大型世界中元学习和理性分析的挑战。

IF 19.3 1区材料科学 Q1 CHEMISTRY, PHYSICAL ACS Energy Letters Pub Date : 2024-09-23 DOI:10.1017/S0140525X24000128

Margherita Calderan, Antonino Visalli

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引用次数: 0

摘要

我们质疑 Binz 等人关于元学习模型在大型世界问题上优于贝叶斯推理的说法。在比较贝叶斯先验与模型训练决策的同时，我们质疑元学习特征的排他性。我们认为，理性贝叶斯方法解决大型世界问题并无特殊理由，我们主张探索认知理性分析之外的多种理论框架，以促进研究。

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Challenges of meta-learning and rational analysis in large worlds.

We challenge Binz et al.'s claim of meta-learned model superiority over Bayesian inference for large world problems. While comparing Bayesian priors to model-training decisions, we question meta-learning feature exclusivity. We assert no special justification for rational Bayesian solutions to large world problems, advocating exploring diverse theoretical frameworks beyond rational analysis of cognition for research advancement.

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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.