Jianyu Wang

Artificial Intelligence · Found. Trends Mach. Learn. · 2018–2022

A researcher in the Science corpus: 10 papers; trusts 468 researchers and is trusted by 63 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 739 citations into it; #7,092 of 35,345 by trust here, #2,620 by citations
• Optimization & Training — 313 citations into it; #369 of 2,601 by trust here, #505 by citations
• Federated & Distributed Learning — 254 citations into it
• Computer Vision and Pattern Recognition — 136 citations into it; #4,078 of 21,575 by trust here, #6,371 by citations
• Computer Networks and Communications — 124 citations into it; #9,644 of 9,886 by trust here, #2,358 by citations
• ML Systems & Hardware — 91 citations into it; #22 of 575 by trust here, #116 by citations
• Efficiency & Compression — 76 citations into it
• Computational Mechanics — 67 citations into it

Whom Jianyu Wang trusts overall

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

1. Alex Krizhevsky
2. Sebastian U. Stich
3. Kaiming He
4. Geoffrey E. Hinton
5. L. Eon Bottou
6. Diederik P. Kingma
7. Hao Yu
8. Yann LeCun
9. Karen Simonyan
10. Ilya Sutskever

Who trusts Jianyu Wang overall

1. Farzin Haddadpour
2. Xiang Li
3. Gauri Joshi
4. Othmane Marfoq
5. Yae Jee Cho
6. Ahmed Khaled
7. Tian Li
8. Hao Yu
9. Wenhao Yang
10. Dmitrii Avdiukhin

Papers from Jianyu Wang's perspective

Ordered by how strongly the researchers Jianyu Wang 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. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
8. Learning Multiple Layers of Features from Tiny Images
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

• Advances and Open Problems in Federated Learning (2019) — 9,019 citations
• Cooperative SGD: A unified Framework for the Design and Analysis of Communication-Efficient SGD Algorithms (2018) — 362 citations
• Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization (2020) — 2,059 citations
• SlowMo: Improving Communication-Efficient Distributed SGD with Slow Momentum (2019) — 224 citations
• Towards Understanding Biased Client Selection in Federated Learning (2022) — 295 citations
• A Field Guide to Federated Optimization (2021) — 487 citations
• Overlap Local-SGD: An Algorithmic Approach to Hide Communication Delays in Distributed SGD (2020) — 35 citations
• Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategies (2020) — 552 citations
• MATCHA: Speeding Up Decentralized SGD via Matching Decomposition Sampling (2019) — 182 citations
• Adaptive Communication Strategies to Achieve the Best Error-Runtime Trade-off in Local-Update SGD (2018) — 256 citations