11 citations · 18 across the 2 of their papers we have counts for
4 papers
FADER: Fast Adversarial Example Rejection
Francesco Crecchi, Marco Melis, Angelo Sotgiu +2
Deep neural networks are vulnerable to adversarial examples, i.e., carefully-crafted inputs that mislead classification at test time. Recent defenses have been shown to improve adv…
Perplexity-free Parametric t-SNE
Francesco Crecchi, Cyril de Bodt, Michel Verleysen +2
The t-distributed Stochastic Neighbor Embedding (t-SNE) algorithm is a ubiquitously employed dimensionality reduction (DR) method. Its non-parametric nature and impressive efficacy…
Detecting Adversarial Examples through Nonlinear Dimensionality Reduction
Francesco Crecchi, Davide Bacciu, Battista Biggio
Deep neural networks are vulnerable to adversarial examples, i.e., carefully-perturbed inputs aimed to mislead classification. This work proposes a detection method based on combin…
DropIn: Making Reservoir Computing Neural Networks Robust to Missing Inputs by Dropout
Davide Bacciu, Francesco Crecchi, Davide Morelli
The paper presents a novel, principled approach to train recurrent neural networks from the Reservoir Computing family that are robust to missing part of the input features at pred…