Masanori Koyama
Masanori Koyama
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Spectral normalization for generative adversarial networks
T Miyato, T Kataoka, M Koyama, Y Yoshida
arXiv preprint arXiv:1802.05957, 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
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
Distributional smoothing with virtual adversarial training
T Miyato, S Maeda, M Koyama, K Nakae, S Ishii
arXiv preprint arXiv:1507.00677, 2015
cGANs with projection discriminator
T Miyato, M Koyama
arXiv preprint arXiv:1802.05637, 2018
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
Robustness to adversarial perturbations in learning from incomplete data
A Najafi, S Maeda, M Koyama, T Miyato
arXiv preprint arXiv:1905.13021, 2019
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
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
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, 2018
Predicting complex traits using a diffusion kernel on genetic markers with an application to dairy cattle and wheat data
G Morota, M Koyama, GJM Rosa, KA Weigel, D Gianola
Genetics Selection Evolution 45 (1), 1-15, 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
Weak error analysis of numerical methods for stochastic models of population processes
DF Anderson, M Koyama
Multiscale Modeling & Simulation 10 (4), 1493-1524, 2012
Out-of-distribution generalization with maximal invariant predictor
M Koyama, S Yamaguchi
arXiv preprint arXiv:2008.01883, 2020
Spectral normalization for generative adversarial networks
M Takeru, K Toshiki, K Masanori, Y Yuichi
International Conference on Learning Representations, 1-26, 2018
Spatially controllable image synthesis with internal representation collaging
R Suzuki, M Koyama, T Miyato, T Yonetsuji, H Zhu
arXiv preprint arXiv:1811.10153, 2018
Graph warp module: an auxiliary module for boosting the power of graph neural networks
K Ishiguro, S Maeda, M Koyama
A graph theoretic framework of recomputation algorithms for memory-efficient backpropagation
M Kusumoto, T Inoue, G Watanabe, T Akiba, M Koyama
arXiv preprint arXiv:1905.11722, 2019
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, 2586-2606, 2020
A Statistical Method of Identifying Interactions in Neuron–Glia Systems Based on Functional Multicell Ca2+ Imaging
K Nakae, Y Ikegaya, T Ishikawa, S Oba, H Urakubo, M Koyama, S Ishii
PLoS computational biology 10 (11), e1003949, 2014
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