deep learning

The lab provides a hands-on experience for users to develop and evaluate Graph Counterfactual Explanation methods using the GRETEL framework, covering challenges, building pipelines, customizing solutions, and analyzing performance in diverse datasets.

The tutorial covers generating counterfactual explanations for Graph Neural Networks, addressing theoretical foundations, challenges, definitions, methods, and benchmarking for improved interpretability.

In this short paper, we explored the challenges and solutions of considering a specific plot as (non)significative from a perceptual point-of-view.

In this presentation, I presented future directions of explainable anomaly detection in daily routine behaviours of older patients.

Online learning environments (OLEs) have seen a continuous increase over the past decade and a sudden surge in the last year, due to …

In this paper we present CoRoNNa a deep sequential framework for epidemic prediction that leverages a flexible combination of …

Autoencoders have been recently applied to outlier detection. However, neural networks are known to be vulnerable to overfitting, and …

CoRoNNa is a deep framework for epidemic prediction that integrates mobility data and demographic information to analyze the impact of containment policies on COVID-19 spread.

We propose CoRoNNa, a deep framework for epidemic prediction to analyse the spread of COVID-19 and, potentially, of other unknown …