Prediction Model Parameter Distributions for Uncertainty-Aware Inference
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Solution Overview
Problem
Prediction models, such as deep neural networks, often make overconfident incorrect determinations when actual input data differs from training samples due to limited training samples, leading to inaccurate prediction accuracy and uncertainty measurement.
Innovation Solution
Introduce randomness to prediction models by training processing units to satisfy an objective parameter distribution, rather than fixed values, allowing the models to reflect uncertainty more accurately through Bayesian inference and reduced sampling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If training samples are increased to improve prediction accuracy, then prediction accuracy is improved, but training cost and time consumption increase
Solution Approach 1:
The patent changes the parameter representation from fixed values to probability distributions. By representing model parameters as distributions rather than fixed values, the model can capture uncertainty and improve prediction accuracy without requiring additional training samples, thus avoiding increased training time.
Solution Approach 2:
The patent applies Monte Carlo sampling to obtain a limited number of parameter groups from the distribution, rather than using all possible parameter variations. This partial action approach provides sufficient uncertainty estimation without the computational burden of exhaustive sampling, balancing accuracy and training efficiency.
2Device complexity
If fixed parameter values are used in prediction models, then model simplicity is maintained, but uncertainty measurement accuracy deteriorates
Solution Approach 1:
The patent transforms parameters from fixed values to probability distributions, enabling the model to represent uncertainty inherently. This parameter change allows the model to maintain its structural simplicity while significantly improving uncertainty measurement accuracy through the distributional representation.
Solution Approach 2:
The patent replaces the traditional deterministic mechanical system (fixed parameters) with a probabilistic system (parameter distributions). This substitution enables the model to naturally capture uncertainty without adding complex uncertainty modeling mechanisms, maintaining simplicity while improving measurement accuracy.
Data Source
AI summary
Embodiments of the present disclosure relate to a method, a device and a computer storage medium for data analysis. The method comprises: obtaining a prediction model, a processing layer of the prediction model comprising a plurality of processing units, parameters of each of the a plurality of processing units satisfying an objective parameter distribution, an output of the prediction model being determined based on a plurality of groups of parameters determined from the parameter distribution; and applying model input data to the prediction model, so as to obtain a prediction for the model input data. In this way, a more accurate prediction result may be obtained.