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

債權(quán)人

書目名稱Database Systems for Advanced Applications影響因子(影響力)




書目名稱Database Systems for Advanced Applications影響因子(影響力)學(xué)科排名




書目名稱Database Systems for Advanced Applications網(wǎng)絡(luò)公開度




書目名稱Database Systems for Advanced Applications網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Database Systems for Advanced Applications被引頻次




書目名稱Database Systems for Advanced Applications被引頻次學(xué)科排名




書目名稱Database Systems for Advanced Applications年度引用




書目名稱Database Systems for Advanced Applications年度引用學(xué)科排名




書目名稱Database Systems for Advanced Applications讀者反饋




書目名稱Database Systems for Advanced Applications讀者反饋學(xué)科排名

notice

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

感激小女

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

漫步

戲法

使成核

使成核

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

慢慢流出

群居動物

決定性