Jiong Zhu

Artificial Intelligence · 2019–2020

A researcher in the Science corpus: 3 papers; trusts 14 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 50 citations into it
• Graph Learning — 38 citations into it
• Statistical and Nonlinear Physics — 24 citations into it
• Representation & Self-Supervised Learning — 11 citations into it

Whom Jiong Zhu trusts overall

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

1. Thomas Kipf
2. Jennifer Neville
3. Max Welling
4. Yoshua Bengio
5. Michaël Defferrard
6. Prithviraj Sen
7. Jure Leskovec
8. Geoffrey E. Hinton
9. Diederik P. Kingma
10. William L. Hamilton

Who trusts Jiong Zhu overall

1. Yao Ma
2. Oleg Platonov
3. Xiang Li
4. Sitao Luan
5. Yujun Yan
6. Xiaorui Liu
7. Denis Kuznedelev
8. Chenqing Hua
9. Erico Tjoa
10. Neil Shah

Papers from Jiong Zhu's perspective

Ordered by how strongly the researchers Jiong Zhu 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

• Graph Neural Networks with Heterophily (2020) — 406 citations
• Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs (2020) — 1,400 citations
• GroupINN: Grouping-based Interpretable Neural Network for Classification of Limited, Noisy Brain Data (2019) — 121 citations