Rademacher Complexity of Neural ODEs via Chen-Fliess Series
Rademacher Complexity of Neural ODEs via Chen-Fliess Series
We show how continuous-depth neural ODE models can be framed as single-layer, infinite-width nets using the Chen--Fliess series expansion for nonlinear ODEs. In this net, the output "weights" are taken from the signature of the control input -- a tool used to represent infinite-dimensional paths as a sequence of tensors …