5 papers
A general approach to compute the relevance of middle-level input features
Andrea Apicella, Salvatore Giugliano, Francesco Isgrò +1
This work proposes a novel general framework, in the context of eXplainable Artificial Intelligence (XAI), to construct explanations for the behaviour of Machine Learning (ML) mode…
Machine learning for beam dynamics studies at the CERN Large Hadron Collider
P. Arpaia, G. Azzopardi, F. Blanc +17
Machine learning entails a broad range of techniques that have been widely used in Science and Engineering since decades. High-energy physics has also profited from the power of th…
A survey on modern trainable activation functions
Andrea Apicella, Francesco Donnarumma, Francesco Isgrò +1
In neural networks literature, there is a strong interest in identifying and defining activation functions which can improve neural network performance. In recent years there has b…
A simple and efficient architecture for trainable activation functions
Andrea Apicella, Francesco Isgrò, Roberto Prevete
Learning automatically the best activation function for the task is an active topic in neural network research. At the moment, despite promising results, it is still difficult to d…
A linear approach for sparse coding by a two-layer neural network
Alessandro Montalto, Giovanni Tessitore, Roberto Prevete
Many approaches to transform classification problems from non-linear to linear by feature transformation have been recently presented in the literature. These notably include spars…