most citedTaking a Stance on Fake News: Towards Automatic Disinformation Assessment via Deep Bidirectional Transformer Language Models for Stance Detection

23 citations · 50 across the 11 of their papers we have counts for

collaborators

11 papers

cs.CV20208 cited

AttendNets: Tiny Deep Image Recognition Neural Networks for the Edge via Visual Attention Condensers

Alexander Wong, Mahmoud Famouri, Mohammad Javad Shafiee

While significant advances in deep learning has resulted in state-of-the-art performance across a large number of complex visual perception tasks, the widespread deployment of deep…

eess.IV2020

COVIDNet-CT: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest CT Images

Hayden Gunraj, Linda Wang, Alexander Wong

The coronavirus disease 2019 (COVID-19) pandemic continues to have a tremendous impact on patients and healthcare systems around the world. In the fight against this novel disease,…

cs.LG2020

Understanding the temporal evolution of COVID-19 research through machine learning and natural language processing

Ashkan Ebadi, Pengcheng Xi, Stéphane Tremblay +3

The outbreak of the novel coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been continuously affecting human live…

cs.CY2020

Investigating the Impact of Inclusion in Face Recognition Training Data on Individual Face Identification

Chris Dulhanty, Alexander Wong

Modern face recognition systems leverage datasets containing images of hundreds of thousands of specific individuals' faces to train deep convolutional neural networks to learn an…

cs.CL201923 cited

Taking a Stance on Fake News: Towards Automatic Disinformation Assessment via Deep Bidirectional Transformer Language Models for Stance Detection

Chris Dulhanty, Jason L. Deglint, Ibrahim Ben Daya +1

The exponential rise of social media and digital news in the past decade has had the unfortunate consequence of escalating what the United Nations has called a global topic of conc…

cs.NE20193 cited

DeepLABNet: End-to-end Learning of Deep Radial Basis Networks with Fully Learnable Basis Functions

Andrew Hryniowski, Alexander Wong

From fully connected neural networks to convolutional neural networks, the learned parameters within a neural network have been primarily relegated to the linear parameters (e.g.,…