2 citations · 3 across the 5 of their papers we have counts for
5 papers
Near-Linear Time Projection onto the Ball; Application to Sparse Autoencoders
Guillaume Perez, Laurent Condat, Michel Barlaud
Looking for sparsity is nowadays crucial to speed up the training of large-scale neural networks. Projections onto the and are among the most efficie…
Constrained Machine Learning: The Bagel Framework
Guillaume Perez, Sebastian Ament, Carla Gomes +1
Machine learning models are widely used for real-world applications, such as document analysis and vision. Constrained machine learning problems are problems where learned models h…
Efficient Projection Algorithms onto the Weighted l1 Ball
Guillaume Perez, Sebastian Ament, Carla Gomes +1
Projected gradient descent has been proved efficient in many optimization and machine learning problems. The weighted ball has been shown effective in sparse system identi…
Compact-Table: Efficiently Filtering Table Constraints with Reversible Sparse Bit-Sets
Jordan Demeulenaere, Renaud Hartert, Christophe Lecoutre +4
In this paper, we describe Compact-Table (CT), a bitwise algorithm to enforce Generalized Arc Consistency (GAC) on table con- straints. Although this algorithm is the default propa…
Relations between MDDs and Tuples and Dynamic Modifications of MDDs based constraints
Guillaume Perez, Jean-Charles Régin
We study the relations between Multi-valued Decision Diagrams (MDD) and tuples (i.e. elements of the Cartesian Product of variables). First, we improve the existing methods for tra…