References in zbMATH (referenced in 38 articles )

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  1. Barakat, Anas; Bianchi, Pascal: Convergence and dynamical behavior of the ADAM algorithm for nonconvex stochastic optimization (2021)
  2. Liu, Yang; Roosta, Fred: Convergence of Newton-MR under inexact Hessian information (2021)
  3. 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)
  4. Ciosek, Kamil; Whiteson, Shimon: Expected policy gradients for reinforcement learning (2020)
  5. Da Silva, Andre Belotto; Gazeau, Maxime: A general system of differential equations to model first-order adaptive algorithms (2020)
  6. Duan, Shiyu; Yu, Shujian; Chen, Yunmei; Principe, Jose C.: On kernel method-based connectionist models and supervised deep learning without backpropagation (2020)
  7. França, Guilherme; Sulam, Jeremias; Robinson, Daniel P.; Vidal, René: Conformal symplectic and relativistic optimization (2020)
  8. Geng, Zhenglin; Johnson, Daniel; Fedkiw, Ronald: Coercing machine learning to output physically accurate results (2020)
  9. Heber, Frederik; Trst’anová, Žofia; Leimkuhler, Benedict: Posterior sampling strategies based on discretized stochastic differential equations for machine learning applications (2020)
  10. He, Juanjuan; Xiang, Song; Zhu, Ziqi: A deep fully residual convolutional neural network for segmentation in EM images (2020)
  11. Jiang, Bo; Lin, Tianyi; Zhang, Shuzhong: A unified adaptive tensor approximation scheme to accelerate composite convex optimization (2020)
  12. Kang, Dongseok; Ahn, Chang Wook: Efficient neural network space with genetic search (2020)
  13. Karumuri, Sharmila; Tripathy, Rohit; Bilionis, Ilias; Panchal, Jitesh: Simulator-free solution of high-dimensional stochastic elliptic partial differential equations using deep neural networks (2020)
  14. Kylasa, Sudhir; Fang, Chih-Hao; Roosta, Fred; Grama, Ananth: Parallel optimization techniques for machine learning (2020)
  15. Lee, Kookjin; Carlberg, Kevin T.: Model reduction of dynamical systems on nonlinear manifolds using deep convolutional autoencoders (2020)
  16. Liu, Hailiang; Markowich, Peter: Selection dynamics for deep neural networks (2020)
  17. Marschall, Owen; Cho, Kyunghyun; Savin, Cristina: A unified framework of online learning algorithms for training recurrent neural networks (2020)
  18. Martens, James: New insights and perspectives on the natural gradient method (2020)
  19. Nguyen, Hoang; Ausín, M. Concepción; Galeano, Pedro: Variational inference for high dimensional structured factor copulas (2020)
  20. Palagi, Laura; Seccia, Ruggiero: Block layer decomposition schemes for training deep neural networks (2020)

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