Nhat-Quang Tran

Computer Vision and Pattern Recognition · 2016–2017

A researcher in the Science corpus: 2 papers; trusts 27 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 15 citations into it; #12,439 of 21,575 by trust here, #17,757 by citations
• Artificial Intelligence — 10 citations into it
• Applied Mathematics — 9 citations into it
• Probabilistic & Bayesian Methods — 6 citations into it; #1,055 of 1,525 by trust here, #1,244 by citations

Whom Nhat-Quang Tran trusts overall

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

1. Andrea Vedaldi
2. Jaume Amores
3. Radford M. Neal
4. Brendan J. Frey
5. Min-Ling Zhang
6. Ronald P. S. Mahler
7. Arthur P. Dempster
8. Thorsten Joachims
9. Kevin P. Murphy
10. Daryl J. Daley

Who trusts Nhat-Quang Tran overall

1. Seyed Hamid Rezatofighi
2. Anton Milan
3. Javen Qinfeng Shi
4. B. Vo

Papers from Nhat-Quang Tran's perspective

Ordered by how strongly the researchers Nhat-Quang Tran 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. ImageNet Large Scale Visual Recognition Challenge
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Model-based classification and novelty detection for point pattern data (2016) — 14 citations
• Clustering for point pattern data (2017) — 13 citations