Neural Network Signal Classification with Iterative Deviation Correction
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Solution Overview
Problem
Machine learning systems, particularly neural networks, often produce incorrect predictions while being highly confident, leading to undesirable outcomes in applications like robotic interactions where accurate confidence in predictions is crucial.
Innovation Solution
A computer-implemented method that determines a feature representation of an input signal, calculates an intermediate signal, predicts the deviation from a desired output, and adapts this intermediate signal to correct predictions, ensuring the output signal accurately reflects the input, using multiple neural networks for feature extraction, intermediate calculation, and deviation prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a machine learning system is used for classification and regression analysis, then prediction performance is improved, but the system outputs high confidence for wrong predictions
Solution Approach 1:
The patent implements a feedback mechanism where the machine learning system's predictions are evaluated against actual outcomes, and the confidence estimates are adjusted based on the discrepancy between predicted confidence and actual accuracy. This closed-loop feedback allows the system to learn from its confidence calibration errors and improve reliability without sacrificing prediction performance.
Solution Approach 2:
The patent modifies the output parameters of the machine learning system by introducing confidence calibration as an additional output dimension. Instead of only predicting class labels or regression values, the system now outputs both predictions and calibrated confidence estimates, changing the parameter space to include reliability metrics that can be independently optimized.
2Measurement precision
If multiple neural networks are used for feature extraction, intermediate calculation, and deviation prediction, then prediction accuracy is improved, but device complexity increases
Solution Approach 1:
The patent divides the machine learning system into distinct functional segments: feature extraction networks, intermediate calculation networks, and deviation prediction networks. Each segment performs a specific task and can be independently trained, optimized, and replaced. This segmentation reduces overall system complexity by modularizing the architecture while maintaining high prediction accuracy through specialized sub-networks.
Solution Approach 2:
The patent introduces an intermediary component that bridges the feature extraction and final prediction stages. This intermediary processes intermediate representations and facilitates information flow between different network components, enabling complex predictions while managing system complexity through structured intermediate representations and controlled information flow.
Data Source
AI summary
A computer-implemented method for determining an output signal characterizing a classification and/or a regression result of an input signal. The method includes: determining a feature representation characterizing the input signal; determining an intermediate signal characterizing a classification and/or regression result of the feature representation; predicting, based on the feature representation and the intermediate signal, a deviation of the intermediate signal from a desired output signal of the input signal; adapting the intermediate signal according to the determined deviation thereby determining an adapted signal; providing the adapted signal as output signal.