Tools
Tools
Bolte, Jérôme
, Castera, Camille, Pauwels, Edouard and Févotte, Cédric
(2019)
An Inertial Newton Algorithm for Deep Learning.
TSE Working Paper, n. 19-1043, Toulouse
Preview |
Text
Download (710kB) | Preview |
Official URL : https://www.tse-fr.eu/sites/default/files/TSE/docu...
Abstract
We devise a learning algorithm for possibly nonsmooth Deep Neural Networks
featuring inertia and Newtonian directional intelligence only by means of a backpropagation
oracle. Our algorithm has an appealing mechanical interpretation making the role of its 2 hyper-parameters transparent. An elementary phase space lifting allows both for its implementation and its theoretical study under very general assumptions. Our algorithm shows high performances and appears to be faster than the state of art on [some genuine problems].
| Item Type: | Monograph (Working Paper) |
|---|---|
| Language: | English |
| Date: | October 2019 |
| Place of Publication: | Toulouse |
| Subjects: | B- ECONOMIE ET FINANCE |
| Divisions: | TSE-R (Toulouse) |
| Institution: | Université Toulouse 1 Capitole |
| Site: | UT1 |
| Date Deposited: | 17 Oct 2019 14:05 |
| Last Modified: | 27 Oct 2021 13:37 |
| OAI Identifier: | oai:tse-fr.eu:123630 |
| URI: | https://publications.ut-capitole.fr/id/eprint/32854 |
