Samuel Gershman

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Joshua B. TenenbaumMITVerifierad e-postadress på mit.edu
Yael NivProfessor of Psychology and Neuroscience, Princeton UniversityVerifierad e-postadress på princeton.edu
Kenneth A. NormanProfessor of Psychology and Neuroscience, Princeton UniversityVerifierad e-postadress på princeton.edu
Eric SchulzMax Planck Institute for Biological CyberneticsVerifierad e-postadress på tue.mpg.de
Matthew BotvinickDeepMind, London, UKVerifierad e-postadress på google.com
Nathaniel D. DawHuo Professor of Computational and Theoretical Neuroscience, Princeton UniversityVerifierad e-postadress på princeton.edu
Tomer UllmanAssistant Professor, HarvardVerifierad e-postadress på fas.harvard.edu
David BleiProfessor of Statistics and Computer Science, Columbia UniversityVerifierad e-postadress på columbia.edu
Fiery CushmanPsychology, Harvard UniversityVerifierad e-postadress på fas.harvard.edu
Kimberly Lauren StachenfeldResearch Scientist, DeepMindVerifierad e-postadress på google.com
Naoshige UchidaProfessor of Molecular and Cellular Biology, Harvard UniversityVerifierad e-postadress på mcb.harvard.edu
Wouter KoolAssistant Professor at Washington University in St. LouisVerifierad e-postadress på wustl.edu
Ishita DasguptaResearch Scientist, DeepMind New York CityVerifierad e-postadress på google.com
Rahul BhuiMassachusetts Institute of TechnologyVerifierad e-postadress på mit.edu
Noah D. GoodmanStanford UniversityVerifierad e-postadress på stanford.edu
Brenden M. LakeAssistant Professor, New York UniversityVerifierad e-postadress på nyu.edu
John G. MikhaelHarvardVerifierad e-postadress på hms.harvard.edu
Marie MonfilsProfessor; The University of Texas at AustinVerifierad e-postadress på utexas.edu
Per B. SederbergProfessor of Psychology, University of VirginiaVerifierad e-postadress på virginia.edu
Momchil S. TomovHarvard UniversityVerifierad e-postadress på g.harvard.edu

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Samuel Gershman

Professor, Harvard University

Verifierad e-postadress på fas.harvard.edu - Startsida

Computational neuroscience cognitive science machine learning


Titel Sortera efter citat Sortera efter år Sortera efter titel	Citeras av Citeras av	År
Building machines that learn and think like people BM Lake, TD Ullman, JB Tenenbaum, SJ Gershman Behavioral and brain sciences 40, e253, 2017	2366	2017
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	1638	2011
A tutorial on Bayesian nonparametric models SJ Gershman, DM Blei Journal of Mathematical Psychology 56, 1-12, 2012	645	2012
The hippocampus as a predictive map KL Stachenfeld, MM Botvinick, SJ Gershman Nature Neuroscience 20, 1643-1653, 2017	639	2017
Computational rationality: A converging paradigm for intelligence in brains, minds, and machines SJ Gershman, EJ Horvitz, JB Tenenbaum Science 349 (6245), 273-278, 2015	628	2015
Reinforcement learning and episodic memory in humans and animals: an integrative framework SJ Gershman, ND Daw Annual review of psychology 68, 101-128, 2017	402	2017
Context, learning, and extinction SJ Gershman, DM Blei, Y Niv Psychological Review 117 (1), 197-209, 2010	378	2010
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	344	2013
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	332	2015
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	326	2017
Amortized Inference in Probabilistic Reasoning SJ Gershman, ND Goodman Proceedings of the 36th Annual Cognitive Science Society, 2013	307	2013
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	270	2014
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	268	2017
Learning latent structure: carving nature at its joints SJ Gershman, Y Niv Current Opinion in Neurobiology 20 (2), 251-256, 2010	268	2010
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	248	2015
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	235	2018
Deep successor reinforcement learning TD Kulkarni, A Saeedi, S Gautam, SJ Gershman arXiv preprint arXiv:1606.02396, 2016	218	2016
Cost-benefit arbitration between multiple reinforcement-learning systems W Kool, SJ Gershman, FA Cushman Psychological science 28 (9), 1321-1333, 2017	216	2017
Nonparametric variational inference S Gershman, M Hoffman, D Blei Proceedings of the 29th International Conference on Machine Learning, 2012	170	2012
Deconstructing the human algorithms for exploration SJ Gershman Cognition 173, 34-42, 2018	168	2018

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