Chenhui Chu

Artificial Intelligence · 2016–2020

A researcher in the Science corpus: 7 papers; trusts 694 researchers and is trusted by 28 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 216 citations into it; #5,860 of 35,345 by trust here, #9,092 by citations
• Natural Language Processing — 190 citations into it; #1,292 of 5,688 by trust here, #2,497 by citations
• Computer Vision and Pattern Recognition — 142 citations into it; #4,731 of 21,575 by trust here, #6,031 by citations
• Evaluation & Benchmarks — 31 citations into it
• Representation & Self-Supervised Learning — 26 citations into it
• Machine Translation — 22 citations into it
• Multimodal Learning — 20 citations into it
• Model Architectures — 19 citations into it

Whom Chenhui Chu trusts overall

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

1. Rico Sennrich
2. Philipp Koehn
3. Yoshua Bengio
4. Geoffrey E. Hinton
5. Ilya Sutskever
6. Kyunghyun Cho
7. Barry Haddow
8. Diederik P. Kingma
9. Dzmitry Bahdanau
10. Franz Joseph Och

Who trusts Chenhui Chu overall

1. Shuhao Gu
2. Raj Dabre
3. Marlies van der Wees
4. David Vilar
5. Zhilin Yang
6. Yang Feng
7. Roee Aharoni
8. Garrett Wilson
9. Christof Monz
10. Paul Michel

Papers from Chenhui Chu's perspective

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

Their papers

• BERT Representations for Video Question Answering (2020) — 118 citations
• KnowIT VQA: Answering Knowledge-Based Questions about Videos (2019) — 97 citations
• An Empirical Comparison of Simple Domain Adaptation Methods for Neural Machine Translation (2017) — 63 citations
• An Empirical Comparison of Domain Adaptation Methods for Neural Machine Translation (2017) — 240 citations
• A Survey of Domain Adaptation for Neural Machine Translation (2018) — 292 citations
• A Survey of Multilingual Neural Machine Translation (2019) — 361 citations
• Integrated Parallel Sentence and Fragment Extraction from Comparable Corpora: A Case Study on Chinese--Japanese Wikipedia (2016) — 23 citations