Quanming Yao

Artificial Intelligence · 2016–2021

A researcher in the Science corpus: 13 papers; trusts 1,070 researchers and is trusted by 24 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 494 citations into it; #8,657 of 35,345 by trust here, #3,816 by citations
• Computer Vision and Pattern Recognition — 268 citations into it; #7,972 of 21,575 by trust here, #8,021 by citations
• Optimization & Training — 117 citations into it; #1,145 of 2,601 by trust here, #672 by citations
• Model Architectures — 96 citations into it; #1,050 of 2,278 by trust here, #1,749 by citations
• Neural Architecture Search & AutoML — 82 citations into it
• Representation & Self-Supervised Learning — 79 citations into it; #2,828 of 3,145 by trust here, #1,638 by citations
• Graph Learning — 53 citations into it
• Computational Theory and Mathematics — 46 citations into it

Whom Quanming Yao trusts overall

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

1. Yoshua Bengio
2. Alex Krizhevsky
3. Geoffrey E. Hinton
4. Kaiming He
5. Diederik P. Kingma
6. Barret Zoph
7. Yann LeCun
8. Quoc V. Le
9. Ilya Sutskever
10. Karen Simonyan

Who trusts Quanming Yao overall

1. Yuhui Ding
2. Hwanjun Song
3. Yi-Wei Chen
4. Qingquan Song
5. Yu Cheng
6. Kaixiong Zhou
7. Tong Yu
8. Tong Zhang
9. Junnan Li
10. Elad Amrani

Papers from Quanming Yao's perspective

Ordered by how strongly the researchers Quanming Yao trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
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. Learning Multiple Layers of Features from Tiny Images
10. Going deeper with convolutions

Their papers

• Co-teaching: Robust training of deep neural networks with extremely noisy labels (2018) — 2,654 citations
• Generalizing from a Few Examples (2019) — 2,151 citations
• Loss-aware Binarization of Deep Networks (2016) — 229 citations
• Efficient Neural Architecture Search via Proximal Iterations (2019) — 108 citations
• Automated Machine Learning: From Principles to Practices (2018) — 264 citations
• Pooling Architecture Search for Graph Classification (2021) — 88 citations
• DiffMG: Differentiable Meta Graph Search for Heterogeneous Graph Neural Networks (2020) — 88 citations
• Simplifying Architecture Search for Graph Neural Network (2020) — 59 citations
• Propagation Model Search for Graph Neural Networks (2020) — 7 citations
• Differentiable Neural Architecture Search via Proximal Iterations (2019) — 14 citations
• Privacy-preserving Transfer Learning for Knowledge Sharing (2018) — 19 citations
• Taking Human out of Learning Applications: A Survey on Automated Machine Learning (2018) — 216 citations
• Meta-Graph Based Recommendation Fusion over Heterogeneous Information Networks (2017) — 562 citations