| Revisiting Graph Neural Networks: All We Have is Low-Pass Filters H NT, T Maehara arXiv preprint arXiv:1905.09550, 2019 | 160 | 2019 |
| Learning Graph Neural Networks with Noisy Labels H NT, CJ Jin, T Murata Workshop on Limited Label Data (LLD - ICLR'19), 2019 | 19 | 2019 |
| A Simple Proof of the Universality of Invariant/Equivariant Graph Neural Networks T Maehara, H NT arXiv preprint arXiv:1910.03802, 2019 | 13 | 2019 |
| Graph homomorphism convolution H NT, T Maehara 37th International Conference on Machine Learning (ICML-20), 7306-7316, 2020 | 12* | 2020 |
| Revisiting graph neural networks: Graph filtering perspective NT Hoang, T Maehara, T Murata 25th International Conference on Pattern Recognition (ICPR-20), 8376-8383, 2021 | 5 | 2021 |
| Stacked graph filter H NT, T Maehara, T Murata arXiv preprint arXiv:2011.10988, 2020 | 5* | 2020 |
| Learning on Random Balls is Sufficient for Estimating (Some) Graph Parameters T Maehara, H NT 35th Conference on Neural Information Processing Systems (NeurIPS-21), 2021 | 1 | 2021 |
| Heterogeneous graph embedding with single-level aggregation and infomax encoding N Chairatanakul, X Liu, NT Hoang, T Murata Machine Learning, 1-30, 2022 | | 2022 |
| Leaping Through Time with Gradient-based Adaptation for Recommendation N Chairatanakul, H NT, X Liu, T Murata 36th AAAI Conference on Artificial Intelligence (AAAI-22), 2021 | | 2021 |
Takanori MaeharaMeta, inc.Verified email at acm.org
