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Joseph A. Gallego-Mejia

2 accepted papers

2023

Efficient Non-parametric Neural Density Estimation and Its Application to Outlier and Anomaly Detection

AAAI 2023technical

The main goal of this thesis is to develop efficient non-parametric density estimation methods that can be integrated with deep learning architectures, for instance, convolutional neural networks and transformers. Density estimation methods can be applied to different problems in statistics and mach…

Cited by 0SourcePDFScholar
2023

LEAN-DMKDE: Quantum Latent Density Estimation for Anomaly Detection (Student Abstract)

AAAI 2023technical

This paper presents an anomaly detection model that combines the strong statistical foundation of density-estimation-based anomaly detection methods with the representation-learning ability of deep-learning models. The method combines an autoencoder, that learns a low-dimensional representation of t…

Cited by 0SourcePDFScholar