Sample generation based on joint probability distribution
AssigneeQUALCOMM Incorporated
InventorsZisis Iason SKORDILIS, Vivek RAJENDRAN, Tushar AGARWAL
Life of this patent
Estimating the term…
Abstract
A device includes a neural network and a sample generator. The neural network is configured to process one or more neural network inputs to generate a joint probability distribution. The one or more neural network inputs include at least first previous sample data and second previous sample data associated with at least one previous data sample of a sequence of data samples. The sample generator is configured to generate first sample data and second sample data based on the joint probability distribution. The first sample data and the second sample data are associated with at least one data sample of the sequence of data samples.
Claims27
A device comprising:
- a neural network configured to process one or more neural network inputs to generate a joint probability distribution, the one or more neural network inputs including at least first previous sample data and second previous sample data associated with at least one previous data sample of a sequence of data samples;
- and
- a sample generator configured to generate first sample data and second sample data based on the joint probability distribution, the first sample data and the second sample data associated with at least one data sample of the sequence of data samples.
(canceled)
-6. (canceled)
-23. (canceled)
-26. (canceled)
(canceled)
A method comprising:
- processing one or more neural network inputs using a neural network to generate a joint probability distribution, the one or more neural network inputs including at least first previous sample data and second previous sample data associated with at least one previous data sample of a sequence of data samples;
- and
- generating first sample data and second sample data based on the joint probability distribution, the first sample data and the second sample data associated with at least one data sample of the sequence of data samples.
(canceled)
A non-transitory computer-readable medium storing instructions that, when executed by one or more processors, cause the one or more processors to:
- process one or more neural network inputs using a neural network to generate a joint probability distribution, the one or more neural network inputs including at least first previous sample data and second previous sample data associated with at least one previous data sample of a sequence of data samples;
- and
- generate first sample data and second sample data based on the joint probability distribution, the first sample data and the second sample data associated with at least one data sample of the sequence of data samples.
-34. (canceled)
Dependencies are inferred from the claim text.