Abstract:Optimization algorithms with momentum have been widely used for building deep learning models because of the fast convergence rate. Momentum helps accelerate Stochastic gradient descent in relevant directions in parameter updating, minifying the oscillations of the parameters update route. The gradient of each step in optimization algorithms with momentum is calculated by a part of the training samples, so there exists stochasticity, which may bring errors to parameter updates. In this case, momentum placing t… Show more
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