Robotics & Machine Learning Daily News2024，Issue(Nov.29) ：117-117.

Researchers from University of Kansas Detail Findings in Machine Learning (Hex: Human-in-the-loop Explainability Via Deep Reinforcement Learning)

堪萨斯大学的研究人员详细介绍了机器学习的发现（Hex：通过深度强化学习的人在回路中的可解释性）

Robotics & Machine Learning Daily News2024，Issue(Nov.29) ：117-117.
✕
  • NETL
  • NSTL

Researchers from University of Kansas Detail Findings in Machine Learning (Hex: Human-in-the-loop Explainability Via Deep Reinforcement Learning)

堪萨斯大学的研究人员详细介绍了机器学习的发现（Hex：通过深度强化学习的人在回路中的可解释性）

扫码查看

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-关于机器学习的详细数据已经呈现。根据新闻报道这项研究起源于堪萨斯州的劳伦斯，由NewsRx编辑撰写，称“机器学习的使用”(ML)决策环境中的模型，特别是那些在高风险决策中使用的模型，是充满了问题和危险,因为一个人,而不是一台机器,必须最终为这个问题负责使用这种系统所做决定的后果。机器学习可解释性(MLX)承诺向决策者提供预测的具体依据，确保模型引发的预测离子产生的原因是正确的，因此是可靠的。

Abstract

By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Data detailed on Machine Learning have been presented. According to news reportingoriginating in Lawrence, Kansas, by NewsRx editors, the research stated, “The use of machine learning(ML) models i n decision-making contexts, particularly those used in high-stakes decision-maki ng, arefraught with issue and peril since a person - not a machine - must ultim ately be held accountable for theconsequences of decisions made using such syst ems. Machine learning explainability (MLX) promises toprovide decision-makers w ith prediction-specific rationale, assuring them that the model-elicited predictions are made for the right reasons and are thus reliable.”

Key words

Lawrence/Kansas/United States/North a nd Central America/Cyborgs/Emerging Technologies/Machine Learning/Reinforcem ent Learning/University of Kansas

引用本文复制引用

出版年

2024
Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

ISSN：
段落导航相关论文