2 citations · 2 across the 8 of their papers we have counts for
8 papers
Generative Unsupervised Downscaling of Climate Models via Domain Alignment: Application to Wind Fields
Julie Keisler, Boutheina Oueslati, Anastase Charantonis +2
General Circulation Models (GCMs) are widely used for future climate projections, but their coarse spatial resolution and systematic biases limit their direct use for impact studie…
SerpentFlow: Generative Unpaired Domain Alignment via Shared-Structure Decomposition
Julie Keisler, Anastase Alexandre Charantonis, Yannig Goude +2
Domain alignment refers broadly to learning correspondences between data distributions from distinct domains. In this work, we focus on a setting where domains share underlying str…
AutoML Algorithms for Online Generalized Additive Model Selection: Application to Electricity Demand Forecasting
Keshav Das, Julie Keisler, Margaux Brégère +1
Electricity demand forecasting is key to ensuring that supply meets demand lest the grid would blackout. Reliable short-term forecasts may be obtained by combining a Generalized Ad…
Automated Spatio-Temporal Weather Modeling for Load Forecasting
Julie Keisler, Margaux Bregere
Electricity is difficult to store, except at prohibitive cost, and therefore the balance between generation and load must be maintained at all times. Electricity is traditionally m…
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…
WindDragon: Enhancing wind power forecasting with Automated Deep Learning
Julie Keisler, Etienne Le Naour
Achieving net zero carbon emissions by 2050 requires the integration of increasing amounts of wind power into power grids. This energy source poses a challenge to system operators…
A Bandit Approach with Evolutionary Operators for Model Selection
Margaux Brégère, Julie Keisler
This work formulates model selection as an infinite-armed bandit problem, namely, a problem in which a decision maker iteratively selects one of an infinite number of fixed choices…
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…