Linqing Liu

Artificial Intelligence · 2019–2021

A researcher in the Science corpus: 3 papers; trusts 99 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 102 citations into it; #27,087 of 35,345 by trust here, #6,229 by citations
• Natural Language Processing — 85 citations into it
• Computer Vision and Pattern Recognition — 52 citations into it; #20,776 of 21,575 by trust here, #4,750 by citations
• Evaluation & Benchmarks — 20 citations into it
• Large Language Models — 20 citations into it
• Representation & Self-Supervised Learning — 19 citations into it
• Model Architectures — 10 citations into it
• Signal Processing — 6 citations into it

Whom Linqing Liu trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Jimmy Ba
4. Yoshua Bengio
5. Ross B. Girshick
6. Richard Socher
7. Alex Krizhevsky
8. Yann LeCun
9. Ilya Sutskever
10. R. Caruana

Who trusts Linqing Liu overall

1. Jimmy J. Lin
2. Zhuyun Dai
3. Victor Sanh

Papers from Linqing Liu's perspective

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

Their papers

• Distilling Task-Specific Knowledge from BERT into Simple Neural Networks (2019) — 459 citations
• PAQ: 65 Million Probably-Asked Questions and What You Can Do With Them (2021) — 275 citations
• MKD: a Multi-Task Knowledge Distillation Approach for Pretrained Language Models (2019) — 24 citations