[ad_1] Adversarial Robustness in Machine Learning Models has become a critical topic of research in recent years due to the susceptibility of machine learning models to attacks from malicious actors. Adversarial attacks involve making small,…
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[ad_1] Introduction: Reinforcement learning has shown significant potential for autonomous driving applications, allowing vehicles to learn complex driving tasks through trial and error. This technology has the capability to improve driving safety, efficiency, and overall…
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[ad_1] Introduction: Bayesian non-parametric models have gained popularity in recent years as a flexible approach to modeling complex data sets. Unlike traditional parametric models, Bayesian non-parametric models do not assume a fixed number of parameters,…
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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: Hierarchical clustering is a widely used method in data analysis for grouping similar data points into clusters based on their distance from each other. This technique has been adapted for multi-resolution data analysis,…
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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: Distributed Optimization for Large-Scale Machine Learning is a vital area within the field of machine learning, particularly as datasets continue to grow exponentially in size and complexity. This thesis aims to explore the…
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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] 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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