17951889. Generative Models for Discrete Datasets Constrained by a Marginal Distribution Specification simplified abstract (GOOGLE LLC)
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 How does this technology compare to other data generation methods in terms of efficiency and accuracy?
- 1.11 What are the potential limitations or challenges of implementing this technology in real-world applications?
- 1.12 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 17951889 titled 'Generative Models for Discrete Datasets Constrained by a Marginal Distribution Specification
Simplified Explanation
The present disclosure describes generative models for datasets constrained by marginal constraints. One method involves generating a target dataset based on a marginal constraint for a source dataset. The source dataset encodes co-occurrence frequencies for object pairs, and a source generative model is accessed and adapted to generate the target dataset.
- Receiving a request to generate a target dataset based on a marginal constraint for a source dataset
- Updating a second module of the source generative model based on the marginal constraint
- Generating an adapted generative model that includes the updated second module
- Generating a 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 data synthesis for machine learning models.
Problems Solved
This technology solves the problem of generating target datasets that adhere to specific marginal constraints, allowing for more precise data generation in various applications.
Benefits
The benefits of this technology include improved data generation accuracy, enhanced model training, and the ability to generate datasets with specific constraints for research and development purposes.
Potential Commercial Applications
Potential commercial applications of this technology include data augmentation services, synthetic data generation tools for machine learning companies, and research tools for academics and scientists.
Possible Prior Art
One possible prior art for this technology could be the use of generative adversarial networks (GANs) for data generation with constraints. However, the specific method described in this disclosure may offer unique advantages and improvements over existing techniques.
Unanswered Questions
How does this technology compare to other data generation methods in terms of efficiency and accuracy?
This article does not provide a direct comparison with other data generation methods, leaving the reader to wonder about the relative performance of this technology.
What are the potential limitations or challenges of implementing this technology in real-world applications?
The article does not address potential limitations or challenges that may arise when implementing this technology in practical settings, leaving room for further exploration and discussion on this topic.
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.
