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

ilustraciones, diagramas

Autores:: Gallego-Mejia, Joseph A.

Tipo de recurso:: Doctoral thesis

Fecha de publicación:: 2023

Institución:: Universidad Nacional de Colombia

Repositorio:: Universidad Nacional de Colombia

Idioma:: eng

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dc.title.eng.fl_str_mv	Efficient Non-Parametric Neural Density Estimation and Its Application to Outlier and Anomaly Detection
dc.title.translated.spa.fl_str_mv	Estimación neuronal no paramétrica eficiente de la densidad y su aplicación a la detección de valores atípicos y anomalías
title	Efficient Non-Parametric Neural Density Estimation and Its Application to Outlier and Anomaly Detection
spellingShingle	Efficient Non-Parametric Neural Density Estimation and Its Application to Outlier and Anomaly Detection 000 - Ciencias de la computación, información y obras generales Aprendizaje profundo Redes Neurales de la Computación Deep Learning Neural Networks, Computer Kernel density estimation Kernel methods Deep Learning Random Fourier Features Machine Learning Deep Kernel Large-scale learning Kernel Density Esitmation Approximation Neural Density Estimation Density Matrix Estimación de la densidad del núcleo Métodos del núcleo Aprendizaje profundo Aprendizaje a gran escala Aproximaciones de la estimación de la densidad del nucleo Matriz de densidad Estimación de la densidad neuronal
title_short	Efficient Non-Parametric Neural Density Estimation and Its Application to Outlier and Anomaly Detection
title_full	Efficient Non-Parametric Neural Density Estimation and Its Application to Outlier and Anomaly Detection
title_fullStr	Efficient Non-Parametric Neural Density Estimation and Its Application to Outlier and Anomaly Detection
title_full_unstemmed	Efficient Non-Parametric Neural Density Estimation and Its Application to Outlier and Anomaly Detection
title_sort	Efficient Non-Parametric Neural Density Estimation and Its Application to Outlier and Anomaly Detection
dc.creator.fl_str_mv	Gallego-Mejia, Joseph A.
dc.contributor.advisor.none.fl_str_mv	González, Fabio A.
dc.contributor.author.none.fl_str_mv	Gallego-Mejia, Joseph A.
dc.contributor.researchgroup.spa.fl_str_mv	Mindlab
dc.contributor.orcid.spa.fl_str_mv	Gallego-Mejia, Joseph A. [0000-0001-8971-4998]
dc.contributor.researchgate.spa.fl_str_mv	https://www.researchgate.net/profile/Joseph-Gallego-Mejia
dc.contributor.googlescholar.spa.fl_str_mv	https://scholar.google.cl/citations?user=DS0IfX4AAAAJ&hl=es&oi=ao
dc.subject.ddc.spa.fl_str_mv	000 - Ciencias de la computación, información y obras generales
topic	000 - Ciencias de la computación, información y obras generales Aprendizaje profundo Redes Neurales de la Computación Deep Learning Neural Networks, Computer Kernel density estimation Kernel methods Deep Learning Random Fourier Features Machine Learning Deep Kernel Large-scale learning Kernel Density Esitmation Approximation Neural Density Estimation Density Matrix Estimación de la densidad del núcleo Métodos del núcleo Aprendizaje profundo Aprendizaje a gran escala Aproximaciones de la estimación de la densidad del nucleo Matriz de densidad Estimación de la densidad neuronal
dc.subject.decs.spa.fl_str_mv	Aprendizaje profundo Redes Neurales de la Computación
dc.subject.decs.eng.fl_str_mv	Deep Learning Neural Networks, Computer
dc.subject.proposal.eng.fl_str_mv	Kernel density estimation Kernel methods Deep Learning Random Fourier Features Machine Learning Deep Kernel Large-scale learning Kernel Density Esitmation Approximation Neural Density Estimation
dc.subject.proposal.none.fl_str_mv	Density Matrix
dc.subject.proposal.spa.fl_str_mv	Estimación de la densidad del núcleo Métodos del núcleo Aprendizaje profundo Aprendizaje a gran escala Aproximaciones de la estimación de la densidad del nucleo Matriz de densidad Estimación de la densidad neuronal
description	ilustraciones, diagramas
publishDate	2023
dc.date.accessioned.none.fl_str_mv	2023-10-06T20:45:17Z
dc.date.available.none.fl_str_mv	2023-10-06T20:45:17Z
dc.date.issued.none.fl_str_mv	2023-10-05
dc.type.spa.fl_str_mv	Trabajo de grado - Doctorado
dc.type.driver.spa.fl_str_mv	info:eu-repo/semantics/doctoralThesis
dc.type.version.spa.fl_str_mv	info:eu-repo/semantics/acceptedVersion
dc.type.coar.spa.fl_str_mv	http://purl.org/coar/resource_type/c_db06
dc.type.content.spa.fl_str_mv	Text
dc.type.redcol.spa.fl_str_mv	http://purl.org/redcol/resource_type/TD
format	http://purl.org/coar/resource_type/c_db06
status_str	acceptedVersion
dc.identifier.uri.none.fl_str_mv	https://repositorio.unal.edu.co/handle/unal/84781
dc.identifier.instname.spa.fl_str_mv	Universidad Nacional de Colombia
dc.identifier.reponame.spa.fl_str_mv	Repositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourl.spa.fl_str_mv	https://repositorio.unal.edu.co/
url	https://repositorio.unal.edu.co/handle/unal/84781 https://repositorio.unal.edu.co/
identifier_str_mv	Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv	eng
language	eng
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dc.publisher.spa.fl_str_mv	Universidad Nacional de Colombia
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dc.publisher.faculty.spa.fl_str_mv	Facultad de Ingeniería
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spelling	Atribución-NoComercial-SinDerivadas 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2González, Fabio A.443fd5ab620c3973efa2055ce58761daGallego-Mejia, Joseph A.150b4a7318e6285f3342c8b8602b8095MindlabGallego-Mejia, Joseph A. [0000-0001-8971-4998]https://www.researchgate.net/profile/Joseph-Gallego-Mejiahttps://scholar.google.cl/citations?user=DS0IfX4AAAAJ&hl=es&oi=ao2023-10-06T20:45:17Z2023-10-06T20:45:17Z2023-10-05https://repositorio.unal.edu.co/handle/unal/84781Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, diagramasThe main goal of this thesis is to propose efficient non-parametric density estimation methods that can be integrated with deep learning architectures, for instance, convolutional neural networks and transformers. A recent approach to non-parametric density estimation is neural density estimation. One advantage of these methods is that they can be integrated with deep learning architectures and trained using gradient descent. Most of these methods are based on neural network implementations of normalizing flows which transform an original simpler distribution to a more complex one. The approach of this thesis is based on a different idea that combines random Fourier features with density matrices to estimate the underlying distribution function. The method can be seen as an approximation of the popular kernel density estimation method but without the inherent computational cost. Density estimation methods can be applied to different problems in statistics and machine learning. They may be used to solve tasks such as anomaly detection, generative models, semi-supervised learning, compression, text-to-speech, among others. This thesis explores the application of the method in anomaly and outlier detection tasks such as medical anomaly detection, fraud detection, video surveillance, time series anomaly detection, industrial damage detection, among others. (Texto tomado de la fuente)El objetivo principal de esta tesis es proponer m´etodos eficientes de estimaci´on de densidad no param´etrica que puedan integrarse con arquitecturas de aprendizaje profundo, por ejemplo, redes neuronales convolucionales y transformadores. Una aproximaci´on reciente a la estimaci´on no param´etrica de la densidad es la estimaci´on de la densidad usando redes neuronales. Una de las ventajas de estos m´etodos es que pueden integrarse con arquitecturas de aprendizaje profundo y entrenarse mediante gradiente descendente. La mayor´ıa de estos m´etodos se basan en implementaciones de redes neuronales de flujos de normalizaci´on que transforman una distribuci´on original m´as simple en una m´as compleja. El enfoque de esta tesis se basa en una nueva idea diferente que combina caracter´ısticas aleatorias de Fourier con matrices de densidad para estimar la funci´on de distribuci´on subyacente. El m´etodo puede considerarse una aproximaci´on al popular m´etodo kernel density estimation, pero sin el coste computacional inherente. Los m´etodos de estimaci´on de la densidad pueden aplicarse a diferentes problemas en estad´ıstica y aprendizaje autom´atico. Pueden ser utilizados para resolver tareas como la detecci´on de anomal´ıas, modelos generativos, aprendizaje semi-supervisado, compresi´on, texto a habla, entre otros. El presente trabajo se centra principalmente en la aplicaci´on del m´etodo en tareas de detecci´on de anomal´ıas y valores at´ıpicos como la detecci´on de anomal´ıas m´edicas, la detecci´on de fraudes, la videovigilancia, la detecci´on de anomal´ıas en series temporales, la detecci´on de da˜nos industriales, entre otras.DoctoradoDoctor en IngenieríaMachine Learningxv, 113 páginasapplication/pdfengUniversidad Nacional de ColombiaBogotá - Ingeniería - Doctorado en Ingeniería - Sistemas y ComputaciónFacultad de IngenieríaBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá000 - Ciencias de la computación, información y obras generalesAprendizaje profundoRedes Neurales de la ComputaciónDeep LearningNeural Networks, ComputerKernel density estimationKernel methodsDeep LearningRandom Fourier FeaturesMachine LearningDeep KernelLarge-scale learningKernel Density Esitmation ApproximationNeural Density EstimationDensity MatrixEstimación de la densidad del núcleoMétodos del núcleoAprendizaje profundoAprendizaje a gran escalaAproximaciones de la estimación de la densidad del nucleoMatriz de densidadEstimación de la densidad neuronalEfficient Non-Parametric Neural Density Estimation and Its Application to Outlier and Anomaly DetectionEstimación neuronal no paramétrica eficiente de la densidad y su aplicación a la detección de valores atípicos y anomalíasTrabajo de grado - Doctoradoinfo:eu-repo/semantics/doctoralThesisinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_db06Texthttp://purl.org/redcol/resource_type/TDKdd cup dataset, 1999. http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html.Andrews Jerone T. 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In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 37, No. 13, pp. 16117-16118).InvestigadoresLICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/84781/3/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD53ORIGINAL1022369610-2023.pdf1022369610-2023.pdfTesis de Doctorado en Ingeniería - Sistemas y Computaciónapplication/pdf11263997https://repositorio.unal.edu.co/bitstream/unal/84781/4/1022369610-2023.pdf235373928c11e7514ce52eba236eb00dMD54THUMBNAIL1022369610-2023.pdf.jpg1022369610-2023.pdf.jpgGenerated Thumbnailimage/jpeg4606https://repositorio.unal.edu.co/bitstream/unal/84781/5/1022369610-2023.pdf.jpgc699c9de2d8b6dc864a1dfa5d1302760MD55unal/84781oai:repositorio.unal.edu.co:unal/847812023-10-06 23:03:55.963Repositorio Institucional Universidad Nacional de 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Efficient Non-Parametric Neural Density Estimation and Its Application to Outlier and Anomaly Detection

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