New Findings from University of Utah in the Area of Machine Learning Described ( Interpreting and Generalizing Deep Learning In Physics-based Problems With Funct ional Linear Models)
New Findings from University of Utah in the Area of Machine Learning Described ( Interpreting and Generalizing Deep Learning In Physics-based Problems With Funct ional Linear Models)
犹他大学在机器学习领域的新发现描述（用函数线性模型解释和推广基于物理问题的深度学习）
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摘要
由一名新闻记者-机器人与机器学习的工作人员新闻编辑每日新闻-研究人员详细介绍了机器学习的新数据。根据NewsRx Ed Itors在犹他州盐湖城的新闻报道，研究表明，“尽管深度学习在各种科学机器学习应用中取得了显著的成功，但其不透明的性质对训练数据之外的可解释性和泛化能力提出了担忧。可解释性是建模物理系统的关键，而且往往是人们所期望的。”
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Researchers detail new data in Machine Learning. According to news reporting out of Salt Lake City, Utah, by NewsRx ed itors, research stated, “Although deep learning has achieved remarkable success in various scientific machine learning applications, its opaque nature poses con cerns regarding interpretability and generalization capabilities beyond the training data. Interpretability is crucial and often desired in modeling physical systems.”
Key words
Salt Lake City/Utah/United States/North and Central America/Cyborgs/Emerging Technologies/Machine Learning/Univer sity of Utah
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