Shin Ishii, Ph.D.

Graduate School of Informatics Kyoto University, Kyōto-shi, Kyōto-fu, Japan 
Computational neuroscience
"Shin Ishii"
Mean distance: 15.62 (cluster 17)
Cross-listing: Computational Biology Tree

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Fujita Y, Yagishita S, Kasai H, et al. (2020) Computational Characteristics of the Striatal Dopamine System Described by Reinforcement Learning With Fast Generalization. Frontiers in Computational Neuroscience. 14: 66
Urakubo H, Bullmann T, Kubota Y, et al. (2019) UNI-EM: An Environment for Deep Neural Network-Based Automated Segmentation of Neuronal Electron Microscopic Images. Scientific Reports. 9: 19413
Miyato T, Maeda SI, Ishii S, et al. (2018) Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning. Ieee Transactions On Pattern Analysis and Machine Intelligence
Liang Z, Hamada Y, Oba S, et al. (2018) Characterization of electroencephalography signals for estimating saliency features in videos. Neural Networks : the Official Journal of the International Neural Network Society. 105: 52-64
Meshgi K, Maeda SI, Oba S, et al. (2017) Constructing a meta-tracker using Dropout to imitate the behavior of an arbitrary black-box tracker. Neural Networks : the Official Journal of the International Neural Network Society. 87: 132-148
Meshgi K, Maeda S, Oba S, et al. (2016) An occlusion-aware particle filter tracker to handle complex and persistent occlusions Computer Vision and Image Understanding. 150: 81-94
Hirayama Ji, Hyvärinen A, Ishii S. (2016) Sparse and low-rank matrix regularization for learning time-varying Markov networks Machine Learning. 1-32
Meshgi K, Ishii S. (2015) The state-of-the-art in handling occlusions for visual object tracking Ieice Transactions On Information and Systems. 1260-1274
Fukushima M, Yamashita O, Kanemura A, et al. (2012) A state-space modeling approach for localization of focal current sources from MEG. Ieee Transactions On Bio-Medical Engineering. 59: 1561-71
Yoshimoto J, Sato M, Ishii S. (2011) Bayesian Normalized Gaussian Network and Hierarchical Model Selection Method Intelligent Automation and Soft Computing. 17: 71-94
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