267 citations · 403 across the 11 of their papers we have counts for
11 papers
NeuroArmor: Safe-Variant-Guided Representation Consistency for Selective Re-Anchoring in Jailbreak Defense
Zhongyang Lin, Ziran Zhao, Feifei Zhai +1
Large language models remain vulnerable to jailbreak attacks that hide harmful intent behind seemingly ordinary requests such as role-play, translation, encoding, adversarial suffi…
COVID-Net S: Towards computer-aided severity assessment via training and validation of deep neural networks for geographic extent and opacity extent scoring of chest X-rays for SARS-CoV-2 lung disease severity
Alexander Wong, Zhong Qiu Lin, Linda Wang +5
Background: A critical step in effective care and treatment planning for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the cause of the COVID-19 pandemic, is the as…
COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-Ray Images
Linda Wang, Alexander Wong
The COVID-19 pandemic continues to have a devastating effect on the health and well-being of the global population. A critical step in the fight against COVID-19 is effective scree…
PuckNet: Estimating hockey puck location from broadcast video
Kanav Vats, William McNally, Chris Dulhanty +3
Puck location in ice hockey is essential for hockey analysts for determining the location of play and analyzing game events. However, because of the difficulty involved in obtainin…
Do Explanations Reflect Decisions? A Machine-centric Strategy to Quantify the Performance of Explainability Algorithms
Zhong Qiu Lin, Mohammad Javad Shafiee, Stanislav Bochkarev +3
There has been a significant surge of interest recently around the concept of explainable artificial intelligence (XAI), where the goal is to produce an interpretation for a decisi…
State of Compact Architecture Search For Deep Neural Networks
Mohammad Javad Shafiee, Andrew Hryniowski, Francis Li +2
The design of compact deep neural networks is a crucial task to enable widespread adoption of deep neural networks in the real-world, particularly for edge and mobile scenarios. Du…
Squeeze-and-Attention Networks for Semantic Segmentation
Zilong Zhong, Zhong Qiu Lin, Rene Bidart +6
The recent integration of attention mechanisms into segmentation networks improves their representational capabilities through a great emphasis on more informative features. Howeve…
EdgeSegNet: A Compact Network for Semantic Segmentation
Zhong Qiu Lin, Brendan Chwyl, Alexander Wong
In this study, we introduce EdgeSegNet, a compact deep convolutional neural network for the task of semantic segmentation. A human-machine collaborative design strategy is leverage…
AttoNets: Compact and Efficient Deep Neural Networks for the Edge via Human-Machine Collaborative Design
Alexander Wong, Zhong Qiu Lin, Brendan Chwyl
While deep neural networks have achieved state-of-the-art performance across a large number of complex tasks, it remains a big challenge to deploy such networks for practical, on-d…
Progressive Label Distillation: Learning Input-Efficient Deep Neural Networks
Zhong Qiu Lin, Alexander Wong
Much of the focus in the area of knowledge distillation has been on distilling knowledge from a larger teacher network to a smaller student network. However, there has been little…
EdgeSpeechNets: Highly Efficient Deep Neural Networks for Speech Recognition on the Edge
Zhong Qiu Lin, Audrey G. Chung, Alexander Wong
Despite showing state-of-the-art performance, deep learning for speech recognition remains challenging to deploy in on-device edge scenarios such as mobile and other consumer devic…