1.6k citations · 4.2k across the 72 of their papers we have counts for
4 papers · 1 filter
Geometric graphs from data to aid classification tasks with graph convolutional networks
Yifan Qian, Paul Expert, Pietro Panzarasa +1
Traditional classification tasks learn to assign samples to given classes based solely on sample features. This paradigm is evolving to include other sources of information, such a…
Semi-supervised classification on graphs using explicit diffusion dynamics
Robert L. Peach, Alexis Arnaudon, Mauricio Barahona
Classification tasks based on feature vectors can be significantly improved by including within deep learning a graph that summarises pairwise relationships between the samples. In…
Extracting information from free text through unsupervised graph-based clustering: an application to patient incident records
M. Tarik Altuncu, Eloise Sorin, Joshua D. Symons +4
The large volume of text in electronic healthcare records often remains underused due to a lack of methodologies to extract interpretable content. Here we present an unsupervised f…
Quantifying the Alignment of Graph and Features in Deep Learning
Yifan Qian, Paul Expert, Tom Rieu +2
We show that the classification performance of graph convolutional networks (GCNs) is related to the alignment between features, graph, and ground truth, which we quantify using a…