Jinman Zhao

Artificial Intelligence · 2018–2020

A researcher in the Science corpus: 2 papers; trusts 33 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 58 citations into it; #27,545 of 35,345 by trust here, #21,316 by citations
• Natural Language Processing — 25 citations into it; #4,527 of 5,688 by trust here, #4,449 by citations
• Information Systems — 24 citations into it; #6,398 of 8,771 by trust here, #5,303 by citations
• Computer Vision and Pattern Recognition — 21 citations into it
• Optimization & Training — 21 citations into it
• Software — 11 citations into it; #888 of 1,227 by trust here, #674 by citations
• Model Architectures — 10 citations into it
• Code Generation & Program Synthesis — 9 citations into it; #154 of 220 by trust here, #160 by citations

Whom Jinman Zhao trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Yann LeCun
5. Kaiming He
6. Sepp Hochreiter
7. Karen Simonyan
8. Uri Alon
9. Veselin Raychev
10. Oriol Vinyals

Who trusts Jinman Zhao overall

1. Maliheh Izadi
2. Baptiste Rozière
3. Matteo Ciniselli
4. Gareth Ari Aye
5. F. Liu
6. Max Schäfer
7. Ge Li
8. Han Peng

Papers from Jinman Zhao's perspective

Ordered by how strongly the researchers Jinman Zhao 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. Long Short-Term Memory
4. Attention is All you Need (Neural Information Processing Systems)
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Gradient-based learning applied to document recognition
7. Learning Multiple Layers of Features from Tiny Images
8. ImageNet: A large-scale hierarchical image database
9. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
10. Going deeper with convolutions

Their papers

• Code Prediction by Feeding Trees to Transformers (2020) — 241 citations
• The Effect of Network Width on the Performance of Large-batch Training (2018) — 24 citations