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 114 for “"Autoencoders"”.
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Approximate Inference in Variational Autoencoders
… standard interpretation of importance-weighted autoencoders is that they maximize a tighter lower bound on the marginal likelihood than the standard evidence lower bound. The first contribution of this thesis is to provide an alternative interpretation: that it optimizes the standard variational …
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Automated Finetuning via Sparse Autoencoders
… a novel method using interpretability in sparse autoencoders to achieve better performance in small models via instruction finetuning. Specifically, we present UnderstandTune, an autonomous method for assembling high-quality instruction finetuning datasets with minimal human intervention, …
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Imputing metabolomics with graph denoising autoencoders
The student, Kowshika Sarker, accepted the attached license on 2024-12-06 at 19:10.
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Towards More Interpretable AI With Sparse Autoencoders
… representations (features) by employing sparse autoencoders (SAEs). An SAE decomposes neural network hidden states into a potentially more interpretable basis. In Chapter 2, we introduce an unsupervised, SAE-based methodology that successfully identifies inherently multi-dimensional features. …
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Variational Autoencoders for Discovering Influential Latent Factors
… 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 random …
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Second chance competitive autoencoders for understanding textual data
… collection of documents. Applying conventional autoencoders on textual data often results in learning trivial and redundant representations due to high text dimensionality, sparsity, and following power-law word distribution. To address these challenges, we introduce three novel autoencoders, …
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Enhancing Microbiome Host Disease Prediction with Variational Autoencoders
… 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 models with …
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Learning to Ground Multi-Agent Communication with Autoencoders
Communication requires having a common language, a lingua franca, between agents. This language could emerge via a consensus process between agents, but this may require many generations of trial and error. Alternatively, the lingua franca can be given by the environment, where agents ground their …
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Anomaly Detection via Latent Variables Learned by Variational Autoencoders
… 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 autoencoders …
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Learning low-dimensional feature dynamics using convolutional recurrent autoencoders
Model reduction of high-dimensional dynamical systems alleviates computational burdens faced in various tasks from design optimization to model predictive control. One popular model reduction approach is based on projecting the governing equations onto a subspace spanned by basis functions obtained …
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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
… 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 nodes. This …
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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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Automated Visual Inspection of Lyophilized Products via Deep Learning and Autoencoders
… location in an image. Furthermore, we show that autoencoders can be used to create classifiers that perform just as well as the pretrained VGG16 and ResNet50 models for our vial image datasets. Lastly, we demonstrate that simple data augmentation techniques do not improve the training of our vial …
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Synonymous question generation: Learning to ask in different ways using variational autoencoders
Recently, there has been significant interest in advancing machine comprehension of text through question answering. Motivated by the idea that machine comprehension should be bidirectional, we explore synonymous question generation from knowledge graphs (KGs) to enable machines to learn how to ask …
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Advances in Probabilistic Modelling: Sparse Gaussian Processes, Autoencoders, and Few-shot Learning
… 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 behaviour of two …
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Bayesian autoencoders for anomaly detection: Design, uncertainty quantification, and explainability with industrial applications
… facilitated the use of powerful models such as autoencoders (AEs), a class of neural networks (NNs), to achieve state-of-the-art results in anomaly detection. Nevertheless, there are growing concerns regarding the safety and trustworthiness of AEs, as recent studies have reported the surprising …
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SAERMA: Stacked Autoencoders Rule Mining Algorithm for the Interpretation of Epistatic Interactions in GWAS of Extreme Obesity
… and explores the use of deep learning stacked autoencoders (SAE) and association rule mining (ARM) to identify epistatic interactions between SNPs. This is achieved using a case-control dataset containing 2,193 observations (962 cases and 1,231 controls) each with 594,034 genetic markers. A …
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Incorporating prior information into geophysical inversion: from regularized inversion of thermal data to a framework using conditional variational autoencoders
Geophysical inversion provides physical property models which are essential to understanding and characterizing the subsurface. However, traditional inversion methods recover models with smooth features that do not resemble geologic structures. Incorporating prior information into inversion can …
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