Google llc (20240232572). NEURAL NETWORKS WITH ADAPTIVE STANDARDIZATION AND RESCALING simplified abstract
Contents
NEURAL NETWORKS WITH ADAPTIVE STANDARDIZATION AND RESCALING
Organization Name
Inventor(s)
NEURAL NETWORKS WITH ADAPTIVE STANDARDIZATION AND RESCALING - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240232572 titled 'NEURAL NETWORKS WITH ADAPTIVE STANDARDIZATION AND RESCALING
Simplified Explanation: The patent application describes methods, systems, and apparatus for processing a network input using a neural network with a normalization block to generate a network output.
Key Features and Innovation:
- Neural network with a normalization block between two layers for processing network input.
- Utilization of adaptive standardization values to standardize the output of the first neural network layer.
- Improved processing of data through standardization neural network layers in the normalization block.
Potential Applications: This technology can be applied in various fields such as image recognition, natural language processing, and predictive analytics.
Problems Solved: The technology addresses the need for more efficient and accurate processing of network inputs using neural networks.
Benefits:
- Enhanced accuracy in generating network outputs.
- Improved performance in processing complex data.
- Increased efficiency in neural network operations.
Commercial Applications: Potential commercial applications include automated image recognition systems, intelligent chatbots, and advanced data analysis tools for businesses.
Prior Art: Prior research in neural network normalization techniques and adaptive standardization methods can provide insights into the development of this technology.
Frequently Updated Research: Stay updated on advancements in neural network normalization techniques and adaptive standardization methods to enhance the performance of this technology.
Questions about Neural Network Normalization: 1. How does the normalization block improve the processing of network inputs in a neural network? 2. What are the key benefits of using adaptive standardization values in standardizing neural network outputs?
Original Abstract Submitted
methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for processing a network input using a neural network to generate a network output. the neural network includes a normalization block that is between a first neural network layer and a second neural network layer in the neural network. processing the network input using the neural network comprises: receiving a first layer output from the first neural network layer; processing data derived from the first layer output using standardization neural network layers of the normalization block to generate one or more adaptive standardization values; standardizing the first layer output using the adaptive standardization values to generate a standardized first layer output; generating a normalization block output from the standardized first layer output; and providing the normalization block output as an input to the second neural network layer.
