Neural Network Loss Function for Uncertainty-Error Correlation
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Solution Overview
Problem
Existing deep learning techniques struggle to accurately correlate aleatoric and epistemic uncertainties with absolute prediction errors, leading to questionable reliability in neural network models.
Innovation Solution
A custom loss function for neural networks that incorporates terms to capture both aleatoric and epistemic uncertainties, explicitly correlating them with prediction bias or error, using a formulation that balances accuracy and uncertainty correlation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional loss functions (e.g., log-likelihood) are used with Bayesian neural networks or dropout techniques, then epistemic and aleatoric uncertainties can be estimated, but the total uncertainty is not well correlated with the absolute prediction error
Solution Approach 1:
The patent modifies the loss function by introducing a custom formulation that changes the parameter optimization objective. The new loss function includes terms that directly penalize the discrepancy between predicted uncertainty and actual absolute error, thereby changing how model parameters are updated during training to achieve better uncertainty-error correlation
Solution Approach 2:
The patent implements a feedback mechanism where the actual absolute error from predictions is used to adjust and refine the uncertainty estimates. The loss function incorporates the absolute error as a reference signal that feeds back into the uncertainty optimization process, allowing the model to learn from the discrepancy between predicted and actual errors
2Reliability
If custom loss functions are designed to improve uncertainty-error correlation, then prediction reliability improves, but model complexity and training difficulty increase
Solution Approach 1:
The patent segments the loss function into distinct components: a primary task loss term and separate uncertainty estimation terms. This segmentation allows each component to be optimized independently while maintaining overall coherence, making the complex loss function more manageable and interpretable during training
Data Source
AI summary
A method and a non-transitory computer readable medium for performing a calculation in a neural network comprise: accepting a data set into the neural network; performing a calculations with the neural network using the data set, wherein the calculations use a loss function and provide an aleatoric and epistemic uncertainty that is correlated to a value; and displaying results of the calculations performed.