Yao Ma

Artificial Intelligence · The Web Conference · 2018–2021

A researcher in the Science corpus: 8 papers; trusts 180 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 234 citations into it; #24,196 of 35,345 by trust here, #7,531 by citations
• Graph Learning — 105 citations into it
• Safety, Robustness & Interpretability — 90 citations into it
• Computer Vision and Pattern Recognition — 84 citations into it
• Statistical and Nonlinear Physics — 40 citations into it
• Molecular Biology — 35 citations into it
• Representation & Self-Supervised Learning — 31 citations into it
• Information Systems — 29 citations into it

Whom Yao Ma trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Yoshua Bengio
4. Yann LeCun
5. Diederik P. Kingma
6. Ilya Sutskever
7. Kaiming He
8. Oriol Vinyals
9. Max Welling
10. Ruslan Salakhutdinov

Who trusts Yao Ma overall

1. Marinka Zitnik
2. Ekagra Ranjan
3. Lan Wei
4. Wenzheng Feng
5. Le Wu
6. Xiao Wang
7. Jianan Zhao
8. Jie Zhou
9. Chen Gao
10. Lichao Sun

Papers from Yao Ma's perspective

Ordered by how strongly the researchers Yao Ma 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
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

• Adversarial Attacks and Defenses in Images, Graphs and Text: A Review (2019) — 757 citations
• Is Homophily a Necessity for Graph Neural Networks? (2021) — 314 citations
• Graph Structure Learning for Robust Graph Neural Networks (2020) — 921 citations
• Traffic Flow Prediction via Spatial Temporal Graph Neural Network (2020) — 661 citations
• Node Similarity Preserving Graph Convolutional Networks (2020) — 328 citations
• Graph Neural Networks for Social Recommendation (2019) — 2,365 citations
• Graph Convolutional Networks with EigenPooling (2019) — 366 citations
• Signed Graph Convolutional Networks (2018) — 325 citations