Samuel Gershman

Citirano

	Sve	Od 2018.
Citati	21127	17768
H-indeks	71	68
i10-indeks	156	155

3700

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Suautori

Joshua B. TenenbaumMITPotvrđena adresa e-pošte na mit.edu
Yael NivProfessor of Psychology and Neuroscience, Princeton UniversityPotvrđena adresa e-pošte na princeton.edu
Kenneth A. NormanProfessor of Psychology and Neuroscience, Princeton UniversityPotvrđena adresa e-pošte na princeton.edu
Eric SchulzMax Planck Institute for Biological CyberneticsPotvrđena adresa e-pošte na tue.mpg.de
Matthew BotvinickGoogle DeepMind, Yale Law School, University College LondonPotvrđena adresa e-pošte na google.com
Nathaniel D. DawHuo Professor of Computational and Theoretical Neuroscience, Princeton UniversityPotvrđena adresa e-pošte na princeton.edu
Tomer UllmanAssistant Professor, HarvardPotvrđena adresa e-pošte na fas.harvard.edu
Fiery CushmanPsychology, Harvard UniversityPotvrđena adresa e-pošte na fas.harvard.edu
David BleiProfessor of Statistics and Computer Science, Columbia UniversityPotvrđena adresa e-pošte na columbia.edu
Kimberly Lauren StachenfeldResearch Scientist, Google DeepMindPotvrđena adresa e-pošte na google.com
Naoshige UchidaProfessor of Molecular and Cellular Biology, Harvard UniversityPotvrđena adresa e-pošte na mcb.harvard.edu
Rahul BhuiMassachusetts Institute of TechnologyPotvrđena adresa e-pošte na mit.edu
Wouter KoolAssistant Professor at Washington University in St. LouisPotvrđena adresa e-pošte na wustl.edu
Ishita DasguptaSenior Research Scientist, DeepMind New York CityPotvrđena adresa e-pošte na google.com
Noah D. GoodmanStanford UniversityPotvrđena adresa e-pošte na stanford.edu
Brenden M. LakeAssistant Professor, New York UniversityPotvrđena adresa e-pošte na nyu.edu
John G. MikhaelHarvardPotvrđena adresa e-pošte na hms.harvard.edu
Marie MonfilsProfessor; The University of Texas at AustinPotvrđena adresa e-pošte na utexas.edu
Momchil S. TomovHarvard UniversityPotvrđena adresa e-pošte na g.harvard.edu
Per B. SederbergProfessor of Psychology, University of VirginiaPotvrđena adresa e-pošte na virginia.edu

Prati

Samuel Gershman

Professor, Harvard University

Potvrđena adresa e-pošte na fas.harvard.edu - Početna stranica

Computational neuroscience cognitive science machine learning


Naslov Poredaj po navodima Poredaj po godini Poredaj po naslovu	Citirano Citirano	Godina
Building machines that learn and think like people BM Lake, TD Ullman, JB Tenenbaum, SJ Gershman Behavioral and brain sciences 40, e253, 2017	2772	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	1793	2011
The hippocampus as a predictive map KL Stachenfeld, MM Botvinick, SJ Gershman Nature Neuroscience 20, 1643-1653, 2017	772	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	721	2015
A tutorial on Bayesian nonparametric models SJ Gershman, DM Blei Journal of Mathematical Psychology 56, 1-12, 2012	695	2012
Reinforcement learning and episodic memory in humans and animals: an integrative framework SJ Gershman, ND Daw Annual review of psychology 68, 101-128, 2017	460	2017
Context, learning, and extinction SJ Gershman, DM Blei, Y Niv Psychological Review 117 (1), 197-209, 2010	409	2010
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	382	2017
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	373	2015
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	368	2013
Amortized Inference in Probabilistic Reasoning SJ Gershman, ND Goodman Proceedings of the 36th Annual Cognitive Science Society, 2013	359	2013
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	304	2017
Learning latent structure: carving nature at its joints SJ Gershman, Y Niv Current Opinion in Neurobiology 20 (2), 251-256, 2010	299	2010
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	287	2018
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	284	2014
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	270	2015
Cost-benefit arbitration between multiple reinforcement-learning systems W Kool, SJ Gershman, FA Cushman Psychological science 28 (9), 1321-1333, 2017	251	2017
Deep successor reinforcement learning TD Kulkarni, A Saeedi, S Gautam, SJ Gershman arXiv preprint arXiv:1606.02396, 2016	232	2016
Deconstructing the human algorithms for exploration SJ Gershman Cognition 173, 34-42, 2018	210	2018
The successor representation: its computational logic and neural substrates SJ Gershman Journal of Neuroscience 38 (33), 7193-7200, 2018	191	2018

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