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Publications

  1. Journal Papers
  2. arXiv Papers
  3. International Conference Papers
  4. Invited Papers & Tutorial Articles
  5. Internal Invited Technical Reports at NTT
  6. Book & Book Chapters
Journal Papers
  1. 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; https://doi.org/10.1785/0220210099.

  2. 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.

  3. 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.

  4. 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.

  5. Kalantar, B., Ueda, N.,Saeidi, V.,Ahmadi, K.,Halin, A.A., and Shabani, F.,"Landslide susceptibility mapping: Machine and ensemble learning based on remote sensing big data. Remote Sensing. 2020, 12(11), 1737; https://doi.org/10.3390/rs12111737.

  6. 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.

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

  8. 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; https://doi.org/10.3390/rs12071081, 2020.

  9. 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.

  10. 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; https://doi.org/10.3390/w11091909.

  11. 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.

  12. 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.

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

  14. Ueda, N., and Naya, F., "," APSIPA Transactions on Signal and Information Processing, Vol.7, No.4, 2018.

  15. 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.

  16. 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.

  17. 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.

  18. Ueda, N., "Proactive People-flow Navigation Based on Spatio-temporal Prediction," Japanese Journal Applied Statistics, Vol.45, No.3, pp.89-104, 2016, (invited).

  19. 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.

  20. 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.

  21. Ueda, N., "Spatio-temporal prediction and its application to proactive people-flow naviation," Journal of the Institute of Image Electronics Engeneers of Japan (in Japanese), Vol.45, No.1, pp4-11, 2016.

  22. 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.

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

  24. 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.

  25. Tanaka, Y., Ueda, N. and Tanaka, T., "Bayesian classifier based on class-specific feature selection," Transactions of IEICEJ, Vol.J96-D-DII, No.11, pp.2755-2764, 2013, (in Japanese).

  26. 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]

  27. 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]

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

  29. 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).

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

  31. 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.

  32. Fujino, A., Ueda, N. and Nagata, M., "Robust Semi-supervised Learning for Labeled Data Selection Bias," Transaction of Information Processing Society of Japan, Vol.4, No.2, pp. 31-42, 2011, (in Japanese).

  33. 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, pp. 1668-1677, 2011. .[IEEE Xplore] [DOI link] [IEEE Copyright Notice]

  34. Fujino, A., Ueda, N. and Nagata, M., "Robust semisupervised learning for data selection bias," Transaction of Information Processing Society of Japan, Vol.2010-MPS-80 No.8, 2010, (in Japanese).

  35. Ishiguro, K., Iwata, T., and Ueda, N.,"Dynamic Infinite Relational Model for Time-dependent Relational Data Analysis," Transaction of Information Processing Society of Japan, Vol.3, No.1, 1-12, 2010, (in Japanese).

  36. Iwata, T., Watanabe, S., Yamada, T., and Ueda, N., "Topic Tracking Model for Purchase Behavior Analysis," Transactions of IEICEJ, Vol.J93-D, No.6, pp.978-987, 2010, (in Japanese).

  37. Iwata, T., Tanaka, T., Yamada, T., and Ueda, N., "Model Learning when Distributions Differ over Time," Transactions of IEICEJ, Vol.J92-D, No.3, 361-370, 2009, (in Japanese).

  38. Iwata, T., Yamada, T., and Ueda, N., "Visualizing Documents based on Topic Models," Journal of Information Processing Society of Japan, Vol.50, No.6,1649-1659, 2009, (in Japanese).

  39. Kawamae, N., Sakano, H., Yamada, T., and Ueda, N., "Collaborative filtering focusing on the dynamics and precedence of user preference," Transactions of IEICEJ, (D-II), Vol. J92-DII, No.6, pp. 767-776, 2009, (in Japanese).

  40. 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), 30(3), 424-437,2008. [IEEE Xplore] [DOI link] [IEEE Copyright Notice]

  41. Ueda, N., Yamada, T., and Kuwata, S., "Co-clustering Discrete Data Based on the Dirichlet Process Mixture Model," Transaction of Information Processing Society of Japan, Vol.1 No.1 (pp. 60-73), 2008, [Transaction of Information Processing Society of Japan],(in Japanese).

  42. Iwata, T., Yamada, T., and Ueda, N., "Collaborative filtering efficiently using purchase orders," Transaction of Information Processing Society of Japan, Vol.49, No.SIG4 (TOM20), pp. 125-134, 2008, (in Japanese).

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

  44. Kuwata, S., and Ueda, N., "One-shot Collaborative Filtering," Transaction of Information Processing Society of Japan, Vol.48, No.SIG_15(TOM_18), pp. 153-162, 2007, [Transaction of Information Processing Society of Japan], (in Japanese).

  45. Kawamae, N., Yamada, T., and Ueda, N., "Personalized Ranking by Identifying, Relative Innovators," FIT2007 Letters, Vol.6, pp.99-102, 2007.

  46. 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.

  47. Fujino, A., Ueda, N., and Saito, K., "Semi-supervised Learning of Multi-class Classifiers for Multi-component Data," Transaction of Information Processing Society of Japan, Vol.48, No.SIG_15(TOM_18), pp. 163-175, 2007, [Transactions of IPSJ.], (in Japanese)

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

  49. 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].

  50. 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: 2536-2556, 2007.

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

  52. Ueda, N. and Yamada, T., "Nonparametric Bayes," Journal of Japanese Applied Mathematics Vol.17, No.3, pp.196-214, 2007.

  53. Fujino A., Ueda, N., and Saito, K., "Text Classification by Effectively Using Additional Information Based on Maximum Entropy Principle (Information Retrieval)," Transaction of Information Processing Society of Japan, Vol.47, No.10, pp. 2929-2937, 2006, [ Transactions of IPSJ.], (in Japanese).

  54. Fujino, A., Ueda, N., and Saito, K., "A hybrid generative/discriminative classifier design for semi-supervised learning," Journal of JSAI,Vol.21, No.3, pp.301-309, 2006, (in Japanese).

  55. 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].
  56. 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]

  57. Ueda, N., "Ensemble Learning," Transactions of IPSJ, CVIM-1036, (invited) Vol.46, No.SIG15(CVIM 12), pp. 11-20, 2005, [Transaction of Information Processing Society of Japan], (in Japanese)

  58. Iwata, T., Saito, K., Ueda, N., Stromsten, S., Griffiths, T., and Tenenbaum, J., "Parametric Embedding for Class Visualization," Neural Computation, vol.46,no.9, pp. 2337-2346, 2005, (in Japanese).

  59. Fujino, A., Ueda, N., and Saito, K., "Semi-supervised learning on hybrid generative/discriminative models," FIT2005 Letters, 2005, (in Japanese).

  60. Iwata, T., Saito, K., and Ueda, N., "Visualization via posterior preserving embedding," FIT2004 Letters, Vol. 3, pp. 119-120, 2004, (in Japanese).

  61. Kaneda, Y., Saito, K., and Ueda, N., "Automatic extraction of correspondences between document taxonomies," FIT2004 Letters, Vol. 3, pp.121-122, 2004, (in Japanese).

  62. Kaneda, Y. and Ueda, N., "A Robust text data clustering method for high-dimensional data," FIT2004 Letters, Vol. 3, pp. 123-124, 2004, (in Japanese).

  63. Fujino, A., Ueda, N., and Saito, K., "Relevance feedback with cross validation," FIT2004, Vol. 3, pp. 53-54, 2004, (in Japanese).

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

  65. 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.

  66. 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.

  67. Ueda, N., and Saito, K., "Parametric Mixture Models for Multi-Topic Text," Transactions of IEICEJ, (D-II), Vol. J87-DII, No.3, pp. 872-883, 2004, (in Japanese)

  68. 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.

  69. Kimura, M., Saito, K., and Ueda, N., "Modeling of growing networks with directional attachment and communities," Transactions of IEICEJ, Vol. J86-DII, No, 10, pp. 1468-1479, 2003, (in Japanese).

  70. Ueda, N. and Saito, K., "Multi - topic Text Model for Topic - based Text Retrieval," Transaction of Information Processing Society of Japan Vol. 44, No. SIG14(TOM9), pp. 1-8, 2003, [Transaction of Information Processing Society of Japan] (in Japanese).

  71. Yamada, T., Saito, K., and Ueda, N., "Embedding networks data based on cross-entropy minimization," Transaction of Information Processing Society of Japan Vol. 44, No. 9, pp. 2401-2408, 2003, [Transaction of Information Processing Society of Japan] (in Japanese).

  72. 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.

  73. Watanabe, S., Minami, Y., Nakamura, A., and Ueda, N., "Selection of Shared-States Hidden Markov Model Structure Using Bayesian Criterion," Transactions of IEICEJ, Vol. J86-DII, No. 6, pp. 776-786, 2003, (in Japanese).

  74. 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.

  75. Inoue, M. and Ueda, N., "Use of Unlabeled Time Series Data in Hidden Markov Models," Transactions of IEICEJ, Vol. J86-DII, No. 2, pp. 173-183, 2003 (in Japanese).

  76. Ueda, N., "Variational Bayesian Learning for Optimal Model Search," Journal of Japanese Society for Artificial Intelligence, Vol.16, No.2, 2001, (in Japanese).

  77. Suzuki, S. and Ueda, N., "Adaptive clustering method using modular learning architecture," Transactions of IEICEJ, Vol. J83_DII, No. 6, pp. 1529-1538, 2000, (in Japanese).

  78. 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.

  79. Suzuki, S., and Ueda, N., "Adaptive clustering method using modular learning architecture," Transactions of IEICEJ, Vol. J83_DII, No. 6, pp. 1529-1538, 2000, (in Japanese).

  80. 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.

  81. 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. 133-140, 2000.

  82. 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.

  83. Ueda, N. and Nakano, R., "Probabilistic Mixture Subspace Method," Transactions of IEICE, Transactions of IEICEJ, Vol. J82-DII, No. 12, pp. 2394-2401, 1999, (in Japanese).

  84. Ueda, N. and Nakano, R., "EM Algorithm with Split and Merge Operations for Mixture Models," Transactions of IEICE, Transactions of IEICEJ, Vol. J82-DII, No. 5, pp. 930-940, 1999, (in Japanese).

  85. Ueda, N., "Optimum Linear Combination of Neural Network Classifiers Based on the Minimum Classification Error Criterion," Transactions of IEICEJ, Vol. J82_DII, No. 3, pp. 522-530, 1999, (in Japanese).

  86. Ueda, N. and Nakano, R., "Deterministic Annealing EM Algorithm," Neural Networks, Vol. 11, No. 2, pp. 271-282, (1998).

  87. Ueda, N. and Nakano, R., "Analysis of Generalization Error on Ensemble Learning," Transactions of IEICEJ, Vol. J80-DII, No. 9, pp. 2512-2521, 1997, (in Japanese).

  88. Ueda, N. and Nakano, R., "Deterministic Annealing EM Algorithm," Transactions of IEICEJ, Vol. J80-DII, No. 1, pp. 267-276, 1997, (in Japanese).

  89. 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.

  90. 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.

  91. Ueda, N. and Nakano, R., "Competitive and Selective Learning method for Vector Quantizer Design - Equidistortion Principle and Its Algorithm -," Transactions of IEICEJ, Vol. J77-DII, No. 11, pp. 2265-2278, 1994, (in Japanese).

  92. 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).

  93. 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.

  94. Ueda, N., Mase K., and Suenaga Y., "A Contour Tracking Method Using Elastic Contour Model and Energy Minimization Approach," Transactions of IEICEJ, Vol. J75-DII, No. 1, pp. 111-120, 1992, (in Japanese).

  95. Ueda, N. and Suzuki, S., "Automatic Shape Model Acquisition Based on A Generalization of Convex/Concave Structure," Transactions of IEICEJ, Vol. J74-DII, No. 2, pp. 220-229, 1991, (in Japanese).

  96. Ueda, N. and Suzuki, S., "A Deformed Line-Drawing Matching Algorithm Using Multiscale Convex/Concave Structures," Transactions of IEICEJ, Vol. J73-DII, No. 7, pp. 992-1000, 1990, (in Japanese).

  97. Ueda, N., Nagura, M., Kosugi, M., and Mori, K., "Image Enhancemen Method for Law Quality Drawings," Transactions of the Institute of Television Engineers of Japan, Vol. 42, No. 8, pp. 831-836, 1988, (in Japanese).

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arXiv Papers

  1. arXiv:1802.03039
     [pdf, other]
    stat.ML
    Few-shot learning of neural networks from scratch by pseudo example optimization
    Authors: Akisato Kimura, Zoubin Ghahramani, Koh Takeuchi, Tomoharu Iwata, Naonori Ueda

  2. arXiv:1711.11526
     [pdf, other]
    astro-ph.IM
    doi
    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
     [pdf, other]
    stat.ML
    Multi-output Polynomial Networks and Factorization Machines
    Authors: Mathieu Blondel, Vlad Niculae, Takuma Otsuka, Naonori Ueda

  4. arXiv:1609.03249
     [pdf, ps, other]
    astro-ph.IM
    doi
    10.1093/pasj/psw096
    Machine-learning Selection of Optical Transients in Subaru/Hyper Suprime-Cam Survey
    Authors: Mikio Morii, Shiro Ikeda, Nozomu Tominaga, Masaomi Tanaka, Tomoki Morokuma, Katsuhiko Ishiguro, Junji Yamato, Naonori Ueda, Naotaka Suzuki, Naoki Yasuda, Naoki Yoshida

  5. arXiv:1607.08810
     [pdf, other]
    stat.ML
    Polynomial Networks and Factorization Machines: New Insights and Efficient Training Algorithms
    Authors: Mathieu Blondel, Masakazu Ishihata, Akinori Fujino, Naonori Ueda

  6. arXiv:1607.07195
     [pdf, other]
    stat.ML
    Higher-Order Factorization Machines
    Authors: Mathieu Blondel, Akinori Fujino, Naonori Ueda, Masakazu Ishihata

  7. arXiv:1409.4757
     [pdf, other]
    cs.LG
    Collapsed Variational Bayes Inference of Infinite Relational Model
    Authors: Katsuhiko Ishiguro, Issei Sato, Naonori Ueda

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

  2. 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.

  3. 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).

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

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  9. 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.

  10. 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.

  11. 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.

  12. 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.

  13. 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

  14. 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.

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

  16. 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.

  17. 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.

  18. 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.

  19. 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.

  20. 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.

  21. 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.

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

  23. 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.

  24. 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.

  25. 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.

  26. 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.

  27. zeez, 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.

  28. 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.

  29. 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.

  30. 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.

  31. 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.

  32. 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.

  33. 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.

  34. 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.

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

  36. 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.

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

  38. 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.

  39. 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.

  40. 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.

  41. 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.

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

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

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

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

  46. 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.

  47. 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.

  48. 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.

  49. 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.

  50. 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.

  51. 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.

  52. 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.

  53. 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.

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

  55. 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.

  56. 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.

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

  58. 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.

  59. 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.

  60. 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.

  61. 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.

  62. 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 (WASPAA2011), pp. 153-156, 2011.

  63. 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. 

  64. 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.

  65. 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.

  66. 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.

  67. 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.

  68. 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.

  69. 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.

  70. 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.

  71. 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.

  72. 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.

  73. 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.

  74. 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.

  75. 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]

  76. 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.

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

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

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

  80. 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.

  81. 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.

  82. 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.

  83. 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.

  84. 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.

  85. 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.

  86. 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.

  87. 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.

  88. 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.

  89. 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.

  90. 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.

  91. 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.

  92. 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.

  93. 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.

  94. 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.

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

  96. 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.

  97. 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.

  98. 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.

  99. 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.

  100. 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.

  101. 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.

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

  103. 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.

  104. 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.

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

  106. 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.

  107. 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.

  108. 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.

  109. 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.

  110. 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.

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

  112. 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.

  113. 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.

  114. 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.

  115. 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.

  116. 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.

  117. 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.

  118. 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.

  119. 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.

  120. 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.

  121. 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.

  122. 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.

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Invited Papers & Tutorial Articles
  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. Ueda, N., "Relational data analysis based on Bayesian models," IEICE-J, Vol.97, No.5, 2014 (in Japanese).

  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, (invited paper).

  5. Ueda, N., "EM algorithm, "Society of Instrument and Control Engineers(SICE)," Vol.44, No.5, pp.333-338, 2005. (In Japanese)

  6. Ueda, N., "The goal of Web science research," OHM, Vol.91, No.10,pp.6-7, 2004.(In Japanese)

  7. Ueda, N., "Bayesian Inference Algorithms -Approximation Methods for High-Dimensional Integrals-," Journal of the Japanese Society for Artificial Intelligence, Vol. 19, No. 6, 2004 (in Japanese).

  8. Ueda, N. and Saito, K., "Probablistic Models for Multi-topic Text," Information Processing Society of Japan, Vol. 45, No. 2, 3, 2004 (in Japanese).

  9. Ueda, N., "Probablistic Models and Statistical Learning," Computer Today, No.114, 2003.

  10. Ueda, N., "Bayeaian Learning I - IV," Journal of IEICEJ, Vol. 85, No. 4, 6, 7, 8, 2002 (in Japanse).

  11. Ueda, N., "Ensemble Learning," Journal of the Society of Instrument and Control Engineers, Vol. 41, pp. 248, 2002.

  12. Ueda, N., "The Front of Bayesian Learning -Variational Bayesian Learning," Information Processing Society of Japan, Vol. 42, No. 1, 2001 (in Japanese).

  13. Ueda, N., "An Inquiry into Statistical Learning Research," Information Processing Society of Japan, Vol. 41, No. 6, pp. 730-733, 2000 (in Japanese).

  14. Ueda, N. and Nakano, R., "Deterministic Annealing EM Algorithm," Journal of the Society of Instrument and Control Engineers, Vol. 38, No. 7, pp. 444-449, 1999 (in Japanse).

  15. Ueda, N. and Nakano, R., "Deterministic Annealing -Another Type of Annealing-," Journal of Japanese Society for Artificial Intelligence, Vol. 12, No. 5, pp. 689-697, 1997 (in Japanse).

  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. Mase, S. and Ueda, N., "Mathematical Morphology and Image Analysis I," Journal of IEICEJ, Vol. 64, No. 2, pp. 166-174 , 1991 (in Japanese).

  21. Ueda, N. and Mase, S., "Mathematical Morphology and Image Analysis II," Journal of IEICEJ, Vol. 64, No. 3, pp. 271-279, 1991 (in Japanse).


Internal Invited Technical Reports at NTT
  1. Ueda, N. ., "Challengers," NTT Technical Journal, Vol.29, No9, pp.38-43, 2017 (In Japanese).

  2. Ueda, N. ., "Challengers," NTT Technical Journal, Vol.25, No.9, pp.40-43, 2013 (In Japanese).

  3. Ueda, N. ., "Machine learning technology making use of big data," NTT Technical Journal, Vol.25, No.4, pp.31-35, 2013 (In Japanese).

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

  5. Fujino, A., Ueda, N. and Saito, K., "Semi-supervised learning forautomatic text classification, " NTT Technical Journal, Vol.19, No.6, pp.26-28, 2007 (In Japanese).

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

  7. Ueda, N., "Web Science Research," NTT Technical Journal, Vol.16, No. 6, p. 22, 2004 (In Japanese).

  8. Ueda, N. and Nakano, R., "Competitive and Selective Learning Method for Optimal Vector Quantizer Design," NTT R&D, Vol. 42, No. 6, 1993 (in Japanese).

  9. Nakano, R., Ueda, N., Saito, K., and Yamada, T., "Research on Learning Aiming at Artificial Intelligence," NTT R&D, Vol. 42, No. 9, pp. 1175-1184, 1993 (in Japanese).

  10. Ueda, N., Mase, K., and Suenaga, Y., "Contour Tracking Method Using Energy-minimizing Elastic Models," NTT R&D, Vol. 42, No. 4, pp. 477-486, 1993 (in Japanese).

  11. Ueda, N. and Suzuki, S., "Multiscale Convex/Concave Structure Matching : MC Matching Method," NTT R&D, Vol. 40, No. 3, pp.399-406, 1991 (in Japanese).

  12. Kawada, E., Ueda, N., Ogawa, H. and Kosugi, M., "A Figure Recognition Method for Hand-Written Drawings," NTT Electrical Communications Laboratories Technical Journal, Vol. 37, No. 3, pp. 217-223, 1988 (in Japanese).

  13. Ueda, N., Nagura, M., Hoshino, T. and Mori, K., "Image Enhancement Method for Hand-Written Line Drawings," NTT Electrical Communications Laboratories Technical Journal, Vol. 37, No. 3, pp. 211-216, 1988 (in Japanese).


Book & Book Chapters
  1. Ueda, N., "Data-driven Science," KADOKAWA Course-Internet (Masao Sakauchi, editor-in-chief), Vol. 7, Part 1, Chap. 5, 2015.(in Japanese)

  2. Ishii, K. and Ueda, N., "Introduction to Pattern Recognition, Part II-Unsupervised Learning-" Ohm-Sha, Tokyo, Japan, 2014.(in Japanese)

  3. Ueda, N., Japanese translation: "Statistics," Science Palette, Maruzen Publishing, 2014.(in Japanese) (Original: David J. Handm, "Statistics: VSI," Oxford Univ. Press.)

  4. Ueda. N., "Variational Bayes method" Science dictionary (2nd ed.), Heisuke Hironaka edu., 2009.(in Japanese)

  5. Kabashima, Y. and Ueda, N., "Frontier of Statistical Science 11, Computational Statistics I - (3)," Iwanami Shoten, Japan, 2003.(in Japanese)

  6. Ishii, K., Ueda, N., Maeda, E. and Murase, H., "Introduction to Pattern Recognition," Ohm-Sha, Tokyo, Japan, 1998.(in Japanese)

  7. Ueda, N., "Chapter 3: Pattern Recognition Theory," in IEICEJ (Ed.),"Handbook of Electronics, Information and Communication," Asakura-Shoten, Tokyo, Japan, 1998.(in Japanese)

  8. Ueda, N., "Chapter 9: Vector Quantization," in Amari, S. (Ed.),"Handbook of Brain Science," Asakura-Shoten, Tokyo, Japan, 1995.(in Japanese)

  9. NTT Human Interface Laboratory, Project RTV, Japanese translation of "Horn, B. K. P., Robot Vision, MIT-Press, 1986" Asakura-Shoten, Tokyo, Japan, 1993. (in Japanese)

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