Hongyi Wang

Artificial Intelligence · 2018–2021

A researcher in the Science corpus: 10 papers; trusts 258 researchers and is trusted by 25 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 486 citations into it; #7,154 of 35,345 by trust here, #3,978 by citations
• Optimization & Training — 193 citations into it; #1,674 of 2,601 by trust here, #924 by citations
• Federated & Distributed Learning — 156 citations into it; #55 of 319 by trust here, #49 by citations
• Computer Vision and Pattern Recognition — 129 citations into it
• Computer Networks and Communications — 83 citations into it
• ML Systems & Hardware — 60 citations into it; #171 of 575 by trust here, #329 by citations
• Computational Mechanics — 57 citations into it
• Privacy & Security — 57 citations into it

Whom Hongyi Wang trusts overall

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

1. Alex Krizhevsky
2. Karen Simonyan
3. Yann LeCun
4. Kaiming He
5. Geoffrey E. Hinton
6. Yoshua Bengio
7. Andrew Zisserman
8. L. Eon Bottou
9. Diederik P. Kingma
10. Ilya Sutskever

Who trusts Hongyi Wang overall

1. Chaoyang He
2. Qinbin Li
3. Deepesh Data
4. Sai Praneeth Karimireddy
5. Konstantin Mishchenko
6. Guodong Long
7. Yue Tan
8. Murali Annavaram
9. Cong Xie
10. Samuel Horváth

Papers from Hongyi Wang's perspective

Ordered by how strongly the researchers Hongyi 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

• Federated Learning with Matched Averaging (2020) — 1,404 citations
• The Effect of Network Width on the Performance of Large-batch Training (2018) — 24 citations
• Attack of the Tails: Yes, You Really Can Backdoor Federated Learning (2020) — 829 citations
• ATOMO: Communication-efficient Learning via Atomic Sparsification (2018) — 392 citations
• A Field Guide to Federated Optimization (2021) — 487 citations
• FedML: A Research Library and Benchmark for Federated Machine Learning (2020) — 716 citations
• DETOX: A Redundancy-based Framework for Faster and More Robust Gradient Aggregation (2019) — 149 citations
• Draco: Robust Distributed Training against Adversaries (2018)
• DRACO: Robust Distributed Training via Redundant Gradients (2018) — 23 citations
• DRACO: Byzantine-resilient Distributed Training via Redundant Gradients (2018) — 274 citations