Thomas Fang Zheng

Artificial Intelligence · 2001–2015

A researcher in the Science corpus: 2 papers; trusts 106 researchers and is trusted by 12 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 70 citations into it; #12,836 of 35,345 by trust here, #19,439 by citations
• Signal Processing — 32 citations into it; #4,018 of 7,572 by trust here, #5,086 by citations
• Natural Language Processing — 27 citations into it; #3,190 of 5,688 by trust here, #4,682 by citations
• Representation & Self-Supervised Learning — 27 citations into it
• Computer Vision and Pattern Recognition — 20 citations into it
• Speech & Audio — 13 citations into it; #584 of 1,076 by trust here, #900 by citations
• Model Architectures — 9 citations into it
• Multimodal Learning — 7 citations into it

Whom Thomas Fang Zheng trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Hynek Hermansky
4. Richard Socher
5. Ruslan Salakhutdinov
6. Alex Krizhevsky
7. Nitish Srivastava
8. Yann LeCun
9. Diederik P. Kingma
10. Ilya Sutskever

Who trusts Thomas Fang Zheng overall

1. Nicholas Donald Lane
2. Petko Georgiev
3. M. Hossan
4. Hong Lu
5. Jiawei Chen
6. X. Gonzalvo
7. John Giorgi
8. Barret Zoph
9. Sheeraz Memon
10. Pegah Ghahremani

Papers from Thomas Fang Zheng's perspective

Ordered by how strongly the researchers Thomas Fang Zheng trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
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. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• Transfer learning for speech and language processing (2015) — 236 citations
• Comparison of different implementations of MFCC (2001) — 640 citations