Training machine-trained models by directly specifying gradient elements

Abstract

A computer-implemented technique performs machine learning that bypasses the traditional design of loss functions. The technique includes receiving plural instances of gradient objective information. Each of the plural instances includes a particular combination of plural gradient elements. The technique produces plural sets of machine-trained parameter values using the plural respective instances of gradient objective information. The technique performs this operation based on the plural instances of gradient objective information as given, without calculating the plural instances of gradient objective information using loss functions. The technique then measures performance of the plural sets of machine-trained parameter values in an application system. Based on the measured performance, the technique provides output information that identifies a particular set of machine-trained parameter values that satisfies a prescribed test.