[ad_1] Introduction: Domain Adaptation for Transfer Learning in Computer Vision is a crucial area of research that focuses on enhancing the performance of computer vision models when they are applied in different domains. It involves…
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[ad_1] Introduction: Reinforcement Learning (RL) has gained significant attention in recent years for its ability to solve complex decision-making problems by learning from interactions with the environment. One important application of RL is in real-time…
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[ad_1] Introduction: Causal inference is a powerful tool for understanding and making decisions in complex systems. In today’s world, decision-makers are faced with a plethora of data and information, making it crucial to accurately determine…
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[ad_1] Introduction: Uncertainty quantification is a crucial aspect of deep learning models, as it allows for a better understanding of the confidence levels associated with model predictions. By quantifying uncertainty, researchers and practitioners can make…
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[ad_1] Introduction: Graph Neural Networks (GNNs) have gained significant attention in recent years for their ability to model complex relational data in various domains such as social networks, biology, and recommender systems. GNNs leverage the…
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[ad_1] Introduction: Interpretable machine learning has gained significant attention in recent years due to the need for transparency and understanding of complex algorithms in decision support systems. The ability to explain how machine learning models…
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[ad_1] Introduction to Gaussian Processes for Regression and Classification: Gaussian Processes (GPs) are a powerful machine learning technique for regression and classification tasks. Unlike traditional methods that assume a specific functional form for the data,…
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[ad_1] Introduction: Deep Learning has emerged as a powerful technique for analyzing and interpreting complex biomedical images. With the advancement of technology, the field of Biomedical Image Analysis has greatly benefited from the application of…
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[ad_1] Introduction: Distributed Representation Learning (DRL) has gained increasing attention in the field of Natural Language Processing (NLP) due to its ability to capture the complex relationships between words in a text. DRL techniques, such…
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[ad_1] Introduction: Meta-Reinforcement Learning (Meta-RL) is a cutting-edge technique that empowers agents to rapidly adapt to new tasks and environments through learning from past experiences. This thesis explores the application of Meta-RL for rapid adaptation…
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