Investigators from University of British Columbia Have Reported New Data on Mach ine Learning (It Is All About Data: a Survey On the Effects of Data On Adversari al Robustness)
Investigators from University of British Columbia Have Reported New Data on Mach ine Learning (It Is All About Data: a Survey On the Effects of Data On Adversari al Robustness)
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-在一份新的报告中讨论了机器学习的研究结果。据NewsRx记者从温哥华发回的Can Ada News报道，研究称：“对抗性示例是机器学习模型的输入，攻击者故意设计这些模型以使模型出错。这类示例对基于机器学习的系统的适用性构成严重威胁，特别是在生命和安全关键领域。”
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Research findings on Machine Learning are discussed in a new report. According to news originating from Vancouver, Can ada, by NewsRx correspondents, research stated, “Adversarial examples are inputs to machine learning models that an attacker has intentionally designed to confu se the model into making a mistake. Such examples pose a serious threat to the a pplicability of machine learning-based systems, especially in life- and safety-c ritical domains.”
Key words
Vancouver/Canada/North and Central Ame rica/Cyborgs/Emerging Technologies/Machine Learning/University of British Co lumbia
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