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We explore the use of autoregressive flows, a type of generative model with tractable likelihood, as a means of efficient generation of physical particle collider events.
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Exhaustive neural importance sampling applied to Monte Carlo event generation ,
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D. Jimenez Rezende and S. Mohamed, · 2015
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MADE: Masked Autoencoder for Distribution Estimation ,
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Adam: A method for stochastic optimization ,
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A Positive Resampler for Monte Carlo Events with Negative Weights (2020),
J. R. Andersen, C. Gütschow, A. Maier and S. Prestel, · 2005
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Invertible Networks or Partons to Detector and Back Again ,
M. Bellagente, A. Butter, G. Kasieczka, T. Plehn, A. Rousselot, R. Winterhalder, L. Ardizzone and U. Köthe, · 2006
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Resummation and simulation of soft gluon effects beyond leading colour (2020),
M. D. Angelis, J. R. Forshaw and S. Plätzer, · 2007
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A Neural Resampler for Monte Carlo Reweighting with Preserved Uncertainties ,
B. Nachman and J. Thaler, · 2007
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Herwig++ Physics and Manual ,
M. Bahr et al. , · 2008
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GANplifying Event Samples (2020),
A. Butter, S. Diefenbacher, G. Kasieczka, B. Nachman and T. Plehn, · 2008
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L. Dinh, J. Sohl-Dickstein and S. Bengio, · 2016
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Improving Variational Inference with Inverse Autoregressive Flow ,
D. P. Kingma, T. Salimans, R. Jozefowicz, X. Chen, I. Sutskever and M. Welling, · 2016
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Effective Sample Size for Importance Sampling based on discrepancy measures ,
L. Martino, V. Elvira and F. Louzada, · 2016
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Triple collinear emissions in parton showers ,
S. Höche and S. Prestel, · 2017
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Implementing NLO DGLAP evolution in Parton Showers ,
S. Höche, F. Krauss and S. Prestel, · 2017
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Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics Synthesis ,
L. de Oliveira, M. Paganini and B. Nachman, · 2017
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Efficient Monte Carlo Integration Using Boosted Decision Trees and Generative Deep Neural Networks (2017),
J. Bendavid, · 2017
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Masked Autoregressive Flow for Density Estimation ,
G. Papamakarios, T. Pavlakou and I. Murray, · 2017
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Leading-Color Fully Differential Two-Loop Soft Corrections to QCD Dipole Showers ,
F. Dulat, S. Höche and S. Prestel, · 2018
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Color matrix element corrections for parton showers ,
S. Plätzer, M. Sjodahl and J. Thorén, · 2018
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Accelerating Science with Generative Adversarial Networks: An Application to 3D Particle Showers in Multilayer Calorimeters ,
M. Paganini, L. de Oliveira and B. Nachman, · 2018
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CaloGAN : Simulating 3D high energy particle showers in multilayer electromagnetic calorimeters with generative adversarial networks ,
M. Paganini, L. de Oliveira and B. Nachman, · 2018
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Three dimensional Generative Adversarial Networks for fast simulation ,
F. Carminati, A. Gheata, G. Khattak, P. Mendez Lorenzo, S. Sharan and S. Vallecorsa, · 2018
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Generative models for fast simulation ,
S. Vallecorsa, · 2018
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Unfolding with Generative Adversarial Networks (2018),
K. Datta, D. Kar and D. Roy, · 2018
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Fast and Accurate Simulation of Particle Detectors Using Generative Adversarial Networks ,
P. Musella and F. Pandolfi, · 2018
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Generating and refining particle detector simulations using the Wasserstein distance in adversarial networks ,
M. Erdmann, L. Geiger, J. Glombitza and D. Schmidt, · 2018
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GANs for generating EFT models (2018),
H. Erbin and S. Krippendorf, · 2018
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Controlling Physical Attributes in GAN-Accelerated Simulation of Electromagnetic Calorimeters ,
L. de Oliveira, M. Paganini and B. Nachman, · 2018
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Deep Learning as a Parton Shower ,
J. Monk, · 2018
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Neural Network-Based Approach to Phase Space Integration (2018),
M. D. Klimek and M. Perelstein, · 2018
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Glow: Generative Flow with Invertible 1x1 Convolutions ,
D. P. Kingma and P. Dhariwal, · 2018
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C.-W. Huang, D. Krueger, A. Lacoste and A. Courville, · 2018
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T. Müller, B. McWilliams, F. Rousselle, M. Gross and J. Novák, · 2018
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Sequential Neural Likelihood: Fast Likelihood-free Inference with Autoregressive Flows ,
G. Papamakarios, D. C. Sterratt and I. Murray, · 2018
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3D convolutional GAN for fast simulation ,
S. Vallecorsa, F. Carminati and G. Khattak, · 2019
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Regressive and generative neural networks for scalar field theory ,
K. Zhou, G. Endr˝odi, L.-G. Pang and H. Stöcker, · 2019
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Generative models for fast cluster simulations in the TPC for the ALICE experiment ,
K. Deja, T. Trzcinski and L. u. Graczykowski, · 2019
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Precise simulation of electromagnetic calorimeter showers using a Wasserstein Generative Adversarial Network ,
M. Erdmann, J. Glombitza and T. Quast, · 2019
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Next Generation Generative Neural Networks for HEP ,
S. Farrell, W. Bhimji, T. Kurth, M. Mustafa, D. Bard, Z. Lukic, B. Nachman and H. Patton, · 2019
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Pytorch: An imperative style, high-performance deep learning library ,
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Kopf et al. , · 2019
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Image-based model parameter optimization using Model-Assisted Generative Adversarial Networks (2018),
S. Alonso-Monsalve and L. H. Whitehead, · 2020
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MINT: A Computer program for adaptive Monte Carlo integration and generation of unweighted distributions (2007),
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S. Frixione, P. Nason and C. Oleari, · 2092
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