Hao Yu

Optimization & Training · with Yang · 2017–2019

A researcher in the Science corpus: 5 papers; trusts 158 researchers and is trusted by 85 researchers here, as inferred from the citation graph.

Subfields

• Optimization & Training — 88 citations into it; #326 of 2,601 by trust here, #575 by citations
• Artificial Intelligence — 74 citations into it; #3,076 of 35,345 by trust here, #7,241 by citations
• Computer Vision and Pattern Recognition — 31 citations into it; #5,556 of 21,575 by trust here, #9,872 by citations
• ML Systems & Hardware — 24 citations into it; #68 of 575 by trust here, #224 by citations
• Computational Mechanics — 20 citations into it; #3,670 of 10,001 by trust here, #8,947 by citations
• Federated & Distributed Learning — 17 citations into it; #47 of 319 by trust here, #197 by citations
• Efficiency & Compression — 15 citations into it
• Electrical and Electronic Engineering — 14 citations into it

Whom Hao Yu trusts overall

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

1. Alex Krizhevsky
2. Sebastian U. Stich
3. Xiangru Lian
4. Kaiming He
5. Yurii E. Nesterov
6. Geoffrey E. Hinton
7. Guanghui Lan
8. Ilya Sutskever
9. Yann LeCun
10. Saeed Ghadimi

Who trusts Hao Yu overall

1. Jianyu Wang
2. Farzin Haddadpour
3. Cong Xie
4. Xiang Li
5. Gauri Joshi
6. Tian Li
7. Dmitrii Avdiukhin
8. Ahmed Khaled
9. Mehrdad Mahdavi
10. Xinwei Zhang

Papers from Hao Yu's perspective

Ordered by how strongly the researchers Hao Yu 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. Gradient-based learning applied to document recognition
5. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Learning Multiple Layers of Features from Tiny Images
8. Attention is All you Need (Neural Information Processing Systems)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• On the Linear Speedup Analysis of Communication Efficient Momentum SGD for Distributed Non-Convex Optimization (2019) — 440 citations
• Online Convex Optimization with Stochastic Constraints (2017) — 269 citations
• On the Computation and Communication Complexity of Parallel SGD with Dynamic Batch Sizes for Stochastic Non-Convex Optimization (2019) — 53 citations
• Parallel Restarted SGD with Faster Convergence and Less Communication: Demystifying Why Model Averaging Works for Deep Learning (2018) — 686 citations
• Parallel Restarted SGD for Non-Convex Optimization with Faster Convergence and Less Communication (2018) — 66 citations