Improving the performance of the network architectures that mimic brain operation is a
research trend. Optimizing the latency on hardware circuits of the artificial neural network …

Many recent works have shown that spiking neural networks (SNNs) are vulnerable to well-
crafted adversarial attacks in the white-box setting, which may lead to misclassification of the …

Spiking neural networks (SNNs) are known as the third generation of neural networks. For
an SNN, the bio-inspired neural dynamics endow the great potential to simulate the neural …

Outside of work, I am extremely lucky in the many friendships I made along the way that
helped me evolve as a person over the years. I am ever in debt for the unconditional comfort …

Spiking Neural Networks (SNNs) have attracted much attention for their energy-efficient
operations and biologically inspired structures, offering potential advantages over Artificial …

Recurrent neural networks (RNNs) have been widely used for supervised pattern
recognition and exploring the underlying spatio-temporal correlation. However, due to the …

Spiking neural networks (SNNs) are a new generation of deep learning models inspired by
biology, which belong to a subset of deep learning and have a strong biological basis to …

In this paper, we propose a new framework to detect adversarial examples motivated by the
observations that random components can improve the smoothness of predictors and make …

Federated Learning (FL) has been widely researched for the growing public data privacy
issues, where only model parameters, instead of private data, are communicated. However …

Le reti neurali Spiking (SNN) sono la terza generazione di reti neurali e stanno diventando
sempre più popolari nella comunità scientifica. Ispirate al funzionamento di reti neurali …