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 6 of 6 for “"Scalable Deep Learning"”.
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Efficient and Scalable Deep Learning
<p>Deep Neural Networks (DNNs) can achieve accuracy superior to traditional machine learning models, because of their large learning capacity and the availability of large amounts of labeled data. In general, larger DNNs can obtain higher accuracy. However, there are two obstacles which hinder us …
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Towards Efficient and Scalable Deep Learning on Graph-Structured Data
… Graph Neural Networks (GNNs), a primary form of deep learning on graphs, is hindered by intertwined challenges of effectiveness and scalability. This thesis, "Towards Effective and Scalable Deep Learning on Graph-Structured Data," proposes novel methodologies to address these limitations across …
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Informatics Strategies for Alzheimer’s Disease Research Through Analyzing Genetics and Neuroimaging Data
… diseases and multimorbidity by developing scalable deep learning and large language models for multi-modal integration, refining advanced imputation methods to manage incomplete data, and incorporating spatial and single-cell multi-omics data. These strategies promise to unravel the complex …
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Deep Learning Domain Adaptation in Brain MRI: Investigating Motion Mitigation in Adult and Neonatal Scans
… non-compliant patients like newborns. While Deep Learning (DL) models have emerged as powerful retrospective solutions for motion mitigation, their performance is expected to degrade when applied to data from different scanners, protocols, or patient populations. This thesis investigates the …
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Scalability Analysis and Optimization for Large-Scale Deep Learning
Despite its growing importance, scalable deep learning (DL) remains a difficult challenge. Scalability of large-scale DL is constrained by many factors, including those deriving from data movement and data processing. DL frameworks rely on large volumes of data to be fed to the computation engines …
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Deep Learning Methods for Built Environment Operational Management
… the development of efficient, reliable, and scalable time series (TS) deep learning (DL) frameworks toward enhancing operational management in the built environment, with case studies on (i) reliable infrastructure anomaly detection (AD) and (ii) scalable energy forecasting. An unsupervised, …