Dong Su
Artificial Intelligence · 2011–2018
A researcher in the Science corpus: 9 papers; trusts 213 researchers and is trusted by 34 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 215 citations into it; #6,207 of 35,345 by trust here, #4,923 by citations
- Safety, Robustness & Interpretability — 88 citations into it; #1,513 of 2,251 by trust here, #799 by citations
- Privacy & Security — 74 citations into it; #312 of 709 by trust here, #552 by citations
- Computer Vision and Pattern Recognition — 58 citations into it
- Computational Theory and Mathematics — 30 citations into it
- Sociology and Political Science — 30 citations into it; #2,663 of 22,756 by trust here, #4,924 by citations
- Model Architectures — 22 citations into it
- Management Science and Operations Research — 16 citations into it
Whom Dong Su trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Cynthia Dwork
- Frank McSherry
- Alex Krizhevsky
- Christian Szegedy
- Kobbi Nissim
- Geoffrey E. Hinton
- Kaiming He
- Adam D. Smith
- Sergey Ioffe
- Ian J. Goodfellow
Who trusts Dong Su overall
- A. Serban
- S. Ji
- Yatharth Dubey
- Danfeng Zhang
- Erik Poll
- Ji Lin
- Maria-Florina Balcan
- Chen Chen
- Aws Albarghouthi
- Yuxin Wang
Papers from Dong Su's perspective
Ordered by how strongly the researchers Dong Su 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)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Learning Multiple Layers of Features from Tiny Images
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Is Robustness the Cost of Accuracy? - A Comprehensive Study on the Robustness of 18 Deep Image Classification Models (2018) — 421 citations
- Evaluating the Robustness of Neural Networks: An Extreme Value Theory Approach (2018) — 524 citations
- Membership privacy: a unifying framework for privacy definitions (2013) — 180 citations
- On sampling, anonymization, and differential privacy or, k-anonymization meets differential privacy (2011) — 306 citations
- Understanding the Sparse Vector Technique for Differential Privacy (2016) — 193 citations
- Defending Against Model Stealing Attacks Using Deceptive Perturbations (2018) — 47 citations
- YerbaBuena: Securing Deep Learning Inference Data via Enclave-based Ternary Model Partitioning (2018) — 29 citations
- Differentially Private K-Means Clustering (2015) — 169 citations
- PrivBasis: Frequent Itemset Mining with Differential Privacy (2012) — 196 citations