16 citations · 28 across the 13 of their papers we have counts for
4 papers · 1 filter
Inter-layer Information Similarity Assessment of Deep Neural Networks Via Topological Similarity and Persistence Analysis of Data Neighbour Dynamics
Andrew Hryniowski, Alexander Wong
The quantitative analysis of information structure through a deep neural network (DNN) can unveil new insights into the theoretical performance of DNN architectures. Two very promi…
Insights into Fairness through Trust: Multi-scale Trust Quantification for Financial Deep Learning
Alexander Wong, Andrew Hryniowski, Xiao Yu Wang
The success of deep learning in recent years have led to a significant increase in interest and prevalence for its adoption to tackle financial services tasks. One particular quest…
Where Does Trust Break Down? A Quantitative Trust Analysis of Deep Neural Networks via Trust Matrix and Conditional Trust Densities
Andrew Hryniowski, Xiao Yu Wang, Alexander Wong
The advances and successes in deep learning in recent years have led to considerable efforts and investments into its widespread ubiquitous adoption for a wide variety of applicati…
How Much Can We Really Trust You? Towards Simple, Interpretable Trust Quantification Metrics for Deep Neural Networks
Alexander Wong, Xiao Yu Wang, Andrew Hryniowski
A critical step to building trustworthy deep neural networks is trust quantification, where we ask the question: How much can we trust a deep neural network? In this study, we take…