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Samuel Gershman
Samuel Gershman
Bestätigte E-Mail-Adresse bei fas.harvard.edu - Startseite
Titel
Zitiert von
Zitiert von
Jahr
Building machines that learn and think like people
BM Lake, TD Ullman, JB Tenenbaum, SJ Gershman
Behavioral and brain sciences 40, 2017
22432017
Model-based influences on humans' choices and striatal prediction errors
ND Daw, SJ Gershman, B Seymour, P Dayan, RJ Dolan
Neuron 69 (6), 1204-1215, 2011
15802011
A tutorial on Bayesian nonparametric models
SJ Gershman, DM Blei
Journal of Mathematical Psychology 56, 1-12, 2012
6352012
Computational rationality: A converging paradigm for intelligence in brains, minds, and machines
SJ Gershman, EJ Horvitz, JB Tenenbaum
Science 349 (6245), 273-278, 2015
6002015
The hippocampus as a predictive map
KL Stachenfeld, MM Botvinick, SJ Gershman
Nature Neuroscience 20, 1643-1653, 2017
5922017
Reinforcement learning and episodic memory in humans and animals: an integrative framework
SJ Gershman, ND Daw
Annual review of psychology 68, 101, 2017
3812017
Context, learning, and extinction
SJ Gershman, DM Blei, Y Niv
Psychological Review 117 (1), 197-209, 2010
3622010
The curse of planning: Dissecting multiple reinforcement learning systems by taxing the central executive
AR Otto, SJ Gershman, AB Markman, ND Daw
Psychological Science 24 (5), 751-761, 2013
3332013
Reinforcement learning in multidimensional environments relies on attention mechanisms
Y Niv, R Daniel, A Geana, SJ Gershman, YC Leong, A Radulescu, ...
Journal of Neuroscience 35 (21), 8145-8157, 2015
3182015
The successor representation in human reinforcement learning
I Momennejad, EM Russek, JH Cheong, MM Botvinick, ND Daw, ...
Nature human behaviour 1 (9), 680-692, 2017
3142017
Amortized Inference in Probabilistic Reasoning
SJ Gershman, ND Goodman
Proceedings of the 36th Annual Cognitive Science Society, 2013
3002013
Retrospective revaluation in sequential decision making: A tale of two systems
SJ Gershman, AB Markman, AR Otto
Journal of Experimental Psychology: General 143, 182-194, 2014
2652014
Predictive representations can link model-based reinforcement learning to model-free mechanisms
EM Russek, I Momennejad, MM Botvinick, SJ Gershman, ND Daw
PLoS computational biology 13 (9), e1005768, 2017
2642017
Learning latent structure: carving nature at its joints
SJ Gershman, Y Niv
Current Opinion in Neurobiology 20 (2), 251-256, 2010
2562010
Interplay of approximate planning strategies
QJM Huys, N Lally, P Faulkner, N Eshel, E Seifritz, SJ Gershman, ...
Proceedings of the National Academy of Sciences 112 (10), 3098-3103, 2015
2482015
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), 1-13, 2018
2152018
Cost-benefit arbitration between multiple reinforcement-learning systems
W Kool, SJ Gershman, FA Cushman
Psychological science 28 (9), 1321-1333, 2017
2052017
Deep successor reinforcement learning
TD Kulkarni, A Saeedi, S Gautam, SJ Gershman
arXiv preprint arXiv:1606.02396, 2016
2042016
Nonparametric variational inference
S Gershman, M Hoffman, D Blei
Proceedings of the 29th International Conference on Machine Learning, 2012
1622012
Deconstructing the human algorithms for exploration
SJ Gershman
Cognition 173, 34-42, 2018
1552018
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