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.

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References in zbMATH (referenced in 26 articles )

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  1. Borisyak, Maxim; Ryzhikov, Artem; Ustyuzhanin, Andrey; Derkach, Denis; Ratnikov, Fedor; Mineeva, Olga: ((1 + \varepsilon))-class classification: an anomaly detection method for highly imbalanced or incomplete data sets (2020)
  2. Ciosek, Kamil; Whiteson, Shimon: Expected policy gradients for reinforcement learning (2020)
  3. Da Silva, Andre Belotto; Gazeau, Maxime: A general system of differential equations to model first-order adaptive algorithms (2020)
  4. Duan, Shiyu; Yu, Shujian; Chen, Yunmei; Principe, Jose C.: On kernel method-based connectionist models and supervised deep learning without backpropagation (2020)
  5. He, Juanjuan; Xiang, Song; Zhu, Ziqi: A deep fully residual convolutional neural network for segmentation in EM images (2020)
  6. Jiang, Bo; Lin, Tianyi; Zhang, Shuzhong: A unified adaptive tensor approximation scheme to accelerate composite convex optimization (2020)
  7. Kang, Dongseok; Ahn, Chang Wook: Efficient neural network space with genetic search (2020)
  8. Kylasa, Sudhir; Fang, Chih-Hao; Roosta, Fred; Grama, Ananth: Parallel optimization techniques for machine learning (2020)
  9. Liu, Hailiang; Markowich, Peter: Selection dynamics for deep neural networks (2020)
  10. Marschall, Owen; Cho, Kyunghyun; Savin, Cristina: A unified framework of online learning algorithms for training recurrent neural networks (2020)
  11. Palagi, Laura; Seccia, Ruggiero: Block layer decomposition schemes for training deep neural networks (2020)
  12. Willmott, Devin; Murrugarra, David; Ye, Qiang: Improving RNA secondary structure prediction via state inference with deep recurrent neural networks (2020)
  13. Chan, Shing; Elsheikh, Ahmed H.: Parametric generation of conditional geological realizations using generative neural networks (2019)
  14. Baydin, Atılım Güneş; Pearlmutter, Barak A.; Radul, Alexey Andreyevich; Siskind, Jeffrey Mark: Automatic differentiation in machine learning: a survey (2018)
  15. Bottou, Léon; Curtis, Frank E.; Nocedal, Jorge: Optimization methods for large-scale machine learning (2018)
  16. Chan, Shing; Elsheikh, Ahmed H.: A machine learning approach for efficient uncertainty quantification using multiscale methods (2018)
  17. Fischer, Thomas; Krauss, Christopher: Deep learning with long short-term memory networks for financial market predictions (2018)
  18. Lee, Seunghye; Ha, Jingwan; Zokhirova, Mehriniso; Moon, Hyeonjoon; Lee, Jaehong: Background information of deep learning for structural engineering (2018)
  19. Tripathy, Rohit K.; Bilionis, Ilias: Deep UQ: learning deep neural network surrogate models for high dimensional uncertainty quantification (2018)
  20. Mandt, Stephan; Hoffman, Matthew D.; Blei, David M.: Stochastic gradient descent as approximate Bayesian inference (2017)

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