5 citations · 6 across the 2 of their papers we have counts for
7 papers
ROPUST: Improving Robustness through Fine-tuning with Photonic Processors and Synthetic Gradients
Alessandro Cappelli, Julien Launay, Laurent Meunier +2
Robustness to adversarial attacks is typically obtained through expensive adversarial training with Projected Gradient Descent. Here we introduce ROPUST, a remarkably simple and ef…
Photonic co-processors in HPC: using LightOn OPUs for Randomized Numerical Linear Algebra
Daniel Hesslow, Alessandro Cappelli, Igor Carron +6
Randomized Numerical Linear Algebra (RandNLA) is a powerful class of methods, widely used in High Performance Computing (HPC). RandNLA provides approximate solutions to linear alge…
Align, then memorise: the dynamics of learning with feedback alignment
Maria Refinetti, Stéphane d'Ascoli, Ruben Ohana +1
Direct Feedback Alignment (DFA) is emerging as an efficient and biologically plausible alternative to the ubiquitous backpropagation algorithm for training deep neural networks. De…
Experimental Approach to Demonstrating Contextuality for Qudits
Adel Sohbi, Ruben Ohana, Isabelle Zaquine +2
We propose a method to experimentally demonstrate contextuality with a family of tests for qudits. The experiment we propose uses a qudit encoded in the path of a single photon and…
Reservoir Computing meets Recurrent Kernels and Structured Transforms
Jonathan Dong, Ruben Ohana, Mushegh Rafayelyan +1
Reservoir Computing is a class of simple yet efficient Recurrent Neural Networks where internal weights are fixed at random and only a linear output layer is trained. In the large…
Impact of epitaxial strain on the topological-nontopological phase diagram and semimetallic behavior of InAs/GaSb composite quantum wells
H. Irie, T. Akiho, F. Couëdo +4
We study the influence of epitaxial strain on the electronic properties of InAs/GaSb composite quantum wells (CQWs), host structures for quantum spin Hall insulators, by transport…