1 citations · 1 across the 5 of their papers we have counts for
4 papers · 1 filter
A combination of noise and bilateral filters achieve supralinear and scalable adversarial robustness in CNNs
Nicolas Stalder, Benjamin F. Grewe, Matteo Saponati +1
The vulnerability of deep neural networks to adversarial examples poses a significant challenge for real-world deployment. Existing techniques to enhance deep network robustness re…
A Predictive Law for On-Policy Self-Distillation From World Feedback
Tommy He, Jerome Sieber, Matteo Saponati
Moving beyond simple scalar rewards toward richer world feedback is a natural path to more scalable RL post-training. On-policy self-distillation (OPSD) is a promising recent appro…
Mixed-signal implementation of feedback-control optimizer for single-layer Spiking Neural Networks
Jonathan Haag, Christian Metzner, Dmitrii Zendrikov +4
On-chip learning is key to scalable and adaptive neuromorphic systems, yet existing training methods are either difficult to implement in hardware or overly restrictive. However, r…
The underlying structures of self-attention: symmetry, directionality, and emergent dynamics in Transformer training
Matteo Saponati, Pascal Sager, Pau Vilimelis Aceituno +2
Self-attention is essential to Transformer architectures, yet how information is embedded in the self-attention matrices and how different objective functions impact this process r…