Beate Sick

According to our database, Beate Sick authored at least 20 papers between 2005 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

NeuroTrialNER: An Annotated Corpus for Neurological Diseases and Therapies in Clinical Trial Registries.

Simona Doneva, Tilia Ellendorff, Beate Sick, Jean-Philippe Goldman, Amelia Cannon, Gerold Schneider, Benjamin Ineichen

Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 2024

Bayesian Semi-structured Subspace Inference.

Daniel Dold, David Rügamer, Beate Sick, Oliver Dürr

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

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, November, 2023

Single-shot Bayesian approximation for neural networks.

Kai Brach, Beate Sick, Oliver Dürr

CoRR, 2023

2022

Distributional anchor regression.

Lucas Kook, Beate Sick, Peter Bühlmann

Stat. Comput., 2022

Deep and interpretable regression models for ordinal outcomes.

Lucas Kook, Lisa Herzog, Torsten Hothorn, Oliver Dürr, Beate Sick

Pattern Recognit., 2022

Deep interpretable ensembles.

Lucas Kook, Andrea Götschi, Philipp F. M. Baumann, Torsten Hothorn, Beate Sick

CoRR, 2022

Bernstein Flows for Flexible Posteriors in Variational Bayes.

Oliver Dürr, Stephan Hörling, Daniel Dold, Ivonne Kovylov, Beate Sick

CoRR, 2022

2021

Transformation Models for Flexible Posteriors in Variational Bayes.

Sefan Hörtling, Daniel Dold, Oliver Dürr, Beate Sick

CoRR, 2021

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

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

Single Shot MC Dropout Approximation.

Kai Brach, Beate Sick, Oliver Dürr

CoRR, 2020

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

Beate Sick, Torsten Hothorn, Oliver Dürr

Proceedings of the 25th International Conference on Pattern Recognition, 2020

2019

Bone erosion scoring for rheumatoid arthritis with deep convolutional neural networks.

Janick Rohrbach, Tobias Reinhard, Beate Sick, Oliver Dürr

Comput. Electr. Eng., 2019

Beyond ImageNet: Deep Learning in Industrial Practice.

Thilo Stadelmann, Vasily Tolkachev, Beate Sick, Jan Stampfli, Oliver Dürr

Proceedings of the Applied Data Science - Lessons Learned for the Data-Driven Business, 2019

2009

Unsupervised assessment of microarray data quality using a Gaussian mixture model.

Brian E. Howard, Beate Sick, Steffen Heber

BMC Bioinform., 2009

Practical Quality Assessment of Microarray Data by Simulation of Differential Gene Expression.

Brian E. Howard, Beate Sick, Steffen Heber

Proceedings of the Bioinformatics Research and Applications, 5th International Symposium, 2009

2007

Quality Assessment of Affymetrix GeneChip Data using the EM Algorithm and a Naive Bayes Classifier.

Brian E. Howard, Beate Sick, Imara Perera, Yang Ju Im, Heike Winter-Sederoff, Steffen Heber

Proceedings of the 7th IEEE International Conference on Bioinformatics and Bioengineering, 2007

2006

Automatic quality assessment of Affymetrix GeneChip data.

Steffen Heber, Beate Sick

Proceedings of the 44st Annual Southeast Regional Conference, 2006

2005

<i>RACE</i>: Remote Analysis Computation for gene Expression data.

Michael Psarros, Steffen Heber, Manuela Sick, Gnanasekaran Thoppae, Keith Harshman, Beate Sick

Nucleic Acids Res., 2005