Graphical Representation for Neural Network Uncertainty Estimation
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Solution Overview
Problem
Current methods for estimating predictive uncertainty in neural networks, particularly aleatoric uncertainty, fail to accurately model flows within deep neural networks, such as skip connections, and require modifications to the original network, limiting their use in safety-critical applications.
Innovation Solution
The use of graphical representations, specifically factor graphs, to model neural network uncertainty propagation by representing layer values as variable nodes and connections as factors, allowing for uncertainty estimation without modifying the neural network architecture.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If prior methods model input and output distributions as Gaussian and propagate uncertainties layer-wise, then uncertainty propagation is enabled, but flows within deep neural networks such as skip connections are not accurately modeled
Solution Approach 1:
The patent introduces graphical models as an intermediary framework to represent uncertainty propagation in neural networks. These graphical models serve as a mediator between the neural network computations and uncertainty analysis, enabling accurate tracking of uncertainty flows through complex architectures including skip connections, without requiring modifications to the original network structure.
2Measurement precision
If neural networks are modified to determine uncertainties, then uncertainty estimation is achieved, but the original network architecture and training procedures must be changed
Solution Approach 1:
The patent segments the uncertainty analysis from the neural network computation itself. By dividing the system into the original neural network component and a separate graphical model component, the method enables uncertainty estimation without requiring any modifications to the neural network's architecture, weights, or training procedures.
3Productivity
If diagonal covariance matrices are used to ignore correlations of weight dimensions, then computational complexity is reduced, but accuracy of uncertainty propagation deteriorates
Solution Approach 1:
The patent implements a dynamic approach where the level of covariance detail can be adjusted based on computational requirements. The graphical model framework allows for representing full covariance matrices when accuracy is paramount, while also supporting simplified representations when computational efficiency is needed, providing flexibility to balance accuracy and performance.
Data Source
AI summary
A method, apparatus, and system for determining an uncertainty estimation of at least one layer of a neural network includes identifying a neural network to be analyzed, representing values of each layer of the neural network as respective variable nodes in a graphical representation of the neural network, and modeling connections among each of the layers of the neural network as different respective factors across the variable nodes in the graphical representation, the graphical representation to be used to determine the uncertainty estimation of at least one layer of the neural network. The method, apparatus, and system can further include propagating data through the graphical representation to determine the uncertainty estimation of the neural network.