7 citations · 11 across the 4 of their papers we have counts for
4 papers
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…
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…
Transfer Learning for Linear Regression: a Statistical Test of Gain
David Obst, Badih Ghattas, Jairo Cugliari +3
Transfer learning, also referred as knowledge transfer, aims at reusing knowledge from a source dataset to a similar target one. While many empirical studies illustrate the benefit…
Textual Data for Time Series Forecasting
David Obst, Badih Ghattas, Sandra Claudel +3
While ubiquitous, textual sources of information such as company reports, social media posts, etc. are hardly included in prediction algorithms for time series, despite the relevan…