[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: Graph embedding techniques have gained popularity in the field of molecular data analysis due to their ability to capture complex relationships and patterns within molecular structures. These techniques involve transforming molecular data into…
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[ad_1] Introduction: Adversarial attacks and defenses for image recognition have become increasingly important in the field of artificial intelligence and computer vision. Adversarial attacks refer to the manipulation of input data in order to trick…
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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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[ad_1] Introduction: Federated Meta-Learning is a cutting-edge approach that combines federated learning and meta-learning to create personalized models for individual users. By leveraging the collective knowledge from multiple devices while also adapting to the unique…
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[ad_1] Introduction: Generative Adversarial Networks (GANs) have gained significant attention in recent years for their ability to generate realistic data samples, including images, audio, and text. In the context of text generation, GANs have been…
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[ad_1] Introduction: Imbalanced data handling is a crucial aspect of data analysis, particularly in scenarios where rare events need to be detected. Rare event detection involves identifying events that occur infrequently in a dataset, but…
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[ad_1] Introduction: Incremental learning is a technique in machine learning where a model is trained continuously over time as new data becomes available. This allows the model to adapt and improve its performance without having…
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[ad_1] Introduction: Graph Neural Networks (GNNs) have emerged as a powerful tool for analyzing and making predictions on graph-structured data. Knowledge graphs, which represent structured information about entities and their relationships, are a common form…
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