Fetching the paper…
Reading the bibliography…
Deep generative models parametrised by neural networks have recently started to provide accurate results in modelling natural images.
Y. LeCun, L. Bottou, Y. Bengio, P. Haffner, Gradient-based learning applied to document recognition, Proceedings of the IEEE 86
1998
Earlier work this paper cites.
DOI 10.1016/S0168-9002(03)01368-8
S. Agostinelli, et al., Geant4 · 2003
Earlier work this paper cites.
DOI 10.1088/1126-6708/2006/05/026
T. Sjostrand, S. Mrenna, P.Z. Skands, PYTHIA 6.4 Physics and Manual, JHEP 05 · 2006
Earlier work this paper cites.
DOI 10.1016/j.physletb.2006.08.037
M. Cacciari, G.P. Salam, Dispelling the N 3 N^{3} myth for the k t k_{t} jet-finder, Phys. Lett. B641 · 2006
Earlier work this paper cites.
DOI 10.1088/1748-0221/3/08/S08003
G. Aad, et al., The ATLAS Experiment at the CERN Large Hadron Collider, JINST 3 · 2008
Earlier work this paper cites.
DOI 10.1088/1748-0221/3/08/S08004
S. Chatrchyan, et al., The CMS Experiment at the CERN LHC, JINST 3 · 2008
Earlier work this paper cites.
URL http://stacks.iop.org/1126-6708/2008/i=04/a=063
M. Cacciari, G.P. Salam, G. Soyez, The anti- k t jet clustering algorithm, Journal of High Energy Physics 2008 · 2008
Earlier work this paper cites.
URL https://arxiv.org/abs/0912.0255
R. Mount, et al., Data Preservation in High Energy Physics, Intermediate report of the ICFA-DPHEP Study Group (2009) · 2009
Earlier work this paper cites.
DOI 10.1007/JHEP03(2011)015
J. Thaler, K. Van Tilburg, Identifying boosted objects with n-subjettiness, Journal of High Energy Physics 2011 · 2011
Earlier work this paper cites.
DOI 10.1140/epjc/s10052-012-1896-2
M. Cacciari, G.P. Salam, G. Soyez, FastJet User Manual, Eur. Phys. J. C72 · 2012
Earlier work this paper cites.
DOI 10.1140/epjc/s10052-012-1896-2
M. Cacciari, G.P. Salam, G. Soyez, FastJet User Manual, Eur. Phys. J. C72 · 2012
Earlier work this paper cites.
A.L. Maas, A.Y. Hannun, A.Y. Ng, in Proc. icml , vol. 30 (2013), vol. 30, p. 3
2013
Earlier work this paper cites.
URL https://arxiv.org/abs/1406.2661
I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative Adversarial Networks (2014) · 2014
Cited alongside, same era.
URL http://arxiv.org/abs/1411.1784
M. Mirza, S. Osindero, Conditional generative adversarial nets, CoRR abs/1411.1784 · 2014
Cited alongside, same era.
DOI 10.1007/JHEP02(2014)057
J. de Favereau, C. Delaere, P. Demin, A. Giammanco, V. Lemaître, A. Mertens, M. Selvaggi, DELPHES 3, A modular framework for fast simulation of a generic collider experiment, JHEP 02 · 2014
Cited alongside, same era.
URL http://arxiv.org/abs/1412.6980
D.P. Kingma, J. Ba, Adam: A method for stochastic optimization, CoRR abs/1412.6980 · 2014
Cited alongside, same era.
A.A. Alves, Jr, et al., A Roadmap for HEP Software and Computing R&D for the 2020s · 2017
Later among the works it cites.
URL http://proceedings.mlr.press/v70/arora17a.html
S. Arora, R. Ge, Y. Liang, T. Ma, Y. Zhang, in Proceedings of the 34th International Conference on Machine Learning , Proceedings of Machine Learning Research , vol. 70, ed. by D. Precup, Y.W. Teh (PMLR, International Convention Centre, Sydney, Australia, 2017), Proceedings of Machine Learning Research , vol. 70, pp. 224–232 · 2017
Later among the works it cites.
URL http://arxiv.org/abs/1704.00028
I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, A.C. Courville, Improved training of wasserstein gans, CoRR abs/1704.00028 · 2017
Later among the works it cites.
URL https://arxiv.org/abs/1701.07875
M. Arjovsky, S. Chintala, L. Bottou, Wasserstein GAN (2017) · 2017
Later among the works it cites.
DOI 10.1103/PhysRevD.96.074034
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
O. Ronneberger, P. Fischer, T. Brox, U-net: Convolutional networks for biomedical image segmentation, CoRR abs/1505.04597 · 2015
Cited alongside, same era.
URL http://arxiv.org/abs/1502.03167
S. Ioffe, C. Szegedy, Batch normalization: Accelerating deep network training by reducing internal covariate shift, CoRR abs/1502.03167 · 2015
Cited alongside, same era.
URL https://arxiv.org/abs/1606.00709
S. Nowozin, B. Cseke, R. Tomioka, f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization (2016) · 2016
Cited alongside, same era.
URL https://arxiv.org/abs/1611.01046
G. Louppe, M. Kagan, K. Cranmer, Learning to pivot with adversarial networks (2016) · 2016
Cited alongside, same era.
URL http://arxiv.org/abs/1611.07004
P. Isola, J. Zhu, T. Zhou, A.A. Efros, Image-to-image translation with conditional adversarial networks, CoRR abs/1611.07004 · 2016
Cited alongside, same era.
URL http://arxiv.org/abs/1606.03498
T. Salimans, I.J. Goodfellow, W. Zaremba, V. Cheung, A. Radford, X. Chen, Improved techniques for training gans, CoRR abs/1606.03498 · 2016
Cited alongside, same era.
URL http://arxiv.org/abs/1605.08695
M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, M. Kudlur, J. Levenberg, R. Monga, S. Moore, D.G. Murray, B. Steiner, P.A. Tucker, V. Vasudevan, P. Warden, M. Wicke, Y. Yu, X. Zhang, Tensorflow: A system for large-scale machine learning, CoRR abs/1605.08695 · 2016
Cited alongside, same era.
DOI 10.23731/CYRM-2017-004
G. Apollinari, et al., High-Luminosity Large Hadron Collider (HL-LHC) (2017) · 2017
Cited alongside, same era.
C. Shimmin, P. Sadowski, P. Baldi, E. Weik, D. Whiteson, E. Goul, A. Søgaard, Decorrelated Jet Substructure Tagging using Adversarial Neural Networks, Phys. Rev. D96 · 2017
Later among the works it cites.
V. Estrade, et al., in NIPS 2017 - workshop Deep Learning for Physical Sciences (Long Beach, United States, 2017), pp. 1–5
2017
Later among the works it cites.
DOI 10.1007/s41781-017-0004-6
L. de Oliveira, M. Paganini, B. Nachman, Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics Synthesis, Comput. Softw. Big Sci. 1 · 2017
Later among the works it cites.
F. Carminati, et al., in NIPS 2017 - workshop Deep Learning for Physical Sciences (Long Beach, United States, 2017), pp. 1–5
2017
Later among the works it cites.
L. de Oliveira, M. Paganini, B. Nachman, in 18th International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT 2017) Seattle, WA, USA, August 21-25, 2017 (2017)
2017
Later among the works it cites.
DOI 10.1103/PhysRevLett.120.042003
M. Paganini, L. de Oliveira, B. Nachman, Accelerating Science with Generative Adversarial Networks: An Application to 3D Particle Showers in Multilayer Calorimeters, Phys. Rev. Lett. 120 · 2018
Closest in time.
URL https://arxiv.org/abs/1802.03325
M. Erdmann, L. Geiger, J. Glombitza, D. Schmidt, Generating and refining particle detector simulations using the Wasserstein distance in adversarial networks (2018) · 2018
Closest in time.