International business machines corporation (20240289608). AUTOMATED DRIFT DETECTION IN MULTIDIMENSIONAL DATA simplified abstract
Contents
- 1 AUTOMATED DRIFT DETECTION IN MULTIDIMENSIONAL DATA
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 AUTOMATED DRIFT DETECTION IN MULTIDIMENSIONAL DATA - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Questions about Automated Drift Detection
- 1.11 Original Abstract Submitted
AUTOMATED DRIFT DETECTION IN MULTIDIMENSIONAL DATA
Organization Name
international business machines corporation
Inventor(s)
Duygu Kabakci Zorlu of Dublin (IE)
AUTOMATED DRIFT DETECTION IN MULTIDIMENSIONAL DATA - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240289608 titled 'AUTOMATED DRIFT DETECTION IN MULTIDIMENSIONAL DATA
Simplified Explanation
Automated drift detection in multidimensional data using deep learning and hypothesis testing to maintain high-quality results from neural networks.
- Techniques for automated drift detection in multidimensional data
- Utilizes deep learning to estimate high-density regions of data
- Combines results with hypothesis testing to test for drift
- Mitigation actions, including retraining, are performed if drift is detected
Key Features and Innovation
- Utilizes deep learning to estimate high-density regions of multidimensional data
- Combines deep learning results with hypothesis testing for drift detection
- Performs mitigation actions, including retraining, to maintain high-quality results from neural networks
Potential Applications
- Data analysis in various industries such as finance, healthcare, and manufacturing
- Monitoring and maintaining the performance of neural networks in real-time
- Identifying and addressing data drift in complex datasets
Problems Solved
- Automated detection of drift in multidimensional data
- Ensuring the reliability and quality of results from neural networks
- Mitigating the impact of drift on data analysis and decision-making processes
Benefits
- Improved accuracy and reliability of data analysis
- Real-time detection and mitigation of data drift
- Enhanced performance and efficiency of neural networks
Commercial Applications
Automated drift detection technology can be applied in industries such as finance for fraud detection, healthcare for patient monitoring, and manufacturing for quality control processes.
Questions about Automated Drift Detection
How does automated drift detection using deep learning improve data analysis processes?
Automated drift detection using deep learning enhances data analysis processes by continuously monitoring data for changes and ensuring the reliability of results.
What are the potential implications of automated drift detection technology in various industries?
Automated drift detection technology can have significant implications in industries such as finance, healthcare, and manufacturing by improving decision-making processes and ensuring data quality.
Original Abstract Submitted
disclosed embodiments provide techniques for automated drift detection in multidimensional data. disclosed embodiments utilize deep learning to estimate high-density regions of multidimensional, multivariate, and/or multimodal data and combine the results with hypothesis testing. a hypothesis of drift or no drift is tested using a mathematical test, and if drift is detected, mitigation actions, including retraining are performed, to enable the continuation of reliable, high-quality results from the neural network.
