Approximating velocity fields with planted attractors via Neural-ODEs for classification purposes
In this work, Neural ODEs equipped with a curated collection of equilibrium points have been successfully employed for classification tasks. The planted attractors serve as indicators for the target classes, while the velocity field leveraging the universal approximation capabilities of the architecture shapes the dynamical landscape. This process defines the basins of attraction of the trained model, effectively directing each input (provided as an initial condition) toward its corresponding destination target.
| Authors | Feliciano Giuseppe Pacifico, Duccio Fanelli, Lorenzo Buffoni et al. |
|---|---|
| Primary category | cond-mat.dis-nn |
| Published | 22 June 2026 |
| Last updated at source | 24 June 2026 |
Silytics shows public metadata from arXiv and links to the original. Information may be incomplete or out of date. Last refreshed 17 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.