3 papers
cs.ET2024
The Inherent Adversarial Robustness of Analog In-Memory Computing
Corey Lammie, Julian Büchel, Athanasios Vasilopoulos +2
A key challenge for Deep Neural Network (DNN) algorithms is their vulnerability to adversarial attacks. Inherently non-deterministic compute substrates, such as those based on Anal…
cs.LG2024
Kernel Approximation using Analog In-Memory Computing
Julian Büchel, Giacomo Camposampiero, Athanasios Vasilopoulos +4
Kernel functions are vital ingredients of several machine learning algorithms, but often incur significant memory and computational costs. We introduce an approach to kernel approx…
cs.CV2018
Ladder Networks for Semi-Supervised Hyperspectral Image Classification
Julian Büchel, Okan Ersoy
We used the Ladder Network [Rasmus et al. (2015)] to perform Hyperspectral Image Classification in a semi-supervised setting. The Ladder Network distinguishes itself from other sem…