29 citations · 33 across the 9 of their papers we have counts for
10 papers
Load Disaggregation under Incomplete Inventory with Structured Unclaimed Demand
Saman Mostafavi, Robert W. Cox
An incomplete inventory creates a specific attribution ambiguity in load disaggregation: unclaimed demand can offset changes in claimed loads without altering meter reconstruction.…
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: Toward 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 electro…
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.…
DACAD: Domain Adaptation Contrastive Learning for Anomaly Detection in Multivariate Time Series
Zahra Zamanzadeh Darban, Yiyuan Yang, Geoffrey I. Webb +4
In time series anomaly detection (TSAD), the scarcity of labeled data poses a challenge to the development of accurate models. Unsupervised domain adaptation (UDA) offers a solutio…
EEG2Rep: Enhancing Self-supervised EEG Representation Through Informative Masked Inputs
Navid Mohammadi Foumani, Geoffrey Mackellar, Soheila Ghane +3
Self-supervised approaches for electroencephalography (EEG) representation learning face three specific challenges inherent to EEG data: (1) The low signal-to-noise ratio which cha…
Series2Vec: Similarity-based Self-supervised Representation Learning for Time Series Classification
Navid Mohammadi Foumani, Chang Wei Tan, Geoffrey I. Webb +2
We argue that time series analysis is fundamentally different in nature to either vision or natural language processing with respect to the forms of meaningful self-supervised lear…