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 | 53 | 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 | 29 | 2019 |
Predicting Opinion Dynamics via Sociologically-Informed Neural Networks M Okawa, T Iwata Proceedings of the 28th ACM SIGKDD International Conference on Knowledge …, 2022 | 18 | 2022 |
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 |
Real-time and proactive navigation via spatio-temporal prediction N Ueda, F Naya, H Shimizu, T Iwata, M Okawa, H Sawada Adjunct Proceedings of the 2015 ACM International Joint Conference on …, 2015 | 17 | 2015 |
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 | 16 | 2019 |
Online traffic flow prediction using convolved bilinear poisson regression M Okawa, H Kim, H Toda 2017 18th IEEE International Conference on Mobile Data Management (MDM), 134-143, 2017 | 15 | 2017 |
Compositional Abilities Emerge Multiplicatively: Exploring Diffusion Models on a Synthetic Task M Okawa, ES Lubana, R Dick, H Tanaka Advances in Neural Information Processing Systems (NeurIPS) 36, 2023 | 10 | 2023 |
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 International Conference on Knowledge …, 2021 | 9 | 2021 |
Context-aware spatio-temporal event prediction via convolutional Hawkes processes M Okawa, T Iwata, Y Tanaka, H Toda, T Kurashima, H Kashima Machine Learning Journal (ECML-PKDD Journal Track), 2022 | 7 | 2022 |
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 | 3 | 2019 |
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 |
Deep Mixture Point Processes M Okawa, T Iwata, T Kurashima, Y Tanaka, H Toda, N Ueda Proceedings of the 25th ACM SIGKDD International Conference on Knowledge …, 2019 | 2 | 2019 |
Towards an Understanding of Stepwise Inference in Transformers: A Synthetic Graph Navigation Model M Khona, M Okawa, J Hula, R Ramesh, K Nishi, R Dick, ES Lubana, ... arXiv preprint arXiv:2402.07757, 2024 | 1 | 2024 |
Spatio-temporal event data estimating device, method, and program M Okawa, H Toda US Patent App. 17/058,613, 2021 | 1 | 2021 |
Meta-Learning for Neural Network-based Temporal Point Processes Y Takimoto, Y Tanaka, T Iwata, M Okawa, H Kim, H Toda, T Kurashima arXiv preprint arXiv:2401.15846, 2024 | | 2024 |
Stepwise Inference in Transformers: Exploring a Synthetic Graph Navigation Task M Khona, M Okawa, R Ramesh, K Nishi, RP Dick, ES Lubana, H Tanaka R0-FoMo: Robustness of Few-shot and Zero-shot Learning in Large Foundation …, 2023 | | 2023 |
Toward a Mechanistic Understanding of Stepwise Inference in Transformers: A Synthetic Graph Navigation Model M Khona, M Okawa, R Ramesh, K Nishi, RP Dick, ES Lubana, H Tanaka | | 2023 |
Learning method, learning apparatus and program M Okawa, H Toda US Patent App. 18/007,696, 2023 | | 2023 |
Learning device, prediction device, learning method, prediction method, and program M Okawa, T Iwata, H Toda, T Kurashima, Y Tanaka US Patent App. 17/624,564, 2022 | | 2022 |