2 citations · 2 across the 4 of their papers we have counts for
5 papers
Interpretable Feature Engineering for Time Series Predictors using Attention Networks
Tianjie Wang, Jie Chen, Joel Vaughan +1
Regression problems with time-series predictors are common in banking and many other areas of application. In this paper, we use multi-head attention networks to develop interpreta…
Supervised Linear Dimension-Reduction Methods: Review, Extensions, and Comparisons
Shaojie Xu, Joel Vaughan, Jie Chen +2
Principal component analysis (PCA) is a well-known linear dimension-reduction method that has been widely used in data analysis and modeling. It is an unsupervised learning techniq…
Supervised Machine Learning Techniques: An Overview with Applications to Banking
Linwei Hu, Jie Chen, Joel Vaughan +4
This article provides an overview of Supervised Machine Learning (SML) with a focus on applications to banking. The SML techniques covered include Bagging (Random Forest or RF), Bo…
Adaptive Explainable Neural Networks (AxNNs)
Jie Chen, Joel Vaughan, Vijayan N. Nair +1
While machine learning techniques have been successfully applied in several fields, the black-box nature of the models presents challenges for interpreting and explaining the resul…
Explainable Neural Networks based on Additive Index Models
Joel Vaughan, Agus Sudjianto, Erind Brahimi +2
Machine Learning algorithms are increasingly being used in recent years due to their flexibility in model fitting and increased predictive performance. However, the complexity of t…