Machine Learning Parameter Tuning via Stopping Criteria
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Solution Overview
Problem
Machine learning models used in natural language processing, such as for speech recognition and understanding, require efficient tuning of parameters to improve performance metrics like accuracy and efficiency, but existing methods lack systematic approaches for optimizing these parameters during retraining or initial configuration.
Innovation Solution
A system that determines a set of values to test for parameters, configures a machine learning model with selected values, and evaluates the model's performance using a metric, incorporating stopping criteria to optimize parameter tuning during retraining or initial configuration, allowing for parallel processing of different model configurations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning model parameters are tuned systematically with multiple values and stopping criteria, then model performance accuracy is improved, but the complexity of the configuration process increases
Solution Approach 1:
The patent segments the parameter tuning process into distinct components: parameter selection module, model configuration module, training module with stopping criteria, and evaluation module. This segmentation allows systematic tuning of multiple parameters (learning rate, batch size, epochs) while managing complexity through modular design, where each component handles a specific aspect of the configuration process.
Solution Approach 2:
The patent systematically changes multiple model parameters (learning rate, batch size, number of epochs, dropout rate) and evaluates their impact on model performance. By implementing structured parameter tuning with stopping criteria based on performance metrics, the system achieves improved accuracy while maintaining manageable complexity through methodical parameter exploration.
2Productivity
If multiple model configurations are evaluated in parallel, then productivity of model optimization is improved, but the use of computational resources increases
Solution Approach 1:
The patent implements preliminary action by establishing stopping criteria before training begins (maximum epochs, performance thresholds). This allows parallel evaluation of multiple configurations to proceed efficiently, terminating training runs that meet criteria or show poor performance, thereby balancing productivity gains from parallel processing with controlled resource consumption.
Solution Approach 2:
The system uses feedback mechanisms by continuously monitoring model performance metrics during training and using this feedback to determine when to stop training runs. This feedback-driven approach enables efficient parallel processing by terminating configurations that have achieved satisfactory performance or are unlikely to improve, optimizing the balance between productivity and resource usage.
Data Source
AI summary
Techniques for tuning parameters for machine learning models are described. Different values for a parameter are tested to determine the value that results in an optimized model. A parameter value may be selected for testing using a search algorithm based on how the model performs with respect to other values for the parameter. Different values may be tested until a stopping criterion (such as time for testing, number of trials, amount of enhancement in performance, etc.) is met. In some embodiments, the techniques may be used to determine parameter values for natural language processing models.