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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
20782017
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
15212011
A tutorial on Bayesian nonparametric models
SJ Gershman, DM Blei
Journal of Mathematical Psychology 56, 1-12, 2012
6112012
Computational rationality: A converging paradigm for intelligence in brains, minds, and machines
SJ Gershman, EJ Horvitz, JB Tenenbaum
Science 349 (6245), 273-278, 2015
5652015
The hippocampus as a predictive map
KL Stachenfeld, MM Botvinick, SJ Gershman
Nature Neuroscience 20, 1643-1653, 2017
5412017
Reinforcement learning and episodic memory in humans and animals: an integrative framework
SJ Gershman, ND Daw
Annual review of psychology 68, 101, 2017
3572017
Context, learning, and extinction
SJ Gershman, DM Blei, Y Niv
Psychological Review 117 (1), 197-209, 2010
3512010
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
3232013
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
2952015
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
2922017
Amortized Inference in Probabilistic Reasoning
SJ Gershman, ND Goodman
Proceedings of the 36th Annual Cognitive Science Society, 2013
2822013
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
2632014
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
2492017
Learning latent structure: carving nature at its joints
SJ Gershman, Y Niv
Current Opinion in Neurobiology 20 (2), 251-256, 2010
2452010
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
2362015
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
1972018
Cost-benefit arbitration between multiple reinforcement-learning systems
W Kool, SJ Gershman, FA Cushman
Psychological science 28 (9), 1321-1333, 2017
1962017
Deep successor reinforcement learning
TD Kulkarni, A Saeedi, S Gautam, SJ Gershman
arXiv preprint arXiv:1606.02396, 2016
1942016
Nonparametric variational inference
S Gershman, M Hoffman, D Blei
Proceedings of the 29th International Conference on Machine Learning, 2012
1562012
Deconstructing the human algorithms for exploration
SJ Gershman
Cognition 173, 34-42, 2018
1442018
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