Narges Armanfard

bahareh.nikpour@mila.quebec

Bahar Bahareh Nikpour

Doctorat - McGill University

Dimitrios Sinodinos

Doctorat - McGill University

dimitrios.sinodinos@mila.quebec

Hadi Hojjati

Doctorat - McGill University

hadi.hojjati@mila.quebec

mohammadreza.sadeghi@mila.quebec

Mohammadreza Sadeghi

Doctorat - McGill University

thi-kieu-khanh.ho@mila.quebec

Khanh Ho Ho

Doctorat - McGill University

Thomas Lai

Maîtrise recherche - McGill University

thomas.lai@mila.quebec

Jack Wei

Maîtrise recherche - McGill University

jack.wei@mila.quebec

Zihan Wang

Maîtrise recherche - McGill University

Co-superviseur⋅e :

Samira Ebrahimi Kahou

zihan.wang@mila.quebec

Site web

Publications

Graph-based Time-Series Anomaly Detection: A Survey

Thi Kieu Khanh Ho

Ali Karami

With the recent advances in technology, a wide range of systems continue to collect a large amount of data over time and thus generate time … (voir plus)series. Time-Series Anomaly Detection (TSAD) is an important task in various time-series applications such as e-commerce, cybersecurity, vehicle maintenance, and healthcare monitoring. However, this task is very challenging as it requires considering both the intra-variable dependency and the inter-variable dependency, where a variable can be defined as an observation in time series data. Recent graph-based approaches have made impressive progress in tackling the challenges of this field. In this survey, we conduct a comprehensive and up-to-date review of Graph-based TSAD (G-TSAD). First, we explore the significant potential of graph representation learning for time-series data. Then, we review state-of-the-art graph anomaly detection techniques in the context of time series and discuss their strengths and drawbacks. Finally, we discuss the technical challenges and potential future directions for possible improvements in this research field.

2023-01-31

ArXiv (prépublication)

arxiv.org

DASVDD: Deep Autoencoding Support Vector Data Descriptor for Anomaly Detection

Hadi Hojjati

Semi-supervised anomaly detection aims to detect anomalies from normal samples using a model that is trained on normal data. With recent adv… (voir plus)ancements in deep learning, researchers have designed efficient deep anomaly detection methods. Existing works commonly use neural networks to map the data into a more informative representation and then apply an anomaly detection algorithm. In this paper, we propose a method, DASVDD, that jointly learns the parameters of an autoencoder while minimizing the volume of an enclosing hyper-sphere on its latent representation. We propose an anomaly score which is a combination of autoencoder's reconstruction error and the distance from the center of the enclosing hypersphere in the latent representation. Minimizing this anomaly score aids us in learning the underlying distribution of the normal class during training. Including the reconstruction error in the anomaly score ensures that DASVDD does not suffer from the common hypersphere collapse issue since the DASVDD model does not converge to the trivial solution of mapping all inputs to a constant point in the latent representation. Experimental evaluations on several benchmark datasets show that the proposed method outperforms the commonly used state-of-the-art anomaly detection algorithms while maintaining robust performance across different anomaly classes.

2023-01-01

IEEE Transactions on Knowledge and Data Engineering (publié)

arxiv.org

Deep Multirepresentation Learning for Data Clustering.

Mohammadreza Sadeghi

Deep clustering incorporates embedding into clustering in order to find a lower-dimensional space suitable for clustering tasks. Conventiona… (voir plus)l deep clustering methods aim to obtain a single global embedding subspace (aka latent space) for all the data clusters. In contrast, in this article, we propose a deep multirepresentation learning (DML) framework for data clustering whereby each difficult-to-cluster data group is associated with its own distinct optimized latent space and all the easy-to-cluster data groups are associated with a general common latent space. Autoencoders (AEs) are employed for generating cluster-specific and general latent spaces. To specialize each AE in its associated data cluster(s), we propose a novel and effective loss function which consists of weighted reconstruction and clustering losses of the data points, where higher weights are assigned to the samples more probable to belong to the corresponding cluster(s). Experimental results on benchmark datasets demonstrate that the proposed DML framework and loss function outperform state-of-the-art clustering approaches. In addition, the results show that the DML method significantly outperforms the SOTA on imbalanced datasets as a result of assigning an individual latent space to the difficult clusters.

2023-01-01

IEEE Transactions on Neural Networks and Learning Systems (publié)

How can intelligent systems revolutionise healthcare?

2023-01-01

Futurum Careers (publié)

Multivariate Time-Series Anomaly Detection with Temporal Self-supervision and Graphs: Application to Vehicle Failure Prediction

Hadi Hojjati

Mohammadreza Sadeghi