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Added doi, updated FAB citation
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VincentStimper committed Jun 12, 2023
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12 changes: 7 additions & 5 deletions paper/paper.bib
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Expand Up @@ -61,7 +61,8 @@ @article{noe2019boltzmann
volume={365},
number={6457},
year={2019},
publisher={American Association for the Advancement of Science}
publisher={American Association for the Advancement of Science},
doi={10.1126/science.aaw1147}
}

@article{kobyzev2021,
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title = {Tensor{F}low {D}istributions},
author = {Joshua V. Dillon and Ian Langmore and Dustin Tran and Eugene Brevdo and Srinivas Vasudevan and Dave Moore and Brian Patton and Alex Alemi and Matt Hoffman and Rif A. Saurous},
journal = {arXiv preprint arXiv:1711.10604},
year = {2017}
year = {2017},
doi={10.48550/arXiv.1711.10604}
}

@software{deepmind2020jax,
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year = {2020}
}

@article{Midgley2022,
@article{Midgley2023,
title={{Flow Annealed Importance Sampling Bootstrap}},
author={Laurence Illing Midgley and Vincent Stimper and Gregor N. C. Simm and Bernhard Schölkopf and José Miguel Hernández-Lobato},
journal={arXiv preprint arXiv:2208.01893},
year={2022}
journal={International Conference on Learning Representations},
year={2023}
}

@article{boltzgen,
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4 changes: 2 additions & 2 deletions paper/paper.md
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Expand Up @@ -67,12 +67,12 @@ distributions. First, sampling layers needed for Stochastic Normalizing Flows
[@wu2020stochasticNF; @nielsen2020] are included. Second, Neural Spline Flows on circular coordinates
are supported [@rezende2020], which can be combined with standard coordinates on bounded or unbounded
intervals. They are needed when modeling the internal coordinates of molecules consisting of angles
and lengths [@Midgley2022]. Furthermore, there is an extension for `normflows` that adds Boltzmann
and lengths [@Midgley2023]. Furthermore, there is an extension for `normflows` that adds Boltzmann
distributions as targets as well as flow layers converting between Cartesian and internal coordinates
[@boltzgen].

Our package has already been used in several scientific projects and publications
[@campbell2021gradient; @stimper2021; @Midgley2022]. Due to its modular nature, `normflows` can be
[@campbell2021gradient; @stimper2021; @Midgley2023]. Due to its modular nature, `normflows` can be
easily extended to house new flow layers, base distributions, or other tools. For instance,
[@stimper2021] extends the package by adding resampled base distributions, which overcome an
architectural weakness of normalizing flows and make them more expressive.
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