* Poster Session, July 22, 16:00 – 18:00 *
Weber, D, Kasneci E., Zell A. (Cluster Innovation Fund Project)
Human-robot interface with eye-tracking and augmented reality to teach mobile robots about the real-world. University of Tübingen, Department of Computer Science
Valera I.1, Utz S.² (Cluster Innovation Fund Project)
Extracting expertise from tweets: Exploring the boundary conditions of ambient awareness). 1Max Planck Institute for Intelligent Systems Tübingen, ² Leibniz-Institut für Wissensmedien
Luxburg U., Wichmann F. (Cluster Innovation Fund Project) Machine learning approaches for psychophysics with ordinal comparisons University of Tübingen, Department of Computer Science
Zabel S.1, Hennig P.2, Nieselt K.1 (Cluster Innovation Fund Project) Visualizing Uncertainty from Data, Model and Algorithm in Large-Scale Omics Data University of Tübingen, 1Center for Bioinformatics Tübingen, ²Department of Computer Science
Karlbauer, M.1, Lensch H.1, Scholten T.², Butz M.1 (Cluster Innovation Fund Project) Short-to-Mid Scale Weather Forecasting with a Distributed, Recurrent Convolutional ANN University of Tübingen, 1Department of Computer Science, ²Department of Geosciences
Behrens, T.1, Schmidt, K.1, Hennig, P.², Scholten, T.1 (Cluster Innovation Fund Project)
Feature engineering for spatial modelling. University of Tübingen, 1Department of Geosciences, ²Department of Computer Science
Baayen H.1, Lensch H.² (Cluster Innovation Fund Project)
Enhancing Machine Learning of Lexical Semantics with Image Mining University of Tübingen, 1Department of Linguistics, ²Department of Computer Science
Macke J.1, Hennig P.², Berens P.³, Oberlaender M.4
Automatic Data-driven Inference of Mechanistic Models 1Technische Universität München, Computational Neuroengineering Group 1 University of Tübingen, ²Department for Computer Science, ³Institute for Ophthalmic Research 4Center of advanced european studies and research
Pawlowski, T.1, Berens, P.², Kelava, A.³
Emotional cues and alcohol use: evidence from football. University of Tübingen, 1Department Institute of Sport Science, ²Institute for Ophthalmic Research, ³ Methods Center Kilian P.
Predicting math student college dropout with sparse information using approaches from statistical learning University of Tübingen, Methods Center
Klopotek M., Oettel M.
Variational autoencoders put up to the test in learning a statistical-mechanical model system University of Tübingen, Institut für Angewandte Physik
Lin SC, Oettel M.
Classical density functionals from machine learning University of Tübingen, Institut für Angewandte Physik
Greco A.1, Starostin V.1, Hinderhofer A.1, Gerlach A.1, Karapanagiotis C.², Liehr S.², Kowarik S.²,
Schreiber F.1
Fast Scattering Data Analysis Using Machine Learning.
1University of Tübingen, Institut für Angewandte Physik, Uni Tübingen, ² Bundesanstalt für Materialforschung
und -prüfung (BAM), Berlin
Sümer Ö.1,2, Kasneci E.1
Attention Flow: End-to-End Joint Attention Estimation University of Tübingen, 1Department of Computer Science, ²Hector Research Institute of Education Sciences and
Psychology (HIB)
Fuhl W., Kasneci G., Rosenstiel W., Kasneci E.
Training decision trees as replacement for convolution layers University of Tübingen, Department of Computer Science Zadaianchuk A., Martius G.
Equation Learning for Extrapolation and Control
Max Planck Institute for Intelligent Systems Tübingen | 
