Qiuling Suo

Artificial Intelligence · 2021–2022

A researcher in the Science corpus: 4 papers; trusts 110 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 135 citations into it; #27,481 of 35,345 by trust here, #25,040 by citations
• Computer Vision and Pattern Recognition — 110 citations into it; #16,946 of 21,575 by trust here, #17,258 by citations
• Meta & Continual Learning — 56 citations into it
• Representation & Self-Supervised Learning — 50 citations into it
• Optimization & Training — 19 citations into it
• Statistics and Probability — 11 citations into it
• Reinforcement Learning — 10 citations into it

Whom Qiuling Suo trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Alex Krizhevsky
4. Yoshua Bengio
5. Chelsea Finn
6. Yann LeCun
7. Oriol Vinyals
8. Marc'Aurelio Ranzato
9. J. Urgen Schmidhuber
10. Kaiming He

Who trusts Qiuling Suo overall

1. Zhenyi Wang

Papers from Qiuling Suo's perspective

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

Their papers

• Meta Learning on a Sequence of Imbalanced Domains with Difficulty Awareness (2021) — 21 citations
• Improving Task-free Continual Learning by Distributionally Robust Memory Evolution (2022) — 54 citations
• Meta-Learning with Less Forgetting on Large-Scale Non-Stationary Task Distributions (2022) — 22 citations
• Meta-learning without data via Wasserstein distributionally-robust model fusion (2022) — 24 citations