Jy-yong Sohn

Artificial Intelligence · 2019–2023

A researcher in the Science corpus: 4 papers; trusts 61 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 233 citations into it
• Computer Vision and Pattern Recognition — 93 citations into it
• Optimization & Training — 40 citations into it
• Federated & Distributed Learning — 33 citations into it
• ML Systems & Hardware — 15 citations into it
• Representation & Self-Supervised Learning — 15 citations into it
• Computational Mechanics — 9 citations into it
• Probabilistic & Bayesian Methods — 6 citations into it

Whom Jy-yong Sohn trusts overall

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

1. Jeremy Bernstein
2. Alex Krizhevsky
3. Geoffrey E. Hinton
4. Diederik P. Kingma
5. Yann LeCun
6. Min Ye
7. Yoshua Bengio
8. Kaiming He
9. Lingjiao Chen
10. Ilya Sutskever

Papers from Jy-yong Sohn's perspective

Ordered by how strongly the researchers Jy-yong Sohn 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. Long Short-Term Memory
4. Attention is All you Need (Neural Information Processing Systems)
5. ImageNet: A large-scale hierarchical image database
6. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
7. Gradient-based learning applied to document recognition
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

• Attack of the Tails: Yes, You Really Can Backdoor Federated Learning (2020) — 829 citations
• Looped Transformers as Programmable Computers (2023) — 222 citations
• LIFT: Language-Interfaced Fine-Tuning for Non-Language Machine Learning Tasks (2022) — 200 citations
• Election Coding for Distributed Learning: Protecting SignSGD against Byzantine Attacks (2019) — 41 citations