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Matt M. Botvinick
Matthew M. Botvinick
Person information
- affiliation: University College London, Gatsby Computational Neuroscience Unit, UK
- affiliation (former): Princeton University, Neuroscience Institute, NJ, USA
- affiliation (former): University of Pennsylvania, Department of Psychiatry, Philadelphia, PA, USA
- affiliation (PhD 2001): Carnegie Mellon University,
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2020 – today
- 2024
- [j17]Ted Moskovitz
, Kevin J. Miller, Maneesh Sahani
Understanding dual process cognition via the minimum description length principle. PLoS Comput. Biol. 20(10): 1012383 (2024) - [i56]Raphael Koster, Miruna Pîslar, Andrea Tacchetti, Jan Balaguer, Leqi Liu, Romuald Elie, Oliver P. Hauser, Karl Tuyls, Matt M. Botvinick, Christopher Summerfield:
Using deep reinforcement learning to promote sustainable human behaviour on a common pool resource problem. CoRR abs/2404.15059 (2024) - 2023
- [j16]Nathan J. Wispinski, Andrew Butcher, Kory Wallace Mathewson, Craig S. Chapman, Matthew M. Botvinick, Patrick M. Pilarski:
Adaptive patch foraging in deep reinforcement learning agents. Trans. Mach. Learn. Res. 2023 (2023) - [c44]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:
Learning to Induce Causal Structure. ICLR 2023 - [c43]Ted Moskovitz, Ta-Chu Kao, Maneesh Sahani, Matt M. Botvinick:
Minimum Description Length Control. ICLR 2023 - [c42]Julian Coda-Forno, Marcel Binz, Zeynep Akata, Matt M. Botvinick, Jane X. Wang, Eric Schulz:
Meta-in-context learning in large language models. NeurIPS 2023 - [c41]Kevin J. Miller, Maria K. Eckstein, Matt M. Botvinick, Zeb Kurth-Nelson:
Cognitive Model Discovery via Disentangled RNNs. NeurIPS 2023 - [i55]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:
DiscoGen: Learning to Discover Gene Regulatory Networks. CoRR abs/2304.05823 (2023) - [i54]Marcel Binz, Ishita Dasgupta, Akshay K. Jagadish, Matthew M. Botvinick, Jane X. Wang, Eric Schulz:
Meta-Learned Models of Cognition. CoRR abs/2304.06729 (2023) - [i53]Julian Coda-Forno, Marcel Binz, Zeynep Akata, Matthew M. Botvinick, Jane X. Wang, Eric Schulz:
Meta-in-context learning in large language models. CoRR abs/2305.12907 (2023) - [i52]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 abs/2312.03759 (2023) - 2022
- [j15]Daniel C. McNamee
Compositional Sequence Generation in the Entorhinal-Hippocampal System. Entropy 24(12): 1791 (2022) - [j14]Angela Langdon, Matthew M. Botvinick, Hiroyuki Nakahara
Meta-learning, social cognition and consciousness in brains and machines. Neural Networks 145: 80-89 (2022) - [c40]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:
Perceiver IO: A General Architecture for Structured Inputs & Outputs. ICLR 2022 - [c39]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:
General-purpose, long-context autoregressive modeling with Perceiver AR. ICML 2022: 8535-8558 - [c38]Michiel A. Bakker, Martin J. Chadwick, Hannah Sheahan, Michael Henry Tessler, Lucy Campbell-Gillingham, Jan Balaguer, Nat McAleese, Amelia Glaese, John Aslanides, Matt M. Botvinick, Christopher Summerfield:
Fine-tuning language models to find agreement among humans with diverse preferences. NeurIPS 2022 - [i51]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:
Human-centered mechanism design with Democratic AI. CoRR abs/2201.11441 (2022) - [i50]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:
General-purpose, long-context autoregressive modeling with Perceiver AR. CoRR abs/2202.07765 (2022) - [i49]Jan Balaguer, Raphael Koster, Ari Weinstein, Lucy Campbell-Gillingham, Christopher Summerfield, Matthew M. Botvinick, Andrea Tacchetti:
HCMD-zero: Learning Value Aligned Mechanisms from Data. CoRR abs/2202.10122 (2022) - [i48]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:
Hierarchical Perceiver. CoRR abs/2202.10890 (2022) - [i47]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:
The Frost Hollow Experiments: Pavlovian Signalling as a Path to Coordination and Communication Between Agents. CoRR abs/2203.09498 (2022) - [i46]Nan Rosemary Ke, Silvia Chiappa, Jane Wang, Jörg Bornschein, Theophane Weber, Anirudh Goyal, Matthew M. Botvinick, Michael Mozer, Danilo Jimenez Rezende:
Learning to Induce Causal Structure. CoRR abs/2204.04875 (2022) - [i45]Ted Moskovitz, Ta-Chu Kao, Maneesh Sahani, Matthew M. Botvinick:
Minimum Description Length Control. CoRR abs/2207.08258 (2022) - [i44]Nathan J. Wispinski, Andrew Butcher, Kory W. Mathewson, Craig S. Chapman, Matthew M. Botvinick, Patrick M. Pilarski
Adaptive patch foraging in deep reinforcement learning agents. CoRR abs/2210.08085 (2022) - [i43]Anthony Zador, Blake A. Richards, Bence Ölveczky, Sean Escola, Yoshua Bengio, Kwabena Boahen, Matthew M. Botvinick, Dmitri B. Chklovskii, Anne Churchland, Claudia Clopath
Toward Next-Generation Artificial Intelligence: Catalyzing the NeuroAI Revolution. CoRR abs/2210.08340 (2022) - [i42]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:
Fine-tuning language models to find agreement among humans with diverse preferences. CoRR abs/2211.15006 (2022) - 2021
- [c37]Samuel Ritter, Ryan Faulkner, Laurent Sartran, Adam Santoro, Matthew M. Botvinick, David Raposo:
Rapid Task-Solving in Novel Environments. ICLR 2021 - [c36]Jane Wang, Michael King, Nicolas Porcel, Zeb Kurth-Nelson, Tina Zhu, Charles Deck, Peter Choy, Mary Cassin, Malcolm Reynolds, H. Francis Song, Gavin Buttimore, David P. Reichert, Neil C. Rabinowitz, Loic Matthey, Demis Hassabis, Alexander Lerchner, Matt M. Botvinick:
Alchemy: A benchmark and analysis toolkit for meta-reinforcement learning agents. NeurIPS Datasets and Benchmarks 2021 - [c35]David Ding, Felix Hill, Adam Santoro, Malcolm Reynolds, Matt M. Botvinick:
Attention over Learned Object Embeddings Enables Complex Visual Reasoning. NeurIPS 2021: 9112-9124 - [c34]DJ Strouse, Kevin R. McKee, Matt M. Botvinick, Edward Hughes, Richard Everett:
Collaborating with Humans without Human Data. NeurIPS 2021: 14502-14515 - [c33]Rishabh Kabra, Daniel Zoran, Goker Erdogan, Loic Matthey, Antonia Creswell, Matt M. Botvinick, Alexander Lerchner, Christopher P. Burgess:
SIMONe: View-Invariant, Temporally-Abstracted Object Representations via Unsupervised Video Decomposition. NeurIPS 2021: 20146-20159 - [i41]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:
Alchemy: A structured task distribution for meta-reinforcement learning. CoRR abs/2102.02926 (2021) - [i40]David Raposo, Samuel Ritter, Adam Santoro, Greg Wayne, Theophane Weber, Matt M. Botvinick, Hado van Hasselt, H. Francis Song:
Synthetic Returns for Long-Term Credit Assignment. CoRR abs/2102.12425 (2021) - [i39]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:
Deep reinforcement learning models the emergent dynamics of human cooperation. CoRR abs/2103.04982 (2021) - [i38]Rishabh Kabra, Daniel Zoran, Goker Erdogan, Loic Matthey, Antonia Creswell, Matthew M. Botvinick, Alexander Lerchner, Christopher P. Burgess:
SIMONe: View-Invariant, Temporally-Abstracted Object Representations via Unsupervised Video Decomposition. CoRR abs/2106.03849 (2021) - [i37]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:
Perceiver IO: A General Architecture for Structured Inputs & Outputs. CoRR abs/2107.14795 (2021) - [i36]DJ Strouse, Kevin R. McKee, Matt M. Botvinick, Edward Hughes, Richard Everett:
Collaborating with Humans without Human Data. CoRR abs/2110.08176 (2021) - [i35]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:
Assessing Human Interaction in Virtual Reality With Continually Learning Prediction Agents Based on Reinforcement Learning Algorithms: A Pilot Study. CoRR abs/2112.07774 (2021) - 2020
- [j13]Will Dabney, Zeb Kurth-Nelson, Naoshige Uchida, Clara Kwon Starkweather, Demis Hassabis, Rémi Munos, Matthew M. Botvinick:
A distributional code for value in dopamine-based reinforcement learning. Nat. 577(7792): 671-675 (2020) - [c32]Andrea Banino, Adrià Puigdomènech Badia, Raphael Köster, Martin J. Chadwick, Vinícius Flores Zambaldi, Demis Hassabis, Caswell Barry, Matthew M. Botvinick, Dharshan Kumaran, Charles Blundell:
MEMO: A Deep Network for Flexible Combination of Episodic Memories. ICLR 2020 - [c31]Anirudh Goyal, Yoshua Bengio, Matthew M. Botvinick, Sergey Levine:
The Variational Bandwidth Bottleneck: Stochastic Evaluation on an Information Budget. ICLR 2020 - [c30]Felix Hill, Andrew K. Lampinen, Rosalia Schneider, Stephen Clark, Matthew M. Botvinick, James L. McClelland, Adam Santoro:
Environmental drivers of systematicity and generalization in a situated agent. ICLR 2020 - [c29]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:
V-MPO: On-Policy Maximum a Posteriori Policy Optimization for Discrete and Continuous Control. ICLR 2020 - [c28]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:
Stabilizing Transformers for Reinforcement Learning. ICML 2020: 7487-7498 - [i34]Andrea Banino, Adrià Puigdomènech Badia, Raphael Köster, Martin J. Chadwick, Vinícius Flores Zambaldi, Demis Hassabis, Caswell Barry, Matthew M. Botvinick, Dharshan Kumaran, Charles Blundell:
MEMO: A Deep Network for Flexible Combination of Episodic Memories. CoRR abs/2001.10913 (2020) - [i33]Anirudh Goyal, Yoshua Bengio, Matthew M. Botvinick, Sergey Levine:
The Variational Bandwidth Bottleneck: Stochastic Evaluation on an Information Budget. CoRR abs/2004.11935 (2020) - [i32]Samuel Ritter, Ryan Faulkner, Laurent Sartran, Adam Santoro, Matt M. Botvinick, David Raposo:
Rapid Task-Solving in Novel Environments. CoRR abs/2006.03662 (2020) - [i31]Matthew M. Botvinick, Jane X. Wang, Will Dabney, Kevin J. Miller, Zeb Kurth-Nelson:
Deep Reinforcement Learning and its Neuroscientific Implications. CoRR abs/2007.03750 (2020) - [i30]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:
Model-free conventions in multi-agent reinforcement learning with heterogeneous preferences. CoRR abs/2010.09054 (2020) - [i29]David Ding, Felix Hill, Adam Santoro, Matt M. Botvinick:
Object-based attention for spatio-temporal reasoning: Outperforming neuro-symbolic models with flexible distributed architectures. CoRR abs/2012.08508 (2020)
2010 – 2019
- 2019
- [j12]José Ribas-Fernandes, Danesh Shahnazian, Clay B. Holroyd
Subgoal- and Goal-related Reward Prediction Errors in Medial Prefrontal Cortex. J. Cogn. Neurosci. 31(1) (2019) - [c27]Tina Zhu, Jessica B. Hamrick, Kevin R. McKee, Raphael Koster, Jan Balaguer, Peter W. Battaglia, Matthew M. Botvinick:
A resource-rational model of physical abstraction for efficient mental simulation. CogSci 2019: 3618 - [c26]Anirudh Goyal, Riashat Islam, Daniel Strouse, Zafarali Ahmed, Hugo Larochelle, Matthew M. Botvinick, Yoshua Bengio, Sergey Levine:
InfoBot: Transfer and Exploration via the Information Bottleneck. ICLR (Poster) 2019 - [c25]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:
Relational Forward Models for Multi-Agent Learning. ICLR (Poster) 2019 - [c24]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:
Deep reinforcement learning with relational inductive biases. ICLR (Poster) 2019 - [c23]Jakob N. Foerster, H. Francis Song, Edward Hughes, Neil Burch, Iain Dunning, Shimon Whiteson, Matthew M. Botvinick, Michael Bowling:
Bayesian Action Decoder for Deep Multi-Agent Reinforcement Learning. ICML 2019: 1942-1951 - [c22]Klaus Greff, Raphaël Lopez Kaufman, Rishabh Kabra, Nick Watters, Chris Burgess, Daniel Zoran, Loic Matthey, Matthew M. Botvinick, Alexander Lerchner:
Multi-Object Representation Learning with Iterative Variational Inference. ICML 2019: 2424-2433 - [i28]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:
Causal Reasoning from Meta-reinforcement Learning. CoRR abs/1901.08162 (2019) - [i27]Anirudh Goyal, Riashat Islam, Daniel Strouse, Zafarali Ahmed, Matthew M. Botvinick, Hugo Larochelle, Sergey Levine, Yoshua Bengio:
InfoBot: Transfer and Exploration via the Information Bottleneck. CoRR abs/1901.10902 (2019) - [i26]Christopher P. Burgess, Loïc Matthey, Nicholas Watters, Rishabh Kabra, Irina Higgins, Matthew M. Botvinick, Alexander Lerchner:
MONet: Unsupervised Scene Decomposition and Representation. CoRR abs/1901.11390 (2019) - [i25]Klaus Greff, Raphaël Lopez Kaufman, Rishabh Kabra, Nick Watters, Chris Burgess, Daniel Zoran, Loïc Matthey, Matthew M. Botvinick, Alexander Lerchner
Multi-Object Representation Learning with Iterative Variational Inference. CoRR abs/1903.00450 (2019) - [i24]Adam Santoro, Felix Hill, David G. T. Barrett, David Raposo, Matthew M. 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 abs/1904.10396 (2019) - [i23]Patrick M. Pilarski, Andrew Butcher, Michael Johanson, Matthew M. Botvinick, Andrew Bolt, Adam S. R. Parker:
Learned human-agent decision-making, communication and joint action in a virtual reality environment. CoRR abs/1905.02691 (2019) - [i22]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:
Meta-learning of Sequential Strategies. CoRR abs/1905.03030 (2019) - [i21]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:
V-MPO: On-Policy Maximum a Posteriori Policy Optimization for Discrete and Continuous Control. CoRR abs/1909.12238 (2019) - [i20]Felix Hill, Andrew K. Lampinen, Rosalia Schneider, Stephen Clark, Matthew M. Botvinick, James L. McClelland, Adam Santoro:
Emergent Systematic Generalization in a Situated Agent. CoRR abs/1910.00571 (2019) - [i19]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:
Stabilizing Transformers for Reinforcement Learning. CoRR abs/1910.06764 (2019) - 2018
- [c21]Samuel Ritter, Jane X. Wang, Zeb Kurth-Nelson, Matthew M. Botvinick:
Episodic Control through Meta-Reinforcement Learning. CogSci 2018 - [c20]Irina Higgins, Nicolas Sonnerat, Loic Matthey, Arka Pal, Christopher P. Burgess, Matko Bosnjak, Murray Shanahan, Matthew M. Botvinick, Demis Hassabis, Alexander Lerchner:
SCAN: Learning Hierarchical Compositional Visual Concepts. ICLR (Poster) 2018 - [c19]Ari S. Morcos, David G. T. Barrett, Neil C. Rabinowitz, Matthew M. Botvinick:
On the importance of single directions for generalization. ICLR (Poster) 2018 - [c18]Neil C. Rabinowitz, Frank Perbet, H. Francis Song, Chiyuan Zhang, S. M. Ali Eslami, Matthew M. Botvinick:
Machine Theory of Mind. ICML 2018: 4215-4224 - [c17]Samuel Ritter, Jane X. Wang, Zeb Kurth-Nelson, Siddhant M. Jayakumar, Charles Blundell, Razvan Pascanu, Matthew M. Botvinick:
Been There, Done That: Meta-Learning with Episodic Recall. ICML 2018: 4351-4360 - [c16]Daniel Strouse, Max Kleiman-Weiner, Josh Tenenbaum, Matthew M. Botvinick, David J. Schwab:
Learning to Share and Hide Intentions using Information Regularization. NeurIPS 2018: 10270-10281 - [i18]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:
Psychlab: A Psychology Laboratory for Deep Reinforcement Learning Agents. CoRR abs/1801.08116 (2018) - [i17]Neil C. Rabinowitz, Frank Perbet, H. Francis Song, Chiyuan Zhang, S. M. Ali Eslami, Matthew M. Botvinick:
Machine Theory of Mind. CoRR abs/1802.07740 (2018) - [i16]Ari S. Morcos, David G. T. Barrett, Neil C. Rabinowitz, Matthew M. Botvinick:
On the importance of single directions for generalization. CoRR abs/1803.06959 (2018) - [i15]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:
Unsupervised Predictive Memory in a Goal-Directed Agent. CoRR abs/1803.10760 (2018) - [i14]Luis Piloto, Ari Weinstein, Dhruva TB, Arun Ahuja, Mehdi Mirza, Greg Wayne, David Amos, Chia-Chun Hung, Matthew M. Botvinick:
Probing Physics Knowledge Using Tools from Developmental Psychology. CoRR abs/1804.01128 (2018) - [i13]Samuel Ritter, Jane X. Wang, Zeb Kurth-Nelson, Siddhant M. Jayakumar, Charles Blundell, Razvan Pascanu, Matthew M. Botvinick:
Been There, Done That: Meta-Learning with Episodic Recall. CoRR abs/1805.09692 (2018) - [i12]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:
Relational inductive biases, deep learning, and graph networks. CoRR abs/1806.01261 (2018) - [i11]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:
Relational Deep Reinforcement Learning. CoRR abs/1806.01830 (2018) - [i10]DJ Strouse, Max Kleiman-Weiner, Josh Tenenbaum, Matthew M. Botvinick, David J. Schwab:
Learning to Share and Hide Intentions using Information Regularization. CoRR abs/1808.02093 (2018) - [i9]Andrea Tacchetti, H. Francis Song, Pedro A. M. Mediano, Vinícius Flores Zambaldi, Neil C. Rabinowitz, Thore Graepel, Matthew M. Botvinick, Peter W. Battaglia:
Relational Forward Models for Multi-Agent Learning. CoRR abs/1809.11044 (2018) - [i8]Jakob N. Foerster, H. Francis Song, Edward Hughes, Neil Burch, Iain Dunning, Shimon Whiteson, Matthew M. Botvinick, Michael Bowling:
Bayesian Action Decoder for Deep Multi-Agent Reinforcement Learning. CoRR abs/1811.01458 (2018) - 2017
- [j11]Evan M. Russek
Predictive representations can link model-based reinforcement learning to model-free mechanisms. PLoS Comput. Biol. 13(9) (2017) - [c15]Matthew M. Botvinick, Peter W. Battaglia:
Tutorial: Recent Advances in Deep Learning. CogSci 2017 - [c14]Jane Wang, Zeb Kurth-Nelson, Hubert Soyer, Joel Z. Leibo, Dhruva Tirumala, Rémi Munos, Charles Blundell, Dharshan Kumaran, Matt M. Botvinick:
Learning to reinforcement learn. CogSci 2017 - [c13]Ari Weinstein, Matthew M. Botvinick:
Structure Learning in Motor Control: A Deep Reinforcement Learning Model. CogSci 2017 - [c12]Irina Higgins, Loïc Matthey, Arka Pal, Christopher P. Burgess, Xavier Glorot
beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework. ICLR (Poster) 2017 - [c11]Yutian Chen, Matthew W. Hoffman, Sergio Gomez Colmenarejo, Misha Denil, Timothy P. Lillicrap, Matthew M. Botvinick, Nando de Freitas:
Learning to Learn without Gradient Descent by Gradient Descent. ICML 2017: 748-756 - [c10]Irina Higgins, Arka Pal, Andrei A. Rusu, Loïc Matthey, Christopher P. Burgess, Alexander Pritzel, Matthew M. Botvinick, Charles Blundell, Alexander Lerchner:
DARLA: Improving Zero-Shot Transfer in Reinforcement Learning. ICML 2017: 1480-1490 - [c9]Samuel Ritter, David G. T. Barrett, Adam Santoro, Matt M. Botvinick:
Cognitive Psychology for Deep Neural Networks: A Shape Bias Case Study. ICML 2017: 2940-2949 - [i7]Ari Weinstein, Matthew M. Botvinick:
Structure Learning in Motor Control: A Deep Reinforcement Learning Model. CoRR abs/1706.06827 (2017) - [i6]Samuel Ritter, David G. T. Barrett, Adam Santoro, Matthew M. Botvinick:
Cognitive Psychology for Deep Neural Networks: A Shape Bias Case Study. CoRR abs/1706.08606 (2017) - [i5]Irina Higgins, Nicolas Sonnerat, Loïc Matthey, Arka Pal, Christopher P. Burgess, Matthew M. Botvinick, Demis Hassabis, Alexander Lerchner:
SCAN: Learning Abstract Hierarchical Compositional Visual Concepts. CoRR abs/1707.03389 (2017) - [i4]Irina Higgins, Arka Pal, Andrei A. Rusu, Loïc Matthey, Christopher P. Burgess, Alexander Pritzel, Matthew M. Botvinick, Charles Blundell, Alexander Lerchner:
DARLA: Improving Zero-Shot Transfer in Reinforcement Learning. CoRR abs/1707.08475 (2017) - [i3]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:
Building Machines that Learn and Think for Themselves: Commentary on Lake et al., Behavioral and Brain Sciences, 2017. CoRR abs/1711.08378 (2017) - 2016
- [c8]Adam Santoro, Sergey Bartunov, Matthew M. Botvinick, Daan Wierstra, Timothy P. Lillicrap:
Meta-Learning with Memory-Augmented Neural Networks. ICML 2016: 1842-1850 - [i2]Adam Santoro, Sergey Bartunov, Matthew M. Botvinick, Daan Wierstra, Timothy P. Lillicrap:
One-shot Learning with Memory-Augmented Neural Networks. CoRR abs/1605.06065 (2016) - [i1]Jane X. Wang, Zeb Kurth-Nelson, Dhruva Tirumala, Hubert Soyer, Joel Z. Leibo, Rémi Munos, Charles Blundell, Dharshan Kumaran, Matthew M. Botvinick:
Learning to reinforcement learn. CoRR abs/1611.05763 (2016) - 2015
- [c7]Samuel Ritter, Cotie Long, Denis Paperno, Marco Baroni
Leveraging Preposition Ambiguity to Assess Compositional Distributional Models of Semantics. *SEM@NAACL-HLT 2015: 199-204 - 2014
- [j10]Matthew M. Botvinick, Jonathan D. Cohen:
The Computational and Neural Basis of Cognitive Control: Charted Territory and New Frontiers. Cogn. Sci. 38(6): 1249-1285 (2014) - [j9]Alec Solway, Carlos Diuk, Natalia Córdova, Debbie Yee, Andrew G. Barto, Yael Niv, Matthew M. Botvinick:
Optimal Behavioral Hierarchy. PLoS Comput. Biol. 10(8) (2014) - [c6]Kimberly L. Stachenfeld, Matthew M. Botvinick, Samuel Gershman:
Design Principles of the Hippocampal Cognitive Map. NIPS 2014: 2528-2536 - 2013
- [j8]Francisco Pereira, Matthew M. Botvinick, Greg Detre:
Using Wikipedia to learn semantic feature representations of concrete concepts in neuroimaging experiments. Artif. Intell. 194: 240-252 (2013) - [j7]Wouter Kool, Sarah J. Getz, Matthew M. Botvinick:
Neural Representation of Reward Probability: Evidence from the Illusion of Control. J. Cogn. Neurosci. 25(6): 852-861 (2013) - [c5]Samuel Gershman, Joshua B. Tenenbaum, Alexandre Pouget, Matthew M. Botvinick, Peter Dayan:
Structured cognitive representations and complex inference in neural systems. CogSci 2013 - [c4]Francisco Pereira, Matthew M. Botvinick:
Simitar: Simplified Searching of Statistically Significant Similarity Structure. PRNI 2013: 1-4 - [p1]Carlos Diuk, Anna C. Schapiro
Divide and Conquer: Hierarchical Reinforcement Learning and Task Decomposition in Humans. Computational and Robotic Models of the Hierarchical Organization of Behavior 2013: 271-291 - 2012
- [j6]Matthew M. Botvinick:
Commentary: Why I Am Not a Dynamicist. Top. Cogn. Sci. 4(1): 78-83 (2012) - [c3]Francisco Pereira, Matthew M. Botvinick:
A systematic approach to extracting semantic information from functional MRI data. NIPS 2012: 2276-2284 - 2011
- [j5]Francisco Pereira, Matthew M. Botvinick:
Information mapping with pattern classifiers: A comparative study. NeuroImage 56(2): 476-496 (2011) - [j4]Nick Yeung
Errors of interpretation and modeling: A reply to Grinband et al. NeuroImage 57(2): 316-319 (2011) - [c2]Francisco Pereira, Matthew M. Botvinick:
Classification of functional magnetic resonance imaging data using informative pattern features. KDD 2011: 940-946
2000 – 2009
- 2009
- [j3]Francisco Pereira, Tom M. Mitchell, Matthew M. Botvinick:
Machine learning classifiers and fMRI: A tutorial overview. NeuroImage 45(1): S199-S209 (2009) - 2008
- [c1]Matthew M. Botvinick, James An:
Goal-directed decision making in prefrontal cortex: a computational framework. NIPS 2008: 169-176 - 2005
- [j2]Matthew M. Botvinick, Amishi P. Jha, Lauren M. Bylsma
Viewing facial expressions of pain engages cortical areas involved in the direct experience of pain. NeuroImage 25(1): 312-319 (2005) - 2001
- [j1]Vincent van Veen, Jonathan D. Cohen, Matthew M. Botvinick, V. Andrew Stenger, Cameron S. Carter:
Anterior Cingulate Cortex, Conflict Monitoring, and Levels of Processing. NeuroImage 14(6): 1302-1308 (2001)
Coauthor Index
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