
Graph Posterior Network: Bayesian Predictive Uncertainty for …
2021年10月26日 · Abstract: The interdependence between nodes in graphs is key to improve class predictions on nodes and utilized in approaches like Label Propagation (LP) or in Graph …
GitHub - stadlmax/Graph-Posterior-Network: Graph Posterior Network ...
Our main Graph Posterior Network model can be found in gpn.models.gpn_base.py. Ablated models can be found in a similar fashion, i.e. PostNet in gpn.models.gpn_postnet.py, …
Graph Posterior Network: Bayesian Predictive Uncertainty for …
In this work, we explore uncertainty quantification for node classification in three ways: (1) We derive three axioms explicitly characterizing the expected predictive uncertainty behavior in …
Graph posterior network | Proceedings of the 35th International ...
The interdependence between nodes in graphs is key to improve class predictions on nodes and utilized in approaches like Label Propagation (LP) or in Graph Neural Networks (GNNs). …
Graph Posterior Network: Bayesian Predictive Uncertainty for …
In this work, we explore uncertainty quantification for node classification in three ways: (1) We derive three axioms explicitly characterizing the expected predictive uncertainty behavior in …
Papers with Code - Graph Posterior Network: Bayesian Predictive ...
The interdependence between nodes in graphs is key to improve class predictions on nodes and utilized in approaches like Label Propagation (LP) or in Graph Neural Networks (GNN). …
Graph Posterior Network
2021年9月28日 · The interdependence between nodes in graphs is key to improve class predictions on nodes and utilized in approaches like abel Propagation (LP) or in Graph Neural …
graph-postnet - Data Analytics and Machine Learning
The interdependence between nodes in graphs is key to improve class predictions on nodes and utilized in approaches like abel Propagation (LP) or in Graph Neural Networks (GNNs). …
Graph Posterior Network: Bayesian Predictive Uncertainty for Node ...
2021年10月26日 · A new model Graph Posterior Network (GPN) is proposed which explicitly performs Bayesian posterior updates for predictions on interdependent nodes and outperforms …
In this work, we make the first attempt to endow a well-trained GNN with the OOD detection ability without modifying its param-eters. To this end, we design a post-hoc framework with Adaptive …