Neural Network Inspection via Neuron Dependency Graphs
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Solution Overview
Problem
Deep Neural Networks (DNNs) are difficult to interpret due to their complex non-linear and interconnected nature, hindering adoption in regulated industries, preventing insight utilization, and complicating debugging and development.
Innovation Solution
A computer-implemented method that inspects neural networks by identifying relevant neurons, assigning relevance values, and generating dependency graphs to show interactions between neurons in adjacent layers, enhancing transparency and interpretability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep neural networks are used for complex decision-making tasks, then decision accuracy is improved, but interpretability deteriorates
Solution Approach 1:
The patent introduces dependency graphs as an intermediary representation that mediates between the complex neural network internal states and human interpreters. The graphs translate neuron activation patterns into visual dependency relationships, allowing users to understand decision-making processes without directly analyzing the complex network architecture. This intermediary visualization layer preserves decision accuracy while improving interpretability.
Solution Approach 2:
The patent extracts specific relevant neurons from the complex neural network based on their contribution to the final decision. By identifying and extracting only the neurons with high relevance values (above a threshold), the system creates a simplified view of the decision-making process. This extraction separates the critical decision-making elements from the rest of the complex network, making interpretation feasible while maintaining decision accuracy.
2Reliability
If the neural network structure is made more complex to handle non-linear relationships, then model performance is improved, but debugging difficulty increases
Solution Approach 1:
The dependency graph serves as an intermediary debugging tool that translates complex neural network interactions into visualizable dependency relationships. By showing which neurons depend on which other neurons in the decision-making process, the graph enables developers to trace and debug complex non-linear relationships without directly analyzing the intricate network structure. This maintains model performance while reducing debugging difficulty.
3Device complexity
If all neurons are analyzed to ensure complete transparency, then interpretability is improved, but computational cost increases
Solution Approach 1:
The patent applies local quality by focusing computational resources on identifying and analyzing only the relevant neurons that contribute significantly to the decision, rather than uniformly analyzing all neurons. By calculating relevance values and filtering for neurons above a threshold, the system concentrates interpretability efforts on the local subset of neurons that matter for the specific decision, reducing overall computational cost while maintaining interpretability where it is most needed.
Solution Approach 2:
The patent implements partial action by performing neuron analysis only to the extent necessary for interpretation. Instead of exhaustively analyzing every neuron's contribution, the system identifies neurons with relevance values above a threshold and focuses the dependency graph construction on these partial results. This partial analysis achieves sufficient interpretability for practical purposes while avoiding the excessive computational cost of complete analysis.
Data Source
AI summary
Broadly speaking, embodiments of the present techniques provide methods for inspecting a neural network, such that a neural network can be made more transparent. The inspection is performed with respect to each decision or output made by the neural network. The method comprises outputting a dependency graph, for each inspection/decision. Each dependency graph shows which neurons are used to make each individual decision made by the neural network, and how those neurons interact with or relate to each other. Specifically, the dependency graph shows the dependencies between neurons in adjacent layers. By understanding which neurons are used to make individual decisions, and the dependencies between neurons, the neural network can be better understood, audited, optimised, and debugged, for example.