Matthew Botvinick

Publication Activity (10 Years)

Years Active: 2005-2022
Publications (10 Years): 64

Top Topics

Reinforcement Learning

Object Representation

Top Venues

Publications

João Carreira, Skanda Koppula, Daniel Zoran, Adrià Recasens, Catalin Ionescu, Olivier J. Hénaff, Evan Shelhamer, Relja Arandjelovic, Matthew Botvinick, Oriol Vinyals, Karen Simonyan, Andrew Zisserman, Andrew Jaegle
Hierarchical Perceiver. CoRR (2022)
Jan Balaguer, Raphael Koster, Ari Weinstein, Lucy Campbell-Gillingham, Christopher Summerfield, Matthew Botvinick, Andrea Tacchetti
HCMD-zero: Learning Value Aligned Mechanisms from Data. CoRR (2022)
Curtis Hawthorne, Andrew Jaegle, Catalina Cangea, Sebastian Borgeaud, Charlie Nash, Mateusz Malinowski, Sander Dieleman, Oriol Vinyals, Matthew Botvinick, Ian Simon, Hannah Sheahan, Neil Zeghidour, Jean-Baptiste Alayrac, João Carreira, Jesse H. Engel
General-purpose, long-context autoregressive modeling with Perceiver AR. CoRR (2022)
Raphael Koster, Jan Balaguer, Andrea Tacchetti, Ari Weinstein, Tina Zhu, Oliver Hauser, Duncan Williams, Lucy Campbell-Gillingham, Phoebe Thacker, Matthew Botvinick, Christopher Summerfield
Human-centered mechanism design with Democratic AI. CoRR (2022)
Angela Langdon, Matthew Botvinick, Hiroyuki Nakahara, Keiji Tanaka, Masayuki Matsumoto, Ryota Kanai
Meta-learning, social cognition and consciousness in brains and machines. Neural Networks 145 (2022)
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 Botvinick
Alchemy: A structured task distribution for meta-reinforcement learning. CoRR (2021)
Rishabh Kabra, Daniel Zoran, Goker Erdogan, Loic Matthey, Antonia Creswell, Matthew Botvinick, Alexander Lerchner, Christopher P. Burgess
SIMONe: View-Invariant, Temporally-Abstracted Object Representations via Unsupervised Video Decomposition. CoRR (2021)
Kevin R. McKee, Edward Hughes, Tina O. Zhu, Martin J. Chadwick, Raphael Koster, Antonio García Castañeda, Charlie Beattie, Thore Graepel, Matthew Botvinick, Joel Z. Leibo
Deep reinforcement learning models the emergent dynamics of human cooperation. CoRR (2021)
Samuel Ritter, Ryan Faulkner, Laurent Sartran, Adam Santoro, Matthew Botvinick, David Raposo
Rapid Task-Solving in Novel Environments. ICLR (2021)
Matthew Botvinick, Jane X. Wang, Will Dabney, Kevin J. Miller, Zeb Kurth-Nelson
Deep Reinforcement Learning and its Neuroscientific Implications. CoRR (2020)
Felix Hill, Andrew K. Lampinen, Rosalia Schneider, Stephen Clark, Matthew Botvinick, James L. McClelland, Adam Santoro
Environmental drivers of systematicity and generalization in a situated agent. ICLR (2020)
Andrea Banino, Adrià Puigdomènech Badia, Raphael Köster, Martin J. Chadwick, Vinícius Flores Zambaldi, Demis Hassabis, Caswell Barry, Matthew Botvinick, Dharshan Kumaran, Charles Blundell
MEMO: A Deep Network for Flexible Combination of Episodic Memories. ICLR (2020)
Will Dabney, Zeb Kurth-Nelson, Naoshige Uchida, Clara Kwon Starkweather, Demis Hassabis, Rémi Munos, Matthew Botvinick
A distributional code for value in dopamine-based reinforcement learning. Nat. 577 (7792) (2020)
Anirudh Goyal, Yoshua Bengio, Matthew Botvinick, Sergey Levine
The Variational Bandwidth Bottleneck: Stochastic Evaluation on an Information Budget. CoRR (2020)
Andrea Banino, Adrià Puigdomènech Badia, Raphael Köster, Martin J. Chadwick, Vinícius Flores Zambaldi, Demis Hassabis, Caswell Barry, Matthew Botvinick, Dharshan Kumaran, Charles Blundell
MEMO: A Deep Network for Flexible Combination of Episodic Memories. CoRR (2020)
Raphael Köster, Kevin R. McKee, Richard Everett, Laura Weidinger, William S. Isaac, Edward Hughes, Edgar A. Duéñez-Guzmán, Thore Graepel, Matthew Botvinick, Joel Z. Leibo
Model-free conventions in multi-agent reinforcement learning with heterogeneous preferences. CoRR (2020)
Anirudh Goyal, Yoshua Bengio, Matthew Botvinick, Sergey Levine
The Variational Bandwidth Bottleneck: Stochastic Evaluation on an Information Budget. ICLR (2020)
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 Botvinick, Nicolas Heess, Raia Hadsell
Stabilizing Transformers for Reinforcement Learning. ICML (2020)
Jakob N. Foerster, H. Francis Song, Edward Hughes, Neil Burch, Iain Dunning, Shimon Whiteson, Matthew Botvinick, Michael Bowling
Bayesian Action Decoder for Deep Multi-Agent Reinforcement Learning. ICML (2019)
Klaus Greff, Raphaël Lopez Kaufman, Rishabh Kabra, Nick Watters, Christopher Burgess, Daniel Zoran, Loic Matthey, Matthew Botvinick, Alexander Lerchner
Multi-Object Representation Learning with Iterative Variational Inference. ICML (2019)
Felix Hill, Andrew K. Lampinen, Rosalia Schneider, Stephen Clark, Matthew Botvinick, James L. McClelland, Adam Santoro
Emergent Systematic Generalization in a Situated Agent. CoRR (2019)
Adam Santoro, Felix Hill, David G. T. Barrett, David Raposo, Matthew Botvinick, Timothy P. Lillicrap
Is coding a relevant metaphor for building AI? A commentary on "Is coding a relevant metaphor for the brain?", by Romain Brette. CoRR (2019)
José Ribas-Fernandes, Danesh Shahnazian, Clay B. Holroyd, Matthew Botvinick
Subgoal- and Goal-related Reward Prediction Errors in Medial Prefrontal Cortex. J. Cogn. Neurosci. 31 (1) (2019)
Andrea Tacchetti, H. Francis Song, Pedro A. M. Mediano, Vinícius Flores Zambaldi, János Kramár, Neil C. Rabinowitz, Thore Graepel, Matthew Botvinick, Peter W. Battaglia
Relational Forward Models for Multi-Agent Learning. ICLR (Poster) (2019)
Ishita Dasgupta, Jane X. Wang, Silvia Chiappa, Jovana Mitrovic, Pedro A. Ortega, David Raposo, Edward Hughes, Peter W. Battaglia, Matthew Botvinick, Zeb Kurth-Nelson
Causal Reasoning from Meta-reinforcement Learning. CoRR (2019)
Tina Zhu, Jessica B. Hamrick, Kevin R. McKee, Raphael Koster, Jan Balaguer, Peter W. Battaglia, Matthew Botvinick
A resource-rational model of physical abstraction for efficient mental simulation. CogSci (2019)
Patrick M. Pilarski, Andrew Butcher, Michael Johanson, Matthew Botvinick, Andrew Bolt, Adam S. R. Parker
Learned human-agent decision-making, communication and joint action in a virtual reality environment. CoRR (2019)
Anirudh Goyal, Riashat Islam, Daniel Strouse, Zafarali Ahmed, Hugo Larochelle, Matthew Botvinick, Yoshua Bengio, Sergey Levine
InfoBot: Transfer and Exploration via the Information Bottleneck. ICLR (Poster) (2019)
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 Botvinick, Shane Legg
Meta-learning of Sequential Strategies. CoRR (2019)
Anirudh Goyal, Riashat Islam, Daniel Strouse, Zafarali Ahmed, Matthew Botvinick, Hugo Larochelle, Sergey Levine, Yoshua Bengio
InfoBot: Transfer and Exploration via the Information Bottleneck. CoRR (2019)
Klaus Greff, Raphaël Lopez Kaufman, Rishabh Kabra, Nick Watters, Christopher Burgess, Daniel Zoran, Loïc Matthey, Matthew Botvinick, Alexander Lerchner
Multi-Object Representation Learning with Iterative Variational Inference. CoRR (2019)
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 Botvinick, Oriol Vinyals, Peter W. Battaglia
Deep reinforcement learning with relational inductive biases. ICLR (Poster) (2019)
Christopher P. Burgess, Loïc Matthey, Nicholas Watters, Rishabh Kabra, Irina Higgins, Matthew Botvinick, Alexander Lerchner
MONet: Unsupervised Scene Decomposition and Representation. CoRR (2019)
Irina Higgins, Nicolas Sonnerat, Loic Matthey, Arka Pal, Christopher P. Burgess, Matko Bosnjak, Murray Shanahan, Matthew Botvinick, Demis Hassabis, Alexander Lerchner
SCAN: Learning Hierarchical Compositional Visual Concepts. ICLR (Poster) (2018)
Daniel Strouse, Max Kleiman-Weiner, Josh Tenenbaum, Matthew Botvinick, David J. Schwab
Learning to Share and Hide Intentions using Information Regularization. NeurIPS (2018)
Andrea Tacchetti, H. Francis Song, Pedro A. M. Mediano, Vinícius Flores Zambaldi, Neil C. Rabinowitz, Thore Graepel, Matthew Botvinick, Peter W. Battaglia
Relational Forward Models for Multi-Agent Learning. CoRR (2018)
Jakob N. Foerster, H. Francis Song, Edward Hughes, Neil Burch, Iain Dunning, Shimon Whiteson, Matthew Botvinick, Michael Bowling
Bayesian Action Decoder for Deep Multi-Agent Reinforcement Learning. CoRR (2018)
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 Botvinick, Oriol Vinyals, Peter W. Battaglia
Relational Deep Reinforcement Learning. CoRR (2018)
DJ Strouse, Max Kleiman-Weiner, Josh Tenenbaum, Matthew Botvinick, David J. Schwab
Learning to Share and Hide Intentions using Information Regularization. CoRR (2018)
Samuel Ritter, Jane X. Wang, Zeb Kurth-Nelson, Matthew Botvinick
Episodic Control through Meta-Reinforcement Learning. CogSci (2018)
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 Botvinick, Oriol Vinyals, Yujia Li, Razvan Pascanu
Relational inductive biases, deep learning, and graph networks. CoRR (2018)
Samuel Ritter, Jane X. Wang, Zeb Kurth-Nelson, Siddhant M. Jayakumar, Charles Blundell, Razvan Pascanu, Matthew Botvinick
Been There, Done That: Meta-Learning with Episodic Recall. ICML (2018)
Neil C. Rabinowitz, Frank Perbet, H. Francis Song, Chiyuan Zhang, S. M. Ali Eslami, Matthew Botvinick
Machine Theory of Mind. ICML (2018)
Ari S. Morcos, David G. T. Barrett, Neil C. Rabinowitz, Matthew Botvinick
On the importance of single directions for generalization. ICLR (Poster) (2018)
Ari S. Morcos, David G. T. Barrett, Neil C. Rabinowitz, Matthew Botvinick
On the importance of single directions for generalization. CoRR (2018)
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 Botvinick
Psychlab: A Psychology Laboratory for Deep Reinforcement Learning Agents. CoRR (2018)
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 Botvinick, Demis Hassabis, Timothy P. Lillicrap
Unsupervised Predictive Memory in a Goal-Directed Agent. CoRR (2018)
Luis Piloto, Ari Weinstein, Dhruva TB, Arun Ahuja, Mehdi Mirza, Greg Wayne, David Amos, Chia-Chun Hung, Matthew Botvinick
Probing Physics Knowledge Using Tools from Developmental Psychology. CoRR (2018)
Neil C. Rabinowitz, Frank Perbet, H. Francis Song, Chiyuan Zhang, S. M. Ali Eslami, Matthew Botvinick
Machine Theory of Mind. CoRR (2018)
Samuel Ritter, Jane X. Wang, Zeb Kurth-Nelson, Siddhant M. Jayakumar, Charles Blundell, Razvan Pascanu, Matthew Botvinick
Been There, Done That: Meta-Learning with Episodic Recall. CoRR (2018)
Yutian Chen, Matthew W. Hoffman, Sergio Gomez Colmenarejo, Misha Denil, Timothy P. Lillicrap, Matthew Botvinick, Nando de Freitas
Learning to Learn without Gradient Descent by Gradient Descent. ICML (2017)
Irina Higgins, Arka Pal, Andrei A. Rusu, Loïc Matthey, Christopher Burgess, Alexander Pritzel, Matthew Botvinick, Charles Blundell, Alexander Lerchner
DARLA: Improving Zero-Shot Transfer in Reinforcement Learning. CoRR (2017)
Ari Weinstein, Matthew Botvinick
Structure Learning in Motor Control: A Deep Reinforcement Learning Model. CoRR (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)
Matthew 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
Building Machines that Learn and Think for Themselves: Commentary on Lake et al., Behavioral and Brain Sciences, 2017. CoRR (2017)
Irina Higgins, Loïc Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew Botvinick, Shakir Mohamed, Alexander Lerchner
beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework. ICLR (Poster) (2017)
Irina Higgins, Nicolas Sonnerat, Loïc Matthey, Arka Pal, Christopher Burgess, Matthew Botvinick, Demis Hassabis, Alexander Lerchner
SCAN: Learning Abstract Hierarchical Compositional Visual Concepts. CoRR (2017)
Matthew Botvinick, Peter W. Battaglia
Tutorial: Recent Advances in Deep Learning. CogSci (2017)
Irina Higgins, Arka Pal, Andrei A. Rusu, Loïc Matthey, Christopher Burgess, Alexander Pritzel, Matthew Botvinick, Charles Blundell, Alexander Lerchner
DARLA: Improving Zero-Shot Transfer in Reinforcement Learning. ICML (2017)
Ari Weinstein, Matthew Botvinick
Structure Learning in Motor Control: A Deep Reinforcement Learning Model. CogSci (2017)
Samuel Ritter, David G. T. Barrett, Adam Santoro, Matthew Botvinick
Cognitive Psychology for Deep Neural Networks: A Shape Bias Case Study. CoRR (2017)
Jane X. Wang, Zeb Kurth-Nelson, Dhruva Tirumala, Hubert Soyer, Joel Z. Leibo, Rémi Munos, Charles Blundell, Dharshan Kumaran, Matthew Botvinick
Learning to reinforcement learn. CoRR (2016)
Adam Santoro, Sergey Bartunov, Matthew Botvinick, Daan Wierstra, Timothy P. Lillicrap
Meta-Learning with Memory-Augmented Neural Networks. ICML (2016)
Adam Santoro, Sergey Bartunov, Matthew Botvinick, Daan Wierstra, Timothy P. Lillicrap
One-shot Learning with Memory-Augmented Neural Networks. CoRR (2016)
Samuel Ritter, Cotie Long, Denis Paperno, Marco Baroni, Matthew Botvinick, Adele E. Goldberg
Leveraging Preposition Ambiguity to Assess Compositional Distributional Models of Semantics. *SEM@NAACL-HLT (2015)
Matthew Botvinick, Jonathan D. Cohen
The Computational and Neural Basis of Cognitive Control: Charted Territory and New Frontiers. Cogn. Sci. 38 (6) (2014)
Alec Solway, Carlos Diuk, Natalia Córdova, Debbie Yee, Andrew G. Barto, Yael Niv, Matthew Botvinick
Optimal Behavioral Hierarchy. PLoS Comput. Biol. 10 (8) (2014)
Kimberly L. Stachenfeld, Matthew Botvinick, Samuel Gershman
Design Principles of the Hippocampal Cognitive Map. NIPS (2014)
Francisco Pereira, Matthew Botvinick
Simitar: Simplified Searching of Statistically Significant Similarity Structure. PRNI (2013)
Francisco Pereira, Matthew Botvinick, Greg Detre
Using Wikipedia to learn semantic feature representations of concrete concepts in neuroimaging experiments. Artif. Intell. 194 (2013)
Samuel Gershman, Joshua B. Tenenbaum, Alexandre Pouget, Matthew Botvinick, Peter Dayan
Structured cognitive representations and complex inference in neural systems. CogSci (2013)
Wouter Kool, Sarah J. Getz, Matthew Botvinick
Neural Representation of Reward Probability: Evidence from the Illusion of Control. J. Cogn. Neurosci. 25 (6) (2013)
Matthew Botvinick
Commentary: Why I Am Not a Dynamicist. Top. Cogn. Sci. 4 (1) (2012)
Francisco Pereira, Matthew Botvinick
A systematic approach to extracting semantic information from functional MRI data. NIPS (2012)
Francisco Pereira, Matthew Botvinick
Classification of functional magnetic resonance imaging data using informative pattern features. KDD (2011)
Francisco Pereira, Matthew Botvinick
Information mapping with pattern classifiers: A comparative study. NeuroImage 56 (2) (2011)
Nick Yeung, Jonathan D. Cohen, Matthew Botvinick
Errors of interpretation and modeling: A reply to Grinband et al. NeuroImage 57 (2) (2011)
Francisco Pereira, Tom M. Mitchell, Matthew Botvinick
Machine learning classifiers and fMRI: A tutorial overview. NeuroImage 45 (1) (2009)
Matthew Botvinick, James An
Goal-directed decision making in prefrontal cortex: a computational framework. NIPS (2008)
Matthew Botvinick, Amishi P. Jha, Lauren M. Bylsma, Sara A. Fabian, Patricia E. Solomon, Kenneth M. Prkachin
Viewing facial expressions of pain engages cortical areas involved in the direct experience of pain. NeuroImage 25 (1) (2005)