Quynh N. Nguyen

Artificial Intelligence · 2016–2017

A researcher in the Science corpus: 4 papers; trusts 363 researchers and is trusted by 26 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 117 citations into it; #6,604 of 35,345 by trust here, #6,229 by citations
• Computer Vision and Pattern Recognition — 68 citations into it; #8,068 of 21,575 by trust here, #6,671 by citations
• Model Architectures — 40 citations into it
• Optimization & Training — 38 citations into it
• Scaling & Learning Theory — 27 citations into it
• Representation & Self-Supervised Learning — 21 citations into it; #1,741 of 3,145 by trust here, #1,338 by citations
• Computational Mechanics — 18 citations into it
• Statistical Learning Theory — 16 citations into it

Whom Quynh N. Nguyen trusts overall

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

1. Yoshua Bengio
2. Yann LeCun
3. Geoffrey E. Hinton
4. Alex Krizhevsky
5. Karen Simonyan
6. Diederik P. Kingma
7. Kaiming He
8. Christian Szegedy
9. Ilya Sutskever
10. Andrew Zisserman

Who trusts Quynh N. Nguyen overall

1. Simon Shaolei Du
2. Jason D. Lee
3. Matthias Hein
4. Charles H. Martin
5. Yu Bai
6. Laura Palagi
7. Huaxiu Yao
8. Wei Hu
9. M. Soltanolkotabi
10. Daniel Soudry

Papers from Quynh N. Nguyen's perspective

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

Their papers

• Latent Embeddings for Zero-Shot Classification (2016) — 750 citations
• The Loss Surface of Deep and Wide Neural Networks (2017) — 306 citations
• The loss surface and expressivity of deep convolutional neural networks (2017) — 29 citations
• Optimization Landscape and Expressivity of Deep CNNs (2017) — 118 citations