AI Training Early Stopping Using Control Limits
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for training artificial intelligence models do not leverage control limits to cease training when desirable performance metrics are met, leading to suboptimal efficiency and computational burden.
Innovation Solution
An iterative training loop that divides training data into subsets, calculates performance metrics, and uses control limits to determine if an improving performance condition is met, halting the loop if this condition is not met within a predetermined number of iterations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If training is continued until convergence using existing methods, then model accuracy is improved, but computational burden and training time increase significantly
Solution Approach 1:
The patent calculates control limits in advance based on historical performance metrics before training reaches convergence. These pre-calculated control limits serve as early stopping criteria, allowing the training process to halt prematurely when performance plateaus are detected, thus reducing training time while maintaining model accuracy
Solution Approach 2:
The patent implements a feedback mechanism where performance metrics are continuously monitored during training and compared against pre-calculated control limits. When metrics fall within the control limits indicating no significant improvement, the system provides feedback to stop training, preventing unnecessary computational waste while preserving model quality
2Manufacturing precision
If training is continued extensively to ensure optimal performance, then model quality is improved, but computational resources are wasted
Solution Approach 1:
Control limits are pre-calculated from historical performance data before the actual training begins. This preliminary action establishes a threshold for meaningful improvement, allowing the system to stop training early when performance plateaus are detected, thereby conserving computational resources without compromising model quality
Solution Approach 2:
The patent applies partial action by stopping training before full convergence is reached. By using control limits to identify when additional training iterations would yield diminishing returns, the system performs just enough training to achieve optimal model quality while avoiding excessive computational resource consumption
Data Source
AI summary
Disclosed herein is a method of training an artificial intelligence model that comprises an iterative training loop. Said iterative training loop comprises: receiving a current set of training data; dividing said current set of training data into a predetermined number of training data subsets; sequentially training said artificial intelligence model with each of said predetermined number of training data subsets using a training portion and calculating a performance metric using a validation portion; and comparing performance metrics from a previous iteration of said iterative training loop to said calculated performance metric to determine if an improving performance metric condition is met. Said method further comprises halting said iterative training loop unless said improving performance metric condition is not met at least once within a predetermined number of previous iterations.