Hannah Dröge

Artificial Intelligence · 2020

A researcher in the Science corpus: 1 paper; trusts 7 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 31 citations into it
• Computer Vision and Pattern Recognition — 12 citations into it
• Optimization & Training — 7 citations into it

Whom Hannah Dröge trusts overall

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

1. Diederik P. Kingma
2. Alex Krizhevsky
3. Geoffrey E. Hinton
4. Sergey Zagoruyko
5. Kaiming He
6. H. Brendan McMahan
7. Jimmy Ba
8. Dong C. Liu

Papers from Hannah Dröge's perspective

Ordered by how strongly the researchers Hannah Dröge 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. ImageNet classification with deep convolutional neural networks
4. Gradient-based learning applied to document recognition
5. Learning Multiple Layers of Features from Tiny Images
6. ImageNet: A large-scale hierarchical image database
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. Intriguing properties of neural networks

Their papers

• Inverting Gradients - How easy is it to break privacy in federated learning? (2020) — 1,706 citations