Google llc (20240112013). Generative Models for Discrete Datasets Constrained by a Marginal Distribution Specification simplified abstract
Contents
- 1 Generative Models for Discrete Datasets Constrained by a Marginal Distribution Specification
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 Generative Models for Discrete Datasets Constrained by a Marginal Distribution Specification - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
Generative Models for Discrete Datasets Constrained by a Marginal Distribution Specification
Organization Name
Inventor(s)
Hanjun Dai of San Jose CA (US)
Mengjiao Yang of Berkeley CA (US)
Dale Eric Schuurmans of Edmonton (CA)
Generative Models for Discrete Datasets Constrained by a Marginal Distribution Specification - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240112013 titled 'Generative Models for Discrete Datasets Constrained by a Marginal Distribution Specification
Simplified Explanation
The present disclosure pertains to generative models for datasets constrained by marginal constraints. One method involves generating a target dataset based on a marginal constraint for a source dataset, where the target dataset is generated by adapting a source generative model to meet the specified constraints.
- Receiving a request to generate a target dataset based on a marginal constraint for a source dataset
- Source dataset encodes co-occurrence frequencies for object pairs
- Accessing a source generative model with modules trained on the source dataset
- Updating the second module of the generative model based on the marginal constraint
- Generating an adapted generative model with the updated modules
- Generating the target dataset based on the adapted generative model
Potential Applications
This technology could be applied in various fields such as natural language processing, image generation, and recommendation systems where generating datasets with specific constraints is required.
Problems Solved
This technology solves the problem of generating datasets that adhere to specific marginal constraints, ensuring that the generated data meets the desired criteria.
Benefits
The benefits of this technology include the ability to generate datasets with tailored constraints, improving the quality and relevance of the generated data for specific applications.
Potential Commercial Applications
Potential commercial applications of this technology include data generation for machine learning models, content generation for media platforms, and personalized recommendation systems.
Possible Prior Art
One possible prior art for this technology could be the use of generative adversarial networks (GANs) for data generation with constraints. Another could be the use of conditional generative models for generating data based on specific criteria.
What are the limitations of this technology in terms of scalability and complexity?
The technology may face challenges in scaling up to large datasets or complex constraints due to computational limitations and training requirements.
How does this technology compare to existing methods for generating constrained datasets?
This technology offers a more flexible and adaptable approach to generating datasets with specific constraints compared to traditional methods, allowing for more precise control over the generated data.
Original Abstract Submitted
the present disclosure is directed to generative models for datasets constrained by marginal constraints. one method includes receiving a request to generate a target dataset based on a marginal constraint for a source dataset. a first object occurs at a source frequency in the source dataset. the marginal constraint indicates a target frequency for the first object. the source dataset encodes a set of co-occurrence frequencies for a plurality of object pairs. a source generative model is accessed. the source generative model includes a first module and a second module that are trained on the source dataset. the second module is updated based on the marginal constraint. an adapted generative model is generated that includes the first module and the updated second module. the target dataset is generated based on the adapted generative model. the first object occurs at the target frequency in the target dataset. the target dataset encodes the set of co-occurrence frequencies for the plurality of object pairs.
