Chelsea Finn

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Soavtorji

Sergey LevineUC Berkeley, Physical IntelligencePreverjeni e-poštni naslov na eecs.berkeley.edu
Pieter AbbeelUC Berkeley | CovariantPreverjeni e-poštni naslov na cs.berkeley.edu
Tianhe YuGoogle DeepMindPreverjeni e-poštni naslov na google.com
Rafael Mitkov RafailovGraduate Student, Stanford UniversityPreverjeni e-poštni naslov na stanford.edu
Archit SharmaPhD Student, Stanford UniversityPreverjeni e-poštni naslov na stanford.edu
Karol HausmanPhysical Intelligence, StanfordPreverjeni e-poštni naslov na physicalintelligence.company
Eric Anthony MitchellPhD Candidate, Stanford UniversityPreverjeni e-poštni naslov na stanford.edu
Frederik EbertUC BerkeleyPreverjeni e-poštni naslov na berkeley.edu
Suraj NairPhysical IntelligencePreverjeni e-poštni naslov na physicalintelligence.company
Christopher D ManningProfessor of Computer Science and Linguistics, Stanford UniversityPreverjeni e-poštni naslov na stanford.edu
Huaxiu YaoAssistant Professor of Computer Science and Data Science, UNC Chapel HillPreverjeni e-poštni naslov na cs.unc.edu
Annie XieGoogle DeepMindPreverjeni e-poštni naslov na stanford.edu
Percy LiangAssociate Professor of Computer Science, Stanford UniversityPreverjeni e-poštni naslov na cs.stanford.edu
Yoonho LeePhD Student, Stanford UniversityPreverjeni e-poštni naslov na stanford.edu
Trevor DarrellProfessor of Computer Science, U.C. BerkeleyPreverjeni e-poštni naslov na eecs.berkeley.edu
Annie S. ChenStanford UniversityPreverjeni e-poštni naslov na stanford.edu
Stefano ErmonStanford UniversityPreverjeni e-poštni naslov na cs.stanford.edu
Mohammad BabaeizadehGoogle BrainPreverjeni e-poštni naslov na google.com
Dumitru ErhanDirector of Research @ Google DeepMindPreverjeni e-poštni naslov na google.com
Aviral KumarCarnegie Mellon UniversityPreverjeni e-poštni naslov na andrew.cmu.edu

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Chelsea Finn

Stanford University, Physical Intelligence

Preverjeni e-poštni naslov na cs.stanford.edu - Domača stran

machine learning robotics reinforcement learning


Naslov Razvrsti po navedbah Razvrsti po letniku Razvrsti po naslovu	Navedeno Navedeno	Leto
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	14890	2017
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	4961	2021
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	4304	2016
Direct preference optimization: Your language model is secretly a reward model R Rafailov, A Sharma, E Mitchell, S Ermon, CD Manning, C Finn Neural Information Processing Systems (NeurIPS), 2023	2803	2023
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	2036*	2022
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	1603	2021
Unsupervised learning for physical interaction through video prediction C Finn, I Goodfellow, S Levine Advances in neural information processing systems 29, 2016	1280	2016
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	1263	2019
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	1246	2016
Gradient surgery for multi-task learning T Yu, S Kumar, A Gupta, S Levine, K Hausman, C Finn Neural Information Processing Systems (NeurIPS), 2020	1191	2020
Model-based reinforcement learning for atari L Kaiser, M Babaeizadeh, P Milos, B Osinski, RH Campbell, ... International Conference on Learning Representations (ICLR), 2020	1085	2020
Rt-2: Vision-language-action models transfer web knowledge to robotic control B Zitkovich, T Yu, S Xu, P Xu, T Xiao, F Xia, J Wu, P Wohlhart, S Welker, ... Conference on Robot Learning, 2165-2183, 2023	1070*	2023
RT-1: Robotics transformer for real-world control at scale A Brohan, N Brown, J Carbajal, Y Chebotar, J Dabis, C Finn, ... Robotics: Science and Systems (RSS), 2022	995	2022
Meta-learning with implicit gradients A Rajeswaran, C Finn, S Kakade, S Levine Neural Information Processing Systems (NeurIPS), 2019	981	2019
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	938	2020
Deep visual foresight for planning robot motion C Finn, S Levine 2017 IEEE international conference on robotics and automation (ICRA), 2786-2793, 2017	927	2017
Probabilistic model-agnostic meta-learning C Finn, K Xu, S Levine Neural Information Processing Systems (NeurIPS), 2018	864	2018
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	799*	2019
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	793	2019
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	762*	2016

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