Variational Graph Convolutional Neural Networks
Estimation of model uncertainty can help improve the explainability of Graph Convolutional Networks and the accuracy of the models at the same time. Uncertainty can also be used in critical applications to verify the results of the model by an expert or additional models. In this paper, we propose Variational Neural Network versions of spatial and spatio-temporal Graph Convolutional Networks. We estimate uncertainty in both outputs and layer-wise attentions of the models, which has the potential for improving model explainability. We showcase the benefits of these models in the social trading analysis and the skeleton-based human action recognition tasks on the Finnish board membership, NTU-60, NTU-120 and Kinetics datasets, where we show improvement in model accuracy in addition to estimated model uncertainties.
Code (0)
등록된 구현이 없습니다.
Tasks
Action RecognitionSimilar Papers 제목 키워드 기반
VEM-GCN: Topology Optimization with Variational EM for Graph Convolutional Networks
Over-smoothing has emerged as a severe problem for node classification with graph convolutional networks (GCNs). In the view of message passing, the over-smoothing issue is caused by the observed noisy graph topology tha…
ClassificationGeneral ClassificationNode ClassificationStochastic Block ModelVariational Graph Auto-Encoders
We introduce the variational graph auto-encoder (VGAE), a framework for unsupervised learning on graph-structured data based on the variational auto-encoder (VAE). This model makes use of latent variables and is capable …
DecoderGraph ClusteringLink PredictionPredictionHierarchical Graph-Convolutional Variational AutoEncoding for Generative Modelling of Human Motion
Models of human motion commonly focus either on trajectory prediction or action classification but rarely both. The marked heterogeneity and intricate compositionality of human motion render each task vulnerable to the d…
Action ClassificationTrajectory PredictionGraph Neural Network, ChebNet, Graph Convolutional Network, and Graph Autoencoder: Tutorial and Survey
This is a tutorial paper on graph neural networks including ChebNet, graph convolutional network, graph attention network, and graph autoencoder. It starts with Laplacian of graph, graph Fourier transform, and graph conv…
Graph AttentionGraph Neural NetworkGraph ReconstructionVariational Inference for Graph Convolutional Networks in the Absence of Graph Data and Adversarial Settings
We propose a framework that lifts the capabilities of graph convolutional networks (GCNs) to scenarios where no input graph is given and increases their robustness to adversarial attacks. We formulate a joint probabilist…
Bayesian InferenceGeneral ClassificationGraph Neural NetworkVariational Inference