1 citations · 1 across the 3 of their papers we have counts for
6 papers
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…
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,…
Don't stop me now: Rethinking Validation Criteria for Model Parameter Selection
Andrea Apicella, Francesco Isgrò, Andrea Pollastro +1
Despite the extensive literature on training loss functions, the evaluation of generalization on the validation set remains underexplored. In this work, we conduct a systematic emp…
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…
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…