Nannan Wang

Artificial Intelligence · 2020–2021

A researcher in the Science corpus: 2 papers; trusts 29 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 68 citations into it
• Computer Vision and Pattern Recognition — 39 citations into it
• Optimization & Training — 33 citations into it
• Representation & Self-Supervised Learning — 10 citations into it
• Statistical Learning Theory — 8 citations into it
• Natural Language Processing — 8 citations into it

Whom Nannan Wang trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Kaiming He
4. Yann LeCun
5. Yoshua Bengio
6. Diederik P. Kingma
7. Christian Szegedy
8. Tongliang Liu
9. Ilya Sutskever
10. Richard Socher

Papers from Nannan Wang's perspective

Ordered by how strongly the researchers Nannan 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. ImageNet: A large-scale hierarchical image database
4. Learning Multiple Layers of Features from Tiny Images
5. Gradient-based learning applied to document recognition
6. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
7. Long Short-Term Memory
8. Attention is All you Need (Neural Information Processing Systems)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Going deeper with convolutions

Their papers

• Robust early-learning: Hindering the memorization of noisy labels (2021) — 338 citations
• Part-dependent Label Noise: Towards Instance-dependent Label Noise (2020) — 313 citations