- Weitere Titel
-
Zur Konvergenz von Struktur und Geometrie in Graph-neuronalen Netzen
- Standort
-
Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
-
Online-Ressource
- Sprache
-
Englisch
- Anmerkungen
-
München, Technische Universität München, Dissertation, 2023
- Schlagwort
-
Graphentheorie
Graphersetzungssystem
Graph
- Ereignis
-
Veröffentlichung
- (wo)
-
München
- (wer)
-
Universitätsbibliothek der TU München
- (wann)
-
2023
- Urheber
-
Gasteiger, Johannes
- Beteiligte Personen und Organisationen
- URN
-
urn:nbn:de:bvb:91-diss-20230303-1660288-1-9
- Rechteinformation
-
Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
-
25.03.2025, 13:48 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
- Gasteiger, Johannes
- Günnemann, Stephan
- Bronstein, Michael
- Universitätsbibliothek der TU München
Entstanden
- 2023
Ähnliche Objekte (12)
Convergence Analysis of Neural Networks
Geometry Regularization in Neural Networks
Explaining Graph Neural Networks
Maneuver Prediction Using Traffic Scene Graphs via Graph Neural Networks and Recurrent Neural Networks
Neural graph distance embedding for molecular geometry generation
Describing neural networks with graph transformations
Adversarial Robustness of Graph Neural Networks
Describing neural networks with graph transformations
Graph neural networks for combinatorial optimization problems on graphs
Knowledge Graphs and Graph Neural Networks for Semantic Parsing
Graph Neural Networks for Maximum Constraint Satisfaction
Explaining and Applying Graph Neural Networks on Text
Convergence Analysis of Neural Networks
Geometry Regularization in Neural Networks
Explaining Graph Neural Networks
Maneuver Prediction Using Traffic Scene Graphs via Graph Neural Networks and Recurrent Neural Networks
Neural graph distance embedding for molecular geometry generation
Describing neural networks with graph transformations
Adversarial Robustness of Graph Neural Networks
Describing neural networks with graph transformations
Graph neural networks for combinatorial optimization problems on graphs
Knowledge Graphs and Graph Neural Networks for Semantic Parsing
Graph Neural Networks for Maximum Constraint Satisfaction
Explaining and Applying Graph Neural Networks on Text
Convergence Analysis of Neural Networks
Geometry Regularization in Neural Networks
Explaining Graph Neural Networks
Maneuver Prediction Using Traffic Scene Graphs via Graph Neural Networks and Recurrent Neural Networks
Neural graph distance embedding for molecular geometry generation
Describing neural networks with graph transformations
Adversarial Robustness of Graph Neural Networks
Describing neural networks with graph transformations
Graph neural networks for combinatorial optimization problems on graphs
Knowledge Graphs and Graph Neural Networks for Semantic Parsing
Graph Neural Networks for Maximum Constraint Satisfaction