论文标题
在开放的实验环境中积极学习：根据深内核学习的可预测性选择正确的信息渠道
Active learning in open experimental environments: selecting the right information channel(s) based on predictability in deep kernel learning
论文作者
论文摘要
主动学习方法正在迅速成为成像，材料合成和计算中自动化实验工作流程的组成部分。许多实验场景的独特方面是存在多个信息通道，包括测量系统的内在方式和外源环境和噪声信号。实验研究中的关键任务之一是建立这些渠道中的哪个可以预测感兴趣的行为。在这里，我们探讨了在主动实验设置中使用深内核学习发现模态选择的结构 - 特性关系的最佳预测通道的问题。我们进一步提出，这种方法可以直接适用于自动合成中类似的主动学习任务，并发现分子系统中定量结构 - 活性关系。
Active learning methods are rapidly becoming the integral component of automated experiment workflows in imaging, materials synthesis, and computation. The distinctive aspect of many experimental scenarios is the presence of multiple information channels, including both the intrinsic modalities of the measurement system and the exogenous environment and noise signals. One of the key tasks in experimental studies is hence establishing which of these channels is predictive of the behaviors of interest. Here we explore the problem of discovery of the optimal predictive channel for structure-property relationships (in microscopy) using deep kernel learning for modality selection in an active experiment setting. We further pose that this approach can be directly applicable to similar active learning tasks in automated synthesis and the discovery of quantitative structure-activity relations in molecular systems.
