Adam

Adam: A Method for Stochastic Optimization. We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. The method is straightforward to implement, is computationally efficient, has little memory requirements, is invariant to diagonal rescaling of the gradients, and is well suited for problems that are large in terms of data and/or parameters. The method is also appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. The hyper-parameters have intuitive interpretations and typically require little tuning. Some connections to related algorithms, on which Adam was inspired, are discussed. We also analyze the theoretical convergence properties of the algorithm and provide a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework. Empirical results demonstrate that Adam works well in practice and compares favorably to other stochastic optimization methods. Finally, we discuss AdaMax, a variant of Adam based on the infinity norm.


References in zbMATH (referenced in 194 articles )

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  1. Abbasi, Babak; Babaei, Toktam; Hosseinifard, Zahra; Smith-Miles, Kate; Dehghani, Maryam: Predicting solutions of large-scale optimization problems via machine learning: a case study in blood supply chain management (2020)
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  5. Barbeiro, Sílvia; Lobo, Diogo: Learning stable nonlinear cross-diffusion models for image restoration (2020)
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  12. Chris Cummins, Zacharias V. Fisches, Tal Ben-Nun, Torsten Hoefler, Hugh Leather: ProGraML: Graph-based Deep Learning for Program Optimization and Analysis (2020) arXiv
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  14. Cui, Ying; He, Ziyu; Pang, Jong-Shi: Multicomposite nonconvex optimization for training deep neural networks (2020)
  15. Edward Ayers, Francisco Eiras, Majd Hawasly, Iain Whiteside: PaRoT: A Practical Framework for Robust Deep NeuralNetwork Training (2020) arXiv
  16. Erway, Jennifer B.; Griffin, Joshua; Marcia, Roummel F.; Omheni, Riadh: Trust-region algorithms for training responses: machine learning methods using indefinite Hessian approximations (2020)
  17. Fangzhou Xie: Pruned Wasserstein Index Generation Model and wigpy Package (2020) arXiv
  18. Gong, Maoguo; Pan, Ke; Xie, Yu; Qin, A. K.; Tang, Zedong: Preserving differential privacy in deep neural networks with relevance-based adaptive noise imposition (2020)
  19. Hayase, Tomohiro: Cauchy noise loss for stochastic optimization of random matrix models via free deterministic equivalents (2020)
  20. Hottung, André; Tanaka, Shunji; Tierney, Kevin: Deep learning assisted heuristic tree search for the container pre-marshalling problem (2020)

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