Xi Wu

Artificial Intelligence · with Jha · 2015–2016

A researcher in the Science corpus: 3 papers; trusts 13 researchers and is trusted by 79 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 44 citations into it; #3,365 of 35,345 by trust here, #2,187 by citations
• Safety, Robustness & Interpretability — 26 citations into it; #252 of 2,251 by trust here, #172 by citations
• Privacy & Security — 19 citations into it; #352 of 709 by trust here, #225 by citations
• Optimization & Training — 14 citations into it; #1,882 of 2,601 by trust here, #1,225 by citations
• Computer Vision and Pattern Recognition — 11 citations into it
• Representation & Self-Supervised Learning — 5 citations into it

Whom Xi Wu trusts overall

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

1. Cynthia Dwork
2. Kamalika Chaudhuri
3. Matt Fredrikson
4. Martin A. Zinkevich
5. Alex Krizhevsky
6. Geoffrey E. Hinton
7. Adam D. Smith
8. Yann LeCun
9. Frank McSherry
10. Kobbi Nissim

Who trusts Xi Wu overall

1. H. Brendan McMahan
2. Nicolas Papernot
3. Om Thakkar
4. Samuel Yeom
5. Nicholas Carlini
6. Ziqi Yang
7. Vitaly Feldman
8. Naveed Akhtar
9. Galen Andrew
10. Stephanie L. Hyland

Papers from Xi Wu's perspective

Ordered by how strongly the researchers Xi Wu 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. Learning Multiple Layers of Features from Tiny Images
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
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. Going deeper with convolutions

Their papers

• Distillation as a Defense to Adversarial Perturbations Against Deep Neural Networks (2015) — 3,315 citations
• A Methodology for Formalizing Model-Inversion Attacks (2016) — 191 citations
• Bolt-on Differential Privacy for Scalable Stochastic Gradient Descent-based Analytics (2016) — 239 citations