Repository: iml-wg/HEP-ML-Resources
Branch: master
Commit: d5b215b54713
Files: 10
Total size: 112.0 KB
Directory structure:
gitextract_a94d46y7/
├── .gitignore
├── CONTRIBUTING.md
├── LICENSE
├── README.md
├── papers.md
└── tex/
├── HEPML.bib
├── HEPML.tex
├── JHEP.bst
├── Makefile
└── uiuchept.bst
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FILE CONTENTS
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FILE: .gitignore
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# Specifies intentionally untracked files that Git should ignore
*~
# Ignore LaTeX auxiliary files and output
*.aux
*.bbl
*.blg
*.log
*.out
*.pdf
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FILE: CONTRIBUTING.md
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# Contributing
To contribute to this project please either start a pull request or an issue with the details of the information that you want added.
## Pull Request Process
1. Beautify your `.md` file using [Tidy Markdown](https://github.com/slang800/tidy-markdown)
2. Verify that your Markdown has been correctly formatted by rendering your `.md` with [Grip](https://github.com/joeyespo/grip)
3. If you have edited the LaTeX file make sure to run `make realclean`
4. You may merge the pull request in once you have the sign-off of at least one maintainer. If you do not have permission to make the merge, request the approving maintainer to merge it for you.
## Areas of Requested Help
1. Adding content across experiments
2. An additional volunteer maintainer
3. Restructuring the layout design to be more readable
## FAQ
### There is a subject not listed that I think should be. How do I get it added to the listing?
If there is content missing that you'd like added please create an issue with as much description as possible (and maybe some examples). A maintainer will add the content once it has been approved. Alternatively, feel free branch the repository and add the content you want and then create a pull request.
### How do I add a paper?
All paper additions should be submitted as a single pull request on a source branch that isn't `master`.
1. Find the paper on [INSPIRE](https://inspirehep.net/?ln=en)
- **N.B.:** If you have already found the paper on [arXiv](https://arxiv.org/) you should be able to find the INSPIRE listing linked under "References & Citations"
2. Get the BibTeX for the paper citation provided by INSPIRE (under "Export" at the bottom of the page)
3. Add this BibTeX entry to [`tex/HEPML.bib`](https://github.com/iml-wg/HEP-ML-Resources/blob/master/tex/HEPML.bib) in the appropriate chronological position
4. Add a citation to [`tex/HEPML.tex`](https://github.com/iml-wg/HEP-ML-Resources/blob/master/tex/HEPML.tex) in the appropriate chronological position
5. `make` and copy the resulting citation into [`papers.md`](https://github.com/iml-wg/HEP-ML-Resources/blob/master/papers.md) in the appropriate chronological position
- You will need to add the appropriate hyperlinks. Follow the style used for previous entries.
6. Add and commit `tex/HEPML.bib`, `tex/HEPML.tex`, and `papers.md` to your pull request
### How do I add a workshop?
1. Update the workshop timeline. Move any workshops that have recently occurred from "Upcoming" to "Past"
2. Paste the full title of the workshop into the correct chronological position in its appropriate section ("Upcoming" or "Past")
3. Hyperlink the full title to the workshop Indico page or website
4. Add the dates of the workshop
5. If the workshop was restricted to a specific experiment add a badge following the entry with
- SUBJECT = restricted
- STATUS = the experiment/group the workshop was restricted to
- COLOR = red
### How do I make a badge/shield?
Badges are made using [shields.io](http://shields.io/). Scroll down to the "Your Badge" section of the page and then fill in the subject, status, and color for the badge that you want and then click "Make Badge". Then simply paste
```
```
into the `.md` file.
Badges do not have to be static. For example, you can have a badge that automatically detects the license for a given repository:
```
[](https://github.com/iml-wg/HEP-ML-Resources/blob/master/LICENSE)
```
generates the linked badge
[](https://github.com/iml-wg/HEP-ML-Resources/blob/master/LICENSE)
================================================
FILE: LICENSE
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MIT License
Copyright (c) 2017 Matthew Feickert
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
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FILE: README.md
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# HEPML Resources
[](https://zenodo.org/badge/latestdoi/89476450)
[](https://opensource.org/licenses/MIT)
Listing of useful (mostly) public learning resources for machine learning applications in high energy physics (HEPML). Listings will be in reverse chronological order (like a CV).
> **N.B.:** This listing will almost certainly be biased towards work done by ATLAS scientists, as the maintainer is a member of ATLAS and so sees ATLAS work the most. However, this is not the desired case and [help to diversify this listing](#contributing) would be greatly appreciated.
## Table of contents
- [Introductory Material](#introductory-material)
- [Lectures](#lectures)
- [Seminar Series](#seminar-series)
- [Tutorials](#tutorials)
- [Schools](#schools)
- [Courses](#courses)
- [Journals](#journals)
- [Software](#software)
- [Public Datasets](#public-datasets)
- [Papers](#papers)
- [Workshops](#workshops)
- [Tweets](#tweets)
- [Other HEP Resource Collections](#other-hep-resource-collections)
- [People](#people)
- [Contributing](#contributing)
## Introductory Material 
### Lectures
- [Introduction to GANs](https://indico.cern.ch/event/655447/contributions/2742176/), by [Luke de Oliveira](https://ldo.io/) (November 3, 2017)
- [Frontiers with GANs](https://indico.cern.ch/event/655447/contributions/2742180/), by [Michela Paganini](http://mickypaganini.github.io) (November 3, 2017)
- [Nikhef Colloquium: "Teaching machines to discover particles"](https://indico.nikhef.nl/event/878/), by [Gilles Louppe](https://glouppe.github.io/) (September 29, 2017)
- [CERN Academic Training Lecture Regular Programme](https://indico.cern.ch/category/72/), April 2017 (Machine Learning):
- [Machine Learning (Lecture 1)](https://indico.cern.ch/event/619370/) --- [Michael Kagan](https://www.linkedin.com/in/michael-kagan-06292616/) (SLAC)
- [Machine Learning (Lecture 2)](https://indico.cern.ch/event/619371/) --- [Michael Kagan](https://www.linkedin.com/in/michael-kagan-06292616/) (SLAC)
- [Deep Learning and Vision](https://indico.cern.ch/event/619372/) --- [Jonathon Shlens](https://research.google.com/pubs/JonathonShlens.html) (Google Research)
- [Deep Learning in High Energy Physics](https://youtu.be/cSxQPFb0yOw), by [Amir Farbin](http://www.uta.edu/physics/pages/faculty/profiles/farbin/index.html)
### Seminar Series
- [CERN Data Science Seminars](https://indico.cern.ch/category/9320/)
- [Inter-Experimental LHC Machine Learning Working Group](https://iml.web.cern.ch/) Guest Seminars:
- [Open challenges for improving Generative Adversarial Networks (GANs)](https://indico.cern.ch/event/673989/), by [Ian Goodfellow](http://www.iangoodfellow.com/) (October 27, 2017)
### Tutorials
- [PyTorch Deep Learning Minicourse](https://github.com/Atcold/pytorch-Deep-Learning-Minicourse) - [CoDaS-HEP 2018](https://indico.cern.ch/event/707498/timetable/), by [Alfredo Canziani](https://github.com/Atcold) [](https://mybinder.org/v2/gh/Atcold/PyTorch-Deep-Learning-Minicourse/master)
- [Intro Tutorial on GANs](https://indico.fnal.gov/event/16720/), by [Michela Paganini](http://mickypaganini.github.io) [](https://mybinder.org/v2/gh/mickypaganini/gan_tutorial/master)
- [Scikit-learn Tutorial](https://indico.cern.ch/event/595059/contributions/2522192/), by [Gilles Louppe](https://glouppe.github.io/) [](https://mybinder.org/v2/gh/glouppe/tutorials-iml2017/master) [](https://nbviewer.jupyter.org/github/glouppe/tutorials-iml2017/tree/master/)
- [TMVA Tutorial](https://indico.cern.ch/event/595059/contributions/2522191/), by [Lorenzo Moneta](https://phonebook.cern.ch/phonebook/#personDetails/?id=415998)
- [Keras and TMVA interfaces Tutorial](https://indico.cern.ch/event/595059/contributions/2522193/), by [Stefan Wunsch](https://www.ims.kit.edu/14_117.php)
- [Boosted Decision Tree Tutorial (using XGBoost)](https://github.com/k-woodruff/bdt-tutorial), by [Katherine Woodruff](https://tele.fnal.gov/cgi-bin/telephone.script?type=name_last&accuracy=contains&string=WOODRUFF) [](https://mybinder.org/v2/gh/k-woodruff/bdt-tutorial/master) [](http://nbviewer.jupyter.org/github/k-woodruff/bdt-tutorial/tree/master/)
- [Introduction to Deep Learning with Keras Tutorial](https://gist.github.com/lukedeo/0654e7310432d6d435126c556b863907), by [Luke de Oliveira](https://ldo.io/)
- [Introduction to Deep Learning with Keras Tutorial](https://indico.cern.ch/event/487416/contributions/2174907/) - [2nd Developers@CERN Forum](https://indico.cern.ch/event/487416/timetable/), by [Michela Paganini](http://mickypaganini.github.io)
### Schools
#### HEP-ML:
##### Upcoming:
- [Deep Learning for Science Summer School 2019, Berkeley, CA, USA](https://dl4sci-school.lbl.gov/home) (July 15-19, 2019)
##### Past:
- [5th Machine Learning in High Energy Physics Summer School 2019](https://indico.cern.ch/event/768915/) (July 1-10, 2019)
- Associated [Yandex School of Data Analysis](https://github.com/yandexdataschool) repo: [mlhep2019](https://github.com/yandexdataschool/mlhep2019)
- [4th Machine Learning in High Energy Physics Summer School 2018](https://indico.cern.ch/event/687473/) (August 6-12, 2018)
- Associated [Yandex School of Data Analysis](https://github.com/yandexdataschool) repo: [mlhep2018](https://github.com/yandexdataschool/mlhep2018)
- [2nd Computational and Data Science school for High Energy Physics (CoDaS-HEP 2018)](https://indico.cern.ch/event/707498/) (July 23-27, 2018)
- [3rd Machine Learning in High Energy Physics Summer School 2017](https://indico.cern.ch/event/613571/) (July 17-23, 2017)
- Associated [Yandex School of Data Analysis](https://github.com/yandexdataschool) repo: [mlhep2017](https://github.com/yandexdataschool/mlhep2017)
- [1st Computational and Data Science School for High Energy Physics (CoDaS-HEP)](https://indico.cern.ch/event/625333/) (July 10-13, 2017)
- [2nd Machine Learning in High Energy Physics Summer School 2016](https://indico.cern.ch/event/497368/overview) (June 20-26, 2016)
- Associated [Yandex School of Data Analysis](https://github.com/yandexdataschool) repo: [mlhep2016](https://github.com/yandexdataschool/mlhep2016)
- [1st Machine Learning in High Energy Physics Summer School 2015](https://www.hse.ru/mlhep2015) (August 27-30, 2015)
- Associated [Yandex School of Data Analysis](https://github.com/yandexdataschool) repo: [mlhep2015](https://github.com/yandexdataschool/mlhep2015)
#### Deep Learning:
##### Upcoming:
- [Machine Learning Summer School 2019, London, UK](https://sites.google.com/view/mlss-2019/home?authuser=0) (July 15–26, 2019)
- [Machine Learning Summer School 2019, Stellenbosch, South Africa](http://mlssafrica.com/) (January 7-18, 2019)
##### Past:
- [Machine Learning Summer School 2018, Madrid, Spain](http://mlss.ii.uam.es/mlss2018/index.html) (August 27 - September 7, 2018)
- [Machine Learning Summer School 2018, Buenos Aires, Argentina](http://mlss2018.net.ar/) (June 18-20, 2018)
- [Deep Learning and Reinforcement Learning Summer School, Toronto, Canada](https://dlrlsummerschool.ca/) (July 25 - August 3, 2018)
- [PAISS: Artificial Intelligence Summer School, Grenoble, France](https://project.inria.fr/paiss/) (July 2-6, 2018)
- [Deep Learning Summer School 2016](https://sites.google.com/site/deeplearningsummerschool2016/) (August 1-7, 2016)
### Courses
- [Advanced Machine Learning](http://www.montefiore.ulg.ac.be/~geurts/Cours/AML/aml2017_2018.html), Pierre Geurts, [Gilles Louppe](https://glouppe.github.io/), and Louis Wehenkel (Spring, 2018 - Université de Liège, Institut Montefiore)
- [Applications of Deep Learning to High Energy Physics](https://wiki.uta.edu/display/~afarbin/Physics+4%285%29391-002+-+Applications+of+Deep+Learning+to+High+Energy+Physics), [Amir Farbin](http://www.uta.edu/physics/pages/faculty/profiles/farbin/index.html) (Spring, 2017 - University of Texas at Arlington)
- Associated GitHub repository: [DSatHEP-Tutorial](https://github.com/UTA-HEP-Computing/DSatHEP-Tutorial)
- [Tensorflow for Deep Learning Research](https://web.stanford.edu/class/cs20si/syllabus.html), (Spring, 2017 - Stanford Univeristy)
- Introduction to Machine Learning and Convolutional Neural Networks for Visual Recognition:
- [Spring, 2017](https://www.youtube.com/watch?v=vT1JzLTH4G4&list=PL3FW7Lu3i5JvHM8ljYj-zLfQRF3EO8sYv) - Stanford University, [Fei-Fei Li](http://vision.stanford.edu/feifeili/), [Justin Johnson](http://cs.stanford.edu/people/jcjohns/), [Serena Yeung](http://ai.stanford.edu/~syyeung/)
- [Winter, 2016](https://www.youtube.com/watch?v=NfnWJUyUJYU) - Stanford University, [Andrej Karpathy](https://cs.stanford.edu/people/karpathy/), [Fei-Fei Li](http://vision.stanford.edu/feifeili/), [Justin Johnson](http://cs.stanford.edu/people/jcjohns/)
### Journals
- [Distill Research Journal](http://distill.pub/)
## Software
#### Common software tools and environments used in HEP for ML
- Python environments for scientific computing
- The [Conda package and environment manager](https://conda.io/docs/) and [Anaconda](https://www.continuum.io/anaconda-overview) Python library collection
- [Using ROOT/PyROOT with Conda and NumPy](https://indico.cern.ch/event/619371/attachments/1450504/2236434/Kagan_Lecture2.pdf#page=99)
- [scikit-learn](http://scikit-learn.org/stable/): General machine learning Python library
- [TMVA](https://root.cern.ch/tmva): ROOT's builtin machine learning package
- [TMVA-branch-adder](https://github.com/pseyfert/tmva-branch-adder): wrapper to add TMVA response to TTree without boiler plate code
### High level deep learning libraries/framework APIs
- [Keras](https://keras.io/)
### Deep learning frameworks
- [TensorFlow](https://www.tensorflow.org/)
- [Theano](http://deeplearning.net/software/theano/)
- [PyTorch](http://pytorch.org/)
- [Caffe2](https://caffe2.ai/)
- [List of Conversion Tools For Saved Networks](https://github.com/ysh329/deep-learning-model-convertor)
### HEP to ML bridge tools
- [lwtnn](https://github.com/lwtnn/lwtnn): Tool to run Keras networks in C++ code
- [sklearn-porter](https://github.com/nok/sklearn-porter): Transpile trained scikit-learn estimators to C, Java, JavaScript and others
- [ONNX](https://onnx.ai) open format to represent deep learning models
- [Scikit-HEP](http://scikit-hep.org/): Toolset of interfaces and Python tools for Particle Physics
- [root_numpy](https://github.com/scikit-hep/root_numpy): The interface between ROOT and numpy
- [root_pandas](https://github.com/scikit-hep/root_pandas): An upgrade of root_numpy to use with pandas
- [uproot](https://github.com/scikit-hep/uproot): Mimimalist ROOT to numpy converter (no dependency on ROOT)
- [ttree2hdf5](https://github.com/dguest/ttree2hdf5): Mimimalist ROOT to HDF5 converter (written in C++)
- [hep_ml](https://github.com/arogozhnikov/hep_ml): Python algorithms and tools for HEP ML use cases
### Images for Containerized Environments
- [ATLAS Machine Learning Docker images](https://gitlab.cern.ch/aml/containers/docker): Base images for a modern Python 3 machine learning environment for physics
<!-- ## Notebooks - [Vince Croft RooFit Notebooks](https://www.nikhef.nl/~vcroft/notebooks.html) -->
## Public Datasets
- [CERN IML public datasets listing](https://iml.web.cern.ch/public-datasets): Listing of public datsets that are used for machine learning studies at the LHC.
## Papers
> A `.bib` file for all papers listed is [available in the `tex` directory](https://github.com/iml-wg/HEP-ML-Resources/blob/master/tex/HEPML.bib).
A listing of papers of applications of machine learning to high energy physics can be found in [`papers.md`](https://github.com/iml-wg/HEP-ML-Resources/blob/master/papers.md).
## Workshops
### Upcoming
- TBA
### Past
- [Machine Learning for Jet Physics (2020)](https://indico.cern.ch/event/809820/) (January 15-17, 2020)
- [Machine Learning and the Physical Sciences at NIPS](https://ml4physicalsciences.github.io/) (December 14, 2019)
- [4th ATLAS Machine Learning Workshop (2019)](https://indico.cern.ch/event/844092/) (November 11-15, 2019)
- [Fast Machine Learning IRIS-HEP Blueprint Workshop](https://indico.cern.ch/event/822126/) (September 10-13, 2019)
- [3rd CMS Machine Learning Workshop (2019)](https://indico.cern.ch/event/798721/) (June 17-19, 2019) 
- [Theoretical Physics for Deep Learning at ICML 2019](https://sites.google.com/view/icml2019phys4dl) (June 14, 2019)
- [3rd IML Machine Learning Workshop (2019)](https://indico.cern.ch/event/766872/) (April 15-18, 2019)
- [Accelerating the Search for Dark Matter with Machine Learning (2019)](http://indico.ictp.it/event/8674/) (April 8-12, 2019)
- [5th Connecting The Dots / Intelligent Trackers (2019)](https://indico.cern.ch/event/742793/) (April 2-5, 2019)
- [19th International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT 2019)](https://indico.cern.ch/event/708041/) (March 11-15, 2019)
- [Machine Learning for Jet Physics (2018)](https://indico.cern.ch/event/745718/) (November 14-16, 2018)
- [3rd ATLAS Machine Learning Workshop (2018)](https://indico.cern.ch/event/735932/) (October 15-17, 2018)
- [2nd CMS Machine Learning Workshop (2018)](https://indico.cern.ch/event/730677/) (July 2-4, 2018) 
- [2nd IML Machine Learning Workshop (2018)](https://indico.cern.ch/event/668017/) (April 9-12, 2018)
- [Machine Learning for Phenomenology (2018)](https://conference.ippp.dur.ac.uk/event/660/) (April 3-6, 2018)
- [4th International Connecting The Dots Workshop (2018)](https://indico.cern.ch/event/658267/) (March 20-22, 2018)
- [Accelerating the Search for Dark Matter with Machine Learning (2018)](https://indico.cern.ch/event/664842/) (January 15-19, 2018)
- [Machine Learning for Jet Physics (2017)](https://indico.physics.lbl.gov/indico/event/546/) (December 11-13, 2017)
- [Deep Learning for Physical Sciences at NIPS](https://dl4physicalsciences.github.io/) (December 8, 2017)
- [18th International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT 2017)](https://indico.cern.ch/event/567550/) (August 21-25, 2017)
- [Hammers & Nails - Machine Learning & HEP](https://www.weizmann.ac.il/conferences/SRitp/Summer2017/) (July 19-28, 2017)
- [CMS Machine Learning Workshop (2017)](https://indico.cern.ch/event/646801) (July 5-6, 2017) 
- [2nd ATLAS Machine Learning Workshop (2017)](https://indico.cern.ch/event/630665/overview) (June 6-9, 2017) 
- [Workshop on Machine Learning and b-tagging](https://indico.cern.ch/event/615994/overview) (May 23-26, 2017) 
- [DS@HEP 2017](https://indico.fnal.gov/conferenceDisplay.py?confId=13497) (May 8-12, 2017)
- [2nd S2I2 HEP/CS Workshop (Parallel Session)](https://indico.cern.ch/event/622920/timetable/#5-parallel-session-machine-lea) (May 1-3, 2017)
- [CERN openlab workshop on Machine Learning and Data Analytics](https://indico.cern.ch/event/627852/) (April 27, 2017)
- [First IML Workshop on Machine Learning](https://indico.cern.ch/event/595059/) (March 20-22, 2017)
- [DS@HEP at the Simons Foundation](https://indico.hep.caltech.edu/indico/conferenceDisplay.py?confId=102) (July 5-7, 2016)
- [ALICE Mini-Workshop 2016: Statistical Methods and Machine Learning Tutorial](https://indico.cern.ch/event/514695/) (May 18, 2016) 
- [ATLAS Machine Learning Workshop (2016)](https://indico.cern.ch/event/483999/) (March 29-31, 2016) 
- [Heavy Flavour Data Mining workshop](https://indico.cern.ch/event/433556/) (February 18-20, 2016)
- [Data Science @ LHC 2015](https://indico.cern.ch/event/395374/) (November 9-13, 2015)
## Tweets
- [#HEPML collection of tweets](https://twitter.com/search?q=HEPML&src=typd)
## People
- [HEPML directory](http://mickypaganini.github.io/HEPML_directory): Opt-in list of people working at the intersection of Machine Learning and High Energy Physics
- Add yourself through the [Google form](https://t.co/jprokVZEiK)
## Other HEP Resource Collections
- [HEP Software Foundation](https://github.com/hsf-training)'s list of [Python Libraries of Interest to Particle Physics](https://github.com/hsf-training/PyHEP-resources)
## Contributing
Contributions to help improve the listing are very much welcome! Please read [CONTRIBUTING.md](https://github.com/matthewfeickert/HEP-ML-Resources/blob/master/CONTRIBUTING.md) for details on the process for submitting pull requests or filing issues.
## Authors
Listing maintainer: [Matthew Feickert](http://www.matthewfeickert.com/)
## Acknowledgments
- Following [PurpleBooth](https://github.com/PurpleBooth)'s [README style](https://gist.github.com/PurpleBooth/109311bb0361f32d87a2)
- All badges made by [shields.io](http://shields.io/)
- Inspiration for this listing came from the [Awesome Machine Learning](https://github.com/josephmisiti/awesome-machine-learning) repo and [Dustin Tran](http://dustintran.com/)'s [Machine Learning Videos](https://github.com/dustinvtran/ml-videos) repo
- Many thanks to [everyone who has contributed their time](https://github.com/iml-wg/HEP-ML-Resources/graphs/contributors) to improve this project
================================================
FILE: papers.md
================================================
# [HEPML Resources ↩](https://github.com/iml-wg/HEP-ML-Resources#papers)
## HEPML Papers
> A `.bib` file for all papers listed is [available in the `tex` directory](https://github.com/iml-wg/HEP-ML-Resources/blob/master/tex/HEPML.bib).
- M. C. Romao, N. Castro, J. Milhano, R. Pedro, and T. Vale, "[Use of a Generalized Energy Mover’s Distance in the Search for Rare Phenomena at Colliders](https://inspirehep.net/literature/1791839)," arXiv:2004.09360 [hep-ph]. (April 21, 2020)
- G. Kanwar, M. S. Albergo, D. Boyda, K. Cranmer, D. C. Hackett, S. Racanière, D. J. Rezende, and P. E. Shanahan, "[Equivariant flow-based sampling for lattice gauge theory](https://inspirehep.net/record/1785309)," arXiv:2003.06413 [hep-lat]. (March 13, 2020)
- J. Hollingsworth and D. Whiteson, "[Resonance Searches with Machine Learned Likelihood Ratios](https://inspirehep.net/record/1779859)," arXiv:2002.04699 [hep-ph]. (February 11, 2020)
- G. C. Strong, "[On the impact of modern deep-learning techniques to the performance and time-requirements of classification models in experimental high-energy physics](https://inspirehep.net/record/1778508)," arXiv:2002.01427 [physics.data-an]. (February 3, 2020)
- M. Romão Crispim, N. Castro, R. Pedro, and T. Vale, "[Transferability of Deep Learning Models in Searches for New Physics at Colliders](https://inspirehep.net/literature/1769198)," Phys. Rev. D 101 (2020) no. 3, 035042, arXiv:1912.04220 [hep-ph]. (December 9, 2019)
- A. Andreassen, P. T. Komiske, E. M. Metodiev, B. Nachman, and J. Thaler, "[OmniFold: A Method to Simultaneously Unfold All Observables](https://inspirehep.net/record/1766424)," arXiv:1911.09107 [hep-ph]. (November 20, 2019)
- B. Nachman and C. Shimmin, "[AI Safety for High Energy Physics](https://inspirehep.net/record/1759867)," arXiv:1910.08606 [hep-ph]. (October 18, 2019)
- M. Borisyak, N. Kazeev, "[Machine Learning on data with sPlot background subtraction](https://inspirehep.net/record/1737258)," arXiv:1905.11719 [cs.LG]. (May 28, 2019)
- S. Caron, T. Heskes, S. Otten and B. Stienen, "[Constraining the Parameters of High-Dimensional Models with Active Learning](https://inspirehep.net/literature/1735784)," Eur. Phys. J. C 79, 944 (2019), arXiv:1905.08628 [cs.LG]. (May 19, 2019)
- R. Di Sipio, M. Faucci Giannelli, S. Ketabchi Haghighat, and S. Palazzo, "[DijetGAN: A Generative-Adversarial Network Approach for the Simulation of QCD Dijet Events at the LHC](https://inspirehep.net/record/1723861)," arXiv:1903.02433 [hep-ex]. (March 6, 2019)
- K. Datta, A. Larkoski, and B. Nachman, "[Automating the Construction of Jet Observables with Machine Learning](https://inspirehep.net/record/1720832)," arXiv:1902.07180 [hep-ph]. (February 19, 2019)
- MicroBooNE Collaboration, "[Deep neural network for pixel-level electromagnetic particle identification in the MicroBooNE liquid argon time projection chamber](https://inspirehep.net/record/1689384)," Phys. Rev. D99 (2019) no. 9, 092001, arXiv:1808.07269 [hep-ex]. (August 22, 2018)
- M. Stoye, J. Brehmer, L. Gilles, J. Paez, and K. Cranmer, "[Likelihood-free inference with an improved cross-entropy estimator](https://inspirehep.net/record/1684960)," arXiv:1808.00973 [stat.ML]. (August 2, 2018)
- D. Bourgeois, C. Fitzpatrick, and S. Stahl, "[Using holistic event information in the Trigger](https://inspirehep.net/record/1684792)," arXiv:1808.00711 [physics.ins-det]. (August 2, 2018)
- M. Andrews, M. Paulini, S. Gleyzer, and B. Poczos, "[End-to-End Physics Event
Classification with the CMS Open Data: Applying Image-based Deep Learning on
Detector Data to Directly Classify Collision Events at the LHC](https://inspirehep.net/record/1684494)," arXiv:1807.11916
[hep-ex]. (July 31, 2018)
- J. Lin, M. Freytsis, I. Moult, and B. Nachman, "[Boosting H → bb̅ with Machine Learning](https://inspirehep.net/record/1684331)," arXiv:1807.10768 [hep-ph]. (July 27, 2018)
- K. Albertsson _et al._, "[Machine Learning in High Energy Physics Community White Paper](https://inspirehep.net/record/1681439)," arXiv:1807.02876 [physics.comp-ph]. (July 8, 2018)
- D. Guest, K. Cranmer, and D. Whiteson, "[Deep Learning and its Application to LHC Physics](https://inspirehep.net/record/1680302)," arXiv:1806.11484 [hep-ex]. (June 29, 2018)
- J. Brehmer, K. Cranmer, G. Louppe, and J. Pavez, "[Constraining Effective Field Theories with Machine Learning](https://inspirehep.net/record/1670936)," arXiv:1805.00013 [hep-ph]. (April 30, 2018)
- J. Brehmer, K. Cranmer, G. Louppe, and J. Pavez, "[A Guide to Constraining Effective Field Theories with Machine Learning](https://inspirehep.net/record/1670939)," arXiv:1805.00020 [hep-ph]. (April 30, 2018)
- CMS Collaboration, A. M. Sirunyan _et al._, "[Identification of heavy-flavour jets with the CMS detector in pp collisions at 13 TeV](https://inspirehep.net/record/1644362)," arXiv:1712.07158 [physics.ins-det]. (December 19, 2017)
- V. Estrade, C. Germain, I. Guyon and D. Rousseau, [Adversarial learning to eliminate systematic errors: a case study in High Energy Physics](https://dl4physicalsciences.github.io/files/nips_dlps_2017_1.pdf), Proceedings of the Deep Learning for Physical Sciences Workshop at NIPS (2017). (December 8, 2017)
- D. Weitekamp III, T. Nguyen, D. Anderson, R. Castello, M.Pierini, M. Spiropulu and J. Vlimant, [Deep topology classifiers for a more efficient trigger selection at the LHC](https://dl4physicalsciences.github.io/files/nips_dlps_2017_3.pdf), Proceedings of the Deep Learning for Physical Sciences Workshop at NIPS (2017). (December 8, 2017)
- L. Hertel, L. Li, P. Baldi and J. Bian, [Convolutional Neural Networks for Electron Neutrino and Electron Shower Energy Reconstruction in the NOvA Detectors](https://dl4physicalsciences.github.io/files/nips_dlps_2017_7.pdf), Proceedings of the Deep Learning for Physical Sciences Workshop at NIPS (2017). (December 8, 2017)
- M. Stoye, J. Kieseler, M. Verzetti, H. Qu, L. Gouskos, A. Stakia and the CMS Collaboration, [DeepJet: Generic physics object based jet multiclass classification for LHC experiments](https://dl4physicalsciences.github.io/files/nips_dlps_2017_10.pdf), Proceedings of the Deep Learning for Physical Sciences Workshop at NIPS (2017). (December 8, 2017)
- B. Hooberman, A. Farbin, G. Khattak, V. Pacela, M. Pierini, J. Vlimant, M. Spiropulu, W. Wei, M. Zhang and S. Vallecorsa, [Calorimetry with Deep Learning: Particle Classification, Energy Regression, and Simulation for High-Energy Physics](https://dl4physicalsciences.github.io/files/nips_dlps_2017_15.pdf), Proceedings of the Deep Learning for Physical Sciences Workshop at NIPS (2017). (December 8, 2017)
- M. Paganini, L. de Oliveira and B. Nachman, [Survey of Machine Learning Techniques for High Energy Electromagnetic Shower Classification](https://dl4physicalsciences.github.io/files/nips_dlps_2017_24.pdf), Proceedings of the Deep Learning for Physical Sciences Workshop at NIPS (2017). (December 8, 2017)
- L. de Oliveira, M. Paganini, and B. Nachman, [Tips and Tricks for Training GANs with Physics Constraints](https://dl4physicalsciences.github.io/files/nips_dlps_2017_26.pdf), Proceedings of the Deep Learning for Physical Sciences Workshop at NIPS (2017). (December 8, 2017)
- S. Farrell, P. Calafiura, M. Mudigonda, Prabhat, D. Anderson, J. Bendavid, M. Spiropoulou, J. Vlimant, S. Zheng, G. Cerati, L. Gray, J. Kowalkowski, P. Spentzouris, A. Tsaris and D. Zurawski, [Particle Track Reconstruction with Deep Learning](https://dl4physicalsciences.github.io/files/nips_dlps_2017_28.pdf), Proceedings of the Deep Learning for Physical Sciences Workshop at NIPS (2017). (December 8, 2017)
- I. Henrion, K. Cranmer, J. Bruna, K. Cho, J. Brehmer, G. Louppe and G. Rochette, [Neural Message Passing for Jet Physics](https://dl4physicalsciences.github.io/files/nips_dlps_2017_29.pdf), Proceedings of the Deep Learning for Physical Sciences Workshop at NIPS (2017). (December 8, 2017)
- T. Cheng, "[Recursive Neural Networks in Quark/Gluon Tagging](https://inspirehep.net/record/1634876)," arXiv:1711.02633 [hep-ph]. (November 7, 2017)
- S. Chang, T. Cohen, and B. Ostdiek, "[What is the Machine Learning?](https://inspirehep.net/record/1627883)," arXiv:1709.10106 [hep-ph]. (September 28, 2017)
- M. Frate, K. Cranmer, S. Kalia, A. Vandenberg-Rodes, and D. Whiteson, “[Modeling Smooth Backgrounds and Generic Localized Signals with Gaussian Processes](https://inspirehep.net/record/1624168),” arXiv:1709.05681 [physics.data-an]. (September 17, 2017)
- E. M. Metodiev, B. Nachman, and J. Thaler, “[Classification without labels: Learning
from mixed samples in high energy physics](https://inspirehep.net/record/1615464),” arXiv:1708.02949 [hep-ph]. (August 9, 2017)
- J. Bendavid, "[Efficient Monte Carlo Integration Using Boosted Decision Trees and Generative Deep Neural Networks](https://inspirehep.net/record/1608392)," arXiv:1707.00028 [hep-ph]. (June 30, 2017)
- T. Cohen, M. Freytsis, and B. Ostdiek, "[(Machine) Learning to Do More with Less](https://inspirehep.net/record/1608029)," arXiv:1706.09451 [hep-ph]. (June 28, 2017)
- M. Paganini, L. de Oliveira, and B. Nachman, "[CaloGAN: Simulating 3D High Energy Particle Showers in Multi-Layer Electromagnetic Calorimeters with Generative Adversarial Networks](https://inspirehep.net/record/1598455)," arXiv:1705.02355 [hep-ex]. (May 5, 2017)
- C. Shimmin, P. Sadowski, P. Baldi, E. Weik, D. Whiteson, E. Goul, and A. Sgaard, "[Decorrelated Jet Substructure Tagging using Adversarial Neural Networks](https://inspirehep.net/record/1516914)," arXiv:1703.03507 [hep-ex]. (March 9, 2017)
- G. Louppe, K. Cho, C. Becot, and K. Cranmer, "[QCD-Aware Recursive Neural Networks for Jet Physics](https://inspirehep.net/record/1511884)," arXiv:1702.00748 [hep-ph]. (February 2, 2017)
- Lecture: [QCD-Aware Neural Networks for Jet Physics](https://indico.cern.ch/event/640111/), by [Kyle Cranmer](http://theoryandpractice.org/)
- L. M. Dery, B. Nachman, F. Rubbo, and A. Schwartzman, "[Weakly Supervised Classification in High Energy Physics](https://inspirehep.net/record/1511880)," JHEP 05 (2017) 145, arXiv:1702.00414 [hep-ph]. (February 1, 2017)
- L. de Oliveira, M. Paganini, and B. Nachman, "[Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics Synthesis](https://inspirehep.net/record/1510258)," arXiv:1701.05927 [stat.ML]. (January 20, 2017)
- L.-G. Pang, K. Zhou, N. Su, H. Petersen, H. Stocker, X.-N. Wang, "[An equation-of-state-meter of QCD transition from deep learning](http://inspirehep.net/record/1503189)," arXiv:1612.04262 [hep-ph]. (December 13, 2016)
- P. T. Komiske, E. M. Metodiev, and M. D. Schwartz, "[Deep learning in color: towards automated quark/gluon jet discrimination](https://inspirehep.net/record/1501944)," JHEP 01 (2017) 110, arXiv:1612.01551 [hep-ph]. (December 5, 2016)
- MicroBooNE Collaboration, R. Acciarri _et al._, "[Convolutional Neural Networks Applied to Neutrino Events in a Liquid Argon Time Projection Chamber](https://inspirehep.net/record/1498561)," JINST 12 (2017) no. 03, P03011, arXiv:1611.05531 [physics.ins-det]. (November 16, 2016)
- M. Kagan, L. d. Oliveira, L. Mackey, B. Nachman, and A. Schwartzman, "[Boosted Jet Tagging with Jet-Images and Deep Neural Networks](http://inspirehep.net/record/1504297/)," EPJ Web Conf. 127 (2016) 00009\. (November 15, 2016)
- G. Bertone, M. P. Deisenroth, J. S. Kim, S. Liem, R. Ruiz de Austri, and M. Welling, "[Accelerating the BSM interpretation of LHC data with machine learning](https://inspirehep.net/record/1496641)," arXiv:1611.02704 [hep-ph]. (November 8, 2016)
- G. Louppe, M. Kagan, and K. Cranmer, "[Learning to Pivot with Adversarial Networks](https://inspirehep.net/record/1495901)," arXiv:1611.01046 [stat.ME]. (November 3, 2016)
- Lecture: [(Machine) Learning to Pivot with Adversarial Networks](http://indico.iihe.ac.be/indico/conferenceDisplay.py?confId=1082), by [Gilles Louppe](https://glouppe.github.io/)
- J. Barnard, E. N. Dawe, M. J. Dolan, and N. Rajcic, ["Parton Shower Uncertainties in Jet Substructure Analyses with Deep Neural Networks](https://inspirehep.net/record/1485081)," Phys. Rev. D95 (2017) no. 1, 014018, arXiv:1609.00607 [hep-ph] (September 2, 2016)
- A. Rogozhnikov, “[Reweighting with Boosted Decision Trees](https://inspirehep.net/record/1482753),” J. Phys. Conf. Ser. 762 (2016) no. 1, 012036, arXiv:1608.05806 [physics.data-an]. (August 20, 2016)
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- P. Baldi, K. Cranmer, T. Faucett, P. Sadowski, and D. Whiteson, “[Parameterized neural networks for high-energy physics](https://inspirehep.net/record/1418479),” Eur. Phys. J. C76 (2016) no. 5, 235, arXiv:1601.07913 [hep-ex]. (January 28, 2016)
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================================================
FILE: tex/HEPML.bib
================================================
# HEPML Papers
% April 21, 2020
@article{Romao:2020ojy,
author = "Romao, M. Crispim and Castro, N.F. and Milhano, J.G. and Pedro, R. and Vale, T.",
archivePrefix = "arXiv",
eprint = "2004.09360",
month = "4",
primaryClass = "hep-ph",
title = "{Use of a Generalized Energy Mover's Distance in the Search for Rare Phenomena at Colliders}",
year = "2020"
}
% March 13, 2020
@article{Kanwar:2003.06413,
key = "1785309",
author = "Kanwar, Gurtej and Albergo, Michael S. and Boyda, Denis
and Cranmer, Kyle and Hackett, Daniel C. and Racanière,
Sébastien and Rezende, Danilo Jimenez and Shanahan,
Phiala E.",
title = "{Equivariant flow-based sampling for lattice gauge
theory}",
year = "2020",
eprint = "2003.06413",
archivePrefix = "arXiv",
primaryClass = "hep-lat",
reportNumber = "MIT-CTP/5181",
SLACcitation = "%%CITATION = ARXIV:2003.06413;%%"
}
% February 11, 2020
@article{Hollingsworth:2020kjg,
author = "Hollingsworth, Jacob and Whiteson, Daniel",
title = "{Resonance Searches with Machine Learned Likelihood
Ratios}",
year = "2020",
eprint = "2002.04699",
archivePrefix = "arXiv",
primaryClass = "hep-ph",
SLACcitation = "%%CITATION = ARXIV:2002.04699;%%"
}
% February 3, 2020
@article{Strong:2020mge,
author = "Strong, Giles Chatham",
title = "{On the impact of modern deep-learning techniques to the
performance and time-requirements of classification models
in experimental high-energy physics}",
year = "2020",
eprint = "2002.01427",
archivePrefix = "arXiv",
primaryClass = "physics.data-an",
SLACcitation = "%%CITATION = ARXIV:2002.01427;%%"
}
% December 9, 2019
@article{Romao:2019dvs,
author = "Romão Crispim, M. and Castro, N.F. and Pedro, R. and Vale, T.",
archivePrefix = "arXiv",
doi = "10.1103/PhysRevD.101.035042",
eprint = "1912.04220",
journal = "Phys.\ Rev.\ D",
number = "3",
pages = "035042",
primaryClass = "hep-ph",
title = "{Transferability of Deep Learning Models in Searches for New Physics at Colliders}",
volume = "101",
year = "2020"
}
% November 20, 2019
@article{Andreassen:2019cjw,
author = "Andreassen, Anders and Komiske, Patrick T. and Metodiev,
Eric M. and Nachman, Benjamin and Thaler, Jesse",
title = "{OmniFold: A Method to Simultaneously Unfold All
Observables}",
year = "2019",
eprint = "1911.09107",
archivePrefix = "arXiv",
primaryClass = "hep-ph",
reportNumber = "MIT-CTP 5155",
SLACcitation = "%%CITATION = ARXIV:1911.09107;%%"
}
% October 18, 2019
@article{Nachman:2019yfl,
author = "Nachman, Benjamin and Shimmin, Chase",
title = "{AI Safety for High Energy Physics}",
year = "2019",
eprint = "1910.08606",
archivePrefix = "arXiv",
primaryClass = "hep-ph",
SLACcitation = "%%CITATION = ARXIV:1910.08606;%%"
}
% May 28, 2019
@article{Borisyak:2019vbz,
author = "Borisyak, Maxim and Kazeev, Nikita",
title = "{Machine Learning on data with sPlot background
subtraction}",
year = "2019",
eprint = "1905.11719",
archivePrefix = "arXiv",
primaryClass = "cs.LG",
SLACcitation = "%%CITATION = ARXIV:1905.11719;%%"
}
% May 19, 2019
@article{Caron:2019xkx,
author = "Caron, Sascha and Heskes, Tom and Otten, Sydney and
Stienen, Bob",
title = "{Constraining the Parameters of High-Dimensional Models
with Active Learning}",
journal = "Eur. Phys. J.",
volume = "C79",
year = "2019",
number = "11",
pages = "944",
doi = "10.1140/epjc/s10052-019-7437-5",
eprint = "1905.08628",
archivePrefix = "arXiv",
primaryClass = "cs.LG",
SLACcitation = "%%CITATION = ARXIV:1905.08628;%%"
}
% March 6, 2019
@article{DiSipio:2019imz,
author = "Di Sipio, Riccardo and Faucci Giannelli, Michele and
Ketabchi Haghighat, Sana and Palazzo, Serena",
title = "{DijetGAN: A Generative-Adversarial Network Approach for
the Simulation of QCD Dijet Events at the LHC}",
year = "2019",
eprint = "1903.02433",
archivePrefix = "arXiv",
primaryClass = "hep-ex",
SLACcitation = "%%CITATION = ARXIV:1903.02433;%%"
}
% February 19, 2019
@article{Datta:2019,
key = "1720832",
author = "Datta, Kaustuv and Larkoski, Andrew and Nachman,
Benjamin",
title = "{Automating the Construction of Jet Observables with
Machine Learning}",
year = "2019",
eprint = "1902.07180",
archivePrefix = "arXiv",
primaryClass = "hep-ph",
SLACcitation = "%%CITATION = ARXIV:1902.07180;%%"
}
% August 22, 2018
@article{Adams:2018bvi,
author = "Adams, C. and others",
title = "{Deep neural network for pixel-level electromagnetic
particle identification in the MicroBooNE liquid argon
time projection chamber}",
collaboration = "MicroBooNE",
journal = "Phys. Rev.",
volume = "D99",
year = "2019",
number = "9",
pages = "092001",
doi = "10.1103/PhysRevD.99.092001",
eprint = "1808.07269",
archivePrefix = "arXiv",
primaryClass = "hep-ex",
reportNumber = "FERMILAB-PUB-18-231-ND",
SLACcitation = "%%CITATION = ARXIV:1808.07269;%%"
}
% August 2, 2018
@article{Stoye:2018ovl,
author = "Stoye, Markus and Brehmer, Johann and Louppe, Gilles and
Pavez, Juan and Cranmer, Kyle",
title = "{Likelihood-free inference with an improved cross-entropy
estimator}",
year = "2018",
eprint = "1808.00973",
archivePrefix = "arXiv",
primaryClass = "stat.ML",
SLACcitation = "%%CITATION = ARXIV:1808.00973;%%"
}
% August 2, 2018
@article{Bourgeois:2018nvk,
author = "Bourgeois, Dylan and Fitzpatrick, Conor and Stahl,
Sascha",
title = "{Using holistic event information in the trigger}",
year = "2018",
eprint = "1808.00711",
archivePrefix = "arXiv",
primaryClass = "physics.ins-det",
reportNumber = "LHCb-PUB-2018-010",
SLACcitation = "%%CITATION = ARXIV:1808.00711;%%"
}
% July 31, 2018
@article{Andrews:2018nwy,
author = "Andrews, Michael and Paulini, Manfred and Gleyzer, Sergei
and Poczos, Barnabas",
title = "{End-to-End Physics Event Classification with the CMS
Open Data: Applying Image-based Deep Learning on Detector
Data to Directly Classify Collision Events at the LHC}",
year = "2018",
eprint = "1807.11916",
archivePrefix = "arXiv",
primaryClass = "hep-ex",
SLACcitation = "%%CITATION = ARXIV:1807.11916;%%"
}
% July 27, 2018
@article{Lin:2018cin,
author = "Lin, Joshua and Freytsis, Marat and Moult, Ian and
Nachman, Benjamin",
title = "{Boosting $H\to b\bar b$ with Machine Learning}",
year = "2018",
eprint = "1807.10768",
archivePrefix = "arXiv",
primaryClass = "hep-ph",
SLACcitation = "%%CITATION = ARXIV:1807.10768;%%"
}
% July 8, 2018
@article{Albertsson:2018maf,
author = "Albertsson, Kim and others",
title = "{Machine Learning in High Energy Physics Community White Paper}",
year = "2018",
eprint = "1807.02876",
archivePrefix = "arXiv",
primaryClass = "physics.comp-ph",
reportNumber = "FERMILAB-PUB-18-318-CD-DI-PPD",
SLACcitation = "%%CITATION = ARXIV:1807.02876;%%"
}
% June 29, 2018
@article{Guest:2018yhq,
author = "Guest, Dan and Cranmer, Kyle and Whiteson, Daniel",
title = "{Deep Learning and its Application to LHC Physics}",
year = "2018",
eprint = "1806.11484",
archivePrefix = "arXiv",
primaryClass = "hep-ex",
SLACcitation = "%%CITATION = ARXIV:1806.11484;%%"
}
% April 30, 2018
@article{Brehmer:2018kdj,
author = "Brehmer, Johann and Cranmer, Kyle and Louppe, Gilles and
Pavez, Juan",
title = "{Constraining Effective Field Theories with Machine
Learning}",
year = "2018",
eprint = "1805.00013",
archivePrefix = "arXiv",
primaryClass = "hep-ph",
SLACcitation = "%%CITATION = ARXIV:1805.00013;%%"
}
% April 30, 2018
@article{Brehmer:2018eca,
author = "Brehmer, Johann and Cranmer, Kyle and Louppe, Gilles and
Pavez, Juan",
title = "{A Guide to Constraining Effective Field Theories with
Machine Learning}",
year = "2018",
eprint = "1805.00020",
archivePrefix = "arXiv",
primaryClass = "hep-ph",
SLACcitation = "%%CITATION = ARXIV:1805.00020;%%"
}
% December 19, 2017
@article{Sirunyan:2017ezt,
author = "Sirunyan, Albert M and others",
title = "{Identification of heavy-flavour jets with the CMS
detector in pp collisions at 13 TeV}",
collaboration = "CMS",
year = "2017",
eprint = "1712.07158",
archivePrefix = "arXiv",
primaryClass = "physics.ins-det",
reportNumber = "CMS-BTV-16-002, CERN-EP-2017-326",
SLACcitation = "%%CITATION = ARXIV:1712.07158;%%"
}
% December 8, 2017
@conference{Estrade:DLPS2017,
author = "Victor Estrade and Cecile Germain and Isabelle Guyon and David Rousseau",
title = "{Adversarial learning to eliminate systematic errors: a case study
in High Energy Physics}",
booktitle = "{Proceedings of the Deep Learning for Physical Sciences Workshop at NIPS (2017)}",
year = "2017",
url = "{https://dl4physicalsciences.github.io/files/nips_dlps_2017_1.pdf}"
}
% December 8, 2017
@conference{Weitekamp:DLPS2017,
author = "Daniel Weitekamp III and Thong Q. Nguyen and Dustin Anderson and
Roberto Castello and Maurizio Pierini and Maria Spiropulu and Jean-Roch Vlimant",
title = "{Deep topology classifiers for a more efficient trigger selection at the LHC}",
booktitle = "{Proceedings of the Deep Learning for Physical Sciences Workshop at NIPS (2017)}",
year = "2017",
url = "{https://dl4physicalsciences.github.io/files/nips_dlps_2017_3.pdf}"
}
% December 8, 2017
@conference{Hertel:DLPS2017,
author = "Lars Hertel and Lingge Li and Pierre Baldi and Jianming Bian",
title = "{Convolutional Neural Networks for Electron Neutrino and Electron
Shower Energy Reconstruction in the NO$\nu$A Detectors}",
booktitle = "{Proceedings of the Deep Learning for Physical Sciences Workshop at NIPS (2017)}",
year = "2017",
url = "{https://dl4physicalsciences.github.io/files/nips_dlps_2017_7.pdf}"
}
% December 8, 2017
@conference{Stoye:DLPS2017,
author = "Markus Stoye and Jan Kieseler and Mauro Verzetti and Huilin Qu and
Loukas Gouskos and Anna Stakia and {CMS Collaboration}",
title = "{DeepJet: Generic physics object based jet multiclass classification
for LHC experiments}",
booktitle = "{Proceedings of the Deep Learning for Physical Sciences Workshop at NIPS (2017)}",
year = "2017",
url = "{https://dl4physicalsciences.github.io/files/nips_dlps_2017_10.pdf}"
}
% December 8, 2017
@conference{Hooberman:DLPS2017,
author = "Benjamin Hooberman and Amir Farbin and Gulrukh Khattak and Vit{\'o}ria Pacela
and Maurizio Pierini and Jean-Roch Vlimant and Maria Spiropulu and Wei Wei and Matt Zhang
and Sofia Vallecorsa",
title = "{Calorimetry with Deep Learning: Particle Classification, Energy Regression,
and Simulation for High-Energy Physics}",
booktitle = "{Proceedings of the Deep Learning for Physical Sciences Workshop at NIPS (2017)}",
year = "2017",
url = "{https://dl4physicalsciences.github.io/files/nips_dlps_2017_15.pdf}"
}
% December 8, 2017
@conference{Paganini:DLPS2017,
author = "Paganini, Michela and de Oliveira, Luke and Nachman,
Benjamin",
title = "{Survey of Machine Learning Techniques for High Energy
Electromagnetic Shower Classification}",
booktitle = "{Proceedings of the Deep Learning for Physical Sciences Workshop at NIPS (2017)}",
year = "2017",
url = "{https://dl4physicalsciences.github.io/files/nips_dlps_2017_24.pdf}"
}
% December 8, 2017
@conference{Oliveira:DLPS2017,
author = "de Oliveira, Luke and Paganini, Michela and Nachman, Benjamin",
title = "{Tips and Tricks for Training GANs with Physics Constraints}",
booktitle = "{Proceedings of the Deep Learning for Physical Sciences Workshop at NIPS (2017)}",
year = "2017",
url = "{https://dl4physicalsciences.github.io/files/nips_dlps_2017_26.pdf}"
}
% December 8, 2017
@conference{Farrell:DLPS2017,
author = "Steven Farrell and Paolo Calafiura and Mayur Mudigonda and Prabhat
and Dustin Anderson and Josh Bendavid and Maria Spiropoulou and Jean-Roch Vlimant
and Stephan Zheng and Giuseppe Cerati and Lindsey Gray and Jim Kowalkowski
and Panagiotis Spentzouris and Aristeidis Tsaris and Daniel Zurawski",
title = "{Particle Track Reconstruction with Deep Learning}",
booktitle = "{Proceedings of the Deep Learning for Physical Sciences Workshop at NIPS (2017)}",
year = "2017",
url = "{https://dl4physicalsciences.github.io/files/nips_dlps_2017_28.pdf}"
}
% December 8, 2017
@conference{Henrion:DLPS2017,
author = "Isaac Henrion and Kyle Cranmer and Joan Bruna and Kyunghyun Cho
and Johann Brehmer and Gilles Louppe and Gaspar Rochette",
title = "{Neural Message Passing for Jet Physics}",
booktitle = "{Proceedings of the Deep Learning for Physical Sciences Workshop at NIPS (2017)}",
year = "2017",
url = "{https://dl4physicalsciences.github.io/files/nips_dlps_2017_29.pdf}"
}
% November 7, 2017
@article{Cheng:2017rdo,
author = "Cheng, Taoli",
title = "{Recursive Neural Networks in Quark/Gluon Tagging}",
year = "2017",
eprint = "1711.02633",
archivePrefix = "arXiv",
primaryClass = "hep-ph",
SLACcitation = "%%CITATION = ARXIV:1711.02633;%%"
}
% September 28, 2017
@article{Chang:2017kvc,
author = "Chang, Spencer and Cohen, Timothy and Ostdiek, Bryan",
title = "{What is the Machine Learning?}",
journal = "Phys. Rev.",
volume = "D97",
year = "2018",
number = "5",
pages = "056009",
doi = "10.1103/PhysRevD.97.056009",
eprint = "1709.10106",
archivePrefix = "arXiv",
primaryClass = "hep-ph",
SLACcitation = "%%CITATION = ARXIV:1709.10106;%%"
}
% September 17, 2017
@article{Frate:2017mai,
author = "Frate, Meghan and Cranmer, Kyle and Kalia, Saarik and
Vandenberg-Rodes, Alexander and Whiteson, Daniel",
title = "{Modeling Smooth Backgrounds and Generic Localized
Signals with Gaussian Processes}",
year = "2017",
eprint = "1709.05681",
archivePrefix = "arXiv",
primaryClass = "physics.data-an",
SLACcitation = "%%CITATION = ARXIV:1709.05681;%%"
}
% August 9, 2017
@article{Metodiev:2017vrx,
author = "Metodiev, Eric M. and Nachman, Benjamin and Thaler,
Jesse",
title = "{Classification without labels: Learning from mixed
samples in high energy physics}",
year = "2017",
eprint = "1708.02949",
archivePrefix = "arXiv",
primaryClass = "hep-ph",
reportNumber = "MIT--CTP-4922",
SLACcitation = "%%CITATION = ARXIV:1708.02949;%%"
}
% June 30, 2017
@article{Bendavid:2017zhk,
author = "Bendavid, Joshua",
title = "{Efficient Monte Carlo Integration Using Boosted Decision
Trees and Generative Deep Neural Networks}",
year = "2017",
eprint = "1707.00028",
archivePrefix = "arXiv",
primaryClass = "hep-ph",
}
% June 28, 2017
@article{Cohen:2017exh,
author = "Cohen, Timothy and Freytsis, Marat and Ostdiek, Bryan",
title = "{(Machine) Learning to Do More with Less}",
year = "2017",
eprint = "1706.09451",
archivePrefix = "arXiv",
primaryClass = "hep-ph",
}
% May 5, 2017
@article{Paganini:2017hrr,
author = "Paganini, Michela and de Oliveira, Luke and Nachman,
Benjamin",
title = "{CaloGAN: Simulating 3D High Energy Particle Showers in
Multi-Layer Electromagnetic Calorimeters with Generative
Adversarial Networks}",
year = "2017",
eprint = "1705.02355",
archivePrefix = "arXiv",
primaryClass = "hep-ex",
}
% April 24, 2017
@article{Caron:2017hku,
author = {Caron, Sascha and Kim, Jong Soo and Rolbiecki, Krzysztof and de Austri, Roberto Ruiz and Stienen, Bob},
doi = {10.1140/epjc/s10052-017-4814-9},
issn = {1434-6052},
journal = {The European Physical Journal C},
number = {4},
pages = {257},
title = {The BSM-AI project: SUSY-AI--generalizing LHC limits on supersymmetry with machine learning},
url = {http://dx.doi.org/10.1140/epjc/s10052-017-4814-9},
volume = {77},
year = {2017},
bdsk-url-1 = {http://dx.doi.org/10.1140/epjc/s10052-017-4814-9}
}
% March 9, 2017
@article{Shimmin:2017mfk,
author = "Shimmin, Chase and Sadowski, Peter and Baldi, Pierre and
Weik, Edison and Whiteson, Daniel and Goul, Edward and
Søgaard, Andreas",
title = "{Decorrelated Jet Substructure Tagging using Adversarial
Neural Networks}",
year = "2017",
eprint = "1703.03507",
archivePrefix = "arXiv",
primaryClass = "hep-ex",
}
% February 2, 2017
@article{Louppe:2017ipp,
author = "Louppe, Gilles and Cho, Kyunghyun and Becot, Cyril and
Cranmer, Kyle",
title = "{QCD-Aware Recursive Neural Networks for Jet Physics}",
year = "2017",
eprint = "1702.00748",
archivePrefix = "arXiv",
primaryClass = "hep-ph",
}
% February 1, 2017
@article{Dery:2017fap,
author = "Dery, Lucio Mwinmaarong and Nachman, Benjamin and Rubbo,
Francesco and Schwartzman, Ariel",
title = "{Weakly Supervised Classification in High Energy
Physics}",
journal = "JHEP",
volume = "05",
year = "2017",
pages = "145",
doi = "10.1007/JHEP05(2017)145",
eprint = "1702.00414",
archivePrefix = "arXiv",
primaryClass = "hep-ph",
}
% January 20, 2017
@article{deOliveira:2017pjk,
author = "de Oliveira, Luke and Paganini, Michela and Nachman,
Benjamin",
title = "{Learning Particle Physics by Example: Location-Aware
Generative Adversarial Networks for Physics Synthesis}",
year = "2017",
eprint = "1701.05927",
archivePrefix = "arXiv",
primaryClass = "stat.ML",
}
% December 13, 2016
@article{Pang:2016vdc,
author = "Pang, Long-Gang and Zhou, Kai and Su, Nan and Petersen,
Hannah and Stöcker, Horst and Wang, Xin-Nian",
title = "{An EoS-meter of QCD transition from deep learning}",
year = "2016",
eprint = "1612.04262",
archivePrefix = "arXiv",
primaryClass = "hep-ph",
}
% December 5, 2016
@article{Komiske:2016rsd,
author = "Komiske, Patrick T. and Metodiev, Eric M. and Schwartz,
Matthew D.",
title = "{Deep learning in color: towards automated quark/gluon
jet discrimination}",
journal = "JHEP",
volume = "01",
year = "2017",
pages = "110",
doi = "10.1007/JHEP01(2017)110",
eprint = "1612.01551",
archivePrefix = "arXiv",
primaryClass = "hep-ph",
reportNumber = "MIT-CTP-4866",
}
% November 16, 2016
@article{Acciarri:2016ryt,
author = "Acciarri, R. and others",
title = "{Convolutional Neural Networks Applied to Neutrino Events
in a Liquid Argon Time Projection Chamber}",
collaboration = "MicroBooNE",
journal = "JINST",
volume = "12",
year = "2017",
number = "03",
pages = "P03011",
doi = "10.1088/1748-0221/12/03/P03011",
eprint = "1611.05531",
archivePrefix = "arXiv",
primaryClass = "physics.ins-det",
reportNumber = "FERMILAB-PUB-16-538-ND",
}
% November 15, 2016
@article{Kagan:2016wnu,
author = "Kagan, Michael and Oliveira, Luke de and Mackey, Lester
and Nachman, Benjamin and Schwartzman, Ariel",
title = "{Boosted Jet Tagging with Jet-Images and Deep Neural
Networks}",
booktitle = "{Proceedings, Connecting The Dots 2016: Vienna, Austria,
February 22-24, 2016}",
journal = "EPJ Web Conf.",
volume = "127",
year = "2016",
pages = "00009",
doi = "10.1051/epjconf/201612700009",
}
% November 8, 2016
@article{Bertone:2016mdy,
author = "Bertone, Gianfranco and Deisenroth, Marc Peter and Kim,
Jong Soo and Liem, Sebastian and Ruiz de Austri, Roberto
and Welling, Max",
title = "{Accelerating the BSM interpretation of LHC data with
machine learning}",
year = "2016",
eprint = "1611.02704",
archivePrefix = "arXiv",
primaryClass = "hep-ph",
}
% November 3, 2016
@article{Louppe:2016ylz,
author = "Louppe, Gilles and Kagan, Michael and Cranmer, Kyle",
title = "{Learning to Pivot with Adversarial Networks}",
year = "2016",
eprint = "1611.01046",
archivePrefix = "arXiv",
primaryClass = "stat.ME",
}
% September 2, 2016
@article{Barnard:2016qma,
author = "Barnard, James and Dawe, Edmund Noel and Dolan, Matthew
J. and Rajcic, Nina",
title = "{Parton Shower Uncertainties in Jet Substructure Analyses
with Deep Neural Networks}",
journal = "Phys. Rev.",
volume = "D95",
year = "2017",
number = "1",
pages = "014018",
doi = "10.1103/PhysRevD.95.014018",
eprint = "1609.00607",
archivePrefix = "arXiv",
primaryClass = "hep-ph",
}
% August 20, 2016
@article{Rogozhnikov:2016bdp,
author = "Rogozhnikov, A.",
title = "{Reweighting with Boosted Decision Trees}",
booktitle = "{Proceedings, 17th International Workshop on Advanced
Computing and Analysis Techniques in Physics Research
(ACAT 2016): Valparaiso, Chile, January 18-22, 2016}",
journal = "J. Phys. Conf. Ser.",
volume = "762",
year = "2016",
number = "1",
pages = "012036",
doi = "10.1088/1742-6596/762/1/012036",
eprint = "1608.05806",
archivePrefix = "arXiv",
primaryClass = "physics.data-an",
SLACcitation = "%%CITATION = ARXIV:1608.05806;%%"
}
% July 28, 2016
@article{Guest:2016iqz,
author = "Guest, Daniel and Collado, Julian and Baldi, Pierre and
Hsu, Shih-Chieh and Urban, Gregor and Whiteson, Daniel",
title = "{Jet Flavor Classification in High-Energy Physics with
Deep Neural Networks}",
journal = "Phys. Rev.",
volume = "D94",
year = "2016",
number = "11",
pages = "112002",
doi = "10.1103/PhysRevD.94.112002",
eprint = "1607.08633",
archivePrefix = "arXiv",
primaryClass = "hep-ex",
SLACcitation = "%%CITATION = ARXIV:1607.08633;%%"
}
% April 5, 2016
@article{Aurisano:2016jvx,
author = "Aurisano, A. and Radovic, A. and Rocco, D. and Himmel, A.
and Messier, M. D. and Niner, E. and Pawloski, G. and
Psihas, F. and Sousa, A. and Vahle, P.",
title = "{A Convolutional Neural Network Neutrino Event
Classifier}",
journal = "JINST",
volume = "11",
year = "2016",
number = "09",
pages = "P09001",
doi = "10.1088/1748-0221/11/09/P09001",
eprint = "1604.01444",
archivePrefix = "arXiv",
primaryClass = "hep-ex",
reportNumber = "FERMILAB-PUB-16-082-ND",
}
% January 28, 2016
@article{Baldi:2016fzo,
author = "Baldi, Pierre and Cranmer, Kyle and Faucett, Taylor and
Sadowski, Peter and Whiteson, Daniel",
title = "{Parameterized neural networks for high-energy physics}",
journal = "Eur. Phys. J.",
volume = "C76",
year = "2016",
number = "5",
pages = "235",
doi = "10.1140/epjc/s10052-016-4099-4",
eprint = "1601.07913",
archivePrefix = "arXiv",
primaryClass = "hep-ex",
SLACcitation = "%%CITATION = ARXIV:1601.07913;%%"
}
% November 16, 2015
@article{deOliveira:2015xxd,
author = "de Oliveira, Luke and Kagan, Michael and Mackey, Lester
and Nachman, Benjamin and Schwartzman, Ariel",
title = "{Jet-images — deep learning edition}",
journal = "JHEP",
volume = "07",
year = "2016",
pages = "069",
doi = "10.1007/JHEP07(2016)069",
eprint = "1511.05190",
archivePrefix = "arXiv",
primaryClass = "hep-ph",
}
% October 15, 2014
@article{Rogozhnikov:2014zea,
author = "Rogozhnikov, Alex and Bukva, Aleksandar and Gligorov, V.
V. and Ustyuzhanin, Andrey and Williams, Mike",
title = "{New approaches for boosting to uniformity}",
journal = "JINST",
volume = "10",
year = "2015",
number = "03",
pages = "T03002",
doi = "10.1088/1748-0221/10/03/T03002",
eprint = "1410.4140",
archivePrefix = "arXiv",
primaryClass = "hep-ex",
SLACcitation = "%%CITATION = ARXIV:1410.4140;%%"
}
% February 19, 2014
@article{Baldi:2014kfa,
author = "Baldi, Pierre and Sadowski, Peter and Whiteson, Daniel",
title = "{Searching for Exotic Particles in High-Energy Physics
with Deep Learning}",
journal = "Nature Commun.",
volume = "5",
year = "2014",
pages = "4308",
doi = "10.1038/ncomms5308",
eprint = "1402.4735",
archivePrefix = "arXiv",
primaryClass = "hep-ph",
}
% May 30, 2013
@article{Stevens:2013dya,
author = "Stevens, Justin and Williams, Mike",
title = "{uBoost: A boosting method for producing uniform
selection efficiencies from multivariate classifiers}",
journal = "JINST",
volume = "8",
year = "2013",
pages = "P12013",
doi = "10.1088/1748-0221/8/12/P12013",
eprint = "1305.7248",
archivePrefix = "arXiv",
primaryClass = "nucl-ex",
SLACcitation = "%%CITATION = ARXIV:1305.7248;%%"
}
% October 25, 2012
@article{Gligorov:2012qt,
author = "Gligorov, V. V. and Williams, Mike",
title = "{Efficient, reliable and fast high-level triggering using
a bonsai boosted decision tree}",
journal = "JINST",
volume = "8",
year = "2013",
pages = "P02013",
doi = "10.1088/1748-0221/8/02/P02013",
eprint = "1210.6861",
archivePrefix = "arXiv",
primaryClass = "physics.ins-det",
SLACcitation = "%%CITATION = ARXIV:1210.6861;%%"
}
% September 20, 1987
@article{Denby:1987rk,
author = "Denby, Bruce H.",
title = "{Neural Networks and Cellular Automata in Experimental
High-energy Physics}",
journal = "Comput. Phys. Commun.",
volume = "49",
year = "1988",
pages = "429-448",
doi = "10.1016/0010-4655(88)90004-5",
reportNumber = "LAL-87-56"
}
================================================
FILE: tex/HEPML.tex
================================================
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\begin{itemize}
\item~\cite{Romao:2020ojy}
\item~\cite{Kanwar:2003.06413}
\item~\cite{Hollingsworth:2020kjg}
\item~\cite{Strong:2020mge}
\item~\cite{Romao:2019dvs}
\item~\cite{Andreassen:2019cjw}
\item~\cite{Nachman:2019yfl}
\item~\cite{Borisyak:2019vbz}
\item~\cite{Caron:2019xkx}
\item~\cite{DiSipio:2019imz}
\item~\cite{Datta:2019}
\item~\cite{Adams:2018bvi}
\item~\cite{Stoye:2018ovl}
\item~\cite{Bourgeois:2018nvk}
\item~\cite{Andrews:2018nwy}
\item~\cite{Lin:2018cin}
\item~\cite{Albertsson:2018maf}
\item~\cite{Guest:2018yhq}
\item~\cite{Brehmer:2018kdj}
\item~\cite{Brehmer:2018eca}
\item~\cite{Sirunyan:2017ezt}
\item~\cite{Estrade:DLPS2017}
\item~\cite{Weitekamp:DLPS2017}
\item~\cite{Hertel:DLPS2017}
\item~\cite{Stoye:DLPS2017}
\item~\cite{Hooberman:DLPS2017}
\item~\cite{Paganini:DLPS2017}
\item~\cite{Oliveira:DLPS2017}
\item~\cite{Farrell:DLPS2017}
\item~\cite{Henrion:DLPS2017}
\item~\cite{Cheng:2017rdo}
\item~\cite{Chang:2017kvc}
\item~\cite{Frate:2017mai}
\item~\cite{Metodiev:2017vrx}
\item~\cite{Bendavid:2017zhk}
\item~\cite{Cohen:2017exh}
\item~\cite{Paganini:2017hrr}
\item~\cite{Caron:2017hku}
\item~\cite{Shimmin:2017mfk}
\item~\cite{Louppe:2017ipp}
\item~\cite{Dery:2017fap}
\item~\cite{deOliveira:2017pjk}
\item~\cite{Pang:2016vdc}
\item~\cite{Komiske:2016rsd}
\item~\cite{Acciarri:2016ryt}
\item~\cite{Kagan:2016wnu}
\item~\cite{Bertone:2016mdy}
\item~\cite{Louppe:2016ylz}
\item~\cite{Barnard:2016qma}
\item~\cite{Rogozhnikov:2016bdp}
\item~\cite{Guest:2016iqz}
\item~\cite{Aurisano:2016jvx}
\item~\cite{Baldi:2016fzo}
\item~\cite{deOliveira:2015xxd}
\item~\cite{Rogozhnikov:2014zea}
\item~\cite{Baldi:2014kfa}
\item~\cite{Stevens:2013dya}
\item~\cite{Gligorov:2012qt}
\item~\cite{Denby:1987rk}
\end{itemize}
\clearpage
\flushbottom
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% References
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\bibliographystyle{uiuchept}
%\bibliographystyle{JHEP}
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FILE: tex/JHEP.bst
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if$
}
FUNCTION {format.eprint.paren}
{ eprint missing$ { "" } { eprint empty$ { "" }
{"[" format.eprint * "]" *}
if$
}
if$
}
FUNCTION {format.report}
{ report empty$
{ ""}
{ report}
if$
}
FUNCTION {format.editors}
{ editor empty$
{ "" }
{ editor format.names
editor num.names$ #1 >
{ ", eds." * }
{ ", ed." * }
if$
}
if$
}
FUNCTION {format.title}
{ title empty$
{ "" }
{ "\emph{" title "t" change.case$ * "}, " * }
if$
}
FUNCTION {format.title.p}
{ title empty$
{ "" }
{ "``" title "t" change.case$ * ".''" * }
if$
}
FUNCTION {n.dashify}
{ 't :=
""
{ t empty$ not }
{ t #1 #1 substring$ "-" =
{ t #1 #2 substring$ "--" = not
{ "--" *
t #2 global.max$ substring$ 't :=
}
{ { t #1 #1 substring$ "-" = }
{ "-" *
t #2 global.max$ substring$ 't :=
}
while$
}
if$
}
{ t #1 #1 substring$ *
t #2 global.max$ substring$ 't :=
}
if$
}
while$
}
FUNCTION {format.date}
{ year empty$
{ month empty$
{ "" }
{ "there's a month but no year in " cite$ * warning$
month
}
if$
}
{ month empty$
'year
{ month ", " * year * }
if$
}
if$
}
FUNCTION {format.date.paren}
{ year empty$
{ month empty$
{ "" }
{ "there's a month but no year in " cite$ * warning$
month
}
if$
}
{ month empty$
{"(" year * ")" *}
{"(" month * ", " * year * ")" *}
if$
}
if$
}
FUNCTION {format.collaboration}
{ collaboration empty$
{ "" }
{ "{\scshape " collaboration * "} " * "collaboration" * }
if$
}
FUNCTION {format.btitle}
{ title emphasize
}
FUNCTION {tie.or.space.connect}
{ duplicate$ text.length$ #3 <
{ "~" }
{ " " }
if$
swap$ * *
}
FUNCTION {either.or.check}
{ empty$
'pop$
{ "can't use both " swap$ * " fields in " * cite$ * warning$ }
if$
}
FUNCTION {format.bvolume}
{ volume empty$
{ "" }
{ "vol.~" volume *
series empty$
'skip$
{ " of " * series emphasize * }
if$
"volume and number" number either.or.check
}
if$
}
FUNCTION {format.number.series}
{ volume empty$
{ number empty$
{ series field.or.null }
{ output.state mid.sentence =
{ "no.~" }
{ "No.~" }
if$
number *
series empty$
{ "there's a number but no series in " cite$ * warning$ }
{ " in " * series * }
if$
}
if$
}
{ "" }
if$
}
FUNCTION {format.edition}
{ edition empty$
{ "" }
{ edition "l" change.case$ "~ed." * }
if$
}
INTEGERS { multiresult }
FUNCTION {multi.page.check}
{ 't :=
#0 'multiresult :=
{ multiresult not
t empty$ not
and
}
{ t #1 #1 substring$
duplicate$ "-" =
swap$ duplicate$ "," =
swap$ "+" =
or or
{ #1 'multiresult := }
{ t #2 global.max$ substring$ 't := }
if$
}
while$
multiresult
}
FUNCTION {format.pages}
{ pages empty$
{ "" }
{ pages multi.page.check
{ "pp.~" pages n.dashify * }
{ "p.~" pages * }
if$
}
if$
}
FUNCTION {format.pages.nopp}
{ pages empty$
{ "" }
{ pages multi.page.check
{ pages n.dashify }
{ pages }
if$
}
if$
}
FUNCTION {format.volume}
{ volume empty$
{ "" }
{ "{\bfseries " volume * "}" * }
if$
}
FUNCTION {format.number}
{ number empty$
{ "" }
{ "no.~" number * }
if$
}
FUNCTION {format.chapter.pages}
{ chapter empty$
'format.pages
{ type empty$
{ "ch.~" chapter * }
{ type "l" change.case$ chapter tie.or.space.connect }
if$
pages empty$
'skip$
{ ", " * format.pages * }
if$
}
if$
}
FUNCTION {format.in.ed.booktitle}
{ booktitle empty$
{ "" }
{ "in " booktitle emphasize *
editor empty$
'skip$
{ " (" * format.editors * ")" * }
if$
}
if$
}
FUNCTION {format.thesis.type}
{ type empty$
'skip$
{ pop$
output.state after.block =
{ type "t" change.case$ }
{ type "l" change.case$ }
if$
}
if$
}
FUNCTION {empty.misc.check}
{ author empty$ title empty$ howpublished empty$
month empty$ year empty$ note empty$
and and and and and
{ "all relevant fields are empty in " cite$ * warning$ }
'skip$
if$
}
FUNCTION {format.tr.number}
{ type empty$
{ "Tech. Rep." }
'type
if$
number empty$
{ "l" change.case$ }
{ number tie.or.space.connect }
if$
}
FUNCTION {format.paddress}
{ address empty$
{ "" }
{ "(" address * ")" * }
if$
}
FUNCTION {format.article.crossref}
{ key empty$
{ journal empty$
{ "need key or journal for " cite$ * " to crossref " * crossref *
warning$
""
}
{ "in \emph{" journal * "\/}" * }
if$
}
{ "in " key * }
if$
" \cite{" * crossref * "}" *
}
FUNCTION {format.crossref.editor}
{ editor #1 "{vv~}{ll}" format.name$
editor num.names$ duplicate$
#2 >
{ pop$ " {et~al.}" * }
{ #2 <
'skip$
{ editor #2 "{ff }{vv }{ll}{ jj}" format.name$ "others" =
{ " {et~al.}" * }
{ " and " * editor #2 "{vv~}{ll}" format.name$ * }
if$
}
if$
}
if$
}
FUNCTION {format.book.crossref}
{ volume empty$
{ "empty volume in " cite$ * "'s crossref of " * crossref * warning$
"In "
}
{ "Vol.~" volume *
" of " *
}
if$
editor empty$
editor field.or.null author field.or.null =
or
{ key empty$
{ series empty$
{ "need editor, key, or series for " cite$ * " to crossref " *
crossref * warning$
"" *
}
{ "{\em " * series * "\/}" * }
if$
}
{ key * }
if$
}
{ format.crossref.editor * }
if$
" \cite{" * crossref * "}" *
}
FUNCTION {format.incoll.inproc.crossref}
{ editor empty$
editor field.or.null author field.or.null =
or
{ key empty$
{ booktitle empty$
{ "need editor, key, or booktitle for " cite$ * " to crossref " *
crossref * warning$
""
}
{ "in {\em " booktitle * "\/}" * }
if$
}
{ "in " key * }
if$
}
{ "in " format.crossref.editor * }
if$
" \cite{" * crossref * "}" *
}
FUNCTION {article}
{ output.bibitem
format.collaboration output
format.authors "author" output.check
format.title "title" output.check
blank.sep
crossref missing$
{ journal missing$
{ format.eprint output }
{ journal empty$ { format.eprint output } {
format.doi * "{" * journal emphasize before.all 'output.state := "journal" output.check
% added \href{doi} and { before journal
% Slv
blank.sep
format.volume output
blank.sep
format.date.paren "year" output.check
%month empty$ { format.number output }
% 'skip$ if$
blank.sep
format.pages.nopp "}" * output }
%% closed parenthesis for href argument
if$
}
if$
report missing$
{ journal empty$ {} { format.eprint.paren output} if$ }
{blank.sep format.report output format.eprint.paren output}
if$
}
{ format.article.crossref output.nonnull
format.pages output
format.eprint.paren output
}
if$
new.sentence
% format.doi output
% note output
fin.entry
}
FUNCTION {book}
{ output.bibitem
format.collaboration output
author empty$
{ format.editors "author and editor" output.check }
{ format.authors output.nonnull
crossref missing$
{ "author and editor" editor either.or.check }
'skip$
if$
}
if$
format.btitle "title" output.check
crossref missing$
{ format.bvolume output
new.block
format.number.series output
new.sentence
publisher "publisher" output.check
address output
}
{ new.block
format.book.crossref output.nonnull
}
if$
format.edition output
format.date "year" output.check
doi empty$
{}
{ format.doi "{" * doi * "}" * "DOI" output.check }
if$
fin.entry
}
FUNCTION {booklet}
{ output.bibitem
format.collaboration output
format.authors output
title empty$
{ "empty title in " cite$ * warning$
howpublished new.sentence.checka
}
{ howpublished empty$ not
address empty$ month empty$ year empty$ and and
or
{ format.title.p output.nonnull }
{ format.title output.nonnull }
if$
blank.sep
}
if$
howpublished output
address output
format.date output
new.block
% note output
doi output
fin.entry
}
FUNCTION {inbook}
{ output.bibitem
format.collaboration output
author empty$
{ format.editors "author and editor" output.check }
{ format.authors output.nonnull
crossref missing$
{ "author and editor" editor either.or.check }
'skip$
if$
}
if$
format.btitle "title" output.check
crossref missing$
{ format.bvolume output
format.chapter.pages "chapter and pages" output.check
new.block
format.number.series output
new.block
publisher "publisher" output.check
address output
}
{ format.chapter.pages "chapter and pages" output.check
new.block
format.book.crossref output.nonnull
}
if$
format.edition output
format.date "year" output.check
new.block
format.eprint output
new.block
% note output
doi output
fin.entry
}
FUNCTION {incollection}
{ output.bibitem
format.collaboration output
format.authors "author" output.check
format.title "title" output.check
blank.sep
crossref missing$
{ format.in.ed.booktitle "booktitle" output.check
format.bvolume output
format.number.series output
format.chapter.pages output
new.block
publisher "publisher" output.check
address output
format.edition output
format.date "year" output.check
}
{ format.incoll.inproc.crossref output.nonnull
format.chapter.pages output
}
if$
new.block
format.eprint output
new.block
% note output
formatfull.doi output
fin.entry
}
FUNCTION {inproceedings}
{ output.bibitem
format.collaboration output
format.authors "author" output.check
format.title "title" output.check
blank.sep
crossref missing$
{ format.in.ed.booktitle "booktitle" output.check
format.bvolume output
format.number.series output
format.paddress output
format.pages output
organization output
publisher output
format.date "year" output.check
}
{ format.incoll.inproc.crossref output.nonnull
format.pages output
}
if$
new.block
format.eprint output
new.block
% note output
formatfull.doi output
fin.entry
}
FUNCTION {conference} { inproceedings }
FUNCTION {manual}
{ output.bibitem
format.collaboration output
author empty$
{ organization empty$
'skip$
{ organization output.nonnull
address output
}
if$
}
{ format.authors output.nonnull }
if$
format.btitle "title" output.check
author empty$
{ organization empty$
{ address new.block.checka
address output
}
'skip$
if$
}
{ organization address new.block.checkb
organization output
address output
}
if$
format.edition output
format.date output
new.block
% note output
doi output
fin.entry
}
FUNCTION {electronic} { manual }
FUNCTION {mastersthesis}
{ output.bibitem
format.authors "author" output.check
format.title "title" output.check
blank.sep
"Master's thesis" format.thesis.type output.nonnull
school "school" output.check
address output
format.date "year" output.check
new.block
% note output
doi output
fin.entry
}
FUNCTION {misc}
{ output.bibitem
format.collaboration output
format.authors output
title empty$
{ howpublished new.sentence.checka }
{ howpublished empty$ not
month empty$ year empty$ and
or
{ format.title.p output.nonnull }
{ format.title output.nonnull }
if$
blank.sep
}
if$
howpublished output
format.date output
new.block
% note output
doi output
fin.entry
empty.misc.check
}
FUNCTION {phdthesis}
{ output.bibitem
format.authors "author" output.check
format.btitle "title" output.check
new.block
"PhD thesis" format.thesis.type output.nonnull
school "school" output.check
address output
format.date "year" output.check
new.block
format.eprint output
new.block
% note output
doi output
fin.entry
}
FUNCTION {proceedings}
{ output.bibitem
editor empty$
{ organization output }
{ format.editors output.nonnull }
if$
format.btitle "title" output.check
format.bvolume output
format.number.series output
format.paddress output
editor empty$
'skip$
{ organization output }
if$
publisher output
format.date "year" output.check
new.block
% note output
doi output
fin.entry
}
FUNCTION {techreport}
{ output.bibitem
format.collaboration output
format.authors "author" output.check
format.title "title" output.check
blank.sep
format.tr.number output.nonnull
institution "institution" output.check
address output
format.date "year" output.check
new.block
% note output
doi output
fin.entry
}
FUNCTION {unpublished}
{ output.bibitem
format.collaboration output
format.authors "author" output.check
format.title.p "title" output.check
blank.sep
% note "note" output.check
format.date output
fin.entry
}
FUNCTION {default.type} { misc }
MACRO {jan} {"Jan."}
MACRO {feb} {"Feb."}
MACRO {mar} {"Mar."}
MACRO {apr} {"Apr."}
MACRO {may} {"May"}
MACRO {jun} {"June"}
MACRO {jul} {"July"}
MACRO {aug} {"Aug."}
MACRO {sep} {"Sept."}
MACRO {oct} {"Oct."}
MACRO {nov} {"Nov."}
MACRO {dec} {"Dec."}
MACRO {nup} {"Nucl. Phys."}
MACRO {cmp} {"Comm. Math. Phys."}
MACRO {prl} {"Phys. Rev. Lett."}
MACRO {pl} {"Phys. Lett."}
MACRO {rmp} {"Rev. Mod. Phys."}
MACRO {ijmp} {"Int. Jour. Mod. Phys."}
MACRO {mpl} {"Mod. Phys. Lett."}
MACRO {pr} {"Phys. Rev."}
READ
STRINGS { longest.label }
INTEGERS { number.label longest.label.width }
FUNCTION {initialize.longest.label}
{ "" 'longest.label :=
#1 'number.label :=
#0 'longest.label.width :=
}
FUNCTION {longest.label.pass}
{ number.label int.to.str$ 'label :=
number.label #1 + 'number.label :=
label width$ longest.label.width >
{ label 'longest.label :=
label width$ 'longest.label.width :=
}
'skip$
if$
}
EXECUTE {initialize.longest.label}
ITERATE {longest.label.pass}
FUNCTION {begin.bib}
{ preamble$ empty$
'skip$
{ preamble$ write$ newline$ }
if$
newline$
"\providecommand{\href}[2]{#2}"
"\begingroup\raggedright\begin{thebibliography}{" * longest.label *
"}" * write$ newline$ }
EXECUTE {begin.bib}
EXECUTE {init.state.consts}
ITERATE {call.type$}
FUNCTION {end.bib}
{ newline$
"\end{thebibliography}\endgroup" write$ newline$
}
EXECUTE {end.bib}
================================================
FILE: tex/Makefile
================================================
driver = HEPML.tex
output_file = $(basename $(driver)).pdf
tex_source = $(wildcard *.tex)
#image_source = $(wildcard Images/*.pdf) $(wildcard Images/*.jpg) $(wildcard Images/*.png)
bib_source = $(wildcard *.bib)
REFERENCES = true
TEX=xelatex
all: document
document: $(driver) $(tex_source) $(bib_source)
$(TEX) $(driver)
$(TEX) $(driver)
if [ "$(REFERENCES)" = true ]; then bibtex $(basename $(driver)); $(TEX) $(driver); $(TEX) $(driver); fi
clean:
\rm -f *~ *.aux *.bbl *.blg *.dvi *.idx *.lof *.log *.lot *.toc *.glg *.gls *.glo *.xdy *.nav *.out *.snm *.vrb *.mp *.synctex.gz
realclean: clean
\rm -f *.pdf
final:
if [ -f *.aux ]; then make clean; fi
make document
make clean
================================================
FILE: tex/uiuchept.bst
================================================
% UT Physics bibliographic style, ver. 2.2. Based on:
%
%X IEEE Transactions bibliography style (29-Jan-88 version)
%X numeric labels, order-of-reference, IEEE abbreviations,
%X quotes around article titles, commas separate all fields
%X except after book titles and before "notes". Otherwise,
%X much like the "plain" family, from which this is adapted.
%X
%X History
%X 9/30/85 (HWT) Original version, by Howard Trickey.
%X 1/29/88 (OP&HWT) Updated for BibTeX version 0.99a, Oren Patashnik;
%X THIS `ieeetr' VERSION DOES NOT WORK WITH BIBTEX 0.98i.
%
% Modifications: 1) added hypertex support and "archive", "eprint"
% and "url" fields.
% 2) parentheses around dates, and no "pp." for article entries
% 3) "publisher, address" instead of "address: publisher"
% 4) added "report" field for article entries.
% 5) particle physics-oriented abbreviations, rather than ieee.
% 6) added "collaboration" field, as per
% Jonathan Flynn' suggestion. SPIRES now supports this field.
% 7) Improved output of Proceedings entries
%
% Modified by Jacques Distler, 4/08
% History: ver 1.0 9/96
% ver 1.1 10/96 - added "collaboration" field
% ver 1.2 7/97 - added a "\providecommand{\href}[2]{#2}"
% to handle case where \href is not defined
% ver 1.3 12/97 - fixed lousy-looking proceedings output.
% ver 1.4 1/98 - fixed format.number, address in
% proceedings entries
% ver 1.5 3/99 - added (nonprinting) CITATION field for
% SLAC internal use
% ver 1.6 4/99 - Fix to ensure %%CITATION output not broken
% across lines. Added new.sentence to ensure
% previous output properly terminated.
% (Moral: test before you release.)
% ver 1.7 10/99 - "et.~al." should be "et al." Morons!
% ver 1.8 11/99 - Changed the Web URL to the more portable
% arxiv.org. The "archive" field functions as
% a true base-URL. This is NOT A
% BACKWARDS-COMPATIBLE change!
% ver 1.8a 12/99 - MACROs for arXiv and cogprints
% BaseURL's defined.
% ver 1.9 6/05 - eprint support for other entry types
% ver 2.0 4/08 - support "new-style" eprint identifiers
% ver 2.1 4/08 - support for "url" field
% ver 2.2 4/08 - support for "doi" field
%
% HyperTeX Wizardry:
%
% The following are equivalent:
% archive = arXiv
% eprint = "hep-th/9605023"
% and
% eprint = "hep-th/9605023"
% both produce
%
% \href{http://arxiv.org/abs/hep-th/9605023}{{\tt hep-th/9605023}}
%
% in the bibliographic output at the appropriate point. More generally,
% if the archive field is present, we produce a URL of the form
% "archive/eprint" as the first argument of the \href. If absent, the base
% URL defaults to "http://arxiv.org/abs"
% If you are using a hypertex macropackage, like hyperref.sty, this command
% will create a link to the eprint at Los Alamos (or wherever).
%
% "New-style" arXiv identifiers are also supported.
%
% archivePrefix = "arXiv",
% eprint = "0707.3168",
% primaryClass = "hep-th",
%
% produces
%
% \href{http://arxiv.org/abs/0707.3168}{{\tt arXiv:0707.3168 [hep-th]}}
%
% Another (non-arXiv) example:
%
% archive = "http://cogprints.org",
% eprint = "5542",
% archivePrefix = "Cogprints",
%
% produces
%
% \href{http://cogprints.org/5542}{{\tt Cogprints:5542}}
%
% If a
%
% doi = "10.xxxx"
%
% field is present, then the journal reference becomes a
% clickable hyperlink to the online journal version of the paper.
%
% The bibtex output produced by SPIRES, while far from perfect, is pretty
% suitable for use with this style. Indeed, this style was designed with
% SPIRES in mind.
ENTRY
{ address
author
booktitle
chapter
edition
editor
howpublished
institution
journal
key
month
note
number
organization
pages
publisher
school
series
title
type
volume
year
archive
eprint
report
collaboration
SLACcitation
archivePrefix
primaryClass
url
doi
}
{}
{ label }
INTEGERS { output.state before.all mid.sentence after.quote after.sentence
after.quoted.block after.block }
FUNCTION {init.state.consts}
{ #0 'before.all :=
#1 'mid.sentence :=
#2 'after.quote :=
#3 'after.sentence :=
#4 'after.quoted.block :=
#5 'after.block :=
}
STRINGS { s t }
FUNCTION {output.nonnull}
{ 's :=
output.state mid.sentence =
{ ", " * write$ }
{ output.state after.quote =
{ " " * write$ }
{ output.state after.block =
{ add.period$ write$
newline$
"\newblock " write$
}
{ output.state before.all =
'write$
{ output.state after.quoted.block =
{ write$
newline$
"\newblock " write$
}
{ add.period$ " " * write$ }
if$
}
if$
}
if$
}
if$
mid.sentence 'output.state :=
}
if$
s
}
FUNCTION {output}
{ duplicate$ empty$
'pop$
'output.nonnull
if$
}
FUNCTION {output.check}
{ 't :=
duplicate$ empty$
{ pop$ "empty " t * " in " * cite$ * warning$ }
'output.nonnull
if$
}
FUNCTION {output.bibitem}
{ newline$
"\bibitem{" write$
cite$ write$
"}" write$
newline$
""
before.all 'output.state :=
}
FUNCTION {blank.sep}
{ after.quote 'output.state :=
}
FUNCTION {fin.entry}
{ output.state after.quoted.block =
'skip$
'add.period$
if$
write$
newline$
}
FUNCTION {new.block}
{ output.state before.all =
'skip$
{ output.state after.quote =
{ after.quoted.block 'output.state := }
{ after.block 'output.state := }
if$
}
if$
}
FUNCTION {new.sentence}
{ output.state after.block =
'skip$
{ output.state before.all =
'skip$
{ after.sentence 'output.state := }
if$
}
if$
}
FUNCTION {not}
{ { #0 }
{ #1 }
if$
}
FUNCTION {and}
{ 'skip$
{ pop$ #0 }
if$
}
FUNCTION {or}
{ { pop$ #1 }
'skip$
if$
}
FUNCTION {new.block.checka}
{ empty$
'skip$
'new.block
if$
}
FUNCTION {new.block.checkb}
{ empty$
swap$ empty$
and
'skip$
'new.block
if$
}
FUNCTION {new.sentence.checka}
{ empty$
'skip$
'new.sentence
if$
}
FUNCTION {field.or.null}
{ duplicate$ empty$
{ pop$ "" }
'skip$
if$
}
FUNCTION {emphasize}
{ duplicate$ empty$
{ pop$ "" }
{ "{\em " swap$ * "}" * }
if$
}
FUNCTION {capitalize}
{ "u" change.case$ "t" change.case$ }
INTEGERS { nameptr namesleft numnames }
FUNCTION {format.names}
{ 's :=
#1 'nameptr :=
s num.names$ 'numnames :=
numnames 'namesleft :=
{ namesleft #0 > }
{ s nameptr "{f.~}{vv~}{ll}{, jj}" format.name$ 't :=
nameptr #1 >
{ namesleft #1 >
{ ", " * t * }
{ numnames #2 >
{ "," * }
'skip$
if$
t "others" =
{ " {\em et al.}" * }
{ " and " * t * }
if$
}
if$
}
't
if$
nameptr #1 + 'nameptr :=
namesleft #1 - 'namesleft :=
}
while$
}
FUNCTION {format.authors}
{ author empty$
{ "" }
{ author format.names }
if$
}
FUNCTION {format.archive}
{
archivePrefix empty$
{ "" }
{ archivePrefix ":" *}
if$
}
FUNCTION {format.primaryClass}
{
primaryClass empty$
{ "" }
{ " [" primaryClass * "]" *}
if$
}
FUNCTION {format.eprint}
{ eprint empty$
{ ""}
{ archive empty$
{"\href{http://arxiv.org/abs/" eprint * "}" *
"{{\tt " * format.archive * eprint *
format.primaryClass * "}}" *}
{"\href{" archive * "/" * eprint * "}" *
"{{\tt " * format.archive * eprint *
format.primaryClass * "}}" *}
if$
}
if$
}
FUNCTION {format.url}
{ url empty$
{ "" }
{"\url{" url * "}" *}
if$
}
FUNCTION {add.doi}
{ duplicate$ empty$
{ skip$ }
{ doi empty$
{}
{"\href{http://dx.doi.org/" doi * "}{" * swap$ * "}" *}
if$
}
if$
}
FUNCTION {format.report}
{ report empty$
{ ""}
{ report}
if$
}
FUNCTION {format.editors}
{ editor empty$
{ "" }
{ editor format.names
editor num.names$ #1 >
{ ", eds." * }
{ ", ed." * }
if$
}
if$
}
FUNCTION {format.title}
{ title empty$
{ "" }
{ "``" title "t" change.case$ * ",''" * }
if$
}
FUNCTION {format.title.p}
{ title empty$
{ "" }
{ "``" title "t" change.case$ * ".''" * }
if$
}
FUNCTION {n.dashify}
{ 't :=
""
{ t empty$ not }
{ t #1 #1 substring$ "-" =
{ t #1 #2 substring$ "--" = not
{ "--" *
t #2 global.max$ substring$ 't :=
}
{ { t #1 #1 substring$ "-" = }
{ "-" *
t #2 global.max$ substring$ 't :=
}
while$
}
if$
}
{ t #1 #1 substring$ *
t #2 global.max$ substring$ 't :=
}
if$
}
while$
}
FUNCTION {format.date}
{ year empty$
{ month empty$
{ "" }
{ "there's a month but no year in " cite$ * warning$
month
}
if$
}
{ month empty$
'year
{ month ", " * year * }
if$
}
if$
}
FUNCTION {format.date.paren}
{ year empty$
{ month empty$
{ "" }
{ "there's a month but no year in " cite$ * warning$
month
}
if$
}
{ month empty$
{"(" year * ") " *}
{"(" month * ", " * year * ") " *}
if$
}
if$
}
FUNCTION {format.collaboration}
{ collaboration empty$
{ "" }
{ "{\bf " collaboration * "} " * "Collaboration" * }
if$
}
FUNCTION {format.SLACcitation}
{ SLACcitation empty$
{""}
{ newline$ SLACcitation output "" newline$ }
if$
}
FUNCTION {format.btitle}
{ title emphasize
}
FUNCTION {tie.or.space.connect}
{ duplicate$ text.length$ #3 <
{ "~" }
{ " " }
if$
swap$ * *
}
FUNCTION {either.or.check}
{ empty$
'pop$
{ "can't use both " swap$ * " fields in " * cite$ * warning$ }
if$
}
FUNCTION {format.bvolume}
{ volume empty$
{ "" }
{ "vol.~" volume *
series empty$
'skip$
{ " of " * series emphasize * }
if$
"volume and number" number either.or.check
}
if$
}
FUNCTION {format.number.series}
{ volume empty$
{ number empty$
{ series field.or.null }
{ output.state mid.sentence =
{ "no.~" }
{ "No.~" }
if$
number *
series empty$
{ "there's a number but no series in " cite$ * warning$ }
{ " in " * series * }
if$
}
if$
}
{ "" }
if$
}
FUNCTION {format.edition}
{ edition empty$
{ "" }
{ edition "l" change.case$ "~ed." * }
if$
}
INTEGERS { multiresult }
FUNCTION {multi.page.check}
{ 't :=
#0 'multiresult :=
{ multiresult not
t empty$ not
and
}
{ t #1 #1 substring$
duplicate$ "-" =
swap$ duplicate$ "," =
swap$ "+" =
or or
{ #1 'multiresult := }
{ t #2 global.max$ substring$ 't := }
if$
}
while$
multiresult
}
FUNCTION {format.pages}
{ pages empty$
{ "" }
{ pages multi.page.check
{ "pp.~" pages n.dashify * }
{ "p.~" pages * }
if$
}
if$
}
FUNCTION {format.pages.nopp}
{ pages empty$
{ "" }
{ pages multi.page.check
{ pages n.dashify }
{ pages }
if$
}
if$
}
FUNCTION {format.volume}
{ volume empty$
{ "" }
{ "{\bf " volume * "} " * }
if$
}
FUNCTION {format.number}
{ number empty$
{ "" }
{ "no.~" number * "," *}
if$
}
FUNCTION {format.chapter.pages}
{ chapter empty$
'format.pages
{ type empty$
{ "ch.~" chapter * }
{ type "l" change.case$ chapter tie.or.space.connect }
if$
pages empty$
'skip$
{ ", " * format.pages * }
if$
}
if$
}
FUNCTION {format.in.ed.booktitle}
{ booktitle empty$
{ "" }
{ "in " booktitle emphasize *
editor empty$
'skip$
{ ", " * format.editors * }
if$
}
if$
}
FUNCTION {format.thesis.type}
{ type empty$
'skip$
{ pop$
output.state after.block =
{ type "t" change.case$ }
{ type "l" change.case$ }
if$
}
if$
}
FUNCTION {empty.misc.check}
{ author empty$ title empty$ howpublished empty$
month empty$ year empty$ note empty$
and and and and and
{ "all relevant fields are empty in " cite$ * warning$ }
'skip$
if$
}
FUNCTION {format.tr.number}
{ type empty$
{ "Tech. Rep." }
'type
if$
number empty$
{ "l" change.case$ }
{ number tie.or.space.connect }
if$
}
FUNCTION {format.paddress}
{ address empty$
{ "" }
{ "(" address * ")" * }
if$
}
FUNCTION {format.article.crossref}
{ key empty$
{ journal empty$
{ "need key or journal for " cite$ * " to crossref " * crossref *
warning$
""
}
{ "in {\em " journal * "\/}" * }
if$
}
{ "in " key * }
if$
" \cite{" * crossref * "}" *
}
FUNCTION {format.crossref.editor}
{ editor #1 "{vv~}{ll}" format.name$
editor num.names$ duplicate$
#2 >
{ pop$ " {\em et.~al.}" * }
{ #2 <
'skip$
{ editor #2 "{ff }{vv }{ll}{ jj}" format.name$ "others" =
{ " {\em et.~al.}" * }
{ " and " * editor #2 "{vv~}{ll}" format.name$ * }
if$
}
if$
}
if$
}
FUNCTION {format.book.crossref}
{ volume empty$
{ "empty volume in " cite$ * "'s crossref of " * crossref * warning$
"In "
}
{ "Vol.~" volume *
" of " *
}
if$
editor empty$
editor field.or.null author field.or.null =
or
{ key empty$
{ series empty$
{ "need editor, key, or series for " cite$ * " to crossref " *
crossref * warning$
"" *
}
{ "{\em " * series * "\/}" * }
if$
}
{ key * }
if$
}
{ format.crossref.editor * }
if$
" \cite{" * crossref * "}" *
}
FUNCTION {format.incoll.inproc.crossref}
{ editor empty$
editor field.or.null author field.or.null =
or
{ key empty$
{ booktitle empty$
{ "need editor, key, or booktitle for " cite$ * " to crossref " *
crossref * warning$
""
}
{ "in {\em " booktitle * "\/}" * }
if$
}
{ "in " key * }
if$
}
{ "in " format.crossref.editor * }
if$
" \cite{" * crossref * "}" *
}
FUNCTION {format.journal}
{ journal missing$
{ "" }
{journal emphasize " " *
format.volume *
format.date.paren *
month empty$
{ format.number }
'skip$
if$ * " " *
format.pages.nopp *
}
if$
}
FUNCTION {article}
{ output.bibitem
format.collaboration output
format.authors "author" output.check
format.title "title" output.check
blank.sep
crossref missing$
{ journal missing$
{}
{ format.journal add.doi "journal" output.check}
if$
report missing$
{format.eprint output}
{blank.sep format.report output format.eprint output}
if$
}
{ format.article.crossref output.nonnull
format.pages output
format.eprint output
}
if$
new.sentence
format.url output
new.sentence
note output
new.sentence
format.SLACcitation output
fin.entry
}
FUNCTION {book}
{ output.bibitem
format.collaboration output
author empty$
{ format.editors "author and editor" output.check }
{ format.authors output.nonnull
crossref missing$
{ "author and editor" editor either.or.check }
'skip$
if$
}
if$
format.btitle add.doi "title" output.check
crossref missing$
{ format.bvolume output
new.block
format.number.series output
new.sentence
publisher "publisher" output.check
address output
}
{ new.block
format.book.crossref output.nonnull
}
if$
format.edition output
format.date "year" output.check
new.block
format.eprint output
new.block
format.url output
new.block
note output
new.sentence
format.SLACcitation output
fin.entry
}
FUNCTION {booklet}
{ output.bibitem
format.collaboration output
format.authors output
title empty$
{ "empty title in " cite$ * warning$
howpublished new.sentence.checka
}
{ howpublished empty$ not
address empty$ month empty$ year empty$ and and
or
{ format.title.p output.nonnull }
{ format.title output.nonnull }
if$
blank.sep
}
if$
howpublished capitalize output
address output
format.date output
new.block
format.eprint output
new.block
format.url output
new.block
note output
new.sentence
format.SLACcitation output
fin.entry
}
FUNCTION {inbook}
{ output.bibitem
format.collaboration output
author empty$
{ format.editors "author and editor" output.check }
{ format.authors output.nonnull
crossref missing$
{ "author and editor" editor either.or.check }
'skip$
if$
}
if$
format.btitle "title" output.check
crossref missing$
{ format.bvolume output
format.chapter.pages add.doi "chapter and pages" output.check
new.block
format.number.series output
new.block
publisher "publisher" output.check
address output
}
{ format.chapter.pages add.doi "chapter and pages" output.check
new.block
format.book.crossref output.nonnull
}
if$
format.edition output
format.date "year" output.check
new.block
format.eprint output
new.block
format.url output
new.block
note output
new.sentence
format.SLACcitation output
fin.entry
}
FUNCTION {incollection}
{ output.bibitem
format.collaboration output
format.authors "author" output.check
format.title add.doi "title" output.check
blank.sep
crossref missing$
{ format.in.ed.booktitle "booktitle" output.check
format.bvolume output
format.number.series output
format.chapter.pages output
new.block
publisher "publisher" output.check
address output
format.edition output
format.date "year" output.check
}
{ format.incoll.inproc.crossref output.nonnull
format.chapter.pages output
}
if$
new.block
format.eprint output
new.block
format.url output
new.block
note output
new.sentence
format.SLACcitation output
fin.entry
}
FUNCTION {inproceedings}
{ output.bibitem
format.collaboration output
format.authors "author" output.check
format.title add.doi "title" output.check
blank.sep
crossref missing$
{ format.in.ed.booktitle "booktitle" output.check
format.bvolume output
format.number.series output
format.pages output
organization output
new.block
publisher output
address output
format.date "year" output.check
}
{ format.incoll.inproc.crossref output.nonnull
format.pages output
}
if$
new.block
format.eprint output
new.block
format.url output
new.block
note output
new.sentence
format.SLACcitation output
fin.entry
}
FUNCTION {conference} { inproceedings }
FUNCTION {manual}
{ output.bibitem
format.collaboration output
author empty$
{ organization empty$
'skip$
{ organization output.nonnull
address output
}
if$
}
{ format.authors output.nonnull }
if$
format.btitle "title" output.check
author empty$
{ organization empty$
{ address new.block.checka
address output
}
'skip$
if$
}
{ organization address new.block.checkb
organization output
address output
}
if$
format.edition output
format.date output
new.block
format.eprint output
new.block
format.url output
new.block
note output
fin.entry
}
FUNCTION {mastersthesis}
{ output.bibitem
format.authors "author" output.check
format.title add.doi "title" output.check
blank.sep
"Master's thesis" format.thesis.type output.nonnull
school "school" output.check
address output
format.date "year" output.check
new.block
format.url output
new.block
note output
fin.entry
}
FUNCTION {misc}
{ output.bibitem
format.collaboration output
format.authors output
title empty$
{ howpublished new.sentence.checka }
{ howpublished empty$ not
month empty$ year empty$ and
or
{ format.title.p output.nonnull }
{ format.title output.nonnull }
if$
blank.sep
}
if$
howpublished capitalize output
format.date output
new.block
format.url output
new.sentence
note output
new.sentence
fin.entry
empty.misc.check
}
FUNCTION {phdthesis}
{ output.bibitem
format.authors "author" output.check
format.btitle add.doi "title" output.check
new.block
"PhD thesis" format.thesis.type output.nonnull
school "school" output.check
address output
format.date "year" output.check
new.block
format.eprint output
new.block
format.url output
new.block
note output
new.sentence
format.SLACcitation output
fin.entry
}
FUNCTION {proceedings}
{ output.bibitem
editor empty$
{ organization output }
{ format.editors output.nonnull }
if$
format.btitle add.doi "title" output.check
format.bvolume output
format.number.series output
editor empty$
'skip$
{ organization output }
if$
new.block
publisher output
address output
format.date "year" output.check
new.block
format.eprint output
new.block
format.url output
new.block
note output
new.sentence
format.SLACcitation output
fin.entry
}
FUNCTION {techreport}
{ output.bibitem
format.collaboration output
format.authors "author" output.check
format.title add.doi "title" output.check
blank.sep
format.tr.number output.nonnull
institution "institution" output.check
address output
format.date "year" output.check
new.block
format.eprint output
new.block
format.url output
new.block
note output
fin.entry
}
FUNCTION {unpublished}
{ output.bibitem
format.collaboration output
format.authors "author" output.check
format.title.p "title" output.check
blank.sep
note "note" output.check
format.date output
new.sentence
format.SLACcitation output
fin.entry
}
FUNCTION {default.type} { misc }
MACRO {jan} {"Jan."}
MACRO {feb} {"Feb."}
MACRO {mar} {"Mar."}
MACRO {apr} {"Apr."}
MACRO {may} {"May"}
MACRO {jun} {"June"}
MACRO {jul} {"July"}
MACRO {aug} {"Aug."}
MACRO {sep} {"Sept."}
MACRO {oct} {"Oct."}
MACRO {nov} {"Nov."}
MACRO {dec} {"Dec."}
MACRO {nup} {"Nucl. Phys."}
MACRO {cmp} {"Comm. Math. Phys."}
MACRO {prl} {"Phys. Rev. Lett."}
MACRO {pl} {"Phys. Lett."}
MACRO {rmp} {"Rev. Mod. Phys."}
MACRO {ijmp} {"Int. Jour. Mod. Phys."}
MACRO {mpl} {"Mod. Phys. Lett."}
MACRO {pr} {"Phys. Rev."}
MACRO {arXiv} {"http://arxiv.org/abs"}
MACRO {cogprints} {"http://cogprints.org"}
MACRO {pubmed} {"http://www.ncbi.nlm.nih.gov/pubmed"}
READ
STRINGS { longest.label }
INTEGERS { number.label longest.label.width }
FUNCTION {initialize.longest.label}
{ "" 'longest.label :=
#1 'number.label :=
#0 'longest.label.width :=
}
FUNCTION {longest.label.pass}
{ number.label int.to.str$ 'label :=
number.label #1 + 'number.label :=
label width$ longest.label.width >
{ label 'longest.label :=
label width$ 'longest.label.width :=
}
'skip$
if$
}
EXECUTE {initialize.longest.label}
ITERATE {longest.label.pass}
FUNCTION {begin.bib}
{ preamble$ empty$
'skip$
{ preamble$ write$ newline$ }
if$
"\providecommand{\href}[2]{#2}"
"\begingroup\raggedright\begin{thebibliography}{" * longest.label *
"}" * write$ newline$ }
EXECUTE {begin.bib}
EXECUTE {init.state.consts}
ITERATE {call.type$}
FUNCTION {end.bib}
{ newline$
"\end{thebibliography}\endgroup" write$ newline$
}
EXECUTE {end.bib}
gitextract_a94d46y7/
├── .gitignore
├── CONTRIBUTING.md
├── LICENSE
├── README.md
├── papers.md
└── tex/
├── HEPML.bib
├── HEPML.tex
├── JHEP.bst
├── Makefile
└── uiuchept.bst
Condensed preview — 10 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (123K chars).
[
{
"path": ".gitignore",
"chars": 147,
"preview": "# Specifies intentionally untracked files that Git should ignore\n*~\n\n# Ignore LaTeX auxiliary files and output\n*.aux\n*.b"
},
{
"path": "CONTRIBUTING.md",
"chars": 3744,
"preview": "# Contributing\n\nTo contribute to this project please either start a pull request or an issue with the details of the inf"
},
{
"path": "LICENSE",
"chars": 1073,
"preview": "MIT License\n\nCopyright (c) 2017 Matthew Feickert\n\nPermission is hereby granted, free of charge, to any person obtaining "
},
{
"path": "README.md",
"chars": 18042,
"preview": "# HEPML Resources\n\n[](https://zenodo.org/badge/latestdoi/89476450)\n[\n\n## HEPML Papers\n\n> A `.bib` file for all paper"
},
{
"path": "tex/HEPML.bib",
"chars": 28581,
"preview": "# HEPML Papers\n\n% April 21, 2020\n@article{Romao:2020ojy,\n author = \"Romao, M. Crispim and Castro, N.F. and Milhano, J"
},
{
"path": "tex/HEPML.tex",
"chars": 2986,
"preview": "\\documentclass[12pt,letterpaper]{article}\n\n\\usepackage[hmargin=1.0in,vmargin=1.0in]{geometry}\n\\usepackage{cite}\n\\usepack"
},
{
"path": "tex/JHEP.bst",
"chars": 19446,
"preview": "% JHEP bibliography style ver. 2.3\n%\n% The bibtex output produced by inSPIRE, while far from perfect, is pretty\n% suitab"
},
{
"path": "tex/Makefile",
"chars": 692,
"preview": "driver = HEPML.tex\noutput_file = $(basename $(driver)).pdf\ntex_source = $(wildcard *.tex)\n#image_source = $(wildcard Ima"
},
{
"path": "tex/uiuchept.bst",
"chars": 25121,
"preview": "% UT Physics bibliographic style, ver. 2.2. Based on:\r\n%\r\n%X IEEE Transactions bibliography style (29-Jan-88 version"
}
]
About this extraction
This page contains the full source code of the iml-wg/HEP-ML-Resources GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 10 files (112.0 KB), approximately 34.0k tokens. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.