| Analysis of function of rectified linear unit used in deep learning K Hara, D Saito, H Shouno 2015 international joint conference on neural networks (IJCNN), 1-8, 2015 | 500 | 2015 |
| Analysis of dropout learning regarded as ensemble learning K Hara, D Saitoh, H Shouno Artificial Neural Networks and Machine Learning–ICANN 2016: 25th …, 2016 | 87 | 2016 |
| Accuracy of the Bethe approximation for hyperparameter estimation in probabilistic image processing K Tanaka, H Shouno, M Okada, DM Titterington Journal of Physics A: Mathematical and General 37 (36), 8675, 2004 | 40 | 2004 |
| A transfer learning method with deep convolutional neural network for diffuse lung disease classification H Shouno, S Suzuki, S Kido Neural Information Processing: 22nd International Conference, ICONIP 2015 …, 2015 | 36 | 2015 |
| Development of multiple core-level XPS spectra decomposition method based on the Bayesian information criterion R Murakami, H Tanaka, H Shinotsuka, K Nagata, H Shouno, H Yoshikawa Journal of Electron Spectroscopy and Related Phenomena 245, 147003, 2020 | 21 | 2020 |
| Distribution estimation of hyperparameters in Markov random field models Y Nakanishi-Ohno, K Nagata, H Shouno, M Okada Journal of Physics A: Mathematical and Theoretical 47 (4), 045001, 2014 | 21 | 2014 |
| Deep feature compression using spatio-temporal arrangement toward collaborative intelligent world S Suzuki, S Takeda, M Takagi, R Tanida, H Kimata, H Shouno IEEE Transactions on Circuits and Systems for Video Technology 32 (6), 3934-3946, 2021 | 20 | 2021 |
| Feature representation analysis of deep convolutional neural network using two-stage feature transfer-an application for diffuse lung disease classification A Suzuki, H Sakanashi, S Kido, H Shouno arXiv preprint arXiv:1810.06282, 2018 | 20 | 2018 |
| Development of spectral decomposition based on Bayesian information criterion with estimation of confidence interval H Shinotsuka, K Nagata, H Yoshikawa, YI Mototake, H Shouno, M Okada Science and technology of advanced materials 21 (1), 402-419, 2020 | 18 | 2020 |
| Mosquito larva classification based on a convolution neural network AS Ortiz, MN Miyatake, H Tünnermann, T Teramoto, H Shouno Proceedings of the International Conference on Parallel and Distributed …, 2018 | 16 | 2018 |
| Deep convolutional network neocognitron: improved interpolating-vector K Fukushima, H Shouno 2015 International Joint Conference on Neural Networks (IJCNN), 1-8, 2015 | 16 | 2015 |
| Recent studies around the neocognitron H Shouno International Conference on Neural Information Processing, 1061-1070, 2007 | 16 | 2007 |
| Training neocognitron to recognize handwritten digits in the real world K Fukushima, K Nagahara, H Shouno Proceedings of IEEE International Symposium on Parallel Algorithms …, 1997 | 16 | 1997 |
| Explainable Deep Learning Approach for Multi-Class Brain Magnetic Resonance Imaging Tumor Classification and Localization Using Gradient-Weighted Class Activation Mapping T Hussain, H Shouno Information 14 (12), 642, 2023 | 13 | 2023 |
| Correlated topographic analysis: estimating an ordering of correlated components H Sasaki, MU Gutmann, H Shouno, A Hyvärinen Machine Learning 92, 285-317, 2013 | 12 | 2013 |
| Bayesian image restoration for medical images using Radon transform H Shouno, M Okada Journal of the Physical Society of Japan 79 (7), 074004, 2010 | 12 | 2010 |
| 胸部単純 X 線写真における経時的差分画像を用いた結節状陰影の自動検出システムの開発 小田敍弘， 木戸尚治， 庄野逸， 上田克彦 電子情報通信学会論文誌 D 87 (1), 208-218, 2004 | 12 | 2004 |
| Bayesian estimation for XPS spectral analysis at multiple core levels A Machida, K Nagata, R Murakami, H Shinotsuka, H Shouno, ... Science and Technology of Advanced Materials: Methods 1 (1), 123-133, 2021 | 11 | 2021 |
| Formation of a direction map by projection learning using Kohonen's self-organization map H Shouno, K Kurata Biological cybernetics 85 (4), 241-246, 2001 | 10 | 2001 |
| Detecting seismic electric signals by LVQ based clustering K Fukuda, M Koganeyama, H Shouno, T Nagao, K Joe 情報処理学会研究報告数理モデル化と問題解決 (MPS) 2001 (63 (2001-MPS-035)), 25-28, 2001 | 10 | 2001 |
MASATO OKADA東京大学確認したメール アドレス: edu.k.u-tokyo.ac.jp
