18335685. PROPAGATING ATTENTION INFORMATION IN EFFICIENT MACHINE LEARNING MODELS simplified abstract (QUALCOMM Incorporated)
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 Unanswered Questions
- 1.11 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 18335685 titled 'PROPAGATING ATTENTION INFORMATION IN EFFICIENT MACHINE LEARNING MODELS
Simplified Explanation
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.
- Improved attention-based machine learning techniques and apparatus
- Generation of attention propagation output using transformer blocks
- Processing input data with self-attention sub-blocks
- Propagation of attention output to subsequent transformer blocks
- Generation of output features based on attention propagation output
Potential Applications
The technology described in this patent application could be applied in various fields such as natural language processing, image recognition, and speech recognition.
Problems Solved
This technology helps in improving the efficiency and accuracy of machine learning models by enhancing attention mechanisms and feature generation processes.
Benefits
The benefits of this technology include better performance of machine learning models, increased interpretability of results, and potential for more advanced applications in artificial intelligence.
Potential Commercial Applications
One potential commercial application of this technology could be in developing advanced recommendation systems for e-commerce platforms, personalized content delivery systems for media companies, and intelligent virtual assistants for various industries.
Possible Prior Art
One possible prior art for this technology could be the use of transformer networks in machine learning models for natural language processing tasks. These networks have been widely used for improving the performance of language translation systems and text generation models.
Unanswered Questions
How does this technology compare to existing attention mechanisms in machine learning models?
This article does not provide a direct comparison with existing attention mechanisms in machine learning models, leaving room for further exploration of the differences and advantages of the proposed approach.
What are the potential limitations or challenges in implementing this technology in real-world applications?
The article does not address the potential limitations or challenges that may arise in implementing this technology in practical settings, such as computational resources required or data availability issues. Further research and experimentation may be needed to address these aspects.
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.
