most citedAn algorithmic framework for the optimization of deep neural networks architectures and hyperparameters

2 citations · 2 across the 2 of their papers we have counts for

collaborators

2 papers

cs.LG2024

Automated Deep Learning for Load Forecasting

Julie Keisler, Sandra Claudel, Gilles Cabriel +1

Accurate forecasting of electricity consumption is essential to ensure the performance and stability of the grid, especially as the use of renewable energy increases. Forecasting e…

cs.NE2023★ 2 cited

An algorithmic framework for the optimization of deep neural networks architectures and hyperparameters

Julie Keisler, El-Ghazali Talbi, Sandra Claudel +1

In this paper, we propose an algorithmic framework to automatically generate efficient deep neural networks and optimize their associated hyperparameters. The framework is based on…