Ryo Yonetani

Computer Vision and Pattern Recognition · 2016–2018

A researcher in the Science corpus: 5 papers; trusts 614 researchers and is trusted by 37 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 99 citations into it; #20,573 of 21,575 by trust here, #13,269 by citations
• Artificial Intelligence — 64 citations into it; #6,179 of 35,345 by trust here, #7,531 by citations
• Human-Computer Interaction — 12 citations into it
• Evaluation & Benchmarks — 10 citations into it
• Privacy & Security — 10 citations into it
• Safety, Robustness & Interpretability — 10 citations into it
• Optimization & Training — 9 citations into it
• Model Architectures — 8 citations into it

Whom Ryo Yonetani trusts overall

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

1. Kaiming He
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Karen Simonyan
5. Yoshua Bengio
6. Andrew Zisserman
7. Diederik P. Kingma
8. Yann LeCun
9. Ross B. Girshick
10. L. Eon Bottou

Who trusts Ryo Yonetani overall

1. Tribhuvanesh Orekondy
2. Praneeth Vepakomma
3. Evonne Ng
4. Jihong Park
5. Georgios Kapidis
6. Li Li
7. Fatemehsadat Mirshghallah
8. Haftay Gebreslasie Abreha
9. Enmao Diao
10. Yin Li

Papers from Ryo Yonetani's perspective

Ordered by how strongly the researchers Ryo Yonetani 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. ImageNet: A large-scale hierarchical image database
5. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Future Person Localization in First-Person Videos (2017) — 183 citations
• Visual Motif Discovery via First-Person Vision (2016) — 15 citations
• Recognizing Micro-Actions and Reactions from Paired Egocentric Videos (2016) — 94 citations
• Privacy-Preserving Visual Learning Using Doubly Permuted Homomorphic Encryption (2017) — 70 citations
• Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge (2018) — 1,773 citations