US 10,482,379 B2Granted

Systems and methods to perform machine learning with feedback consistency

AssigneeGoogle Inc.

InventorsJason E. Holt, Marcello Mathias Herreshoff

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Abstract

The present disclosure provides systems and methods that enable training of an encoder model based on a decoder model that performs an inverse transformation relative to the encoder model. In one example, an encoder model can receive a first set of inputs and output a first set of outputs. The encoder model can be a neural network. The decoder model can receive the first set of outputs and output a second set of outputs. A loss function can describe a difference between the first set of inputs and the second set of outputs. According to an aspect of the present disclosure, the loss function can be sequentially backpropagated through the decoder model without modifying the decoder model and then through the encoder model while modifying the encoder model, thereby training the encoder model. Thus, an encoder model can be trained to have enforced consistency relative to the inverse decoder model.

Claims20

Dependent claims

A computer-implemented method to perform machine learning, the method comprising:

obtaining, by one or more computing devices, data descriptive of an encoder model that is configured to receive a first set of inputs and, in response to receipt of the first set of inputs, output a first set of outputs;
obtaining, by the one or more computing devices, data descriptive of a decoder model that is configured to receive the first set of outputs and, in response to receipt of the first set of outputs, output a second set of outputs;
determining, by the one or more computing devices, a loss function that describes a difference between the first set of inputs and the second set of outputs;
backpropagating, by the one or more computing devices, the loss function through the decoder model without modifying the decoder model;
and
after backpropagating, by the one or more computing devices, the loss function through the decoder model, continuing to backpropagate, by the one or more computing devices, the loss function through the encoder model to train the encoder model;
wherein continuing to backpropagate, by the one or more computing devices, the loss function through the encoder model to train the encoder model comprises adjusting, by the one or more computing devices, at least one weight included in the encoder model.

4 more elements

A computing system to perform machine learning, the computing system comprising:

at least one processor;
and
at least one tangible, non-transitory computer-readable medium that stores instructions that, when executed by the at least one processor, cause the computing system to:
obtain data descriptive of a model that comprises an encoder model and a decoder model, wherein the encoder model is configured to receive a first set of inputs and, in response to receipt of the first set of inputs, output a first set of outputs, and wherein the decoder model is configured to receive the first set of outputs and, in response to receipt of the first set of outputs, output a second set of outputs;
determine a loss function that describes a difference between the first set of inputs and the second set of outputs;
backpropagate the loss function through the decoder model without modifying the decoder model;
and
after backpropagating the loss function through the decoder model, continue to backpropagate the loss function through the encoder model while modifying the encoder model to train the encoder model.

5 more elements

Dependencies are inferred from the claim text.

Facts

Application: 15/222,997Filed 29 Jul 2016
Published: US 2018/0032871 A11 Feb 2018
Granted: 19 Nov 2019
Term ends: Not estimatedThe source records don’t support an estimate.

Classification6 codes

G06N 3/084
Learning methods: backpropagation, e.g. using gradient descent
G06N 3/045
Architecture, e.g. interconnection topology: combinations of networks
G06N 3/0455
Combinations of networks: auto-encoder networks; Encoder-decoder networks
G06N 3/0499
Architecture, e.g. interconnection topology: feedforward networks

Family

US 2018/0032871 A1Pre-grant publication · published 1 Feb 2018
US 10,482,379 B2This patent

From the US filing history. Foreign family members aren’t listed.

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