17949710. NEURAL GRAPHICAL MODELS FOR GENERIC DATA TYPES simplified abstract (Microsoft Technology Licensing, LLC)
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 Unanswered Questions
- 1.11 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 17949710 titled 'NEURAL GRAPHICAL MODELS FOR GENERIC DATA TYPES
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. The model is generated from input data that includes generic input data and a combination of different data types. The neural view of the model is used for inference and sampling tasks.
- Neural graphical model generation:
- Utilizes input data from a domain - Includes generic input data and different data types - Represents domain features using a neural network
- Inference and sampling tasks:
- Utilize the neural graphical model - Perform tasks based on learned functions of domain features
Potential Applications
The technology can be applied in various fields such as: - Machine learning - Data analysis - Pattern recognition
Problems Solved
- Efficient representation of domain features - Improved inference and sampling tasks - Handling different data types effectively
Benefits
- Enhanced modeling capabilities - Better performance in inference tasks - Increased accuracy in sampling tasks
Potential Commercial Applications
- Data analytics software - AI-driven decision-making tools - Predictive modeling platforms
Possible Prior Art
One possible prior art could be the use of neural networks for modeling complex data structures in machine learning applications.
Unanswered Questions
1. How does the neural graphical model handle noisy input data? 2. What are the limitations of the neural network in representing complex domain features accurately?
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.
