Search Results for author:
Found 12 papers, 2 papers with code
VC dimension of Graph Neural Networks with Pfaffian activation functions
Graph Neural Networks (GNNs) have emerged in recent years as a powerful tool to learn tasks across a wide range of graph domains in a data-driven fashion; based on a message passing mechanism, GNNs have gained increasing popularity due to their intuitive formulation, closely linked with the Weisfeiler-Lehman (WL) test for graph isomorphism, to which they have proven equivalent.
A topological description of loss surfaces based on Betti Numbers
In the context of deep learning models, attention has recently been paid to studying the surface of the loss function in order to better understand training with methods based on gradient descent.
Graph Neural Networks for temporal graphs: State of the art, open challenges, and opportunities
Graph Neural Networks (GNNs) have become the leading paradigm for learning on (static) graph-structured data.
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 the number of drug-related hospitalizations low and to improve drug discovery processes.
Modular multi-source prediction of drug side-effects with DruGNN
Drug Side-Effects (DSEs) have a high impact on public health, care system costs, and drug discovery processes.
On the approximation capability of GNNs in node classification/regression tasks
Furthermore, all current results are dedicated to graph classification/regression tasks, where the GNN must produce a single output for the whole graph, while also node classification/regression problems, in which an output is returned for each node, are very common.
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 for computer aided diagnosis systems.
Molecular graph generation with Graph Neural Networks
The use of graph neural networks maximizes the information in input at each generative step, which consists of the subgraph produced during the previous steps.
Weak Supervision for Generating Pixel-Level Annotations in Scene Text Segmentation
Providing pixel-level supervisions for scene text segmentation is inherently difficult and costly, so that only few small datasets are available for this task.
A Two Stage GAN for High Resolution Retinal Image Generation and Segmentation
In this paper, we use Generative Adversarial Networks (GANs) for synthesizing high quality retinal images, along with the corresponding semantic label-maps, to be used instead of real images during the training process.
COCO_TS Dataset: Pixel-level Annotations Based on Weak Supervision for Scene Text Segmentation
The generated annotations are used to train a deep convolutional neural network for semantic segmentation.
Modeling Taxi Drivers' Behaviour for the Next Destination Prediction
In this paper, we study how to model taxi drivers' behaviour and geographical information for an interesting and challenging task: the next destination prediction in a taxi journey.
