Symplectic Spectrum Gaussian Processes: Learning Hamiltonians from Noisy and Sparse Data - researchr publication authors

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Yusuke Tanaka 0002, Tomoharu Iwata, Naonori Ueda. Symplectic Spectrum Gaussian Processes: Learning Hamiltonians from Noisy and Sparse Data. In Sanmi Koyejo, S. Mohamed, A. Agarwal, Danielle Belgrave, K. Cho, A. Oh, editors, Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, NeurIPS 2022, New Orleans, LA, USA, November 28 - December 9, 2022. 2022. [doi]

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