Shinichi Nakajima
Shinichi Nakajima
Machine Learning Group, Technische Universität Berlin
Verified email at - Homepage
Cited by
Cited by
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
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
Semi-supervised local Fisher discriminant analysis for dimensionality reduction
M Sugiyama, T Idé, S Nakajima, J Sese
Machine learning 78, 35-61, 2010
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
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
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
Bayesian group-sparse modeling and variational inference
SD Babacan, S Nakajima, MN Do
IEEE transactions on signal processing 62 (11), 2906-2921, 2014
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
Pool-based active learning in approximate linear regression
M Sugiyama, S Nakajima
Machine Learning 75, 249-274, 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
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
Theoretical analysis of Bayesian matrix factorization
S Nakajima, M Sugiyama
The Journal of Machine Learning Research 12, 2583-2648, 2011
Perfect dimensionality recovery by variational Bayesian PCA
S Nakajima, R Tomioka, M Sugiyama, S Babacan
Advances in neural information processing systems 25, 2012
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
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
Variational Bayesian learning theory
S Nakajima, K Watanabe, M Sugiyama
Cambridge University Press, 2019
Variational Bayes solution of linear neural networks and its generalization performance
S Nakajima, S Watanabe
Neural Computation 19 (4), 1112-1153, 2007
Noisegrad—enhancing explanations by introducing stochasticity to model weights
K Bykov, A Hedström, S Nakajima, MMC Höhne
Proceedings of the AAAI Conference on Artificial Intelligence 36 (6), 6132-6140, 2022
How Much Can I Trust You?--Quantifying Uncertainties in Explaining Neural Networks
K Bykov, MMC Höhne, KR Müller, S Nakajima, M Kloft
arXiv preprint arXiv:2006.09000, 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
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