108 citations · 234 across the 12 of their papers we have counts for
5 papers · 1 filter
Module-wise Training of Neural Networks via the Minimizing Movement Scheme
Skander Karkar, Ibrahim Ayed, Emmanuel de Bézenac +1
Greedy layer-wise or module-wise training of neural networks is compelling in constrained and on-device settings where memory is limited, as it circumvents a number of problems of…
An operator preconditioning perspective on training in physics-informed machine learning
Tim De Ryck, Florent Bonnet, Siddhartha Mishra +1
In this paper, we investigate the behavior of gradient descent algorithms in physics-informed machine learning methods like PINNs, which minimize residuals connected to partial dif…
Representation Equivalent Neural Operators: a Framework for Alias-free Operator Learning
Francesca Bartolucci, Emmanuel de Bézenac, Bogdan Raonić +3
Recently, operator learning, or learning mappings between infinite-dimensional function spaces, has garnered significant attention, notably in relation to learning partial differen…
Unifying GANs and Score-Based Diffusion as Generative Particle Models
Jean-Yves Franceschi, Mike Gartrell, Ludovic Dos Santos +4
Particle-based deep generative models, such as gradient flows and score-based diffusion models, have recently gained traction thanks to their striking performance. Their principle…
Convolutional Neural Operators for robust and accurate learning of PDEs
Bogdan Raonić, Roberto Molinaro, Tim De Ryck +5
Although very successfully used in conventional machine learning, convolution based neural network architectures -- believed to be inconsistent in function space -- have been large…