ML Control System Hyperparameter Search Against Overfitting
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Solution Overview
Problem
Machine learning systems, such as neural networks, often memorize training data rather than generalizing it, which is problematic for safety-critical applications like automated driving, as they may not perform well with new data points.
Innovation Solution
The method automatically determines architectural and optimization parameters through a parameter search, preventing the system from memorizing training data and enhancing its generalization capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine learning systems are trained with traditional methods using fixed hyperparameters, then training can be completed efficiently, but the system memorizes training data and fails to generalize to new data points
Solution Approach 1:
The system performs self-service by automatically determining its own hyperparameters through the parameter search. The control system autonomously identifies optimal values for learning rate, batch size, and other hyperparameters without requiring manual intervention or external expertise, enabling the system to improve its own generalization capability while maintaining operational simplicity for users
Solution Approach 2:
The invention applies parameter changes by systematically varying hyperparameters (learning rate, batch size, number of epochs) during the training process. The control system explores different parameter combinations to identify optimal settings that prevent overfitting and enhance generalization, transforming fixed-parameter training into adaptive parameter optimization
2Reliability
If hyperparameters are chosen based on empirical knowledge or validation dataset performance, then training efficiency is maintained, but the system may still memorize training data due to insufficient parameter optimization
Solution Approach 1:
The system performs preliminary action by conducting a comprehensive parameter search before final model deployment. The control system pre-identifies optimal hyperparameter combinations through systematic exploration, ensuring that the model is properly configured for generalization before being deployed to production environments, thereby preventing time loss during later validation phases
Solution Approach 2:
The invention implements feedback mechanisms where the control system continuously monitors training performance metrics and uses this information to adjust hyperparameter selection. The system learns from validation performance and iteratively refines parameter choices, creating a closed-loop optimization process that balances exploration time with generalization improvement
3Adaptability or versatility
If the number of parameters in the neural network is much larger than the number of data points, then the model capacity increases, but the risk of memorizing training data increases significantly
Solution Approach 1:
The system applies parameter changes by dynamically adjusting both architectural parameters (network depth, width, filter sizes) and optimization parameters (learning rate schedules, regularization strengths) during training. This allows the model to adapt its capacity to match the complexity of the underlying data patterns rather than blindly increasing parameters, thereby maintaining high adaptability while controlling overfitting risk through data-driven parameter selection
Data Source
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AI summary
The invention relates to a method for parameterizing a machine learning system (60), in particular a neural network, which is configured to determine an associated class (y) of a plurality of classes from input data (x), wherein the machine learning system (60) is trained once with correctly labeled training data, and once with not correctly labeled training data. Hyperparameters (θH) of the machine learning system (60) are selected in such a way that the respective trained machine learning system (60) can reproduce actual classifications (y T ) of the correctly labeled training data better than actual classifications of the not correctly labeled training data (y T ).