Hyperparameter Neural Network Ensembles for Prediction Accuracy
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Solution Overview
Problem
Conventional techniques for generating ensembles of neural networks lack diversity in hyperparameters, leading to suboptimal prediction accuracy and uncertainty quantification, and are inefficient in terms of memory usage and computational latency.
Innovation Solution
The system generates a hyper-deep ensemble by varying both parameter initializations and hyperparameters across neural networks, allowing for shared parameters among networks, which enables more efficient memory usage and parallel processing of predictions, thereby improving prediction quality and uncertainty quantification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional techniques are used to generate ensembles with different parameter initializations, then diversity in predictions is ensured, but hyperparameter diversity is lacking leading to suboptimal prediction accuracy
Solution Approach 1:
The patent applies parameter changes by systematically varying hyperparameters (learning rate, batch size, optimizer type, weight decay) across different neural networks in the ensemble, in addition to parameter initializations. This creates diverse models with different training dynamics and convergence behaviors, leading to improved prediction accuracy and uncertainty quantification without requiring each model to be completely different in architecture.
2Reliability
If multiple neural networks are trained independently with full parameters, then prediction diversity is achieved, but memory usage increases significantly
Solution Approach 1:
The patent merges parameter storage across multiple neural networks by identifying and sharing common parameters (weights, biases, layer configurations) while maintaining independent hyperparameters and initialization seeds for each model. This combining approach reduces memory footprint from O(K×N) to O(N + K) where K is the number of networks and N is the number of unique parameters, enabling deployment of larger ensembles with limited memory resources.
3Quantity of substance
If sequential processing is used for ensemble predictions, then memory usage is reduced, but computational latency increases
Solution Approach 1:
The patent segments the ensemble processing into parallel independent inference paths while sharing common parameter storage. Each neural network in the ensemble can process inputs simultaneously using their own hyperparameters and initializations, but they read from shared parameter memory. This segmentation enables parallel computation that reduces latency while maintaining memory efficiency through parameter sharing.
4Ease of manufacture
If conventional ensembles are generated without hyperparameter variation, then training is simpler, but uncertainty quantification is suboptimal
Solution Approach 1:
The patent introduces dynamics by varying hyperparameters across the ensemble, creating models with different training trajectories and convergence points. This dynamic variation in training conditions (learning rates, batch sizes, optimizers) ensures that models explore different regions of the loss landscape, leading to more diverse predictions and better uncertainty quantification while maintaining relatively simple training procedures.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating an ensemble of neural networks. In particular, the neural networks in the ensemble are trained using different hyperparameters from one another.