Ryuichiro Hataya

Artificial Intelligence · 2019–2020

A researcher in the Science corpus: 3 papers; trusts 217 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 63 citations into it
• Computer Vision and Pattern Recognition — 63 citations into it
• Generative Models — 22 citations into it
• Optimization & Training — 21 citations into it
• Representation & Self-Supervised Learning — 14 citations into it
• Model Architectures — 11 citations into it
• Safety, Robustness & Interpretability — 10 citations into it

Whom Ryuichiro Hataya trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Alex Krizhevsky
2. Diederik P. Kingma
3. Kaiming He
4. Geoffrey E. Hinton
5. Yoshua Bengio
6. Karen Simonyan
7. Ilya Sutskever
8. Yann LeCun
9. Sergey Zagoruyko
10. Jimmy Ba

Who trusts Ryuichiro Hataya overall

1. David Berthelot
2. Yi Xu
3. Mingle Xu
4. Longhui Wei
5. Jie Qin
6. Nicholas Carlini
7. Raphael Gontijo Lopes
8. Qizhe Xie
9. Lei Shang
10. Sook Yoon

Papers from Ryuichiro Hataya's perspective

Ordered by how strongly the researchers Ryuichiro Hataya trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Faster AutoAugment: Learning Augmentation Strategies using Backpropagation (2019) — 238 citations
• Unsupervised Brain Abnormality Detection Using High Fidelity Image Reconstruction Networks (2020) — 5 citations
• Unifying semi-supervised and robust learning by mixup (2019) — 22 citations