855 citations · 2.1k across the 21 of their papers we have counts for
22 papers
Interpretable Artificial Intelligence through the Lens of Feature Interaction
Michael Tsang, James Enouen, Yan Liu
Interpretation of deep learning models is a very challenging problem because of their large number of parameters, complex connections between nodes, and unintelligible feature repr…
MIMIC-IF: Interpretability and Fairness Evaluation of Deep Learning Models on MIMIC-IV Dataset
Chuizheng Meng, Loc Trinh, Nan Xu +1
The recent release of large-scale healthcare datasets has greatly propelled the research of data-driven deep learning models for healthcare applications. However, due to the nature…
Feature Interaction Interpretability: A Case for Explaining Ad-Recommendation Systems via Neural Interaction Detection
Michael Tsang, Dehua Cheng, Hanpeng Liu +3
Recommendation is a prevalent application of machine learning that affects many users; therefore, it is important for recommender models to be accurate and interpretable. In this w…
How does this interaction affect me? Interpretable attribution for feature interactions
Michael Tsang, Sirisha Rambhatla, Yan Liu
Machine learning transparency calls for interpretable explanations of how inputs relate to predictions. Feature attribution is a way to analyze the impact of features on prediction…
Interpretable and Trustworthy Deepfake Detection via Dynamic Prototypes
Loc Trinh, Michael Tsang, Sirisha Rambhatla +1
In this paper we propose a novel human-centered approach for detecting forgery in face images, using dynamic prototypes as a form of visual explanations. Currently, most state-of-t…
Physics-aware Spatiotemporal Modules with Auxiliary Tasks for Meta-Learning
Sungyong Seo, Chuizheng Meng, Sirisha Rambhatla +1
Modeling the dynamics of real-world physical systems is critical for spatiotemporal prediction tasks, but challenging when data is limited. The scarcity of real-world data and the…