Results 31 to 40 of about 46,859 (313)
Scaling Laws for Hyperparameter Optimization
Advances in Neural Information Processing Systems 36, 2023 Accepted at NeurIPS ...
Arlind Kadra +3 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
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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
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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 [PDF]
, 2019 Recent interest in complex and computationally expensive machine learning models with many hyperparameters, such as automated machine learning (AutoML) frameworks and deep neural networks, has resulted in a resurgence of research on hyperparameter optimization (HPO). In this chapter, we give an overview of the most prominent approaches for HPO.
Feurer, Matthias, Hutter, Frank
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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
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IEEE Access, 2021 As the main driving equipment of modern industrial production activities, if a motor fails, it causes serious consequences. Bearings are the component with the highest motor failure frequency.
Zhen Li, Yang Wang, Jianeng Ma
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Impact of Hyperparameter Optimization on Cross-Version Defect Prediction: An Empirical Study [PDF]
Jisuanji kexue yu tansuo, 2023 In the field of machine learning, hyperparameters are one of the key factors that affect prediction performance. Previous studies have shown that optimizing hyperparameters can improve the performance of inner-version defect prediction and cross-project ...
HAN Hui, YU Qiao, ZHU Yi
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A Comparison of AutoML Hyperparameter Optimization Tools For Tabular Data
Proceedings of the International Florida Artificial Intelligence Research Society Conference, 2023 The performance of machine learning (ML) methods for classification and regression tasks applied to tabular datasets is sensitive to hyperparameters values.
Prativa Pokhrel, Alina Lazar
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Rectangularization of Gaussian process regression for optimization of hyperparameters
Machine Learning with Applications, 2023 Gaussian process regression (GPR) is a powerful machine learning method which has recently enjoyed wider use, in particular in physical sciences. In its original formulation, GPR uses a square matrix of covariances among training data and can be viewed ...
Sergei Manzhos, Manabu Ihara
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