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Hennies, J (2017). Improvement And Validation Of Neural Em Volume Image Segmentation By High-Level Information. University of Heidelberg
Heitz, D, Mémin, E and Schnörr, C (2010). Variational fluid flow measurements from image sequences: synopsis and perspectives. Exp.~Fluids. 48 369-393PDF icon Technical Report (1.91 MB)
Heitz, D, Mémin, E and Schnörr, C (2010). Variational fluid flow measurements from image sequences: synopsis and perspectives. Exp. Fluids. 48 369-393
Heinz, G (1986). Messung Der Diffusionskonstanten Von In Wasser Gelösten Gasen Mit Einem Modifizierten Barrerverfahren. Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg
Heiler, M, Cremers, D and Schnörr, C (2001). Efficient Feature Subset Selection For Support Vector Machines. Dept. Math. and Comp. Science, University of Mannheim, Germany
Heiler, M, Keuchel, J and Schnörr, C (2005). Semidefinite Clustering for Image Segmentation with A-priori Knowledge. Pattern Recognition, Proc. 27th DAGM Symposium. Springer. 3663 309–317
Heiler, M and Schnörr, C (2006). Learning Sparse Representations by Non-Negative Matrix Factorization and Sequential Cone Programming. J. Mach. Learning Res. 7 1385–1407. http://www.cvgpr.uni-mannheim.de/Publications
Heiler, M and Schnörr, C (2005). Natural Image Statistics for Natural Image Segmentation. Int. J. Comp. Vision. 63 5–19
Heiler, M and Schnörr, C (2005). Learning Sparse Image Codes by Convex Programming. Proc. Tenth IEEE Int. Conf. Computer Vision (ICCV'05). Beijing, China. 1667-1674
Heiler, M and Schnörr, C (2005). Reverse-Convex Programming for Sparse Image Codes. Proc. Int. Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR'05). Springer. 3757 600-616
Heiler, M and Schnörr, C (2003). Natural Statistics for Natural Image Segmentation. Proc. IEEE Int. Conf. Computer Vision (ICCV 2003). Nice, France. 1259-1266
Heiler, M and Schnörr, C (2006). Controlling Sparseness in Non-negative Tensor Factorization. Computer Vision -- ECCV 2006. Springer. 3951 56-67PDF icon Technical Report (568.86 KB)
Heikkonen, J, Koikkalainen, P and Schnörr, C (1994). Building Trajectories via Selforganization from Spatiotemporal Features. 12th Int. Conf. on Pattern Recognition. Jerusalem, Israel
Hehn, T (2017). A Probabilistic Approach To Learn Complex Differentiable Split Functions In Decision Trees Using Gradient Ascent. Heidelberg University
Hehn, T and Hamprecht, F A (2018). End-to-end Learning of Deterministic Decision Trees. German Conference on Pattern Recognition. Proceedings. Springer. LNCS 11269 612-627PDF icon Technical Report (1.4 MB)
Hehn, T M, Kooij, J F P and Hamprecht, F A (2020). End-to-End Learning of Decision Trees and Forests. International Journal of Computer Vision. 128 997-1011
Heers, J, Schnörr, C and Stiehl, H S (2001). Globally–Convergent Iterative Numerical Schemes for Non–Linear Variational Image Smoothing and Segmentation on a Multi–Processor Machine. IEEE Trans. Image Proc. 10 852–864
Heers, J, Schnörr, C and Stiehl, H S (1999). Investigating A Class Of Iterative Schemes And Their Parallel Implementation For Nonlinear Variational Image Smoothing And Segmentation. Comp. Sci. Dept., AB KOGS, University of Hamburg, Germany
Heers, J, Schnörr, C and Stiehl, H S (1998). A class of parallel algorithms for nonlinear variational image segmentation. Proc. Noblesse Workshop on Non–Linear Model Based Image Analysis (NMBIA'98). Glasgow, Scotland
Heers, J, Schnörr, C and Stiehl, H –S (1998). Investigation of Parallel and Globally Convergent Iterative Schemes for Nonlinear Variational Image Smoothing and Segmentation. Proc. IEEE Int. Conf. Image Proc. Chicago
Heers, J, Schnörr, C and Stiehl, H –S (1998). Parallele und global konvergente iterative Minimierung nichtlinearer Variationsansätze zur adaptiven Glättung und Segmentation von Bildern. Mustererkennung 1998. Springer, Heidelberg
Heck, H (2011). Bildverarbeitendes Verfahren Zur Detektion Und Vermessung Von Luftblasen An Der Wasseroberfläche Eines Blasentanks. Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg
Heck, D (2004). Proximity Graphs For Nonlinear Dimension Reduction. University of Heidelberg
He, X, Wang, H, Zhang, F, Wang, G and Zhou, K (2014). Robust Simulation of Small-Scale Thin Features in SPH-based Free Surface Flows. Life.Kunzhou.Net. 1 1–8. http://doi.acm.org/10.1145/XXXXXXX.YYYYYYY http://life.kunzhou.net/2013/SPHsurfacetension.pdf
He, K, Rhemann, C, Rother, C, Tang, X and Sun, J (2011). A global sampling method for alpha matting. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2049–2056
Hayn, M (2007). Statistical Analysis Of Spatio-Temporal Patterns In Global Nox Satellite Data. University of Heidelberg
Hayn, M, Beirle, S, Hamprecht, F A, Platt, U, Menze, B H and Wagner, T (2009). Analysing spatio-temporal patterns of the global NO2-distribution retrieved frome GOME satellite observations using a generalized additive model. Atmospheric Chemistry and Physics. 9 9367-9398PDF icon Technical Report (2.52 MB)
Haußmann, (2021). Bayesian Neural Networks for Probabilistic Machine Learning. Heidelberg University
Haußmann, M, Gerwinn, S, Look, A, Rakitsch, B and Kandemir, M (2021). Learning Partially Known Stochastic Dynamics with Empirical PAC Bayes. International Conference on Artificial Intelligence and Statistics . PMLR 130 478-486
Haußmann, M, Gerwinn, S and Kandemir, M (2020). Bayesian Evidential Deep Learning with PAC Regularization . 3rd Symposium on Advances in Approximate Bayesian Inference
Haußmann, M, Hamprecht, F A and Kandemir, M (2017). Variational Bayesian Multiple Instance Learning with Gaussian Processes. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 6570-6579PDF icon Technical Report (1.29 MB)
Haußmann, (2016). Weakly Supervised Detection With Gaussian Processes. University of Heidelberg
Haußmann, M, Hamprecht, F A and Kandemir, M (2019). Sampling-Free Variational Inference of Bayesian Neural Networks by Variance Backpropagation. UAI. Proceedings. 563-573PDF icon Technical Report (1.04 MB)
Haußmann, M, Hamprecht, F A and Kandemir, M (2019). Deep Active Learning with Adaptive Acquisition. IJCAI. Proceedings. 2470-2476PDF icon Technical Report (137.6 KB)
Haußmann, M, Gerwinn, S and Kandemir, M (2019). Bayesian Prior Networks with PAC Training. arXiv preprint arXiv:1906.00816

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