[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: 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: Few-shot learning is an emerging area in machine learning that focuses on training models with only a small amount of labeled data. This approach is particularly valuable for applications where collecting extensive labeled…
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[ad_1] Introduction: Tensor decomposition is a powerful technique used in high-dimensional data analysis to extract meaningful patterns and relationships from complex datasets. By decomposing a tensor into a set of simpler components, researchers can gain…
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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: 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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