Nvidia corporation (20240095534). NEURAL NETWORK PROMPT TUNING simplified abstract
Contents
- 1 NEURAL NETWORK PROMPT TUNING
NEURAL NETWORK PROMPT TUNING
Organization Name
Inventor(s)
Anima Anandkumar of Pasadena CA (US)
Weili Nie of Sunnyvale CA (US)
De-An Huang of Cupertino CA (US)
Zhiding Yu of Cupertino CA (US)
Manli Shu of Greenbelt MD (US)
NEURAL NETWORK PROMPT TUNING - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240095534 titled 'NEURAL NETWORK PROMPT TUNING
Simplified Explanation
The patent application describes apparatuses, systems, and techniques for performing neural networks, specifically selecting the most consistent output of pre-trained neural networks based on variances of inputs.
- Neural networks are pre-trained to perform specific tasks efficiently.
- The selection of the most consistent output is based on analyzing the variances of inputs.
- This technology aims to improve the accuracy and reliability of neural network outputs.
Potential Applications
This technology could be applied in various fields such as:
- Image recognition
- Natural language processing
- Autonomous vehicles
Problems Solved
- Enhances the accuracy of neural network outputs
- Reduces errors in decision-making processes
- Improves the overall performance of neural networks
Benefits
- Increased efficiency in processing data
- Enhanced reliability in decision-making
- Improved performance in complex tasks
Potential Commercial Applications
Optimizing neural network outputs can benefit industries such as:
- Healthcare for medical image analysis
- Finance for fraud detection
- Manufacturing for quality control
Possible Prior Art
Prior art may include:
- Research papers on neural network optimization techniques
- Patents related to improving neural network performance
Unanswered Questions
How does this technology compare to existing methods of selecting neural network outputs based on input variances?
This article does not provide a direct comparison with existing methods, leaving uncertainty about the specific advantages of this technology over others.
What are the potential limitations or challenges of implementing this technology in real-world applications?
The article does not address any potential obstacles or difficulties that may arise when implementing this technology, leaving room for speculation on its practicality and scalability.
Original Abstract Submitted
apparatuses, systems, and techniques to perform neural networks. in at least one embodiment, a most consistent output of one or more pre-trained neural networks is to be selected. in at least one embodiment, a most consistent output of one or more pre-trained neural networks is to be selected based, at least in part, on a plurality of variances of one or more inputs to the one or more neural networks.
