Huawei Technologies Co., Ltd. (20240273338). NEURAL NETWORK CONSTRUCTION METHOD AND APPARATUS simplified abstract

NEURAL NETWORK CONSTRUCTION METHOD AND APPARATUS

Organization Name

Huawei Technologies Co., Ltd.

Inventor(s)

Jiajun Wu of Beijing (CN)

Chengjian Sun of Beijing (CN)

Chenyang Yang of Beijing (CN)

Jian Wang of Hangzhou (CN)

Rong Li of Boulogne Billancourt (FR)

NEURAL NETWORK CONSTRUCTION METHOD AND APPARATUS - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240273338 titled 'NEURAL NETWORK CONSTRUCTION METHOD AND APPARATUS

The present disclosure pertains to methods for constructing neural networks. One example method involves generating parameters for a target neural network using a parameter generation network. The input of the parameter generation network includes information about the relative number of a neuron in the target neural network, which represents the neuron's relative location at a specific layer. The target neural network is then constructed based on these parameters.

• Parameters for a target neural network are generated using a parameter generation network.
• Input to the parameter generation network includes information about the relative number of a neuron in the target neural network.
• The relative number of the neuron indicates its position at a specific layer in the network.
• The target neural network is built using the generated parameters.

Potential Applications: - Artificial intelligence - Machine learning - Data analysis

Problems Solved: - Efficient construction of neural networks - Improved accuracy in neural network modeling

Benefits: - Enhanced performance in neural network applications - Streamlined network construction process

Commercial Applications: Title: "Optimized Neural Network Construction for Enhanced AI Applications" This technology can be utilized in industries such as: - Healthcare for medical diagnosis - Finance for fraud detection - Marketing for customer behavior analysis

Questions about Neural Network Construction: 1. How does the parameter generation network improve the efficiency of constructing neural networks? - The parameter generation network streamlines the process by generating parameters based on the relative number of neurons in the target network, optimizing network construction.

2. What are the key advantages of using this method for neural network construction? - This method enhances accuracy and efficiency in building neural networks, leading to improved performance in various applications.

Original Abstract Submitted

the present disclosure relates to neural network construction methods. one example method includes generating parameters of a target neural network based on a parameter generation network, where input of the parameter generation network includes information about a relative number of a neuron in the target neural network, the relative number of the neuron represents a relative location of the neuron at a first neural network layer, and the first neural network layer is a layer at which the neuron in the target neural network is located. the target neural network is constructed based on the parameters of the target neural network, where the parameters of the target neural network are generated by inputting the information about the relative number of the neuron into the target neural network.