Deeply Uncertain: Comparing Methods of Uncertainty Quantification in Deep Learning Algorithms

Aleatoric and epistemic uncertainty in machine learning: A tutorial introduction

Original

Eyke Hüllermeier and Willem Waegeman · 1910

Earlier work this paper cites.

Detecting extrapolation with local ensembles

Original

David Madras, James Atwood, and Alex D’Amour · 1910

Earlier work this paper cites.

Evaluating scalable uncertainty estimation methods for dnn-based molecular property prediction

Original

Gabriele Scalia, Colin A. Grambow, Barbara Pernici, Yi-Pei Li, and William H. Green · 1910

Earlier work this paper cites.

Pitfalls of in-domain uncertainty estimation and ensembling in deep learning

Original

Arsenii Ashukha, Alexander Lyzhov, Dmitry Molchanov, and Dmitry P. Vetrov · 2002

Earlier work this paper cites.

Bayesian neural networks with maximum mean discrepancy regularization

Original

Jary Pomponi, Simone Scardapane, and Aurelio Uncini · 2003

Earlier work this paper cites.

Evaluation of measurement data — Guide to the expression of uncertainty in measurement

JCGM/WG1 · 2008

Earlier work this paper cites.

Practical variational inference for neural networks

Alex Graves · 2011

Earlier work this paper cites.

Improving neural networks by preventing co-adaptation of feature detectors

Original

Geoffrey E. Hinton, Nitish Srivastava, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2012

Earlier work this paper cites.

TensorFlow: Large-scale machine learning on heterogeneous systems, 2015

Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Jozefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dandelion Mané, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viégas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng · 2015

Cited alongside, same era.

Adam: A method for stochastic optimization

Original

Diederik P. Kingma and Jimmy Ba · 2015

Cited alongside, same era.

Uncertainty in Deep Learning

Yarin Gal · 2016

Cited alongside, same era.

Dropout as a bayesian approximation: Representing model uncertainty in deep learning

Yarin Gal and Zoubin Ghahramani · 2016

Cited alongside, same era.

Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles

What uncertainties do we need in bayesian deep learning for computer vision?

Original

Alex Kendall and Yarin Gal · 2017

Later among the works it cites.

Flipout: Efficient pseudo-independent weight perturbations on mini-batches

Original

Yeming Wen, Paul Vicol, Jimmy Ba, Dustin Tran, and Roger B. Grosse · 2018

Later among the works it cites.

Parameters Estimation for the Cosmic Microwave Background with Bayesian Neural Networks

Original

Hector J. Hortua, Riccardo Volpi, Dimitri Marinelli, and Luigi Malagò · 2019

Later among the works it cites.

A high-bias, low-variance introduction to Machine Learning for physicists

Pankaj Mehta, Marin Bukov, Ching-Hao Wang, Alexandre G. R. Day, Clint Richardson, Charles K. Fisher, and David J. Schwab · 2019

Later among the works it cites.

Can you trust your model’s uncertainty? evaluating predictive uncertainty under dataset shift