18276438. OUT OF DISTRIBUTION SAMPLES REPORTING FOR NEURAL NETWORK OPTIMIZATION simplified abstract (QUALCOMM Incorporated)
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 Unanswered Questions
- 1.11 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 18276438 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, detects OOD events, and reports the OOD dataset based on the configuration. It 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.
- Configuration for reporting OOD samples for neural network optimization
- Apparatus receives configuration from base station
- Detects OOD events and reports OOD dataset based on configuration
- Receives updates to machine learning model
- OOD dataset may contain raw or extracted latent data related to OOD events
Potential Applications
The technology could be applied in various fields such as anomaly detection, fraud detection, and cybersecurity where identifying OOD samples is crucial for model optimization.
Problems Solved
1. Improved neural network optimization by reporting OOD samples. 2. Enhanced accuracy and reliability of machine learning models by detecting and addressing OOD events.
Benefits
1. Increased model performance and efficiency. 2. Better identification and handling of OOD samples. 3. Enhanced overall system security and reliability.
Potential Commercial Applications
"Enhancing Machine Learning Models with OOD Sample Reporting Technology"
Possible Prior Art
There are existing methods for detecting anomalies in machine learning models, but the specific configuration for reporting OOD samples as described in this patent application may be a novel approach.
Unanswered Questions
How does the apparatus differentiate between different types of OOD events?
The patent application does not provide details on how the apparatus distinguishes between various OOD events and whether it treats them differently in reporting.
What is the impact of reporting OOD samples on the overall performance of the machine learning model?
The application does not discuss the potential effects of reporting OOD samples on the accuracy and efficiency of the machine learning model.
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.
