1.2k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- CentraleSupélecFR21 papers
- Institut national de recherche en sciences et technologies du numériqueFR13 papers
- Université Paris-SaclayFR11 papers
- OPIS: OPtimisation Imagerie et SantéFR8 papers
- École Centrale ParisFR6 papers
- Bouygues (France)FR4 papers
- Centre National de la Recherche ScientifiqueFR4 papers
- Laboratoire d'Informatique Gaspard-MongeFR4 papers
- ESIEE ParisFR3 papers
- KU LeuvenBE3 papers
- Université de MontréalCA3 papers
- Centre Inria de SaclayFR2 papers
6 papers · 1 filter
TNE: A Latent Model for Representation Learning on Networks
Abdulkadir Çelikkanat, Fragkiskos D. Malliaros
Network representation learning (NRL) methods aim to map each vertex into a low dimensional space by preserving the local and global structure of a given network, and in recent yea…
Compressing the Input for CNNs with the First-Order Scattering Transform
Edouard Oyallon, Eugene Belilovsky, Sergey Zagoruyko +1
We study the first-order scattering transform as a candidate for reducing the signal processed by a convolutional neural network (CNN). We show theoretical and empirical evidence t…
Scattering Networks for Hybrid Representation Learning
Edouard Oyallon, Sergey Zagoruyko, Gabriel Huang +4
Scattering networks are a class of designed Convolutional Neural Networks (CNNs) with fixed weights. We argue they can serve as generic representations for modelling images. In par…
Rational Optimization for Nonlinear Reconstruction with Approximate Penalization
Marc Castella, Jean-Christophe Pesquet, Arthur Marmin
Recovering nonlinearly degraded signal in the presence of noise is a challenging problem. In this work, this problem is tackled by minimizing the sum of a non convex least-squares…
Nonlinear Acceleration of CNNs
Damien Scieur, Edouard Oyallon, Alexandre d'Aspremont +1
The Regularized Nonlinear Acceleration (RNA) algorithm is an acceleration method capable of improving the rate of convergence of many optimization schemes such as gradient descend,…
A Proximal Approach for a Class of Matrix Optimization Problems
A. Benfenati, E. Chouzenoux, J. -C. Pesquet
In recent years, there has been a growing interest in mathematical models leading to the minimization, in a symmetric matrix space, of a Bregman divergence coupled with a regulariz…