Publications

Khurram Azeem Hashmi; Rakshith Bymana Ponnappa; Syed Saqib Bukhari; Martin Jenckel; Andreas Dengel

Feedback Learning: Automating the Process of Correcting and Completing the Extracted Information

In: International Conference on Document Analysis and Recognition. International Conference on Document Analysis and Recognition Workshops (ICDARW), September 22-25, Sydney, NSW, Australia, ISBN 978-1-7281-5054-3, IEEE, 9/2019.

Noshaba Cheema

In Silico User Testing for Mid-Air Interactions with Deep Reinforcement Learning

Mastersthesis, Saarland University, 9/2019.

Jens Krauth; Stefan Gerlach; Christian Marzahl; Jörn Voigt; Heinz Handels

Synthetic Training with Generative Adversarial Networks for Segmentation of Microscopies

In: Heinz Handels; Thomas M. Deserno; Andreas Maier; Klaus Hermann Maier-Hein; Christoph Palm; Thomas Tolxdorff (Hrsg.). Bildverarbeitung für die Medizin 2019. Workshop Bildverarbeitung für die Medizin (BVM-2019), March 20-22, Lübeck, Germany, Pages 37-42, ISBN 978-3-658-25326-4, Springer Fachmedien Wiesbaden, 2019.

Timo Kepp; Jan Ehrhardt; Mattias P. Heinrich; Gereon Hüttmann; Heinz Handels

Topology-Preserving Shape-Based Regression Of Retinal Layers In Oct Image Data Using Convolutional Neural Networks

In: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019). IEEE International Symposium on Biomedical Imaging (ISBI-2019), April 8-11, Venice, Italy, Pages 1437-1440, IEEE, 2019.

Timo Kepp; Christine Droigk; Malte Casper; Michael Evers; Gereon Hüttmann; Nunciada Salma; Dieter Manstein; Mattias P. Heinrich; Heinz Handels

Segmentation of mouse skin layers in optical coherence tomography image data using deep convolutional neural networks

In: Christoph Hitzenberger (Hrsg.). Biomedical Optics Express, Vol. 10, No. 7, Pages 3484-3496, OSA, 7/2019.

Michael Benedikt; Kristian Kersting; Phokion G. Kolaitis; Daniel Neider

Logic and Learning (Dagstuhl Seminar 19361)

In: Dagstuhl Reports, Vol. 9, No. 9, Pages 1-22, Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik, 2019.

Alejandro Molina; Antonio Vergari; Karl Stelzner; Robert Peharz; Pranav Subramani; Nicola Di Mauro; Pascal Poupart; Kristian Kersting

SPFlow: An Easy and Extensible Library for Deep Probabilistic Learning using Sum-Product Networks

In: Computing Research Repository eprint Journal (CoRR), Vol. abs/1901.03704, Pages 0-10, arXiv, 2019.

Robert Peharz; Antonio Vergari; Karl Stelzner; Alejandro Molina; Martin Trapp; Xiaoting Shao; Kristian Kersting; Zoubin Ghahramani

Random Sum-Product Networks: A Simple and Effective Approach to Probabilistic Deep Learning

In: Amir Globerson; Ricardo Silva (Hrsg.). Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence. Conference in Uncertainty in Artificial Intelligence (UAI-2019), July 22-25, Tel Aviv, Israel, Pages 334-344, Proceedings of Machine Learning Research, Vol. 115, AUAI Press, 2019.

Claas Völcker; Alejandro Molina; Johannes Neumann; Dirk Westermann; Kristian Kersting

DeepNotebooks: Deep Probabilistic Models Construct Python Notebooks for Reporting Datasets

In: Peggy Cellier; Kurt Driessens (Hrsg.). Machine Learning and Knowledge Discovery in Databases. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD-2019), International Workshops of ECML PKDD 2019, Proceedings, Part I, September 16-20, Würzburg, Germany, Pages 28-43, Communications in Computer and Information Science, Vol. 1167, Springer, 2019.

Navdeep Kaur; Gautam Kunapuli; Saket Joshi; Kristian Kersting; Sriraam Natarajan

Neural Networks for Relational Data

In: Dimitar Kazakov; Can Erten (Hrsg.). Inductive Logic Programming - 29th International Conference, Proceedings. International Conference on Inductive Logic Programming (ILP-2019), September 3-5, Plovdiv, Bulgaria, Pages 62-71, Lecture Notes in Computer Science (LNAI), Vol. 11770, Springer, 2019.