58 citations · 255 across the 11 of their papers we have counts for
8 papers · 1 filter
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…
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…
D-City: A Large-Scale Dashcam Video Dataset of Diverse Traffic Scenarios
Zhengping Che, Guangyu Li, Tracy Li +7
Driving datasets accelerate the development of intelligent driving and related computer vision technologies, while substantial and detailed annotations serve as fuels and powers to…
Benchmark of Deep Learning Models on Large Healthcare MIMIC Datasets
Sanjay Purushotham, Chuizheng Meng, Zhengping Che +1
Deep learning models (aka Deep Neural Networks) have revolutionized many fields including computer vision, natural language processing, speech recognition, and is being increasingl…
Boosting Deep Learning Risk Prediction with Generative Adversarial Networks for Electronic Health Records
Zhengping Che, Yu Cheng, Shuangfei Zhai +2
The rapid growth of Electronic Health Records (EHRs), as well as the accompanied opportunities in Data-Driven Healthcare (DDH), has been attracting widespread interests and attenti…