71 citations · 208 across the 27 of their papers we have counts for
8 papers · 1 filter
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…
Survival Seq2Seq: A Survival Model based on Sequence to Sequence Architecture
Ebrahim Pourjafari, Navid Ziaei, Mohammad R. Rezaei +5
This paper introduces a novel non-parametric deep model for estimating time-to-event (survival analysis) in presence of censored data and competing risks. The model is designed bas…
Does Form Follow Function? An Empirical Exploration of the Impact of Deep Neural Network Architecture Design on Hardware-Specific Acceleration
Saad Abbasi, Mohammad Javad Shafiee, Ellick Chan +1
The fine-grained relationship between form and function with respect to deep neural network architecture design and hardware-specific acceleration is one area that is not well stud…
Residual Error: a New Performance Measure for Adversarial Robustness
Hossein Aboutalebi, Mohammad Javad Shafiee, Michelle Karg +2
Despite the significant advances in deep learning over the past decade, a major challenge that limits the wide-spread adoption of deep learning has been their fragility to adversar…
Self-Gradient Networks
Hossein Aboutalebi, Mohammad Javad Shafiee Alexander Wong
The incredible effectiveness of adversarial attacks on fooling deep neural networks poses a tremendous hurdle in the widespread adoption of deep learning in safety and security-cri…