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Use of Static Surrogates in Hyperparameter Optimization [PDF]
Operations Research Forum, 2022 http://www.optimization-online.org/DB_HTML/2021/03/8296 ...
Dounia Lakhmiri, Sébastien Le Digabel
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Tuning hyperparameters of doublet‐detection methods for single‐cell RNA sequencing data
Quantitative Biology, 2023 Doublet is a major confounder in single‐cell RNA sequencing data analysis. Computational doublet‐detection methods aim to remove doublets from scRNA‐seq data. The performance of those methods relies on the appropriate setting of their hyperparameters. In
Nan Miles Xi, Angelos Vasilopoulos
doaj +1 more source
Bayesian Optimized Echo State Network Applied to Short-Term Load Forecasting
Energies, 2020 Load forecasting impacts directly financial returns and information in electrical systems planning. A promising approach to load forecasting is the Echo State Network (ESN), a recurrent neural network for the processing of temporal dependencies.
Gabriel Trierweiler Ribeiro +4 more
doaj +1 more source
Symbolic Explanations for Hyperparameter Optimization
International Conference on AutoML, 2023 Hyperparameter optimization (HPO) methods can determine well-performing hyperparameter configurations efficiently but often lack insights and transparency. We propose to apply symbolic regression to meta-data collected with Bayesian optimization (BO) during HPO.
Segel, Sarah +4 more
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RHOASo: An Early Stop Hyper-Parameter Optimization Algorithm
Mathematics, 2021 This work proposes a new algorithm for optimizing hyper-parameters of a machine learning algorithm, RHOASo, based on conditional optimization of concave asymptotic functions.
Ángel Luis Muñoz Castañeda +2 more
doaj +1 more source
Is one hyperparameter optimizer enough? [PDF]
Proceedings of the 4th ACM SIGSOFT International Workshop on Software Analytics, 2018 Hyperparameter tuning is the black art of automatically finding a good combination of control parameters for a data miner. While widely applied in empirical Software Engineering, there has not been much discussion on which hyperparameter tuner is best for software analytics.
Huy Tu, Vivek Nair
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Hyperparameters and the average optimization runtime per epoch of the best performing model.
, 2021 Hyperparameters and the average optimization runtime per epoch of the best performing model.
Ethel Dominique Viray (11564842) +6 more
core +1 more source
IEEE Access, 2023 Deep Reinforcement Learning (DRL) allows agents to make decisions in a specific environment based on a reward function, without prior knowledge. Adapting hyperparameters significantly impacts the learning process and time.
Ebrahim Hamid Hasan Sumiea +6 more
doaj +1 more source
Optimization of hyperparameters for SMS reconstruction [PDF]
Magnetic Resonance Imaging, 2020 Simultaneous multi-slice (SMS) imaging accelerates MRI data acquisition by exciting multiple image slices simultaneously. Overlapping slices are then separated using a mathematical model. Several parameters used in SMS reconstruction impact the quality of final images. Therefore, finding an optimal set of reconstruction parameters is critical to ensure
Muftuler, L. Tugan +7 more
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Hyperparameter Optimization: A Spectral Approach
CoRR, 2017 We give a simple, fast algorithm for hyperparameter optimization inspired by techniques from the analysis of Boolean functions. We focus on the high-dimensional regime where the canonical example is training a neural network with a large number of hyperparameters.
Elad Hazan +2 more
openaire +4 more sources