元学习与元认知相辅相成。

IF 19.3 1区材料科学 Q1 CHEMISTRY, PHYSICAL ACS Energy Letters Pub Date : 2024-09-23 DOI:10.1017/S0140525X24000256

Chris Fields, James F Glazebrook

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引用次数: 0

摘要

Binz等人提出了元学习的一般框架，并将其与手工建立的贝叶斯模型进行了对比。我们对该方法的一些架构假设、它与主动推理框架的关系、它对一般生命系统的潜在适用性以及后者在解决解释问题方面的优势进行了评论。

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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Meta-learning goes hand-in-hand with metacognition.

Binz et al. propose a general framework for meta-learning and contrast it with built-by-hand Bayesian models. We comment on some architectural assumptions of the approach, its relation to the active inference framework, its potential applicability to living systems in general, and the advantages of the latter in addressing the explanation problem.

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来源期刊

ACS Energy Letters Energy-Renewable Energy, Sustainability and the Environment

CiteScore

31.20

自引率

5.00%

发文量

469

审稿时长

1 months

期刊介绍： ACS Energy Letters is a monthly journal that publishes papers reporting new scientific advances in energy research. The journal focuses on topics that are of interest to scientists working in the fundamental and applied sciences. Rapid publication is a central criterion for acceptance, and the journal is known for its quick publication times, with an average of 4-6 weeks from submission to web publication in As Soon As Publishable format. ACS Energy Letters is ranked as the number one journal in the Web of Science Electrochemistry category. It also ranks within the top 10 journals for Physical Chemistry, Energy & Fuels, and Nanoscience & Nanotechnology. The journal offers several types of articles, including Letters, Energy Express, Perspectives, Reviews, Editorials, Viewpoints and Energy Focus. Additionally, authors have the option to submit videos that summarize or support the information presented in a Perspective or Review article, which can be highlighted on the journal's website. ACS Energy Letters is abstracted and indexed in Chemical Abstracts Service/SciFinder, EBSCO-summon, PubMed, Web of Science, Scopus and Portico.