US 2021/0065007 A1 · NEURAL ADAPTER FOR CLASSICAL MACHINE LEARNING (ML) MODELS

Abstract

Solutions for adapting machine learning (ML) models to neural networks (NNs) include receiving an ML pipeline comprising a plurality of operators; determining operator dependencies within the ML pipeline; determining recognized operators; for each of at least two recognized operators, selecting a corresponding NN module from a translation dictionary; and wiring the selected NN modules in accordance with the operator dependencies to generate a translated NN. Some examples determine a starting operator for translation, which is the earliest recognized operator having parameters. Some examples connect inputs of the translated NN to upstream operators of the ML pipeline that had not been translated. Some examples further tune the translated NN using backpropagation. Some examples determine whether an operator is trainable or non-trainable and flag related parameters accordingly for later training. Some examples determine whether an operator has multiple corresponding NN modules within the translation dictionary and make an optimized selection.

Claims20

Dependent claims

A system for adapting machine learning (ML) models to neural networks (NNs), the system comprising:

a processor;
and
a computer-readable medium storing instructions that are operative upon execution by the processor to:
receive an ML pipeline comprising a plurality of operators;
determine operator dependencies within the ML pipeline;
determine a plurality of recognized operators within the plurality of operators;
for each of at least two recognized operators, select a corresponding NN module from a translation dictionary;
and
wire the selected NN modules in accordance with the operator dependencies to generate a translated NN.

6 more elements

One or more computer storage devices having computer-executable instructions stored thereon for adapting machine learning (ML) models to neural networks (NNs), which, on execution by a computer, cause the computer to perform operations comprising:

receiving an ML pipeline comprising a plurality of operators;
determining operator dependencies within the ML pipeline;
determining a plurality of recognized operators within the plurality of operators;
for each of at least two recognized operators, selecting a corresponding NN module from a translation dictionary;
and
wiring the selected NN modules in accordance with the operator dependencies to generate a translated NN.

3 more elements

Dependencies are inferred from the claim text.

Neural adapter for classical machine learning (ml) models

Life of this patent

Neural adapter for classical machine learning (ml) models

Life of this patent

Abstract

Claims20