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Found 9 papers, 7 papers with code
Variational Learning is Effective for Large Deep Networks
We give extensive empirical evidence against the common belief that variational learning is ineffective for large neural networks.
ResolvNet: A Graph Convolutional Network with multi-scale Consistency
To remedy these shortcomings, we introduce ResolvNet, a flexible graph neural network based on the mathematical concept of resolvents.
Beyond In-Domain Scenarios: Robust Density-Aware Calibration
While existing post-hoc calibration methods achieve impressive results on in-domain test datasets, they are limited by their inability to yield reliable uncertainty estimates in domain-shift and out-of-domain (OOD) scenarios.
A Graph Is More Than Its Nodes: Towards Structured Uncertainty-Aware Learning on Graphs
Current graph neural networks (GNNs) that tackle node classification on graphs tend to only focus on nodewise scores and are solely evaluated by nodewise metrics.
What Makes Graph Neural Networks Miscalibrated?
Furthermore, based on the insights from this study, we design a novel calibration method named Graph Attention Temperature Scaling (GATS), which is tailored for calibrating graph neural networks.
Deep Combinatorial Aggregation
In this work, we explore a combinatorial generalization of deep ensemble called deep combinatorial aggregation (DCA).
Explicit Pairwise Factorized Graph Neural Network for Semi-Supervised Node Classification
To learn the direct influence among output nodes in a graph, we propose the Explicit Pairwise Factorized Graph Neural Network (EPFGNN), which models the whole graph as a partially observed Markov Random Field.
A Chain Graph Interpretation of Real-World Neural Networks
It is thus a promising framework that deepens our understanding of neural networks and provides a coherent theoretical formulation for future deep learning research.
Probabilistic Discriminative Learning with Layered Graphical Models
Probabilistic graphical models are traditionally known for their successes in generative modeling.
