Microsoft technology licensing, llc (20240112000). NEURAL GRAPHICAL MODELS simplified abstract

NEURAL GRAPHICAL MODELS

Organization Name

microsoft technology licensing, llc

Inventor(s)

Harsh Shrivastava of Redmond WA (US)

Urszula Stefania Chajewska of Issaquah WA (US)

NEURAL GRAPHICAL MODELS - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240112000 titled 'NEURAL GRAPHICAL MODELS

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 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 using neural network training for the neural view. 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 input data
• Representation of domain features using a neural network
• Learning functions for domain features using input graph structure
• Utilizing neural graphical model for inference and sampling tasks

Potential Applications

The technology can be applied in various fields such as:

• Machine learning
• Data analysis
• Pattern recognition
• Predictive modeling

Problems Solved

The technology helps in:

• Efficiently representing domain features
• Performing inference tasks accurately
• Conducting sampling tasks effectively

Benefits

The technology offers benefits such as:

• Improved accuracy in modeling
• Enhanced performance in inference and sampling tasks
• Increased efficiency in data analysis

Potential Commercial Applications

The technology can be utilized in industries like:

• Healthcare
• Finance
• E-commerce
• Marketing

Possible Prior Art

One possible prior art could be the use of neural networks for graphical modeling in machine learning applications.

What are the specific neural network architectures used in this technology?

The specific neural network architectures used in this technology are not explicitly mentioned in the abstract. Further details on the neural network structures employed for generating the neural graphical model would provide a clearer understanding of the innovation.

How does the dependency structure of the input graph impact the learning of functions for domain features?

The abstract mentions the utilization of a dependency structure of an input graph for learning functions of domain features. Exploring the specific mechanisms through which the dependency structure influences the learning process would enhance the comprehension of this aspect of the technology.

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 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 using neural network training for the neural view. 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.