| 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 | 3254 | 2018 |
| A Bayesian missing value estimation method for gene expression profile data S Oba, M Sato, I Takemasa, M Monden, K Matsubara, S Ishii Bioinformatics 19 (16), 2088-2096, 2003 | 949 | 2003 |
| Bayesian brain: Probabilistic approaches to neural coding K Doya MIT press, 2007 | 929 | 2007 |
| A critical time window for dopamine actions on the structural plasticity of dendritic spines S Yagishita, A Hayashi-Takagi, GCR Ellis-Davies, H Urakubo, S Ishii, ... Science 345 (6204), 1616-1620, 2014 | 660 | 2014 |
| Distributional smoothing with virtual adversarial training T Miyato, S Maeda, M Koyama, K Nakae, S Ishii arXiv preprint arXiv:1507.00677, 2015 | 571 | 2015 |
| On-line EM algorithm for the normalized Gaussian network M Sato, S Ishii Neural computation 12 (2), 407-432, 2000 | 392 | 2000 |
| Control of exploitation–exploration meta-parameter in reinforcement learning S Ishii, W Yoshida, J Yoshimoto Neural networks 15 (4-6), 665-687, 2002 | 291 | 2002 |
| Resolution of uncertainty in prefrontal cortex W Yoshida, S Ishii Neuron 50 (5), 781-789, 2006 | 262 | 2006 |
| Expression profiling using a tumor-specific cDNA microarray predicts the prognosis of intermediate risk neuroblastomas M Ohira, S Oba, Y Nakamura, E Isogai, S Kaneko, A Nakagawa, T Hirata, ... Cancer cell 7 (4), 337-350, 2005 | 175 | 2005 |
| Reinforcement learning for a biped robot based on a CPG-actor-critic method Y Nakamura, T Mori, M Sato, S Ishii Neural networks 20 (6), 723-735, 2007 | 159 | 2007 |
| Dopamine D2 receptors in discrimination learning and spine enlargement Y Iino, T Sawada, K Yamaguchi, M Tajiri, S Ishii, H Kasai, S Yagishita Nature 579 (7800), 555-560, 2020 | 156 | 2020 |
| Spiking network simulation code for petascale computers S Kunkel, M Schmidt, JM Eppler, HE Plesser, G Masumoto, J Igarashi, ... Frontiers in neuroinformatics 8, 78, 2014 | 148 | 2014 |
| Molecular-based prediction of early recurrence in hepatocellular carcinoma Y Kurokawa, R Matoba, I Takemasa, H Nagano, K Dono, S Nakamori, ... Journal of hepatology 41 (2), 284-291, 2004 | 134 | 2004 |
| Learning a common dictionary for subject-transfer decoding with resting calibration H Morioka, A Kanemura, J Hirayama, M Shikauchi, T Ogawa, S Ikeda, ... NeuroImage 111, 167-178, 2015 | 122 | 2015 |
| Reinforcement learning for cpg-driven biped robot T Mori, Y Nakamura, M Sato, S Ishii AAAI 4, 623-630, 2004 | 115 | 2004 |
| Multi-agent reinforcement learning: An approach based on the other agent's internal model Y Nagayuki, S Ishii, K Doya Proceedings Fourth International Conference on MultiAgent Systems, 215-221, 2000 | 111 | 2000 |
| An unsupervised EEG decoding system for human emotion recognition Z Liang, S Oba, S Ishii Neural Networks 116, 257-268, 2019 | 103 | 2019 |
| Novel risk stratification of patients with neuroblastoma by genomic signature, which is independent of molecular signature N Tomioka, S Oba, M Ohira, A Misra, J Fridlyand, S Ishii, Y Nakamura, ... Oncogene 27 (4), 441-449, 2008 | 100 | 2008 |
| A diffusion‐based neurite length‐sensing mechanism involved in neuronal symmetry breaking M Toriyama, Y Sakumura, T Shimada, S Ishii, N Inagaki Molecular systems biology 6 (1), 394, 2010 | 99 | 2010 |
| A network of chaotic elements for information processing S Ishi, K Fukumizu, S Watanabe Neural Networks 9 (1), 25-40, 1996 | 99 | 1996 |
