Systems and methods to perform machine learning with feedback consistency
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United States of America Patent
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Nov 19, 2019
Grant Date -
Feb 1, 2018
app pub date -
Jul 29, 2016
filing date -
Jul 29, 2016
priority date (Note) -
In Force
status (Latency Note)
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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.
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- 1 Citation Count
- G06N Class
- 30.66 % this patent is cited more than
- 6 Age
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