18455717. MODEL OPTIMIZATION METHOD AND APPARATUS, ELECTRONIC DEVICE, COMPUTER-READABLE STORAGE MEDIUM, AND COMPUTER PROGRAM PRODUCT simplified abstract (Tencent Technology (Shenzhen) Company Limited)
Contents
- 1 MODEL OPTIMIZATION METHOD AND APPARATUS, ELECTRONIC DEVICE, COMPUTER-READABLE STORAGE MEDIUM, AND COMPUTER PROGRAM PRODUCT
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 MODEL OPTIMIZATION METHOD AND APPARATUS, ELECTRONIC DEVICE, COMPUTER-READABLE STORAGE MEDIUM, AND COMPUTER PROGRAM PRODUCT - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Original Abstract Submitted
MODEL OPTIMIZATION METHOD AND APPARATUS, ELECTRONIC DEVICE, COMPUTER-READABLE STORAGE MEDIUM, AND COMPUTER PROGRAM PRODUCT
Organization Name
Tencent Technology (Shenzhen) Company Limited
Inventor(s)
MODEL OPTIMIZATION METHOD AND APPARATUS, ELECTRONIC DEVICE, COMPUTER-READABLE STORAGE MEDIUM, AND COMPUTER PROGRAM PRODUCT - A simplified explanation of the abstract
This abstract first appeared for US patent application 18455717 titled 'MODEL OPTIMIZATION METHOD AND APPARATUS, ELECTRONIC DEVICE, COMPUTER-READABLE STORAGE MEDIUM, AND COMPUTER PROGRAM PRODUCT
Simplified Explanation
The patent application describes a method for adjusting a model in a project by encapsulating a model operator in a project model to obtain a super-model. The super-model has a dynamically variable space structure and is trained based on a configuration search space and the project model. The method aims to find an adjusted model corresponding to the project model by searching the convergence super-model.
- The method involves encapsulating a model operator in a project model to obtain a super-model.
- The super-model has a dynamically variable space structure.
- A configuration search space is determined based on the model operator and a control parameter.
- The super-model is trained using the configuration search space and the project model.
- A convergence super-model is obtained when a training end condition is reached.
- The convergence super-model is searched for an adjusted model corresponding to the project model.
Potential Applications
- This method can be applied in various fields where model adjustment is required, such as machine learning, data analysis, and optimization.
- It can be used to fine-tune models in complex projects to improve their performance and accuracy.
Problems Solved
- The method solves the problem of adjusting a model in a project by providing a systematic approach to encapsulate a model operator and train a super-model.
- It addresses the challenge of finding an adjusted model that corresponds to the project model by searching the convergence super-model.
Benefits
- The method allows for the adjustment of models in a project by encapsulating a model operator, providing flexibility in the model's structure.
- It enables the training of a super-model based on a configuration search space, allowing for optimization and fine-tuning.
- The method provides a systematic approach to finding an adjusted model corresponding to the project model, improving the overall performance and accuracy of the project.
Original Abstract Submitted
A model adjustment method includes: encapsulating a model operator in a project model to obtain a super-model corresponding to the project model, the model operator at least including: a network layer in the project model, the super-model being a model with a dynamically variable space structure; determining a configuration search space corresponding to the project model according to the model operator and a control parameter; training the super-model based on the configuration search space and the project model and obtaining a convergence super-model corresponding to the project model in response to that a training end condition is reached; and searching the convergence super-model for an adjusted model corresponding to the project model.
