Qualcomm incorporated (20240121621). OUT OF DISTRIBUTION SAMPLES REPORTING FOR NEURAL NETWORK OPTIMIZATION simplified abstract
Contents
- 1 OUT OF DISTRIBUTION SAMPLES REPORTING FOR NEURAL NETWORK OPTIMIZATION
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 OUT OF DISTRIBUTION SAMPLES REPORTING FOR NEURAL NETWORK OPTIMIZATION - 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
OUT OF DISTRIBUTION SAMPLES REPORTING FOR NEURAL NETWORK OPTIMIZATION
Organization Name
Inventor(s)
OUT OF DISTRIBUTION SAMPLES REPORTING FOR NEURAL NETWORK OPTIMIZATION - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240121621 titled 'OUT OF DISTRIBUTION SAMPLES REPORTING FOR NEURAL NETWORK OPTIMIZATION
Simplified Explanation
The patent application describes a configuration for reporting out-of-distribution (OOD) samples for neural network optimization. The apparatus receives a configuration from a base station to report an OOD dataset for a machine learning model. It detects OOD events and reports the OOD dataset based on the received configuration. The apparatus can also receive updates to the machine learning model and the OOD dataset may contain raw data or extracted latent data related to the OOD events.
- Explanation of the patent:
* Apparatus receives configuration to report OOD dataset * Detects OOD events * Reports OOD dataset based on configuration * Can receive updates to machine learning model * OOD dataset may contain raw or latent data related to OOD events
Potential Applications
The technology can be applied in various fields such as anomaly detection, fraud detection, and quality control in manufacturing processes.
Problems Solved
1. Efficient reporting of OOD samples for neural network optimization 2. Improved detection and handling of OOD events in machine learning models
Benefits
1. Enhances the performance and accuracy of machine learning models 2. Enables proactive identification and mitigation of OOD events 3. Streamlines the optimization process for neural networks
Potential Commercial Applications
Optimizing neural networks for improved performance in industries such as healthcare, finance, and cybersecurity.
Possible Prior Art
Prior art may include existing methods for handling OOD samples in machine learning models, such as outlier detection algorithms and anomaly detection techniques.
Unanswered Questions
How does the apparatus differentiate between different types of OOD events?
The patent application does not specify the mechanism by which the apparatus distinguishes between various OOD events and their corresponding datasets.
What is the impact of the configuration settings on the reporting process?
The patent application does not elaborate on how different configurations provided by the base station affect the reporting of OOD datasets and the optimization of neural networks.
Original Abstract Submitted
a configuration for reporting ood samples for neural network optimization. the apparatus receives, from a base station, a configuration to report an ood dataset for a machine learning model. the apparatus detects an occurrence of one or more ood events. the apparatus reports the ood dataset comprising the one or more ood events based on the configuration to report ood dataset. the apparatus receives, from the base station, an update to the machine learning model. the ood dataset may comprise raw data related to the one or more ood events, or may comprise extracted latent data corresponding to features of raw data related to the one or more ood events.
