Linear Probes for Interpretable Models via Confidence Profiles
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Solution Overview
Problem
Complex deep neural networks lack transparency, limiting their use in domains requiring human expert decisions, and simpler models like lasso and decision trees have low performance due to their low complexity and accuracy.
Innovation Solution
The method involves using linear probes to generate confidence scores from pre-trained deep neural networks and theoretically-justified weighting of samples during training to enhance the performance of simpler, interpretable models by transferring information from high-performing complex models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If deep neural networks are used to achieve high test accuracy, then model performance is improved, but model transparency and interpretability deteriorate
Solution Approach 1:
The patent extracts useful information from complex deep neural networks by using linear probes to generate confidence scores from intermediate representations. This allows transferring knowledge from the complex model to simpler models without copying the entire complex architecture, thus maintaining accuracy while reducing complexity.
Solution Approach 2:
The patent introduces linear probes as intermediary components that bridge complex deep neural networks and simple interpretable models. These probes extract confidence scores from intermediate layers, serving as a mediator that transfers information without requiring direct use of the complex model.
2Device complexity
If simple models like lasso and decision trees are used to maintain interpretability, then model transparency is improved, but test accuracy deteriorates
Solution Approach 1:
The patent applies preliminary action by pre-training deep neural networks and extracting confidence scores before training simple models. This pre-computed information is then used to weight training samples, giving simple models a head start that improves their accuracy without increasing their complexity.
Solution Approach 2:
The patent copies confidence score information from complex deep neural networks to simple models through weighted sampling. Instead of copying the complex model structure, it copies the essential decision-making signals (confidence scores) to guide the simple model's training process.
3Reliability
If complex models are used to achieve high performance, then test accuracy is improved, but ease of deployment in memory-constrained environments deteriorates
Solution Approach 1:
The patent replaces expensive, complex deep neural networks with cheap, simple models that can be deployed in resource-constrained environments. By using linear probes to extract essential information, the system achieves acceptable performance with much lighter models that are easier and cheaper to deploy.
Data Source
AI summary
A method, system, and computer program product, including generating, using a linear probe, confidence scores through flattened intermediate representations and theoretically-justified weighting of samples during a training of the simple model using the confidence scores of the intermediate representations.