59 citations · 209 across the 50 of their papers we have counts for
7 papers · 1 filter
Parameter-efficient Quantum Multi-task Learning
Hevish Cowlessur, Chandra Thapa, Tansu Alpcan +1
Multi-task learning (MTL) improves generalization and data efficiency by jointly learning related tasks through shared representations. In the widely used hard-parameter-sharing se…
Computable Model-Independent Bounds for Adversarial Quantum Machine Learning
Bacui Li, Tansu Alpcan, Chandra Thapa +1
By leveraging the principles of quantum mechanics, QML opens doors to novel approaches in machine learning and offers potential speedup. However, machine learning models are well-d…
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…
Failure-tolerant Distributed Learning for Anomaly Detection in Wireless Networks
Marc Katzef, Andrew C. Cullen, Tansu Alpcan +2
The analysis of distributed techniques is often focused upon their efficiency, without considering their robustness (or lack thereof). Such a consideration is particularly importan…
Local Intrinsic Dimensionality Signals Adversarial Perturbations
Sandamal Weerasinghe, Tansu Alpcan, Sarah M. Erfani +2
The vulnerability of machine learning models to adversarial perturbations has motivated a significant amount of research under the broad umbrella of adversarial machine learning. S…
Defending Distributed Classifiers Against Data Poisoning Attacks
Sandamal Weerasinghe, Tansu Alpcan, Sarah M. Erfani +1
Support Vector Machines (SVMs) are vulnerable to targeted training data manipulations such as poisoning attacks and label flips. By carefully manipulating a subset of training samp…