Ruotian Luo

Computer Vision and Pattern Recognition · 2018–2020

A researcher in the Science corpus: 3 papers; trusts 612 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 88 citations into it; #10,907 of 21,575 by trust here, #10,500 by citations
• Artificial Intelligence — 54 citations into it; #18,831 of 35,345 by trust here, #33,294 by citations
• Multimodal Learning — 30 citations into it
• Natural Language Processing — 27 citations into it
• Evaluation & Benchmarks — 17 citations into it
• Media Technology — 9 citations into it
• Reinforcement Learning — 8 citations into it; #1,360 of 2,242 by trust here, #2,201 by citations
• Model Architectures — 6 citations into it

Whom Ruotian Luo trusts overall

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

1. Ronald J. Williams
2. Diederik P. Kingma
3. Kaiming He
4. Geoffrey E. Hinton
5. Alex Krizhevsky
6. Yoshua Bengio
7. Karen Simonyan
8. Andrej Karpathy
9. Marc'Aurelio Ranzato
10. Ilya Sutskever

Who trusts Ruotian Luo overall

1. Pierre L. Dognin
2. Xu Yang
3. Dora Zhao
4. Anna Rohrbach
5. Inkit Padhi
6. Sheng Shen
7. Yuanen Zhou
8. Hanwang Zhang
9. Igor Melnyk
10. Angelina Wang

Papers from Ruotian Luo's perspective

Ordered by how strongly the researchers Ruotian Luo 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. 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

• Discriminability Objective for Training Descriptive Captions (2018) — 211 citations
• DIODE: A Dense Indoor and Outdoor DEpth Dataset (2019) — 365 citations
• A Better Variant of Self-Critical Sequence Training (2020) — 43 citations