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@@ -117,6 +117,8 @@ Listing of useful (mostly) public learning resources for machine learning applic
 
  ## 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. 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)
 
 - 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)