Zobrazeno 1 - 10
of 246
pro vyhledávání: '"Salehi, Mahsa"'
Autor:
Darban, Zahra Zamanzadeh, Yang, Yiyuan, Webb, Geoffrey I., Aggarwal, Charu C., Wen, Qingsong, Salehi, Mahsa
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 solution by leveraging labeled data from a related domain to detect anomalies
Externí odkaz:
http://arxiv.org/abs/2404.11269
In this paper we study the variations in human brain activity when listening to real and fake audio. Our preliminary results suggest that the representations learned by a state-of-the-art deepfake audio detection algorithm, do not exhibit clear disti
Externí odkaz:
http://arxiv.org/abs/2402.14982
Autor:
Foumani, Navid Mohammadi, Mackellar, Geoffrey, Ghane, Soheila, Irtza, Saad, Nguyen, Nam, Salehi, Mahsa
Self-supervised approaches for electroencephalography (EEG) representation learning face three specific challenges inherent to EEG data: (1) The low signal-to-noise ratio which challenges the quality of the representation learned, (2) The wide range
Externí odkaz:
http://arxiv.org/abs/2402.17772
Utility companies are increasingly leveraging residential demand flexibility and the proliferation of smart/IoT devices to enhance the effectiveness of residential demand response (DR) programs through automated device scheduling. However, the adopti
Externí odkaz:
http://arxiv.org/abs/2312.08646
Autor:
Foumani, Navid Mohammadi, Tan, Chang Wei, Webb, Geoffrey I., Rezatofighi, Hamid, Salehi, Mahsa
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 learning tasks that can be defined. Motivated by this insight, we introduc
Externí odkaz:
http://arxiv.org/abs/2312.03998
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 (referred to as seen anomalies) to detect both seen anomalies and unseen
Externí odkaz:
http://arxiv.org/abs/2311.06835
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 learning the normal behaviour of unlabelled time series in an unsupervised ma
Externí odkaz:
http://arxiv.org/abs/2308.09296
Transformers have demonstrated outstanding performance in many applications of deep learning. When applied to time series data, transformers require effective position encoding to capture the ordering of the time series data. The efficacy of position
Externí odkaz:
http://arxiv.org/abs/2305.16642
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, frequency, magnitude, and various patterns. To address this challenge
Externí odkaz:
http://arxiv.org/abs/2304.05800
Autor:
Foumani, Navid Mohammadi, Miller, Lynn, Tan, Chang Wei, Webb, Geoffrey I., Forestier, Germain, Salehi, Mahsa
Time Series Classification and Extrinsic Regression are important and challenging machine learning tasks. Deep learning has revolutionized natural language processing and computer vision and holds great promise in other fields such as time series ana
Externí odkaz:
http://arxiv.org/abs/2302.02515