Forecasting remaining useful life: Interpretable deep learning approach
via variational Bayesian inferences

Forecasting remaining useful life: Interpretable deep learning approach via variational Bayesian inferences

11 July 2019

Mathias Kraus

Stefan Feuerriegel

Papers citing "Forecasting remaining useful life: Interpretable deep learning approach via variational Bayesian inferences"

5 / 5 papers shown

Title
Physics-Informed Deep Learning: A Promising Technique for System Reliability Assessment Taotao Zhou E. Droguett A. Mosleh AI4CE 11 25 0 24 Aug 2021
A Holistic Approach to Interpretability in Financial Lending: Models, Visualizations, and Summary-Explanations Chaofan Chen Kangcheng Lin Cynthia Rudin Yaron Shaposhnik Sijia Wang Tong Wang 29 41 0 04 Jun 2021
An interpretable neural network model through piecewise linear approximation Mengzhuo Guo Qingpeng Zhang Xiuwu Liao D. Zeng MILM FAtt 24 7 0 20 Jan 2020
Deep learning in business analytics and operations research: Models, applications and managerial implications Mathias Kraus Stefan Feuerriegel A. Oztekin 26 286 0 28 Jun 2018
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 289 9,167 0 06 Jun 2015