Neural Process Uncertainty Assessment With Softmax Latent Weighting
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Solution Overview
Problem
Existing neural processes for predicting model uncertainties, such as those used in safety-critical systems, are inefficient in terms of parameter usage and do not effectively weight latent observations based on their ambiguity, leading to suboptimal predictive performance.
Innovation Solution
A method and architecture that uses softmax aggregation, parameterized by a hyperparameter T, to unevenly weight latent observations without requiring a separate encoder network, thereby simplifying Bayesian context aggregation and reducing the number of parameters needed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Bayesian context aggregation with a second encoder network is used, then predictive performance is improved and latent observations are weighted according to task ambiguity, but the number of parameters and device complexity increases
Solution Approach 1:
The patent extracts the task ambiguity quantification function from the second encoder network and implements it through a simplified variance calculation based on latent observations. Instead of using a separate neural network to learn task ambiguity, the invention directly computes variance from the latent observations themselves, thereby removing the complex second encoder while preserving the ambiguity-weighted aggregation capability
Solution Approach 2:
The patent changes the parameters used for aggregation by introducing a temperature parameter T that controls the softmax aggregation behavior. This single parameter replacement allows the system to achieve adaptive weighting of latent observations based on their reliability, replacing the need for complex learned parameters in a second encoder network
2Device complexity
If mean aggregation is used, then the aggregation mechanism is simple, but predictive performance deteriorates because all latent observations are weighted uniformly regardless of task ambiguity
Solution Approach 1:
The patent transforms the simple mean aggregation into an adaptive softmax aggregation by introducing a temperature parameter T. This parameter change enables the aggregation mechanism to automatically adjust weights based on latent observation variance, achieving both simplicity and high predictive performance by maintaining a straightforward aggregation formula while enhancing its adaptivity through parameter control
Data Source
AI summary
A computer-implemented method for assessing uncertainties using a neural network, in particular a neural process, in a model, The model models a technical system and/or system behavior of the technical system. An architecture of the neural network for assessing uncertainties is also described.