Joshua Tenenbaum

Joshua Brett Tenenbaum (Josh Tenenbaum) is Professor of Computational Cognitive Science at the Massachusetts Institute of Technology. He is known for contributions to mathematical psychology and Bayesian cognitive science. According to the MacArthur Foundation, which named him a MacArthur Fellow in 2019, "Tenenbaum is one of the first to develop and apply probabilistic and statistical modeling to the study of human learning, reasoning, and perception, and to show how these models can explain a fundamental challenge of cognition: how our minds understand so much from so little, so quickly." Provided by Wikipedia
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  1. 1
    Unsupervised learning of latent physical properties using perception-prediction networks

    Unsupervised learning of latent physical properties using perception-prediction networks by Zheng, David Y., M. Eng. Massachusetts Institute of Technology

    Published 2018
    Other Authors: “…Joshua Tenenbaum.…”
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    Thesis
  2. 2
    The acquisition of inductive constraints

    The acquisition of inductive constraints by Kemp, Charles, Ph. D. Massachusetts Institute of Technology

    Published 2008
    Other Authors: “…Joshua Tenenbaum.…”
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    Thesis
  3. 3
    Stochastic Digital Circuits for Probabilistic Inference

    Stochastic Digital Circuits for Probabilistic Inference by Tenenbaum, Joshua B., Jonas, Eric M., Mansinghka, Vikash K.

    Published 2008
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  4. 4
    Fragment Grammars: Exploring Computation and Reuse in Language

    Fragment Grammars: Exploring Computation and Reuse in Language by O'Donnell, Timothy J., Tenenbaum, Joshua B., Goodman, Noah D.

    Published 2009
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  5. 5
    Learnability, representation, and language : a Bayesian approach

    Learnability, representation, and language : a Bayesian approach by Perfors, Amy (Amy Francesca)

    Published 2009
    Other Authors: “…Joshua Tenenbaum.…”
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    Thesis
  6. 6
    Meaning and compositionality as statistical induction of categories and constraints

    Meaning and compositionality as statistical induction of categories and constraints by Schmidt, Lauren A

    Published 2010
    Other Authors: “…Joshua Tenenbaum.…”
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    Thesis
  7. 7
    An Efficient Learning Procedure for Deep Boltzmann Machines

    An Efficient Learning Procedure for Deep Boltzmann Machines by Salakhutdinov, Ruslan, Hinton, Geoffrey

    Published 2010
    Other Authors: “…Joshua Tenenbaum…”
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  8. 8
    Bayesian perceptual inference in linear Gaussian models

    Bayesian perceptual inference in linear Gaussian models by Battaglia, Peter W.

    Published 2010
    Other Authors: “…Joshua Tenenbaum…”
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  9. 9
    One-Shot Learning with a Hierarchical Nonparametric Bayesian Model

    One-Shot Learning with a Hierarchical Nonparametric Bayesian Model by Salakhutdinov, Ruslan, Tenenbaum, Josh, Torralba, Antonio

    Published 2010
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  10. 10
    Compositional Policy Priors

    Compositional Policy Priors by Wingate, David, Diuk, Carlos, O'Donnell, Timothy, Tenenbaum, Joshua, Gershman, Samuel

    Published 2013
    Other Authors: “…Joshua Tenenbaum…”
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  11. 11
    Generative probabilistic models of neuron morphology

    Generative probabilistic models of neuron morphology by Serene, Stephen Rothrock

    Published 2014
    Other Authors: “…Joshua Tenenbaum.…”
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    Thesis
  12. 12
    An experimental and theoretical tool for studying the language of geometric concepts

    An experimental and theoretical tool for studying the language of geometric concepts by Dhariwal, Manuj

    Published 2019
    Other Authors: “…Laura Schulz and Joshua Tenenbaum.…”
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    Thesis

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