Lirong Wu

Artificial Intelligence · 2021–2022

A researcher in the Science corpus: 4 papers; trusts 416 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 128 citations into it
• Computer Vision and Pattern Recognition — 123 citations into it
• Representation & Self-Supervised Learning — 50 citations into it
• Graph Learning — 27 citations into it
• Generative Models — 25 citations into it
• Model Architectures — 18 citations into it
• Computational Theory and Mathematics — 15 citations into it
• Molecular Biology — 15 citations into it

Whom Lirong Wu trusts overall

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

1. Geoffrey E. Hinton
2. Kaiming He
3. Alex Krizhevsky
4. Yoshua Bengio
5. Diederik P. Kingma
6. Yann LeCun
7. Ross B. Girshick
8. Ilya Sutskever
9. Max Welling
10. Thomas Kipf

Papers from Lirong Wu's perspective

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

Their papers

• SimGRACE: A Simple Framework for Graph Contrastive Learning without Data Augmentation (2022) — 452 citations
• SimVP: Simpler yet Better Video Prediction (2022) — 438 citations
• Decoupled Mixup for Data-efficient Learning (2022) — 20 citations
• Self-Supervised Learning on Graphs: Contrastive, Generative, or Predictive (2021) — 336 citations