Predictive Model Compression Using Simulated Data Distillation
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Solution Overview
Problem
Complex predictive models are opaque, brittle, and storage inefficient, making them difficult to understand, update, and deploy in resource-constrained environments while maintaining high predictive accuracy.
Innovation Solution
A computer-implemented method and system for compressing predictive models by generating an unlabeled simulated data set, creating a labeled data set using a complex model, and training a neural network model to reproduce the predictive capabilities of the complex model, thereby reducing model size and complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a complex predictive model is used to achieve high predictive accuracy, then the model can make sophisticated predictions, but the model size and storage requirements increase significantly
Solution Approach 1:
The complex predictive model is divided into multiple sub-models, each handling specific aspects of the prediction task. This segmentation allows the overall model to maintain high accuracy while each individual component remains manageable in size, enabling efficient storage and deployment.
Solution Approach 2:
The patent implements a hierarchical structure where sub-models are nested within the overall predictive system. Each sub-model processes specific input features and passes results to subsequent sub-models, creating a nested architecture that reduces the computational and storage burden of the complete model while preserving predictive accuracy.
2Measurement precision
If multiple sub-models are combined to improve predictive capabilities, then the model can handle complex transformations, but the model becomes opaque and difficult to understand
Solution Approach 1:
By segmenting the model into distinct sub-models with specific functions, the patent makes the complex system more comprehensible. Each sub-model can be independently understood and explained, and their individual contributions to the final prediction can be tracked and interpreted.
Solution Approach 2:
The patent introduces intermediate representations and transformations between sub-models that serve as interpretable mediators. These intermediate steps provide meaningful interpretations of the data flow and transformations, making the overall complex model more transparent while maintaining its predictive power.
3Measurement precision
If a complex model with many sub-models is used to achieve accurate predictions, then the model can be highly sophisticated, but the model becomes brittle and difficult to update
Solution Approach 1:
The segmented architecture allows individual sub-models to be updated independently without requiring changes to the entire system. This modularity enables selective retraining and updating of specific components based on new data or changing requirements, significantly improving adaptability.
Solution Approach 2:
The patent implements a dynamic model structure where sub-models can be added, removed, or modified based on performance requirements and available data. This dynamic architecture allows the system to adapt to changing conditions and incorporate new information without rigid constraints.
4Measurement precision
If numerous mathematical transformations are applied to input parameters to improve predictions, then the model can capture complex relationships, but the model requires more storage space
Solution Approach 1:
The patent extracts and applies only the most relevant mathematical transformations and features needed for accurate predictions, rather than using all possible transformations. This selective approach maintains predictive accuracy while reducing the storage requirements by eliminating redundant or less important model components.
Data Source
AI summary
A computer-implemented method for improving compression of predictive models can include providing a labeled data set. The computer-implemented method also can include training, using the labeled data set, a neural network model associated with one or more training parameters to create a trained neural network model. Other embodiments are disclosed.