Meta-learning: Data, architecture, and both

Binz, M; Dasgupta, I; Jagadish, A; Botvinick, M; Wang, JX; Schulz, E

doi:10.1017/S0140525X24000311

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Meta-learning: Data, architecture, and both

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Binz, M
Research Group Computational Principles of Intelligence, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Jagadish, A
Research Group Computational Principles of Intelligence, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Schulz, E
Research Group Computational Principles of Intelligence, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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https://www.cambridge.org/core/journals/behavioral-and-brain-sciences/article/metalearning-data-architecture-and-both/7DC1497065FAFD61FD7D3906C3EA3A23#
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Citation

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.

Cite as: https://hdl.handle.net/21.11116/0000-000F-E06B-C

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.