Samsung electronics co., ltd. (20240119712). METHOD AND APPARATUS FOR CORRECTING ERRORS IN OUTPUTS OF MACHINE LEARNING MODELS simplified abstract
Contents
- 1 METHOD AND APPARATUS FOR CORRECTING ERRORS IN OUTPUTS OF MACHINE LEARNING MODELS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 METHOD AND APPARATUS FOR CORRECTING ERRORS IN OUTPUTS OF MACHINE LEARNING MODELS - 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 AND APPARATUS FOR CORRECTING ERRORS IN OUTPUTS OF MACHINE LEARNING MODELS
Organization Name
Inventor(s)
Cristina Cornelio of Milton Keynes (GB)
Timothy Hospedales of London (GB)
Jan Stuehmer of Heidelberg (DE)
METHOD AND APPARATUS FOR CORRECTING ERRORS IN OUTPUTS OF MACHINE LEARNING MODELS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240119712 titled 'METHOD AND APPARATUS FOR CORRECTING ERRORS IN OUTPUTS OF MACHINE LEARNING MODELS
Simplified Explanation
The present techniques aim to reduce errors in machine learning models by resolving inconsistencies in potential outputs before final results are generated. This ensures that the final result adheres to a set of rules or constraints, including logical constraints, to prevent dangerous or poor user experiences.
- Machine learning models are used to generate outputs for various tasks.
- Errors in these outputs can occur due to inconsistencies or violations of rules.
- The techniques aim to identify and resolve these errors before final results are produced.
- By enforcing constraints, the risk of generating incorrect or unsafe outputs is minimized.
Potential Applications
The technology can be applied in various fields such as healthcare, finance, and autonomous vehicles to ensure accurate and reliable results from machine learning models.
Problems Solved
1. Reducing errors in machine learning outputs. 2. Ensuring compliance with rules and constraints in model outputs.
Benefits
1. Improved accuracy and reliability of machine learning results. 2. Enhanced user experience by preventing unsafe or incorrect outputs.
Potential Commercial Applications
The technology can be utilized in industries such as e-commerce, cybersecurity, and manufacturing to enhance the performance of machine learning systems.
Possible Prior Art
There may be prior art related to error detection and correction in machine learning models, but specific examples are not provided in this context.
Unanswered Questions
How does this technology impact the efficiency of machine learning models?
The article does not delve into the potential impact of these techniques on the efficiency of machine learning models. It would be interesting to explore whether the error resolution process adds any computational overhead or if it improves the overall efficiency of the models.
What are the limitations of this error resolution method in complex machine learning tasks?
The article does not address the potential limitations of the error resolution method, especially in complex machine learning tasks with multiple constraints. It would be valuable to understand the scalability and adaptability of these techniques in such scenarios.
Original Abstract Submitted
broadly speaking, embodiments of the present techniques provide a method for reducing errors in the outputs of machine learning, ml, models on a potential output of the models to resolve any inconsistencies before outputting a final result from the models. the final result respects a set of rules or constraints, which may include logical constraints. advantageously, this reduces the risk of a model outputting a result which violates some rules associated with the overall task of the model, which could be dangerous or provide a poor user experience.
