575 citations · 880 across the 16 of their papers we have counts for
15 papers · 1 filter
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…
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,…
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…
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…
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…