Chen Wu

Artificial Intelligence · with Wang · 2018–2020

A researcher in the Science corpus: 5 papers; trusts 235 researchers and is trusted by 11 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 117 citations into it; #14,967 of 35,345 by trust here, #9,275 by citations
• Natural Language Processing — 115 citations into it; #4,156 of 5,688 by trust here, #2,888 by citations
• Computer Vision and Pattern Recognition — 51 citations into it; #9,943 of 21,575 by trust here, #6,873 by citations
• Large Language Models — 25 citations into it
• Evaluation & Benchmarks — 24 citations into it
• Model Architectures — 15 citations into it
• Representation & Self-Supervised Learning — 13 citations into it
• Information Systems — 6 citations into it

Whom Chen 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. Richard Socher
6. Alex Krizhevsky
7. Kaiming He
8. Yann LeCun
9. Christopher D. Manning
10. Jimmy Ba

Who trusts Chen Wu overall

1. Chenliang Li
2. Ming Ding
3. Reham Osama
4. Bin Bi
5. Minghao Hu
6. Kai Sun
7. Yiming Cui
8. Rajarshi Das
9. Hao Cheng
10. Ming Yan

Papers from Chen Wu's perspective

Ordered by how strongly the researchers Chen 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. Long Short-Term Memory
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
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. 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

• StructBERT: Incorporating Language Structures into Pre-training for Deep Language Understanding (2019) — 301 citations
• Multi-Granularity Hierarchical Attention Fusion Networks for Reading Comprehension and Question Answering (2018) — 178 citations
• PALM: Pre-training an Autoencoding&autoregressive Language Model for Context-conditioned Generation (2020) — 78 citations
• IDST at TREC 2019 Deep Learning Track: Deep Cascade Ranking with Generation-based Document Expansion and Pre-trained Language Modeling (2019) — 40 citations
• Incorporating External Knowledge into Machine Reading for Generative Question Answering (2019) — 46 citations