Qiankun Gao

Artificial Intelligence · 2022–2023

A researcher in the Science corpus: 2 papers; trusts 169 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 67 citations into it
• Computer Vision and Pattern Recognition — 57 citations into it
• Meta & Continual Learning — 27 citations into it
• Representation & Self-Supervised Learning — 19 citations into it
• Model Architectures — 9 citations into it
• Optimization & Training — 5 citations into it

Whom Qiankun Gao trusts overall

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

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

Who trusts Qiankun Gao overall

1. Da-Wei Zhou
2. Doyoung Kim
3. Hai-Long Sun
4. Han-Jia Ye

Papers from Qiankun Gao's perspective

Ordered by how strongly the researchers Qiankun Gao 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. ImageNet Large Scale Visual Recognition Challenge
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• A Unified Continual Learning Framework with General Parameter-Efficient Tuning (2023) — 161 citations
• R-DFCIL: Relation-Guided Representation Learning for Data-Free Class Incremental Learning (2022) — 102 citations