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 273 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)
  2. Aggarwal, Charu C.: Linear algebra and optimization for machine learning. A textbook (2020)
  3. Agnelli, J. P.; Çöl, A.; Lassas, Matti; Murthy, Rashmi; Santacesaria, Matteo; Siltanen, Samuli: Classification of stroke using neural networks in electrical impedance tomography (2020)
  4. Akyildiz, Ömer Deniz; Crisan, Dan; Míguez, Joaquín: Parallel sequential Monte Carlo for stochastic gradient-free nonconvex optimization (2020)
  5. Ali Shahin Shamsabadi, Adria Gascon, Hamed Haddadi, Andrea Cavallaro: PrivEdge: From Local to Distributed Private Training and Prediction (2020) arXiv
  6. Anderson, Ross; Huchette, Joey; Ma, Will; Tjandraatmadja, Christian; Vielma, Juan Pablo: Strong mixed-integer programming formulations for trained neural networks (2020)
  7. Bacciu, Davide; Errica, Federico; Micheli, Alessio: Probabilistic learning on graphs via contextual architectures (2020)
  8. Baguer, Daniel Otero; Leuschner, Johannes; Schmidt, Maximilian: Computed tomography reconstruction using deep image prior and learned reconstruction methods (2020)
  9. Baines, W.; Kuchment, P.; Ragusa, J.: Deep learning for 2D passive source detection in presence of complex cargo (2020)
  10. Banert, Sebastian; Ringh, Axel; Adler, Jonas; Karlsson, Johan; Öktem, Ozan: Data-driven nonsmooth optimization (2020)
  11. Barbeiro, Sílvia; Lobo, Diogo: Learning stable nonlinear cross-diffusion models for image restoration (2020)
  12. Bertocchi, Carla; Chouzenoux, Emilie; Corbineau, Marie-Caroline; Pesquet, Jean-Christophe; Prato, Marco: Deep unfolding of a proximal interior point method for image restoration (2020)
  13. Boehmke, Brad; Greenwell, Brandon M.: Hands-on machine learning with R (2020)
  14. Bougie, Nicolas; Ichise, Ryutaro: Skill-based curiosity for intrinsically motivated reinforcement learning (2020)
  15. Cai, Wei; Li, Xiaoguang; Liu, Lizuo: A phase shift deep neural network for high frequency approximation and wave problems (2020)
  16. Chan, Shing; Elsheikh, Ahmed H.: Data-driven acceleration of multiscale methods for uncertainty quantification: application in transient multiphase flow in porous media (2020)
  17. Cheng, M.; Fang, F.; Pain, C. C.; Navon, I. M.: Data-driven modelling of nonlinear spatio-temporal fluid flows using a deep convolutional generative adversarial network (2020)
  18. Cheng, Xiang; Jin, Zhuo; Yang, Hailiang: Optimal insurance strategies: a hybrid deep learning Markov chain approximation approach (2020)
  19. 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)
  20. 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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