Aleksei Triastcyn

Artificial Intelligence · 2018–2019

A researcher in the Science corpus: 3 papers; trusts 215 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 75 citations into it; #14,580 of 35,345 by trust here, #22,463 by citations
• Privacy & Security — 34 citations into it
• Computer Vision and Pattern Recognition — 28 citations into it
• Safety, Robustness & Interpretability — 23 citations into it
• Generative Models — 12 citations into it
• Sociology and Political Science — 11 citations into it; #9,312 of 22,756 by trust here, #16,952 by citations
• Federated & Distributed Learning — 9 citations into it
• Statistics and Probability — 9 citations into it

Whom Aleksei Triastcyn trusts overall

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

1. Cynthia Dwork
2. Diederik P. Kingma
3. H. Brendan McMahan
4. Yann LeCun
5. Kaiming He
6. Nicolas Papernot
7. Geoffrey E. Hinton
8. Latanya Sweeney
9. Alex Krizhevsky
10. Yoshua Bengio

Who trusts Aleksei Triastcyn overall

1. Sergey I. Nikolenko
2. Ahmed El Ouadrhiri
3. Chaoyang He
4. Xuefei Yin
5. Qinbin Li
6. Jamie Hayes
7. Rodolfo Stoffel Antunes
8. Ho Bae
9. Sean Augenstein
10. Ahmed M Abdelhadi

Papers from Aleksei Triastcyn's perspective

Ordered by how strongly the researchers Aleksei Triastcyn 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. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. GENERATIVE ADVERSARIAL NETS

Their papers

• Federated Learning with Bayesian Differential Privacy (2019) — 212 citations
• Federated Generative Privacy (2019) — 72 citations
• Generating Differentially Private Datasets Using GANs (2018) — 41 citations