Sampling-free Uncertainty Estimation in Gated Recurrent Units with Exponential Families

TL;DR

This paper introduces a novel, sampling-free method for estimating uncertainty in Gated Recurrent Units (GRUs) using exponential family distributions, enabling efficient and deterministic uncertainty quantification in sequence prediction tasks.

Contribution

It presents the first sampling-free approach to uncertainty estimation in GRUs by leveraging exponential family models, improving computational efficiency over Bayesian methods.

Findings

Effective uncertainty measurement in unsupervised image sequence prediction

Deterministic estimation avoids costly sampling procedures

Applicable to powerful sequential models like GRUs

Abstract

There has recently been a concerted effort to derive mechanisms in vision and machine learning systems to offer uncertainty estimates of the predictions they make. Clearly, there are enormous benefits to a system that is not only accurate but also has a sense for when it is not sure. Existing proposals center around Bayesian interpretations of modern deep architectures -- these are effective but can often be computationally demanding. We show how classical ideas in the literature on exponential families on probabilistic networks provide an excellent starting point to derive uncertainty estimates in Gated Recurrent Units (GRU). Our proposal directly quantifies uncertainty deterministically, without the need for costly sampling-based estimation. We demonstrate how our model can be used to quantitatively and qualitatively measure uncertainty in unsupervised image sequence prediction. To our knowledge, this is the first result describing sampling-free uncertainty estimation for powerful sequential models such as GRUs.