Yaqing Wang

Artificial Intelligence · with Kwok · 2019

A researcher in the Science corpus: 1 paper; trusts 400 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 97 citations into it; #31,049 of 35,345 by trust here, #12,761 by citations
• Computer Vision and Pattern Recognition — 64 citations into it
• Representation & Self-Supervised Learning — 40 citations into it; #2,824 of 3,145 by trust here, #1,638 by citations
• Meta & Continual Learning — 26 citations into it
• Optimization & Training — 11 citations into it
• Model Architectures — 10 citations into it
• Natural Language Processing — 10 citations into it
• Probabilistic & Bayesian Methods — 8 citations into it

Whom Yaqing Wang trusts overall

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

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

Who trusts Yaqing Wang overall

1. Maximilian Pichler
2. Oleksiy Ostapenko
3. Maryam Karimi Mamaghan
4. Yinheng Li
5. Han-Jia Ye
6. Joseph Paul Cohen
7. Florian Hartig
8. Pau Rodr'iguez L'opez
9. Patrick Meyer
10. Shaofei Wang

Papers from Yaqing Wang's perspective

Ordered by how strongly the researchers Yaqing Wang 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. Learning Multiple Layers of Features from Tiny Images

Their papers

• Generalizing from a Few Examples (2019) — 2,151 citations