2.7k citations · 3k across the 33 of their papers we have counts for
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Attacking Binarized Neural Networks
Angus Galloway, Graham W. Taylor, Medhat Moussa
Neural networks with low-precision weights and activations offer compelling efficiency advantages over their full-precision equivalents. The two most frequently discussed benefits…
Discovery Radiomics with CLEAR-DR: Interpretable Computer Aided Diagnosis of Diabetic Retinopathy
Devinder Kumar, Graham W. Taylor, Alexander Wong
Objective: Radiomics-driven Computer Aided Diagnosis (CAD) has shown considerable promise in recent years as a potential tool for improving clinical decision support in medical onc…
Opening the Black Box of Financial AI with CLEAR-Trade: A CLass-Enhanced Attentive Response Approach for Explaining and Visualizing Deep Learning-Driven Stock Market Prediction
Devinder Kumar, Graham W Taylor, Alexander Wong
Deep learning has been shown to outperform traditional machine learning algorithms across a wide range of problem domains. However, current deep learning algorithms have been criti…
Improved Regularization of Convolutional Neural Networks with Cutout
Terrance DeVries, Graham W. Taylor
Convolutional neural networks are capable of learning powerful representational spaces, which are necessary for tackling complex learning tasks. However, due to the model capacity…
Structure Optimization for Deep Multimodal Fusion Networks using Graph-Induced Kernels
Dhanesh Ramachandram, Michal Lisicki, Timothy J. Shields +2
A popular testbed for deep learning has been multimodal recognition of human activity or gesture involving diverse inputs such as video, audio, skeletal pose and depth images. Deep…
Explaining the Unexplained: A CLass-Enhanced Attentive Response (CLEAR) Approach to Understanding Deep Neural Networks
Devinder Kumar, Alexander Wong, Graham W. Taylor
In this work, we propose CLass-Enhanced Attentive Response (CLEAR): an approach to visualize and understand the decisions made by deep neural networks (DNNs) given a specific input…