Yue Wang
Artificial Intelligence · with Wu · 2013–2018
A researcher in the Science corpus: 5 papers; trusts 37 researchers and is trusted by 5 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 65 citations into it; #17,281 of 35,345 by trust here, #17,110 by citations
- Privacy & Security — 29 citations into it; #531 of 709 by trust here, #499 by citations
- Statistics and Probability — 23 citations into it; #9,611 of 12,932 by trust here, #10,275 by citations
- Public Health, Environmental and Occupational Health — 9 citations into it
- Computer Science Applications — 8 citations into it
- Sociology and Political Science — 7 citations into it
- Molecular Biology — 6 citations into it
Whom Yue Wang trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Cynthia Dwork
- Kamalika Chaudhuri
- Adam D. Smith
- Frank McSherry
- Kobbi Nissim
- Jing Lei
- Aaron Johnson
- Caroline Uhler
- Peter D. Hoff
- David A. Edwards
Who trusts Yue Wang overall
- Nhathai Phan
- Jayadev Acharya
- S. Ji
- Marco Gaboardi
- Vishesh Karwa
- Clément Louis Canonne
- Xintao Wu
- Simon Couch
- Ryan M. Rogers
- Ting Wang
Papers from Yue Wang's perspective
Ordered by how strongly the researchers Yue Wang trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Gradient-based learning applied to document recognition
- Learning Multiple Layers of Features from Tiny Images
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Calibrating Noise to Sensitivity in Private Data Analysis
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Differential Privacy Preserving Spectral Graph Analysis (2013) — 127 citations
- Statistical Approximating Distributions Under Differential Privacy (2018) — 23 citations
- Differential Privacy Preservation for Deep Auto-Encoders: an Application of Human Behavior Prediction (2016) — 252 citations
- Regression Model Fitting under Differential Privacy and Model Inversion Attack (2015) — 62 citations
- Revisiting Differentially Private Hypothesis Tests for Categorical Data (2015) — 58 citations