Shi Pu

Computer Networks and Communications · 2018–2019

A researcher in the Science corpus: 4 papers; trusts 49 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Computer Networks and Communications — 87 citations into it; #4,863 of 9,886 by trust here, #5,733 by citations
• Optimization & Training — 50 citations into it
• Artificial Intelligence — 40 citations into it; #19,560 of 35,345 by trust here, #23,731 by citations
• Computational Mechanics — 35 citations into it; #6,573 of 10,001 by trust here, #6,877 by citations
• ML Systems & Hardware — 13 citations into it
• Mathematical Optimization — 12 citations into it
• Management Science and Operations Research — 11 citations into it
• Algorithms & Complexity — 8 citations into it

Whom Shi Pu trusts overall

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

1. Angelia Nedić
2. Ali H. Sayed
3. Alexander Olshevsky
4. Wei Shi
5. Dusan Jakovetic
6. P. Lorenzo
7. Jianshu Chen
8. Guannan Qu
9. John C. Duchi
10. John N. Tsitsiklis

Who trusts Shi Pu overall

1. Angelia Nedić
2. Alexander Olshevsky
3. Ioannis Ch. Paschalidis
4. Guannan Qu
5. Anastasia Koloskova

Papers from Shi Pu's perspective

Ordered by how strongly the researchers Shi Pu 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Distributed Subgradient Methods for Multi-Agent Optimization
5. Gradient-based learning applied to document recognition
6. Learning Multiple Layers of Features from Tiny Images
7. Long Short-Term Memory
8. Distributed Asynchronous Deterministic and Stochastic Gradient Optimization Algorithms
9. A Stochastic Approximation Method
10. ImageNet: A large-scale hierarchical image database

Their papers

• A Non-Asymptotic Analysis of Network Independence for Distributed Stochastic Gradient Descent (2019) — 18 citations
• A Sharp Estimate on the Transient Time of Distributed Stochastic Gradient Descent (2019) — 78 citations
• Distributed stochastic gradient tracking methods (2018) — 380 citations
• Push–Pull Gradient Methods for Distributed Optimization in Networks (2018) — 415 citations