Explainable artificial intelligence has rapidly emerged since lawmakers have started
requiring interpretable models for safety-critical domains. Concept-based neural networks …

The large and still increasing popularity of deep learning clashes with a major limit of neural
network architectures, that consists in their lack of capability in providing human …

Recent research on graph neural network (GNN) models successfully applied GNNs to
classical graph algorithms and combinatorial optimisation problems. This has numerous …

Background Not being well controlled by therapy with inhaled corticosteroids and long‐
acting β2 agonist bronchodilators is a major concern for severe‐asthma patients. The …

Interpretability in machine learning is critical for the safe deployment of learned policies
across legally-regulated and safety-critical domains. While gradient-based approaches in …

Algorithms are easy... as long as they are kept in a textbook. In their universe, the world is
often oversimplified and things are always represented in a simple numerical form. Sadly …

Ratio and proportion have a fundamental role in understanding mathematics and science.
However, the fact is still found that students still face difficulties in carrying out the stages of …

Gradient-based approaches in reinforcement learning (RL) have achieved tremendous
success in learning policies for continuous control problems. While the performance of these …

Deep Learning (DL) is a special class of Artificial Intelligence (AI) algorithms, studying the
training of Deep Neural Networks (DNNs). Thanks to the modularity of their structure, these …