4 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…
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…
Towards More Trustworthy Deep Code Models by Enabling Out-of-Distribution Detection
Yanfu Yan, Viet Duong, Huajie Shao +1
Numerous machine learning (ML) models have been developed, including those for software engineering (SE) tasks, under the assumption that training and testing data come from the sa…
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…