| Learning to detect unseen object classes by between-class attribute transfer CH Lampert, H Nickisch, S Harmeling 2009 IEEE conference on computer vision and pattern recognition, 951-958, 2009 | 3085 | 2009 |
| Attribute-based classification for zero-shot visual object categorization CH Lampert, H Nickisch, S Harmeling IEEE transactions on pattern analysis and machine intelligence 36 (3), 453-465, 2013 | 1980 | 2013 |
| Gaussian processes for machine learning (GPML) toolbox CE Rasmussen, H Nickisch The Journal of Machine Learning Research 11, 3011-3015, 2010 | 1296 | 2010 |
| Kernel interpolation for scalable structured Gaussian processes (KISS-GP) A Wilson, H Nickisch International conference on machine learning, 1775-1784, 2015 | 656 | 2015 |
| Approximations for binary Gaussian process classification H Nickisch, CE Rasmussen Journal of Machine Learning Research 9 (Oct), 2035-2078, 2008 | 515 | 2008 |
| Comparison of deep learning approaches for multi-label chest X-ray classification IM Baltruschat, H Nickisch, M Grass, T Knopp, A Saalbach Scientific reports 9 (1), 6381, 2019 | 505 | 2019 |
| Additive gaussian processes DK Duvenaud, H Nickisch, C Rasmussen Advances in neural information processing systems 24, 2011 | 449 | 2011 |
| Optimization of k‐space trajectories for compressed sensing by Bayesian experimental design M Seeger, H Nickisch, R Pohmann, B Schölkopf Magnetic Resonance in Medicine: An Official Journal of the International …, 2010 | 190 | 2010 |
| Compressed sensing and Bayesian experimental design MW Seeger, H Nickisch Proceedings of the 25th international conference on Machine learning, 912-919, 2008 | 147 | 2008 |
| Thoughts on massively scalable Gaussian processes AG Wilson, C Dann, H Nickisch arXiv preprint arXiv:1511.01870, 2015 | 130 | 2015 |
| Fast Kronecker inference in Gaussian processes with non-Gaussian likelihoods S Flaxman, A Wilson, D Neill, H Nickisch, A Smola International conference on machine learning, 607-616, 2015 | 117 | 2015 |
| Blind retrospective motion correction of MR images A Loktyushin, H Nickisch, R Pohmann, B Schölkopf Magnetic resonance in medicine 70 (6), 1608-1618, 2013 | 114 | 2013 |
| Scalable log determinants for Gaussian process kernel learning K Dong, D Eriksson, H Nickisch, D Bindel, AG Wilson Advances in Neural Information Processing Systems 30, 2017 | 110 | 2017 |
| Large scale Bayesian inference and experimental design for sparse linear models MW Seeger, H Nickisch SIAM Journal on Imaging Sciences 4 (1), 166-199, 2011 | 91 | 2011 |
| Automatic classification of auroral images from the Oslo Auroral THEMIS (OATH) data set using machine learning LBN Clausen, H Nickisch Journal of Geophysical Research: Space Physics 123 (7), 5640-5647, 2018 | 79 | 2018 |
| Method of determining the blood flow through coronary arteries M Grass, H Schmitt, H Nickisch US Patent 9,867,584, 2018 | 67 | 2018 |
| Smart chest X-ray worklist prioritization using artificial intelligence: a clinical workflow simulation I Baltruschat, L Steinmeister, H Nickisch, A Saalbach, M Grass, G Adam, ... European radiology 31, 3837-3845, 2021 | 64 | 2021 |
| Blind multirigid retrospective motion correction of MR images A Loktyushin, H Nickisch, R Pohmann, B Schölkopf Magnetic resonance in medicine 73 (4), 1457-1468, 2015 | 52 | 2015 |
| Motion artifact recognition and quantification in coronary CT angiography using convolutional neural networks T Lossau, H Nickisch, T Wissel, R Bippus, H Schmitt, M Morlock, M Grass Medical image analysis 52, 68-79, 2019 | 49 | 2019 |
| Motion estimation and correction in cardiac CT angiography images using convolutional neural networks T Lossau, H Nickisch, T Wissel, R Bippus, H Schmitt, M Morlock, M Grass Computerized Medical Imaging and Graphics 76, 101640, 2019 | 44 | 2019 |
Michael GrassPhilips Innovative Technologies, Hamburg, Germanyphilips.com의 이메일 확인됨