Sortout Network Units for Neural Network Overfitting
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Solution Overview
Problem
Neural networks often overfit to labeled training data, leading to poor performance on unlabeled data, even when the training and test data come from the same distribution, due to the large number of tunable parameters and limited training data size.
Innovation Solution
The introduction of 'sortout' network units that produce a sorted list of output values, where each value represents the performance of a respective model, allowing subsequent units to utilize information from all models, not just the maximum value, thereby reducing overfitting by implicitly conveying the significance of each value through its position in the sorted list.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If neural networks use a large number of tunable parameters to approximate complex functions, then the network can capture detailed patterns in training data, but the network overfits to sampling noise and fails to generalize to unlabeled data
Solution Approach 1:
The patent segments the output values from multiple models into a sorted sequence, where each position in the sorted list represents a different level of confidence or performance. This segmentation allows the system to preserve information from all models while organizing it in a structured way that reduces overfitting to any single model's noise
Solution Approach 2:
The patent transforms the parameter representation by sorting output values and using their positions in the sorted list rather than treating them as independent weighted parameters. This parameter transformation changes how information is conveyed from one layer to the next, implicitly encoding significance through position rather than magnitude alone
2Measurement precision
If neural networks are trained extensively on labeled training data, then the network achieves high accuracy on training data, but the network becomes too closely tuned to training data characteristics and produces meaningless results on different unlabeled data
Solution Approach 1:
The patent performs preliminary sorting of output values before they are passed to subsequent layers, organizing the information in advance to highlight the most significant results. This preliminary organization helps the network generalize better by ensuring that the most reliable predictions are prioritized before further processing occurs
Solution Approach 2:
The sorted list of output values acts as an intermediary representation between multiple models and subsequent network layers. Instead of directly combining all model outputs, the sorting process creates an intermediate structured representation that filters and organizes information, reducing the direct transmission of noise from individual models
Data Source
AI summary
According to some aspects, a method of classifying speech recognition results is provided, using a neural network comprising a plurality of interconnected network units, each network unit having one or more weight values, the method comprising using at least one computer, performing acts of providing a first vector as input to a first network layer comprising one or more network units of the neural network, transforming, by a first network unit of the one or more network units, the input vector to produce a plurality of values, the transformation being based at least in part on a plurality of weight values of the first network unit, sorting the plurality of values to produce a sorted plurality of values, and providing the sorted plurality of values as input to a second network layer of the neural network.