Jinliang Wei

Artificial Intelligence · 2013–2022

A researcher in the Science corpus: 7 papers; trusts 9 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 76 citations into it; #34,328 of 35,345 by trust here, #11,750 by citations
• Computer Vision and Pattern Recognition — 59 citations into it; #20,909 of 21,575 by trust here, #6,206 by citations
• Optimization & Training — 43 citations into it
• Computer Networks and Communications — 31 citations into it
• ML Systems & Hardware — 29 citations into it; #530 of 575 by trust here, #286 by citations
• Hardware and Architecture — 22 citations into it
• Information Systems — 20 citations into it; #8,418 of 8,771 by trust here, #1,515 by citations
• Natural Language Processing — 12 citations into it

Whom Jinliang Wei trusts overall

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

1. Joseph E. Gonzalez
2. Qirong Ho
3. Yucheng Low
4. James Cipar
5. T. Griffiths
6. Geoffrey E. Hinton
7. Alex Krizhevsky
8. Yoshua Bengio
9. L. Eon Bottou
10. Henggang Cui

Who trusts Jinliang Wei overall

1. Eric P. Xing
2. Henggang Cui
3. Wei Dai
4. Aaron Harlap
5. Qirong Ho

Papers from Jinliang Wei's perspective

Ordered by how strongly the researchers Jinliang Wei 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. Going deeper with convolutions
5. Attention is All you Need (Neural Information Processing Systems)
6. ImageNet Large Scale Visual Recognition Challenge
7. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. Long Short-Term Memory
9. ImageNet: A large-scale hierarchical image database
10. Gradient-based learning applied to document recognition

Their papers

• Overlap Communication with Dependent Computation via Decomposition in Large Deep Learning Models (2022) — 117 citations
• Petuum: A New Platform for Distributed Machine Learning on Big Data (2013) — 526 citations
• Poseidon: An Efficient Communication Architecture for Distributed Deep Learning on GPU Clusters (2017) — 378 citations
• Addressing the straggler problem for iterative convergent parallel ML (2016) — 148 citations
• Exploiting iterative-ness for parallel ML computations (2014) — 40 citations
• Exploiting Bounded Staleness to Speed Up Big Data Analytics (2014) — 171 citations
• High-Performance Distributed ML at Scale through Parameter Server Consistency Models (2014) — 123 citations