71 citations · 168 across the 10 of their papers we have counts for
9 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,…
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…
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…
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…