Quantization Model Online Adaptation via Time Scaling
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Solution Overview
Problem
Neural network models exhibit weak learning capability and low accuracy in online recommendation and prediction due to their inability to effectively adapt to new data features generated in actual application scenarios after offline training.
Innovation Solution
The method involves determining jump ratios of embedding layer parameters in a trained quantization model over a predetermined time range, fitting a jump curve to obtain a time scaling parameter, and optimizing the initial optimization algorithm based on this parameter to train the quantization model, thereby enhancing its ability to learn new data features online.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If an initial optimization algorithm is used to train the quantization model after offline training, then the model structure is simple and easy to implement, but the learning capability is weak and the accuracy of recommendation and prediction is low
Solution Approach 1:
The patent changes the parameters of the optimization algorithm by introducing a time scaling parameter that adjusts the learning rate dynamically based on the age of training data. This allows the model to adapt to new data features without fundamentally changing the optimization algorithm structure, thereby improving accuracy while maintaining relative simplicity.
Solution Approach 2:
The patent introduces dynamic adjustment mechanisms where the optimization algorithm adapts its behavior over time. The time scaling parameter makes the learning rate dynamic rather than static, allowing the model to learn new data features more effectively while the quantization mechanism provides dynamic adaptation to data distribution changes.
2Adaptability or versatility
If the quantization model is trained offline with a large amount of training data, then the model has initial prediction capability, but it cannot adapt to new data features generated in online actual use
Solution Approach 1:
The patent performs preliminary quantization processing on the model during offline training, preparing the model structure in advance. This preliminary action enables the model to quickly adapt to online data features without requiring extensive retraining, as the quantization framework is already in place to handle data distribution changes.
Solution Approach 2:
The patent implements continuous learning through the time scaling parameter mechanism that operates throughout the model's deployment. The optimization algorithm continuously adapts to new data features by adjusting learning rates based on data age, maintaining useful learning action without interruption or full retraining cycles.
3Speed
If the learning rate is increased to accelerate learning of new data features, then the convergence speed improves, but the model stability decreases and oscillation occurs
Solution Approach 1:
The patent applies dynamic adjustment of the learning rate through the time scaling parameter. The learning rate automatically increases when training on newer data features (where faster convergence is needed) and decreases when training on older, more stable data (where stability is prioritized), thus achieving both speed and stability at different stages.
Solution Approach 2:
The patent changes the learning rate parameter dynamically based on the time scaling factor derived from data age. This parameter transformation allows the system to accelerate convergence on new features while maintaining stability on established patterns, resolving the trade-off between speed and stability.
Data Source
AI summary
An optimization method comprises determining jump ratios of embedding layer parameters of a trained quantization model in a predetermined time range. The quantization model comprises a neural network model obtained after quantization processing on the embedding layer parameters. The method also comprises determining a jump curve in the predetermined time range according to the jump ratios, and fitting the jump curve to obtain a corresponding time scaling parameter. The method also comprises optimizing an initial algorithm of the quantization model based on the time scaling parameter to obtain an optimized target optimization algorithm, and training the quantization model based on the target optimization algorithm.