Deep transformation models: Tackling complex regression problems with neural network based transformation models

1 April 2020

Papers citing "Deep transformation models: Tackling complex regression problems with neural network based transformation models"

9 / 9 papers shown

Title
An interpretable neural network-based non-proportional odds model for ordinal regression Akifumi Okuno Kazuharu Harada 32 1 0 31 Mar 2023
Deep conditional transformation models for survival analysis Gabriele Campanella Lucas Kook I. Häggström Torsten Hothorn Thomas J. Fuchs 21 2 0 20 Oct 2022
Sample-based Uncertainty Quantification with a Single Deterministic Neural Network T. Kanazawa Chetan Gupta UQCV 37 4 0 17 Sep 2022
Deep interpretable ensembles Lucas Kook Andrea Götschi Philipp F. M. Baumann Torsten Hothorn Beate Sick UQCV 32 8 0 25 May 2022
Short-Term Density Forecasting of Low-Voltage Load using Bernstein-Polynomial Normalizing Flows M. Arpogaus Marcus Voss Beate Sick Mark Nigge-Uricher Oliver Durr 33 16 0 29 Apr 2022
Probabilistic Time Series Forecasts with Autoregressive Transformation Models David Rügamer Philipp F. M. Baumann Thomas Kneib Torsten Hothorn AI4TS 63 13 0 15 Oct 2021
Transformation Models for Flexible Posteriors in Variational Bayes Sefan Hörtling Daniel Dold Oliver Durr Beate Sick 26 0 0 01 Jun 2021
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles Balaji Lakshminarayanan Alexander Pritzel Charles Blundell UQCV BDL 278 5,695 0 05 Dec 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 289 9,167 0 06 Jun 2015