Stephan Sigg

Artificial Intelligence · 2021

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
• Privacy & Security — 16 citations into it
• Computational Theory and Mathematics — 7 citations into it
• Safety, Robustness & Interpretability — 7 citations into it

Whom Stephan Sigg trusts overall

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

1. Cynthia Dwork
2. Andrew L. Maas
3. Keith B. Frikken
4. Ivan Bjerre Damgård

Papers from Stephan Sigg's perspective

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

1. Deep Learning with Differential Privacy
2. Calibrating Noise to Sensitivity in Private Data Analysis
3. ImageNet classification with deep convolutional neural networks
4. Differential Privacy
5. CryptoNets: Applying Neural Networks to Encrypted Data with High Throughput and Accuracy
6. Practical Secure Aggregation for Privacy-Preserving Machine Learning
7. Rectifier Nonlinearities Improve Neural Network Acoustic Models
8. On Ideal Lattices and Learning with Errors over Rings
9. Gradient-based learning applied to document recognition
10. Our Data, Ourselves: Privacy Via Distributed Noise Generation

Their papers

• Privacy‐preserving federated learning based on multi‐key homomorphic encryption (2021) — 388 citations