Single Model Uncertainty Estimation via Stochastic Data Centering

• URL: http://arxiv.org/abs/2207.07235v1
• Date: Thu, 14 Jul 2022 23:54:54 GMT
• Title: Single Model Uncertainty Estimation via Stochastic Data Centering
• Authors: Jayaraman J. Thiagarajan, Rushil Anirudh, Vivek Narayanaswamy and Peer-Timo Bremer
• Abstract summary: We are interested in estimating the uncertainties of deep neural networks. We present a striking new finding that an ensemble of neural networks with the same weight initialization, trained on datasets that are shifted by a constant bias gives rise to slightly inconsistent trained models. We show that $Delta-$UQ's uncertainty estimates are superior to many of the current methods on a variety of benchmarks.
• Score: 39.71621297447397
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We are interested in estimating the uncertainties of deep neural networks, which play an important role in many scientific and engineering problems. In this paper, we present a striking new finding that an ensemble of neural networks with the same weight initialization, trained on datasets that are shifted by a constant bias gives rise to slightly inconsistent trained models, where the differences in predictions are a strong indicator of epistemic uncertainties. Using the neural tangent kernel (NTK), we demonstrate that this phenomena occurs in part because the NTK is not shift-invariant. Since this is achieved via a trivial input transformation, we show that it can therefore be approximated using just a single neural network -- using a technique that we call $\Delta-$UQ -- that estimates uncertainty around prediction by marginalizing out the effect of the biases. We show that $\Delta-$UQ's uncertainty estimates are superior to many of the current methods on a variety of benchmarks -- outlier rejection, calibration under distribution shift, and sequential design optimization of black box functions.

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