Adam: A Method for Stochastic Optimization

22 Dec 2014 Diederik P. Kingma Jimmy Ba

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... (read more)

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METHOD TYPE
AdaMax
Stochastic Optimization
Adam
Stochastic Optimization