289 citations · 395 across the 10 of their papers we have counts for
5 papers · 1 filter
Leveraging Joint-Diagonalization in Transform-Learning NMF
Sixin Zhang, Emmanuel Soubies, Cédric Févotte
Non-negative matrix factorization with transform learning (TL-NMF) is a recent idea that aims at learning data representations suited to NMF. In this work, we relate TL-NMF to the…
Data Assimilation Networks
Pierre Boudier, Anthony Fillion, Serge Gratton +2
Data assimilation (DA) aims at forecasting the state of a dynamical system by combining a mathematical representation of the system with noisy observations taking into account thei…
Statistical learning of geometric characteristics of wireless networks
Antoine Brochard, Bartłomiej Błaszczyszyn, Stéphane Mallat +1
Motivated by the prediction of cell loads in cellular networks, we formulate the following new, fundamental problem of statistical learning of geometric marks of point processes: A…
Distributed stochastic optimization for deep learning (thesis)
Sixin Zhang
We study the problem of how to distribute the training of large-scale deep learning models in the parallel computing environment. We propose a new distributed stochastic optimizati…
Deep learning with Elastic Averaging SGD
Sixin Zhang, Anna Choromanska, Yann LeCun
We study the problem of stochastic optimization for deep learning in the parallel computing environment under communication constraints. A new algorithm is proposed in this setting…