Wenxuan Zhou

Artificial Intelligence · with Neves · 2019–2020

A researcher in the Science corpus: 4 papers; trusts 96 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 105 citations into it
• Natural Language Processing — 84 citations into it
• Computer Vision and Pattern Recognition — 34 citations into it
• Representation & Self-Supervised Learning — 21 citations into it
• Model Architectures — 18 citations into it
• Optimization & Training — 12 citations into it
• Large Language Models — 10 citations into it
• Molecular Biology — 9 citations into it

Whom Wenxuan Zhou trusts overall

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

1. Yoshua Bengio
2. Geoffrey E. Hinton
3. Yann LeCun
4. Ilya Sutskever
5. Diederik P. Kingma
6. Alex Krizhevsky
7. Christopher D. Manning
8. Tomas Mikolov
9. Kaiming He
10. Sepp Hochreiter

Who trusts Wenxuan Zhou overall

1. Shirui Pan
2. Dong-Ho Lee

Papers from Wenxuan Zhou's perspective

Ordered by how strongly the researchers Wenxuan Zhou 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. 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

• Learning from Explanations with Neural Execution Tree (2019) — 41 citations
• NERO: A Neural Rule Grounding Framework for Label-Efficient Relation Extraction (2020) — 58 citations
• Document-Level Relation Extraction with Adaptive Thresholding and Localized Context Pooling (2020) — 379 citations
• Learning to Annotate: Modularizing Data Augmentation for Text Classifiers with Natural Language Explanations (2019)