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Aug 15, 2023 · In International Conference on Learning Representations; 2022. (20) Li, Q.; Lin, T.; Shen, Z. Deep Learning via Dynamical Systems: An Approximation Perspective.
Dec 15, 2023 · Shen, Deep learning via dynamical systems: An approximation perspective, J. Eur. Math. Soc., (2022), pp. 1671–1709. Crossref · Google Scholar. 40. D. Liberzon ...
Feb 17, 2024 · Deep learning via dynamical systems: An approximation perspective. Q Li, T ... Deep neural network approximation of invariant functions through dynamical systems.
Dec 27, 2023 · Deep learning via dynamical systems: An approximation perspective. Q Li, T Lin, Z Shen. Journal of the European Mathematical Society 25 (5), 1671-1709, 2022 ...
Sep 12, 2023 · Abstract. We investigate the expressive power of deep residual neural networks idealized as continuous dynamical systems through control theory.
4 days ago · Modeling complex physical dynamics is a fundamental task in science and engineering. Traditional physics-based models are first-principled, explainable, ...
Jan 15, 2024 · Li et al. Deep learning via dynamical systems: an approximation perspective. J. Eur. Math. Soc. (2022). M.E. Sander et al. Momentum residual neural networks. P ...
Jan 15, 2024 · Deep learning via dynamical systems: An approximation perspective. arXiv ... A proposal on machine learning via dynamical systems. Communications in.
Feb 16, 2024 · Deep learning via dynamical systems: An approximation perspective. J. Eur ... Deep Learning approximation of diffeomorphisms via linear-control systems.
Aug 31, 2023 · Shen, Deep learning via dynamical systems: An approximation perspec- tive ... Pinkus, Approximation theory of the MLP model in neural networks, Acta Numerica, 8.