29 citations · 33 across the 9 of their papers we have counts for
9 papers
A Simple State Space Model Excels at Multivariate Time Series Classification
Hassan Saadatmand, Geoffrey I. Webb, Hamid Rezatofighi +1
Structured state space models (SSMs) have recently emerged as a promising foundation for sequence modeling, with Mamba-based architectures demonstrating strong performance through…
CEDL: Centre-Enhanced Discriminative Learning for Anomaly Detection
Zahra Zamanzadeh Darban, Qizhou Wang, Charu C. Aggarwal +3
Supervised anomaly detection methods perform well in identifying known anomalies that are well represented in the training set. However, they often struggle to generalise beyond th…
EEG-X: Device-Agnostic and Noise-Robust Foundation Model for EEG
Navid Mohammadi Foumani, Soheila Ghane, Nam Nguyen +3
Foundation models for EEG analysis are still in their infancy, limited by two key challenges: (1) variability across datasets caused by differences in recording devices and configu…
MONSTER: Monash Scalable Time Series Evaluation Repository
Angus Dempster, Navid Mohammadi Foumani, Chang Wei Tan +6
We introduce MONSTER-the MONash Scalable Time Series Evaluation Repository-a collection of large datasets for time series classification. The field of time series classification ha…
GenIAS: Generator for Instantiating Anomalies in time Series
Zahra Zamanzadeh Darban, Qizhou Wang, Geoffrey I. Webb +3
Synthetic anomaly injection is a recent and promising approach for time series anomaly detection (TSAD), but existing methods rely on ad hoc, hand-crafted strategies applied to raw…
MTP: A Dataset for Multi-Modal Turning Points in Casual Conversations
Gia-Bao Dinh Ho, Chang Wei Tan, Zahra Zamanzadeh Darban +3
Detecting critical moments, such as emotional outbursts or changes in decisions during conversations, is crucial for understanding shifts in human behavior and their consequences.…