no code implementations • 29 Feb 2024 • Yang Xu, Yunlin Tan, Cheng Zhang, Kai Chi, Peng Sun, Wenyuan Yang, Ju Ren, Hongbo Jiang, Yaoxue Zhang
This paper presents a robust watermark embedding scheme, named RobWE, to protect the ownership of personalized models in PFL.
no code implementations • 10 Dec 2023 • Yongheng Deng, Ziqing Qiao, Ju Ren, Yang Liu, Yaoxue Zhang
While large language models (LLMs) are empowered with broad knowledge, their task-specific performance is often suboptimal.
no code implementations • 16 Sep 2023 • Fucheng Jia, Shiqi Jiang, Ting Cao, Wei Cui, Tianrui Xia, Xu Cao, Yuanchun Li, Deyu Zhang, Ju Ren, Yunxin Liu, Lili Qiu, Mao Yang
Web applications are increasingly becoming the primary platform for AI service delivery, making in-browser deep learning (DL) inference more prominent.
no code implementations • 9 Feb 2023 • Sheng Yue, Guanbo Wang, Wei Shao, Zhaofeng Zhang, Sen Lin, Ju Ren, Junshan Zhang
This work aims to tackle a major challenge in offline Inverse Reinforcement Learning (IRL), namely the reward extrapolation error, where the learned reward function may fail to explain the task correctly and misguide the agent in unseen environments due to the intrinsic covariate shift.
no code implementations • SenSys 2022 • Yongjian Fu, Shuning Wang, Linghui Zhong, Lili Chen, Ju Ren, Yaoxue Zhang
The design of introduces a new model that provides the unique mapping relationship between ultrasound and speech signals, so that the audible speech can be successfully reconstructed from the silent speech.
no code implementations • 14 Aug 2021 • Sheng Yue, Ju Ren, Jiang Xin, Deyu Zhang, Yaoxue Zhang, Weihua Zhuang
After that, we formulate a resource allocation problem integrating NUFM in multi-access wireless systems to jointly improve the convergence rate and minimize the wall-clock time along with energy cost.
no code implementations • 16 Dec 2020 • Sheng Yue, Ju Ren, Jiang Xin, Sen Lin, Junshan Zhang
To overcome these challenges, we explore continual edge learning capable of leveraging the knowledge transfer from previous tasks.