Sampling Model Generation for Neural Network Uncertainty
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Solution Overview
Problem
Current deep learning models are unable to predict uncertainty, which is crucial in applications like medical diagnostics and autonomous vehicles, where providing uncertainty information can lead to more accurate decisions.
Innovation Solution
A sampling model generation apparatus and method that uses stochastic gradient to discover an approximate posterior distribution of neural network weights, allowing for uncertainty prediction by training an artificial neural network with sampling models and updating weights based on proximity to local minimum values, either by adding Gaussian noise or not, depending on gradient certainty.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional deep learning models are used for prediction, then computational efficiency is maintained, but uncertainty prediction capability is lost
Solution Approach 1:
The model is segmented into two distinct components: a deterministic artificial neural network for efficient prediction and multiple sampling models for uncertainty estimation. This segmentation allows each component to specialize - the main model maintains computational efficiency while the sampling models provide uncertainty information through stochastic gradient sampling.
Solution Approach 2:
Sampling models act as intermediaries between the deterministic neural network and uncertainty prediction requirements. These sampling models use stochastic gradient sampling to approximate the posterior distribution of weights, serving as a bridge that enables uncertainty estimation without requiring the main model to be fully probabilistic.
2Reliability
If sampling models are introduced for uncertainty prediction, then reliability improves, but computational overhead increases
Solution Approach 1:
Instead of performing full Bayesian inference which would be computationally prohibitive, the method uses partial action by sampling only the essential weight parameters with stochastic gradients. This partial sampling approach provides sufficient uncertainty information without the excessive computational cost of complete posterior distribution estimation.
Solution Approach 2:
The method changes the parameter representation by working with sampled weight values rather than fixed deterministic weights. By introducing stochastic variations in weight parameters through sampling, the system can estimate uncertainty while maintaining computational feasibility through efficient gradient-based sampling methods.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables the provision of uncertainty information for artificial neural network predictions, effectively expressing the uncertainty of deep learning models, as demonstrated by improved performance in uncertainty tests on MNIST and not MNIST datasets compared to conventional methods like SGLD and Dropout.
Implementation Method 1
a sampling model generation apparatus and method for uncertainty prediction to discover an approximate posterior distribution of a neural network weight using a stochastic gradient
Implementation Method 2
generating a sampling model modeled with the corresponding weight and updating the weight through addition of Gaussian noise when it is determined that the weight is in close proximity to the local minimum value
Data Source
AI summary
An uncertainty prediction apparatus includes an artificial neural network model trained based on deep learning, sampling models modeled by at least two weights obtained through sampling during a training process for the artificial neural network model, and an output generation unit configured to generate a result value reflecting an uncertainty degree by aggregating values output from the artificial neural network model and the sampling models after the same data is input to the artificial neural network model and the sampling models.