Takeru Miyato

Takeru Miyato

(Last updated: Nov. 16, 2021)

Researcher at Preferred Networks, Inc.

Contact

E-mail : takeru.miyato(at)gmail.com

Links

GitHub
Google Scholar
ResearchGate

Affiliation

I am now working at Preferred Networks, Inc. as a part-time researcher.
I was a Master student in Integrated System Biology Labratory,
Department of System Science, Graduate school of Informatics, Kyoto University.

Research Interests

I am engaged in machine learning research.
I have strong interest in scalable and simple machine learning algorithm.
My current focuses are

Education

Publications

  1. Masanori Koyama, Kentaro Minami, Takeru Miyato, and Yarin Gal
    Contrastive Representation Learning with Trainable Augmentation Channel.
    {Bayesian Deep Learning/Self-Supervised Learning - Theory and Practice} Workshop at NeurIPS. 2021.
    [arXiv]
  2. Takeru Miyato, Masanori Koyama
    Generative Adversarial Networks.
    A book chapter in Computer Vision: A Reference Guide. Ed. by Katsushi Ikeuchi. Springer. 2021
    [paper link]
  3. Amir Najafi, Shin-ichi Maeda, Masanori Koyama, and Takeru Miyato
    Robustness to adversarial perturbations in learning from incomplete data.
    Advances in Neural Information Processing Systems (NeurIPS). 2019
  4. [code] [paper link]
  5. Takeru Miyato, Shin-ichi Maeda, Masanori Koyama and Shin Ishii
    Virtual Adversarial Training : A Regularization Method for Supervised and Semi-Supervised Learning.
    IEEE TPAMI, 2019. (extended version of the paper published at ICLR2016)
    [code (TF)] [code (Chainer)] [arXiv]
  6. Ken Nakanishi, Shin-ichi Maeda, Takeru Miyato and Daisuke Okanohara
    Neural Multi-scale Image Compression.
    ACCV, 2018. (accepted for oral presentation)
    [arXiv]
  7. Takeru Miyato, Toshiki Kataoka, Masanori Koyama and Yuichi Yoshida
    Spectral Normalization for Generative Adversarial Networks.
    ICLR, 2018. (accepted for oral presentation)
    [code] [code (on ImageNet)] [OpenReview]
  8. Takeru Miyato and Masanori Koyama
    cGANs with Projection Discriminator.
    ICLR, 2018.
    [code] [OpenReview]
  9. Weihua Hu, Takeru Miyato, Seiya Tokui, Eiichi Matsumoto and Masashi Sugiyama
    Learning Discrete Representations via Information Maximizing Self Augmented Training.
    ICML, 2017.
    [code (by @weihua916)] [arXiv]
  10. Takeru Miyato, Andrew M. Dai and Ian Goodfellow
    Adversarial Training Methods for Semi-Supervised Text Classification.
    ICLR, 2017.
    [code (Chainer, by @aonotas)] [code (TF)] [arXiv] [slide] [poster] [OpenReview]
  11. Takeru Miyato, Daisuke Okanohara, Shin-ichi Maeda and Masanori Koyama
    Synthetic gradient methods with Virtual Forward-Backward Networks.
    Workshop at ICLR, 2017.
    [poster] [OpenReview]
  12. Takeru Miyato, Shin-ichi Maeda, Masanori Koyama, Ken Nakae and Shin Ishii
    Distributional Smoothing with Virtual Adversarial Training.
    ICLR, 2016.
    [code] [arXiv] [slide] [poster]

Working Experiences

Skills

Python, MATLAB, Objective-C, C#, C, C++, OpenGL.

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