Zhao Chen

Artificial Intelligence · with Badrinarayanan · 2017–2020

A researcher in the Science corpus: 3 papers; trusts 318 researchers and is trusted by 21 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 66 citations into it; #8,947 of 35,345 by trust here, #8,210 by citations
• Computer Vision and Pattern Recognition — 63 citations into it; #7,778 of 21,575 by trust here, #6,371 by citations
• Optimization & Training — 28 citations into it; #1,173 of 2,601 by trust here, #878 by citations
• Model Architectures — 19 citations into it
• Safety, Robustness & Interpretability — 12 citations into it
• Representation & Self-Supervised Learning — 11 citations into it
• Management Science and Operations Research — 5 citations into it

Whom Zhao Chen trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Kaiming He
4. Diederik P. Kingma
5. Karen Simonyan
6. Yoshua Bengio
7. Ilya Sutskever
8. Yann LeCun
9. Ross B. Girshick
10. Christian Szegedy

Who trusts Zhao Chen overall

1. Michael Crawshaw
2. Yunhui Guo
3. Christopher Fifty
4. Simon Vandenhende
5. Kevis-Kokitsi Maninis
6. Jeremy Howard
7. Ozan Sener
8. Andrea Gesmundo
9. Menelaos Kanakis
10. Stamatios Georgoulis

Papers from Zhao Chen's perspective

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

Their papers

• GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks (2017) — 1,947 citations
• Just Pick a Sign: Optimizing Deep Multitask Models with Gradient Sign Dropout (2020) — 300 citations
• Gradient Adversarial Training of Neural Networks (2018) — 41 citations