[ad_1] Introduction: Federated Learning is a novel machine learning approach that allows multiple edge devices to collaboratively train a shared machine learning model, without exchanging their raw data with a centralized server. This decentralized approach…
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[ad_1] Introduction: Explainable Artificial Intelligence (AI) refers to the ability of AI systems to provide understandable explanations for their decisions and actions. In high-stakes decision-making scenarios, such as healthcare, finance, and criminal justice, it is…
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[ad_1] Introduction: Tensor methods have emerged as powerful tools for analyzing high-dimensional data in various fields such as machine learning, signal processing, and image processing. Tensors, which are generalizations of matrices, allow for the representation…
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[ad_1] Introduction: Fairness and Bias Mitigation in AI Systems is a critical topic in the field of artificial intelligence. As AI becomes increasingly integrated into various aspects of society, it is essential to ensure that…
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[ad_1] Introduction: Uncertainty quantification is a critical aspect of machine learning models that is often overlooked but can greatly impact the reliability and accuracy of predictions. By quantifying uncertainty, we can gain a better understanding…
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[ad_1] Introduction: Automated Machine Learning (AutoML) is a rapidly growing field in artificial intelligence and data science. It aims to automate the process of model selection, hyperparameter tuning, and feature engineering, making machine learning more…
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[ad_1] Introduction: Active Learning is a machine learning approach that aims to efficiently label large datasets by selecting the most informative data points for manual annotation. In this thesis, we will explore the use of…
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[ad_1] Introduction: Continual Learning is an important aspect of machine learning that allows models to adapt to evolving data over time. In a rapidly changing world where data is constantly being updated and new trends…
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[ad_1] In the field of machine learning, Semi-Supervised Learning (SSL) is a powerful technique that utilizes a combination of labeled and unlabeled data to improve model performance. This approach is particularly useful in scenarios where…
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[ad_1] Introduction: Differentially Private Machine Learning is a rapidly growing field in the realm of data privacy, especially when dealing with sensitive data. With the increasing concerns about data breaches and privacy violations, there is…
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