Feng Yu

Artificial Intelligence · The Web Conference · 2016–2020

A researcher in the Science corpus: 4 papers; trusts 112 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 76 citations into it
• Computer Vision and Pattern Recognition — 45 citations into it
• Representation & Self-Supervised Learning — 45 citations into it
• Graph Learning — 31 citations into it
• Statistical and Nonlinear Physics — 19 citations into it
• Information Systems — 17 citations into it; #5,862 of 8,771 by trust here, #2,406 by citations
• Molecular Biology — 10 citations into it
• Optimization & Training — 8 citations into it

Whom Feng Yu trusts overall

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

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

Who trusts Feng Yu overall

1. Dietmar Jannach
2. Malte Ludewig
3. Xu Chen
4. Shengsheng Qian
5. Massimo Quadrana
6. Shoujin Wang
7. Jin Huang
8. Yukuo Cen
9. Yongfeng Zhang
10. Xiaowen Huang

Papers from Feng Yu's perspective

Ordered by how strongly the researchers Feng Yu 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. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• A Dynamic Recurrent Model for Next Basket Recommendation (2016) — 514 citations
• A Convolutional Approach for Misinformation Identification (2017) — 508 citations
• Graph Contrastive Learning with Adaptive Augmentation (2020) — 1,475 citations
• Deep Graph Contrastive Representation Learning (2020) — 1,103 citations