Yan Kyaw Tun

Artificial Intelligence · 2019

A researcher in the Science corpus: 1 paper; trusts 4 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 18 citations into it
• Optimization & Training — 13 citations into it
• Federated & Distributed Learning — 9 citations into it
• Electrical and Electronic Engineering — 5 citations into it

Whom Yan Kyaw Tun trusts overall

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

1. Jakub Konecný
2. Sebastian U. Stich

Papers from Yan Kyaw Tun's perspective

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

1. Communication-Efficient Learning of Deep Networks from Decentralized Data
2. Communication-Efficient Distributed Optimization using an Approximate Newton-type Method
3. Accelerated proximal stochastic dual coordinate ascent for regularized loss minimization
4. Distributed stochastic optimization and learning
5. Distributed optimization with arbitrary local solvers
6. Convex Optimization
7. Local SGD Converges Fast and Communicates Little
8. Federated Learning: Collaborative Machine Learning without Centralized Training Data
9. FireCaffe: Near-Linear Acceleration of Deep Neural Network Training on Compute Clusters
10. Federated Optimization: Distributed Machine Learning for On-Device Intelligence

Their papers

• A Crowdsourcing Framework for On-Device Federated Learning (2019) — 296 citations