| Deep learning with topological signatures C Hofer, R Kwitt, M Niethammer, A Uhl Advances in neural information processing systems, 1634-1644, 2017 | 101 | 2017 |
| Connectivity-optimized representation learning via persistent homology C Hofer, R Kwitt, M Niethammer, M Dixit International Conference on Machine Learning, 2751-2760, 2019 | 18 | 2019 |
| Learning Representations of Persistence Barcodes. CD Hofer, R Kwitt, M Niethammer Journal of Machine Learning Research 20 (126), 1-45, 2019 | 13 | 2019 |
| Factors affecting volume changes of the somatosensory cortex in patients with spinal cord injury: to be considered for future neuroprosthetic design Y Höller, A Tadzic, AC Thomschewski, P Höller, S Leis, SO Tomasi, ... Frontiers in Neurology 8, 662, 2017 | 5 | 2017 |
| Simple domain adaptation for cross-dataset analyses of brain MRI data C Hofer, R Kwitt, Y Höller, E Trinka, A Uhl 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017 …, 2017 | 5 | 2017 |
| Constructing shape spaces from a topological perspective C Hofer, R Kwitt, M Niethammer, Y Höller, E Trinka, A Uhl International Conference on Information Processing in Medical Imaging, 106-118, 2017 | 3 | 2017 |
| An empirical assessment of appearance descriptors applied to MRI for automated diagnosis of TLE and MCI C Hofer, R Kwitt, Y Höller, E Trinka, A Uhl Computers in Biology and Medicine 117, 103592, 2020 | | 2020 |
| Connectivity-Optimized Representation Learning via Persistent Homology–Supplementary material– CD Hofer, R Kwitt, M Dixit, M Niethammer | | |
Roland KwittProfessor, Department of Computer Science, University of Salzburg, AustriaVerified email at sbg.ac.at
