Multi-projector Resolution Enhancement Through Biased Interpolation. - Andrew Hryniowski; Ibrahim Ben Daya; Ahmed Gawish; Mark Lamm; Alexander Wong; Paul W. Fieguth (CRV, 2018)

PolyNeuron: Automatic Neuron Discovery via Learned Polyharmonic Spline Activations. - Andrew Hryniowski; Alexander Wong (CoRR, 2018)

Seeing Convolution Through the Eyes of Finite Transformation Semigroup Theory: An Abstract Algebraic Interpretation of Convolutional Neural Networks. - Andrew Hryniowski; Alexander Wong (CoRR, 2019)

State of Compact Architecture Search For Deep Neural Networks. - Mohammad Javad Shafiee; Andrew Hryniowski; Francis Li; Zhong Qiu Lin; Alexander Wong (CoRR, 2019)

DeepLABNet: End-to-end Learning of Deep Radial Basis Networks with Fully Learnable Basis Functions. - Andrew Hryniowski; Alexander Wong (CoRR, 2019)

How Much Can We Really Trust You? Towards Simple, Interpretable Trust Quantification Metrics for Deep Neural Networks. - Alexander Wong; Xiao Yu Wang 010; Andrew Hryniowski (CoRR, 2020)

Where Does Trust Break Down? A Quantitative Trust Analysis of Deep Neural Networks via Trust Matrix and Conditional Trust Densities. - Andrew Hryniowski; Xiao Yu Wang 010; Alexander Wong (CoRR, 2020)

Insights into Fairness through Trust: Multi-scale Trust Quantification for Financial Deep Learning. - Alexander Wong; Andrew Hryniowski; Xiao Yu Wang 010 (CoRR, 2020)

Inter-layer Information Similarity Assessment of Deep Neural Networks Via Topological Similarity and Persistence Analysis of Data Neighbour Dynamics. - Andrew Hryniowski; Alexander Wong (CoRR, 2020)

AttendSeg: A Tiny Attention Condenser Neural Network for Semantic Segmentation on the Edge. - Xiaoyu Wen; Mahmoud Famouri; Andrew Hryniowski; Alexander Wong (CoRR, 2021)

COVID-Net Clinical ICU: Enhanced Prediction of ICU Admission for COVID-19 Patients via Explainability and Trust Quantification. - Audrey G. Chung; Mahmoud Famouri; Andrew Hryniowski; Alexander Wong (CoRR, 2021)

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; Adrian Florea; Andrew Hryniowski; Alexander Wong (CoRR, 2022)

Systematic Architectural Design of Scale Transformed Attention Condenser DNNs via Multi-Scale Class Representational Response Similarity Analysis. - Andrew Hryniowski; Alexander Wong (CVPR Workshops, 2023)

DVQI: A Multi-task, Hardware-integrated Artificial Intelligence System for Automated Visual Inspection in Electronics Manufacturing. - Audrey G. Chung; Francis Li; Jeremy Ward; Andrew Hryniowski; Alexander Wong (CoRR, 2023)

A Representational Response Analysis Framework For Convolutional Neural Networks. - Andrew Hryniowski (2024)