Browsing by Author Wang, Xuesong

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  • Authors: Wang, Xuesong;  Advisor: -;  Participants: Gu, Yang; Cheng, Yuhu; Liu, Aiping; Chen, C. L. Philip (2019)

  • In recent years, the deep reinforcement learning (DRL) algorithms have been developed rapidly and have achieved excellent performance in many challenging tasks. However, due to the complexity of network structure and a large amount of network parameters, the training of deep network is timeconsuming, and consequently, the learning efficiency of DRL is limited. In this paper, aiming to speed up the learning process of DRL agent, we propose a novel approximate policy-based accelerated (APA) algorithm from the viewpoint of the error analysis of approximate policy iteration reinforcement learning algorithms. The proposed APA is proven to be convergent even with a more aggressive learning ...

  • BB

  • Authors: Wang, Xuesong;  Advisor: -;  Participants: Li, Qianyu; Gong, Ping; Cheng, Yuhu (2019)

  • Since the learning of attribute classifiers is independent of the learning of object classifier in zero-shot learning, it is difficult to guarantee that the learned attribute classifiers are optimal for the subsequent object recognition tasks. Therefore, a novel zero-shot learning method based on multitask extended attribute groups (MTEAGs) is proposed by using the multitask learning framework and grouping idea. First, we used an unsupervised clustering method to group the attributes and object classes of training images. Then, based on the obtained attribute and class groups, we constructed the group-based attribute/object classifier collaborative learning model where the class group...