1.8k citations
- Zhejiang UniversityCN42 papers
- Tsinghua UniversityCN19 papers
- Peking UniversityCN18 papers
- Shanghai Jiao Tong UniversityCN17 papers
- Alibaba Group (United States)US16 papers
- University of Science and Technology of ChinaCN16 papers
- Chinese Academy of SciencesCN12 papers
- Nanyang Technological UniversitySG10 papers
- University of Chinese Academy of SciencesCN9 papers
- University of Electronic Science and Technology of ChinaCN8 papers
- CAS Key Laboratory of Urban Pollutant ConversionCN7 papers
- Huazhong University of Science and TechnologyCN7 papers
45 papers · 1 filter
Which Channel to Ask My Question? Personalized Customer Service Request Stream Routing using Deep Reinforcement Learning
Zining Liu, Chong Long, Xiaolu Lu +3
Customer services are critical to all companies, as they may directly connect to the brand reputation. Due to a great number of customers, e-commerce companies often employ multipl…
A New Ensemble Adversarial Attack Powered by Long-term Gradient Memories
Zhaohui Che, Ali Borji, Guangtao Zhai +3
Deep neural networks are vulnerable to adversarial attacks.
Distribution Context Aware Loss for Person Re-identification
Zhigang Chang, Qin Zhou, Mingyang Yu +3
To learn the optimal similarity function between probe and gallery images in Person re-identification, effective deep metric learning methods have been extensively explored to obta…
Learning To Characterize Adversarial Subspaces
Xiaofeng Mao, Yuefeng Chen, Yuhong Li +2
Deep Neural Networks (DNNs) are known to be vulnerable to the maliciously generated adversarial examples. To detect these adversarial examples, previous methods use artificially de…
Query-bag Matching with Mutual Coverage for Information-seeking Conversations in E-commerce
Zhenxin Fu, Feng Ji, Wenpeng Hu +4
Information-seeking conversation system aims at satisfying the information needs of users through conversations. Text matching between a user query and a pre-collected question is…
Learning Disentangled Representations for Recommendation
Jianxin Ma, Chang Zhou, Peng Cui +2
User behavior data in recommender systems are driven by the complex interactions of many latent factors behind the users' decision making processes. The factors are highly entangle…