Shin-ichi Maeda
Shin-ichi Maeda
Preferred Networks, Inc.
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Cited by
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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
Distributional smoothing with virtual adversarial training
T Miyato, S Maeda, M Koyama, K Nakae, S Ishii
arXiv preprint arXiv:1507.00677, 2015
Robustness to adversarial perturbations in learning from incomplete data
A Najafi, S Maeda, M Koyama, T Miyato
Advances in Neural Information Processing Systems, 5541-5551, 2019
An occlusion-aware particle filter tracker to handle complex and persistent occlusions
K Meshgi, S Maeda, S Oba, H Skibbe, Y Li, S Ishii
Computer Vision and Image Understanding 150, 81-94, 2016
Superresolution with compound Markov random fields via the variational EM algorithm
A Kanemura, S Maeda, S Ishii
Neural Networks 22 (7), 1025-1034, 2009
DQN-TAMER: Human-in-the-Loop Reinforcement Learning with Intractable Feedback
R Arakawa, S Kobayashi, Y Unno, Y Tsuboi, S Maeda
arXiv preprint arXiv:1810.11748, 2018
Semaphorin 3A induces Ca V 2.3 channel-dependent conversion of axons to dendrites
M Nishiyama, K Togashi, MJ Von Schimmelmann, CS Lim, S Maeda, ...
Nature cell biology 13 (6), 676-685, 2011
A Bayesian encourages dropout
S Maeda
arXiv preprint arXiv:1412.7003, 2014
Gaussian process regression for rendering music performance
K Teramura, H Okuma, Y Taniguchi, S Makimoto, S Maeda
Proc. ICMPC, 167-172, 2008
Exploring Unexplored Tensor Network Decompositions for Convolutional Neural Networks
K Hayashi, T Yamaguchi, Y Sugawara, S Maeda
Advances in Neural Information Processing Systems, 5552-5562, 2019
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
Clipped action policy gradient
Y Fujita, S Maeda
International Conference on Machine Learning, 1597-1606, 2018
Neural multi-scale image compression
KM Nakanishi, S Maeda, T Miyato, D Okanohara
Asian Conference on Computer Vision, 718-732, 2018
Markov and semi-Markov switching of source appearances for nonstationary independent component analysis
J Hirayama, S Maeda, S Ishii
IEEE transactions on neural networks 18 (5), 1326-1342, 2007
Generalized TD learning
T Ueno, S Maeda, M Kawanabe, S Ishii
Journal of Machine Learning Research 12 (Jun), 1977-2020, 2011
Maximum a posteriori X-ray computed tomography using graph cuts
S Maeda, W Fukuda, A Kanemura, S Ishii
Journal of Physics: Conference Series 233 (1), 012023, 2010
Edge-preserving Bayesian image superresolution based on compound Markov random fields
A Kanemura, S Maeda, S Ishii
International Conference on Artificial Neural Networks, 611-620, 2007
Rebuilding factorized information criterion: Asymptotically accurate marginal likelihood
K Hayashi, S Maeda, R Fujimaki
International Conference on Machine Learning, 1358-1366, 2015
A semiparametric statistical approach to model-free policy evaluation
T Ueno, M Kawanabe, T Mori, S Maeda, S Ishii
Proceedings of the 25th international conference on Machine learning, 1072-1079, 2008
Hyperparameter estimation in Bayesian image superresolution with a compound Markov random field prior
A Kanemura, S Maeda, S Ishii
2007 IEEE Workshop on Machine Learning for Signal Processing, 181-186, 2007
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