[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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[ad_1] Introduction: Bayesian Deep Learning has gained significant attention in recent years due to its ability to provide uncertainty estimates in deep neural networks. Uncertainty estimation is crucial in several applications such as autonomous driving,…
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[ad_1] Introduction: Secure Multi-Party Computation (SMPC) is a cryptographic technique that allows multiple parties to jointly compute a function over their private inputs without revealing any individual input to the other parties. This technology has…
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[ad_1] Introduction: In recent years, the rapid advancement of artificial intelligence (AI) and data science technologies has led to significant ethical concerns regarding the implications of their use in various applications. It is essential for…
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[ad_1] Bayesian Optimization is a popular method used in machine learning for hyperparameter tuning, which aims to find the best configuration of parameters for a given model. This approach utilizes a probabilistic model to predict…
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[ad_1] Introduction: Multi-Agent Reinforcement Learning (MARL) has gained significant attention in recent years due to its ability to model complex collaborative systems where multiple agents interact with each other to achieve a common goal. MARL…
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[ad_1] Introduction: Differential Privacy is a comprehensive approach to data privacy that aims to protect the privacy of individuals while still allowing meaningful analysis to be conducted on the data. With the increasing amount of…
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[ad_1] Introduction: Quantum Machine Learning (QML) is an emerging field that combines the principles of quantum computing with classical machine learning techniques. One of the key applications of QML is in solving optimization problems, where…
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[ad_1] Introduction: Federated Learning is a cutting-edge machine learning technique that enables multiple parties to collaboratively train a shared model without sharing their raw data. This approach is particularly useful in scenarios where data privacy…
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