[ad_1] Introduction: Tensor factorization is a powerful tool used in signal processing to extract relevant information from high-dimensional data. By decomposing a tensor into a set of lower-dimensional factors, tensor factorization allows for efficient representation,…
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[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: 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: Interpretable Machine Learning has become increasingly important as the use of complex machine learning models continues to grow. Model debugging, in particular, is a crucial aspect of machine learning model development as it…
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[ad_1] Introduction: Topological Data Analysis (TDA) has emerged as a powerful tool in the field of scientific visualization, allowing researchers to analyze complex datasets and extract meaningful patterns and structures. By studying the topological properties…
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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: Meta-reinforcement learning has emerged as a promising approach for enabling rapid adaptation in robotics, allowing robots to efficiently learn new tasks with minimal human intervention. This thesis explores the application of meta-reinforcement learning…
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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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