Deyu Bo

Artificial Intelligence · 2020–2021

A researcher in the Science corpus: 4 papers; trusts 33 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 122 citations into it
• Statistical and Nonlinear Physics — 58 citations into it
• Graph Learning — 53 citations into it
• Computer Vision and Pattern Recognition — 44 citations into it
• Information Systems — 29 citations into it
• Representation & Self-Supervised Learning — 28 citations into it
• Molecular Biology — 17 citations into it
• Generative Models — 6 citations into it

Whom Deyu Bo trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Thomas Kipf
4. Yoshua Bengio
5. Max Welling
6. Yann LeCun
7. Alex Krizhevsky
8. Jimmy Ba
9. Kaiming He
10. Andrew Y. Ng

Who trusts Deyu Bo overall

1. Oleg Platonov
2. Yujun Yan
3. Ruosong Ye
4. Anton Tsitsulin
5. Sitao Luan
6. Yue Liu
7. Denis Kuznedelev
8. Milad Hashemi

Papers from Deyu Bo's perspective

Ordered by how strongly the researchers Deyu Bo 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. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Beyond Low-frequency Information in Graph Convolutional Networks (2021) — 808 citations
• AM-GCN: Adaptive Multi-channel Graph Convolutional Networks (2020) — 616 citations
• A Survey on Heterogeneous Graph Embedding: Methods, Techniques, Applications and Sources (2020) — 454 citations
• Structural Deep Clustering Network (2020) — 684 citations