Shin-ichi Maeda
Shin-ichi Maeda
Preferred Networks, Inc.
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Cited by
Cited by
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
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
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
Superresolution with compound Markov random fields via the variational EM algorithm
A Kanemura, S Maeda, S Ishii
Neural Networks 22 (7), 1025-1034, 2009
A Bayesian encourages dropout
S Maeda
arXiv preprint arXiv:1412.7003, 2014
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
Clipped action policy gradient
Y Fujita, S Maeda
International Conference on Machine Learning, 1597-1606, 2018
Gaussian process regression for rendering music performance
K Teramura, H Okuma, Y Taniguchi, S Makimoto, S Maeda
Proc. ICMPC, 167-172, 2008
Neural multi-scale image compression
KM Nakanishi, S Maeda, T Miyato, D Okanohara
Computer Vision–ACCV 2018: 14th Asian Conference on Computer Vision, Perth …, 2019
Graph warp module: an auxiliary module for boosting the power of graph neural networks
K Ishiguro, S Maeda, M Koyama
arXiv preprint arXiv:1902.01020, 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
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
Semi-supervised learning of hierarchical representations of molecules using neural message passing
H Nguyen, S Maeda, K Oono
arXiv preprint arXiv:1711.10168, 2017
Generalized TD learning
T Ueno, S Maeda, M Kawanabe, S Ishii
Journal of Machine Learning Research 12 (Jun), 1977-2020, 2011
Uncertainty-aware Self-supervised Target-mass Grasping of Granular Foods
K Takahashi, W Ko, A Ummadisingu, S Maeda
2021 IEEE International Conference on Robotics and Automation (ICRA), 2620-2626, 2021
Warp-Refine Propagation: Semi-Supervised Auto-labeling via Cycle-consistency
A Ganeshan, A Vallet, Y Kudo, S Maeda, T Kerola, R Ambrus, D Park, ...
Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021
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
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