View source for Adobe Inc. (20240330682). SYSTEMS AND METHODS FOR GENERATING SYNTHETIC TABULAR DATA FOR MACHINE LEARNING AND OTHER APPLICATIONS simplified abstract

=SYSTEMS AND METHODS FOR GENERATING SYNTHETIC TABULAR DATA FOR MACHINE LEARNING AND OTHER APPLICATIONS=


==Organization Name==

[[:Category:Adobe Inc.|Adobe Inc.]]


[[Category:Adobe Inc.]]

==Inventor(s)==

[[:Category:Surgan Jandial of Noida Uttar Pradesh (IN)|Surgan Jandial of Noida Uttar Pradesh (IN)]][[Category:Surgan Jandial of Noida Uttar Pradesh (IN)]]

[[:Category:Siddarth Ramesh of Hyderabad Telangana (IN)|Siddarth Ramesh of Hyderabad Telangana (IN)]][[Category:Siddarth Ramesh of Hyderabad Telangana (IN)]]

[[:Category:Piyush Gupta of Noida Uttar Pradesh (IN)|Piyush Gupta of Noida Uttar Pradesh (IN)]][[Category:Piyush Gupta of Noida Uttar Pradesh (IN)]]

[[:Category:Gauri Gupta of Cambridge MA (US)|Gauri Gupta of Cambridge MA (US)]][[Category:Gauri Gupta of Cambridge MA (US)]]

[[:Category:Balaji Krishnamurthy of Noida Uttar Pradesh (IN)|Balaji Krishnamurthy of Noida Uttar Pradesh (IN)]][[Category:Balaji Krishnamurthy of Noida Uttar Pradesh (IN)]]

==SYSTEMS AND METHODS FOR GENERATING SYNTHETIC TABULAR DATA FOR MACHINE LEARNING AND OTHER APPLICATIONS - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240330682 titled 'SYSTEMS AND METHODS FOR GENERATING SYNTHETIC TABULAR DATA FOR MACHINE LEARNING AND OTHER APPLICATIONS

'''Simplified Explanation:'''
This patent application describes systems and methods for generating synthetic tabular data for machine learning and other applications. It involves training a variational autoencoder to learn inter-feature correlations in real tabular data, then using this information to train a generator model in a generative adversarial network (GAN) to create synthetic tabular data with similar correlations.

* Trains a variational autoencoder to learn inter-feature correlations in real tabular data.
* Uses the trained autoencoder to train a generator model in a GAN to generate synthetic tabular data.
* The synthetic data exhibits the same inter-feature correlation distribution as the real data.
* Involves receiving tabular data records, training machine learning models, and generating synthetic data based on learned correlations.

'''Key Features and Innovation:'''
- Utilizes a variational autoencoder to capture inter-feature correlations in tabular data.
- Employs a generative adversarial network (GAN) to generate synthetic tabular data.
- Mimics the correlation distribution of real data in the synthetic data.
- Enables the creation of large datasets for machine learning training.

'''Potential Applications:'''
This technology can be applied in various fields such as:
- Data augmentation for machine learning models.
- Privacy-preserving data sharing.
- Testing and validation of algorithms without access to real data.

'''Problems Solved:'''
- Overcoming limitations in generating diverse and realistic synthetic data.
- Addressing the need for large and diverse datasets for training machine learning models.
- Providing a solution for data privacy concerns in sharing sensitive information.

'''Benefits:'''
- Enhances the performance of machine learning models by providing diverse training data.
- Facilitates research and development in data-driven applications.
- Improves data privacy and security by enabling the use of synthetic data for testing and validation.

'''Commercial Applications:'''
Potential commercial applications include:
- Data science and analytics companies.
- Healthcare and finance industries for data analysis.
- Research institutions for algorithm development and testing.

'''Questions about Synthetic Tabular Data Generation:'''
1. How does the use of a variational autoencoder improve the generation of synthetic tabular data?
2. What are the key advantages of using a generative adversarial network (GAN) in this context?


==Original Abstract Submitted==

systems and methods for generating synthetic tabular data for machine learning and other applications are provided. in some embodiments, a variational autoencoder is trained to learn inter-feature correlations found in tabular data collected from real data sources. the trained variational autoencoder is used to train a generator model of a generative adversarial network (gan) to generate synthetic tabular data that exhibits the inter-feature correlation distribution found in the tabular data collected from real data sources. in some embodiments, processing devices perform operations comprising: receiving a set of tabular data records, each record comprising a plurality of features; training a first machine learning model using the tabular data records to learn correlations between the plurality of features; and training a second machine learning model, using the first machine learning model, to generate a synthetic tabular data records based at least on the one or more correlations between the plurality of features.



[[Category:G06N3/08]]
[[Category:G06N3/0455]]
[[Category:CPC_G06N3/08]]