Yang Zhang

Artificial Intelligence · Conference on Computer and Communications Security · 2018–2023

A researcher in the Science corpus: 11 papers; trusts 548 researchers and is trusted by 28 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 439 citations into it; #6,438 of 35,345 by trust here, #4,393 by citations
• Safety, Robustness & Interpretability — 244 citations into it
• Computer Vision and Pattern Recognition — 138 citations into it; #11,916 of 21,575 by trust here, #8,021 by citations
• Privacy & Security — 102 citations into it
• Signal Processing — 77 citations into it
• Generative Models — 38 citations into it
• Natural Language Processing — 36 citations into it
• Optimization & Training — 31 citations into it

Whom Yang Zhang trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Diederik P. Kingma
4. Yoshua Bengio
5. Kaiming He
6. Yann LeCun
7. Ilya Sutskever
8. Karen Simonyan
9. Ian J. Goodfellow
10. Christian Szegedy

Who trusts Yang Zhang overall

1. Maria Rigaki
2. Liwei Song
3. Hongsheng Hu
4. Ahmed Salem
5. Nicholas Carlini
6. Florian Tramèr
7. Fatemehsadat Mireshghallah
8. Zheng Li
9. Sebastian Garcia
10. Prateek Mittal

Papers from Yang Zhang's perspective

Ordered by how strongly the researchers Yang Zhang 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models (2018) — 1,184 citations
• GAN-Leaks: A Taxonomy of Membership Inference Attacks against Generative Models (2019) — 514 citations
• MemGuard: Defending against Black-Box Membership Inference Attacks via Adversarial Examples (2019) — 475 citations
• Dynamic Backdoor Attacks Against Machine Learning Models (2020) — 335 citations
• "Do Anything Now": Characterizing and Evaluating In-The-Wild Jailbreak Prompts on Large Language Models (2023) — 675 citations
• Membership Leakage in Label-Only Exposures (2020) — 315 citations
• When Machine Unlearning Jeopardizes Privacy (2020) — 325 citations
• BadNL: Backdoor Attacks against NLP Models with Semantic-preserving Improvements (2020) — 363 citations
• Updates-Leak: Data Set Inference and Reconstruction Attacks in Online Learning (2019) — 298 citations
• Gradient-Leaks: Understanding and Controlling Deanonymization in Federated Learning (2018) — 43 citations
• MLCapsule: Guarded Offline Deployment of Machine Learning as a Service (2018) — 124 citations