Lingjuan Lyu

Artificial Intelligence · 2019–2021

A researcher in the Science corpus: 10 papers; trusts 194 researchers and is trusted by 22 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 344 citations into it; #7,810 of 35,345 by trust here, #7,471 by citations
• Privacy & Security — 128 citations into it; #256 of 709 by trust here, #478 by citations
• Safety, Robustness & Interpretability — 120 citations into it
• Federated & Distributed Learning — 93 citations into it; #83 of 319 by trust here, #237 by citations
• Computer Vision and Pattern Recognition — 48 citations into it
• Signal Processing — 45 citations into it
• Computer Networks and Communications — 34 citations into it
• Computer Science Applications — 30 citations into it

Whom Lingjuan Lyu trusts overall

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

1. H. Brendan McMahan
2. Cynthia Dwork
3. Reza Shokri
4. Alex Krizhevsky
5. Geoffrey E. Hinton
6. Yann LeCun
7. Yoshua Bengio
8. Kaiming He
9. John C. Duchi
10. Martín Abadi

Who trusts Lingjuan Lyu overall

1. Lichao Sun
2. Han Yu
3. Mang Ye
4. Haftay Gebreslasie Abreha
5. Jihong Park
6. Qiang Yang
7. Alysa Ziying Tan
8. Chaoyang He
9. Wenke Huang
10. Zhuangdi Zhu

Papers from Lingjuan Lyu's perspective

Ordered by how strongly the researchers Lingjuan Lyu 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. ImageNet: A large-scale hierarchical image database
6. Learning Multiple Layers of Features from Tiny Images
7. Long Short-Term Memory
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Local Differential Privacy-Based Federated Learning for Internet of Things (2020) — 414 citations
• A Novel Attribute Reconstruction Attack in Federated Learning (2021) — 47 citations
• Neural Attention Distillation: Erasing Backdoor Triggers from Deep Neural Networks (2021) — 575 citations
• Anti-Backdoor Learning: Training Clean Models on Poisoned Data (2021) — 473 citations
• Privacy and Robustness in Federated Learning: Attacks and Defenses (2020) — 538 citations
• Lightweight Crypto-Assisted Distributed Differential Privacy for Privacy-Preserving Distributed Learning (2020) — 11 citations
• Federated Model Distillation with Noise-Free Differential Privacy (2020) — 133 citations
• Threats to Federated Learning: A Survey (2020) — 545 citations
• Privacy-Preserving Blockchain-Based Federated Learning for IoT Devices (2019) — 508 citations
• Towards Fair and Privacy-Preserving Federated Deep Models (2019) — 244 citations