Constrained Neural Network Training for Sparse, Explainable Models
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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 functionality, and they lack the ability to enforce regulatory constraints and desired properties during training, leading to compliance issues and reduced model explainability.
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 model explainability and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If neural networks are trained to achieve high prediction accuracy, then predictive performance is improved, but model complexity increases and explainability deteriorates
Solution Approach 1:
The training process is segmented into two distinct phases: an initial training phase that focuses on achieving high prediction accuracy without constraints, and a subsequent constrained training phase that enforces structural constraints to improve explainability. This segmentation allows the model to first learn optimal predictive patterns, then refine its structure for interpretability without completely retraining from scratch.
Solution Approach 2:
The preliminary training phase performs the action of learning predictive patterns before the constrained training phase is applied. By pre-training the network to achieve good predictive performance first, the subsequent constraint application can focus on structural refinement rather than learning basic predictive relationships, preserving accuracy while improving explainability.
2Measurement precision
If neural networks are trained to achieve high prediction accuracy, then predictive performance is improved, but the ability to enforce regulatory constraints deteriorates
Solution Approach 1:
The training approach dynamically transitions from an unconstrained training regime to a constrained training regime. The network is first trained without constraints to achieve high accuracy, then dynamically switched to a constrained training mode where regulatory requirements are enforced through modified loss functions and optimization procedures, ensuring both accuracy and compliance.
Solution Approach 2:
The constrained training phase maintains continuity of the useful predictive action learned during initial training while adding constraint satisfaction to the optimization objective. The network continues to minimize prediction error while simultaneously satisfying regulatory constraints, ensuring that the model remains both accurate and compliant without requiring complete retraining.
3Reliability
If constraints are applied during training to ensure compliance, then regulatory compliance is improved, but training complexity increases
Solution Approach 1:
The constraint satisfaction problem is extracted and handled separately from the main predictive training objective. By formulating constraints as distinct components (such as separate penalty terms in the loss function or post-training adjustments), the training system can focus on predictive accuracy while constraint compliance is addressed through dedicated mechanisms, reducing overall training complexity compared to fully integrated approaches.
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 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.