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Keyword: Adamax ×Clear all

3 claims from 1 paper

1. Computer Science › Stochastic Gradient Optimization Techniques

   Adam: A Method for Stochastic Optimization

   Kingma and Ba · arXiv (Cornell University) · 2014

   The paper introduces Adam, a first-order stochastic optimisation algorithm using adaptive moment estimates, with a convergence regret bound and empirical results comparing it favourably with other methods.

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   1. UncheckedThe authors describe Adam as simple to implement, efficient, light on memory, unaffected by diagonal gradient rescaling, and suited to large problems.“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.”
   2. UncheckedThe Adam method is described as suitable for objectives that change over time and for problems with very noisy or sparse gradients.“The method is also appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients.”
   3. UncheckedThe authors report that Adam performs well in practice and compares favourably with other stochastic optimisation methods in their experiments.“Empirical results demonstrate that Adam works well in practice and compares favorably to other stochastic optimization methods.”