A review of deep learning with special emphasis on architectures, applications and recent trends
Deep learning (DL) has solved a problem that a few years ago was thought to be intractable—
the automatic recognition of patterns in spatial and temporal data with an accuracy superior …
the automatic recognition of patterns in spatial and temporal data with an accuracy superior …
Vehicle detection from UAV imagery with deep learning: A review
Vehicle detection from unmanned aerial vehicle (UAV) imagery is one of the most important
tasks in a large number of computer vision-based applications. This crucial task needed to …
tasks in a large number of computer vision-based applications. This crucial task needed to …
Deep learning for anomaly detection: A survey
R Chalapathy, S Chawla - arXiv preprint arXiv:1901.03407, 2019 - arxiv.org
Anomaly detection is an important problem that has been well-studied within diverse
research areas and application domains. The aim of this survey is two-fold, firstly we present …
research areas and application domains. The aim of this survey is two-fold, firstly we present …
DeepAnT: A deep learning approach for unsupervised anomaly detection in time series
Traditional distance and density-based anomaly detection techniques are unable to detect
periodic and seasonality related point anomalies which occur commonly in streaming data …
periodic and seasonality related point anomalies which occur commonly in streaming data …
Quant GANs: deep generation of financial time series
Modeling financial time series by stochastic processes is a challenging task and a central
area of research in financial mathematics. As an alternative, we introduce Quant GANs, a …
area of research in financial mathematics. As an alternative, we introduce Quant GANs, a …
Artificial intelligence co-piloted auditing
This paper proposes the concept of artificial intelligence co-piloted auditing, emphasizing
the collaborative potential of auditors and foundation models in the auditing domain. The …
the collaborative potential of auditors and foundation models in the auditing domain. The …
Good-d: On unsupervised graph out-of-distribution detection
Most existing deep learning models are trained based on the closed-world assumption,
where the test data is assumed to be drawn iid from the same distribution as the training …
where the test data is assumed to be drawn iid from the same distribution as the training …
DCT-GAN: dilated convolutional transformer-based GAN for time series anomaly detection
Time series anomaly detection (TSAD) is an essential problem faced in several fields, eg,
fault detection, fraud detection, and intrusion detection, etc. Although TSAD is a crucial …
fault detection, fraud detection, and intrusion detection, etc. Although TSAD is a crucial …
Assembly line anomaly detection and root cause analysis using machine learning
O Abdelrahman, P Keikhosrokiani - IEEE Access, 2020 - ieeexplore.ieee.org
Anomaly detection is becoming widely used in Manufacturing Industry to enhance product
quality. At the same time, it plays a great role in several other domains due to the fact that …
quality. At the same time, it plays a great role in several other domains due to the fact that …
Graph regularized autoencoder and its application in unsupervised anomaly detection
Dimensionality reduction is a crucial first step for many unsupervised learning tasks
including anomaly detection and clustering. Autoencoder is a popular mechanism to …
including anomaly detection and clustering. Autoencoder is a popular mechanism to …