Johnny Tian-Zheng Wei

Artificial Intelligence · with Bojar · 2018–2021

A researcher in the Science corpus: 4 papers; trusts 92 researchers and is trusted by 22 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 84 citations into it; #8,649 of 35,345 by trust here, #9,369 by citations
• Natural Language Processing — 77 citations into it; #1,819 of 5,688 by trust here, #2,069 by citations
• Evaluation & Benchmarks — 73 citations into it; #947 of 3,147 by trust here, #1,420 by citations
• Computer Vision and Pattern Recognition — 11 citations into it
• Language and Linguistics — 8 citations into it
• Information Systems — 7 citations into it; #1,891 of 8,771 by trust here, #1,542 by citations

Whom Johnny Tian-Zheng Wei trusts overall

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

1. Ondrej Bojar
2. Philipp Koehn
3. Matt Post
4. Yvette Graham
5. Chin-Yew Lin
6. Qingsong Ma
7. Jacob Devlin
8. Maja Popovic
9. Alon Lavie
10. Nitika Mathur

Who trusts Johnny Tian-Zheng Wei overall

1. Sebastian Gehrmann
2. Thibault Sellam
3. Ricardo Rei
4. Ananya B. Sai
5. Nitika Mathur
6. Daniel Deutsch
7. Elizabeth Clark
8. Markus Freitag
9. Amy Pu
10. Thuy Vu

Papers from Johnny Tian-Zheng Wei's perspective

Ordered by how strongly the researchers Johnny Tian-Zheng Wei trusts endorse each paper. Same corpus, a different order for every researcher.

1. Attention is All you Need (Neural Information Processing Systems)
2. Long Short-Term Memory
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
5. ImageNet classification with deep convolutional neural networks
6. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
7. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
8. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. Bleu: a Method for Automatic Evaluation of Machine Translation
10. Gradient-based learning applied to document recognition

Their papers

• Results of the WMT19 Metrics Shared Task: Segment-Level and Strong MT Systems Pose Big Challenges (2019) — 191 citations
• Results of the WMT20 Metrics Shared Task (2020) — 170 citations
• Twitter Universal Dependency Parsing for African-American and Mainstream American English (2018) — 76 citations
• The statistical advantage of automatic NLG metrics at the system level (2021) — 25 citations