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 192 for “"Autoencoder"”.
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Autoencoder-based image dimensionality reduction methods
… we use a machine-learning--based method called autoencoder for learning these compact representations of images.
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Semantic autoencoder for modeling dielectric lifetime distributions
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms
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A FEDERATED DEEP AUTOENCODER FOR DETECTING IOT CYBER ATTACKS
… approach, this will employ a deep autoencoder to detect botnet attacks using on-device decentralized traffic data. This suggested federated learning solution will be able to address the privacy and security of data by ensuring that the device’s data is not transferred or moved off the …
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Land Cover Quantification using Autoencoder based Unsupervised Deep Learning
… hyperspectral unmixing using an unsupervised autoencoder. Land cover identification and classification is instrumental in urban planning, environmental monitoring and land management. With the technological advancements in remote sensing, hyperspectral imagery which captures high resolution …
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Adaptive surrogate modeling for high dimensional problems using Autoencoder Gaussian Process
Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-12-01
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Converting Autoencoder Based Energy-Efficient and Secure DNN Inference on Edge Devices
… a novel approach to DNN compression using autoencoders, leveraging the idea that not all images require the same level of complexity for classification. Our method trains an autoencoder to transform complex images into simpler representations, enabling a more efficient DNN. To optimize this …
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Modified Kernel Principal Component Analysis and Autoencoder Approaches to Unsupervised Anomaly Detection
… Kernel Principal Component Analysis (KPCA) and Autoencoders (AE), and proposes novel solutions to improve both of their performances in the unsupervised settings. Anomaly detection has several useful applications, such as intrusion detection, fault monitoring, and vision processing. More …
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Generalized flow-based variational autoencoder networks for anomaly detection in multivariate time series
… 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 distributions, and methods to improve these approximations are explored through the use of …
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Autoencoder-based multivariate time series anomaly detection and clustering for diagnosis of mechatronic systems
… Deep Artificial Neural Network (DANN) called the Autoencoder to quantify the degree of fault, failure or malfunction (anomaly) of a mechatronic system using raw sensory data is analyzed. The Autoencoder approach follows the one-class classification paradigm and is trained unsupervised by …
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Autoencoder, análise via componentes principais e independentes aplicados no reconhecimento de padrões de populações
… artificial, como as redes neurais, sendo o Autoencoder um tipo de rede neural que também busca reduzir o espaço dimensional e reconstruir os dados com perda mínima de informação. Assim, o primeiro capítulo desta tese é uma revisão bibliográfica sobre os métodos estatísticos e baseados em …
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Product perceptual mapping on fashion designs with Gaussian mixture variational autoencoder and triplet loss
… 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 dataset of simple …
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Autoencoder-based techniques for improved classification in settings with high dimensional and small sized data
… relationships between the features such as the autoencoders. This thesis investigates the problem of predicting from small sizedhigh dimensional datasets by introducing novel autoencoder-basedtechniques to increase the classification accuracy of the data. Twoautoencoder-based methods for …
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Wildfire path spread prediction system using machine learning: use of ANN, convolutional autoencoder, and ConvLSTM ML model
… Artificial Neural Networks(ANN), Convolutional Autoencoder, and Convolution Long Short-Term Memory(convLSTM), in predicting wildfire spread. The three ML models developed were based on domain knowledge of fire behaviour and utilised Google Earth Engine(GEE) weather data and Sentinel Hub burned …
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Integration of Spatial Transcriptomics with Chromatin Images Using Graph-Based Autoencoder Identifies Joint Biomarkers for Alzheimer’s Disease
… data using over-parameterized graph-based Autoencoders with Chromatin Imaging data (STACI) to identify molecular and functional alterations in tissues. STACI represents multiple data modalities with a single joint representation, which allows for the simultaneous incorporation of the …
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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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Geo-Informed Deep Learning for Spatial Downscaling of Solute Transport in Heterogeneous Porous Media
… learning architecture comprising a dual-branch autoencoder and a Geo-informed super-resolution generative adversarial network (Gi-SRGAN) to address this dual challenge. The dual-branch autoencoder addresses the issue of sparsity by constructing a continuous, but coarse representation of …
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Anomaly detection in a mobile data network
… “cleaned” data set was then used to train an autoencoder in an semi-supervised approach. The resultant autoencoder was able to indentify normal observations. A subsequent data set was then analysed by the autoencoder. The resultant reconstruction errors were then compared to the ground truth …
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The role of representations in human activity recognition
… we develop convolutional and recurrent autoencoder architectures for feature learning and compare their performance to a distribution-based representation as well as a supervised deep learning representation based on the DeepConvLSTM architecture. This is motivated by the promises deep …
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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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