18529620. NEURAL NETWORK METHOD AND APPARATUS simplified abstract (Samsung Electronics Co., Ltd.)
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 18529620 titled 'NEURAL NETWORK METHOD AND APPARATUS
Simplified Explanation
The patent application describes a neural network method and apparatus that involves determining the precision of a layer in the network based on the number of output classes it has, and processing new parameters for the layer with the determined precision.
- The method involves obtaining information about the layer in the memory, such as the number of output classes.
- Based on this information, the precision for the layer is determined proportionally to the number of output classes.
- New parameters for the layer are then processed with the determined precision based on the stored parameters.
Potential Applications
This technology could be applied in various fields such as image recognition, natural language processing, and autonomous driving systems.
Problems Solved
This technology helps in optimizing the precision of neural network layers based on the number of output classes, leading to improved performance and efficiency in machine learning tasks.
Benefits
The benefits of this technology include enhanced accuracy, faster processing speeds, and better utilization of computational resources in neural network operations.
Potential Commercial Applications
One potential commercial application of this technology could be in developing advanced AI systems for industries such as healthcare, finance, and e-commerce.
Possible Prior Art
Prior art in the field of neural networks and precision optimization techniques may include research papers, patents, and existing software tools that address similar issues.
What is the impact of this technology on the field of machine learning?
This technology could potentially revolutionize the field of machine learning by improving the efficiency and accuracy of neural network operations, leading to significant advancements in AI applications.
How does this technology compare to existing methods for precision optimization in neural networks?
This technology stands out by dynamically determining the precision of neural network layers based on the number of output classes, which could result in more tailored and optimized models compared to traditional fixed-precision approaches.
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.
