2 citations · 5 across the 6 of their papers we have counts for
6 papers
Personality-affected Emotion Generation in Dialog Systems
Zhiyuan Wen, Jiannong Cao, Jiaxing Shen +3
Generating appropriate emotions for responses is essential for dialog systems to provide human-like interaction in various application scenarios. Most previous dialog systems tried…
BAGEL: Backdoor Attacks against Federated Contrastive Learning
Yao Huang, Kongyang Chen, Jiannong Cao +5
Federated Contrastive Learning (FCL) is an emerging privacy-preserving paradigm in distributed learning for unlabeled data. In FCL, distributed parties collaboratively learn a glob…
Take Your Pick: Enabling Effective Personalized Federated Learning within Low-dimensional Feature Space
Guogang Zhu, Xuefeng Liu, Shaojie Tang +3
Personalized federated learning (PFL) is a popular framework that allows clients to have different models to address application scenarios where clients' data are in different doma…
Unlocking the Potential of Federated Learning for Deeper Models
Haolin Wang, Xuefeng Liu, Jianwei Niu +2
Federated learning (FL) is a new paradigm for distributed machine learning that allows a global model to be trained across multiple clients without compromising their privacy. Alth…
Automatically Select Emotion for Response via Personality-affected Emotion Transition
Wen Zhiyuan, Cao Jiannong, Yang Ruosong +2
To provide consistent emotional interaction with users, dialog systems should be capable to automatically select appropriate emotions for responses like humans. However, most exist…
EPARS: Early Prediction of At-risk Students with Online and Offline Learning Behaviors
Yu Yang, Zhiyuan Wen, Jiannong Cao +3
Early prediction of students at risk (STAR) is an effective and significant means to provide timely intervention for dropout and suicide. Existing works mostly rely on either onlin…