Tao Zhang

Artificial Intelligence · 2 papers · 2016–2017

A researcher in the Science corpus: 2 papers; trusts 21 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 20 citations into it; #12,809 of 35,345 by trust here, #25,040 by citations
• Privacy & Security — 14 citations into it
• Computational Theory and Mathematics — 6 citations into it
• Algorithms & Complexity — 5 citations into it

Whom Tao Zhang trusts overall

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

1. Shai Shalev-Shwartz
2. Michael Collins
3. Leslie G. Valiant
4. Muthu Dayalan
5. Kobbi Nissim
6. Cynthia Dwork
7. Ryan T. McDonald
8. Pedro A. Forero
9. Frank McSherry
10. Shiva Prasad Kasiviswanathan

Who trusts Tao Zhang overall

1. Xueru Zhang
2. Yaochen Hu
3. Mohammad Mahdi Khalili
4. Peng Liu
5. Zonghao Huang
6. M. Liu
7. Linglong Kong
8. Di Niu
9. Rui Hu
10. Chunlei Zhang

Papers from Tao Zhang's perspective

Ordered by how strongly the researchers Tao Zhang 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. Gradient-based learning applied to document recognition
3. ImageNet classification with deep convolutional neural networks
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. Learning Multiple Layers of Features from Tiny Images
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
10. ImageNet: A large-scale hierarchical image database

Their papers

• Dynamic Differential Privacy for ADMM-Based Distributed Classification Learning (2017) — 194 citations
• A Dual Perturbation Approach for Differential Private ADMM-Based Distributed Empirical Risk Minimization (2016) — 17 citations