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Systems and methods to perform machine learning with feedback consistency

  • US 10,482,379 B2
  • Filed: 07/29/2016
  • Issued: 11/19/2019
  • Est. Priority Date: 07/29/2016
  • Status: Active Grant
First Claim
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1. 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.

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