C. Wang

Artificial Intelligence · with Cen · 2017–2022

A researcher in the Science corpus: 2 papers; trusts 5 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 46 citations into it; #34,142 of 35,345 by trust here, #19,018 by citations
• Graph Learning — 29 citations into it
• Statistical and Nonlinear Physics — 21 citations into it; #9,748 of 10,127 by trust here, #4,633 by citations
• Molecular Biology — 15 citations into it; #16,641 of 17,773 by trust here, #11,410 by citations
• Generative Models — 5 citations into it

Whom C. Wang trusts overall

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

1. Mark Newman
2. Michelle Girvan
3. Thomas Kipf
4. Jonathan D. Chang
5. Max Welling
6. Geoffrey E. Hinton
7. Shaosheng Cao
8. Yoshua Bengio
9. Minmin Chen
10. Deng Cai

Who trusts C. Wang overall

1. Shirui Pan
2. Kaveh Hassani
3. Yaochen Xie
4. Xiaotong Zhang
5. Jie Zhou
6. Chun Wang
7. Ruiqi Hu
8. Amir Hosein Khas Ahmadi
9. Guodong Long
10. Ganqu Cui

Papers from C. Wang's perspective

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

Their papers

• GraphMAE: Self-Supervised Masked Graph Autoencoders (2022) — 977 citations
• MGAE: Marginalized Graph Autoencoder for Graph Clustering (2017) — 521 citations