Biblio

Export 52 results:
Author Title [ Type(Desc)] Year
Filters: First Letter Of Last Name is D  [Clear All Filters]
Book
Meier, B. Bruno, Stadelmann T., & Dürr O. (2018).  Learning to Cluster. PDF icon learning_to_cluster.pdf (1.82 MB)
Dürr, O., Sick B., & Murina E. (2020).  Probabilistic deep learning: With python, keras and tensorflow probability.
Dürr, O., Sick B., & Murina E. (2020).  Probabilistic Deep Learning: With Python, Keras and TensorFlow Probability.
Book Chapter
Stadelmann, T., Tolkachev V., Sick B., & Dürr O. (2019).  Beyond ImageNet: Deep Learning in Industrial Practice. Applied Data Science. 205-232.
Chart
Dold, D., Arpogaus M., & Dürr O. (2023).  Deep probabilistic modelling for energy forecasting. PDF icon Poster_Deep probabilistic modelling for energy forecasting TTT.pdf (839.27 KB)
Dold, D., Arpogaus M., & Dürr O. (2023).  Deep probabilistic modelling for energy forecasting. PDF icon Poster_Deep probabilistic modelling for energy forecasting TTT.pdf (839.27 KB)
Conference Paper
Caputo, M., Denker K., Dums B., & Umlauf G. (2012).  3d hand gesture recognition based on sensor fusion of commodity hardware. (Reiterer, H., & Deussen O., Ed.).Mensch und Computer. PDF icon GestureRecognition.pdf (378.26 KB)
Caputo, M., Denker K., Dums B., & Umlauf G. (2012).  3d hand gesture recognition based on sensor fusion of commodity hardware. (Reiterer, H., & Deussen O., Ed.).Mensch und Computer. PDF icon GestureRecognition.pdf (378.26 KB)
Caputo, M., Denker K., Dums B., & Umlauf G. (2012).  3d hand gesture recognition based on sensor fusion of commodity hardware. (Reiterer, H., & Deussen O., Ed.).Mensch und Computer. PDF icon GestureRecognition.pdf (378.26 KB)
Scharpf, P., Hong C. Lap, & Dürr O. (2021).  Accelerating Active Learning Image Labeling Through Bulk Shift Recommendations. 2021 International Conference on Data Mining Workshops (ICDMW). 398–404.
Scharpf, P., Hong C. Lap, & Duerr O. (2021).  Accelerating Active Learning Image Labeling Through Bulk Shift Recommendations. 2021 International Conference on Data Mining Workshops (ICDMW). 398-404.
Seepold, R., Dermati C., Kostka A., Pfeil L., Lange R., Hermann M., et al. (2016).  Analyzing environmental conditions and vital signs to increase healthy living. Mobile Networks for Biometric Data Analysis.
Casanova, R., Murina E., Haberecker M., Honcharova-Biletska H., Vrugt B., Dürr O., et al. (2018).  Automatic classification of non-small cell lung cancer histologic sub-types by deep learning. VIRCHOWS ARCHIV. 108-108.
Sick, B., Hothorn T., & Dürr O. (2021).  Deep transformation models: Tackling complex regression problems with neural network based transformation models. Accepted for Proceedings of the 25th International Conference on Pattern Recognition (ICPR), Milan/Online, 2021.
Hermann, M., Dold D., Umlauf G., & Dürr O. (2022).  DeepDoubt - Improving uncertainty measures in machine learning to improve explainability and transparency. 2022 All-Hands-Meeting of the BMBF-funded AI Research Projects at Munich Center for Machine Learning. PDF icon AHM2022_DeepDoubt.pdf (238.98 KB)
Hermann, M., Dold D., Umlauf G., & Dürr O. (2022).  DeepDoubt - Improving uncertainty measures in machine learning to improve explainability and transparency. 2022 All-Hands-Meeting of the BMBF-funded AI Research Projects at Munich Center for Machine Learning. PDF icon AHM2022_DeepDoubt.pdf (238.98 KB)
Hoher, P., Reuter J., Dold D., Griesser D., Govaers F., & Koch W. (2023).  Extended Target Tracking With a Lidar Sensor Using Random Matrices and a Gaussian Processes Regression Model. International Conference on Information Fusion (FUSION). 1-8.
Bobach, T., Constantiniu A., Steinmann P., & Umlauf G. (2010).  Geometric properties of the adaptice Delaunay tessellation. (Dæhlen, M., Floater M.S., Lyche T., Merrien J.-L., Morken K., & Schumaker L.L., Ed.).Mathematical Methods of Curves and Surfaces, Tondsberg 2008. PDF icon ADTProperties.pdf (335.14 KB)
Bender, C., Denker K., Friedrich M., Hirt K., & Umlauf G. (2012).  A hand-held laser scanner based on multi-camera stereo-matching. Visualization of Large and Unstructured Data Sets - Applications in Geospatial Planning, Modeling and Engineering (IRTG 1131 Workshop. PDF icon LaserScannerStereoMatching.pdf (584.47 KB)
Bobach, T., & Umlauf G. (2006).  Natural neighbor interpolation and order of continuity. (Hagen, H., Kerren A., & Dannenmann P., Ed.).GI Lecture Notes in Informatics, Visualization of Large and Unstructured Data Sets. PDF icon NatNeighborInterp.pdf (1.47 MB)
Denker, K., Hamann B., & Umlauf G. (2015).  On-line CAD Reconstruction with Accumulated Means of Local Geometric Properties. (Boissonnat, J-D., Cohen A., Gibaru O., Gout C., Lyche T., Mazure M-L., et al., Ed.).Curves and Surfaces, 8th International Conference, Paris 2014. 181-201.PDF icon OnlineCADReconst.pdf (3.18 MB)
Denker, K., Hagel D., Raible J., Umlauf G., & Hamann B. (2013).  On-line reconstruction of CAD geometry. International Conference on 3d Vision. PDF icon OnlineReconstruction.pdf (392.38 KB)
Denker, K., Lehner B., & Umlauf G. (2008).  Online triangulation of laser-scan data. (Garimella, R., Ed.).Proceedings of the 17th International Meshing Roundtable 2008. PDF icon OnlineTriang.pdf (8.39 MB)
Arpogaus, M., Voß M., Sick B., Nigge-Uricher M., & Dürr O. (2021).  Probabilistic short-term low-voltage load forecasting using bernstein-polynomial normalizing flows. ICML 2021, Workshop Tackling Climate Change with Machine Learning, June 26, 2021, virtual.
Arpogaus, M., Voß M., Sick B., Nigge-Uricher M., & Dürr O. (2021).  Probabilistic Short-Term Low-Voltage Load Forecasting using Bernstein-Polynomial Normalizing Flows. ICML 2021, Workshop Tackling Climate Change with Machine Learning, June 26, 2021, virtual. PDF icon Arpogaus2021_Probabilistic_Forecasting.pdf (427.35 KB)

Pages