Attention Loss Regularization for Interpretable Manufacturing Data Models
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Solution Overview
Problem
Manufacturing datasets are challenging due to diverse value types, non-Gaussian distributions, extreme outliers, missing/undefined data, and inefficiencies in standard tokenization, leading to poor performance and interpretability in conventional neural networks and transformers.
Innovation Solution
A regularization method for attention-based neural networks (ABNNs) using softmax1 function and L1-L2 penalty on attention layers, combined with data preprocessing to handle undefined values and relational embeddings, enhances convergence stability and interpretability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional neural networks and transformers are used to process manufacturing data, then the models can handle diverse value types and non-Gaussian distributions, but the interpretability and convergence stability deteriorate due to overfitting and noise
Solution Approach 1:
The patent applies L1-L2 regularization penalty to the softmax function parameters in attention layers, modifying the parameter space to balance model complexity and generalization. This regularization approach constrains the attention weights to prevent overfitting while maintaining the model's ability to handle diverse manufacturing data types
Solution Approach 2:
The patent introduces relational embeddings as an intermediary representation layer between raw manufacturing data and the attention mechanism. This intermediary structure facilitates the handling of diverse data types (categorical, numerical, temporal) while improving interpretability through explicit relationship modeling
2Productivity
If standard tokenization and attention layers are used, then the processing speed is maintained, but the interpretability deteriorates due to difficulty in understanding model predictions
Solution Approach 1:
The patent segments the attention mechanism into interpretable components by applying regularization to individual attention heads and layers. This segmentation allows separate analysis of different attention patterns while maintaining overall processing efficiency
Solution Approach 2:
The patent enhances interpretability by visualizing attention weights through color-coded heatmaps and gradient representations, allowing stakeholders to visually detect and measure attention patterns without sacrificing processing speed
3Reliability
If regularization is applied to improve convergence stability, then the generalizability improves, but the complexity of the training process increases
Solution Approach 1:
The patent merges L1 and L2 regularization penalties into a unified L1-L2 regularization framework applied to the softmax function. This combination achieves robust generalization while simplifying the training process by using a single regularization parameter instead of separate L1 and L2 parameters
Data Source
AI summary
A systems and methods for implementing attention-based neural networks, attention modules, regularization techniques, and unique data encoding such as for sequential tabular data and/or manufacturing data is provided. The attention-based neural networks may include a high dropout and unique softmax regularization. The encoding may attend to missing or undefined data as well as numerous data types common to manufacturing data.