Wenqi Wei

Artificial Intelligence · 2018–2023

A researcher in the Science corpus: 5 papers; trusts 58 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 175 citations into it; #16,977 of 35,345 by trust here, #12,244 by citations
• Computer Vision and Pattern Recognition — 67 citations into it
• Safety, Robustness & Interpretability — 56 citations into it; #1,473 of 2,251 by trust here, #1,673 by citations
• Privacy & Security — 37 citations into it
• Generative Models — 35 citations into it
• Signal Processing — 18 citations into it
• Optimization & Training — 16 citations into it
• Computational Theory and Mathematics — 15 citations into it

Whom Wenqi Wei trusts overall

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

1. Diederik P. Kingma
2. Alex Krizhevsky
3. Geoffrey E. Hinton
4. Yann LeCun
5. Reza Shokri
6. Yoshua Bengio
7. Roberto Battiti
8. Ning Qian
9. John C. Duchi
10. H. Brendan McMahan

Who trusts Wenqi Wei overall

1. Yangsibo Huang
2. Luca Melis

Papers from Wenqi Wei's perspective

Ordered by how strongly the researchers Wenqi Wei 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. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. GENERATIVE ADVERSARIAL NETS

Their papers

• A Framework for Evaluating Gradient Leakage Attacks in Federated Learning (2020) — 172 citations
• Towards Demystifying Membership Inference Attacks (2018) — 120 citations
• Machine Learning for Synthetic Data Generation: a Review (2023) — 297 citations
• LDP-Fed: federated learning with local differential privacy (2020) — 534 citations
• Demystifying Membership Inference Attacks in Machine Learning as a Service (2019) — 330 citations