Essays about: "Gaussian Mixture Models"

Showing result 6 - 10 of 57 essays containing the words Gaussian Mixture Models.

  1. 6. Exploring Normalizing Flow Modifications for Improved Model Expressivity

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Marcel Juschak; [2023]
    Keywords : Normalizing Flows; Motion Synthesis; Invertible Neural Networks; Glow; MoGlow; Maximum Likelihood Estimation; Generative models; normaliserande flöden; rörelsesyntes; inverterbara neurala nätverk; Glow; MoGlow; maximum likelihood-skattning generativa modeller;

    Abstract : Normalizing flows represent a class of generative models that exhibit a number of attractive properties, but do not always achieve state-of-the-art performance when it comes to perceived naturalness of generated samples. To improve the quality of generated samples, this thesis examines methods to enhance the expressivity of discrete-time normalizing flow models and thus their ability to capture different aspects of the data. READ MORE

  3. 7. A study about Active Semi-Supervised Learning for Generative Models

    University essay from Linköpings universitet/Institutionen för datavetenskap

    Author : Elisio Fernandes de Almeida Quintino; [2023]
    Keywords : Semi-Supervised Learning; Active Learning; Generative Models; Mixture Models; Semi-Övervakad Inlärning; Aktiv Inlärning; Generativa Modeller; Mixturmodeller;

    Abstract : In many relevant scenarios, there is an imbalance between abundant unlabeled data and scarce labeled data to train predictive models. Semi-Supervised Learning and Active Learning are two distinct approaches to deal with this issue. READ MORE

  4. 8. Out-of-distribution Recognition and Classification of Time-Series Pulsed Radar Signals

    University essay from KTH/Matematisk statistik

    Author : Paul Hedvall; [2022]
    Keywords : Out-of-Distribution; Gaussian Mixture Models; Dirichlet Process Mixture Models; Deinterleaving; Radar classification; Time-series analysis; Pulsed radar signals; Out-of-Distribution; Gaussian Mixture Models; Dirichlet Process Mixture Models; Deinterleaving; Radar classification; Time-series analysis; Pulsed radar signals;

    Abstract : This thesis investigates out-of-distribution recognition for time-series data of pulsedradar signals. The classifier is a naive Bayesian classifier based on Gaussian mixturemodels and Dirichlet process mixture models. READ MORE

  5. 9. Predicting Quality of Experience from Performance Indicators : Modelling aggregated user survey responses based on telecommunications networks performance indicators

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Christian Vestergaard; [2022]
    Keywords : Quality of Experience; Telecommunication; Regression; Long Short Term Memmory; Clustering; K-means; Gaussian Mixture Models; Användarupplevelse; Telekommunikation; Regression; Long Short Term Memmory; Klusteranalys; K-means; Gaussian Mixture Models;

    Abstract : As user experience can be a competitive edge, it lies in the interest of businesses to be aware of how users perceive the services they provide. For telecommunications operators, how network performance influences user experience is critical. To attain this knowledge, one can survey users. READ MORE

  6. 10. Neural Ordinary Differential Equations for Anomaly Detection

    University essay from KTH/Matematisk statistik

    Author : Jón Hlöðver Friðriksson; Erik Ågren; [2021]
    Keywords : Anomaly detection; Neural ordinary differential equations; Statistical modelling; Autoregression; Variational autoencoder; Multivariate time series; Anomalidetektion; Neurala ordinära differentialekvationer; Statistisk modellering; Autoregression; Variational autoencoder; Multivariat tidsserie;

    Abstract : Today, a large amount of time series data is being produced from a variety of different devices such as smart speakers, cell phones and vehicles. This data can be used to make inferences and predictions. Neural network based methods are among one of the most popular ways to model time series data. READ MORE