Zijun Zhang

Artificial Intelligence · with Li · 2017–2019

A researcher in the Science corpus: 3 papers; trusts 104 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 39 citations into it; #10,240 of 35,345 by trust here, #23,731 by citations
• Computer Vision and Pattern Recognition — 34 citations into it; #7,040 of 21,575 by trust here, #15,529 by citations
• Optimization & Training — 25 citations into it; #1,772 of 2,601 by trust here, #2,313 by citations
• Generative Models — 21 citations into it
• Model Architectures — 12 citations into it
• Computational Mechanics — 6 citations into it
• Management Science and Operations Research — 6 citations into it
• Representation & Self-Supervised Learning — 6 citations into it

Whom Zijun Zhang trusts overall

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

1. Alex Krizhevsky
2. Diederik P. Kingma
3. Yoshua Bengio
4. Geoffrey E. Hinton
5. Sergey Zagoruyko
6. John C. Duchi
7. Kaiming He
8. Matthew D. Zeiler
9. Jimmy Ba
10. Sepp Hochreiter

Who trusts Zijun Zhang overall

1. Ilya Loshchilov
2. Mohamed Reyad
3. Dylan Turpin
4. Chen Yan
5. Frank Hutter
6. Nitish Shirish Keskar
7. Amany M. Sarhan
8. Jason Li
9. Liquang Wang
10. M. Arafa

Papers from Zijun Zhang's perspective

Ordered by how strongly the researchers Zijun Zhang 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. Gradient-based learning applied to document recognition
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Going deeper with convolutions

Their papers

• Normalized Direction-preserving Adam (2017) — 31 citations
• Perceptual Generative Autoencoders (2019) — 32 citations
• Improved Adam Optimizer for Deep Neural Networks (2018) — 1,333 citations