Neural Network Training With Constraints for Explainable Prediction
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Solution Overview
Problem
Current machine learning models, particularly neural networks, are complex and opaque, making it difficult for humans to understand their rationale and weights, and they lack the ability to enforce regulatory constraints and desired properties during training, leading to unpredictable behavior in production.
Innovation Solution
Implement constraints on the model's coefficients during training to ensure compliance with regulatory requirements and desired properties, such as monotonicity and sparse connectivity, by iteratively adjusting weights and biases to zero non-conforming connections, thereby improving explainability and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If neural networks are trained to maximize predictive accuracy, then prediction accuracy improves, but model complexity and opacity increase making the models unexplainable
Solution Approach 1:
The patent segments the training process into distinct phases: an initial training phase that maximizes predictive accuracy without constraints, followed by a constraint application phase that enforces sparsity and monotonicity constraints on specific weight matrices. This segmentation allows the model to first learn optimal predictive patterns, then selectively simplify specific connection layers to improve explainability while preserving overall performance.
Solution Approach 2:
The patent applies constraints locally to specific weight matrices rather than uniformly across the entire network. By identifying which weight matrices correspond to specific input features or connection layers, the patent applies sparsity and monotonicity constraints only to those local regions, preserving the predictive accuracy of the overall model while making specific portions explainable and interpretable.
2Ease of operation
If constraints are applied to ensure regulatory compliance and explainability, then model explainability improves, but predictive performance may deteriorate
Solution Approach 1:
The patent employs dynamic constraint application where sparsity and monotonicity constraints are enforced during the training process rather than being static. The constraint strength and application are adjusted dynamically based on training progress and performance metrics, allowing the model to adaptively balance between explainability requirements and predictive performance throughout the training phases.
Solution Approach 2:
The patent changes key parameters of the neural network during training, specifically modifying weight matrices to satisfy sparsity and monotonicity constraints. By systematically adjusting weight values, applying thresholding operations, and enforcing parameter bounds, the patent transforms the model parameters to achieve regulatory compliance while monitoring and maintaining acceptable predictive performance through iterative optimization.
3Ease of operation
If the model structure is simplified to improve understandability, then ease of interpretation improves, but predictive power decreases
Solution Approach 1:
The patent extracts and removes non-essential or redundant weight connections from the neural network by applying sparsity constraints that force certain weight matrices to zero or near-zero values. This extraction process eliminates complex, hard-to-interpret connections while preserving the essential predictive pathways, thereby improving interpretability without significantly degrading predictive power.
Solution Approach 2:
The patent performs preliminary training without constraints to establish optimal predictive patterns, then subsequently applies sparsity and monotonicity constraints to simplify the structure. This preliminary action ensures that the model first achieves maximum predictive capability before structural simplification, allowing the subsequent simplification to remove only redundant elements rather than essential predictive features.
Data Source
AI summary
Systems and methods for training a machine learning model implemented over a network configured to represent the machine learning model are provided. At least one or more directed edges connect the one or more nodes with an edge representing a connection between a first node and a second node, the second node computing an activation depending on the values of activations on first nodes and values associated with the connections, the connection being either conforming or non-conforming. The machine learning model may be trained by iteratively adjusting parameters w and b, respectively associated with weights and biases associated with edges connecting computational nodes. Connections between nodes may be sparsified by adjusting the parameter w to a first value for non-conforming connections during the training phase to reduce complexity of the connections among the plurality of nodes, or to ensure the input-output function of the network adheres to additional constraints.