| Fully neural network based model for general temporal point processes T Omi, N Ueda, K Aihara Advances in Neural Information Processing Systems 33, 2122-2132, 2019 | 154 | 2019 |
| Forecasting large aftershocks within one day after the main shock T Omi, Y Ogata, Y Hirata, K Aihara Scientific reports 3 (1), 2218, 2013 | 119 | 2013 |
| Intermediate‐term forecasting of aftershocks from an early aftershock sequence: Bayesian and ensemble forecasting approaches T Omi, Y Ogata, Y Hirata, K Aihara Journal of Geophysical Research: Solid Earth 120 (4), 2561-2578, 2015 | 67 | 2015 |
| Estimating the ETAS model from an early aftershock sequence T Omi, Y Ogata, Y Hirata, K Aihara Geophysical Research Letters 41 (3), 850-857, 2014 | 66 | 2014 |
| Automatic aftershock forecasting: A test using real‐time seismicity data in Japan T Omi, Y Ogata, K Shiomi, B Enescu, K Sawazaki, K Aihara Bulletin of the Seismological Society of America 106 (6), 2450-2458, 2016 | 42 | 2016 |
| Hawkes process model with a time-dependent background rate and its application to high-frequency financial data T Omi, Y Hirata, K Aihara Physical Review E 96 (1), 012303, 2017 | 41 | 2017 |
| Optimizing time histograms for non-Poissonian spike trains T Omi, S Shinomoto Neural computation 23 (12), 3125-3144, 2011 | 40 | 2011 |
| Implementation of a real‐time system for automatic aftershock forecasting in Japan T Omi, Y Ogata, K Shiomi, B Enescu, K Sawazaki, K Aihara Seismological Research Letters 90 (1), 242-250, 2019 | 32 | 2019 |
| Can distributed delays perfectly stabilize dynamical networks? T Omi, S Shinomoto Physical Review E 77 (4), 046214, 2008 | 20 | 2008 |
| Deciphering elapsed time and predicting action timing from neuronal population signals S Shinomoto, T Omi, A Mita, H Mushiake, K Shima, Y Matsuzaka, J Tanji Frontiers in computational neuroscience 5, 29, 2011 | 15 | 2011 |
| A non-universal aspect in the temporal occurrence of earthquakes X Zhao, T Omi, N Matsuno, S Shinomoto New Journal of Physics 12 (6), 063010, 2010 | 13 | 2010 |
| Information transmission using non-Poisson regular firing S Koyama, T Omi, RE Kass, S Shinomoto Neural computation 25 (4), 854-876, 2013 | 6 | 2013 |
| Statistical monitoring and early forecasting of the earthquake sequence: Case studies after the 2019 M 6.4 Searles Valley earthquake, California Y Ogata, T Omi Bulletin of the Seismological Society of America 110 (4), 1781-1798, 2020 | 5 | 2020 |
| Reverberating activity in a neural network with distributed signal transmission delays T Omi, S Shinomoto Physical Review E 76 (5), 051908, 2007 | 5 | 2007 |
| Optimal observation time window for forecasting the next earthquake T Omi, I Kanter, S Shinomoto Physical Review E 83 (2), 026101, 2011 | 4 | 2011 |
| Pretraining and updating language-and domain-specific large language model: A case study in japanese business domain K Takahashi, T Omi, K Arima, T Ishigaki arXiv preprint arXiv:2404.08262, 2024 | 1 | 2024 |
| Training Generative Question-Answering on Synthetic Data Obtained from an Instruct-tuned Model K Takahashi, T Omi, K Arima, T Ishigaki Proceedings of the 37th Pacific Asia Conference on Language, Information and …, 2023 | | 2023 |
Yosihiko OgataThe Institute of Statistical MathematicsDirección de correo verificada de ism.ac.jp
