Jamie Hayes

Artificial Intelligence · 2015–2023

A researcher in the Science corpus: 9 papers; trusts 646 researchers and is trusted by 29 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 271 citations into it; #10,005 of 35,345 by trust here, #7,172 by citations
• Computer Vision and Pattern Recognition — 142 citations into it; #5,678 of 21,575 by trust here, #4,555 by citations
• Safety, Robustness & Interpretability — 133 citations into it; #1,053 of 2,251 by trust here, #889 by citations
• Generative Models — 54 citations into it; #1,466 of 1,956 by trust here, #1,021 by citations
• Privacy & Security — 53 citations into it; #392 of 709 by trust here, #273 by citations
• Optimization & Training — 39 citations into it
• Signal Processing — 36 citations into it
• Computer Networks and Communications — 21 citations into it

Whom Jamie Hayes trusts overall

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

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

Who trusts Jamie Hayes overall

1. Kevin Alex Zhang
2. Congzheng Song
3. Ning Yu
4. Naveed Akhtar
5. Andrew Ilyas
6. Liwei Song
7. Reza Shokri
8. Katja Auernhammer
9. Nicholas Carlini
10. Maria Rigaki

Papers from Jamie Hayes's perspective

Ordered by how strongly the researchers Jamie Hayes 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. 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Extracting Training Data from Diffusion Models (2023) — 986 citations
• LOGAN: Evaluating Privacy Leakage of Generative Models Using Generative Adversarial Networks (2017) — 107 citations
• Generating steganographic images via adversarial training (2017) — 337 citations
• LOGAN: Membership Inference Attacks Against Generative Models (2017) — 600 citations
• Unlocking High-Accuracy Differentially Private Image Classification through Scale (2022) — 286 citations
• Contamination Attacks and Mitigation in Multi-Party Machine Learning (2018) — 76 citations
• Learning Universal Adversarial Perturbations with Generative Models (2017) — 170 citations
• Machine Learning as an Adversarial Service: Learning Black-Box Adversarial Examples (2017) — 56 citations
• k-fingerprinting: A Robust Scalable Website Fingerprinting Technique (2015) — 493 citations