Hochschulschrift
Towards efficient convolutional neural architecture design : exploring the properties of neural architectures using spectral decomposition and logistic regression probes
- Standort
-
Deutsche Nationalbibliothek Frankfurt am Main
- Maße
-
30 cm
- Umfang
-
xxvii, 196 Seiten
- Sprache
-
Englisch
- Anmerkungen
-
Diagramme
University of Osnabrück, Dissertation, 2022
- Klassifikation
-
Informatik
- Inhaltsverzeichnis
- Rechteinformation
-
Bei diesem Objekt liegt nur das Inhaltsverzeichnis digital vor. Der Zugriff darauf ist unbeschränkt möglich.
- Letzte Aktualisierung
-
11.03.2025, 12:29 MEZ
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.
Objekttyp
- Hochschulschrift
Beteiligte
Entstanden
- April 21, 2022
Ähnliche Objekte (12)
Grasping Unknown Objects Using Convolutional Neural Networks
Estimating optical flow with convolutional neural networks
Face image analysis with convolutional neural networks
Physics field prediction using convolutional neural networks
Convolutional Neural Networks as 2-D systems
Grasping Unknown Objects Using Convolutional Neural Networks
Stationary flow predictions using convolutional neural networks
Towards energy-efficient convolutional neural network inference
Multi-view classification with convolutional neural networks
Harmonic analysis of deep convolutional neural networks
Silicon MEMS technologies for novel neural probes
Energy efficient convolutional neural networks for arrhythmia detection
Grasping Unknown Objects Using Convolutional Neural Networks
Estimating optical flow with convolutional neural networks
Face image analysis with convolutional neural networks
Physics field prediction using convolutional neural networks
Convolutional Neural Networks as 2-D systems
Grasping Unknown Objects Using Convolutional Neural Networks
Stationary flow predictions using convolutional neural networks
Towards energy-efficient convolutional neural network inference
Multi-view classification with convolutional neural networks
Harmonic analysis of deep convolutional neural networks
Silicon MEMS technologies for novel neural probes
Energy efficient convolutional neural networks for arrhythmia detection
Grasping Unknown Objects Using Convolutional Neural Networks
Estimating optical flow with convolutional neural networks
Face image analysis with convolutional neural networks
Physics field prediction using convolutional neural networks
Convolutional Neural Networks as 2-D systems
Grasping Unknown Objects Using Convolutional Neural Networks
Stationary flow predictions using convolutional neural networks
Towards energy-efficient convolutional neural network inference
Multi-view classification with convolutional neural networks
Harmonic analysis of deep convolutional neural networks
Silicon MEMS technologies for novel neural probes