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Found 6 papers, 3 papers with code
Efficient Training Under Limited Resources
Training time budget and size of the dataset are among the factors affecting the performance of a Deep Neural Network (DNN).
Scaling Deep Networks with the Mesh Adaptive Direct Search algorithm
Deep neural networks are getting larger.
A Stochastic Proximal Method for Nonsmooth Regularized Finite Sum Optimization
We consider the problem of training a deep neural network with nonsmooth regularization to retrieve a sparse and efficient sub-structure.
Use of static surrogates in hyperparameter optimization
Optimizing the hyperparameters and architecture of a neural network is a long yet necessary phase in the development of any new application.
Tuning a variational autoencoder for data accountability problem in the Mars Science Laboratory ground data system
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 operations center making it prone to volume loss and data corruption.
HyperNOMAD: Hyperparameter optimization of deep neural networks using mesh adaptive direct search
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.
