Meta-learning: Data, architecture, and both.

IF 16.6 1区心理学 Q1 BEHAVIORAL SCIENCES

Behavioral and Brain Sciences Pub Date : 2024-09-23 DOI:10.1017/S0140525X24000311

Marcel Binz, Ishita Dasgupta, Akshay Jagadish, Matthew Botvinick, Jane X Wang, Eric Schulz

引用次数: 0

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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元学习：数据、架构和两者。

对我们目标文章的许多积极评论使我们深受鼓舞。在这篇回应中，我们将重述提出的一些观点，并确定它们之间的协同作用。我们根据元学习框架中出现的数据与架构之间的矛盾来安排我们的回应。此外，我们还就与基础模型的联系进行了简短讨论。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Behavioral and Brain Sciences 医学-行为科学

CiteScore

1.40

自引率

1.70%

发文量

353

期刊介绍： Behavioral and Brain Sciences (BBS) is a highly respected journal that employs an innovative approach called Open Peer Commentary. This format allows for the publication of noteworthy and contentious research from various fields including psychology, neuroscience, behavioral biology, and cognitive science. Each article is accompanied by 20-40 commentaries from experts across these disciplines, as well as a response from the author themselves. This unique setup creates a captivating forum for the exchange of ideas, critical analysis, and the integration of research within the behavioral and brain sciences, spanning topics from molecular neurobiology and artificial intelligence to the philosophy of the mind.