Neural Network Gating for Interpretable Synthesized Data Sets
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Solution Overview
Problem
Existing data processing systems struggle to efficiently identify and process large numbers of diverse data sets to find useful correlations and generate understandable synthesized data sets for predicting a target metric due to combinatorial explosion and lack of human-understandability.
Innovation Solution
A neural network system that processes input data sets, iteratively trains to converge on significant nodes and operations, selects relevant data sets and operations, and generates human-understandable names for synthesized data sets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all possible data set combinations are processed to find useful correlations, then the completeness of analysis is improved, but the processing time and computational resources increase exponentially due to combinatorial explosion
Solution Approach 1:
The neural network is trained in advance on the diverse data sets to learn patterns and relationships before actual prediction tasks. This preliminary training enables the network to quickly process new data without requiring exhaustive combination analysis at prediction time, thus reducing processing time while maintaining analysis completeness
Solution Approach 2:
The system uses synthesized data sets generated by the neural network as copies or representations of complex real-world data relationships. These synthesized data sets capture essential patterns without requiring processing of all original data combinations, enabling efficient analysis while preserving reliability
2Measurement precision
If synthesized data sets are generated using complex neural network operations, then the predictive accuracy is improved, but the human-understandability of the results deteriorates
Solution Approach 1:
The system introduces an intermediary layer that translates complex neural network operations into human-understandable synthesized data sets. The neural network generates intermediate representations that maintain predictive accuracy while being structured in a way that can be interpreted and understood by humans, bridging the gap between complex computation and human comprehension
3Measurement precision
If the neural network processes all input data sets with all processing operations, then the accuracy of synthesized data sets is improved, but the data processing speed deteriorates
Solution Approach 1:
The neural network applies processing operations selectively rather than exhaustively to all data sets. During training and inference, the network learns to apply only the necessary subset of operations needed to achieve accurate synthesized data sets, avoiding redundant computations and maintaining high processing speed while preserving accuracy
Data Source
AI summary
A computer system stores data sets, a target metric, and a parameter that indicates a desired number of synthesized data sets, and a neural network. The neural network includes a summing node and multiple processing nodes. One or more hardware processors is configured to perform operations where each processing node of a neural network weights input data set values, determines gating operations to select processing operations, and generates a node output by applying the gating operations to weighted input data set values. Weighted node outputs from the processing nodes produce a value for the target parameter. The neural network is trained until the neural network converges. One or more nodes is selected, and for each selected node, a subset of the input data sets and a subset of the gating operations are selected. The selected input data set values are processed with the selected processing nodes using the selected subset of gating operations to produce synthesized data sets.