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 & Max Planck Institute for Informatics, GermanyVerified email at tu-berlin.de
Motoaki KawanabeSenior Researcher, Advanced Telecommunications Research Institute InternationalVerified email at atr.jp
Wojciech SamekProfessor at TU Berlin, Head of AI Department at Fraunhofer HHI, BIFOLD FellowVerified email at tu-berlin.de
S. Derin BabacanAmazonVerified email at amazon.com
Marius KloftTU KaiserslauternVerified email at cs.uni-kl.de
Hisashi KashimaProfessor, Kyoto UniversityVerified email at i.kyoto-u.ac.jp
Alexander BinderAssociate Professor, Singapore Institute of Technology (SIT) and University of Oslo (UiO)Verified email at singaporetech.edu.sg
Paul von Bünauidalab GmbHVerified email at idalab.de
Ryota TomiokaMicrosoft Research CambridgeVerified email at microsoft.com
Tsuyoshi IdeIBM T. J. Watson Research CenterVerified email at us.ibm.com
Ulf BrefeldLeuphana Universität LüneburgVerified email at leuphana.de
Jun SeseHumanome Lab Inc.Verified email at humanome.jp
Marc ToussaintProfessor of Computer Science, TU Berlin, GermanyVerified email at tu-berlin.de
Kazuho WatanabeToyohashi University of TechnologyVerified email at cs.tut.ac.jp
Christoph LippertProfessor, Hasso Plattner Institute, Universität PotsdamVerified email at hpi.de
Stephan MandtAssociate Professor, University of California, IrvineVerified email at uci.edu
Takeuchi IchiroProfessor, Nagoya UniversityVerified email at mae.nagoya-u.ac.jp
John CunninghamProfessor, Columbia UniversityVerified email at columbia.edu

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 Buenau, M Kawanabe Advances in neural information processing systems 20, 2007	930	2007
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, 699-746, 2008	427	2008
Semi-supervised local Fisher discriminant analysis for dimensionality reduction M Sugiyama, T Idé, S Nakajima, J Sese Machine learning 78, 35-61, 2010	357	2010
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	174	2009
Bayesian group-sparse modeling and variational inference SD Babacan, S Nakajima, MN Do IEEE transactions on signal processing 62 (11), 2906-2921, 2014	135	2014
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	115	2020
Higher-order explanations of graph neural networks via relevant walks T Schnake, O Eberle, J Lederer, S Nakajima, KT Schütt, KR Müller, ... IEEE transactions on pattern analysis and machine intelligence 44 (11), 7581 …, 2021	106	2021
Pool-based active learning in approximate linear regression M Sugiyama, S Nakajima Machine Learning 75, 249-274, 2009	103	2009
Global analytic solution of fully-observed variational Bayesian matrix factorization S Nakajima, M Sugiyama, SD Babacan, R Tomioka The Journal of Machine Learning Research 14 (1), 1-37, 2013	102	2013
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	73	2020
Estimation of thermodynamic observables in lattice field theories with deep generative models KA Nicoli, CJ Anders, L Funcke, T Hartung, K Jansen, P Kessel, ... Physical review letters 126 (3), 032001, 2021	64	2021
Theoretical analysis of Bayesian matrix factorization S Nakajima, M Sugiyama The Journal of Machine Learning Research 12, 2583-2648, 2011	59	2011
Perfect dimensionality recovery by variational Bayesian PCA S Nakajima, R Tomioka, M Sugiyama, S Babacan Advances in neural information processing systems 25, 2012	45	2012
Nikon Multimedia Event Detection System. T Matsuo, S Nakajima TRECVID, 2010	44	2010
Variational Bayes solution of linear neural networks and its generalization performance S Nakajima, S Watanabe Neural Computation 19 (4), 1112-1153, 2007	36	2007
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	35	2017
XAI for graphs: explaining graph neural network predictions by identifying relevant walks T Schnake, O Eberle, J Lederer, S Nakajima, KT Schütt, KR Müller, ... arXiv preprint arXiv:2006.03589, 2020	34	2020
Variational Bayesian learning theory S Nakajima, K Watanabe, M Sugiyama Cambridge University Press, 2019	31	2019
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	30	2015
Implicit Regularization in Variational Bayesian Matrix Factorization. S Nakajima, M Sugiyama ICML, 815-822, 2010	28	2010

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