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Toward Next-Generation Artificial Intelligence: Catalyzing the NeuroAI Revolution


Oct 15, 2022
Anthony Zador, Blake Richards, Bence Ölveczky, Sean Escola, Yoshua Bengio, Kwabena Boahen, Matthew Botvinick, Dmitri Chklovskii, Anne Churchland, Claudia Clopath, James DiCarlo, Surya Ganguli, Jeff Hawkins, Konrad Koerding, Alexei Koulakov, Yann LeCun, Timothy Lillicrap, Adam Marblestone, Bruno Olshausen, Alexandre Pouget, Cristina Savin, Terrence Sejnowski, Eero Simoncelli, Sara Solla, David Sussillo, Andreas S. Tolias, Doris Tsao

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* White paper, 8 pages + 3 pages of references, 0 figures 

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HCMD-zero: Learning Value Aligned Mechanisms from Data


Feb 21, 2022
Jan Balaguer, Raphael Koster, Ari Weinstein, Lucy Campbell-Gillingham, Christopher Summerfield, Matthew Botvinick, Andrea Tacchetti

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General-purpose, long-context autoregressive modeling with Perceiver AR


Feb 15, 2022
Curtis Hawthorne, Andrew Jaegle, Cătălina 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 Engel

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Human-centered mechanism design with Democratic AI


Jan 27, 2022
Raphael Koster, Jan Balaguer, Andrea Tacchetti, Ari Weinstein, Tina Zhu, Oliver Hauser, Duncan Williams, Lucy Campbell-Gillingham, Phoebe Thacker, Matthew Botvinick, Christopher Summerfield

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* 18 pages, 4 figures, 54 pages including supplemental materials 

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SIMONe: View-Invariant, Temporally-Abstracted Object Representations via Unsupervised Video Decomposition


Jun 07, 2021
Rishabh Kabra, Daniel Zoran, Goker Erdogan, Loic Matthey, Antonia Creswell, Matthew Botvinick, Alexander Lerchner, Christopher P. Burgess

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* Animated figures are available at https://sites.google.com/view/simone-scene-understanding/ 

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Alchemy: A structured task distribution for meta-reinforcement learning


Feb 04, 2021
Jane X. Wang, Michael King, Nicolas Porcel, Zeb Kurth-Nelson, Tina Zhu, Charlie Deck, Peter Choy, Mary Cassin, Malcolm Reynolds, Francis Song, Gavin Buttimore, David P. Reichert, Neil Rabinowitz, Loic Matthey, Demis Hassabis, Alexander Lerchner, Matthew Botvinick

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* 16 pages, 9 figures 

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Deep Reinforcement Learning and its Neuroscientific Implications


Jul 07, 2020
Matthew Botvinick, Jane X. Wang, Will Dabney, Kevin J. Miller, Zeb Kurth-Nelson

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* 22 pages, 5 figures 

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The Variational Bandwidth Bottleneck: Stochastic Evaluation on an Information Budget


Apr 24, 2020
Anirudh Goyal, Yoshua Bengio, Matthew Botvinick, Sergey Levine

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* Published as a conference paper at ICLR 2020 

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MEMO: A Deep Network for Flexible Combination of Episodic Memories


Jan 29, 2020
Andrea Banino, Adrià Puigdomènech Badia, Raphael Köster, Martin J. Chadwick, Vinicius Zambaldi, Demis Hassabis, Caswell Barry, Matthew Botvinick, Dharshan Kumaran, Charles Blundell

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* 9 pages, 2 figures, 3 tables, to be published as a conference paper at ICLR 2020 

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Emergent Systematic Generalization in a Situated Agent


Oct 28, 2019
Felix Hill, Andrew Lampinen, Rosalia Schneider, Stephen Clark, Matthew Botvinick, James L. McClelland, Adam Santoro

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