Detecting adversarial examples through nonlinear dimensionality reduction F Crecchi, D Bacciu, B Biggio arXiv preprint arXiv:1904.13094, 2019 | 39 | 2019 |
Augmenting recurrent neural networks resilience by dropout D Bacciu, F Crecchi IEEE transactions on neural networks and learning systems 31 (1), 345-351, 2019 | 30 | 2019 |
Fader: Fast adversarial example rejection F Crecchi, M Melis, A Sotgiu, D Bacciu, B Biggio Neurocomputing 470, 257-268, 2022 | 24 | 2022 |
Perplexity-free Parametric t-SNE F Crecchi, C de Bodt, M Verleysen, JA Lee, D Bacciu arXiv preprint arXiv:2010.01359, 2020 | 13 | 2020 |
DropIn: Making reservoir computing neural networks robust to missing inputs by dropout D Bacciu, F Crecchi, D Morelli 2017 International Joint Conference on Neural Networks (IJCNN), 2080-2087, 2017 | 8 | 2017 |
Deep Learning Safety under Non-Stationarity Assumptions F Crecchi Università degli Studi di Pisa, 2021 | | 2021 |
Augmenting Recurrent Neural Networks Resiliency by Dropout F CRECCHI | | 2017 |