RMSprop

RMSprop: Divide the gradient by a running average of its recent magnitude. RmsProp [tieleman2012rmsprop] is an optimizer that utilizes the magnitude of recent gradients to normalize the gradients.

Keywords for this software

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References in zbMATH (referenced in 63 articles )

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  1. Cui, Tao; Wang, Ziming; Xiang, Xueshuang: An efficient neural network method with plane wave activation functions for solving Helmholtz equation (2022)
  2. Goda, Takashi; Hironaka, Tomohiko; Kitade, Wataru; Foster, Adam: Unbiased MLMC stochastic gradient-based optimization of Bayesian experimental designs (2022)
  3. Kim, Sehwan; Song, Qifan; Liang, Faming: Stochastic gradient Langevin dynamics with adaptive drifts (2022)
  4. Reiners, Malena; Klamroth, Kathrin; Heldmann, Fabian; Stiglmayr, Michael: Efficient and sparse neural networks by pruning weights in a multiobjective learning approach (2022)
  5. Schnürch, Simon; Korn, Ralf: Point and interval forecasts of death rates using neural networks (2022)
  6. Barakat, Anas; Bianchi, Pascal: Convergence and dynamical behavior of the ADAM algorithm for nonconvex stochastic optimization (2021)
  7. Barakat, Anas; Bianchi, Pascal; Hachem, Walid; Schechtman, Sholom: Stochastic optimization with momentum: convergence, fluctuations, and traps avoidance (2021)
  8. Chakraborty, Souvik: Transfer learning based multi-fidelity physics informed deep neural network (2021)
  9. Duruisseaux, Valentin; Schmitt, Jeremy; Leok, Melvin: Adaptive Hamiltonian variational integrators and applications to symplectic accelerated optimization (2021)
  10. Flori, Andrea; Regoli, Daniele: Revealing pairs-trading opportunities with long short-term memory networks (2021)
  11. 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)
  12. Gunnarsson, Björn Rafn; vanden Broucke, Seppe; Baesens, Bart; Óskarsdóttir, María; Lemahieu, Wilfried: Deep learning for credit scoring: do or don’t? (2021)
  13. Hyde, David A. B.; Bao, Michael; Fedkiw, Ronald: On obtaining sparse semantic solutions for inverse problems, control, and neural network training (2021)
  14. Jomaa, Hadi S.; Schmidt-Thieme, Lars; Grabocka, Josif: Dataset2Vec: learning dataset meta-features (2021)
  15. Kovachki, Nikola B.; Stuart, Andrew M.: Continuous time analysis of momentum methods (2021)
  16. Lakhmiri, Dounia; Digabel, Sébastien Le; Tribes, Christophe: HyperNOMAD. Hyperparameter optimization of deep neural networks using mesh adaptive direct search (2021)
  17. Liu, Yang; Roosta, Fred: Convergence of Newton-MR under inexact Hessian information (2021)
  18. Prazeres, Mariana; Oberman, Adam M.: Stochastic gradient descent with Polyak’s learning rate (2021)
  19. Tang, Xueying; Zhang, Susu; Wang, Zhi; Liu, Jingchen; Ying, Zhiliang: ProcData: an R package for process data analysis (2021)
  20. Wang, Yating; Deng, Wei; Lin, Guang: Bayesian sparse learning with preconditioned stochastic gradient MCMC and its applications (2021)

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