Search Results for author: Valentino Crespi

Found 2 papers, 2 papers with code

Implicit SVD for Graph Representation Learning

1 code implementation NeurIPS 2021 Sami Abu-El-Haija, Hesham Mostafa, Marcel Nassar, Valentino Crespi, Greg Ver Steeg, Aram Galstyan

Recent improvements in the performance of state-of-the-art (SOTA) methods for Graph Representational Learning (GRL) have come at the cost of significant computational resource requirements for training, e. g., for calculating gradients via backprop over many data epochs.

Graph Representation Learning

Fast Graph Learning with Unique Optimal Solutions

1 code implementation ICLR Workshop GTRL 2021 Sami Abu-El-Haija, Valentino Crespi, Greg Ver Steeg, Aram Galstyan

We consider two popular Graph Representation Learning (GRL) methods: message passing for node classification and network embedding for link prediction.

Graph Learning Graph Representation Learning +3

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