Built on

S. Otten et al
Original
1901
Earlier work this paper cites.
B. Hashemi, N. Amin, K. Datta, D. Olivito, and M. Pierini, LHC analysis-specific datasets with Generative Adversarial Networks
Original
1901
Earlier work this paper cites.
R. Di Sipio, M. Faucci Giannelli, S. Ketabchi Haghighat, and S. Palazzo, DijetGAN: A Generative-Adversarial Network Approach for the Simulation of QCD Dijet Events at the LHC
Original
1903
Earlier work this paper cites.
J. Lin, W. Bhimji, and B. Nachman, Machine Learning Templates for QCD Factorization in the Search for Physics Beyond the Standard Model
Original
1903
Earlier work this paper cites.
S. Bollweg, M. Haußmann, G. Kasieczka, M. Luchmann, T. Plehn, and J. Thompson, Deep-Learning Jets with Uncertainties and More
Original
1904
Earlier work this paper cites.
A. Butter, T. Plehn, and R. Winterhalder, How to GAN LHC Events
Original
1907
Earlier work this paper cites.
I. Kobyzev, S. Prince, and M. A. Brubaker, Normalizing flows: An introduction and review of current methods
Original
1908
Earlier work this paper cites.
B. Nachman, A guide for deploying Deep Learning in LHC searches: How to achieve optimality and account for uncertainty
Original
1909
Earlier work this paper cites.
G. Papamakarios, E. Nalisnick, D. J. Rezende, S. Mohamed, and B. Lakshminarayanan, Normalizing flows for probabilistic modeling and inference
Original
1912
Earlier work this paper cites.
F. Bishara and M. Montull, (Machine) Learning Amplitudes for Faster Event Generation
Original
1912
Earlier work this paper cites.
A. Butter, T. Plehn, and R. Winterhalder, How to GAN Event Subtraction
Original
1912
Earlier work this paper cites.
D. Belayneh et al
Original
1912
Earlier work this paper cites.
M. Bellagente, A. Butter, G. Kasieczka, T. Plehn, and R. Winterhalder, How to GAN away Detector Effects
Original
1912
Earlier work this paper cites.
Curran Associates, Inc., 2019
Original
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Kopf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala, Pytorch: An imperative style, high-performance deep learning library · 1912
Earlier work this paper cites.
D. MacKay, Probable Networks and Plausible Predictions – A Review of Practical Bayesian Methods for Supervised Neural Networks
1995
Earlier work this paper cites.
PhD thesis, Toronto,
R. M. Neal, Bayesian learning for neural networks · 1995
Earlier work this paper cites.
D. J. MacKay, Probable networks and plausible predictions—a review of practical bayesian methods for supervised neural networks
1995
Earlier work this paper cites.
E. Bothmann, T. Janßen, M. Knobbe, T. Schmale, and S. Schumann, Exploring phase space with Neural Importance Sampling
Original
2001
Earlier work this paper cites.
C. Gao, J. Isaacson, and C. Krause, i-flow: High-dimensional Integration and Sampling with Normalizing Flows
Original
2001
Earlier work this paper cites.
C. Gao, S. Höche, J. Isaacson, C. Krause, and H. Schulz, Event Generation with Normalizing Flows
Original
2001
Earlier work this paper cites.
Y. Alanazi, N. Sato, T. Liu, W. Melnitchouk, M. P. Kuchera, E. Pritchard, M. Robertson, R. Strauss, L. Velasco, and Y. Li, Simulation of electron-proton scattering events by a Feature-Augmented and Transformed Generative Adversarial Network (FAT-GAN)
Original
2001
Earlier work this paper cites.

Similar

B. Nachman and D. Shih, Anomaly Detection with Density Estimation
Original
2001
Cited alongside, same era.
S. Badger and J. Bullock, Using neural networks for efficient evaluation of high multiplicity scattering amplitudes
Original
2002
Cited alongside, same era.
F. A. Di Bello, S. Ganguly, E. Gross, M. Kado, M. Pitt, L. Santi, and J. Shlomi, Towards a Computer Vision Particle Flow
Original
2003
Cited alongside, same era.
J. Brehmer and K. Cranmer, Flows for simultaneous manifold learning and density estimation
Original
2003
Cited alongside, same era.
S. T. Radev, U. K. Mertens, A. Voss, L. Ardizzone, and U. Köthe, Bayesflow: Learning complex stochastic models with invertible neural networks

Then

J. Alwall et al
Original
2014
Later among the works it cites.
PMLR, Lille, France, 07–09 Jul, 2015
Original
D. Rezende and S. Mohamed, Variational inference with normalizing flows · 2015
Later among the works it cites.
D. P. Kingma and J. Ba, Adam: A method for stochastic optimization
Original
2015
Later among the works it cites.
PhD thesis, Cambridge,
Y. Gal, Uncertainty in Deep Learning · 2016
Later among the works it cites.
Curran Associates, Inc.,
J. T. Springenberg, A. Klein, S. Falkner, and F. Hutter, Bayesian optimization with robust bayesian neural networks · 2016
Later among the works it cites.
L. de Oliveira, M. Paganini, and B. Nachman, Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics Synthesis

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2021

Understanding Event-Generation Networks via Uncertainties

Bellagente, Marco, Haußmann, Manuel, Luchmann, Michel et al.

Understand

Following the growing success of generative neural networks in LHC simulations, the crucial question is how to control the networks and assign uncertainties to their event output.

We show how Bayesian normalizing flow or invertible networks capture uncertainties from the training and turn them into an uncertainty on the event weight.
Fundamentally, the interplay between density and uncertainty estimates indicates that these networks learn functions in analogy to parameter fits rather than binned event counts.

Built on

S. Otten et al
Original
1901
Earlier work this paper cites.
B. Hashemi, N. Amin, K. Datta, D. Olivito, and M. Pierini, LHC analysis-specific datasets with Generative Adversarial Networks
Original
1901
Earlier work this paper cites.
R. Di Sipio, M. Faucci Giannelli, S. Ketabchi Haghighat, and S. Palazzo, DijetGAN: A Generative-Adversarial Network Approach for the Simulation of QCD Dijet Events at the LHC
Original
1903
Earlier work this paper cites.
J. Lin, W. Bhimji, and B. Nachman, Machine Learning Templates for QCD Factorization in the Search for Physics Beyond the Standard Model
Original
1903
Earlier work this paper cites.
S. Bollweg, M. Haußmann, G. Kasieczka, M. Luchmann, T. Plehn, and J. Thompson, Deep-Learning Jets with Uncertainties and More
Original
1904
Earlier work this paper cites.
A. Butter, T. Plehn, and R. Winterhalder, How to GAN LHC Events
Original
1907
Earlier work this paper cites.
I. Kobyzev, S. Prince, and M. A. Brubaker, Normalizing flows: An introduction and review of current methods
Original
1908
Earlier work this paper cites.
B. Nachman, A guide for deploying Deep Learning in LHC searches: How to achieve optimality and account for uncertainty
Original
1909
Earlier work this paper cites.
G. Papamakarios, E. Nalisnick, D. J. Rezende, S. Mohamed, and B. Lakshminarayanan, Normalizing flows for probabilistic modeling and inference
Original
1912
Earlier work this paper cites.
F. Bishara and M. Montull, (Machine) Learning Amplitudes for Faster Event Generation
Original
1912
Earlier work this paper cites.
A. Butter, T. Plehn, and R. Winterhalder, How to GAN Event Subtraction
Original
1912
Earlier work this paper cites.
D. Belayneh et al
Original
1912
Earlier work this paper cites.
M. Bellagente, A. Butter, G. Kasieczka, T. Plehn, and R. Winterhalder, How to GAN away Detector Effects
Original
1912
Earlier work this paper cites.
Curran Associates, Inc., 2019
Original
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Kopf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala, Pytorch: An imperative style, high-performance deep learning library · 1912
Earlier work this paper cites.
D. MacKay, Probable Networks and Plausible Predictions – A Review of Practical Bayesian Methods for Supervised Neural Networks
1995
Earlier work this paper cites.
PhD thesis, Toronto,
R. M. Neal, Bayesian learning for neural networks · 1995
Earlier work this paper cites.
D. J. MacKay, Probable networks and plausible predictions—a review of practical bayesian methods for supervised neural networks
1995
Earlier work this paper cites.
E. Bothmann, T. Janßen, M. Knobbe, T. Schmale, and S. Schumann, Exploring phase space with Neural Importance Sampling
Original
2001
Earlier work this paper cites.
C. Gao, J. Isaacson, and C. Krause, i-flow: High-dimensional Integration and Sampling with Normalizing Flows
Original
2001
Earlier work this paper cites.
C. Gao, S. Höche, J. Isaacson, C. Krause, and H. Schulz, Event Generation with Normalizing Flows
Original
2001
Earlier work this paper cites.
Y. Alanazi, N. Sato, T. Liu, W. Melnitchouk, M. P. Kuchera, E. Pritchard, M. Robertson, R. Strauss, L. Velasco, and Y. Li, Simulation of electron-proton scattering events by a Feature-Augmented and Transformed Generative Adversarial Network (FAT-GAN)
Original
2001
Earlier work this paper cites.

Similar

B. Nachman and D. Shih, Anomaly Detection with Density Estimation
Original
2001
Cited alongside, same era.
S. Badger and J. Bullock, Using neural networks for efficient evaluation of high multiplicity scattering amplitudes
Original
2002
Cited alongside, same era.
F. A. Di Bello, S. Ganguly, E. Gross, M. Kado, M. Pitt, L. Santi, and J. Shlomi, Towards a Computer Vision Particle Flow
Original
2003
Cited alongside, same era.
J. Brehmer and K. Cranmer, Flows for simultaneous manifold learning and density estimation
Original
2003
Cited alongside, same era.
S. T. Radev, U. K. Mertens, A. Voss, L. Ardizzone, and U. Köthe, Bayesflow: Learning complex stochastic models with invertible neural networks

Then

J. Alwall et al
Original
2014
Later among the works it cites.
PMLR, Lille, France, 07–09 Jul, 2015
Original
D. Rezende and S. Mohamed, Variational inference with normalizing flows · 2015
Later among the works it cites.
D. P. Kingma and J. Ba, Adam: A method for stochastic optimization
Original
2015
Later among the works it cites.
PhD thesis, Cambridge,
Y. Gal, Uncertainty in Deep Learning · 2016
Later among the works it cites.
Curran Associates, Inc.,
J. T. Springenberg, A. Klein, S. Falkner, and F. Hutter, Bayesian optimization with robust bayesian neural networks · 2016
Later among the works it cites.
L. de Oliveira, M. Paganini, and B. Nachman, Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics Synthesis