[ad_1] Introduction: Reinforcement Learning (RL) has emerged as a powerful tool for optimizing complex systems by learning from interactions with the environment. One such application is in the field of energy management, where RL algorithms…
Read More
[ad_1] Introduction: Generative Adversarial Networks (GANs) have gained significant attention in the field of machine learning and artificial intelligence for their ability to generate realistic and high-quality images, text, and even videos. GANs consist of…
Read More
[ad_1] Introduction: Bayesian Non-Parametric Models for Clustering is a powerful tool in machine learning and data analysis that allows for flexible and adaptive clustering without the need for specifying the number of clusters in advance.…
Read More
[ad_1] Introduction: Gaussian Processes (GPs) are a powerful tool for modeling spatial data. They allow for the flexible modeling of complex spatial patterns and relationships, making them particularly well-suited for tasks such as spatial interpolation,…
Read More
[ad_1] Introduction: Kernel methods have gained popularity in the field of structured data analysis due to their ability to handle non-linear relationships and high-dimensional datasets efficiently. These methods use kernel functions to map input data…
Read More
[ad_1] Introduction: Deep learning has shown significant promise in the field of medical image segmentation, a critical task in medical image analysis for disease diagnosis and treatment planning. Medical image segmentation involves partitioning an image…
Read More
[ad_1] Introduction: Collaborative filtering is a popular technique used in recommendation systems to provide personalized suggestions to users based on their preferences. In recent years, research has focused on improving collaborative filtering for sequential recommendation,…
Read More
[ad_1] Introduction: Meta-reinforcement learning has emerged as a promising approach for enabling rapid adaptation in robotics, allowing robots to efficiently learn new tasks with minimal human intervention. This thesis explores the application of meta-reinforcement learning…
Read More
[ad_1] Introduction: Multi-Agent Reinforcement Learning (MARL) has gained significant attention in recent years due to its ability to enable autonomous agents to learn and adapt in dynamic and complex environments. In the field of swarm…
Read More
[ad_1] Introduction: Numerical optimization plays a crucial role in large-scale machine learning, as it allows us to efficiently optimize complex models and algorithms used in data analysis and prediction. In this thesis, we will explore…
Read More