构建强大的机器学习系统:当前进展,研究挑战和机遇

J. Zhang, Kang Liu, Faiq Khalid, Muhammad Abdullah Hanif, Semeen Rehman, T. Theocharides, Alessandro Artussi, M. Shafique, S. Garg
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引用次数: 28

摘要

机器学习,特别是深度学习,几乎被用于生活的各个方面,以方便人类,特别是在移动和基于物联网(IoT)的应用中。由于其最先进的性能,深度学习也被用于安全关键应用,例如自动驾驶汽车。可靠性和安全性是这些应用程序所需的两个关键特性,因为它们可能对人类的生活产生影响。为此,在本文中,我们重点介绍了基于机器学习应用的鲁棒系统领域的当前进展、挑战和研究机会。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Building Robust Machine Learning Systems: Current Progress, Research Challenges, and Opportunities
Machine learning, in particular deep learning, is being used in almost all the aspects of life to facilitate humans, specifically in mobile and Internet of Things (IoT)-based applications. Due to its state-of-the-art performance, deep learning is also being employed in safety-critical applications, for instance, autonomous vehicles. Reliability and security are two of the key required characteristics for these applications because of the impact they can have on human's life. Towards this, in this paper, we highlight the current progress, challenges and research opportunities in the domain of robust systems for machine learning-based applications.
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