M. Chen

Artificial Intelligence · with Wu · 2018–2022

A researcher in the Science corpus: 8 papers; trusts 111 researchers and is trusted by 11 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 274 citations into it; #12,659 of 35,345 by trust here, #2,711 by citations
• Natural Language Processing — 203 citations into it; #2,485 of 5,688 by trust here, #670 by citations
• Computer Vision and Pattern Recognition — 142 citations into it; #10,371 of 21,575 by trust here, #3,124 by citations
• Model Architectures — 64 citations into it
• Evaluation & Benchmarks — 36 citations into it
• Optimization & Training — 36 citations into it
• Efficiency & Compression — 11 citations into it

Whom M. Chen trusts overall

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

1. Diederik P. Kingma
2. Yoshua Bengio
3. Geoffrey E. Hinton
4. Ilya Sutskever
5. Jimmy Ba
6. Kaiming He
7. Alex Krizhevsky
8. Alex Graves
9. J. Urgen Schmidhuber
10. Yann LeCun

Who trusts M. Chen overall

1. Ankur Bapna
2. Orhan Firat
3. Naveen Arivazhagan
4. Felix Stahlberg
5. Biao Zhang
6. Nicholas Carlini
7. Aditya Siddhant
8. Florian Tramèr
9. Julia Kreutzer
10. Colin Cherry

Papers from M. Chen's perspective

Ordered by how strongly the researchers M. 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
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

• The Best of Both Worlds: Combining Recent Advances in Neural Machine Translation (2018) — 479 citations
• Massively Multilingual Neural Machine Translation in the Wild: Findings and Challenges (2019) — 477 citations
• Lingvo: a Modular and Scalable Framework for Sequence-to-Sequence Modeling (2019) — 215 citations
• Training Deeper Neural Machine Translation Models with Transparent Attention (2018) — 147 citations
• Building Machine Translation Systems for the Next Thousand Languages (2022) — 120 citations
• Gmail Smart Compose: Real-Time Assisted Writing (2019) — 248 citations
• Leveraging Monolingual Data with Self-Supervision for Multilingual Neural Machine Translation (2020) — 88 citations
• Faster Transformer Decoding: N-gram Masked Self-Attention (2020) — 20 citations