TRANSFORMING SOURCE DISTRIBUTION TO TARGET DISTRIBUTION USING SOBOLEV DESCENT
First Claim
1. A system, comprising:
- a memory that stores computer executable components;
a processor that executes the computer executable components stored in the memory, wherein the computer executable components comprise;
a sampling component that receives a source distribution having a source sample and a target distribution having a target sample;
an optimizer component that employs a neural network to find a critic that dynamically discriminates between the source sample and the target sample, while constraining a gradient of the neural network; and
a morphing component that generates a first product distribution by morphing the source distribution along the gradient of the neural network to the target distribution.
1 Assignment
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Accused Products
Abstract
Systems, computer-implemented methods, and computer program products for transforming a source distribution to a target distribution. A system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise a sampling component that receives a source distribution having a source sample and a target distribution having a target sample. The computer executable components can further comprise an optimizer component that employs a neural network to find a critic that dynamically discriminates between the source sample and the target sample, while constraining a gradient of the neural network. The computer executable components can further comprise a morphing component that generates a first product distribution by morphing the source distribution along the gradient of the neural network to the target distribution.
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Citations
25 Claims
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1. A system, comprising:
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a memory that stores computer executable components; a processor that executes the computer executable components stored in the memory, wherein the computer executable components comprise; a sampling component that receives a source distribution having a source sample and a target distribution having a target sample; an optimizer component that employs a neural network to find a critic that dynamically discriminates between the source sample and the target sample, while constraining a gradient of the neural network; and a morphing component that generates a first product distribution by morphing the source distribution along the gradient of the neural network to the target distribution. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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9. A computer-implemented method, comprising:
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receiving, by a system operatively coupled to a processor, a source distribution having a source sample, and a target distribution having a target sample; employing, by the system operatively coupled to the processor, a neural network to find a critic that dynamically discriminates between the source sample and the target sample, while constraining a gradient of the neural network; and generating, by the system operatively coupled to the processor, a first product distribution by morphing the source distribution along the gradient of the neural network to the target distribution. - View Dependent Claims (10, 11, 12, 13, 14, 15, 16)
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17. A computer program product for transforming a source distribution to a target distribution using Sobolev Descent, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to:
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receive, by the processor, the source distribution having a source sample and the target distribution having a target sample; employ, by the processor, a neural network to find a critic that dynamically discriminates between the source sample and the target sample, while constraining a gradient of the neural network; and generate, by the processor, a first product distribution by morphing the source distribution along the gradient of the neural network to the target distribution. - View Dependent Claims (18, 19)
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20. A system, comprising:
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a memory that stores computer executable components; a processor that executes the computer executable components stored in the memory, wherein the computer executable components comprise; a sampling component that receives a source distribution having a source sample and a target distribution having a target sample; an optimizer component that optimizes a neural network, wherein the optimizer component; determines a critic that dynamically discriminates between the source sample and the target sample, iteratively optimizes the critic, and updates one or more parameters of the critic; and a morphing component that iteratively transports particles of the source distribution along a gradient of the target distribution to create a product distribution, wherein the product distribution is stored in the memory. - View Dependent Claims (21, 22)
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23. A computer-implemented method, comprising:
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receiving, by a sampling component operatively coupled to a processor, a source distribution having a source sample and a target distribution having a target sample; optimizing, by an optimizer component operatively coupled to the processor, a neural network, wherein optimizing comprises; determining a critic that dynamically discriminates between the source sample and the target sample, and iteratively updating the critic; and iteratively transporting, by a morphing component operatively coupled to the processor, one or more particles of the source distribution along a gradient of the target distribution to create a product distribution that is stored in a memory. - View Dependent Claims (24, 25)
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Specification