| Deep mixture point processes: Spatio-temporal event prediction with rich contextual information M Okawa, T Iwata, T Kurashima, Y Tanaka, H Toda, N Ueda Proceedings of the 25th ACM SIGKDD International Conference on Knowledge …, 2019 | 81 | 2019 |
| Spatially aggregated Gaussian processes with multivariate areal outputs Y Tanaka, T Tanaka, T Iwata, T Kurashima, M Okawa, Y Akagi, H Toda Advances in Neural Information Processing Systems 32, 2019 | 37 | 2019 |
| Inferring latent triggers of purchases with consideration of social effects and media advertisements Y Tanaka, T Kurashima, Y Fujiwara, T Iwata, H Sawada Proceedings of the ninth ACM international conference on web search and data …, 2016 | 28 | 2016 |
| Dynamic hawkes processes for discovering time-evolving communities' states behind diffusion processes M Okawa, T Iwata, Y Tanaka, H Toda, T Kurashima, H Kashima Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data …, 2021 | 24 | 2021 |
| Estimating latent people flow without tracking individuals. Y Tanaka, T Iwata, T Kurashima, H Toda, N Ueda IJCAI 18, 3556-3563, 2018 | 24 | 2018 |
| Predicting traffic accidents with event recorder data Y Takimoto, Y Tanaka, T Kurashima, S Yamamoto, M Okawa, H Toda Proceedings of the 3rd ACM SIGSPATIAL international workshop on prediction …, 2019 | 23 | 2019 |
| Refining coarse-grained spatial data using auxiliary spatial data sets with various granularities Y Tanaka, T Iwata, T Tanaka, T Kurashima, M Okawa, H Toda Proceedings of the AAAI Conference on Artificial Intelligence 33 (01), 5091-5099, 2019 | 20 | 2019 |
| Few-shot learning for spatial regression via neural embedding-based Gaussian processes T Iwata, Y Tanaka Machine Learning 111 (4), 1239-1257, 2022 | 19 | 2022 |
| Symplectic spectrum gaussian processes: Learning hamiltonians from noisy and sparse data Y Tanaka, T Iwata Advances in neural information processing systems 35, 20795-20808, 2022 | 18 | 2022 |
| Meta-learning of physics-informed neural networks for efficiently solving newly given pdes T Iwata, Y Tanaka, N Ueda arXiv preprint arXiv:2310.13270, 2023 | 14 | 2023 |
| Time-delayed collective flow diffusion models for inferring latent people flow from aggregated data at limited locations Y Tanaka, T Iwata, T Kurashima, H Toda, N Ueda, T Tanaka Artificial Intelligence 292, 103430, 2021 | 10 | 2021 |
| Context-aware spatio-temporal event prediction via convolutional Hawkes processes M Okawa, T Iwata, Y Tanaka, T Kurashima, H Toda, H Kashima Machine Learning 111 (8), 2929-2950, 2022 | 9 | 2022 |
| Understanding the Expressivity and Trainability of Fourier Neural Operator: A Mean-Field Perspective T Koshizuka, M Fujisawa, Y Tanaka, I Sato The Thirty-eighth Annual Conference on Neural Information Processing Systems, 2024 | 6 | 2024 |
| Few-shot learning for spatial regression T Iwata, Y Tanaka arXiv preprint arXiv:2010.04360, 2020 | 6 | 2020 |
| Exact and efficient inference for collective flow diffusion model via minimum convex cost flow algorithm Y Akagi, T Nishimura, Y Tanaka, T Kurashima, H Toda Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 3163-3170, 2020 | 6 | 2020 |
| Robust naive Bayes combination of multiple classifications N Ueda, Y Tanaka, A Fujino The Impact of Applications on Mathematics: Proceedings of the Forum of …, 2014 | 6 | 2014 |
| Probabilistic optimal transport based on collective graphical models Y Akagi, Y Tanaka, T Iwata, T Kurashima, H Toda arXiv preprint arXiv:2006.08866, 2020 | 3 | 2020 |
| Meta-Learning under Task Shift L Sun, Y Tanaka, T Iwata Transactions on Machine Learning Research, 2024 | 2 | 2024 |
| Machine Learning That Reproduces Physical Phenomena from Data T Yusuke NTT Technical Review 21 (10), 15-19, 2023 | 2 | 2023 |
| Deep Mixture Point Processes M Okawa, T Iwata, T Kurashima, Y Tanaka, H Toda, N Ueda, H Kashima Transactions of the Japanese Society for Artificial Intelligence 36 (5), C-L37, 2021 | 2 | 2021 |
Tomoharu IwataNTTVerified email at hco.ntt.co.jp
