Huawei technologies co., ltd. (20240135176). TRAINING METHOD AND APPARATUS FOR NEURAL NETWORK MODEL, AND DATA PROCESSING METHOD AND APPARATUS simplified abstract
Contents
- 1 TRAINING METHOD AND APPARATUS FOR NEURAL NETWORK MODEL, AND DATA PROCESSING METHOD AND APPARATUS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 TRAINING METHOD AND APPARATUS FOR NEURAL NETWORK MODEL, AND DATA PROCESSING METHOD AND APPARATUS - 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 Unanswered Questions
- 1.11 Original Abstract Submitted
TRAINING METHOD AND APPARATUS FOR NEURAL NETWORK MODEL, AND DATA PROCESSING METHOD AND APPARATUS
Organization Name
Inventor(s)
Qingchun Meng of Shenzhen (CN)
TRAINING METHOD AND APPARATUS FOR NEURAL NETWORK MODEL, AND DATA PROCESSING METHOD AND APPARATUS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240135176 titled 'TRAINING METHOD AND APPARATUS FOR NEURAL NETWORK MODEL, AND DATA PROCESSING METHOD AND APPARATUS
Simplified Explanation
The abstract describes a neural network training method where a training device trains a neural network model based on a second training data set to obtain a target neural network model. The neural network model includes an expert network layer with a first expert network of a first service field. The training device determines an initial weight of the first expert network based on a first word vector matrix and obtains the first word vector matrix through training based on a first training data set of the first service field.
- Neural network training method with expert network layer
- Training device trains neural network model based on second training data set
- Expert network of a specific service field
- Initial weight determined by first word vector matrix
- First word vector matrix obtained through training on first training data set
Potential Applications
The technology described in this patent application could be applied in various fields such as natural language processing, machine translation, and sentiment analysis.
Problems Solved
This technology helps in improving the accuracy and efficiency of neural network models by incorporating expert networks specific to different service fields.
Benefits
The benefits of this technology include enhanced performance of neural network models, better understanding of domain-specific data, and improved overall results in various applications.
Potential Commercial Applications
The potential commercial applications of this technology include developing advanced AI systems for language processing, creating specialized recommendation engines, and enhancing customer service chatbots.
Possible Prior Art
One possible prior art for this technology could be the use of domain-specific expert networks in neural network models for improving performance and accuracy in specialized tasks.
Unanswered Questions
How does the initial weight determination process impact the overall training of the neural network model?
The abstract mentions that the initial weight of the first expert network is determined based on a first word vector matrix. It would be interesting to know how this initial weight impacts the training process and the final performance of the neural network model.
What are the specific characteristics of the first service field that make it suitable for training the first word vector matrix?
The abstract mentions that the first word vector matrix is obtained through training based on a first training data set of the first service field. It would be helpful to understand the specific characteristics of this service field that make it ideal for training the word vector matrix.
Original Abstract Submitted
in a neural network training method, a training device trains a neural network model based on a second training data set to obtain a target neural network model. the neural network model includes an expert network layer, which includes a first expert network of a first service field. the training device determines an initial weight of the first expert network based on a first word vector matrix, and obtains the first word vector matrix through training based on a first training data set of the first service field.
