I am now working at Preferred Networks, Inc. as a researcher.
I am engaged in machine learning research. Research Interests
I was Master student in Integrated System Biology Labratory,
Department of System Science, Graduate school of Informatics, Kyoto University.
I have strong interest in scalable and simple machine learning algorithm.
My current focuses are
- Semi-supervised and unsupervised learning with neural networks
- Generative adversarial networks and implicit models
- Learning on extremely large distributed systems
- M.S. Informatics – Graduate School of Informatics, Kyoto University, April 2014 - March 2016.
- B.S. Electronic Engineering – Kyoto University, April 2010 - March 2014.
Ken Nakanishi, Shin-ichi Maeda, Takeru Miyato and Daisuke Okanohara
Neural Multi-scale Image Compression.
ACCV, 2018. (accepted for oral presentation).
Takeru Miyato, Shin-ichi Maeda, Masanori Koyama and Shin Ishii
Virtual Adversarial Training : A Regularization Method for Supervised and Semi-Supervised Learning.
IEEE TPAMI, 2018.
(extended version of the paper published at ICLR2016)
[code (TF)] [code (Chainer)] [arXiv]
Takeru Miyato, Toshiki Kataoka, Masanori Koyama and Yuichi Yoshida
Spectral Normalization for Generative Adversarial Networks.
ICLR, 2018. (accepted for oral presentation)
[code (on ImageNet)]
Takeru Miyato and Masanori Koyama
cGANs with Projection Discriminator.
Weihua Hu, Takeru Miyato, Seiya Tokui, Eiichi Matsumoto and Masashi Sugiyama
Learning Discrete Representations via Information Maximizing Self Augmented Training.
[code (by @weihua916)]
Takeru Miyato, Andrew M. Dai and Ian Goodfellow
Adversarial Training Methods for Semi-Supervised Text Classification.
[code (Chainer, by @aonotas)]
Takeru Miyato, Daisuke Okanohara, Shin-ichi Maeda and Masanori Koyama
Synthetic gradient methods with Virtual Forward-Backward Networks.
Workshop at ICLR, 2017
Takeru Miyato, Shin-ichi Maeda, Masanori Koyama, Ken Nakae and Shin Ishii
Distributional Smoothing with Virtual Adversarial Training.
Jiren Jin, Richard G. Calland, Takeru Miyato, Brian K. Vogel and Hideki Nakayama
Parameter Reference Loss for Unsupervised Domain Adaptation.
arXiv preprint arXiv:1711.07170, 2017.
Yuichi Yoshida and Takeru Miyato
Spectral Norm Regularization for Improving the Generalizability of Deep Learning.
arXiv preprint arXiv:1705.10941, 2017.
- Preferred Networks Inc., Tokyo, Japan, 09/2016 - (now)
Working as a full-time researcher.
- Advanced Telecommunications Research Institute International (ATR), Kyoto, Japan, 06/2016 - 03/2018
Visiting researcher, 04/2017 - 03/2018
Visiting research engineer, 09/2016 - 03/2017
Full-time research engineer, 06/2016 - 09/2016
- Google Inc., Mountain View CA, 01/2016 - 05/2016.
Software Engineer Intern (Research oriented) at Google Brain, under supervision of Dr. Andrew M. Dai and Dr. Ian Goodfellow.
- Suntex, Inc., Osaka, 02/2013 - 10/2013.
- Used C# to program the movement and the UI of an industrial bending machine.
- Jeyes, Inc., Osaka 04/2012 - 08/2013.
- Used OpenGL and Objective-C for the geometry processing in the development of iPhone and Android application for augmented reality(AR).
Python, MATLAB, Objective-C, C#, C, C++, OpenGL.
- Information processing practice A, in Kyoto University, Japan, 10/2013-03/2014.
- Lecture on deep neural networks, in Kyoto University, Japan, 07/2014.
- Especially skilled in Theano, Tensorflow and Chainer, python based deep learning frameworks.
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