Shusen Wang
Artificial Intelligence · with Li · 2017–2019
A researcher in the Science corpus: 6 papers; trusts 171 researchers and is trusted by 20 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 168 citations into it; #8,372 of 35,345 by trust here, #6,911 by citations
- Optimization & Training — 150 citations into it; #1,711 of 2,601 by trust here, #1,509 by citations
- Computational Mechanics — 65 citations into it; #3,529 of 10,001 by trust here, #3,908 by citations
- Federated & Distributed Learning — 65 citations into it; #69 of 319 by trust here, #68 by citations
- Computer Networks and Communications — 41 citations into it; #3,944 of 9,886 by trust here, #1,003 by citations
- Computer Vision and Pattern Recognition — 41 citations into it
- Computational Theory and Mathematics — 30 citations into it
- ML Systems & Hardware — 30 citations into it
Whom Shusen Wang trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Yuchen Zhang
- Michael W. Mahoney
- E.J. Candes
- Ohad Shamir
- Yurii E. Nesterov
- Robert Tibshirani
- Muthu Dayalan
- Petros Drineas
- John C. Duchi
- Farbod Roosta-Khorasani
Who trusts Shusen Wang overall
- Xiang Li
- Wenhao Yang
- Zhihua Zhang
- Tian Li
- Dong Yin
- Yuanming Shi
- Avishek Ghosh
- Celestine Mendler-Dünner
- Kai Yang
- Anit Kumar Sahu
Papers from Shusen Wang's perspective
Ordered by how strongly the researchers Shusen Wang 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
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- Learning Multiple Layers of Features from Tiny Images
- 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
- Do Subsampled Newton Methods Work for High-Dimensional Data? (2019) — 16 citations
- Communication-Efficient Local Decentralized SGD Methods (2019) — 76 citations
- A Sharper Generalization Bound for Divide-and-Conquer Ridge Regression (2019) — 12 citations
- GIANT: Globally Improved Approximate Newton Method for Distributed Optimization (2017) — 148 citations