Kleomenis Katevas

Artificial Intelligence · 2017–2021

A researcher in the Science corpus: 4 papers; trusts 159 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 127 citations into it; #17,469 of 35,345 by trust here, #13,163 by citations
• Computer Vision and Pattern Recognition — 59 citations into it
• Privacy & Security — 42 citations into it
• Safety, Robustness & Interpretability — 40 citations into it
• Representation & Self-Supervised Learning — 17 citations into it
• Sociology and Political Science — 16 citations into it; #18,907 of 22,756 by trust here, #12,387 by citations
• Model Architectures — 14 citations into it
• Computer Networks and Communications — 12 citations into it

Whom Kleomenis Katevas trusts overall

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

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

Who trusts Kleomenis Katevas overall

1. Fatemehsadat Mireshghallah
2. Mohammadkazem Taram

Papers from Kleomenis Katevas's perspective

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

Their papers

• Deep Private-Feature Extraction (2018) — 107 citations
• PPFL: privacy-preserving federated learning with trusted execution environments (2021) — 317 citations
• DarkneTZ: towards model privacy at the edge using trusted execution environments (2020) — 240 citations
• A Hybrid Deep Learning Architecture for Privacy-Preserving Mobile Analytics (2017) — 266 citations