Su Zhu

Artificial Intelligence · with Yu · 2019–2020

A researcher in the Science corpus: 4 papers; trusts 73 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 79 citations into it
• Natural Language Processing — 71 citations into it
• Computer Vision and Pattern Recognition — 27 citations into it
• Model Architectures — 13 citations into it
• Molecular Biology — 5 citations into it
• Reinforcement Learning — 5 citations into it

Whom Su Zhu trusts overall

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

1. Alex Graves
2. Diederik P. Kingma
3. Oriol Vinyals
4. Yoshua Bengio
5. Geoffrey E. Hinton
6. Richard S. Sutton
7. Ilya Sutskever
8. Sepp Hochreiter
9. Alex Krizhevsky
10. Jimmy Ba

Who trusts Su Zhu overall

1. Lu Chen
2. Silei Xu
3. Victor Zhong

Papers from Su Zhu's perspective

Ordered by how strongly the researchers Su Zhu 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. Attention is All you Need (Neural Information Processing Systems)
3. Long Short-Term Memory
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. ImageNet classification with deep convolutional neural networks
6. Gradient-based learning applied to document recognition
7. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
8. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
9. ImageNet: A large-scale hierarchical image database
10. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)

Their papers

• Schema-Guided Multi-Domain Dialogue State Tracking with Graph Attention Neural Networks (2020) — 144 citations
• Line Graph Enhanced AMR-to-Text Generation with Mix-Order Graph Attention Networks (2020) — 31 citations
• Data Augmentation with Atomic Templates for Spoken Language Understanding (2019) — 29 citations
• Semantic Parsing with Dual Learning (2019) — 65 citations