Sökning: "variational approach"

Visar resultat 1 - 5 av 34 uppsatser innehållade orden variational approach.

1. 1. Branching Out with Mixtures: Phylogenetic Inference That’s Not Afraid of a Little Uncertainty

   Master-uppsats, KTH/Matematisk statistik

   Författare :Ricky Molén; [2023]
   Nyckelord :Phylogeny; Bayesian analysis; Markov chain Monte Carlo; Variational inference; Mixture of proposal distributions; Fylogeni; Bayesiansk analys; Markov Chain Monte Carlo; Variationsinferens; Mixturer av förslagsfördelningar;

   Sammanfattning : Phylogeny, the study of evolutionary relationships among species and other taxa, plays a crucial role in understanding the history of life. Bayesian analysis using Markov chain Monte Carlo (MCMC) is a widely used approach for inferring phylogenetic trees, but it suffers from slow convergence in higher dimensions and is slow to converge. LÄS MER

3. 2. Quantum Reinforcement Learning for Sensor-Assisted Robot Navigation Tasks

   Master-uppsats, Lunds universitet/Fysiska institutionen

   Författare :Joyce Cobussen; [2023]
   Nyckelord :Physics and Astronomy;

   Sammanfattning : Quantum computing has advanced rapidly throughout the past decade, both from a hardware and software point of view. A variety of algorithms have been developed that are suitable for the current generation of quantum devices, which are referred to as noisy intermediate-scale quantum devices. LÄS MER

4. 3. EVALUATING PERFORMANCE OF GENERATIVE MODELS FOR TIME SERIES SYNTHESIS

   Master-uppsats, Mälardalens universitet/Akademin för innovation, design och teknik

   Författare :Muhammad Junaid Haris; [2023]
   Nyckelord :GAN; Generative Adversarial Network; VQ-VAE; Vector Quantized Variational AutoEncoder; AutoEncoder; VAE; Time Series; Synthesizing; Data Synthesis;

   Sammanfattning : Motivated by successes in the image generation domain, this thesis presents a novel Hybrid VQ-VAE (H-VQ-VAE) approach for generating realistic synthetic time series data with categorical features. The primary motivation behind this work is to address the limitations of existing generative models in accurately capturing the underlying structure and patterns of time series data, especially when dealing with categorical features. LÄS MER

5. 4. Improving Change Point Detection Using Self-Supervised VAEs : A Study on Distance Metrics and Hyperparameters in Time Series Analysis

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Daniel Workinn; [2023]
   Nyckelord :Change point detection; Time series data; Segmentation; Machine learning; Data mining; Detektion av brytpunkter; Tidsseriedata; Segmentering; Maskininlärning; Datautvinning;

   Sammanfattning : This thesis addresses the optimization of the Variational Autoencoder-based Change Point Detection (VAE-CP) approach in time series analysis, a vital component in data-driven decision making. We evaluate the impact of various distance metrics and hyperparameters on the model’s performance using a systematic exploration and robustness testing on diverse real-world datasets. LÄS MER

7. 5. Image generation through feature extraction and learning using a deep learning approach

   Master-uppsats, Linnéuniversitetet/Institutionen för datavetenskap och medieteknik (DM)

   Författare :Tibo Bruneel; [2023]
   Nyckelord :Deep Learning; Neural Networks; Deep Generative Learning; Variational Autoencoders; Generative Adversarial Networks; Flow-based Models; Triplet Image Generation; Triplet Loss; Tree Log End Generation; Forestry Application;

   Sammanfattning : With recent advancements, image generation has become more and more possible with the introduction of stronger generative artificial intelligence (AI) models. The idea and ability of generating non-existing images that highly resemble real world images is interesting for many use cases. LÄS MER