127 citations · 296 across the 22 of their papers we have counts for
3 papers · 1 filter
Latent-Graph Learning for Disease Prediction
Luca Cosmo, Anees Kazi, Seyed-Ahmad Ahmadi +2
Recently, Graph Convolutional Networks (GCNs) have proven to be a powerful machine learning tool for Computer-Aided Diagnosis (CADx) and disease prediction. A key component in thes…
LIMP: Learning Latent Shape Representations with Metric Preservation Priors
Luca Cosmo, Antonio Norelli, Oshri Halimi +2
In this paper, we advocate the adoption of metric preservation as a powerful prior for learning latent representations of deformable 3D shapes. Key to our construction is the intro…
Differentiable Graph Module (DGM) for Graph Convolutional Networks
Anees Kazi, Luca Cosmo, Seyed-Ahmad Ahmadi +2
Graph deep learning has recently emerged as a powerful ML concept allowing to generalize successful deep neural architectures to non-Euclidean structured data. Such methods have sh…