[ad_1] Introduction: Uncertainty quantification is a crucial aspect of predictive maintenance, as it allows for the assessment of the reliability and accuracy of predictive models. By quantifying uncertainties, maintenance planners can make informed decisions about…
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[ad_1] Introduction: Bayesian Non-Parametric Models for Clustering is a powerful tool in machine learning and data analysis that allows for flexible and adaptive clustering without the need for specifying the number of clusters in advance.…
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[ad_1] Introduction: Deep learning has shown significant promise in the field of medical image segmentation, a critical task in medical image analysis for disease diagnosis and treatment planning. Medical image segmentation involves partitioning an image…
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[ad_1] Introduction: Bayesian Deep Learning has emerged as a powerful tool for uncertainty quantification in machine learning models. By incorporating Bayesian principles into deep learning algorithms, researchers can not only make more accurate predictions but…
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[ad_1] Introduction: Distributed representation learning for multimodal data is a cutting-edge research area that aims to develop efficient and effective algorithms for extracting meaningful representations from data that combine information from multiple modalities, such as…
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[ad_1] Introduction: Heterogeneous data integration in healthcare applications is a crucial aspect of modern healthcare systems, as it involves the integration of different types of data from various sources to provide a comprehensive view of…
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[ad_1] Introduction: Numerical optimization plays a crucial role in large-scale machine learning, as it allows us to efficiently optimize complex models and algorithms used in data analysis and prediction. In this thesis, we will explore…
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[ad_1] Introduction: With the advancements in genomic research, there is a growing need for sharing genomic data among researchers and institutions. However, ensuring the privacy and confidentiality of this sensitive data poses a significant challenge.…
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[ad_1] Introduction: Domain generalization is a critical component of robust machine learning, allowing models to perform well on unseen data from different domains. In this thesis, we will explore the concept of domain generalization and…
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[ad_1] Introduction: Disentangled representation learning has emerged as a powerful tool for domain adaptation, allowing for the extraction of meaningful and interpretable features from data. This thesis explores the use of disentangled representation learning for…
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