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2010.08376
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Deep and interpretable regression models for ordinal outcomes
16 October 2020
Lucas Kook
L. Herzog
Torsten Hothorn
Oliver Durr
Beate Sick
Re-assign community
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Papers citing
"Deep and interpretable regression models for ordinal outcomes"
7 / 7 papers shown
Title
Single-shot Bayesian approximation for neural networks
K. Brach
Beate Sick
Oliver Durr
BDL
UQCV
29
0
0
24 Aug 2023
A New PHO-rmula for Improved Performance of Semi-Structured Networks
David Rügamer
55
10
0
01 Jun 2023
mixdistreg: An R Package for Fitting Mixture of Experts Distributional Regression with Adaptive First-order Methods
David Rügamer
75
0
0
04 Feb 2023
Deep interpretable ensembles
Lucas Kook
Andrea Götschi
Philipp F. M. Baumann
Torsten Hothorn
Beate Sick
UQCV
58
9
0
25 May 2022
Distributional Gradient Boosting Machines
Alexander März
Thomas Kneib
AI4CE
66
7
0
02 Apr 2022
Transformation Models for Flexible Posteriors in Variational Bayes
Sefan Hörtling
Daniel Dold
Oliver Durr
Beate Sick
40
0
0
01 Jun 2021
Deep Conditional Transformation Models
Philipp F. M. Baumann
Torsten Hothorn
David Rügamer
46
29
0
15 Oct 2020
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