575 citations · 836 across the 16 of their papers we have counts for
7 papers · 1 filter
Toward the application of XAI methods in EEG-based systems
Andrea Apicella, Francesco Isgrò, Andrea Pollastro +1
An interesting case of the well-known Dataset Shift Problem is the classification of Electroencephalogram (EEG) signals in the context of Brain-Computer Interface (BCI). The non-st…
On The Effects Of Data Normalisation For Domain Adaptation On EEG Data
Andrea Apicella, Francesco Isgrò, Andrea Pollastro +1
In the Machine Learning (ML) literature, a well-known problem is the Dataset Shift problem where, differently from the ML standard hypothesis, the data in the training and test set…
Exploiting auto-encoders and segmentation methods for middle-level explanations of image classification systems
Andrea Apicella, Salvatore Giugliano, Francesco Isgrò +1
A central issue addressed by the rapidly growing research area of eXplainable Artificial Intelligence (XAI) is to provide methods to give explanations for the behaviours of Machine…
Adaptive Filters in Graph Convolutional Neural Networks
Andrea Apicella, Francesco Isgrò, Andrea Pollastro +1
Over the last few years, we have witnessed the availability of an increasing data generated from non-Euclidean domains, which are usually represented as graphs with complex relatio…
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…
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…