Giorgio Patrini

Artificial Intelligence · 2014–2018

A researcher in the Science corpus: 9 papers; trusts 378 researchers and is trusted by 31 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 156 citations into it; #7,155 of 35,345 by trust here, #4,772 by citations
• Computer Vision and Pattern Recognition — 53 citations into it; #6,343 of 21,575 by trust here, #6,089 by citations
• Optimization & Training — 39 citations into it
• Statistical Learning Theory — 35 citations into it
• Management Science and Operations Research — 32 citations into it
• Representation & Self-Supervised Learning — 27 citations into it
• Privacy & Security — 23 citations into it; #606 of 709 by trust here, #342 by citations
• Statistics and Probability — 23 citations into it

Whom Giorgio Patrini trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Alex Smola
4. Kaiming He
5. Yoshua Bengio
6. Peter L. Bartlett
7. Michael I. Jordan
8. Yann LeCun
9. Diederik P. Kingma
10. Ilya Sutskever

Who trusts Giorgio Patrini overall

1. Richard Nock
2. Stephen Hardy
3. Hwanjun Song
4. Dan Hendrycks
5. Xingjun Ma
6. Xiaobo Xia
7. Yisen Wang
8. Pengfei Chen
9. Bo Han
10. Himanshu Kumar

Papers from Giorgio Patrini's perspective

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

Their papers

• Making Deep Neural Networks Robust to Label Noise: A Loss Correction Approach (2016) — 1,704 citations
• Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption (2017) — 622 citations
• Fast Learning from Distributed Datasets without Entity Matching (2016) — 12 citations
• Loss factorization, weakly supervised learning and label noise robustness (2016) — 120 citations
• Entity Resolution and Federated Learning get a Federated Resolution (2018) — 95 citations
• Three Tools for Practical Differential Privacy (2018) — 43 citations
• Weakly supervised learning via statistical sufficiency (2016) — 4 citations
• Rademacher Observations, Private Data, and Boosting (2015) — 13 citations
• (Almost) No Label No Cry (2014) — 122 citations