Hannes Nickisch
Zitiert von
Zitiert von
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
Approximations for binary Gaussian process classification
H Nickisch, CE Rasmussen
Journal of Machine Learning Research 9 (Oct), 2035-2078, 2008
Kernel interpolation for scalable structured Gaussian processes (KISS-GP)
A Wilson, H Nickisch
International Conference on Machine Learning, 1775-1784, 2015
Additive gaussian processes
D Duvenaud, H Nickisch, CE Rasmussen
arXiv preprint arXiv:1112.4394, 2011
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
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
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
Thoughts on massively scalable Gaussian processes
AG Wilson, C Dann, H Nickisch
arXiv preprint arXiv:1511.01870, 2015
Scalable log determinants for Gaussian process kernel learning
K Dong, D Eriksson, H Nickisch, D Bindel, AG Wilson
arXiv preprint arXiv:1711.03481, 2017
Blind retrospective motion correction of MR images
A Loktyushin, H Nickisch, R Pohmann, B Schölkopf
Magnetic resonance in medicine 70 (6), 1608-1618, 2013
Method of determining the blood flow through coronary arteries
M Grass, H Schmitt, H Nickisch
US Patent 9,867,584, 2018
Fast convergent algorithms for expectation propagation approximate Bayesian inference
M Seeger, H Nickisch
Proceedings of the Fourteenth International Conference on Artificial …, 2011
Bayesian experimental design of magnetic resonance imaging sequences
M Seeger, H Nickisch, R Pohmann, B Schölkopf
Proceedings of the 22nd Annual Conference on Neural Information Processing …, 2009
User-centric learning and evaluation of interactive segmentation systems
P Kohli, H Nickisch, C Rother, C Rhemann
International journal of computer vision 100 (3), 261-274, 2012
Convex variational Bayesian inference for large scale generalized linear models
H Nickisch, MW Seeger
Proceedings of the 26th Annual International Conference on Machine Learning …, 2009
Learning an interactive segmentation system
H Nickisch, C Rother, P Kohli, C Rhemann
Proceedings of the Seventh Indian Conference on Computer Vision, Graphics …, 2010
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