Stochastic Gradient Made Stable: A Manifold Propagation Approach for Large-Scale Optimization

28 Jun 2015Yadong MuWei LiuWei Fan

Stochastic gradient descent (SGD) holds as a classical method to build large scale machine learning models over big data. A stochastic gradient is typically calculated from a limited number of samples (known as mini-batch), so it potentially incurs a high variance and causes the estimated parameters bounce around the optimal solution... (read more)

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