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The success of methods based on artificial neural networks in creating intelligent machines seems like it might pose a challenge to explanations of human cognition in terms of Bayesian inference.
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“Generalization, similarity, and Bayesian inference”
J.. Tenenbaum and Thomas. Griffiths · 2001
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Thomas. Griffiths and J.. Tenenbaum · 2006
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“Optimal predictions in everyday cognition”
Thomas. Griffiths and J.. Tenenbaum · 2006
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“Theory-based causal induction”
Thomas. Griffiths and J.. Tenenbaum · 2009
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“Neural implementation of hierarchical Bayesian inference by importance sampling”
L. Shi and Thomas. Griffiths · 2009
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“Theory-based causal induction”
Thomas. Griffiths and J.. Tenenbaum · 2009
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“Neural implementation of hierarchical Bayesian inference by importance sampling”
L. Shi and Thomas. Griffiths · 2009
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“Probabilistic models of cognition: Exploring representations and inductive biases”
Thomas. Griffiths et al · 2010
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“Letting structure emerge: Connectionist and dynamical systems approaches to understanding cognition”
J.. McClelland et al · 2010
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“Rational approximations to rational models: Alternative algorithms for category learning”
A.. Sanborn, Thomas. Griffiths and D.. Navarro · 2010
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“Probabilistic models of cognition: Exploring representations and inductive biases”
Thomas. Griffiths et al · 2010
“Recasting gradient-based meta-learning as hierarchical Bayes”
Erin Grant et al · 2018
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“Meta-trained agents implement Bayes-optimal agents”
Vladimir Mikulik et al · 2020
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“The child as hacker”
Joshua Rule, Joshua Tenenbaum and Steven Piantadosi · 2020
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“Score-based generative modeling through stochastic differential equations”
Yang Song et al · 2020
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“Meta-trained agents implement Bayes-optimal agents”
Vladimir Mikulik et al · 2020
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“The child as hacker”
Joshua Rule, Joshua Tenenbaum and Steven Piantadosi · 2020
Later among the works it cites.
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Cited alongside, same era.
“Letting structure emerge: Connectionist and dynamical systems approaches to understanding cognition”
J.. McClelland et al · 2010
Cited alongside, same era.
“Rational approximations to rational models: Alternative algorithms for category learning”
A.. Sanborn, Thomas. Griffiths and D.. Navarro · 2010
Cited alongside, same era.
“Learning a theory of causality.”
Noah Goodman, Tomer Ullman and Joshua Tenenbaum · 2011
Cited alongside, same era.
“Learning a theory of causality.”
Noah Goodman, Tomer Ullman and Joshua Tenenbaum · 2011
Cited alongside, same era.
“Auto-encoding variational Bayes”
Diederik Kingma and Max Welling · 2013
Cited alongside, same era.
“Auto-encoding variational Bayes”
Diederik Kingma and Max Welling · 2013
Cited alongside, same era.
Yang Song et al · 2020
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“Memory as a computational resource”
Ishita Dasgupta and Samuel Gershman · 2021
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“Cosmological parameter inference with Bayesian statistics”
Luis Padilla, Luis Tellez, Luis Escamilla and Jose Vazquez · 2021
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“Memory as a computational resource”
Ishita Dasgupta and Samuel Gershman · 2021
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“Cosmological parameter inference with Bayesian statistics”
Luis Padilla, Luis Tellez, Luis Escamilla and Jose Vazquez · 2021
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“Gaussian process surrogate models for neural networks”
Michael Li, Erin Grant and Thomas Griffiths · 2023
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“Modeling rapid language learning by distilling Bayesian priors into artificial neural networks”
R McCoy and Thomas Griffiths · 2023
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R McCoy et al · 2023
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“Pretraining task diversity and the emergence of non-Bayesian in-context learning for regression”
Allan Raventós, Mansheej Paul, Feng Chen and Surya Ganguli · 2023
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“Gaussian process probes (GPP) for uncertainty-aware probing”
Zi Wang et al · 2023
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“Gaussian process surrogate models for neural networks”
Michael Li, Erin Grant and Thomas Griffiths · 2023
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“Modeling rapid language learning by distilling Bayesian priors into artificial neural networks”
R McCoy and Thomas Griffiths · 2023
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R McCoy et al · 2023
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“Pretraining task diversity and the emergence of non-Bayesian in-context learning for regression”
Allan Raventós, Mansheej Paul, Feng Chen and Surya Ganguli · 2023
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“Gaussian process probes (GPP) for uncertainty-aware probing”
Zi Wang et al · 2023
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