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H. Haußecker, Spies, H., and Jähne, B., “Tensor-based image sequence processing techniques for the study of dynamical processes”, in Proc. Intern. Symp. On Real-time Imaging and Dynamic Analysis, 1998, p. 704--711.
H. Haußecker, Tizhoosh, H. R., Jähne, B., Geißler, P., and Haußecker, H., “Fuzzy image processing”, Handbook of Computer Vision and Applications, vol. 2: Signal Processing and Pattern Recognition. Academic Press, p. 683--727, 1999.
H. Haußecker, Tizhoosh, H. R., and Jähne, B., “Fuzzy image processing”, Computer Vision and Applications - A Guide for Students and Practitioners. Academic Press, p. 541--576, 2000.
M. Haußmann, “Bayesian Neural Networks for Probabilistic Machine Learning”. Heidelberg University, 2021.
M. Haußmann, Gerwinn, S., Look, A., Rakitsch, B., and Kandemir, M., “Learning Partially Known Stochastic Dynamics with Empirical PAC Bayes”, International Conference on Artificial Intelligence and Statistics , vol. PMLR 130. pp. 478-486, 2021.
M. Haußmann, Gerwinn, S., and Kandemir, M., “Bayesian Evidential Deep Learning with PAC Regularization ”, 3rd Symposium on Advances in Approximate Bayesian Inference . 2020.
M. Haußmann, Hamprecht, F. A., and Kandemir, M., “Variational Bayesian Multiple Instance Learning with Gaussian Processes”, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 6570-6579, 2017.PDF icon Technical Report (1.29 MB)
M. Haußmann, “Weakly Supervised Detection with Gaussian Processes”, University of Heidelberg, 2016.
M. Haußmann, Hamprecht, F. A., and Kandemir, M., “Sampling-Free Variational Inference of Bayesian Neural Networks by Variance Backpropagation”, UAI. Proceedings. pp. 563-573, 2019.PDF icon Technical Report (1.04 MB)
M. Haußmann, Hamprecht, F. A., and Kandemir, M., “Deep Active Learning with Adaptive Acquisition”, IJCAI. Proceedings. pp. 2470-2476, 2019.PDF icon Technical Report (137.6 KB)
M. Haußmann, Gerwinn, S., and Kandemir, M., “Bayesian Prior Networks with PAC Training”, arXiv preprint arXiv:1906.00816, 2019.
M. Hayn, “Statistical analysis of spatio-temporal patterns in global NOX satellite data”, University of Heidelberg, 2007.
M. Hayn, Beirle, S., Hamprecht, F. A., Platt, U., Menze, B. H., and Wagner, T., “Analysing spatio-temporal patterns of the global NO2-distribution retrieved frome GOME satellite observations using a generalized additive model”, Atmospheric Chemistry and Physics, vol. 9, pp. 9367-9398, 2009.PDF icon Technical Report (2.52 MB)
X. He, Wang, H., Zhang, F., Wang, G., and Zhou, K., “Robust Simulation of Small-Scale Thin Features in SPH-based Free Surface Flows”, Life.Kunzhou.Net, vol. 1, pp. 1–8, 2014.
K. He, Rhemann, C., Rother, C., Tang, X., and Sun, J., “A global sampling method for alpha matting”, in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2011, pp. 2049–2056.
D. Heck, “Proximity Graphs for Nonlinear Dimension Reduction”, University of Heidelberg, 2004.
H. Heck, “Bildverarbeitendes Verfahren zur Detektion und Vermessung von Luftblasen an der Wasseroberfläche eines Blasentanks”, Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg, 2011.
J. Heers, Schnörr, C., and Stiehl, H. S., “Globally–Convergent Iterative Numerical Schemes for Non–Linear Variational Image Smoothing and Segmentation on a Multi–Processor Machine”, IEEE Trans. Image Proc., vol. 10, pp. 852–864, 2001.
J. Heers, Schnörr, C., and Stiehl, H. S., “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, 283/99, 1999.
J. Heers, Schnörr, C., and Stiehl, H. S., “A class of parallel algorithms for nonlinear variational image segmentation”, in Proc. Noblesse Workshop on Non–Linear Model Based Image Analysis (NMBIA'98), Glasgow, Scotland, 1998.
J. Heers, Schnörr, C., and Stiehl, H. –S., “Investigation of Parallel and Globally Convergent Iterative Schemes for Nonlinear Variational Image Smoothing and Segmentation”, in Proc. IEEE Int. Conf. Image Proc., Chicago, 1998.
J. Heers, Schnörr, C., and Stiehl, H. –S., “Parallele und global konvergente iterative Minimierung nichtlinearer Variationsansätze zur adaptiven Glättung und Segmentation von Bildern”, in Mustererkennung 1998, Heidelberg, 1998.
T. Hehn, “A probabilistic approach to learn complex differentiable split functions in decision trees using gradient ascent”, Heidelberg University, 2017.
T. Hehn and Hamprecht, F. A., “End-to-end Learning of Deterministic Decision Trees”, German Conference on Pattern Recognition. Proceedings, vol. LNCS 11269. Springer, pp. 612-627, 2018.PDF icon Technical Report (1.4 MB)
T. M. Hehn, Kooij, J. F. P., and Hamprecht, F. A., “End-to-End Learning of Decision Trees and Forests”, International Journal of Computer Vision, vol. 128, pp. 997-1011, 2020.
J. Heikkonen, Koikkalainen, P., and Schnörr, C., “Building Trajectories via Selforganization from Spatiotemporal Features”, in 12th Int. Conf. on Pattern Recognition, Jerusalem, Israel, 1994.
M. Heiler, Cremers, D., and Schnörr, C., “Efficient Feature Subset Selection for Support Vector Machines”, Dept. Math. and Comp. Science, University of Mannheim, Germany, 21/2001, 2001.
M. Heiler, Keuchel, J., and Schnörr, C., “Semidefinite Clustering for Image Segmentation with A-priori Knowledge”, Pattern Recognition, Proc. 27th DAGM Symposium, vol. 3663. Springer, pp. 309–317, 2005.
M. Heiler and Schnörr, C., “Learning Sparse Representations by Non-Negative Matrix Factorization and Sequential Cone Programming”, J. Mach. Learning Res., vol. 7, pp. 1385–1407, 2006.
M. Heiler and Schnörr, C., “Natural Image Statistics for Natural Image Segmentation”, Int. J. Comp. Vision, vol. 63, pp. 5–19, 2005.
M. Heiler and Schnörr, C., “Learning Sparse Image Codes by Convex Programming”, in Proc. Tenth IEEE Int. Conf. Computer Vision (ICCV'05), Beijing, China, 2005, pp. 1667-1674.
M. Heiler and Schnörr, C., “Reverse-Convex Programming for Sparse Image Codes”, in Proc. Int. Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR'05), 2005, vol. 3757, pp. 600-616.
M. Heiler and Schnörr, C., “Natural Statistics for Natural Image Segmentation”, in Proc. IEEE Int. Conf. Computer Vision (ICCV 2003), Nice, France, 2003, pp. 1259-1266.
M. Heiler and Schnörr, C., “Controlling Sparseness in Non-negative Tensor Factorization”, in Computer Vision -- ECCV 2006, 2006, vol. 3951, pp. 56-67.PDF icon Technical Report (568.86 KB)
G. Heinz, “Messung der Diffusionskonstanten von in Wasser gelösten Gasen mit einem modifizierten Barrerverfahren”, Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg, 1986.

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