Adam: A Method for Stochastic Optimization
We propose Adam , a method for efcient stochastic optimization that only requires rst-order gra-dients with little memory requirement. The method computes individual adaptive learning rates for different parameters from estimates of rst and second moments of the gradients; the name Adam is derived from adaptive moment estimation.
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