Matthew M. Botvinick

104 papers A* 18B 5Journal 69Unranked 11

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Modeling human reputation-seeking behavior in a spatio-temporally complex public good provision game. CoRR Edward Hughes, Tina O. Zhu, Martin J. Chadwick, Raphael Koster, Antonio García Castañeda, Charles Beattie, Thore Graepel, Matthew M. Botvinick, Joel Z. Leibo
2024	J	`jnl`	Understanding dual process cognition via the minimum description length principle. PLoS Comput. Biol. Ted Moskovitz, Kevin J. Miller, Maneesh Sahani, Matthew M. Botvinick
2023	J	`jnl`	Adaptive patch foraging in deep reinforcement learning agents. Trans. Mach. Learn. Res. Nathan J. Wispinski, Andrew Butcher, Kory Wallace Mathewson, Craig S. Chapman, Matthew M. Botvinick, Patrick M. Pilarski
2023	J	`jnl`	DiscoGen: Learning to Discover Gene Regulatory Networks. CoRR Nan Rosemary Ke, Sara-Jane Dunn, Jörg Bornschein, Silvia Chiappa, Mélanie Rey, Jean-Baptiste Lespiau, Albin Cassirer, Jane X. Wang, Theophane Weber, David G. T. Barrett, Matthew M. Botvinick, Anirudh Goyal, Michael Mozer, Danilo J. Rezende
2023	J	`jnl`	How should the advent of large language models affect the practice of science? CoRR Marcel Binz, Stephan Alaniz, Adina Roskies, Balazs Aczel, Carl T. Bergstrom, Colin Allen, Daniel Schad, Dirk U. 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
2023	A*	`conf`	Learning to Induce Causal Structure. ICLR Nan Rosemary Ke, Silvia Chiappa, Jane X. Wang, Jörg Bornschein, Anirudh Goyal, Mélanie Rey, Theophane Weber, Matthew M. Botvinick, Michael Curtis Mozer, Danilo Jimenez Rezende
2023	J	`jnl`	Meta-Learned Models of Cognition. CoRR Marcel Binz, Ishita Dasgupta, Akshay K. Jagadish, Matthew M. Botvinick, Jane X. Wang, Eric Schulz
2023	J	`jnl`	Meta-in-context learning in large language models. CoRR Julian Coda-Forno, Marcel Binz, Zeynep Akata, Matthew M. Botvinick, Jane X. Wang, Eric Schulz
2022	J	`jnl`	Adaptive patch foraging in deep reinforcement learning agents. CoRR Nathan J. Wispinski, Andrew Butcher, Kory W. Mathewson, Craig S. Chapman, Matthew M. Botvinick, Patrick M. Pilarski
2022	J	`jnl`	Compositional Sequence Generation in the Entorhinal-Hippocampal System. Entropy Daniel C. McNamee, Kimberly L. Stachenfeld, Matthew M. Botvinick, Samuel J. Gershman
2022	J	`jnl`	Fine-tuning language models to find agreement among humans with diverse preferences. CoRR Michiel A. Bakker, Martin J. Chadwick, Hannah R. Sheahan, Michael Henry Tessler, Lucy Campbell-Gillingham, Jan Balaguer, Nat McAleese, Amelia Glaese, John Aslanides, Matthew M. Botvinick, Christopher Summerfield
2022	A*	`conf`	General-purpose, long-context autoregressive modeling with Perceiver AR. ICML Curtis Hawthorne, Andrew Jaegle, Catalina Cangea, Sebastian Borgeaud, Charlie Nash, Mateusz Malinowski, Sander Dieleman, Oriol Vinyals, Matthew M. Botvinick, Ian Simon, Hannah Sheahan, Neil Zeghidour, Jean-Baptiste Alayrac, João Carreira, Jesse H. Engel
2022	J	`jnl`	General-purpose, long-context autoregressive modeling with Perceiver AR. CoRR Curtis Hawthorne, Andrew Jaegle, Catalina Cangea, Sebastian Borgeaud, Charlie Nash, Mateusz Malinowski, Sander Dieleman, Oriol Vinyals, Matthew M. Botvinick, Ian Simon, Hannah Sheahan, Neil Zeghidour, Jean-Baptiste Alayrac, João Carreira, Jesse H. Engel
2022	J	`jnl`	HCMD-zero: Learning Value Aligned Mechanisms from Data. CoRR Jan Balaguer, Raphael Koster, Ari Weinstein, Lucy Campbell-Gillingham, Christopher Summerfield, Matthew M. Botvinick, Andrea Tacchetti
2022	J	`jnl`	Hierarchical Perceiver. CoRR João Carreira, Skanda Koppula, Daniel Zoran, Adrià Recasens, Catalin Ionescu, Olivier J. Hénaff, Evan Shelhamer, Relja Arandjelovic, Matthew M. Botvinick, Oriol Vinyals, Karen Simonyan, Andrew Zisserman, Andrew Jaegle
2022	J	`jnl`	Human-centered mechanism design with Democratic AI. CoRR Raphael Koster, Jan Balaguer, Andrea Tacchetti, Ari Weinstein, Tina Zhu, Oliver P. Hauser, Duncan Williams, Lucy Campbell-Gillingham, Phoebe Thacker, Matthew M. Botvinick, Christopher Summerfield
2022	J	`jnl`	Learning to Induce Causal Structure. CoRR Nan Rosemary Ke, Silvia Chiappa, Jane Wang, Jörg Bornschein, Theophane Weber, Anirudh Goyal, Matthew M. Botvinick, Michael Mozer, Danilo Jimenez Rezende
2022	J	`jnl`	Meta-learning, social cognition and consciousness in brains and machines. Neural Networks Angela Langdon, Matthew M. Botvinick, Hiroyuki Nakahara, Keiji Tanaka, Masayuki Matsumoto, Ryota Kanai
2022	J	`jnl`	Minimum Description Length Control. CoRR Ted Moskovitz, Ta-Chu Kao, Maneesh Sahani, Matthew M. Botvinick
2022	A*	`conf`	Perceiver IO: A General Architecture for Structured Inputs & Outputs. ICLR Andrew Jaegle, Sebastian Borgeaud, Jean-Baptiste Alayrac, Carl Doersch, Catalin Ionescu, David Ding, Skanda Koppula, Daniel Zoran, Andrew Brock, Evan Shelhamer, Olivier J. Hénaff, Matthew M. Botvinick, Andrew Zisserman, Oriol Vinyals, João Carreira
2022	J	`jnl`	The Frost Hollow Experiments: Pavlovian Signalling as a Path to Coordination and Communication Between Agents. CoRR Patrick M. Pilarski, Andrew Butcher, Elnaz Davoodi, Michael Bradley Johanson, Dylan J. A. Brenneis, Adam S. R. Parker, Leslie Acker, Matthew M. Botvinick, Joseph Modayil, Adam White
2022	J	`jnl`	Toward Next-Generation Artificial Intelligence: Catalyzing the NeuroAI Revolution. CoRR Anthony Zador, Blake A. Richards, Bence Ölveczky, Sean Escola, Yoshua Bengio, Kwabena Boahen, Matthew M. Botvinick, Dmitri B. Chklovskii, Anne Churchland, Claudia Clopath, James DiCarlo, Surya Ganguli, Jeff Hawkins, Konrad P. Körding, Alexei A. Koulakov, Yann LeCun, Timothy P. Lillicrap, Adam H. Marblestone, Bruno A. Olshausen, Alexandre Pouget, Cristina Savin, Terrence J. Sejnowski, Eero P. Simoncelli, Sara A. Solla, David Sussillo, Andreas S. Tolias, Doris Tsao
2021	J	`jnl`	Alchemy: A structured task distribution for meta-reinforcement learning. CoRR Jane X. Wang, Michael King, Nicolas Porcel, Zeb Kurth-Nelson, Tina Zhu, Charlie Deck, Peter Choy, Mary Cassin, Malcolm Reynolds, H. Francis Song, Gavin Buttimore, David P. Reichert, Neil C. Rabinowitz, Loic Matthey, Demis Hassabis, Alexander Lerchner, Matthew M. Botvinick
2021	J	`jnl`	Assessing Human Interaction in Virtual Reality With Continually Learning Prediction Agents Based on Reinforcement Learning Algorithms: A Pilot Study. CoRR Dylan J. A. Brenneis, Adam S. R. Parker, Michael Bradley Johanson, Andrew Butcher, Elnaz Davoodi, Leslie Acker, Matthew M. Botvinick, Joseph Modayil, Adam White, Patrick M. Pilarski
2021	J	`jnl`	Deep reinforcement learning models the emergent dynamics of human cooperation. CoRR Kevin R. McKee, Edward Hughes, Tina O. Zhu, Martin J. Chadwick, Raphael Koster, Antonio García Castañeda, Charlie Beattie, Thore Graepel, Matthew M. Botvinick, Joel Z. Leibo
2021	J	`jnl`	Perceiver IO: A General Architecture for Structured Inputs & Outputs. CoRR Andrew Jaegle, Sebastian Borgeaud, Jean-Baptiste Alayrac, Carl Doersch, Catalin Ionescu, David Ding, Skanda Koppula, Daniel Zoran, Andrew Brock, Evan Shelhamer, Olivier J. Hénaff, Matthew M. Botvinick, Andrew Zisserman, Oriol Vinyals, João Carreira
2021	A*	`conf`	Rapid Task-Solving in Novel Environments. ICLR Samuel Ritter, Ryan Faulkner, Laurent Sartran, Adam Santoro, Matthew M. Botvinick, David Raposo
2021	J	`jnl`	SIMONe: View-Invariant, Temporally-Abstracted Object Representations via Unsupervised Video Decomposition. CoRR Rishabh Kabra, Daniel Zoran, Goker Erdogan, Loic Matthey, Antonia Creswell, Matthew M. Botvinick, Alexander Lerchner, Christopher P. Burgess
2020	J	`jnl`	A distributional code for value in dopamine-based reinforcement learning. Nat. Will Dabney, Zeb Kurth-Nelson, Naoshige Uchida, Clara Kwon Starkweather, Demis Hassabis, Rémi Munos, Matthew M. Botvinick
2020	J	`jnl`	Deep Reinforcement Learning and its Neuroscientific Implications. CoRR Matthew M. Botvinick, Jane X. Wang, Will Dabney, Kevin J. Miller, Zeb Kurth-Nelson
2020	A*	`conf`	Environmental drivers of systematicity and generalization in a situated agent. ICLR Felix Hill, Andrew K. Lampinen, Rosalia Schneider, Stephen Clark, Matthew M. Botvinick, James L. McClelland, Adam Santoro
2020	A*	`conf`	MEMO: A Deep Network for Flexible Combination of Episodic Memories. ICLR Andrea Banino, Adrià Puigdomènech Badia, Raphael Köster, Martin J. Chadwick, Vinícius Flores Zambaldi, Demis Hassabis, Caswell Barry, Matthew M. Botvinick, Dharshan Kumaran, Charles Blundell
2020	J	`jnl`	MEMO: A Deep Network for Flexible Combination of Episodic Memories. CoRR Andrea Banino, Adrià Puigdomènech Badia, Raphael Köster, Martin J. Chadwick, Vinícius Flores Zambaldi, Demis Hassabis, Caswell Barry, Matthew M. Botvinick, Dharshan Kumaran, Charles Blundell
2020	J	`jnl`	Model-free conventions in multi-agent reinforcement learning with heterogeneous preferences. CoRR Raphael Köster, Kevin R. McKee, Richard Everett, Laura Weidinger, William S. Isaac, Edward Hughes, Edgar A. Duéñez-Guzmán, Thore Graepel, Matthew M. Botvinick, Joel Z. Leibo
2020	A*	`conf`	Stabilizing Transformers for Reinforcement Learning. ICML Emilio Parisotto, H. Francis Song, Jack W. Rae, Razvan Pascanu, Çaglar Gülçehre, Siddhant M. Jayakumar, Max Jaderberg, Raphaël Lopez Kaufman, Aidan Clark, Seb Noury, Matthew M. Botvinick, Nicolas Heess, Raia Hadsell
2020	A*	`conf`	The Variational Bandwidth Bottleneck: Stochastic Evaluation on an Information Budget. ICLR Anirudh Goyal, Yoshua Bengio, Matthew M. Botvinick, Sergey Levine
2020	J	`jnl`	The Variational Bandwidth Bottleneck: Stochastic Evaluation on an Information Budget. CoRR Anirudh Goyal, Yoshua Bengio, Matthew M. Botvinick, Sergey Levine
2020	A*	`conf`	V-MPO: On-Policy Maximum a Posteriori Policy Optimization for Discrete and Continuous Control. ICLR H. Francis Song, Abbas Abdolmaleki, Jost Tobias Springenberg, Aidan Clark, Hubert Soyer, Jack W. Rae, Seb Noury, Arun Ahuja, Siqi Liu, Dhruva Tirumala, Nicolas Heess, Dan Belov, Martin A. Riedmiller, Matthew M. Botvinick
2019	B	`conf`	A resource-rational model of physical abstraction for efficient mental simulation. CogSci Tina Zhu, Jessica B. Hamrick, Kevin R. McKee, Raphael Koster, Jan Balaguer, Peter W. Battaglia, Matthew M. Botvinick
2019	A*	`conf`	Bayesian Action Decoder for Deep Multi-Agent Reinforcement Learning. ICML Jakob N. Foerster, H. Francis Song, Edward Hughes, Neil Burch, Iain Dunning, Shimon Whiteson, Matthew M. Botvinick, Michael Bowling
2019	J	`jnl`	Causal Reasoning from Meta-reinforcement Learning. CoRR Ishita Dasgupta, Jane X. Wang, Silvia Chiappa, Jovana Mitrovic, Pedro A. Ortega, David Raposo, Edward Hughes, Peter W. Battaglia, Matthew M. Botvinick, Zeb Kurth-Nelson
2019	—	`conf`	Deep reinforcement learning with relational inductive biases. ICLR (Poster) Vinícius Flores Zambaldi, David Raposo, Adam Santoro, Victor Bapst, Yujia Li, Igor Babuschkin, Karl Tuyls, David P. Reichert, Timothy P. Lillicrap, Edward Lockhart, Murray Shanahan, Victoria Langston, Razvan Pascanu, Matthew M. Botvinick, Oriol Vinyals, Peter W. Battaglia
2019	J	`jnl`	Emergent Systematic Generalization in a Situated Agent. CoRR Felix Hill, Andrew K. Lampinen, Rosalia Schneider, Stephen Clark, Matthew M. Botvinick, James L. McClelland, Adam Santoro
2019	—	`conf`	InfoBot: Transfer and Exploration via the Information Bottleneck. ICLR (Poster) Anirudh Goyal, Riashat Islam, Daniel Strouse, Zafarali Ahmed, Hugo Larochelle, Matthew M. Botvinick, Yoshua Bengio, Sergey Levine
2019	J	`jnl`	InfoBot: Transfer and Exploration via the Information Bottleneck. CoRR Anirudh Goyal, Riashat Islam, Daniel Strouse, Zafarali Ahmed, Matthew M. Botvinick, Hugo Larochelle, Sergey Levine, Yoshua Bengio
2019	J	`jnl`	Is coding a relevant metaphor for building AI? A commentary on "Is coding a relevant metaphor for the brain?", by Romain Brette. CoRR Adam Santoro, Felix Hill, David G. T. Barrett, David Raposo, Matthew M. Botvinick, Timothy P. Lillicrap
2019	J	`jnl`	Learned human-agent decision-making, communication and joint action in a virtual reality environment. CoRR Patrick M. Pilarski, Andrew Butcher, Michael Johanson, Matthew M. Botvinick, Andrew Bolt, Adam S. R. Parker
2019	J	`jnl`	MONet: Unsupervised Scene Decomposition and Representation. CoRR Christopher P. Burgess, Loïc Matthey, Nicholas Watters, Rishabh Kabra, Irina Higgins, Matthew M. Botvinick, Alexander Lerchner
2019	J	`jnl`	Meta-learning of Sequential Strategies. CoRR Pedro A. Ortega, Jane X. Wang, Mark Rowland, Tim Genewein, Zeb Kurth-Nelson, Razvan Pascanu, Nicolas Heess, Joel Veness, Alexander Pritzel, Pablo Sprechmann, Siddhant M. Jayakumar, Tom McGrath, Kevin J. Miller, Mohammad Gheshlaghi Azar, Ian Osband, Neil C. Rabinowitz, András György, Silvia Chiappa, Simon Osindero, Yee Whye Teh, Hado van Hasselt, Nando de Freitas, Matthew M. Botvinick, Shane Legg
2019	A*	`conf`	Multi-Object Representation Learning with Iterative Variational Inference. ICML Klaus Greff, Raphaël Lopez Kaufman, Rishabh Kabra, Nick Watters, Chris Burgess, Daniel Zoran, Loic Matthey, Matthew M. Botvinick, Alexander Lerchner
2019	J	`jnl`	Multi-Object Representation Learning with Iterative Variational Inference. CoRR Klaus Greff, Raphaël Lopez Kaufman, Rishabh Kabra, Nick Watters, Chris Burgess, Daniel Zoran, Loïc Matthey, Matthew M. Botvinick, Alexander Lerchner
2019	—	`conf`	Relational Forward Models for Multi-Agent Learning. ICLR (Poster) Andrea Tacchetti, H. Francis Song, Pedro A. M. Mediano, Vinícius Flores Zambaldi, János Kramár, Neil C. Rabinowitz, Thore Graepel, Matthew M. Botvinick, Peter W. Battaglia
2019	J	`jnl`	Stabilizing Transformers for Reinforcement Learning. CoRR Emilio Parisotto, H. Francis Song, Jack W. Rae, Razvan Pascanu, Çaglar Gülçehre, Siddhant M. Jayakumar, Max Jaderberg, Raphael Lopez Kaufman, Aidan Clark, Seb Noury, Matthew M. Botvinick, Nicolas Heess, Raia Hadsell
2019	J	`jnl`	Subgoal- and Goal-related Reward Prediction Errors in Medial Prefrontal Cortex. J. Cogn. Neurosci. José Ribas-Fernandes, Danesh Shahnazian, Clay B. Holroyd, Matthew M. Botvinick
2019	J	`jnl`	V-MPO: On-Policy Maximum a Posteriori Policy Optimization for Discrete and Continuous Control. CoRR H. Francis Song, Abbas Abdolmaleki, Jost Tobias Springenberg, Aidan Clark, Hubert Soyer, Jack W. Rae, Seb Noury, Arun Ahuja, Siqi Liu, Dhruva Tirumala, Nicolas Heess, Dan Belov, Martin A. Riedmiller, Matthew M. Botvinick
2018	J	`jnl`	Bayesian Action Decoder for Deep Multi-Agent Reinforcement Learning. CoRR Jakob N. Foerster, H. Francis Song, Edward Hughes, Neil Burch, Iain Dunning, Shimon Whiteson, Matthew M. Botvinick, Michael Bowling
2018	A*	`conf`	Been There, Done That: Meta-Learning with Episodic Recall. ICML Samuel Ritter, Jane X. Wang, Zeb Kurth-Nelson, Siddhant M. Jayakumar, Charles Blundell, Razvan Pascanu, Matthew M. Botvinick
2018	J	`jnl`	Been There, Done That: Meta-Learning with Episodic Recall. CoRR Samuel Ritter, Jane X. Wang, Zeb Kurth-Nelson, Siddhant M. Jayakumar, Charles Blundell, Razvan Pascanu, Matthew M. Botvinick
2018	B	`conf`	Episodic Control through Meta-Reinforcement Learning. CogSci Samuel Ritter, Jane X. Wang, Zeb Kurth-Nelson, Matthew M. Botvinick
2018	A*	`conf`	Learning to Share and Hide Intentions using Information Regularization. NeurIPS Daniel Strouse, Max Kleiman-Weiner, Josh Tenenbaum, Matthew M. Botvinick, David J. Schwab
2018	J	`jnl`	Learning to Share and Hide Intentions using Information Regularization. CoRR DJ Strouse, Max Kleiman-Weiner, Josh Tenenbaum, Matthew M. Botvinick, David J. Schwab
2018	A*	`conf`	Machine Theory of Mind. ICML Neil C. Rabinowitz, Frank Perbet, H. Francis Song, Chiyuan Zhang, S. M. Ali Eslami, Matthew M. Botvinick
2018	J	`jnl`	Machine Theory of Mind. CoRR Neil C. Rabinowitz, Frank Perbet, H. Francis Song, Chiyuan Zhang, S. M. Ali Eslami, Matthew M. Botvinick
2018	—	`conf`	On the importance of single directions for generalization. ICLR (Poster) Ari S. Morcos, David G. T. Barrett, Neil C. Rabinowitz, Matthew M. Botvinick
2018	J	`jnl`	On the importance of single directions for generalization. CoRR Ari S. Morcos, David G. T. Barrett, Neil C. Rabinowitz, Matthew M. Botvinick
2018	J	`jnl`	Probing Physics Knowledge Using Tools from Developmental Psychology. CoRR Luis Piloto, Ari Weinstein, Dhruva TB, Arun Ahuja, Mehdi Mirza, Greg Wayne, David Amos, Chia-Chun Hung, Matthew M. Botvinick
2018	J	`jnl`	Psychlab: A Psychology Laboratory for Deep Reinforcement Learning Agents. CoRR Joel Z. Leibo, Cyprien de Masson d'Autume, Daniel Zoran, David Amos, Charles Beattie, Keith Anderson, Antonio García Castañeda, Manuel Sanchez, Simon Green, Audrunas Gruslys, Shane Legg, Demis Hassabis, Matthew M. Botvinick
2018	J	`jnl`	Relational Deep Reinforcement Learning. CoRR Vinícius Flores Zambaldi, David Raposo, Adam Santoro, Victor Bapst, Yujia Li, Igor Babuschkin, Karl Tuyls, David P. Reichert, Timothy P. Lillicrap, Edward Lockhart, Murray Shanahan, Victoria Langston, Razvan Pascanu, Matthew M. Botvinick, Oriol Vinyals, Peter W. Battaglia
2018	J	`jnl`	Relational Forward Models for Multi-Agent Learning. CoRR Andrea Tacchetti, H. Francis Song, Pedro A. M. Mediano, Vinícius Flores Zambaldi, Neil C. Rabinowitz, Thore Graepel, Matthew M. Botvinick, Peter W. Battaglia
2018	J	`jnl`	Relational inductive biases, deep learning, and graph networks. CoRR Peter W. Battaglia, Jessica B. Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Vinícius Flores Zambaldi, Mateusz Malinowski, Andrea Tacchetti, David Raposo, Adam Santoro, Ryan Faulkner, Çaglar Gülçehre, H. Francis Song, Andrew J. Ballard, Justin Gilmer, George E. Dahl, Ashish Vaswani, Kelsey R. Allen, Charles Nash, Victoria Langston, Chris Dyer, Nicolas Heess, Daan Wierstra, Pushmeet Kohli, Matthew M. Botvinick, Oriol Vinyals, Yujia Li, Razvan Pascanu
2018	—	`conf`	SCAN: Learning Hierarchical Compositional Visual Concepts. ICLR (Poster) Irina Higgins, Nicolas Sonnerat, Loic Matthey, Arka Pal, Christopher P. Burgess, Matko Bosnjak, Murray Shanahan, Matthew M. Botvinick, Demis Hassabis, Alexander Lerchner
2018	J	`jnl`	Unsupervised Predictive Memory in a Goal-Directed Agent. CoRR Greg Wayne, Chia-Chun Hung, David Amos, Mehdi Mirza, Arun Ahuja, Agnieszka Grabska-Barwinska, Jack W. Rae, Piotr Mirowski, Joel Z. Leibo, Adam Santoro, Mevlana Gemici, Malcolm Reynolds, Tim Harley, Josh Abramson, Shakir Mohamed, Danilo Jimenez Rezende, David Saxton, Adam Cain, Chloe Hillier, David Silver, Koray Kavukcuoglu, Matthew M. Botvinick, Demis Hassabis, Timothy P. Lillicrap
2017	J	`jnl`	Building Machines that Learn and Think for Themselves: Commentary on Lake et al., Behavioral and Brain Sciences, 2017. CoRR Matthew M. Botvinick, David G. T. Barrett, Peter W. Battaglia, Nando de Freitas, Dharshan Kumaran, Joel Z. Leibo, Tim Lillicrap, Joseph Modayil, S. Mohamed, Neil C. Rabinowitz, Danilo Jimenez Rezende, Adam Santoro, Tom Schaul, Christopher Summerfield, Greg Wayne, Theophane Weber, Daan Wierstra, Shane Legg, Demis Hassabis
2017	J	`jnl`	Cognitive Psychology for Deep Neural Networks: A Shape Bias Case Study. CoRR Samuel Ritter, David G. T. Barrett, Adam Santoro, Matthew M. Botvinick
2017	A*	`conf`	DARLA: Improving Zero-Shot Transfer in Reinforcement Learning. ICML Irina Higgins, Arka Pal, Andrei A. Rusu, Loïc Matthey, Christopher P. Burgess, Alexander Pritzel, Matthew M. Botvinick, Charles Blundell, Alexander Lerchner
2017	J	`jnl`	DARLA: Improving Zero-Shot Transfer in Reinforcement Learning. CoRR Irina Higgins, Arka Pal, Andrei A. Rusu, Loïc Matthey, Christopher P. Burgess, Alexander Pritzel, Matthew M. Botvinick, Charles Blundell, Alexander Lerchner
2017	A*	`conf`	Learning to Learn without Gradient Descent by Gradient Descent. ICML Yutian Chen, Matthew W. Hoffman, Sergio Gomez Colmenarejo, Misha Denil, Timothy P. Lillicrap, Matthew M. Botvinick, Nando de Freitas
2017	J	`jnl`	Predictive representations can link model-based reinforcement learning to model-free mechanisms. PLoS Comput. Biol. Evan M. Russek, Ida Momennejad, Matthew M. Botvinick, Samuel J. Gershman, Nathaniel D. Daw
2017	J	`jnl`	SCAN: Learning Abstract Hierarchical Compositional Visual Concepts. CoRR Irina Higgins, Nicolas Sonnerat, Loïc Matthey, Arka Pal, Christopher P. Burgess, Matthew M. Botvinick, Demis Hassabis, Alexander Lerchner
2017	B	`conf`	Structure Learning in Motor Control: A Deep Reinforcement Learning Model. CogSci Ari Weinstein, Matthew M. Botvinick
2017	J	`jnl`	Structure Learning in Motor Control: A Deep Reinforcement Learning Model. CoRR Ari Weinstein, Matthew M. Botvinick
2017	B	`conf`	Tutorial: Recent Advances in Deep Learning. CogSci Matthew M. Botvinick, Peter W. Battaglia
2017	—	`conf`	beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework. ICLR (Poster) Irina Higgins, Loïc Matthey, Arka Pal, Christopher P. Burgess, Xavier Glorot, Matthew M. Botvinick, Shakir Mohamed, Alexander Lerchner
2016	J	`jnl`	Learning to reinforcement learn. CoRR Jane X. Wang, Zeb Kurth-Nelson, Dhruva Tirumala, Hubert Soyer, Joel Z. Leibo, Rémi Munos, Charles Blundell, Dharshan Kumaran, Matthew M. Botvinick
2016	A*	`conf`	Meta-Learning with Memory-Augmented Neural Networks. ICML Adam Santoro, Sergey Bartunov, Matthew M. Botvinick, Daan Wierstra, Timothy P. Lillicrap
2016	J	`jnl`	One-shot Learning with Memory-Augmented Neural Networks. CoRR Adam Santoro, Sergey Bartunov, Matthew M. Botvinick, Daan Wierstra, Timothy P. Lillicrap
2015	—	`conf`	Leveraging Preposition Ambiguity to Assess Compositional Distributional Models of Semantics. *SEM@NAACL-HLT Samuel Ritter, Cotie Long, Denis Paperno, Marco Baroni, Matthew M. Botvinick, Adele E. Goldberg
2014	—	`conf`	Design Principles of the Hippocampal Cognitive Map. NIPS Kimberly L. Stachenfeld, Matthew M. Botvinick, Samuel Gershman
2014	J	`jnl`	Optimal Behavioral Hierarchy. PLoS Comput. Biol. Alec Solway, Carlos Diuk, Natalia Córdova, Debbie Yee, Andrew G. Barto, Yael Niv, Matthew M. Botvinick
2014	J	`jnl`	The Computational and Neural Basis of Cognitive Control: Charted Territory and New Frontiers. Cogn. Sci. Matthew M. Botvinick, Jonathan D. Cohen
2013	—	`ch.`	Divide and Conquer: Hierarchical Reinforcement Learning and Task Decomposition in Humans. Computational and Robotic Models of the Hierarchical Organization of Behavior Carlos Diuk, Anna C. Schapiro, Natalia Córdova, José Ribas-Fernandes, Yael Niv, Matthew M. Botvinick
2013	J	`jnl`	Neural Representation of Reward Probability: Evidence from the Illusion of Control. J. Cogn. Neurosci. Wouter Kool, Sarah J. Getz, Matthew M. Botvinick
2013	—	`conf`	Simitar: Simplified Searching of Statistically Significant Similarity Structure. PRNI Francisco Pereira, Matthew M. Botvinick
2013	B	`conf`	Structured cognitive representations and complex inference in neural systems. CogSci Samuel Gershman, Joshua B. Tenenbaum, Alexandre Pouget, Matthew M. Botvinick, Peter Dayan
2013	J	`jnl`	Using Wikipedia to learn semantic feature representations of concrete concepts in neuroimaging experiments. Artif. Intell. Francisco Pereira, Matthew M. Botvinick, Greg Detre
2012	—	`conf`	A systematic approach to extracting semantic information from functional MRI data. NIPS Francisco Pereira, Matthew M. Botvinick
2012	J	`jnl`	Commentary: Why I Am Not a Dynamicist. Top. Cogn. Sci. Matthew M. Botvinick
2011	A*	`conf`	Classification of functional magnetic resonance imaging data using informative pattern features. KDD Francisco Pereira, Matthew M. Botvinick
2011	J	`jnl`	Errors of interpretation and modeling: A reply to Grinband et al. NeuroImage Nick Yeung, Jonathan D. Cohen, Matthew M. Botvinick
2011	J	`jnl`	Information mapping with pattern classifiers: A comparative study. NeuroImage Francisco Pereira, Matthew M. Botvinick
2009	J	`jnl`	Machine learning classifiers and fMRI: A tutorial overview. NeuroImage Francisco Pereira, Tom M. Mitchell, Matthew M. Botvinick
2008	—	`conf`	Goal-directed decision making in prefrontal cortex: a computational framework. NIPS Matthew M. Botvinick, James An
2005	J	`jnl`	Viewing facial expressions of pain engages cortical areas involved in the direct experience of pain. NeuroImage Matthew M. Botvinick, Amishi P. Jha, Lauren M. Bylsma, Sara A. Fabian, Patricia E. Solomon, Kenneth M. Prkachin
2001	J	`jnl`	Anterior Cingulate Cortex, Conflict Monitoring, and Levels of Processing. NeuroImage Vincent van Veen, Jonathan D. Cohen, Matthew M. Botvinick, V. Andrew Stenger, Cameron S. Carter