Samuel J. Gershman

Publication Activity (10 Years)

Years Active: 2015-2024
Publications (10 Years): 42

Top Topics

Reinforcement Learning

Hierarchical Bayesian

Neural Mechanisms

Biologically Plausible

Top Venues

PLoS Comput. Biol.

J. Cogn. Neurosci.

Publications

Samuel J. Gershman
What have we learned about artificial intelligence from studying the brain? Biol. Cybern. 118 (1) (2024)
Alicia M. Chen, Andrew Palacci, Natalia Vélez, Robert D. Hawkins, Samuel J. Gershman
A Hierarchical Bayesian Model of Adaptive Teaching. Cogn. Sci. 48 (7) (2024)
Rick Dale, Ruth M. J. Byrne, Emma Cohen, Ophelia Deroy, Samuel J. Gershman, Janet H. Hsiao, Ping Li, Padraic Monaghan, David C. Noelle, Iris van Rooij, Priti Shah, Michael J. Spivey, Sashank Varma
Introduction to Progress and Puzzles of Cognitive Science. Cogn. Sci. 48 (7) (2024)
Tanishq Kumar, Blake Bordelon, Samuel J. Gershman, Cengiz Pehlevan
Grokking as the transition from lazy to rich training dynamics. ICLR (2024)
Wilka Carvalho, Momchil S. Tomov, William de Cothi, Caswell Barry, Samuel J. Gershman
Predictive representations: building blocks of intelligence. CoRR (2024)
Qihong Lu, Tan T. Nguyen, Qiong Zhang, Uri Hasson, Thomas L. Griffiths, Jeffrey M. Zacks, Samuel J. Gershman, Kenneth A. Norman
Toward a More Biologically Plausible Neural Network Model of Latent Cause Inference. CoRR (2023)
Samuel J. Gershman
The molecular memory code and synaptic plasticity: A synthesis. Biosyst. 224 (2023)
Marcel Binz, Stephan Alaniz, Adina Roskies, Balazs Aczel, Carl T. Bergstrom, Colin Allen, Daniel Schad, Dirk Wulff, Jevin D. West, Qiong Zhang, Richard M. Shiffrin, Samuel J. Gershman, Ven Popov, Emily M. Bender, Marco Marelli, Matthew M. Botvinick, Zeynep Akata, Eric Schulz
How should the advent of large language models affect the practice of science? CoRR (2023)
Changmin Yu, Neil Burgess, Maneesh Sahani, Samuel J. Gershman
Successor-Predecessor Intrinsic Exploration. NeurIPS (2023)
Haoxue Fan, Taylor Burke, Deshawn Chatman Sambrano, Emily Dial, Elizabeth A. Phelps, Samuel J. Gershman
Pupil Size Encodes Uncertainty during Exploration. J. Cogn. Neurosci. 35 (9) (2023)
Jay A. Hennig, Sandra A. Romero Pinto, Takahiro Yamaguchi, Scott W. Linderman, Naoshige Uchida, Samuel J. Gershman
Emergence of belief-like representations through reinforcement learning. PLoS Comput. Biol. 19 (9) (2023)
Tanishq Kumar, Blake Bordelon, Samuel J. Gershman, Cengiz Pehlevan
Grokking as the Transition from Lazy to Rich Training Dynamics. CoRR (2023)
Daniel C. McNamee, Kimberly L. Stachenfeld, Matthew M. Botvinick, Samuel J. Gershman
Compositional Sequence Generation in the Entorhinal-Hippocampal System. Entropy 24 (12) (2022)
Tuan Anh Le, Katherine M. Collins, Luke Hewitt, Kevin Ellis, N. Siddharth, Samuel J. Gershman, Joshua B. Tenenbaum
Hybrid Memoised Wake-Sleep: Approximate Inference at the Discrete-Continuous Interface. CoRR (2021)
Nikhil X. Bhattasali, Momchil S. Tomov, Samuel J. Gershman
CCNLab: A Benchmarking Framework for Computational Cognitive Neuroscience. NeurIPS Datasets and Benchmarks (2021)
John G. Mikhael, Lucy Lai, Samuel J. Gershman
Rational inattention and tonic dopamine. PLoS Comput. Biol. 17 (3) (2021)
Pedro A. Tsividis, João Loula, Jake Burga, Nathan Foss, Andres Campero, Thomas Pouncy, Samuel J. Gershman, Joshua B. Tenenbaum
Human-Level Reinforcement Learning through Theory-Based Modeling, Exploration, and Planning. CoRR (2021)
Samuel J. Gershman, Marc Guitart-Masip, James F. Cavanagh
Neural signatures of arbitration between Pavlovian and instrumental action selection. PLoS Comput. Biol. 17 (2) (2021)
Thomas Pouncy, Pedro Tsividis, Samuel J. Gershman
What Is the Model in Model-Based Planning? Cogn. Sci. 45 (1) (2021)
Ishita Dasgupta, Demi Guo, Samuel J. Gershman, Noah D. Goodman
Analyzing Machine-Learned Representations: A Natural Language Case Study. Cogn. Sci. 44 (12) (2020)
Ruocheng Wang, Jiayuan Mao, Samuel J. Gershman, Jiajun Wu
Language-Mediated, Object-Centric Representation Learning. CoRR (2020)
Momchil S. Tomov, Samyukta Yagati, Agni Kumar, Wanqian Yang, Samuel J. Gershman
Discovery of hierarchical representations for efficient planning. PLoS Comput. Biol. 16 (4) (2020)
Zoran Tiganj, Samuel J. Gershman, Per B. Sederberg, Marc W. Howard
Estimating Scale-Invariant Future in Continuous Time. Neural Comput. 31 (4) (2019)
Isaac Lage, Emily Chen, Jeffrey He, Menaka Narayanan, Been Kim, Samuel J. Gershman, Finale Doshi-Velez
Human Evaluation of Models Built for Interpretability. HCOMP (2019)
Samuel J. Gershman
The Generative Adversarial Brain. Frontiers Artif. Intell. 2 (2019)
Ishita Dasgupta, Demi Guo, Samuel J. Gershman, Noah D. Goodman
Analyzing machine-learned representations: A natural language case study. CoRR (2019)
Isaac Lage, Andrew Slavin Ross, Been Kim, Samuel J. Gershman, Finale Doshi-Velez
Human-in-the-Loop Interpretability Prior. CoRR (2018)
Isaac Lage, Andrew Slavin Ross, Samuel J. Gershman, Been Kim, Finale Doshi-Velez
Human-in-the-Loop Interpretability Prior. NeurIPS (2018)
Ishita Dasgupta, Demi Guo, Andreas Stuhlmüller, Samuel J. Gershman, Noah D. Goodman
Evaluating Compositionality in Sentence Embeddings. CoRR (2018)
Wouter Kool, Samuel J. Gershman, Fiery Cushman
Planning Complexity Registers as a Cost in Metacontrol. J. Cogn. Neurosci. 30 (10) (2018)
Zoran Tiganj, Samuel J. Gershman, Per B. Sederberg, Marc W. Howard
Estimating scale-invariant future in continuous time. CoRR (2018)
Alexander J. Millner, Samuel J. Gershman, Matthew K. Nock, Hanneke E. M. den Ouden
Pavlovian Control of Escape and Avoidance. J. Cogn. Neurosci. 30 (10) (2018)
Pedro Tsividis, Thomas Pouncy, Jaqueline L. Xu, Joshua B. Tenenbaum, Samuel J. Gershman
Human Learning in Atari. AAAI Spring Symposia (2017)
Ardavan Saeedi, Tejas D. Kulkarni, Vikash K. Mansinghka, Samuel J. Gershman
Variational Particle Approximations. J. Mach. Learn. Res. 18 (2017)
Samuel J. Gershman
Dopamine, Inference, and Uncertainty. Neural Comput. 29 (12) (2017)
Ishita Dasgupta, Eric Schulz, Noah D. Goodman, Samuel J. Gershman
Amortized Hypothesis Generation. CogSci (2017)
Evan M. Russek, Ida Momennejad, Matthew Botvinick, Samuel J. Gershman, Nathaniel D. Daw
Predictive representations can link model-based reinforcement learning to model-free mechanisms. PLoS Comput. Biol. 13 (9) (2017)
Samuel J. Gershman, Jimmy Zhou, Cody Kommers
Imaginative Reinforcement Learning: Computational Principles and Neural Mechanisms. J. Cogn. Neurosci. 29 (12) (2017)
Wouter Kool, Fiery Cushman, Samuel J. Gershman
When Does Model-Based Control Pay Off? PLoS Comput. Biol. 12 (8) (2016)
Tejas D. Kulkarni, Ardavan Saeedi, Simanta Gautam, Samuel J. Gershman
Deep Successor Reinforcement Learning. CoRR (2016)
Brenden M. Lake, Tomer D. Ullman, Joshua B. Tenenbaum, Samuel J. Gershman
Building Machines That Learn and Think Like People. CoRR (2016)
Eric Schulz, Josh Tenenbaum, David Duvenaud, Maarten Speekenbrink, Samuel J. Gershman
Probing the Compositionality of Intuitive Functions. NIPS (2016)
Samuel J. Gershman, Yael Niv
Novelty and Inductive Generalization in Human Reinforcement Learning. Top. Cogn. Sci. 7 (3) (2015)
Samuel J. Gershman
A Unifying Probabilistic View of Associative Learning. PLoS Comput. Biol. 11 (11) (2015)