45 citations · 59 across the 5 of their papers we have counts for
7 papers
Drill the Cork of Information Bottleneck by Inputting the Most Important Data
Xinyu Peng, Jiawei Zhang, Fei-Yue Wang +1
Deep learning has become the most powerful machine learning tool in the last decade. However, how to efficiently train deep neural networks remains to be thoroughly solved. The wid…
Defining Digital Quadruplets in the Cyber-Physical-Social Space for Parallel Driving
Teng Liu, Yang Xing, Long Chen +2
Parallel driving is a novel framework to synthesize vehicle intelligence and transport automation. This article aims to define digital quadruplets in parallel driving. In the cyber…
Digital Quadruplets for Cyber-Physical-Social Systems based Parallel Driving: From Concept to Applications
Teng Liu, Xing Yang, Hong Wang +4
Digital quadruplets aiming to improve road safety, traffic efficiency, and driving cooperation for future connected automated vehicles are proposed with the enlightenment of ACP ba…
A Negotiation-based Right-of-way Assignment Strategy to Ensure Traffic Safety and Efficiency in Lane Change
Can Zhao, Zhiheng Li, Li Li +3
It is widely acknowledged that verifying the safety of autonomous driving strategies requires a substantial body of simulation testing and road testing. In recent years, the formal…
Accelerating Minibatch Stochastic Gradient Descent using Typicality Sampling
Xinyu Peng, Li Li, Fei-Yue Wang
Machine learning, especially deep neural networks, has been rapidly developed in fields including computer vision, speech recognition and reinforcement learning. Although Mini-batc…
An Efficient Deep Reinforcement Learning Model for Urban Traffic Control
Yilun Lin, Xingyuan Dai, Li Li +1
Urban Traffic Control (UTC) plays an essential role in Intelligent Transportation System (ITS) but remains difficult. Since model-based UTC methods may not accurately describe the…