Samuel Ritter
Samuel Ritter
Research Scientist, DeepMind
Потвърден имейл адрес: google.com - Начална страница
Reinforcement learning, fast and slow
M Botvinick, S Ritter, JX Wang, Z Kurth-Nelson, C Blundell, D Hassabis
Trends in cognitive sciences 23 (5), 408-422, 2019
Toward a universal decoder of linguistic meaning from brain activation
F Pereira, B Lou, B Pritchett, S Ritter, SJ Gershman, N Kanwisher, ...
Nature communications 9 (1), 963, 2018
Cognitive psychology for deep neural networks: A shape bias case study
S Ritter, DGT Barrett, A Santoro, MM Botvinick
Proceedings of the 34th International Conference on Machine Learning-Volume …, 2017
A comparative evaluation of off-the-shelf distributed semantic representations for modelling behavioural data
F Pereira, S Gershman, S Ritter, M Botvinick
Cognitive neuropsychology 33 (3-4), 175-190, 2016
Been there, done that: Meta-learning with episodic recall
S Ritter, J Wang, Z Kurth-Nelson, S Jayakumar, C Blundell, R Pascanu, ...
International conference on machine learning, 4354-4363, 2018
Synthetic returns for long-term credit assignment
D Raposo, S Ritter, A Santoro, G Wayne, T Weber, M Botvinick, ...
arXiv preprint arXiv:2102.12425, 2021
Rapid task-solving in novel environments
S Ritter, R Faulkner, L Sartran, A Santoro, M Botvinick, D Raposo
arXiv preprint arXiv:2006.03662, 2020
Episodic Control as Meta-Reinforcement Learning
S Ritter, JX Wang, Z Kurth-Nelson, MM Botvinick
bioRxiv, 360537, 2018
Toward a universal decoder of linguistic meaning from brain activation. Nat Commun 9: 963
F Pereira, B Lou, B Pritchett, S Ritter, SJ Gershman, N Kanwisher, ...
Leveraging preposition ambiguity to assess compositional distributional models of semantics
S Ritter, C Long, D Paperno, M Baroni, M Botvinick, A Goldberg
Proceedings of the Fourth Joint Conference on Lexical and Computational …, 2015
Causation, force, and the sense of touch
P Wolff, S Ritter, K Holmes
Proceedings of the Annual Meeting of the Cognitive Science Society 36 (36), 2014
Meta-reinforcement learning with episodic recall: An integrative theory of reward-driven learning
S Ritter
Princeton University, 2019
Leveraging preposition ambiguity to assess representation of semantic interaction in cdsm
AG Samuel Ritter, Cotie Long, Denis Paperno, Marco Baroni, Matthew Botvinick
NIPS Workshop on Learning Semantics, 2014
Mixture-of-Depths: Dynamically allocating compute in transformer-based language models
D Raposo, S Ritter, B Richards, T Lillicrap, PC Humphreys, A Santoro
arXiv preprint arXiv:2404.02258, 2024
Generating implicit plans for accomplishing goals in an environment using attention operations over planning embeddings
S Ritter, R Faulkner, DN Raposo
US Patent App. 17/794,780, 2023
Selecting actions by reverting to previous learned action selection policies
S Ritter, XJ Wang, S Jayakumar, R Pascanu, C Blundell, M Botvinick
US Patent 11,423,300, 2022
How to Learn and Represent Abstractions: An Investigation using Symbolic Alchemy
B AlKhamissi, A Srinivasan, ZK Nelson, S Ritter
arXiv preprint arXiv:2112.08360, 2021
Controlling agents using state associative learning for long-term credit assignment
S Ritter, DN Raposo
US Patent App. 18/275,542, 2024
Scientific Life 363 Emerging Opportunities for Advancing Cognitive Neuroscience Science & Society 365 Holding Robots Responsible
A Waytz, R Alterovitz, K Gray, P Van Dessel, B Gawronski, J De Houwer, ...
Trends in Cognitive Sciences 23 (5), 2019
Blog post
D Barrett, S Ritter
Interpreting, 2017
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