[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 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: 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: 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: Automated Feature Engineering for Machine Learning is a field of study that focuses on developing algorithms and techniques to automatically extract and create predictive features from raw data. By automating this process, researchers…
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[ad_1] Introduction: Generative Models for Data Augmentation is a rapidly growing field in machine learning and artificial intelligence that focuses on generating new training data from existing data to improve the performance of machine learning…
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[ad_1] Introduction: Anomaly detection in sensor data plays a crucial role in ensuring the security and reliability of IoT networks. With the increasing number of devices connected to the internet, the need for efficient anomaly…
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[ad_1] Introduction: Monte Carlo methods are computational algorithms that rely on random sampling to obtain numerical results. These methods are widely used in various fields such as physics, engineering, finance, and statistics for simulating complex…
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[ad_1] Introduction: Streaming data processing techniques have become increasingly important in the field of data analytics as the volume and velocity of data continue to grow exponentially. This has led to the development of various…
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