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 “"Unsupervised Deep Learning"”.
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Identifying Cancer Subtypes Using Unsupervised Deep Learning
<p>Glioblastoma multiforme (GBM) is the most fatal malignant type of brain tumor with a very poor prognosis with a median survival of around one year. Numerous studies have reported tumor subtypes that consider different characteristics on individual patients, which may play important roles in …
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Fault detection in manufacturing equipment using unsupervised deep learning
We investigate the use of unsupervised deep learning to create a general purpose automated fault detection system for manufacturing equipment. Unexpected equipment faults can be costly to manufacturing lines, but data driven fault detection systems often require a high level of application specific …
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Land Cover Quantification using Autoencoder based Unsupervised Deep Learning
This work aims to develop a deep learning model for land cover quantification through hyperspectral unmixing using an unsupervised autoencoder. Land cover identification and classification is instrumental in urban planning, environmental monitoring and land management. With the technological …
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In-Memory Computing Architecture for Deep Learning Acceleration
<p>The ever-increasing demands of deep learning applications, especially the more powerful but intensive unsupervised deep learning models, overwhelm computation capability, communication capability, and storage capability of the modern general-purpose CPUs and GPUs. To accommodate the memory and …
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Vision-based human action recognition using machine learning techniques
… categorized into handcrafted feature-based and deep learning-based approaches. The proposed action recognition framework is then based on these handcrafted and deep learning based techniques, which are then adopted throughout the thesis by embedding novel algorithms for action recognition, both …
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Anomalous Motion Detection of Vehicles on Highway using Deep Learning
… an area of interest. Despite the challenges of learning from a single class of data in an unsupervised learning paradigm, previous research shows promising results in detecting spatial as well as temporal anomalies in crowded environments. The advent of self-driving cars provides an opportunity …
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Visual Simultaneous Localization and Mapping: From Geometry to Deep Learning
… that merge data driven approaches, such as deep learning, with visual SLAM techniques are proposed in order to gain better performance. Firstly, a novel model-based SLAM method based on points and plane-patches is proposed. Evaluational experiments on multiple benchmark dataset are performed …
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Constraining the Major Merging History of Massive Galaxies: A Comprehensive Analysis of Close Pairs and Tidal Features Using Empirical and Simulated Data
… et al., 2019). Finally, using supervised and unsupervised deep-learning models, we also investigate the automated characterization of different morphological substructures hosted within the parametric light-profile subtracted residual images of 10,000 massive galaxies from the HST CANDELS …
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Fibrous Microstructure and Biomechanics of Healthy and Diseased Aortic Tissues
… Secondly, a computational framework based on the unsupervised deep learning UNet model was proposed to characterise the heterogeneity of vessels. Lastly, a simulation framework was developed and used for parameter studies to estimate artery material performances according to a few critical …
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Multi-layer Optimization Aspects of Deep Learning and MIMO-based Communication Systems
… of multiple input multiple output (MIMO) and deep learning-based communication systems. The initial focus is on the rate optimization for multi-user MIMO (MU-MIMO) configurations; specifically, multiple access channel (MAC) and interference channel (IC). First, the ergodic sum rates of MIMO …
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From Fossils to Function: Exploring the Evolutionary And Functional Diversity Of HERV Envelope Proteins Across Primate Lineages
… to identify these interactions, we developed an unsupervised deep learning autoencoder model, which revealed several human cellular receptors that interact with HERV Env proteins. Key interactions were predicted for Syncytin-1, HERV-W, HERV-T, HML2-Rec, and Np9, highlighting their roles in …
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Unsupervised Feature Learning for Point Cloud by Contrasting and Clustering with Graph Convolutional Neural Network
<p>Recently, deep graph neural networks (GNNs) have attracted significant attention for point cloud understanding tasks, including classification, segmentation, and detection. However, the training of such deep networks still requires a large amount of annotated data, which is both expensive and …