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We propose to apply several gradient estimation techniques to enable the differentiation of programs with discrete randomness in High Energy Physics.
S. Carrazza and F. A. Dreyer, “Jet grooming through reinforcement learning,” Phys. Rev. D 100
1903
Earlier work this paper cites.
1906
Earlier work this paper cites.
R. Williams, “Simple statistical gradient-following algorithms for connectionist reinforcement learning,” Machine Learning 8
1992
Earlier work this paper cites.
E. Greensmith, P. L. Bartlett, and J. Baxter, “Variance reduction techniques for gradient estimates in reinforcement learning,” Journal of Machine Learning Research 5
2004
Earlier work this paper cites.
H. M. Bücker, G. F. Corliss, P. D. Hovland, U. Naumann, and B. Norris, eds., Automatic Differentiation: Applications, Theory, and Implementations , Lecture Notes in Computational Science and Engineering (Springer, New York, NY, 2005)
2005
Earlier work this paper cites.
2011
Earlier work this paper cites.
P. Glasserman, Monte Carlo Methods in Financial Engineering (Springer, 2013)
2013
Earlier work this paper cites.
2014
Earlier work this paper cites.
D. Rezende, S. Mohamed, and D. Wierstra, “Stochastic backpropagation and approximate inference in deep generative models,” in Proceedings of the 31st International Conference on International Conference on Machine Learning - Volume 32 , ICML’14 (2014) p. II–1278–II–1286
2014
Earlier work this paper cites.
Martín Abadi et al. , “TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems,” (2015), software available from tensorflow.org
2015
Earlier work this paper cites.
J. Schulman, N. Heess, T. Weber, and P. Abbeel, “Gradient estimation using stochastic computation graphs,” in Advances in Neural Information Processing Systems , Vol. 28 (Curran Associates, Inc., 2015)
2015
Earlier work this paper cites.
D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” in 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015)
2015
Earlier work this paper cites.
A. Radovic et al. , “Machine learning at the energy and intensity frontiers of particle physics,” Nature 560
2018
Earlier work this paper cites.
A. G. Baydin, B. A. Pearlmutter, A. A. Radul, and J. M. Siskind, “Automatic differentiation in machine learning: a survey,” Journal of Machine Learning Research 18
2018
Cited alongside, same era.
J. Bradbury et al. , “JAX: composable transformations of Python+NumPy programs,” (2018)
2018
Cited alongside, same era.
Adam Paszke et al. , “PyTorch: An Imperative Style, High-Performance Deep Learning Library,” in Advances in Neural Information Processing Systems 32 (Curran Associates, Inc., 2019) pp. 8024–8035
2019
Cited alongside, same era.
K. Cranmer, J. Brehmer, and G. Louppe, “The frontier of simulation-based inference,” Proceedings of the National Academy of Sciences 117
2020
Cited alongside, same era.
J. Shlomi, P. Battaglia, and J.-R. Vlimant, “Graph neural networks in particle physics,” Machine Learning: Science and Technology 2
2020
P. Calafiura, D. Rousseau, and K. Terao, Artificial Intelligence for High Energy Physics (WORLD SCIENTIFIC, 2022)
2022
Later among the works it cites.
2022
Later among the works it cites.
2022
Later among the works it cites.
S. Cheong et al. , “Novel light field imaging device with enhanced light collection for cold atom clouds,” Journal of Instrumentation 17
2022
Later among the works it cites.
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Cited alongside, same era.
L. Heinrich and N. Simpson, “pyhf/neos: initial zenodo release [ Link
2020
Cited alongside, same era.
S. Shirobokov et al. , “Black-box optimization with local generative surrogates,” in Advances in Neural Information Processing Systems , Vol. 33 (Curran Associates, Inc., 2020) pp. 14650–14662
2020
Cited alongside, same era.
E. Krieken, J. Tomczak, and A. Ten Teije, “Storchastic: A framework for general stochastic automatic differentiation,” in Advances in Neural Information Processing Systems , Vol. 34 (Curran Associates, Inc., 2021) pp. 7574–7587
2021
Cited alongside, same era.
S. Bangaru, J. Michel, K. Mu, G. Bernstein, T.-M. Li, and J. Ragan-Kelley, “Systematically differentiating parametric discontinuities,” ACM Trans. Graph. 40
2021
Cited alongside, same era.
L. Heinrich, M. Feickert, G. Stark, and K. Cranmer, “pyhf: pure-Python implementation of HistFactory statistical models,” Journal of Open Source Software 6
2021
Cited alongside, same era.
S. Carrazza, J. M. Cruz-Martinez, and M. Rossi, “Pdfflow: Parton distribution functions on gpu,” Computer Physics Communications 264
2021
Cited alongside, same era.
2021
Cited alongside, same era.
2022
Later among the works it cites.
2022
Later among the works it cites.
2022
Later among the works it cites.
A. Butter et al. , “Machine learning and LHC event generation,” SciPost Phys. 14
2023
Closest in time.
L. Heinrich and M. Kagan, “Differentiable Matrix Elements with MadJax,” J. Phys. Conf. Ser. 2438
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
A. K. Lew, M. Huot, S. Staton, and V. K. Mansinghka, “ADEV: Sound automatic differentiation of expected values of probabilistic programs,” Proceedings of the ACM on Programming Languages 7
2023
Closest in time.