Wenhao Yang
Artificial Intelligence · 2019
A researcher in the Science corpus: 3 papers; trusts 77 researchers and is trusted by 9 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 117 citations into it; #16,433 of 35,345 by trust here, #8,007 by citations
- Optimization & Training — 93 citations into it
- Federated & Distributed Learning — 57 citations into it; #128 of 319 by trust here, #76 by citations
- Computer Networks and Communications — 31 citations into it; #3,941 of 9,886 by trust here, #1,003 by citations
- Computer Vision and Pattern Recognition — 29 citations into it
- Computational Mechanics — 25 citations into it
- ML Systems & Hardware — 23 citations into it
- Efficiency & Compression — 19 citations into it
Whom Wenhao Yang trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Yuchen Zhang
- Shusen Wang
- Sebastian U. Stich
- Jianyu Wang
- H. Brendan McMahan
- John C. Duchi
- Hao Yu
- Alex Krizhevsky
- L. Eon Bottou
- Jakub Konecný
Who trusts Wenhao Yang overall
- Farzin Haddadpour
- Yae Jee Cho
- Tian Li
- Jianyu Wang
- John Nguyen
- Canh T. Dinh
- Deepesh Data
- Anastasia Koloskova
- Othmane Marfoq
- Zachary B. Charles
Papers from Wenhao Yang's perspective
Ordered by how strongly the researchers Wenhao Yang trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Gradient-based learning applied to document recognition
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- On the Convergence of FedAvg on Non-IID Data (2019) — 3,042 citations
- Communication Efficient Decentralized Training with Multiple Local Updates (2019) — 56 citations
- Communication-Efficient Local Decentralized SGD Methods (2019) — 76 citations