Qirong Ho

Artificial Intelligence · 2011–2017

A researcher in the Science corpus: 11 papers; trusts 85 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 109 citations into it; #20,886 of 35,345 by trust here, #8,893 by citations
• Optimization & Training — 71 citations into it
• Computer Vision and Pattern Recognition — 59 citations into it; #19,453 of 21,575 by trust here, #5,987 by citations
• Computer Networks and Communications — 40 citations into it
• Computational Mechanics — 38 citations into it; #6,260 of 10,001 by trust here, #5,813 by citations
• ML Systems & Hardware — 24 citations into it; #172 of 575 by trust here, #100 by citations
• Statistics and Probability — 19 citations into it
• Representation & Self-Supervised Learning — 17 citations into it

Whom Qirong Ho trusts overall

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

1. Michael I. Jordan
2. Geoffrey E. Hinton
3. L. Eon Bottou
4. Alex Krizhevsky
5. Radford M. Neal
6. Amr Ahmed
7. Yoshua Bengio
8. Robert Tibshirani
9. T. Griffiths
10. Yucheng Low

Who trusts Qirong Ho overall

1. Cong Xie
2. Henggang Cui
3. Eric P. Xing
4. Wei Dai
5. Oluwasanmi Koyejo
6. Aaron Harlap
7. Ilya Trofimov
8. Mu Li
9. Indranil Gupta
10. G. R. Ganger

Papers from Qirong Ho's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. Going deeper with convolutions

Their papers

• Petuum: A New Platform for Distributed Machine Learning on Big Data (2013) — 526 citations
• On Model Parallelization and Scheduling Strategies for Distributed Machine Learning (2014) — 129 citations
• Distributed Multi-Task Relationship Learning (2017) — 75 citations
• Poseidon: An Efficient Communication Architecture for Distributed Deep Learning on GPU Clusters (2017) — 378 citations
• Multiscale Community Blockmodel for Network Exploration (2011) — 46 citations
• Strategies and Principles of Distributed Machine Learning on Big Data (2015) — 167 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
• More Effective Distributed ML via a Stale Synchronous Parallel Parameter Server (2013) — 813 citations
• Solving the Straggler Problem with Bounded Staleness (2013) — 149 citations