17949721. NEURAL GRAPHICAL MODELS simplified abstract (Microsoft Technology Licensing, LLC)

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 17949721 titled 'NEURAL GRAPHICAL MODELS

Simplified Explanation

The present disclosure describes methods and systems for creating a neural graphical model that represents the functions of different features of a domain using a neural network. This model is generated for input data and is used for inference and sampling tasks.

• Neural graphical model represents functions of domain features using a neural network
• Functions are learned using a dependency structure of an input graph
• Neural network training is used to create the neural view of the model
• Model is used 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 complex relationships in data
• Performing inference tasks accurately
• Generating samples from the model effectively

Benefits

The benefits of this technology include:

• Improved accuracy in modeling complex data
• Enhanced performance in inference tasks
• Efficient sampling from the model

Potential Commercial Applications

The technology could be used in:

• Predictive analytics software
• Financial modeling tools
• Healthcare data analysis platforms

Possible Prior Art

One possible prior art for this technology could be:

• Existing neural network models for data analysis and prediction

What are the specific applications of this technology in the healthcare industry?

This technology could be used in healthcare for:

• Predictive modeling of patient outcomes
• Disease diagnosis based on complex data patterns

How does this technology compare to traditional statistical modeling methods?

This technology offers:

• More flexibility in modeling complex relationships
• Better performance in handling large datasets

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.