6 citations · 12 across the 10 of their papers we have counts for
5 papers · 1 filter
Listening to the Noise: Blind Denoising with Gibbs Diffusion
David Heurtel-Depeiges, Charles C. Margossian, Ruben Ohana +1
In recent years, denoising problems have become intertwined with the development of deep generative models. In particular, diffusion models are trained like denoisers, and the dist…
xVal: A Continuous Numerical Tokenization for Scientific Language Models
Siavash Golkar, Mariel Pettee, Michael Eickenberg +11
Due in part to their discontinuous and discrete default encodings for numbers, Large Language Models (LLMs) have not yet been commonly used to process numerically-dense scientific…
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…
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…