Obserwuj
Chelsea Finn
Chelsea Finn
Stanford University, Google
Zweryfikowany adres z cs.stanford.edu - Strona główna
Tytuł
Cytowane przez
Cytowane przez
Rok
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
109362017
End-to-end training of deep visuomotor policies
S Levine, C Finn, T Darrell, P Abbeel
Journal of Machine Learning Research 17 (1), 1334-1373, 2016
36512016
On the opportunities and risks of foundation models
R Bommasani, DA Hudson, E Adeli, R Altman, S Arora, S von Arx, ...
arXiv preprint arXiv:2108.07258, 2021
19592021
Unsupervised learning for physical interaction through video prediction
C Finn, I Goodfellow, S Levine
Advances in neural information processing systems 29, 2016
11302016
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
10082016
Wilds: A benchmark of in-the-wild distribution shifts
PW Koh, S Sagawa, H Marklund, SM Xie, M Zhang, A Balsubramani, ...
International Conference on Machine Learning (ICML), 5637-5664, 2021
9392021
Deep visual foresight for planning robot motion
C Finn, S Levine
2017 IEEE International Conference on Robotics and Automation (ICRA), 2786-2793, 2017
7932017
Model-based reinforcement learning for atari
L Kaiser, M Babaeizadeh, P Milos, B Osinski, RH Campbell, ...
International Conference on Learning Representations (ICLR), 2020
7912020
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
7612019
Probabilistic model-agnostic meta-learning
C Finn, K Xu, S Levine
Neural Information Processing Systems (NeurIPS), 2018
7462018
Meta-learning with implicit gradients
A Rajeswaran, C Finn, S Kakade, S Levine
Neural Information Processing Systems (NeurIPS), 2019
7032019
Do as I can, not as I say: Grounding language in robotic affordances
M Ahn, A Brohan, N Brown, Y Chebotar, O Cortes, B David, C Finn, ...
Conference on Robot Learning (CoRL), 2022
673*2022
Gradient surgery for multi-task learning
T Yu, S Kumar, A Gupta, S Levine, K Hausman, C Finn
Neural Information Processing Systems (NeurIPS), 2020
6692020
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
662*2016
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
612*2019
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
5752018
Mopo: Model-based offline policy optimization
T Yu, G Thomas, L Yu, S Ermon, J Zou, S Levine, C Finn, T Ma
Neural Information Processing Systems (NeurIPS), 2020
5732020
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
5672019
One-shot visual imitation learning via meta-learning
C Finn, T Yu, T Zhang, P Abbeel, S Levine
Conference on Robot Learning (CoRL), 2017
5632017
Stochastic variational video prediction
M Babaeizadeh, C Finn, D Erhan, RH Campbell, S Levine
International Conference on Learning Representations (ICLR), 2017
5362017
Nie można teraz wykonać tej operacji. Spróbuj ponownie później.
Prace 1–20