18 citations · 30 across the 10 of their papers we have counts for
15 papers · 1 filter
On Discprecncies between Perturbation Evaluations of Graph Neural Network Attributions
Razieh Rezaei, Alireza Dizaji, Ashkan Khakzar +3
Neural networks are increasingly finding their way into the realm of graphs and modeling relationships between features. Concurrently graph neural network explanation approaches ar…
Latent Graph Inference using Product Manifolds
Haitz Sáez de Ocáriz Borde, Anees Kazi, Federico Barbero +1
Graph Neural Networks usually rely on the assumption that the graph topology is available to the network as well as optimal for the downstream task. Latent graph inference allows m…
Unsupervised pre-training of graph transformers on patient population graphs
Chantal Pellegrini, Nassir Navab, Anees Kazi
Pre-training has shown success in different areas of machine learning, such as Computer Vision, Natural Language Processing (NLP), and medical imaging. However, it has not been ful…
Graph-in-Graph (GiG): Learning interpretable latent graphs in non-Euclidean domain for biological and healthcare applications
Kamilia Mullakaeva, Luca Cosmo, Anees Kazi +3
Graphs are a powerful tool for representing and analyzing unstructured, non-Euclidean data ubiquitous in the healthcare domain. Two prominent examples are molecule property predict…
Unsupervised Pre-Training on Patient Population Graphs for Patient-Level Predictions
Chantal Pellegrini, Anees Kazi, Nassir Navab
Pre-training has shown success in different areas of machine learning, such as Computer Vision (CV), Natural Language Processing (NLP) and medical imaging. However, it has not been…
GKD: Semi-supervised Graph Knowledge Distillation for Graph-Independent Inference
Mahsa Ghorbani, Mojtaba Bahrami, Anees Kazi +3
The increased amount of multi-modal medical data has opened the opportunities to simultaneously process various modalities such as imaging and non-imaging data to gain a comprehens…