546 citations · 940 across the 9 of their papers we have counts for
13 papers
Open-Set Graph Anomaly Detection via Normal Structure Regularisation
Qizhou Wang, Guansong Pang, Mahsa Salehi +2
This paper considers an important Graph Anomaly Detection (GAD) task, namely open-set GAD, which aims to train a detection model using a small number of normal and anomaly nodes (r…
CARLA: Self-supervised Contrastive Representation Learning for Time Series Anomaly Detection
Zahra Zamanzadeh Darban, Geoffrey I. Webb, Shirui Pan +2
One main challenge in time series anomaly detection (TSAD) is the lack of labelled data in many real-life scenarios. Most of the existing anomaly detection methods focus on learnin…
Improving Position Encoding of Transformers for Multivariate Time Series Classification
Navid Mohammadi Foumani, Chang Wei Tan, Geoffrey I. Webb +1
Transformers have demonstrated outstanding performance in many applications of deep learning. When applied to time series data, transformers require effective position encoding to…
Proximity Forest 2.0: A new effective and scalable similarity-based classifier for time series
Matthieu Herrmann, Chang Wei Tan, Mahsa Salehi +1
Time series classification (TSC) is a challenging task due to the diversity of types of feature that may be relevant for different classification tasks, including trends, variance,…
Deep Learning for Time Series Classification and Extrinsic Regression: A Current Survey
Navid Mohammadi Foumani, Lynn Miller, Chang Wei Tan +3
Time Series Classification and Extrinsic Regression are important and challenging machine learning tasks. Deep learning has revolutionized natural language processing and computer…
Cross-Domain Graph Anomaly Detection via Anomaly-aware Contrastive Alignment
Qizhou Wang, Guansong Pang, Mahsa Salehi +2
Cross-domain graph anomaly detection (CD-GAD) describes the problem of detecting anomalous nodes in an unlabelled target graph using auxiliary, related source graphs with labelled…