[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: Anomaly detection in time series data is a critical task in various fields such as finance, healthcare, cybersecurity, and manufacturing. Detecting anomalies in time series data can help identify potential issues, prevent fraud,…
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[ad_1] Introduction: Graph Neural Networks (GNNs) have emerged as a powerful tool for analyzing and making predictions on graph-structured data. Knowledge graphs, which represent structured information about entities and their relationships, are a common form…
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[ad_1] Introduction: Transfer learning has gained significant attention in recent years as a powerful technique for improving the performance of natural language understanding systems. By leveraging knowledge learned from one task or domain and applying…
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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: Federated Transfer Learning for Collaborative Modeling is a cutting-edge research field that combines transfer learning and federated learning techniques to improve model performance in collaborative settings. This thesis aims to explore the potential…
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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: Self-supervised learning has gained significant attention in recent years as a method for unsupervised representation learning in machine learning. By leveraging the inherent structure of the data itself, self-supervised learning techniques can learn…
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[ad_1] Introduction: Multimodal learning is an emerging field in machine learning that aims to combine various data sources, such as text, images, and sensors, to improve the performance of models. By fusing multiple sources of…
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