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ArticlesPeer ReviewedFull Text
Human-like systematic generalization through a meta-learning neural network
Author / Creator:Lake, Brenden M. ; Baroni, Marco
Publisher:London: Nature Publishing Group UK
Journal title:Nature (London), 2023-11, Vol.623 (7985), p.115-121
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ArticlesPeer ReviewedFull Text
Human-level concept learning through probabilistic program induction
Author / Creator:Lake, Brenden M. ; Salakhutdinov, Ruslan ; Tenenbaum, Joshua B.
Publisher:United States: American Association for the Advancement of Science
Journal title:Science (American Association for the Advancement of Science), 2015-12, Vol.350 (6266), p.1332-1338
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ArticlesPeer ReviewedFull Text
Building machines that learn and think like people
Author / Creator:Lake, Brenden M. ; Ullman, Tomer D. ; Tenenbaum, Joshua B. ; Gershman, Samuel J.
Publisher:New York, USA: Cambridge University Press
Journal title:The Behavioral and brain sciences, 2017, Vol.40, p.e253-e253, Article e253
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ArticlesPeer ReviewedFull Text
Learning high-level visual representations from a child’s perspective without strong inductive biases
Author / Creator:Orhan, A. Emin ; Lake, Brenden M.
Publisher:London: Nature Publishing Group UK
Journal title:Nature machine intelligence, 2024-03, Vol.6 (3), p.271-283
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ArticlesPeer ReviewedFull Text
Ingredients of intelligence: From classic debates to an engineering roadmap
Author / Creator:Lake, Brenden M. ; Ullman, Tomer D. ; Tenenbaum, Joshua B. ; Gershman, Samuel J.
Publisher:New York, USA: Cambridge University Press
Journal title:The Behavioral and brain sciences, 2017, Vol.40, p.e281-e281, Article e281
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ArticlesFull Text
Neural networks that overcome classic challenges through practice
Author / Creator:Irie, Kazuki ; Lake, Brenden M
Publisher:Ithaca: Cornell University Library, arXiv.org
Journal title:arXiv.org, 2024-12
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ArticlesFull Text
A systematic investigation of learnability from single child linguistic input
Author / Creator:Qin, Yulu ; Wang, Wentao ; Lake, Brenden M
Publisher:Ithaca: Cornell University Library, arXiv.org
Journal title:arXiv.org, 2024-05
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ArticlesFull Text
Compositional learning of functions in humans and machines
Author / Creator:Zhou, Yanli ; Lake, Brenden M ; Williams, Adina
Publisher:Ithaca: Cornell University Library, arXiv.org
Journal title:arXiv.org, 2024-03
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ArticlesFull Text
Learning high-level visual representations from a child's perspective without strong inductive biases
Author / Creator:Orhan, A Emin ; Lake, Brenden M
Publisher:Ithaca: Cornell University Library, arXiv.org
Journal title:arXiv.org, 2023-09
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ArticlesFull Text
Compositional diversity in visual concept learning
Author / Creator:Zhou, Yanli ; Feinman, Reuben ; Lake, Brenden M
Publisher:Ithaca: Cornell University Library, arXiv.org
Journal title:arXiv.org, 2023-05
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ArticlesFull Text
Compositional generalization through meta sequence-to-sequence learning
Author / Creator:Lake, Brenden M
Publisher:Ithaca: Cornell University Library, arXiv.org
Journal title:arXiv.org, 2019-10
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ArticlesFull Text
Flexible Compositional Learning of Structured Visual Concepts
Author / Creator:Zhou, Yanli ; Lake, Brenden M
Publisher:Ithaca: Cornell University Library, arXiv.org
Journal title:arXiv.org, 2021-05
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ArticlesFull Text
Modeling question asking using neural program generation
Author / Creator:Wang, Ziyun ; Lake, Brenden M
Publisher:Ithaca: Cornell University Library, arXiv.org
Journal title:arXiv.org, 2021-05
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ArticlesFull Text
Learning Task-General Representations with Generative Neuro-Symbolic Modeling
Author / Creator:Feinman, Reuben ; Lake, Brenden M
Publisher:Ithaca: Cornell University Library, arXiv.org
Journal title:arXiv.org, 2021-01
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ArticlesFull Text
Mutual exclusivity as a challenge for deep neural networks
Author / Creator:Gandhi, Kanishk ; Lake, Brenden M
Publisher:Ithaca: Cornell University Library, arXiv.org
Journal title:arXiv.org, 2020-10
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ArticlesFull Text
Generating new concepts with hybrid neuro-symbolic models
Author / Creator:Feinman, Reuben ; Lake, Brenden M
Publisher:Ithaca: Cornell University Library, arXiv.org
Journal title:arXiv.org, 2020-06
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ArticlesFull Text
Investigating Simple Object Representations in Model-Free Deep Reinforcement Learning
Author / Creator:Davidson, Guy ; Lake, Brenden M
Publisher:Ithaca: Cornell University Library, arXiv.org
Journal title:arXiv.org, 2020-05
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ArticlesFull Text
Beyond the Doors of Perception: Vision Transformers Represent Relations Between Objects
Author / Creator:Lepori, Michael A ; Tartaglini, Alexa R ; Wai Keen Vong ; Serre, Thomas ; Lake, Brenden M ; Pavlick, Ellie
Publisher:Ithaca: Cornell University Library, arXiv.org
Journal title:arXiv.org, 2024-11
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ArticlesFull Text
Self-supervised learning of video representations from a child's perspective
Author / Creator:Orhan, A Emin ; Wang, Wentao ; Wang, Alex N ; Ren, Mengye ; Lake, Brenden M
Publisher:Ithaca: Cornell University Library, arXiv.org
Journal title:arXiv.org, 2024-10
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ArticlesFull Text
Word meaning in minds and machines
Author / Creator:Lake, Brenden M ; Murphy, Gregory L
Publisher:Ithaca: Cornell University Library, arXiv.org
Journal title:arXiv.org, 2021-04
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Human-like systematic generalization through a meta-learning neural network

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Human-level concept learning through probabilistic program induction

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Building machines that learn and think like people

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Learning high-level visual representations from a child’s perspective without strong inductive biases

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Ingredients of intelligence: From classic debates to an engineering roadmap

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Neural networks that overcome classic challenges through practice

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A systematic investigation of learnability from single child linguistic input

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Compositional learning of functions in humans and machines

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Learning high-level visual representations from a child's perspective without strong inductive biases

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Compositional diversity in visual concept learning

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Compositional generalization through meta sequence-to-sequence learning

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Flexible Compositional Learning of Structured Visual Concepts

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Modeling question asking using neural program generation

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Learning Task-General Representations with Generative Neuro-Symbolic Modeling

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Mutual exclusivity as a challenge for deep neural networks

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Generating new concepts with hybrid neuro-symbolic models

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Investigating Simple Object Representations in Model-Free Deep Reinforcement Learning

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Beyond the Doors of Perception: Vision Transformers Represent Relations Between Objects

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Self-supervised learning of video representations from a child's perspective

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Word meaning in minds and machines

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