Hyperparameter optimisation in deep learning from ensemble methods: applications to proton structure

Hyperparameter optimisation in deep learning from ensemble methods: applications to proton structure

Deep learning models are defined in terms of a large number of hyperparameters, such as network architectures and optimiser settings. These hyperparameters must be determined separately from the model parameters such as network weights, and are often fixed by ad-hoc methods or by manual inspection o...

Full description

Saved in:
Bibliographic Details
Main Authors: Juan Cruz-Martinez, Aron Jansen, Gijs van Oord, Tanjona R Rabemananjara, Carlos M R Rocha, Juan Rojo, Roy Stegeman
Format: Article
Language:English
Published: IOP Publishing 2025-01-01
Series:Machine Learning: Science and Technology
Subjects:
hyperoptimisation
machine learning
hardware acceleration
proton structure
parton distribution functions
GPU
Online Access:https://doi.org/10.1088/2632-2153/adcd39
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

https://doi.org/10.1088/2632-2153/adcd39

Similar Items