View source for Qualcomm incorporated (20240214578). REGULARIZING NEURAL NETWORKS WITH DATA QUANTIZATION USING EXPONENTIAL FAMILY PRIORS simplified abstract

=REGULARIZING NEURAL NETWORKS WITH DATA QUANTIZATION USING EXPONENTIAL FAMILY PRIORS=


==Organization Name==

[[:Category:qualcomm incorporated|qualcomm incorporated]]


[[Category:qualcomm incorporated]]

==Inventor(s)==

[[:Category:Thomas Alexander Ryder of San Diego CA (US)|Thomas Alexander Ryder of San Diego CA (US)]][[Category:Thomas Alexander Ryder of San Diego CA (US)]]

[[:Category:Muhammed Zeyd Coban of Carlsbad CA (US)|Muhammed Zeyd Coban of Carlsbad CA (US)]][[Category:Muhammed Zeyd Coban of Carlsbad CA (US)]]

[[:Category:Marta Karczewicz of San Diego CA (US)|Marta Karczewicz of San Diego CA (US)]][[Category:Marta Karczewicz of San Diego CA (US)]]

==REGULARIZING NEURAL NETWORKS WITH DATA QUANTIZATION USING EXPONENTIAL FAMILY PRIORS - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240214578 titled 'REGULARIZING NEURAL NETWORKS WITH DATA QUANTIZATION USING EXPONENTIAL FAMILY PRIORS

'''Simplified Explanation:'''
The patent application describes systems and techniques for processing video data using a neural network-based video encoder with quantization steps and an exponential-family prior.

* Neural network-based video encoder processes video data with quantization steps.
* Exponential-family prior is applied to the output of the first layer of the encoder.
* Total loss value is generated based on the loss value and the first layer output evaluation.
* The encoder is trained based on the total loss value.

'''Key Features and Innovation:'''
* Utilization of a neural network-based video encoder for processing video data.
* Incorporation of quantization steps in the video encoding process.
* Application of an exponential-family prior to enhance the output evaluation.
* Training the encoder based on the total loss value to improve performance.
* Overall, the innovation focuses on optimizing video data processing using advanced techniques.

'''Potential Applications:'''
The technology can be applied in various fields such as video streaming, surveillance systems, video editing software, and virtual reality applications.

'''Problems Solved:'''
* Efficient processing of video data.
* Improved video quality through advanced encoding techniques.
* Enhanced training methods for neural network-based video encoders.

'''Benefits:'''
* Higher quality video output.
* Faster video processing speeds.
* Enhanced performance of video encoding systems.
* Improved training processes for neural networks.

'''Commercial Applications:'''
The technology can be utilized in industries such as entertainment, security, education, and telecommunications for enhancing video processing capabilities and improving overall user experience.

'''Prior Art:'''
Researchers can explore prior art related to neural network-based video encoders, video processing techniques, and quantization methods in the field of video encoding.

'''Frequently Updated Research:'''
Stay updated on advancements in neural network-based video encoding, video processing algorithms, and training methods for improving video quality and efficiency.

'''Questions about Video Data Processing:'''
1. What are the key benefits of using a neural network-based video encoder for processing video data?
2. How does the application of an exponential-family prior enhance the output evaluation in video encoding systems?


==Original Abstract Submitted==

systems and techniques are described for processing video data. for instance, a process can include processing a frame of video data using a first layer of a neural network-based video encoder, the neural network-based video encoder performing at least one quantization step. the process can further include applying an exponential-family prior to an output of the first layer of the neural network-based video encoder to generate a first layer output evaluation, generating a total loss value for the neural network-based video encoder based on a sum of a loss value for the neural network-based video encoder and the first layer output evaluation, and training the neural network-based video encoder based on the total loss value.



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