research
∙
02/02/2023
Graph Neural Networks for temporal graphs: State of the art, open challenges, and opportunities
Graph Neural Networks (GNNs) have become the leading paradigm for learni...
share
research
∙
11/30/2022
A Deep Learning Approach to the Prediction of Drug Side-Effects on Molecular Graphs
Predicting drug side-effects before they occur is a key task in keeping ...
research
∙
02/15/2022
Modular multi-source prediction of drug side-effects with DruGNN
Drug Side-Effects (DSEs) have a high impact on public health, care syste...
research
∙
06/16/2021
A unifying point of view on expressive power of GNNs
Graph Neural Networks (GNNs) are a wide class of connectionist models fo...
research
∙
06/09/2021
A multi-stage GAN for multi-organ chest X-ray image generation and segmentation
Multi-organ segmentation of X-ray images is of fundamental importance fo...
research
∙
12/14/2020
Molecular graph generation with Graph Neural Networks
The generation of graph-structured data is an emerging problem in the fi...
research
∙
11/19/2019
Weak Supervision for Generating Pixel-Level Annotations in Scene Text Segmentation
Providing pixel-level supervisions for scene text segmentation is inhere...
research
∙
07/29/2019
A Two Stage GAN for High Resolution Retinal Image Generation and Segmentation
In recent years, the use of deep learning is becoming increasingly popul...
research
∙
04/01/2019
COCO_TS Dataset: Pixel-level Annotations Based on Weak Supervision for Scene Text Segmentation
The absence of large scale datasets with pixel-level supervisions is a s...
research
∙
07/21/2018
