[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: 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: 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: 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: Fairness and bias mitigation in machine learning is a critical topic in the field of artificial intelligence and data science. As machine learning models become more prevalent in decision-making processes across various industries,…
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[ad_1] Introduction: In recent years, artificial intelligence (AI) has become increasingly integrated into various aspects of our daily lives, from healthcare and finance to autonomous vehicles and social media. However, as AI systems become more…
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