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@INPROCEEDINGS{Nicoli:619664,
      author       = {Nicoli, Kim A. and Anders, Christopher J. and Funcke, Lena
                      and Jansen, Karl and Nakajima, Shinichi and Kessel, Pan},
      title        = {{N}eu{L}at: a toolbox for neural samplers in lattice field
                      theories},
      journal      = {Proceedings of Science / International School for Advanced
                      Studies},
      volume       = {(LATTICE2023)},
      issn         = {1824-8039},
      address      = {Trieste},
      publisher    = {SISSA},
      reportid     = {PUBDB-2024-07803},
      series       = {2752003},
      pages        = {286},
      year         = {2024},
      abstract     = {The application of normalizing flows for sampling in
                      lattice field theory has garnered considerable attention in
                      recent years. Despite the growing community at the
                      intersection of machine learning (ML) and lattice field
                      theory, there is currently a lack of a software package that
                      facilitates efficient software development for new ideas in
                      this field. We present NeuLat, a fully customizable software
                      package that unifies recent advances in the fast-growing
                      field of deep generative models for lattice field theory in
                      a single software library. NeuLat is designed to be modular,
                      supports a variety of lattice field theories as well as
                      normalizing flow architectures, and is easily extensible. We
                      believe that NeuLat has the potential to considerably
                      simplify the application and benchmarking of ML methods for
                      lattice quantum field theories and beyond},
      month         = {Jul},
      date          = {2023-07-30},
      organization  = {40th International Symposium on
                       Lattice Field Theory, Batavia (United
                       States), 30 Jul 2023 - 5 Aug 2023},
      keywords     = {lattice field theory (INSPIRE) / programming (INSPIRE) /
                      flow (INSPIRE) / lattice (INSPIRE) / modular (INSPIRE) /
                      machine learning (INSPIRE)},
      cin          = {CQTA},
      ddc          = {530},
      cid          = {I:(DE-H253)CQTA-20221102},
      pnm          = {611 - Fundamental Particles and Forces (POF4-611) / QUEST -
                      QUantum computing for Excellence in Science and Technology
                      (101087126)},
      pid          = {G:(DE-HGF)POF4-611 / G:(EU-Grant)101087126},
      experiment   = {EXP:(DE-MLZ)NOSPEC-20140101},
      typ          = {PUB:(DE-HGF)16 / PUB:(DE-HGF)8 / PUB:(DE-HGF)7},
      doi          = {10.22323/1.453.0286},
      url          = {https://bib-pubdb1.desy.de/record/619664},
}