4 citations · 10 across the 21 of their papers we have counts for
8 papers · 1 filter
R^3: On-device Real-Time Deep Reinforcement Learning for Autonomous Robotics
Zexin Li, Aritra Samanta, Yufei Li +3
Autonomous robotic systems, like autonomous vehicles and robotic search and rescue, require efficient on-device training for continuous adaptation of Deep Reinforcement Learning (D…
RT-LM: Uncertainty-Aware Resource Management for Real-Time Inference of Language Models
Yufei Li, Zexin Li, Wei Yang +1
Recent advancements in language models (LMs) have gained substantial attentions on their capability to generate human-like responses. Though exhibiting a promising future for vario…
RED: A Systematic Real-Time Scheduling Approach for Robotic Environmental Dynamics
Zexin Li, Tao Ren, Xiaoxi He +1
Intelligent robots are designed to effectively navigate dynamic and unpredictable environments laden with moving mechanical elements and objects. Such environment-induced dynamics,…
PIMbot: Policy and Incentive Manipulation for Multi-Robot Reinforcement Learning in Social Dilemmas
Shahab Nikkhoo, Zexin Li, Aritra Samanta +2
Recent research has demonstrated the potential of reinforcement learning (RL) in enabling effective multi-robot collaboration, particularly in social dilemmas where robots face a t…
MIMONet: Multi-Input Multi-Output On-Device Deep Learning
Zexin Li, Xiaoxi He, Yufei Li +3
Future intelligent robots are expected to process multiple inputs simultaneously (such as image and audio data) and generate multiple outputs accordingly (such as gender and emotio…
Dynamic Transformers Provide a False Sense of Efficiency
Yiming Chen, Simin Chen, Zexin Li +4
Despite much success in natural language processing (NLP), pre-trained language models typically lead to a high computational cost during inference. Multi-exit is a mainstream appr…
White-Box Multi-Objective Adversarial Attack on Dialogue Generation
Yufei Li, Zexin Li, Yingfan Gao +1
Pre-trained transformers are popular in state-of-the-art dialogue generation (DG) systems. Such language models are, however, vulnerable to various adversarial samples as studied i…
Sibling-Attack: Rethinking Transferable Adversarial Attacks against Face Recognition
Zexin Li, Bangjie Yin, Taiping Yao +4
A hard challenge in developing practical face recognition (FR) attacks is due to the black-box nature of the target FR model, i.e., inaccessible gradient and parameter information…