Wei Fang

Artificial Intelligence · with Chung · 2017–2019

A researcher in the Science corpus: 2 papers; trusts 85 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 47 citations into it; #20,031 of 35,345 by trust here, #17,110 by citations
• Computer Vision and Pattern Recognition — 31 citations into it; #17,913 of 21,575 by trust here, #13,542 by citations
• Natural Language Processing — 18 citations into it
• Representation & Self-Supervised Learning — 12 citations into it
• Speech & Audio — 12 citations into it
• Model Architectures — 9 citations into it
• Signal Processing — 9 citations into it; #5,121 of 7,572 by trust here, #5,829 by citations

Whom Wei Fang trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Ilya Sutskever
4. Kaiming He
5. Diederik P. Kingma
6. Alex Krizhevsky
7. Sepp Hochreiter
8. Alex Graves
9. Yann LeCun
10. J. Urgen Schmidhuber

Who trusts Wei Fang overall

1. Hamed Hemati
2. Xu Tan
3. Brooke Stephenson
4. Yujia Xiao
5. Guanghui Xu
6. Ali Cheraghian
7. Damian Borth
8. Frank K. Soong
9. Tao Qin
10. Lei He

Papers from Wei Fang's perspective

Ordered by how strongly the researchers Wei Fang 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. Long Short-Term Memory
4. Attention is All you Need (Neural Information Processing Systems)
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Multi-label Zero-Shot Learning with Structured Knowledge Graphs (2017) — 307 citations
• Towards Transfer Learning for End-to-End Speech Synthesis from Deep Pre-Trained Language Models (2019) — 29 citations