- Weitere Titel
-
Lernen, neuronale Repräsentationen mit begrenzten Daten und Überwachung zu lernen
- Standort
-
Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
-
Online-Ressource
- Sprache
-
Englisch
- Anmerkungen
-
München, Technische Universität München, Dissertation, 2024
- Schlagwort
-
Maschinelles Lernen
Neuronales Netz
Deep learning
Data Mining
- Ereignis
-
Veröffentlichung
- (wo)
-
München
- (wer)
-
Universitätsbibliothek der TU München
- (wann)
-
2024
- Urheber
-
Farshad, Azade
- Beteiligte Personen und Organisationen
- URN
-
urn:nbn:de:bvb:91-diss-20240229-1721532-1-6
- Rechteinformation
-
Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
-
25.03.2025, 13:49 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.
Beteiligte
- Farshad, Azade
- Navab, Nassir
- Adeli, Ehsan
- Glocker, Ben
- Universitätsbibliothek der TU München
Entstanden
- 2024
Ähnliche Objekte (12)
Visual Similarity Using Limited Supervision
Visual Representation Learning with Limited Supervision
Deep Visual Parsing with Limited Supervision
Neural representations of materials and their properties
The cognitive and neural representations of actions
Understanding memory representations through deep neural networks
Can Neural Quantum States Learn Volume-Law Ground States?
Representations in sustainability science : tools to analyze, envision, engage and learn
Learning Neural Graph Representations in Non-Euclidean Geometries
How aging shapes neural representations of continuous spaces
On layer-wise representations in deep neural networks
Compact and efficient representations of deep neural networks
Visual Similarity Using Limited Supervision
Visual Representation Learning with Limited Supervision
Deep Visual Parsing with Limited Supervision
Neural representations of materials and their properties
The cognitive and neural representations of actions
Understanding memory representations through deep neural networks
Can Neural Quantum States Learn Volume-Law Ground States?
Representations in sustainability science : tools to analyze, envision, engage and learn
Learning Neural Graph Representations in Non-Euclidean Geometries
How aging shapes neural representations of continuous spaces
On layer-wise representations in deep neural networks
Compact and efficient representations of deep neural networks
Visual Similarity Using Limited Supervision
Visual Representation Learning with Limited Supervision
Deep Visual Parsing with Limited Supervision
Neural representations of materials and their properties
The cognitive and neural representations of actions
Understanding memory representations through deep neural networks
Can Neural Quantum States Learn Volume-Law Ground States?
Representations in sustainability science : tools to analyze, envision, engage and learn
Learning Neural Graph Representations in Non-Euclidean Geometries
How aging shapes neural representations of continuous spaces
On layer-wise representations in deep neural networks