Qitian Wu

Artificial Intelligence · 2019–2023

A researcher in the Science corpus: 3 papers; trusts 34 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 82 citations into it
• Computer Vision and Pattern Recognition — 34 citations into it
• Graph Learning — 28 citations into it
• Representation & Self-Supervised Learning — 27 citations into it
• Information Systems — 26 citations into it
• Computer Networks and Communications — 9 citations into it
• Management Science and Operations Research — 6 citations into it

Whom Qitian Wu trusts overall

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

1. Diederik P. Kingma
2. Thomas Kipf
3. Geoffrey E. Hinton
4. Ronald J. Williams
5. Yoshua Bengio
6. Max Welling
7. Kaiming He
8. Alex Krizhevsky
9. Oriol Vinyals
10. Yann LeCun

Who trusts Qitian Wu overall

1. Le Wu
2. Ruosong Ye
3. Chen Gao

Papers from Qitian Wu's perspective

Ordered by how strongly the researchers Qitian 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. 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

• Dual Graph Attention Networks for Deep Latent Representation of Multifaceted Social Effects in Recommender Systems (2019) — 362 citations
• From Canonical Correlation Analysis to Self-supervised Graph Neural Networks (2021) — 301 citations
• NodeFormer: A Scalable Graph Structure Learning Transformer for Node Classification (2023) — 429 citations