Model Output Combining Program With Dynamic Calibration
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Solution Overview
Problem
Current methods for combining machine learning model outputs from different architectures and training techniques, such as supervised learning (SL) and reinforcement learning (RL), are inefficient and non-optimal, particularly when dealing with minimally or partially trained models.
Innovation Solution
A method for combining model outputs using a combining program that receives outputs from multiple models, performs calibration to improve results, and applies various combining techniques such as pairwise max functions, dynamic weighted averages, customizable rules, and dynamic adapting and throttling to generate a final output.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple models with different architectures and training techniques are used, then the algorithm can incorporate benefits of supervised, unsupervised, and reinforcement learning, but the complexity of combining their outputs increases
Solution Approach 1:
The combining program segments the output combination process into distinct functional modules: receiving outputs from multiple models, calibrating individual model outputs, combining calibrated outputs using various techniques (pairwise max, dynamic weighted average, etc.), and generating final outputs. This segmentation allows complex multi-model integration to be managed through systematic, isolated processing stages.
Solution Approach 2:
The system dynamically adjusts combining parameters such as weights in dynamic weighted averages and thresholds in pairwise max functions based on model confidence levels and performance metrics. This allows the combining strategy to adapt automatically to different model states, resolving the complexity through parameter optimization rather than structural simplification.
2Productivity
If traditional combining methods are used, then the process is simple, but the prediction accuracy and efficiency are insufficient
Solution Approach 1:
The combining program incorporates feedback mechanisms where model outputs are calibrated based on their confidence levels and historical performance. The system continuously learns from prediction results and adjusts combining strategies accordingly, improving accuracy through iterative optimization rather than relying on complex static combining rules.
Solution Approach 2:
The system employs dynamic combining strategies that adapt in real-time based on model performance and input characteristics. Dynamic weighted averages adjust weights based on current model confidence, and pairwise max functions dynamically select between models based on their outputs, replacing static combining methods with adaptive, performance-driven approaches.
3Quantity of substance
If low-confidence outputs are included in the combining process, then more model outputs are utilized, but the final output quality is reduced
Solution Approach 1:
The combining program applies different processing quality levels to different model outputs based on their confidence characteristics. High-confidence outputs receive full processing weight while low-confidence outputs are either calibrated heavily or excluded from certain combining operations. This local differentiation ensures that output quality is maintained while still utilizing multiple model perspectives.
Solution Approach 2:
The calibration module acts as an intermediary between raw model outputs and the final combining process. It mediates the influence of low-confidence outputs by adjusting their effectiveness through calibration based on confidence levels, preventing them from directly degrading final output quality while still allowing them to contribute information where appropriate.
Data Source
AI summary
A method, a structure, and a computer system for combining model outputs. The exemplary embodiments may include receiving two or more outputs from two or more models, combining the two or more outputs, and generating a final output based on the combining.