Mingqing Chen

Artificial Intelligence · 2019

A researcher in the Science corpus: 3 papers; trusts 26 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 68 citations into it
• Computer Vision and Pattern Recognition — 23 citations into it
• Privacy & Security — 18 citations into it
• Optimization & Training — 15 citations into it
• Signal Processing — 13 citations into it
• Natural Language Processing — 12 citations into it
• Model Architectures — 11 citations into it
• Federated & Distributed Learning — 9 citations into it

Whom Mingqing Chen trusts overall

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

1. J. Urgen Schmidhuber
2. Sepp Hochreiter
3. H. Brendan McMahan
4. Yurii E. Nesterov
5. Ilya Sutskever
6. Geoffrey E. Hinton
7. Hasim Sak
8. Yoshua Bengio
9. Klaus Greff
10. Jimmy Ba

Who trusts Mingqing Chen overall

1. Fan Lai
2. Yinwei Dai
3. Xinle Liang
4. Sai Praneeth Karimireddy
5. Andrew Straiton Hard
6. Mehryar Mohri
7. Jiahuan Luo
8. Xiangfeng Zhu
9. Li Li
10. Yang Liu

Papers from Mingqing Chen's perspective

Ordered by how strongly the researchers Mingqing Chen trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Long Short-Term Memory
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Federated Learning Of Out-Of-Vocabulary Words (2019) — 182 citations
• Generative Models for Effective ML on Private, Decentralized Datasets (2019) — 211 citations
• Federated Learning of N-Gram Language Models (2019) — 80 citations