Lingfei Wu

Artificial Intelligence · 2008–2023

A researcher in the Science corpus: 9 papers; trusts 225 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 148 citations into it; #19,979 of 35,345 by trust here, #11,624 by citations
• Natural Language Processing — 89 citations into it
• Computer Vision and Pattern Recognition — 35 citations into it
• Information Systems — 29 citations into it; #4,758 of 8,771 by trust here, #3,098 by citations
• Graph Learning — 28 citations into it
• Model Architectures — 18 citations into it
• Management of Technology and Innovation — 12 citations into it
• Materials Chemistry — 12 citations into it

Whom Lingfei Wu trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Diederik P. Kingma
4. Ilya Sutskever
5. Quoc V. Le
6. Alex Krizhevsky
7. Alex Graves
8. Sepp Hochreiter
9. Yann LeCun
10. Tomas Mikolov

Who trusts Lingfei Wu overall

1. Iddo Drori
2. John Pougué-Biyong
3. Qitian Wu
4. Zhanming Jie
5. Jinghao Zhang

Papers from Lingfei Wu's perspective

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

Their papers

• Improving Graph Neural Network Representations of Logical Formulae with Subgraph Pooling (2019) — 40 citations
• Improved Code Summarization via a Graph Neural Network (2020) — 335 citations
• A Joint Neural Model for Information Extraction with Global Features (2020) — 466 citations
• Graph-to-Tree Neural Networks for Learning Structured Input-Output Translation with Applications to Semantic Parsing and Math Word Problem (2020) — 83 citations
• Knowledge Graph-Augmented Abstractive Summarization with Semantic-Driven Cloze Reward (2020) — 173 citations
• Graph Neural Networks for Natural Language Processing: A Survey (2023) — 393 citations
• Iterative Deep Graph Learning for Graph Neural Networks: Better and Robust Node Embeddings (2020) — 532 citations
• Large teams develop and small teams disrupt science and technology (2019) — 959 citations
• The impact of higher education on entrepreneurial intentions of university students in China (2008) — 522 citations