10 citations · 13 across the 5 of their papers we have counts for
6 papers
Efficient Training Under Limited Resources
Mahdi Zolnouri, Dounia Lakhmiri, Christophe Tribes +2
Training time budget and size of the dataset are among the factors affecting the performance of a Deep Neural Network (DNN). This paper shows that Neural Architecture Search (NAS),…
Scaling Deep Networks with the Mesh Adaptive Direct Search algorithm
Dounia Lakhmiri, Mahdi Zolnouri, Vahid Partovi Nia +2
Deep neural networks are getting larger. Their implementation on edge and IoT devices becomes more challenging and moved the community to design lighter versions with similar perfo…
A Stochastic Proximal Method for Nonsmooth Regularized Finite Sum Optimization
Dounia Lakhmiri, Dominique Orban, Andrea Lodi
We consider the problem of training a deep neural network with nonsmooth regularization to retrieve a sparse and efficient sub-structure. Our regularizer is only assumed to be lowe…
Use of static surrogates in hyperparameter optimization
Dounia Lakhmiri, Sébastien Le Digabel
Optimizing the hyperparameters and architecture of a neural network is a long yet necessary phase in the development of any new application. This consuming process can benefit from…
Tuning a variational autoencoder for data accountability problem in the Mars Science Laboratory ground data system
Dounia Lakhmiri, Ryan Alimo, Sebastien Le Digabel
The Mars Curiosity rover is frequently sending back engineering and science data that goes through a pipeline of systems before reaching its final destination at the mission operat…
HyperNOMAD: Hyperparameter optimization of deep neural networks using mesh adaptive direct search
Dounia Lakhmiri, Sébastien Le Digabel, Christophe Tribes
The performance of deep neural networks is highly sensitive to the choice of the hyperparameters that define the structure of the network and the learning process. When facing a ne…