23 citations · 104 across the 19 of their papers we have counts for
5 papers · 1 filter
GraphPPD: Posterior Predictive Modelling for Graph-Level Inference
Soumyasundar Pal, Liheng Ma, Amine Natik +2
Accurate modelling and quantification of predictive uncertainty is crucial in deep learning since it allows a model to make safer decisions when the data is ambiguous and facilitat…
Node Copying: A Random Graph Model for Effective Graph Sampling
Florence Regol, Soumyasundar Pal, Jianing Sun +3
There has been an increased interest in applying machine learning techniques on relational structured-data based on an observed graph. Often, this graph is not fully representative…
RNN with Particle Flow for Probabilistic Spatio-temporal Forecasting
Soumyasundar Pal, Liheng Ma, Yingxue Zhang +1
Spatio-temporal forecasting has numerous applications in analyzing wireless, traffic, and financial networks. Many classical statistical models often fall short in handling the com…
Non-Parametric Graph Learning for Bayesian Graph Neural Networks
Soumyasundar Pal, Saber Malekmohammadi, Florence Regol +3
Graphs are ubiquitous in modelling relational structures. Recent endeavours in machine learning for graph-structured data have led to many architectures and learning algorithms. Ho…
Bayesian graph convolutional neural networks for semi-supervised classification
Yingxue Zhang, Soumyasundar Pal, Mark Coates +1
Recently, techniques for applying convolutional neural networks to graph-structured data have emerged. Graph convolutional neural networks (GCNNs) have been used to address node an…