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.
@article{arxiv.2204.13933,
title = {Latent Space Explorer: Unsupervised Data Pattern Discovery on the Cloud},
author = {T. Cecconello and C. Bordiu and F. Bufano and L. Puerari and S. Riggi and E. Schisano and E. Sciacca and Y. Maruccia and G. Vizzari},
journal= {arXiv preprint arXiv:2204.13933},
year = {2022}
}
Comments
4 pages, 3 figures, proceedings of ADASS XXXI conference, to be published in ASP Conference Series