Guanghui Qin

Artificial Intelligence · 2021–2022

A researcher in the Science corpus: 2 papers; trusts 141 researchers and is trusted by 58 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 53 citations into it; #4,289 of 35,345 by trust here, #9,275 by citations
• Natural Language Processing — 36 citations into it
• Computer Vision and Pattern Recognition — 21 citations into it; #4,171 of 21,575 by trust here, #6,542 by citations
• Large Language Models — 19 citations into it; #103 of 799 by trust here, #484 by citations
• Model Architectures — 16 citations into it
• Evaluation & Benchmarks — 15 citations into it
• Prompting & Reasoning — 6 citations into it; #18 of 359 by trust here, #195 by citations
• Efficiency & Compression — 5 citations into it

Whom Guanghui Qin 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. Karen Simonyan
8. Mandar Joshi
9. Yann LeCun
10. Kaiming He

Who trusts Guanghui Qin overall

1. Boshi Wang
2. Hyojin Bahng
3. Renrui Zhang
4. Zonghan Yang
5. Xiao Liu
6. Benjamin Van Durme
7. Xiang Deng
8. Ohad Rubin
9. Colin Wei
10. Patrick Schramowski

Papers from Guanghui Qin's perspective

Ordered by how strongly the researchers Guanghui Qin 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. ImageNet classification with deep convolutional neural networks
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
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. Gradient-based learning applied to document recognition
9. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• Learning How to Ask: Querying LMs with Mixtures of Soft Prompts (2021) — 602 citations
• The NLP Task Effectiveness of Long-Range Transformers (2022) — 42 citations