Hybrid Neural Gaussian Process Model for Uncertainty Calibration
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Solution Overview
Problem
Modern machine learning systems, particularly those based on neural networks, often fail to provide well-calibrated uncertainty in their predictions, leading to high confidence in incorrect or imprecise outputs.
Innovation Solution
A method that combines features of Gaussian processes and neural networks to train machine learning systems, allowing them to ascertain well-calibrated uncertainty in predictions by using a context representation and loss functions based on probability density functions, which improves performance by considering multiple input and output signals in the training process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If neural networks are used for machine learning systems, then prediction performance is improved, but uncertainty calibration deteriorates
Solution Approach 1:
The patent combines neural networks with Gaussian processes to create a hybrid model that achieves both high prediction accuracy and well-calibrated uncertainty. The neural network component captures complex patterns for accurate predictions, while the Gaussian process component provides probabilistic predictions with calibrated uncertainty estimates, resolving the contradiction between accuracy and reliability.
Solution Approach 2:
The machine learning system uses a composite approach by integrating two different modeling paradigms (neural networks and Gaussian processes) into a unified framework. This composite structure allows the system to leverage the strengths of both approaches: the representational power of neural networks and the uncertainty calibration of Gaussian processes.
2Reliability
If Gaussian processes are used for machine learning systems, then uncertainty calibration is improved, but computational complexity increases
Solution Approach 1:
The patent segments the computational task by dividing the machine learning system into two components: a neural network for feature extraction and pattern recognition, and a Gaussian process for probabilistic prediction. This segmentation allows each component to handle specific aspects of the problem, reducing the overall computational burden while maintaining uncertainty calibration.
Solution Approach 2:
The neural network acts as an intermediary that processes input data and extracts features before passing them to the Gaussian process. This intermediary structure allows the computationally intensive Gaussian process to work with compressed, meaningful representations rather than raw high-dimensional data, reducing computational complexity.
3Loss of time
If deterministic training is used, then training time is reduced, but adaptability to uncertain contexts deteriorates
Solution Approach 1:
The patent changes the parameter being optimized during training from purely deterministic loss minimization to a probabilistic loss function that incorporates uncertainty calibration. The training process optimizes both prediction accuracy and uncertainty calibration simultaneously by modifying the loss function to include terms that penalize poor calibration, enabling the system to adapt to uncertain contexts while maintaining efficient training.
Data Source
AI summary
A computer-implemented method for training a machine learning system in which the machine learning system is configured to ascertain, based on at least a first input signal and a multiplicity of second input signals and second output signals corresponding to the second input signals, a first output signal corresponding to the first input signal, the first output signal characterizing a classification encumbered with an uncertainty and/or a regression encumbered with an uncertainty.