Results 61 to 70 of about 5,001,817 (246)
A Hybrid Sparrow Search Algorithm of the Hyperparameter Optimization in Deep Learning
Mathematics, 2022 Deep learning has been widely used in different fields such as computer vision and speech processing. The performance of deep learning algorithms is greatly affected by their hyperparameters.
Yanyan Fan +5 more
doaj +1 more source
Optimization of Annealed Importance Sampling Hyperparameters
, 2023 AbstractAnnealed Importance Sampling (AIS) is a popular algorithm used to estimates the intractable marginal likelihood of deep generative models. Although AIS is guaranteed to provide unbiased estimate for any set of hyperparameters, the common implementations rely on simple heuristics such as the geometric average bridging distributions between ...
Shirin Goshtasbpour +1 more
openaire +4 more sources
Neighbor Regularized Bayesian Optimization for Hyperparameter Optimization
CoRR, 2022 Bayesian Optimization (BO) is a common solution to search optimal hyperparameters based on sample observations of a machine learning model. Existing BO algorithms could converge slowly even collapse when the potential observation noise misdirects the optimization.
Lei Cui +4 more
openaire +4 more sources
Advanced Engineering Materials, EarlyView.Building machine‐readable vocabularies for materials science is slow, expert‐driven work. This study benchmarks 13 large language models on two of its first steps: finding candidate terms in engineering articles and deciding where they belong in a class hierarchy.
Thomas Bjarsch +3 more
wiley +1 more source
Advanced Engineering Materials, EarlyView.We develop a data‐driven method to derive the mathematical expressions of the Flory–Huggins interaction parameter χ for the swelling behavior of temperature–responsive hydrogels. Starting from initial assumptions of χ, our workflow combines Bayesian optimization, Flory–Rehner theory, and symbolic regression to generate candidate χ expressions.
Yawen Wang +2 more
wiley +1 more source
Hyperparameter Optimization for Machine Learning Models Based on Bayesian Optimizationb
Journal of Electronic Science and Technology, 2019 Hyperparameters are important for machine learning algorithms since they directly control the behaviors of training algorithms and have a significant effect on the performance of machine learning models.
Jia Wu +5 more
doaj +1 more source
Advanced Engineering Materials, EarlyView.This study applies machine learning regression to predict chromium layer thickness in decorative trivalent chromium electroplating, using 441 experiments from laboratory‐scale (1L) and pilot‐scale (14L) setups. Tree‐based models, particularly CatBoost, outperformed linear regression by capturing nonlinear parameter interactions (R2$R^2$ up to 0.77 ...
Christoph Baumer +4 more
wiley +1 more source
Tuning Bayesian optimization for materials synthesis: simulating two- and three-dimensional cases
Science and Technology of Advanced Materials: Methods, 2023 Compared to the optimization of a 1D synthesis parameter in materials synthesis, the optimization of multi-dimensional synthesis parameters is challenging for researchers.
Han Xu +8 more
doaj +1 more source
Robust optimization of SVM hyper-parameters for spillway type selection
Ain Shams Engineering Journal, 2021 Spillways, which play a vital role in dams, can be built in various types. Although several studies have been conducted on hydraulic calculations of spillways, studies on type selection that require heuristics knowledge were limited.
Enes Gul, Nuh Alpaslan, M. Emin Emiroglu
doaj +1 more source
Optimizing Hyperparameters with Conformal Quantile Regression
CoRR, 2023 Many state-of-the-art hyperparameter optimization (HPO) algorithms rely on model-based optimizers that learn surrogate models of the target function to guide the search. Gaussian processes are the de facto surrogate model due to their ability to capture uncertainty but they make strong assumptions about the observation noise, which might not be ...
David Salinas +4 more
openaire +4 more sources