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To make progress in science, we often build abstract representations of physical systems that meaningfully encode information about the systems.
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“Discovering physical concepts with neural networks”, Preprint (2018), arXiv: 1807.10300
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M. Kissner and H. Mayer, · 2019
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“Guiding new physics searches with unsupervised learning”, The European Physical Journal C 79, 289 (2019)
A. De Simone and T. Jacques, · 2019
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“Learning New Physics from a Machine”, Physical Review D 99, 015014 (2019)
R. T. D’Agnolo and A. Wulzer, · 2019
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“Combined Reinforcement Learning via Abstract Representations”, The Thirty-Third AAAI Conference on Artificial Intelligence, AAAI 2019, 3582 (2019)
V. François-Lavet, Y. Bengio, D. Precup, and J. Pineau, · 2019
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“Optimizing Quantum Error Correction Codes with Reinforcement Learning”, Quantum 3, 215 (2019)
H. Poulsen Nautrup, N. Delfosse, V. Dunjko, H. J. Briegel, and N. Friis, · 2019
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“Modelling collective motion based on the principle of agency: General framework and the case of marching locusts”, PLOS ONE 14, 1 (2019)
K. Ried, T. Müller, and H. J. Briegel, · 2019
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Modeling and Optimization in Science and Technologies
S. Patnaik, I. K. Sethi, and X. Li, · 2020
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