[ad_1] Introduction: Causal inference has become increasingly important in the field of recommendation systems, as it allows us to understand not just correlations between user preferences and recommendations, but also the causal relationships that drive…
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[ad_1] Introduction: Differential Privacy is a promising approach for protecting sensitive data while allowing for accurate analysis and information extraction. With the increasing use of data analysis in various fields such as healthcare, finance, and…
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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: Meta-learning for automated machine learning is a cutting-edge approach to optimizing the process of developing machine learning models. By leveraging meta-learning techniques, researchers and practitioners can automate the selection of algorithms, hyperparameters, and…
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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: 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] 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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[ad_1] Introduction: Distributed deep learning has become increasingly popular in recent years due to the growing size of training data and the complexity of deep learning models. Large-scale training requires distributing the workload across multiple…
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[ad_1] Introduction: Multi-Agent Reinforcement Learning (MARL) is a relatively new approach that involves multiple agents learning to interact and collaborate with each other in order to achieve a common goal. When applied to collaborative robotics,…
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