ML Applications

A Simulation-based Framework for Characterizing Predictive Distributions for Deep Learning

July 17, 2020

Abstract

Characterizing the confidence of machine learning predictions unlocks models that know when they do not know. In this study, we propose a framework for assessing the quality of predictive distributions obtained using deep learning models. The framework enables representation of aleatory and epistemic uncertainty, and relies on simulated data to generate different sources of uncertainty. Finally, it enables quantitative evaluation of the performance of uncertainty estimation techniques. We demonstrate the proposed framework with a case study highlighting the insights one can gain from using this framework.

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AUTHORS

Written by

Jessica Ai

Beliz Gokkaya

Ilknur Kaynar Kabul

Audrey Flower

Ehsan Emamjomeh-Zadeh

Hannah Li

Li Chen

Neamah Hussein

Ousmane Dia

Sevi Baltaoglu

Erik Meijer

Publisher

International Conference on Machine Learning (ICML)

Research Topics

Human & Machine Intelligence

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