Model Training Correlation Analysis for Accuracy Optimization

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Solution Overview

Problem

Current computer model training processes face challenges in tracking and correlating changes in training data, hyperparameters, and tools with performance and accuracy, especially when multiple instances of models are distributed across different developers, leading to difficulties in identifying trends and optimizing model performance.

Innovation Solution

A method and system that perform multiple instances of training using different data sets and hyperparameters, record changes, and analyze differences to generate correlations, providing recommendations for improving model accuracy and automatically implementing these changes, while also allowing for version tracking to revert to previous data sets if needed.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Manufacturing precision

If multiple instances of training are performed with different training data sets and hyperparameters across different developers, then model performance and accuracy can be improved through diverse training approaches, but it becomes difficult to track and correlate changes in training data, hyperparameters, and tools with performance metrics

Engineering Contradiction:

Improvemodel accuracyVSAvoidtracking information

Core Design Contradiction:

Manufacturing precisionVSLoss of information

Solution Approach 1:

The patent introduces an intermediary system that acts as a central repository and analysis platform between the distributed training processes and the performance metrics. This intermediary automatically collects, stores, and correlates training data, hyperparameters, tool versions, and performance results, enabling tracking and analysis without burdening the individual training processes.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system implements automated feedback loops where performance metrics are continuously monitored and correlated with training configurations. The system provides feedback by identifying which training data sets, hyperparameters, and tools contribute to improved model accuracy, enabling iterative optimization of the training process.

Inventive Principle:

Principle #23Feedback

2Reliability

If manual tracking of training changes is performed, then correlation between training parameters and performance can be maintained, but time and resources are consumed and efficiency is reduced

Engineering Contradiction:

Improvecorrelation accuracyVSAvoidtracking time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system enables self-service automation where the training infrastructure automatically tracks and correlates its own parameters and performance metrics without requiring manual intervention. The system self-monitors training data versions,hyperparameter changes, tool configurations, and performance results, eliminating the need for manual tracking while maintaining high correlation accuracy.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces manual mechanical tracking processes with automated computational systems. Instead of manually recording and correlating training parameters, the system uses automated data collection, storage, and analysis mechanisms that efficiently process and correlate training information without human intervention.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Productivity

If centralized analysis of training data is implemented, then trends and correlations can be identified to improve model performance, but system complexity and infrastructure requirements increase

Engineering Contradiction:

Improvetraining efficiencyVSAvoidsystem complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent implements a universal centralized analysis platform that serves multiple functions: collecting training data, storing configurations, analyzing performance metrics, identifying trends, and providing recommendations. This multi-functional system consolidates various training management tasks into a single platform, improving productivity while managing complexity through integration rather than proliferation of separate systems.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS20240020579A1Computer Model Machine Learning Based on Correlations of Training Data with Performance Trends

Publication Date: 2024.01.18 INTERNATIONAL BUSINESS MACHINE CORPORATION

AI summary

Mechanisms are provided for training a computer implemented model. The mechanisms perform multiple instances of training of the computer implemented model, where each instance of training of the computer implemented model comprises training the computer implemented model using a different training data set to generate a different instance of a trained computer implemented model. The mechanisms generate computer implemented model results after each instance of training by executing the corresponding instance of the trained computer implemented model. The mechanisms record differences in the instances of training of the computer implemented model in association with corresponding identifiers of the instances of trained computer implemented model and corresponding computer implemented model results. The mechanisms analyze the recorded differences and the corresponding computer implemented model results, and generate an output indicating a correlation between recorded differences and corresponding computer implemented model results.