Yunwen Lei

Artificial Intelligence · 2016–2019

A researcher in the Science corpus: 2 papers; trusts 16 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 36 citations into it; #35,296 of 35,345 by trust here, #29,412 by citations
• Computational Mechanics — 23 citations into it
• Optimization & Training — 20 citations into it; #2,601 of 2,601 by trust here, #2,378 by citations
• Representation & Self-Supervised Learning — 14 citations into it
• Statistical Learning Theory — 11 citations into it
• Statistics and Probability — 11 citations into it
• Computer Vision and Pattern Recognition — 7 citations into it

Whom Yunwen Lei trusts overall

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

1. Tong Zhang
2. Olivier Bousquet
3. Andreas Maurer
4. Chih-Jen Lin
5. Zeyuan Allen-Zhu
6. Peter L. Bartlett
7. Yurii E. Nesterov
8. Jonathan Baxter
9. Andreas Argyriou
10. Simon Lacoste-Julien

Who trusts Yunwen Lei overall

1. Nicolas Loizou
2. Tao Sun
3. Ahmed Khaled
4. Vivak Patel
5. Dongsheng Li
6. Sharan Vaswani

Papers from Yunwen Lei's perspective

Ordered by how strongly the researchers Yunwen Lei 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. Learning Multiple Layers of Features from Tiny Images
8. ImageNet: A large-scale hierarchical image database
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Stochastic Gradient Descent for Nonconvex Learning Without Bounded Gradient Assumptions (2019) — 144 citations
• Local Rademacher Complexity-based Learning Guarantees for Multi-Task Learning (2016) — 33 citations