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.
Results
Showing 1 to 20 of 989 for “"learning methods"”.
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ONLINE LEARNING METHODS FOR DIGITAL MARKETS
… digital markets and economics into an online learning framework, where a learner sequentially interacts with an unknown environment, trying to discover its relevant features to maximize her cumulative reward. After an introduction to online learning in Chapter 1, we start with a study of the …
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Machine Learning Methods for Cancer Immunology
Tumours are highly heterogeneous collections of tissues characterised by a repertoire of heavily mutated and rapidly proliferating cells. Evading immune destruction is a fundamental hallmark of cancer, and elucidating the contextual basis of tumour-infiltrating leukocytes is pivotal for improving …
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Machine learning methods for architected materials
… of this work, we explore graph-based machine learning as an alternative method to obtain the constitutive relationship of periodic lattice materials. We design a Physics-constrained graph neural network which can predict the stiffness tensor of periodic lattices with arbitrary crystal …
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Machine Learning Methods for Wastewater Treatment Plants
… thesis introduces a solution that uses machine learning to detect anomalies within wastewater treatment plants, focusing on activated sludge compartments and systems that utilize intermittent aeration based on ammonia and oxygen measurements. The study analyzes the main anomalies that may arise …
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Advanced Statistical Learning Methods in Image Processing
… in which we develop advanced statistical methods to address daunting challenges in three key imaging processing problems. First, in the image compression problem, we develop a scalable and model-based method called Compression through Adaptive Recursive Partitioning (CARP) to compress …
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Machine Learning Methods for Brain Image Analysis
… manual human-level performance. These automated methods essentially need to generalize well to be able to accommodate data from different species. Also, novel approaches and techniques are becoming a necessity to reveal the correlations between different data modalities in the brain at the global …
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Deep learning methods for large-scale physics
… the 21st century has generated the need for new methods and approaches to mathematical modeling. Machine learning leads the forefront of this change but still requires theoretical frameworks to manage large-scale data and increase the usefulness of models, especially when using deep learning. The …
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Machine learning methods for detecting positive selection
… remains a persistent challenge. Traditional methods for detecting interspecific positive selection are grounded in statistical, likelihood-based methods, typically employing codon substitution models. These approaches infer rates of nonsynonymous to synonymous substitutions (dN/dS) from …
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Certified robustness of modern machine learning methods
Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2025-05-01
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Machine learning methods based on diffusion processes
This thesis presents three distinct machine learning algorithms based on the mathematical formalism and physical idea of diffusion processes. First, the idea of using heat diffusion on a hypersphere to measure similarity has been previously proposed and tested by computer scientists, demonstrating …
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Machine Learning Methods for Modeling Synthesizable Molecules
… designed next. This thesis proposes new machine learning (ML) methods to augment chemists in the design and make steps of this process, focusing on the tasks of (a) how to use ML to predict chemical reaction outcomes, and (b) how to build generative models to search for new molecules. We take a …
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Machine Learning Methods for Learning Genetic Dependencies
… to identify SL pairs automatically using machine learning. We build on a recently developed class of feature learning kernel machines known as Recursive Feature Machines (RFMs) to develop a pipeline for identifying SL pairs based on CRISPR viability data from DepMap. In particular, we first train …
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Interpretable Machine Learning Methods for Landslide Analysis
… them is urgent. In this project, we use machine learning to computationally study landslide detection, the likelihood of past landslides occurrence, and landslide susceptibility, or risk, in the Mocoa region. The region’s geographical and climate features make it elusive to remote-sensing …
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Predicting competitor restructuring using machine learning methods
… dataset and application of various machine learning methods to predict future restructuring. Literature review emphasizes understanding of current methods benefits and shortcomings in relation to forecasting, and proposed methods seeks to fill in gaps. Depending on the competitor, the …
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Interpretable machine learning methods for stroke prediction
Machine learning has long been touted as the next big tool, revolutionizing scientific endeavors as well as impacting industries like retail and finance. Naturally, there is much interest in the potential of next improving healthcare. However, using traditional machine learning approaches in this …
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Enhancing genomic data quality through deep learning methods
… further, I developed GI-Joe, a novel deep learning architecture augmented with transformer blocks and convolutional blocks for scalable genotype imputation. GI-Joe extended the context window to over one million variants, markedly improving accuracy for rare and structural variants while …
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Robust Machine Learning Methods in Solving Inverse Problems
… not unique and are sensitive to noise. Classical methods for solving inverse problems typically involve minimizing a least-squares data fidelity term combined with a handcrafted regularization function. However, without careful design of the regularizer, such optimization approaches often yield …
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Machine Learning Methods for Hypervelocity Fragment Flyout Characterization
… overall goal of this work is to employ machine learning to estimate distribution characteristics of a space debris cloud resulting from on-orbit breakup events by supplementing existing space debris data with simulated fragmentation events and augmented datasets.</p> <p>The first proposed …
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