ML Model Training With Validation-Guided Back Propagation Skipping
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Solution Overview
Problem
Training machine learning models is time-consuming and resource-intensive due to the necessity of performing back propagation for every training sample, even when the model is already accurate.
Innovation Solution
Implement a validation function to determine the accuracy of model predictions during training, skipping back propagation for samples where the model exceeds a predetermined threshold, thereby reducing unnecessary computations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If back propagation is performed for every training sample, then model training accuracy is improved, but training time and resource usage increase
Solution Approach 1:
The patent applies local quality by differentiating the treatment of training samples based on their individual characteristics. Instead of uniformly applying back propagation to all samples, the system selectively applies it only to samples where the model's prediction accuracy falls below a threshold, thereby optimizing resource allocation while maintaining training effectiveness
Solution Approach 2:
The patent implements partial action by performing back propagation only on a subset of training samples rather than all samples. By identifying and skipping samples that already meet the accuracy threshold, the system reduces unnecessary computational effort while still achieving adequate model training through partial application of the back propagation process
2Reliability
If back propagation is performed for every training sample, then model training completeness is improved, but computing resource usage increases
Solution Approach 1:
The system applies local quality by customizing the training process for different samples based on their individual accuracy levels. Samples that already meet the threshold receive minimal processing (skipping back propagation), while only samples below the threshold receive full back propagation treatment, optimizing computing resource usage
Solution Approach 2:
The patent extracts and removes the unnecessary back propagation steps from the training process for samples that already meet the accuracy threshold. By taking out these redundant computational operations, the system reduces computing resource usage while maintaining training completeness for samples that actually need it
3Productivity
If back propagation is skipped for accurate samples, then training efficiency is improved, but model accuracy may deteriorate
Solution Approach 1:
The patent implements feedback by continuously evaluating model prediction accuracy against a threshold during training. This feedback mechanism determines whether back propagation should be applied or skipped for each sample, ensuring that accuracy requirements are met while optimizing training efficiency through selective processing
Solution Approach 2:
The system applies partial action by performing back propagation only when necessary (for samples below the accuracy threshold) and skipping it when sufficient (for samples meeting the threshold). This selective application maintains model accuracy while improving training efficiency by avoiding excessive processing
Data Source
AI summary
An example operation may include one or more of storing a validation function and a training set comprising a plurality of training samples, executing a machine learning (ML) model on the plurality of training samples using a software application during an epoch of a multi-epoch training process performed by the software application, wherein the executing includes retrieving a training sample from the training set, executing the ML model on the training sample to generate a model prediction for the training sample, determining an accuracy of the ML model for the training sample based on the model prediction, an expected output, and the validation function, skipping a back propagation process of the ML model for the training sample based on the accuracy exceeding a pre-determined threshold value, and retrieving a next training sample from the training set based on the skipping.