Web: http://arxiv.org/abs/2209.10633

Sept. 23, 2022, 1:11 a.m. | Duo Yu, Hongyu Miao, Hulin Wu

cs.LG updates on arXiv.org arxiv.org

Deep residual networks (ResNets) have shown state-of-the-art performance in
various real-world applications. Recently, the ResNets model was
reparameterized and interpreted as solutions to a continuous ordinary
differential equation or Neural-ODE model. In this study, we propose a neural
generalized ordinary differential equation (Neural-GODE) model with
layer-varying parameters to further extend the Neural-ODE to approximate the
discrete ResNets. Specifically, we use nonparametric B-spline functions to
parameterize the Neural-GODE so that the trade-off between the model complexity
and computational efficiency can be …

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