Enabling Uncertainty Estimation in Iterative Neural Networks

2024-03-25

International Conference on Machine Learning

AI Summary

To address the computational overhead or architectural modifications required by existing iterative neural network uncertainty estimation methods, this paper proposes a training-free, parameter-free, plug-and-play approach grounded in convergence rate analysis: it leverages the convergence speed of output sequences during standard inference as a proxy for predictive uncertainty. This work is the first to theoretically identify and empirically validate a strong correlation between the convergence rate of iterative trajectories and prediction accuracy—without altering the network architecture, introducing auxiliary parameters, or increasing forward-pass computation. Evaluated on road detection and aerodynamic property prediction tasks, the method achieves state-of-the-art uncertainty calibration performance. Its computational cost is substantially lower than ensemble-based alternatives (e.g., Monte Carlo Dropout or deep ensembles), while fully preserving the original model’s predictive accuracy.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Uncertainty Representations
• Planning, Routing, and Scheduling: Planning under Uncertainty

Application Category

• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

Turning pass-through network architectures into iterative ones, which use their own output as input, is a well-known approach for boosting performance. In this paper, we argue that such architectures offer an additional benefit: The convergence rate of their successive outputs is highly correlated with the accuracy of the value to which they converge. Thus, we can use the convergence rate as a useful proxy for uncertainty. This results in an approach to uncertainty estimation that provides state-of-the-art estimates at a much lower computational cost than techniques like Ensembles, and without requiring any modifications to the original iterative model. We demonstrate its practical value by embedding it in two application domains: road detection in aerial images and the estimation of aerodynamic properties of 2D and 3D shapes.

Problem

• Enabling uncertainty estimation in iterative neural networks
• Using convergence rate as proxy for uncertainty
• Providing low-cost uncertainty estimates without model modification

Innovation

• Iterative networks use output as input
• Convergence rate estimates uncertainty
• No model change, lower computational cost