Lingxiao Wang

Artificial Intelligence · 2018

A researcher in the Science corpus: 2 papers; trusts 32 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 68 citations into it; #34,698 of 35,345 by trust here, #23,077 by citations
• Optimization & Training — 20 citations into it; #2,576 of 2,601 by trust here, #1,942 by citations
• Privacy & Security — 16 citations into it; #664 of 709 by trust here, #615 by citations
• Computer Vision and Pattern Recognition — 15 citations into it; #21,204 of 21,575 by trust here, #15,162 by citations
• Statistical Learning Theory — 14 citations into it; #1,034 of 1,038 by trust here, #770 by citations
• Scaling & Learning Theory — 14 citations into it
• Computational Theory and Mathematics — 10 citations into it
• Computational Mechanics — 9 citations into it

Whom Lingxiao Wang trusts overall

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

1. Cynthia Dwork
2. Rong Ge
3. Alex Krizhevsky
4. M. Soltanolkotabi
5. Simon Shaolei Du
6. Geoffrey E. Hinton
7. Yehuda Lindell
8. Jason D. Lee
9. Kamalika Chaudhuri
10. Reza Shokri

Who trusts Lingxiao Wang overall

1. César Sabater
2. Lun Wang

Papers from Lingxiao Wang's perspective

Ordered by how strongly the researchers Lingxiao Wang 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. Learning Multiple Layers of Features from Tiny Images
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Attention is All you Need (Neural Information Processing Systems)
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

• Learning One-hidden-layer ReLU Networks via Gradient Descent (2018) — 140 citations
• Distributed Learning without Distress: Privacy-Preserving Empirical Risk Minimization (2018) — 174 citations