Jihun Hamm

Artificial Intelligence · 2015–2016

A researcher in the Science corpus: 3 papers; trusts 61 researchers and is trusted by 45 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 57 citations into it; #4,700 of 35,345 by trust here, #10,430 by citations
• Privacy & Security — 32 citations into it; #249 of 709 by trust here, #394 by citations
• Sociology and Political Science — 17 citations into it
• Optimization & Training — 15 citations into it
• Computer Vision and Pattern Recognition — 12 citations into it
• Safety, Robustness & Interpretability — 11 citations into it
• Computer Networks and Communications — 10 citations into it
• Management Science and Operations Research — 10 citations into it

Whom Jihun Hamm trusts overall

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

1. Cynthia Dwork
2. Frank McSherry
3. L. Eon Bottou
4. Graham Cormode
5. Kamalika Chaudhuri
6. Kobbi Nissim
7. Latanya Sweeney
8. Shai Shalev-Shwartz
9. Leo Breiman
10. Alex Krizhevsky

Who trusts Jihun Hamm overall

1. Congzheng Song
2. Ying Zhao
3. Lichao Sun
4. Jamie Hayes
5. Nicolas Papernot
6. Abhishek Singh
7. Lingjuan Lyu
8. Vitaly Shmatikov
9. Aleksei Triastcyn
10. Cynthia Dwork

Papers from Jihun Hamm's perspective

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

Their papers

• Learning privately from multiparty data (2016) — 169 citations
• Minimax Filter: Learning to Preserve Privacy from Inference Attacks (2016) — 84 citations
• Crowd-ML: A Privacy-Preserving Learning Framework for a Crowd of Smart Devices (2015) — 65 citations