| 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 | 11726 | 2019 |
| Spectral normalization for generative adversarial networks T Miyato, T Kataoka, M Koyama, Y Yoshida arXiv preprint arXiv:1802.05957, 2018 | 6408 | 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 | 3695 | 2018 |
| cGANs with projection discriminator T Miyato, M Koyama arXiv preprint arXiv:1802.05637, 2018 | 737 | 2018 |
| Distributional smoothing with virtual adversarial training T Miyato, S Maeda, M Koyama, K Nakae, S Ishii arXiv preprint arXiv:1507.00677, 2015 | 596 | 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 | 250 | 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 | 170 | 2020 |
| 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 | 169 | 2019 |
| 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 | 151 | 2019 |
| Optuna: A Next-generation Hyperparameter Optimization Framework. arXiv 2019 T Akiba, S Sano, T Yanase, T Ohta, M Koyama arXiv preprint arXiv:1907.10902 10, 1907 | 131 | 1907 |
| Out-of-distribution generalization with maximal invariant predictor M Koyama, S Yamaguchi | 106 | 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 | 91 | 2015 |
| When is invariance useful in an out-of-distribution generalization problem? M Koyama, S Yamaguchi arXiv preprint arXiv:2008.01883, 2020 | 75 | 2020 |
| 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 |
| 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 | 52 | 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 | 49 | 2018 |
| 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), 17, 2013 | 48 | 2013 |
| Non-explosivity of stochastically modeled reaction networks that are complex balanced DF Anderson, D Cappelletti, M Koyama, TG Kurtz Bulletin of mathematical biology 80 (10), 2561-2579, 2018 | 40 | 2018 |
| 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 | 39 | 2019 |
| 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 |
