Deep Learning

R^3: On-device Real-Time Deep Reinforcement Learning for Autonomous Robotics
Autonomous robotic systems, like autonomous vehicles and robotic search and rescue, require efficient on-device training for continuous …
Zexin Li, Aritra Samanta, Yufei Li, Andrea Soltoggio, Hyoseung Kim, Cong Liu
PDF Cite Code DOI
RED: A Systematic Real-Time Scheduling Approach for Robotic Environmental Dynamics
Intelligent robots are designed to effectively navigate dynamic and unpredictable environments laden with moving mechanical elements …
Zexin Li, Tao Ren, Xiaoxi He, Cong Liu
PDF Cite DOI
PIMbot: Policy and Incentive Manipulation for Multi-Robot Reinforcement Learning in Social Dilemmas
Recent research has demonstrated the potential of reinforcement learning (RL) in enabling effective multi-robot collaboration, …
Shahab Nikkhoo, Zexin Li, Aritra Samanta, Yufei Li, Cong Liu
PDF Cite Code Video DOI
Dynamic Transformers Provide a False Sense of Efficiency
Despite much success in natural language processing (NLP), pre-trained language models typically lead to a high computational cost …
Yiming Chen, Simin Chen, Zexin Li, Wei Yang, Cong Liu, Robby Tan, Haizhou Li
PDF Cite Code DOI
White-Box Multi-Objective Adversarial Attack on Dialogue Generation
Pre-trained transformers are popular in state-of-the-art dialogue generation (DG) systems. Such language models are, however, …
Yufei Li, Zexin Li, Yingfan Gao, Cong Liu
PDF Cite Code DOI
Sibling-Attack: Rethinking Transferable Adversarial Attacks against Face Recognition
A hard challenge in developing practical face recognition (FR) attacks is due to the black-box nature of the target FR model, i.e., …
Zexin Li*, Bangjie Yin, Taiping Yao, Junfeng Guo, Shouhong Ding, Simin Chen, Cong Liu
PDF Cite Code Video DOI