Event Generation and Statistical Sampling for Physics with Deep Generative Models and a Density Information Buffer

Bobak Hashemi, Nick Amin, Kaustuv Datta, Dominick Olivito, and Maurizio Pierini, “LHC analysis-specific datasets with Generative Adversarial Networks,” (2019), arXiv:1901.05282 [hep-ex]

Original

1901

Earlier work this paper cites.

K. Pearson, “On lines and planes of closest fit to systems of points in space,” Philosophical Magazine 2

1901

Earlier work this paper cites.

Riccardo Di Sipio, Michele Faucci Giannelli, Sana Ketabchi Haghighat, and Serena Palazzo, “DijetGAN: A Generative-Adversarial Network Approach for the Simulation of QCD Dijet Events at the LHC,” JHEP 08

Original

1903

Earlier work this paper cites.

Bin Dai and David Wipf, “Diagnosing and Enhancing VAE Models,” arXiv e-prints , arXiv:1903.05789 (2019), arXiv:1903.05789 [cs.LG]

Original

1903

Earlier work this paper cites.

Herbert Robbins, “An empirical bayes approach to statistics,” in Proceedings of the Third Berkeley Symposium on Mathematical Statistics and Probability, Volume 1: Contributions to the Theory of Statistics (University of California Press, Berkeley, Calif., 1956) pp. 157–163

1956

Earlier work this paper cites.

Emanuel Parzen, “On estimation of a probability density function and mode,” Ann. Math. Statist. 33

1962

Earlier work this paper cites.

G. Peter Lepage, “VEGAS: AN ADAPTIVE MULTIDIMENSIONAL INTEGRATION PROGRAM,” (1980)

1980

Earlier work this paper cites.

D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Parallel distributed processing: Explorations in the microstructure of cognition, vol. 1,” (MIT Press, Cambridge, MA, USA, 1986) Chap. Learning Internal Representations by Error Propagation, pp. 318–362

1986

Earlier work this paper cites.

G. Corcella, I. G. Knowles, G. Marchesini, S. Moretti, K. Odagiri, P. Richardson, M. H. Seymour, and B. R. Webber, “HERWIG 6: An Event generator for hadron emission reactions with interfering gluons (including supersymmetric processes),” JHEP 01

2001

Earlier work this paper cites.

Frank Dellaert College and Frank Dellaert, The Expectation Maximization Algorithm , Tech. Rep. (2002)

2002

Earlier work this paper cites.

Michelangelo L. Mangano, Mauro Moretti, Fulvio Piccinini, Roberto Pittau, and Antonio D. Polosa, “ALPGEN, a generator for hard multiparton processes in hadronic collisions,” JHEP 07

2003

Earlier work this paper cites.

Johan Alwall et al. , “A Standard format for Les Houches event files,” Monte Carlos for the LHC: A Workshop on the Tools for LHC Event Simulation (MC4LHC) Geneva, Switzerland, July 17-16, 2006 , Comput. Phys. Commun. 176

2007

Earlier work this paper cites.

Frank C. Porter, “Testing Consistency of Two Histograms,” (2008), arXiv:0804.0380 [physics.data-an]

Original

2008

Earlier work this paper cites.

T. Gleisberg, Stefan. Hoeche, F. Krauss, M. Schonherr, S. Schumann, F. Siegert, and J. Winter, “Event generation with SHERPA 1.1,” JHEP 02

Original

2009

Earlier work this paper cites.

Johan Alwall, Michel Herquet, Fabio Maltoni, Olivier Mattelaer, and Tim Stelzer, “MadGraph 5 : Going Beyond,” JHEP 06

Original

2011

Earlier work this paper cites.

Wolfgang Kilian, Thorsten Ohl, and Jurgen Reuter, “WHIZARD: Simulating Multi-Particle Processes at LHC and ILC,” Eur. Phys. J. C71

Original

2011

Earlier work this paper cites.

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay, “Scikit-learn: Machine learning in Python,” Journal of Machine Learning Research 12

2011

Earlier work this paper cites.

Matteo Cacciari, Gavin P. Salam, and Gregory Soyez, “FastJet User Manual,” Eur. Phys. J. C72

Original

2012

Earlier work this paper cites.

Alexander Belyaev, Neil D. Christensen, and Alexander Pukhov, “CalcHEP 3.4 for collider physics within and beyond the Standard Model,” Comput. Phys. Commun. 184

Original

2013

Earlier work this paper cites.

Diederik P Kingma and Max Welling, “Auto-Encoding Variational Bayes,” arXiv e-prints , arXiv:1312.6114 (2013), arXiv:1312.6114 [stat.ML]

Original

2013

Earlier work this paper cites.

J. de Favereau, C. Delaere, P. Demin, A. Giammanco, V. Lemaître, A. Mertens, and M. Selvaggi (DELPHES 3), “DELPHES 3, A modular framework for fast simulation of a generic collider experiment,” JHEP 02

Original

2014

Cited alongside, same era.

Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio, “Generative Adversarial Networks,” arXiv e-prints , arXiv:1406.2661 (2014), arXiv:1406.2661 [stat.ML]

Original

2014

Cited alongside, same era.

Jonathon Shlens, “A Tutorial on Principal Component Analysis,” arXiv e-prints , arXiv:1404.1100 (2014), arXiv:1404.1100 [cs.LG]

Original

2014

Cited alongside, same era.

Sharan Chetlur, Cliff Woolley, Philippe Vandermersch, Jonathan Cohen, John Tran, Bryan Catanzaro, and Evan Shelhamer, “cuDNN: Efficient Primitives for Deep Learning,” arXiv e-prints , arXiv:1410.0759 (2014), arXiv:1410.0759 [cs.NE]

Original

2014

Cited alongside, same era.

Martin Arjovsky, Soumith Chintala, and Léon Bottou, “Wasserstein GAN,” arXiv e-prints , arXiv:1701.07875 (2017), arXiv:1701.07875 [stat.ML]

Original

2017

Later among the works it cites.

Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron Courville, “Improved Training of Wasserstein GANs,” arXiv e-prints , arXiv:1704.00028 (2017), arXiv:1704.00028 [cs.LG]

Original

2017

Later among the works it cites.

Chun-Liang Li, Wei-Cheng Chang, Yu Cheng, Yiming Yang, and Barnabás Póczos, “Mmd gan: Towards deeper understanding of moment matching network,” (2017), arXiv:1705.08584 [cs.LG]

Original

2017

Later among the works it cites.

Nitish Shirish Keskar and Richard Socher, “Improving Generalization Performance by Switching from Adam to SGD,” arXiv e-prints , arXiv:1712.07628 (2017), arXiv:1712.07628 [cs.LG]

Original

2017

Later among the works it cites.