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 “"Variational Auto Encoder"”.
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Neural network libor market model for pricing and hedging interest rate derivatives
… we will introduce a new formulation of variational auto-encoders in order to generate the data we require. Our variational auto-encoder is based on data generation principles from elementary probability i.e. finding the inverse cumulative distribution function and using uniform inputs to …
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Learning distributions with Particle Mirror Descent
… multi-modal posterior, we introduce a variant of Variational Auto-encoder model that uses a mixture of customized kernels as posterior distribution in latent space. Our deep generative model produces visually plausible images as well as good clustering performance using latent representations.
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Towards Learning the Geometry of Data: From Diffusion Models to Riemannian Geometry
… In chapter 6, we introduce ScoreVAE, a novel Variational Auto-encoder (VAE) that alleviates the typical VAE limitation of blurry reconstructions by combining a frozen pretrained diffusion model with a learnable time-dependent encoder to model the reconstruction distribution. In chapter 5, we …
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Deep reinforcement learning for adaptive monitarizacion and patrolling of water resources with unmanned surface vehicles
… human efforts and high costs. The use of autonomous surface vehicles equipped with water quality measurement equipment and pollution variables increases efficiency and improves the precision and validity of biological models for such resources. Deploying these vehicles requires special …
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Cold-start universal information extraction
… the desire to endow machines with the ability to automatically extract, assess, and understand text in order to answer these fundamental questions. IE has been serving as one of the most important components for many downstream natural language processing (NLP) tasks, such as knowledge base …
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Applications of Probabilistic Machine Learning Models to Semiconductor Fabrication
… one-class support vector machines, as well as variational auto encoder based anomaly detection methods. Finally, we investigate the use of Bayesian optimization and Gaussian process models to improve thickness uniformity in sputtering deposition processes. Here, we use Gaussian processes to …
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Advancing semantic modeling: addressing coordination, interpretability, and data scarcity in domain representation
… leverages large language models and prefix-tuned autoencoders to enrich sparse inputs and produce coherent topics under extreme document-level scarcity. Low-Resource Topic Modeling (LRTM) presents DALTA, a domain-adaptation framework that transfers knowledge from data-rich corpora while preserving …
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Using Deep Learning to Extract Multicellular Aggregation Features of Myxococcus xanthus
… integrating ResNet and StyleGAN2 into a Variational AutoEncoders (VAEs), and using Siamese architectures as the similarity metric. This pipeline transforms high-resolution microscopy data into low-dimensional phenotypic feature vectors. Human evaluations confirmed the model captures …
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RESOLVING TUMOR HETEROGENEITY THROUGH HIGH RESOLUTION MULTI-MODAL TRANSCRIPTOMICS: INTEGRATIVE SINGLE-CELL ATLASES AND GENERATIVE SPATIAL MODELING
Understanding tumor heterogeneity is essential for improving diagnosis, treatment, and drug development in oncology. This thesis explores how cutting-edge transcriptomic technologies and new computational frameworks can be used to dissect, interpret, and model the cellular complexity of solid …