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stat.ML2023
Implicit regularization of deep residual networks towards neural ODEs
Pierre Marion, Yu-Han Wu, Michael E. Sander +1
Residual neural networks are state-of-the-art deep learning models. Their continuous-depth analog, neural ordinary differential equations (ODEs), are also widely used. Despite thei…
stat.ML2023★ 8 cited
Generalization bounds for neural ordinary differential equations and deep residual networks
Pierre Marion
Neural ordinary differential equations (neural ODEs) are a popular family of continuous-depth deep learning models. In this work, we consider a large family of parameterized ODEs w…