| 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 | 3169 | 2018 |
| Distributional smoothing with virtual adversarial training T Miyato, S Maeda, M Koyama, K Nakae, S Ishii arXiv preprint arXiv:1507.00677, 2015 | 569 | 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 | 133 | 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 | 85 | 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 | 83 | 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 | 79 | 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 | 71 | 2009 |
| A Bayesian encourages dropout S Maeda arXiv preprint arXiv:1412.7003, 2014 | 55 | 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 | 55 | 2011 |
| Clipped action policy gradient Y Fujita, S Maeda International Conference on Machine Learning, 1597-1606, 2018 | 50 | 2018 |
| Gaussian process regression for rendering music performance K Teramura, H Okuma, Y Taniguchi, S Makimoto, S Maeda Proc. ICMPC, 167-172, 2008 | 46 | 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 | 40 | 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 | 35 | 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 | 32 | 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 | 23 | 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 | 21 | 2017 |
| Generalized TD learning T Ueno, S Maeda, M Kawanabe, S Ishii Journal of Machine Learning Research 12 (Jun), 1977-2020, 2011 | 20 | 2011 |
| A Scaling Law for Syn2real Transfer: How Much Is Your Pre-training Effective? H Mikami, K Fukumizu, S Murai, S Suzuki, Y Kikuchi, T Suzuki, S Maeda, ... Joint European Conference on Machine Learning and Knowledge Discovery in …, 2022 | 19 | 2022 |
| 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 | 19 | 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 | 18 | 2021 |
