16 citations · 28 across the 13 of their papers we have counts for
7 papers · 1 filter
COVID-Net Biochem: An Explainability-driven Framework to Building Machine Learning Models for Predicting Survival and Kidney Injury of COVID-19 Patients from Clinical and Biochemistry Data
Hossein Aboutalebi, Maya Pavlova, Mohammad Javad Shafiee +3
Since the World Health Organization declared COVID-19 a pandemic in 2020, the global community has faced ongoing challenges in controlling and mitigating the transmission of the SA…
COVID-Net Clinical ICU: Enhanced Prediction of ICU Admission for COVID-19 Patients via Explainability and Trust Quantification
Audrey Chung, Mahmoud Famouri, Andrew Hryniowski +1
The COVID-19 pandemic continues to have a devastating global impact, and has placed a tremendous burden on struggling healthcare systems around the world. Given the limited resourc…
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…
Seeing Convolution Through the Eyes of Finite Transformation Semigroup Theory: An Abstract Algebraic Interpretation of Convolutional Neural Networks
Andrew Hryniowski, Alexander Wong
Researchers are actively trying to gain better insights into the representational properties of convolutional neural networks for guiding better network designs and for interpretin…