Progressive Regularization for Faster Machine-Learned Model Training
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Solution Overview
Problem
The training of large and complex machine-learned models, such as neural networks, requires significant computational resources and is often prohibitively expensive, with existing methods either being slow or requiring excessive parameters, limiting the availability of resources for other tasks.
Innovation Solution
A method involving progressive adjustment of regularization during training, combined with architecture search, to optimize training speed and efficiency by initially using weak regularization and data complexity, progressively increasing both, and employing Fused-MBConv stages to enhance model training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If large and complex machine-learned models are trained to improve accuracy, then model performance is improved, but computational cost and training time increase prohibitively
Solution Approach 1:
The patent applies dynamic adjustment of regularization strength during training. The system transitions from strong regularization in early stages to weak regularization in later stages, allowing the model to achieve good accuracy with fewer computational resources. This dynamic approach resolves the contradiction by adapting the training process to reduce computational cost while maintaining accuracy.
Solution Approach 2:
The patent changes the regularization parameter dynamically during training iterations. By progressively reducing the regularization strength from high initial values to lower final values, the system enables faster convergence and reduces overall computational cost while maintaining model accuracy. This parameter change strategy directly addresses the trade-off between accuracy and computational expense.
2Reliability
If strong regularization is applied during training to prevent overfitting, then model generalization is improved, but training speed decreases
Solution Approach 1:
The system dynamically adjusts regularization strength based on training progress. Strong regularization is applied initially to ensure good generalization, then progressively reduced to accelerate training speed in later stages. This dynamic adjustment resolves the contradiction between generalization and training speed by timing the regularization strength appropriately throughout the training process.
Solution Approach 2:
The patent implements periodic adjustment of regularization strength during training iterations. The system alternates between periods of strong regularization for generalization and weak regulation for speed, creating a rhythmic training pattern that balances both objectives. This periodic action allows the model to achieve good generalization while maintaining acceptable training speed.
3Reliability
If more training iterations are performed to improve model accuracy, then model performance is improved, but training time increases
Solution Approach 1:
The patent changes the regularization parameter over training iterations to accelerate convergence. By reducing regularization strength as training progresses, the system achieves better accuracy in fewer iterations, thereby reducing training time. This parameter evolution strategy resolves the contradiction between accuracy and training time by making the training process more efficient in later stages.
Solution Approach 2:
The system dynamically adapts the training process by adjusting regularization strength based on iteration count. This dynamic approach allows the model to converge faster and achieve high accuracy with fewer training iterations, directly reducing training time while maintaining or improving model accuracy compared to static regularization approaches.
Data Source
AI summary
Systems and methods of the present disclosure can include a computer-implemented method for efficient machine-learned model training. The method can include obtaining a plurality of training samples for a machine-learned model. The method can include, for one or more first training iterations, training, based at least in part on a first regularization magnitude configured to control a relative effect of one or more regularization techniques, the machine-learned model using one or more respective first training samples of the plurality of training samples. The method can include, for one or more second training iterations, training, based at least in part on a second regularization magnitude greater than the first regularization magnitude, the machine-learned model using one or more respective second training samples of the plurality of training samples.