Yu Sun

Artificial Intelligence · with Wang · 2019–2021

A researcher in the Science corpus: 9 papers; trusts 247 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 271 citations into it; #20,453 of 35,345 by trust here, #3,279 by citations
• Natural Language Processing — 199 citations into it; #4,387 of 5,688 by trust here, #1,351 by citations
• Computer Vision and Pattern Recognition — 144 citations into it; #13,825 of 21,575 by trust here, #4,317 by citations
• Large Language Models — 95 citations into it; #530 of 799 by trust here, #364 by citations
• Evaluation & Benchmarks — 76 citations into it
• Representation & Self-Supervised Learning — 39 citations into it
• Model Architectures — 26 citations into it
• Graph Learning — 15 citations into it

Whom Yu Sun trusts overall

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

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

Who trusts Yu Sun overall

1. Zhuang Liu
2. Ruiyang Ren
3. Junqiu Wei
4. Emily M. Bender
5. Shuohuan Wang
6. Wayne Xin Zhao
7. Yi Mao
8. Xiaodong Liu
9. Xu Han
10. Pengcheng He

Papers from Yu Sun's perspective

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

Their papers

• ERNIE-ViL: Knowledge Enhanced Vision-Language Representations Through Scene Graph (2020) — 419 citations
• ERNIE-M: Enhanced Multilingual Representation by Aligning Cross-lingual Semantics with Monolingual Corpora (2020) — 121 citations
• ERNIE: Enhanced Representation through Knowledge Integration (2019) — 1,025 citations
• ERNIE 2.0: A Continual Pre-training Framework for Language Understanding (2019) — 902 citations
• ERNIE 3.0 Titan: Exploring Larger-scale Knowledge Enhanced Pre-training for Language Understanding and Generation (2021) — 88 citations
• Masked Label Prediction: Unified Massage Passing Model for Semi-Supervised Classification (2020) — 1,148 citations
• ERNIE 3.0: Large-scale Knowledge Enhanced Pre-training for Language Understanding and Generation (2021) — 607 citations
• ERNIE-GEN: An Enhanced Multi-Flow Pre-training and Fine-tuning Framework for Natural Language Generation (2020) — 135 citations
• ERNIE-Gram: Pre-Training with Explicitly N-Gram Masked Language Modeling for Natural Language Understanding (2020) — 43 citations