Changnan Xiao

Artificial Intelligence · 2022–2023

A researcher in the Science corpus: 4 papers; trusts 716 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 244 citations into it; #22,113 of 35,345 by trust here, #22,463 by citations
• Computer Vision and Pattern Recognition — 177 citations into it; #15,159 of 21,575 by trust here, #15,955 by citations
• Meta & Continual Learning — 154 citations into it
• Representation & Self-Supervised Learning — 72 citations into it
• Optimization & Training — 31 citations into it
• Radiology, Nuclear Medicine and Imaging — 27 citations into it
• Reinforcement Learning — 26 citations into it
• Model Architectures — 17 citations into it

Whom Changnan Xiao trusts overall

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

1. Alex Krizhevsky
2. Kaiming He
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Diederik P. Kingma
6. Yann LeCun
7. Ilya Sutskever
8. Ross B. Girshick
9. Arslan Chaudhry
10. Karen Simonyan

Who trusts Changnan Xiao overall

1. Gyuhak Kim
2. Da-Wei Zhou

Papers from Changnan Xiao's perspective

Ordered by how strongly the researchers Changnan Xiao 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. ImageNet: A large-scale hierarchical image database
6. Gradient-based learning applied to document recognition
7. Long Short-Term Memory
8. Learning Multiple Layers of Features from Tiny Images
9. ImageNet Large Scale Visual Recognition Challenge
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Open-World Continual Learning: Unifying Novelty Detection and Continual Learning (2023) — 34 citations
• Learnability and Algorithm for Continual Learning (2023) — 37 citations
• Continual Learning Based on OOD Detection and Task Masking (2022) — 29 citations
• A Theoretical Study on Solving Continual Learning (2022) — 111 citations