View source for 18289100. NEURAL NETWORKS WITH ADAPTIVE STANDARDIZATION AND RESCALING simplified abstract (GOOGLE LLC)

=NEURAL NETWORKS WITH ADAPTIVE STANDARDIZATION AND RESCALING=


==Organization Name==

[[:Category:GOOGLE LLC|GOOGLE LLC]]


[[Category:GOOGLE LLC]]

==Inventor(s)==

[[:Category:Qifei Wang|Qifei Wang]][[Category:Qifei Wang]]

[[:Category:Junjie Ke|Junjie Ke]][[Category:Junjie Ke]]

[[:Category:Feng Yang|Feng Yang]][[Category:Feng Yang]]

[[:Category:Boqing Gong|Boqing Gong]][[Category:Boqing Gong]]

[[:Category:Xinjie Fan|Xinjie Fan]][[Category:Xinjie Fan]]

==NEURAL NETWORKS WITH ADAPTIVE STANDARDIZATION AND RESCALING - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18289100 titled 'NEURAL NETWORKS WITH ADAPTIVE STANDARDIZATION AND RESCALING

'''Simplified Explanation''': 

This patent application describes methods, systems, and apparatus for processing a network input using a neural network with a normalization block. The neural network includes layers that standardize data to generate adaptive standardization values, which are then used to standardize the input data for further processing.

* The patent application focuses on processing network inputs using a neural network with a normalization block.
* The neural network includes layers that standardize data to generate adaptive standardization values.
* These adaptive standardization values are used to standardize the input data for further processing.
* The normalization block plays a crucial role in ensuring the data is processed efficiently and accurately.
* The technology aims to improve the performance and accuracy of neural networks in processing network inputs.

'''Potential Applications''':

The technology described in this patent application has potential applications in various fields, including:

* Image and speech recognition systems
* Natural language processing applications
* Autonomous vehicles
* Healthcare diagnostics
* Financial forecasting models

'''Problems Solved''':

This technology addresses the following specific problems:

* Improving the efficiency and accuracy of neural networks in processing network inputs
* Enhancing the performance of machine learning models
* Standardizing data to ensure consistent processing results

'''Benefits''':

The benefits of this technology include:

* Improved accuracy in processing network inputs
* Enhanced performance of neural networks
* Consistent and reliable data standardization
* Increased efficiency in data processing tasks

'''Commercial Applications''':

Title: Enhanced Neural Network Processing for Improved Data Standardization

This technology has significant commercial potential in industries such as:

* Technology and software development companies
* Data analytics and machine learning firms
* Healthcare and medical imaging companies
* Financial services and investment firms
* Automotive and transportation industries

'''Questions about Enhanced Neural Network Processing for Improved Data Standardization''':

1. How does the normalization block in the neural network improve data processing efficiency?
2. What are the key advantages of using adaptive standardization values in standardizing input data?


==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.



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