Fetching the paper…
Reading the bibliography…
We derive a novel sensitivity analysis of input variables for predictive epistemic and aleatoric uncertainty.
Sensitivity analysis for input vector in multilayer feedforward neural networks
Li Fu and Tinghuai Chen · 1993
Earlier work this paper cites.
Aleatory or epistemic? does it matter?
Armen Der Kiureghian and Ove Ditlevsen · 2009
Earlier work this paper cites.
UCI machine learning repository
Moshe Lichman · 2013
Earlier work this paper cites.
Learning and policy search in stochastic dynamical systems with bayesian neural networks
Stefan Depeweg, José Miguel Hernández-Lobato, Finale Doshi-Velez, and Steffen Udluft · 2016
Cited alongside, same era.
Black-box
José Miguel Hernández-Lobato, Yingzhen Li, Mark Rowland, Daniel Hernández-Lobato, Thang Bui, and Richard E Turner · 2016
Cited alongside, same era.
Methods for interpreting and understanding deep neural networks
Grégoire Montavon, Wojciech Samek, and Klaus-Robert Müller · 2017
Cited alongside, same era.
Stefan Depeweg, José Miguel Hernández-Lobato, Finale Doshi-Velez, and Steffen Udluft · 2017
Closest in time.
What uncertainties do we need in bayesian deep learning for computer vision?
Alex Kendall and Yarin Gal · 2017
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…