Qualcomm incorporated (20240160896). PROPAGATING ATTENTION INFORMATION IN EFFICIENT MACHINE LEARNING MODELS simplified abstract
Contents
- 1 PROPAGATING ATTENTION INFORMATION IN EFFICIENT MACHINE LEARNING MODELS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 PROPAGATING ATTENTION INFORMATION IN EFFICIENT MACHINE LEARNING MODELS - 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 Original Abstract Submitted
PROPAGATING ATTENTION INFORMATION IN EFFICIENT MACHINE LEARNING MODELS
Organization Name
Inventor(s)
Shashanka Venkataramanan of Amsterdam (NL)
Amir Ghodrati of Amsterdam (NL)
Amirhossein Habibian of Amsterdam (NL)
PROPAGATING ATTENTION INFORMATION IN EFFICIENT MACHINE LEARNING MODELS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240160896 titled 'PROPAGATING ATTENTION INFORMATION IN EFFICIENT MACHINE LEARNING MODELS
Simplified Explanation
The present disclosure relates to techniques for improved attention-based machine learning using transformer blocks.
- The first transformer block processes input data using a self-attention sub-block to generate a first attention propagation output.
- The first attention propagation output is then passed to a second transformer block for further processing.
- The output features for the second transformer block are generated based on the first attention propagation output.
Potential Applications
This technology can be applied in various fields such as natural language processing, image recognition, and speech recognition.
Problems Solved
This technology helps improve the efficiency and accuracy of machine learning models by enhancing attention mechanisms.
Benefits
The benefits of this technology include improved performance of machine learning models, better understanding of relationships in data, and increased scalability.
Potential Commercial Applications
Potential commercial applications of this technology include developing advanced AI systems for industries such as healthcare, finance, and autonomous vehicles.
Possible Prior Art
Prior art in this field includes research on transformer networks, attention mechanisms, and machine learning algorithms.
Unanswered Questions
How does this technology compare to other attention-based machine learning techniques currently available in the market?
This article does not provide a direct comparison with other attention-based machine learning techniques, so it is unclear how this technology stands out in the current landscape.
What are the specific limitations or challenges that may arise when implementing this technology in real-world applications?
The article does not address potential limitations or challenges that may arise when implementing this technology, leaving room for further exploration in this area.
Original Abstract Submitted
certain aspects of the present disclosure provide techniques and apparatus for improved attention-based machine learning. a first attention propagation output is generated using a first transformer block of a plurality of transformer blocks, this generation including processing input data for the first transformer block using a first self-attention sub-block of the first transformer block. the first attention propagation output is propagated to a second transformer block of the plurality of transformer blocks. an output for the second transformer block is generated, this generation including generating output features for the second transformer block based on the first attention propagation output.
