Meta-learning as a bridge between neural networks and symbolic Bayesian models
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Updated: Jun 12, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
Published on: December 15, 2023
R Thomas McCoy1, Thomas L Griffiths2
1Department of Linguistics, Yale University, New Haven, CT, USA tom.mccoy@yale.eduhttps://rtmccoy.com/.
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.
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