Robotics & Machine Learning Daily News2024,Issue(Jun.11) :121-122.

Studies from Washington State University Describe New Findings in Machine Learni ng (Improve Robustness of Machine Learning Via Efficient Optimization and Confor mal Prediction)

华盛顿州立大学的研究描述了机器学习的新发现(通过有效的优化和常规预测提高机器学习的鲁棒性)

Robotics & Machine Learning Daily News2024,Issue(Jun.11) :121-122.

Studies from Washington State University Describe New Findings in Machine Learni ng (Improve Robustness of Machine Learning Via Efficient Optimization and Confor mal Prediction)

华盛顿州立大学的研究描述了机器学习的新发现(通过有效的优化和常规预测提高机器学习的鲁棒性)

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摘要

一位新闻记者兼机器人与机器学习的工作人员新闻编辑每日新闻-机器学习的新研究是一篇报道的主题。根据NewsRx编辑从华盛顿普尔曼发回的新闻报道,该研究指出,“机器学习(ML)系统在现实场景中的进步通常期望在关键决策过程的高风险应用(例如医疗诊断)中安全部署。通常需要ML的可证明的稳健性来衡量和了解部署的ML系统有多可靠,以及他们的预测有多可信。

Abstract

By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – New research on Machine Learning is the subject o f a report. According to news reporting originating from Pullman, Washington, by NewsRx editors, the research stated, “The advance of machine learning (ML) syst ems in real-world scenarios usually expects safe deployment in high-stake applic ations (e.g., medical diagnosis) for critical decision-making process. To this e nd, provable robustness of ML is usually required to measure and understand how reliable the deployed ML system is and how trustworthy their predictions can be. ”

Key words

Pullman/Washington/United States/North and Central America/Algorithms/Cyborgs/Emerging Technologies/Machine Learn ing/Optimization Algorithms/Washington State University

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出版年

2024
Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

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