论文标题
潜在空间探索器：云上无监督的数据模式发现
Latent Space Explorer: Unsupervised Data Pattern Discovery on the Cloud
论文作者
论文摘要
从原始数据中提取信息可能是实验科学企业的中心活动之一。这项工作是关于一个管道，在该管道中，训练特定模型以提供训练数据的紧凑，必不可少的表示，这是可视化和分析旨在检测模式，数据规律性的分析的起点。为了使研究人员能够利用这种方法，正在尼亚尼亚项目中开发和测试一个基于云的系统，这是将提供给EOSC的主题服务的ML工具之一。在这里，我们描述了系统的体系结构，并在天文学背景下介绍了两个示例用例。
Extracting information from raw data is probably one of the central activities of experimental scientific enterprises. This work is about a pipeline in which a specific model is trained to provide a compact, essential representation of the training data, useful as a starting point for visualization and analyses aimed at detecting patterns, regularities among data. To enable researchers exploiting this approach, a cloud-based system is being developed and tested in the NEANIAS project as one of the ML-tools of a thematic service to be offered to the EOSC. Here, we describe the architecture of the system and introduce two example use cases in the astronomical context.
