Hui Tang

Artificial Intelligence · with Jia · 2019–2020

A researcher in the Science corpus: 2 papers; trusts 509 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 60 citations into it; #13,192 of 35,345 by trust here, #19,018 by citations
• Computer Vision and Pattern Recognition — 50 citations into it; #10,950 of 21,575 by trust here, #18,266 by citations
• Representation & Self-Supervised Learning — 47 citations into it
• Radiology, Nuclear Medicine and Imaging — 12 citations into it; #1,540 of 3,265 by trust here, #2,257 by citations
• Cancer Research — 10 citations into it; #440 of 1,780 by trust here, #1,648 by citations

Whom Hui Tang trusts overall

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

1. Geoffrey E. Hinton
2. Kaiming He
3. Yoshua Bengio
4. Alex Krizhevsky
5. Ross B. Girshick
6. Mingsheng Long
7. Yann LeCun
8. Ilya Sutskever
9. Diederik P. Kingma
10. Jia Deng

Who trusts Hui Tang overall

1. Shiqi Yang
2. Jaemin Na
3. Yaxing Wang
4. Heechul Jung
5. Mohammad Rostami
6. Toshihiko Matsuura
7. Joost van de Weijer
8. HyungJin Chang
9. Shuhao Cui
10. Hyeonseob Nam

Papers from Hui Tang's perspective

Ordered by how strongly the researchers Hui Tang 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. Gradient-based learning applied to document recognition
5. ImageNet: A large-scale hierarchical image database
6. Attention is All you Need (Neural Information Processing Systems)
7. Long Short-Term Memory
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. GENERATIVE ADVERSARIAL NETS

Their papers

• Unsupervised Domain Adaptation via Structurally Regularized Deep Clustering (2020) — 326 citations
• Domain-Symmetric Networks for Adversarial Domain Adaptation (2019) — 354 citations