Ruiqi Hu

Artificial Intelligence · 2018–2019

A researcher in the Science corpus: 3 papers; trusts 42 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 78 citations into it; #34,613 of 35,345 by trust here, #19,018 by citations
• Statistical and Nonlinear Physics — 49 citations into it; #9,935 of 10,127 by trust here, #3,979 by citations
• Graph Learning — 43 citations into it
• Computer Vision and Pattern Recognition — 30 citations into it
• Representation & Self-Supervised Learning — 27 citations into it
• Molecular Biology — 17 citations into it
• Generative Models — 16 citations into it
• Information Systems — 7 citations into it

Whom Ruiqi Hu trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Thomas Kipf
4. Max Welling
5. Yoshua Bengio
6. L. V. D. van der Maaten
7. Individualized Treat
8. Shaosheng Cao
9. Yann LeCun
10. Alex Krizhevsky

Who trusts Ruiqi Hu overall

1. Ganqu Cui
2. Shirui Pan
3. Dongkwan Kim
4. Yue Liu
5. Zonghan Wu

Papers from Ruiqi Hu's perspective

Ordered by how strongly the researchers Ruiqi Hu 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. 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. GENERATIVE ADVERSARIAL NETS
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

• Adversarially Regularized Graph Autoencoder (2018) — 489 citations
• Attributed Graph Clustering: A Deep Attentional Embedding Approach (2019) — 645 citations
• Learning Graph Embedding With Adversarial Training Methods (2019) — 347 citations