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 10 of 10 for “"Variational auto-encoders"”.
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AI-Assisted Pipeline for 3D Face Avatar Generation
… like Generative Adversarial Networks (GANs) and Variational Auto-Encoders (VAEs) to automate avatar creation. Our pipeline offers control over three aspects: face shape, skin color, and fine details like beards or wrinkles. This provides artists flexibility in avatar creation and can integrate …
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Three ploys for robust co-generation with generative adversarial nets
Generative adversarial nets (GANs) and variational auto-encoders enable accurate modeling of high-dimensional data distributions by forward propagating a sample drawn from a latent space. However, an often overlooked shortcoming is their inability to find an arbitrary marginal distribution, which …
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Neural network libor market model for pricing and hedging interest rate derivatives
… we will introduce a new formulation of variational auto-encoders in order to generate the data we require. Our variational auto-encoder is based on data generation principles from elementary probability i.e. finding the inverse cumulative distribution function and using uniform inputs to …
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Generative models for predictive UI design tools
… be used queried multiple times in succession to autocomplete an entire UI screen. To power this design interaction, we present two types of models: generative adversarial networks (GANs) [7] and variational auto-encoders (VAEs) [15]. We train the GAN and VAE models over 1949 mobile UIs that …
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Machine learning for cryo-EM structure determination
… My doctoral work focused on ModelAngelo, an automated model-building and protein-identification program for cryo-EM. I designed specialised graph-neural-network architectures that allow ModelAngelo to build atomic models in high-resolution maps with accuracy matching that of human experts. I …
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Deep Generative Models and Biological Applications
… distributions. </p><p>The recent proposed Variational auto-encoders (VAE) framework is an efficient high-dimensional inference method to modeling complicated data manifold in an approximate Bayesian way, i.e., variational inference. </p><p>We first discuss how to design fast stochastic …
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Perturbation Modeling for Molecular Design of Protein Tyrosine Kinase Inhibitors using Unsupervised Machine Learning
… of a generative learning technique called Variational Auto-Encoders and Unsupervised Machine Learning techniques. This study focuses specifically using the methods above to transform molecules from various kinase inhibitor families to SRC Kinase Inhibitors. These generated molecules are …
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Learning joint latent representations for images and language
… the image captioning problem using conditional variational auto-encoders (CVAEs). Standard CVAEs with a fixed Gaussian prior yield descriptions with too little variability. Instead, we propose two models that explicitly structure the latent space with K components corresponding to different …
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Learning multiple solutions to computer vision problems
… therefore we also develop a method that uses automatically generated (or learned) latent proposals. Our latent proposal method uses a combination of variational auto-encoders [30] and mixture density net- works [31] to perform multiple colorization. To the best of our knowledge, this is the …
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Using generative modelling in healthcare
… two popular classes of generative models, namely Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs) and their variants. Subsequently, we shall review some new developed imputation methods which are based on GANs and VAEs. We shall assess their performance under various …