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在多维分类中,输出空间中存在多个类变量,每个类变量对应一个异构类空间。由于类空间的异质性,在从MDC示例中学习时,考虑类变量之间的依赖关系非常具有挑战性。本文提出了一种新的多目标预测方法,即SLEM方法,它在编码的标签空间中学习预测模型,而不是在异构的标签空间中学习预测模型。具体来说,SLEM在编码-训练-解码框架中工作。在编码阶段,通过成对分组、一次热转换和稀疏线性编码三种级联操作,将每个类向量映射为实值向量。在训练阶段,在编码标签空间内学习多输出回归模型。在解码阶段,通过对学习的多输出回归模型的输出进行正交匹配追踪,得到预测的类向量。实验结果清楚地验证了SLEM相对于最先进的MDC方法的优越性。

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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.

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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.

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