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 12 of 12 for “"Conditional variational autoencoder"”.
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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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Latent variable models for understanding user behavior in software applications
… to large-scale 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 …
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Synonymous question generation: Learning to ask in different ways using variational autoencoders
… questions 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, …
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Computational creativity applications in engineering
… materials 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 …
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DL-based defense against polymorphic network attacks
… that leverages the best characteristics of a Conditional Variational Autoencoder (CVAE) and a Generative Adversarial Network (GAN). Our system generates adversarial polymorphic attacks against the IDS to examine its performance and incrementally retrains it to strengthen its detection of new …
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Incorporating prior information into geophysical inversion: from regularized inversion of thermal data to a framework using conditional variational autoencoders
… from a set of example models. I train a conditional variational autoencoder to incorporate learned information into geophysical inversion and generate models that resemble the example models while honoring the specific data to be inverted. I apply this framework to two different problems. …
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Learning Heuristics for Combinatorial Optimization Problems with Deep Neural Networks
… learning. The third approach uses a conditional variational autoencoder to learn a mapping from discrete routing problem solutions to a continuous space that can be searched using any continuous optimization method. The fourth approach presents a simple technique for extending …
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Machine Learning Enabled Inorganic Synthesis Planning and Materials Design
… this regression-based condition modeling with a conditional variational autoencoder (CVAE) neural network which can generate appropriate distributions for the synthesis conditions of interest. We evaluate model interpretability using the SHAP (SHapley Additive exPlanations) approach to gain …
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Cyber-Physical Attacks and Detection Methods in Water Distribution Systems
… we propose a deep learn- ing model based on a conditional variational autoencoder (CVAE) to detect cyber-physical attacks. The CVAE model shows a highly effective way to maximize the chance of generating the desired output and detecting CPA attacks quickly. We also train CVAE on (BATADAL) real …
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Non-linear Multi Omics Data Integration Method Using Conditional Variational Autoencoders
… et al., 2019 introduced techniques that use Variational Autoencoders (VAEs) for data integration. Similarly, Zarayeneh et al., 2017 proposed a method called the Integrative Gene Regulatory Network (iGRN), which combines multiple layers of omics data using a network made up entirely of gene …
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Informing decision-making in single-objective, mixed-variable design problems
… by the author, which trains the data on a conditional variational autoencoder (cVAE), approximates gradients on the model, and summarizes gradients into “influence metrics” using a Gaussian mixture model (GMM) (in contrast to a mean absolute value). Through this comparison, this thesis …
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Deep generative models for speech editing
… by modeling different speech components using autoencoders. Multi-channel speech enhancement with ad-hoc sensors has been a challenging task. Speech model guided beamforming algorithms can recover natural-sounding speech, but the speech models tend to be oversimplified to prevent the inference …