Results 41 to 50 of about 56,420 (266)
Bayesian Optimization Based on K-Optimality [PDF]
Entropy, 2018 Bayesian optimization (BO) based on the Gaussian process (GP) surrogate model has attracted extensive attention in the field of optimization and design of experiments (DoE). It usually faces two problems: the unstable GP prediction due to the ill-conditioned Gram matrix of the kernel and the difficulty of determining the trade-off parameter between ...
Liang Yan 0003 +3 more
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Bayesian Optimization with Expensive Integrands
SIAM Journal on Optimization, 2022 We propose a Bayesian optimization algorithm for objective functions that are sums or integrals of expensive-to-evaluate functions, allowing noisy evaluations. These objective functions arise in multi-task Bayesian optimization for tuning machine learning hyperparameters, optimization via simulation, and sequential design of experiments with random ...
Saul Toscano-Palmerin, Peter I. Frazier
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FEBS Open Bio, EarlyView.The dFoCC pipeline starts with observed DED and resting‐state coordinates, which are then used to generate a library of triggered states. Correlation analysis of the calculated DED features of each candidate vs observed DED permits quantitative evaluation of candidate structural quality.
Meng Iao Fong +3 more
wiley +1 more source
A Bayesian optimization approach for reliability-based design of prestressed concrete structures
Data-Centric EngineeringThis paper presents a reliability-constrained Bayesian optimization framework for structural design under uncertainty, addressing challenges in stochastic optimization where the objectives and constraints are defined implicitly by potentially expensive ...
James Whiteley, Jurgen Becque
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Real-time load forecasting model for the smart grid using bayesian optimized CNN-BiLSTM
Frontiers in Energy Research, 2023 A smart grid is a new type of power system based on modern information technology, which utilises advanced communication, computing and control technologies and employs advanced sensors, measurement, communication and control devices that can monitor the
Daohua Zhang +4 more
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On Batch Bayesian Optimization
CoRR, 2019 All of Bayesian Nonparametrics workshop, Neural Information Processing Systems ...
Sayak Ray Chowdhury, Aditya Gopalan
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Bayesian Optimization for Optimizing Retrieval Systems [PDF]
Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, 2018 The effectiveness of information retrieval systems heavily depends on a large number of hyperparameters that need to be tuned. Hyperparameters range from the choice of different system components, e.g., stopword lists, stemming methods, or retrieval models, to model parameters, such as k1 and b in BM25, or the number of query expansion terms.
Dan Li 0015, Evangelos Kanoulas
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Directed evolution of enzymes at the crossroads of tradition and innovation
FEBS Open Bio, EarlyView.An iterative cycle of data‐driven enzyme optimization comprising four stages: genetic diversification of a template enzyme, expression of protein variants, high‐throughput evaluation, and machine‐learning‐guided redesign of the next variant library.
Maria Tomkova +2 more
wiley +1 more source
MathematicsOptimizing process outcomes by tuning parameters through an automated system is common in industry. Ideally, this optimization is performed as efficiently as possible, using the minimum number of steps to achieve an optimal configuration.
Santiago Ramos Garces +5 more
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Application of Bayesian optimized XGBoost in seismic interpretation of small-scale faults
Meitan xuebao, 2023 In order to further improve the identification accuracy of small-scale faults in seismic interpretation, Bayesian optimized extreme gradient boosting (XGBoost) model was constructed to recognize small-scale faults across coalbeds using reduced seismic ...
Changwei DING +3 more
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