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A recent paradigm views deep neural networks as discretizations of certain controlled ordinary differential equations, sometimes called neural ordinary differential equations.
E. Dupont, A. Doucet, and Y. W. Teh · 1904
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E. Dupont, A. Doucet, and Y. W. Teh · 1904
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Approximation capabilities of neural ordinary differential equations
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W. E · 2017
Neural ordinary differential equations
T. Q. Chen, Y. Rubanova, J. Bettencourt, and D. K. Duvenaud · 2018
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Overfitting or perfect fitting? risk bounds for classification and regression rules that interpolate
M. Belkin, D. J. Hsu, and P. Mitra · 2018
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Controlled differential equations on convenient spaces
C. Cuchiero, M. Larsson, and J. Teichmann · 2019
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Selection dynamics for deep neural networks
H. Liu and P. Markowich · 2019
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