Titre :

PAC bounds of continuous Linear Parameter-Varying systems related to neural ODEs

Auteur(s) :

Rácz, Dániel [Auteur]
Institute for Computer Science and Control [Budapest] [SZTAKI]
Petreczky, Mihály [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Daróczy, Bálint [Auteur]
Institute for Computer Science and Control [Budapest] [SZTAKI]

Date de publication :

2023

Mot(s)-clé(s) en anglais :

Machine Learning (cs.LG)
FOS: Computer and information sciences
I.2.0
68

Discipline(s) HAL :

Statistiques [stat]/Machine Learning [stat.ML]
Informatique [cs]/Intelligence artificielle [cs.AI]

Résumé en anglais : [en]

We consider the problem of learning Neural Ordinary Differential Equations (neural ODEs) within the context of Linear Parameter-Varying (LPV) systems in continuous-time. LPV systems contain bilinear systems which are known ...
Lire la suite >We consider the problem of learning Neural Ordinary Differential Equations (neural ODEs) within the context of Linear Parameter-Varying (LPV) systems in continuous-time. LPV systems contain bilinear systems which are known to be universal approximators for non-linear systems. Moreover, a large class of neural ODEs can be embedded into LPV systems. As our main contribution we provide Probably Approximately Correct (PAC) bounds under stability for LPV systems related to neural ODEs. The resulting bounds have the advantage that they do not depend on the integration interval.Lire moins >

Langue :

Anglais

Collections :

• Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL