| Maximal margin labeling for multi-topic text categorization H Kazawa, T Izumitani, H Taira, E Maeda Advances in neural information processing systems 17, 2004 | 186 | 2004 |
| A background music detection method based on robust feature extraction T Izumitani, R Mukai, K Kashino 2008 IEEE International Conference on Acoustics, Speech and Signal …, 2008 | 37 | 2008 |
| Bayesian semi-supervised audio event transcription based on Markov Indian buffet process Y Ohishi, D Mochihashi, T Matsui, M Nakano, H Kameoka, T Izumitani, ... 2013 IEEE international conference on acoustics, speech and signal …, 2013 | 36 | 2013 |
| A Robust Musical Audio Search Method Based on Diagonal Dynamic Programming Matching of Self-Similarity Matrices. T Izumitani, K Kashino ISMIR, 609-613, 2008 | 15 | 2008 |
| Assigning gene ontology categories (go) to yeast genes using text-based supervised learning methods T Izumitani, H Taira, H Kazawa, E Maeda Proceedings. 2004 IEEE Computational Systems Bioinformatics Conference, 2004 …, 2004 | 15 | 2004 |
| NTT Communication Science Laboratories and National Institute of Informatics at TRECVID 2012 Instance Search and Multimedia Event Detection Tasks. M Murata, T Izumitani, H Nagano, R Mukai, K Kashino, ... TRECVID, 2012 | 8 | 2012 |
| Effective nonlinear feature selection method based on hsic lasso and with variational inference K Koyama, K Kiritoshi, T Okawachi, T Izumitani International Conference on Artificial Intelligence and Statistics, 10407-10421, 2022 | 7 | 2022 |
| Estimating individual-level optimal causal interventions combining causal models and machine learning models K Kiritoshi, T Izumitani, K Koyama, T Okawachi, K Asahara, S Shimizu The KDD'21 Workshop on Causal Discovery, 55-77, 2021 | 7 | 2021 |
| Causal discovery for non-stationary non-linear time series data using just-in-time modeling D Fujiwara, K Koyama, K Kiritoshi, T Okawachi, T Izumitani, S Shimizu Conference on Causal Learning and Reasoning, 880-894, 2023 | 6 | 2023 |
| L1-Norm Gradient Penalty for Noise Reduction of Attribution Maps. K Kiritoshi, R Tanno, T Izumitani CVPR Workshops, 118-121, 2019 | 6 | 2019 |
| Capturing time-varying influence using an attribution map method for neural networks K Kiritoshi, K Ito, T Izumitani IJCAI Workshop on AI for Internet of Things (AI4IoT), 2018 | 4 | 2018 |
| 機械学習を用いた工場機器の故障予測 切通恵介， 泉谷知範 DEIM Forum, H2-1, 2017 | 4 | 2017 |
| Frequency component restoration for music sounds using a Markov random field and maximum entropy learning T Izumitani, K Kashino 2006 IEEE International Conference on Acoustics Speech and Signal Processing …, 2006 | 4 | 2006 |
| Information processing device, information processing method, and information processing program T Okawachi, T Izumitani, K Kiritoshi, K Koyama US Patent App. 17/711,032, 2022 | 1 | 2022 |
| A Musical Audio Search Method Based on Self-Similarity Features T Izumitani, K Kashino 2007 IEEE International Conference on Multimedia and Expo, 68-71, 2007 | 1 | 2007 |
| 最大マージン原理に基づく多重ラベリング学習 賀沢秀人， 泉谷知範， 平博順， 前田英作， 磯崎秀樹 電子情報通信学会論文誌 D 88 (11), 2246-2259, 2005 | 1 | 2005 |
| 最大マージン原理にもとづく多重トピック文書の自動分類 賀沢秀人， 泉谷知範， 平博順， 前田英作 情報処理学会研究報告自然言語処理 (NL) 2004 (93 (2004-NL-163)), 53-60, 2004 | 1 | 2004 |
| Assigning Gene Ontology (GO) Codes to Yeast Genes using Text-based Super-vised Learning Methods T Izumitani Proc. of IEEE Bioinformatics Conference (CSB-2004), 2004 | 1 | 2004 |
| 構造的因果モデルに基づく繰り返しの介入による最適化と制御応用 藤原大悟， 泉谷知範， 清水昌平 人工知能学会全国大会論文集 第 38 回 (2024), 4Xin231-4Xin231, 2024 | | 2024 |
| 分割時系列デザインに基づく低頻度繰り返し介入の効果推定手法 石山隼， 藤原大悟， 片島健博， 泉谷知範 人工知能学会全国大会論文集 第 38 回 (2024), 4Xin271-4Xin271, 2024 | | 2024 |
Kunio KashinoFellow, NTT Corporation在 ieee.org 的电子邮件经过验证
