Quan Hoang

Computer Vision and Pattern Recognition · 2017–2018

A researcher in the Science corpus: 2 papers; trusts 78 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 42 citations into it; #18,879 of 21,575 by trust here, #17,757 by citations
• Generative Models — 34 citations into it; #1,708 of 1,956 by trust here, #1,382 by citations
• Artificial Intelligence — 27 citations into it; #30,868 of 35,345 by trust here, #27,551 by citations
• Optimization & Training — 12 citations into it; #2,418 of 2,601 by trust here, #1,661 by citations
• Model Architectures — 9 citations into it

Whom Quan Hoang trusts overall

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

1. Alex Krizhevsky
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Christian Szegedy
5. Sergey Ioffe
6. Yoshua Bengio
7. Ilya Sutskever
8. Alec Radford
9. Luke Metz
10. Yann LeCun

Who trusts Quan Hoang overall

1. Ngoc-Trung Tran
2. Garrett Wilson
3. Divya Saxena
4. Hao He
5. Kevin J Liang
6. Viet-Hung Tran
7. Wei Li
8. Jiannong Cao
9. Diane Joyce Cook
10. Ngoc-Bao Nguyen

Papers from Quan Hoang's perspective

Ordered by how strongly the researchers Quan Hoang 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. ImageNet Large Scale Visual Recognition Challenge
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

• Multi-Generator Generative Adversarial Nets (2017) — 69 citations
• MGAN: Training Generative Adversarial Nets with Multiple Generators (2018) — 250 citations