Phan-Minh Nguyen

Artificial Intelligence · 2018–2019

A researcher in the Science corpus: 2 papers; trusts 46 researchers and is trusted by 53 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 49 citations into it; #3,865 of 35,345 by trust here, #7,858 by citations
• Optimization & Training — 26 citations into it
• Scaling & Learning Theory — 25 citations into it; #166 of 428 by trust here, #374 by citations
• Computer Vision and Pattern Recognition — 18 citations into it
• Statistical and Nonlinear Physics — 12 citations into it; #499 of 10,127 by trust here, #1,016 by citations
• Model Architectures — 9 citations into it; #421 of 2,278 by trust here, #827 by citations
• Statistics and Probability — 8 citations into it; #1,516 of 12,932 by trust here, #1,523 by citations

Whom Phan-Minh Nguyen trusts overall

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

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

Who trusts Phan-Minh Nguyen overall

1. D. Araújo
2. Greg Yang
3. Justin A. Sirignano
4. Mario Geiger
5. Roberto Imbuzeiro Oliveira
6. Lénaïc Chizat
7. Jiaoyang Huang
8. Song Mei
9. Cong Fang
10. Daniel Yukimura

Papers from Phan-Minh Nguyen's perspective

Ordered by how strongly the researchers Phan-Minh Nguyen 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. Learning Multiple Layers of Features from Tiny Images
9. ImageNet: A large-scale hierarchical image database
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• A mean field view of the landscape of two-layer neural networks (2018) — 1,021 citations
• Mean Field Limit of the Learning Dynamics of Multilayer Neural Networks (2019) — 75 citations