Ngoc Duy Nguyen

Artificial Intelligence · 2018–2019

A researcher in the Science corpus: 2 papers; trusts 76 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 63 citations into it; #18,672 of 35,345 by trust here, #19,439 by citations
• Reinforcement Learning — 60 citations into it
• Control and Systems Engineering — 16 citations into it
• Computational Theory and Mathematics — 13 citations into it; #6,096 of 12,182 by trust here, #4,531 by citations
• Computer Vision and Pattern Recognition — 12 citations into it
• Management Science and Operations Research — 11 citations into it
• Electrical and Electronic Engineering — 6 citations into it
• Computer Networks and Communications — 5 citations into it

Whom Ngoc Duy Nguyen trusts overall

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

1. Richard S. Sutton
2. Geoffrey E. Hinton
3. John Schulman
4. Volodymyr Mnih
5. Sergey Levine
6. David Silver
7. Yoshua Bengio
8. Alex Krizhevsky
9. Diederik P. Kingma
10. Andrew Barto

Who trusts Ngoc Duy Nguyen overall

1. Jieyi Long
2. Thanh Thi Nguyen
3. Amal Feriani
4. Chao Yu
5. Roxana Rădulescu
6. Vijay Janappa Reddi

Papers from Ngoc Duy Nguyen's perspective

Ordered by how strongly the researchers Ngoc Duy Nguyen 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. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Reinforcement Learning: An Introduction
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Deep Reinforcement Learning for Multiagent Systems: A Review of Challenges, Solutions, and Applications (2018) — 1,032 citations
• A New Tensioning Method using Deep Reinforcement Learning for Surgical Pattern Cutting (2019) — 42 citations