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20182023
most citedHow Much Can We Really Trust You? Towards Simple, Interpretable Trust Quantification Metrics for Deep Neural Networks

16 citations · 28 across the 13 of their papers we have counts for

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Showing 2022Show all

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cs.LG2022

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…