Xian Li

Artificial Intelligence · with Wang · 2018–2022

A researcher in the Science corpus: 18 papers; trusts 589 researchers and is trusted by 54 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 722 citations into it; #4,469 of 35,345 by trust here, #2,734 by citations
• Natural Language Processing — 516 citations into it; #1,489 of 5,688 by trust here, #1,205 by citations
• Computer Vision and Pattern Recognition — 325 citations into it
• Large Language Models — 196 citations into it; #695 of 799 by trust here, #169 by citations
• Evaluation & Benchmarks — 131 citations into it; #1,261 of 3,147 by trust here, #2,545 by citations
• Representation & Self-Supervised Learning — 58 citations into it
• Efficiency & Compression — 57 citations into it
• Model Architectures — 44 citations into it

Whom Xian Li trusts overall

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

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

Who trusts Xian Li overall

1. Changhan Wang
2. Juan Pino
3. Jiatao Gu
4. Jungo Kasai
5. Hang Le
6. Yun Tang
7. Shijie Wu
8. Yuqing Tang
9. Junliang Guo
10. Peng-Jen Chen

Papers from Xian Li's perspective

Ordered by how strongly the researchers Xian Li 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. Attention is All you Need (Neural Information Processing Systems)
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. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
8. ImageNet: A large-scale hierarchical image database
9. Gradient-based learning applied to document recognition
10. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers

• Multilingual Denoising Pre-training for Neural Machine Translation (2020) — 2,142 citations
• Efficient Large Scale Language Modeling with Mixtures of Experts (2021) — 248 citations
• OPT-IML: Scaling Language Model Instruction Meta Learning through the Lens of Generalization (2022) — 307 citations
• Cross-lingual Retrieval for Iterative Self-Supervised Training (2020) — 76 citations
• FlowSeq: Non-Autoregressive Conditional Sequence Generation with Generative Flow (2019) — 203 citations
• Do Language Models Have Beliefs? Methods for Detecting, Updating, and Visualizing Model Beliefs (2021) — 92 citations
• Multilingual Translation with Extensible Multilingual Pretraining and Finetuning (2020) — 563 citations
• Deep Transformers with Latent Depth (2020) — 31 citations
• A Corpus for Multilingual Document Classification in Eight Languages (2018) — 150 citations
• Few-shot Learning with Multilingual Generative Language Models (2021) — 384 citations
• Recipes for Adapting Pre-trained Monolingual and Multilingual Models to Machine Translation (2020) — 51 citations
• Lifting the Curse of Multilinguality by Pre-training Modular Transformers (2022) — 181 citations
• Multilingual Translation from Denoising Pre-Training (2021) — 146 citations
• Multilingual Speech Translation from Efficient Finetuning of Pretrained Models (2021) — 167 citations
• Improving Speech Translation by Understanding and Learning from the Auxiliary Text Translation Task (2021) — 96 citations
• OPT: Open Pre-trained Transformer Language Models (2022) — 4,844 citations
• Few-shot Learning with Multilingual Language Models (2021) — 281 citations
• Gender bias amplification during Speed-Quality optimization in Neural Machine Translation (2021) — 42 citations