This is the Proceedings of ICML 2021 Workshop on Theoretic Foundation, Criticism, and Application Trend of Explainable AI. Deep neural networks (DNNs) have undoubtedly brought great success to a wide range of applications in computer vision, computational linguistics, and AI. However, foundational principles underlying the DNNs' success and their resilience to adversarial attacks are still largely missing. Interpreting and theorizing the internal mechanisms of DNNs becomes a compelling yet controversial topic. This workshop pays a special interest in theoretic foundations, limitations, and new application trends in the scope of XAI. These issues reflect new bottlenecks in the future development of XAI.
翻译：这是ICML 2021年关于理论基础、批评和可解释的AI的应用趋势研讨会的议事录。深神经网络无疑在计算机视野、计算语言学和AI的广泛应用中取得了巨大成功。然而,DNN的成功及其抵御对抗性攻击的能力所依据的基本原则仍然基本缺乏。DNN的内部机制的解释和理论化成为一个令人信服但有争议的主题。该研讨会对XAI范围内的理论基础、限制和新的应用趋势特别感兴趣。这些问题反映了XAI未来发展中的新瓶颈。
相关内容
微信扫码咨询专知VIP会员