Nanqing Dong

Artificial Intelligence · 2018–2022

A researcher in the Science corpus: 5 papers; trusts 571 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 68 citations into it; #22,978 of 35,345 by trust here, #28,481 by citations
• Computer Vision and Pattern Recognition — 62 citations into it; #14,170 of 21,575 by trust here, #18,782 by citations
• Computer Networks and Communications — 40 citations into it
• Optimization & Training — 28 citations into it
• Representation & Self-Supervised Learning — 22 citations into it; #2,272 of 3,145 by trust here, #2,910 by citations
• Model Architectures — 20 citations into it
• Evaluation & Benchmarks — 8 citations into it
• Management Science and Operations Research — 6 citations into it

Whom Nanqing Dong trusts overall

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

1. Kaiming He
2. Alex Krizhevsky
3. Diederik P. Kingma
4. Geoffrey E. Hinton
5. Yoshua Bengio
6. Karen Simonyan
7. Ronald J. Williams
8. Yann LeCun
9. Ross B. Girshick
10. Oriol Vinyals

Who trusts Nanqing Dong overall

1. Mennatullah Siam
2. Fabio Cermelli
3. Yongqin Xian
4. Umberto Michieli
5. Khoi Duc Minh Nguyen
6. Boris N. Oreshkin
7. Juhong Min
8. Naoki Kato
9. Chi Zhang
10. Yunchao Wei

Papers from Nanqing Dong's perspective

Ordered by how strongly the researchers Nanqing Dong 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. ImageNet: A large-scale hierarchical image database
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. Attention is All you Need (Neural Information Processing Systems)
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Domain Adaption in One-Shot Learning (2018) — 49 citations
• Edge Computing with Artificial Intelligence: A Machine Learning Perspective (2022) — 441 citations
• Neural Architecture Search for Adversarial Medical Image Segmentation (2019) — 42 citations
• Few-Shot Semantic Segmentation with Prototype Learning (2018) — 565 citations
• Toward Understanding the Impact of Staleness in Distributed Machine Learning (2018) — 96 citations