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  Meta-learning: Data, architecture, and both

Binz, M., Dasgupta, I., Jagadish, A., Botvinick, M., Wang, J., & Schulz, E. (2024). Meta-learning: Data, architecture, and both. Behavioral and Brain Sciences, 47: e170. doi:10.1017/S0140525X24000311.

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Item Permalink: https://hdl.handle.net/21.11116/0000-000F-E06B-C Version Permalink: https://hdl.handle.net/21.11116/0000-000F-E06C-B
Genre: Journal Article

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 Creators:
Binz, M1, Author                 
Dasgupta, I, Author
Jagadish, A1, Author                 
Botvinick, M, Author
Wang, JX, Author
Schulz, E1, Author                 
Affiliations:
1Research Group Computational Principles of Intelligence, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_3189356              

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 Abstract: We are encouraged by the many positive commentaries on our target article. In this response, we recapitulate some of the points raised and identify synergies between them. We have arranged our response based on the tension between data and architecture that arises in the meta-learning framework. We additionally provide a short discussion that touches upon connections to foundation models.

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 Dates: 2024-09
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1017/S0140525X24000311
 Degree: -

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Title: Behavioral and Brain Sciences
Source Genre: Journal
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Publ. Info: New York : Cambridge University Press.
Pages: - Volume / Issue: 47 Sequence Number: e170 Start / End Page: - Identifier: ISSN: 0140-525X
CoNE: https://pure.mpg.de/cone/journals/resource/954925341730