Google llc (20240256862). NOISE SCHEDULING FOR DIFFUSION NEURAL NETWORKS simplified abstract
Contents
NOISE SCHEDULING FOR DIFFUSION NEURAL NETWORKS
Organization Name
Inventor(s)
Ting Chen of Mountain View CA (US)
NOISE SCHEDULING FOR DIFFUSION NEURAL NETWORKS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240256862 titled 'NOISE SCHEDULING FOR DIFFUSION NEURAL NETWORKS
Simplified Explanation: The patent application describes methods, systems, and apparatus for generating a network output using a diffusion neural network and training the network with a modified noise scheduling strategy.
Key Features and Innovation:
- Utilizes a diffusion neural network for generating network output.
- Implements a modified noise scheduling strategy for training the network.
- Includes computer programs encoded on computer storage media for execution.
Potential Applications: The technology can be applied in various fields such as image recognition, natural language processing, and financial forecasting.
Problems Solved: Addresses the challenge of efficiently generating network output and training neural networks with improved strategies.
Benefits: Enhances the performance and accuracy of neural networks, leading to more reliable results in various applications.
Commercial Applications: The technology can be utilized in industries such as healthcare, finance, and marketing for data analysis and prediction tasks.
Prior Art: Researchers can explore existing literature on diffusion neural networks and noise scheduling strategies in the field of artificial intelligence.
Frequently Updated Research: Stay updated on advancements in diffusion neural networks and noise scheduling techniques for neural network training.
Questions about the Technology: 1. How does the modified noise scheduling strategy improve the training of diffusion neural networks? 2. What are the potential limitations of using diffusion neural networks for generating network output?
Original Abstract Submitted
methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a network output using a diffusion neural network and for training a diffusion neural network with a modified noise scheduling strategy.
