| 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 | 5664 | 2019 |
| Spectral normalization for generative adversarial networks T Miyato, T Kataoka, M Koyama, Y Yoshida arXiv preprint arXiv:1802.05957, 2018 | 5349 | 2018 |
| 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 | 3169 | 2018 |
| cGANs with projection discriminator T Miyato, M Koyama arXiv preprint arXiv:1802.05637, 2018 | 637 | 2018 |
| Distributional smoothing with virtual adversarial training T Miyato, S Maeda, M Koyama, K Nakae, S Ishii arXiv preprint arXiv:1507.00677, 2015 | 569 | 2015 |
| 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 | 189 | 2018 |
| 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 | 134 | 2020 |
| 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 | 133 | 2019 |
| 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 | 116 | 2019 |
| Out-of-distribution generalization with maximal invariant predictor M Koyama, S Yamaguchi | 91 | 2020 |
| 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 | 81 | 2015 |
| Spectral normalization for generative adversarial networks M Takeru, K Toshiki, K Masanori, Y Yuichi arXiv preprint arXiv:1802.05957, 2018 | 60 | 2018 |
| 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 | 55 | 2018 |
| When is invariance useful in an out-of-distribution generalization problem? M Koyama, S Yamaguchi arXiv preprint arXiv:2008.01883, 2020 | 49 | 2020 |
| 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 | 47 | 2013 |
| 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 | 46 | 2019 |
| Spatially controllable image synthesis with internal representation collaging R Suzuki, M Koyama, T Miyato, T Yonetsuji, H Zhu arXiv preprint arXiv:1811.10153, 2018 | 41 | 2018 |
| 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 | 38 | 2018 |
| Graph warp module: an auxiliary module for boosting the power of graph neural networks K Ishiguro, S Maeda, M Koyama arXiv preprint arXiv:1902.01020, 2019 | 35 | 2019 |
| Graph warp module: an auxiliary module for boosting the power of graph neural networks in molecular graph analysis K Ishiguro, S Maeda, M Koyama arXiv preprint arXiv:1902.01020, 2019 | 32 | 2019 |
Takeru MiyatoUniversity of Tübingen, Preferred Networks, Inc.Verified email at uni-tuebingen.de
