Interpretable Neural Network Using Sparsity-Inducing Probability Distributions
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Solution Overview
Problem
Neural networks lack interpretability, making it difficult to determine which input data features are relevant for predictions, particularly in critical domains like healthcare, where transparent decision-making is essential.
Innovation Solution
A neural network design that uses a mixture of sparsity-inducing and non-sparsity-inducing probability distributions across layers, allowing for the identification of relevant input features by driving less contributing nodes towards zero, thereby providing interpretable predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional neural networks are used to achieve high predictive performance, then prediction accuracy is improved, but interpretability deteriorates
Solution Approach 1:
The patent applies different probability distribution characteristics to different layers of the neural network. The first hidden layer uses sparsity-inducing distributions to promote interpretability, while subsequent hidden layers use non-sparsity-inducing distributions to maintain predictive performance. This local differentiation allows each layer to specialize in different functions.
Solution Approach 2:
The neural network is segmented into distinct functional zones: an input layer, a first hidden layer with sparsity-inducing distributions for feature selection, additional hidden layers with non-sparsity-inducing distributions for complex pattern recognition, and an output layer. This segmentation allows the network to simultaneously achieve interpretability in feature selection and accuracy in prediction.
2Reliability
If all input features are processed through the network, then prediction completeness is improved, but computational complexity increases
Solution Approach 1:
The first hidden layer extracts and identifies the most relevant input features by applying sparsity-inducing probability distributions that drive less contributing features toward zero. This extraction process separates important features from irrelevant ones, allowing the network to focus computational resources on meaningful inputs while maintaining prediction completeness.
Solution Approach 2:
The network processes all input features through the first hidden layer to ensure none are missed, but the sparsity-inducing mechanism then selectively de-emphasizes irrelevant features. This partial action approach ensures completeness in feature consideration while reducing computational burden by focusing subsequent processing on the most relevant features identified by the sparsity mechanism.
Data Source
AI summary
A method of operating a neural network, comprising: at each input node of an input layer, weighting a respective input element received by that node by applying a first class of probability distribution, thereby generating a respective set of output parameters describing an output probability distribution; and from each input node, outputting the respective set of output parameters to one or more nodes in a next, hidden layer of the network, thereby propagating the respective set of output parameters through the hidden layers to an output layer; the propagating comprising, at one or more nodes of at least one hidden layer, combining the sets of input parameters and weighting the combination by applying a second class of probability distribution, thereby generating a respective set of output parameters describing an output probability distribution, wherein the first class of probability distribution is more sparsity inducing than the second class of probability distribution.