Microsoft technology licensing, llc (20240281643). NEURAL GRAPH REVEALERS simplified abstract

NEURAL GRAPH REVEALERS

Organization Name

microsoft technology licensing, llc

Inventor(s)

Harsh Shrivastava of Redmond WA (US)

Urszula Stefania Chajewska of Camano Island WA (US)

NEURAL GRAPH REVEALERS - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240281643 titled 'NEURAL GRAPH REVEALERS

The present disclosure involves recovering a sparse feature graph from input data containing samples and associated features using a fully connected neural network. The neural network learns a regression of the input data to determine direct connections between features while satisfying sparsity constraints. This regression helps recover a feature graph showing direct connections between input data features, which can be interactively presented for user exploration.

• Utilizes a fully connected neural network to learn a regression of input data
• Determines direct connections between features while satisfying sparsity constraints
• Recovers a feature graph indicating direct connections between input data features
• Enables interactive presentation for user navigation of the feature graph
• Provides insights into the input data and associated features through the graph

Potential Applications: - Data analysis and visualization - Pattern recognition and feature extraction - Machine learning and artificial intelligence

Problems Solved: - Identifying direct connections between features in input data - Handling sparse feature graphs efficiently - Enhancing user understanding of complex data relationships

Benefits: - Improved data interpretation and analysis - Enhanced visualization of feature connections - Facilitates decision-making based on data insights

Commercial Applications: Title: "Enhanced Data Visualization and Analysis Tool for Machine Learning Applications" This technology can be used in various industries such as finance, healthcare, and marketing for data analysis, pattern recognition, and predictive modeling. It can help businesses make informed decisions based on insights derived from complex data relationships.

Questions about the technology: 1. How does the neural network determine direct connections between features in the input data? 2. What are the potential limitations of using a fully connected neural network for recovering sparse feature graphs?

Original Abstract Submitted

the present disclosure relates to recovering a sparse feature graph based on input data having a collection of samples and associated features. in particular, the systems described herein utilize a fully connected neural network to learn a regression of the input data and determine direct connections between features of the input data while the neural network satisfies one or more sparsity constraints. this regression may be used to recover a feature graph indicating direct connections between the features of the input data. in addition, the feature graph may be presented via an interactive presentation that enables a user to navigate nodes and edges of the graph to gain insights of the input data and associated features.