@article{1482612,
      note = {Includes index.},
      author = {Bufano, Filomena, and Riggi, Simone, and Sciacca, Eva, and  Schilliro, Francesco,},
      url = {http://library.usi.edu/record/1482612},
      title = {Machine learning for astrophysics : proceedings of the  ML4Astro International Conference 30 May-1 June 2022 /.  International Conference on Machine Learning for  Astrophysics},
      abstract = {This book reviews the state of the art in the exploitation  of machine learning techniques for the astrophysics  community and gives the reader a complete overview of the  field. The contributed chapters allow the reader to easily  digest the material through balanced theoretical and  numerical methods and tools with applications in different  fields of theoretical and observational astronomy. The book  helps the reader to really understand and quantify both the  opportunities and limitations of using machine learning in  several fields of astrophysics.},
      doi = {https://doi.org/10.1007/978-3-031-34167-0},
      recid = {1482612},
      pages = {1 online resource (180 pages) :},
}