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2015 | Conference Paper | IST-REx-ID: 1424 |
R. Kwitt, S. Huber, M. Niethammer, W. Lin, and U. Bauer, “Statistical topological data analysis-A kernel perspective,” presented at the NIPS: Neural Information Processing Systems, Montreal, Canada, 2015, vol. 28, pp. 3070–3078.View | Download Submitted Version (ext.)
2014 | Book Chapter | IST-REx-ID: 2044 |
U. Bauer, M. Kerber, and J. Reininghaus, “Clear and Compress: Computing Persistent Homology in Chunks,” in Topological Methods in Data Analysis and Visualization III, P.-T. Bremer, I. Hotz, V. Pascucci, and R. Peikert, Eds. Springer, 2014, pp. 103–117.View | DOI | Download Submitted Version (ext.)
2014 | Journal Article | IST-REx-ID: 1929
V. V. Alexeev, V. G. Bogaevskaya, M. M. Preobrazhenskaya, A. Y. Ukhalov, H. Edelsbrunner, and O. Yakimova, “An algorithm for cartographic generalization that preserves global topology,” Journal of Mathematical Sciences (United States), vol. 203, no. 6, pp. 754–760, 2014.View | DOI
2014 | Journal Article | IST-REx-ID: 1930
D. Günther, A. Jacobson, J. Reininghaus, H. Seidel, O. Sorkine Hornung, and T. Weinkauf, “Fast and memory-efficient topological denoising of 2D and 3D scalar fields,” IEEE Transactions on Visualization and Computer Graphics, vol. 20, no. 12, pp. 2585–2594, 2014.View | DOI
2013 | Conference Paper | IST-REx-ID: 2812 |
D. Attali, U. Bauer, O. Devillers, M. Glisse, and A. Lieutier, “Homological reconstruction and simplification in R3,” in Proceedings of the 29th annual symposium on Computational Geometry, Rio de Janeiro, Brazil, 2013, pp. 117–125.View | Files available | DOI | Download Submitted Version (ext.)
2013 | Journal Article | IST-REx-ID: 2822 |
C. Topp et al., “3D phenotyping and quantitative trait locus mapping identify core regions of the rice genome controlling root architecture,” PNAS, vol. 110, no. 18, pp. E1695–E1704, 2013.View | DOI | Download Submitted Version (ext.) | PubMed | Europe PMC
2013 | Conference Paper | IST-REx-ID: 2901 |
C. Chen, V. Kolmogorov, Z. Yan, D. Metaxas, and C. Lampert, “Computing the M most probable modes of a graphical model,” presented at the AISTATS: Conference on Uncertainty in Artificial Intelligence, Scottsdale, AZ, United States, 2013, vol. 31, pp. 161–169.View | Download None (ext.)