Minghong Fang

Artificial Intelligence · 2018–2021

A researcher in the Science corpus: 5 papers; trusts 73 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 113 citations into it; #16,903 of 35,345 by trust here, #11,750 by citations
• Safety, Robustness & Interpretability — 69 citations into it; #2,178 of 2,251 by trust here, #2,041 by citations
• Federated & Distributed Learning — 34 citations into it; #268 of 319 by trust here, #209 by citations
• Computer Networks and Communications — 33 citations into it; #9,676 of 9,886 by trust here, #7,140 by citations
• Optimization & Training — 30 citations into it
• Information Systems — 28 citations into it; #8,572 of 8,771 by trust here, #6,501 by citations
• Computer Vision and Pattern Recognition — 21 citations into it
• Signal Processing — 18 citations into it

Whom Minghong Fang trusts overall

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

1. Alex Krizhevsky
2. Kaiming He
3. H. Brendan McMahan
4. Geoffrey E. Hinton
5. Neil Zhenqiang Gong
6. Yann LeCun
7. Yoshua Bengio
8. El Mahdi El Mhamdi
9. L. Eon Bottou
10. Nicolas Papernot

Who trusts Minghong Fang overall

1. Xiaoyu Cao
2. Zaixi Zhang
3. Neil Zhenqiang Gong
4. Lichao Sun
5. Zhiyi Tian
6. T. D. Nguyen
7. Lingjuan Lyu
8. Binghui Wang
9. Hongbin Liu
10. Xinmeng Huang

Papers from Minghong Fang's perspective

Ordered by how strongly the researchers Minghong Fang 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Learning Multiple Layers of Features from Tiny Images
6. ImageNet: A large-scale hierarchical image database
7. Long Short-Term Memory
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

• Achieving Linear Speedup with Partial Worker Participation in Non-IID Federated Learning (2021) — 319 citations
• FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping (2020) — 1,101 citations
• Local Model Poisoning Attacks to Byzantine-Robust Federated Learning (2019) — 1,621 citations
• Byzantine-Resilient Stochastic Gradient Descent for Distributed Learning: A Lipschitz-Inspired Coordinate-wise Median Approach (2019) — 52 citations
• Poisoning Attacks to Graph-Based Recommender Systems (2018) — 233 citations