论文标题
Skillearn：由人类的学习技能启发的机器学习
Skillearn: Machine Learning Inspired by Humans' Learning Skills
论文作者
论文摘要
作为地球上最有力的学习者，人类已经积累了许多学习技能，例如通过测试，交织学习，自我解释，积极回忆，等等。这些学习技能和方法使人类能够更有效，有效地学习新的主题。我们有兴趣调查人类的学习技能是否可以借用以帮助机器学习更好。具体来说，我们旨在使这些技能形式化并利用它们来训练更好的机器学习（ML）模型。为了实现这一目标，我们开发了一个通用框架 - Skillearn，该框架提供了一种有原则的方式来数学地代表人类的学习技能，并使用正式代表的技能来改善ML模型的培训。在两个案例研究中，我们将Skillearn应用于正式的人类学习技能：通过通过测试和交织学习，并使用正式的技能来改善神经体系结构搜索。各种数据集的实验表明，使用Skillearn正式的技能进行培训，ML模型的性能明显更好。
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.
