Jinhyun So

Artificial Intelligence · 2018–2020

A researcher in the Science corpus: 6 papers; trusts 70 researchers and is trusted by 12 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 250 citations into it; #11,119 of 35,345 by trust here, #9,170 by citations
• Federated & Distributed Learning — 100 citations into it; #150 of 319 by trust here, #146 by citations
• Privacy & Security — 78 citations into it; #477 of 709 by trust here, #462 by citations
• Computer Networks and Communications — 38 citations into it
• Computer Vision and Pattern Recognition — 33 citations into it
• Optimization & Training — 32 citations into it
• Safety, Robustness & Interpretability — 32 citations into it
• Information Systems — 24 citations into it

Whom Jinhyun So trusts overall

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

1. Johannes Blömer
2. Alex Krizhevsky
3. H. Brendan McMahan
4. Qian Yu
5. Lisandro Dalcin
6. Ivan Bjerre Damgård
7. Amir Salman Avestimehr
8. Zuzana Beerliová-Trubíniová
9. Whitfield Diffie
10. Gergely Ács

Who trusts Jinhyun So overall

1. Basak Guler
2. Amir Salman Avestimehr
3. Chaoyang He
4. Qinbin Li
5. Khaled Ben Letaief
6. Omar Abdel Wahab
7. Hongwei Li
8. Ahmed Roushdy Elkordy
9. Sai Praneeth Karimireddy
10. Zeyi Wen

Papers from Jinhyun So's perspective

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

Their papers

• Lagrange Coded Computing: Optimal Design for Resiliency, Security and Privacy (2018) — 461 citations
• FedML: A Research Library and Benchmark for Federated Machine Learning (2020) — 716 citations
• Turbo-Aggregate: Breaking the Quadratic Aggregation Barrier in Secure Federated Learning (2020) — 387 citations
• A Scalable Approach for Privacy-Preserving Collaborative Machine Learning (2020) — 57 citations
• Byzantine-Resilient Secure Federated Learning (2020) — 330 citations
• CodedPrivateML: A Fast and Privacy-Preserving Framework for Distributed Machine Learning (2019) — 133 citations