Infinite-Width Neural Network Hyperparameter Transfer for Low-Cost Tuning
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Solution Overview
Problem
Training large neural networks with billions of parameters is computationally expensive and energy-intensive, making multiple tuning and training passes impractical.
Innovation Solution
Tune hyperparameters indirectly on a narrower model and transfer them to the full-size model, leveraging the theory of infinitely wide neural networks to maintain a stable hyperparameter landscape across different widths.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If hyperparameter tuning is performed on a large neural network model with billions of parameters, then the tuning accuracy is improved, but the computational cost and energy consumption increase significantly
Solution Approach 1:
The patent creates a scaled-down copy of the large neural network model with reduced width (fewer parameters) while maintaining the same architecture and hyperparameter relationships. This smaller copy serves as a surrogate for hyperparameter tuning, allowing multiple tuning passes to be performed efficiently. The tuned hyperparameters from the small model are then transferred to the large model, achieving accurate tuning without the prohibitive computational cost of tuning the full-sized model directly.
2Reliability
If multiple tuning passes are performed on a large neural network model, then the model performance is improved, but the training time and computational resources required increase impractically
Solution Approach 1:
The patent performs hyperparameter tuning in advance on a small, computationally efficient model before deploying the large model. By completing the iterative tuning process on the small model first, the optimal hyperparameters are determined beforehand. These pre-determined hyperparameters are then applied to the large model, eliminating the need for multiple time-consuming tuning passes on the full-sized model and enabling faster deployment.
3Productivity
If the width of neural network layers is reduced to create a smaller model, then the computational efficiency is improved, but the model capacity may be reduced
Solution Approach 1:
The patent systematically varies the width parameter of neural network layers to create a sequence of models with different sizes. By analyzing how model performance changes with width, the method identifies the optimal width that balances computational efficiency and model capacity. This parameter study allows the selection of a small model width sufficient for accurate hyperparameter tuning while maintaining the ability to transfer results to larger models when needed.
Data Source
AI summary
Systems and method are provided that are directed to tuning a hyperparameter associated with a small neural network model and transferring the hyperparameter to a large neural network model. At least one neural network model may be received along with a request for one or more tuned hyperparameters. Prior to scaling the large neural network, the large neural network is parameterized in accordance with a parameterizing scheme. The large neural network is then scaled and reduced in size such that a hyperparameter tuning process may be performed. A tuned hyperparameter may then be provided to a requestor such that the hyperparameter can be directly input into the large neural network. By tuning a hyper parameter using a small neural network, significant computation cycles and energy may be saved.