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Humans have a remarkable ability to rapidly generalize to new tasks that is difficult to reproduce in artificial learning systems.
GQA: A New Dataset for Real-World Visual Reasoning and Compositional Question Answering
Drew A. Hudson and Christopher D. Manning · 1902
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Routing Networks and the Challenges of Modular and Compositional Computation
Clemens Rosenbaum, Ignacio Cases, Matthew Riemer, and Tim Klinger · 1904
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Compositional generalization through meta sequence-to-sequence learning
Brenden M. Lake · 1906
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Statistical parametric maps in functional imaging: A general linear approach
K. J. Friston, A. P. Holmes, K. J. Worsley, J.-P. Poline, C. D. Frith, and R. S. J. Frackowiak · 1994
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Nonparametric Permutation Tests for Functional Neuroimaging Experiments: A Primer with examples
T E Nichols and Andrew P Holmes · 2001
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E-Prime: User’s guide
Walter Schneider, Amy Eschman, and Anthony Zuccolotto · 2002
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Learning Compositional Rules via Neural Program Synthesis
Maxwell I. Nye, Armando Solar-Lezama, Joshua B. Tenenbaum, and Brenden M. Lake · 2003
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Measuring functional connectivity during distinct stages of a cognitive task
Jesse Rissman, Adam Gazzaley, and Mark D’Esposito · 2004
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A component based noise correction method (CompCor) for BOLD and perfusion based fMRI
Yashar Behzadi, Khaled Restom, Joy Liau, and Thomas T Liu · 2007
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A Neural Circuit Model of Flexible Sensorimotor Mapping: Learning and Forgetting on Multiple Timescales
Stefano Fusi, Wael F. Asaad, Earl K. Miller, and Xiao-Jing Wang · 2007
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Rapid Transfer of Abstract Rules to Novel Contexts in Human Lateral Prefrontal Cortex
Michael W. Cole, Joset A. Etzel, Jeffrey M. Zacks, Walter Schneider, and Todd S. Braver · 2011
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Rapid instructed task learning: A new window into the human brain’s unique capacity for flexible cognitive control
Michael W. Cole, Patryk Laurent, and Andrea Stocco · 2012
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Compositionality of Rule Representations in Human Prefrontal Cortex
Carlo Reverberi, Kai Görgen, and John-Dylan Haynes · 2012
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Linguistic regularities in continuous space word representations
Tomáš Mikolov, Wen-tau Yih, and Geoffrey Zweig · 2013
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The importance of mixed selectivity in complex cognitive tasks
Mattia Rigotti, Omri Barak, Melissa R Warden, Xiao-Jing Wang, Nathaniel D Daw, Earl K Miller, and Stefano Fusi · 2013
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Linguistic regularities in sparse and explicit word representations
Omer Levy and Yoav Goldberg · 2014
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Why neurons mix: high dimensionality for higher cognition
Stefano Fusi, Earl K Miller, and Mattia Rigotti · 2016
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beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework
Irina Higgins, Loic Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew Botvinick, Shakir Mohamed, and Alexander Lerchner · 2016
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Situating the default-mode network along a principal gradient of macroscale cortical organization
Daniel S. Margulies, Satrajit S. Ghosh, Alexandros Goulas, Marcel Falkiewicz, Julia M. Huntenburg, Georg Langs, Gleb Bezgin, Simon B. Eickhoff, F. Xavier Castellanos, Michael Petrides, Elizabeth Jefferies, and Jonathan Smallwood · 2016
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Benchmarking of participant-level confound regression strategies for the control of motion artifact in studies of functional connectivity
Rastko Ciric, Daniel H Wolf, Jonathan D Power, David R Roalf, Graham L Baum, Kosha Ruparel, Russell T Shinohara, Mark A Elliott, Simon B Eickhoff, Christos Davatzikos, Ruben C Gur, Raquel E Gur, Danielle S Bassett, and Theodore D Satterthwaite · 2017
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Adam: A Method for Stochastic Optimization
Diederik P. Kingma and Jimmy Ba · 2017
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The role of Disentanglement in Generalisation
Milton Llera Montero, Casimir JH Ludwig, Rui Ponte Costa, Gaurav Malhotra, and Jeffrey Bowers · 2020
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The Computational Origin of Representation
Steven T. Piantadosi · 2020
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A Benchmark for Systematic Generalization in Grounded Language Understanding
Laura Ruis, Jacob Andreas, Marco Baroni, Diane Bouchacourt, and Brenden M Lake · 2020
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Multi-task representations in human cortex transform along a sensory-to-motor hierarchy
Takuya Ito and John D. Murray · 2021
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Abstract representations emerge naturally in neural networks trained to perform multiple tasks
W. Jeffrey Johnston and Stefano Fusi · 2021
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Curriculum learning as a tool to uncover learning principles in the brain
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Building machines that learn and think like people
Brenden M. Lake, Tomer D. Ullman, Joshua B. Tenenbaum, and Samuel J. Gershman · 2017
Cited alongside, same era.
Routing Networks: Adaptive Selection of Non-linear Functions for Multi-Task Learning
Clemens Rosenbaum, Tim Klinger, and Matthew Riemer · 2017
Cited alongside, same era.
Comparing continual task learning in minds and machines
Timo Flesch, Jan Balaguer, Ronald Dekker, Hamed Nili, and Christopher Summerfield · 2018
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Towards a Definition of Disentangled Representations
Irina Higgins, David Amos, David Pfau, Sebastien Racaniere, Loic Matthey, Danilo Rezende, and Alexander Lerchner · 2018
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Mapping the human brain’s cortical-subcortical functional network organization
Jie Lisa Ji, Marjolein Spronk, Kaustubh Kulkarni, Grega Repovš, Alan Anticevic, and Michael W Cole · 2018
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Generalization without Systematicity: On the Compositional Skills of Sequence-to-Sequence Recurrent Networks
Brenden Lake and Marco Baroni · 2018
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Prefrontal cortex as a meta-reinforcement learning system
Jane X. Wang, Zeb Kurth-Nelson, Dharshan Kumaran, Dhruva Tirumala, Hubert Soyer, Joel Z. Leibo, Demis Hassabis, and Matthew Botvinick · 2018
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A dataset and architecture for visual reasoning with a working memory
Guangyu Robert Yang, Igor Ganichev, Xiao-Jing Wang, Jonathon Shlens, and David Sussillo · 2018
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Daniel R. Kepple, Rainer Engelken, and Kanaka Rajan · 2021
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Efficient and robust multi-task learning in the brain with modular task primitives
Christian David Marton, Guillaume Lajoie, and Kanaka Rajan · 2021
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An Analytical Theory of Curriculum Learning in Teacher-Student Networks
Luca Saglietti, Stefano Sarao Mannelli, and Andrew Saxe · 2021
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Determinants of human compositional generalization
Ronald Boris Dekker, Fabian Otto, and Christopher Summerfield · 2022
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Orthogonal representations for robust context-dependent task performance in brains and neural networks
Timo Flesch, Keno Juechems, Tsvetomira Dumbalska, Andrew Saxe, and Christopher Summerfield · 2022
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A neural model of task compositionality with natural language instructions
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Unsupervised deep learning identifies semantic disentanglement in single inferotemporal face patch neurons
Irina Higgins, Le Chang, Victoria Langston, Demis Hassabis, Christopher Summerfield, Doris Tsao, and Matthew Botvinick · 2041
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Cognitive task information is transferred between brain regions via resting-state network topology
Takuya Ito, Kaustubh R. Kulkarni, Douglas H. Schultz, Ravi D. Mill, Richard H. Chen, Levi I. Solomyak, and Michael W. Cole · 2041
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Constructing neural network models from brain data reveals representational transformations linked to adaptive behavior
Takuya Ito, Guangyu Robert Yang, Patryk Laurent, Douglas H. Schultz, and Michael W. Cole · 2041
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Predictive learning as a network mechanism for extracting low-dimensional latent space representations
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Concepts and Compositionality: In Search of the Brain’s Language of Thought
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