most citedQuantum density estimation with density matrices: Application to quantum anomaly detection

6 citations · 10 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG2024★ 1 cited

Latent Anomaly Detection Through Density Matrices

Joseph Gallego-Mejia, Oscar Bustos-Brinez, Fabio A. González

This paper introduces a novel anomaly detection framework that combines the robust statistical principles of density-estimation-based anomaly detection methods with the representat…

cs.LG2022★ 2 cited

LEAN-DMKDE: Quantum Latent Density Estimation for Anomaly Detection

Joseph Gallego-Mejia, Oscar Bustos-Brinez, Fabio A. González

This paper presents an anomaly detection model that combines the strong statistical foundation of density-estimation-based anomaly detection methods with the representation-learnin…

cs.LG2022

AD-DMKDE: Anomaly Detection through Density Matrices and Fourier Features

Oscar Bustos-Brinez, Joseph Gallego-Mejia, Fabio A. González

This paper presents a novel density estimation method for anomaly detection using density matrices (a powerful mathematical formalism from quantum mechanics) and Fourier features.…

cs.LG2022★ 1 cited

InQMAD: Incremental Quantum Measurement Anomaly Detection

Joseph Gallego-Mejia, Oscar Bustos-Brinez, Fabio Gonzalez

Streaming anomaly detection refers to the problem of detecting anomalous data samples in streams of data. This problem poses challenges that classical and deep anomaly detection me…

quant-ph2022★ 6 cited

Quantum density estimation with density matrices: Application to quantum anomaly detection

Diego H. Useche, Oscar A. Bustos-Brinez, Joseph A. Gallego-Mejia +1

Density estimation is a central task in statistics and machine learning. This problem aims to determine the underlying probability density function that best aligns with an observe…