Wen-bing Huang

Computer Vision and Pattern Recognition · 2015–2020

A researcher in the Science corpus: 4 papers; trusts 327 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 65 citations into it
• Artificial Intelligence — 54 citations into it
• Graph Learning — 31 citations into it
• Molecular Biology — 25 citations into it
• Representation & Self-Supervised Learning — 22 citations into it
• Probabilistic & Bayesian Methods — 8 citations into it
• Evaluation & Benchmarks — 6 citations into it

Whom Wen-bing Huang trusts overall

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

1. Geoffrey E. Hinton
2. Kaiming He
3. Yoshua Bengio
4. Ross B. Girshick
5. Alex Krizhevsky
6. Yann LeCun
7. Radford M. Neal
8. Diederik P. Kingma
9. Ilya Sutskever
10. Ruslan Salakhutdinov

Who trusts Wen-bing Huang overall

1. Yu Rong
2. Christopher Morris
3. Lukas Galke Poech
4. José Jiménez-Luna
5. Austin Tripp
6. Ibrahim Aljarah
7. Si Zhang
8. Rex Ying
9. Hanqing Zeng
10. Wenbing Huang

Papers from Wen-bing Huang's perspective

Ordered by how strongly the researchers Wen-bing Huang 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. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. ImageNet Large Scale Visual Recognition Challenge
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Deep Feature Pyramid Reconfiguration for Object Detection (2018) — 218 citations
• Adaptive Sampling Towards Fast Graph Representation Learning (2018) — 553 citations
• Self-Supervised Graph Transformer on Large-Scale Molecular Data (2020) — 1,044 citations
• Scalable Gaussian Process Regression Using Deep Neural Networks (2015) — 70 citations