I've been wanting to add some semi-automated way to handle papers and bibliography information in this website. I found pelican-bibtex and pelican-cite. I'll need to look into pelican-bibtex to see if I can make a nice automated page based on a
.bib file, but pelican-cite looked pretty easy to setup.
Ok, let's see how this works. I got the .bib file from INSPIRE here.
I am doing a simple
grep "@" content/kyle-10authors-2020.bib and then I can use a keyboard macro in VS Code to get the necessary markdown, which should complete into a nicer looking reference. Here are the papers with less than 10 authors that I wrote in 2020:
Brehmer et al. (2020) Brehmer et al. (2020) MishraSharma and Cranmer (2020) Brehmer and Cranmer (2020) Boyda et al. (2020) Shlomi et al. (2020) Cranmer et al. (2020) Brehmer et al. (2020) Brehmer and Cranmer (2020) Kanwar et al. (2020) Greenberg et al. (2020) Serviansky et al. (2020) Rezende et al. (2020)
Well, I had to edit some entries in the
.bib file that had missing
journal information or it lead to a critical failure. This is nice, but not exactly what I'm looking for.
... time passes...
Ok, I played with pelican-bibtex some. I added a custom template and I hacked pelican-bibtex with this:
#hack added by Kyle Cranmer to avoid problems in bib with no journal entry for entry in bibdata_all.entries.values(): if 'journal' not in entry.fields: entry.fields['journal']='' if 'booktitle' not in entry.fields: entry.fields['booktitle']='' formatted_entries = plain_style.format_entries(bibdata_all.entries.values())
so that I don't have to modify the
.bib file. That produces this page
Denis Boyda, Gurtej Kanwar, Sébastien Racanière, Danilo Jimenez Rezende, Michael S. Albergo, Kyle Cranmer, Daniel C. Hackett, and Phiala E. Shanahan. Sampling using $SU(N)$ gauge equivariant flows. 8 2020. arXiv:2008.05456. ↩
Johann Brehmer and Kyle Cranmer. Simulation-based inference methods for particle physics. To appear in Artificial Intelligence for Particle Physics, World Scientific Publishing Co, 10 2020. arXiv:2010.06439. ↩
Johann Brehmer, Kyle Cranmer, Irina Espejo, Alexander Held, Felix Kling, Gilles Louppe, and Juan Pavez. Constraining effective field theories with machine learning. EPJ Web Conf., 245:06026, 2020. doi:10.1051/epjconf/202024506026. ↩
Johann Brehmer, Sebastian Macaluso, Duccio Pappadopulo, and Kyle Cranmer. Hierarchical clustering in particle physics through reinforcement learning. In 34th Conference on Neural Information Processing Systems. 11 2020. arXiv:2011.08191. ↩
Miles Cranmer, Alvaro Sanchez-Gonzalez, Peter Battaglia, Rui Xu, Kyle Cranmer, David Spergel, and Shirley Ho. Discovering Symbolic Models from Deep Learning with Inductive Biases. NeurIPS2020, 6 2020. arXiv:2006.11287. ↩
Craig S. Greenberg, Sebastian Macaluso, Nicholas Monath, Ji-Ah Lee, Patrick Flaherty, Kyle Cranmer, Andrew McGregor, and Andrew McCallum. Data Structures \& Algorithms for Exact Inference in Hierarchical Clustering. 2 2020. arXiv:2002.11661. ↩
Gurtej Kanwar, Michael S. Albergo, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Sébastien Racanière, Danilo Jimenez Rezende, and Phiala E. Shanahan. Equivariant flow-based sampling for lattice gauge theory. Phys. Rev. Lett., 125(12):121601, 2020. arXiv:2003.06413, doi:10.1103/PhysRevLett.125.121601. ↩
Siddharth Mishra-Sharma and Kyle Cranmer. Semi-parametric γ-ray modeling with Gaussian processes and variational inference. In 34th Conference on Neural Information Processing Systems. 10 2020. arXiv:2010.10450. ↩
Danilo Jimenez Rezende, George Papamakarios, Sébastien Racanière, Michael S. Albergo, Gurtej Kanwar, Phiala E. Shanahan, and Kyle Cranmer. Normalizing Flows on Tori and Spheres. ICML 2020, 2 2020. arXiv:2002.02428. ↩
Jonathan Shlomi, Sanmay Ganguly, Eilam Gross, Kyle Cranmer, Yaron Lipman, Hadar Serviansky, Haggai Maron, and Nimrod Segol. Secondary Vertex Finding in Jets with Neural Networks. 8 2020. arXiv:2008.02831. ↩