Publications

Daniel Wirtz, and Bernard Haasdonk. Efficient a-posteriori error estimation for nonlinear kernel-based reduced systems. Systems & Control Letters, (61)1:203--211, Elsevier BV, 2012. [PUMA: subspace error dynamical kernel a-posteriori from:britsteiner methods, ians systems, nonlinear offline/online decomposition, projection estimates, model anm reduction,] URL

Daniel Wirtz, and Bernard Haasdonk. A-posteriori error estimation for parameterized kernel-based systems. Proc. MATHMOD 2012 - 7th Vienna International Conference on Mathematical Modelling, 2012. [PUMA: subspace error dynamical kernel a-posteriori methods, systems, nonlinear offline/online decomposition, parameterized projection estimates, model vorlaeufig reduction,] URL

Daniel Wirtz, and Bernard Haasdonk. Efficient a-posteriori error estimation for nonlinear kernel-based reduced systems. Systems & Control Letters, (61)1:203--211, Elsevier BV, 2012. [PUMA: subspace error dynamical kernel a-posteriori methods, ians systems, nonlinear offline/online decomposition, projection estimates, model anm reduction,] URL

Daniel Wirtz, and Bernard Haasdonk. A-posteriori error estimation for parameterized kernel-based systems. Proc. MATHMOD 2012 - 7th Vienna International Conference on Mathematical Modelling, 2012. [PUMA: subspace error dynamical kernel a-posteriori methods, ians systems, nonlinear offline/online decomposition, parameterized projection estimates, model anm reduction,] URL

D. Wirtz, and B. Haasdonk. Efficient a-posteriori error estimation for nonlinear kernel-based reduced systems. Systems and Control Letters, (61)1:203 - 211, 2012. [PUMA: subspace error dynamical kernel a-posteriori methods, systems, nonlinear offline/online decomposition, projection estimates, model vorlaeufig reduction,] URL

Daniel Wirtz, and Bernard Haasdonk. A-posteriori error estimation for parameterized kernel-based systems. Proc. MATHMOD 2012 - 7th Vienna International Conference on Mathematical Modelling, 2012. [PUMA: subspace error dynamical kernel a-posteriori methods, ians systems, nonlinear offline/online from:mhartmann decomposition, parameterized projection estimates, model vorlaeufig reduction,] URL

D. Wirtz, and B. Haasdonk. Efficient a-posteriori error estimation for nonlinear kernel-based reduced systems. Systems and Control Letters, (61)1:203 - 211, 2012. [PUMA: subspace error dynamical kernel a-posteriori methods, ians systems, nonlinear offline/online from:mhartmann decomposition, projection estimates, model vorlaeufig reduction,] URL

Roman Klinger, and Philipp Cimiano. Instance Selection Improves Cross-Lingual Model Training for Fine-Grained Sentiment Analysis. Proceedings of the Nineteenth Conference on Computational Natural Language Learning, 153-163, Association for Computational Linguistics, Beijing, China, July 2015. [PUMA: sentiment nlp projection multilingual] URL

Jeremy Barnes, Roman Klinger, and Sabine Schulte im Walde. Bilingual Sentiment Embeddings: Joint Projection of Sentiment Across Languages. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, Association for Computational Linguistics, Melbourne, Australia, July 2018. [PUMA: sentiment myown bilingual projection] URL

Markus Funk, Lars Lischke, Sven Mayer, Alireza Sahami Shirazi, and Albrecht Schmidt. Teach Me How! Interactive Assembly Instructions Using Demonstration and In-Situ Projection. In Jochen Huber, Roy Shilkrot, Pattie Maes, and Suranga Nanayakkara (Eds.), 49--73, Springer Singapore, Singapore, 2018. [PUMA: myown system vis-mci In-Situ visus:schmiat visus:sahamsi visus:mayersn assistive visus:funkms projection vis-sks visus:lischkls]

Christoph Stach. VAULT: A Privacy Approach towards High-Utility Time Series Data. In Stefan Rass, and George Yee (Eds.), Proceedings of the Thirteenth International Conference on Emerging Security Information, Systems and Technologies, 41–46, IARIA, Nice, October 2019. [PUMA: selection time_series interpolation privacy aggregation projection noise smoothing information_emphasization] URL