34 citations · 111 across the 8 of their papers we have counts for
8 papers
DHEN: A Deep and Hierarchical Ensemble Network for Large-Scale Click-Through Rate Prediction
Buyun Zhang, Liang Luo, Xi Liu +14
Learning feature interactions is important to the model performance of online advertising services. As a result, extensive efforts have been devoted to designing effective architec…
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…
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…
Extracting Interpretable Concept-Based Decision Trees from CNNs
Conner Chyung, Michael Tsang, Yan Liu
In an attempt to gather a deeper understanding of how convolutional neural networks (CNNs) reason about human-understandable concepts, we present a method to infer labeled concept…