Basak Guler

Artificial Intelligence · 2019–2020

A researcher in the Science corpus: 4 papers; trusts 57 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 119 citations into it; #18,984 of 35,345 by trust here, #16,771 by citations
• Privacy & Security — 64 citations into it; #573 of 709 by trust here, #642 by citations
• Federated & Distributed Learning — 48 citations into it; #206 of 319 by trust here, #250 by citations
• Safety, Robustness & Interpretability — 20 citations into it
• Computational Theory and Mathematics — 18 citations into it
• Optimization & Training — 18 citations into it
• Computer Vision and Pattern Recognition — 14 citations into it
• Computer Networks and Communications — 13 citations into it

Whom Basak Guler trusts overall

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

1. Johannes Blömer
2. Alex Krizhevsky
3. Jinhyun So
4. H. Brendan McMahan
5. Lisandro Dalcin
6. Ivan Bjerre Damgård
7. Zuzana Beerliová-Trubíniová
8. Qian Yu
9. Whitfield Diffie
10. Gergely Ács

Who trusts Basak Guler overall

1. Jinhyun So
2. Amir Salman Avestimehr
3. Chaoyang He
4. Qinbin Li
5. Khaled Ben Letaief
6. Omar Abdel Wahab
7. Hongwei Li
8. Ahmed Roushdy Elkordy

Papers from Basak Guler's perspective

Ordered by how strongly the researchers Basak Guler trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Learning Multiple Layers of Features from Tiny Images
3. Gradient-based learning applied to document recognition
4. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. ImageNet: A large-scale hierarchical image database
7. Long Short-Term Memory
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Going deeper with convolutions

Their papers

• Turbo-Aggregate: Breaking the Quadratic Aggregation Barrier in Secure Federated Learning (2020) — 387 citations
• A Scalable Approach for Privacy-Preserving Collaborative Machine Learning (2020) — 57 citations
• Byzantine-Resilient Secure Federated Learning (2020) — 330 citations
• CodedPrivateML: A Fast and Privacy-Preserving Framework for Distributed Machine Learning (2019) — 133 citations