Jianzhong Qi

Artificial Intelligence · 2018–2020

A researcher in the Science corpus: 6 papers; trusts 291 researchers and is trusted by 22 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 100 citations into it; #21,096 of 35,345 by trust here, #16,771 by citations
• Computer Vision and Pattern Recognition — 52 citations into it; #20,365 of 21,575 by trust here, #18,782 by citations
• Natural Language Processing — 38 citations into it; #3,931 of 5,688 by trust here, #4,361 by citations
• Model Architectures — 19 citations into it
• Representation & Self-Supervised Learning — 18 citations into it
• Graph Learning — 17 citations into it
• Evaluation & Benchmarks — 15 citations into it
• Generative Models — 15 citations into it; #944 of 1,956 by trust here, #1,460 by citations

Whom Jianzhong Qi trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Alex Krizhevsky
5. Yann LeCun
6. Ilya Sutskever
7. Richard Socher
8. Kaiming He
9. Jimmy Ba
10. Alex Graves

Who trusts Jianzhong Qi overall

1. Leonardo F. R. Ribeiro
2. Bayu Distiawan Trisedya
3. Chao Zhao
4. Zezhi Shao
5. Zequn Sun
6. Linfeng Zhang
7. Seyed Iman Mirzadeh

Papers from Jianzhong Qi's perspective

Ordered by how strongly the researchers Jianzhong Qi 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• GTR-LSTM: A Triple Encoder for Sentence Generation from RDF Data (2018) — 91 citations
• Entity Alignment between Knowledge Graphs Using Attribute Embeddings (2019) — 332 citations
• Semi-Supervised Dialogue Policy Learning via Stochastic Reward Estimation (2020) — 21 citations
• DBSVEC: Density-Based Clustering Using Support Vector Expansion (2019) — 16 citations
• GMAN: A Graph Multi-Attention Network for Traffic Prediction (2019) — 1,978 citations
• KDGAN: Knowledge Distillation with Generative Adversarial Networks (2018) — 191 citations