Skillearn: Machine Learning Inspired by Humans' Learning Skills

by   Pengtao Xie, et al.

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.


page 1

page 2

page 3

page 4


Small-Group Learning, with Application to Neural Architecture Search

Small-group learning is a broadly used methodology in human learning and...

Self-directed Machine Learning

Conventional machine learning (ML) relies heavily on manual design from ...

Learning by Self-Explanation, with Application to Neural Architecture Search

Learning by self-explanation, where students explain a learned topic to ...

Fostering learners' self-regulation and collaboration skills and strategies for mobile language learning beyond the classroom

Many language learners need to be supported in acquiring a second or for...

Evaluation Methodologies for Code Learning Tasks

There has been a growing interest in developing machine learning (ML) mo...

Amanuensis: The Programmer's Apprentice

This document provides an overview of the material covered in a course t...

Learning to Complement Humans

A rising vision for AI in the open world centers on the development of s...