Hannes Nickisch
Cited by
Cited by
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
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
Gaussian processes for machine learning (GPML) toolbox
CE Rasmussen, H Nickisch
The Journal of Machine Learning Research 11, 3011-3015, 2010
Kernel interpolation for scalable structured Gaussian processes (KISS-GP)
A Wilson, H Nickisch
International conference on machine learning, 1775-1784, 2015
Approximations for binary Gaussian process classification
H Nickisch, CE Rasmussen
Journal of Machine Learning Research 9 (Oct), 2035-2078, 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), 1-10, 2019
Additive gaussian processes
DK Duvenaud, H Nickisch, C Rasmussen
Advances in neural information processing systems 24, 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
Compressed sensing and Bayesian experimental design
MW Seeger, H Nickisch
Proceedings of the 25th international conference on Machine learning, 912-919, 2008
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
Thoughts on massively scalable Gaussian processes
AG Wilson, C Dann, H Nickisch
arXiv preprint arXiv:1511.01870, 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
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
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
Method of determining the blood flow through coronary arteries
M Grass, H Schmitt, H Nickisch
US Patent 9,867,584, 2018
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
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
Learning patient-specific lumped models for interactive coronary blood flow simulations
H Nickisch, Y Lamash, S Prevrhal, M Freiman, M Vembar, L Goshen, ...
Medical Image Computing and Computer-Assisted Intervention--MICCAI 2015 …, 2015
Attribute-based classification for zero-shot learning of object categories
CH Lampert, H Nickisch, S Harmeling
IEEE Transactions on Pattern Analysis & Machine Intelligence, 1-1, 2013
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
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