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 | 1043 | 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 | 493 | 2008 |
Semi-supervised local Fisher discriminant analysis for dimensionality reduction M Sugiyama, T Idé, S Nakajima, J Sese Machine learning 78, 35-61, 2010 | 380 | 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 | 218 | 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 | 215 | 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 | 169 | 2020 |
Bayesian group-sparse modeling and variational inference SD Babacan, S Nakajima, MN Do IEEE transactions on signal processing 62 (11), 2906-2921, 2014 | 146 | 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 | 122 | 2013 |
Pool-based active learning in approximate linear regression M Sugiyama, S Nakajima Machine Learning 75, 249-274, 2009 | 114 | 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 | 102 | 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 | 101 | 2021 |
Theoretical analysis of Bayesian matrix factorization S Nakajima, M Sugiyama The Journal of Machine Learning Research 12, 2583-2648, 2011 | 65 | 2011 |
Perfect dimensionality recovery by variational Bayesian PCA S Nakajima, R Tomioka, M Sugiyama, S Babacan Advances in neural information processing systems 25, 2012 | 52 | 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 | 50 | 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 | 48 | 2017 |
Variational Bayesian learning theory S Nakajima, K Watanabe, M Sugiyama Cambridge University Press, 2019 | 39 | 2019 |
Variational Bayes solution of linear neural networks and its generalization performance S Nakajima, S Watanabe Neural Computation 19 (4), 1112-1153, 2007 | 37 | 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 | 31 | 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 | 30 | 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 | 29 | 2015 |