Qualcomm incorporated (20240121165). TECHNIQUES FOR REPORTING CORRELATION METRICS FOR MACHINE LEARNING REPRODUCIBILITY simplified abstract
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 Unanswered Questions
- 1.11 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 20240121165 titled 'TECHNIQUES FOR REPORTING CORRELATION METRICS FOR MACHINE LEARNING REPRODUCIBILITY
Simplified Explanation
The patent application describes methods, systems, and devices for wireless communication using machine learning to generate low-dimensional parameter sets for training and testing data in different communication environments or channel environments. The machine learning server calculates a reproducibility metric based on the correlation between the training and testing data parameter sets, and transmits this metric to devices for communication procedures.
- Explanation of the patent:
- Machine learning server generates low-dimensional parameter sets for training and testing data in communication or channel environments.
- Reproducibility metric is calculated based on the correlation between training and testing data parameter sets.
- Metric is transmitted to devices for communication procedures.
Potential Applications
This technology can be applied in: - Wireless communication systems - IoT devices - Network optimization
Problems Solved
- Improving communication reliability - Enhancing network performance - Automating communication procedures
Benefits
- Increased efficiency in wireless communication - Better adaptation to changing environments - Enhanced data transmission reliability
Potential Commercial Applications
Optimized Wireless Communication Systems for IoT Devices
Possible Prior Art
Prior art may include research on machine learning in wireless communication systems and network optimization.
Unanswered Questions
How does the machine learning server handle real-time data updates in communication environments?
The patent application does not specify the process of updating data in real-time for communication procedures.
What are the limitations of using low-dimensional parameter sets in complex communication environments?
The patent application does not address the potential challenges or constraints of using low-dimensional parameter sets in intricate communication scenarios.
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.
