2 citations · 7 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…
Machine Unlearning in Large Language Models
Kongyang Chen, Zixin Wang, Bing Mi +4
Recently, large language models (LLMs) have emerged as a notable field, attracting significant attention for its ability to automatically generate intelligent contents for various…
PreConfig: A Pretrained Model for Automating Network Configuration
Fuliang Li, Haozhi Lang, Jiajie Zhang +2
Manual network configuration automation (NCA) tools face significant challenges in versatility and flexibility due to their reliance on extensive domain expertise and manual design…
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…