Quantile Pooling for Neural Networks Handling Set-Structured Data
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Solution Overview
Problem
Traditional pooling methods like max pooling and average pooling fail to capture the full spectrum of feature importance in set-structured data, often overlooking critical nuances or being misled by anomalies.
Innovation Solution
A quantile data pooling method that segments input tensors into equal-sized segments, determines quantile levels, calculates quantile values for each segment, and concatenates these values to create a pooled output vector, allowing for adjustable pooling that balances between max and average pooling methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional max pooling is used, then the most prominent features are captured, but critical nuances and anomalies are overlooked
Solution Approach 1:
The patent segments the feature aggregation process into multiple quantile levels (e.g., 0.1, 0.3, 0.5, 0.7, 0.9), where each quantile captures different aspects of the data distribution. This segmentation allows simultaneous capture of both prominent features (higher quantiles) and subtle nuances (lower quantiles), resolving the contradiction between capturing dominant features and preserving critical details.
Solution Approach 2:
The patent changes the aggregation parameter from a single value (max or mean) to multiple quantile levels. By computing statistics at different quantile positions across the data distribution, the method captures a spectrum of feature importances, thereby preserving both dominant and subtle features that would be lost in traditional single-parameter pooling.
2Stability of the object's composition
If traditional average pooling is used, then overall context is maintained, but the method is misled by anomalies and outliers
Solution Approach 1:
The patent employs multiple quantile levels rather than a single average, allowing the system to select appropriate quantiles that are robust to outliers while maintaining context. Higher quantiles (e.g., 0.7, 0.9) provide outlier-resistant representations that maintain stability without being misled by anomalies, thus resolving the contradiction between context preservation and precision.
Solution Approach 2:
Different quantile levels serve different local quality requirements: lower quantiles capture subtle contextual information while higher quantiles provide robustness against outliers. By combining multiple quantile-based pooled outputs, the system achieves both contextual stability and measurement precision simultaneously.
3Ease of operation
If a single pooling method is used, then the process is simple, but the full spectrum of feature importance cannot be captured
Solution Approach 1:
The patent creates a universal pooling framework that incorporates multiple quantile levels within a single operation. This multi-functional approach allows the same pooling layer to capture the full spectrum of feature importances (from subtle to dominant) while maintaining a unified, relatively simple computational structure that can be integrated into standard neural network architectures.
Solution Approach 2:
The patent merges multiple quantile-based pooled outputs into a single comprehensive representation. By concatenating or otherwise combining the results from different quantile levels, the method captures the full spectrum of feature importance while presenting a unified output to subsequent network layers, balancing simplicity with versatility.
Data Source
AI summary
A computer implemented method of data pooling in a neural network. The method comprises receiving an input tensor formed from a set of data points from an input space, the input tensor having a plurality of input space dimensions. The method includes: segmenting the input tensor over each of its input space dimensions into equal-sized segments, each set of corresponding segments over the input space dimensions comprising a partition; determining, selecting, or calculating a respective quantile level for each partition; determining or calculating a quantile value for each segment based on the quantile level for its partition; and creating a pooled output vector by concatenating the quantile values for the segments of each partition, the output vector comprising a pooled output for each partition.