1 citations · 2 across the 5 of their papers we have counts for
5 papers
Understanding the Theoretical Foundations of Deep Neural Networks through Differential Equations
Hongjue Zhao, Yizhuo Chen, Yuchen Wang +4
Deep neural networks (DNNs) have achieved remarkable empirical success, yet the absence of a principled theoretical foundation continues to hinder their systematic development. In…
A Generalizable Physics-guided Causal Model for Trajectory Prediction in Autonomous Driving
Zhenyu Zong, Yuchen Wang, Haohong Lin +2
Trajectory prediction for traffic agents is critical for safe autonomous driving. However, achieving effective zero-shot generalization in previously unseen domains remains a signi…
A Generalizable Physics-Enhanced State Space Model for Long-Term Dynamics Forecasting in Complex Environments
Yuchen Wang, Hongjue Zhao, Haohong Lin +3
This work aims to address the problem of long-term dynamic forecasting in complex environments where data are noisy and irregularly sampled. While recent studies have introduced so…
TrafficKAN-GCN: Graph Convolutional-based Kolmogorov-Arnold Network for Traffic Flow Optimization
Jiayi Zhang, Yiming Zhang, Yuan Zheng +5
Urban traffic optimization is critical for improving transportation efficiency and alleviating congestion, particularly in large-scale dynamic networks. Traditional methods, such a…
Fermi-Bose Machine achieves both generalization and adversarial robustness
Mingshan Xie, Yuchen Wang, Haiping Huang
Distinct from human cognitive processing, deep neural networks trained by backpropagation can be easily fooled by adversarial examples. To design a semantically meaningful represen…