Using Community Structure for Complex Network Layout - Oliver Dürr; Arnd Brandenburg (CoRR, 2012)

Potential and Limitations of Commercial Sentiment Detection Tools. - Mark Cieliebak; Oliver Dürr; Fatih Uzdilli (ESSEM@AI*IA, 2013)

Meta-Classifiers Easily Improve Commercial Sentiment Detection Tools. - Mark Cieliebak; Oliver Dürr; Fatih Uzdilli (LREC, 2014)

JOINT_FORCES: Unite Competing Sentiment Classifiers with Random Forest. - Oliver Dürr; Fatih Uzdilli; Mark Cieliebak (SemEval@COLING, 2014)

Deep Learning on a Raspberry Pi for Real Time Face Recognition. - Oliver Dürr; Yves Pauchard; Diego Browarnik; Rebekka Axthelm; Martin Loeser (Eurographics (Posters), 2015)

Speaker identification and clustering using convolutional neural networks. - Yanick X. Lukic; Carlo Vogt; Oliver Durr; Thilo Stadelmann (MLSP, 2016)

Learning embeddings for speaker clustering based on voice equality. - Yanick X. Lukic; Carlo Vogt; Oliver Durr; Thilo Stadelmann (MLSP, 2017)

Learning Neural Models for End-to-End Clustering. - Benjamin Bruno Meier; Ismail Elezi; Mohammadreza Amirian; Oliver Dürr; Thilo Stadelmann (ANNPR, 2018)

Capturing Suprasegmental Features of a Voice with RNNs for Improved Speaker Clustering. - Thilo Stadelmann; Sebastian Glinski-Haefeli; Patrick Gerber; Oliver Dürr (ANNPR, 2018)

Beyond ImageNet: Deep Learning in Industrial Practice. - Thilo Stadelmann; Vasily Tolkachev; Beate Sick; Jan Stampfli; Oliver Dürr (Applied Data Science, 2019)

Bone erosion scoring for rheumatoid arthritis with deep convolutional neural networks. - Janick Rohrbach; Tobias Reinhard; Beate Sick; Oliver Dürr (Comput. Electr. Eng., 2019)

Deep transformation models: Tackling complex regression problems with neural network based transformation models. - Beate Sick; Torsten Hothorn; Oliver Dürr (ICPR, 2020)

Single Shot MC Dropout Approximation. - Kai Brach; Beate Sick; Oliver Dürr (CoRR, 2020)

Ordinal Neural Network Transformation Models: Deep and interpretable regression models for ordinal outcomes. - Lucas Kook; Lisa Herzog; Torsten Hothorn; Oliver Dürr; Beate Sick (CoRR, 2020)

Integrating uncertainty in deep neural networks for MRI based stroke analysis. - Lisa Herzog; Elvis Murina; Oliver Dürr; Susanne Wegener; Beate Sick (Medical Image Anal., 2020)

Transformation Models for Flexible Posteriors in Variational Bayes. - Sefan Hörtling; Daniel Dold; Oliver Dürr; Beate Sick (CoRR, 2021)

Bernstein Flows for Flexible Posteriors in Variational Bayes. - Oliver Dürr; Stephan Hörling; Daniel Dold; Ivonne Kovylov; Beate Sick (CoRR, 2022)

Deep and interpretable regression models for ordinal outcomes. - Lucas Kook; Lisa Herzog; Torsten Hothorn; Oliver Dürr; Beate Sick (Pattern Recognit., 2022)

Bayesian Calibration of MEMS Accelerometers. - Oliver Dürr; Po-Yu Fan; Zong-Xian Yin (CoRR, 2023)

Single-shot Bayesian approximation for neural networks. - Kai Brach; Beate Sick; Oliver Dürr (CoRR, 2023)

Short-Term Density Forecasting of Low-Voltage Load Using Bernstein-Polynomial Normalizing Flows. - Marcel Arpogaus; Marcus Voss; Beate Sick; Mark Nigge-Uricher; Oliver Dürr (IEEE Trans. Smart Grid, 2023)

Bayesian Semi-structured Subspace Inference. - Daniel Dold; David Rügamer; Beate Sick; Oliver Dürr (AISTATS, 2024)

Estimating Conditional Distributions with Neural Networks Using R Package deeptrafo. - Lucas Kook; Philipp F. M. Baumann; Oliver Dürr; Beate Sick; David Rügamer (J. Stat. Softw., 2024)

Paths and Ambient Spaces in Neural Loss Landscapes. - Daniel Dold; Julius Kobialka; Nicolai Palm; Emanuel Sommer; David Rügamer; Oliver Dürr (AISTATS, 2025)

Interpretable Neural Causal Models with TRAM-DAGs. - Beate Sick; Oliver Dürr (CLeaR, 2025)

Assessing deep learning: a work program for the humanities in the age of artificial intelligence. - Jan Segessenmann; Thilo Stadelmann; Andrew Davison; Oliver Dürr (AI Ethics, 2025)