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Maya Okawa
Maya Okawa
NTT Research, Inc
Verified email at fas.harvard.edu
Title
Cited by
Cited by
Year
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
512019
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
292019
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
172019
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
162019
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
162015
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
152017
Predicting Opinion Dynamics via Sociologically-Informed Neural Networks
M Okawa, T Iwata
Proceedings of the 28th ACM SIGKDD International Conference on Knowledge …, 2022
142022
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
92021
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
62022
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
52023
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
22021
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
22019
Spatio-temporal event data estimating device, method, and program
M Okawa, H Toda
US Patent App. 17/058,613, 2021
12021
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
12019
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
2024
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
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