meta-learning

A subset of machine learning that discovers or improves a learning algorithm. A meta-learning system can also aim to train a model to quickly learn a new task from a small amount of data or from experience gained in previous tasks. Meta-learning algorithms generally try to achieve the following: - Improve or learn hand-engineered features (such as an initializer or an optimizer). - Be more data-efficient and compute-efficient. - Improve generalization. Meta-learning is related to few-shot learning.

Practitioners refer to meta-learning when building, training, or evaluating machine learning systems. It appears in research papers, product documentation, and technical discussions about AI capabilities and limitations.