18045614. TECHNIQUES FOR REPORTING CORRELATION METRICS FOR MACHINE LEARNING REPRODUCIBILITY simplified abstract (QUALCOMM Incorporated)
Contents
- 1 TECHNIQUES FOR REPORTING CORRELATION METRICS FOR MACHINE LEARNING REPRODUCIBILITY
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 TECHNIQUES FOR REPORTING CORRELATION METRICS FOR MACHINE LEARNING REPRODUCIBILITY - 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
TECHNIQUES FOR REPORTING CORRELATION METRICS FOR MACHINE LEARNING REPRODUCIBILITY
Organization Name
Inventor(s)
Vasanthan Raghavan of West Windsor Township NJ (US)
Tianyang Bai of Somerville NJ (US)
TECHNIQUES FOR REPORTING CORRELATION METRICS FOR MACHINE LEARNING REPRODUCIBILITY - A simplified explanation of the abstract
This abstract first appeared for US patent application 18045614 titled 'TECHNIQUES FOR REPORTING CORRELATION METRICS FOR MACHINE LEARNING REPRODUCIBILITY
Simplified Explanation
The abstract describes methods, systems, and devices for wireless communication using machine learning to generate low-dimensional parameter sets for training and testing data in communication environments or channel environments. A reproducibility metric is generated to evaluate the correlation between the training and testing data parameter sets, which is then used to optimize communication procedures.
- Machine learning server generates low-dimensional parameter sets for training and testing data in communication environments or channel environments.
- Reproducibility metric is calculated based on the correlation between training and testing data parameter sets.
- Message indicating the reproducibility metric is transmitted to devices for communication procedures optimization.
Potential Applications
The technology can be applied in various wireless communication systems to improve performance and reliability based on machine learning algorithms.
Problems Solved
1. Optimization of communication procedures in different environments. 2. Enhancing the reproducibility of training and testing data for better performance.
Benefits
1. Improved communication reliability. 2. Enhanced performance in varying communication environments. 3. Efficient utilization of machine learning for wireless communication systems.
Potential Commercial Applications
Optimizing wireless communication systems for industries such as telecommunications, IoT, and smart devices.
Possible Prior Art
Prior art may include research on machine learning applications in wireless communication systems and reproducibility metrics for data analysis.
Unanswered Questions
How does this technology compare to traditional methods of optimizing communication procedures in wireless systems?
This technology utilizes machine learning algorithms to generate reproducibility metrics for training and testing data, which may provide more accurate and efficient optimization compared to traditional methods.
What are the potential limitations or challenges in implementing this technology in real-world wireless communication systems?
Some potential challenges could include the complexity of machine learning algorithms, data privacy concerns, and the need for continuous updates and maintenance of the system to adapt to changing communication environments.
Original Abstract Submitted
Methods, systems, and devices for wireless communication are described. A machine learning server may generate a low-dimensional parameter set representing training data for the machine learning server, the training data being associated with one or more communication environments or one or more channel environments, or a combination thereof. The machine learning server may receive, from one or more devices within a communication environment or within a channel environment, or both, a low-dimensional parameter set representing testing data associated with the communication environment or the channel environment, or both. The machine learning server may generate a reproducibility metric according to a correlation between the parameter set representing the training data and the parameter set representing the testing data. The machine learning server may transmit a message indicating the reproducibility metric to the one or more devices, and the one or more devices may perform communication procedures based on the reproducibility metric.
