Abstract
Humans, as the most powerful learners on the planet, have accumulated a
lot of learning skills, such as learning through tests, interleaving
learning, self-explanation, active recalling, to name a few. These
learning skills and methodologies enable humans to learn new topics more
effectively and efficiently. We are interested in investigating whether
humans’ learning skills can be borrowed to help machines to learn
better. Specifically, we aim to formalize these skills and leverage them
to train better machine learning (ML) models. To achieve this goal, we
develop a general framework – Skillearn, which provides a principled
way to represent humans’ learning skills mathematically and use the
formally-represented skills to improve the training of ML models. In two
case studies, we apply Skillearn to formalize two learning skills of
humans: learning by passing tests and interleaving learning, and use the
formalized skills to improve neural architecture search. Experiments on
various datasets show that trained using the skills formalized by
Skillearn, ML models achieve significantly better performance.