1 citations · 2 across the 7 of their papers we have counts for
9 papers
MAPLE-X: Latency Prediction with Explicit Microprocessor Prior Knowledge
Saad Abbasi, Alexander Wong, Mohammad Javad Shafiee
Deep neural network (DNN) latency characterization is a time-consuming process and adds significant cost to Neural Architecture Search (NAS) processes when searching for efficient…
MAPLE-Edge: A Runtime Latency Predictor for Edge Devices
Saeejith Nair, Saad Abbasi, Alexander Wong +1
Neural Architecture Search (NAS) has enabled automatic discovery of more efficient neural network architectures, especially for mobile and embedded vision applications. Although re…
COVID-Net MLSys: Designing COVID-Net for the Clinical Workflow
Audrey G. Chung, Maya Pavlova, Hayden Gunraj +7
As the COVID-19 pandemic continues to devastate globally, one promising field of research is machine learning-driven computer vision to streamline various parts of the COVID-19 cli…
COVID-Net US: A Tailored, Highly Efficient, Self-Attention Deep Convolutional Neural Network Design for Detection of COVID-19 Patient Cases from Point-of-care Ultrasound Imaging
Alexander MacLean, Saad Abbasi, Ashkan Ebadi +6
The Coronavirus Disease 2019 (COVID-19) pandemic has impacted many aspects of life globally, and a critical factor in mitigating its effects is screening individuals for infections…
COVID-Net CT-S: 3D Convolutional Neural Network Architectures for COVID-19 Severity Assessment using Chest CT Images
Hossein Aboutalebi, Saad Abbasi, Mohammad Javad Shafiee +1
The health and socioeconomic difficulties caused by the COVID-19 pandemic continues to cause enormous tensions around the world. In particular, this extraordinary surge in the numb…
OutlierNets: Highly Compact Deep Autoencoder Network Architectures for On-Device Acoustic Anomaly Detection
Saad Abbasi, Mahmoud Famouri, Mohammad Javad Shafiee +1
Human operators often diagnose industrial machinery via anomalous sounds. Automated acoustic anomaly detection can lead to reliable maintenance of machinery. However, deep learning…