Boe technology group co., ltd. (20240176992). METHOD, APPARATUS AND DEVICE FOR EXPLAINING MODEL AND COMPUTER STORAGE MEDIUM simplified abstract
Contents
- 1 METHOD, APPARATUS AND DEVICE FOR EXPLAINING MODEL AND COMPUTER STORAGE MEDIUM
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 METHOD, APPARATUS AND DEVICE FOR EXPLAINING MODEL AND COMPUTER STORAGE MEDIUM - 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 Original Abstract Submitted
METHOD, APPARATUS AND DEVICE FOR EXPLAINING MODEL AND COMPUTER STORAGE MEDIUM
Organization Name
boe technology group co., ltd.
Inventor(s)
METHOD, APPARATUS AND DEVICE FOR EXPLAINING MODEL AND COMPUTER STORAGE MEDIUM - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240176992 titled 'METHOD, APPARATUS AND DEVICE FOR EXPLAINING MODEL AND COMPUTER STORAGE MEDIUM
Simplified Explanation
The method described in the patent application involves explaining a model by transforming input samples, generating perturbation data, acquiring weights, training an explainable model, and obtaining an explanation result.
- Transforming input sample from original feature space to an embedding space
- Generating perturbation data set in the embedding space
- Acquiring weight of a neighborhood vector in the perturbation data set
- Transforming perturbation data set back to original feature space
- Acquiring an explainable model of a target data analysis model by training based on acquired data
- Obtaining an explanation result based on the explainable model
Potential Applications
This technology could be applied in the fields of machine learning, data analysis, and artificial intelligence for model explanation and interpretability.
Problems Solved
This technology helps in explaining complex models and making their decision-making process more transparent and understandable.
Benefits
The benefits of this technology include improved model transparency, better understanding of model decisions, and increased trust in AI systems.
Potential Commercial Applications
- Enhancing model interpretability in AI systems
- Improving decision-making processes in data analysis
Possible Prior Art
There may be prior art related to model explanation techniques in machine learning and data analysis, but specific examples are not provided in this article.
Unanswered Questions
How does this method compare to existing model explanation techniques in terms of accuracy and efficiency?
This article does not provide a direct comparison with existing model explanation techniques, so it is unclear how this method performs in comparison.
What are the limitations or constraints of implementing this method in real-world applications?
The article does not address any potential limitations or constraints that may arise when implementing this method in practical scenarios.
Original Abstract Submitted
disclosed is a method for explaining a model. the method comprises: transforming a target input sample from an original feature space to an embedding space; generating a perturbation data set in the embedding space; acquiring a weight of a neighborhood vector in the perturbation data set; after that, transforming the perturbation data set back to the original feature space; acquiring an explainable model of a target data analysis model by training based on the acquired data; and acquiring an explanation result based on the explainable model.
