Data reconstruction using machine-learning predictive coding

A method comprising:

• generating a first reconstructed data sample corresponding to a reconstructed version of a first data sample in a time series of data samples of a portion of a data stream;
• generating a second reconstructed data sample corresponding to a reconstructed version of a second data sample in the time series of data samples;
• and
• providing the first reconstructed data sample and the second reconstructed data sample as inputs to a neural network, the neural network configured to use machine-learning predictive coding to generate a network-predicted data sample, the network-predicted data sample corresponding to a predicted version of a particular data sample in the time series of data samples, and the particular data sample positioned between the first data sample and the second data sample.

A non-transitory computer-readable medium comprising instructions that, when executed by one or more processors, cause the one or more processors to:

• generate a first reconstructed data sample corresponding to a reconstructed version of a first data sample in a time series of data samples of a portion of a data stream;
• generate a second reconstructed data sample corresponding to a reconstructed version of a second data sample in the time series of data samples;
• and
• provide the first reconstructed data sample and the second reconstructed data sample as inputs to a neural network, the neural network configured to use machine-learning predictive coding to generate a network-predicted data sample, the network-predicted data sample corresponding to a predicted version of a particular data sample in the time series of data samples, and the particular data sample positioned between the first data sample and the second data sample.