xu, S Levine
Neural Information Processing Systems (NeurIPS), 2018
622018
Universal Planning Networks
A Srinivas, A Jabri, P Abbeel, S Levine, C Finn
International Conference on Machine Learning (ICML), 2018
602018
Self-Supervised Visual Planning with Temporal Skip Connections
F Ebert, C Finn, AX Lee, S Levine
Conference on Robot Learning (CoRL), 2017
602017
Learning Deep Neural Network Policies with Continuous Memory States
M Zhang, Z McCarthy, C Finn, S Levine, P Abbeel
International Conference on Robotics and Automation (ICRA), 2016
50*2016
Adapting deep visuomotor representations with weak pairwise constraints
E Tzeng, C Devin, J Hoffman, C Finn, P Abbeel, S Levine, K Saenko, ...
Workshop on the Algorithmic Foundations of Robotics (WAFR), 2016
472016
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, 2019
46*2019
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
422018
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Articles 1–20