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 260 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. Anderson, Ross; Huchette, Joey; Ma, Will; Tjandraatmadja, Christian; Vielma, Juan Pablo: Strong mixed-integer programming formulations for trained neural networks (2020)
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  15. Cheng, Xiang; Jin, Zhuo; Yang, Hailiang: Optimal insurance strategies: a hybrid deep learning Markov chain approximation approach (2020)
  16. Cheung, Siu Wun; Chung, Eric T.; Efendiev, Yalchin; Gildin, Eduardo; Wang, Yating; Zhang, Jingyan: Deep global model reduction learning in porous media flow simulation (2020)
  17. Chris Cummins, Zacharias V. Fisches, Tal Ben-Nun, Torsten Hoefler, Hugh Leather: ProGraML: Graph-based Deep Learning for Program Optimization and Analysis (2020) arXiv
  18. Christoph Heindl, Lukas Brunner, Sebastian Zambal, Josef Scharinger: BlendTorch: A Real-Time, Adaptive Domain Randomization Library (2020) arXiv
  19. Ciosek, Kamil; Whiteson, Shimon: Expected policy gradients for reinforcement learning (2020)
  20. Cohen, William; Yang, Fan; Mazaitis, Kathryn Rivard: TensorLog: a probabilistic database implemented using deep-learning infrastructure (2020)

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