37 citations · 143 across the 34 of their papers we have counts for
5 papers · 2 filters
On the Convergence of Gradient Descent in GANs: MMD GAN As a Gradient Flow
Youssef Mroueh, Truyen Nguyen
We consider the maximum mean discrepancy () GAN problem and propose a parametric kernelized gradient flow that mimics the min-max game in gradient regularized $\mathr…
Tabular Transformers for Modeling Multivariate Time Series
Inkit Padhi, Yair Schiff, Igor Melnyk +6
Tabular datasets are ubiquitous in data science applications. Given their importance, it seems natural to apply state-of-the-art deep learning algorithms in order to fully unlock t…
Unbalanced Sobolev Descent
Youssef Mroueh, Mattia Rigotti
We introduce Unbalanced Sobolev Descent (USD), a particle descent algorithm for transporting a high dimensional source distribution to a target distribution that does not necessari…
Active learning of deep surrogates for PDEs: Application to metasurface design
Raphaël Pestourie, Youssef Mroueh, Thanh V. Nguyen +2
Surrogate models for partial-differential equations are widely used in the design of meta-materials to rapidly evaluate the behavior of composable components. However, the training…
Improving Efficiency in Large-Scale Decentralized Distributed Training
Wei Zhang, Xiaodong Cui, Abdullah Kayi +9
Decentralized Parallel SGD (D-PSGD) and its asynchronous variant Asynchronous Parallel SGD (AD-PSGD) is a family of distributed learning algorithms that have been demonstrated to p…