11 citations · 13 across the 2 of their papers we have counts for
9 papers
Population Based Training for Data Augmentation and Regularization in Speech Recognition
Daniel Haziza, Jérémy Rapin, Gabriel Synnaeve
Varying data augmentation policies and regularization over the course of optimization has led to performance improvements over using fixed values. We show that population based tra…
Black-Box Optimization Revisited: Improving Algorithm Selection Wizards through Massive Benchmarking
Laurent Meunier, Herilalaina Rakotoarison, Pak Kan Wong +5
Existing studies in black-box optimization for machine learning suffer from low generalizability, caused by a typically selective choice of problem instances used for training and…
EvolGAN: Evolutionary Generative Adversarial Networks
Baptiste Roziere, Fabien Teytaud, Vlad Hosu +4
We propose to use a quality estimator and evolutionary methods to search the latent space of generative adversarial networks trained on small, difficult datasets, or both. The new…
Versatile Black-Box Optimization
Jialin Liu, Antoine Moreau, Mike Preuss +4
Choosing automatically the right algorithm using problem descriptors is a classical component of combinatorial optimization. It is also a good tool for making evolutionary algorith…
Variance Reduction for Better Sampling in Continuous Domains
Laurent Meunier, Carola Doerr, Jeremy Rapin +1
Design of experiments, random search, initialization of population-based methods, or sampling inside an epoch of an evolutionary algorithm use a sample drawn according to some prob…
On averaging the best samples in evolutionary computation
Laurent Meunier, Yann Chevaleyre, Jeremy Rapin +2
Choosing the right selection rate is a long standing issue in evolutionary computation. In the continuous unconstrained case, we prove mathematically that a single parent lea…