Amazon technologies, inc. (20240202587). UN-LEARNING OF TRAINING DATA FOR MACHINE LEARNING MODELS simplified abstract
Contents
- 1 UN-LEARNING OF TRAINING DATA FOR MACHINE LEARNING MODELS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 UN-LEARNING OF TRAINING DATA FOR MACHINE LEARNING MODELS - 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 Prior Art
- 1.11 Frequently Updated Research
- 1.12 Questions about Machine Learning Model Training System
- 1.13 Original Abstract Submitted
UN-LEARNING OF TRAINING DATA FOR MACHINE LEARNING MODELS
Organization Name
Inventor(s)
Vinayshekhar Bannihatti Kumar of Santa Clara CA (US)
Rashmi Gangadharaiah of San Jose CA (US)
Dan Roth of Philadelphia PA (US)
UN-LEARNING OF TRAINING DATA FOR MACHINE LEARNING MODELS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240202587 titled 'UN-LEARNING OF TRAINING DATA FOR MACHINE LEARNING MODELS
Simplified Explanation
The patent application describes a machine learning model training system that can efficiently remove specific data points by training multiple instances of the model on different data shards. When a removal request is received, the system can remove the data point from the corresponding shard and retrain only that specific shard's model instance.
- The system trains multiple instances of a machine learning model on disjoint shards of data.
- Data points can be expunged from their respective shards upon removal requests.
- Each shard can be divided into data slices, with model checkpoints saved after training each slice.
- The system can retrain the model instance starting from the last checkpoint before the removed data point for efficient training.
Key Features and Innovation
- Efficient removal of specific data points from machine learning models.
- Training multiple model instances on disjoint data shards.
- Retraining only the affected shard's model instance upon data point removal.
- Saving model checkpoints after training each data slice for easy retraining.
Potential Applications
The technology can be applied in various fields such as:
- Data analysis
- Predictive modeling
- Anomaly detection
- Pattern recognition
Problems Solved
- Efficient removal of specific data points without retraining the entire model.
- Simplified training process for machine learning models.
- Improved model accuracy by focusing on individual data shards.
Benefits
- Time and resource efficiency in data point removal.
- Enhanced model performance through targeted retraining.
- Scalability for large datasets with disjoint data shards.
Commercial Applications
- Predictive maintenance in manufacturing industries.
- Fraud detection in financial services.
- Personalized recommendations in e-commerce platforms.
- Medical diagnosis and treatment planning in healthcare.
Prior Art
Readers can explore prior research on machine learning model training systems and data point removal techniques in the field of artificial intelligence and data science.
Frequently Updated Research
Stay updated on advancements in machine learning model training systems, data point removal algorithms, and optimization techniques for improving model performance.
Questions about Machine Learning Model Training System
How does the system ensure the accuracy of retrained model instances after data point removal?
The system saves model checkpoints after training each data slice, allowing for precise retraining from the last checkpoint before the removed data point.
What are the potential challenges in implementing this technology in real-world applications?
Some challenges may include managing large datasets, optimizing training processes, and ensuring seamless integration with existing machine learning workflows.
Original Abstract Submitted
methods and systems are disclosed for a machine learning (ml) model training system that can remove the influence of specific data points in an efficient way. an ml training system can train multiple instances of a machine learning model on disjoint shards of data. upon receiving a request to remove a specific data point, the ml training system can expunge the data point from its corresponding shard and only retrain the model instance for that specific shard. each shard can be further divided into data slices, with each slice containing a portion of the data from the shard. during the training of each instance of the machine learning model, the ml training system can save model checkpoints after completion of training for each slice. upon receiving a removal request, the related data point is removed from its respective slice, and the relevant model instance can be retrained starting from the last checkpoint before that slice had been previously used for training.
