Le Zhang

Artificial Intelligence · with Suganthan · 2014–2016

A researcher in the Science corpus: 4 papers; trusts 120 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 49 citations into it; #13,837 of 35,345 by trust here, #11,327 by citations
• Computer Vision and Pattern Recognition — 40 citations into it; #12,863 of 21,575 by trust here, #15,529 by citations
• Statistical Learning Theory — 15 citations into it
• Model Architectures — 14 citations into it
• Statistics and Probability — 13 citations into it
• Optimization & Training — 12 citations into it; #2,451 of 2,601 by trust here, #1,715 by citations
• Evaluation & Benchmarks — 11 citations into it
• Computational Mechanics — 10 citations into it

Whom Le Zhang trusts overall

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

1. Leo Breiman
2. Geoffrey E. Hinton
3. Yann LeCun
4. E.J. Candes
5. Ali Rahimi
6. Yoshua Bengio
7. Monther Alhamdoosh
8. Alex Krizhevsky
9. Matthew D. Zeiler
10. Simon S. Haykin

Who trusts Le Zhang overall

1. Xueheng Qiu
2. Ponnuthurai Nagaratnam Suganthan
3. Simone Scardapane
4. Ye Ren
5. Ibomoiye Domor Mienye
6. Kasun Amarasinghe
7. Bilal Mirza
8. Hua Shen
9. M. A. Ganaie
10. Milos Manic

Papers from Le Zhang's perspective

Ordered by how strongly the researchers Le Zhang 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. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
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
• Ensemble Classification and Regression-Recent Developments, Applications and Future Directions [Review Article] (2016) — 592 citations
• A comprehensive evaluation of random vector functional link networks (2016) — 443 citations
• A survey of randomized algorithms for training neural networks (2016) — 336 citations