Wei Jin

Artificial Intelligence · 2020–2023

A researcher in the Science corpus: 4 papers; trusts 21 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 114 citations into it; #33,048 of 35,345 by trust here, #18,186 by citations
• Graph Learning — 52 citations into it
• Statistical and Nonlinear Physics — 19 citations into it
• Molecular Biology — 18 citations into it
• Transportation — 9 citations into it

Whom Wei Jin trusts overall

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

1. Daniel Zügner
2. Thomas Kipf
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Max Welling
6. Michaël Defferrard
7. Diederik P. Kingma
8. Yann LeCun
9. Stephan Günnemann
10. Alex Krizhevsky

Who trusts Wei Jin overall

1. Marinka Zitnik
2. Lichao Sun
3. Yaochen Xie
4. Yixin Liu
5. Yingtong Dou
6. Jinghao Zhang
7. Jianan Zhao
8. Philip S. Yu
9. Ji Wang

Papers from Wei Jin's perspective

Ordered by how strongly the researchers Wei Jin 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Exploring the Potential of Large Language Models (LLMs)in Learning on Graphs (2023) — 443 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