Xiaoqian Jiang

Artificial Intelligence · 2006–2019

A researcher in the Science corpus: 16 papers; trusts 99 researchers and is trusted by 30 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 219 citations into it; #6,910 of 35,345 by trust here, #4,445 by citations
• Privacy & Security — 50 citations into it
• Computer Vision and Pattern Recognition — 44 citations into it
• Information Systems — 30 citations into it; #7,472 of 8,771 by trust here, #8,676 by citations
• Optimization & Training — 28 citations into it
• Computational Theory and Mathematics — 27 citations into it
• Computational Mathematics — 24 citations into it
• Health Information Management — 22 citations into it; #336 of 344 by trust here, #16 by citations

Whom Xiaoqian Jiang trusts overall

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

1. Jaideep Vaidya
2. Matthew D. Zeiler
3. Geoffrey E. Hinton
4. Thorsten Joachims
5. Alex Krizhevsky
6. Chris Clifton
7. Yoshua Bengio
8. Cynthia Dwork
9. Yann LeCun
10. glenn. fung

Who trusts Xiaoqian Jiang overall

1. M. Sadegh Riazi
2. Payman Mohassel
3. Mahdi Pakdaman Naeini
4. Volodymyr Kuleshov
5. Qinbin Li
6. Percy Liang
7. Gregory F. Cooper
8. Zhanglong Ji
9. Tingting Chen
10. Hao Chen

Papers from Xiaoqian Jiang's perspective

Ordered by how strongly the researchers Xiaoqian Jiang 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. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Going deeper with convolutions
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• Calibrating predictive model estimates to support personalized medicine (2011) — 250 citations
• Deep learning for healthcare: review, opportunities and challenges (2018) — 2,642 citations
• Predicting accurate probabilities with a ranking loss (2012) — 79 citations
• Privacy-preserving SVM classification (2008) — 194 citations
• Differentially private distributed logistic regression using private and public data (2014) — 58 citations
• Privacy-Preserving Tensor Factorization for Collaborative Health Data Analysis (2019) — 55 citations
• Discriminative and Distinct Phenotyping by Constrained Tensor Factorization (2017) — 40 citations
• Secure Logistic Regression Based on Homomorphic Encryption: Design and Evaluation (2018) — 296 citations
• Privacy Preserving Federated Big Data Analysis (2018) — 28 citations
• Secure Outsourced Matrix Computation and Application to Neural Networks (2018) — 362 citations
• L G ] 1 1 A pr 2 01 7 Federated Tensor Factorization for Computational Phenotyping (2018)
• Federated Tensor Factorization for Computational Phenotyping (2017) — 129 citations
• VERTIcal Grid lOgistic regression (VERTIGO) (2016) — 55 citations
• Fast and Robust Parallel SGD Matrix Factorization (2015) — 53 citations
• Privacy-preserving SVM using nonlinear kernels on horizontally partitioned data (2006) — 225 citations
• Privacy-Preserving SVM Classification on Vertically Partitioned Data (2006) — 232 citations