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We develop a graph generative adversarial network to generate sparse data sets like those produced at the CERN Large Hadron Collider (LHC).
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S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift”, in Proceedings of the 32nd International Conference on Machine Learning · 2015
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2016
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M. Arjovsky, S. Chintala, and L. Bottou, “Wasserstein GAN”, (2017). arXiv:1701.07875
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I. Gulrajani et al., “Improved training of Wasserstein GANs”, in Advances in Neural Information Processing Systems 30 · 2017
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F. Monti et al., “Geometric deep learning on graphs and manifolds using mixture model CNNs”, in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · 2017
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J. Gilmer et al., “Neural message passing for quantum chemistry”, in Proceedings of the 34th International Conference on Machine Learning · 2017
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M. Heusel et al., “GANs trained by a two time-scale update rule converge to a local nash equilibrium”, in Advances in Neural Information Processing Systems 30 · 2017
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J. Duarte et al., “Fast inference of deep neural networks in FPGAs for particle physics”, J. Instrum
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E. Coleman et al., “The importance of calorimetry for highly-boosted jet substructure”, J. Instrum
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ATLAS Collaboration, “Fast simulation of the ATLAS calorimeter system with generative adversarial networks”, Technical Report ATL-SOFT-PUB-2020-006, 2020
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doi: 10.5281/zenodo.3601436
M. Pierini, J. M. Duarte, N. Tran, and M. Freytsis, “ hls4ml · 2020
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R. Kansal, “rkansal47/graph-gan: v0.1.0”, 2020 · 2020
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J. Shlomi, P. Battaglia, and J.-R. Vlimant, “Graph neural networks in particle physics”, Machine Learning: Science and Technology
2021
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