Titlebook: Database Systems for Advanced Applications; 24th International C Guoliang Li,Jun Yang,Yongxin Tong Conference proceedings 2019 Springer Nat

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An Exploration of Cross-Modal Retrieval for Unseen Concepts of the existing models are trained and tested in a close-set circumstance, which may easily fail on the newly emerged concepts that are never present in the training stage. In this paper, we propose a novel cross-modal hashing model, named Cross-Modal Attribute Hashing (CMAH), which can handle cros

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Continuous Patient-Centric Sequence Generation via Sequentially Coupled Adversarial Learningnt of these data is limited and the access to EHRs is difficult due to the issue of patient privacy. Thus high quality synthetic EHRs data is necessary to alleviate these issues. In this paper, we propose a Sequentially Coupled Generative Adversarial Network (SC-GAN) to generate continuous patient-c

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Reinforcement Learning to Diversify Top-N RecommendationTraditional approaches are to treat the process of constructing the recommendation list as a problem of greedy sequential items selection, which are inevitably sub-optimal. In this paper, we propose a reinforcement learning and neural networks based framework – .ersify top-N Recommendation with .ast

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