References in zbMATH (referenced in 53 articles )

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  1. Cowen-Rivers, Alexander I.; Lyu, Wenlong; Tutunov, Rasul; Wang, Zhi; Grosnit, Antoine; Griffiths, Ryan Rhys; Maraval, Alexandre Max; Jianye, Hao; Wang, Jun; Peters, Jan; Bou-Ammar, Haitham: \textttHEBO: Pushing the limits of sample-efficient hyper-parameter optimisation (2022)
  2. Fraccaroli, Michele; Lamma, Evelina; Riguzzi, Fabrizio: Symbolic DNN-tuner (2022)
  3. García Trillos, Nicolás; Morales, Javier: Semi-discrete optimization through semi-discrete optimal transport: a framework for neural architecture search (2022)
  4. Guo, Mengwu; Manzoni, Andrea; Amendt, Maurice; Conti, Paolo; Hesthaven, Jan S.: Multi-fidelity regression using artificial neural networks: efficient approximation of parameter-dependent output quantities (2022)
  5. Hertel, Lars; Baldi, Pierre; Gillen, Daniel L.: Reproducible hyperparameter optimization (2022)
  6. Kontolati, Katiana; Loukrezis, Dimitrios; Giovanis, Dimitrios G.; Vandanapu, Lohit; Shields, Michael D.: A survey of unsupervised learning methods for high-dimensional uncertainty quantification in black-box-type problems (2022)
  7. Lakhmiri, Dounia; Le Digabel, Sébastien: Use of static surrogates in hyperparameter optimization (2022)
  8. Ozaki, Yoshihiko; Tanigaki, Yuki; Watanabe, Shuhei; Nomura, Masahiro; Onishi, Masaki: Multiobjective tree-structured Parzen estimator (2022)
  9. Sun, Xuxiang; Cao, Wenbo; Liu, Yilang; Zhu, Linyang; Zhang, Weiwei: High Reynolds number airfoil turbulence modeling method based on machine learning technique (2022)
  10. Watanabe, Satoru; Yamana, Hayato: Topological measurement of deep neural networks using persistent homology (2022)
  11. Ali Haidar, Matthew Field, Jonathan Sykes, Martin Carolan, Lois Holloway: PSPSO: A package for parameters selection using particle swarm optimization (2021) not zbMATH
  12. Blanchard, Antoine; Sapsis, Themistoklis: Bayesian optimization with output-weighted optimal sampling (2021)
  13. Bliek, Laurens; Verwer, Sicco; de Weerdt, Mathijs: Black-box combinatorial optimization using models with integer-valued minima (2021)
  14. Chen, Xiaoli; Duan, Jinqiao; Karniadakis, George Em: Learning and meta-learning of stochastic advection-diffusion-reaction systems from sparse measurements (2021)
  15. Corazza, Marco; di Tollo, Giacomo; Fasano, Giovanni; Pesenti, Raffaele: A novel hybrid PSO-based metaheuristic for costly portfolio selection problems (2021)
  16. Gouk, Henry; Frank, Eibe; Pfahringer, Bernhard; Cree, Michael J.: Regularisation of neural networks by enforcing Lipschitz continuity (2021)
  17. Guo, Liang; Liu, Jianya; Lu, Ruodan: Subsampling bias and the best-discrepancy systematic cross validation (2021)
  18. Jomaa, Hadi S.; Schmidt-Thieme, Lars; Grabocka, Josif: Dataset2Vec: learning dataset meta-features (2021)
  19. Kafka, Dominic; Wilke, Daniel N.: Resolving learning rates adaptively by locating stochastic non-negative associated gradient projection points using line searches (2021)
  20. Kafka, Dominic; Wilke, Daniel N.: An empirical study into finding optima in stochastic optimization of neural networks (2021)

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