Ke M. Tran

Natural Language Processing · 2016–2023

A researcher in the Science corpus: 4 papers; trusts 100 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Natural Language Processing — 52 citations into it; #2,144 of 5,688 by trust here, #2,808 by citations
• Artificial Intelligence — 49 citations into it; #10,961 of 35,345 by trust here, #12,387 by citations
• Model Architectures — 32 citations into it
• Computer Vision and Pattern Recognition — 19 citations into it; #7,796 of 21,575 by trust here, #11,083 by citations
• Evaluation & Benchmarks — 11 citations into it
• Large Language Models — 6 citations into it
• Optimization & Training — 5 citations into it

Whom Ke M. Tran trusts overall

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

1. Yoshua Bengio
2. Alex Graves
3. Geoffrey E. Hinton
4. Ilya Sutskever
5. Alex Krizhevsky
6. Tomas Mikolov
7. Diederik P. Kingma
8. Kyunghyun Cho
9. J. Urgen Schmidhuber
10. Yann LeCun

Who trusts Ke M. Tran overall

1. Yonatan Belinkov
2. Elena Voita
3. Mostafa Dehghani
4. Alessandro Raganato
5. Samira Abnar
6. Nadir Durrani
7. Michal Daniluk
8. Ivan Titov
9. James R. Glass
10. Zhiqiang Tao

Papers from Ke M. Tran's perspective

Ordered by how strongly the researchers Ke M. Tran trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Long Short-Term Memory
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers

• The Importance of Being Recurrent for Modeling Hierarchical Structure (2018) — 160 citations
• Recurrent Memory Networks for Language Modeling (2016) — 92 citations
• Recurrent Memory Network for Language Modeling (2016) — 21 citations
• SeamlessM4T: Massively Multilingual&Multimodal Machine Translation (2023) — 202 citations