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Thomas L Griffiths

Showing results (21-30 of 207) with videos related to

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Neural Computation|June 7, 2008
Latent features in similarity judgments: a nonparametric bayesian approachDaniel J Navarro, Thomas L Griffiths
Cognitive Psychology|September 20, 2005
Structure and strength in causal inductionThomas L Griffiths, Joshua B Tenenbaum
Psychological Science|September 21, 2006
Optimal predictions in everyday cognitionThomas L Griffiths, Joshua B Tenenbaum
Journal of Experimental Psychology. Learning, Memory, and Cognition|April 24, 2013
Why are people bad at detecting randomness? A statistical argumentJoseph J Williams, Thomas L Griffiths
Psychological Review|October 21, 2009
Theory-based causal inductionThomas L Griffiths, Joshua B Tenenbaum
Psychological Review|June 5, 2025
Computation-limited Bayesian updating: A resource-rational analysis of approximate Bayesian inferenceJian-Qiao Zhu, Thomas L Griffiths
Cognitive Science|June 4, 2011
Language evolution by iterated learning with bayesian agentsThomas L Griffiths, Michael L Kalish
Nature Communications|May 20, 2025
Modeling rapid language learning by distilling Bayesian priors into artificial neural networksR Thomas McCoy, Thomas L Griffiths
Cognitive Science|June 25, 2021
The Challenges of Large-Scale, Web-Based Language Datasets: Word Length and Predictability RevisitedStephan C Meylan, Thomas L Griffiths
Cognitive Science|May 14, 2011
Learning the form of causal relationships using hierarchical bayesian modelsChristopher G Lucas, Thomas L Griffiths
Pageof 21

Showing results (21-30 of 207) with videos related to

Sort By:
Pageof 21
Neural Computation|June 7, 2008
Latent features in similarity judgments: a nonparametric bayesian approachDaniel J Navarro, Thomas L Griffiths
Cognitive Psychology|September 20, 2005
Structure and strength in causal inductionThomas L Griffiths, Joshua B Tenenbaum
Psychological Science|September 21, 2006
Optimal predictions in everyday cognitionThomas L Griffiths, Joshua B Tenenbaum
Journal of Experimental Psychology. Learning, Memory, and Cognition|April 24, 2013
Why are people bad at detecting randomness? A statistical argumentJoseph J Williams, Thomas L Griffiths
Psychological Review|October 21, 2009
Theory-based causal inductionThomas L Griffiths, Joshua B Tenenbaum
Psychological Review|June 5, 2025
Computation-limited Bayesian updating: A resource-rational analysis of approximate Bayesian inferenceJian-Qiao Zhu, Thomas L Griffiths
Cognitive Science|June 4, 2011
Language evolution by iterated learning with bayesian agentsThomas L Griffiths, Michael L Kalish
Nature Communications|May 20, 2025
Modeling rapid language learning by distilling Bayesian priors into artificial neural networksR Thomas McCoy, Thomas L Griffiths
Cognitive Science|June 25, 2021
The Challenges of Large-Scale, Web-Based Language Datasets: Word Length and Predictability RevisitedStephan C Meylan, Thomas L Griffiths
Cognitive Science|May 14, 2011
Learning the form of causal relationships using hierarchical bayesian modelsChristopher G Lucas, Thomas L Griffiths
Pageof 21