System and method for increasing efficiency of gradient descent while training machine-learning models

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United States of America Patent

PATENT NO 11763151
SERIAL NO

17405458

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Systems and methods of the present disclosure provide processes for determining how much to adjust machine-learning parameter values in a direction of a gradient for gradient-descent steps in training processes for machine-learning models. Current parameter values of a machine-learning model are vector components that define an initial estimate for a local extremum of a cost function used to measure how well the machine-learning model performs. The initial estimate and the gradient of the cost function for the initial estimate are used to define an auxiliary function. A root estimate is determined for the auxiliary function of the gradient. The parameters are adjusted in the direction of the gradient by an amount specified by the root estimate.

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  • INTUIT, INC.

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Inventor(s)

Inventor Name Address # of filed Patents Total Citations
Laaser, William T Mountain View, US 31 531

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