Mehmet Emre Gursoy

Artificial Intelligence · 2018–2020

A researcher in the Science corpus: 6 papers; trusts 61 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 158 citations into it; #15,964 of 35,345 by trust here, #9,369 by citations
• Safety, Robustness & Interpretability — 79 citations into it; #1,474 of 2,251 by trust here, #1,673 by citations
• Privacy & Security — 51 citations into it
• Computer Vision and Pattern Recognition — 41 citations into it
• Optimization & Training — 22 citations into it
• Computer Networks and Communications — 15 citations into it; #5,939 of 9,886 by trust here, #4,405 by citations
• Signal Processing — 13 citations into it
• Sociology and Political Science — 9 citations into it

Whom Mehmet Emre Gursoy trusts overall

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

1. Cynthia Dwork
2. Alex Krizhevsky
3. Yann LeCun
4. Geoffrey E. Hinton
5. Reza Shokri
6. Yoshua Bengio
7. Battista Biggio
8. Nicolas Papernot
9. Diederik P. Kingma
10. Christian Szegedy

Who trusts Mehmet Emre Gursoy overall

1. Luca Melis
2. Lichao Sun
3. Congzheng Song
4. Aditya Golatkar

Papers from Mehmet Emre Gursoy's perspective

Ordered by how strongly the researchers Mehmet Emre Gursoy 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Learning Multiple Layers of Features from Tiny Images
6. ImageNet: A large-scale hierarchical image database
7. Long Short-Term Memory
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Attention is All you Need (Neural Information Processing Systems)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• A Framework for Evaluating Gradient Leakage Attacks in Federated Learning (2020) — 172 citations
• Towards Demystifying Membership Inference Attacks (2018) — 120 citations
• Data Poisoning Attacks Against Federated Learning Systems (2020) — 946 citations
• LDP-Fed: federated learning with local differential privacy (2020) — 534 citations
• Demystifying Membership Inference Attacks in Machine Learning as a Service (2019) — 330 citations
• Differentially Private Model Publishing for Deep Learning (2019) — 296 citations