Takeru Miyato

According to our database, Takeru Miyato authored at least 20 papers between 2016 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Artificial Kuramoto Oscillatory Neurons.

Takeru Miyato, Sindy Löwe, Andreas Geiger, Max Welling

CoRR, 2024

GTA: A Geometry-Aware Attention Mechanism for Multi-View Transformers.

Takeru Miyato, Bernhard Jaeger, Max Welling, Andreas Geiger

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Neural Fourier Transform: A General Approach to Equivariant Representation Learning.

Masanori Koyama, Kenji Fukumizu, Kohei Hayashi, Takeru Miyato

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2022

Invariance-adapted decomposition and Lasso-type contrastive learning.

Masanori Koyama, Takeru Miyato, Kenji Fukumizu

CoRR, 2022

Unsupervised Learning of Equivariant Structure from Sequences.

Takeru Miyato, Masanori Koyama, Kenji Fukumizu

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021

Contrastive Representation Learning with Trainable Augmentation Channel.

Masanori Koyama, Kentaro Minami, Takeru Miyato, Yarin Gal

CoRR, 2021

2019

Unsupervised Discrete Representation Learning.

Weihua Hu, Takeru Miyato, Seiya Tokui, Eiichi Matsumoto, Masashi Sugiyama

Proceedings of the Explainable AI: Interpreting, 2019

Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning.

Takeru Miyato, Shin-ichi Maeda, Masanori Koyama, Shin Ishii

IEEE Trans. Pattern Anal. Mach. Intell., 2019

Robustness to Adversarial Perturbations in Learning from Incomplete Data.

Amir Najafi, Shin-ichi Maeda, Masanori Koyama, Takeru Miyato

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2018

Collaging on Internal Representations: An Intuitive Approach for Semantic Transfiguration.

Ryohei Suzuki, Masanori Koyama, Takeru Miyato, Taizan Yonetsuji

CoRR, 2018

Spectral Normalization for Generative Adversarial Networks.

Takeru Miyato, Toshiki Kataoka, Masanori Koyama, Yuichi Yoshida

Proceedings of the 6th International Conference on Learning Representations, 2018

cGANs with Projection Discriminator.

Takeru Miyato, Masanori Koyama

Proceedings of the 6th International Conference on Learning Representations, 2018

Neural Multi-scale Image Compression.

Ken M. Nakanishi, Shin-ichi Maeda, Takeru Miyato, Daisuke Okanohara

Proceedings of the Computer Vision - ACCV 2018, 2018

2017

Parameter Reference Loss for Unsupervised Domain Adaptation.

Jiren Jin, Richard G. Calland, Takeru Miyato, Brian K. Vogel, Hideki Nakayama

CoRR, 2017

Spectral Norm Regularization for Improving the Generalizability of Deep Learning.

Yuichi Yoshida, Takeru Miyato

CoRR, 2017

Learning Discrete Representations via Information Maximizing Self-Augmented Training.

Weihua Hu, Takeru Miyato, Seiya Tokui, Eiichi Matsumoto, Masashi Sugiyama

Proceedings of the 34th International Conference on Machine Learning, 2017

Synthetic Gradient Methods with Virtual Forward-Backward Networks.

Takeru Miyato, Daisuke Okanohara, Shin-ichi Maeda, Masanori Koyama

Proceedings of the 5th International Conference on Learning Representations, 2017

Adversarial Training Methods for Semi-Supervised Text Classification.

Takeru Miyato, Andrew M. Dai, Ian J. Goodfellow

Proceedings of the 5th International Conference on Learning Representations, 2017

2016

Distributional Smoothing by Virtual Adversarial Examples.

Takeru Miyato, Shin-ichi Maeda, Masanori Koyama, Ken Nakae, Shin Ishii

Proceedings of the 4th International Conference on Learning Representations, 2016

Virtual Adversarial Training for Semi-Supervised Text Classification.

Takeru Miyato, Andrew M. Dai, Ian J. Goodfellow

CoRR, 2016