Hongkun Yu

Artificial Intelligence · 2019–2022

A researcher in the Science corpus: 5 papers; trusts 47 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 175 citations into it; #25,841 of 35,345 by trust here, #2,382 by citations
• Natural Language Processing — 90 citations into it; #4,826 of 5,688 by trust here, #2,172 by citations
• Computer Vision and Pattern Recognition — 67 citations into it; #18,199 of 21,575 by trust here, #6,671 by citations
• Large Language Models — 65 citations into it; #458 of 799 by trust here, #216 by citations

Whom Hongkun Yu trusts overall

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

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

Who trusts Hongkun Yu overall

1. Canwen Xu
2. Gaurav Menghani
3. Zhiyuan Fang
4. Jianfeng Wang
5. Sam Shleifer
6. Ronen Eldan
7. Wenhui Wang
8. Zi-Mei Yao
9. Sehoon Kim
10. Mousumi Akter

Papers from Hongkun Yu's perspective

Ordered by how strongly the researchers Hongkun Yu 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. Attention is All you Need (Neural Information Processing Systems)
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. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Scaling Instruction-Finetuned Language Models (2022) — 4,301 citations
• Large Language Models Can Self-Improve (2022) — 879 citations
• MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices (2020) — 1,053 citations
• On the Transformer Growth for Progressive BERT Training (2020) — 61 citations
• MobileBERT: Task-Agnostic Compression of BERT by Progressive Knowledge Transfer (2019) — 20 citations