Microsoft technology licensing, llc (20240256922). FAST ADAPTATION FOR DEEP LEARNING APPLICATION THROUGH BACKPROPAGATION simplified abstract
Contents
- 1 FAST ADAPTATION FOR DEEP LEARNING APPLICATION THROUGH BACKPROPAGATION
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 FAST ADAPTATION FOR DEEP LEARNING APPLICATION THROUGH BACKPROPAGATION - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Prior Art
- 1.11 Frequently Updated Research
- 1.12 Questions about 5G Inferencing
- 1.13 Original Abstract Submitted
FAST ADAPTATION FOR DEEP LEARNING APPLICATION THROUGH BACKPROPAGATION
Organization Name
microsoft technology licensing, llc
Inventor(s)
Ganesh Ananthanarayanan of Sammamish WA (US)
Yuanchao Shu of Kirkland WA (US)
FAST ADAPTATION FOR DEEP LEARNING APPLICATION THROUGH BACKPROPAGATION - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240256922 titled 'FAST ADAPTATION FOR DEEP LEARNING APPLICATION THROUGH BACKPROPAGATION
Simplified Explanation
The patent application describes a method for dynamically adjusting configuration settings for capturing content as input data for inferencing in a 5G telecommunication network using a deep neural network.
- The method involves determining the gradient of a change in inference data over a change in configuration settings and updating the configuration settings based on this gradient.
- The gradient is a combination of an input-configuration gradient and an inference-input gradient, indicating changes in input data and inference results, respectively.
Key Features and Innovation
- Dynamic adaptation of configuration settings for capturing content as input data for inferencing in a 5G network.
- Use of a deep neural network for inferencing.
- Calculation of gradients to update configuration settings based on changes in input data and inference results.
Potential Applications
This technology can be applied in various industries such as telecommunications, artificial intelligence, and edge computing.
Problems Solved
- Efficiently adapt configuration settings for capturing content in a 5G network.
- Improve inferencing accuracy using a deep neural network.
- Enhance the overall performance of edge computing systems.
Benefits
- Increased accuracy in inferencing results.
- Improved efficiency in capturing and processing input data.
- Enhanced performance of 5G telecommunication networks.
Commercial Applications
- Title: "Dynamic Configuration Setting Adaptation for 5G Inferencing"
- This technology can be utilized by telecommunications companies to enhance their 5G networks' performance.
- It can also be integrated into AI systems for more accurate inferencing results.
Prior Art
There may be prior research on dynamic configuration adaptation in edge computing and inferencing systems using neural networks. Researchers can explore academic journals and patent databases for relevant information.
Frequently Updated Research
Researchers are constantly exploring ways to optimize configuration settings for inferencing in 5G networks. Stay updated on the latest advancements in edge computing and deep learning technologies.
Questions about 5G Inferencing
How does dynamic configuration setting adaptation improve inferencing accuracy in 5G networks?
Dynamic adaptation allows for real-time adjustments to configuration settings based on changes in input data and inference results, leading to more accurate predictions.
What are the potential challenges in implementing this technology in real-world applications?
Challenges may include ensuring compatibility with existing network infrastructure, optimizing computational resources, and addressing privacy and security concerns.
Original Abstract Submitted
systems and methods are provided for dynamically adapting configuration setting associated with capturing content as input data for inferencing in the multi-access edge computing in a 5g telecommunication network. the inferencing is based on a use of a deep neural network. in particular, the method includes determining a gradient of a change in inference data over a change in configuration setting for capturing input data (the inference-configuration gradient). the method further updates the configuration setting based on the gradient of a change in inference data over a change in the configuration setting. the inference-configuration gradient is based on a combination of an input-configuration gradient and an inference-input gradient. the input-configuration gradient indicates a change in input data as the configuration setting value changes. the inference-input gradient indicates, as a saliency of the deep neural network, a change in inference result of the input data as the input data changes.
