References in zbMATH (referenced in 13 articles )

Showing results 1 to 13 of 13.
Sorted by year (citations)

  1. Palagi, Laura; Seccia, Ruggiero: Block layer decomposition schemes for training deep neural networks (2020)
  2. Chan, Shing; Elsheikh, Ahmed H.: Parametric generation of conditional geological realizations using generative neural networks (2019)
  3. Baydin, Atılım Güneş; Pearlmutter, Barak A.; Radul, Alexey Andreyevich; Siskind, Jeffrey Mark: Automatic differentiation in machine learning: a survey (2018)
  4. Bottou, Léon; Curtis, Frank E.; Nocedal, Jorge: Optimization methods for large-scale machine learning (2018)
  5. Chan, Shing; Elsheikh, Ahmed H.: A machine learning approach for efficient uncertainty quantification using multiscale methods (2018)
  6. Fischer, Thomas; Krauss, Christopher: Deep learning with long short-term memory networks for financial market predictions (2018)
  7. Lee, Seunghye; Ha, Jingwan; Zokhirova, Mehriniso; Moon, Hyeonjoon; Lee, Jaehong: Background information of deep learning for structural engineering (2018)
  8. Tripathy, Rohit K.; Bilionis, Ilias: Deep UQ: learning deep neural network surrogate models for high dimensional uncertainty quantification (2018)
  9. Mandt, Stephan; Hoffman, Matthew D.; Blei, David M.: Stochastic gradient descent as approximate Bayesian inference (2017)
  10. Mohammad Emtiyaz Khan, Zuozhu Liu, Voot Tangkaratt, Yarin Gal: Vprop: Variational Inference using RMSprop (2017) arXiv
  11. Sainbayar Sukhbaatar, Arthur Szlam, Gabriel Synnaeve, Soumith Chintala, Rob Fergus: MazeBase: A Sandbox for Learning from Games (2015) arXiv
  12. Schmidhuber, Jürgen: Deep learning in neural networks: an overview (2015) ioport
  13. Diederik P. Kingma, Jimmy Ba: Adam: A Method for Stochastic Optimization (2014) arXiv