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 39 for “"Variational Autoencoders"”.
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Approximate Inference in Variational Autoencoders
… approximate inference of the latent variable. A variational autoencoder (VAE) is a framework for learning both the generative and inference models for a latent variable model. This thesis provides novel analyses, applications, and interpretations of approximate inference in VAEs. This thesis …
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Variational Autoencoders for Discovering Influential Latent Factors
… other ways of understanding data. In this vein, variational autoencoders (VAEs) and their variants are one technique of generative modeling (and therefore representation learning) using variational inference under the assumption that the underlying data distribution is composed of a few latent …
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Enhancing Microbiome Host Disease Prediction with Variational Autoencoders
… We show the use of dimensionality reduction and variational autoencoders (VAE) in generating synthetic microbiome profiles as a potential method to deal with this issue and increase existing disease classification model performance. Results are compared across various baseline machine learning …
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Anomaly Detection via Latent Variables Learned by Variational Autoencoders
… critical tasks for effective anomaly detection. Variational autoencoders are state of the art modeling techniques that incorporate latent variables, hidden variables that are not directly observed but instead inferred from observed variables. Approaches to anomaly detection via variational …
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JoVA-hinge: joint variational autoencoders for personalized recommendation with implicit feedback
Recently, Variational Autoencoders (VAEs) have shown remarkable performance in collaborative filtering (CF) with implicit feedback. These existing recommendation models learn user representations to reconstruct or predict user preferences. However, existing VAE-based recommendation models learn …
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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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Networked time series imputation via position-aware graph enhanced variational autoencoders
Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2025-05-01
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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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Incorporating prior information into geophysical inversion: from regularized inversion of thermal data to a framework using conditional variational autoencoders
… 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. First, I …
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Modelling non-linearity in 3D shapes: A comparative study of Gaussian process morphable models and variational autoencoders for 3D shape data
… in using principal component analysis (PCA) and autoencoders (AE) shape modelling methods. The data identified to have linear and non-linear shape variations is used to compare two sophisticated techniques: linear Gaussian process morphable models (GPMM) and non-linear variational autoencoders …
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Capsule Networks: Framework and Application to Disentanglement for Generative Models
… of the generated data. Since the architecture of variational autoencoders is centered around latent variables and their objective function directly governs the generative factors, they are the perfect choice for creating a more disentangled representation. However, these architectures generate …
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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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Efficient Gaussian Random Number Generators in HLS4ML
… implementation of neural networks, such as Variational Autoencoders (VAEs), often relies on FPGAs for their balance of performance and energy efficiency. VAEs require accurate Gaussian distributions for latent space sampling, but traditional methods like the Central Limit Theorem (CLT) are …
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Adversarial attacks and defenses for generative models
… We study some attacks for generative models like Autoencoders and Variational Autoencoders. We discuss the relative effectiveness of the attack methods, and explore some simple defense methods against the attacks.
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Advances in Probabilistic Modelling: Sparse Gaussian Processes, Autoencoders, and Few-shot Learning
… functions, learning flexible priors for variational autoencoders, and probabilistic approaches for few-shot learning. As inference is rarely tractable, we discuss variational inference methods as a secondary theme. First, we disentangle the theoretical properties and optimisation …
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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 …
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Using heterogeneous Graph Neural Networks(hGNN) to predict cell-cell communication
… gene-gene edges, Contrastive Learning, and Variational Autoencoders (VAEs) across multiple datasets. Our study compares these methods and establishes benchmarks for assessing their effectiveness beyond traditional case studies. By integrating extensive signaling pathway data, we aim to …
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Interpreting Deep Learning for cell differentiation. Supervised and Unsupervised models viewed through the lens of information and perturbation theory.
… biologically interpretable framework based on Variational Autoencoders. The application and validation of the methods has proven to be successful, but questions regarding the learning process and generative nature of the results remained unanswered. I use information theory to define a new …
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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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Generative AI for Human-Centric Design in Extended Reality Applications
… Finally, a generative framework using variational autoencoders and latent diffusion is proposed for generating anthropometrically plausible yet diverse 3D human body models, also referred to as avatars. Model evaluation combines quantitative measures of geometric accuracy and …
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