18401738. TRAINING METHOD AND APPARATUS FOR NEURAL NETWORK MODEL, AND DATA PROCESSING METHOD AND APPARATUS simplified abstract (Huawei Technologies Co., Ltd.)
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 18401738 titled 'TRAINING METHOD AND APPARATUS FOR NEURAL NETWORK MODEL, AND DATA PROCESSING METHOD AND APPARATUS
Simplified Explanation
The abstract of the patent application describes a method for training a neural network model with an expert network layer based on a second training data set to achieve a target neural network model.
- The training device determines the initial weight of the expert network based on a first word vector matrix.
- The first word vector matrix is obtained through training with a first training data set of the first service field.
Potential Applications
This technology 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 knowledge from specific service fields.
Benefits
The benefits of this technology include enhanced performance of neural network models, better utilization of expert knowledge, and improved results in specialized tasks.
Potential Commercial Applications
Potential commercial applications of this technology include developing advanced chatbots, improving search engine algorithms, and enhancing recommendation systems.
Possible Prior Art
One possible prior art could be the use of expert networks in neural network models for specific domains, but the method described in this patent application may have unique features or improvements.
Unanswered Questions
How does the initial weight determination process affect the overall performance of the neural network model?
The abstract mentions determining the initial weight of the expert network based on a first word vector matrix, but it does not elaborate on how this process impacts the final performance of the neural network model.
What are the specific characteristics of the first training data set that make it suitable for training the first word vector matrix?
The abstract mentions training the first word vector matrix based on a first training data set of the first service field, but it does not provide details on the specific characteristics or composition of this training data set.
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.
