Ryota Tomioka
Ryota Tomioka
Microsoft Research Cambridge
Verified email at - Homepage
Cited by
Cited by
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
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
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
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
Norm-based capacity control in neural networks
B Neyshabur, R Tomioka, N Srebro
Conference on Learning Theory, 1376-1401, 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
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
Estimation of low-rank tensors via convex optimization
R Tomioka, K Hayashi, H Kashima
arXiv preprint arXiv:1010.0789, 2010
A regularized discriminative framework for EEG analysis with application to brain–computer interface
R Tomioka, KR Müller
NeuroImage 49 (1), 415-432, 2010
Tensor factorization using auxiliary information
A Narita, K Hayashi, R Tomioka, H Kashima
Data Mining and Knowledge Discovery 25 (2), 298-324, 2012
Statistical performance of convex tensor decomposition
R Tomioka, T Suzuki, K Hayashi, H Kashima
Advances in neural information processing systems 24, 2011
Convex tensor decomposition via structured schatten norm regularization
R Tomioka, T Suzuki
Advances in neural information processing systems 26, 2013
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
Logistic regression for single trial EEG classification
R Tomioka, K Aihara, KR Müller
Advances in neural information processing systems 19, 2006
Geometry of optimization and implicit regularization in deep learning
B Neyshabur, R Tomioka, R Salakhutdinov, N Srebro
arXiv preprint arXiv:1705.03071, 2017
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
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
Spectrally weighted common spatial pattern algorithm for single trial EEG classification
R Tomioka, G Dornhege, G Nolte, B Blankertz, K Aihara, KR Müller
Dept. Math. Eng., Univ. Tokyo, Tokyo, Japan, Tech. Rep 40, 2006
Super-Linear Convergence of Dual Augmented Lagrangian Algorithm for Sparsity Regularized Estimation.
R Tomioka, T Suzuki, M Sugiyama
Journal of Machine Learning Research 12 (5), 2011
Multivariate analysis of noise in genetic regulatory networks
R Tomioka, H Kimura, TJ Kobayashi, K Aihara
Journal of theoretical biology 229 (4), 501-521, 2004
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