Model Selection via Accumulated Activation Scores
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Solution Overview
Problem
Selecting an appropriate machine learning model and learning rate for new data is computationally intensive and time-consuming, often resulting in suboptimal model performance due to high computation costs and the risk of overfitting or slow convergence.
Innovation Solution
Calculating an accumulated model activation score (AMAS) and accumulated layer activation score (ALAS) for existing trained models to efficiently select the best model for new data and determine an optimal learning rate, reducing computational requirements and improving model selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If model selection and learning rate selection are performed through extensive training and evaluation, then model performance and accuracy are improved, but computation time and computational resources are significantly increased
Solution Approach 1:
The patent pre-calculates activation scores for all models in the set using a subset of training data before the actual model selection process. This preliminary computation of model activation scores and layer activation scores allows for rapid comparison and selection during fine-tuning, avoiding the need for extensive trial training of each model
Solution Approach 2:
The patent uses activation score patterns as proxies or copies of actual model performance characteristics. Instead of measuring full model performance through extensive training, the system computes activation scores that replicate the selection criteria, enabling fast model comparison without complete training runs
2Speed
If a high learning rate is used to speed up learning, then convergence time is reduced, but the risk of overfitting and algorithm instability increases
Solution Approach 1:
The patent dynamically determines the learning rate by analyzing the accumulated layer activation scores from the selected model. The system adjusts the learning rate parameter based on the magnitude of activation scores, using higher rates when activations are small and lower rates when activations are large, thereby adapting the learning speed to the specific model-data combination
Solution Approach 2:
The patent uses the accumulated activation scores as feedback to automatically select an appropriate learning rate. The system measures the activation pattern and uses this information to adjust the learning rate parameter, creating a closed-loop control mechanism that balances convergence speed and stability
3Measurement precision
If extensive training is performed to select the best model for new data, then model matching accuracy is improved, but computational resources and energy consumption are significantly increased
Solution Approach 1:
The patent extracts only the essential information needed for model selection by computing activation scores on a subset of training data rather than processing the entire dataset. This extraction of key activation patterns enables model comparison with reduced computational energy while maintaining selection accuracy
Data Source
AI summary
A method, computer system, and computer program product for model selection for training a new dataset is provided. The present invention may include choosing a model from a set of models to be evaluated for training the new dataset, selecting a sample input from a subset of the new dataset, calculating a model activation score for each of the sample inputs in the chosen model, calculating an accumulated model activation score for the chosen model, depending on the model activation score of each of the sample inputs in the chosen model, calculating an accumulated model activation score for each model from the set of models to be evaluated for training the new dataset, and selecting the model for training the new dataset with the highest accumulated model activation score.