2 citations · 4 across the 5 of their papers we have counts for
5 papers
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…
Vulnerability Under Adversarial Machine Learning: Bias or Variance?
Hossein Aboutalebi, Mohammad Javad Shafiee, Michelle Karg +2
Prior studies have unveiled the vulnerability of the deep neural networks in the context of adversarial machine learning, leading to great recent attention into this area. One inte…
Learn2Perturb: an End-to-end Feature Perturbation Learning to Improve Adversarial Robustness
Ahmadreza Jeddi, Mohammad Javad Shafiee, Michelle Karg +2
While deep neural networks have been achieving state-of-the-art performance across a wide variety of applications, their vulnerability to adversarial attacks limits their widesprea…
StressedNets: Efficient Feature Representations via Stress-induced Evolutionary Synthesis of Deep Neural Networks
Mohammad Javad Shafiee, Brendan Chwyl, Francis Li +4
The computational complexity of leveraging deep neural networks for extracting deep feature representations is a significant barrier to its widespread adoption, particularly for us…
Non-contact transmittance photoplethysmographic imaging (PPGI) for long-distance cardiovascular monitoring
Robert Amelard, Christian Scharfenberger, Farnoud Kazemzadeh +4
Photoplethysmography (PPG) devices are widely used for monitoring cardiovascular function. However, these devices require skin contact, which restrict their use to at-rest short-te…