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

v1v2 (latest)

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

11 July 2019

Mathias Kraus

Stefan Feuerriegel

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

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Title
Rethinking Log Odds: Linear Probability Modelling and Expert Advice in Interpretable Machine Learning Danial Dervovic Nicolas Marchesotti Freddy Lecue Daniele Magazzeni 62 0 0 11 Nov 2022
GAM(e) changer or not? An evaluation of interpretable machine learning models based on additive model constraints Patrick Zschech Sven Weinzierl Nico Hambauer Sandra Zilker Mathias Kraus 143 14 0 19 Apr 2022
Physics-Informed Deep Learning: A Promising Technique for System Reliability Assessment Taotao Zhou E. Droguett A. Mosleh AI4CE 24 26 0 24 Aug 2021
Explainable AI (XAI) for PHM of Industrial Asset: A State-of-The-Art, PRISMA-Compliant Systematic Review Ahmad Nazrie Mohd Nor S. R. Pedapati M. Muhammad 64 16 0 08 Jul 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 76 41 0 04 Jun 2021
Multi-agent maintenance scheduling based on the coordination between central operator and decentralized producers in an electricity market Pegah Rokhforoz B. Gjorgiev G. Sansavini Olga Fink 31 22 0 27 Feb 2020
An interpretable neural network model through piecewise linear approximation Mengzhuo Guo Qingpeng Zhang Xiuwu Liao D. Zeng MILM FAtt 51 7 0 20 Jan 2020
Deep learning in business analytics and operations research: Models, applications and managerial implications Mathias Kraus Stefan Feuerriegel A. Oztekin 77 294 0 28 Jun 2018