Split-net ML Model for Constraint-Aware Predictive Modeling
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Solution Overview
Problem
Deep Learning models struggle to incorporate industry-specific constraints, leading to inefficient optimization and incorrect generalization in predictive tasks, particularly in domains like demand planning and workforce management, where constraints are complex and data is limited.
Innovation Solution
A machine learning system employing a split-net configuration, combining a deep learning model and a wide learning model, where the deep learning model learns fixed features and the wide learning model is constrained to memorize domain-specific constraints, allowing for efficient implementation of arbitrary constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning models are used for predictive modeling, then modeling flexibility and accuracy are improved, but the ability to incorporate industry-specific constraints deteriorates
Solution Approach 1:
The model is segmented into two distinct components: an unconstrained deep learning model for learning complex patterns from data, and a constrained wide learning model for enforcing industry-specific constraints. This segmentation allows each component to specialize in its strength while working together to achieve both accuracy and constraint compliance.
Solution Approach 2:
The patent merges the deep learning model and wide learning model into a unified predictive modeling system where both models operate simultaneously on the same input data. The deep learning model captures complex non-linear relationships while the wide learning model ensures constraint satisfaction, combining their outputs to achieve both high accuracy and constraint adherence.
2Reliability
If domain-specific constraints are incorporated into deep learning models, then constraint satisfaction is improved, but optimization efficiency deteriorates
Solution Approach 1:
The optimization process is segmented into two parallel training procedures: one for the deep learning model that optimizes for predictive accuracy without constraint penalties, and another for the wide learning model that optimizes for constraint satisfaction. This segmentation avoids the computational burden of integrating constraints directly into the deep learning optimization.
Solution Approach 2:
The wide learning model acts as an intermediary that handles constraint enforcement separately from the main deep learning optimization process. Instead of directly constraining the complex neural network, the wide learning model learns constraint representations that can be applied to guide the deep learning model's predictions, making optimization more efficient.
3Reliability
If weaker models with incorporated constraints are used, then constraint compliance is improved, but predictive accuracy deteriorates
Solution Approach 1:
The patent combines the strengths of both model types by merging their outputs. The deep learning model provides high predictive accuracy by learning complex patterns, while the wide learning model ensures constraint compliance. Their combined prediction achieves both high accuracy and constraint satisfaction, overcoming the limitations of using either model alone.
Data Source
AI summary
A machine learning system that uses a split net configuration to incorporate arbitrary constraints receives a set of input data and a set of functional constraints. The machine learning system jointly optimizes a deep learning model by using the set of input data and a wide learning model by using the set of constraints. The deep learning model includes an input layer, an output layer, and an intermediate layer between the input layer and the output layer. The wide learning model includes an input layer and an output layer but no intermediate layer. The machine learning system provides a machine learning model comprising the optimized deep learning model and the optimized wide learning model.