Lingxiao Zhao

Artificial Intelligence · 2019–2020

A researcher in the Science corpus: 2 papers; trusts 46 researchers and is trusted by 12 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 35 citations into it; #10,279 of 35,345 by trust here, #20,377 by citations
• Graph Learning — 23 citations into it
• Statistical and Nonlinear Physics — 15 citations into it; #2,114 of 10,127 by trust here, #4,439 by citations
• Computer Vision and Pattern Recognition — 9 citations into it
• Representation & Self-Supervised Learning — 9 citations into it
• Molecular Biology — 8 citations into it
• Model Architectures — 7 citations into it
• Information Systems — 6 citations into it

Whom Lingxiao Zhao trusts overall

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

1. Prithviraj Sen
2. Kaiming He
3. Diederik P. Kingma
4. Tim Salimans
5. Hoang NT
6. Yoshua Bengio
7. Geoffrey E. Hinton
8. Qimai Li
9. X. Zhang
10. Petar Velickovic

Who trusts Lingxiao Zhao overall

1. Uri Alon
2. Kaixiong Zhou
3. Chen Cai
4. T. Konstantin Rusch
5. Shaked Brody
6. Eran Yahav
7. Yujun Yan
8. Gabriele Corso
9. Xiao Huang
10. Yusu Wang

Papers from Lingxiao Zhao's perspective

Ordered by how strongly the researchers Lingxiao 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. 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Learning Multiple Layers of Features from Tiny Images
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• PairNorm: Tackling Oversmoothing in GNNs (2019) — 611 citations
• Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs (2020) — 1,400 citations