In this work, we introduce the concept of Active Representation Learning, a novel class of problems that intertwines exploration and representation learning within partially observable environments. We extend ideas from Active Simultaneous Localization and Mapping (active SLAM), and translate them to scientific discovery problems, exemplified by adaptive microscopy. We explore the need for a framework that derives exploration skills from representations that are in some sense actionable, aiming to enhance the efficiency and effectiveness of data collection and model building in the natural sciences.

翻译：本文提出了主动表征学习的概念，这是一类在部分可观测环境中将探索与表征学习相结合的新型问题。我们拓展了主动同步定位与建图（active SLAM）的思想，并将其转化为以自适应显微镜为代表的科学发现问题。我们探讨了构建一种框架的必要性，该框架需从具有可操作性的表征中衍生出探索能力，旨在提升自然科学领域数据采集与模型构建的效率和效果。