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We pursue the use of deep learning methods to improve state-of-the-art computations in theoretical high-energy physics.
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G. W. Merz, F. Charton, T. Cai, K. Cranmer, L. Dixon, N. Nolte et al., Transformers for scattering amplitudes , in Machine Learning and the Physical Sciences: Workshop at the 37th conference on Neural Information Processing Systems (NeurIPS). Non-archival. , 2023, https://ml4physicalsciences.github.io/2023/files/NeurIPS_ML4PS_2023_72.pdf
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L. Dixon, M. Wilhelm, G. Merz, T. Cai, F. Charton, N. Nolte et al., Symbol Representation of the Three Gluon Form Factor in N=4 Planar Super Yang-Mills Theory , May, 2024 · 2024
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