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|Abstract:||Preconditioned gradient methods are among the most general and powerful tools in optimization. However, preconditioning requires storing and manipulating prohibitively large matrices. We describe and analyze a new structure-aware preconditioning algorithm, called Shampoo, for stochastic optimization over tensor spaces. Shampoo maintains a set of preconditioning matrices, each of which operates on a single dimension, contracting over the remaining dimensions. We establish convergence guarantees in the stochastic convex setting, the proof of which builds upon matrix trace inequalities. Our experiments with state-of-the-art deep learning models show that Shampoo is capable of converging considerably faster than commonly used optimizers. Surprisingly, although it involves a more complex update rule, Shampoo’s runtime per step is comparable in practice to that of simple gradient methods such as SGD, AdaGrad, and Adam.|
|Citation:||Gupta, Vineet, Tomer Koren, and Yoram Singer. "Shampoo: Preconditioned Stochastic Tensor Optimization." In Proceedings of the 35th International Conference on Machine Learning 80 (2018): pp. 1842-1850.|
|Pages:||1842 - 1850|
|Type of Material:||Conference Article|
|Journal/Proceeding Title:||Proceedings of the 35th International Conference on Machine Learning|
|Version:||Final published version. Article is made available in OAR by the publisher's permission or policy.|
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