Neural Network Intermediate Prediction Ensemble
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Solution Overview
Problem
Machine learning models face challenges in real-time classification tasks due to limited computational budgets, where the quality of predictions is not guaranteed to improve with increased computational resources, and traditional neural networks do not satisfy the interruptibility requirement, leading to unpredictable outcomes.
Innovation Solution
A trained machine learning model with input, intermediate, and output layers that generates ensemble predictions by partially propagating input data through a subset of layers, using weighted and normalized intermediate predictions to produce a reliable output, even with limited computational resources, without modifying the training process or model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional neural networks are used for real-time classification, then model completeness is maintained, but interruptibility and computational efficiency deteriorate because the model cannot produce intermediate outputs with limited computational budget
Solution Approach 1:
The neural network is divided into multiple independent layers that can be executed sequentially. Each layer can produce intermediate outputs that serve as predictions when computational budget is limited. This segmentation allows the model to be interrupted at any layer while still providing useful classification results, thereby achieving interruptibility without sacrificing model completeness.
2Productivity
If early-exit models are used to enable intermediate predictions, then interruptibility is achieved, but prediction reliability deteriorates because prediction quality is not guaranteed to improve monotonically with increased computational budget
Solution Approach 1:
The system implements a confidence-based feedback mechanism where each layer's output is evaluated against a confidence threshold. If the intermediate prediction meets the threshold, it is returned immediately; otherwise, computation continues to deeper layers. This feedback loop ensures that intermediate predictions are only returned when they are sufficiently reliable, guaranteeing monotonic improvement in prediction quality with increased computational budget.
Solution Approach 2:
The model dynamically adjusts the computational budget allocation based on input data characteristics and confidence levels. By changing the parameter of how many layers to execute based on real-time assessment, the system ensures that prediction quality improves monotonically with computational resources while maintaining interruptibility when confidence thresholds are met.
3Measurement precision
If full model execution is performed to ensure prediction accuracy, then prediction quality is maximized, but computational cost and time consumption increase beyond limited budgets
Solution Approach 1:
The system performs partial execution of the neural network by stopping at the shallowest layer that produces a prediction meeting the confidence threshold. This partial action principle allows the model to consume only the necessary computational resources required to achieve satisfactory prediction accuracy, avoiding the excessive computational cost of always executing the full model while maintaining high prediction quality when needed.
Data Source
AI summary
Generating intermediate predictions in a trained machine learning model. The trained machine learning models may include one or more input layers, multiple intermediate layers, and one or more output layers, which generate an output. A computer-implemented method may perform classification using the trained machine learning model, which includes the steps of feeding input data to the trained machine learning model, propagating the input data through a part of the trained machine learning model, obtaining intermediate predictions from the layers in the part of the trained machine learning model, ensembling these intermediate predictions to obtain an ensemble prediction, and using the ensemble prediction as a substitute for the output of the trained machine learning model in the classification. The ensembling phase may include determining the ensemble prediction as a product of weighted versions of the obtained intermediate predictions, and having a normalized probability density.