2.7k citations · 4k across the 68 of their papers we have counts for
12 papers · 1 filter
Learning with less data via Weakly Labeled Patch Classification in Digital Pathology
Eu Wern Teh, Graham W. Taylor
In Digital Pathology (DP), labeled data is generally very scarce due to the requirement that medical experts provide annotations. We address this issue by learning transferable fea…
Learning Permutation Invariant Representations using Memory Networks
Shivam Kalra, Mohammed Adnan, Graham Taylor +1
Many real-world tasks such as classification of digital histopathology images and 3D object detection involve learning from a set of instances. In these cases, only a group of inst…
Skip-Clip: Self-Supervised Spatiotemporal Representation Learning by Future Clip Order Ranking
Alaaeldin El-Nouby, Shuangfei Zhai, Graham W. Taylor +1
Deep neural networks require collecting and annotating large amounts of data to train successfully. In order to alleviate the annotation bottleneck, we propose a novel self-supervi…
A Nonparametric Bayesian Model for Sparse Dynamic Multigraphs
Elahe Ghalebi, Hamidreza Mahyar, Radu Grosu +2
As the availability and importance of temporal interaction data--such as email communication--increases, it becomes increasingly important to understand the underlying structure th…
Learning Temporal Attention in Dynamic Graphs with Bilinear Interactions
Boris Knyazev, Carolyn Augusta, Graham W. Taylor
Reasoning about graphs evolving over time is a challenging concept in many domains, such as bioinformatics, physics, and social networks. We consider a common case in which edges c…
Image Classification with Hierarchical Multigraph Networks
Boris Knyazev, Xiao Lin, Mohamed R. Amer +1
Graph Convolutional Networks (GCNs) are a class of general models that can learn from graph structured data. Despite being general, GCNs are admittedly inferior to convolutional ne…
On the Evaluation of Conditional GANs
Terrance DeVries, Adriana Romero, Luis Pineda +2
Conditional Generative Adversarial Networks (cGANs) are finding increasingly widespread use in many application domains. Despite outstanding progress, quantitative evaluation of su…
Batch Normalization is a Cause of Adversarial Vulnerability
Angus Galloway, Anna Golubeva, Thomas Tanay +2
Batch normalization (batch norm) is often used in an attempt to stabilize and accelerate training in deep neural networks. In many cases it indeed decreases the number of parameter…
Understanding Attention and Generalization in Graph Neural Networks
Boris Knyazev, Graham W. Taylor, Mohamed R. Amer
We aim to better understand attention over nodes in graph neural networks (GNNs) and identify factors influencing its effectiveness. We particularly focus on the ability of attenti…
Beyond Explainability: Leveraging Interpretability for Improved Adversarial Learning
Devinder Kumar, Ibrahim Ben-Daya, Kanav Vats +3
In this study, we propose the leveraging of interpretability for tasks beyond purely the purpose of explainability. In particular, this study puts forward a novel strategy for leve…
Similarity Learning Networks for Animal Individual Re-Identification -- Beyond the Capabilities of a Human Observer
Stefan Schneider, Graham W. Taylor, Stefan Linquist +1
Deep learning has become the standard methodology to approach computer vision tasks when large amounts of labeled data are available. One area where traditional deep learning appro…
SISC: End-to-end Interpretable Discovery Radiomics-Driven Lung Cancer Prediction via Stacked Interpretable Sequencing Cells
Vignesh Sankar, Devinder Kumar, David A. Clausi +2
Objective: Lung cancer is the leading cause of cancer-related death worldwide. Computer-aided diagnosis (CAD) systems have shown significant promise in recent years for facilitatin…