Epistemic Machine Learning Models for Single-Pass Uncertainty
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Solution Overview
Problem
Conventional machine learning models fail to distinguish between aleatoric and epistemic uncertainty, leading to unreliable predictions in real-world applications.
Innovation Solution
Augmenting a base machine learning model with an epistemic model that consumes fewer resources, allowing for improved uncertainty indications through a single forward pass, thereby enhancing the base model's output with epistemic uncertainty information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional machine learning models are used to generate predictions, then the models can provide output predictions, but they cannot distinguish between aleatoric uncertainty and epistemic uncertainty
Solution Approach 1:
The patent segments the uncertainty analysis into two distinct components by using separate neural network models: one for predicting aleatoric uncertainty (input data ambiguity) and another for predicting epistemic uncertainty (model knowledge gaps). This segmentation allows the system to distinguish between different types of uncertainty that conventional single-model approaches cannot separate.
Solution Approach 2:
The patent introduces intermediary neural network models that act as mediators between the input data and the final prediction. These intermediary models specifically predict uncertainty metrics without directly generating the primary prediction, allowing the system to capture uncertainty information that would otherwise be lost in conventional end-to-end prediction models.
2Loss of information
If ensembles of multiple models are used to provide uncertainty information, then uncertainty indications can be improved, but computational cost increases significantly
Solution Approach 1:
The patent employs lightweight neural network models designed specifically for uncertainty prediction that consume significantly fewer computational resources than full-scale predictive models. These simplified uncertainty models are trained independently and can be executed efficiently, providing accurate uncertainty estimates without the prohibitive computational cost of running multiple full model ensembles.
3Reliability
If additional forward passes through the base model are performed to incorporate uncertainty information, then prediction reliability improves, but computational overhead increases
Solution Approach 1:
The patent performs uncertainty prediction in advance by training dedicated uncertainty models during the offline training phase. These pre-trained models can then quickly provide uncertainty estimates during inference without requiring additional forward passes through the expensive base prediction model, thus maintaining high computational efficiency during deployment.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing inputs using an epistemic machine learning model that improves the quality of outputs generated by a base machine learning model.