Kuan-Hao Huang

Artificial Intelligence · 2019–2021

A researcher in the Science corpus: 2 papers; trusts 64 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 50 citations into it; #23,955 of 35,345 by trust here, #16,771 by citations
• Natural Language Processing — 39 citations into it; #3,997 of 5,688 by trust here, #3,421 by citations
• Safety, Robustness & Interpretability — 19 citations into it
• Computer Vision and Pattern Recognition — 14 citations into it; #15,303 of 21,575 by trust here, #20,271 by citations
• Representation & Self-Supervised Learning — 11 citations into it

Whom Kuan-Hao Huang trusts overall

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

1. John Wieting
2. Christopher D. Manning
3. Rico Sennrich
4. Geoffrey E. Hinton
5. Yoshua Bengio
6. Richard Socher
7. Jeffrey Pennington
8. Diederik P. Kingma
9. Regina Barzilay
10. Tomas Mikolov

Who trusts Kuan-Hao Huang overall

1. Jiao Sun
2. Xuezhe Ma
3. Nanyun Peng

Papers from Kuan-Hao Huang's perspective

Ordered by how strongly the researchers Kuan-Hao Huang trusts endorse each paper. Same corpus, a different order for every researcher.

1. Attention is All you Need (Neural Information Processing Systems)
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. ImageNet classification with deep convolutional neural networks
6. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
7. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
8. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. Gradient-based learning applied to document recognition
10. ImageNet: A large-scale hierarchical image database

Their papers

• Examining Gender Bias in Languages with Grammatical Gender (2019) — 115 citations
• Generating Syntactically Controlled Paraphrases without Using Annotated Parallel Pairs (2021) — 82 citations