Yinbo Chen

Computer Vision and Pattern Recognition · 2018–2020

A researcher in the Science corpus: 4 papers; trusts 524 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 100 citations into it
• Artificial Intelligence — 75 citations into it
• Representation & Self-Supervised Learning — 40 citations into it; #2,483 of 3,145 by trust here, #2,299 by citations
• Computational Mechanics — 17 citations into it
• Generative Models — 14 citations into it
• Media Technology — 14 citations into it
• Meta & Continual Learning — 14 citations into it
• Optimization & Training — 14 citations into it

Whom Yinbo Chen trusts overall

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

1. Kaiming He
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Alex Krizhevsky
5. Yoshua Bengio
6. Yann LeCun
7. Sergey Ioffe
8. Christian Szegedy
9. Ilya Sutskever
10. Ross B. Girshick

Who trusts Yinbo Chen overall

1. Wei-Hong Li
2. Yisheng Song
3. Yingfei Liu
4. Xialei Liu
5. Carl Doersch
6. Renrui Zhang
7. Chieh Hubert Lin
8. Tero Karras
9. Zhen Wang
10. Eleni Triantafillou

Papers from Yinbo Chen's perspective

Ordered by how strongly the researchers Yinbo Chen 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. ImageNet: A large-scale hierarchical image database
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Learning Continuous Image Representation with Local Implicit Image Function (2020) — 970 citations
• Rethinking Knowledge Graph Propagation for Zero-Shot Learning (2018) — 340 citations
• Meta-Baseline: Exploring Simple Meta-Learning for Few-Shot Learning (2020) — 467 citations
• A New Meta-Baseline for Few-Shot Learning (2020) — 224 citations