8 citations · 24 across the 11 of their papers we have counts for
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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…
math.OC2023
Leveraging the two timescale regime to demonstrate convergence of neural networks
Pierre Marion, Raphaël Berthier
We study the training dynamics of shallow neural networks, in a two-timescale regime in which the stepsizes for the inner layer are much smaller than those for the outer layer. In…