Yasuhide Miura

Artificial Intelligence · 2020

A researcher in the Science corpus: 2 papers; trusts 200 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 68 citations into it; #32,005 of 35,345 by trust here, #10,065 by citations
• Computer Vision and Pattern Recognition — 60 citations into it; #21,078 of 21,575 by trust here, #7,499 by citations
• Natural Language Processing — 32 citations into it
• Multimodal Learning — 25 citations into it; #1,079 of 1,211 by trust here, #583 by citations
• Evaluation & Benchmarks — 20 citations into it
• Representation & Self-Supervised Learning — 19 citations into it; #3,075 of 3,145 by trust here, #1,415 by citations
• Model Architectures — 6 citations into it
• Medical Imaging — 5 citations into it

Whom Yasuhide Miura trusts overall

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

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

Who trusts Yasuhide Miura overall

1. Shruthi Bannur
2. Benedikt Boecking
3. Fahad Shamshad
4. Stephanie L. Hyland
5. Naoto Usuyama

Papers from Yasuhide Miura's perspective

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

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
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. ImageNet Large Scale Visual Recognition Challenge
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)

Their papers

• Contrastive Learning of Medical Visual Representations from Paired Images and Text (2020) — 1,105 citations
• Improving Factual Completeness and Consistency of Image-to-Text Radiology Report Generation (2020) — 235 citations