Geyong Min

Optimization & Training · with Hu · 2020

A researcher in the Science corpus: 1 paper; trusts 1 researcher and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Optimization & Training — 7 citations into it

Whom Geyong Min trusts

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

1. Daniel Ramage (in Optimization & Training)

Papers from Geyong Min's perspective

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

1. Federated Learning for Mobile Keyboard Prediction
2. Federated Learning of Deep Networks using Model Averaging
3. Federated Learning: Collaborative Machine Learning without Centralized Training Data
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet classification with deep convolutional neural networks
8. ImageNet: A large-scale hierarchical image database
9. Large Scale Distributed Deep Networks
10. Dropout: a simple way to prevent neural networks from overfitting

Their papers

• Communication-Efficient Federated Learning for Wireless Edge Intelligence in IoT (2020) — 383 citations