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Found 5 papers, 1 papers with code
Transductive Kernels for Gaussian Processes on Graphs
To address this, we present a novel, generalized kernel for graphs with node feature data for semi-supervised learning.
Gaussian Processes on Graphs via Spectral Kernel Learning
We propose a graph spectrum-based Gaussian process for prediction of signals defined on nodes of the graph.
Bayesian Semi-supervised Learning with Graph Gaussian Processes
We propose a data-efficient Gaussian process-based Bayesian approach to the semi-supervised learning problem on graphs.
A Dynamic Edge Exchangeable Model for Sparse Temporal Networks
We propose a dynamic edge exchangeable network model that can capture sparse connections observed in real temporal networks, in contrast to existing models which are dense.
Scaling Factorial Hidden Markov Models: Stochastic Variational Inference without Messages
Factorial Hidden Markov Models (FHMMs) are powerful models for sequential data but they do not scale well with long sequences.
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