References in zbMATH (referenced in 98 articles )

Showing results 1 to 20 of 98.
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  1. Ayensa-Jiménez, Jacobo; Doweidar, Mohamed H.; Sanz-Herrera, Jose A.; Doblaré, Manuel: Prediction and identification of physical systems by means of physically-guided neural networks with meaningful internal layers (2021)
  2. Binder, Martin; Pfisterer, Florian; Lang, Michel; Schneider, Lennart; Kotthoff, Lars; Bischl, Bernd: mlr3pipelines -- flexible machine learning pipelines in R (2021)
  3. Bliek, Laurens; Verwer, Sicco; de Weerdt, Mathijs: Black-box combinatorial optimization using models with integer-valued minima (2021)
  4. Chen, Xiaoli; Duan, Jinqiao; Karniadakis, George Em: Learning and meta-learning of stochastic advection-diffusion-reaction systems from sparse measurements (2021)
  5. Corazza, Marco; di Tollo, Giacomo; Fasano, Giovanni; Pesenti, Raffaele: A novel hybrid PSO-based metaheuristic for costly portfolio selection problems (2021)
  6. Ellenbach, Nicole; Boulesteix, Anne-Laure; Bischl, Bernd; Unger, Kristian; Hornung, Roman: Improved outcome prediction across data sources through robust parameter tuning (2021)
  7. Grosnit, Antoine; Cowen-Rivers, Alexander I.; Tutunov, Rasul; Griffiths, Ryan-Rhys; Wang, Jun; Bou-Ammar, Haitham: Are we forgetting about compositional optimisers in Bayesian optimisation? (2021)
  8. Huang, Junhao; Sun, Weize; Huang, Lei: Joint structure and parameter optimization of multiobjective sparse neural network (2021)
  9. Jakubik, Johannes; Binding, Adrian; Feuerriegel, Stefan: Directed particle swarm optimization with Gaussian-process-based function forecasting (2021)
  10. Järvenpää, Marko; Gutmann, Michael U.; Vehtari, Aki; Marttinen, Pekka: Parallel Gaussian process surrogate Bayesian inference with noisy likelihood evaluations (2021)
  11. Kafka, Dominic; Wilke, Daniel N.: Resolving learning rates adaptively by locating stochastic non-negative associated gradient projection points using line searches (2021)
  12. Kim, Jungtaek; McCourt, Michael; You, Tackgeun; Kim, Saehoon; Choi, Seungjin: Bayesian optimization with approximate set kernels (2021)
  13. Masti, Daniele; Bemporad, Alberto: Learning nonlinear state-space models using autoencoders (2021)
  14. Müller, Juliane; Park, Jangho; Sahu, Reetik; Varadharajan, Charuleka; Arora, Bhavna; Faybishenko, Boris; Agarwal, Deborah: Surrogate optimization of deep neural networks for groundwater predictions (2021)
  15. Nam, Jaehyun; Yong, Hwanmoo; Hwang, Jungho; Choi, Jongeun: Training an artificial neural network for recognizing electron collision patterns (2021)
  16. Qian, Zhaozhi; Alaa, Ahmed M.; van der Schaar, Mihaela: CPAS: the UK’s national machine learning-based hospital capacity planning system for COVID-19 (2021)
  17. Shi, Junjie; Bian, Jiang; Richter, Jakob; Chen, Kuan-Hsun; Rahnenführer, Jörg; Xiong, Haoyi; Chen, Jian-Jia: MODES: model-based optimization on distributed embedded systems (2021)
  18. Škrlj, Blaž; Martinc, Matej; Lavrač, Nada; Pollak, Senja: autoBOT: evolving neuro-symbolic representations for explainable low resource text classification (2021)
  19. Sudermann-Merx, Nathan; Rebennack, Steffen: Leveraged least trimmed absolute deviations (2021)
  20. Wang, Qihan; Wu, Di; Li, Guoyin; Gao, Wei: A virtual model architecture for engineering structures with twin extended support vector regression (T-X-SVR) method (2021)

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