35 citations · 40 across the 6 of their papers we have counts for
6 papers
Overcoming the Limitations of Localization Uncertainty: Efficient & Exact Non-Linear Post-Processing and Calibration
Moussa Kassem Sbeyti, Michelle Karg, Christian Wirth +2
Robustly and accurately localizing objects in real-world environments can be challenging due to noisy data, hardware limitations, and the inherent randomness of physical systems. T…
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…
Detecting Falls with X-Factor Hidden Markov Models
Shehroz S. Khan, Michelle E. Karg, Dana Kulic +1
Identification of falls while performing normal activities of daily living (ADL) is important to ensure personal safety and well-being. However, falling is a short term activity th…