[ad_1] Introduction: Causal inference is the process of determining the causal relationship between variables in a given system. Counterfactual reasoning is a powerful tool in causal inference, as it allows researchers to analyze what might…
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[ad_1] Introduction: Federated learning is a decentralized machine learning approach that enables training models across multiple edge devices while keeping the data localized. This allows for improved privacy and reduced latency, making it ideal for…
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[ad_1] Introduction: Uncertainty quantification is a crucial aspect of predictive maintenance, as it allows for the assessment of the reliability and accuracy of predictive models. By quantifying uncertainties, maintenance planners can make informed decisions about…
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[ad_1] Introduction: Adversarial attacks have become a significant concern in the field of machine learning and artificial intelligence, as attackers can manipulate models to produce incorrect predictions by introducing small, carefully crafted perturbations to inputs.…
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[ad_1] Introduction: Graph Neural Networks (GNNs) have gained popularity in recent years for their ability to effectively model graph-structured data. One of the emerging applications of GNNs is in traffic prediction, where they can be…
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[ad_1] Introduction: Gaussian Processes (GPs) are a powerful tool for modeling spatial data. They allow for the flexible modeling of complex spatial patterns and relationships, making them particularly well-suited for tasks such as spatial interpolation,…
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[ad_1] Introduction: Kernel methods have gained popularity in the field of structured data analysis due to their ability to handle non-linear relationships and high-dimensional datasets efficiently. These methods use kernel functions to map input data…
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[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: 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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