ML Output Signal Estimation With Calibrated Prediction Uncertainty
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Solution Overview
Problem
Neural networks often fail to provide well-calibrated uncertainty in their predictions, leading to high certainty for incorrect or imprecise outputs.
Innovation Solution
A method combining Gaussian processes and neural networks to ascertain a well-calibrated uncertainty by using latent representations and incorporating context from multiple input signals and their corresponding outputs, with separate neural networks for encoding and decoding these representations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If neural networks are used for prediction, then high performance is achieved, but well-calibrated uncertainty cannot be ascertained
Solution Approach 1:
The patent combines neural networks with Gaussian processes to create a hybrid system that leverages the predictive power of neural networks while incorporating the uncertainty modeling capabilities of Gaussian processes. The encoder processes input data through a neural network and outputs parameters for a Gaussian process, merging both approaches to achieve both high performance and reliable uncertainty estimation.
Solution Approach 2:
The patent introduces latent representations as an intermediary between the neural network encoder and the Gaussian process. These latent representations serve as a bridge, transforming neural network outputs into parameters that the Gaussian process can use for calibrated uncertainty estimation, rather than directly connecting the two systems.
2Reliability
If neural networks output high certainty, then confidence is provided, but false or imprecise predictions are made
Solution Approach 1:
The patent uses the variance output from the Gaussian process as feedback to adjust the confidence assessment of predictions. When the Gaussian process indicates high variance (uncertainty), the system appropriately reduces confidence in the prediction, even if the neural network output appears certain, thereby preventing overconfident false predictions.
Solution Approach 2:
The patent creates a composite prediction system where the final output combines the mean prediction from the neural network-Gaussian process hybrid with the variance information from the Gaussian process. This composite approach ensures that predictions are accompanied by appropriately calibrated uncertainty measures that reflect true prediction reliability.
Data Source
AI summary
A computer-implemented method for ascertaining, using a machine learning system, a first output signal characterizing a classification and/or a regression of a first input signal, and the output signal includes a first representation which characterizes an expected value of the classification or the regression, and a second representation, which characterizes a variance of the classification or regression. The method includes: ascertaining, using an encoder, latent representations each ascertained based on a second input signal and a second output signal that corresponds to the second input signal, and the second input signal and the second output signal characterize a context, the latent representation includes a first representation characterizing an expected value and a second representation characterizing a variance; ascertaining a third representation characterizing an accumulation of the first representations; ascertaining a fourth representation characterizing an accumulation of the second representations; ascertaining the first output signal using a decoder.