Yin-Wen Chang

Artificial Intelligence · 2010–2020

A researcher in the Science corpus: 4 papers; trusts 56 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 75 citations into it; #20,635 of 35,345 by trust here, #10,207 by citations
• Natural Language Processing — 44 citations into it
• Computer Vision and Pattern Recognition — 34 citations into it
• Evaluation & Benchmarks — 13 citations into it
• Information Retrieval & Search — 11 citations into it; #626 of 1,022 by trust here, #448 by citations
• Optimization & Training — 6 citations into it

Whom Yin-Wen Chang trusts overall

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

1. Philipp Koehn
2. Franz Joseph Och
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Joakim Nivre
6. Diederik P. Kingma
7. Ilya Sutskever
8. Martin J. Wainwright
9. Alex Krizhevsky
10. Yann LeCun

Who trusts Yin-Wen Chang overall

1. Alexander M. Rush
2. Luyu Gao
3. Dipanjan Das
4. Andrew Chisholm
5. Sohee Yang
6. Sheng-syun Shen
7. Huifeng Guo
8. Zhuang Wang
9. Si Si
10. Michael Collins

Papers from Yin-Wen Chang's perspective

Ordered by how strongly the researchers Yin-Wen Chang trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Attention is All you Need (Neural Information Processing Systems)
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
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. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
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. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• Pre-training Tasks for Embedding-based Large-scale Retrieval (2020) — 327 citations
• Training and Testing Low-degree Polynomial Data Mappings via Linear SVM (2010) — 590 citations
• $O(n)$ Connections are Expressive Enough: Universal Approximability of Sparse Transformers (2020) — 108 citations
• Exact Decoding of Phrase-Based Translation Models through Lagrangian Relaxation (2011) — 60 citations