Nvidia corporation (20240185034). GENERATING GLOBAL HIERARCHICAL SELF-ATTENTION simplified abstract
Contents
- 1 GENERATING GLOBAL HIERARCHICAL SELF-ATTENTION
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 GENERATING GLOBAL HIERARCHICAL SELF-ATTENTION - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Unanswered Questions
- 1.11 Original Abstract Submitted
GENERATING GLOBAL HIERARCHICAL SELF-ATTENTION
Organization Name
Inventor(s)
Ali Hatamizadeh of Los Angeles CA (US)
Gregory Heinrich of Aix-en-Provence (FR)
Hongxu Yin of San Jose CA (US)
Jose Manuel Alvarez Lopez of Mountain View CA (US)
Jan Kautz of Lexington MA (US)
Pavlo Molchanov of Mountain View CA (US)
GENERATING GLOBAL HIERARCHICAL SELF-ATTENTION - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240185034 titled 'GENERATING GLOBAL HIERARCHICAL SELF-ATTENTION
Simplified Explanation
The patent application describes the use of machine learning processes, such as neural networks, to process data using hierarchical self-attention. In one embodiment, image data is classified using hierarchical self-attention generated using carrier tokens associated with windowed subregions of the image data, along with local attention generated using local tokens within the windowed subregions and the carrier tokens.
- Explanation of the patent/innovation:
- Utilizes machine learning processes like neural networks for data processing
- Hierarchical self-attention is used for classification of image data
- Carrier tokens are associated with windowed subregions of the image data
- Local attention is generated using local tokens within the windowed subregions and carrier tokens
Potential Applications
This technology could be applied in various fields such as image recognition, object detection, and natural language processing.
Problems Solved
This technology helps in improving the accuracy and efficiency of data classification tasks, especially in complex datasets like images.
Benefits
- Enhanced accuracy in data classification
- Improved efficiency in processing large datasets
- Versatile application in different domains
Potential Commercial Applications
Optimized Data Processing using Hierarchical Self-Attention in Image Classification
Possible Prior Art
There may be prior art related to the use of self-attention mechanisms in machine learning processes for data classification tasks.
Unanswered Questions
How does this technology compare to traditional methods of data processing?
This article does not provide a direct comparison between this technology and traditional methods of data processing.
Are there any limitations or challenges associated with implementing this technology?
The article does not address any potential limitations or challenges that may arise in the implementation of this technology.
Original Abstract Submitted
apparatuses, systems, and techniques of using one or more machine learning processes (e.g., neural network(s)) to process data (e.g., using hierarchical self-attention). in at least one embodiment, image data is classified using hierarchical self-attention generated using carrier tokens that are associated with windowed subregions of the image data, and local attention generated using local tokens within the windowed subregions and the carrier tokens.
