Surrogate Model Predicts ML Performance from Weights
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Solution Overview
Problem
Current methods for predicting machine learning model performance, such as neural networks, rely heavily on computationally expensive margin approximations in intermediate layers, which are not always accurate and require inference passes over the training set, limiting their efficiency and accuracy.
Innovation Solution
A machine-learned performance prediction model is trained to predict performance values based on model parameter values, allowing for early stopping of training procedures by using gradient boosting machines, logit-linear models, or fully-connected neural networks, reducing the need for costly computations and providing accurate predictions with minimal resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If margin approximation methods are used to predict model performance, then prediction capability is provided, but computational cost increases and accuracy is not guaranteed
Solution Approach 1:
The patent creates a surrogate model (copy) that mimics the behavior of the original complex machine learning model. Instead of computing margins through expensive inference passes on the original model, the surrogate model predicts performance metrics directly from parameter values, providing accurate predictions with minimal computational resources.
Solution Approach 2:
The patent performs preliminary computation by pre-calculating and storing the relationship between parameter values and performance metrics in a surrogate model. This preliminary action eliminates the need for expensive real-time margin approximations during model evaluation, as the surrogate model can quickly predict performance from parameters alone.
2Measurement precision
If inference passes over training set are performed for margin approximation, then performance prediction is enabled, but computational efficiency decreases
Solution Approach 1:
The patent extracts the essential performance-determining information from the complex inference process and encapsulates it in the surrogate model. By taking out the critical relationship between parameters and performance, the system eliminates the need for repeated inference passes over the training set, dramatically improving computational efficiency.
Solution Approach 2:
The surrogate model serves as a computational copy that captures the performance prediction capability without requiring access to the original training data or execution of inference passes. This copy enables fast predictions using only parameter values.
3Measurement precision
If complex margin approximation procedures are used, then performance prediction is achieved, but system complexity increases
Solution Approach 1:
The patent replaces the complex margin approximation procedure with a simpler surrogate model that copies the essential prediction capability. The surrogate model uses straightforward parameter-based predictions instead of complex inference procedures, reducing system complexity while maintaining prediction accuracy.
4Measurement precision
If traditional performance evaluation methods are used, then accurate performance measurement is obtained, but training time increases
Solution Approach 1:
The patent performs preliminary modeling of the performance-parameter relationship in the surrogate model. This preliminary action enables fast predictions during training without requiring time-consuming traditional evaluation methods, thus reducing training time while maintaining measurement accuracy.
Solution Approach 2:
The surrogate model provides a simplified copy of the performance evaluation capability that operates directly on parameter values without requiring full model inference. This copying approach maintains accuracy while dramatically reducing the time required for performance measurement during training.
Data Source
AI summary
Provided are systems and methods for predicting machine learning model performance from the model parameter values, including for use in making improved decisions with regard to early stopping of training procedures. As one example, the present disclosure discusses the prediction of the accuracy (e.g., relative to a defined task and testing dataset such as a computer vision task) of trained neural networks (e.g., convolutional neural networks (CNNs)), using only the parameter values (e.g., the values of the network's weights) as inputs. As such, one example aspect of the present disclosure is directed to computing systems that include and use a machine-learned performance prediction model that has been trained to predict performance values of machine-learned models based on their parameter values (e.g., weight values and/or hyperparameter values).