One Representative-Shot Learning Using a Population-Driven Template with Application to Brain Connectivity Classification and Evolution Prediction

1 code implementation • 6 Oct 2021 • Umut Guvercin, Mohammed Amine Gharsallaoui, Islem Rekik

Using a one-representative CBT as a training sample, we alleviate the training load of GNN models while boosting their performance across a variety of classification and regression tasks.

One-Shot Learning

Quantifying the Reproducibility of Graph Neural Networks using Multigraph Brain Data

1 code implementation • 6 Sep 2021 • Mohammed Amine Gharsallaoui, Islem Rekik

While prior studies have focused on boosting the model accuracy, quantifying the reproducibility of the most discriminative features identified by GNNs is still an intact problem that yields concerns about their reliability in clinical applications in particular.

Predicting cognitive scores with graph neural networks through sample selection learning

1 code implementation • 17 Jun 2021 • Martin Hanik, Mehmet Arif Demirtaş, Mohammed Amine Gharsallaoui, Islem Rekik

On top of that, we introduce a novel, fully modular sample selection method to select the best samples to learn from for our target prediction task.