575 citations · 988 across the 22 of their papers we have counts for
24 papers
The Impact of Stochasticity on the Rashomon Effect in Machine Learning
Andrea Apicella, Francesco Isgrò, Andrea Pollastro +1
Neural network training is inherently stochastic, with factors such as weight initialization leading to distinct models despite comparable predictive performance. This phenomenon i…
It's All in the Way You Say It: The Role of Information Representation in LLM-Based Glycemic-Event Prediction
Andrea Apicella, Pasquale Arpaia, Matteo Orefice +2
Large Language Models (LLMs) are increasingly being investigated for physiological time-series prediction, yet their effectiveness may depend not only on the model itself, but also…
Instance-Adaptive Parametrization for Amortized Variational Inference
Andrea Pollastro, Andrea Apicella, Francesco Isgrò +1
Variational autoencoders (VAEs) rely on amortized variational inference to enable efficient posterior approximation, but this efficiency comes at the cost of a shared parametrizati…
Don't stop me now: How Validation Criteria Affect Checkpoint Selection and Early Stopping
Andrea Apicella, Francesco Isgrò, Andrea Pollastro +1
Checkpoint selection is a standard component of neural network training, yet the validation criterion used to select a checkpoint is often chosen heuristically. Moreover, the same…
IMPACTX: improving model performance by appropriately constraining the training with teacher explanations
Andrea Apicella, Salvatore Giugliano, Francesco Isgrò +2
The eXplainable Artificial Intelligence (XAI) research predominantly concentrates to provide explainations about AI model decisions, especially Deep Learning (DL) models. However,…
SincVAE: A new semi-supervised approach to improve anomaly detection on EEG data using SincNet and variational autoencoder
Andrea Pollastro, Francesco Isgrò, Roberto Prevete
Over the past few decades, electroencephalography (EEG) monitoring has become a pivotal tool for diagnosing neurological disorders, particularly for detecting seizures. Epilepsy, o…
Towards a general framework for improving the performance of classifiers using XAI methods
Andrea Apicella, Salvatore Giugliano, Francesco Isgrò +1
Modern Artificial Intelligence (AI) systems, especially Deep Learning (DL) models, poses challenges in understanding their inner workings by AI researchers. eXplainable Artificial…
Don't Push the Button! Exploring Data Leakage Risks in Machine Learning and Transfer Learning
Andrea Apicella, Francesco Isgrò, Roberto Prevete
Machine Learning (ML) has revolutionized various domains, offering predictive capabilities in several areas. However, with the increasing accessibility of ML tools, many practition…
Strategies to exploit XAI to improve classification systems
Andrea Apicella, Luca Di Lorenzo, Francesco Isgrò +2
Explainable Artificial Intelligence (XAI) aims to provide insights into the decision-making process of AI models, allowing users to understand their results beyond their decisions.…
Hidden Classification Layers: Enhancing linear separability between classes in neural networks layers
Andrea Apicella, Francesco Isgrò, Roberto Prevete
In the context of classification problems, Deep Learning (DL) approaches represent state of art. Many DL approaches are based on variations of standard multi-layer feed-forward neu…
Toward cross-subject and cross-session generalization in EEG-based emotion recognition: Systematic review, taxonomy, and methods
Andrea Apicella, Pasquale Arpaia, Giovanni D'Errico +4
A systematic review on machine-learning strategies for improving generalizability (cross-subjects and cross-sessions) electroencephalography (EEG) based in emotion classification w…
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…
Semi-supervised detection of structural damage using Variational Autoencoder and a One-Class Support Vector Machine
Andrea Pollastro, Giusiana Testa, Antonio Bilotta +1
In recent years, Artificial Neural Networks (ANNs) have been introduced in Structural Health Monitoring (SHM) systems. A semi-supervised method with a data-driven approach allows t…
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…
JEM-EUSO Collaboration contributions to the 37th International Cosmic Ray Conference
G. Abdellaoui, S. Abe, J. H. Adams +289
Compilation of papers presented by the JEM-EUSO Collaboration at the 37th International Cosmic Ray Conference (ICRC), held on July 12-23, 2021 (online) in Berlin, Germany.
Beam Measurements and Machine Learning at the CERN Large Hadron Collider
P. Arpaia, G. Azzopardi, F. Blanc +16
This paper presents a review of the recent Machine Learning activities carried out on beam measurements performed at the CERN Large Hadron Collider. This paper has been accepted fo…
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…
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…
Neural Networks with Non-Uniform Embedding and Explicit Validation Phase to Assess Granger Causality
Alessandro Montalto, Sebastiano Stramaglia, Luca Faes +3
A challenging problem when studying a dynamical system is to find the interdependencies among its individual components. Several algorithms have been proposed to detect directed dy…