Jiaqi Mu

Natural Language Processing · 2017

A researcher in the Science corpus: 2 papers; trusts 83 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Natural Language Processing — 58 citations into it; #2,411 of 5,688 by trust here, #3,529 by citations
• Artificial Intelligence — 49 citations into it; #11,436 of 35,345 by trust here, #16,166 by citations
• Representation & Self-Supervised Learning — 28 citations into it; #1,342 of 3,145 by trust here, #1,982 by citations
• Evaluation & Benchmarks — 12 citations into it
• Computer Vision and Pattern Recognition — 9 citations into it; #17,223 of 21,575 by trust here, #20,271 by citations

Whom Jiaqi Mu trusts overall

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

1. Richard Socher
2. Eneko Agirre
3. Tomas Mikolov
4. Geoffrey E. Hinton
5. Yoshua Bengio
6. Ilya Sutskever
7. Christopher D. Manning
8. Diederik P. Kingma
9. Jeffrey Pennington
10. Yoon Kim

Who trusts Jiaqi Mu overall

1. João Sedoc
2. Nina Poerner
3. William Timkey
4. Lyle Ungar
5. Tianlin Liu
6. Dilin Wang
7. Benjamin Robaidek
8. Fangyu Liu
9. Nikunj Saunshi
10. Daniel Matthew Cer

Papers from Jiaqi Mu's perspective

Ordered by how strongly the researchers Jiaqi Mu 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. Attention is All you Need (Neural Information Processing Systems)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)

Their papers

• All-but-the-Top: Simple and Effective Postprocessing for Word Representations (2017) — 407 citations
• Representing Sentences as Low-Rank Subspaces (2017) — 27 citations