International business machines corporation (20240211727). LOCAL INTERPRETABILITY ARCHITECTURE FOR A NEURAL NETWORK simplified abstract
Contents
LOCAL INTERPRETABILITY ARCHITECTURE FOR A NEURAL NETWORK
Organization Name
international business machines corporation
Inventor(s)
LOCAL INTERPRETABILITY ARCHITECTURE FOR A NEURAL NETWORK - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240211727 titled 'LOCAL INTERPRETABILITY ARCHITECTURE FOR A NEURAL NETWORK
The patent application describes a computer-implemented process for training a neural network with multiple transform layers.
- Input data is transformed by each transform layer into output data.
- A neural-backed decision tree is generated for each transform layer.
- The process is repeated for all transform layers.
- A neural-backed decision tree map converts output data into interpretable words from a generative search domain.
Potential Applications: - Natural language processing - Data analysis and interpretation - Machine learning algorithms
Problems Solved: - Enhancing the interpretability of neural network outputs - Improving decision-making processes in complex data sets
Benefits: - Increased accuracy in data analysis - Enhanced understanding of neural network decisions - Improved transparency in machine learning models
Commercial Applications: Title: "Enhancing Data Interpretation with Neural Network Training" This technology could be used in industries such as finance, healthcare, and marketing for data analysis, pattern recognition, and predictive modeling.
Prior Art: Researchers in the field of machine learning and artificial intelligence have explored methods to improve the interpretability of neural networks, including decision trees and generative models.
Frequently Updated Research: Stay updated on advancements in neural network training techniques, interpretability in machine learning, and applications in various industries.
Questions about Neural Network Training: 1. How does this technology improve the interpretability of neural network outputs? 2. What are the potential real-world applications of this patent innovation?
Original Abstract Submitted
a computer-implemented process for training a neural network having a plurality of transform layers includes the following operations. input data for one transform layer of the plurality of transform layers is transformed by the one transform layer into output data. a neural-backed decision tree is generated for the transform layer, a neural-backed decision tree. the transforming and the generating are repeated for each of the plurality of transform layers. a neural-backed decision tree map for a particular one of the plurality of transform layers maps output data of the particular one of the plurality of transform layers into a list of interpretable words from a generative search domain of facts and evidence.
