Ming Tan

Natural Language Processing · with Xiang · 2003–2019

A researcher in the Science corpus: 5 papers; trusts 109 researchers and is trusted by 239 researchers here, as inferred from the citation graph.

Subfields

• Natural Language Processing — 69 citations into it; #3,999 of 5,688 by trust here, #4,449 by citations
• Artificial Intelligence — 67 citations into it; #8,610 of 35,345 by trust here, #11,921 by citations
• Computer Vision and Pattern Recognition — 23 citations into it
• Model Architectures — 19 citations into it; #1,367 of 2,278 by trust here, #1,215 by citations
• Information Retrieval & Search — 9 citations into it; #709 of 1,022 by trust here, #727 by citations
• Representation & Self-Supervised Learning — 8 citations into it
• Large Language Models — 5 citations into it
• Signal Processing — 5 citations into it

Whom Ming Tan trusts overall

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

1. Alex Graves
2. Ilya Sutskever
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Oriol Vinyals
6. Sepp Hochreiter
7. Dzmitry Bahdanau
8. Jason Aaron Edward Weston
9. Tomas Mikolov
10. J. Urgen Schmidhuber

Who trusts Ming Tan overall

1. Tabish Rashid
2. Georgios Papoudakis
3. Filippos Christianos
4. Laëtitia Matignon
5. Junling Hu
6. Shimon Whiteson
7. Wendelin Böhmer
8. Gregory Farquhar
9. Jakob N. Foerster
10. Jiachen Yang

Papers from Ming Tan's perspective

Ordered by how strongly the researchers Ming Tan 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. Long Short-Term Memory
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
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. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)

Their papers

• LSTM-based Deep Learning Models for non-factoid answer selection (2015) — 439 citations
• Improved Representation Learning for Question Answer Matching (2016) — 254 citations
• Extracting Multiple-Relations in One-Pass with Pre-Trained Transformers (2019) — 104 citations
• Attentive Pooling Networks (2016) — 364 citations
• Multi Agent Reinforcement Learning Independent vs Cooperative Agents (2003)