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Masanori Koyama
Masanori Koyama
Preferred Networks
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Title
Cited by
Cited by
Year
Spectral normalization for generative adversarial networks
T Miyato, T Kataoka, M Koyama, Y Yoshida
arXiv preprint arXiv:1802.05957, 2018
49082018
Optuna: A next-generation hyperparameter optimization framework
T Akiba, S Sano, T Yanase, T Ohta, M Koyama
Proceedings of the 25th ACM SIGKDD international conference on knowledge …, 2019
42552019
Virtual adversarial training: a regularization method for supervised and semi-supervised learning
T Miyato, S Maeda, M Koyama, S Ishii
IEEE transactions on pattern analysis and machine intelligence 41 (8), 1979-1993, 2018
28832018
cGANs with projection discriminator
T Miyato, M Koyama
arXiv preprint arXiv:1802.05637, 2018
6102018
Distributional smoothing with virtual adversarial training
T Miyato, S Maeda, M Koyama, K Nakae, S Ishii
arXiv preprint arXiv:1507.00677, 2015
5512015
Big data analytics and precision animal agriculture symposium: Machine learning and data mining advance predictive big data analysis in precision animal agriculture
G Morota, RV Ventura, FF Silva, M Koyama, SC Fernando
Journal of animal science 96 (4), 1540-1550, 2018
1692018
Spectral normalization for generative adversarial networks. arXiv 2018
T Miyato, T Kataoka, M Koyama, Y Yoshida
arXiv preprint arXiv:1802.05957, 1802
1371802
Robustness to adversarial perturbations in learning from incomplete data
A Najafi, S Maeda, M Koyama, T Miyato
Advances in Neural Information Processing Systems 32, 2019
1202019
Train sparsely, generate densely: Memory-efficient unsupervised training of high-resolution temporal gan
M Saito, S Saito, M Koyama, S Kobayashi
International Journal of Computer Vision 128 (10), 2586-2606, 2020
1082020
A wrapped normal distribution on hyperbolic space for gradient-based learning
Y Nagano, S Yamaguchi, Y Fujita, M Koyama
International Conference on Machine Learning, 4693-4702, 2019
1042019
Out-of-distribution generalization with maximal invariant predictor
M Koyama, S Yamaguchi
832020
Deep learning of fMRI big data: a novel approach to subject-transfer decoding
S Koyamada, Y Shikauchi, K Nakae, M Koyama, S Ishii
arXiv preprint arXiv:1502.00093, 2015
802015
Spectral normalization for generative adversarial networks
M Takeru, K Toshiki, K Masanori, Y Yuichi
arXiv preprint arXiv:1802.05957, 2018
532018
Machine learning and data mining advance predictive big data analysis in precision animal agriculture
G Morota, RV Ventura, FF Silva, M Koyama, SC Fernando
Journal of Animal Science 96 (4), 1540-1550, 2018
502018
Optuna: a next-generation hyperparameter optimization framework (2019)
T Akiba, S Sano, T Yanase, T Ohta, M Koyama
arXiv preprint arXiv:1907.10902 9, 1907
491907
Predicting complex traits using a diffusion kernel on genetic markers with an application to dairy cattle and wheat data
G Morota, M Koyama, GJ M Rosa, KA Weigel, D Gianola
Genetics Selection Evolution 45, 1-15, 2013
472013
A graph theoretic framework of recomputation algorithms for memory-efficient backpropagation
M Kusumoto, T Inoue, G Watanabe, T Akiba, M Koyama
Advances in Neural Information Processing Systems 32, 2019
422019
Spatially controllable image synthesis with internal representation collaging
R Suzuki, M Koyama, T Miyato, T Yonetsuji, H Zhu
arXiv preprint arXiv:1811.10153, 2018
392018
When is invariance useful in an out-of-distribution generalization problem?
M Koyama, S Yamaguchi
arXiv preprint arXiv:2008.01883, 2020
372020
Non-explosivity of stochastically modeled reaction networks that are complex balanced
DF Anderson, D Cappelletti, M Koyama, TG Kurtz
Bulletin of mathematical biology 80, 2561-2579, 2018
352018
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