Area of research
Artificial Intelligence · Computer Vision and Pattern Recognition
Research interest
Research interests include Neural Networks and Applications, Advanced Graph Neural Networks, Face and Expression Recognition, and Computational Drug Discovery Methods.
On the application of neural networks for structured domains to fMRI data
D4: Distance diffusion for a truly equivariant molecular design
Advances in artificial neural networks, machine learning and computational intelligence
A Spectral Interpretation of Redundancy in a Graph Reservoir
Empowering Simple Graph Convolutional Networks
TumFlow: An AI Model for Predicting New Anticancer Molecules
Investigating over-parameterized randomized graph networks
Beyond the Additive Nodes' Convolutions: a Study on High-Order Multiplicative Integration
Improving Soft Skill Extraction via Data Augmentation and Embedding Manipulation
TumFlow: An AI Model for Predicting New Anticancer Molecules
TumFlow: An AI Model for Predicting New Anticancer Molecules.
TumFlow: An AI Model for Predicting New Anticancer Molecules
Topology preserving maps as aggregations for Graph Convolutional Neural Networks
A unified framework for backpropagation-free soft and hard gated graph neural networks
An Untrained Neural Model for Fast and Accurate Graph Classification
Towards learning trustworthily, automatically, and with guarantees on graphs: An overview
Multiresolution Reservoir Graph Neural Network
Conditional Variational Capsule Network for Open Set Recognition
Multiresolution Reservoir Graph Neural Network
Author Correction: Suppression of a SARS-CoV-2 outbreak in the Italian municipality of Vo’
Polynomial-based graph convolutional neural networks for graph classification
Author Correction: Suppression of a SARS-CoV-2 outbreak in the Italian municipality of Vo'.
Suppression of a SARS-CoV-2 outbreak in the Italian municipality of Vo’
Suppression of a SARS-CoV-2 outbreak in the Italian municipality of Vo'.
Multi-task learning for the prediction of wind power ramp events with deep neural networks
Heterogeneous networks integration for disease–gene prioritization with node kernels
Conditional Constrained Graph Variational Autoencoders for MoleculeDesign
Universal Readout for Graph Convolutional Neural Networks
On Filter Size in Graph Convolutional Networks