Yanfang Ye

Artificial Intelligence · 2017–2021

A researcher in the Science corpus: 4 papers; trusts 55 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 130 citations into it; #23,820 of 35,345 by trust here, #10,942 by citations
• Graph Learning — 51 citations into it
• Statistical and Nonlinear Physics — 51 citations into it; #8,467 of 10,127 by trust here, #2,158 by citations
• Information Systems — 43 citations into it
• Signal Processing — 34 citations into it; #6,671 of 7,572 by trust here, #5,570 by citations
• Computer Vision and Pattern Recognition — 31 citations into it
• Computer Networks and Communications — 30 citations into it; #8,480 of 9,886 by trust here, #7,140 by citations
• Representation & Self-Supervised Learning — 25 citations into it

Whom Yanfang Ye trusts overall

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

1. Yoshua Bengio
2. Leo Breiman
3. Ulrich Bayer
4. Thomas G. Dietterich
5. William W. Cohen
6. Yoav Freund
7. Fabrizio Sebastiani
8. Alain Rakotomamonjy
9. Lior Rokach
10. Geoffrey E. Hinton

Who trusts Yanfang Ye overall

1. Raghavendra Chalapathy
2. Ori Or-Meir
3. Sanjay Chawla
4. Davide Maiorca
5. Nir Nissim

Papers from Yanfang Ye's perspective

Ordered by how strongly the researchers Yanfang Ye 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. Gradient-based learning applied to document recognition
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• A Survey on Heterogeneous Graph Embedding: Methods, Techniques, Applications and Sources (2020) — 454 citations
• Heterogeneous Graph Structure Learning for Graph Neural Networks (2021) — 319 citations
• Heterogeneous Graph Attention Network (2019) — 3,241 citations
• A Survey on Malware Detection Using Data Mining Techniques (2017) — 612 citations