Eugene Bagdasarian

Artificial Intelligence · 2018–2020

A researcher in the Science corpus: 4 papers; trusts 362 researchers and is trusted by 20 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 189 citations into it; #9,396 of 35,345 by trust here, #4,747 by citations
• Safety, Robustness & Interpretability — 93 citations into it; #747 of 2,251 by trust here, #415 by citations
• Computer Vision and Pattern Recognition — 51 citations into it
• Signal Processing — 42 citations into it
• Privacy & Security — 38 citations into it
• Optimization & Training — 36 citations into it
• Federated & Distributed Learning — 34 citations into it
• Natural Language Processing — 13 citations into it

Whom Eugene Bagdasarian trusts overall

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

1. Alex Krizhevsky
2. Kaiming He
3. Cynthia Dwork
4. Geoffrey E. Hinton
5. H. Brendan McMahan
6. Yann LeCun
7. Yoshua Bengio
8. Christian Szegedy
9. Ian J. Goodfellow
10. Diederik P. Kingma

Who trusts Eugene Bagdasarian overall

1. Vitaly Feldman
2. Li Li
3. Clement Fung
4. Khoa D Doan
5. Xiaoyu Cao
6. Saurav Prakash
7. Nuria Rodr'iguez-Barroso
8. Qiang Yang
9. Chien-Lun Chen
10. Muhammad Shayan

Papers from Eugene Bagdasarian's perspective

Ordered by how strongly the researchers Eugene Bagdasarian 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. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Differential Privacy Has Disparate Impact on Model Accuracy (2019) — 599 citations
• Blind Backdoors in Deep Learning Models (2020) — 391 citations
• How To Backdoor Federated Learning (2018) — 2,584 citations
• Salvaging Federated Learning by Local Adaptation (2020) — 315 citations