A Deeper Look into Aleatoric and Epistemic Uncertainty Disentanglement

Principles of risk minimization for learning theory

Vladimir Vapnik · 1991

Earlier work this paper cites.

Ideal-observer computation in medical imaging with use of markov-chain monte carlo techniques

Matthew A Kupinski, John W Hoppin, Eric Clarkson, and Harrison H Barrett · 2003

Earlier work this paper cites.

Challenges in representation learning: A report on three machine learning contests

Ian J Goodfellow, Dumitru Erhan, Pierre Luc Carrier, Aaron Courville, Mehdi Mirza, Ben Hamner, Will Cukierski, Yichuan Tang, David Thaler, Dong-Hyun Lee, et al · 2013

Earlier work this paper cites.

Reliable classification: Learning classifiers that distinguish aleatoric and epistemic uncertainty

Robin Senge, Stefan Bösner, Krzysztof Dembczyński, Jörg Haasenritter, Oliver Hirsch, Norbert Donner-Banzhoff, and Eyke Hüllermeier · 2014

Earlier work this paper cites.

Weight uncertainty in neural network

Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, and Daan Wierstra · 2015

Earlier work this paper cites.

Uncertainty in Deep Learning

Yarin Gal · 2016

Earlier work this paper cites.

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

Yarin Gal and Zoubin Ghahramani · 2016

Earlier work this paper cites.

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

Alex Kendall and Yarin Gal · 2017

Earlier work this paper cites.

Simple and scalable predictive uncertainty estimation using deep ensembles

Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell · 2017

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Oludare Isaac Abiodun, Aman Jantan, Abiodun Esther Omolara, Kemi Victoria Dada, Nachaat AbdElatif Mohamed, and Humaira Arshad · 2018

Cited alongside, same era.

Deep learning in natural language processing

Li Deng and Yang Liu · 2018

Cited alongside, same era.

Decomposition of uncertainty in bayesian deep learning for efficient and risk-sensitive learning

Stefan Depeweg, Jose-Miguel Hernandez-Lobato, Finale Doshi-Velez, and Steffen Udluft · 2018

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

Original

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

Cited alongside, same era.

Computer vision for autonomous vehicles: Problems, datasets and state of the art

Joel Janai, Fatma Güney, Aseem Behl, Andreas Geiger, et al · 2020

Later among the works it cites.

A review of uncertainty quantification in deep learning: Techniques, applications and challenges

Moloud Abdar, Farhad Pourpanah, Sadiq Hussain, Dana Rezazadegan, Li Liu, Mohammad Ghavamzadeh, Paul Fieguth, Xiaochun Cao, Abbas Khosravi, U. Rajendra Acharya, Vladimir Makarenkov, and Saeid Nahavandi · 2021

Later among the works it cites.

A state-of-the-art review on mobile robotics tasks using artificial intelligence and visual data

Sergio Cebollada, Luis Payá, María Flores, Adrián Peidró, and Oscar Reinoso · 2021

Later among the works it cites.

Facial expression recognition with grid-wise attention and visual transformer

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Later among the works it cites.

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