• DL
• DL in Physics
• DL in HEP

Deep Learning

• Geometric Deep Learning

Deep Learning in Physics

Papers

A more comprehensive list of resources can be found here (more related to condensed matter physics)

Basics

• Machine learning and the physical sciences, arXiv:1903.10563
• A high-bias, low-variance introduction to Machine Learning for physicists, Pankaj Mehta, Marin Bukov, Ching-Hao Wang, Alexandre G.R. Day, Clint Richardson, Charles K. Fisher, David J. Schwab. arXiv:1803.08823

Specific Topics

• Neural Network Renormalization Group, Shuo-Hui Li, Lei Wang. arXiv:1802.02840
• Mutual Information, Neural Networks and the Renormalization Group. Maciej Koch-Janusz, Zohar Ringel. arXiv:1704.06279
• Why does deep and cheap learning work so well? Henry W. Lin (Harvard), Max Tegmark (MIT), David Rolnick (MIT). arXiv: 1608.08225
• Accelerate Monte Carlo Simulations with Restricted Boltzmann Machines. Li Huang, Lei Wang. arXiv:1610.02746
• Discovering Phase Transitions with Unsupervised Learning, Lei Wang. arXiv:1606.00318

Confs and Workshops

• Physics for Deep Learning Workshop, ICML 2019
• Machine Learning and Physics 2018, IASTU. [Video Recording]
• Many Boby Physics Workshop 2017

Deep Learning in HEP

An active list maintained by IML working group can be found here

Papers

• Machine Learning in High Energy Physics Community White Paper, arXiv:1807.02876. [inspire]

Model Inference

• A Guide to Constraining Effective Field Theories with Machine Learning, Johann Brehmer, Kyle Cranmer (New York U.), Gilles Louppe (Liege U.), Juan Pavez (Santa Maria U., Valparaiso). arXiv:1805.00020. [inspire]. Main letter: https://arxiv.org/abs/1805.00013

Jet Physics

See relevant blocks in another repository: jet-physics-notes

Signal Detection

• CWoLa Hunting: Extending the Bump Hunt with Machine Learning, Jack H. Collins (Maryland U. & Johns Hopkins U.), Kiel Howe (Fermilab), Benjamin Nachman (UC, Berkeley & LBL, Berkeley). arXiv:1805.02664 [inspire]

Domain Adaption

• Learning to Pivot with Adversarial Networks, Gilles Louppe (New York U. (main)), Michael Kagan, Kyle Cranmer (New York U.). arXiv:1611.01046. [inspire]
• Domain-Adversarial Training of Neural Networks, Yaroslav Ganin, et al. arXiv:1505.07818.

Pile-up Mitigation

• Pileup Mitigation with Machine Learning (PUMML), Patrick T. Komiske, Eric M. Metodiev (MIT, Cambridge, CTP), Benjamin Nachman (LBL, Berkeley), Matthew D. Schwartz (Harvard U., Phys. Dept.). arXiv: arXiv:1707.08600. [inspire]

Conferences and Workshops

• 3rd IML Machine Learning Workshop