Ye Ren

Artificial Intelligence · 2014–2017

A researcher in the Science corpus: 4 papers; trusts 56 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 50 citations into it; #16,994 of 35,345 by trust here, #11,490 by citations
• Computer Vision and Pattern Recognition — 31 citations into it
• Evaluation & Benchmarks — 13 citations into it
• Statistics and Probability — 13 citations into it
• Statistical Learning Theory — 11 citations into it
• Optimization & Training — 11 citations into it; #2,453 of 2,601 by trust here, #1,715 by citations
• Management Science and Operations Research — 6 citations into it
• Model Architectures — 6 citations into it

Whom Ye Ren trusts overall

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

1. Leo Breiman
2. Robert E. Schapire
3. Jerome H. Friedman
4. Tin Kam Ho
5. Yoshua Bengio
6. Geoffrey E. Hinton
7. Yann LeCun
8. David Hilton Wolpert
9. Pedro M. Domingos
10. Jimmy Ba

Who trusts Ye Ren overall

1. M. A. Ganaie
2. Hieu-Thi Luong
3. André Gensler
4. Ibomoiye Domor Mienye
5. Ponnuthurai Nagaratnam Suganthan
6. Minghui Hu
7. Le Zhang
8. Xin Eric Wang
9. Yongquan Yang
10. Mengmeng Cai

Papers from Ye Ren's perspective

Ordered by how strongly the researchers Ye Ren 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
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. Going deeper with convolutions

Their papers

• Ensemble deep learning for regression and time series forecasting (2014) — 353 citations
• Empirical Mode Decomposition based ensemble deep learning for load demand time series forecasting (2017) — 421 citations
• Ensemble Classification and Regression-Recent Developments, Applications and Future Directions [Review Article] (2016) — 592 citations
• Ensemble methods for wind and solar power forecasting—A state-of-the-art review (2015) — 404 citations