Meta-Learning based efficient framework for diagnosing rare disorders: A comprehensive survey
Kuljeet Singh, Deepti Malhotra · AIP conference proceedings · 2024
This state-of-the-art review paper provides a comprehensive overview of meta-learning techniques for learning to learn in machine learning.Meta-learning called "learning to learn," is an approach that enables models to learn from new tasks learning quickly and efficiently, based on prior knowledge and experience.The paper discusses various meta-learning techniques, such as model-based meta-learning, metric-based meta-learning, optimization-based meta-learning, and memory-based meta-learning.The review also covers the applications of meta-learning in different domains, including natural language processing, computer vision, and reinforcement learning.Additionally, the paper discusses the benchmark datasets, challenges along with the future directions of meta-learning research, highlighting the need for developing more efficient and scalable meta-learning algorithms.Moreover, the proposed Meta-Learn framework incorporates a hierarchical structure to facilitate knowledge transfer across multiple levels of abstraction, and it also includes a memory component that enables the system to store and reuse previous experiences.Overall, this review aims to provide a comprehensive overview of Meta-Learning techniques for learning to learn in Machine Learning and proposes a novel Meta-Learn framework that could potentially enhance the performance and scalability of Meta-Learning algorithms.