- Standort
-
Deutsche Nationalbibliothek Frankfurt am Main
- ISSN
-
2197-9847
- Umfang
-
Online-Ressource
- Sprache
-
Englisch
- Anmerkungen
-
online resource.
- Erschienen in
-
Deep limits of residual neural networks ; volume:10 ; number:1 ; day:16 ; month:12 ; year:2022 ; pages:1-44 ; date:3.2023
Research in the mathematical sciences ; 10, Heft 1 (16.12.2022), 1-44, 3.2023
- Urheber
-
Thorpe, Matthew
van Gennip, Yves
- Beteiligte Personen und Organisationen
-
SpringerLink (Online service)
- DOI
-
10.1007/s40687-022-00370-y
- URN
-
urn:nbn:de:101:1-2023022721024714021694
- Rechteinformation
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
-
14.08.2025, 10:55 MESZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Beteiligte
- Thorpe, Matthew
- van Gennip, Yves
- SpringerLink (Online service)
Ähnliche Objekte (12)
Correction to: Deep limits of residual neural networks
Continuous limits of residual neural networks in case of large input data
Universal flow approximation with deep residual networks
RESIDUAL SHUFFLING CONVOLUTIONAL NEURAL NETWORKS FOR DEEP SEMANTIC IMAGE SEGMENTATION USING MULTI-MODAL DATA
Smaller generalization error derived for a deep residual neural network compared with shallow networks
DeepBase: Deep Inspection of Neural Networks
Training deep quantum neural networks
Adventitious respiratory classification using attentive residual neural networks
Fundamental limits of deep neural network learning
Reduced-Order Modeling of Subsurface Multi-phase Flow Models Using Deep Residual Recurrent Neural Networks
DeepMCGCN: Multi-channel Deep Graph Neural Networks
Deep Neural Networks motivated by PDEs
Correction to: Deep limits of residual neural networks
Continuous limits of residual neural networks in case of large input data
Universal flow approximation with deep residual networks
RESIDUAL SHUFFLING CONVOLUTIONAL NEURAL NETWORKS FOR DEEP SEMANTIC IMAGE SEGMENTATION USING MULTI-MODAL DATA
Smaller generalization error derived for a deep residual neural network compared with shallow networks
DeepBase: Deep Inspection of Neural Networks
Training deep quantum neural networks
Adventitious respiratory classification using attentive residual neural networks
Fundamental limits of deep neural network learning
Reduced-Order Modeling of Subsurface Multi-phase Flow Models Using Deep Residual Recurrent Neural Networks
DeepMCGCN: Multi-channel Deep Graph Neural Networks
Deep Neural Networks motivated by PDEs
Correction to: Deep limits of residual neural networks
Continuous limits of residual neural networks in case of large input data
Universal flow approximation with deep residual networks
RESIDUAL SHUFFLING CONVOLUTIONAL NEURAL NETWORKS FOR DEEP SEMANTIC IMAGE SEGMENTATION USING MULTI-MODAL DATA
Smaller generalization error derived for a deep residual neural network compared with shallow networks
DeepBase: Deep Inspection of Neural Networks
Training deep quantum neural networks
Adventitious respiratory classification using attentive residual neural networks
Fundamental limits of deep neural network learning
Reduced-Order Modeling of Subsurface Multi-phase Flow Models Using Deep Residual Recurrent Neural Networks
DeepMCGCN: Multi-channel Deep Graph Neural Networks