Shinichi Nakajima

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Masashi SugiyamaDirector, RIKEN Center for Advanced Intelligence Project / Professor, The University of TokyoVerified email at k.u-tokyo.ac.jp
Klaus-Robert MüllerTU Berlin & Korea University & Google Brain & MPIIVerified email at tu-berlin.de
Motoaki KawanabeSenior Researcher, Advanced Telecommunications Research Institute InternationalVerified email at atr.jp
S. Derin BabacanAmazonVerified email at amazon.com
Wojciech SamekHead of AI Department, Fraunhofer HHI, Germany, BIFOLD Fellow & ELLIS BerlinVerified email at hhi.fraunhofer.de
Hisashi KashimaProfessor, Kyoto UniversityVerified email at i.kyoto-u.ac.jp
Marius KloftTU KaiserslauternVerified email at cs.uni-kl.de
Ryota TomiokaMicrosoft Research CambridgeVerified email at microsoft.com
Paul von Bünauidalab GmbHVerified email at idalab.de
Alexander BinderAssociate Professor, University of Oslo (UiO)Verified email at ifi.uio.no
Tsuyoshi IdeIBM T. J. Watson Research CenterVerified email at us.ibm.com
Jun SeseHumanome Lab Inc.Verified email at humanome.jp
Ulf BrefeldLeuphana Universität LüneburgVerified email at leuphana.de
Marc ToussaintProfessor of Computer Science, TU Berlin, GermanyVerified email at tu-berlin.de
Christoph LippertProfessor, Hasso Plattner Institute, Universität PotsdamVerified email at hpi.de
Stephan MandtAssistant Professor, University of California, IrvineVerified email at uci.edu
Takeuchi IchiroProfessor of Computer Science, Nagoya Institute of TechnologyVerified email at nitech.ac.jp
John CunninghamAssociate Professor, Columbia UniversityVerified email at columbia.edu
Kazuho WatanabeToyohashi University of TechnologyVerified email at cs.tut.ac.jp

Shinichi Nakajima

Machine Learning Group, Technische Universität Berlin

Verified email at tu-berlin.de - Homepage

Machine Learning Computer Vision


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Direct importance estimation with model selection and its application to covariate shift adaptation. M Sugiyama, S Nakajima, H Kashima, P Von Buenau, M Kawanabe NIPS 7, 1433-1440, 2007	766	2007
Semi-supervised local Fisher discriminant analysis for dimensionality reduction M Sugiyama, T Idé, S Nakajima, J Sese Machine learning 78 (1), 35-61, 2010	330	2010
Direct importance estimation for covariate shift adaptation M Sugiyama, T Suzuki, S Nakajima, H Kashima, P von Bünau, ... Annals of the Institute of Statistical Mathematics 60 (4), 699-746, 2008	314	2008
Multi-class image segmentation using conditional random fields and global classification N Plath, M Toussaint, S Nakajima Proceedings of the 26th Annual International Conference on Machine Learning …, 2009	128	2009
Bayesian group-sparse modeling and variational inference SD Babacan, S Nakajima, MN Do IEEE transactions on signal processing 62 (11), 2906-2921, 2014	112	2014
Global analytic solution of fully-observed variational Bayesian matrix factorization S Nakajima, M Sugiyama, SD Babacan, R Tomioka Journal of Machine Learning Research 14 (Jan), 1-37, 2013	82	2013
Pool-based active learning in approximate linear regression M Sugiyama, S Nakajima Machine Learning 75 (3), 249-274, 2009	79	2009
Theoretical analysis of Bayesian matrix factorization S Nakajima, M Sugiyama The Journal of Machine Learning Research 12, 2583-2648, 2011	52	2011
Towards best practice in explaining neural network decisions with LRP M Kohlbrenner, A Bauer, S Nakajima, A Binder, W Samek, S Lapuschkin 2020 International Joint Conference on Neural Networks (IJCNN), 1-7, 2020	40	2020
Perfect dimensionality recovery by variational Bayesian PCA S Nakajima, R Tomioka, M Sugiyama, S Babacan Advances in Neural Information Processing Systems 25, 971-979, 2012	32	2012
Variational Bayes solution of linear neural networks and its generalization performance S Nakajima, S Watanabe Neural Computation 19 (4), 1112-1153, 2007	32	2007
On Bayesian PCA: Automatic dimensionality selection and analytic solution S Nakajima, M Sugiyama, SD Babacan ICML, 2011	30	2011
Implicit Regularization in Variational Bayesian Matrix Factorization. S Nakajima, M Sugiyama ICML, 2010	26	2010
Support Vector Data Descriptions and -Means Clustering: One Class? N Görnitz, LA Lima, KR Müller, M Kloft, S Nakajima IEEE transactions on neural networks and learning systems 29 (9), 3994-4006, 2017	25	2017
Multiple kernel learning for object classification S Nakajima, A Binder, C Müller, W Wojcikiewicz, M Kloft, U Brefeld, ... Proceedings of the 12th Workshop on Information-based Induction Sciences 24, 2009	24	2009
Asymptotically unbiased estimation of physical observables with neural samplers KA Nicoli, S Nakajima, N Strodthoff, W Samek, KR Müller, P Kessel Physical Review E 101 (2), 023304, 2020	22	2020
An exhaustive search and stability of sparse estimation for feature selection problem K Nagata, J Kitazono, S Nakajima, S Eifuku, R Tamura, M Okada IPSJ Online Transactions 8, 25-32, 2015	22	2015
Global analytic solution for variational Bayesian matrix factorization S Nakajima, M Sugiyama, R Tomioka Advances in Neural Information Processing Systems 23, 1768-1776, 2010	21	2010
Probabilistic Low-Rank Subspace Clustering. SD Babacan, S Nakajima, MN Do NIPS, 2753-2761, 2012	20	2012
Variational Bayesian sparse additive matrix factorization S Nakajima, M Sugiyama, SD Babacan Machine learning 92 (2-3), 319-347, 2013	19	2013

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