Robotics & Machine Learning Daily News2024,Issue(Nov.21) :12-12.

Study Data from Texas A&M University Provide New Insights into Mach ine Learning (Adaptive Loss Weighting for Machine Learning Interatomic Potential s)

德克萨斯农工大学的研究数据为我们提供了新的见解机器学习的自适应损失加权(Machine Learning)原子间势

Robotics & Machine Learning Daily News2024,Issue(Nov.21) :12-12.

Study Data from Texas A&M University Provide New Insights into Mach ine Learning (Adaptive Loss Weighting for Machine Learning Interatomic Potential s)

德克萨斯农工大学的研究数据为我们提供了新的见解机器学习的自适应损失加权(Machine Learning)原子间势

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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。根据消息来源来自德克萨斯州大学站,由NewsRx记者报道,研究表明,“训练机器学习”原子间p电位通常需要优化由三个变量组成的损失函数:势能量、力量和压力。每个变量对总损失的贡献通常用固定系数。

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 originatingfrom College Station, Texas, by NewsRx correspondents, research stated, “Training machine learninginteratomic p otentials often requires optimizing a loss function composed of three variables: potentialenergies, forces, and stress. The contribution of each variable to th e total loss is typically weighted usingfixed coefficients.”

Key words

College Station/Texas/United States/N orth and Central America/Cyborgs/Emerging Technologies/Machine Learning/Texa s A&M University

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出版年

2024
Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

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