Bayesian Optimization in Action 出版社:Manning 出版年:2023-11-4 页数:424 定价:USD 59.99 装帧:Paperback ISBN:9781633439078 豆瓣评分 目前无人评价 评价: 写笔记 写书评 加入购书单 分享到 推荐 内容简介· ··· Bayesian Optimization in Action teaches you how to create efficient machine learning process...
Bayesian Optimization in Action shows you how to optimize hyperparameter tuning, A/B testing, and other aspects of the machine learning process by applying cutting-edge Bayesian techniques. Using clear language, illustrations, and concrete examples, this book proves that Bayesian optimization doesn’t...
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Awesome-AutoML-Papersincludes very up-to-date overviews of the bread-and-butter techniques we need inAutoML: Automated Data Clean (Auto Clean) Automated Feature Engineering (Auto FE) Hyperparameter Optimization (HPO) Meta-Learning Neural Architecture Search (NAS) ...
Optimizing multiple, non-preferential objectives for mixed variable, expensive black-box problems is important in many areas of engineering and science. The expensive, noisy, black-box nature of these problems makes them ideal candidates for Bayesian optimization (BO). mixed variable and multi-objectiv...
Bayesian Optimization is employed for hyperparameter tuning, streamlining the model’s training process. The study shows that our proposed framework exhibits 100% resilience against external faults and disturbances, achieving an average recognition accuracy rate of 99.04% in diverse testing scenarios. ...
These prior beliefs are encoded in a probability distribution function. The most widely used distribution function in Bayesian optimization is the Gaussian process (GP) function. A Gaussian process is a collection of random variables, each of which, when taken in any finite linear combination, ...
Adam: A method for stochastic optimization. In Proceedings of the 3rd International Conference On Learning Representations, ICLR 2015—Conference Track Proceedings, San Diego, CA, USA, 7–9 May 2015. [Google Scholar] Pelikan, M.; Goldberg, D.; Cantú-Paz, E. BOA: The Bayesian optimization ...
Adam: a method for stochastic optimization. Preprint at https://arxiv.org/abs/1412.6980 (2014). Li, Y., Hernández-Lobato, J. M. & Turner, R. E. Stochastic expectation propagation. Adv. Neural Inf. Process. Syst. 28, 2323–2331 (2015). Google Scholar Liang, F., Paulo, R., ...
As an alternative route to developing an ITR, direct optimization of the population average over a class of ITRs [9, 10, 15, 16, see, e.g.,] may be considered. Substantial developments have been made in the statistical methodology on the ITE estimation [17, see, e.g., review provided...