DropIn: Making reservoir computing neural networks robust to missing inputs by dropout. - Davide Bacciu; Francesco Crecchi; Davide Morelli (IJCNN, 2017)

Detecting Black-box Adversarial Examples through Nonlinear Dimensionality Reduction. - Francesco Crecchi; Davide Bacciu; Battista Biggio (ESANN, 2019)

Detecting Adversarial Examples through Nonlinear Dimensionality Reduction. - Francesco Crecchi; Davide Bacciu; Battista Biggio (CoRR, 2019)

Perplexity-free Parametric t-SNE. - Francesco Crecchi; Cyril de Bodt; Michel Verleysen; John A. Lee 001; Davide Bacciu (ESANN, 2020)

Augmenting Recurrent Neural Networks Resilience by Dropout. - Davide Bacciu; Francesco Crecchi (IEEE Trans. Neural Networks Learn. Syst., 2020)

Deep Learning Safety under Non-Stationarity Assumptions. - Francesco Crecchi (2021)

FADER: Fast adversarial example rejection. - Francesco Crecchi; Marco Melis; Angelo Sotgiu; Davide Bacciu; Battista Biggio (Neurocomputing, 2022)