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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 2021Show all

2 papers · 1 filter

cs.LG2021

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…

cs.CV2021★ 3 cited

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

Xiaoyu Wen, Mahmoud Famouri, Andrew Hryniowski +1

In this study, we introduce \textbf{AttendSeg}, a low-precision, highly compact deep neural network tailored for on-device semantic segmentation. AttendSeg possesses a self-attenti…