Merge branch 'JOSS-2023' of https://github.com/jet-net/JetNet into JO…

…SS-2023
jet-net · Aug 18, 2023 · 1cb2d40 · 1cb2d40
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diff --git a/paper.bib b/paper.bib
@@ -9,16 +9,16 @@ @article{Chen:2021euv
  year = "2021"
 }
 
-@incollection{NEURIPS2019_9015, 
- title = {PyTorch: An Imperative Style, High-Performance Deep Learning Library}, 
- author = {Paszke, Adam and Gross, Sam and Massa, Francisco and Lerer, Adam and Bradbury, James and Chanan, Gregory and Killeen, Trevor and Lin, Zeming and Gimelshein, Natalia and Antiga, Luca and Desmaison, Alban and Kopf, Andreas and Yang, Edward and DeVito, Zachary and Raison, Martin and Tejani, Alykhan and Chilamkurthy, Sasank and Steiner, Benoit and Fang, Lu and Bai, Junjie and Chintala, Soumith}, 
+@incollection{NEURIPS2019_9015,
+ title = {PyTorch: An Imperative Style, High-Performance Deep Learning Library},
+ author = {Paszke, Adam and Gross, Sam and Massa, Francisco and Lerer, Adam and Bradbury, James and Chanan, Gregory and Killeen, Trevor and Lin, Zeming and Gimelshein, Natalia and Antiga, Luca and Desmaison, Alban and Kopf, Andreas and Yang, Edward and DeVito, Zachary and Raison, Martin and Tejani, Alykhan and Chilamkurthy, Sasank and Steiner, Benoit and Fang, Lu and Bai, Junjie and Chintala, Soumith},
  booktitle = {Advances in Neural Information Processing Systems},
  volume = {32},
- pages = {8024}, 
+ pages = {8024},
  year = {2019},
  eprint = {1912.01703},
  archivePrefix = {arXiv},
- publisher = {Curran Associates, Inc.}, 
+ publisher = {Curran Associates, Inc.},
  url = {http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf},
  editor = {H. Wallach and H. Larochelle and A. Beygelzimer and F. d\textquotesingle Alch\'{e}-Buc and E. Fox and R. Garnett}
 }

diff --git a/paper.md b/paper.md
@@ -28,7 +28,7 @@ bibliography: paper.bib
 
 # Summary
 
-`JetNet` is a Python package that aims to increase accessibility and reproducibility for machine learning (ML) research in high energy physics (HEP), especially related to particle jets. Based on the popular PyTorch ML framework, it provides easy-to-access and standardized interfaces for multiple heterogeneous HEP datasets as well as implementations of evaluation metrics, loss functions, and more general utilities relevant to HEP. 
+`JetNet` is a Python package that aims to increase accessibility and reproducibility for machine learning (ML) research in high energy physics (HEP), especially related to particle jets. Based on the popular PyTorch ML framework, it provides easy-to-access and standardized interfaces for multiple heterogeneous HEP datasets as well as implementations of evaluation metrics, loss functions, and more general utilities relevant to HEP.