Yun Chen

Artificial Intelligence · with Li · 2017–2020

A researcher in the Science corpus: 6 papers; trusts 342 researchers and is trusted by 30 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 176 citations into it; #6,726 of 35,345 by trust here, #6,468 by citations
• Natural Language Processing — 147 citations into it; #1,645 of 5,688 by trust here, #1,636 by citations
• Computer Vision and Pattern Recognition — 88 citations into it; #6,118 of 21,575 by trust here, #5,236 by citations
• Model Architectures — 34 citations into it
• Evaluation & Benchmarks — 17 citations into it
• Representation & Self-Supervised Learning — 16 citations into it
• Optimization & Training — 6 citations into it

Whom Yun Chen trusts overall

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

1. Kyunghyun Cho
2. Yoshua Bengio
3. Geoffrey E. Hinton
4. Ilya Sutskever
5. Alex Graves
6. Dzmitry Bahdanau
7. Diederik P. Kingma
8. Rico Sennrich
9. Oriol Vinyals
10. Kaiming He

Who trusts Yun Chen overall

1. Kyunghyun Cho
2. Jiatao Gu
3. Ilia Kulikov
4. Naveen Arivazhagan
5. Raj Dabre
6. Victor O. K. Li
7. Maruan Al-Shedivat
8. Ankur Bapna
9. Yong Wang
10. Xiangyu Duan

Papers from Yun Chen's perspective

Ordered by how strongly the researchers Yun Chen 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. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)

Their papers

• Meta-Learning for Low-Resource Neural Machine Translation (2018) — 364 citations
• A Stable and Effective Learning Strategy for Trainable Greedy Decoding (2018) — 31 citations
• A Teacher-Student Framework for Zero-Resource Neural Machine Translation (2017) — 156 citations
• Perturbed Masking: Parameter-free Probing for Analyzing and Interpreting BERT (2020) — 216 citations
• Zero-Resource Neural Machine Translation with Multi-Agent Communication Game (2018) — 49 citations
• Zero-Shot Cross-Lingual Neural Headline Generation (2018) — 54 citations