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Ryuichiro Hataya’s webpage.

About

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I am Ryuichiro Hataya, PhD (Information Science and Technology).

older news
  • Our paper “Sketch-based Semantic Retrieval of Medical Images” (Kobayashi et al.) has been accepted at Medical Image Analysis.

  • We will present “Non-commutative $C^\ast$-algebra Net” at QTML 2023 @ Geneve, Switzerland.

  • Our paper “An Empirical Investigation of Pre-trained Model Selection for Out-of-Distribution Generalization and Calibration” (Naganuma & Hataya) has been accepted at ICCV Workshop 2023 on Uncertainty Quantification for Computer Vision.

  • I will visit Nicolaus Copernicus University Poland from Sep 25th to 30th.

  • Our paper “Will Large-scale Generative Models Corrupt Future Datasets?” has been accepted at ICCV 2023.

  • Our paper “Towards AI-driven radiology education: A self-supervised segmentation-based framework for high-precision medical image editing” (Kobayashi et al.) has been accepted at MICCAI 2023 as an oral presentation.

  • I will visit MILA at Montreal and attend CVPR in June.

  • I will give a talk at UTokyo ICEPP.

  • I will visit IIT at Genova in May.

  • I will attend AISTATS at Valencia in April.

  • I will visit Vietnam Institute for Advanced Study in Mathematics at Hanoi in April.

  • I will visit EPFL CIS (Switzerland) and Fraunhofer IIS (Germany) from 8th to 15th March 2023.

  • Our paper “Nyström Method for Accurate and Scalable Implicit Differentiation” has been accepted at AISTATS 2023.

  • I joined RIKEN ADSP and RIKEN AIP as a postdoctral researcher
  • I recieved a doctal degree as a representative student of the graduate school of Information Science and Techonology, UTokyo.
  • I defended my PhD thesis.
  • I visited IIT (Genova, Italy) from July 8th.
  • Our paper “DJMix: Unsupervised Task-agnostic Image Augmentation for Improving Robustness of Convolutional Neural Networks” is accepted to IJCNN 2022.
  • I visited IIT (Genova, Italy) from October 1st to December 17th.
  • Our paper “Meta Approach to Data Augmentation Optimization” is accepted to WACV 2022.
  • My research proposal has been accepted in JST’s ACT-X.
  • Call for NeurIPS meetups is now out!
  • I will present about Faster AutoAugment and its applications at AIP Open seminar.
  • Our paper “Graph Energy-based Model for Molecular Graph Generation” is accepted at EBM workshop 2021 as a contributed talk.
  • I will serve as a meetup chair for NeurIPS 2021.
  • My research proposal has been accepted in JSPS’s travel grant.
  • My research proposals have been accepted by Microsoft Research Asia, and RIISE at UTokyo.
  • We organized a NeurIPS meetup and Women in ML in Japan: https://neuripsmeetupjapan.github.io.
  • Our paper “Decomposing Normal and Abnormal Features of Medical Images for Content-based Image Retrieval” is accepted at ML4H 2020.

Projects


Latest Post

Jan 5, 2024

Useful information when you come to Japan

Public Transportation When you use public transporation, such as buses and trains in Japan, I highly recommend to use “Pasmo”, prepaid and rechargebale contactless smar cards, which can also be installable on Android smartphones or iPhones. Because Tokyo’s transporation system is run by several companies, it is so complex; buying tickets every time at the machine is not a good idea. Instead, if you prepare Pasmo once, you can ride almost all buses and tranins in Tokyo or other big cities. Read more
All Posts

Publications

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  • Han Bao, Ryuichiro Hataya, Ryo Karakida, “Self-attention Networks Localize When QK-eigenspectrum Concentrates,” International Conference on Machine Learning, 2024. arXiv
  • Ryuichiro Hataya, Han Bao, and Hiromi Arai, “Will Large-scale Generative Models Corrupt Future Datasets?,” International Conference on Computer Vision, France, 2023.
  • Kazuma Kobayashi, Lin Gu, Ryuichiro Hataya, Mototaka Miyake, Yasuyuki Takamizawa, Sono Ito, Hirokazu Watanabe, Yukihiro Yoshida, Hiroki Yoshimura, Tatsuya Harada, Ryuji Hamamoto, “Towards AI-driven radiology education: A self-supervised segmentation-based framework for high-precision medical image editing,” Medical Image Computing and Computer-Assisted Intervention, Canada, 2023.
  • Ryuichiro Hataya, and Makoto Yamada, “Nyström Method for Accurate and Scalable Implicit Differentiation,” International Conference on Artificial Intelligence and Statistics, Spain, 2023.
  • Ryuichiro Hataya, and Hideki Nakayama, “DJMix: Unsupervised Task-agnostic Image Augmentation for Improving Robustness of Convolutional Neural Networks,” International Joint Conference on Neural Networks, 2022.
  • Ryuichiro Hataya, Jan Zdenek, Kazuki Yoshizoe, and Hideki Nakayama, “Meta Approach to Data Augmentation Optimization.” Winter Conference on Applications of Computer Vision, 2022.
  • Taiga Kashima, Ryuichiro Hataya, and Hideki Nakayama, “Visualizing Association in Exemplar-based Classification.” International Conference on Acoustics, Speech, and Signal Processing, 2021.
  • Ryuichiro Hataya, Jan Zdenek, Kazuki Yoshizoe, and Hideki Nakayama, “Faster AutoAugment: Learning Augmentation Strategies using Backpropagation.” European Conference on Computer Vision, 2020.
  • Ryuichiro Hataya, and Hideki Nakayama, “LOL: Learning To Optimize Loss Switching Under Label Noise.” International Conference on Image Processing, 2019.
Preprints and others
  • Yuka Hashimoto${}^\star$, Ryuichiro Hataya${}^\star$, “Quantum Circuit $C^\ast$-algebra Net,” 2024. arXiv
  • Ryuichiro Hataya, Yoshinobu Kawahara, “Glocal Hypergradient Estimation with Koopman Operator,” 2024. arXiv
  • Hiroki Naganuma${}^\star$, Ryuichiro Hataya${}^\star$, Ioannis Mitliagkas, “An Empirical Investigation of Pre-trained Model Selection for Out-of-Distribution Generalization and Calibration,” 2023. arXiv
  • Leonardo Placidi, Ryuichiro Hataya, Toshio Mori, Koki Aoyama, Hayata Morisaki, Kosuke Mitarai, and Keisuke Fujii, “MNISQ: A Large-Scale Quantum Circuit Dataset for Machine Learning on/for Quantum Computers in the NISQ era,” 2023. arXiv
  • Ryuichiro Hataya${}^\star$, and Yuka Hashimoto${}^\star$, “Noncommutative $C^\ast$-algebra Net: Learning Neural Networks with Powerful Product Structure in $C^\ast$-algebra,” 2023. arXiv
  • Ryuichiro Hataya, Hideki Nakayama, and Kazuki Yoshizoe, “Graph Energy-based Model for Substructure Preserving Molecular Design.” 2021. arxiv

(${}^\star$ indicates equal contribution)

  • Ryuichiro Hataya, Kota Matsui, Masaaki Imaizumi, “Automatic Domain Adaptation by Transformers in In-Context Learning,” 2024. arXiv
  • Ryuichiro Hataya, Yuka Hashimoto, “Noncommutative $C^\ast$-algebra Nets,” International Conference on Quantum Techinques in Machine Learning, 2023. (Peer Reviewed Etended Abstract)
  • Hiroki Naganuma, Ryuichiro Hataya, “An Empirical Investigation of Pre-trained Model Selection for Out-of-Distribution Generalization and Calibration,” ICCV 2023 Workshop on Uncertainty Quantification for Computer Vision, 2023. (Peer Reviewed Etended Abstract arXiv)
  • Ryuichiro Hataya, Hideki Nakayama, and Kazuki Yoshizoe, “Graph Energy-based Model for Molecular Graph Generation.” EBM Workshop at ICLR 2021, 2021. (Peer Reviewed, Contributed Talk)
  • Kazuma Kobayashi, Ryuichiro Hataya, Yusuke Kurose, Tatsuya Harada, and Ryuji Hamamoto, “Decomposing Normal and Abnormal Features of Medical Images for Content-based Image Retrieval.” Machine Learning for Health Workshop at NeurIPS 2020. (Peer Reviewed, Extended Abstract)
  • Ryuichiro Hataya, Kumiko Matsui, and Tomoki Karasawa, “Learning to Identify Large Fossils using Deep Convolutional Neural Networks”, Geological Society of America Abstracts with Programs. Vol 52, No. 6, 2020.
  • Ryuichiro Hataya, and Hideki Nakayama, “Unifying semi-supervised and robust leaning by mixup.” Workshop on Learning from Limited Labeled Data at ICLR 2019, 2019. (Peer Reviewed, Spotlight)

Other Research Activities

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  • “Determinantal point processes and their applications in machine learning,” (in Japanese) ICEPP seminar, June 2023.
  • “Towards accurate and scalable gradient-based hyperparameter optimization,” Istituto Italiano di Tecnologia, May, 2023.
  • “Noncommutative $C^\ast$-algebra Nets,” Japan-Vietnam AI Forum, April 2023.
  • “Bayesian Model Selection for Deep Learning” (in Japanese), Zapping Seminar, 2022.
  • “Data Augmentation for Deep Learning” (in Japanese), Ehime University DS Research Seminar, 2021.
  • “Data Augmentation for Deep Learning” (in Japanese), Symposium on Sensing via Image Information, 2021.
others
  • “Data Augmentation for Deep Learning” (in Japanese), StatsML Symposium, 2020.
  • “Gradient-based Hyperparameter Optimization” (in Japanese), Zapping Seminar, 2020.
  • Japan Science and Technology Agency, ACT-X Acceleration phase, ¥5.0M, 2024.
  • Grant-in-Aid for Research Activity Start-up, Japan Society for the Promotion of Science, ¥2.2M, 2023-2024.
  • Japan Science and Technology Agency, ACT-X, ¥4.5M, 2021-2024.
  • Overseas Challenge Program for Young Researchers by JSPS, ¥1.4M, 2021.
  • Microsoft Research Asia Collaborative Research Program (D-CORE 2021) by MSRA, ¥1.0M, 2021.
  • Sprouting Research RA’s in Value Exchange Engineering by RIISE@UTokyo, ¥2.0M, 2020-2022.
  • Microsoft Research Asia D-Core Award, 2020.
  • Best Student Paper Award, The 23rd Meeting on Image Recognition and Understanding, 2020.

Contact

Contact me via ${firstname}.${lastname}@riken.jp.