Ha Quang Minh

Computer Vision and Pattern Recognition · 2012–2016

A researcher in the Science corpus: 3 papers; trusts 81 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 25 citations into it
• Artificial Intelligence — 21 citations into it; #21,932 of 35,345 by trust here, #31,361 by citations
• Statistical Learning Theory — 10 citations into it
• Statistics and Probability — 9 citations into it; #8,052 of 12,932 by trust here, #10,275 by citations
• Applied Mathematics — 7 citations into it
• Computational Mechanics — 7 citations into it; #6,000 of 10,001 by trust here, #8,566 by citations
• Media Technology — 6 citations into it
• Optimization & Training — 6 citations into it

Whom Ha Quang Minh trusts overall

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

1. Chih-Jen Lin
2. Mehrtash Harandi
3. G. Larotonda
4. Bernhard Schölkopf
5. Sadeep Jayasumana
6. Ming Yuan
7. Mathieu Salzmann
8. Shaohua Kevin Zhou
9. Ali Rahimi
10. Geoffrey E. Hinton

Who trusts Ha Quang Minh overall

1. Jacopo Cavazza
2. Vittorio Murino
3. Marco San-Biagio
4. Keerthiram Murugesan
5. Michel Besserve
6. Zhiwu Huang
7. Pietro Morerio
8. Andrea Zunino

Papers from Ha Quang Minh's perspective

Ordered by how strongly the researchers Ha Quang Minh 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. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
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

• Scalable Matrix-valued Kernel Learning for High-dimensional Nonlinear Multivariate Regression and Granger Causality (2012) — 63 citations
• Approximate Log-Hilbert-Schmidt Distances between Covariance Operators for Image Classification (2016) — 23 citations
• Log-Hilbert-Schmidt metric between positive definite operators on Hilbert spaces (2014) — 83 citations