| Hierarchical reinforcement learning with unlimited recursive subroutine calls Y Ichisugi, N Takahashi, H Nakada, T Sano Artificial Neural Networks and Machine Learning–ICANN 2019: Deep Learning …, 2019 | 14 | 2019 |
| U A (1) breaking and phase transition in chiral random matrix model T Sano, H Fujii, M Ohtani Physical Review D 80 (3), 034007, 2009 | 11 | 2009 |
| Random matrix model at nonzero chemical potentials with anomaly effects H Fujii, T Sano Physical Review D 83 (1), 014005, 2011 | 5 | 2011 |
| Regularization methods for the restricted bayesian network besom Y Ichisugi, T Sano Neural Information Processing: 23rd International Conference, ICONIP 2016 …, 2016 | 4 | 2016 |
| Random matrix model for chiral and color-flavor locking condensates T Sano, K Yamazaki Physical Review D 85 (9), 094032, 2012 | 3 | 2012 |
| Chiral random matrix model with 2+ 1 flavors at finite temperature and density H Fujii, T Sano Physical Review D 81 (3), 037502, 2010 | 3 | 2010 |
| An ODE-based neural network with Bayesian optimization H Honda, T Sano, S Nakamura, M Ueno, H Hanazawa, NMD Tuan JSIAM Letters 15, 101-104, 2023 | 2 | 2023 |
| A noniterative solution to the inverse Ising problem using a convex upper bound on the partition function T Sano Journal of Statistical Mechanics: Theory and Experiment 2022 (2), 023406, 2022 | 1 | 2022 |
| Translation-Invariant Neural Responses as Variational Messages in a Bayesian Network Model T Sano, Y Ichisugi Artificial Neural Networks and Machine Learning–ICANN 2017: 26th …, 2017 | 1 | 2017 |
| Complex Langevin simulation applied to chiral random matrix model at finite density T Sano Proceedings of the XXIX International Symposium on Lattice Field Theory …, 2011 | 1 | 2011 |
| From Coupled Oscillators to Graph Neural Networks: Reducing Over-smoothing via a Kuramoto Model-based Approach T Nguyen, H Honda, T Sano, V Nguyen, S Nakamura, TM Nguyen International Conference on Artificial Intelligence and Statistics, 2710-2718, 2024 | | 2024 |
| Understanding Neural ODE prediction decision using SHAP P Dinh, D Jobson, T Sano, H Honda, S Nakamura Northern Lights Deep Learning Conference, 53-58, 2024 | | 2024 |
| Detecting Fraudulent Cryptocurrencies Using Natural Language Processing Techniques M Ueno, T Sano, H Honda, S Nakamura Transactions of the Japanese Society for Artificial Intelligence 38 (5), E-N34, 2023 | | 2023 |
| Stochastic Neural Variational Learning of Noisy-OR Bayesian Networks for Images T Sano, Y Ichisugi Proceedings of the 5th International Conference on Advances in Artificial …, 2021 | | 2021 |
| An Analytic Solution to the Inverse Ising Problem in the Tree-reweighted Approximation T Sano 2018 International Joint Conference on Neural Networks (IJCNN), 1-8, 2018 | | 2018 |
| A solution to the inverse Ising problem in tree-reweighted approximation T Sano IEICE Technical Report; IEICE Tech. Rep. 117 (293), 309-313, 2017 | | 2017 |
| A study of message propagation algorithms for approximate MAP inference of large scale probabilistic models T Sano, Y Ichisugi IEICE Technical Report; IEICE Tech. Rep. 116 (300), 81-86, 2016 | | 2016 |
| Report of chiral and diquark condensates by random matrix model K Yamazaki, T Sano Genshikaku Kenkyu 56 (suppl. 2), 109-112, 2012 | | 2012 |
| 21pBD-12 ランダム行列模型による有限温度密度 QCD 相構造の解析 (21pBD クォーク物質・QCD 相図, 理論核物理領域) 佐野崇， 藤井宏次 日本物理学会講演概要集 65.1. 1, 56, 2010 | | 2010 |
| Chiral phase transition in a random matrix model with three flavors H Fujii, M Ohtani, T Sano arXiv preprint arXiv:1001.3640, 2010 | | 2010 |
