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Tomonori Izumitani
Tomonori Izumitani
NTT Communications Corporation
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Title
Cited by
Cited by
Year
Maximal margin labeling for multi-topic text categorization
H Kazawa, T Izumitani, H Taira, E Maeda
Advances in neural information processing systems 17, 2004
1842004
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
382008
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
372013
A Robust Musical Audio Search Method Based on Diagonal Dynamic Programming Matching of Self-Similarity Matrices.
T Izumitani, K Kashino
ISMIR, 609-613, 2008
152008
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
152004
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
82012
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
72022
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
72021
L1-Norm Gradient Penalty for Noise Reduction of Attribution Maps.
K Kiritoshi, R Tanno, T Izumitani
CVPR Workshops, 118-121, 2019
62019
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
42018
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
42017
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
42006
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
22023
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
12007
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
12005
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
12004
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
12004
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
Prospects of Continual Causality for Industrial Applications
D Fujiwara, K Koyama, K Kiritoshi, T Okawachi, T Izumitani, S Shimizu
AAAI Bridge Program on Continual Causality, 18-24, 2023
2023
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