http://arxiv.org/abs/2204.13933
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.
T. Cecconello, C. Bordiu, F. Bufano, et. al.
Mon, 2 May 22
22/52
Comments: 4 pages, 3 figures, proceedings of ADASS XXXI conference, to be published in ASP Conference Series