18258314. OUT-OF-DISTRIBUTION DETECTION AND REPORTING FOR MACHINE LEARNING MODEL DEPLOYMENT simplified abstract (QUALCOMM Incorporated)
OUT-OF-DISTRIBUTION DETECTION AND REPORTING FOR MACHINE LEARNING MODEL DEPLOYMENT
Organization Name
Inventor(s)
OUT-OF-DISTRIBUTION DETECTION AND REPORTING FOR MACHINE LEARNING MODEL DEPLOYMENT - A simplified explanation of the abstract
This abstract first appeared for US patent application 18258314 titled 'OUT-OF-DISTRIBUTION DETECTION AND REPORTING FOR MACHINE LEARNING MODEL DEPLOYMENT
Simplified Explanation
Methods, systems, and devices for wireless communications are described in this patent application. The innovation involves a user equipment (UE) detecting whether a data sample falls outside of a dataset used to train a machine learning model configured by a network device. The UE receives control signaling from a base station indicating an out-of-distribution (OOD) detection rule configuration. This configuration is used by the UE to determine if any data sample falls outside of the dataset. If at least one data sample is found to be outside the dataset using the OOD detection rule configuration, the UE transmits an indication to the base station, signaling that an OOD event has occurred for that sample. The base station may provide parameters for determining the OOD event, OOD detection patterns to indicate when to monitor for the OOD event, or a combination of both.
- User equipment (UE) detects if a data sample falls outside a dataset used for training a machine learning model.
- Base station transmits control signaling to UE indicating an out-of-distribution (OOD) detection rule configuration.
- UE uses the OOD detection rule configuration to determine if any data sample falls outside the dataset.
- If at least one data sample is found to be outside the dataset, UE transmits an indication to the base station about the OOD event.
- Base station can provide parameters and OOD detection patterns for determining and monitoring the OOD event.
Potential applications of this technology:
- Enhancing wireless communication systems by detecting and handling out-of-distribution events.
- Improving the accuracy and reliability of machine learning models used in wireless communications.
- Enabling proactive measures to be taken when data samples fall outside the trained dataset.
Problems solved by this technology:
- Addressing the challenge of detecting and handling out-of-distribution events in wireless communications.
- Overcoming limitations of traditional machine learning models that may not account for data samples outside the trained dataset.
- Providing a mechanism for user equipment to communicate OOD events to the base station.
Benefits of this technology:
- Improved performance and reliability of wireless communication systems.
- Enhanced ability to detect anomalies and outliers in data samples.
- Proactive identification and handling of out-of-distribution events, leading to more efficient network management.
Original Abstract Submitted
Methods, systems, and devices for wireless communications are described. A user equipment (UE) may detect whether a data sample falls outside of a dataset used to train a machine learning model configured by a network device. For example, a base station may transmit control signaling to the UE to indicate an out-of-distribution (OOD) detection rule configuration that the UE uses to determine whether at least one data sample falls outside of the dataset. If the at least one data sample is determined to fall outside of the dataset using the OOD detection rule configuration, the UE may transmit an indication to the base station that indicates an OOD event has occurred for the at least one sample. Additionally, the base station may parameters for determining the OOD event, OOD detection patterns to indicate when to monitor for the OOD event, or a combination thereof.
