3 papers
cs.LG2025
Expressivity of Quadratic Neural ODEs
Joshua Hanson, Maxim Raginsky
This work focuses on deriving quantitative approximation error bounds for neural ordinary differential equations having at most quadratic nonlinearities in the dynamics. The simple…
quant-ph2024
Constructing Noise-Robust Quantum Gates via Pontryagin's Maximum Principle
Joshua Hanson, Dennis Lucarelli
Reliable quantum information technologies depend on precise actuation and techniques to mitigate the effects of undesired disturbances such as environmental noise and imperfect cal…
stat.ML2024
Rademacher Complexity of Neural ODEs via Chen-Fliess Series
Joshua Hanson, Maxim Raginsky
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 outpu…