18397909. SYSTEMS AND METHODS FOR AN ACCELERATED AND ENHANCED TUNING OF A MODEL BASED ON PRIOR MODEL TUNING DATA simplified abstract (Intel Corporation)
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 18397909 titled 'SYSTEMS AND METHODS FOR AN ACCELERATED AND ENHANCED TUNING OF A MODEL BASED ON PRIOR MODEL TUNING DATA
Simplified Explanation
The patent application abstract describes a method for generating a joint model based on first and second subject models, selecting the joint model after meeting certain criteria, and providing tuning data associated with the joint model to a tuning session of a target model.
- Joint model generated based on first and second subject models
- Selection of joint model from multiple options meeting entropy data point threshold
- Tuning data provided to tuning session of target model
Potential Applications
This technology could be applied in various fields such as machine learning, data analysis, and predictive modeling.
Problems Solved
This technology helps in improving the accuracy and efficiency of models by selecting the best joint model based on specific criteria.
Benefits
The benefits of this technology include enhanced model performance, optimized tuning processes, and improved overall predictive capabilities.
Potential Commercial Applications
One potential commercial application of this technology could be in the development of advanced predictive analytics software for industries such as finance, healthcare, and marketing.
Possible Prior Art
One possible prior art could be the use of ensemble modeling techniques in machine learning to improve model performance and accuracy.
Unanswered Questions
How does this technology compare to existing model selection methods in terms of efficiency and accuracy?
This article does not provide a direct comparison to existing model selection methods, leaving the reader to wonder about the specific advantages of this technology over traditional approaches.
What are the specific criteria used to determine the threshold for entropy data points in selecting the joint model?
The article does not delve into the exact criteria or methodology behind setting the threshold for entropy data points, leaving a gap in understanding the decision-making process behind this aspect of the technology.
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.
