Samuel Yeom

Artificial Intelligence · 2017

A researcher in the Science corpus: 2 papers; trusts 45 researchers and is trusted by 16 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 45 citations into it; #9,768 of 35,345 by trust here, #7,858 by citations
• Safety, Robustness & Interpretability — 20 citations into it; #689 of 2,251 by trust here, #768 by citations
• Privacy & Security — 18 citations into it; #263 of 709 by trust here, #194 by citations
• Statistical Learning Theory — 9 citations into it
• Sociology and Political Science — 8 citations into it
• Statistics and Probability — 8 citations into it
• Computer Vision and Pattern Recognition — 6 citations into it

Whom Samuel Yeom trusts overall

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

1. Cynthia Dwork
2. Diederik P. Kingma
3. Matt Fredrikson
4. Alex Krizhevsky
5. Karen Simonyan
6. Olivier Bousquet
7. Jimmy Ba
8. Andrew Zisserman
9. Yann LeCun
10. Geoffrey E. Hinton

Who trusts Samuel Yeom overall

1. Nicholas Carlini
2. Liwei Song
3. Reza Shokri
4. Pratyush Maini
5. Matthew Jagielski
6. Milad Nasr
7. Congzheng Song
8. Ahmed Salem
9. Florian Tramèr
10. Alexandre Sablayrolles

Papers from Samuel Yeom's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
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. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
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
• The Unintended Consequences of Overfitting: Training Data Inference Attacks (2017) — 41 citations