[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: 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
[ad_1] Introduction: Reinforcement learning is a type of machine learning that enables an agent to learn how to behave in an environment by performing actions and receiving rewards. It has gained significant attention in recent…
Read More
[ad_1] Introduction: Disentangled representation learning has emerged as a powerful tool for domain adaptation, allowing for the extraction of meaningful and interpretable features from data. This thesis explores the use of disentangled representation learning for…
Read More
[ad_1] Introduction: Few-shot learning is an emerging area in machine learning that focuses on training models with only a small amount of labeled data. This approach is particularly valuable for applications where collecting extensive labeled…
Read More
[ad_1] Introduction: Tensor decomposition is a powerful technique used in high-dimensional data analysis to extract meaningful patterns and relationships from complex datasets. By decomposing a tensor into a set of simpler components, researchers can gain…
Read More
[ad_1] Introduction: Fairness and bias mitigation in machine learning is a critical topic in the field of artificial intelligence and data science. As machine learning models become more prevalent in decision-making processes across various industries,…
Read More
[ad_1] Introduction: Multimodal learning is a growing field in machine learning that focuses on integrating information from multiple modalities to improve the performance of learning systems. Multimodal data fusion refers to the process of combining…
Read More