To build a large-scale dataset for ADMOS, we collected anomalous operating sounds of miniature machines (toys) by deliberately damaging them.
This way, new solutions to monitor and detect security events are needed addressing new challenges coming from this scenario that are, among others, the number of devices to monitor, the huge amount of data to manage and the real time requirement to provide a quick security event detection and, consequently, quick attack reaction.
There exist few text-specific methods for unsupervised anomaly detection, and for those that do exist, none utilize pre-trained models for distributed vector representations of words.
We introduce Gluon Time Series (GluonTS, available at https://gluon-ts. mxnet. io), a library for deep-learning-based time series modeling.
SOTA for Time Series on Bitcoin-Alpha
To fight against real-life image forgery, which commonly involves different types and combined manipulations, we propose a unified deep neural architecture called ManTra-Net.
In this work, we examine the effects of contaminating training data with anomalies for state-of-the-art GAN-based anomaly detection methods.
To address these problems, we introduce the Machine Learning Bazaar, a new approach to developing machine learning and automated machine learning software systems.
The machine learning (ML) algorithms that have been used here are Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and Artificial Neural Network (ANN).
For the purpose of monitoring the behavior of complex infrastructures (e. g. aircrafts, transport or energy networks), high-rate sensors are deployed to capture multivariate data, generally unlabeled, in quasi continuous-time to detect quickly the occurrence of anomalies that may jeopardize the smooth operation of the system of interest.