A New Method for Predicting RNA Secondary Structure. - Hirotoshi Taira; Tomonori Izumitani; Eisaku Maeda; Takeshi Suzuki (CSB, 2003)

Experiments in TREC 2003 Genomics Track at NTT. - Hirotoshi Taira; Tomonori Izumitani; Tsutomu Hirao; Hideki Isozaki; Hideto Kazawa; Eisaku Maeda (TREC, 2003)

Assigning Gene Ontology Categories (GO) to Yeast Genes Using Text-Based Supervised Learning Methods. - Tomonori Izumitani; Hirotoshi Taira; Hideto Kazawa; Eisaku Maeda (CSB, 2004)

Maximal Margin Labeling for Multi-Topic Text Categorization. - Hideto Kazawa; Tomonori Izumitani; Hirotoshi Taira; Eisaku Maeda (NIPS, 2004)

Frequency Component Restoration for Music Sounds using a Markov Random Field and Maximum Entropy Learning. - Tomonori Izumitani; Kunio Kashino (ICASSP (5), 2006)

Frequency component restoration for music sounds using local probabilistic models with maximum entropy learning. - Tomonori Izumitani; Kunio Kashino (SAPA@INTERSPEECH, 2006)

A Musical Audio Search Method Based on Self-Similarity Features. - Tomonori Izumitani; Kunio Kashino (ICME, 2007)

A background music detection method based on robust feature extraction. - Tomonori Izumitani; Ryo Mukai; Kunio Kashino (ICASSP, 2008)

A Robust Musical Audio Search Method Based on Diagonal Dynamic Programming Matching of Self-Similarity Matrices. - Tomonori Izumitani; Kunio Kashino (ISMIR, 2008)

NTT Communication Science Laboratories and National Institute of Informatics at TRECVID 2012 Instance Search and Multimedia Event Detection Tasks. - Masaya Murata; Tomonori Izumitani; Hidehisa Nagano; Ryo Mukai; Kunio Kashino; Shin'ichi Satoh 001 (TRECVID, 2012)

Bayesian semi-supervised audio event transcription based on Markov indian buffet process. - Yasunori Ohishi; Daichi Mochihashi; Tomoko Matsui; Masahiro Nakano; Hirokazu Kameoka; Tomonori Izumitani; Kunio Kashino (ICASSP, 2013)

L1-Norm Gradient Penalty for Noise Reduction of Attribution Maps. - Keisuke Kiritoshi; Ryosuke Tanno; Tomonori Izumitani (CVPR Workshops, 2019)

Discovering Sparse and Ununiform Lag Structure Using VAR Models with Latent Group LASSO. - Kazuki Koyama; Keisuke Kiritoshi; Tomonori Izumitani (ICDM Workshops, 2019)

Estimating individual-level optimal causal interventions combining causal models and machine learning models. - Keisuke Kiritoshi; Tomonori Izumitani; Kazuki Koyama; Tomomi Okawachi; Keisuke Asahara; Shohei Shimizu (CD@KDD, 2021)

Effective Nonlinear Feature Selection Method based on HSIC Lasso and with Variational Inference. - Kazuki Koyama; Keisuke Kiritoshi; Tomomi Okawachi; Tomonori Izumitani (AISTATS, 2022)

Prospects of Continual Causality for Industrial Applications. - Daigo Fujiwara; Kazuki Koyama; Keisuke Kiritoshi; Tomomi Okawachi; Tomonori Izumitani; Shohei Shimizu (AAAI Bridge Program, 2023)

Causal Discovery for Non-stationary Non-linear Time Series Data Using Just-In-Time Modeling. - Daigo Fujiwara; Kazuki Koyama; Keisuke Kiritoshi; Tomomi Okawachi; Tomonori Izumitani; Shohei Shimizu (CLeaR, 2023)

XicorAttention: Time Series Transformer Using Attention with Nonlinear Correlation. - Daichi Kimura; Tomonori Izumitani; Hisashi Kashima (CoRR, 2025)