Adam: A Method for Stochastic Optimization
Published as a conference paper at ICLR 2015ADAM: A Method FORSTOCHASTICOPTIMIZATIONDiederik P. Kingma*University of Amsterdam, Lei Ba University of introduceAdam, an algorithm for first-order gradient-based Optimization ofstochastic objective functions, based on adaptive estimates of lower-order mo-ments. 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 Method is also appropriate for non-stationary objectives and problems withvery noisy and/or sparse gradients.
Published as a conference paper at ICLR 2015 otherwise. The rst case only happens in the most severe case of sparsity: when a gradient has been zero at all timesteps except at the current timestep.
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