主动推理中的元学习

IF 19.3 1区材料科学 Q1 CHEMISTRY, PHYSICAL ACS Energy Letters Pub Date : 2024-09-23 DOI:10.1017/S0140525X24000074

O Penacchio, A Clemente

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引用次数: 0

摘要

Binz等人提出元学习（meta-learning）是建立人类认知模型的一条大有可为的途径。他们深入反思了元学习相对于其他认知计算模型的优势，包括对他们的建议如何适应神经科学洞察力的合理讨论。我们认为，主动推理具有类似的计算优势，同时具有更强的机理解释能力和生物学合理性。

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 in active inference.

Binz et al. propose meta-learning as a promising avenue for modelling human cognition. They provide an in-depth reflection on the advantages of meta-learning over other computational models of cognition, including a sound discussion on how their proposal can accommodate neuroscientific insights. We argue that active inference presents similar computational advantages while offering greater mechanistic explanatory power and biological plausibility.

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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.