国际自动计划和调度会议(ICAPS)是规划和调度领域的研究人员和实践者的主要论坛,这两项技术对制造业、空间系统、软件工程、机器人、教育和娱乐至关重要。ICAPS会议合并了两个双年度会议,即国际人工智能规划与调度会议(AIPS)和欧洲规划会议(ECP)。 官网地址:http://dblp.uni-trier.de/db/conf/aips/

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We propose an interpretable Capsule Network, iCaps, for image classification. A capsule is a group of neurons nested inside each layer, and the one in the last layer is called a class capsule, which is a vector whose norm indicates a predicted probability for the class. Using the class capsule, existing Capsule Networks already provide some level of interpretability. However, there are two limitations which degrade its interpretability: 1) the class capsule also includes classification-irrelevant information, and 2) entities represented by the class capsule overlap. In this work, we address these two limitations using a novel class-supervised disentanglement algorithm and an additional regularizer, respectively. Through quantitative and qualitative evaluations on three datasets, we demonstrate that the resulting classifier, iCaps, provides a prediction along with clear rationales behind it with no performance degradation.

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