Andrew Hryniowski
According to our database1,
Andrew Hryniowski
authored at least 15 papers
between 2018 and 2024.
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Bibliography
2024
PhD thesis, 2024
2023
DVQI: A Multi-task, Hardware-integrated Artificial Intelligence System for Automated Visual Inspection in Electronics Manufacturing.
CoRR, 2023
Systematic Architectural Design of Scale Transformed Attention Condenser DNNs via Multi-Scale Class Representational Response Similarity Analysis.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023
2022
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.
CoRR, 2022
2021
COVID-Net Clinical ICU: Enhanced Prediction of ICU Admission for COVID-19 Patients via Explainability and Trust Quantification.
CoRR, 2021
AttendSeg: A Tiny Attention Condenser Neural Network for Semantic Segmentation on the Edge.
CoRR, 2021
2020
Inter-layer Information Similarity Assessment of Deep Neural Networks Via Topological Similarity and Persistence Analysis of Data Neighbour Dynamics.
CoRR, 2020
Insights into Fairness through Trust: Multi-scale Trust Quantification for Financial Deep Learning.
CoRR, 2020
Where Does Trust Break Down? A Quantitative Trust Analysis of Deep Neural Networks via Trust Matrix and Conditional Trust Densities.
CoRR, 2020
How Much Can We Really Trust You? Towards Simple, Interpretable Trust Quantification Metrics for Deep Neural Networks.
CoRR, 2020
2019
DeepLABNet: End-to-end Learning of Deep Radial Basis Networks with Fully Learnable Basis Functions.
CoRR, 2019
Seeing Convolution Through the Eyes of Finite Transformation Semigroup Theory: An Abstract Algebraic Interpretation of Convolutional Neural Networks.
CoRR, 2019
2018
CoRR, 2018
Proceedings of the 15th Conference on Computer and Robot Vision, 2018