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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


Apr 24, 2022
Hossein Aboutalebi, Maya Pavlova, Mohammad Javad Shafiee, Adrian Florea, Andrew Hryniowski, Alexander Wong

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COVID-Net Clinical ICU: Enhanced Prediction of ICU Admission for COVID-19 Patients via Explainability and Trust Quantification


Sep 14, 2021
Audrey Chung, Mahmoud Famouri, Andrew Hryniowski, Alexander Wong

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* 5 pages 

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AttendSeg: A Tiny Attention Condenser Neural Network for Semantic Segmentation on the Edge


Apr 29, 2021
Xiaoyu Wen, Mahmoud Famouri, Andrew Hryniowski, Alexander Wong

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* 5 pages 

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Inter-layer Information Similarity Assessment of Deep Neural Networks Via Topological Similarity and Persistence Analysis of Data Neighbour Dynamics


Dec 07, 2020
Andrew Hryniowski, Alexander Wong

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* 6 pages 

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Insights into Fairness through Trust: Multi-scale Trust Quantification for Financial Deep Learning


Nov 03, 2020
Alexander Wong, Andrew Hryniowski, Xiao Yu Wang

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* 9 pages 

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Where Does Trust Break Down? A Quantitative Trust Analysis of Deep Neural Networks via Trust Matrix and Conditional Trust Densities


Sep 30, 2020
Andrew Hryniowski, Xiao Yu Wang, Alexander Wong

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* 5 pages 

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How Much Can We Really Trust You? Towards Simple, Interpretable Trust Quantification Metrics for Deep Neural Networks


Sep 20, 2020
Alexander Wong, Xiao Yu Wang, Andrew Hryniowski

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* 13 pages 

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DeepLABNet: End-to-end Learning of Deep Radial Basis Networks with Fully Learnable Basis Functions


Nov 21, 2019
Andrew Hryniowski, Alexander Wong

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* 10 pages 

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State of Compact Architecture Search For Deep Neural Networks


Oct 15, 2019
Mohammad Javad Shafiee, Andrew Hryniowski, Francis Li, Zhong Qiu Lin, Alexander Wong

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* 6 pages 

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