Epistemic Deep Learning

Shireen Kudukkil Manchingal; Fabio Cuzzolin

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arXiv · 2206.07609

Epistemic Deep Learning

Shireen Kudukkil Manchingal·Fabio Cuzzolin

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Abstract

The belief function approach to uncertainty quantification as proposed in the Demspter-Shafer theory of evidence is established upon the general mathematical models for set-valued observations, called random sets. Set-valued predictions are the most natural representations of uncertainty in machine learning. In this paper, we introduce a concept called epistemic deep learning based on the random-set interpretation of belief functions to model epistemic learning in deep neural networks. We propose a novel random-set convolutional neural network for classification that produces scores for sets of classes by learning set-valued ground truth representations. We evaluate different formulations of entropy and distance measures for belief functions as viable loss functions for these random-set networks. We also discuss methods for evaluating the quality of epistemic predictions and the performance of epistemic random-set neural networks. We demonstrate through experiments that the epistemic approach produces better performance results when compared to traditional approaches of estimating uncertainty.

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cs.LG cs.NE stat.ML

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BibTeX RIS

Shireen Kudukkil Manchingal, Fabio Cuzzolin. 2022-06-15. Epistemic Deep Learning. https://arxiv.org/abs/2206.07609

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