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Meta-learning as a bridge between neural networks and symbolic Bayesian models

Published online by Cambridge University Press:  23 September 2024

R. Thomas McCoy  and
Thomas L. Griffiths Open the ORCID record for Thomas L. Griffiths [Opens in a new window]
R. Thomas McCoy
Affiliation:
Department of Linguistics, Yale University, New Haven, CT, USA tom.mccoy@yale.edu https://rtmccoy.com/
Thomas L. Griffiths*
Affiliation:
Departments of Psychology and Computer Science, Princeton University, Princeton, NJ, USA tomg@princeton.edu http://cocosci.princeton.edu/tom/
*
*Corresponding author.
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Abstract

Meta-learning is even more broadly relevant to the study of inductive biases than Binz et al. suggest: Its implications go beyond the extensions to rational analysis that they discuss. One noteworthy example is that meta-learning can act as a bridge between the vector representations of neural networks and the symbolic hypothesis spaces used in many Bayesian models.

Information

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 47 , 2024 , e155
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Copyright
Copyright © The Author(s), 2024. Published by Cambridge University Press

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