Dell products l.p. (20240303491). EFFICIENT PARALLEL SEARCH FOR PRUNED MODEL IN EDGE ENVIRONMENTS simplified abstract
Contents
- 1 EFFICIENT PARALLEL SEARCH FOR PRUNED MODEL IN EDGE ENVIRONMENTS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 EFFICIENT PARALLEL SEARCH FOR PRUNED MODEL IN EDGE ENVIRONMENTS - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Prior Art
- 1.11 Frequently Updated Research
- 1.12 Questions about the Technology
- 1.13 Original Abstract Submitted
EFFICIENT PARALLEL SEARCH FOR PRUNED MODEL IN EDGE ENVIRONMENTS
Organization Name
Inventor(s)
Vinicius Michel Gottin of Rio de Janeiro (BR)
Paulo Abelha Ferreira of Rio de Janeiro (BR)
Pablo Nascimento Da Silva of Niterói (BR)
EFFICIENT PARALLEL SEARCH FOR PRUNED MODEL IN EDGE ENVIRONMENTS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240303491 titled 'EFFICIENT PARALLEL SEARCH FOR PRUNED MODEL IN EDGE ENVIRONMENTS
Simplified Explanation
The patent application describes a method for searching for a model, where source nodes generate pruned candidate models that are tested at generalization nodes in a distributed manner. The winning candidate model with the lowest loss score is then deployed to target nodes.
- Source nodes generate pruned candidate models.
- Central node receives pruned candidate models and their loss values.
- Pruned candidate models are tested at generalization nodes.
- Winning candidate model with lowest loss score is selected and deployed to target nodes.
Key Features and Innovation
- Generation of pruned candidate models from a distribution of models.
- Distributed testing of candidate models at generalization nodes.
- Selection of winning candidate model based on lowest loss score.
Potential Applications
The technology can be applied in various fields such as machine learning, artificial intelligence, and data analysis.
Problems Solved
The technology addresses the need for efficient model selection and deployment in distributed systems.
Benefits
- Improved model selection process.
- Enhanced efficiency in deploying models to target nodes.
Commercial Applications
The technology can be used in industries such as finance, healthcare, and e-commerce for optimizing data analysis and decision-making processes.
Prior Art
Readers can explore prior research on distributed model testing and selection methods in the field of machine learning and artificial intelligence.
Frequently Updated Research
Stay updated on the latest advancements in distributed model testing and selection techniques to enhance the efficiency of model deployment in various applications.
Questions about the Technology
How does the technology improve the model selection process?
The technology improves the model selection process by generating pruned candidate models and testing them in a distributed manner to select the best-performing model.
What are the potential applications of this technology in different industries?
The technology can be applied in industries such as finance, healthcare, and e-commerce for optimizing data analysis and decision-making processes.
Original Abstract Submitted
searching for a model is disclosed. source nodes are configured to generate pruned candidate models starting from a distribution of models. a central node receives the pruned candidate models and their associated loss values. the central mode causes the pruned candidate models to be tested in a distributed manner at generalization nodes. loss values returned to the central mode are associated with the pruned candidate models. the pruned candidate model with a lowest loss score, based on the distributed generalization testing, is selected as a winning candidate model and deployed to target nodes.
