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SLURM-Managed HyperParameter Optimization.

Hyperparameter optimization (HPO) is essential for achieving state-of-the-art performance in machine learning, yet it is computationally demanding, particularly on shared or resource-constrained clusters. We present a system that integrates the Asynchronous Successive Halving Algorithm (ASHA) with SEML, the SLURM Experiment Management Library - an ...
Chattopadhyay, Anusha   +3 more
openaire   +2 more sources

Scalable Meta-Bayesian Based Hyperparameters Optimization for Machine Learning

Communications in Computer and Information Science, 2023
Mohamed Hamlich   +2 more
exaly  

How to tune the RBF SVM hyperparameters? An empirical evaluation of 18 search algorithms

Artificial Intelligence Review, 2021
Jacques Wainer, Pablo Fonseca
exaly  

Hyperparameters optimization of convolutional neural network based on local autonomous competition harmony search algorithm

Journal of Computational Design and Engineering, 2023
Haibin Ouyang   +2 more
exaly  

Computer Optimization of ANN Hyperparameters for Retrospective Information Processing

Lecture Notes in Networks and Systems, 2022
Aleksey F. Rogachev, Elena V Melikhova
exaly  

Automatic tuning of hyperparameters using Bayesian optimization

Evolving Systems, 2020
G Maragatham, A Helen Victoria
exaly  

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