Model Parameter Weighting for Prior-Posterior Accuracy Balance
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Solution Overview
Problem
Current model training methods face challenges in achieving high model accuracy due to limited prior data, leading to an exponential concentration of the prior distribution near optimal parameters, thereby minimizing the impact of posterior data and resulting in low model performance.
Innovation Solution
A method that traverses reference values based on preset model evaluation information and data pairs to determine target values that meet specific loss and accuracy bounds, allowing for optimized parameter updates that consider both prior and posterior data, thus balancing data quantities and improving model accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the amount of prior data is increased, then the prior distribution becomes more concentrated near optimal parameters, but the impact of posterior data on the posterior distribution decreases, resulting in low model accuracy
Solution Approach 1:
The patent applies dynamics by making the weight coefficient adaptive rather than fixed. The weight coefficient dynamically adjusts based on the relative amounts of prior and posterior data, allowing the optimization algorithm to automatically balance the influence of each data type. This dynamic adjustment resolves the contradiction by preventing prior data from dominating when posterior data is scarce, thereby maintaining model accuracy while still utilizing prior knowledge effectively.
Solution Approach 2:
The patent changes the parameter of data weighting by introducing a weight coefficient that modifies how prior and posterior data are combined. This parameter change allows the system to flexibly control the influence of prior data distribution on the final parameter optimization, preventing the harmful effect of excessive concentration while preserving the benefits of prior knowledge when appropriate.
2Extent of automation
If sampling-based Bayesian optimization is used to obtain posterior distribution, then parameter optimization can be performed, but the posterior data has little impact on the posterior distribution when prior data is abundant, leading to suboptimal model performance
Solution Approach 1:
The patent implements feedback by using the weight coefficient to regulate the interaction between prior and posterior data. The optimization process continuously evaluates the relative importance of each data type and adjusts the weighting accordingly, ensuring that posterior data maintains appropriate influence on the final parameter optimization even when prior data is abundant. This feedback mechanism prevents performance degradation while maintaining automated optimization.
3Ease of manufacture
If a fixed weight coefficient is used in parameter optimization, then the optimization process is simple, but it cannot adapt to different data quantities, resulting in poor model accuracy
Solution Approach 1:
The patent transforms the static weight coefficient into a dynamic one that automatically adapts to different data quantities. This dynamic approach maintains the simplicity of the optimization process while eliminating the need for manual tuning, as the weight coefficient automatically adjusts based on the relative amounts of prior and posterior data available, thereby achieving both ease of implementation and high model accuracy.
Data Source
AI summary
Embodiments of this application provide a model parameter optimization method, a related system, and a storage medium, to avoid introducing a complex hyperparameter for balancing an amount of prior data and an amount of posterior data during parameter optimization, and improve model accuracy.