Visualizing Machine-Learning Model Input Influence
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Solution Overview
Problem
Current machine-learning models are poorly understood, leading to difficulties in optimizing their complexity and efficiency, resulting in processing errors, unnecessary resource consumption, and suboptimal performance due to a lack of insight into their inner workings.
Innovation Solution
A method and system for visualizing machine-learning model behavior through a graphical user interface (GUI) that analyzes and displays the influence of input elements on model outputs, allowing designers to identify crucial and redundant inputs, thereby optimizing model structure and resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine-learning models are made more complex to improve accuracy, then model performance is improved, but processing time and resource consumption increase
Solution Approach 1:
The patent segments the machine-learning model into individual input elements and evaluates their influence separately. By analyzing each input element's contribution to the model output independently, the system identifies which segments (inputs) are most critical, allowing for selective optimization without requiring full model retraining.
Solution Approach 2:
The patent extracts and removes redundant or less influential input elements from the model. By identifying inputs with minimal impact on the output and eliminating them, the model complexity is reduced while maintaining acceptable accuracy, thereby decreasing processing time and resource consumption.
2Reliability
If machine-learning models are made more complex to improve accuracy, then model performance is improved, but memory consumption increases
Solution Approach 1:
The patent extracts and removes redundant or less influential input elements from the model. By identifying inputs with minimal impact on the output and eliminating them, the model complexity is reduced while maintaining acceptable accuracy, thereby decreasing processing time and resource consumption.
Solution Approach 2:
The patent applies local quality by treating different input elements differently based on their influence. Instead of uniformly processing all inputs, the system identifies high-influence inputs that require detailed processing and low-influence inputs that can be simplified or removed, optimizing memory usage by allocating resources proportionally to input importance.
3Reliability
If machine-learning models are made more complex to improve accuracy, then model performance is improved, but device complexity increases
Solution Approach 1:
The patent segments the machine-learning model into individual input elements and evaluates their influence separately. By analyzing each input element's contribution to the model output independently, the system identifies which segments (inputs) are most critical, allowing for selective optimization without requiring full model retraining.
Solution Approach 2:
The patent changes the parameter of input selection by using influence scoring to determine which inputs to retain or remove. By adjusting the threshold for input inclusion based on their measured influence on the output, the system can dynamically optimize model complexity while maintaining performance.
4Reliability
If machine-learning models are made more complex to improve accuracy, then model performance is improved, but difficulty in understanding and optimizing increases
Solution Approach 1:
The patent segments the machine-learning model into individual input elements and evaluates their influence separately. By analyzing each input element's contribution to the model output independently, the system identifies which segments (inputs) are most critical, allowing for selective optimization without requiring full model retraining.
Solution Approach 2:
The patent changes the parameter of input selection by using influence scoring to determine which inputs to retain or remove. By adjusting the threshold for input inclusion based on their measured influence on the output, the system can dynamically optimize model complexity while maintaining performance.
Data Source
AI summary
Machine-learning models and behavior can be visualized. For example, a machine-learning model can be taught using a teaching dataset. A test input can then be provided to the machine-learning model to determine a baseline confidence-score of the machine-learning model. Next, weights for elements in the teaching dataset can be determined. An analysis dataset can be generated that includes a subset of the elements that have corresponding weights above a predefined threshold. For each overlapping element in both the analysis dataset and the test input, (i) a modified version of the test input can be generated that excludes the overlapping element, and (ii) the modified version of the test input can be provided to the machine-learning model to determine an effect of the overlapping element on the baseline confidence-score. A graphical user interface can be generated that visually depicts the test input and various elements' effects on the baseline confidence-score.