Hannes Nickisch

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Michael GrassPhilips Research, Hambug, GermanyVerified email at philips.com
Carl Edward RasmussenProfessor of Machine Learning, University of CambridgeVerified email at cam.ac.uk
Andrew Gordon WilsonNew York UniversityVerified email at cims.nyu.edu
Matthias SeegerPrincipal Applied Scientist, Amazon, BerlinVerified email at amazon.de
Christoph H. LampertProfessor at IST AustriaVerified email at ist.ac.at
Stefan HarmelingProfessor at Technische Universität DortmundVerified email at tu-dortmund.de
Ivo Matteo BaltruschatScientist, Deutsches Elektronen-Synchrotron DESYVerified email at desy.de
Tobias KnoppProfessor for Biomedical Imaging, UKE and TUHH, HamburgVerified email at knoppweb.de
Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems; Professor at ETH Zürich, and DistinguishedVerified email at tuebingen.mpg.de
Sven PrevrhalPhilips Research HamburgVerified email at philips.com
Daniel B. NeillNew York UniversityVerified email at nyu.edu
Moti FreimanBiomedical Engineering, Technion - Israel Institute of TechnologyVerified email at technion.ac.il
Michael MorlockTUHHVerified email at tuhh.de
Seth Flaxmanassociate professor at OxfordVerified email at cs.ox.ac.uk
Gerhard AdamRadiologe, UKE, HamburgVerified email at uke.de
David DuvenaudAssistant Professor, University of TorontoVerified email at cs.toronto.edu
Christoph RhemannResearch Scientist, GoogleVerified email at google.com
Carsten RotherProfessor Uni Heidelberg / GermanyVerified email at iwr.uni-heidelberg.de
Alex SmolaAmazon Web ServicesVerified email at smola.org
Jürgen WeeseResearch Fellow, Philips ResearchVerified email at philips.com

Hannes Nickisch

Senior Scientist at Philips Research

Verified email at philips.com - Homepage

Biophysical Simulations Medical Image Processing Probabilistic Pattern Recognition Machine Learning


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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	2330	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	1418	2013
Gaussian processes for machine learning (GPML) toolbox CE Rasmussen, H Nickisch The Journal of Machine Learning Research 11, 3011-3015, 2010	963	2010
Approximations for binary Gaussian process classification H Nickisch, CE Rasmussen Journal of Machine Learning Research 9 (Oct), 2035-2078, 2008	411	2008
Kernel interpolation for scalable structured Gaussian processes (KISS-GP) A Wilson, H Nickisch International conference on machine learning, 1775-1784, 2015	381	2015
Additive gaussian processes DK Duvenaud, H Nickisch, C Rasmussen Advances in neural information processing systems 24, 2011	303	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	254	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	151	2010
Compressed sensing and Bayesian experimental design MW Seeger, H Nickisch Proceedings of the 25th international conference on Machine learning, 912-919, 2008	127	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	95	2015
Thoughts on massively scalable Gaussian processes AG Wilson, C Dann, H Nickisch arXiv preprint arXiv:1511.01870, 2015	84	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	83	2011
Blind retrospective motion correction of MR images A Loktyushin, H Nickisch, R Pohmann, B Schölkopf Magnetic resonance in medicine 70 (6), 1608-1618, 2013	79	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	74	2017
Method of determining the blood flow through coronary arteries M Grass, H Schmitt, H Nickisch US Patent 9,867,584, 2018	51	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	38	2015
Fast convergent algorithms for expectation propagation approximate Bayesian inference M Seeger, H Nickisch Proceedings of the Fourteenth International Conference on Artificial …, 2011	33	2011
Learning an interactive segmentation system H Nickisch, C Rother, P Kohli, C Rhemann Proceedings of the Seventh Indian Conference on Computer Vision, Graphics …, 2010	33	2010
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	33	2009
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	32	2018

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