Wenlong Mou

Artificial Intelligence · 2017

A researcher in the Science corpus: 3 papers; trusts 47 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 52 citations into it; #16,955 of 35,345 by trust here, #21,878 by citations
• Privacy & Security — 26 citations into it
• Statistics and Probability — 24 citations into it; #9,527 of 12,932 by trust here, #7,508 by citations
• Optimization & Training — 10 citations into it
• Computational Theory and Mathematics — 9 citations into it
• Computational Mechanics — 8 citations into it; #7,191 of 10,001 by trust here, #6,582 by citations
• Computer Vision and Pattern Recognition — 8 citations into it
• Statistical Learning Theory — 6 citations into it

Whom Wenlong Mou trusts overall

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

1. Adam D. Smith
2. Cynthia Dwork
3. David A. McAllester
4. John C. Duchi
5. Moritz Hardt
6. Raef Bassily
7. Peter Kairouz
8. Kamalika Chaudhuri
9. Arnak S. Dalalyan
10. Frank McSherry

Who trusts Wenlong Mou overall

1. Di Wang
2. Ankit Pensia
3. Simon Shaolei Du
4. Pengfei Chen
5. Yeming Wen
6. Sanjeev Arora
7. Jeffrey Negrea
8. Marco Gaboardi
9. Jinhui Xu
10. Varun Jog

Papers from Wenlong Mou's perspective

Ordered by how strongly the researchers Wenlong Mou 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. 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Generalization Bounds of SGLD for Non-convex Learning: Two Theoretical Viewpoints (2017) — 177 citations
• Differentially Private Clustering in High-Dimensional Euclidean Spaces (2017) — 92 citations
• Collect at Once, Use Effectively: Making Non-interactive Locally Private Learning Possible (2017) — 43 citations