Alexander Wong

Do Explanations Reflect Decisions? A Machine-centric Strategy to Quantify the Performance of Explainability Algorithms

2019 · 68 Zit. · arXiv (Cornell University)

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

2020 · 16 Zit. · arXiv (Cornell University)

Explaining with Impact: A Machine-centric Strategy to Quantify the Performance of Explainability Algorithms

2019 · 4 Zit. · arXiv (Cornell University)

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

2020 · 3 Zit. · arXiv (Cornell University)

Why Do I Trust Your Model? Building and Explaining Predictive Models for Peritoneal Dialysis Eligibility

2019 · 2 Zit. · Journal of Computational Vision and Imaging Systems

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)