Irene Giacomelli

Artificial Intelligence · 2017–2018

A researcher in the Science corpus: 2 papers; trusts 16 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 36 citations into it; #10,141 of 35,345 by trust here, #8,525 by citations
• Privacy & Security — 24 citations into it; #264 of 709 by trust here, #194 by citations
• Safety, Robustness & Interpretability — 11 citations into it; #690 of 2,251 by trust here, #768 by citations
• Statistical Learning Theory — 5 citations into it
• Sociology and Political Science — 5 citations into it

Whom Irene Giacomelli trusts overall

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

1. Pascal Paillier
2. Oded Regev
3. Donald Beaver
4. Cynthia Dwork
5. Alex Krizhevsky
6. Ivan Bjerre Damgård
7. Matt Fredrikson
8. Diederik P. Kingma
9. Karen Simonyan
10. Olivier Bousquet

Who trusts Irene Giacomelli overall

1. Nicholas Carlini
2. Liwei Song
3. Reza Shokri
4. Pratyush Maini
5. Matthew Jagielski
6. Qiang Yang
7. Milad Nasr
8. Edward Chou
9. Congzheng Song
10. Florian Tramèr

Papers from Irene Giacomelli's perspective

Ordered by how strongly the researchers Irene Giacomelli 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
5. Gradient-based learning applied to document recognition
6. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
7. ImageNet: A large-scale hierarchical image database
8. Long Short-Term Memory
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

• Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting (2017) — 1,558 citations
• Privacy-Preserving Ridge Regression with only Linearly-Homomorphic Encryption (2018) — 103 citations