Microsoft technology licensing, llc (20240111988). NEURAL GRAPHICAL MODELS FOR GENERIC DATA TYPES simplified abstract
Contents
- 1 NEURAL GRAPHICAL MODELS FOR GENERIC DATA TYPES
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 NEURAL GRAPHICAL MODELS FOR GENERIC DATA TYPES - 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
NEURAL GRAPHICAL MODELS FOR GENERIC DATA TYPES
Organization Name
microsoft technology licensing, llc
Inventor(s)
Harsh Shrivastava of Redmond WA (US)
Urszula Stefania Chajewska of Issaquah WA (US)
NEURAL GRAPHICAL MODELS FOR GENERIC DATA TYPES - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240111988 titled 'NEURAL GRAPHICAL MODELS FOR GENERIC DATA TYPES
Simplified Explanation
The present disclosure relates to methods and systems for providing a neural graphical model. The methods and systems generate a neural view of the neural graphical model for a domain. The input data is generated from the domain and includes generic input data. The input data also includes a combination of different data types of input data. The neural view of the neural graphical model represents the functions of the different features of the domain using a neural network. The functions are learned for the features of the domain using a dependency structure of an input graph for the input data and the neural network. The methods and systems use the neural graphical model to perform inference tasks. The methods and systems also use the neural graphical model to perform sampling tasks.
- Neural graphical model generation for a domain
- Input data includes generic and different data types
- Neural view represents functions of domain features using a neural network
- Functions learned using dependency structure of input graph
- Utilized for inference and sampling tasks
Potential Applications
The technology could be applied in various fields such as:
- Machine learning
- Data analysis
- Pattern recognition
Problems Solved
The technology helps in:
- Efficiently representing domain features
- Performing inference tasks accurately
- Handling different data types in input data
Benefits
The benefits of this technology include:
- Improved accuracy in modeling domain features
- Enhanced performance in inference tasks
- Ability to handle diverse data types effectively
Potential Commercial Applications
The technology could be beneficial for industries such as:
- Healthcare
- Finance
- Marketing
Possible Prior Art
One possible prior art could be the use of neural networks for modeling complex data structures in various domains.
What are the specific industries that could benefit from this technology?
Industries such as healthcare, finance, and marketing could benefit from the technology due to its ability to handle diverse data types effectively and improve accuracy in modeling domain features.
How does this technology compare to traditional methods of graphical modeling?
This technology offers a more advanced approach by using neural networks to represent functions of domain features, allowing for more accurate modeling and efficient handling of different data types in input data compared to traditional methods of graphical modeling.
Original Abstract Submitted
the present disclosure relates to methods and systems for providing a neural graphical model. the methods and systems generate a neural view of the neural graphical model for a domain. the input data is generated from the domain and includes generic input data. the input data also includes a combination of different data types of input data. the neural view of the neural graphical model represents the functions of the different features of the domain using a neural network. the functions are learned for the features of the domain using a dependency structure of an input graph for the input data and the neural network. the methods and systems use the neural graphical model to perform inference tasks. the methods and systems also use the neural graphical model to perform sampling tasks.
