Machine Learning Papers

A not at all comprehensive list of interesting machine learning papers

I come from a background in Physics and Astrophysics, but I started getting interested in and applying Machine Learning at the beginning of my graduate studies, in 2013. I stay up to date with the field by attending conferences such as NIPS and reading interesting papers. Here is a small selection of a few of them that I find helpful.


End-To-End Memory Networks, Sukhbaatar, Szlam, Weston, Fergus (NIPS 2015).


Spatial Transformer Networks, Jaderberg, Simonyan, Zisserman, Kavukcuoglu (NIPS 2015).
Character-level Convolutional Networks for Text Classification, Zhang, Zhao, LeCun (NIPS 2015).



Adversarial Learning


Other Deep Learning

Training Very Deep Networks, Srivastava, Greff, Schmidhuber (NIPS 2015).
Semi-Supervised Learning with Ladder Networks, Rasmus, Valpola, Honkala, Berglund, Raiko (NIPS 2015).

Machine Learning in HEP

Jet Substructure Classification in High-Energy Physics with Deep Neural Networks, Baldi, Bauer, Eng, Sadowski, Whiteson.
Parameterized Machine Learning for High-Energy Physics, Baldi, Cranmer, Faucett, Sadowski, Whiteson.
Jet-Images – Deep Learning Edition, de Oliveira, Kagan, Mackey, Nachman, Schwartzman.