| 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 | 67 | 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 | 30 | 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 |
| 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 | 19 | 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 | 17 | 2019 |
| 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 | 12 | 2021 |
| 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 | 11 | 2022 |
| Few-shot learning for spatial regression via neural embedding-based Gaussian processes T Iwata, Y Tanaka Machine Learning, 1-19, 2022 | 10 | 2022 |
| 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 | 8 | 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 | 7 | 2022 |
| 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 | 7 | 2020 |
| Few-shot learning for spatial regression T Iwata, Y Tanaka arXiv preprint arXiv:2010.04360, 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 |
| メタ学習に基づく加速度センサからの看護師行動識別 上田修功， 田中佑典， 中島直樹 マルチメディア, 分散協調とモバイルシンポジウム 2013 論文集 2013, 663-667, 2013 | 4 | 2013 |
| 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 | 2 | 2023 |
| 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 |
| Probabilistic optimal transport based on collective graphical models Y Akagi, Y Tanaka, T Iwata, T Kurashima, H Toda arXiv preprint arXiv:2006.08866, 2020 | 2 | 2020 |
| Marked temporal point processes for trip demand prediction in bike sharing systems M Okawa, Y Tanaka, T Kurashima, H Toda, T Yamada IEICE TRANSACTIONS on Information and Systems 102 (9), 1635-1643, 2019 | 2 | 2019 |
Tomoharu IwataNTTZweryfikowany adres z hco.ntt.co.jp
