4 papers · 1 filter
Leveraging AutoML for Sustainable Deep Learning: A Multi-Objective HPO Approach on Deep Shift Neural Networks
Leona Hennig, Marius Lindauer
Deep Learning (DL) has advanced various fields by extracting complex patterns from large datasets. However, the computational demands of DL models pose environmental and resource c…
Dynamic Priors in Bayesian Optimization for Hyperparameter Optimization
Lukas Fehring, Marcel Wever, Maximilian Spliethöver +3
Bayesian optimization (BO) is a widely used approach to hyperparameter optimization (HPO). However, most existing HPO methods only incorporate expert knowledge during initializatio…
carps: A Framework for Comparing N Hyperparameter Optimizers on M Benchmarks
Carolin Benjamins, Helena Graf, Sarah Segel +14
Hyperparameter Optimization (HPO) is crucial to develop well-performing machine learning models. In order to ease prototyping and benchmarking of HPO methods, we propose carps, a b…
Guidelines for the Quality Assessment of Energy-Aware NAS Benchmarks
Nick Kocher, Christian Wassermann, Leona Hennig +5
Neural Architecture Search (NAS) accelerates progress in deep learning through systematic refinement of model architectures. The downside is increasingly large energy consumption d…