Tomás Maul

Data Augmentation by AutoEncoders for Unsupervised Anomaly Detection

2019 · arXiv (Cornell University)

This paper proposes an autoencoder (AE) that is used for improving the performance of once-class classifiers for the purpose of detecting anomalies. Traditional one-class classifiers (OCCs) perform poorly under certain conditions such as high-dimensionality and …

AEGR: A simple approach to gradient reversal in autoencoders for network anomaly detection

2019 · arXiv (Cornell University)

Anomaly detection is referred to as a process in which the aim is to detect data points that follow a different pattern from the majority of data points. Anomaly detection methods suffer from several well-known …

Can lies be faked? Comparing low-stakes and high-stakes deception video datasets from a Machine Learning perspective

2022 · arXiv (Cornell University)

Despite the great impact of lies in human societies and a meager 54% human accuracy for Deception Detection (DD), Machine Learning systems that perform automated DD are still not viable for proper application in real-life …