[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…
Read More
[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…
Read More
[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…
Read More
[ad_1] Generative Adversarial Networks (GANs) have shown remarkable success in generating realistic images through a competitive process between two neural networks: a generator and a discriminator. This innovative approach has revolutionized the field of image…
Read More
[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,…
Read More
[ad_1] Introduction: Kernel methods are powerful tools in machine learning and data analysis that enable the modeling of non-linear relationships in data. These methods transform data into a higher-dimensional space where it may be easier…
Read More
[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,…
Read More
[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,…
Read More
[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…
Read More
[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…
Read More