[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: 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: 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: 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: 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: 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: Reinforcement learning is an area of Machine Learning where an agent learns to make decisions by interacting with an environment and receiving rewards for its actions. This type of learning has shown promising…
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