Hua He

Natural Language Processing · with Lin · 2015–2017

A researcher in the Science corpus: 4 papers; trusts 248 researchers and is trusted by 38 researchers here, as inferred from the citation graph.

Subfields

• Natural Language Processing — 97 citations into it; #1,203 of 5,688 by trust here, #2,045 by citations
• Artificial Intelligence — 88 citations into it; #5,353 of 35,345 by trust here, #9,275 by citations
• Computer Vision and Pattern Recognition — 30 citations into it; #5,954 of 21,575 by trust here, #12,992 by citations
• Evaluation & Benchmarks — 23 citations into it
• Model Architectures — 19 citations into it
• Representation & Self-Supervised Learning — 10 citations into it
• Information Retrieval & Search — 8 citations into it
• Computational Linguistics & Semantics — 5 citations into it

Whom Hua He trusts overall

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

1. L. Eon Bottou
2. Richard Socher
3. Alex Graves
4. Christopher D. Manning
5. Eneko Agirre
6. Geoffrey E. Hinton
7. Yoshua Bengio
8. Alex Krizhevsky
9. Ilya Sutskever
10. Jeffrey Pennington

Who trusts Hua He overall

1. Yang Shao
2. Jimmy J. Lin
3. John Wieting
4. Shuohang Wang
5. Dhivya Chandrasekaran
6. Like Hui
7. Eneko Agirre
8. Vikas Yadav
9. Siddhant Garg
10. Zhiguo Wang

Papers from Hua He's perspective

Ordered by how strongly the researchers Hua He 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. 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. 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

• A Continuously Growing Dataset of Sentential Paraphrases (2017) — 181 citations
• Multi-Perspective Sentence Similarity Modeling with Convolutional Neural Networks (2015) — 383 citations
• Pairwise Word Interaction Modeling with Deep Neural Networks for Semantic Similarity Measurement (2016) — 246 citations
• UMD-TTIC-UW at SemEval-2016 Task 1: Attention-Based Multi-Perspective Convolutional Neural Networks for Textual Similarity Measurement (2016) — 31 citations