Zhenhua Dong

Artificial Intelligence · with Chen · 2018

A researcher in the Science corpus: 2 papers; trusts 22 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 42 citations into it
• Computer Vision and Pattern Recognition — 22 citations into it
• Representation & Self-Supervised Learning — 10 citations into it
• Federated & Distributed Learning — 9 citations into it; #180 of 319 by trust here, #132 by citations
• Meta & Continual Learning — 8 citations into it; #657 of 1,041 by trust here, #355 by citations
• Reinforcement Learning — 6 citations into it

Whom Zhenhua Dong trusts overall

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

1. Chelsea Finn
2. Yann LeCun
3. Zhenguo Li
4. Geoffrey E. Hinton
5. Oriol Vinyals
6. Diederik P. Kingma
7. Pieter Abbeel
8. Yoshua Bengio
9. Alex Krizhevsky
10. Hugo Larochelle

Who trusts Zhenhua Dong overall

1. Mikhail Khodak
2. Alireza Fallah
3. Canh T. Dinh
4. Alessio Zappone
5. Yutao Huang
6. Qinbin Li

Papers from Zhenhua Dong's perspective

Ordered by how strongly the researchers Zhenhua Dong 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Federated Meta-Learning with Fast Convergence and Efficient Communication (2018) — 475 citations
• Federated Meta-Learning for Recommendation (2018) — 140 citations