6 citations · 6 across the 6 of their papers we have counts for
7 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…
CAT: Interpretable Concept-based Taylor Additive Models
Viet Duong, Qiong Wu, Zhengyi Zhou +5
As an emerging interpretable technique, Generalized Additive Models (GAMs) adopt neural networks to individually learn non-linear functions for each feature, which are then combine…
: Confidence Calibration Model Cascade for Inference-Efficient Cross-Lingual Natural Language Understanding
Taixi Lu, Haoyu Wang, Huajie Shao +2
Cross-lingual natural language understanding (NLU) is a critical task in natural language processing (NLP). Recent advancements have seen multilingual pre-trained language models (…
Enhancing Compositional Generalization via Compositional Feature Alignment
Haoxiang Wang, Haozhe Si, Huajie Shao +1
Real-world applications of machine learning models often confront data distribution shifts, wherein discrepancies exist between the training and test data distributions. In the com…