Yuxiang Wu

Artificial Intelligence · 2017–2022

A researcher in the Science corpus: 6 papers; trusts 254 researchers and is trusted by 77 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 186 citations into it; #3,427 of 35,345 by trust here, #2,070 by citations
• Natural Language Processing — 162 citations into it; #2,083 of 5,688 by trust here, #4,250 by citations
• Computer Vision and Pattern Recognition — 89 citations into it
• Evaluation & Benchmarks — 43 citations into it
• Information Retrieval & Search — 34 citations into it
• Large Language Models — 28 citations into it; #184 of 799 by trust here, #204 by citations
• Representation & Self-Supervised Learning — 16 citations into it
• Model Architectures — 15 citations into it

Whom Yuxiang Wu trusts overall

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

1. Yoshua Bengio
2. Geoffrey E. Hinton
3. Ronald J. Williams
4. Diederik P. Kingma
5. Ilya Sutskever
6. Richard S. Sutton
7. Yann LeCun
8. Alex Krizhevsky
9. Samuel R. Bowman
10. Tomas Mikolov

Who trusts Yuxiang Wu overall

1. Brielen Madureira
2. Kristjan Arumae
3. Wojciech Kryscinski
4. Yang Gao
5. Ming Zhong
6. Yue Dong
7. Timo Schick
8. Nora Kassner
9. Harrison Lee
10. Rafael Rafailov

Papers from Yuxiang Wu's perspective

Ordered by how strongly the researchers Yuxiang 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. Attention is All you Need (Neural Information Processing Systems)
4. ImageNet classification with deep convolutional neural networks
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

• Language Models as Knowledge Bases? (2019) — 3,230 citations
• Learning to Extract Coherent Summary via Deep Reinforcement Learning (2018) — 180 citations
• PAQ: 65 Million Probably-Asked Questions and What You Can Do With Them (2021) — 275 citations
• Generating Data to Mitigate Spurious Correlations in Natural Language Inference Datasets (2022) — 76 citations
• How Context Affects Language Models' Factual Predictions (2020) — 271 citations
• End-to-End Adversarial Memory Network for Cross-domain Sentiment Classification (2017) — 256 citations