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 9 of 9 for “"Data-Efficient Machine Learning"”.
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Data-Efficient Machine Learning for Computational Imaging
This thesis presents a method that improves data efficiency in computational imaging by incorporating prior knowledge from physical models into machine learning algorithms. Our approach optimizes image reconstruction from sparse and noisy datasets by utilizing physical constraints to guide deep …
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Data-Efficient Machine Learning with Applications to Cardiology
Deep learning models have demonstrated impressive capabilities in many settings including computer vision, natural language generation, and speech processing. However, an important shortcoming of these models is that they often need to be trained on large datasets in order to be most effective. In …
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Data-Efficient Machine Learning with Focus on Transfer Learning
<p>Machine learning (ML) has attracted a significant amount of attention from the artificial intelligence community. ML has shown state-of-art performance in various fields, such as signal processing, healthcare system, and natural language processing (NLP). However, most conventional ML algorithms …
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Data-efficient machine learning for decision-making in smart manufacturing
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-09-16 without embargo terms
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Data-efficient machine learning for decision-making in smart manufacturing
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-09-16 without embargo terms
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TOWARDS DATA-EFFICIENT DEEP LEARNING
This thesis advances data-efficient machine learning by tackling the limitations of current dataset distillation (DD) methods, which aim to compress large datasets into compact synthetic ones for faster training and enhanced privacy. First, it introduces Dataset Factorization, a novel framework …
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Machine learning-based stress-strain prediction and process parameter optimisation in material extrusion additive manufacturing
… Despite progress in empirical modelling and data-driven approaches, most existing methods focus on isolated mechanical indicators such as yield strength or modulus. These approaches often demand large datasets and fail to generalise across changing process parameters or testing conditions. …
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Towards Data Efficiency and Controllable Representations for Deep Learning in Resource-Constrained Domains
… two fundamental challenges: label scarcity and data scarcity. Label scarcity stems from the high cost of expert annotation, the scarcity of domain experts, and the infeasibility of crowdsourcing, particularly in complex tasks requiring specialised knowledge. In parallel, data scarcity stems from …
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Data and Computation Efficient Meta-Learning
… with high accuracy, conventional deep learning systems require large training datasets consisting of thousands or millions of examples and long training times measured in hours or days, consuming high levels of electricity with a negative impact on our environment. It is desirable to …