Samuel Gershman

发表

Explaining Human Decision Making in Optimal Stopping Tasks

Christiane Baumann, Henrik Singmann, Vassilios E. Kaxiras, 2018, CogSci.

Behavioural Analysis, Bayesian

Samuel Gershman, S. Gershman, 2014, Encyclopedia of Computational Neuroscience.

Distance Dependent Infinite Latent Feature Models

Peter I. Frazier, David M. Blei, Samuel Gershman, 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

A Bayesian Analysis of Dynamics in Free Recall

Adler J. Perotte, Richard Socher, David M. Blei, 2009, NIPS.

Learning to act by integrating mental simulations and physical experiments

Joshua B. Tenenbaum, Kevin A. Smith, Samuel Gershman, 2018, bioRxiv.

Multistability and Perceptual Inference

Joshua B. Tenenbaum, Ed Vul, Samuel Gershman, 2012, Neural Computation.

Assessing the Perceived Predictability of Functions

Joshua B. Tenenbaum, Samuel Gershman, Maarten Speekenbrink, 2015, CogSci.

Dopamine, Inference, and Uncertainty

Samuel Gershman, S. Gershman, 2017, bioRxiv.

Amortized Inference in Probabilistic Reasoning

Noah D. Goodman, Samuel Gershman, S. Gershman, 2014, CogSci.

Novelty and Inductive Generalization in Human Reinforcement Learning

Samuel Gershman, Yael Niv, Y. Niv, 2015, Top. Cogn. Sci..

Statistical Computations Underlying the Dynamics of Memory Updating

Samuel Gershman, Kenneth A. Norman, Yael Niv, 2014, PLoS Comput. Biol..

Probing the Compositionality of Intuitive Functions

Joshua B. Tenenbaum, David Duvenaud, Samuel Gershman, 2016, NIPS.

Pavlovian Control of Escape and Avoidance

Samuel Gershman, Hanneke E. M. den Ouden, Alexander J. Millner, 2018, Journal of Cognitive Neuroscience.

Design Principles of the Hippocampal Cognitive Map

Samuel Gershman, Matthew Botvinick, Kimberly L. Stachenfeld, 2014, NIPS.

Structured cognitive representations and complex inference in neural systems

Alexandre Pouget, Peter Dayan, Joshua B. Tenenbaum, 2013, CogSci.

Evaluating Compositionality in Sentence Embeddings pdf

Noah D. Goodman, Samuel Gershman, Andreas Stuhlmüller, 2018, CogSci.

Predictive representations can link model-based reinforcement learning to model-free mechanisms

Samuel Gershman, Nathaniel D. Daw, Matthew Botvinick, 2017, bioRxiv.

Coalescing the Vapors of Human Experience into a Viable and Meaningful Comprehension

Joshua B. Tenenbaum, Samuel Gershman, Tomer Ullman, 2016, CogSci.

Computational, Neuroscientific, and Lifespan Perspectives on the Exploration-Exploitation Dilemma

Bradley C. Love, Richard M. Shiffrin, Jerome R. Busemeyer, 2011, CogSci.

Imaginative Reinforcement Learning: Computational Principles and Neural Mechanisms

Samuel Gershman, Cody Kommers, Jimmy Zhou, 2017, Journal of Cognitive Neuroscience.

Joshua B. Tenenbaum, Samuel Gershman, J. Tenenbaum, 2015, CogSci.

Human Learning in Atari

Joshua B. Tenenbaum, Samuel Gershman, Pedro Tsividis, 2017, AAAI Spring Symposia.

Nonparametric variational inference pdf

David M. Blei, Matthew D. Hoffman, Samuel Gershman, 2012, ICML.

Deep Successor Reinforcement Learning pdf

Samuel Gershman, Ardavan Saeedi, Tejas D. Kulkarni, 2016, ArXiv.

Nonparametric Spherical Topic Modeling with Word Embeddings

Nematollah Batmanghelich, Samuel Gershman, Ardavan Saeedi, 2016, ACL.

Bayesian vector analysis and the perception of hierarchical motion

Frank Jäkel, Joshua B. Tenenbaum, Samuel Gershman, 2013, CogSci.

Decomposing spatiotemporal brain patterns into topographic latent sources

David M. Blei, Per B. Sederberg, Samuel Gershman, 2014, NeuroImage.

Information Selection in Noisy Environments with Large Action Spaces

Joshua B. Tenenbaum, Laura Schulz, Samuel Gershman, 2014, CogSci.

Dopamine Ramps Are a Consequence of Reward Prediction Errors

Samuel Gershman, S. Gershman, 2014, Neural Computation.

Planning Complexity Registers as a Cost in Metacontrol

Wouter Kool, Fiery Cushman, Samuel Gershman, 2018, Journal of Cognitive Neuroscience.

A topographic latent source model for fMRI data

David M. Blei, Francisco Pereira, Samuel Gershman, 2011, NeuroImage.

The Neural Costs of Optimal Control

Samuel Gershman, Robert Wilson, Robert C. Wilson, 2010, NIPS.

Perceptual Multistability as Markov Chain Monte Carlo Inference

Joshua B. Tenenbaum, Ed Vul, Samuel Gershman, 2009, NIPS.

Estimating Scale-Invariant Future in Continuous Time

Per B. Sederberg, Samuel Gershman, Marc W. Howard, 2018, Neural Computation.

Time representation in reinforcement learning models of the basal ganglia

Samuel Gershman, Ahmed A. Moustafa, Elliot A. Ludvig, 2014, Front. Comput. Neurosci..

When Does Model-Based Control Pay Off?

Wouter Kool, Fiery Cushman, Samuel Gershman, 2016, PLoS Comput. Biol..

Neural and Psychological Maturation of Decision-making in Adolescence and Young Adulthood

Michael J. Brammer, Samuel Gershman, Andrew Simmons, 2013, Journal of Cognitive Neuroscience.

A Unifying Probabilistic View of Associative Learning

Samuel Gershman, S. Gershman, 2015, PLoS Comput. Biol..

The Successor Representation and Temporal Context

Per B. Sederberg, Samuel Gershman, Kenneth A. Norman, 2012, Neural Computation.

Editors' Introduction: Computational Approaches to Social Cognition

Fiery Cushman, Samuel Gershman, S. Gershman, 2019, Top. Cogn. Sci..

Moral Dynamics: A Computational Model of Moral Judgment

Joshua B. Tenenbaum, Samuel Gershman, Tomer Ullman, 2018, CogSci.

Amortized Hypothesis Generation

Noah D. Goodman, Samuel Gershman, Eric Schulz, 2017, bioRxiv.

Computation with Dopaminergic Modulation

Samuel Gershman, S. Gershman, 2014, Encyclopedia of Computational Neuroscience.

A comparative evaluation of off-the-shelf distributed semantic representations for modelling behavioural data

Francisco Pereira, Samuel Gershman, Samuel Ritter, 2016, Cognitive neuropsychology.