Andrew Hryniowski

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

2020 · 16 Zit. · arXiv (Cornell University)

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

2023 · 7 Zit. · Scientific Reports

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

2020 · 3 Zit. · arXiv (Cornell University)

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

2021 · 2 Zit. · arXiv (Cornell University)

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

2022 · 0 Zit. · arXiv (Cornell University)

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

2021 · 0 Zit. · arXiv (Cornell University)