Yae Jee Cho

Artificial Intelligence · 2020–2022

A researcher in the Science corpus: 2 papers; trusts 34 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 52 citations into it
• Optimization & Training — 32 citations into it
• Federated & Distributed Learning — 31 citations into it
• Computer Science Applications — 11 citations into it
• Computer Vision and Pattern Recognition — 9 citations into it
• Computer Networks and Communications — 8 citations into it
• Electrical and Electronic Engineering — 6 citations into it
• Computational Mechanics — 5 citations into it

Whom Yae Jee Cho trusts overall

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

1. Sebastian U. Stich
2. Michael M. Mitzenmacher
3. Jianyu Wang
4. Tian Li
5. Sai Praneeth Karimireddy
6. Sashank J. Reddi
7. Xiang Li
8. Anit Kumar Sahu
9. Alex Krizhevsky
10. Farzin Haddadpour

Papers from Yae Jee Cho's perspective

Ordered by how strongly the researchers Yae Jee Cho 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. 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. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Attention is All you Need (Neural Information Processing Systems)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Towards Understanding Biased Client Selection in Federated Learning (2022) — 295 citations
• Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategies (2020) — 552 citations