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 13 of 13 for “"Generative Machine Learning"”.
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Principled Methods for Advancing Generative Machine Learning
… generating novel data beyond collected samples, generative machine learning (ML) has become one of the most actively explored research areas in recent years, witnessing a series of groundbreaking advances. In computer vision (CV), diffusion models mark a major milestone — they can synthesize …
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Generative Machine Learning Models for RNA Structure Prediction and Design
… data. Inspired by recent advances in deep learning for protein folding and design, this thesis explores novel geometric and generative architectures for modeling RNA. We first present a systematic study on RNA structure prediction using equivariant neural networks within diffusion …
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Machine Learning-Aided Aerospace Applications with Generative Adversarial Networks
<p>Applied generative machine-learning models have demonstrated exceptional accuracy at recreating realistic data, becoming a highly researched field in aerospace and defense technologies. Generative Adversarial Networks (GANs), a subset of generative models, have shown remarkable proficiency at …
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Neural Network Models for Generating Synthetic Flight Data
… challenges. Over the past few decades, generative machine learning has a emerged as a popular tool for data augmentation. In this thesis, several neural network architectures were investigated as methods of generating synthetic flight data that is consistent with the aircraft …
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Data-Driven Bicycle Design using Performance-Aware Deep Generative Models
This treatise explores the application of Deep Generative Machine Learning Models to bicycle design and optimization. Deep Generative Models have been growing in popularity across the design community thanks to their ability to learn and mimic complex data distributions. This work addresses several …
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Investigating the Capacity of Generative AI to Learn Genotype-by-Environment Interactions in Brachypodium distachyon
… of regulation. This thesis investigates whether generative machine learning modeling, specifically the use of transformers, can extract biologically meaningful representations of gene expression dynamics in plants. Inspired by the successes of the scGPT model for human genomics, I developed and …
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Modulating Cariogenic Interfaces Through Machine Learning-Guided Peptide Design
… interactions with hydroxyapatite through a generative machine-learning approach with augmentation for small data sets. Silver-binding peptide sequences are selected through a process that includes tailored physiochemical analysis and in silico assessments to discover candidates with …
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Development of Machine Learning Models for Generation and Activity Prediction of the Protein Tyrosine Kinase Inhibitors
… continues to grow at a rapid pace, using generative machine learning approaches to present us with solutions to high dimensional and complex problems in drug discovery and design. In this work, we present a platform of Machine Learning based approaches for generation and scoring of novel …
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Machine Aided Biological Discovery and Design
… desired objectives. First, we introduce a generative machine learning model for inferring cellular developmental landscapes from cross-sectional sequencing of in vitro differentiation time-series. We validate this model with ground-truth experimental lineage tracing experiments, and we show …
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Physics-Informed Deep Learning for Pilot Parameter Estimation and Pilot-Induced Oscillation Characterization
… <p>This research also employs proven benchmark machine learning models for the purpose of pilot-induced-oscillation monitoring with the use of the Neal-Smith Criterion. These models help add context to any pilot parameter estimation as a direct metric related to PIO can be generated to determine …
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Modeling Astrophysical and Large-Scale Structure Signatures in Axion Cosmologies
… Moving to astrophysical implications, we deploy generative machine learning models (normalizing flows) to characterize neutral hydrogen distributions in post-reionization FDM model Universes. Our findings indicate that extreme FDM models can be ruled out based solely on their low neutral hydrogen …
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Machine Learning Approaches to Assessing Future Flood & Storm Risk
This thesis describes the application of machine learning to hydrology problems in the face of imminent and long term climate change, in particular through the lens of data minimalism. First, we note that with the dawn of the Anthropocene the world's climate is changing, primarily due to human …
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Machine-Learning-Enabled Gestural Interaction in Mixed Reality
… keyboard and mouse used on personal computers. Machine learning has significantly advanced various technologies; thus the central hypothesis in this thesis is that Machine learning enables fast and accurate gestural interaction systems in Mixed Reality. The design of machine-learning-enabled …