CPC G06N 3/084 (2013.01) [G06N 3/04 (2013.01)] | 20 Claims |
1. A method of training a machine learning model having a plurality of model parameters and configured to receive a model input and to process the model input in accordance with the model parameters to generate a model output based on the model input, the method comprising:
obtaining a training input and a corresponding ground truth output;
processing the training input using the machine learning model and in accordance with current values of the model parameters to generate a training output based on the training input;
computing a loss for the training output by evaluating an objective function that measures a difference between the training output and the ground truth output, wherein the objective function is composed of a base loss and a link function; and
determining an update to current values of the model parameters, comprising:
determining, with respect to the model parameters, a first partial gradient of the loss with respect to the base loss and a second partial gradient of the loss with respect to the link function;
regularizing the first partial gradient of the loss to generate a regularized first partial gradient of the loss;
generating a recomposition of the regularized partial first gradient of the loss and the second partial gradient of the loss; and
computing the update from the generated recomposition.
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