Active Meta-Learning

Abstract

This thesis outlines the methods used in machine learning to generate models which are effective on a variety of tasks. We begin with a quick overview of the field of machine learning, covering the topics necessary to understand more complex learning algorithms. We then discuss the field of transfer learning and the common strategies used to train models so that they can function in different domains and solve different tasks. We then continue with current research in the field of meta-learning, which aims to find optimal solutions to the problem of learning how to learn. We introduce model-agnostic meta-learning, or MAML, as an algorithm which addresses the difficulty of few-shot learning on a variety of tasks. We then consider PLATIPUS as a tool for reasoning about model uncertainty in meta-learned models. Using this uncertainty,we explore active learning as a means of selecting the optimal examples to train our models. Using PLATIPUS and active learning, we seek to address the problem of exploring the space of chemical reactions efficiently.