Nguyen Cong Luong

Artificial Intelligence · 2016–2019

A researcher in the Science corpus: 4 papers; trusts 20 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 130 citations into it
• Computer Networks and Communications — 96 citations into it
• Computer Vision and Pattern Recognition — 48 citations into it
• Reinforcement Learning — 33 citations into it
• Optimization & Training — 21 citations into it
• Management Science and Operations Research — 17 citations into it
• Privacy & Security — 15 citations into it
• ML Systems & Hardware — 8 citations into it

Whom Nguyen Cong Luong trusts overall

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

1. Hado van Hasselt
2. Richard S. Sutton
3. Alex Krizhevsky
4. Geoffrey E. Hinton
5. Yoshua Bengio
6. Yann LeCun
7. Volodymyr Mnih
8. David Silver
9. Christopher J. C. H. Watkins
10. Diederik P. Kingma

Who trusts Nguyen Cong Luong overall

1. Mingzhe Chen
2. Yasar Sinan Nasir
3. Khaled Ben Letaief
4. Chongwen Huang
5. Jiawen Kang
6. Deniz Gündüz
7. Dinh C. Nguyen
8. Wei Yang Bryan Lim
9. Ursula Challita
10. Haftay Gebreslasie Abreha

Papers from Nguyen Cong Luong's perspective

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

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

Their papers

• Applications of Deep Reinforcement Learning in Communications and Networking: A Survey (2018) — 1,781 citations
• Federated Learning in Mobile Edge Networks: A Comprehensive Survey (2019) — 2,306 citations
• Data Collection and Wireless Communication in Internet of Things (IoT) Using Economic Analysis and Pricing Models: A Survey (2016) — 297 citations
• Efficient Training Management for Mobile Crowd-Machine Learning: A Deep Reinforcement Learning Approach (2018) — 82 citations