Xiao-Ming Wu

Artificial Intelligence · with Li · 2018–2021

A researcher in the Science corpus: 7 papers; trusts 348 researchers and is trusted by 30 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 147 citations into it; #7,436 of 35,345 by trust here, #5,317 by citations
• Computer Vision and Pattern Recognition — 83 citations into it
• Representation & Self-Supervised Learning — 63 citations into it
• Graph Learning — 45 citations into it; #208 of 693 by trust here, #183 by citations
• Meta & Continual Learning — 29 citations into it
• Statistical and Nonlinear Physics — 21 citations into it
• Radiology, Nuclear Medicine and Imaging — 18 citations into it
• Molecular Biology — 11 citations into it

Whom Xiao-Ming Wu trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Yoshua Bengio
4. Alex Krizhevsky
5. Yann LeCun
6. Kaiming He
7. Ilya Sutskever
8. Max Welling
9. Ruslan Salakhutdinov
10. Karen Simonyan

Who trusts Xiao-Ming Wu overall

1. Hao Zhu
2. Deyu Bo
3. Junlin Yang
4. Yuhui Ding
5. Uri Alon
6. Xiao Wang
7. Chen Cai
8. Lingxiao Zhao
9. Le Wu
10. Ganqu Cui

Papers from Xiao-Ming Wu's perspective

Ordered by how strongly the researchers Xiao-Ming 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. Learning Multiple Layers of Features from Tiny Images

Their papers

• Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning (2018) — 3,507 citations
• Reconstructing Capsule Networks for Zero-shot Intent Classification (2019) — 49 citations
• Overcoming Catastrophic Forgetting in Incremental Few-Shot Learning by Finding Flat Minima (2021) — 219 citations
• Label Efficient Semi-Supervised Learning via Graph Filtering (2019) — 179 citations
• Slake: A Semantically-Labeled Knowledge-Enhanced Dataset For Medical Visual Question Answering (2021) — 635 citations
• Unknown Intent Detection Using Gaussian Mixture Model with an Application to Zero-shot Intent Classification (2020) — 99 citations
• Attributed Graph Clustering via Adaptive Graph Convolution (2019) — 349 citations