Improving SGD convergence by online linear regression of gradients in multiple statistically relevant directions

31 Jan 2019 Jarek Duda

Deep neural networks are usually trained with stochastic gradient descent (SGD), which minimizes objective function using very rough approximations of gradient, only averaging to the real gradient. Standard approaches like momentum or ADAM only consider a single direction, and do not try to model distance from extremum - neglecting valuable information from calculated sequence of gradients, often stagnating in some suboptimal plateau... (read more)

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