Results 11 to 20 of about 4,909,083 (351)
Generative Multiform Bayesian Optimization
IEEE Transactions on Cybernetics, 2023 Many real-world problems, such as airfoil design, involve optimizing a black-box expensive objective function over complex structured input space (e.g., discrete space or non-Euclidean space). By mapping the complex structured input space into a latent space of dozens of variables, a two-stage procedure labeled as generative model based optimization ...
Zhendong Guo +5 more
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Tuning of Bayesian optimization for materials synthesis: simulation of the one-dimensional case
Science and Technology of Advanced Materials: Methods, 2022 Materials exploration requires the optimization of a multidimensional space including the chemical composition and synthesis parameters such as temperature and pressure.
Ryo Nakayama +8 more
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Active Learning and Bayesian Optimization: A Unified Perspective to Learn with a Goal [PDF]
Archives of Computational Methods in Engineering, 2023 Science and Engineering applications are typically associated with expensive optimization problem to identify optimal design solutions and states of the system of interest.
Francesco Di Fiore +2 more
semanticscholar +1 more source
Enhanced machine learning tree classifiers for lithology identification using Bayesian optimization
Applied Computing and Geosciences, 2022 Lithology identification is a fundamental activity in oil and gas exploration. The application of artificial intelligence (AI) is currently being adopted as a state-of-the-art means of automating lithology identification.
Solomon Asante-Okyere +2 more
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Journal of Fluid Science and Technology, 2021 We attempt to optimize the control parameters of traveling wave-like wall deformation for turbulent friction drag reduction using the Bayesian optimization. The Bayesian optimization is an optimization method based on stochastic processes, and it is good
Yusuke NABAE, Koji FUKAGATA
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Sparse Bayesian Optimization [PDF]
, 2022 Bayesian optimization (BO) is a powerful approach to sample-efficient optimization of black-box objective functions. However, the application of BO to areas such as recommendation systems often requires taking the interpretability and simplicity of the configurations into consideration, a setting that has not been previously studied in the BO ...
Sulin Liu +4 more
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Accelerating Bayesian Optimization for Biological Sequence Design with Denoising Autoencoders [PDF]
International Conference on Machine Learning, 2022 Bayesian optimization (BayesOpt) is a gold standard for query-efficient continuous optimization. However, its adoption for drug design has been hindered by the discrete, high-dimensional nature of the decision variables. We develop a new approach (LaMBO)
S. Stanton +6 more
semanticscholar +1 more source
Comparison of High-Dimensional Bayesian Optimization Algorithms on BBOB [PDF]
ACM Transactions on Evolutionary Learning and Optimization, 2023 Bayesian Optimization (BO) is a class of surrogate-based black-box optimization heuristics designed to efficiently locate high-quality solutions for problems that are expensive to evaluate, and therefore allow only small evaluation budgets.
M. Santoni +3 more
semanticscholar +1 more source
Entropy, 2023 In order to reduce the errors caused by the idealization of the conventional analytical model in the transient planar source (TPS) method, a finite element model that more closely represents the actual heat transfer process was constructed.
Hualin Ji +4 more
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Quantum Gaussian process regression for Bayesian optimization [PDF]
Quantum Machine Intelligence, 2023 Gaussian process regression is a well-established Bayesian machine learning method. We propose a new approach to Gaussian process regression using quantum kernels based on parameterized quantum circuits.
Frederic Rapp, Marco Roth
semanticscholar +1 more source