| Relating neuronal firing patterns to functional differentiation of cerebral cortex S Shinomoto, H Kim, T Shimokawa, N Matsuno, S Funahashi, K Shima, ... PLoS computational biology 5 (7), e1000433, 2009 | 262 | 2009 |
| Elemental spiking neuron model for reproducing diverse firing patterns and predicting precise firing times S Yamauchi, H Kim, S Shinomoto Frontiers in computational neuroscience 5, 42, 2011 | 39 | 2011 |
| Tracking temporal dynamics of purchase decisions via hierarchical time-rescaling model H Kim, N Takaya, H Sawada Proceedings of the 23rd ACM International Conference on Conference on …, 2014 | 25 | 2014 |
| Estimating nonstationary input signals from a single neuronal spike train H Kim, S Shinomoto Physical Review E—Statistical, Nonlinear, and Soft Matter Physics 86 (5 …, 2012 | 25 | 2012 |
| Estimating nonstationary inputs from a single spike train based on a neuron model with adaptation H Kim, S Shinomoto Mathematical Biosciences & Engineering 11 (1), 49-62, 2013 | 20 | 2013 |
| 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 | 17 | 2017 |
| Read the Silence: Well-Timed Recommendation via Admixture Marked Point Processes H Kim, T Iwata, Y Fujiwara, N Ueda Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17), http …, 2017 | 9 | 2017 |
| Neurons as ideal change-point detectors H Kim, BJ Richmond, S Shinomoto Journal of computational neuroscience 32, 137-146, 2012 | 7 | 2012 |
| Fast Bayesian inference for Gaussian Cox processes via path integral formulation H Kim Advances in Neural Information Processing Systems 34, 26130-26142, 2021 | 6 | 2021 |
| Integrated optimization of bipartite matching and its stochastic behavior: New formulation and approximation algorithm via min-cost flow optimization Y Hikima, Y Akagi, H Kim, M Kohjima, T Kurashima, H Toda Proceedings of the AAAI Conference on Artificial Intelligence 35 (5), 3796-3805, 2021 | 6 | 2021 |
| Scaling locally linear embedding Y Fujiwara, N Marumo, M Blondel, K Takeuchi, H Kim, T Iwata, N Ueda Proceedings of the 2017 ACM International Conference on Management of Data …, 2017 | 6 | 2017 |
| Online Matching with Controllable Rewards and Arrival Probabilities Y Hikima, Y Akagi, N Marumo, H Kim Proceedings of the Thirty-First International Joint Conference on Artificial …, 2022 | 5 | 2022 |
| Fast Bayesian estimation of point process intensity as function of covariates H Kim, T Asami, H Toda Advances in Neural Information Processing Systems 35, 25711-25724, 2022 | 3 | 2022 |
| Optimization of maintenance by failure prediction considering instantaneous and cumulative effects of external environments K Kanetani, M Yamazaki, T Babasaki, H Kim, T Matsubayashi 2018 International Power Electronics Conference (IPEC-Niigata 2018-ECCE Asia …, 2018 | 2 | 2018 |
| Analyzing Temporal Dynamics of Consumer's Behavior Based on Hierarchical Time-Rescaling H Kim, N Takaya, H Sawada IEICE TRANSACTIONS on Information and Systems 100 (4), 693-703, 2017 | 2 | 2017 |
| An improved approximation algorithm for wage determination and online task allocation in crowd-sourcing Y Hikima, Y Akagi, H Kim, T Asami Proceedings of the AAAI Conference on Artificial Intelligence 37 (4), 3977-3986, 2023 | 1 | 2023 |
| Non-approximate inference for collective graphical models on path graphs via discrete difference of convex algorithm Y Akagi, N Marumo, H Kim, T Kurashima, H Toda Advances in Neural Information Processing Systems 34, 25812-25823, 2021 | 1 | 2021 |
| SVD-Based Screening for the Graphical Lasso. Y Fujiwara, N Marumo, M Blondel, K Takeuchi, H Kim, T Iwata, N Ueda IJCAI, 1682-1688, 2017 | 1 | 2017 |
| Survival permanental processes for survival analysis with time-varying covariates H Kim Advances in Neural Information Processing Systems 36, 2024 | | 2024 |
| MAP inference algorithms without approximation for collective graphical models on path graphs via discrete difference of convex algorithm Y Akagi, N Marumo, H Kim, T Kurashima, H Toda Machine Learning 112 (1), 99-129, 2023 | | 2023 |
Shigeru ShinomotoATR Institute InternationalVerified email at scphys.kyoto-u.ac.jp
