Linting Xue

Artificial Intelligence · 2020–2022

A researcher in the Science corpus: 4 papers; trusts 74 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 185 citations into it; #30,697 of 35,345 by trust here, #3,261 by citations
• Computer Vision and Pattern Recognition — 122 citations into it
• Natural Language Processing — 119 citations into it; #5,082 of 5,688 by trust here, #1,031 by citations
• Evaluation & Benchmarks — 42 citations into it
• Large Language Models — 34 citations into it; #670 of 799 by trust here, #272 by citations
• Model Architectures — 17 citations into it

Whom Linting Xue trusts overall

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

1. Taku Kudo
2. Yoshua Bengio
3. Geoffrey E. Hinton
4. Alex Graves
5. Noam Shazeer
6. Diederik P. Kingma
7. Ilya Sutskever
8. Alex Krizhevsky
9. Alexis Conneau
10. Yann LeCun

Who trusts Linting Xue overall

1. Yi Tay
2. Katikapalli Subramanyam Kalyan
3. Sebastian Ruder
4. Vinh Q. Tran
5. Barret Zoph
6. Zewen Chi
7. Li Dong
8. Mostafa Dehghani
9. Jianmo Ni
10. Jinlan Fu

Papers from Linting Xue's perspective

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

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

Their papers

• PaLI: A Jointly-Scaled Multilingual Language-Image Model (2022) — 1,015 citations
• mT5: A Massively Multilingual Pre-trained Text-to-Text Transformer (2020) — 3,278 citations
• ByT5: Towards a Token-Free Future with Pre-trained Byte-to-Byte Models (2021) — 725 citations
• nmT5 - Is parallel data still relevant for pre-training massively multilingual language models? (2021) — 28 citations