References in zbMATH (referenced in 28 articles )

Showing results 1 to 20 of 28.
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  1. Ali Haidar, Matthew Field, Jonathan Sykes, Martin Carolan, Lois Holloway: PSPSO: A package for parameters selection using particle swarm optimization (2021) not zbMATH
  2. Guo, Liang; Liu, Jianya; Lu, Ruodan: Subsampling bias and the best-discrepancy systematic cross validation (2021)
  3. Kafka, Dominic; Wilke, Daniel N.: Resolving learning rates adaptively by locating stochastic non-negative associated gradient projection points using line searches (2021)
  4. Vlassis, Nikolaos N.; Sun, WaiChing: Sobolev training of thermodynamic-informed neural networks for interpretable elasto-plasticity models with level set hardening (2021)
  5. Zöller, Marc-André; Huber, Marco F.: Benchmark and survey of automated machine learning frameworks (2021)
  6. Binois, Mickael; Picheny, Victor; Taillandier, Patrick; Habbal, Abderrahmane: The Kalai-Smorodinsky solution for many-objective Bayesian optimization (2020)
  7. Du, Xin; Pei, Yulong; Duivesteijn, Wouter; Pechenizkiy, Mykola: Exceptional spatio-temporal behavior mining through Bayesian non-parametric modeling (2020)
  8. Erway, Jennifer B.; Griffin, Joshua; Marcia, Roummel F.; Omheni, Riadh: Trust-region algorithms for training responses: machine learning methods using indefinite Hessian approximations (2020)
  9. Gao, Kaifeng; Mei, Gang; Piccialli, Francesco; Cuomo, Salvatore; Tu, Jingzhi; Huo, Zenan: Julia language in machine learning: algorithms, applications, and open issues (2020)
  10. Kandasamy, Kirthevasan; Vysyaraju, Karun Raju; Neiswanger, Willie; Paria, Biswajit; Collins, Christopher R.; Schneider, Jeff; Poczos, Barnabas; Xing, Eric P.: Tuning hyperparameters without grad students: scalable and robust Bayesian optimisation with Dragonfly (2020)
  11. Mao, Zhiping; Jagtap, Ameya D.; Karniadakis, George Em: Physics-informed neural networks for high-speed flows (2020)
  12. Mello, Alexandre R.; Stemmer, Marcelo R.; Koerich, Alessandro L.: Incremental and decremental fuzzy bounded twin support vector machine (2020)
  13. Moriconi, Riccardo; Deisenroth, Marc Peter; Sesh Kumar, K. S.: High-dimensional Bayesian optimization using low-dimensional feature spaces (2020)
  14. Rohan Anand, Joeran Beel: Auto-Surprise: An Automated Recommender-System (AutoRecSys) Library with Tree of Parzens Estimator (TPE) Optimization (2020) arXiv
  15. Sandeep Singh Sandha, Mohit Aggarwal, Igor Fedorov, Mani Srivastava: MANGO: A Python Library for Parallel Hyperparameter Tuning (2020) arXiv
  16. Yokoi, Soma; Otsuka, Takuma; Sato, Issei: Weak approximation of transformed stochastic gradient MCMC (2020)
  17. Zhan, Dawei; Xing, Huanlai: Expected improvement for expensive optimization: a review (2020)
  18. Ariafar, Setareh; Coll-Font, Jaume; Brooks, Dana; Dy, Jennifer: ADMMBO: Bayesian optimization with unknown constraints using ADMM (2019)
  19. Mariappan, Ragunathan; Rajan, Vaibhav: Deep collective matrix factorization for augmented multi-view learning (2019)
  20. Ray, Deep; Hesthaven, Jan S.: Detecting troubled-cells on two-dimensional unstructured grids using a neural network (2019)

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