Tsubasa Hirakawa

Computer Vision and Pattern Recognition · 2018–2019

A researcher in the Science corpus: 4 papers; trusts 460 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 106 citations into it; #18,980 of 21,575 by trust here, #14,822 by citations
• Artificial Intelligence — 72 citations into it; #30,947 of 35,345 by trust here, #25,809 by citations
• Model Architectures — 35 citations into it
• Safety, Robustness & Interpretability — 17 citations into it
• Natural Language Processing — 16 citations into it
• Efficiency & Compression — 11 citations into it
• Optimization & Training — 10 citations into it
• Evaluation & Benchmarks — 8 citations into it

Whom Tsubasa Hirakawa trusts overall

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

1. Geoffrey E. Hinton
2. Kaiming He
3. Alex Krizhevsky
4. Yoshua Bengio
5. Ross B. Girshick
6. Yann LeCun
7. Ilya Sutskever
8. Diederik P. Kingma
9. Karen Simonyan
10. Jia Deng

Who trusts Tsubasa Hirakawa overall

1. Lin Wang
2. Yichao Liu
3. Zequn Qin

Papers from Tsubasa Hirakawa's perspective

Ordered by how strongly the researchers Tsubasa Hirakawa 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
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. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Knowledge Transfer Graph for Deep Collaborative Learning (2019) — 13 citations
• Deep learning-based image recognition for autonomous driving (2019) — 390 citations
• Embedding Human Knowledge into Deep Neural Network via Attention Map (2019) — 81 citations
• Attention Branch Network: Learning of Attention Mechanism for Visual Explanation (2018) — 462 citations