20 citations · 22 across the 3 of their papers we have counts for
5 papers
Explainable Recommendation Systems by Generalized Additive Models with Manifest and Latent Interactions
Yifeng Guo, Yu Su, Zebin Yang +1
In recent years, the field of recommendation systems has attracted increasing attention to developing predictive models that provide explanations of why an item is recommended to a…
Unwrapping The Black Box of Deep ReLU Networks: Interpretability, Diagnostics, and Simplification
Agus Sudjianto, William Knauth, Rahul Singh +2
The deep neural networks (DNNs) have achieved great success in learning complex patterns with strong predictive power, but they are often thought of as "black box" models without a…
An Effective and Efficient Initialization Scheme for Training Multi-layer Feedforward Neural Networks
Zebin Yang, Hengtao Zhang, Agus Sudjianto +1
Network initialization is the first and critical step for training neural networks. In this paper, we propose a novel network initialization scheme based on the celebrated Stein's…
Enhancing Explainability of Neural Networks through Architecture Constraints
Zebin Yang, Aijun Zhang, Agus Sudjianto
Prediction accuracy and model explainability are the two most important objectives when developing machine learning algorithms to solve real-world problems. The neural networks are…
Interval-valued Data Prediction via Regularized Artificial Neural Network
Zebin Yang, Dennis K. J. Lin, Aijun Zhang
A regularized artificial neural network (RANN) is proposed for interval-valued data prediction. The ANN model is selected due to its powerful capability in fitting linear and nonli…