| Optimizing spatial filters for robust EEG single-trial analysis B Blankertz, R Tomioka, S Lemm, M Kawanabe, KR Muller IEEE Signal processing magazine 25 (1), 41-56, 2007 | 2314 | 2007 |
| f-gan: Training generative neural samplers using variational divergence minimization S Nowozin, B Cseke, R Tomioka Advances in Neural Information Processing Systems, 271-279, 2016 | 1878 | 2016 |
| QSGD: Communication-efficient SGD via gradient quantization and encoding D Alistarh, D Grubic, J Li, R Tomioka, M Vojnovic Advances in neural information processing systems 30, 2017 | 1831 | 2017 |
| In search of the real inductive bias: On the role of implicit regularization in deep learning B Neyshabur, R Tomioka, N Srebro arXiv preprint arXiv:1412.6614, 2014 | 697 | 2014 |
| Norm-based capacity control in neural networks B Neyshabur, R Tomioka, N Srebro Conference on learning theory, 1376-1401, 2015 | 623 | 2015 |
| Invariant common spatial patterns: Alleviating nonstationarities in brain-computer interfacing B Blankertz, M Kawanabe, R Tomioka, F Hohlefeld, K Müller, V Nikulin Advances in neural information processing systems 20, 2007 | 322 | 2007 |
| Multi-level variational autoencoder: Learning disentangled representations from grouped observations D Bouchacourt, R Tomioka, S Nowozin Proceedings of the AAAI Conference on Artificial Intelligence 32 (1), 2018 | 321 | 2018 |
| Estimation of low-rank tensors via convex optimization R Tomioka, K Hayashi, H Kashima arXiv preprint arXiv:1010.0789, 2010 | 229 | 2010 |
| Tensor factorization using auxiliary information A Narita, K Hayashi, R Tomioka, H Kashima Data Mining and Knowledge Discovery 25, 298-324, 2012 | 198 | 2012 |
| A regularized discriminative framework for EEG analysis with application to brain–computer interface R Tomioka, KR Müller NeuroImage 49 (1), 415-432, 2010 | 193 | 2010 |
| Statistical performance of convex tensor decomposition R Tomioka, T Suzuki, K Hayashi, H Kashima Advances in neural information processing systems 24, 2011 | 188 | 2011 |
| Continuous hierarchical representations with poincaré variational auto-encoders E Mathieu, C Le Lan, CJ Maddison, R Tomioka, YW Teh Advances in neural information processing systems 32, 2019 | 182 | 2019 |
| Convex tensor decomposition via structured schatten norm regularization R Tomioka, T Suzuki Advances in neural information processing systems 26, 2013 | 165 | 2013 |
| Geometry of optimization and implicit regularization in deep learning B Neyshabur, R Tomioka, R Salakhutdinov, N Srebro arXiv preprint arXiv:1705.03071, 2017 | 156 | 2017 |
| Discovering emerging topics in social streams via link-anomaly detection T Takahashi, R Tomioka, K Yamanishi IEEE Transactions on Knowledge and Data Engineering 26 (1), 120-130, 2012 | 140 | 2012 |
| Logistic regression for single trial EEG classification R Tomioka, K Aihara, KR Müller Advances in neural information processing systems 19, 2006 | 132 | 2006 |
| Large-scale EEG/MEG source localization with spatial flexibility S Haufe, R Tomioka, T Dickhaus, C Sannelli, B Blankertz, G Nolte, ... NeuroImage 54 (2), 851-859, 2011 | 127 | 2011 |
| Modeling sparse connectivity between underlying brain sources for EEG/MEG S Haufe, R Tomioka, G Nolte, KR Müller, M Kawanabe IEEE transactions on biomedical engineering 57 (8), 1954-1963, 2010 | 123 | 2010 |
| 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 | 121 | 2013 |
| The algebraic combinatorial approach for low-rank matrix completion. FJ Király, L Theran, R Tomioka J. Mach. Learn. Res. 16 (1), 1391-1436, 2015 | 116 | 2015 |
Klaus-Robert MüllerGoogle DeepMind & TU Berlin & Korea University & Max Planck Institute for Informatics, Germany在 tu-berlin.de 的电子邮件经过验证
