3 papers
cs.AI2026
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…
cs.CR2025
VISAT: Benchmarking Adversarial and Distribution Shift Robustness in Traffic Sign Recognition with Visual Attributes
Simon Yu, Peilin Yu, Hongbo Zheng +3
We present VISAT, a novel open dataset and benchmarking suite for evaluating model robustness in the task of traffic sign recognition with the presence of visual attributes. Built…
cs.LG2025
Neural Probabilistic Circuits: Enabling Compositional and Interpretable Predictions through Logical Reasoning
Weixin Chen, Simon Yu, Huajie Shao +2
End-to-end deep neural networks have achieved remarkable success across various domains but are often criticized for their lack of interpretability. While post hoc explanation meth…