Meta-Learning: A Survey
Meta-learning, or learning to learn, is the science of systematically observing how different machine learning approaches perform on a wide range of learning tasks, and then learning from this experience, or meta-data, to learn new tasks much faster than otherwise possible. Not only does this dramatically speed up and improve the design of machine learning pipelines or neural architectures, it also allows us to replace hand-engineered algorithms with novel approaches learned in a data-driven way. In this chapter, we provide an overview of the state of the art in this fascinating and continuously evolving field.
Code (0)
등록된 구현이 없습니다.
Tasks
BIG-bench Machine LearningMeta-LearningSurveySimilar Papers 제목 키워드 기반
Meta-Learning in Neural Networks: A Survey
The field of meta-learning, or learning-to-learn, has seen a dramatic rise in interest in recent years. Contrary to conventional approaches to AI where tasks are solved from scratch using a fixed learning algorithm, meta…
Few-Shot LearningHyperparameter OptimizationMeta-LearningMulti-Task Learning+3Meta Learning for Natural Language Processing: A Survey
Deep learning has been the mainstream technique in the natural language processing (NLP) area. However, deep learning requires many labeled data and is less generalizable across domains. Meta-learning is an arising field…
Meta-LearningSurveyMeta-learning in healthcare: A survey
As a subset of machine learning, meta-learning, or learning to learn, aims at improving the model's capabilities by employing prior knowledge and experience. A meta-learning paradigm can appropriately tackle the conventi…
Few-Shot LearningMeta-LearningSurveyAI and 6G into the Metaverse: Fundamentals, Challenges and Future Research Trends
Since Facebook was renamed Meta, a lot of attention, debate, and exploration have intensified about what the Metaverse is, how it works, and the possible ways to exploit it. It is anticipated that Metaverse will be a con…
Mixed RealitySurveyA Survey of Meta-Reinforcement Learning
While deep reinforcement learning (RL) has fueled multiple high-profile successes in machine learning, it is held back from more widespread adoption by its often poor data efficiency and the limited generality of the pol…
Deep Reinforcement LearningMeta Reinforcement Learningreinforcement-learningReinforcement Learning+2