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Self-driving laboratories for chemistry and materials science
Self-driving laboratories (SDLs) promise an accelerated application of the scientific method.
Through the automation of experimental workflows, along with autonomous experimental …
Through the automation of experimental workflows, along with autonomous experimental …
Hyperparameter optimization: Foundations, algorithms, best practices, and open challenges
Most machine learning algorithms are configured by a set of hyperparameters whose values
must be carefully chosen and which often considerably impact performance. To avoid a time …
must be carefully chosen and which often considerably impact performance. To avoid a time …
Tree-structured parzen estimator: Understanding its algorithm components and their roles for better empirical performance
S Watanabe - arxiv preprint arxiv:2304.11127, 2023 - arxiv.org
Recent advances in many domains require more and more complicated experiment design.
Such complicated experiments often have many parameters, which necessitate parameter …
Such complicated experiments often have many parameters, which necessitate parameter …
Scene text recognition with permuted autoregressive sequence models
Context-aware STR methods typically use internal autoregressive (AR) language models
(LM). Inherent limitations of AR models motivated two-stage methods which employ an …
(LM). Inherent limitations of AR models motivated two-stage methods which employ an …
Unexpected improvements to expected improvement for bayesian optimization
Expected Improvement (EI) is arguably the most popular acquisition function in Bayesian
optimization and has found countless successful applications, but its performance is often …
optimization and has found countless successful applications, but its performance is often …
SMAC3: A versatile Bayesian optimization package for hyperparameter optimization
Algorithm parameters, in particular hyperparameters of machine learning algorithms, can
substantially impact their performance. To support users in determining well-performing …
substantially impact their performance. To support users in determining well-performing …
Bayesian optimization is superior to random search for machine learning hyperparameter tuning: Analysis of the black-box optimization challenge 2020
This paper presents the results and insights from the black-box optimization (BBO)
challenge at NeurIPS2020 which ran from July–October, 2020. The challenge emphasized …
challenge at NeurIPS2020 which ran from July–October, 2020. The challenge emphasized …
[HTML][HTML] A review of uncertainty quantification in deep learning: Techniques, applications and challenges
Uncertainty quantification (UQ) methods play a pivotal role in reducing the impact of
uncertainties during both optimization and decision making processes. They have been …
uncertainties during both optimization and decision making processes. They have been …
Deep learning for time series forecasting: a survey
Time series forecasting has become a very intensive field of research, which is even
increasing in recent years. Deep neural networks have proved to be powerful and are …
increasing in recent years. Deep neural networks have proved to be powerful and are …
SMT 2.0: A Surrogate Modeling Toolbox with a focus on hierarchical and mixed variables Gaussian processes
Abstract The Surrogate Modeling Toolbox (SMT) is an open-source Python package that
offers a collection of surrogate modeling methods, sampling techniques, and a set of sample …
offers a collection of surrogate modeling methods, sampling techniques, and a set of sample …