Samsung electronics co., ltd. (20240112030). NEURAL NETWORK METHOD AND APPARATUS simplified abstract
Contents
- 1 NEURAL NETWORK METHOD AND APPARATUS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 NEURAL NETWORK 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 Original Abstract Submitted
NEURAL NETWORK METHOD AND APPARATUS
Organization Name
Inventor(s)
Junhaeng Lee of Hwaseong-si (KR)
Seungwon Lee of Hwaseong-si (KR)
NEURAL NETWORK METHOD AND APPARATUS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240112030 titled 'NEURAL NETWORK METHOD AND APPARATUS
Simplified Explanation
The abstract describes a neural network method that involves determining the precision of a layer in the network based on the number of output classes, and processing new parameters for the layer with the determined precision.
- Neural network method for determining precision of a layer based on the number of output classes.
- Processing new parameters for the layer with the determined precision.
Potential Applications
This technology could be applied in various fields such as image recognition, natural language processing, and predictive analytics.
Problems Solved
This technology helps in optimizing the precision of neural network layers, leading to improved accuracy and efficiency in tasks such as classification and regression.
Benefits
The benefits of this technology include enhanced performance of neural networks, better classification results, and increased computational efficiency.
Potential Commercial Applications
Potential commercial applications of this technology include in industries such as healthcare (medical image analysis), finance (fraud detection), and e-commerce (recommendation systems).
Possible Prior Art
One possible prior art could be methods for determining precision in neural networks based on different parameters such as activation functions or network architectures.
What are the specific parameters used to determine the precision of the layer in the neural network method described in the abstract?
The specific parameters used to determine the precision of the layer are the number of output classes of the layer, which is obtained from the information stored in memory.
How does the precision of the layer affect the processing of new parameters in the neural network method?
The precision of the layer, determined based on the number of output classes, proportionally influences the processing of new parameters with a set precision, ensuring optimal performance of the neural network.
Original Abstract Submitted
a neural network method and apparatus is provided. a processor-implemented neural network method includes a processor and a memory storing information, including stored predetermined precision parameters of a layer of a n neural network, about the layer, the method includes obtaining information about the layer in the memory indicative of the number of output classes; determining, based on the obtained information, a precision for the layer based on the number of output classes of the layer, wherein the precision is determined proportionally with respect to the obtained number of output classes; and processing new parameters, with a set precision, for the layer based on the stored parameter.
