Robotics & Machine Learning Daily News2024,Issue(Dec.4) :158-159.

Researchers from China Academy of Engineering Physics Describe Findings in Machi ne Learning (Structural Mechanism of Glass Transition Uncovered By Unsupervised Machine Learning)

中国工程物理研究院的研究人员描述了机械学习(无监督机器学习揭示的玻璃化转变结构机制)的发现

Robotics & Machine Learning Daily News2024,Issue(Dec.4) :158-159.

Researchers from China Academy of Engineering Physics Describe Findings in Machi ne Learning (Structural Mechanism of Glass Transition Uncovered By Unsupervised Machine Learning)

中国工程物理研究院的研究人员描述了机械学习(无监督机器学习揭示的玻璃化转变结构机制)的发现

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摘要

由一名新闻记者-机器人与机器学习日报的工作人员新闻编辑每日新闻-调查人员讨论机器学习的新发现。根据新闻报道来自中华人民共和国四川,由NewsRx记者报道,研究称,"揭露普遍存在的玻璃化转变动态停滞现象的结构起源一直是人们关注的焦点由于难以从无序的介质中识别出合理的结构表征而带来的挑战。为了解决这一问题,我们提出了一种基于无监督学习的nov el方法来定义一组结构指尖"。

Abstract

By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Investigators discuss new findings in Machine Learning. According to news reportingfrom Sichuan, People’s Republic of China, by NewsRx journalists, research stated, “Uncovering thestructural origi ns of the ubiquitous dynamic arrest phenomenon at the glass transition has long been achallenge due to the difficulty in identifying a rational structural repr esentation from a disordered medium.To address this challenge, we propose a nov el approach based on unsupervised learning to define a set ofstructural fingerp rints.”

Key words

Sichuan/People’s Republic of China/Asi a/Cyborgs/Emerging Technologies/Machine Learning/China Academy of Engineerin g Physics

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出版年

2024
Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

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