Publications

Peer-reviewed Papers

  1. Toshimitsu Aritake, Hideitsu Hino, Noboru Murata, “Learning Ancestral Atom via Sparse Coding,” in IEEE Journal of Selected Topics in Signal Processing, vol. 7, no. 4, pp. 586-594, Aug. 2013
  2. Toshimitsu Aritake, Hideitsu Hino, Shigeyuki Namiki, Daisuke Asanuma, Kenzoh Hirose, Noboru Murata, “Single-molecule localization by voxel-wise regression using convolutional neural network,” in Results in Optics, Volume 1, 2020
  3. Toshimitsu Aritake, Hideitsu Hino, Shigeyuki Namiki, Daisuke Asanuma, Kenzoh Hirose, Noboru Murata, “Fast and robust multiplane single-molecule localization microscopy using a deep neural network,” in Neurocomputing, Volume 451, pp. 279-289, 2021
  4. Mizuo Nagayama, Toshimitsu Aritake, Hideitsu Hino, Takeshi Kanda, Takehiro Miyazaki, Masashi Yanagisawa, Shotaro Akaho, Noboru Murata, “Detecting cell assemblies by NMF-based clustering from calcium imaging data,” in Neural Networks, Volume 149, Pages 29-39, 2022
  5. Toshimitsu Aritake, Hideitsu Hino, “Unsupervised Domain Adaptation for Extra Features in the Target Domain Using Optimal Transport.” in Neural Computation, to be appear.

Peer-reviewed International Conferences

  1. Naja L. Holten Møller, Pernille Bjørn, Jonas Christoffer Villumsen, Tine C. Hansen Hancock, Toshimitsu Aritake, and Shigeyuki Tani, “Data Tracking in Search of Workflows”, Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing, pp2153-2165, 2017
  2. Toshimitsu Aritake, Noboru Murata, “Learning Scale and Shift-Invariant Dictionary for Sparse Representation,” in Machine Learning, Optimization, and Data Science. LOD 2019, Lecture Notes in Computer Science, vol 11943. Springer, Cham, 2019
  3. Mizuo Nagayama, Toshimitsu Aritake, Hideitsu Hino, Takeshi Kanda, Takehiro Miyazaki, Masashi Yanagisawa, Shotaro Akaho, Noboru Murata, “Sleep State Analysis Using Calcium Imaging Data by Non-negative Matrix Factorization,” in ICANN 2019: Theoretical Neural Computation, Lecture Notes in Computer Science, vol 11727. Springer, Cham, 2019
  4. Toshimitsu Aritake, Hideitsu Hino, “Domain Adaptation with Optimal Transport for Extended Variable Space”, in IEEE World Congress on Computational Intelligence (WCCI2022), 2022.

Domestic Coferences/Workshops

  1. 有竹 俊光，日野 英逸，村田 昇，“スパースコーディングにおける基底生成のための単一母基底の学習”，情報論的学習理論と機械学習研究会 (IBISML)，2012, 東京.
  2. 有竹 俊光，大塚 理恵子，“人流シミュレーションを用いたサテライトオ フィスの導入効果分析，” 第 52回土木計画学研究・講演集", 52 巻, No.(P5), 2015., 秋田.
  3. 永山 瑞生，有竹 俊光，日野 英逸，上田 壮志，宮崎 峻弘，柳沢 正史，赤穂 昭太郎，村田 昇，“非負値行列因子分解を用いたカルシウムイメージングデータからの睡眠状態解析”，情報論的学習理論と機械学習研究会 (IBISML)，2019. 沖縄.
  4. 有竹 俊光，日野 英逸，並木 繁行，浅沼 大祐，廣瀬 謙造，村田 昇，“深層ニューラルネットワークを用いた多焦点顕微鏡のリアルタイム3次元局在化”, 情報論的学習理論と機械学習研究会 (IBISML)，2020, 東京.
  5. 有竹 俊光，日野 英逸，“変数の拡張に対する最適輸送を用いたドメイン適応”, 情報論的学習理論と機械学習研究会 (IBISML)，2022. オンライン.

Preprints

  1. Toshimitsu Aritake, Hideitsu Hino, Shigeyuki Namiki, Daisuke Asanuma, Kenzoh Hirose, Noboru Murata, “Fast and robust multiplane single-molecule localization microscopy using a deep neural network,” arXiv, 2020.
  2. Toshimitsu Aritake, Hideitsu Hino, “Unsupervised Domain Adaptation for Extra Features in the Target Domain Using Optimal Transport.” arXiv, 2022.

Others

  1. 谷 繁幸，Jonas Christoffer Villumsen，有竹 俊光，Bruno Hannibal Taylor，“データ流通により加速する社会変革”, 日立評論, Vol.99, No.3, 2017.
  2. 有竹 俊光, 鴨志田 亮太,“データ処理システム、及び、データ処理方法”, 特許 6454222, 2019