2k citations · 6.6k across the 78 of their papers we have counts for
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Catalyst Acceleration for First-order Convex Optimization: from Theory to Practice
Hongzhou Lin, Julien Mairal, Zaid Harchaoui
We introduce a generic scheme for accelerating gradient-based optimization methods in the sense of Nesterov. The approach, called Catalyst, builds upon the inexact accelerated prox…
BlitzNet: A Real-Time Deep Network for Scene Understanding
Nikita Dvornik, Konstantin Shmelkov, Julien Mairal +1
Real-time scene understanding has become crucial in many applications such as autonomous driving. In this paper, we propose a deep architecture, called BlitzNet, that jointly perfo…
Group Invariance, Stability to Deformations, and Complexity of Deep Convolutional Representations
Alberto Bietti, Julien Mairal
The success of deep convolutional architectures is often attributed in part to their ability to learn multiscale and invariant representations of natural signals. However, a precis…
Catalyst Acceleration for Gradient-Based Non-Convex Optimization
Courtney Paquette, Hongzhou Lin, Dmitriy Drusvyatskiy +2
We introduce a generic scheme to solve nonconvex optimization problems using gradient-based algorithms originally designed for minimizing convex functions. Even though these method…
Stochastic Subsampling for Factorizing Huge Matrices
Arthur Mensch, Julien Mairal, Bertrand Thirion +1
We present a matrix-factorization algorithm that scales to input matrices with both huge number of rows and columns. Learned factors may be sparse or dense and/or non-negative, whi…