Structure Functions and Parton Densities: a Session Summary
B. Nachman, K. Wichmann, P. Zurita
Proceedings for the DIS2021 Conference
Scaffolding Simulations with Deep Learning for High-Dimensional Deconvolution
B. Nachman
Nature Reviews Physics (2021)
Scaffolding Simulations with Deep Learning for High-Dimensional Deconvolution
A. Andreassen, P. T. Komiske, E. M. Metodiev, B. Nachman, A. Suresh, J. Thaler
ICLR simDL (2021)
Amplifying Statistics with Ensembles of Generative Models
A. Butter, T. Plehn, S. Diefenbacher, G. Kasieczka, B. Nachman
ICLR simDL (2021)
Radiation effects in the LHC experiments: Impact on detector performance and operation
I. Dawson (ed) et al. (Sensor measurements chapter co-edited by B. Nachman)
AI Safety for High Energy Physics
C. Shimmin and B. Nachman
Deep Learning for Physical Sciences, NeurlPS 2019
Tips and Tricks for Training GANs with PhysicsConstraints
L. de Oliveira, M. Paganini, B. Nachman
Deep Learning for Physical Sciences, NeurlPS 2017
Survey of Machine Learning Techniques for HighEnergy Electromagnetic Shower Classification
M. Paganini, L. de Oliveira, B. Nachman
Deep Learning for Physical Sciences, NeurlPS 2017
Controlling Physical Attributes in GAN-Accelerated Simulation of Electromagnetic Calorimeters
L. de Oliveira, M. Paganini, B. Nachman
Journal of Physics: Conference Series 1085 (2018) 042017 (ACAT 2017)
Deep Learning usage by Large Experiments
B. Nachman
Journal of Physics: Conference Series 1085 (2018) 022002 (ACAT 2017)