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People can learn rich, general-purpose conceptual representations from only raw perceptual inputs.
The role of theories in conceptual coherence
G. L. Murphy and D. L. Medin · 1985
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Perception of dynamic information in static handwritten forms
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Neural networks and the bias/variance dilemma
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The big book of concepts
G. L. Murphy · 2002
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Visual presentation of single letters activates a premotor area involved in writing
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The Algebraic Mind: Integrating Connectionism and Cognitive Science
G. F. Marcus · 2003
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A rational analysis of rule-based concept learning
N. D. Goodman, J. B. Tenenbaum, J. Feldman, and T. L. Griffiths · 2008
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When writing impairs reading: Letter perception’s susceptibility to motor interference
K. H. James and I. Gauthier · 2009
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Structured statistical models of inductive reasoning
C. Kemp and J. B. Tenenbaum · 2009
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Letting structure emerge: Connectionist and dynamical systems approaches to cognition
J. L. McClelland, M. M. Botvinick, D. C. Noelle, D. C. Plaut, T. T. Rogers, M. S. Seidenberg, and L. B. Smith · 2010
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Learning Structured Generative Concepts
A. Stuhlmuller, J. B. Tenenbaum, and N. D. Goodman · 2010
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Learning a theory of causality
N. D. Goodman, T. D. Ullman, and J. B. Tenenbaum · 2011
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The learnability of abstract syntactic principles
A. Perfors, J. B. Tenenbaum, and T. Regier · 2011
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How to grow a mind: Statistics, structure, and abstraction
J. B. Tenenbaum, C. Kemp, T. L. Griffiths, and N. D. Goodman · 2011
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Generating sequences with recurrent neural networks
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Bayesian Data Analysis (3rd ed.)
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Concepts in a probabilistic language of thought
N. D. Goodman, J. B. Tenenbaum, and T. Gerstenberg · 2015
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DRAW: A recurrent neural network for image generation
K. Gregor, I. Danihelka, A. Graves, D. J. Rezende, and D. Wierstra · 2015
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Picture: A probabilistic programming language for scene perception
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Human-level concept learning through probabilistic program induction
B. M. Lake, R. Salakhutdinov, and J. B. Tenenbaum · 2015
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Deep learning
Y. LeCun, Y. Bengio, and G. Hinton · 2015
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Attend, infer, repeat: Fast scene understanding with generative models
S. M. A. Eslami, N. Heess, T. Weber, Y. Tassa, D. Szepesvari, K. Kavukcuoglu, and G. E. Hinton · 2016
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Learning to infer graphics programs from hand-drawn images
K. Ellis, D. Ritchie, A. Solar-lezama, and J. B. Tenenbaum · 2018
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Synthesizing programs for images using reinforced adversarial learning
Y. Ganin, T. Kulkarni, I. Babuschkin, S. M. A. Eslami, and O. Vinyals · 2018
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Few-shot learning with graph neural networks
V. Garcia and J. Bruna · 2018
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A neural representation of sketch drawings
D. Ha and D. Eck · 2018
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The Variational Homoencoder: Learning to learn high capacity generative models from few examples
L. B. Hewitt, M. I. Nye, A. Gane, T. Jaakkola, and J. B. Tenenbaum · 2018
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Generalization without systematicity: On the compositional skills of sequence-to-sequence recurrent networks
B. M. Lake and M. Baroni · 2018
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The logical primitives of thought: Empirical foundations for compositional cognitive models
S. T. Piantadosi, J. B. Tenenbaum, and N. D. Goodman · 2016
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Neural programmer-interpreters
S. Reed and N. de Freitas · 2016
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One-Shot generalization in deep generative models
D. J. Rezende, S. Mohamed, I. Danihelka, K. Gregor, and D. Wierstra · 2016
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Matching networks for one shot learning
O. Vinyals, C. Blundell, T. Lillicrap, and D. Wierstra · 2016
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Building machines that learn and think for themselves
M. Botvinick, D. Barrett, P. Battaglia, N. de Freitas, D. Kumaran, and et al · 2017
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Robustfill: Neural program learning under noisy i/o
J. Devlin, J. Uesato, S. Bhupatiraju, R. Singh, A.-R. Mohamed, and P. Kohli · 2017
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HOUDINI: Lifelong learning as program synthesis
L. Valkov, D. Chaudhari, A. Srivastava, C. Sutton, and S. Chaudhuri · 2018
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Neural-symbolic VQA: Disentangling reasoning from vision and language understanding
K. Yi, J. Wu, C. Gan, A. Torralba, P. Kohli, and J. B. Tenenbaum · 2018
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Multi-object representation learning with iterative variational inference
K. Greff, R. L. Kaufman, R. Kabra, N. Watters, C. Burgess, D. Zoran, L. Matthey, M. Botvinick, and A. Lerchner · 2019
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Stacked Capsule Autoencoders
A. R. Kosiorek, S. Sabour, Y. W. Teh, and G. E. Hinton · 2019
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People infer recursive visual concepts from just a few examples
B. M. Lake and S. T. Piantadosi · 2019
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The Omniglot challenge: A 3-year progress report
B. M. Lake, R. Salakhutdinov, and J. B. Tenenbaum · 2019
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The neuro-symbolic concept learner: Interpreting scenes, words, and sentences from natural supervision
J. Mao, C. Gan, P. Kohli, J. B. Tenenbaum, and J. Wu · 2019
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Generating new concepts with hybrid neuro-symbolic models
R. Feinman and B. M. Lake · 2020
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Learning to learn generative programs with Memoised Wake-Sleep
L. B. Hewitt, T. A. Le, and J. B. Tenenbaum · 2020
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Learning compositional rules via neural program synthesis
M. I. Nye, A. Solar-Lezama, J. B. Tenenbaum, and B. M. Lake · 2020
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