Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 67 for “"Variational Autoencoder"”.
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Generalized flow-based variational autoencoder networks for anomaly detection in multivariate time series
… temporal data formulates the problem as one of variational inference. To solve this problem, such approaches have used variational autoencoders to try to learn the probability distribution of multiple time series. Variational autoencoders are used as a way to approximate intractable …
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Product perceptual mapping on fashion designs with Gaussian mixture variational autoencoder and triplet loss
… consumers, we propose and use a Gaussian mixture variational autoencoder (GMVAE) with triplet loss to create product embeddings. These product embeddings are then flattened into a 2D perceptual map able to be interpreted by human judgment. We test the GMVAE approach on three datasets: (1) a …
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Latent Dirichlet Variational Autoencoder: a novel approach for hyperspectral image analysis and pixel unmixing exploring deep learning architectures
… the research delves into the application of autoencoders for spectral dimensionality reduction, culminating in a comparative analysis demonstrating their efficacy in preserving crucial information for classification while achieving significant data compression. Building upon these findings, …
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Inference of electromagnetic system behavior in the presence of variability
… requirement. Specifically, for the first time, a variational autoencoder based method is used for the generative modeling of high-dimensional S-parameters data. The generation accuracy is shown to be superior to that of existing methods. The passive variational autoencoder, a variational …
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Deep Time: Deep Learning Extensions to Time Series Factor Analysis with Applications to Uncertainty Quantification in Economic and Financial Modeling
… principal-based improvements on the standard autoencoder and variational autoencoder. While the first-principal improvements on the standard variational autoencoder provide additional means of explainability, we ultimately look to non-variational methods for quantifying uncertainty under the …
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Unsupervised learning of disentangled representations for speech with neural variational inference models
… data. We start with investigating an existing variational autoencoder (VAE) model for learning latent representations, and derive novel latent space operations for speech transformation. The transformation method is applied to unsupervised domain adaptation problems, which addresses the …
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Causal Representation Learning for Predicting Genetic Perturbation Effects on Single Cells
… grounded in the gene regulatory network, with variational deep learning techniques. The proposed mechanistic model utilizes a learned gene regulatory network to represent perturbational effects as shift interventions that propagate through the network. This approach operates within a …
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Spatiaalinen multiomiikka ja syväoppiminen: Datan haasteet ja menetelmäkehitys
… menetelmäryhmään: variaatioautoenkooderit (variational autoencoder, VAE), graafipohjaiset neuroverkot (graph neural network, GNN) sekä hybridimallit, jotka voivat yhdistellä useampaa eri arkkitehtuuria yhdeksi kokonaisuudeksi. Kirjallisuuskatsauksessa käsiteltyjen tutkimusten pohjalta on …
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Weakly Supervised Representation Learning for Trauma Injury Pattern Discovery
… Quality Improvement Program with a disentangled variational autoencoder, weakly supervised by a latent-space classifier of auxiliary features. We also develop a novel scoring metric that serves as a proxy for clinical intuition in extracting clusters with clinically meaningful injury patterns. We …
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Advances in Probabilistic Deep Learning and Their Applications
… deep learning method based on a Bayesian variational autoencoder, where a full distribution is inferred over the model parameters, rather than just a point estimate. We then use information-theoretic measures to detect out-of-distribution inputs with this model. The second application is …
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Interpreting and optimizing data
… to nd beneficial interventions. To this end, variational autoencoders were used. We found that while the Gaussian process technique was able to successfully identify interventions in both simulations and practical applications, the variational autoencoder approach did not retain enough …
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Latent variable models for understanding user behavior in software applications
… user datasets. Next, using a conditional variational autoencoder and some related models, I introduce a framework for automating the user interaction with a software application. I focus on photo enhancement applications, but this framework can be applied to any domain where segmentation, …
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Synonymous question generation: Learning to ask in different ways using variational autoencoders
… associated with KG triples through a conditional variational autoencoder-based model, the Template VAE (T-VAE). Evaluating the generated questions via the Fre'chet InferSent Distance (FID) and the Multiset-Jaccard-k-gram (MS-Jaccard-k) Measure, two joint diversity-quality metrics, demonstrates …
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Learning to draw vector graphics : applying generative modeling to font glyphs
… dataset of 2552 font faces. Our approach uses a variational autoencoder to learn sequences of SVG drawing commands and is capable of both recreating ground truth inputs and generating unseen, editable SVG outputs. To investigate improvements to model performance, we perform two experiments: one …
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Imitation Learning with Superhuman Policy Gradient Optimization for Sequential Cancer Treatment Decisions
… A pre-trained clinical simulator—combining a variational autoencoder (VAE) and gradient boosting (XGBoost) models—generates complete, temporally consistent patient trajec- tories, enabling safe and reproducible training. Unlike conventional behavior cloning, SPGO optimizes a subdominance loss …
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Biologically Interpretable Representation Learning for Mechanistic Insights into Cancer Immunotherapy Resistance
… introduces the Biologically Disentangled Variational Autoencoder (BDVAE)—an interpretable deep learning framework designed to uncover mechanistic drivers of ICI resistance through multi-omic data integration. Using RNA-seq and wholeexome sequencing data from 366 patients across melanoma, …
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Experimental and analytical techniques for studying mechanotransduction in articular cartilage
… classifiers, and unsupervised clustering via a variational autoencoder to identify and categorize cell phenotypes. Time series data collected from thousands of chondrocytes \textit{in situ} during and after impact allow me to probe responses through the lenses of calcium signaling, mitochondrial …
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Graph Attention Mechanisms for Modeling Pathway-Level Importance from Gene Expression
… performance. This work presents a pathway-aware variational autoencoder which integrates curated gene-pathway structure into latent representation learning. This includes the integration of gene interactions through graph neural networks, and attentionbased hierarchical pooling from genes to …
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Computational creativity applications in engineering
… with desired properties, we use a conditional variational autoencoder (CVAE), a type of semisupervised generative model. Our model is trained using open data from the UCI Machine Learning Repository joined with environmental impact data computed using a web-based tool. We demonstrate that the …
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Searches for New Physics at the Large Hadron Collider
… we show that a cutting edge technique known as a variational autoencoder, can be used to effectively parametrize composite images of events detected at the planned XENONnT dark matter detector. The variational autoencoder model is trained exclusively on electron recoil background images and builds …
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