Intel corporation (20240127124). SYSTEMS AND METHODS FOR AN ACCELERATED AND ENHANCED TUNING OF A MODEL BASED ON PRIOR MODEL TUNING DATA simplified abstract
Contents
- 1 SYSTEMS AND METHODS FOR AN ACCELERATED AND ENHANCED TUNING OF A MODEL BASED ON PRIOR MODEL TUNING DATA
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 SYSTEMS AND METHODS FOR AN ACCELERATED AND ENHANCED TUNING OF A MODEL BASED ON PRIOR MODEL TUNING DATA - 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
SYSTEMS AND METHODS FOR AN ACCELERATED AND ENHANCED TUNING OF A MODEL BASED ON PRIOR MODEL TUNING DATA
Organization Name
Inventor(s)
Michael Mccourt of San Francisco CA (US)
Ben Hsu of San Francisco CA (US)
Patrick Hayes of San Francisco CA (US)
Scott Clark of San Francisco CA (US)
SYSTEMS AND METHODS FOR AN ACCELERATED AND ENHANCED TUNING OF A MODEL BASED ON PRIOR MODEL TUNING DATA - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240127124 titled 'SYSTEMS AND METHODS FOR AN ACCELERATED AND ENHANCED TUNING OF A MODEL BASED ON PRIOR MODEL TUNING DATA
Simplified Explanation
The patent application describes a method for generating a joint model based on first and second subject models, selecting the joint model from a plurality of joint models after meeting a threshold, and providing tuning data associated with the joint model to a tuning session of a target model.
- Joint model generation based on first and second subject models
- Selection of joint model after meeting entropy data points threshold
- Providing tuning data to a target model tuning session
Potential Applications
The technology described in this patent application could be applied in various fields such as machine learning, data analysis, predictive modeling, and optimization algorithms.
Problems Solved
This technology helps in improving the accuracy and efficiency of models by generating joint models based on relationships between subject models and providing tuning data to enhance the performance of target models.
Benefits
The benefits of this technology include improved model accuracy, enhanced predictive capabilities, optimized tuning processes, and increased overall performance of machine learning algorithms.
Potential Commercial Applications
Potential commercial applications of this technology could include predictive analytics software, data science platforms, optimization tools, and machine learning services.
Possible Prior Art
One possible prior art for this technology could be related to ensemble learning methods in machine learning, where multiple models are combined to improve predictive performance.
Unanswered Questions
How does this technology compare to existing methods of model tuning and optimization?
This article does not provide a direct comparison with existing methods of model tuning and optimization, leaving the reader to wonder about the specific advantages and differences of this technology.
What are the specific industries or use cases where this technology would be most beneficial?
The article does not delve into the specific industries or use cases where this technology would be most beneficial, leaving the reader to speculate on its potential applications.
Original Abstract Submitted
disclosed examples including generating a joint model based on first and second subject models, the first and second subject models selected based on a relationship between the first and second subject models; selecting the joint model from a plurality of joint models after a determination that entropy data points of the joint model satisfy a threshold, the entropy data points based on multiple tuning trials of the joint model; and providing tuning data associated with the joint model to a tuning session of a target model.
