Zhengdao Chen

Artificial Intelligence · 2017–2020

A researcher in the Science corpus: 4 papers; trusts 58 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 74 citations into it; #23,038 of 35,345 by trust here, #14,718 by citations
• Graph Learning — 59 citations into it; #300 of 693 by trust here, #409 by citations
• Computer Vision and Pattern Recognition — 31 citations into it
• Computational Theory and Mathematics — 24 citations into it
• Model Architectures — 24 citations into it
• Statistical and Nonlinear Physics — 22 citations into it
• Molecular Biology — 21 citations into it
• Materials Chemistry — 19 citations into it; #8,813 of 11,513 by trust here, #9,657 by citations

Whom Zhengdao Chen trusts overall

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

1. Diederik P. Kingma
2. Yann LeCun
3. Haggai Maron
4. Geoffrey E. Hinton
5. Joan Bruna
6. Sergey Ioffe
7. Jure Leskovec
8. Yoshua Bengio
9. William L. Hamilton
10. Sepp Hochreiter

Who trusts Zhengdao Chen overall

1. Pan Li
2. Vikas K. Garg
3. Vijay Prakash Dwivedi
4. Johannes Klicpera
5. Xiyuan Wang
6. Soledad Villar
7. Lei Chen
8. Rex Ying
9. Jiaxuan You
10. Christopher Morris

Papers from Zhengdao Chen's perspective

Ordered by how strongly the researchers Zhengdao Chen trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
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. 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

• On the equivalence between graph isomorphism testing and function approximation with GNNs (2019) — 311 citations
• Can graph neural networks count substructures? (2020) — 381 citations
• Supervised Community Detection with Line Graph Neural Networks (2017) — 371 citations
• Multimodal Surface Matching with Higher-Order Smoothness Constraints☆ (2017) — 312 citations