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論文(査読有りま たは招待)および著書

  1. ジャーナル論文
  2. アーカイブ論文
  3. 国際会議論文
  4. 招待解説論文・記事
  5. NTT機関論文誌および研究実用化報告
  6. 登録特許
  7. 著書・訳書
ジャーナル論文
  1. Saeidi,V.,Seydi,S.T.,Kalantar,B.,Ueda,N.,Tajfirooz,B.,& Shabani,F.,”Water depth estimation from Sentinel-2 imagery using advanced machine learning methods and explainable artificial intelligence”,Geomatics,Natural Hazards and Risk,14(1),2225691,2023.

  2. Takahashi,A.,Hokari,H.,Doi,M.,Yoshikawa,N.,Mariyama,T.,Ueda,N.,and Hirai,N.,“Using active cooling/heating for 1C1R gray-box model parameter identification in actual environment: a proof-of-concept study,” Building Services Engineering Research & Technology (Sage Journals) (in preparation)

  3. Mulia,I.E.,Ueda,N.,Miyoshi,T.,Iwamoto,T.& Heidarzadeh,M.A novel deep learning approach for typhoon-induced storm surge modeling through efficient emulation of wind and pressure fields. Scientific Reports 13,7918,2023. doi:10.1038/s41598-023-35093-9.

  4. Hachiya,H.,Nagayoshi,K.,Iwaki,A.,Maeda,T.,Ueda,N.,Fujiwara,H.,“Position-dependent partial convolutions for supervised spatial interpolation,”Machine Learning with Applications,100514-100514,2023.

  5. Hachiya,H.,Masumoto,Y.,Kudo,A.,and Ueda,N.,“Encoder–decoder-based image transformation approach for integrating multiple spatial forecasts,”Machine Learning with Applications 12(100473) 1-11,2023.

  6. Murakami,S.,Fujita,K.,Ichimura,T.,Hori,T.,Hori,M.,Lalith,M.,and Ueda,N.,“Development of 3D viscoelastic crustal deformation analysis solver with data-driven method on GPU, Lecture Notes in Computer Science, vol 14074,2023, https://doi.org/10.1007/978-3-031-36021-3_45

  7. Mulia, I. E., Ueda, N., Miyoshi, T., Iwamoto, T. & Heidarzadeh, M. A novel deep learning approach for typhoon-induced storm surge modeling through efficient emulation of wind and pressure fields. Scientific Reports 13, 7918 (2023). doi: 10.1038/s41598-023-35093-9

  8. Hachiya, H., Masumoto, Y., Kudo, A., and Ueda,N.,"Encoder-decoder-based image transformation approach for integrating multiple spatial forecasts,," Machine Learning with Applications, Vol.12, No.5, 2023.

  9. Okazaki, T., Ito, T., Hirahara., and Ueda, N., "Physics-informed deep learning approach for modeling crustal deformation," Nature Communications, 13, 7092, 2022.

  10. Mulia, I., Ueda, N., Miyoshi, T., Gusman, A.R. and Satake, K., "Machine learning-based tsunami inundation prediction derived from offshore observations," Nature Communications, 13, 5489, 2022.

  11. Okazaki, T., Fukuhata, Y., and Ueda, N., "Time variable stress inversion of centroid moment tensor using Gaussian processes," Journal of Geophysical Research (JGR): Solid Earth, 2022.

  12. Takahashi, I., Hamasaki, R., Ueda, N., Tanaka, M., Tominaga, N., Sako, Shigeyuki, Ohsawa, R., and Yoshida, N., "Deep-learning real/bogus classification for the Tomo-e Gozen transient survey," Publication of the Astronomical Society of Japan, Vol.74, Issue 4, pp.946--960, 2022.

  13. Saed, F. G., Noori, A. M., Kalantar, B., Oader, W. M., and Ueda, N., "Earthquake-induced ground deformation assenment via sentinel-1 rader aided at Darbandikhan town," Journal of Sensors, Vol. 2022, Article ID 2020069, 2022.

  14. Seydi, S. T., Saeidi, V., Kalantar, B., Ueda., N Genderen, V., Maskouni, F. H., and Aria, F. A.,"Fusion of the multisource datasets for flood extent mapping based on ensemble convolutional neural network (CNN) model," Journal of Sensors, Vol.2022, ID 2887502, 2022.

  15. Okazaki, T., N. Morikawa, A. Iwaki, H. Fujiwara, T. Iwata, N. Ueda., "Ground-Motion Prediction Model Based on Neural Networks to Extract Site Properties from Observational Records," Bulletin of the Seismological Society of America, 2021.

  16. Okazaki, T., H. Hachiya, A. Iwaki, T. Maeda, H. Fujiwara, N. Ueda., "Broad-band ground motions with consistent long-period and short-period components using Wasserstein interpolation of acceleration envelopes," Geophysical Journal International, 2021.

  17. Kalantar, B., Ueda, N., Saeidi, V., Janizadeh, S., Shabani, F., Ahmadi, K., & Shabani, F., "Deep neural network utilizing remote sensing datasets for flood hazard susceptibility mapping in Brisbane," Australia. Remote Sensing, 13(13), 2021.

  18. Ojogbane, S. S., Mansor, S., Kalantar, B., Khuzaimah, Z. B., Shafri, H. Z. M., & Ueda, N., "Automated Building Detection from Airborne LiDAR and Very High-Resolution Aerial Imagery with Deep Neural Network," Remote Sensing, 13(23), 2021.

  19. Al-Dogom, D., Al-Ruzouq, R., Kalantar, B., Schuckman, K., Al-Mansoori, S., Mukherjee, S., & Ueda, N., "Geospatial multicriteria analysis for earthquake risk assessment: Case Study of Fujairah City in the UAE," Journal of Sensors, 2021.

  20. Jumaah, H. J., Kalantar, B., Halin, A. A., Mansor, S., Ueda, N., & Jumaah, S. J., "Development of UAV-based PM2. 5 monitoring system. Drones," 5(3), 2021.

  21. Tehrany, M. S., Özener, H., Kalantar, B., Ueda, N., Habibi, M. R., Shabani, F., & Shabani, F., "Application of an ensemble statistical approach in spatial predictions of bushfire probability and risk mapping," Journal of Sensors, 2021.

  22. Ameen, M. H., Jumaah, H. J., Kalantar, B., Ueda, N., Halin, A. A., Tais, A. S., & Jumaah, S. J., "Evaluation of PM2. 5 particulate matter and noise pollution in Tikrit University based on GIS and statistical modeling, Sustainability," 13(17), 2021.

  23. Hamed, H. H., Jumaah, H. J., Kalantar, B., Ueda, N., Saeidi, V., Mansor, S., & Khalaf, Z. A.,  "Predicting PM2. 5 levels over the north of Iraq using regression analysis and geographical information system (GIS) techniques. Geomatics, Natural Hazards and Risk," 12(1), pp.1778-1796, 2021.

  24. Futami, F., Iwata, T., Ueda, N., and Sato, I., "Accelerated diffusion-based sampling by the non-reversible dynamics with skew-symmetric matrices," Special Issue Approximate Bayesian Inference, Entropy, 2021.

  25. Okazaki, T., Morikawa, N., Fujiwara, H., and Ueda, N., "Monotonic neural network for ground ｍotion predictions to avoid overfitting to recorded site, " Seismological Research Letters, 2021.

  26. Okawa, M., Owata, T., Kurashima, T., Tanaka, Y., Toda, H., and Ueda, N., "Deep mixture point processes, " Transaction of the Japanese Society for Artificial Intelligence. 2021.

  27. Fujiwara, Y., Kanai, S., Ida, Y., Kumagai, A., and Ueda, N.,"Fast algorithm for anchor graph hashing," Proc. of the VLDB　Endowment, Vol.14, Issue 6, 2021.

  28. Tanaka, Y., Iwata, T., Kurashima, T., Ueda, N., Tanaka, T., "Time-delayed collective flow diffusion models for inferring latent people flow from aggregated data at limited locations," Artificial Intelligence, Vol.292, 103430, 2021.

  29. Natsume-Kitatani, Y., Mizuguchi, K., and Ueda, N., "Subset-binding: A novel algorithm to detect paired items from heterogeneous data including biological datasets," Research Square, April 12th, 2021.
    DOI: https://doi.org/10.21203/rs.3.rs-405195/v1

  30. Kalantar, B., Ueda, N.,Saeidi, V.,Ahmadi, K.,Halin, A.A., & Shabani, F., "Landslide susceptibility mapping: Machine and ensemble learning based on remote sensing big data," Remote Sensing, 12(11), 1737, 2020.

  31. Takahashi, I, Suzuki, Nao, Yasuda, N., Kimura, A., Ueda, N., Tanaka, M., Tominaga, N., Yoshida, N., "Photometric classification of hyper suprime-cam transients using machine learning," Publications of the Astronomical Society of Japan, Vol.72, Issue 5, 89, pp.1-22, 2020.

  32. Iwata, T., Toyoda,M., Tora,S., and Ueda,N., "Anomaly Detection with Inexact Labels," Machine Learning, Vol.109, Issue. 8, pp.1617-1633, 2020.

  33. Gibril, M. B. A., Kalantar, B., Al-Ruzouq, R., Ueda, N., Saeidi, V., Shanableh, A., Mansor, S., and Shafri, H. Z. M., "Mapping heterogeneous urban landscapes from the fusion of digital surface model and unmanned aerial vehicle-based images using adaptive multiscale image segmentation and classification," Remote Sensing, 2020,12(7), 1081, 2020.

  34. Yamamoto, Y., Tsuzuki, T., Akatsuka, J., Ueki, M., Morikawa, H., Numata, Y., Takahara, T., Tsuyuki, T., Shimizu, A., Maeda, K., Tsuchiya, S., Kanno, H., Kondo, Y., Tamiya, G., Ueda, N., and Kimura, G., "Automated acquisition of explainable knowledge from unannotated," Nature Communications, 10, 5642, 2019.

  35. Kalantar, B., Al-Najjar, H.A.H., Pradhan, B., Saeidi, V., Halin, A.A, Ueda, N., Naghibi., S.A.,"Optimized conditioning factors and machine learning for groundwater potential mapping,"Water Journal 2019, 11(9), 1909, 2019.

  36. Al-Najjar, H. A., Kalantar, B., Pradhan, B., Saeidi, V., Halin, A. A., Ueda, N., and Mansor, S.,"Land cover classification from fused DSM and UAV images using convolutional neural networks," Remote Sensing, 11(12), 1461, 2019.

  37. Yasuda, N., Tanaka, M., Tominaga, N., Jiang, J., Moriya, T., Morokuma, T., Suzuki, N., Takahashi, I., Yamaguchi, M., Maeda, K., Sako, M., Ikeda, S.,Kimura, A., Morii, M., Ueda, N., Yoshida, N., Lee, C., Suyu, S., Komiyama, Y., Regnault, N., and Rubin, D., "The Hyper Suprime-cam SSP transient survey in COSMOS: Overview,"Publications of the Astronomical Society of Japan, vol.71, No.4, pp.1--16, 2019.

  38. Ueda, N., asd Fujino, A., "Partial auc maximization via nonlinear scoring functions," Xiv submit/2294250, 2018.

  39. Ueda, N., and Naya, F., "Spatio-temporal multidimensional collective data analysis for providing comfortable living anytime and anywhere," APSIPA Transactions on Signal and Information Processing, Vol.7, No.4, 2018.

  40. Iwata, T., Hirao, T., Ueda, N.,"Topic models for unsupervised cluster matching," IEEE Transactions on Knowledge and Data Engineering, Volume:30, Issue:4, pages 786--795, 2018.

  41. Iwata, T., Shimizu, H., Naya, F. and Ueda, N., "Estimating people flow from spatio-temporal population data via collective graphical mixture models," ACM Transactions on Spatial Algorithms and Systems, Vol. 3, Issue 1, Article 39. 2017.

  42. Ishiguro, K., Sato, I. and Ueda, N., "Averaged collapsed variational Bayes inference," Journal of Machine Learning Resaerch (JMLR), Volume 18, Number 1, pp.1--29, 2017.

  43. 上田修功, "時空間予測技術に基づく先行的集団最良誘導," 応用統計学(招待論文), Vol.45, No.3, pp.89-104, 2016.

  44. Morii, M., Ikeda, S., Tominaga, N., Tanaka, M., Morokuma, T., Ishiguro, K., Yamato, J., Ueda, N., Suzuki, N., Yasuda, N. and Yoshida, N., "Machine-learning selection of optical transients in Subaru/hyper suprime-cam survey," Publication of Astronomical Society of Japan, Vol.68, No.6, pp.104-112, 2016.

  45. Inoue, S., Ueda, N., Nohara, Y. and Nakashima, N., "Recognizing and understanding nursing activities for a whole day with a big data set," Journal of Information Processing, Vol.57, No.10, 2016.

  46. 上田修功,"時空間予測技術とその先行的人流誘導への応用,"画像電子学会誌(招待論文), Vol.45, No.1, pp.4-11, 2016.

  47. Iwata, T., Hirao T. and Ueda, N., " Unsupervised many-to-many object matching via probabilistic latent variable models,"Information Processing & Management, Volume 52, Issue 4, pp682-697, July 2016.

  48. Blondel, M., Onogi, A., Iwata, H. and Ueda, N., "A Ranking Approach to Genomic Selection,"PLOS ONE (peer-reviewed open access journal), Public Library of Science, 2015.

  49. Nohara, Y., Kai, E., Ghosh, P., Islam, R., Ahmed, A., Kuroda, M., Inoue, S., Hiramatsu, T., Kimura, M., Shimizu, S., Kobayashi, K., Baba, Y., Kashima, H., Tsuda, K., Sugiyama, M., Blondel, M., Ueda, N., Kitsuregawa, M. and Nakashima, N., "Health Checkup and Telemedical Intervention Program for Preventive Medicine in Developing Countries: Verification Study," Journal of Medical Internet Research, Vol.17, No.1 January 2015.

  50. 田中祐典,上田修功.田中利幸, "クラス固有の特徴選択に基づくベイズ分類器,"　電子情報通信学会和文論文誌(D-II), Vol.J96-D, No.11, pp.2755-2764, 2013.

  51. Sun, X. Kashima, H. and Ueda, N., "Large-Scale Personalized Human Activity Recognition using Online Multi-Task Learning, "IEEE Transactions on Knowledge and Data Engineering (TKDE), Vol.25, No.11, pp.2551-2563, 2013. [IEEE Copyright Notice]

  52. Sawada, H., Kameoka, H., Araki, S. and Ueda, N., "Multichannel Extensions of Non-negative Matrix Factorization with Complex-valued Data," IEEE Transactions on Audio, Speech, and Language Processing, Vol.21, No.5, pp.971-982, 2013. [IEEE Copyright Notice]

  53. Iwata, T., Yamada, T. and Ueda, N., "Modeling Noisy Annotated Data with Application to Social Annotation," IEEE Transactions on Knowledge and Data Engineering, Vol.25, No.7, pp.1601-1613, 2013. [IEEE Copyright Notice]

  54. Fujino, A., Ueda, N. and Nagata, M., "Adaptive semi-supervised learning on labeled and unlabeled data with different distributions," Knowledge and Information Systems(KAIS), Vol. 37, Issue 1, pp. 129-154, Springer, 2013, (invited paper).

  55. Iwata, T., Yamada, T., SakuraI, Y. and Ueda, N., "Sequential Modeling of Topics Dynamics with Multiple Timiscales," ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 5 Issue 4, 19:1-19:27, 2012.

  56. Hachiya, H., Sugiyama, M. and Ueda, N., "Importance-weighted least-squares probabilistic classifier for covariate shift adaptation with application to human activity recognition," Neurocomputing, Vol. 80, pp 93-101, 2012.

  57. 藤野昭典, 上田修功, 永田昌明, "ラベルありデータの選択バイアスに頑健な 半教師あり学習," 情報処理学会論文誌, Vol.4, No.2, pp. 31-42, 2011.

  58. Iwata, T., Tanaka, T., Yamada, T. and Ueda, N., "Improving Classifier Performance Using Data with Different Taxonomies," IEEE Transactions on Knowledge and Data Engineering (TKDE), Vol.23, No.11, 1668-1677, 2011.[IEEE Xplore] [IEEE Copyright Notice],

  59. 藤野昭典, 上田修功, 永田昌明, "ラベルありデータの選択バイアスに頑健な半教師あり学習," 情報処理学会論文誌  数理モデル化と応用, Vol.2010-MPS-80 No.8, 2010.

  60. 石黒 勝彦, 岩田具治, 上田修功, "時間依存関係データ分析のための動的無限関係モデル,"情報処理学会論文誌 数理モデル化と応用, Vol.3, No.1, pp. 1-12, 2010.

  61. 岩田具治, 渡部晋治, 山田武士, 上田修功, "購買行動解析のためのトピック追跡モデル," 電子情報通信学会, Vol.J93-D, No.6, pp.978-987, 2010

  62. 岩田具治, 田中利幸, 山田武士, 上田修功, "分布が変化するデータにおけるモデル学習法," 電子情報通信学会, Vol.J-92D, No.3, 361-370, 2009.

  63. 岩田具治, 山田武士, 上田修功, "トピックモデルに基づく文書群の可視化," 情報処理学会論文誌, Vol.50, No.6, 1649-1659, 2009.

  64. 川前徳章, 坂野　鋭, 山田武士, 上田修功, "ユーザの嗜好の時系列性と先行性に着目した協調フィルタリング," 電子情報通信学会論文誌 (D-II), Vol.J92-DII, No.6, pp. 767-776, 2009.

  65. Fujino, A., Ueda, N. and Saito, K., "Semisupervised learning for a hybrid generative/discriminative classifier based on the maximum entropy principle," IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), Vol.30, No.3, pp.424-437, 2008, [IEEE Xplore] [DOI link] [IEEE Copyright Notice].

  66. 桑田修平, 山田武士, 上田修功, "ディリクレ過程混合モデルに基づく離散データの共クラスタリング, "情報処理学会論文誌：数理モデル化と応用, pp. 60-73, 2008, [情報処理学会].

  67. 岩田具治, 山田武士, 上田修功, "購買順序を効率的に用いた協調フィルタリング," 情報処理学会論文誌：数理モデル化と応用 Vol.49, No.SIG4 (TOM20), pp. 125-134, 2008.

  68. Naud, A., Usui, S., Ueda, N. and Taniguchi T., "Visualization of documents and concepts in Neuroinformatics with the 3D-SE Viewer," Neuroinformatics, 2007.

  69. 桑田修平, 上田修功, "一括予測型協調フィルタリング," 情報処理学会論文誌, Vol.48, No.SIG 15(TOM 18), pp. 153-162, 2007, [情報処理学会].

  70. 川前徳章, 山田武士, 上田修功, "Relative Innovator の発見によるパーソナライズ手法の提案," FIT2007 Letters, Vol.6, pp.99-102, 2007.
  71. Kuwata, S. and Ueda, N., "An efficient collaborative filtering algorithm based on marginal rating distributions," International Journal of IT & IC, IEEE CIS, Vol.2, No.1, 2007.

  72. 藤野昭典, 上田修功, 斉藤和巳, "複数の構成要素データを扱う多クラス 分類器の半教師有り学習法," 情報処理学会論文誌, 数理モデル化と応用(TOM), 48(SIG 15), pp. 163-175, 2007, [情報処理学会].

  73. Fujino, A., Ueda, N. and Saito, K., "A hybrid generative/discriminative approach to text classification with additional information," Information Processing & Management, Elisevier, Vol.43, No.2, pp. 379-392, 2007.

  74. Usui, S., Plames, P., Nagata, K., Taniguchi, T. and Ueda, N., "Keyword extraction, ranking, and organization for the neuroinfomatics platform," Biosystems, Elsevier Science, Vol.88, Issue 3, pp. 334-342, 2007, [Biosystems].
  75. Iwata, T., Saito, K., Ueda, N., Stromsten, S., Griffiths, T. and Tenenbaum, J., "Parametric Embedding for Class Visualization," Neural Computation Vol. 19, No. 9, pp. 2536-2556, 2007.

  76. Kawamae, N., Yamada, T. and Ueda, N.,"Personalized Ranking by Identifying, RelativeInnovators," FIT2007 L, Vol.6, pp.99-102, 2007.

  77. 上田修功, 山田武士, "ノンパラメトリックベイズモデル," 応用数理 Vol.17, No.3, pp.196-214, 2007.

  78. 藤野昭典, 上田修功, 斉藤和巳, "最大エントロピー原理に基づく付加情報 の効率的な利用によるテキスト分類," 情報処理学会論文誌, Vol.47, No.10, pp. 2929-2937, 2006, [情報処理学会].
  79. 藤野昭典, 上田修功, 斉藤和巳, "半教師あり学習のための生成・識別ハイブリッド分類器の設計法人工知能学会論文誌, Vol.21, No.3, pp. 301-309, 2006.

  80. Ueda, N. and Saito, K., "Parametric mixture models for multi-topic text," Systems and Computers in Japan, Vol.37, No.2, pp. 56-66, 2006, [Systems and Computers in Japan].
  81. 上田 修功, "アンサンブル学習," 情報処理学会論文誌(招待論文), Vol.46, No.SIG15(CVIM 12), pp. 11-20, 2005, [情報処理学会 ].
  82. 岩田具治, 斎藤和巳, 上田修功, "パラメトリック埋め込み法によるクラス構造の可視化," 情報処理学会論文誌, vol.46,no.9, pp. 2337-2346, 2005.

  83. 藤野 昭典, 上田 修功, 　斉藤 和巳, "生成・識別ハイブリッドモデルに基づく半教師あり学習," FIT2005 情報科学技術レターズ, 2005.

  84. Kimura, M., Saito K. and Ueda, N., "Modeling network growth with directional attachment and communities," Systems and Computers in Japan, Vol. 35, No. 8, pp. 1-11, 2004, [Systems and Computers in Japan].

  85. 岩田具治, 斉藤和巳, 上田修功, "事後確率構造の可視化," 情報科学技術レターズ, Vol. 3, pp. 119-120, 2004.

  86. 金田 有二, 斉藤 和巳, 上田 修功, "文書分類体系間の対応関係の自動抽出," FIT2004 情報科学技術レターズ, Vol. 3, pp.121-122, 2004.

  87. 金田 有二, 上田 修功, "高次元データに対して頑健な文書クラスタリング手法," FIT2004 情報科学技術レターズ, Vol. 3, pp. 123-124, 2004.

  88. 藤野昭典, 上田修功, 斉藤和巳, "交差確認法に基づく適合性フィードバック," FIT2004 情報科学技術レターズ, Vol. 3, pp. 53-54, 2004.

  89. Kimura, M., Saito, K. and Ueda, N., "Modeling share dynamics by extracting competition structure," Physica D, Vol.198, pp. 51-73, 2004.

  90. Watanabe, S., Minami, Y., Nakamura, A. and Ueda, N., "Variational Bayesian Estimation and Clustering for Speech Recognition," IEEE transaction on Speech and Audio Processing, Vol. 12, pp.365-381, 2004.

  91. Kimura, M., Saito, K. and Ueda, N., "Modeling of growing networks with directional attachment and communities," Neural Networks, Vol. 17, No. 7, pp. 975--988, 2004.

  92. 上田修功, 斉藤和己, "多重トピックテキストの確率モデル --パラメトリック混合モデル--," 電子情報通信学会論文誌 (D-II), Vol. J87-DII, No.3, pp. 872-883, 2004.

  93. Ueda, N. and Inoue, M., "Extended Tied-Mixture HMMs for Both Labeled and Unlabeled Time Series Data," Journal of VLSI Signal Processing Systems, Vol. 37, pp. 189-197, 2004.

  94. 木村昌弘, 斉藤和巳, 上田修功, "指向性アタッチメントとコミュニティをもつ成長ネットワークモデル," 電子情報通信学会論文誌, Vol. J86-DII, No, 10, pp. 1468-1479, 2003.

  95. 上田修功, 斉藤和己, "類似テキスト検索のための多重トピックテキストモデル" 情報処理学会論文誌, Vol. 44, No. SIG14(TOM9), pp. 1-8, 2003, [情報処理学会].

  96. 山田武士, 斉藤和巳, 上田修功, "クロスエントロピー最小化に基づくネットワークデータの埋め込み," 情報処理学会論文誌, Vol. 44, No. 9, pp. 2401-2408, 2003, [情報処理学会]

  97. Inoue, M. and Ueda, N., "Exploitation of unlabeled sequences in hidden markov models," IEEE Transaction on Pattern Analysis and Machine Intelligence (PAMI), Vol. 25, No. 12, pp1570-1581, 2003.

  98. 渡部晋治, 南泰浩, 中村篤, 上田修功, "ベイズ的基準を用いた 状態共有型HMM構造の選択" 電子情報通信学会論文誌, (D-II), Vol. J86-DII, No. 6, pp. 776-786, 2003.

  99. 井上雅史, 上田修功, "隠れマルコフモデルにおけるクラスラベル無しデータの利用 " 電子情報通信学会論文誌, (D-II), Vol. J86-DII, No.2, pp. 173-183, 2003.

  100. Ueda, N. and Ghahramani, Z., "Bayesian model search for mixture models based on optimizing variational bounds," Neural Networks, Vol. 15, No.10, pp. 1223-1241, 2002.

  101. 上田修功,"最良モデル探索の変分ベイズ法," 人工知能学会論文誌, Vol.16, No.2, SP-F, 2001.

  102. 鈴木 敏, 上田修功, "混合回帰モデルのためのSMEMアルゴリズム" 電子情報通信学会論文誌, (D-II), Vol. J83-DII, No. 12, pp. 2777-2785, 2000.

  103. Ueda, N., "EM algorithm with split and merge operations for mixture models (invited)," Transactions of IEICE, Vol. E83-D, No. 12, pp. 2047-2055, 2000.

  104. 鈴木 敏, 上田修功, "モジュール競合学習を用いた適応的クラスタリング" 電子情報通信学会論文誌, (D-II), Vol. J83-DII, No. 6, pp. 1529-1538, 2000.

  105. Ueda, N., Nakano, R., Ghahramani, Z. and Hinton, G. E., "SMEM algorithm for mixture models," Neural Computation, Vol. 12, No. 9, pp. 2109-2128, 2000.

  106. Ueda, N., Nakano, R., Ghahramani, Z. and Hinton, G. E., "Split and merge EM algorithm for improving Gaussian mixture density estimates (invited), "Journal of VLSI Signal Processing, Vol. 26, pp.133-140Z, 2000.

  107. Ueda, N., "Optimal linear combination of neural networks for improving classification performance," IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), Vol. 22, No. 2, pp. 207-215, 2000.

  108. 上田修功, 中野良平, "確率的混合部分空間法" 電子情報通信学会論文誌, (D-II), Vol. J82-DII, No. 12, pp. 2394-2401, 1999.

  109. 上田修功, 中野良平, "混合モデルのための併合分割操作付きEMアルゴリズム" 電子情報通信学会論文誌(D-II), Vol. J82-DII, No. 5, pp. 930-940, 1999.

  110. 上田修功, "分類誤り最小基準に基づくニューラルネット識別機の最適線形統合法,"電子情報通信学会論文誌(D-II), Vol. J82_DII, No. 3, pp. 522-530, 1999.

  111. Ueda, N. and Nakano, R., "Deterministic annealing EM algorithm," Neural Networks, Vol.11, No. 2, pp.271-282, 1998.

  112. 上田修功, 中野良平, "アンサンブル学習の汎化誤差解析," 電子情報通信学会論文誌(D-II), Vol. J80-DII, No. 9, pp. 2512-2521, 1997.

  113. 上田修功, 中野良平, "確定的アニーリングEMアルゴリズム," 電子情報通信学会論文誌(D-II), Vol.J80-DII, No.1, pp. 267-276, 1997.

  114. Ueda, N. and Mase, K., "Tracking moving contours using energy-minimizing elastic contour models," International Journal of Pattern Recognition and Artificial Intelligence, Vol. 9, No. 3, pp. 465-484, 1995.

  115. Ueda, N. and Nakano, R., "A new competitive learning approach based on an equidistortion principle for designing optimal vector quantizers," Neural Networks, Vol.7, No.8, pp. 1211-1227, 1994.

  116. 上田修功, 中野良平, "ベクトル量子化器設計のための淘汰型競合学習法 -等ひずみ原理とその実現アルゴリズム-" 電子情報通信学会論文誌(D-II), Vol. J77-DII, No. 11, pp. 2265-2278, 1994.

  117. Ueda, N. and Suzuki, S., "Learning visual models from shape contours using multiscale convex/concave structure matching," IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), Vol. 15, No. 4, pp. 337-352, 1993, [IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI)].

  118. Suzuki, S., Ueda, N. and Sklansky, J., "Graph-Based Thinning for Binary Images," International Journal of Pattern Recognition and Artificial Intelligence, Vol. 7, No. 5 pp. 1009-1030, 1993.

  119. 上田修功, 間瀬健二, 末永康仁, "弾性輪郭モデルとエネルギー最小化原理に基づく動輪郭追跡手法," 電子情報通信学会論文誌(D-II), Vol. J75-DII, No. 1, pp. 111-120, 1992.

  120. 上田修功, 鈴木智, "凹凸構造の一般化に基づく輪郭形状モデルの自動獲得," 電子情報通信学会論文誌(D-II), Vol. J74-DII, No. 2, pp. 220-229, 1991.

  121. 上田修功, 鈴木智, "多重スケールの凹凸構造を用いた変形図形のマッチングアルゴリズム," 電子情報通信論文誌(D-II),  Vol. J73-DII, No. 7, pp. 992-1000, 1990.

  122. 上田修功, 名倉正計, 小杉 信, 森 克己, "図面の2値化のための画質改善," テレビジョン学会誌, Vol. 42, No. 8, pp. 831-836, 1988.

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アーカイブ論文

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    Few-shot learning of neural networks from scratch by pseudo example optimization
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  2. arXiv:1711.11526
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    10.1109/ICDCSW.2017.47
    Single-epoch supernova classification with deep convolutional neural networks
    Authors: Akisato Kimura, Ichiro Takahashi, Masaomi Tanaka, Naoki Yasuda, Naonori Ueda, Naoki Yoshida

  3. arXiv:1705.07603
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    Multi-output Polynomial Networks and Factorization Machines
    Authors: Mathieu Blondel, Vlad Niculae, Takuma Otsuka, Naonori Ueda

  4. arXiv:1609.03249
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  5. arXiv:1607.08810
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    Polynomial Networks and Factorization Machines: New Insights and Efficient Training Algorithms
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  6. arXiv:1607.07195
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    Higher-Order Factorization Machines
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  7. arXiv:1409.4757
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    Authors: Katsuhiko Ishiguro, Issei Sato, Naonori Ueda

国際会議論文

  1. Kalantar,B.,Ueda,N.,Zand,M.,& Al-Najjar,H.,”Moving object detection by low-rank analysis of region-based correlated motion fields,” GARSS 2023-2023 IEEE International Geoscience and Remote Sensing Symposium, (pp. 5874-5877), 2023.

  2. Fujiwara, Y., Nakano, M., Kumagai, A., Ida, Y., Kimura, A., and Ueda, N., "Fast binary network hashing via graph clustering," Proc. of IEEE Conference on Bigdata, 2022.

  3. Ichimura, T., Fujita, K., Koyama, K., Kusakabe, R., Kikuchi, Y., Hori, T., Hori, M., Maddegedara, L., Ohi, N., Nishiki, T., Inoue, H., Minami, K., Nishizawa, S., Tsuji, M., and Ueda, N., "152K-computer-node parallel scalable implicit solver for dynamic nonlinear earthquake simulation, " Proc. of the International Conference on High Performance Computing in Asia-Pacific Region, HPC Asia 2022. (Best Paper Finalists)

  4. Tanaka, Y., Iwata, T., and Ueda, N., "Symplectic spectrum Gaussian processes: Learning a Hamiltonian from Noisy and sparse data, " Proc. of Neural Information Processing Systems, NeurIPS2022.

  5. Mulia, I. E., Ueda, N., Miyoshi, T., Gusman, A. R., Satake, K., "Method for real-time prediction of tsunami inundation directly from offshore observations using machine learning," AGU Fall Meeting 2021, virtual meeting, 13-17 December 2021.

  6. Hachiya, H., Nagayoshi, K., Iwasaki, A., Maeda, T., Ueda, N., and Fujiwara, H, "Position-dependent partial convolutions for supervised spatial interpolation, " Proc. of The 14th Asian Conference on Machine Learning (ACML), 2022.

  7. Tanaka, Y., Iwata, T., and Ueda, N., "Symplectic Spectrum Gaussian Processes: Learning a Hamiltonian from Noisy and Sparse Data," Proc of Neural Information Processing Systems, NeurIPS2022.

  8. Kalantar, B., Ojogbane,S. S., Seydi,S. T., Halin, A., Mansor, S., Ueda,N, "A deep learning approach for automated building outlines extraction in compact urban environments," Proc. of IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2022.

  9. Kalantar, B., Seydi, S. T., Ueda,N., Saeidi, V., Halin, A. A., Shabani, F.,"Deep ensemble learning for land cover classification based on hyperspectral prisma image," Proc. of IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2022.

  10. Nakano, M., Nishikimi, R., Fujiwara, Y., Kimura, A., Yamada, T., and Ueda, N., "Nonparametric relational models with superrectangulation," Proc. of the 25th International Conference on Artificial Intelligence and Statistics (AISTATS2022), 2022.

  11. Fujiwara, Y., Ida, Y., Kumagai, A., kanai, S., and Ueda, N., "Fast and accurate anchor graph-based label prediction,"
    Proc of the 30th ACM International Conference on Information and Knowledge Management (CIKM), pp.504--513, 2021.

  12. Jumaah, H. J., Kalantar, B., Ueda, N., Sani, O. S., Ajaj, Q. M., & Jumaah, S. J., "The effect of war on land use dynamics in mosul Iraq using remote sensing and GIS techniques," In 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS (pp. 6476-6479), 2021.

  13. Fujita, K., Kikuchi, Y., Ichimura, T., Hori, M., Maddegedara, L., and Ueda, N.,"GPU porting of scalable implicit solver with Green's function-based neural networks by open ACC," Proc. of Eighth Workshop on Accelerator Programming using Directives (WACCPD), 2021.（Honorable Mention）

  14. Futami, F., Iwata, T., Ueda, N., Sato, I., and Sugiyama, M., "Loss function based second-order Jensen inequality and its application to particle variational inference, " Proc. of Neural Information Processing Systems, NeurIPS 2021.

  15. Nakano, M., Fujiwara, Y., Kimura, A., Yamada, T., and Ueda, N., "Permuton-induced Chinese restaurant process," Proc. of Neural Information Processing Systems, NeurIPS 2021.

  16. Yamagishi, Y., Saito, K., Hirahara, K., and Ueda, N., "Constructing weighted networks of earthquakes with multiple-parent nodes based on correlation-metric," Proc. of International Conference on Complex Networks and their Applications, COMLEX NETWROKS2021.

  17. Nakano, M., Fujiwara, Y., Kimura, A., Yamada, T., and Ueda, N., "Bayesian nonparametric model for arbitrary cubic partitioning," Proc. of Asian Conference on Machine Learning (ACML2021), 2021.

  18. Futami, F., Iwata, T., Sato, I,, and Ueda, N., ”Skew symmetrically perturbed gradient flow for convex optimization," Proc. of Asian Conference on Machine Learning (ACML2021), 2021.

  19. Hachiya, H., Masamoto, Y., Mori, Y., and Ueda, N., "Encoder-decoder-based image transformation approach for integrating precipitation forecasts," Proc. of Asian Conference on Machine Learning (ACML2021), 2021.

  20. Yamagishi, Y., Saito,K., Hirahara, K., and Ueda, N., "Magnitude-weighted mean-shift clustering with leave-one-out bandwidth estimation," Proc. of Pacific Rim International Conference on Artificial Intelligence (PRICAI2021), 2021.

  21. Nakano, M., Kimura, A., Yamada, T, and Ueda, N., "Baxter permutation process, " Proc. of Neural Information Processing Systems, NeurIPS 2020.

  22. Yamaguchi, Y., Saito, K., Hirahara, K., and Ueda, N., "Spatio-temporal clustering of earthquakes based on average magnitudes," Proc. of International Conference on Complex Networks and their Applications, 2020.

  23. Yamaguchi, T., Ichimura, T., Fujita, K., Hori, M., Wijerathne, L., and Ueda, N., "Data-driven approach to inversion analysis of three-dimensional inner soil structure via wave propagation analysis," Proc. of International Conference on Computational Science (ICCS-2020).

  24. Fujiwara、Y., Kumagai, A., Kanai, S., Ida, Y., and Ueda, N.,"Efficient algorithm for the b-matching graph," proc. of ACM SIG-KDD 2020.

  25. Miyoshi, T., Honda,T., Otsuka,S., Amemiya, A., Maejima,Y., Ishikawa, Y., Seko, H.,Yoshizaki,Y., Ueda,N., Tomita, H., Ishikawa,Y., Satoh,S., Ushio,T., Koike,K., and Nakada, Y., "Big data assimilation: Real-time workflow for 30-second-update forecasting and perspectives toward DA-AI integration," Proc. of EGU General Assembly, EGU2020-2483, 2020.

  26. Kalantar, B., Ueda, N., Al-Najjar, H. A. H.Saeidi, V., Gibril, M. B. A, Halin, A.,"A comparison between three conditioning factors dataset for landslide prediction in the Sajadrood Catchment of Iran," Proc. of ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences (ISPRS), 2020.

  27. Yamaguchi, T., Ichimura, T., Fujita, K., Naruse,A., Wells,J.C., Zimmer, C. J., Straatsma,T.P., Hori,M., Lalith, W., and Ueda, N., "Implicit low-order finite element solver with small matrix-matrix multiplication accelerated by AI-specific hardware," Proc. Of Platform for Advanced Scientific Computing Conference (PASC2020), 2020.

  28. Ichimura, T., Fujita, K., Yamaguchi, T., Hori, M., Wijerathne, L., and Ueda, N, “Fast multi-step optimization with deep learning for data-centric supercomputing,” The 4th International Conference on High Performance Compilation, Computing and Communications, 2020.

  29. Iwata, T., Fujino, A., Ueda, N., "Semi-supervised Learning for maximizing the partial AUC," Proc. of Association for the Advancement of Artificial Intelligence (AAAI2020), 2020.

  30. Okawa, M., Iwata, T., Kurashima, T., Tanaka, Y., Yoda, H., and Ueda, N., "Deep mixture point processes: Spatio-temporal event prediction with rich contextual information," Proc. of ACM SIG-KDD2019, 2019.

  31. Hachiya, H., Hirahara,K.,and Ueda, N.,"Machine learning approach for adaptive integration of multiple relative intensity models toward improved earthquake forecasts in Japan," International Union of Geodesy and Geophysics (IUGG2019), 2019.

  32. Hachiya, H., Yamamoto, Y., Hirahara, K, and Ueda, N., "Adaptive truncated residual regression for fine-grained regression problems," Proc. of Asian Conference on Machine Learning (ACML), 2019.

  33. Miyoshi, T., Otsuka, S., Honda, T., Lien, G., Maejima, Y., Ohhigashi, M., Yoshizaki, Y., Seko, H., Tomita, H., Satoh, S., Ushio, T., Gerofi, B., Ishikawa, Y., Ueda, N., Koike, K., Nakada, Y., “Big data assimilation: Past 6 years and future plans,” AMS 39th Conference on Radar Meteorology, 2019. *AMS: American Meteorological Society

  34. Otsuka,T.,Shimizu,H.,Iwata,T.,Naya,F.,Sawada,H., and Ueda,N., "Bayesian optimization for crowd traffic control using multi-agent simulation," Proc. Intelligent transportation systems conference (ITSC), 2019.

  35. Omi, T, Ueda, N, and Aihara, K,"Fully neural based model for general temporal point processes," Proc. Neural Information Processing Systems, NeuriPS 2019.

  36. Okazaki, T, Hachiya, H, Ueda, N., Iwaki, A., Maeda, T. and Fujiwara, H.,"Synthesis of broadband ground motions using embedding and neural networks," Geophysical Research Abstracts, Vol. 21, EGU2019-4590, EGU General Assembly 2019.

  37. Ichimura,T., Fujita, K.,Yamaguchi, T., Naruse,A., Wells, J.C., Zimmer, C. J.,Straatsma,T.,Hori, T., Puel,S., Becker, T.W., Hori,M., and  Ueda, T,"2416-PFLOPS fast scalable implicit solver on low-ordered unstructured finite elements accelerated by 1.10-ExaFLOPS kernel with reformulated AI-like algorithm: For equation-based earthquake modeling," Proc. of International Conference for High Performance Computing, Networking, Storage, and Analysis (SC2019) 2019.

  38. Kalantar, B., Ueda, N., Al-Najjar, H.A.H., Gibril M. B. A., Lay, U.S., Motevalli, A.,"An evaluation of landslide susceptibility mapping using remote sensing data and machine learning algorithms in Iran. ISPRS Annals of the Photogrammetry", Remote Sensing and Spatial Information Sciences, 2019.

  39. Kalantar, B., Ueda, N., Al-Najjar, H.A.H., Moayedi. H., Halin, A.A., Mansor, S.,"UAV and LiDAR image registration: A surf-based approach for ground control points selection", International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, 2019.

  40. Kalantar, B., Ueda, N., Lay, U.S., Al-Najjar, A.H.A., Halin, A.A., "Conditioning factors determination for landslide susceptibility mapping using support vector machine learning",IEEE International Geoscience and Remote Sensing Symposium, 2019.

  41. Fujiwara,Y., Ida, Y., Kanai,S., Kumagai,A., Arai, J., and Ueda, N., "Fast random forest algorithm via incremental upper bound," Proc. of the 28th ACM International Conference on Information and Knowledge Management (CIKM2019) , 2019.

  42. Okita, T., Hachiya,H.,Inoue, S.,and Ueda, N.,"Translation between waves, wave2wave," Proc. of the 22nd International Conference on Discovery Science (DS2019) , 2019.

  43. Fujiwara, Y., Kanai, S., Arai, J., Ida, Y., and Ueda, N., "Efficient data point pruning for one-class SVM," Proc. of Association for the Advancement of Artificial Intelligence (AAAI2019), 2019.

  44. Shimizu, H., Matsubayashi, T., Tanaka, Y., Iwata1, T., Ueda, N., and Sawada, H.,"Improving route traffic estimation by considering staying population," The 21st International Conference on Principles and Practice of Multi-Agent Systems (PRIMA), 2018.

  45. Kalantar, B., Mansor, S., Halin, A. A., Ueda, N., Shafri, H. Z. M. and Zand, M., "A graph-based approach for moving objects detection from UAV videos," Proc. of SPIE Image and Signal Processing for Remote Sensing, Vol.10789, 2018.

  46. Kalantar, B., Ueda, N., AL-Najjar, H. A. H., Idrees, M. O., Motevalli, A. and Pradhan, B., "Landslide susceptibility mapping at dodangeh watershed, Iran, using LR and ANN models in GIS," Proc. of SPIE Earth Resources and Environmental Remote Sensing, Vlo.10790, 2018.

  47. Azeez, O. S., Kalantar, B., Al-Najjar, H. A. H., Halin, A. A., Ueda, N. and Mansor, S., "Object boundaries regularization using the dynamic polyline compression algorithm," The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Science (ISPRS2018),  Vol.XLII-4, pp. 541-546, 2018.

  48. Yonezawa, T., Takeuchi, K., Itoh, T., Sakamura, N., Kishino, Y., Naya, F, Ueda, N. and Nakazawa, J., "Accelerating urban science by crowdsensing with civil officers," Proc. of ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp2018), 2018.

  49. Kishino, Y., Shirai, Y., Takeuchi, K., Suyama, T., Naya, F. and Ueda, N., "Regional Garbage Amount Estimation and Analysis using Car-Mounted Motion Sensor," Proc. of ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp2018), 2018.

  50. Fujiwara, Y., Arai, J., Kanai, S., Ida, Y. and Ueda, N., "Adaptive data pruning for support vector machines," Proc. of IEEE International Conference on Big Data, 2018.

  51. Choffin, B., and Ueda, N.,"Scaling Bayesian optimization up to higher dimensions: A review and comparison of recent algorithms," Proc. of IEEE International Workshop on Machine Learning for Signal Processing (MLSP2018), 2018.

  52. Kimura, A., Gharamani, Z., Takeuchi, K., Iwata, T., and Ueda, N., "Few-shot learning of neural networks from scratch by pseudo example optimization, " Proc. of 29th British Machine Vison Confernece （BMVC), 2018.

  53. Tanaka, Y., Iwata, T, Kurashima, T., Toda, H., and Ueda, N., "Estimating latent people flow without tracking individuals," International Conference on Artificial Intelligence  (IJCAI), July 2018.

  54. Kimura, A., Takahashi, I., Tanaka, M., Yasuda, N., Ueda, N., and Yoshida, N., "Single-epoch supernova classification with deep convolutional neural networks," Proc. US-Japan Workshop on Collaborative Global Research on Applying Information Technology, in conjunction with ICDCS 2017.

  55. Blondel, M., Niculae, V., Otsuka, T., and Ueda, N., "Multi-output polynomial networks and factorization machine, "Proc. Neural Information Processing Systems (NIPS2017), 2017.

  56. Kishino, Y., Takeuchi, K., Shirai, Y., Naya, F., and Ueda, N., "Datafying city: detecting and accumulating sptio-temporal events by vehicle-mounted sensors, "Proc of International Workshop on Smart Cities (IWSC2017), 2017.

  57. Takeuchi, K., Kashima, H., and Ueda, N., "Autoregressive tensor factorization for spatio-temporal predictions," Proc. of IEEE Ineternationl Conference on Data Mining (ICDM2017), 2017.

  58. Fujiwara, Y., Marumo, N., Blondel, M., Takeuchi, K., Kim, H., Iwata, T. and Ueda, N., "Scaling Locally Linear Embedding," In Proc. SIGMOD 2017, pp. 1479-1492, 2017.

  59. Kim, H., Iwata, T., Fujiwara, Y. and Ueda, N., "Read the Silence: Well-Timed Recommendation via Admixture Marked Point Processes," In Proc. AAAI 2017, pp. 132-139, 2017.

  60. Ichimura1, T., Fujita1, K., Yamaguchi, T., Hori1, M., Lalith1, M. and Ueda, N., "AI with Super-computed Data for Monte Carlo Earthquake Hazard Classification," Proc. of the international conference for high performance computing, networking, storage and analysis (SC2017), 2017.

  61. Kimura, A., Takahashi, I., Tanaka, M., Yasuda, N., Ueda, N. and Yoshida, N., "Single-epoch supernova classification with deep convolutional neural networks," The 1st US-Japan Workshop 2017, 2017.

  62. Toda, T., Inoue, S. and Ueda, N., "Mobile activity recognition through training labelswith inaccurate activity segments," 13th Annual International Conference on Mobile and Ubiquitous Systems 2016.(MobiQuious2016), 2016.

  63. Blondel, M., Ishihata, M., Fujino, A. and Ueda, N., "Higher-order factorization machines," Advances in Neural Information Processing Systems (NIPS2016), 2016.

  64. Fujino, A. and Ueda, N., "A semi-supervised AUC optimization method with generative models," IEEE International Conference on Data Mining (ICDM2016), 2016.

  65. Takeuchi, K. and Ueda, N., "Graph regularized non-negative tensor completion for spatio-temporal data analysis," The Second International Workshop on Smart Cities, 2016.

  66. M. Blondel, Fujino, A. and Ueda, N., "Polynomial Networks and Factorization Machines: New Insights and Efficient Training Algorithms," International Conference on Machine Learning (ICML2016), 2016.

  67. Ishiguro, K., Sato, I., Ueda, N., Nakano, M. and Kimura, S., "Infinite plaid models for infinite bi-clustering," Proc. the 27th AAAI Conference on Artificial Intelligence (AAAI2016), 2016.

  68. Ueda, N., Naya, F., Shimizu, H., Iwata, T., Okawa, M. and Sawada, H., "Real-time and proactive navigation via spatio-temporal prediction, "Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2015 ACM International Symposium on Wearable Computers(UbiComp 2015), pp. 1559-1566, 2015.

  69. Inoue, S., Ueda., N., Nohara, Y. and Nakashima, N., "Mobile activity recognition for a whole day: recognizing real nursing activities with big dataset," Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing(UbiComp2015), pp. 1269-1280, 2015.

  70. Baba, Y., Kashima, H., Nohara, Y., Kai, E., Ghosh, P., Islam, R., Ahmed, A., Kuroda, M., Inoue, S., Hiramatsu, T., Kimura, M., Shimizu, S., Kobayashi, K., Tsuda, K., Sugiyama, M., Blondel, M., Ueda, N., Kitsuregawa, M. and Nakashima, N., "Predictive Approaches for Low-Cost Preventive Medicine Program in Developing Countries," Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD2015), pp. 1681-1690, 2015.

  71. Blondel, M., Fujino, A. and Ueda, N., "Convex Factorization Machines," Proceedings of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases(ECML PKDD), Part II, LNAI 9285, pp. 19-35, 2015.

  72. Matsubara, Y., Sakurai, Y., Ueda, N. and Yoshikawa M., "Fast and Exact Monitoring of Co-Evolving Data Streams," 2014 IEEE International Conference on Data Mining(ICDM), pp. 390-399, 2014.

  73. Blondel, M. Fujino, A. and Ueda, N., "Large-scale Multiclass Support Vector Machine Training via Euclidean Projection onto the Simplex," 22nd International Conference on Pattern Recognition (ICPR2014), pp. 1289-1294, 2014.

  74. Nakano, M., Ishiguro, K., Kimura, A., Yamada, T. and Ueda, N., "Rectangular tiling process," Proceedings of The 31st International Conference on Machine Learning (ICML2014), pp. 361–369, 2014.

  75. Blondel, M., Kubota, Y. and Ueda, N., "Online Passive-Aggressive Algorithms for Non-Negative Matrix Factorization and Completion," Proc. 17th International Conference on Artificial Intelligence and Statistics (AISTATS2014), Vol.33, pp. 96-104, 2014.

  76. Ueda, N., Tanaka, Y. and Fujino, A., "Robust Naive Bayes Combination of Multiple Classifications," The Impact of Applications on Mathematics, Proceedings of the Forum of Mathematics for Industry 2013, Springer, pp. 141-156, 2014.

  77. Iwata, T., Hirao, T. and Ueda, N., "Unsupervised Cluster Matching via Probabilistic Latent Variable Models," Proc. the 24th AAAI Conference on Artificial Intelligence (AAAI2013), pp. 445-451, 2013.

  78. Ishiguro, K., Ueda, N. and Sawada, H., "Subset Infinite Relational Models," Proc. International Conference on Artificial Intelligence and Statistics (AISTATS 2012), Society for AI and Statistics, Vo. 22, pp. 547-555, 2012.

  79. Sawada, H., Kameoka, H., Araki, S. and Ueda, N., "Efficient Algorithms for Multichannel Extensions of Itakura-Saito Nonnegative Matrix Factorization," IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2012), pp. 261-264, 2012.

  80. Sun, X., Kashima, H., Tomioka, R. and Ueda, N., "Large Scale Real-life Action Recognition Using Conditional Random Fields with Stochastic Training," 15th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2011), Part II, LNAI 6635, pp. 222–233, 2011.

  81. Sun, X., Kashima, H., Tomioka, R., Ueda, N. and Li, P., "A New Multi-Task Learning Method for Personalized Activity Recognition," 11th IEEE International Conference on Data Mining (ICDM 2011), pp. 1218-1223, 2011.

  82. Sawada, H., Kameoka, H., Araki, S. and Ueda, N., "New Formulations and Efficient Algorithms for Multichannel NMF," 2011 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA 2011), pp. 153-156, 2011.

  83. Aoyama, K., Sawada, H., Ueda, N. and Saito, K., "Fast approximate similarity seach based in degree-reduced neighborhood graphs," Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD 2011), pp. 1055-1063, 2011.

  84. Sawada, H., Kameoka, H., Araki, S. and Ueda, N., "Formulations and Algorithms for Multichannel Complex NMF," IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2011) , pp. 229-232, May, 2011.

  85. Iwata, T., Yamada, T., Sakurai, Y. and Ueda, N., "Online Multiscale Dynamic Topic Models," Proc. the 16th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD 2010), pp. 663-672, 2010.

  86. Sun, X., Kashima, H., Matsuzaki, T. and Ueda, N., "A Robust, Accurate, and Fast Stochastic Gradient Training Method for Modeling Latent-Information in Data," IEEE International Conference on Data Mining (ICDM2010), pp.1067-1072, Sydney, Australia, 2010.

  87. Hachiya, H., Sugiyama, M. and Ueda, N., "Coping with new user problems: Transfer learning in accelerometer-based human activity recognition," NIPS 2010 Workshop on Transfer Learning by Learning Rich Generative Models, 2010.

  88. Fujino, A., Ueda, N. and Nagata, M., "A Robust Semi-supervised Classification Method for Transfer Learning," Proc. of the 19th ACM international conference on Information and knowledge management (CIKM 2010), pp. 379-388, 2010.

  89. Ishiguro, K., Iwata, T., Ueda, N. and Tenenbaum, J. B., "Dynamic Infinite Relational Model for Time-varying Relational Data Analysis," Advances in Neural Information Processing Systems 23 (NIPS2010), 2010.

  90. Aoyama, K., Watanabe, S., Sawada, H., Minami, Y., Ueda, N. and Saito, K.,"Fast Similarity Search On A Large Speech Data Set With Neighborhood Graph Indexing," International Conference on Acoustics, Speech, and Signal Processing(ICASSP2010), pp. 5358-5361, 2010.

  91. Usui, S., Kamiji, N. L., Taniguchi, T. and Ueda N., "RAST: A Related Abstract Search Tool," International Conference on Neural Information Processing (ICONIP2009), Part II, LNCS 5864, pp. 189–195, 2009.

  92. Iwata, T., Yamada, T. and Ueda, N., "Modeling Social Annotation Data with Content Relevance using a Topic Model," Advances in Neural Information Processing Systems (NIPS2009), pp. 835-843, 2009.

  93. Iwata, T., Watanabe, S., Yamada, T. and Ueda, N., "Topic Tracking Model for Analyzing Consumer Purchase Behavior," Proc. of 21st International Joint Conference on Artificial Intelligence (IJCAI-09), pp. 1427-1432, 2009.

  94. Iwata, T., Yamada, T. and Ueda, N., "Probabilistic Latent Semantic Visualization: Topic Model for Visualizing Documents," Proc. of 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Minig (KDD2008), pp.363-371, 2008.

  95. Ishiguro, K., Yamada, T. and Ueda, N., "Simultaneous Clustering and Tracking Unknown Number of Objects," Proc. of the 19th IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR08), pp. 1-8, 2008, [CVPR08]

  96. Usui, S., Naud, A., Ueda, N. and Taniguchi, T., "3D-SE Viewer: A Text Mining Tool based on Bipartite Graph Visualization," 20th International Joint Conference on Neural Networks (IJCNN'07), 2007.

  97. Kuwata, S. and Ueda, N., "One-shot Collaborative Filtering," IEEE CIDM2007, Vol.1, No.1, pp.300-307, 2007.

  98. Fujino, A., Ueda, N. and Saito, K., "Semi-superviged learning for multi-component data classification,"Proc. of International Joint Conference on Artificial Intelligence (IJCAI2007), pp. 2754-2759, 2007.

  99. Kuwata, S. and Ueda N., "One-shot collaborative filtering," Proc. of IEEE Symposium on Compututational Intelligence and Data Mining (CIDM2007), pp. 300-307, 2007.

  100. Kemp, C., Tenenbaum, J. B., Griffiths, T. L., Yamada, T. and Ueda, N., "Learning systems of concepts with an infinite relational model," Proc. of the 21st National Conference on Artificial Intelligence(AAAI-06), pp. 381-388, 2006.

  101. Iwata, T., Saito, K. and Ueda, N., "Visual nonlinear discriminant analysis for classifier design," Proc. of the 14th European Symposium on Artificial Neural Networks (ESANN2006), pp.283-288, 2006.

  102. Usui, S., Palmes P., Nagata, K., Taniguchi, T. and Ueda, N., "Extracting Keywords from Research Abstracts for the Neuroinformatics Platform Index Tree," Proc. of International Joint Conference on Neural Networks (IJCNN2006), pp. 5045-5050, 2006.

  103. Ueda, N., "Bayesian probabilistic models for data partitioning and their applications," Proc. of the 17th International Symposium on Mathematical Theory of Networks and Systems (MTNS2006), MoP08.3, pp. 364-369, 2006.

  104. Saito, K. and Ueda, N., "Filtering Search Engine Spam based on Anomaly Detection Approach," Proc. of the KDD2005 Workshop on Data Mining Methods for Anomaly Detection, pp.62-67, 2005.

  105. Usui, S., Palmes, P., Nagata, K., Taniguchi, T. and Ueda, N., "Relevance keyword extraction, ranking, and organization for the neuroinformatics platform, Proc. of International Conference on Biological Computation," Proc. of BIOCOMP, 2005.

  106. Fujino, A., Ueda, N. and Saito, K., "A Classifier design based on combining multiple components by maximum entropy principle," Proc. of the 2nd Asia Information Retrieval Symposium (AIRS2005), LNCS 3689, pp. 423–438, 2005.

  107. Kimura, M., Saito, K. and Ueda, N., "Multinomial PCA for extracting major latent topics from document streams," Proc. International Joint Conference on Neural Networks ( IJCNN2005), Vol. 1, pp.238-243, 2005.

  108. Fujino, A., Ueda, N. and Saito, K., "A hybrid generative/discriminative approach to semi-supervised classifier design," Proc. of the 20th National Conference on Artificial Intelligence (AAAI-05), pp. 764-769, 2005.

  109. Inoue, M. and Ueda, N., "Retrieving lightly annotated images using image similarities," ACM Symposium on Applied Computing (SAC), Special Track on Information Access and Retrieval (IAR)Santa Fe, March 14-17, pp.1031-1037, 2005.

  110. Iwata, T., Saito, K., Ueda, N., Stromsten, S., Griffiths, T. L. and Tenenbaum, J. B., "Parametric Embedding for Class Visualization, " Advances in Neural Information Processing Systems 17 (NIPS2004), pp. 617-624, 2005.

  111. Kimura, M., K. Saito and N. Ueda, "Modeling share dynamics by extracting competition structure," Proc. of the 5th International Conference on Complex Systems, p. 72, 2004.

  112. Kaneda, Y., Ueda, N. and Saito, K., "Extended Parametric Mixture Model for Robust Multi-labeled Text Categorization," Proc. of the 8-th International Conference on Knowledge-Based Intelligent Information & Engineering Systems, Vol. 3214 of Lecture Notes in Computer Science, pp. 616-623, 2004.

  113. Ueda, N. and Saito, K., "Simplex mixture models for multi-topic text," In Science of Modeling, ISM Report on Research and Education No.17, The Institute of Statisitical Mathematics, pp. 380-381, 2003.

  114. Kimura, M., Saito, K. and Ueda, N., "Modeling share dynamics by extracting competition structure," In Science of Modeling, ISM Report on Research and Education No.17, The Institute of Statisitical Mathematics, pp.366-367, 2003.

  115. Yamada, T., Saito, K. and Ueda, N., "Cross-entropy based embedding for relational data," International Conference on Machine Learning (ICML2003), pp. 832-839, 2003.

  116. Watanabe, S., Minami, Y., Nakamura, A. and N. Ueda, "Bayesian acoustic modeling for spontaneous speech recognition," IEEE Workshop on Spontaneous Speech Processing and Recognition (SSPR03), pp. 47-50, 2003.

  117. Watanabe, S., Minami, Y., Nakamura, A. and Ueda, N., "Application of variational bayesian estimation and clustering to acoustic model adaptation," IEEE International Conference on Acoustic, Speech, and Signal Processing (ICASSP03), Vol. 1, pp. 568-571, 2003.

  118. Kimura, M., Saito, K. and Ueda, N., "Modeling of growing networks with directional attachment and communities," European Symposium on Artificial Neural Networks (ESANN03), pp. 15-20, 2003.

  119. Kimura, M., Saito, K. and Ueda, N., "Modeling of growing networks with communities," IEEE International Workshop on Neural Networks for Signal Processing (NNSP2002), pp. 189-198, 2002.

  120. Watanabe, S., Minami, Y., Nakamura, A. and Ueda, N., "Application of Variational Bayesian Approach to Speech Recognition," Proc. of Advances in Neural Information Processing Systems 15 (NIPS15), MIT Press, pp. 1261-1268, 2002.

  121. Watanabe, S., Minami, Y., Nakamura, A. and Ueda, N., "Constructing shared-state HMMs based on a Bayesian approach," International Conference on Spoken Language Processing (ICSLP02), Vol. 4, pp. 2669-2672, 2002.

  122. Ueda, N. and Saito, K., "Parametric mixture models for multi-topic text," Neural Information Processing Systems 15(NIPS15), MIT Press, pp. 737-744, 2002.

  123. Ueda, N. and Saito, K., "Singleshot detection of multi-category text using parametric mixture models," ACM SIG Knowledge Discovery and Data Mining (SIGKDD2002), pp. 626-631, 2002.

  124. Inoue, M. and Ueda, N., "HMMs for both labeled and unlabed time series data," IEEE Neural Networks for Signal Processing (NNSP2001), pp. 93-102, 2001.

  125. Ueda, N. and Ghahramani, Z., "Optimal model inference for Bayesian mixture of experts," IEEE Neural Networks for Signal Processing (NNSP2000), pp. 145-154, 2000.

  126. Ueda, N., Nakano, R., Ghahramani, Z. and Hinton, G. E., "Pattern classification using a mixture of factor analyzers," IEEE Neural Networks for Signal Processing (NNSP99), pp. 525-534, 1999.

  127. Ueda, N., Nakano, R., Ghahramani, Z. and Hinton, G. E., "SMEM algorithm for mixture models," Neural Information Processing Systems 11 (NIPS11), pp. 599-605, 1999.

  128. Ueda, N., Nakano, R., Ghahramani, Z. and Hinton, G. E., "Split and merge EM algorithm for improving Gaussian mixture density estimates," IEEE Neural Networks for Signal Processing (NNSP98), pp. 274-283, 1998.

  129. Ueda, N. and Nakano, R., "Combining discriminant-based classifiers using the minimum classification error discrimininant,"IEEE Neural Networks for Signal Processing (NNSP97), pp. 365-374, 1997.

  130. Suzuki, S. and Ueda, N., "Self-organization of feature columns and its application to object classification," Proceedings of International Conference on Neural Information Processing (ICONIP97), pp. 1166-1169, 1997.

  131. Ueda, N. and Nakano, R, "Generalization error of ensemble estimators," Proceedings of International Conference on Neural Networks (ICNN96), pp. 90-95, 1996.

  132. Ueda, N. and Nakano, R, "Deterministic annealing variant of the EM algorithm," Neural Information Processing Systems 7 (NIPS7), MIT Press, Cambridge MA, pp. 545-552, 1995.

  133. Ueda, N. and Nakano, R, "A new maximum likelihood training and application to probabilistic neural networks," Proceedings of International Conference on Artificial Neural Networks (ICANN95), pp. 497-504, 1995.

  134. Ueda, N. and Nakano, R, "Estimating expected error rates of neural network classifiers in small sample size situations: A comparison of cross-validation and bootstrap," International Conference on Neural Networks (ICNN95), pp. 101-104, 1995.

  135. Ueda, N. and Nakano, R, "Mixture density estimation via EM algorithm with deterministic annealing," Proceedings of IEEE Neural Networks for Signal Processing (NNSP94), pp. 69-77, 1994.

  136. Ueda, N. and Nakano, R, "A new learning approach based on equidistortion principle for optimal vector quantizer Design," Proceedings of IEEE Neural Networks for Signal Processing (NNSP93), pp. 362-371, 1993.

  137. Ueda, N. and Nakano, R, "Competitive and selective learning method for designing optimal vector quantizers," Proceedings of IEEE International Conference on Neural Networks (ICNN93), pp. 1444-1450, 1993.

  138. Ueda, N. and Mase, K., "Tracking moving contours using energy-minimizing elastic contour models,"Proceedings of European Conference on Computer Vision (ECCV92), pp. 453-457, 1992.

  139. Suzuki, S. and Ueda, N., "Robust vectorization using graph-based thnning and reliability-based line approximation," Proceedings of IEEE Conference on Computer Vision (CVPR91), pp. 494-500, 1991.

  140. Ueda, N. and Suzuki, S., "Automatic shape model acquisition using multiscale segment matching," Proceedings of International Conference on Pattern Recognition (ICPR90), pp. 897-902, 1990.

  141. Ogawa, H. Kawada, E. and Ueda, N., "Application of image processing equipment with multiprocessors to line-drawing recognition," Proceedings of SPIE-845, pp. 97-103, 1987.

  142. Okudaira, M. Ueda, N. and Aoki, U., "Image enhancement of handwritten drawings and their recognition followed by interactive processing," Proceedings of SPIE-707, pp. 42-50, 1986.

招待解説論文・記事
  1. Ueda, N, Tanaka, Y. and Fujino,A., "Bayesian Meta-Learning and its Application to High-Level Real Nursing Activity Recognition Using Accelerometers"　Springer, 2014.

  2. 上田修功, "ベイズモデルに基づく関係データ解析技術," 電子情報通信学会誌, H26年5月号特集：データを読み解く技術──ビッグデータ, e-サイエンス, 潜在的ダイナミクス──

  3. Ueda, N,　"Cyber Physical Systems, Creating New Value from Sensor Network Information, NII Today, No.45, 2013.

  4. Fujino, A., Ueda, N. and Nagata, M., "Adaptive semi-supervised learning on labeled and unlabeled data with different distributions," Knowledge and Information Systems(KAIS), Vol. 37, Issue 1, pp. 129-154, Springer, 2013.

  5. 上田修功, "汎用学習手法 EMアルゴリズム," 計測自動制御学会誌, Vol. 44, No. 5, pp. 333-338, 2005.

  6. 上田修功, "Webサイエンス研究が目指すもの," 技術情報誌OHM, ．Vol. 91, No. 10, pp. 6-7, 2004.

  7. 上田修功, "ベイズ学習のアルゴリズム -高次元積分の近似手法-," 人工知能学会誌, 特集(統計モデルと学習の数理), Vol. 19, No. 6, 2004.

  8. 上田修功, 斉藤和巳, "多重トピックテキストの確率モデル,"（全２回） 情報処理学会誌, Vol. 45, No. 2, 3, 2004.

  9. 上田修功, "確率モデルと統計的学習," Computer Today, No.114, 2003.

  10. 上田修功, "ベイズ学習,"（全４回）電子情報通信学会誌, Vol. 85, No. 4,6,7,8, 2002.

  11. 上田修功, "アンサンブル学習," 計測自動制御学会誌, Vol. 41, pp.248, 2002.

  12. 上田修功, "ベイズ学習法の 最前線 -変分ベイズ法-," 情報処理学会誌, Vol. 42, No. 1, 2001.

  13. 上田修功, "統計的学習研究探訪," 情報処理学会誌, Vol. 41, No. 6, pp. 730-733, 2000.

  14. 上田修功, 中野良平, "確定的アニーリングEMアルゴリズム," 計測と制御, Vol. 38, No. 7, pp. 444-449, 1999.

  15. 上田修功, 中野良平, "確定的アニーリング -もうひとつのアニーリング-," 人工知能学会誌, Vol. 12, No. 5, pp. 689-697, 1997.

  16. Ueda, N. and Nakano, R., "Competitive and selective learning method for vector quantizer design -Equidistortion principle and its algorithm," Systems and computers in Japan, Scripta Technica, Inc., Vol. 26, No. 9, pp. 34-49, 1995.

  17. Ueda, N., Mase, K. and Suenaga, Y., "A contour tracking method using elastic contour model and energy minimizing approach," Systems and computers in Japan, Scripta Technica, Inc., Vol. 24, No. 8, pp. 59-70, 1993.

  18. Ueda, N. and Suzuki, S., "Automatic shape model acquisition based on a generalization of convex/concave structure," Systems and computers in Japan, Scripta Technica, Inc. Vol. 23, No. 1, pp. 89-100, 1992.

  19. Ueda, N. and Suzuki, S., "A matching algorithm of deformed planar curves using multiscale convex/concave structures," Systems and computers in Japan, Scripta Technica, Inc. Vol. 22, No. 5, pp. 94-104, 1991.

  20. 間瀬 茂, 上田修功, "モルフォロジーと画像解析I,"電子情報通信学会誌, Vol. 64, No. 2, pp. 166-174, 1991.

  21. 上田修功, 間瀬 茂, "モルフォロジーと画像解析 II," 電子情報通信学会誌, Vol. 64, No. 3, pp. 271-279, 1991


NTT機関論文誌および研究実用化報告

  1. 上田修功, "挑戦する研究者たち," NTT技術ジャーナル インタビュー記事, Vol.29 No.9, pp. 38-43, 2017.

  2. 上田修功, "挑戦する研究者たち," NTT技術ジャーナル インタビュー記事, Vol.25 No.9, pp. 40-43, 2013.

  3. 上田修功, "ビッグデータを活かす機械学習技術," NTT技術ジャーナル 特集 NTT R&Dフォーラム2013 ワークショップ, Vol.25, No.4, pp 31-35, 2013.

  4. Ueda, N., "Communication Science for the Big Data Era," NTT Technical Review, Vol. 10, No. 11, 2012.

  5. 藤野昭典, 上田修功, 斎藤和巳, "テキスト自動分類のための半教師あり学習技術," NTT技術ジャーナル, Vol. 19, No. 6, pp. 26-28, 2007.

  6. Ueda, N., "Web Science Research," NTT Technical Review}, Vol. 3, No. 3, pp. 12-14, 2005.

  7. 上田修功, "Webサイエンス研究," NTT技術ジャーナル, Vol. 16, No. 6, pp. 22, 2004.

  8. 上田修功, 中野良平, "最適ベクトル量子化を実現する淘汰型競 合学習法,"NTT R&D, Vol.42. No.6, 1993.

  9. 中野良平, 上田修功, 斎藤和巳, 山田武士, "学習機構の 研究,"NTT R&D, Vol. 42, No. 9, pp. 1175-1184, 1993.

  10. 上田修功, 間瀬健二, 末永康仁, "エネルギー最小化弾性モデル による動輪郭追跡法," NTT R&D, Vol. 42, No. 4, pp. 477-486, 1993.

  11. 上田修功, 鈴木智, "多重スケール凹凸構造マッチ ング, " NTT R&D, Vol. 40, No. 3, pp.399-406, 1991.

  12. 河田悦生, 上田修功, 小川 博, 小杉 信, 手書き図面の図形認識法," NTT研究実用化報告第37巻, 第3号, pp. 217-223, 1988.

  13. 上田修功, 名倉正計, 星野肇夫, 森 克己, "手書き図面の画質改善法," 研究実用化報告第37巻, 第3号, pp. 211-216, (1988).

登録特許
  1. 特願第2018-006711, 特許第6988504号,“データ生成装置、データ作成方法及びプログラム”

  2. 特願第2018-006710, 特許第6988504号,“データ生成装置、誘導モデル学習装置、誘導推定装置、誘導推定方法及びプログラム”

  3. 特願2018-060685, 特許第06813527号, “ 推定装置、推定方法、及びプログラム.”

  4. 特願2017-208508, 特許第06723967号, “ 推定装置、推定方法、及びプログラム.”

  5. 特願2017-039148, 特許第06757280号, “ パラメータ出力装置、パラメータ出力方法及びプログラム.”

  6. 特願2016-239885, 特許第06389836号, “解析装置、方法、及びプログラム.”

  7. 特願2016-151446, 特許第06522561号, “予測装置、予測方法、及びプログラム.”

  8. 特願2016-100885, 特許第06660248号, “目的変数予測装置、方法及びプログラム.”

  9. 特願2016-029201, 特許第06389836号, “解析装置、方法、及びプログラム.”

  10. 特願2016-004441, 特許第06482481号, "2値分類学習装置, 2値分類装置, 方法, 及びプログラム."

  11. 特願2015-233199, 特許第06498107号, “分類装置、方法、及びプログラム.”

  12. 特願2015-173931, 特許第06509685号, “移動速度推定装置、方法、及びプログラム.”

  13. 特願2015-166038, 特許第06464057号, “予測装置、方法、及びプログラム.”

  14. 特願2015-151299, 特許第06397380号, “時空間変数予測装置及びプログラム.”

  15. 特願2015-124687, 特許第06343591号, “部分行列領域抽出装置、方法、及びプログラム.”

  16. 特願2015-128917, 特許第06283331号, “流れ推定装置、予測装置、及びプログラム.”

  17. 特願2014-146172, 特許第06161581号, “モデルパラメータ推定装置、方法、及びプログラム.”

  18. 特願2013-139244, 特許第05728534号, “統合識別器学習装置、統合識別器学習方法、及び統合識別器学習プログラム.”

  19. 特願2012-094780, 特許第05710539号, “グラフ生成装置、方法、及びプログラム.”

  20. 特願2012-094117, 特許第05746086号, “クラスタリング装置、方法、及びプログラム.”

  21. 特願2011-240542, 特許第05851205号, “クラスタリング装置、方法、及びプログラム.”

  22. 特願2011-110463, 特許第05520883号, “信号分解装置、方法、及びプログラム.”

  23. 特願2009-195882, 特許第05281990号, “クラスタリング装置、クラスタリング方法、およびプログラム.”

  24. 特願2002-204434, 特許第3868344号, "テキストの多重トピックス抽出方法および装置, テキストの多重トピックス抽出プログラム, ならびに該プログラムを記録した記録媒体."


著書・訳書
  1. 坂内正夫 監修, 上田修功　著, 角川インターネット講座 "ビッグデータを開拓せよ 解析が生む新しい価値," 第7巻 第1部 第5章 データに語らせる科学, KADOKAWA, 2015.

  2. 石井健一郎, 上田修功　共著, "続・わかりやすい　パターン認識 ―教師なし学習入門―," オーム社, 2014

  3. 上田修功　訳：サイエンスパレットシリーズ "統計学", 丸善出版, 2014
    (原著："Statistics: VSI," David J. Hand著、"Statistics," Oxford Univ. Press）

  4. 上田修功　"科学事典（第２版）,　変分ベイズ法"執筆, 編集代表　広中平祐　丸善株式会社, 2007.

  5. 樺島祥介, 上田修功 共著, "統計科学のフロンティア11, 計算統計I (第III部)," 岩波書店, 2003.

  6. 石井健一郎, 上田修功, 前田英作, 村瀬洋 共著, "わかりやすいパターン認識," オーム社, 1998.

  7. 電子情報通信学会 編, "電子情報通信ハンドブック," 第3.2編(パターン認識理論)一部執筆, 1998.

  8. 甘利, 外山 編, "脳科学ハンドブック," 第9章(学習ベクトル量子化)執筆, 朝倉書店, 1995.

  9. B. K. P. Horn 著, "ROBOT VISION (The MIT Press)," 共訳, 朝倉書店, 1993.

https://doi.org/10.1007/978-3-031-36021-3_45

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