Guannan Liang

Artificial Intelligence · 2020

A researcher in the Science corpus: 1 paper; trusts 19 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 23 citations into it
• Optimization & Training — 17 citations into it
• Federated & Distributed Learning — 11 citations into it
• Computer Vision and Pattern Recognition — 6 citations into it
• Computational Mechanics — 5 citations into it

Whom Guannan Liang trusts overall

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

1. Karen Simonyan
2. Kaiming He
3. Sai Praneeth Karimireddy
4. H. Brendan McMahan
5. Sashank J. Reddi
6. Diederik P. Kingma
7. Alex Krizhevsky
8. Geoffrey E. Hinton
9. Andrew Zisserman
10. Satyen Kale

Papers from Guannan Liang's perspective

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

Their papers

• Effective Federated Adaptive Gradient Methods with Non-IID Decentralized Data (2020) — 29 citations