485 citations · 1.5k across the 72 of their papers we have counts for
5 papers · 2 filters
OIL-AD: An Anomaly Detection Framework for Sequential Decision Sequences
Chen Wang, Sarah Erfani, Tansu Alpcan +1
Anomaly detection in decision-making sequences is a challenging problem due to the complexity of normality representation learning and the sequential nature of the task. Most exist…
Unlearnable Examples For Time Series
Yujing Jiang, Xingjun Ma, Sarah Monazam Erfani +1
Unlearnable examples (UEs) refer to training samples modified to be unlearnable to Deep Neural Networks (DNNs). These examples are usually generated by adding error-minimizing nois…
Be Persistent: Towards a Unified Solution for Mitigating Shortcuts in Deep Learning
Hadi M. Dolatabadi, Sarah M. Erfani, Christopher Leckie
Deep neural networks (DNNs) are vulnerable to shortcut learning: rather than learning the intended task, they tend to draw inconclusive relationships between their inputs and outpu…
End-to-End Anti-Backdoor Learning on Images and Time Series
Yujing Jiang, Xingjun Ma, Sarah Monazam Erfani +2
Backdoor attacks present a substantial security concern for deep learning models, especially those utilized in applications critical to safety and security. These attacks manipulat…
LDReg: Local Dimensionality Regularized Self-Supervised Learning
Hanxun Huang, Ricardo J. G. B. Campello, Sarah Monazam Erfani +3
Representations learned via self-supervised learning (SSL) can be susceptible to dimensional collapse, where the learned representation subspace is of extremely low dimensionality…